Pantograph net off-line electromagnetic interference multichannel data acquisition and analysis method and device

Abstract

The invention discloses a bow net offline electromagnetic interference multichannel data acquisition and analysis method and device, and belongs to the technical field of electromagnetic interference test and analysis. A transient electromagnetic signal of pantograph-catenary discharge is acquired through cooperation of multiple antennas, multiple probes and multiple channels, and strict alignment of multi-source data under the same discharge event is realized through unified triggering and time base line unification; in the off-line stage, denoising, feature extraction and multi-event statistical modeling are carried out on data generated by triggering, so that multi-path and multi-mode electromagnetic characteristics of pantograph-catenary discharge and statistical laws of the multi-path and multi-mode electromagnetic characteristics of pantograph-catenary discharge are obtained; the device comprises a data acquisition unit, a synchronous trigger unit, an analysis processing unit and an interactive display unit, and can improve data processing efficiency and analysis depth while ensuring high sampling rate and broadband coverage; the device solves the problems of insufficient space coverage, poor synchronism and limited analysis capability in the existing pantograph-catenary discharge test, and is suitable for pantograph-catenary discharge mechanism research, electromagnetic interference evaluation and related engineering application.

Description

Method and apparatus for multi-channel data acquisition and analysis of offline electromagnetic interference by pantograph-catenary Technical Field

[0001] This application belongs to the field of electromagnetic interference testing and analysis technology, and specifically relates to a method and device for multi-channel data acquisition and analysis of offline electromagnetic interference from pantograph-catenary system. Background Technology

[0002] During high-speed railway operation, the electrical contact state between the pantograph and the overhead contact line is extremely complex. Influenced by factors such as environmental humidity, wind pressure disturbances, fluctuations in pantograph-catenary contact pressure, wear of the contact plate, and non-uniformity of the contact line structure, arc discharge is easily generated at the pantograph-catenary interface. The transient electromagnetic energy radiated from this arc discharge can couple to onboard electronic equipment, communication subsystems, and signaling facilities along the line, posing a potential threat to the electromagnetic safety of high-speed railways. Pantograph-catenary discharge is characterized by its steep transient response, high randomness, wide spectrum, and poor repeatability, making it difficult to comprehensively and reliably measure its transient electromagnetic behavior using traditional single-point, single-channel testing methods. Existing technologies have significant shortcomings in spatial sampling capabilities, synchronous triggering capabilities, and multi-modal characteristic analysis, failing to meet the testing requirements for multi-channel, multi-mode electromagnetic characteristics of pantograph-catenary discharge.

[0003] Traditional testing typically relies on a single point or a limited number of monitoring channels, making it impossible to simultaneously capture the transient radiation characteristics of pantograph-catenary discharges from multiple locations and directions. The lack of multi-point synchronous measurement methods makes it difficult to obtain the spatial diffusion path, coupling mode, and energy distribution of the discharge.

[0004] The rise edge of pantograph-catenary discharge is extremely steep and has abundant high-frequency components. However, traditional test equipment has limited bandwidth and sampling rate, which can easily lead to loss of high-frequency details, waveform distortion, or noise masking, thus failing to truly reflect the energy structure, peak amplitude, and spectral spread characteristics of the discharge transient.

[0005] The lack of a unified trigger and time baseline among multiple machines and channels makes it impossible to align multi-source data. At the same time, the lack of an offline analysis system suitable for a large number of events makes it difficult to extract key features, construct statistical laws, or build models that can be used for research and engineering analysis from multimodal data. Summary of the Invention

[0006] This application aims to address the testing difficulties posed by the wide range of influence, strong transient characteristics, large spectral span, and high randomness of pantograph-catenary discharges (PCDs), particularly the inability of existing methods to simultaneously acquire multi-location, multi-physical-quantity, and broadband transient information, as well as the inability to achieve time alignment among multiple devices. To this end, this application provides a pantograph-catenary discharge testing method based on multi-antenna, multi-probe, and multi-channel collaboration. By achieving multi-channel synchronous acquisition, broadband high-fidelity recording, and multi-modal characteristic analysis, it enables multi-dimensional, multi-mode, and repeatable measurement of the transient electromagnetic characteristics of PCDs, providing a reliable technical foundation for the research, evaluation, and application of complex transient interference behavior.

[0007] This application provides a method for multi-channel data acquisition and analysis of pantograph-catenary offline electromagnetic interference, comprising: connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; setting up a master triggering device, which sends a sampling freeze command to the slave devices when a trigger occurs, calibrating the sampling rate of each slave device to be consistent with its internal clock, and storing the data generated by the trigger in units of events; parsing the acquired event data, extracting multi-dimensional features, and performing statistical analysis; and displaying the acquired waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, supporting multi-event comparative analysis, and allowing users to configure parameters.

[0008] Furthermore, the step of connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions also includes: unified monitoring and management of multiple antennas and electromagnetic probes, establishing a long-term stable communication link; when an acquisition command is issued, sending a unified parameter configuration to each data acquisition device, the parameter configuration including sampling rate, channel switch, bandwidth mode, triggering method, and sampling depth; and when the data acquisition device receives the acquisition command, it enters a ready-to-trigger state.

[0009] Furthermore, the setting of the master triggering device includes: setting any data acquisition device as the master triggering device, configuring the same triggering mode and delay parameters for the slave devices, configuring the master triggering device as a high-sensitivity threshold triggering mode, capturing the steep rise edge current, voltage or electromagnetic field change generated by the discharge, and immediately latching the trigger time and setting the trigger status register when the trigger comparator inside the master triggering device detects that the signal exceeds the threshold.

[0010] Furthermore, the step of sending a sampling freeze command to the slave devices when a trigger occurs, calibrating the sampling rate of each slave device against its internal clock, and storing the triggered data in units of events includes: when a trigger occurs, sending the internally stored original sampling waveform to the host computer in the form of a binary data stream, and sending a sampling freeze command to the slave devices through the host computer; calibrating the sampling rate of each slave device against its internal clock, and correcting time drift through a periodic verification mechanism; storing the triggered data in units of events, each event including multiple devices, multiple channels, and multiple data segments, wherein the data segments include the sampling waveform, sampling rate, trigger mode, trigger point position, channel gain, timestamp, device serial number, and operator configuration.

[0011] Furthermore, the process of parsing the collected event data, extracting multidimensional features, and performing statistical analysis includes: reading and parsing the collected event data, performing denoising, feature enhancement, and compressed storage; extracting multidimensional features of pantograph-catenary discharge based on differential, envelope extraction, integral, and peak search algorithms, and performing strict unification processing on the offline data, including resampling, trigger point alignment, filtering, and noise suppression steps; mapping transient signals to the time-frequency plane using short-time Fourier transform and wavelet transform methods to analyze the rise, decay, and expansion patterns of energy over time; and constructing a statistical model of pantograph-catenary discharge by collecting features from a large amount of event data, using histogram, distribution fitting, and probability density estimation methods to study the repetitiveness, suddenness, and spectral expansion patterns of discharge events.

[0012] Furthermore, the process of denoising the collected event data includes: using mode decomposition technology to decompose the signal read from the event data into several sub-modes, separating low-frequency trends, high-frequency noise and main energy components, performing FFT transformation on the decomposed signal to facilitate feature extraction and observation, and then reconstructing the sub-modes of the main energy components into a purified signal to obtain the denoised signal.

[0013] Furthermore, the system displays the acquired waveforms, spectrum, and analysis results through a unified user interface, supports multi-event comparative analysis, and allows users to configure parameters. These features include: displaying waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, and supporting the viewing of historical events and comparisons of multiple events; providing multiple analysis views such as event browsing, result export, and feature tables; and allowing users to set parameters such as sampling rate, channel status, sampling depth, triggering method, and device selection in the user interface.

[0014] Based on the same inventive concept, this application also provides a multi-channel data acquisition and analysis device for pantograph-catenary offline electromagnetic interference, comprising: a data acquisition unit for connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; a synchronization triggering unit for setting a master triggering device, sending a sampling freeze command to the slave devices when a trigger occurs, calibrating the sampling rate of each slave device to be consistent with its internal clock, and storing the data generated by the trigger in units of events; an analysis and processing unit for parsing the acquired event data, extracting multi-dimensional features, and performing statistical analysis; and an interactive display unit for displaying the acquired waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, supporting multi-event comparative analysis, and allowing users to configure parameters.

[0015] Furthermore, the data acquisition unit also includes: a device management unit, used for unified management of multiple antennas and electromagnetic probes, specifically including: unified monitoring and management of multiple antennas and electromagnetic probes, establishing a long-term stable communication link; when an acquisition command is issued, sending unified parameter configurations to each data acquisition device, the parameter configurations including sampling rate, channel switch, bandwidth mode, triggering method, and sampling depth; when the data acquisition device receives the acquisition command, it enters a ready-to-trigger state.

[0016] Furthermore, the device also includes a low-level communication unit, which includes a communication control unit and a multi-threaded scheduling unit, and is responsible for low-level communication, command scheduling, data transfer, and parallel computing.

[0017] Furthermore, the underlying communication unit is used to provide underlying communication support for device interaction, specifically including: interacting with the device through SCPI commands, including parameter setting, sampling control, status query and data reading functions; translating the commands generated by the upper layer logic into device-recognizable statements and sending them through the VISA / TCP-Socket channel.

[0018] Furthermore, the multi-threaded scheduling unit specifically includes: dividing the device functions into independent threads, wherein the independent threads include device control threads, data receiving threads, parsing threads, drawing threads, feature threads, frequency domain threads, time-frequency threads, compression threads, and storage threads; wherein each independent thread is driven by an independent task loop, and the independent threads exchange data through a thread-safe queue, and the execution order of the independent threads is dynamically managed through task priority, thread queue length, and system status indicators.

[0019] In another aspect, this application also provides a computing device, including: at least one processor and a memory; the memory is used to store one or more programs; when the one or more programs are executed by the one or more processors, a method for multi-channel data acquisition and analysis of offline electromagnetic interference by pantograph-catenary system as described above is implemented.

[0020] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-described method for multi-channel data acquisition and analysis of offline electromagnetic interference by a pantograph-catenary system.

[0021] Compared with existing technologies, this application has the following advantages: This application provides a method and apparatus for multi-channel data acquisition and analysis of pantograph-catenary offline electromagnetic interference, comprising: connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; setting up a master triggering device, which, when triggered, sends a sampling freeze command to the slave devices, calibrates the sampling rate and internal clock of each slave device to ensure consistency, and stores the triggered data in units of events; analyzing the acquired event data, extracting multi-dimensional features, and performing statistical analysis; displaying the acquired waveforms, spectrum, time-frequency diagrams, and analysis results through a unified user interface, supporting multi-event comparative analysis, and allowing users to configure parameters; and achieving synchronous observation of pantograph-catenary discharge transient signals through multi-antenna, multi-probe, and multi-channel collaborative acquisition, enabling simultaneous observation from different positions and directions. Each acquisition node adopts a unified trigger and time baseline consistency, ensuring strict temporal alignment of data for the same discharge event. This technology solves the problem that traditional single-point or limited-number probes cannot fully capture spatial distribution and multimodal characteristics. It supports high sampling rates and wideband channels, accurately recording the steep rising edges, high-frequency components, and wide-spectrum transient signals of pantograph-catenary discharges. Combining modal decomposition denoising and adaptive compression techniques reduces data volume while preserving key features. This approach ensures accurate capture of transient signals, providing a reliable foundation for subsequent feature extraction and multi-event analysis. It offers a unified offline analysis workflow, enabling feature extraction, time-frequency analysis, and statistical modeling of numerous discharge events. Through multi-threaded parallel processing, it achieves efficient data management and real-time visualization, including waveforms, spectra, and time-frequency plots. This technology supports the extraction of event statistical patterns and spatial distribution characteristics, providing a protective technical solution for pantograph-catenary discharge mechanism research and electromagnetic interference assessment.

[0022] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 shows a flowchart of the implementation of a pantograph-catenary offline electromagnetic interference multi-channel data acquisition and analysis method provided in this application; Figure 2 shows a structural diagram of a pantograph-catenary offline electromagnetic interference multi-channel data acquisition and analysis device provided in this application; Figure 3 shows a distributed system architecture diagram provided in this application; Figure 4 shows a device addition diagram provided in this application; Figure 5 shows a device connection diagram provided in this application; Figure 6 shows a data to be analyzed diagram provided in this application; Figure 7 shows a trigger source device control diagram provided in this application; Figure 8 shows a data statistical analysis diagram provided in this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Example 1 This application provides a method for multi-channel data acquisition and analysis of pantograph-catenary offline electromagnetic interference, as shown in Figure 1. The method includes: connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; setting a master triggering device, which sends a sampling freeze command to slave devices when a trigger occurs; calibrating the sampling rate of each slave device to be consistent with its internal clock; and storing the data generated by the trigger in units of events; parsing the acquired event data, extracting multi-dimensional features, and performing statistical analysis; and displaying the acquired waveforms, spectrum, time-frequency diagrams, and analysis results through a unified user interface, supporting multi-event comparative analysis, and allowing users to configure parameters.

[0027] 1. Multi-probe, multi-channel synchronous acquisition: Further, the step of connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions also includes: unified monitoring and management of multiple antennas and electromagnetic probes, establishing a long-term stable communication link; when an acquisition command is issued, a unified parameter configuration is sent to each data acquisition device, the parameter configuration including sampling rate, channel switch, bandwidth mode, triggering method, and sampling depth; when the data acquisition device receives the acquisition command, it enters a ready-to-trigger state.

[0028] 2.1. Master Trigger Detection and Event Triggering Further, setting the master trigger device includes: setting any data acquisition device as the master trigger device, configuring the same trigger mode and delay parameters for the slave devices, configuring the master trigger device in a high-sensitivity threshold trigger mode, capturing the steep rise edge current, voltage, or electromagnetic field change generated by discharge, and immediately latching the trigger time and setting the trigger status register when the trigger comparator inside the master trigger device detects that the signal exceeds the threshold.

[0029] 2.2. Cross-Device Sampling Freeze and Time Consistency Further, when a trigger occurs, a sampling freeze command is sent to the slave devices, the sampling rate of each slave device is calibrated to be consistent with its internal clock, and the data generated by the trigger is stored in units of events, including: when a trigger occurs, the original sampling waveform stored internally is sent to the host computer in the form of a binary data stream, and the host computer sends a sampling freeze command to the slave devices; the sampling rate of each slave device is calibrated to be consistent with its internal clock, and time drift is corrected through a periodic verification mechanism.

[0030] 2.3. Event-based data acquisition stores the triggered data in units of events. Each event includes multiple devices, multiple channels, and multiple data segments. The data segments include the sampled waveform, sampling rate, trigger mode, trigger point location, channel gain, timestamp, device serial number, and operator configuration.

[0031] 3.1. Offline Data Analysis and Preprocessing Further, the analysis of the collected event data, extraction of multidimensional features, and statistical analysis include: performing denoising, feature enhancement, and compressed storage on the collected event data; further, the denoising process on the collected event data includes: using modal decomposition technology to decompose the signal into several sub-modes, separating low-frequency trends, high-frequency noise, and main energy components, performing FFT transformation on the decomposed signal to facilitate feature extraction and observation, and then reconstructing the main modes into a purified signal to reduce noise.

[0032] 3.2. Feature Extraction and Time-Frequency Analysis: Multidimensional features of pantograph-catenary discharge are calculated based on differential, envelope extraction, integral and peak search algorithms. Offline data is subjected to strict unification processing, which includes resampling, trigger point alignment, filtering and noise suppression steps. The transient signal is mapped to the time-frequency plane using short-time Fourier transform and wavelet transform methods to analyze the rise, decay and expansion modes of energy along time.

[0033] 3.3. Multi-event statistical analysis: By collecting the characteristics of a large amount of event data, a statistical model of pantograph-catenary discharge is constructed using histogram, distribution fitting, and probability density estimation methods to study the repetitiveness, suddenness, and spectral expansion of discharge events.

[0034] 4. Result Display and Interactive Configuration Furthermore, the system displays the acquired waveforms, spectrum, and analysis results through a unified user interface, supporting user parameter configuration. This includes: displaying waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, and supporting viewing historical events and comparing multiple events; providing multiple analysis views such as event browsing, result export, and feature tables; and allowing users to set parameters such as sampling rate, channel status, sampling depth, triggering method, and device selection in the user interface.

[0035] Example 2, based on the same inventive concept, provides a multi-channel data acquisition and analysis device for pantograph-catenary offline electromagnetic interference, as shown in Figure 2. It includes: a data acquisition unit for connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; a synchronization triggering unit for setting a master triggering device, which, when triggered, sends a sampling freeze command to the slave devices, calibrates the sampling rate of each slave device to be consistent with its internal clock, and stores the triggered data in units of events; an analysis and processing unit for parsing the acquired event data, extracting multi-dimensional features, and performing statistical analysis; and an interactive display unit for displaying the acquired waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, supporting multi-event comparative analysis and user parameter configuration.

[0036] This application unfolds from three interrelated directions: First, it achieves simultaneous observation of multiple locations, multiple probes, and multiple channels through a multi-device collaborative acquisition architecture to obtain the complete spatial response of pantograph-catenary discharge. Second, through unified triggering and time baseline consistency processing, it ensures precise alignment of data collected by different devices on the time axis, enabling comparison, overlay, and correlation analysis along the same event dimension. Finally, it constructs an offline data processing platform for centralized management and in-depth analysis of multi-source data, enabling the extraction of transient characteristics, spectral properties, statistical regularities, and commonalities / differences between events. These three aspects together form a complete closed loop: front-end collaborative acquisition provides full information, time alignment ensures information correlation, and offline analysis further mines information value, thus providing systematic and quantifiable technical support for pantograph-catenary discharge mechanism research and electromagnetic protection design. The specific setup is shown in Figure 3.

[0037] 1. The data acquisition unit constitutes the core of the front end of this application, responsible for the unified management of multiple measuring devices, and realizing real-time monitoring, data extraction, and event storage of multiple electromagnetic probes. This module covers a complete functional chain including device connection management, acquisition parameter configuration, data reading, waveform plotting, and event archiving.

[0038] 1.1 The Device Management and Connection Unit is responsible for establishing long-term and stable communication links with multiple data acquisition devices. At the initial stage of system operation, it automatically scans the preset address list and attempts to establish a connection, as shown in Figures 4 and 5. After establishing communication, it immediately reads basic information such as device model, number of channels, sampling range, and storage depth, and registers it in the device management table. This unit continuously and periodically probes the online status of devices, buffer usage, and internal error flags. In the event of an anomaly, it automatically executes reconnection or reset commands to ensure the long-term stability of the system.

[0039] The 1.2 Data Acquisition and Parsing Unit is responsible for parsing the received data frames. As shown in Figure 6, when an acquisition command is issued, the system sends unified parameter configurations to each device, including sampling rate, channel switch, bandwidth mode, triggering method, and sampling depth. Upon receiving the command, the device enters a wait-to-trigger state. After triggering, the device sends the internally stored raw sampled waveform to the host computer as a binary data stream. This unit obtains fields including header identifier, timestamp, vertical scale, sampling interval, trigger point position, and effective data length, and converts the raw voltage sequence of each channel into a floating-point physical quantity sequence, providing a standardized data structure for subsequent thread processing.

[0040] 1.3 The real-time waveform display unit is responsible for displaying the acquired waveforms on the interface in real time. The system adopts a double-buffering rendering mechanism. After the background parsing thread completes data updates, it delivers the complete waveform to the foreground rendering thread. The rendering thread adaptively adjusts the range of the horizontal and vertical coordinates according to the current display mode, providing interactive functions such as scaling, dragging, multi-channel overlay, and multi-device switching. To ensure smooth display, the system automatically adjusts the refresh rate to avoid blocking the data parsing thread in high-frequency acquisition scenarios.

[0041] The 1.4 Data Storage and Event Archiving unit is responsible for persistently storing the data generated by each trigger on an event-by-event basis. Each event contains multiple devices, multiple channels, and multiple data segments. The system generates a unique number for each event and writes it into a unified directory structure. In addition to the sampled waveform, the stored content also includes metadata such as sampling rate, trigger method, trigger point location, channel gain, timestamp, device serial number, and operator configuration, ensuring complete traceability for each event.

[0042] 2. The synchronous triggering unit ensures that multiple devices can perform sampling and freezing with a strictly consistent time boundary when detecting the same pantograph-catenary discharge event. It consists of three parts: a trigger source detection unit, a trigger consistency control unit, and a time baseline calibration unit.

[0043] 2.1 Trigger Source Detection Unit: Any data acquisition device can be set as the master trigger device. The master trigger device is configured in a high-sensitivity threshold trigger mode, capable of capturing steep rising edges of current, voltage, or electromagnetic field changes generated by discharge. When the trigger comparator inside the master trigger device detects that the signal exceeds the threshold, it immediately latches the trigger time and sets the trigger status register. The host computer continuously polls or monitors its trigger status, and once a trigger occurs, it immediately enters the event processing flow.

[0044] 2.2 Cross-Device Trigger Consistency Control Unit ensures that the acquisition windows of all devices remain consistent by pre-configuring the same trigger mode and delay parameters for the slave devices, as shown in Figure 7. After the master trigger device triggers, the host computer immediately sends a "sampling freeze" command to the slave devices. Since the slave devices continuously acquire data and maintain their internal circular buffers in the wait-to-trigger mode, the freeze command can lock the data content before and after the master event. Therefore, the waveforms acquired by all devices are strictly aligned in time, and their trigger points are at the same position.

[0045] 2.3 The time baseline calibration unit performs a consistency calibration on the sampling rate and internal clock of all devices before system operation, ensuring that the time base is consistent across devices. Through a periodic verification mechanism, it promptly corrects time drift issues, ensuring that the timing information collected by different devices always corresponds to the same time axis, providing a foundation for subsequent multi-channel data alignment.

[0046] 3. The analysis and processing unit performs denoising, feature enhancement, and compressed storage on the data, making it particularly suitable for wideband, high-sampling-depth scenarios. It provides deep processing capabilities such as frequency domain analysis, time-frequency analysis, feature calculation, model fitting, and statistical analysis for a large number of offline events. This includes a modal decomposition denoising unit, a feature parameter extraction and time-frequency analysis unit, and a multi-event statistical analysis and model building unit.

[0047] 3.1 The mode decomposition and denoising unit uses mode decomposition technology to decompose the signal into several sub-modes, separate low-frequency trends, high-frequency noise and main energy components, perform FFT transformation on the decomposed signal to facilitate feature extraction and observation, and then reconstruct the main modes into the purified signal, so that the noise is significantly reduced and the features are preserved.

[0048] 3.2 Feature Parameter Extraction and Time-Frequency Analysis Unit: Based on algorithms such as differential, envelope extraction, integration, and peak search, the unit calculates multidimensional features of pantograph-catenary discharge, extracting features such as peak amplitude, pulse width, rising edge slope, high-frequency energy proportion, characteristic frequency band amplitude, and energy center frequency. These features can be used for classification, source tracing, and statistical modeling. Strict unification processing is performed on offline data, including resampling, trigger point alignment, filtering, and noise suppression. Subsequently, short-time Fourier transform and wavelet transform methods are used to map the transient signal to the time-frequency plane, analyzing the rise, decay, and expansion patterns of energy over time.

[0049] 3.3 Multi-event statistical analysis and model building unit: By collecting the characteristics of a large number of events, the unit uses methods such as histogram, distribution fitting, and probability density estimation to build a statistical model of pantograph-catenary discharge, as shown in Figure 8, to study the repetitiveness, suddenness and spectral expansion of discharge events.

[0050] 4. The interactive display unit provides a unified interface for data acquisition, display, analysis and management. It displays complete data content through dynamic curves, spectrum, time-frequency graphs and event lists. It consists of a parameter configuration interface unit, a data display unit and an analysis view organization unit.

[0051] 4.1 The parameter configuration interface unit allows users to set parameters such as sampling rate, channel status, sampling depth, triggering method, and device selection in the interface.

[0052] 4.2 The data display unit supports real-time plotting of waveforms, spectra, and analysis results, and also supports viewing historical events and comparing multiple events.

[0053] 4.3 The analysis view organization unit supports providing multiple views such as event browsing, result export, feature tables, and time-frequency graphs, making data analysis more intuitive.

[0054] 5. In addition, this device also includes a low-level communication unit, which is responsible for core operations such as low-level communication, command scheduling, data transfer, and parallel computing. It is the key to the system's high real-time performance and high throughput, and includes a communication control unit and a multi-threaded scheduling unit.

[0055] 5.1 The communication control unit provides underlying communication support for device interaction. The system interacts with the device via SCPI commands, including functions such as parameter setting, sampling control, status query, and data reading. The communication control unit translates the commands generated by the upper-layer logic into device-recognizable statements and sends them through the VISA / TCP-Socket channel. This unit has response timeout management, command queue scheduling, automatic retransmission, and connection recovery logic to ensure the reliability of command interaction during long-term operation.

[0056] 5.2 The multi-threaded scheduling unit divides system functions into independent threads, including device control threads, data receiving threads, parsing threads, plotting threads, feature threads, frequency domain threads, time-frequency threads, compression threads, and storage threads, to ensure continuous processing of high-sampling-rate data. Each thread is driven by an independent task loop, and threads exchange data through a thread-safe queue. The scheduling unit dynamically manages the thread execution order based on indicators such as task priority, thread queue length, and system status to avoid data backlog and interface lag.

[0057] Example 3: Based on the same inventive concept, this application also provides an electronic device. The electronic device of this application embodiment includes at least one processor and at least one storage medium electrically connected to the processor. The storage medium is electrically connected to the processor, wherein the storage medium stores instructions executable by at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0058] Example 4 Based on the same inventive concept, this application also provides a storage medium storing instructions executable by at least one processor, the instructions being executed by at least one processor to enable at least one processor to perform the method described above.

[0059] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for offline electromagnetic interference multi-channel data acquisition and analysis using a pantograph-catenary system, characterized in that, include: Multiple antennas and electromagnetic probes are connected to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions. Configure the master trigger device. When a trigger occurs, send a sampling freeze command to the slave devices, calibrate the sampling rate of each slave device to be consistent with the internal clock, and store the data generated by the trigger in units of events. The collected event data is parsed, multidimensional features are extracted, and statistical analysis is performed. The system displays the acquired waveforms, spectra, time-frequency graphs, and analysis results through a unified user interface, supports multi-event comparative analysis, and allows users to configure parameters.

2. The method according to claim 1, characterized in that, The method of connecting multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions also includes: unified monitoring and management of multiple antennas and electromagnetic probes, and establishing a long-term stable communication link; when an acquisition command is issued, a unified parameter configuration is sent to each data acquisition device, including sampling rate, channel switch, bandwidth mode, triggering method, and sampling depth; when the data acquisition device receives the acquisition command, it enters a ready-to-trigger state.

3. The method according to claim 1, characterized in that, The setting of the master triggering device includes: setting any data acquisition device as the master triggering device, configuring the same triggering mode and delay parameters for the slave devices, configuring the master triggering device as a high-sensitivity threshold triggering mode, capturing the steep rise edge current, voltage or electromagnetic field change generated by discharge, and immediately latching the trigger time and setting the trigger status register when the trigger comparator inside the master triggering device detects that the signal exceeds the threshold.

4. A method according to claim 1 or 3, characterized in that, When a trigger occurs, a sampling freeze command is sent to the slave devices. The sampling rate of each slave device is calibrated to be consistent with its internal clock. The data generated by the trigger is stored in units of events. This includes: when a trigger occurs, sending the internally stored original sampling waveform to the host computer in the form of a binary data stream, and the host computer sending a sampling freeze command to the slave devices; calibrating the sampling rate of each slave device to be consistent with its internal clock, and correcting time drift through a periodic verification mechanism; storing the data generated by the trigger in units of events, each event including multiple devices, multiple channels, and multiple data segments, wherein the data segments include sampling waveform, sampling rate, trigger mode, trigger point position, channel gain, timestamp, device serial number, and operator configuration.

5. A method according to claim 1, characterized in that, The process of parsing the collected event data, extracting multidimensional features, and performing statistical analysis includes: reading and parsing the collected event data, performing denoising, feature enhancement, and compression storage; extracting multidimensional features of pantograph-catenary discharge based on differential, envelope extraction, integral, and peak search algorithms, and performing strict unification processing on the offline data, including resampling, trigger point alignment, filtering, and noise suppression steps; mapping transient signals to the time-frequency plane using short-time Fourier transform and wavelet transform methods to analyze the rise, decay, and expansion patterns of energy over time; and constructing a statistical model of pantograph-catenary discharge by collecting features from a large amount of event data, using histogram, distribution fitting, and probability density estimation methods to study the repetitiveness, suddenness, and spectral expansion patterns of discharge events.

6. A method according to claim 5, characterized in that, The process of denoising the collected event data includes: using mode decomposition technology to decompose the signal read from the event data into several sub-modes, separating low-frequency trends, high-frequency noise and main energy components, performing FFT transformation on the decomposed signal to facilitate feature extraction and observation, and then reconstructing the sub-modes of the main energy components into the purified signal to obtain the denoised signal.

7. The method according to claim 1, characterized in that, The system displays the acquired waveforms, spectrum, and analysis results through a unified user interface, supports multi-event comparative analysis, and allows users to configure parameters. These features include: displaying waveforms, spectrum, time-frequency graphs, and analysis results through a unified user interface, and supporting the viewing of historical events and comparisons of multiple events; providing multiple analysis views such as event browsing, result export, and feature tables; and allowing users to set parameters such as sampling rate, channel status, sampling depth, triggering method, and device selection in the user interface.

8. A multi-channel data acquisition and analysis device for offline electromagnetic interference of pantograph-catenary system, characterized in that, include: The data acquisition unit is used to connect multiple antennas and electromagnetic probes to the acquisition channels of multiple data acquisition devices to synchronously acquire transient electromagnetic signals of pantograph-catenary discharge from different positions and directions; the synchronization triggering unit is used to set the master triggering device, and when the trigger is generated, it sends a sampling freeze command to the slave devices, calibrates the sampling rate of each slave device to be consistent with the internal clock, and stores the data generated by the trigger in units of events; The analysis and processing unit is used to parse the collected event data, extract multidimensional features, and perform statistical analysis. The interactive display unit is used to display the acquired waveforms, spectrum, time-frequency graphs and analysis results through a unified user interface. It supports multi-event comparison analysis and allows users to configure parameters.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the pantograph-catenary offline electromagnetic interference multi-channel data acquisition and analysis method according to any one of claims 1-7.

10. An electronic device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer programs. When the processor executes the program stored in the memory, it implements the steps of the pantograph-catenary offline electromagnetic interference multi-channel data acquisition and analysis method as described in any one of claims 1-7.

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