Double-train state monitoring system and method based on phi-OTDR (Optical Time Domain Reflectometer)

Abstract

The system comprises a phi-OTDR distributed optical fiber sensing optical path, an AD acquisition module, an FPGA circuit and a host, a Rayleigh scattering light signal is generated through the distributed optical fiber sensing optical path, digital orthogonal demodulation is executed by the FPGA circuit after photoelectric conversion and AD acquisition module digitization, and the Rayleigh scattering light signal is transmitted to the host through the phi-OTDR distributed optical fiber sensing optical path. Extracting phase data; processing the phase data by adopting a Hilbert transform algorithm, and establishing a mapping relation between the phase difference and the vibration amplitude and frequency; the host receives the data and generates a time-position two-dimensional waterfall plot, and the running directions of the double trains are judged by analyzing the homonymy or reversibility of vibration point tracks; and calculating the displacement variation and the time difference of the two trains based on a time sequence analysis algorithm, optimizing speed data in combination with moving average filtering and Kalman filtering, and outputting the real-time positions and speeds of the two trains. According to the invention, accurate monitoring of the running state of the double trains is realized, and the accuracy, real-time performance and reliability of railway safety monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed optical fiber vibration sensing, and in particular to a dual-train status monitoring system and method based on Φ-OTDR. Background Art

[0002] With the rapid development of railway transportation and the continuous increase in train speed, the safety of railway transportation has become a crucial issue. Therefore, real-time monitoring of train operation status, including location, speed and direction, is of great significance to ensure the safety of railway transportation.

[0003] Traditional train monitoring technologies rely primarily on track circuits, video surveillance, or amplitude-based fiber optic sensing systems, but these methods have significant limitations:

[0004] Limitations of single-train monitoring: Existing amplitude-based distributed fiber optic sensing systems (such as traditional OTDR) can only locate a single train by vibration intensity and cannot distinguish the vibration signals of two trains traveling in the same or opposite directions, resulting in data confusion in multi-train scenarios.

[0005] Insufficient direction and speed recognition: Existing technologies lack the ability to accurately determine the direction of a train, and speed calculation relies on single sensor data, which is susceptible to noise interference, resulting in insufficient accuracy.

[0006] Poor environmental adaptability: Harsh environments such as electromagnetic interference and extreme temperatures may affect the stability of traditional sensors, while existing phase demodulation algorithms are highly complex and difficult to process massive amounts of data in real time.

[0007] As an advanced distributed fiber optic sensor, Φ-OTDR uses phase shifts in Rayleigh scattered light within an optical fiber to detect external disturbances. It offers high sensitivity, high spatial resolution, and a wide measurement range. However, existing Φ-OTDR applications still focus on single-target monitoring, and have yet to address the separation, direction determination, and speed calculation of dual-train vibration signals.

[0008] Therefore, an innovative method based on Φ-OTDR is urgently needed to achieve accurate monitoring of the running status of dual trains, including real-time calculation of position, direction and speed in the same and opposite direction running scenarios, while improving anti-interference ability and system robustness. Summary of the Invention

[0009] In view of this, the present invention provides a dual-train status monitoring system and method based on Φ-OTDR, which can realize accurate monitoring of the running status of dual trains, including real-time calculation of position, direction and speed in same-direction and opposite-direction driving scenarios, while improving anti-interference ability and system robustness.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] In a first aspect, an embodiment of the present invention provides a dual-train status monitoring system based on Φ-OTDR, comprising: a Φ-OTDR distributed optical fiber sensing optical path, an AD acquisition module, an FPGA circuit, and a host:

[0012] The Φ-OTDR distributed optical fiber sensing optical path is buried beside the railway track and is used to generate scattered light signals when a train passes by and convert them into electrical signals;

[0013] The AD acquisition module digitizes the electrical signal;

[0014] The FPGA circuit includes:

[0015] The signal demodulation module is used to perform digital quadrature demodulation on the digitized signal and extract the vibration phase data along the rail;

[0016] The Rayleigh phase processing module uses the Hilbert transform algorithm to calculate the phase difference and establish a mapping relationship between the phase difference and the vibration amplitude and frequency;

[0017] The data interaction module transmits the data processed by the Rayleigh phase processing module to the host;

[0018] The host generates a two-dimensional time-position waterfall diagram based on the received data, and determines the travel direction of the two trains by the same or opposite direction of the vibration point trajectory; and calculates the displacement change and time difference based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

[0019] Furthermore, the Φ-OTDR distributed optical fiber sensing optical path includes: an ultra-narrow linewidth laser, an acousto-optic modulator (AOM), an erbium-doped fiber amplifier (EDFA), a splitter, a circulator, a 3dB coupler, and a balanced detector (BPD);

[0020] Wherein, the ultra-narrow linewidth laser is connected to an acousto-optic modulator (AOM) via a coupler;

[0021] The output end of the acousto-optic modulator AOM is connected to the erbium-doped fiber amplifier EDFA;

[0022] The output end of the erbium-doped fiber amplifier EDFA is connected to the first port of the circulator;

[0023] The second port of the circulator is connected to an optical cable laid beside the railway track, and the third port is connected to a balanced detector BPD through a 3dB coupler.

[0024] Furthermore, the signal demodulation module includes:

[0025] The digital orthogonal demodulation unit is used to mix the digitized signal with the same-frequency orthogonal carrier to generate I and Q signals;

[0026] A low-pass filtering unit, configured to filter out high-frequency components from the I and Q signals through a low-pass filter;

[0027] The CORDIC demodulation unit is used to filter out the high-frequency components of the I and Q signals and obtain phase information through the inverse tangent function of the cordic IP core.

[0028] Furthermore, the Rayleigh phase processing module includes:

[0029] The Hilbert transform unit constructs a Hilbert transformer h(n) for the demodulated rail vibration data sequence x(n) input, whose unit impulse response satisfies h(0)=0; through the convolution operation y(n)=x(n)*h(n), the analytical signal z(n)=x(n)+jy(n) is obtained, and the phase of the signal is extracted

[0030] Phase difference calculation unit, based on Define the continuous phase after unwrapping

[0031] Calculate phase difference

[0032] Data combination unit for determining phase difference It is linearly related to the vibration amplitude A: k1 is the proportional coefficient determined by experimental calibration; and according to the phase difference Calculation of the rate of change over time, i.e. frequency Δt is the time interval between two adjacent samples; the phase difference is combined with the vibration amplitude and frequency to form the final data.

[0033] Furthermore, the host includes:

[0034] A data receiving unit is used to receive processed data from the data interaction module of the FPGA circuit, including vibration position, phase difference, amplitude, and frequency information;

[0035] A graphics rendering unit displays the received data in the form of a waterfall chart, with the horizontal axis representing the position along the optical fiber and the vertical axis representing time, with color or grayscale representing vibration intensity;

[0036] A direction determination unit is used to extract the vibration point sets of the two trains from the waterfall diagram by analyzing the trajectory characteristics of the vibration points in the waterfall diagram, calculate the displacement direction of adjacent time segments, and determine the travel direction of the trains;

[0037] The speed calculation unit calculates the displacement change and time difference of the train based on time series analysis, and calculates the preliminary speed using the speed formula;

[0038] The filtering optimization unit uses sliding average filtering and Kalman filtering to smooth and optimize the preliminary velocity data and eliminate noise interference;

[0039] The result output module is used to output the final dual-train status information, including position, speed, and direction.

[0040] In a second aspect, an embodiment of the present invention further provides a dual-train status monitoring method based on Φ-OTDR, applying the dual-train status monitoring system based on Φ-OTDR as described in any one of the first aspects, the method comprising:

[0041] S1. Rayleigh scattered light signals are generated through a distributed fiber-optic sensing optical path. After being digitized by the photoelectric conversion and AD acquisition modules, the FPGA circuit performs digital quadrature demodulation to extract vibration phase data along the rail.

[0042] S2. Processing the phase data using the Hilbert transform algorithm to construct an analytical signal and calculate the phase difference, establishing a mapping relationship between the phase difference and the vibration amplitude and frequency;

[0043] S3. The host receives the processed data and generates a two-dimensional time-position waterfall chart. It determines the direction of travel of the two trains by analyzing the unidirectional or reverse directions of the vibration point trajectories.

[0044] S4. The host calculates the displacement change and time difference of the two trains based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

[0045] Furthermore, in step S1, the FPGA circuit performs digital quadrature demodulation to extract vibration phase data along the rail, including:

[0046] The digitized signal is mixed with the same frequency orthogonal carrier respectively to generate I and Q signals; the I and Q signals are filtered through a low-pass filter to remove high-frequency components;

[0047] The I and Q signals with high-frequency components filtered out are used to obtain phase information through the arc tangent function of the cordic IP core.

[0048] Furthermore, the step S2 includes:

[0049] For the demodulated rail vibration data sequence x(n) input, a Hilbert transformer h(n) is constructed, whose unit impulse response satisfies h(0)=0; through the convolution operation y(n)=x(n)*h(n), the analytical signal z(n)=x(n)+jy(n) is obtained, and the phase of the signal is extracted

[0050] based on Define the continuous phase after unwrapping Calculate phase difference

[0051] Determine the phase difference It is linearly related to the vibration amplitude A: k1 is the proportional coefficient determined by experimental calibration; and according to the phase difference Calculation of the rate of change over time, i.e. frequency Δt is the time interval between two adjacent samplings;

[0052] The phase difference is combined with the vibration amplitude and frequency to form the final data.

[0053] Furthermore, step S3 includes:

[0054] Receive processed data from the data interaction module of the FPGA circuit, including vibration position, phase difference, amplitude, and frequency information;

[0055] The received data is displayed in the form of a waterfall chart, with the horizontal axis representing the position along the fiber and the vertical axis representing time. The color or grayscale represents the vibration intensity.

[0056] By analyzing the trajectory characteristics of the vibration points in the waterfall diagram, the vibration point sets of the two trains are extracted from the waterfall diagram;

[0057] When the displacement directions of the two trains are the same and the speeds are similar, they are traveling in the same direction; when the displacement directions of the two trains are opposite, they are traveling in opposite directions.

[0058] Furthermore, the step S4 includes:

[0059] Based on time series analysis, the displacement changes and time differences of the two trains are calculated, and the preliminary speeds are calculated using the speed formula respectively;

[0060] Use sliding average filter and Kalman filter to smooth and optimize the preliminary velocity data to eliminate noise interference;

[0061] Output the final dual train status information, including position, speed, and direction.

[0062] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following technical advantages:

[0063] 1. Breakthrough in multi-train monitoring capabilities

[0064] This method uses the high-sensitivity phase detection of a Φ-OTDR combined with Hilbert transform to extract phase difference characteristics and separate the vibration signals of two trains. The method also uses the trajectory characteristics of the vibration points in the waterfall diagram (parallel movement in the same direction or crossing in opposite directions) to accurately distinguish the travel directions of the two trains. This method is suitable for scenarios where two trains are running parallel or intersecting, such as high-speed rail double-track and busy railway sections.

[0065] 2. High-precision direction and speed calculation

[0066] The present invention uses a trajectory analysis algorithm based on a time-position waterfall diagram, combined with a dynamic threshold mechanism (such as sliding window counting), to automatically determine the train's direction of travel. It extracts displacement differences (ΔS) and time differences (Δt) through a time series analysis algorithm, and optimizes data using sliding average filtering and Kalman filtering to eliminate transient noise. It can accurately determine direction and speed.

[0067] 3. Improved real-time performance and system robustness

[0068] The present invention uses FPGA parallel processing and implements hardware acceleration in the FPGA circuit based on core algorithms such as signal demodulation and phase extraction, resulting in low processing latency. In addition, it has a dynamic adaptive mechanism, and the sliding average filter window length and Kalman gain parameters can be dynamically adjusted according to environmental noise, improving system adaptability, real-time performance, and system robustness.

[0069] Through phase demodulation algorithm, multi-stage filtering optimization, FPGA hardware acceleration and visual interactive design, this invention solves the core problems of traditional technologies such as insufficient multi-train monitoring capabilities, manual reliance on direction judgment, and speed calculation susceptible to interference. It significantly improves the accuracy, real-time performance and reliability of railway safety monitoring, and has clear technological advancement and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0071] Figure 1 This is a structural diagram of the dual-train status monitoring system based on Φ-OTDR provided by the present invention.

[0072] Figure 2 This is a flow chart of the speed detection algorithm provided by the present invention.

[0073] Figure 3 This is a flow chart of the train running direction algorithm provided by the present invention.

[0074] Figure 4This is a flow chart of the dual-train status monitoring method based on Φ-OTDR provided by the present invention.

[0075] Figure 5 This is the optical path diagram of the distributed optical fiber sensing optical path system provided by the present invention. DETAILED DESCRIPTION

[0076] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0077] Reference Figure 1 As shown, the embodiment of the present invention discloses a dual-train status monitoring system based on Φ-OTDR, which is composed of a Φ-OTDR distributed optical fiber sensing optical path, an AD acquisition module, an FPGA circuit, and a host. The FPGA circuit includes a signal demodulation module, a Rayleigh phase processing module, and a data interaction module.

[0078] The Φ-OTDR distributed optical fiber sensing optical path is buried beside the railway track and is used to generate scattered light signals when a train passes by and convert them into electrical signals; the AD acquisition module digitizes the electrical signals;

[0079] The signal demodulation module is used to perform digital orthogonal demodulation on the digitized signal and extract the vibration phase data along the rail. The Rayleigh phase processing module uses the Hilbert transform algorithm to calculate the phase difference and establish a mapping relationship between the phase difference and the vibration amplitude and frequency. The data interaction module transmits the data processed by the Rayleigh phase processing module to the host. The host generates a time-position two-dimensional waterfall diagram based on the received data, and determines the direction of travel of the two trains by the same or opposite direction of the vibration point trajectory. The host also calculates the displacement change and time difference based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

[0080] The optical path of the Φ-OTDR distributed fiber optic sensing optical path system consists of an ultra-narrow linewidth laser, an acousto-optic modulator (AOM), an erbium-doped amplifier (EDFA), a splitter, a circulator, a 3dB coupler, and a balanced detector (BPD). The ultra-narrow linewidth laser is connected to the acousto-optic modulator (AOM) via a coupler, the acousto-optic modulator is connected to the erbium-doped amplifier (EDFA), the erbium-doped amplifier (EDFA) is connected to port 1 of the circulator, port 2 of the circulator is connected to the laid optical cable, port 3 of the circulator is connected to the 3dB coupler, the coupler is connected to the 3dB coupler, and the 3dB coupler is connected to the balanced detector (BPD).

[0081] Working Principle: The components of the Φ-OTDR distributed fiber optic sensing optical path operate in a specific connection configuration, generating scattered light signals that are converted into electrical signals. The acquisition module digitizes the electrical signals, separates them through signal processing, and then inputs them into the signal demodulation module. A Hilbert transform algorithm is then used to construct a Hilbert transformer to extract phase features from the demodulated vibration data sequence along the track. A conversion relationship between phase difference, vibration amplitude, and frequency is established, and the data is combined into the final data. The host computer receives the final data collected and preliminarily processed by the sensing optical path, quantifies the vibration intensity, and uses a graphics rendering algorithm to display the data in the form of a waterfall chart. This data visually illustrates the difference in vibration intensity at different locations and times by correlating color or grayscale with vibration intensity. The direction of the train's travel is determined based on the trajectory of the vibration points in the waterfall chart in a two-dimensional coordinate system based on time and fiber position. When traveling in the same direction, the displacement of the vibration points sets in the same direction and at similar rates. When traveling in opposite directions, the vibration points move in opposite directions. The host computer uses a speed detection algorithm based on time series analysis to record the vibration times and positions of the vibration points on both trains. The distance change is used as the displacement, and the time difference as the time. The speed is then calculated using a speed calculation formula. A sliding average filter algorithm is used to process displacement and time data to ensure accurate speed calculations. A Kalman filter algorithm is used to predict and correct speed data. Through repeated prediction and updating processes, the final output train speed data accurately reflects the actual train speed, thus enabling monitoring of the operating status, position, and speed of both trains.

[0082] In one embodiment, the signal demodulation module includes:

[0083] The digital orthogonal demodulation unit is used to mix the digitized signal with the same-frequency orthogonal carrier to generate I and Q signals;

[0084] A low-pass filtering unit, configured to filter out high-frequency components from the I and Q signals through a low-pass filter;

[0085] The CORDIC demodulation unit is used to filter out the high-frequency components of the I and Q signals and obtain phase information through the inverse tangent function of the cordic IP core.

[0086] In this embodiment, digital quadrature demodulation primarily includes mixing, filtering, and Cordic demodulation. The signal demodulation module performs digital domain demodulation on the digitized signal. The digitized signal is multiplied with a co-frequency orthogonal signal to generate two mixed signals. These signals are then low-pass filtered to obtain the orthogonal I and Q signals. The I and Q signals are then squared using the Cordic IP core to obtain amplitude information. The phase information of these two signals is then obtained using the inverse tangent function of the Cordic IP core.

[0087] In one embodiment, the Rayleigh phase processing module uses the Hilbert transform algorithm to extract phase features. For the input demodulated rail vibration data sequence x(n), a Hilbert transformer h(n) is constructed, whose unit impulse response satisfies h(0)=0. Through the convolution operation y(n)=x(n)*h(n), the analytical signal z(n)=x(n)+jy(n) is obtained, where j represents the imaginary unit. The phase of the signal is

[0088] Based on theoretical derivation and experimental calibration, the conversion relationship between phase difference, vibration amplitude and frequency is established; Phase difference It satisfies the linear relationship with the vibration amplitude A Where k1 is the proportional coefficient determined by experimental calibration; when the laser center wavelength is 1550.12 nm and the fiber core refractive index is 1.46, k1 = 110.37 nε·m / rad, and ε is the strain. The vibration frequency f is calculated based on the rate of change of the phase difference with time, that is, Where Δt is the time interval between two adjacent samples. The phase difference is combined with the vibration amplitude and frequency to form the final data: {position pos(n), time t(n), amplitude A(n), frequency f(n)}.

[0089] In one embodiment, the host comprises:

[0090] A data receiving unit is used to receive processed data from the data interaction module of the FPGA circuit, including vibration position, phase difference, amplitude, and frequency information;

[0091] A graphics rendering unit displays the received data in the form of a waterfall chart, with the horizontal axis representing the position along the optical fiber and the vertical axis representing time, with color or grayscale representing vibration intensity;

[0092] The direction determination unit is used to analyze the trajectory characteristics of the vibration points in the waterfall plot, extract the set of vibration points of the two trains from the waterfall plot, calculate the displacement direction of adjacent time segments, and determine the train's direction of travel. For example, if two trains are traveling in the same direction, their vibration point trajectories appear as two parallel moving curves in the waterfall plot (for example, the displacement rate of each is 150m / s). If the trains are traveling in opposite directions, the trajectories will intersect in opposite directions (for example, one moving eastward at +120m / s and the other moving westward at -130m / s).

[0093] The speed calculation unit calculates the displacement change and time difference of the train based on time series analysis, and calculates the preliminary speed using the speed formula;

[0094] The filtering optimization unit uses sliding average filtering and Kalman filtering to smooth and optimize the preliminary velocity data and eliminate noise interference;

[0095] The result output module is used to output the final dual-train status information, including position, speed, and direction.

[0096] In this embodiment, the host receives the final data collected and preliminarily processed from the sensor optical path. These data contain relevant information at different times and different positions along the optical fiber. When collecting and processing vibration data, the host quantifies the vibration intensity and sets the vibration intensity value range, from the minimum value I min To the maximum value I max , to cover all possible vibration intensities. The host computer uses OpenGL or CUDA-accelerated graphics rendering algorithms to generate waterfall charts. The collected data is displayed in waterfall chart form. The waterfall chart constructs a two-dimensional coordinate system, with time on the vertical axis and position along the fiber on the horizontal axis.

[0097] For the correspondence between color or grayscale and vibration intensity, a matching range is predefined. In terms of color, a gradient color system is used from the color representing weaker vibration intensity to the color representing stronger vibration intensity; in terms of grayscale, a transition is used from the grayscale corresponding to weaker vibration intensity to the grayscale corresponding to stronger vibration intensity. When the vibration intensity I at a certain position and time is collected, its position within the vibration intensity range [I min ,I max ], by the formula A proportional value between 0 and 1 is obtained. This proportional value corresponds to the corresponding position in the pre-set color or grayscale range, thereby showing the difference in vibration intensity at different positions and times in the waterfall chart, providing an intuitive data display basis for subsequent analysis.

[0098] The vibration points captured and processed by the Φ-OTDR, as reflected in the waterfall plot, exhibit highly similar trajectories in the two-dimensional coordinate system of time and fiber position. Because the two trains are traveling in the same direction, the vibration excitation they generate on the fiber exhibits isotropic motion in the time series. Specifically, in consecutive time slices, the displacement changes of the corresponding vibration points on the horizontal axis (the fiber position) for the two trains are in the same direction, and the displacement change rates are similar within the same time interval. Because the two trains are traveling in opposite directions, the vibration excitations they generate on the fiber exhibit opposite characteristics in the time series. As reflected in the waterfall plot, the corresponding vibration points on the two trains move in opposite directions in the two-dimensional coordinate system of time and fiber position. At the beginning of the waterfall plot, the vibration points of the two trains are located in different regions along the fiber. As time progresses, the two sets of vibration points gradually approach each other. As they approach, their displacement changes on the horizontal axis move in opposite directions, forming two curves in the waterfall plot that extend in opposite directions and gradually converge.

[0099] The real-time speed of the train is calculated by the ratio of the change in the distance between the train center and the time. Figure 2The recorded current vibration position center pos_start_A of train A is shown, and at the same time, the current system time time_start_A is recorded using the timestamp function. Traverse the next train phase curve. Position information can be obtained from the phase information. Confirm the amplitude center position pos_end_A of the next vibration position of train A, and record the current time time_end_A. Confirm the train running direction based on the relationship between the center positions of the train at two time points. If pos_end_A > pos_start_A, calculate the forward running speed of the train as (pos_end_A - pos_start_A) / (time_end_A - time_start_A); if pos_end_A < pos_start_A, calculate the reverse running speed of the train as (pos_start_A - pos_end_A) / (time_end_A - time_start_A).

[0100] Through Figure 3 The algorithm can determine the movement directions of two trains. Set the number of comparisons N. The number of times when pos_end > pos_start is recorded as time_1 and time_2 respectively. On the premise of confirming that it is the vibration of train A, obtain the position information according to the phase information, and record the amplitude center point of the current train A, denoted as pos_start_A = (pos + i) / 2. Traverse the next phase curve of train A, and confirm the amplitude center position of the next vibration position of train A according to the phase information, denoted as pos_end_A = (pos + i) / 2. Then compare pos_end_A and pos_start_A: if pos_end_A > pos_start_A, let time_1 = time_1 + 1; if pos_end_A < pos_start_A, let time_1 = time_1 - 1. Continuously update pos_start_A, pos_end_A and compare them according to the above steps, and continuously update time_1. When time_1 > N is satisfied, it means that train A continuously moves in the direction of increasing position within a period of time, and it can be judged that train A is moving forward; when time_1 < -N is satisfied, it means that train A continuously moves in the direction of decreasing position within a period of time, and it can be judged that train A is moving in reverse. Repeat the above steps to obtain the running direction of train B. When time_2 > N is satisfied, it means that train B is moving forward; when time_2 < -N is satisfied, it means that train B is moving in reverse. After judging the movement directions of train A and train B respectively, if (time_1 > N and time_2 > N) or (time_1 < -N and time_2 < -N) is satisfied, it can be judged that the two trains are moving in the same direction; in other cases, it is judged that the two trains are moving in opposite directions.

[0101] N is the comparison threshold, which is related to data stability and system response speed requirements. Different types of trains have different vibration characteristics during operation. For example, high-speed trains operate relatively smoothly, with strong regularity in vibration data, so the N value can be relatively small. However, freight trains may experience more complex vibrations due to factors such as load and road conditions, so a larger N value may be required. Assuming that a high-speed train's N value of 10-12 is sufficient for accurate direction determination, a freight train might require 15-20.

[0102] After determining the train's direction of travel, the host computer uses a speed detection algorithm based on time series analysis to calculate the speed. This algorithm records the vibration time and position information of the two train vibration points in real time. The change in distance between the two train vibration points at different times is used as displacement S, and the time difference between the first detection of the two vibration points is used as time t. The speed calculation formula v = S / t is used to calculate the speed of the two trains.

[0103] The host will use the sliding average filter algorithm to process the displacement and time data of the collected data, and perform multiple checks and corrections. The sliding average filter algorithm processes the displacement and time data within a fixed length time window. The time window contains n consecutive data points. For the displacement data sequence {S1, S2, ..., S n}, displacement data after sliding average filtering is the average value of these n data points, that is As time goes by, the time window slides forward, new data points enter the window, and old data points move out of the window. The new average value is calculated in the same way as above to smooth the displacement data and eliminate the instantaneous fluctuations caused by noise and interference. n}, similar method is used to calculate the average value of n time points in the time window Smooth time data.

[0104] Kalman filter algorithm predicts and corrects speed data; based on the speed estimate at the previous moment And the prediction model of speed change, predict the speed at the current moment When the new velocity measurement value v k After the previous calculation, the Kalman filter algorithm calculates the Kalman gain K based on the difference between the measured value and the predicted value, as well as the statistical characteristics of the measurement noise and system noise. k . Using Kalman gain, the predicted value and the measured value v k Perform weighted fusion to obtain a more accurate speed estimate at the current moment Right now By repeatedly predicting and updating, the speed data is continuously optimized and the final output train speed data accurately reflects the actual operating speed of the train.

[0105] For example: the host records the initial position pos of the vibration point start and time t start After 10 consecutive samplings, the displacement difference ΔS and time difference Δt are calculated, resulting in a preliminary velocity v = ΔS / Δt. The data is smoothed using a sliding average filter (window length 8), and the velocity values are then corrected using a Kalman filter (process noise variance 0.1, observation noise variance 0.01). The final output is the real-time velocity of the two trains (e.g., Train 1: 153.2 ± 0.5 km / h, Train 2: 148.7 ± 0.5 km / h).

[0106] Based on the same inventive concept, the embodiment of the present invention also provides a dual-train status monitoring method based on Φ-OTDR, which is applied to the dual-train status monitoring system based on Φ-OTDR as in the above embodiment. Since the principle of the problem solved by this method is similar to that of the aforementioned dual-train status monitoring system based on Φ-OTDR, the implementation of this method can refer to the implementation of the aforementioned system, and the repeated parts will not be repeated. Figure 4 As shown, the method includes:

[0107] S1. Rayleigh scattered light signals are generated through a distributed fiber-optic sensing optical path. After being digitized by the photoelectric conversion and AD acquisition modules, the FPGA circuit performs digital quadrature demodulation to extract vibration phase data along the rail.

[0108] S2. Processing the phase data using the Hilbert transform algorithm to construct an analytical signal and calculate the phase difference, establishing a mapping relationship between the phase difference and the vibration amplitude and frequency;

[0109] S3. The host receives the processed data and generates a two-dimensional time-position waterfall chart. It determines the direction of travel of the two trains by analyzing the unidirectional or reverse directions of the vibration point trajectories.

[0110] S4. The host calculates the displacement change and time difference of the two trains based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

[0111] This method relies on a monitoring system consisting of a Φ-OTDR distributed fiber optic sensor optical circuit, an AD acquisition module, an FPGA circuit, and a host computer. The FPGA circuit integrates signal demodulation, Rayleigh phase method processing, and data exchange modules. The Φ-OTDR distributed fiber optic sensor optical circuit generates scattered light signals, which undergo photoelectric conversion, digitization, and a series of signal processing and demodulation steps to obtain phase data along the track. The Rayleigh phase processing module uses the Hilbert transform algorithm to extract phase characteristics based on the phase characteristics of Rayleigh scattered light. After receiving the final data, the host computer uses a waterfall plot to visually display the vibration status. The travel direction is determined based on the different trajectory characteristics of the vibration points in the waterfall plot when the two trains are traveling in the same and opposite directions, and the train speed is calculated using a time series analysis algorithm. Through this algorithm and system architecture, accurate monitoring of the operating status of the two trains is achieved.

[0112] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0113] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dual-train status monitoring system based on Φ-OTDR, characterized in that: include: Φ-OTDR distributed optical fiber sensing path, AD acquisition module, FPGA circuit and host: The Φ-OTDR distributed optical fiber sensing optical path is buried beside the railway track and is used to generate scattered light signals when a train passes by and convert them into electrical signals; The AD acquisition module digitizes the electrical signal; The FPGA circuit includes: The signal demodulation module is used to perform digital quadrature demodulation on the digitized signal and extract the vibration phase data along the rail; The Rayleigh phase processing module uses the Hilbert transform algorithm to calculate the phase difference and establish a mapping relationship between the phase difference and the vibration amplitude and frequency; The data interaction module transmits the data processed by the Rayleigh phase processing module to the host; The host generates a two-dimensional time-position waterfall diagram based on the received data, and determines the travel direction of the two trains by the same or opposite direction of the vibration point trajectory; and calculates the displacement change and time difference based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

2. A dual-train status monitoring system based on Φ-OTDR according to claim 1, characterized in that: The Φ-OTDR distributed optical fiber sensing optical path includes: an ultra-narrow linewidth laser, an acousto-optic modulator (AOM), an erbium-doped fiber amplifier (EDFA), a splitter, a circulator, a 3dB coupler, and a balanced detector (BPD); Wherein, the ultra-narrow linewidth laser is connected to an acousto-optic modulator (AOM) via a coupler; The output end of the acousto-optic modulator AOM is connected to the erbium-doped fiber amplifier EDFA; The output end of the erbium-doped fiber amplifier EDFA is connected to the first port of the circulator; The second port of the circulator is connected to an optical cable laid beside the railway track, and the third port is connected to a balanced detector BPD through a 3dB coupler.

3. A dual-train status monitoring system based on Φ-OTDR according to claim 1, characterized in that: The signal demodulation module includes: The digital orthogonal demodulation unit is used to mix the digitized signal with the same-frequency orthogonal carrier to generate I and Q signals; A low-pass filtering unit, configured to filter out high-frequency components from the I and Q signals through a low-pass filter; The CORDIC demodulation unit is used to filter out the high-frequency components of the I and Q signals and obtain phase information through the inverse tangent function of the cordic IP core.

4. A dual-train status monitoring system based on Φ-OTDR according to claim 1, characterized in that: The Rayleigh phase processing module includes: The Hilbert transform unit constructs a Hilbert transformer h(n) for the demodulated rail vibration data sequence x(n) input, whose unit impulse response satisfies The analytic signal z(n)=x(n)+jy(n) is obtained by convolution operation y(n)=x(n)*h(n), and the phase of the signal is extracted. Phase difference calculation unit, based on Define the continuous phase after unwrapping Calculate phase difference Data combination unit for determining phase difference It is linearly related to the vibration amplitude A: k1 is the proportional coefficient determined by experimental calibration; and according to the phase difference Calculation of the rate of change over time, i.e. frequency Δt is the time interval between two adjacent samples; the phase difference is combined with the vibration amplitude and frequency to form the final data.

5. The dual-train status monitoring system based on Φ-OTDR according to claim 1, characterized in that: The host comprises: A data receiving unit is used to receive processed data from the data interaction module of the FPGA circuit, including vibration position, phase difference, amplitude, and frequency information; A graphics rendering unit displays the received data in the form of a waterfall chart, with the horizontal axis representing the position along the optical fiber and the vertical axis representing time, with color or grayscale representing vibration intensity; A direction determination unit is used to extract the vibration point sets of the two trains from the waterfall diagram by analyzing the trajectory characteristics of the vibration points in the waterfall diagram, calculate the displacement direction of adjacent time segments, and determine the travel direction of the trains; The speed calculation unit calculates the displacement change and time difference of the train based on time series analysis, and calculates the preliminary speed using the speed formula; The filtering optimization unit uses sliding average filtering and Kalman filtering to smooth and optimize the preliminary velocity data and eliminate noise interference; The result output module is used to output the final dual-train status information, including position, speed, and direction.

6. A dual-train status monitoring method based on Φ-OTDR, characterized in that: The dual-train status monitoring system based on Φ-OTDR according to any one of claims 1 to 5 is applied, and the method comprises: S1. Rayleigh scattered light signals are generated through a distributed fiber-optic sensing optical path. After being digitized by the photoelectric conversion and AD acquisition modules, the FPGA circuit performs digital quadrature demodulation to extract vibration phase data along the rail. S2. Processing the phase data using the Hilbert transform algorithm to construct an analytical signal and calculate the phase difference, establishing a mapping relationship between the phase difference and the vibration amplitude and frequency; S3. The host receives the processed data and generates a two-dimensional time-position waterfall chart. It determines the direction of travel of the two trains by analyzing the same or opposite directions of the vibration point trajectories. S4. The host calculates the displacement change and time difference of the two trains based on the time series analysis algorithm, combines the sliding average filter and Kalman filter to optimize the speed data, and outputs the real-time position and speed of the two trains.

7. The dual-train status monitoring method based on Φ-OTDR according to claim 6, characterized in that: In step S1, the FPGA circuit performs digital quadrature demodulation to extract vibration phase data along the rail, including: The digitized signal is mixed with the same frequency orthogonal carrier respectively to generate I and Q signals; the I and Q signals are filtered through a low-pass filter to remove high-frequency components; The I and Q signals with high-frequency components filtered out are used to obtain phase information through the arc tangent function of the cordic IP core.

8. The dual-train status monitoring method based on Φ-OTDR according to claim 6, characterized in that: The step S2 includes: For the demodulated rail vibration data sequence x(n) input, a Hilbert transformer h(n) is constructed, whose unit impulse response satisfies The analytic signal z(n)=x(n)+jy(n) is obtained by convolution operation y(n)=x(n)*h(n), and the phase of the signal is extracted. based on Define the continuous phase after unwrapping Calculate phase difference Determine the phase difference It is linearly related to the vibration amplitude A: k1 is the proportional coefficient determined by experimental calibration; and according to the phase difference Calculation of the rate of change over time, i.e. frequency Δt is the time interval between two adjacent samplings; The phase difference is combined with the vibration amplitude and frequency to form the final data.

9. The dual-train status monitoring method based on Φ-OTDR according to claim 6, characterized in that: The step S3 comprises: Receive processed data from the data interaction module of the FPGA circuit, including vibration position, phase difference, amplitude, and frequency information; The received data is displayed in the form of a waterfall chart, with the horizontal axis representing the position along the fiber and the vertical axis representing time. The color or grayscale represents the vibration intensity. By analyzing the trajectory characteristics of the vibration points in the waterfall diagram, the vibration point sets of the two trains are extracted from the waterfall diagram; When the displacement directions of the two trains are the same and the speeds are similar, they are traveling in the same direction; when the displacement directions of the two trains are opposite, they are traveling in opposite directions.

10. The dual-train status monitoring method based on Φ-OTDR according to claim 6, characterized in that: The step S4 comprises: Based on time series analysis, the displacement changes and time differences of the two trains are calculated, and the preliminary speeds are calculated using the speed formula respectively; Use sliding average filtering and Kalman filtering to smooth and optimize the preliminary velocity data to eliminate noise interference; Output the final dual train status information, including position, speed, and direction.

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