PyQt-based optical fiber distributed sound wave sensing data visualization processing system

Abstract

The invention discloses an optical fiber distributed sound wave sensing data visualization processing system based on PyQt, which belongs to the technical field of optical fiber sensing, seismic data processing, traffic monitoring and signal processing, and comprises a data access module, a user interface module, a data conversion module, a basic processing module and an advanced analysis module. The user interface module performs visualization operation, the data conversion module realizes format unification of sound wave sensing data in different formats, and the basic processing module and the advanced analysis module jointly realize processing and visualization of the distributed sound wave sensing data. According to the system, the functions of data loading, visualization, signal processing and advanced analysis are integrated through a graphical interface, and multi-field application is supported.

Description

Technical Field

[0001] This invention relates to the fields of fiber optic sensing, seismic data processing, traffic monitoring and signal processing, and in particular to a fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt. Background Technology

[0002] Fiber optic distributed acoustic sensing (DAS) is an advanced monitoring technology that utilizes optical fibers as continuous distributed sensors. DAS technology, with its advantages of high spatial resolution, high sampling density, wide monitoring range, and cost-effectiveness, has been widely applied in various fields such as earthquake detection, environmental noise imaging, pipeline leak detection, traffic flow monitoring, structural health monitoring, and urban underground imaging. However, DAS data is characterized by its massive volume, ultra-dense sampling, and heterogeneous format, which is fundamentally different from traditional seismic records, posing significant challenges to data visualization and signal processing.

[0003] The existing DAS data processing tools have the following shortcomings: (1) Traditional tools are mostly based on command line or script programming, which has a high threshold for non-programming professionals; (2) Although there are open source DAS data processing libraries (such as DASPy, DASCore, Xdas, etc.), they lack integrated and interactive graphical interfaces, which makes it difficult to meet the needs of rapid data exploration and visualization; (3) Data format compatibility is limited, and the data formats generated by different manufacturers' equipment are highly heterogeneous, which increases the difficulty of data interoperability; (4) There is a lack of dedicated advanced analysis functions for the characteristics of DAS data, such as integrated support for automatic vehicle trajectory extraction, long-term traffic statistics and accurate seismic phase picking. Summary of the Invention

[0004] The purpose of this invention is to propose a PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system to solve the problems mentioned in the background art: in order to reduce the technical threshold of DAS data processing and improve data processing efficiency and user experience, this system integrates data loading, visualization, signal processing and advanced analysis functions through a graphical interface, supporting applications in multiple fields.

[0005] To achieve the above objectives, this invention provides a PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system, including a data processing platform for fiber optic distributed acoustic wave sensor data. The data processing platform provides visualization functions, and the modules are as follows: Data access module: It can directly access the existing communication optical fiber through the spare fiber core or independently deploy a dedicated sensing optical cable along the monitoring path to convert the corresponding optical fiber into a continuously distributed auditory sensing array. It sets up a distributed acoustic wave sensor demodulator directly connected to the optical fiber, and then demodulates the vibration data of the optical cable to obtain DAS data in different formats. The user interface module accepts user input commands and provides a visual interface that displays related images, waveforms, and spectrum analysis. It integrates the Matplotlib plotting tool and supports image export and custom resolution settings. The data conversion module reads distributed acoustic wave sensor data in different formats and converts the data into standardized DAS data and structured metadata; the standardized data is then pushed to the basic processing module for preprocessing. The basic processing module performs data preprocessing and pushes the processed data to the user interface module for content display. The advanced analysis module performs non-destructive processing on the data from the basic processing module, and then analyzes it using the interactive pickup submodule and the vehicle tracking submodule. The results are then displayed through the user interface module.

[0006] Preferably, the user interface module is developed using the PyQt5 framework and adopts a three-region layout, which includes: Data area: Loads distributed acoustic wave sensor data in different formats, and integrates operation logs and file attribute display components; Image display area: Displays a two-dimensional waterfall plot of distributed acoustic wave sensing data, provides single-channel time series and spectrum plots, and enables interactive operation; Signal processing control area: regulates the operation status of the basic processing module and the advanced analysis module, and processes the distributed acoustic wave sensing data.

[0007] Preferably, the specific content of the data area is as follows: It loads distributed acoustic wave sensing data in several mainstream file formats. The distributed acoustic wave sensing data is vibration data, and the data format is a two-dimensional array. In the two-dimensional array, the columns are channels and the actions are time. To simplify the initial access process, the operation log and file attribute display components can provide users with real-time feedback and key metadata information. The key metadata information includes start time, sampling rate, gauge length, device type, and channel spacing, which is implemented using the DASPy reader. The specific content of the image display area is as follows: The two-dimensional waterfall chart shows the time and channel number on the horizontal and vertical axes, respectively, which can display the spatiotemporal evolution characteristics of DAS data along the optical fiber; the single-channel view displays the time series waveform and spectrum of the user-selected channel, supports image scaling and panning interaction, and supports exporting custom image size and resolution. Basic processing module: Implements spatial coordinate calibration, data downsampling, data extraction, data conversion, and common-mode noise removal for standardized DAS data volumes and structured metadata; The advanced analytics module includes phase pickup and vehicle trajectory recognition, and the aforementioned signal processing techniques are selectively applied through the user interface module.

[0008] Preferably, the data conversion module reads data based on the DASPy library, supports loading distributed acoustic sensor data in different formats, converts the data into NumPy arrays through the NumPy module to achieve data standardization, and simultaneously parses metadata through Python's dictionary structure. The metadata includes data source information, acquisition parameters, and file attributes, and outputs standardized DAS data and structured metadata.

[0009] Preferably, the basic processing module receives the standardized DAS data body and structured metadata for preprocessing, as follows: Spatial coordinate calibration: Achieve precise mapping between channel number and physical spatial coordinates; Data downsampling: reducing the amount of data through extraction algorithms; Data extraction: Supports bandpass and lowpass filtering, and custom frequency range; Data conversion: Enables bidirectional conversion between strain and strain rate data; Common-mode noise removal: Attenuates spatially correlated noise common to all channels.

[0010] Preferably, the advanced analysis module receives the data processed by the basic processing module. The advanced analysis module includes an interactive picking submodule and a vehicle tracking submodule. The two submodules perform specialized analysis and generate a vehicle trajectory map with the number of channels on the horizontal axis and time on the vertical axis through the user interface module.

[0011] Preferably, the interactive picking submodule provides manual picking and event marking functions when phase arrives, and supports the export of the results of manual picking and event marking; The vehicle tracking submodule is based on Kalman filtering and smoothing algorithms to achieve automatic extraction of vehicle trajectory and estimation of motion parameters, including vehicle speed and direction.

[0012] Preferably, the specific implementation process of the Kalman filter and smoothing algorithm in the vehicle tracking submodule includes: signal preprocessing: extracting low-frequency quasi-static strain signals of 0.01-1Hz and detecting significant peak values ​​of signals in each channel; Define the state vector: Define the first... The state vector of each channel for: ; in, For the vehicle to arrive at the The time of each channel The time derivative is indicated by the superscript. Represents the transpose of a matrix; State evolution is used to describe the state changes of a vehicle between consecutive channels: ; ; in, The distance between the channels. To address process noise, observation modeling is performed, using candidate arrival times obtained from channel peak detection as observation values. The observation relationship is established through the following model: ; ; in, To observe the arrival time, To observe noise; Trajectory optimization: Vehicle state is recursively estimated using a Kalman filter, and trajectory is optimized using a Rauch-Ton-Stribel smoother; trajectory generation is performed, and the overall vehicle trajectory is drawn by generating smooth vehicle positions labeled in each channel. Starting from the initial channel, multiple distinct vehicle trajectories can be identified.

[0013] Therefore, the fiber-optic distributed acoustic wave sensing data visualization and processing system based on PyQt described above has the following advantages: (1) Lowering the barrier to entry: It provides a graphical operation that is "click and play", allowing non-programming professionals to quickly explore, process and analyze DAS data; (2) Improve data interoperability: natively supports multiple mainstream DAS data formats, and achieves unified analysis across data sources through standardized data conversion; (3) Fully integrated functions: covering the entire process from data loading and basic processing to advanced analysis (seismic phase picking, vehicle tracking), without the need to switch between multiple tools; (4) Expand application scenarios: Integrate long-term traffic statistical analysis functions to help DAS technology be deeply applied in fields such as smart transportation, urban planning, and near-surface imaging.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the main interface layout of the fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt mentioned in the embodiments of the present invention. Figure 2 This describes the specific workflow of the data loading and format conversion module in the PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system mentioned in this embodiment of the invention. Figure 3This describes the process of creating dual views in the PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system mentioned in this embodiment of the invention. Figure 4 This is an example diagram of the data processing flow of the PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system mentioned in the embodiments of the present invention; Figure 5 This is an example diagram of the interactive pickup module of the PyQt-based fiber optic distributed acoustic wave sensor data visualization processing system mentioned in the embodiments of the present invention. Figure 6 This is a diagram showing the parameter settings and trajectory extraction results of the vehicle tracking module in the PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system mentioned in this embodiment of the invention. Figure 7 This is an example diagram of the long-term traffic statistical analysis results of the PyQt-based fiber optic distributed acoustic wave sensor data visualization processing system mentioned in the embodiments of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0017] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0018] Example like Figure 1-7 As shown, this invention provides a PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system, including a data processing platform for fiber optic distributed acoustic wave sensor data. The data processing platform provides visualization functions, and the modules are as follows: Data access module: It can be directly connected to the existing communication optical fiber through the spare fiber core or a dedicated sensing optical cable can be independently deployed along the monitoring path. Using the existing optical fiber can reduce the construction cost, while using the independently deployed one can effectively improve the monitoring effect. The corresponding optical fiber is converted into a continuously distributed auditory sensing array. A distributed acoustic wave sensor demodulator is set up to be directly connected to the optical fiber. Then, the vibration data of the optical cable is demodulated to obtain DAS data in different formats. The user interface module accepts user input commands and provides a visual interface that displays related images, waveforms, and spectrum analysis. It integrates the Matplotlib plotting tool and supports image export and custom resolution settings. The data conversion module reads distributed acoustic wave sensor data in different formats and converts the data into standardized DAS data and structured metadata; the standardized data is then pushed to the basic processing module for preprocessing. The basic processing module performs data preprocessing and pushes the processed data to the user interface module for content display. The advanced analysis module performs non-destructive processing on the data from the basic processing module, and then analyzes it using the interactive pickup submodule and the vehicle tracking submodule. The results are then displayed through the user interface module.

[0019] Through the collaborative work of the above modules, the entire process of DAS data, from loading, processing, analysis to visualization, is covered.

[0020] The user interface module receives user operation commands and schedules the data loading and format conversion module to read DAS data in several formats and convert it into a standardized DAS data body and structured metadata. The standardized DAS data body and structured metadata are simultaneously output to the interactive visualization module to create dual views for display, and are also pushed to the basic processing module for non-destructive processing. The processed data from the basic processing module flows back to the interactive visualization module to update the display effect, and also serves as input data for the advanced analysis module. After completing specialized analysis through the interactive picking submodule and the vehicle tracking submodule, the advanced analysis module overlays the analysis results onto the interactive visualization module for presentation. The execution status and data results of all modules are fed back to the user through the user interface module, together realizing the entire process of DAS data reading, visualization, processing and advanced analysis.

[0021] The user interface module is developed using the PyQt5 framework. It supports cross-platform operation on Windows, macOS, and Linux. It is responsible for receiving user commands and scheduling the core functions of the data loading and format conversion module, interactive visualization module, basic processing module, and advanced analysis module. It also provides feedback on the execution results of each module to the user. Figure 1 As shown, the user interface module adopts a three-area layout, naturally guiding users through the workflow from data loading to visualization and processing. The three areas, from left to right, are: Data Area: Loads distributed acoustic wave sensor data in different formats, and integrates operation logs and file attribute display components. The specific content of the data area is as follows: It loads distributed acoustic wave sensor data in several mainstream file formats. The distributed acoustic wave sensor data is vibration data, and the data format is a two-dimensional array. In the two-dimensional array, the columns are channels, and the action is time. To simplify the initial access process, the operation log and file attribute display components can provide users with real-time feedback and key metadata information. The key metadata information includes start time, sampling rate, gauge length, device type, and channel spacing, which is implemented using the DASPy reader. Image Display Area: Displays a two-dimensional waterfall plot of distributed acoustic wave sensing data, providing single-channel time series and spectrum plots, enabling interactive operation; the horizontal and vertical axes of the two-dimensional waterfall plot represent time and channel number, respectively, and can show the spatiotemporal evolution characteristics of DAS data along the optical fiber; the single-channel view displays the time series waveform and spectrum plot of the user-selected channel, supports image zooming and panning interaction, and supports exporting custom image size and resolution; Signal processing control area: regulates the operation status of the basic processing module and the advanced analysis module, and processes the distributed acoustic wave sensing data.

[0022] The data conversion module is based on the DASPy library to read data, supports loading distributed acoustic wave sensor data in different formats, converts the data into NumPy arrays through the NumPy module to achieve data standardization, and simultaneously parses the metadata through Python dictionary structure. The metadata includes data source information, acquisition parameters, and file attributes, and outputs standardized DAS data body and structured metadata.

[0023] The basic processing module receives standardized DAS data and structured metadata for preprocessing. All processing operations are performed in memory, and the results are displayed in a dual-view architecture. The core functions of the basic processing module include: Spatial coordinate calibration: Achieve precise mapping between channel number and physical spatial coordinates; Data downsampling: reducing the amount of data through extraction algorithms; Digital filtering: Supports bandpass and low-pass filtering, and allows for custom frequency ranges; Data conversion: Enables bidirectional conversion between strain and strain rate data; Common-mode noise removal: Attenuates spatially correlated noise common to all channels.

[0024] like Figure 4As shown, a representative example is provided using the open-source RAPID dataset. Original DAS records ( Figure 4 a) It exhibits typical background noise and a prevalent coherent noise pattern. A 15-27Hz bandpass filter is applied ( Figure 4 b) It effectively separated the frequency range containing most meaningful seismic signals and significantly suppressed out-of-band noise. Based on this, subsequent common-mode noise removal ( Figure 4 c) By attenuating the spatially correlated noise components common to all channels, the signal-to-noise ratio is further improved. This sequential processing flow of "filtering-common-mode noise suppression" demonstrates the tool's ability to progressively extract the seismic signal of interest.

[0025] The advanced analysis module receives data processed by the basic processing module. It includes an interactive pickup submodule and a vehicle tracking submodule. These two submodules perform specialized analyses, and the results are then overlaid onto the interactive visualization module for presentation. The specific specialized analyses of the two submodules are as follows: The interactive picking submodule provides manual picking and event tagging functionality for seismic phase arrival times. It offers sophisticated navigation controls, including horizontal or vertical zoom and precise scrolling, and supports exporting the results of manual picking and event tagging. This submodule is specifically designed for seismic phase arrival time analysis and for labeling datasets to support deep learning tasks. When activated, this mode provides an interactive environment where users can manually pick any arrival time or tag any event on a 2D waterfall plot. The interface is equipped with dedicated sophisticated navigation controls, including horizontal and vertical zoom and precise scrolling, which are crucial for accurate picking in noisy datasets.

[0026] The interactive picking submodule supports two key interpretation workflows, such as Figure 5 As shown. In earthquake analysis Figure 5 In this context, it is possible to manually pick the phase-to-time ratio in seismic experiments. This is also applicable to signal annotation, such as... Figure 5 (a) and Figure 5 (b) This module provides labeling functionality for identifying specific features (such as vehicle traffic), and this paper presents an example from an urban traffic monitoring dataset. All interpreted results (including waveforms and user annotations) can be exported as graphs suitable for publication, supporting reproducible analysis and subsequent processing.

[0027] The vehicle tracking submodule is based on Kalman filtering and smoothing algorithms to achieve automatic extraction of vehicle trajectories and estimation of motion parameters, and supports long-term traffic flow statistical analysis. The specific implementation process of the Kalman filter and smoothing algorithm in the vehicle tracking submodule includes: Define the state vector: Define the first... The state vector of each channel for: ; in, For the vehicle to arrive at the The time of each channel The time derivative is indicated by the superscript. Represents the transpose of a matrix; State evolution is used to describe the state changes of a vehicle between consecutive channels: ; ; in, The distance between the channels. To address process noise, observation modeling is performed, using candidate arrival times obtained from channel peak detection as observation values. The observation relationship is established through the following model: ; ; in, To observe the arrival time, To observe noise; Trajectory optimization: Vehicle state is recursively estimated using a Kalman filter, and trajectory is optimized using a Rauch-Ton-Stribel smoother; trajectory generation is performed, and the overall vehicle trajectory is drawn by generating smooth vehicle positions labeled in each channel. Starting from the initial channel, multiple distinct vehicle trajectories can be identified.

[0028] The vehicle tracking workflow begins with user-defined parameter configuration—including minimum peak saliency, minimum interval, process noise, filter type (a 0.01-1Hz bandpass filter is used in this example) and tracking direction—to adapt to different DAS acquisition geometries and traffic conditions. Figure 6 a). These parameters control the detection of quasi-static strain (or strain rate) caused by vehicles traveling within the selected channel interval. The algorithm uses configured settings to perform spatiotemporal tracking on the target segment (e.g., channels 1105-1200); the trajectory is estimated using a Kalman filter and optimized using fixed-interval smoothing to suppress kinematic noise. Crucially, the reconstructed trajectory is consistent with the time and location of strain rate peaks (a). Figure 6 b, 6c) confirm that the tracking output can accurately capture vehicle passage events. Figure 6 b, 6c).

[0029] To evaluate the adaptability of the tracking framework under different field conditions, this invention applies the workflow to two independent DAS deployments. In each case, the user first configures scenario-specific parameters via an interface—including the process noise covariance (sigma) in the Kalman filter and the salience / interval threshold for peak detection (see [link to DAS deployment]). Figure 6 a), thereby optimizing for local data features (such as signal-to-noise ratio, traffic density, and cable coupling).

[0030] A specific implementation process is as follows: like Figure 7 As shown, this embodiment provides a long-term traffic monitoring application in a city. Based on real-time vehicle tracking capabilities, this invention further enables users to conduct long-term traffic analysis, which is crucial for urban planning and near-surface time monitoring.

[0031] This example demonstrates the application of a vehicle tracking model on a specific roadside DAS dataset, which contains 10 consecutive days of records to reveal daily variation patterns in traffic flow and vehicle behavior.

[0032] The analysis primarily uses channels 1305-1355 of the urban fiber optic dataset, which are distributed along a major arterial road.

[0033] Figure 7 The green dashed box in Figure a shows traffic statistics for a single representative date, with vehicle numbers and average speed calculated every 10 minutes, presenting the diurnal variation characteristics of traffic activity. Unlike typical arterial roads that exhibit a clear bimodal pattern (e.g., around 08:00 and 18:00), the vehicle number time series here remains consistently high throughout the day, increasing only slightly during typical morning and evening commuting periods. This phenomenon is attributed to the high baseline traffic load on the monitored arterial roads: dense vehicle spacing during peak hours leads to signal overlap, causing adjacent vehicle characteristics to merge and be counted as fewer, longer events, thus effectively suppressing the apparent amplitude of the bimodal peak in the detection statistics. In contrast, the average speed curve ( Figure 7 (a, the red curve) shows a more pronounced diurnal variation: speed is lowest between 07:00-09:00 and 17:00-19:00 (consistent with congestion), increases significantly at night, and reaches its maximum between 00:00-04:00. Figure 7 b (Nighttime Free Flow) and Figure 7 A single tracking example in c (daytime congestion) confirms this trend: the nighttime track shows vehicles traveling at a uniformly high speed and smoothly along the cable, while the daytime track shows a decrease in speed. This periodic pattern highlights the reliability of DAS-derived traffic statistics and identifies periods of high signal abundance for seismic interferometry.

[0034] Extend the analysis to a longer time scale Figure 7The data for 10 days shows the average daily vehicle count. The time series data reveals a relatively stable traffic pattern with minimal fluctuations between weekdays and weekends, indicating sustained stable traffic activity during the observation period. Unlike typical urban traffic patterns (where traffic decreases significantly on weekends), the average daily vehicle count here remains fairly uniform across all days, likely reflecting frequent weekend travel in the study area. This long-term perspective is crucial for understanding the temporal stability of noise sources in seismic imaging and identifying anomalous periods that may distort analysis results. The average vehicle speed over the 10 days stabilized at approximately 45 km / h, consistent with the speed limit on this main road. Daily speed variation was small (<5%), and the daily speed pattern (lower during the day and higher at night) reliably repeated throughout the day.

[0035] In summary, long-term tracking capabilities transform DAS fiber optics from a simple seismic sensor into a continuous, comprehensive traffic monitoring system. Insights gained from such analyses—including diurnal variation patterns, periodicity, and velocity statistics—are not only valuable for traffic studies but also provide a foundational framework for optimizing near-surface seismic imaging workflows—maximizing signal quality for specific targets through intelligent selection of time windows and vehicle categories.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PyQt-based fiber optic distributed acoustic wave sensor data visualization and processing system, characterized in that: This includes a data processing platform for fiber optic distributed acoustic wave sensing data. The platform provides visualization capabilities and its modules are as follows: Data access module: It can directly access the existing communication optical fiber through the spare fiber core or independently deploy a dedicated sensing optical cable along the monitoring path to convert the corresponding optical fiber into a continuously distributed auditory sensing array. It sets up a distributed acoustic wave sensor demodulator directly connected to the optical fiber, and then demodulates the vibration data of the optical cable to obtain DAS data in different formats. The user interface module accepts user input commands and provides a visual interface that displays related images, waveforms, and spectrum analysis. It integrates the Matplotlib plotting tool and supports image export and custom resolution settings. The data conversion module reads distributed acoustic wave sensor data in different formats and converts the data into standardized DAS data and structured metadata; the standardized data is then pushed to the basic processing module for preprocessing. The basic processing module performs data preprocessing and pushes the processed data to the user interface module for content display. The advanced analysis module performs non-destructive processing on the data from the basic processing module, and then analyzes it using the interactive pickup submodule and the vehicle tracking submodule. The results are then displayed through the user interface module.

2. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 1, characterized in that: The user interface module is developed using the PyQt5 framework and adopts a three-region layout, which includes: Data area: Loads distributed acoustic wave sensor data in different formats, and integrates operation logs and file attribute display components; Image display area: Displays a two-dimensional waterfall plot of distributed acoustic wave sensing data, provides single-channel time series and spectrum plots, and enables interactive operation; Signal processing control area: regulates the operation status of the basic processing module and the advanced analysis module, and processes the distributed acoustic wave sensing data.

3. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 2, characterized in that: The specific contents of the data area are as follows: It loads distributed acoustic wave sensor data in several mainstream file formats. The distributed acoustic wave sensor data is vibration data, and the data format is a two-dimensional array. In the two-dimensional array, the columns are channels and the action is time. To simplify the initial access process, the operation log and file attribute display components can provide users with real-time feedback and key metadata information. The key metadata information includes start time, sampling rate, gauge length, device type, and channel spacing, which is implemented using the DASPy reader. The specific content of the image display area is as follows: The two-dimensional waterfall chart shows the time and channel number on the horizontal and vertical axes, respectively, which can display the spatiotemporal evolution characteristics of DAS data along the optical fiber; the single-channel view displays the time series waveform and spectrum of the user-selected channel, supports image scaling and panning interaction, and supports exporting custom image size and resolution. Basic processing module: Implements spatial coordinate calibration, data downsampling, data extraction, data conversion, and common-mode noise removal for standardized DAS data volumes and structured metadata; The advanced analytics module includes phase pickup and vehicle trajectory recognition, and the aforementioned signal processing techniques are selectively applied through the user interface module.

4. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 1, characterized in that: The data conversion module is based on the DASPy library to read data, supports loading distributed acoustic wave sensor data in different formats, converts the data into NumPy arrays through the NumPy module to achieve data standardization, and simultaneously parses the metadata through Python dictionary structure. The metadata includes data source information, acquisition parameters, and file attributes, and outputs standardized DAS data body and structured metadata.

5. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 1, characterized in that: The basic processing module receives the standardized DAS data body and structured metadata and performs preprocessing, as follows: Spatial coordinate calibration: Achieve precise mapping between channel number and physical spatial coordinates; Data downsampling: reducing the amount of data through extraction algorithms; Data extraction: Supports bandpass and lowpass filtering, and custom frequency range; Data conversion: Enables bidirectional conversion between strain and strain rate data; Common-mode noise removal: Attenuates spatially correlated noise common to all channels.

6. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 1, characterized in that: The advanced analysis module receives the data processed by the basic processing module. The advanced analysis module includes an interactive picking submodule and a vehicle tracking submodule. Through these two submodules, specialized analysis is performed, and the analysis results are used by the user interface module to generate a vehicle trajectory graph with the number of channels on the horizontal axis and time on the vertical axis.

7. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 6, characterized in that: The interactive pickup submodule provides manual pickup and event marking functions for phase arrival, and supports the export of the results of manual pickup and event marking; The vehicle tracking submodule is based on Kalman filtering and smoothing algorithms to achieve automatic extraction of vehicle trajectory and estimation of motion parameters, including vehicle speed and direction.

8. The fiber optic distributed acoustic wave sensing data visualization and processing system based on PyQt according to claim 6, characterized in that: The specific implementation process of the Kalman filter and smoothing algorithm in the vehicle tracking submodule includes: signal preprocessing: extracting low-frequency quasi-static strain signals of 0.01-1Hz and detecting significant peak values ​​of signals in each channel; Define the state vector: Define the first... The state vector of each channel for: ； in, For the vehicle to arrive at the The time of each channel The time derivative is indicated by the superscript. Represents the transpose of a matrix; State evolution is used to describe the state changes of a vehicle between consecutive channels: ； ； in, The distance between the channels. To address process noise, observation modeling is performed, using candidate arrival times obtained from channel peak detection as observation values. The observation relationship is established through the following model: ； ； in, To observe the arrival time, To observe noise; Trajectory optimization: Vehicle state is recursively estimated using a Kalman filter, and trajectory is optimized using a Rauch-Ton-Stribel smoother; trajectory generation is performed, and the overall vehicle trajectory is drawn by generating smooth vehicle positions labeled in each channel. Starting from the initial channel, multiple distinct vehicle trajectories can be identified.

Images