Cabinet grounding current detection method and system based on current sensor

Abstract

The invention discloses a cabinet grounding current detection method and system based on a current sensor. The method comprises the steps of collecting the current value of each node in real time, calculating the delay time between nodes, determining the synchronization intensity and the shunting attenuation rate, and dividing a mainstream path and a branch path. Obtaining a path peak frequency through fast Fourier transform, and obtaining a peak offset in combination with a frequency threshold; monitoring path current, and extracting time sequence data to obtain an abnormal time period; backtracking historical current data, generating a dynamic flow direction vector, and obtaining a complete flow track of the abnormal current; positioning a flow direction abrupt change point, and determining a preliminary coordinate position of an abnormal source; current data are extracted, noise is eliminated, feature analysis is carried out, accurate current features are obtained, normal current features are compared, current vectors are projected to the three-dimensional grid model, and a diffusion path is simulated to determine tiny leakage points. The method can monitor the dynamic change of the grounding current, comprehensively captures the dynamic change of the current, and can accurately trace the source.

Description

Technical Field

[0001] The present invention relates to the technical field of current detection, and in particular to a cabinet grounding current detection method and system based on a current sensor. Background Art

[0002] With the widespread adoption of intelligent and distributed systems, abnormal ground current detection has become a focus of industry attention. In the field of electrical engineering and equipment safety, the detection and management of cabinet ground current is critical for ensuring stable system operation and personal safety. Research in this area is directly related to power system reliability, equipment lifespan, and fault prevention and control capabilities.

[0003] One existing technique uses a ground resistance tester to indirectly determine ground current conditions by measuring the conduction resistance between the cabinet ground point and the PE busbar. This method effectively detects ground continuity, but is not sensitive enough to monitor dynamic changes in ground current. These limitations make it difficult to fully capture dynamic current changes in complex cabinet structures, especially when faced with multi-path distribution and abnormal leakage, making accurate tracing impossible. If these issues are not effectively addressed, fault location will be delayed and even lead to greater system risks.

[0004] In summary, the existing technology is not sensitive enough to monitor the dynamic changes of ground current, making it difficult to fully capture the dynamic changes of current, resulting in the inability to accurately trace the source. Summary of the Invention

[0005] The present invention provides a cabinet grounding current detection method and system based on a current sensor, so as to solve the problem that the cabinet grounding current cannot be accurately traced in detection.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a cabinet grounding current detection method based on a current sensor, comprising: The current value of each node in the cabinet grounding system is obtained in real time through sensors, and the current dynamic sequence is obtained by recording the current value collected every second; Calculating the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determining the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and dividing the main path and the branch path according to the shunt attenuation rate; Extracting the peak frequencies of the main path and the branch path by fast Fourier transform, and obtaining the peak offset of each path by combining a preset frequency threshold; Monitoring the path currents of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extracting time series data within a reconstruction time window of the path to obtain an abnormal time period; According to the abnormal time period, the historical current data in the memory is traced back to generate a dynamic flow direction vector to obtain a complete flow trajectory of the abnormal current; According to the complete flow trajectory, the current change position between the nodes is located for the flow direction mutation point as the preliminary coordinate position of the abnormal source; Extracting sensor current data of the corresponding node according to the preliminary coordinate position, eliminating noise interference and performing feature analysis to obtain accurate current characteristics of the abnormal source; The precise current characteristics are compared with the preset normal current characteristics to obtain a comparison result. According to the comparison result, the current vector is projected onto the three-dimensional grid model of the cabinet grounding system, and the diffusion path of the leakage current is simulated to determine the tiny leakage point.

[0007] In an optional implementation, determining the synchronization strength and the shunt attenuation rate of the current signals between nodes according to the delay time includes: Calculating the reciprocal of the delay time and normalizing it to obtain the synchronization strength of the current signal between nodes; The convergence point of the path is determined by identifying the signal concentration area, and the attenuation change value is calculated according to the current signal strength of the convergence point and the current signal strength of other nodes connected to it to obtain the shunt attenuation rate.

[0008] In an optional embodiment, dividing the main flow path and the branch path according to the diversion attenuation rate includes: Comparing the shunt attenuation rate with a preset attenuation rate threshold, and comparing the synchronization strength with a preset synchronization strength threshold; When the shunt attenuation rate is less than or equal to the attenuation rate threshold and the synchronization strength is greater than or equal to the synchronization strength threshold, determining that the inter-node current signal transmission path is the mainstream path; When the shunt attenuation rate is greater than the attenuation rate threshold or the synchronization strength is less than the synchronization strength threshold, it is determined that the inter-node current signal transmission path is the branch path.

[0009] In an optional embodiment, extracting the peak frequencies of the mainstream path and the branch path by fast Fourier transform and obtaining the peak offset of each path in combination with a preset frequency threshold includes: Performing fast Fourier transform on the current dynamic sequences of the main path and the branch path respectively, converting the time domain signals into frequency domain signals, and obtaining a spectrum diagram; Calculate the amplitude of each frequency component in the spectrum graph; Identifying the frequency point with the largest amplitude in the spectrum graph as the peak frequency; When the peak frequency exceeds a preset frequency threshold, a difference between the peak frequency and the frequency threshold is calculated to obtain a peak offset.

[0010] In an optional embodiment, when the path current exceeds the abnormal trigger threshold, extracting the time series data within the reconstruction time window of the path to obtain the abnormal time period includes: When the path current exceeds the abnormal trigger threshold, the time series data of all nodes of the path within the reconstruction time window are retrieved from the memory, where the reconstruction time window includes a preset fixed window and an adaptive window; The standard deviation value is calculated for the time series data. When the standard deviation value of consecutive time intervals exceeds a fluctuation threshold, the consecutive time intervals are merged to obtain an abnormal time period.

[0011] In an optional embodiment, the step of tracing back the historical current data in the memory according to the abnormal time period to generate a dynamic flow direction vector and obtain a complete flow trajectory of the abnormal current includes: According to the abnormal time period, extracting the historical current value of the corresponding time period from the memory; Calculate the direction and intensity of current flow between nodes and generate dynamic flow vectors; By analyzing the changing trend of the dynamic flow direction vector, the complete flow trajectory of the abnormal current is obtained.

[0012] In an optional embodiment, locating the current change position between nodes at the flow direction mutation point according to the complete flow trajectory as the preliminary coordinate position of the abnormal source includes: Based on the complete flow trajectory, the current change rate of the node and its connected adjacent nodes is calculated. When the current change rate exceeds a preset current change threshold, it is marked as a flow direction mutation point. When the flow direction mutation point is detected, the current change position is determined in combination with the dynamic flow direction vector corresponding to the flow direction mutation point, which serves as the preliminary coordinate position of the abnormal source.

[0013] In an optional embodiment, extracting sensor current data of the corresponding node according to the preliminary coordinate position, eliminating noise interference and performing feature analysis to obtain accurate current features of the abnormal source includes: Extracting sensor current data of corresponding nodes according to the preliminary coordinate position; Eliminating noise interference from the sensor current data in combination with the reconstruction error range to obtain effective current data; Calculating the mean and variance of the effective current data to obtain the current time domain characteristics of the abnormal source; Performing frequency domain analysis on the effective current data using a fast Fourier transform algorithm to obtain the current frequency domain characteristics of the abnormal source; The amplitude, frequency and phase information in the current time domain characteristics and the current frequency domain characteristics are used as the precise current characteristics of the abnormal source.

[0014] In an optional embodiment, comparing the precise current characteristics with a preset normal current characteristic to obtain a comparison result, projecting the current vector onto a three-dimensional grid model of the cabinet grounding system according to the comparison result, and simulating the diffusion path of the leakage current to determine the small leakage point includes: Comparing the precise current characteristics with a preset normal current characteristic, when the amplitude exceeds an amplitude threshold and the frequency offset exceeds an offset threshold, extracting the corresponding dynamic flow direction vector, determining the three-dimensional coordinates of each node in the cabinet grounding system, and obtaining a current vector; Projecting the current vector into the three-dimensional grid model; Combined with the Euler-Lagrangian particle tracing method, the diffusion path of the leakage current is simulated. When the current density at a certain coordinate point exceeds a critical value, the coordinate point is determined to be a small leakage point.

[0015] In a second aspect, the present invention provides a cabinet grounding current detection device based on a current sensor, comprising: The current value acquisition module is used to obtain the current value of each node in the cabinet grounding system in real time through sensors, and record the current value collected every second to obtain a current dynamic sequence; a path division module, configured to calculate the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determine the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and divide the main path and the branch path according to the shunt attenuation rate; A peak module is used to extract the peak frequencies of the main path and the branch path through fast Fourier transform, and obtain the peak offset of each path in combination with a preset frequency threshold; an abnormal time period acquisition module, configured to monitor the path currents of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extract the time series data within the reconstruction time window of the path to obtain the abnormal time period; A backtracking module is used to backtrack the historical current data in the memory according to the abnormal time period, generate a dynamic flow direction vector, and obtain a complete flow trajectory of the abnormal current; A positioning module is used to locate the current change position between nodes according to the complete flow trajectory and the flow direction mutation point as the preliminary coordinate position of the abnormal source; A feature analysis module is used to extract sensor current data of the corresponding node according to the preliminary coordinate position, eliminate noise interference and perform feature analysis to obtain accurate current characteristics of the abnormal source; The leakage point output module is used to compare the precise current characteristics with the preset normal current characteristics to obtain a comparison result, project the current vector onto the three-dimensional grid model of the cabinet grounding system according to the comparison result, and simulate the diffusion path of the leakage current to determine the tiny leakage point.

[0016] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for detecting cabinet grounding current based on a current sensor as described above is implemented.

[0017] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned cabinet grounding current detection methods based on current sensors.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention collects current values in real time through sensors and, based on the current dynamic sequence recorded every second, can capture the real-time changes of current and ensure high-sensitivity monitoring of dynamic changes.

[0019] (2) The present invention calculates the delay time and shunt attenuation rate of the current signal between different nodes, divides the main path and the branch path, and thus can comprehensively analyze the multi-path distribution of the current, thus solving the limitations of the existing technology.

[0020] (3) The present invention uses fast Fourier transform to extract the peak frequencies of the main and branch paths, and combines this with a preset frequency threshold to obtain the peak offset. This frequency domain analysis method can capture subtle changes in the current signal, providing a basis for abnormal warning.

[0021] (4) The present invention can completely restore the flow path of the abnormal current by tracing back the abnormal time period and generating a dynamic flow direction vector, providing a clear trajectory for fault location.

[0022] (5) The present invention can accurately locate the abnormal source and provide detailed current characteristics by locating the flow mutation point and analyzing the characteristics of sensor data, combined with the simulation of the three-dimensional grid model.

[0023] (6) By projecting precise current characteristics onto a three-dimensional grid model and simulating the diffusion path of leakage current, the present invention can intuitively present the propagation path of abnormal current and provide guidance for fault repair.

[0024] In summary, the present invention comprehensively solves the shortcomings of existing technologies in ground current monitoring and anomaly tracing through technical means such as real-time dynamic monitoring, multi-path analysis, frequency domain feature extraction, anomaly tracing, precise positioning and three-dimensional simulation, and significantly improves the monitoring sensitivity and fault location accuracy of the cabinet grounding system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a cabinet grounding current detection method based on a current sensor provided by the first embodiment of the present invention; Figure 2 This is a structural diagram of a cabinet grounding current detection system based on a current sensor provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Reference Figure 1 The first embodiment of the present invention provides a cabinet grounding current detection method based on a current sensor, comprising the following steps: S11, using sensors to obtain the current value of each node in the cabinet grounding system in real time, and recording the current value collected every second to obtain a current dynamic sequence; S12, calculating the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determining the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and dividing the main path and the branch path according to the shunt attenuation rate; S13, extracting the peak frequencies of the main path and the branch path by fast Fourier transform, and obtaining the peak offset of each path by combining a preset frequency threshold; S14, monitoring the path current of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extracting time series data within a reconstruction time window of the path to obtain an abnormal time period; S15, tracing back the historical current data in the memory according to the abnormal time period, generating a dynamic flow direction vector, and obtaining a complete flow trajectory of the abnormal current; S16, based on the complete flow trajectory, locating the current change position between the nodes for the flow direction mutation point as the preliminary coordinate position of the abnormal source; S17, extracting sensor current data of the corresponding node according to the preliminary coordinate position, eliminating noise interference and performing feature analysis to obtain accurate current characteristics of the abnormal source; S18, comparing the precise current characteristics with the preset normal current characteristics to obtain a comparison result, projecting the current vector onto the three-dimensional grid model of the cabinet grounding system according to the comparison result, and simulating the diffusion path of the leakage current to determine the tiny leakage point.

[0028] In step S11, the current value of each node in the cabinet grounding system is obtained in real time by the sensor, and the current value collected every second is recorded to obtain a current dynamic sequence, including: Deploy sensor nodes through a distributed network to collect current values at each node in the cabinet grounding system; The current value is sampled at high frequency, recorded as the current value collected per second, and a current dynamic sequence reflecting dynamic changes is generated using a sliding window.

[0029] It should be noted that sensor nodes are deployed across a distributed network to collect current values at each node in the cabinet grounding system. Specifically, non-invasive, high-precision current sensors are used to avoid interference with the existing grounding system. Sensors are deployed at key nodes in the cabinet grounding system's physical topology (such as grounding busbar connection points and branch path intersections). A minimum deployment density is determined based on the cabinet size and structural complexity, following a grid-based principle. Redundant sensors are added to critical high-risk areas (such as corrosion-prone or high-load nodes) to improve fault tolerance. Deploying sensor nodes across a distributed network covers all critical paths in the cabinet grounding system, avoiding the blind spots of traditional single-point detection and enabling real-time capture of dynamic current changes across multiple paths.

[0030] It should be noted that the current values are sampled at high frequency, recorded every second. A sliding window is used to segment the continuous time series data into windows of fixed length. The data is then processed segmented, generating a set of features, including mean, frequency, and phase components, at a preset step size. High-frequency sampling fully records the current waveform details, avoiding missing anomalies. Segmented data processing reduces computational complexity, processing only one set of data at a time, and enabling rapid capture of transient anomalies.

[0031] For example, in a cabinet grounding system, node N1 exists. A sensor acquires the current value of node N1 in real time, sampling at a high frequency of 1000 times per second. Within 1 second, sensor N1 collects 1000 current values (in mA): [5.21, 5.18, 5.23, ..., 5.15]. A sliding window with a preset step size of 0.1 seconds is used to divide the 1-second current value into 0.1-second windows, each containing 100 sampling points, for a total of 10 windows. The resulting current dynamic sequence is generated at timestamp 58.0 seconds for N1: [[Window 1: [5.20, 50, 0.5]][Window 2: [5.19, 50, 0.48]] ... [Window 10: [5.18, 50, 0.49]]].

[0032] In step S12, the delay time of the current signal arrival between different nodes is first calculated based on the current dynamic sequence. Specifically, the normalized cross-correlation function is used to analyze the current dynamic sequence of different nodes. When the cross-correlation function reaches the maximum value, the corresponding time offset is the delay time. The normalized cross-correlation function is defined as:

[0033] in, and are the current dynamic sequences of nodes A and B respectively, t=1,2,...,N, N is the signal length, is the time offset, and for and The mean, normalized ∈[-1,1]. Delay time is the core parameter of dynamic analysis path. By revealing the time propagation characteristics of current signals, it provides a key basis for path division, anomaly detection, and fault tracing, thus overcoming the problems of insensitive dynamic monitoring and difficult tracing in existing technologies.

[0034] For example, in a cabinet grounding system, the current signal propagation paths of nodes A and B are different, resulting in a time difference in the signals. The current dynamic sequence of node A is: =[10.0,12.0,15.0,14.0,13.0] (unit: mA) The current dynamic sequence of the node B sequence is =[10.0,10.0,12.0,11.0,10.0] (unit: mA). By calculation, when =0.001 seconds, =0.814, The maximum value is obtained, indicating that the signal of node B is delayed by 0.001 seconds compared with that of node A.

[0035] In one embodiment, determining the synchronization strength and the shunt attenuation rate of the current signals between nodes according to the delay time includes: Calculating the reciprocal of the delay time and normalizing it to obtain the synchronization strength of the current signal between nodes; The convergence point of the path is determined by identifying the signal concentration area, and the attenuation change value is calculated according to the current signal strength of the convergence point and the current signal strength of other nodes connected to it to obtain the shunt attenuation rate.

[0036] It should be noted that, in this embodiment, the synchronization strength of the current signal between nodes is obtained by calculating the inverse of the delay time and performing normalization processing. in, is the delay time between node A and node B, The maximum delay time allowed by the system is determined based on the cabinet size and signal propagation speed. Less than Synchronization strength is a quantitative indicator of the consistency of current signal propagation time between nodes. Its essence is to infer the timing consistency of the current propagation path through the delay time. It can optimize the anomaly detection range, enhance the ability to capture dynamic anomalies, provide dual basis for fault tracing, and overcome the limitations of traditional static methods.

[0037] It should be noted that in the cabinet grounding system, a node with the highest current signal strength and stable signal fluctuations exists in the signal concentration area. This node is identified as the convergence point. After the convergence point is determined, the current signal strength of the other nodes connected to it is subtracted from the current signal strength of the convergence point to obtain the attenuation change value. The absolute value of the ratio of the attenuation change value to the current signal strength of the convergence point is used to obtain the shunt attenuation rate.

[0038] For example, in a cabinet grounding system, the cross-correlation function calculates that the delay time between nodes A and B is 0.001 seconds, with a synchronization strength of 90%. The delay time between nodes A and C is 0.005 seconds, with a synchronization strength of 30%. The signal concentration area is identified and node B is determined as the convergence point. The current signal strength of node B is 9mA, and the current strength of node C is 3mA. The shunt attenuation rate is 67%, and the current intensity of node A is 10mA. The shunt attenuation rate is found to be 10%.

[0039] In one embodiment, dividing the main flow path and the branch flow path according to the diversion attenuation rate includes: Comparing the shunt attenuation rate with a preset attenuation rate threshold, and comparing the synchronization strength with a preset synchronization strength threshold; When the shunt attenuation rate is less than or equal to the attenuation rate threshold and the synchronization strength is greater than or equal to the synchronization strength threshold, determining that the inter-node current signal transmission path is the mainstream path; When the shunt attenuation rate is greater than the attenuation rate threshold or the synchronization strength is less than the synchronization strength threshold, it is determined that the inter-node current signal transmission path is the branch path.

[0040] It should be noted that the synchronization strength indicates the synchronization of current signals between nodes, reflects the real-time nature of signal transmission, and helps identify the physical connection quality of the current path; the shunt attenuation rate quantifies the current distribution ratio and is used to locate abnormal shunts. The current in the mainstream path propagates through a low-impedance trunk line. The path is short and the impedance is uniform, the signal delay time is small, the synchronization strength is high, and the current is concentratedly transmitted, with little shunt and a low shunt attenuation rate. When the current passes through a branch path, the delay time may increase due to tortuous paths, contact impedance, or electromagnetic interference, and the synchronization strength is low. At the same time, the current is distributed to multiple branches, or the line impedance is high, and the shunt attenuation is high. Therefore, paths with high synchronization strength and low shunt attenuation are identified as mainstream paths, and paths with low synchronization strength or high shunt attenuation are determined to be branch paths.

[0041] For example, based on experimental data, the attenuation rate threshold is set to 10%, and the synchronization strength threshold is set to 80%. For example, the shunt attenuation rate from node A to node B is 10%, the synchronization strength is 90%, and the shunt attenuation rate from node B to node C is 67%, the synchronization strength is 30%. The shunt attenuation rate from node A to node B is equal to the attenuation rate threshold, and the synchronization strength is greater than the synchronization strength threshold. Therefore, node A to node B is a mainstream path. The shunt attenuation rate from node B to node C is greater than the attenuation rate threshold, and the synchronization strength is less than the synchronization strength threshold. Therefore, node B to node C is a branch path.

[0042] In step S13, the peak frequencies of the main path and the branch path are extracted by fast Fourier transform, and the peak offset of each path is obtained by combining a preset frequency threshold, including: Performing fast Fourier transform on the current dynamic sequences of the main path and the branch path respectively, converting the time domain signals into frequency domain signals, and obtaining a spectrum diagram; Calculate the amplitude of each frequency component in the spectrum graph; Identifying the frequency point with the largest amplitude in the spectrum graph as the peak frequency; When the peak frequency exceeds a preset frequency threshold, a difference between the peak frequency and the frequency threshold is calculated to obtain a peak offset.

[0043] It should be noted that in this embodiment, a fast Fourier transform is performed on the current dynamic sequence, and the time domain current signal is converted into a spectrum diagram. The current signal of the current dynamic sequence can be decomposed into a superposition of sine waves of different frequencies, and a mainstream spectrum diagram and a branch spectrum diagram are obtained respectively. Specifically, a fast Fourier transform is performed on the current dynamic sequence of the mainstream path and the branch path, wherein the sampling frequency is 1000 Hz and the number of sampling points is 1024, to obtain a mainstream spectrum diagram and a branch spectrum diagram. Normal current signals usually have stable frequency components, while abnormal conditions may cause changes in frequency components or the emergence of new frequency components. By using fast Fourier transform to obtain a spectrum diagram, these abnormal frequency components can be quickly identified.

[0044] It should be noted that in this embodiment, the amplitude of each frequency component in the spectrum is calculated. Specifically, the amplitude is calculated for the complex value of each frequency point. The amplitude can identify high-frequency noise or low-frequency fluctuations in the current signal and capture dynamic abnormal characteristics.

[0045] It should be noted that the frequency point with the largest amplitude in the spectrum is identified as the peak frequency. The peak frequency includes both the mainstream peak frequency and the branch peak frequency. The mainstream peak frequency is used to determine whether the current is within the normal operating frequency range. The peak frequency reflects the core mode of current oscillation during normal operation or abnormal conditions. Specifically, the peak frequency of normal ground current is typically stable near the operating frequency (50 Hz), while leakage current caused by insulation degradation will generate high-frequency harmonic components.

[0046] Exemplarily, the amplitude of each frequency component in the mainstream spectrum and the branch spectrum is calculated respectively. The frequency point with the largest amplitude in the mainstream spectrum is identified as 50 Hz as the mainstream peak frequency, and the frequency point with the largest amplitude in the branch spectrum is identified as 60 Hz as the branch peak frequency.

[0047] It should be noted that, in this embodiment, when the peak frequency exceeds the preset frequency threshold, the difference between the peak frequency and the frequency threshold is calculated to obtain the peak offset. The peak offset includes the mainstream peak offset and the branch peak offset. The peak offset quantifies the degree of frequency abnormality and provides a quantitative basis for fault classification and positioning.

[0048] Exemplarily, the frequency threshold can be set to 50 Hz, the mainstream peak frequency in the mainstream spectrum diagram is 50 Hz, the branch peak frequency in the branch spectrum diagram is 60 Hz, the mainstream peak frequency is equal to the frequency threshold, the mainstream path has no peak offset, the branch peak frequency is greater than the frequency threshold, the difference between the branch peak frequency and the frequency threshold is calculated, and the branch peak offset is 10 Hz.

[0049] In step S14, the peak offset is first used to monitor the path currents of the main path and the branch path. Specifically, monitoring resources are dynamically allocated based on the peak offset, prioritizing high-risk paths and improving detection efficiency. This is particularly applicable to complex, multi-branch cabinet grounding systems, significantly improving the fault detection speed and location accuracy of the cabinet grounding system, and reducing operation and maintenance costs.

[0050] In one embodiment, when the path current exceeds the abnormal trigger threshold, extracting the time series data within the reconstruction time window of the path to obtain the abnormal time period includes: When the path current exceeds the abnormal trigger threshold, the time series data of all nodes of the path within the reconstruction time window are retrieved from the memory; The standard deviation value is calculated for the time series data. When the standard deviation value of consecutive time intervals exceeds the fluctuation threshold, the consecutive intervals are merged to obtain an abnormal time period.

[0051] It should be noted that when the path current exceeds the abnormality triggering threshold, the time series data of all nodes of the path within the reconstruction time window are retrieved from the memory. Among them, the reconstruction time window includes a preset fixed window and an adaptive window. The preset fixed window is a time range pre-set according to the system design, and the adaptive window is dynamically adjusted according to the dynamic characteristics of the signal. Specifically, only when the abnormality is triggered, the time series data is retrieved instead of continuously storing the full amount of data, providing a high-quality data foundation for subsequent tracing, saving memory resources, and suitable for long-term monitoring systems.

[0052] For example, based on experimental data, the abnormality trigger threshold is set at 1 Hz, and the path currents of the main path and the branch path are monitored. The branch peak offset is set to 10 Hz. When the abnormality trigger threshold is exceeded, the time series data within the reconstruction time window is extracted. The fixed window is 1 second, and the adaptive window is dynamically adjusted by 0.5 seconds based on the signal fluctuation frequency. The path currents from 0 to 1.4 seconds are obtained.

[0053] It should be noted that in this embodiment, the standard deviation of the time series data is calculated by time interval to measure the current fluctuation amplitude. When the standard deviation exceeds the fluctuation threshold, the interval is marked as an abnormal candidate interval, and multiple consecutive abnormal candidate intervals are merged to obtain the abnormal time period. Specifically, the abnormal time period is shortened from hours to minutes, significantly improving operation and maintenance efficiency and accurately capturing abnormal time periods. Through dynamic window and standard deviation analysis, misjudgments caused by transient interference are avoided, and only the continuous time period of true abnormality is extracted.

[0054] For example, the fluctuation threshold can be set to 0.5 mA. The standard deviation of the current within each 0.5-second time series is calculated, resulting in a standard deviation of 0.1414 mA within the time interval 0.0-0.5 seconds, a standard deviation of 2.5 mA within the time interval 0.5-1.0 seconds, and a standard deviation of 0.1414 mA within the time interval 1.0-1.5 seconds. The standard deviation of 2.5 mA within the time interval 0.5-1.0 seconds exceeds the fluctuation threshold, resulting in an abnormal time period of 0.5-1.0 seconds.

[0055] In step S15, the process of tracing back the historical current data in the memory during the abnormal time period to generate a dynamic flow direction vector and obtain a complete flow trajectory of the abnormal current includes: According to the abnormal time period, extracting the historical current value of the corresponding time period from the memory; Calculate the direction and intensity of current flow between nodes and generate dynamic flow vectors; By analyzing the changing trend of the dynamic flow direction vector, the complete flow trajectory of the abnormal current is obtained.

[0056] It should be noted that, based on the abnormal time period, historical current values corresponding to the time period are extracted from memory. Current values must be aligned by timestamp to ensure the correlation of current values at different nodes at the same time. This time-series correlation eliminates transient interference (such as random noise) and improves fault location accuracy.

[0057] For example, in a cabinet grounding system, there are four nodes A, B, C, and D. The connection relationship between the nodes is that node A is connected to nodes B and C, node B is connected to node D, and node C is connected to node D. Based on the abnormal time period of 0.5-1.0 seconds, historical current values within the corresponding time period are extracted from the memory.

[0058] It should be noted that the current flow direction is determined by the temporal relationship between current changes at adjacent nodes. The integral of the current change within the sliding window is used as the flow intensity. A dynamic flow direction vector set is generated for the abnormal time period window. The vector elements are (source node, target node, flow intensity). The resulting dynamic flow direction vector represents the current direction and magnitude. In cabinet grounding current detection, the dynamic flow direction vector quantifies the current flow direction and intensity changes, intuitively reflecting the actual current distribution. Its role is to clarify the propagation path of abnormal current and identify the abnormal starting point through analysis in spatial and temporal dimensions.

[0059] There are four nodes A, B, C, and D in the cabinet grounding system. In the abnormal time window of 0.5-1.0 seconds, the flow intensity from node A to node B is 1mA, and the flow direction is from node A to node B. The dynamic flow direction vector is obtained. =(A,B,1A), the flow intensity from node A to node C is 2mA, and the flow direction is from node A to node C, and the dynamic flow direction vector is obtained. =(A,C,1A), the flow intensity from node D to node B is 1mA, and the flow direction is from node B to node D, and the dynamic flow direction vector is obtained. =(B,D,1A), the flow intensity from node C to node D is 1mA, the flow direction is from node C to node D, and the dynamic flow direction vector is obtained =(C,D,1A).

[0060] It should be noted that by analyzing the changing trends of the dynamic flow direction vectors and connecting them in chronological order to form a current propagation chain, the complete flow trajectory of the abnormal current is obtained. This enables real-time tracking of the propagation path of the abnormal source and avoids cascading failures caused by grounding failure.

[0061] For example, there are four nodes A, B, C, and D in the cabinet grounding system. The dynamic flow direction vector is analyzed. =(A,B,1A), =(A,C,1A), =(B,D,1A) and =(C,D,1A), starting from node A, the current flows from node A to node B, and then from node B to node D. The current flows from node A to node C, and then from node C to node D. The complete flow trajectory of the abnormal current is obtained. The mainstream path is A → B → D, and the branch path is A → C → D.

[0062] In step S16, the current change position between nodes is located according to the complete flow trajectory for the flow direction mutation point as the preliminary coordinate position of the abnormal source, including: Calculating the current change rate of the node and its connected adjacent nodes based on the complete flow trajectory, and marking the node as a flow direction mutation point when the current change rate exceeds a preset current change threshold; When the flow direction mutation point is detected, the current change position is determined in combination with the dynamic flow direction vector corresponding to the flow direction mutation point, and is used as the preliminary coordinate position of the abnormal source.

[0063] It should be noted that the current change rate of a node and its connected adjacent nodes is calculated. When the current change rate exceeds a preset current change threshold, it is marked as a flow mutation point. Specifically, the absolute value of the current difference between adjacent nodes is calculated, and the quotient is taken from a fixed time interval to obtain the current change rate of the adjacent nodes. The current change rate reflects the energy attenuation or enhancement characteristics of the current in the flow. When the current change rate exceeds a preset current change threshold, it is marked as a flow mutation point. Specifically, the preset current change threshold can be adjusted according to the system characteristics to balance the sensitivity and false alarm rate. After marking the mutation point, the investigation scope can be quickly narrowed to guide subsequent feature analysis. It is suitable for complex topologies and solves the problem of multi-path current being difficult to trace.

[0064] For example, the current change threshold can be set to 3A / S. There are two adjacent nodes A and node B in the cabinet grounding system. The current difference between node A and node B is 5.0mA. The fixed time interval is 1 second. 5.0 / 1 results in a current change rate of 5A / s per unit distance between node A and node B. When the current change threshold is exceeded, node B is marked as a flow direction mutation point.

[0065] It should be noted that in this embodiment, when a flow direction mutation point is detected, the current change location is determined by combining the dynamic flow direction vector corresponding to the flow direction mutation point, and used as the preliminary coordinate location of the abnormal source. This is applicable to complex topologies and solves the problem of multi-path current tracing difficulties.

[0066] For example, in the cabinet grounding system including node A (0, 0, 0) and node B (1, 0, 0), the flow direction mutation points are detected as nodes A and B, and the center of AB (0.5, 0, 0) is taken, combined with the dynamic flow direction vector =(A,B,1A), determine the current change location as node B, and determine the physical position coordinates of node B (1,0,0) as the preliminary coordinate position of the abnormal source.

[0067] In step S17, the sensor current data of the corresponding node is extracted according to the preliminary coordinate position, noise interference is eliminated, and feature analysis is performed to obtain the accurate current feature of the abnormal source, including: Extracting sensor current data of corresponding nodes according to the preliminary coordinate position; Eliminating noise interference from the sensor current data in combination with the reconstruction error range to obtain effective current data; Calculating the mean and variance of the effective current data to obtain the current time domain characteristics of the abnormal source; Performing frequency domain analysis on the effective current data using a fast Fourier transform algorithm to obtain the current frequency domain characteristics of the abnormal source; The amplitude, frequency and phase information in the current time domain characteristics and the current frequency domain characteristics are used as the precise current characteristics of the abnormal source.

[0068] It should be noted that, in this embodiment, the sensor current data of the corresponding node is extracted according to the preliminary coordinate position, wherein the sensor current data includes time domain signals and frequency domain signals, which can improve the accuracy of abnormal source positioning, narrow the scope of the abnormal source, and provide detailed data support for accurate current feature analysis.

[0069] It should be noted that a wavelet threshold denoising algorithm is used, the db4 wavelet basis function is selected, and the number of decomposition layers is 5 to separate the high-frequency noise and low-frequency features in the signal. By setting the wavelet threshold to 0.15, the system performs soft threshold processing on the high-frequency component to eliminate noise interference and retain the valid signal. The reconstruction error range is set. When the reconstructed signal of a frequency band exceeds the reconstruction error range, the frequency band is judged to be noise and set to zero to obtain valid current data. The reconstruction error range can be set to ±0.01mA. Electromagnetic interference often exists in the cabinet grounding system. Direct analysis of the original data may lead to misjudgment. In combination with the reconstruction error range, the noise interference of the sensor current data is eliminated to retain the true fault characteristics.

[0070] For example, the sensor current data of node B fluctuates dramatically within 1 second, ranging from [0.5A, 1.8A, 0.6A, 2.1A, ...]. After denoising, the low-frequency trend component [0.5A, 0.7A, 0.6A, 0.8A, ...] is retained, while the spikes caused by high-frequency noise are eliminated.

[0071] It should be noted that in this embodiment, the effective current data is calculated by calculating the mean and variance to obtain the current time-domain characteristics of the abnormal source. The mean reflects the average amplitude level of the current and is used to detect whether there is a continuous leakage or abnormal ground resistance. The variance reflects the intensity of current fluctuations and is used to detect intermittent faults. By combining these two parameters, the stability and fluctuation pattern of the abnormal source can be quickly identified.

[0072] It should be noted that the effective current data is analyzed in the frequency domain using a fast Fourier transform algorithm to obtain the current frequency domain signature of the abnormal source. By converting the current signal from the time domain to the frequency domain, harmonic components outside the normal spectrum range, such as high-frequency noise and low-frequency oscillation, are detected. Current frequency domain signatures are an important supplement to current time domain analysis, enabling fault types to be distinguished by differences in spectral distribution. They are particularly suitable for detecting latent, periodic, or high-frequency transient anomalies.

[0073] It should be noted that in this embodiment, the amplitude, frequency, and phase information in the current time-domain characteristics and the current frequency-domain characteristics are used as the precise current characteristics of the abnormal source. Specifically, through the combined analysis of the current time-domain characteristics and the current frequency-domain characteristics, the stability and fluctuation pattern of the abnormal source can be quickly identified.

[0074] For example, the coordinates of the anomaly source were initially located at node B. Current data from node B was extracted and noise interference was removed from the sensor current data within a reconstruction error range of ±0.01 mA to obtain effective current data. The mean and variance of this effective current data were calculated to obtain the time-domain characteristics of the anomaly source, with a mean of 4.25 mA and a variance of 0.0292. A fast Fourier transform (FFT) algorithm was used to perform frequency-domain analysis on the effective current data, yielding the frequency-domain characteristics of the anomaly source with an amplitude of 4.8 dB, a frequency of 55 Hz, and a phase of 20°, providing the precise current characteristics of the anomaly source.

[0075] In step S18, the precise current characteristics are compared with the preset normal current characteristics to obtain a comparison result, the current vector is projected onto the three-dimensional grid model of the cabinet grounding system according to the comparison result, and the diffusion path of the leakage current is simulated to determine the small leakage point, including: Comparing the precise current characteristics with a preset normal current characteristic, when the amplitude exceeds an amplitude threshold and the frequency offset exceeds an offset threshold, extracting the corresponding dynamic flow direction vector, determining the three-dimensional coordinates of each node in the cabinet grounding system, and obtaining a current vector; Projecting the current vector into the three-dimensional grid model; Combined with the Euler-Lagrangian particle tracing method, the diffusion path of the leakage current is simulated. When the current density exceeds a critical value at a certain coordinate, the coordinate point is determined to be a small leakage point.

[0076] It should be noted that the precise current characteristics are compared with the preset normal current characteristics, and the comparison results include the amplitude exceeding the amplitude threshold and the frequency offset exceeding the offset threshold, the amplitude not exceeding the amplitude threshold or the frequency offset not exceeding the offset threshold. When the amplitude exceeds the amplitude threshold and the frequency offset exceeds the offset threshold, the corresponding dynamic flow direction vector is extracted; when the amplitude does not exceed the amplitude threshold or the frequency offset does not exceed the offset threshold, it is determined to be a normal fluctuation, and the current characteristics of the path are marked as "normal state", and the leakage point location process is not triggered to avoid misjudgment due to instantaneous interference or normal fluctuations. Comparing the precise current characteristics with the preset normal characteristics can more comprehensively identify abnormal situations and avoid misdiagnosis due to misjudgment of a single feature. The amplitude threshold is set according to the fluctuation range of the current amplitude during normal operation, and is used to distinguish normal fluctuations from abnormal situations. The frequency offset threshold is used to identify abnormal changes in current frequency.

[0077] Specifically, when the amplitude exceeds the amplitude threshold and the frequency offset exceeds the offset threshold, the corresponding dynamic flow direction vector is extracted, the three-dimensional coordinates of each node in the cabinet grounding system are determined, and the current vector is obtained. The current vector contains the direction and magnitude information of the current and can describe the flow of the current in three-dimensional space. The flow of current can be converted from an abstract current characteristic into a specific three-dimensional space vector, and the source and flow path of the abnormal current can be accurately located, which helps to quickly determine the fault location and reduce the troubleshooting time. Among them, the three-dimensional coordinates of each node are pre-established according to the structure of the cabinet grounding system and the layout of the sensor. The three-dimensional coordinates are determined based on the physical layout of the cabinet and the actual installation position of the sensor with the geometric center of the cabinet as the origin, and are used to accurately locate each node in three-dimensional space.

[0078] For example, the normal current feature can be set to an amplitude range of [3.5, 4.5] dB, a frequency range of [49, 51] Hz, a phase range of [0, 10]°, and an offset threshold of 2 Hz. The precise current feature of node B is extracted, with a mean of 4.25 mA, a variance of 0.0292, an amplitude of 4.8 dB, a frequency of 55 Hz, and a phase of 20°. The precise current feature is compared with the preset normal current feature. If the amplitude exceeds the amplitude threshold and the frequency offset exceeds the offset threshold, the corresponding dynamic flow direction vector is extracted. =(A,B,1A / s), according to the layout of the cabinet grounding system, determine the three-dimensional coordinates of node B (1, 2, 3), and obtain the current vector: the current intensity is 4.8mA, the current flows from node A to B, and the direction vector is (0,0,1).

[0079] It should be noted that the current vector is projected onto the 3D grid model. Specifically, the 3D grid model is a pre-established 3D model of the cabinet grounding system, used to visualize and analyze current flow. This model divides the cabinet grounding system into multiple grid cells, each corresponding to a set of nodes. The generated current vector is mapped onto the 3D grid model, with each current vector corresponding to a grid cell. This allows the current flow path and intensity to be visually displayed in the 3D model, allowing the identification of interference signals from adjacent nodes.

[0080] It should be noted that the Euler-Lagrangian particle tracking method is combined to simulate the diffusion path of the leakage current. In the three-dimensional grid model, the initial coordinate position of the abnormal source is used as the current source, and a preset leakage current value is injected. These particles move according to the direction and magnitude of the current vector. When the current density exceeds the critical value at a certain coordinate, the coordinate point is determined to be a small leakage point. The critical value is based on the statistical significance principle 3 Criteria, set the critical value to ,in, is the average current density under normal conditions, The standard deviation of the current density under normal conditions. By simulating the diffusion path of the leakage current, the location of small leaks can be more accurately determined, increasing the detection sensitivity of small leaks by more than 10 times and improving the accuracy of fault diagnosis.

[0081] For example, in a normal area, the particle density is uniformly distributed, with a mean density of 50 particles per unit volume and a standard deviation of 5. Therefore, the critical value can be set to 65. The three-dimensional coordinates of node B are (1, 2, 3), the current vector is 4.8 mA, the current flows from node A to node B, and the direction vector is (0, 0, 1). The current vector is projected onto the three-dimensional grid model from (1, 2, 3) to (1, 2, 4). Using the Euler-Lagrangian particle tracing method, a preset leakage current value of 0.1 mA is injected at the node B position. Based on the current vector and the current field in the three-dimensional grid model, the motion trajectory of each particle is observed and the current density of each grid point is calculated. The current density near node B (1, 2, 4) increases significantly to 70 particles per unit volume, exceeding the critical value, and the coordinate point is determined to be a small leakage point.

[0082] In summary, the present invention discloses a cabinet grounding current detection method and system based on a current sensor. By real-time monitoring of the current value of each node in the cabinet grounding system, it can capture slight changes in the current in a timely manner, solving the problem of insufficient sensitivity to dynamic change monitoring in the prior art. By dividing the mainstream path and the branch path, the distribution of current in the cabinet grounding system can be understood more accurately, and the high-energy, low-attenuation mainstream path is analyzed first to reduce the impact of branch path noise on tracing. It also combines time domain (dynamic sequence), frequency domain (peak offset), and spatial domain (three-dimensional grid) analysis to avoid the limitations of single-dimensional information. At the same time, the dynamic flow direction vector constructs a causal chain by correlating historical data with real-time anomalies, avoiding misjudgment based solely on instantaneous data. The spatial projection of the three-dimensional model further binds the electrical characteristics to the physical location, improving positioning accuracy, thereby improving the accuracy of anomaly tracing.

[0083] Reference Figure 2 A second embodiment of the present invention provides a cabinet grounding current detection system based on a current sensor, comprising: The current value acquisition module is used to obtain the current value of each node in the cabinet grounding system in real time through sensors, and record the current value collected every second to obtain a current dynamic sequence; a path division module, configured to calculate the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determine the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and divide the main path and the branch path according to the shunt attenuation rate; A peak module is used to extract the peak frequencies of the main path and the branch path through fast Fourier transform, and obtain the peak offset of each path in combination with a preset frequency threshold; an abnormal time period acquisition module, configured to monitor the path currents of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extract the time series data within the reconstruction time window of the path to obtain the abnormal time period; A backtracking module is used to backtrack the historical current data in the memory according to the abnormal time period, generate a dynamic flow direction vector, and obtain a complete flow trajectory of the abnormal current; A positioning module is used to locate the current change position between nodes according to the complete flow trajectory and the flow direction mutation point as the preliminary coordinate position of the abnormal source; A feature analysis module is used to extract sensor current data of the corresponding node according to the preliminary coordinate position, eliminate noise interference and perform feature analysis to obtain accurate current characteristics of the abnormal source; The leakage point output module is used to compare the precise current characteristics with the preset normal current characteristics to obtain a comparison result, project the current vector onto the three-dimensional grid model of the cabinet grounding system according to the comparison result, and simulate the diffusion path of the leakage current to determine the tiny leakage point.

[0084] It should be noted that the cabinet grounding current detection system based on a current sensor provided in an embodiment of the present invention is used to execute all the process steps of the cabinet grounding current detection method based on a current sensor in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0085] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in each of the above-mentioned embodiments of the cabinet grounding current detection method based on the current sensor are implemented, for example: Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments, such as the peak module, are realized.

[0086] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0087] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0088] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0089] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0090] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0091] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0092] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A cabinet grounding current detection method based on a current sensor, characterized in that: include: The current value of each node in the cabinet grounding system is obtained in real time through sensors, and the current dynamic sequence is obtained by recording the current value collected every second; Calculating the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determining the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and dividing the main path and the branch path according to the shunt attenuation rate; Extracting the peak frequencies of the main path and the branch path by fast Fourier transform, and obtaining the peak offset of each path by combining a preset frequency threshold; Monitoring the path currents of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extracting time series data within a reconstruction time window of the path to obtain an abnormal time period; According to the abnormal time period, the historical current data in the memory is traced back to generate a dynamic flow direction vector to obtain a complete flow trajectory of the abnormal current; According to the complete flow trajectory, the current change position between the nodes is located for the flow direction mutation point as the preliminary coordinate position of the abnormal source; Extracting sensor current data of the corresponding node according to the preliminary coordinate position, eliminating noise interference and performing feature analysis to obtain accurate current characteristics of the abnormal source; The precise current characteristics are compared with the preset normal current characteristics to obtain a comparison result. According to the comparison result, the current vector is projected onto the three-dimensional grid model of the cabinet grounding system, and the diffusion path of the leakage current is simulated to determine the tiny leakage point.

2. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: Determining the synchronization strength and the shunt attenuation rate of the current signals between nodes according to the delay time includes: Calculating the reciprocal of the delay time and normalizing it to obtain the synchronization strength of the current signal between nodes; The convergence point of the path is determined by identifying the signal concentration area, and the attenuation change value is calculated according to the current signal strength of the convergence point and the current signal strength of other nodes connected to it to obtain the shunt attenuation rate.

3. The cabinet grounding current detection method based on current sensor according to claim 2, characterized in that: The dividing of the main flow path and the branch flow path according to the diversion attenuation rate includes: Comparing the shunt attenuation rate with a preset attenuation rate threshold, and comparing the synchronization strength with a preset synchronization strength threshold; When the shunt attenuation rate is less than or equal to the attenuation rate threshold and the synchronization strength is greater than or equal to the synchronization strength threshold, determining that the inter-node current signal transmission path is the mainstream path; When the shunt attenuation rate is greater than the attenuation rate threshold or the synchronization strength is less than the synchronization strength threshold, it is determined that the inter-node current signal transmission path is the branch path.

4. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: The extracting the peak frequencies of the mainstream path and the branch path by fast Fourier transform and obtaining the peak offset of each path by combining a preset frequency threshold comprises: Performing fast Fourier transform on the current dynamic sequences of the main path and the branch path respectively, converting the time domain signals into frequency domain signals, and obtaining a spectrum diagram; Calculate the amplitude of each frequency component in the spectrum graph; Identifying the frequency point with the largest amplitude in the spectrum graph as the peak frequency; When the peak frequency exceeds a preset frequency threshold, a difference between the peak frequency and the frequency threshold is calculated to obtain a peak offset.

5. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: When the path current exceeds the abnormal trigger threshold, extracting the time series data within the reconstruction time window of the path to obtain the abnormal time period includes: When the path current exceeds the abnormal trigger threshold, the time series data of all nodes of the path within the reconstruction time window are retrieved from the memory, where the reconstruction time window includes a preset fixed window and an adaptive window; The standard deviation value is calculated for the time series data. When the standard deviation value of consecutive time intervals exceeds a fluctuation threshold, the consecutive time intervals are merged to obtain an abnormal time period.

6. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: The step of tracing back the historical current data in the memory according to the abnormal time period to generate a dynamic flow direction vector and obtain a complete flow trajectory of the abnormal current includes: According to the abnormal time period, extracting the historical current value of the corresponding time period from the memory; Calculate the direction and intensity of current flow between nodes and generate dynamic flow vectors; By analyzing the changing trend of the dynamic flow direction vector, the complete flow trajectory of the abnormal current is obtained.

7. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: The method of locating the current change position between nodes based on the complete flow trajectory and the flow direction mutation point as the preliminary coordinate position of the abnormal source includes: Calculating the current change rate of the node and its connected adjacent nodes based on the complete flow trajectory, and marking the node as a flow direction mutation point when the current change rate exceeds a preset current change threshold; When the flow direction mutation point is detected, the current change position is determined in combination with the dynamic flow direction vector corresponding to the flow direction mutation point, and is used as the preliminary coordinate position of the abnormal source.

8. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: The step of extracting sensor current data of the corresponding node according to the preliminary coordinate position, removing noise interference and performing feature analysis to obtain accurate current features of the abnormal source includes: Extracting sensor current data of corresponding nodes according to the preliminary coordinate position; Eliminating noise interference from the sensor current data in combination with the reconstruction error range to obtain effective current data; Calculating the mean and variance of the effective current data to obtain the current time domain characteristics of the abnormal source; Performing frequency domain analysis on the effective current data using a fast Fourier transform algorithm to obtain the current frequency domain characteristics of the abnormal source; The amplitude, frequency and phase information in the current time domain characteristics and the current frequency domain characteristics are used as the precise current characteristics of the abnormal source.

9. The cabinet grounding current detection method based on current sensor according to claim 1, characterized in that: The precise current characteristics are compared with preset normal current characteristics, and according to the comparison result, the current vector is projected onto a three-dimensional grid model of the cabinet grounding system, and the diffusion path of the leakage current is simulated to determine the small leakage point, including: Comparing the precise current characteristics with a preset normal current characteristic, when the amplitude exceeds an amplitude threshold and the frequency offset exceeds an offset threshold, extracting the corresponding dynamic flow direction vector, determining the three-dimensional coordinates of each node in the cabinet grounding system, and obtaining a current vector; Projecting the current vector into the three-dimensional grid model; Combined with the Euler-Lagrangian particle tracing method, the diffusion path of the leakage current is simulated. When the current density at a certain coordinate point exceeds a critical value, the coordinate point is determined to be a small leakage point.

10. A cabinet grounding current detection system based on a current sensor, characterized in that: include: The current value acquisition module is used to obtain the current value of each node in the cabinet grounding system in real time through sensors, and record the current value collected every second to obtain a current dynamic sequence; a path division module, configured to calculate the delay time of the current signal arrival between different nodes according to the current dynamic sequence, determine the synchronization strength and shunt attenuation rate of the current signal between the nodes according to the delay time, and divide the main path and the branch path according to the shunt attenuation rate; A peak module is used to extract the peak frequencies of the main path and the branch path through fast Fourier transform, and obtain the peak offset of each path in combination with a preset frequency threshold; an abnormal time period acquisition module, configured to monitor the path currents of the main path and the branch path by using the peak offset, and when the path current exceeds an abnormal trigger threshold, extract the time series data within the reconstruction time window of the path to obtain the abnormal time period; A backtracking module is used to backtrack the historical current data in the memory according to the abnormal time period, generate a dynamic flow direction vector, and obtain a complete flow trajectory of the abnormal current; A positioning module is used to locate the current change position between nodes according to the complete flow trajectory and the flow direction mutation point as the preliminary coordinate position of the abnormal source; A feature analysis module is used to extract sensor current data of the corresponding node according to the preliminary coordinate position, eliminate noise interference and perform feature analysis to obtain accurate current characteristics of the abnormal source; The leakage point output module is used to compare the precise current characteristics with the preset normal current characteristics to obtain a comparison result, project the current vector onto the three-dimensional grid model of the cabinet grounding system according to the comparison result, and simulate the diffusion path of the leakage current to determine the tiny leakage point.

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