Tunnel vehicle monitoring method and system based on chirp DAS

Abstract

The application relates to the technical field of optical fiber distributed sensing and traffic monitoring, and discloses a tunnel vehicle monitoring method and system based on a linear frequency modulation pulse DAS. The method comprises the following steps: collecting a beat frequency signal based on a linear frequency modulation phi-OTDR device and performing filtering processing; a two-dimensional space-time matrix is constructed, and an envelope is extracted from the two-dimensional space-time matrix through Hilbert transformation to obtain a two-dimensional envelope matrix; after the two-dimensional envelope matrix is converted into an image, an edge detection algorithm is used to extract a vibration curve of a vehicle; whether overload occurs is judged according to the vibration curve, and the speed state of the vehicle is identified. The application relies on the time shift characteristics of the linear frequency modulation pulse DAS, establishes the accurate correlation between vehicle overload and vibration waveform, and can realize efficient discrimination of abnormal states of the vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical fiber distributed sensing and traffic monitoring, in particular to a tunnel vehicle state monitoring method and system based on linear frequency modulation pulse DAS (distributed optical fiber acoustic wave sensing). BACKGROUND

[0002] As a key node in the traffic network, the tunnel has the characteristics of strong closure, narrow space and complex environment, and its traffic safety is directly related to the safety of people's life and property and the efficient operation of the traffic system. When vehicles drive in the tunnel, they are easily affected by factors such as light changes, air flow disturbances, and dense traffic flow, which can cause sudden failures, overspeeds, low-speed stagnation, and other abnormal driving states. If not monitored and responded to in time, it may cause congestion, rear-end collisions, and other chain accidents. Therefore, in order to improve road safety, it is necessary to monitor the driving state of vehicles in the tunnel in real time, accurately and comprehensively.

[0003] Vehicle driving state monitoring technology mainly includes video monitoring technology, inductive coil technology, etc., but it is easily affected by the complex tunnel environment. The Chinese patent with publication number CN113327248B discloses a tunnel vehicle flow monitoring method based on video monitoring. This method is based on the video images of vehicles in the tunnel, and an improved vehicle recognition and detection model is constructed. After training, vehicle tracking and vehicle flow statistics are realized through feature extraction, weight and offset prediction, the data dimension is comprehensive, and it has the ability of vehicle flow and vehicle attribute correlation analysis, but it is more dependent on the lighting environment. The dim, strong light and exhaust in the tunnel will reduce the image quality, resulting in a decrease in detection accuracy.

[0004] In contrast, optical fiber sensing technology can cover the entire area without dead angles, simultaneously sense vibrations, and is not affected by environmental factors such as light and fog. The Chinese patent with publication number CN114049777A discloses a tunnel vehicle driving early warning method based on distributed optical fiber sensing technology. By laying optical fibers on the tunnel road surface to collect vehicle vibration data, combining with a license plate recognition system, the vehicle driving state is restored through scene fitting, realizing full-process monitoring and abnormal early warning of overspeed and congestion, and having the advantages of no monitoring blind area, stable operation in low-visibility, power failure and other extreme environments. However, the sensing distance of the DAS system is restricted by the detection pulse width, and as the sensing distance increases, the pulse signal attenuation will cause the accuracy of the remote data acquisition to decrease, affecting the reliability of vehicle flow statistics and state discrimination.

[0005] In order to realize the comprehensive monitoring of the driving state of the vehicle in the tunnel, improve the accuracy and real-time of the vehicle abnormal driving state judgment, it is necessary to improve the existing technology of the vehicle abnormal state monitoring method in the tunnel, so as to capture the subtle vibration change of the vehicle, thereby more accurately judging the driving state of the vehicle in the tunnel, and overcoming the problems of limited sensing distance and insufficient anti-interference ability of the traditional DAS system. SUMMARY

[0006] In order to solve the bottleneck of the DAS system in the prior art in terms of detection distance, monitoring accuracy and anti-interference performance, and realize accurate monitoring of the abnormal state of the vehicle in the tunnel such as overspeed, overload and illegal parking, the present application provides a tunnel vehicle monitoring method and system based on linear frequency pulse DAS.

[0007] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows: a tunnel vehicle monitoring method based on linear frequency pulse DAS, comprising the following steps:

[0008] Step 1: based on the linear frequency φ-OTDR device, collect beat frequency signals and perform filtering processing to remove ultra-low frequency noise less than 20Hz in the beat frequency signals;

[0009] Step 2: based on the beat frequency signals after filtering processing, construct a two-dimensional space-time matrix according to the detection pulse period and the sensing distance, the two-dimensional space-time matrix takes the time axis as the matrix row and the distance axis as the matrix column, and extracts the envelope of the two-dimensional space-time matrix through Hilbert transform to obtain a two-dimensional envelope matrix;

[0010] Step 3: after converting the two-dimensional envelope matrix into an image, the image edge is extracted by using an edge detection algorithm to obtain a vibration curve of the vehicle;

[0011] Step 4: according to the vibration curve, judge whether overload occurs and identify the speed state of the vehicle;

[0012] The method for judging whether overload occurs is:

[0013] Step 4.1.1: traverse the vibration curve to determine the small fluctuation period;

[0014] Step 4.1.2: split the vibration curve into a plurality of continuous small fluctuation periods;

[0015] Step 4.1.3: count the number N1 of all small periods whose peak-to-peak value is greater than the peak-to-peak value threshold, if N1 is greater than the number threshold N0, it is determined that overload occurs;

[0016] The method for identifying the speed state of the vehicle is:

[0017] Step 4.2.1: traverse each time sampling point on the vibration curve, find all valid peak points, and record the coordinates;

[0018] Step 4.2.2: Calculate the speed of the vehicle based on the coordinates of all valid peak points.

[0019] Step 4.2.3: Determine the speed state of the vehicle according to the speed of the vehicle.

[0020] In the step 1, the filtering formula is:

[0021] ;

[0022] wherein, represents the beat frequency signal after filtering, n represents the discrete time index of the beat frequency signal, represents the discrete impulse response of the filter, k represents the sampling point index of the filter, represents the signal value of the original beat frequency signal delayed by k, N represents the number of discrete time indexes corresponding to the original beat frequency signal, and M represents the order of the filter.

[0023] In the step 2, the method for extracting the envelope to obtain the two-dimensional envelope matrix is:

[0024] Step 2.1: Perform Hilbert transform on the data of each row of the two-dimensional space-time matrix, and the transform formula is:

[0025] ;

[0026] wherein, represents the Hilbert transform, represents the row data of the two-dimensional space-time matrix, and t represents time, represents the signal after Hilbert transform;

[0027] Step 2.2: Calculate the signal envelope, and the calculation formula is:

[0028] ;

[0029] wherein, represents the signal envelope;

[0030] Step 2.3: Obtain the two-dimensional envelope matrix according to the signal envelope corresponding to the data of each row of the two-dimensional space-time matrix.

[0031] The step 3 specifically includes the following steps:

[0032] Step 3.1: After converting the two-dimensional envelope matrix into an image format, perform normalization processing on the image;

[0033] Step 3.2: Convolve the discrete LoG kernel with the normalized image to obtain the LoG response map; the convolution formula is:

[0034] ;

[0035] wherein, represents the response value of LoG response map corresponding to coordinate point x , y , represents the pixel value of normalized image at coordinate point , represents discrete LoG kernel, (i, j) represents the position index of discrete LoG kernel, and a and b represent the half-width range index values of discrete LoG kernel in two coordinate directions, respectively;

[0036] Step 3.3: Traverse the entire LoG response map to find effective zero-crossing points as edge points;

[0037] Step 3.4: Connect the edge points in sequence to generate a binary edge image, and then restore the image coordinate points where the edge contour is located to time-distance coordinate axis parameters to obtain the vibration curve of the vehicle.

[0038] In step 3.1, the method for converting the two-dimensional envelope matrix into an image is as follows:

[0039] The time dimension in the matrix is converted into the horizontal pixel position of the image, the distance dimension is converted into the vertical pixel position of the image, and the intensity value of each position in the matrix is mapped to the color information of the corresponding pixel point to obtain the image.

[0040] In step 3.1, the obtained image is subjected to R, G, and B channel decomposition, and the channel with the highest weight is selected for normalization processing, and the formula for normalization processing is as follows:

[0041] ;

[0042] wherein, represents the pixel value of the selected channel of the image at coordinate point x , y , represents the pixel value of the normalized image, min represents the minimum value, and max represents the maximum value.

[0043] In step 3.3, the specific method for finding effective zero-crossing points is as follows:

[0044] Traverse all coordinate points of the entire LoG response map ,

[0045] ;

[0046] wherein, represents the coordinate point The response value of the neighborhood pixel point of the corresponding coordinate point, T represents the zero-crossing threshold, if it is satisfied, the corresponding coordinate point is determined as an effective zero-crossing point.

[0047] In step 4.1.1, the method for determining a single small fluctuation period is:

[0048] Traverse the vibration curve, determine the position of the wave peak, and count the time interval of adjacent wave peaks, and calculate the average value of the time interval of each adjacent wave peak as the small fluctuation period ;

[0049] In step 4.1.3, the number threshold N0 is determined according to the number of small fluctuation periods of the vibration curve.

[0050] The specific method of step 4.2.2 is:

[0051] Based on the coordinates of all effective peak points, the vibration curve is divided into continuous calculation sections according to time, and the least squares method is used to fit a straight line according to the coordinates of the effective peak points in each calculation section to obtain the average speed of each calculation section ;

[0052] If the average speed of a certain calculation section increases or decreases, a quadratic polynomial fitting is performed using the least squares method to obtain the instantaneous speed of each time section ;

[0053] The specific method of step 4.2.3 is:

[0054] Determine the highest speed threshold , the lowest speed threshold and the parking determination threshold Z in the tunnel;

[0055] If there is a calculation section with an average speed , and the fluctuation amplitude of the vibration curve in this calculation section is lower than the preset parking determination threshold Z, it is determined as illegal parking;

[0056] If the average speed of the continuous three calculation sections , or there is a time section with instantaneous speed , it is determined as speeding;

[0057] If the average speed of the continuous three calculation sections , it is determined as low-speed driving;

[0058] Otherwise, it is determined as normal driving.

[0059] Furthermore, the application also provides a vehicle monitoring system in a tunnel based on a linear frequency modulation pulse DAS, comprising a linear frequency modulation phi-OTDR device and a data processor, the linear frequency modulation phi-OTDR device comprising a narrow linewidth laser, a first coupler, a Mach-Zehnder modulator, a bias controller, a beam splitter, a first erbium-doped fiber amplifier, an optical circulator, a sensing optical fiber, a second erbium-doped fiber amplifier, a polarization controller, a second coupler and a balanced photodetector and a data acquisition device.

[0060] The narrow linewidth laser emits laser light, which is divided into probe light and reference light after the first coupler, the probe light is modulated into a linear frequency modulation light pulse by the Mach-Zehnder modulator, and the linear frequency modulation light pulse is divided into two beams after the beam splitter, one of which is output to the bias controller to bias the intensity modulator of the Mach-Zehnder modulator to zero and the phase shifter to the quadrature point; the other is output to the optical circulator and enters the sensing optical fiber after the optical circulator.

[0061] The backscattered Rayleigh scattering light generated in the sensing optical fiber is output after the optical circulator, amplified by the second erbium-doped fiber amplifier and then incident to the first input end of the second coupler; the reference light is incident to the second input end of the second coupler after the polarization controller; the backscattered Rayleigh scattering light and the reference light generate beat frequency in the second coupler, and the output beat frequency light signal is detected by the balanced photodetector and collected and sent to the data processor by the data acquisition device.

[0062] The data processor is used to execute the vehicle monitoring method in the tunnel according to the beat frequency signal collected by the data acquisition device to obtain the driving state of the vehicle in the tunnel.

[0063] The vehicle monitoring system in the tunnel based on the linear frequency modulation pulse DAS also comprises an arbitrary waveform generator, which is used to emit a linear frequency modulation signal to drive the Mach-Zehnder modulator.

[0064] Compared with the prior art, the application has the following beneficial effects:

[0065] (1) The application provides a vehicle monitoring method and system in a tunnel based on a linear frequency modulation pulse DAS, which relies on the time shift characteristics of the linear frequency modulation pulse DAS to establish a precise correlation between vehicle overload and vibration waveform, realizes efficient discrimination of the vehicle overload state, extracts the peak-to-peak value of the periodic fluctuation, combines the double judgment criteria of the peak-to-peak value exceeding the preset threshold and the number of times of exceeding the threshold being at least ten, accurately distinguishes the vibration difference caused by overload, effectively eliminates accidental interference such as road bumps, and realizes precise monitoring of the overloaded vehicle in the tunnel.

[0066] (2) The application constructs a two-dimensional space-time matrix through "detection pulse period + sensing distance", realizes synchronous monitoring of the whole domain of the sensing optical fiber along the line, does not need any relay equipment, reduces the deployment cost, and moreover, the linear frequency modulation pulse DAS device takes the linear frequency modulation pulse as the detection signal, the frequency linearly changes with time, the characteristics break through the limitation of the pulse width of the traditional DAS system, and the effective monitoring distance of the sensing optical fiber is greatly increased;

[0067] (3) The application generates stable light pulses through the narrow linewidth laser and the LFM pulse modulation, matches the balanced photoelectric detector to suppress the common mode noise, filters the ultra-low frequency noise interference in the pretreatment stage, enhances the vibration characteristics through the Hilbert transform, combines the electromagnetic resistance and corrosion resistance of the optical fiber itself, ensures stable work in the dim, fog and other environments, and the anti-interference ability is significantly better than that of the traditional monitoring technology. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 A flowchart of a tunnel vehicle monitoring method based on the linear frequency modulation pulse DAS is provided for the embodiment one of the application;

[0069] Figure 2 A structure schematic diagram of the linear frequency modulation phi-OTDR device used in the embodiment one of the application is provided;

[0070] Figure 3 A two-dimensional space-time matrix construction principle diagram based on the vibration signal in the embodiment one of the application is provided;

[0071] Figure 4 Timing waterfall diagrams corresponding to the two-dimensional envelope matrix of the vibration signal under different running states of the vehicle in the embodiment one of the application are provided, wherein (a) is a timing waterfall diagram under the normal driving state of the vehicle, (b) is a timing waterfall diagram under the overload state of the vehicle, (c) is a timing waterfall diagram corresponding to the overspeed driving of the vehicle, (d) is a timing waterfall diagram corresponding to the low-speed driving state of the vehicle, and (e) is a timing waterfall diagram when the vehicle is parked in violation of the rules;

[0072] Figure 5 A vibration curve under the normal driving state of the vehicle extracted based on the LOG edge detection algorithm in the embodiment one of the application is provided;

[0073] Figure 6 An intensity feature diagram of the local periodic fluctuation of the vibration curve obtained in the embodiment one of the application is provided, wherein (a) is a local diagram of the vibration curve under the non-overload state of the vehicle, and (b) is a local diagram of the vibration curve under the overload state of the vehicle;

[0074] Figure 7A trend line chart obtained by fitting vibration curves in different running states of the vehicle in the first embodiment of the present application: (a) corresponds to normal driving of the vehicle; (b) corresponds to overspeed driving of the vehicle; (c) corresponds to low-speed driving of the vehicle; (d) corresponds to illegal parking of the vehicle;

[0075] Figure 8 A structure schematic diagram of a tunnel vehicle monitoring system based on linear frequency modulation pulse DAS provided in the second embodiment of the present application;

[0076] In the figure: 1 is a narrow line width laser, 2 is a first coupler, 3 is an arbitrary waveform generator, 4 is a Mach-Zehnder modulator, 5 is a bias controller, 6 is a beam splitter, 7 is a first erbium-doped fiber amplifier, 8 is an optical circulator, 9 is a sensing optical fiber, 10 is a second erbium-doped fiber amplifier, 11 is a polarization controller, 12 is a second coupler, 13 is a balanced photodetector, 14 is an acquisition card, and 15 is a data processor. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0078] Embodiment 1

[0079] As shown in the figure, the present application provides a tunnel vehicle monitoring method based on linear frequency modulation pulse DAS, which comprises the following steps: Figure 1

[0080] Step 1: Based on the linear frequency modulation φ-OTDR device, the beat frequency signal is collected and filtered to remove the ultra-low frequency noise in the beat frequency signal.

[0081] Specifically, as shown in the figure, Figure 2 ​As shown, the linear frequency modulation φ-OTDR device in the embodiment includes a narrow linewidth laser 1, a first coupler 2, a Mach-Zehnder modulator 4, a bias controller 5, a beam splitter 6, a first erbium-doped fiber amplifier 7, an optical circulator 8, a sensing optical fiber 9, a second erbium-doped fiber amplifier 10, a polarization controller 11, a second coupler 12, a balanced photodetector 13, and a data acquisition device 14. The narrow linewidth laser 1 emits laser light, which is divided into probe light and reference light after passing through the first coupler 2. The probe light is modulated into a linear frequency modulation light pulse by the Mach-Zehnder modulator 4. The linear frequency modulation light pulse is divided into two beams after passing through the beam splitter 6. One of the beams is output to the bias controller 5 to bias the intensity modulator of the Mach-Zehnder modulator 4 to zero and the phase shifter to the quadrature point. The other beam is output to the optical circulator 8 and then enters the sensing optical fiber 9. The backscattered Rayleigh scattering light generated in the sensing optical fiber 9 is output from the optical circulator 8, amplified by the second erbium-doped fiber amplifier 10, and then incident on the first input end of the second coupler 12. The reference light is incident on the second input end of the second coupler 12 after passing through the polarization controller 11. In the second coupler 12, the backscattered Rayleigh scattering light and the reference light are beat, and the output beat signal is detected by the balanced photodetector 13 and then sent to the data acquisition device 14, which acquires the beat signal.

[0082] In step 1, the filtering formula is:

[0083] (1)

[0084] wherein, represents the beat signal after filtering, n represents the discrete time index of the beat signal, and n takes values from 0 to N-1 (a total of N points); M represents the order of the filter, and N represents the number of discrete time indexes of the original beat signal. The filter has a discrete impulse response h(k), and k represents the sampling point index of the filter. The filter has a discrete impulse response h(k), and k represents the sampling point index of the filter. represents the signal value of the original beat signal delayed by k, and during filtering, the input signal and the filter are “sliding weighted superimposed” by summing all the sampling points of the filter impulse response, thereby suppressing the ultra-low frequency noise with a frequency less than 20 Hz in the beat signal. Step 2: Based on the detection pulse period and the sensing distance, a two-dimensional space-time matrix is constructed based on the beat signal after filtering, and the envelope is extracted by Hilbert transform to obtain a two-dimensional envelope matrix.

[0085] Step 2: Based on the detection pulse period and the sensing distance, a two-dimensional space-time matrix is constructed based on the beat signal after filtering, and the envelope is extracted by Hilbert transform to obtain a two-dimensional envelope matrix.

[0086] ​​Specifically, in step 2, the method of constructing a two-dimensional space-time matrix is as follows: the beat frequency signal after filtering is a one-dimensional signal with time as the horizontal axis and intensity as the vertical axis. As shown in Figure 3 , by segmenting the time axis according to the period of the probe pulse, the response signal of each linear frequency modulation pulse on the entire sensing fiber can be extracted; accordingly, the response signal of each linear frequency modulation pulse on the entire sensing fiber obtained after filtering can be arranged and spliced in the order of actual sampling time and the distribution order of sensing distance converted based on the propagation time of the optical pulse, and then the one-dimensional signal after filtering is reconstructed into a two-dimensional space-time matrix with time and distance as dimensions; the matrix has the time axis as the matrix row and the distance axis as the matrix column, the row represents the actual sampling time, and the column represents the sensing distance converted based on the propagation time of the optical pulse, corresponding to the spatial position of the vehicle in the tunnel.

[0087] Specifically, in the embodiment, the beat frequency signal after filtering is a discrete domain, which can be converted to a continuous domain to construct a two-dimensional space-time matrix, and the row data of the two-dimensional space-time matrix can be regarded as a corresponding denoised beat frequency signal of a specific distance position, where t represents time.

[0088] Specifically, in step 2, the method of extracting the envelope to obtain a two-dimensional envelope matrix is as follows:

[0089] Step 2.1: Hilbert transform is performed on the data of each row of the two-dimensional space-time matrix, and the transform formula is:

[0090] ; (2)

[0091] wherein, represents the Hilbert transform, represents the row data of the two-dimensional space-time matrix , wherein t represents time, represents the signal after Hilbert transform;

[0092] Step 2.2: Calculate the signal envelope, and the calculation formula is:

[0093] ; (3)

[0094] wherein, represents the signal envelope, which describes the amplitude profile of the row data of the two-dimensional space-time matrix with time.

[0095] Step 2.3: Obtain the two-dimensional envelope matrix through the calculated signal envelope. Specifically, by calculating the signal envelope corresponding to the data of each row of the two-dimensional space-time matrix, the two-dimensional envelope matrix can be obtained.

[0096] Specifically, in this embodiment, after obtaining the two-dimensional envelope matrix, the two-dimensional envelope matrix of the vehicle vibration signal under multiple different operating states is visualized to obtain a time sequence waterfall diagram, as shown in Figure 4 (a) is a time sequence waterfall diagram under a normal driving state of the vehicle, the trajectory slope of which is consistent with the normal driving speed, and the overall performance shows stable regularity; (b) is a time sequence waterfall diagram under an overloaded state of the vehicle, and compared with (a), the periodic fluctuation amplitude of the vibration curve is enhanced, but since the actual fluctuation amplitude is much smaller than the distance axis range, the fluctuation difference between the two is not obvious in the diagram; (c) is a time sequence waterfall diagram corresponding to an overspeed driving state of the vehicle, and since the speed is increased, the trajectory slope is significantly increased; (d) is a time sequence waterfall diagram corresponding to a low-speed driving state of the vehicle, and the trajectory slope is obviously reduced; (e) is a time sequence waterfall diagram when the vehicle is parked in violation of regulations, and after 50 seconds, the vehicle is in a stationary state, the trajectory slope is zero, and in the space-time matrix, it is represented as continuous stay at a fixed spatial position. By comparing the time sequence waterfall diagrams under different driving states, the differentiated characteristics of the vehicle vibration signal can be distinguished from the space-time dimension, thereby providing an analysis basis for subsequent vehicle abnormal state recognition.

[0097] Step 3: After converting the two-dimensional envelope matrix into an image, an edge detection algorithm is used to extract the edges of the image to obtain the vibration curve of the vehicle.

[0098] The step 3 specifically includes the following steps:

[0099] Step 3.1: After converting the two-dimensional envelope matrix into an image format, the image is normalized.

[0100] In the step 3.1, the method for converting the two-dimensional envelope matrix into an image is as follows: the time dimension in the matrix is converted into the horizontal pixel position of the image, the distance dimension is converted into the vertical pixel position of the image, and the intensity value of each position in the matrix is mapped to the pixel information of the corresponding pixel point to obtain the image. After obtaining the image, the pixel value is normalized.

[0101] Specifically, in this embodiment, the Viridis sequential color mapping table is used for pseudo-color mapping of the two-dimensional envelope matrix to obtain a color image containing R, G and B channels. Then, the obtained image is decomposed into R, G and B channels, the channel with the highest weight is selected for normalization, and the formula for normalization is:

[0102] ; (4)

[0103] wherein, represents the pixel value of the selected channel of the image, x , y represents a coordinate point, This represents the pixel values ​​of the normalized image, where min represents the minimum value and max represents the maximum value.

[0104] Specifically, the method for calculating channel weights is as follows: The gradient magnitude of all pixels in each channel is calculated using the gradient operator and summed. The resulting scalar is used as the channel weight. The channel with the largest weight is selected as the basis for subsequent edge detection algorithms and normalized. Edge detection itself involves finding areas with large gradients (rates of change) in an image. A larger gradient reflects greater vibration and contains more vibration information. Since the R, G, and B channels all contain vibration signals but also their own noise, selecting the channel with the largest gradient (i.e., more vibration information) as the edge detection benchmark reduces the impact of noise, decreases the complexity of subsequent processing, and improves computational efficiency.

[0105] Step 3.2: Convolve the discrete LoG kernel with the normalized image to obtain the LoG response map; the convolution formula is:

[0106] (5)

[0107] in, This indicates the LoG response map at coordinate point ( x , y The corresponding response value, This indicates the normalized image in Pixel value at that location, represents the discrete LoG kernel; (i, j) represents the position index of the discrete LoG kernel, used to traverse each element in the convolution kernel and mark the coordinates of different positions within the convolution kernel; a and b represent the half-width range index values ​​of the discrete LoG kernel in two coordinate directions, respectively.

[0108] In the LoG response graph, the response value of each point represents the intensity of the second derivative of that point after Gaussian smoothing. Edges will appear when the response value is 0, i.e. The location.

[0109] Specifically, the discrete LoG kernel is a discrete convolution kernel generated based on the LoG kernel, and its expression can be written as:

[0110] (6)

[0111] Where s represents the total number of elements in the discrete kernel; The LoG kernel is obtained by using the Laplace result of a Gaussian function:

[0112] (7)

[0113] in, The standard deviation of the Gaussian distribution, which is the key parameter to control the smoothness.

[0114] Step 3.3: Traverse the entire LoG response map to find valid zero-crossing points as edge points.

[0115] In step 3.3, the specific way to find valid zero-crossing points is:

[0116] Traverse all coordinate points in the entire LoG response map ( x , y ), check their neighborhoods, if there is a pixel point in its neighborhood whose sign of LoG response value is opposite, and the absolute value of the difference between the LoG response values of the coordinate point and its neighborhood pixel points is greater than the set threshold T, then the coordinate point is a valid zero-crossing point, mark it as an edge point. That is, the judgment condition:

[0117] ; (8)

[0118] Where, represents the response value of the neighborhood pixel point of coordinate point , where, = or = . and respectively represent the response values of the neighborhood pixel points of coordinate point ( x ±1, y ) and ( x, y ±1). T represents the zero-crossing threshold, which is adjusted according to the numerical range of the image , and can be set to a value between .

[0119] Step 3.4: Connect the edge points in turn to generate a binary edge image, and then restore the image coordinate points where the edge contour is located to time-distance coordinate axis parameters to obtain the vibration curve of the vehicle.

[0120] Specifically, in the binarized edge image, the value of a pixel point is 1, and the value of a background pixel point is 0; an edge pixel point in the selected image is selected as a starting point, and then edge pixel points around the starting point are searched in a clockwise or counterclockwise direction until the boundary of the image is reached; in the searching process, the coordinates of all the passing edge pixel points are recorded and extracted one by one. After the preliminary edge tracking is completed, an edge thinning algorithm is used to process the obtained edge contour, and the original multi-pixel width contour is compressed to a single-pixel dimension. Finally, the edge pixel points in the vibration region after thinning are connected according to their spatial position order in the image to form a continuous edge contour.

[0121] After the vibration edge detection and thinning processing are completed, the image coordinate system in which the edge contour is located is restored to the "time-distance" coordinate axis parameters before the edge detection, to ensure that the edge position is consistent with the time and position attributes of the original signal, and the vibration curve of the vehicle is obtained, as shown in Figure 5 In the time-distance coordinate system, the vibration curve presents an approximately straight line segment at a macro scale. This is mainly because the actual amplitude of the vehicle vibration is much smaller than the range of the distance axis in the coordinate system, and the vibration frequency is high, which causes the micro fluctuations to be greatly compressed in the overall view, and thus the vibration curve tends to be a straight line in vision. By zooming in on a local area of the curve, the detailed vibration characteristics contained therein can be clearly observed, as shown in Figure 6

[0122] Specifically, in step 3, the horizontal coordinate of the obtained vibration curve of the vehicle is time t, representing the actual sampling time, and the vertical coordinate is distance d, representing the sensing distance converted based on the propagation time of the light pulse, corresponding to the spatial position of the vehicle in the tunnel. The vibration curve presents the characteristics of "overall inclined upward / downward trend + periodic small fluctuations". The overall inclined upward / downward trend corresponds to the normal driving trajectory of the vehicle along the sensing optical fiber in the tunnel; the periodic small fluctuations are caused by the vibration generated by the tire friction with the ground and the compression and rebound of the suspension system when the vehicle is driving, directly reflecting the vibration intensity of the vehicle.

[0123] Step 4: judging whether overloading occurs according to the vibration curve, and identifying the speed state of the vehicle.

[0124] Specifically, in this embodiment, the vibration curve is analyzed from two independent dimensions to determine the driving state of the vehicle: one is around the fluctuation amplitude associated with the weight, and the other is around the driving trajectory associated with the speed. The peak-to-peak value of the periodic fluctuation is counted, and if the number of times exceeds a preset threshold, it is determined that the vehicle is overloaded. The slope of the trend line is calculated by fitting the trend line, and the speed of the vehicle is obtained. In combination with the preset threshold, it is identified whether the vehicle is driving at a high speed, a low speed, normally or illegally parked. Finally, the two types of discrimination results are fused to determine the complete driving state of the vehicle in the tunnel. ​

[0125] In the LFM pulse-based DAS system, there is a relationship expression as follows:

[0126] (9)

[0127] wherein, represents the fiber strain, represents the system correlation coefficient, represents the starting frequency of the LFM pulse, represents the modulation slope of the LFM pulse, represents the time shift amount. It can be known from the expression that the sensing fiber strain caused by the vehicle vibration has a direct physical correlation with the time shift amount When the intensity of the vehicle vibration changes, the strain of the sensing fiber will directly change correspondingly, and the change of the fiber strain will further drive the time shift amount to quantitatively change synchronously. Since the two-dimensional space-time matrix is a signal carrier based on the "detection pulse period (time dimension) and the position of the sensing fiber along the line (distance dimension)", it forms a "time-distance" two-dimensional signal distribution structure by sequentially splicing the response signals of each detection pulse at different sensing distances. This splicing method makes the change of the time shift amount directly change the time delay distribution characteristics of the backscattering Rayleigh signal in the matrix.

[0128] The above signal distribution adjustment in the matrix caused by the change of the time shift amount will be completely and distortionlessly mapped to the "time-distance" vehicle vibration curve after the signal preprocessing, envelope extraction, LoG edge detection and other processing links. Specifically, when the vehicle is overloaded, the tire deformation is intensified, the load of the suspension system is increased, and the vibration intensity generated during driving will be significantly higher than that generated under normal load, and the difference in vibration intensity will be converted into the amplitude change of the local periodic fluctuation in the vibration curve through the chain action of "fiber strain-time shift amount-matrix signal distribution". The peak-to-peak value of the periodic fluctuation in the vibration curve under overload is significantly larger than that under normal load.

[0129] The discrimination of the vehicle overload in this embodiment is based on the physical correlation between the vehicle vibration intensity and the peak-to-peak value of the fluctuation. Specifically, the vehicle overload will cause the vibration to intensify, and directly manifest as the increase of the peak-to-peak value of the periodic fluctuation in the vibration curve. According to this mechanism, the overload state can be identified by monitoring whether the peak-to-peak value is out of limit, and the specific method is as follows:

[0130] Step 4.1.1: Traverse the vibration curve of the vehicle to determine the small period of fluctuation.

[0131] In step 4.1.1, the method for determining a single small fluctuation period is to traverse the vibration curve of the vehicle, determine the position of the peak, and count the time interval between adjacent peaks, and calculate the average of the time interval of each adjacent peak as the small fluctuation period .

[0132] wherein the determination of the peak position is to traverse each time sampling point on the vibration curve, and if the distance value of a certain point satisfies and , it is determined that the point is a peak. Wherein , , respectively represent the distance values corresponding to time sampling points i-1, i, i+1.

[0133] Step 4.1.2: Split the vibration curve into multiple continuous small fluctuation periods to ensure that enough fluctuations are covered to exclude accidental interference.

[0134] Step 4.1.3: Count the number N1 of peak-to-peak values greater than the peak-to-peak threshold value in all small periods, and if N1 is greater than the number threshold N0, it is determined to be overloaded. Otherwise, it is determined to be not overloaded.

[0135] For each small period, extract the maximum and minimum values of the vibration curve in the period, and calculate the difference between the two as the peak-to-peak value ; a predetermined overload peak-to-peak threshold value D (based on the statistical vibration peak-to-peak value of a normal load vehicle) is set, and the number N1 of peak-to-peak values greater than the peak-to-peak threshold value D in all small fluctuation periods is counted.

[0136] Specifically, in step 4.1.3, the number threshold N0 is determined according to the number of small fluctuation periods of the vibration curve; for example, it can be set to half of the number of small fluctuation periods.

[0137] As shown in Figure 6 , wherein (a) is a local schematic diagram of the vibration curve of the vehicle in the unloaded state; (b) is a local schematic diagram of the vibration curve of the vehicle in the overloaded state, as can be seen from the figure, in the overloaded state, the amplitude peak-to-peak value of the vibration curve is significantly increased. The peak-to-peak value of the local periodic fluctuation of the vibration curve of the vehicle in the overloaded state is significantly greater than that in the unloaded state, and this quantitative difference directly reflects the vibration intensity characteristics of the vehicle in different load states.

[0138] Specifically, in this embodiment, the method for identifying the speed state of the vehicle is:

[0139] Step 4.2.1: Traverse each time sampling point on the vibration curve, find all valid peak points, and record the coordinates.

[0140] In this embodiment, each time sampling point on the vibration curve is traversed to determine whether the following conditions are met:

[0141] , and , and T2; (9)

[0142] wherein, , , respectively represent the distance values corresponding to time sampling points i, i-1, i+1, T2 represents a peak detection threshold value, and if the conditions are met, the point is marked as an effective peak point.

[0143] Step 4.2.2: Based on the coordinates of all effective peak points, a trend line fitting is performed to calculate the speed of the vehicle.

[0144] The specific method of step 4.2.2 is:

[0145] (1) Based on the coordinates of all effective peak points, the vibration curve is divided into continuous calculation sections according to time, and the least squares method is used to perform linear fitting to obtain the average speed of each calculation section according to the coordinates of the effective peak points in each calculation section .

[0146] (2) If the average speed of a calculation section increases or decreases, a quadratic polynomial fitting is performed using the least squares method to obtain the instantaneous speed of each time section .

[0147] The speed of the vehicle in the tunnel can be defined as the rate of change of the spatial position of the vehicle on the sensing optical fiber laid in the tunnel with respect to time; at the same time, the trend line of the vibration curve is a fitting line that accurately anchors the vehicle displacement-time relationship after removing local periodic fluctuations such as tire friction, and the slope is defined as the ratio of the change in the vertical coordinate to the change in the horizontal coordinate of any two points on the trend line; since the global trend line has excluded the interference of local fluctuations on displacement, the vertical coordinate change is the true displacement of the vehicle, so the average speed of the vehicle can be calculated from the slope of the fitted straight line; the positive and negative of the straight line slope reflects the driving direction of the vehicle (positive for driving towards the far end of the optical fiber, negative for driving towards the near end).

[0148] Step 4.2.3: Determine the speed state of the vehicle according to the speed of the vehicle.

[0149] Specifically, the specific method of step 4.2.3 is:

[0150] (1) Determine the highest speed threshold , the lowest speed threshold , and the parking determination threshold Z in the tunnel;

[0151] (2) If there is a calculation section of average speed , and the fluctuation amplitude of the vibration curve in the calculation section is lower than the preset parking determination threshold Z, it is determined as illegal parking;

[0152] If the average speed of the continuous three calculation sections , or there is a time section of instantaneous speed , it is determined as overspeed driving;

[0153] If the average speed of the continuous three calculation sections , it is determined as low-speed driving;

[0154] Otherwise, it is determined as normal driving.

[0155] Specifically, in step 4.2.3, the determination method of the parking determination threshold Z is: when there is no vehicle passing through the tunnel, the background noise signal of the sensing system is collected, and the statistical characteristics (such as the maximum value or the mean value plus a multiple of the standard deviation) of the noise signal are taken as the basis, and a proper safety margin is reserved to ensure reliable identification of the static state in the noise environment.

[0156] Figure 7 The trend lines obtained by fitting the vibration curves under different running states of the vehicle in the embodiment are shown. Among them, (a) corresponds to the trend line of the vehicle in normal driving, which has a stable slope and matches the preset normal speed interval; (b) corresponds to the trend line of the vehicle in overspeed driving, whose slope increases significantly, which intuitively reflects the speed overrun feature; (c) corresponds to the trend line of the vehicle in low-speed driving, whose slope is significantly smaller than that in normal driving; (d) corresponds to the trend line of the vehicle in illegal parking, which tends to be horizontal after 45s, and the slope is 0, which clearly reflects the static state of the vehicle. Through the comparison of these trend lines, it can be seen directly that: the slope of overspeed driving is the largest, the slope of normal driving is at an intermediate level, the slope of low-speed driving is smaller, and the slope of illegal parking is 0, and the slope difference of the four states constitutes an intuitive feature to distinguish the running state of the vehicle.

[0157] Finally, the overload discrimination result (overload / non-overload) and the speed state discrimination result (overspeed / normal driving / low-speed driving / illegal parking) can be fused to determine the complete driving state of the vehicle in the tunnel:

[0158] (1) If the vehicle has been determined to be overloaded, and the speed state is overspeed, it is finally determined as "overloaded and overspeed";

[0159] (2) If the vehicle has been determined to be overloaded, and the speed state is normal driving, it is finally determined as "overloaded";

[0160] (3) If the vehicle has been determined to be overloaded, and the speed state is low-speed driving, it is finally determined as "overloaded and low-speed driving";

[0161] (4) if the vehicle has been determined to be overloaded and the speed state is illegal parking, then the final determination is "overloaded and illegal parking";

[0162] (5) if the vehicle has been determined to be not overloaded and the speed state is overspeed, then the final determination is "overspeed";

[0163] (6) if the vehicle has been determined to be not overloaded and the speed state is normal driving, then the final determination is "normal driving";

[0164] (7) if the vehicle has been determined to be not overloaded and the speed state is low-speed driving, then the final determination is "low-speed driving";

[0165] (8) if the vehicle has been determined to be not overloaded and the speed state is illegal parking, then the final determination is "illegal parking".

[0166] The monitoring method of the present application can accurately capture the vibration information of the vehicle in the tunnel, break through the bottleneck of the traditional scheme in the detection distance, monitoring accuracy and anti-interference performance, and realize accurate monitoring of the abnormal state of the vehicle in the tunnel, such as overspeed, overload, illegal parking and low-speed driving.

[0167] Embodiment two

[0168] As shown in Figure 8 Embodiment two of the present application provides a tunnel vehicle monitoring device based on linear frequency pulse DAS, which comprises a linear frequency φ-OTDR device and a data processor 15, the data processor 15 is used to calculate the driving state of the vehicle in the tunnel by using the monitoring method of embodiment one according to the signal collected by the linear frequency φ-OTDR device.

[0169] Specifically, the linear frequency modulation φ-OTDR device comprises a narrow linewidth laser 1, a first coupler 2, a Mach-Zehnder modulator 4, a bias controller 5, a beam splitter 6, a first erbium-doped fiber amplifier 7, an optical circulator 8, a sensing optical fiber 9, a second erbium-doped fiber amplifier 10, a polarization controller 11, a second coupler 12, and a balanced photodetector 13. The narrow linewidth laser 1 emits laser light, which is divided into probe light and reference light after passing through the first coupler 2. The probe light is modulated into a linear frequency modulation light pulse by the Mach-Zehnder modulator 4. The linear frequency modulation light pulse is divided into two beams after passing through the beam splitter 6. One of the two beams is output to the bias controller 5 to bias the intensity modulator of the Mach-Zehnder modulator 4 to zero and the phase shifter to the quadrature point. The other beam is output to the optical circulator 8 and then enters the sensing optical fiber 9. The backscattered Rayleigh scattering light generated in the sensing optical fiber 9 is output from the optical circulator 8, amplified by the second erbium-doped fiber amplifier 10, and then incident on the first input end of the second coupler 12. The reference light is incident on the second input end of the second coupler 12 after passing through the polarization controller 11. In the second coupler 12, the backscattered Rayleigh scattering light and the reference light are beat, and the output beat light signal is detected by the balanced photodetector 13, collected by the data acquisition device 14, and sent to the data processor 15. The data processor 15 calculates the driving state of the vehicle in the tunnel according to the beat signal collected by the data acquisition device 14.

[0170] Further, as shown in the figure, the linear frequency modulation pulse DAS-based tunnel vehicle monitoring system further comprises an arbitrary waveform generator 3, which is used to generate a linear frequency modulation signal to drive the Mach-Zehnder modulator 4 to modulate the signal light output by the first coupler 2 into a linear frequency modulation light pulse. Figure 2

[0171] Specifically, in this embodiment, the narrow linewidth laser 1 outputs laser light with a center wavelength of 1550.12 nm and a linewidth of less than 0.1 kHz. The arbitrary waveform generator 3 generates a digital linear frequency modulation single pulse signal with a pulse width of 300 ns and a repetition period of 330 us. The Mach-Zehnder modulator 4, the bias controller 5, and the beam splitter 6 together constitute an IQ modulator, which is used to load an electrical linear frequency modulation signal onto an optical carrier to generate an optical pulse with a linearly changing frequency over time and coherent phase. Specifically, the beam splitter 6 is an optical coupler with a splitting ratio of 1:99, of which the 1% part is connected to the bias controller 5 to provide feedback, and the 99% part outputs the linear frequency modulation light pulse. The first erbium-doped fiber amplifier 7 is a pulse-type optical fiber amplifier, and the second erbium-doped fiber amplifier 10 is a small-signal-type optical fiber amplifier, which is used to amplify the backscattered Rayleigh scattering signal output by the sensing optical fiber 9.

[0172] ​It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS, characterized in that, Includes the following steps: Step 1: Acquire beat frequency signals based on linear frequency modulated φ-OTDR devices and perform filtering processing to remove ultra-low frequency noise less than 20Hz from the beat frequency signals; Step 2: Based on the detection pulse period and sensing distance, construct a two-dimensional spatiotemporal matrix based on the filtered beat frequency signal. The two-dimensional spatiotemporal matrix has the time axis as the matrix row and the distance axis as the matrix column. Extract the envelope of the two-dimensional spatiotemporal matrix through Hilbert transform to obtain a two-dimensional envelope matrix. Step 3: After converting the two-dimensional envelope matrix into an image, use an edge detection algorithm to extract the image edges and obtain the vehicle's vibration curve; Step 4: Determine whether overload has occurred based on the vibration curve, and simultaneously identify the vehicle's speed status; The method for determining whether an overload has occurred is as follows: Step 4.1.1: Traverse the vibration curves to determine the small period of fluctuation; Step 4.1.2: Divide the vibration curve into multiple continuous small oscillation cycles; Step 4.1.3: Count the number of times N1 is greater than the peak-to-peak value threshold in all small cycles. If N1 is greater than the number threshold N0, it is determined to be overload. The method for identifying vehicle speed status is as follows: Step 4.2.1: Traverse each time sampling point on the vibration curve, find all valid peak points, and record their coordinates; Step 4.2.2: Calculate the vehicle speed based on the coordinates of all valid peak points; Step 4.2.3: Determine the vehicle's speed status based on its speed; In step 2, the method for extracting the envelope to obtain the two-dimensional envelope matrix is ​​as follows: Step 2.1: Perform a Hilbert transform on the data in each row of the two-dimensional spatiotemporal matrix. The transformation formula is as follows: ； in, Represents the Hilbert transform. This represents the row data of a two-dimensional spatiotemporal matrix, where t represents time. This represents the signal after the Hilbert transform; Step 2.2: Calculate the signal envelope. The calculation formula is as follows: ； in, Indicates the signal envelope; Step 2.3: Obtain the two-dimensional envelope matrix based on the signal envelope corresponding to the data in each row of the two-dimensional spatiotemporal matrix; Step 3 specifically includes the following steps: Step 3.1: After converting the two-dimensional envelope matrix into an image format, normalize the image. Step 3.2: Convolve the discrete LoG kernel with the normalized image to obtain the LoG response map; the convolution formula is: ； in, This indicates the LoG response map at coordinate point ( x , y The corresponding response value, This indicates the normalized image at coordinate points. Pixel value at that location, Let (i, j) represent the discrete LoG kernel, (i, j) represent the position index of the discrete LoG kernel, and a and b represent the half-width range index values ​​of the discrete LoG kernel in the two coordinate directions, respectively. Step 3.3: Traverse the entire LoG response graph and find valid zero-crossing points as edge points; Step 3.4: Connect each edge point sequentially to generate a binarized edge image, and then restore the image coordinate points where the edge contour is located to time-distance coordinate axis parameters to obtain the vehicle vibration curve.

2. The method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS according to claim 1, characterized in that, In step 1, the filtering formula is as follows: ； in, This represents the beat frequency signal after filtering, where n represents the discrete-time index of the beat frequency signal. This represents the discrete impulse response of the filter, where k represents the sampling point index of the filter. The signal value is represented by k after a delay from the original beat frequency signal, N represents the number of discrete-time indices corresponding to the original beat frequency signal, and M represents the filter order.

3. The method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS according to claim 1, characterized in that, In step 3.1, the method for converting the two-dimensional envelope matrix into an image is as follows: The time dimension in the matrix is ​​transformed into the horizontal axis pixel position of the image, the distance dimension is transformed into the vertical axis pixel position of the image, and the intensity value of each position in the matrix is ​​mapped to the color information of the corresponding pixel to obtain the image. In step 3.1, the obtained image is decomposed into R, G, and B channels, and the channel with the highest weight is selected for normalization. The normalization formula is as follows: ； in, This indicates that the selected channel of the image is at coordinate point ( x , y The pixel value of ) This represents the pixel values ​​of the normalized image, where min represents the minimum value and max represents the maximum value.

4. The method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS according to claim 1, characterized in that, In step 3.3, the specific method for finding effective zero-crossing points is as follows: Traverse the entire LoG response graph Given all coordinate points, determine if any of them satisfy the following condition: in, Represents coordinate points The response value of the neighboring pixels is T, which represents the zero-crossing threshold. If the threshold is met, the corresponding coordinate point is determined to be a valid zero-crossing point.

5. The method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS according to claim 1, characterized in that, In step 4.1.1, the method for determining a single small fluctuation cycle is as follows: Traverse the vibration curves to determine the peak positions, and count the time intervals between adjacent peaks. Calculate the average of the time intervals between adjacent peaks as the small oscillation period. ; In step 4.1.3, the number of times threshold N0 is determined based on the number of small cycles of the vibration curve.

6. The method for vehicle monitoring in tunnels based on linear frequency modulated pulse DAS according to claim 1, characterized in that, The specific method for step 4.2.2 is as follows: Based on the coordinates of all effective peak points, the vibration curve is divided into continuous calculation segments according to time. The average velocity of each calculation segment is obtained by performing linear fitting using the least squares method based on the coordinates of the effective peak points within that segment. ; If the average vehicle speed increases or decreases in a certain calculation segment, the least squares method is used to perform a quadratic polynomial fitting to obtain the instantaneous speed for each time period. ; The specific method for step 4.2.3 is as follows: Determine the maximum speed threshold inside the tunnel Minimum speed threshold And the parking determination threshold Z; If an average velocity exists If the calculation segment is less than the preset parking judgment threshold Z, then it is judged as illegal parking. If the average speed of three consecutive calculation segments Or there may be instantaneous speed If the time period is specified, it is considered speeding; If the average speed of three consecutive calculation segments If so, it is determined to be driving at low speed; Otherwise, it is considered normal driving.

7. A vehicle monitoring system in a tunnel based on linear frequency modulated pulse DAS, characterized in that, The device includes a linear frequency modulated φ-OTDR device and a data processor (15). The linear frequency modulated φ-OTDR device includes a narrow linewidth laser (1), a first coupler (2), a Mach-Zehnder modulator (4), a bias controller (5), a beam splitter (6), a first erbium-doped fiber amplifier (7), an optical circulator (8), a sensing fiber (9), a second erbium-doped fiber amplifier (10), a polarization controller (11), a second coupler (12), a balanced photodetector (13), and a data acquisition device (14). The narrow linewidth laser (1) emits a laser beam that is split into two beams, a probe beam and a reference beam, after passing through the first coupler (2). The probe beam is modulated into a linear frequency modulated light pulse by the Mach-Zehnder modulator (4). The linear frequency modulated light pulse is split into two beams by the beam splitter (6). One beam is biased to the intensity modulator of the Mach-Zehnder modulator (4) to zero and the phase shifter to the quadrature point by the output control signal of the bias controller (5). The other beam is output to the optical circulator (8) and enters the sensing fiber (9) after passing through the optical circulator (8). The backscattered Rayleigh light generated in the sensing fiber (9) is output through the optical circulator (8), amplified by the second erbium-doped fiber amplifier (10), and then incident on the first input end of the second coupler (12); the reference light is incident on the second input end of the second coupler (12) after passing through the polarization controller (11); the backscattered Rayleigh light and the reference light beat within the second coupler (12), and the output beat frequency light signal is detected by the balanced photodetector (13), collected by the data acquisition device (14), and sent to the data processor (15). The data processor (15) is used to execute the vehicle monitoring method in the tunnel according to any one of claims 1 to 6 based on the beat frequency signal collected by the data acquisition device (14) to obtain the driving status of the vehicle in the tunnel.

8. A vehicle monitoring system in a tunnel based on linear frequency modulated pulse DAS according to claim 7, characterized in that, It also includes an arbitrary waveform generator (3), which is used to generate a linear frequency modulation signal to drive the Mach-Zehnder modulator (4).

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