Early warning system based on distributed prefabricated optical cable

Abstract

The invention relates to the technical field of optical fiber sensing, in particular to an early warning system based on a distributed prefabricated optical cable, which comprises a spatial domain analysis module, a time domain analysis module, a vibration monitoring module and a signal generation module, according to the invention, through real-time monitoring of the distributed prefabricated optical cable and the optical time domain reflectometer, filtering and noise reduction are carried out by using an OTDR test curve and a sliding flat weight algorithm, so that the detection precision is improved; through feature extraction and reflection peak positioning, the position of an abnormal point can be accurately determined; according to the method, optical fiber detection characteristic parameters are considered, a skewness factor and a kurtosis factor are introduced, and the asymmetry and non-uniformity of reflection point distribution are quantitatively analyzed; the multi-dimensional analysis can reflect the state of the optical cable more comprehensively and accurately; the detection curve sequence group provided by the invention adopts a dynamic updating mode, and the latest state of the optical cable can be reflected in real time.

Description

Technical Field

[0001] The present invention relates to the field of optical fiber sensing technology, in particular to an early warning system based on distributed prefabricated optical cables. Background Art

[0002] Fiber optic sensing technology is a new type of sensing technology based on optical fibers developed in the mid-1970s. It is an emerging technology developed on the basis of optical information processing technology and electrical information processing technology. Fiber optic sensing technology makes optical fiber both an information carrier and a sensitive medium for sensing external signals; it not only maintains the characteristics of optical fiber with high-speed information transmission with large capacity, but also has the sensing characteristics of sensitively sensing subtle changes in various physical and chemical quantities in the outside world through modulation and processing of various parameters such as amplitude, phase, frequency, and polarization state of light. Fiber optic sensing technology has the characteristics of long-term reliability and continuous monitoring, and transmits data to the target system quickly, thereby improving the efficiency and response speed of data collection, and is suitable for oil and gas pipeline safety management. By real-time sensing of the vibration signals of oil and gas pipelines and combining with innovative perception algorithm engines, accurate identification and early warning of construction events can be achieved, so that timely measures can be taken to prevent threats or carry out repairs, and the efficiency and response speed of event processing can be improved.

[0003] However, existing fiber optic sensing technology still has many problems. For example, during operation, the output signal of the fiber optic sensor will be affected by factors such as light source fluctuations, changes in fiber optic transmission loss, and detector aging. These factors will reduce the accuracy of fiber optic sensor measurements.

[0004] Secondly, the data analysis of optical fiber sensors involves comprehensive knowledge of signal-to-noise ratio, OTDR system, coupler signal demodulation, etc. The data of optical fiber sensors is poorly interpretable, and its specific use requires professional training, or even only professionals can operate it. This is mainly because the technology is still in the development stage, there are many types of sensors, poor standardization, and lack of mandatory standards.

[0005] In view of the above problems, it is necessary to propose an early warning system based on distributed prefabricated optical cables. Summary of the invention

[0006] The purpose of the present invention is to solve the problems existing in the background technology and to propose an early warning system based on distributed prefabricated optical cables.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The early warning system based on distributed prefabricated optical cables includes a space domain analysis module, a time domain analysis module, a vibration monitoring module and a signal generation module.

[0009] The spatial domain analysis module numbers the distributed prefabricated optical cables, and the numbering symbol is i, i=1, 2, 3, ..., n; wherein n is the total number of the distributed prefabricated optical cables.

[0010] For each distributed prefabricated optical cable i, a detection light is emitted to it at a preset detection period T. Subsequently, the Rayleigh scattered light of the detection light is monitored in real time by an optical time domain reflectometer to obtain the distance and amplitude data of the Rayleigh scattered light. The propagation distance of the Rayleigh scattered light is the horizontal axis x, and the amplitude A of the Rayleigh scattered light is the vertical axis to generate a distance-amplitude curve of the scattered light, that is, the OTDR test curve.

[0011] A total of k OTDR test curves generated after k detection cycles are recorded as a detection curve sequence group, where k is the preset curve group capacity. In this group, the earliest generated OTDR test curve is marked as OTDR (1), and the most recently generated OTDR test curve is marked as OTDR (k).

[0012] At the end of each detection cycle T, the detection curve sequence group is dynamically updated. The specific operation is: OTDR (1), that is, the earliest generated OTDR test curve, is removed from the detection curve sequence group, and the numbers k of all remaining OTDR test curves are reduced by one. At the same time, the OTDR test curve generated in the previous detection cycle T is marked as the new OTDR (k) and added to the detection curve sequence group.

[0013] As a preferred embodiment of the present invention, whenever the detection curve sequence group completes a dynamic update, all OTDR detection curves included in the detection curve sequence group are immediately filtered and denoised by a sliding equal weight algorithm. All detection curves in the detection curve sequence group are numbered, and the number symbol is j.

[0014] Obtain the image analysis formula fj(x)=f1(x),f2(x),f3(x),...,fk(x) of each OTDR test curve OTDR(j)=OTDR(1),OTDR(2),OTDR(3),...,OTDR(k) in the detection curve sequence group. Calculate the second-order moment Ej=E1, E2, E3, ..., Ek of each image analytical expression, where L is the total length of the distributed prefabricated optical cable i.

[0015] As a preferred embodiment of the present invention, by formula Calculate the dynamic weight μj = μ1, μ2, μ3, ..., μk. By formula A sliding equalization noise reduction operation is performed to obtain the image analytical expression g(x) of the OTDR test curve after noise reduction and draw its function graph, which is recorded as the noise reduction curve OTDR(0).

[0016] Feature extraction is performed on the noise reduction curve OTDR (0), and the peak image is located therein, and the peak image whose area is greater than the preset threshold AMin is screened and recorded as the reflection peak. The reflection peaks are numbered, and the number symbol is p, p = 1, 2, 3, ..., q; q is the total number of reflection peaks. The point with the largest amplitude A value on each reflection peak is recorded as the reflection peak value A1p, the horizontal coordinate corresponding to the reflection peak is marked as the reflection point xp, and the point with the smallest amplitude A value on the reflection peak is recorded as the reflection valley value A2p.

[0017] By formula Calculate the optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i. Calculate the mean value Ei(xp) and variance σi(xp) of the distribution of reflection point xp in distributed prefabricated optical cable i.

[0018] As a preferred embodiment of the present invention, by formula Calculate the skewness factor S1i and the kurtosis factor S2i of the distribution of the reflection point xp.

[0019] The optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i, the skewness factor S1ixp and the kurtosis factor S2i of the distribution of the reflection point xp are sent to the time domain analysis module.

[0020] After receiving the optical fiber detection characteristic parameter C1i, the skewness factor S1i and the kurtosis factor S2i, the time domain analysis module records the receiving time t and generates an optical fiber detection characteristic parameter-time diagram, a skewness factor-time diagram and a kurtosis factor-time diagram.

[0021] As a preferred embodiment of the present invention, in the optical fiber detection characteristic parameter-time diagram, the expression C1i=F1i(t) of the image is obtained, and the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R1 is the preset maximum threshold. Calculate the first abnormal index α1i, where K1 and K2 are preset weight factors;

[0022] In the skewness factor-time diagram, the expression of the image is obtained S1i=F2i(t), through the formula Calculate the cumulative anomaly index of the skewness factor and transient anomaly index Where R2 is the preset maximum threshold. Calculate the second anomaly index α2i, where K3 and K4 are preset circle factors;

[0023] In the kurtosis factor-time diagram, the expression of the image is obtained, C2i=F3i(t), through the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R13 is the preset maximum threshold. The third abnormality index α3i is calculated, where K5 and K6 are preset weight factors.

[0024] The generated first, second and third abnormality indexes are sent to the signal generation module.

[0025] The vibration monitoring module collects vibration acceleration data through accelerometers arranged in each distributed prefabricated optical cable i. Each accelerometer collects acceleration data of the distributed prefabricated optical cable 1000 times per second, records the collection time t and the specific value of the acceleration data Acc, and records 1000 acceleration data points (t, Acc) per second. t ). Every preset monitoring period T, all recorded acceleration data points are input into the discrete Fourier transform formula, the acceleration data points are converted from time domain signals to frequency domain signals, the main frequency components are identified and the characteristic data containing abnormal vibration information is extracted. The discrete Fourier transform formula is: Where N is the total number of acceleration data points and N=1000×T; T is the total number of seconds of the preset monitoring period; where j is an imaginary unit and j×j=-1; where f is the frequency, DFT(f) is the Fourier transform result of the acceleration data point at the frequency f.

[0026] According to the Fourier transform result, an acceleration frequency spectrum, i.e., f-DFT(f) diagram, is generated with frequency f as the horizontal axis and Fourier transform result FFT(f) as the vertical axis to obtain the fitting function DFT(f)=X(f) of the acceleration frequency spectrum.

[0027] Furthermore, vibration signal detection is performed by extracting abnormal feature data, specifically:

[0028] The highest point in the peak image in the acceleration frequency spectrum is extracted and recorded as the frequency peak point. All frequency peak points are numbered with the numbering symbol q, q = 1, 2, 3, ..., u; where u is the number of peak images in the acceleration frequency spectrum, and the coordinates of all frequency peak points are obtained (fq, DFT(fq)) = (f1, DFT(f1)), (f1, DFT(f1)), (f2, DFT(f2)), ..., (fu, DFT(fu)).

[0029] By formula The root mean square eigenvalue RMS and the peak eigenvalue PF of the acceleration frequency spectrum are calculated, and the peak eigenvalue PF of each distributed prefabricated optical cable i is output to the signal generation module.

[0030] It should be noted that the root mean square eigenvalue reflects the energy of the frequency domain signal contained in the acceleration frequency spectrum, and the peak eigenvalue reflects the sharpness of the peak image in the acceleration frequency spectrum.

[0031] It should be further explained that if the peak characteristic value PF exceeds the preset threshold PFMax, it is determined that the acceleration frequency spectrum is abnormal at a specific frequency.

[0032] The signal generation module performs numerical analysis after receiving the first, second, and third abnormal indexes and the peak characteristic value PF. For the same distributed prefabricated optical cable number symbol i, if the first abnormal index α1i is greater than the preset threshold α1Max or the second abnormal index α2i is greater than the preset threshold α2Max or the third abnormal index α3i is greater than the preset threshold α3Max, an early warning signal is generated and the distributed prefabricated optical cable number symbol i contained in the first, second, or third abnormal index exceeding the threshold is output.

[0033] As a preferred embodiment of the present invention, for the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the second abnormality index is greater than the preset threshold value α2Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output;

[0034] For the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the third abnormality index is greater than the preset threshold value α3Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output.

[0035] After the early warning signal is generated, it indicates that an abnormality in Rayleigh scattered light detection occurs on the outputted prefabricated optical cable number symbol i, and it is determined that an abnormality occurs in the operation of the optical transmission line of the prefabricated optical cable i.

[0036] Furthermore, a numerical judgment is made on the peak characteristic value PF of the distributed prefabricated optical cable number symbol i that is synchronously output with the early warning signal. If the peak characteristic value exceeds the preset threshold value PFmax, it is determined that for the distributed prefabricated optical cable i, while generating abnormal operation of the optical transmission line, abnormal vibrations including excessive background noise, human attack of oil transportation sound, vibration of electric tools, construction of excavation equipment, vibration of impact equipment and blasting are generated.

[0037] At this time, the early warning signal is converted into an attack alarm signal, and the attack alarm signal and the number symbol i of the distributed prefabricated optical cable are output and displayed correspondingly through the display screen.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The present invention can timely detect abnormal conditions along the optical cable, such as construction damage, natural disasters, etc., through real-time monitoring of distributed prefabricated optical cables and optical time domain reflectometers, thereby providing early warning and reducing potential losses; the OTDR test curve and sliding equal weight algorithm are used for filtering and noise reduction to improve the detection accuracy. The location of the abnormal point can be accurately determined through feature extraction and reflection peak positioning, providing an important basis for subsequent maintenance and processing;

[0040] (2) The present invention not only takes into account the characteristic parameters of optical fiber detection (such as amplitude, distance, etc.), but also introduces the skewness factor and the kurtosis factor to quantitatively analyze the asymmetry and unevenness of the reflection point distribution. This multi-dimensional analysis can more comprehensively and accurately reflect the state of the optical cable; and the present invention can more accurately determine the degree of abnormality of the optical cable by calculating the cumulative abnormality index and the instantaneous abnormality index, as well as the first, second and third abnormality indexes. When the abnormality index exceeds the preset threshold, an early warning signal or a damage alarm signal is automatically generated, thereby improving the timeliness and accuracy of the early warning;

[0041] (3) The detection curve sequence group proposed in the present invention adopts a dynamic update method, which can reflect the latest status of the optical cable in real time. At the same time, by continuously optimizing the algorithm and parameter settings, the stability and reliability of the system can be further improved. It is not only suitable for the safety monitoring of optical cable lines, but also can be extended to other fields that require long-distance and high-precision monitoring, such as perimeter security, large-scale structural health monitoring, etc.; through the complete technical solution of data collection, analysis, and response, the real-time monitoring, high-precision positioning, multi-dimensional analysis, abnormal index calculation and early warning signal generation functions of distributed prefabricated optical cables are realized, reducing the difficulty of data interpretation and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0043] Figure 1 is a system block diagram of the present invention;

[0044] Figure 2 A schematic diagram of Rayleigh scattered light of the present invention;

[0045] Figure 3 Schematic diagram of the distance-amplitude curve of scattered light;

[0046] Figure 4 Schematic diagram of the detection curve sequence group of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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 creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, the early warning system based on distributed prefabricated optical cables includes a space domain analysis module, a time domain analysis module, a vibration monitoring module and a signal generation module.

[0049] The spatial domain analysis module numbers the distributed prefabricated optical cables, and the numbering symbol is i, i=1, 2, 3, ..., n; wherein n is the total number of the distributed prefabricated optical cables.

[0050] See also Figure 2 and Figure 3 As shown in the figure, for each distributed prefabricated optical cable i, a detection light is emitted to it every preset detection period T. Subsequently, the Rayleigh scattered light of the detection light is monitored in real time by an optical time domain reflectometer to obtain the distance and amplitude data of the Rayleigh scattered light. The propagation distance of the Rayleigh scattered light is the horizontal axis x, and the amplitude A of the Rayleigh scattered light is the vertical axis to generate a distance-amplitude curve of the scattered light, that is, the OTDR test curve.

[0051] See also Figure 4 As shown, a total of k OTDR test curves generated after k detection cycles are recorded as a detection curve sequence group, where k is a preset curve group capacity. In this group, the earliest generated OTDR test curve is marked as OTDR (1), and the most recently generated OTDR test curve is marked as OTDR (k).

[0052] At the end of each detection cycle T, the detection curve sequence group is dynamically updated. The specific operation is: OTDR (1), that is, the earliest generated OTDR test curve, is removed from the detection curve sequence group, and the numbers k of all remaining OTDR test curves are reduced by one. At the same time, the OTDR test curve generated in the previous detection cycle T is marked as the new OTDR (k) and added to the detection curve sequence group.

[0053] Furthermore, whenever the detection curve sequence group completes a dynamic update, all OTDR detection curves included in the detection curve sequence group are immediately filtered and denoised using a sliding equal weight algorithm. All detection curves in the detection curve sequence group are numbered, and the number symbol is j.

[0054] Obtain the image analysis formula fj(x)=f1(x),f2(x),f3(x),...,fk(x) of each OTDR test curve OTDR(j)=OTDR(1),OTDR(2),OTDR(3),...,OTDR(k) in the detection curve sequence group. Calculate the second-order moment Ej=E1, E2, E3, ..., Ek of each image analytical expression, where L is the total length of the distributed prefabricated optical cable i.

[0055] Furthermore, through the formula Calculate the dynamic weight μj = μ1, μ2, μ3, ..., μk. By formula A sliding equalization noise reduction operation is performed to obtain the image analytical expression g(x) of the OTDR test curve after noise reduction and draw its function graph, which is recorded as the noise reduction curve OTDR(0).

[0056] Feature extraction is performed on the noise reduction curve OTDR (0), and the peak image is located therein, and the peak image whose area is greater than the preset threshold AMin is screened and recorded as the reflection peak. The reflection peaks are numbered, and the number symbol is p, p = 1, 2, 3, ..., q; q is the total number of reflection peaks. The point with the largest amplitude A value on each reflection peak is recorded as the reflection peak value A1p, the horizontal coordinate corresponding to the reflection peak is marked as the reflection point xp, and the point with the smallest amplitude A value on the reflection peak is recorded as the reflection valley value A2p.

[0057] By formula Calculate the optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i. Calculate the mean value Ei(xp) and variance σi(xp) of the distribution of reflection point xp in distributed prefabricated optical cable i.

[0058] Furthermore, through the formula Calculate the skewness factor S1i and the kurtosis factor S2i of the distribution of the reflection point xp.

[0059] It should be noted that the skewness factor is a quantification of the asymmetry of the distribution of reflection points xp, and the kurtosis factor is a quantification of the unevenness of the distribution of reflection points xp. When the skewness factor and the kurtosis factor are large, it indicates that the distribution of the reflection points along the total length L is more skewed; when the kurtosis factor is large, it indicates that the reflection points are steeper than the normal distribution and are more concentrated within a certain length range.

[0060] The optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i, the skewness factor S1ixp and the kurtosis factor S2i of the distribution of the reflection point xp are sent to the time domain analysis module.

[0061] After receiving the optical fiber detection characteristic parameter C1i, the skewness factor S1i and the kurtosis factor S2i, the time domain analysis module records the receiving time t and generates an optical fiber detection characteristic parameter-time diagram, a skewness factor-time diagram and a kurtosis factor-time diagram.

[0062] Further, in the optical fiber detection characteristic parameter-time diagram, the expression of the image C1i=F1i(t) is obtained, and the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R1 is the preset maximum threshold. Calculate the first abnormal index α1i, where K1 and K2 are preset weight factors;

[0063] In the skewness factor-time diagram, the expression of the image is obtained S1i=F2i(t), through the formula Calculate the cumulative anomaly index of the skewness factor and transient anomaly index Where R2 is the preset maximum threshold. Calculate the second anomaly index α2i, where K3 and K4 are preset circle factors;

[0064] In the kurtosis factor-time diagram, the expression of the image is obtained, C2i=F3i(t), through the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R13 is the preset maximum threshold. The third abnormality index α3i is calculated, where K5 and K6 are preset weight factors.

[0065] The generated first, second and third abnormality indexes are sent to the signal generation module.

[0066] The vibration monitoring module collects vibration acceleration data through accelerometers arranged in each distributed prefabricated optical cable i. Each accelerometer collects acceleration data of the distributed prefabricated optical cable 1000 times per second, records the collection time t and the specific value of the acceleration data Acc, and records 1000 acceleration data points (t, Acc) per second. t ). Every preset monitoring period T, all recorded acceleration data points are input into the discrete Fourier transform formula, the acceleration data points are converted from time domain signals to frequency domain signals, the main frequency components are identified and the characteristic data containing abnormal vibration information is extracted. The discrete Fourier transform formula is: Where N is the total number of acceleration data points and N=1000×T; T is the total number of seconds of the preset monitoring period; where j is an imaginary unit and j×j=-1; where f is the frequency, DFT(f) is the Fourier transform result of the acceleration data point at the frequency f.

[0067] According to the Fourier transform result, an acceleration frequency spectrum, i.e., f-DFT(f) diagram, is generated with frequency f as the horizontal axis and Fourier transform result FFT(f) as the vertical axis to obtain the fitting function DFT(f)=X(f) of the acceleration frequency spectrum.

[0068] Furthermore, vibration signal detection is performed by extracting abnormal feature data, specifically:

[0069] The highest point in the peak image in the acceleration frequency spectrum is extracted and recorded as the frequency peak point. All frequency peak points are numbered with the numbering symbol q, q = 1, 2, 3, ..., u; where u is the number of peak images in the acceleration frequency spectrum, and the coordinates of all frequency peak points are obtained (fq, DFT(fq)) = (f1, DFT(f1)), (f1, DFT(f1)), (f2, DFT(f2)), ..., (fu, DFT(fu)).

[0070] By formula The root mean square eigenvalue RMS and the peak eigenvalue PF of the acceleration frequency spectrum are calculated, and the peak eigenvalue PF of each distributed prefabricated optical cable i is output to the signal generation module.

[0071] It should be noted that the root mean square eigenvalue reflects the energy of the frequency domain signal contained in the acceleration frequency spectrum, and the peak eigenvalue reflects the sharpness of the peak image in the acceleration frequency spectrum.

[0072] It should be further explained that if the peak characteristic value PF exceeds the preset threshold PFMax, it is determined that the acceleration frequency spectrum is abnormal at a specific frequency.

[0073] The signal generation module performs numerical analysis after receiving the first, second, and third abnormal indexes and the peak characteristic value PF. For the same distributed prefabricated optical cable number symbol i, if the first abnormal index α1i is greater than the preset threshold α1Max or the second abnormal index α2i is greater than the preset threshold α2Max or the third abnormal index α3i is greater than the preset threshold α3Max, an early warning signal is generated and the distributed prefabricated optical cable number symbol i contained in the first, second, or third abnormal index exceeding the threshold is output.

[0074] As a preferred embodiment of the present invention, for the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the second abnormality index is greater than the preset threshold value α2Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output;

[0075] For the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the third abnormality index is greater than the preset threshold value α3Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output.

[0076] After the early warning signal is generated, it indicates that an abnormality in Rayleigh scattered light detection occurs on the outputted prefabricated optical cable number symbol i, and it is determined that an abnormality occurs in the operation of the optical transmission line of the prefabricated optical cable i.

[0077] Furthermore, a numerical judgment is made on the peak characteristic value PF of the distributed prefabricated optical cable number symbol i that is synchronously output with the early warning signal. If the peak characteristic value exceeds the preset threshold value PFmax, it is determined that for the distributed prefabricated optical cable i, while generating abnormal operation of the optical transmission line, abnormal vibrations including excessive background noise, human attack of oil transportation sound, vibration of electric tools, construction of excavation equipment, vibration of impact equipment and blasting are generated.

[0078] At this time, the early warning signal is converted into an attack alarm signal, and the attack alarm signal and the number symbol i of the distributed prefabricated optical cable are output and displayed correspondingly through the display screen.

[0079] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0080] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0081] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The early warning system based on distributed prefabricated optical cables includes a spatial domain analysis module and a vibration monitoring module, and is characterized by: The spatial domain analysis module numbers the distributed prefabricated optical cables, and the number symbol is i, i=1, 2, 3, ..., n; wherein n is the total number of distributed prefabricated optical cables; for each distributed prefabricated optical cable i, a detection light is emitted to it at every preset detection period T; the Rayleigh scattered light of the detection light is monitored in real time by an optical time domain reflectometer to obtain the distance and amplitude data of the Rayleigh scattered light; the propagation distance of the Rayleigh scattered light is the horizontal axis x, and the amplitude A of the Rayleigh scattered light is the vertical axis, and a distance-amplitude curve of the scattered light, i.e., an OTDR test curve, is generated; The OTDR test curves generated in each time period are grouped and denoised by a sliding equal weight algorithm to obtain a denoised curve; feature extraction is performed on the denoised curve to locate the peak image therein, and the peak image whose area is greater than a preset threshold value AMin is screened and recorded as a reflection peak; the reflection peaks are numbered, and the number symbol is p, p=1, 2, 3, ..., q; q is the total number of reflection peaks; the point with the largest amplitude A value on each reflection peak is recorded as a reflection peak value A1p, the horizontal coordinate corresponding to the reflection peak is marked as a reflection point xp, and the point with the smallest amplitude A value on the reflection peak is recorded as a reflection valley value A2p; the optical fiber detection characteristic parameters, the skewness factor S1i and the kurtosis factor S2i of the distribution of the reflection point xp are obtained by calculation; the optical fiber detection characteristic parameters C1i of each distributed prefabricated optical cable i, the skewness factor S1ixp and the kurtosis factor S2i of the distribution of the reflection point xp are sent to the time domain analysis module; The vibration monitoring module collects vibration acceleration data at a preset frequency through accelerometers distributed in each distributed prefabricated optical cable, and records the collection time t and the specific value of the acceleration data Acc; every preset monitoring period T, all recorded acceleration data points are input into the discrete Fourier transform formula, the acceleration data points are converted from time domain signals to frequency domain signals, and an acceleration frequency spectrum is further generated, all frequency peak points in the acceleration frequency spectrum are numbered and their coordinates are obtained; the root mean square eigenvalue RMS and peak eigenvalue PF of the acceleration frequency spectrum are calculated, and the peak eigenvalue PF is output to the signal generation module.

2. The early warning system based on distributed prefabricated optical cables according to claim 1 is characterized in that: It also includes a time domain analysis module and a signal generation module; After receiving the optical fiber detection characteristic parameter C1i, the skewness factor S1i and the kurtosis factor S2i, the time domain analysis module records the receiving time t and generates an optical fiber detection characteristic parameter-time diagram, a skewness factor-time diagram and a kurtosis factor-time diagram; Generate first, second and third abnormality indexes by extracting the characteristics of the optical fiber detection characteristic parameter-time graph, the skewness factor-time graph and the kurtosis factor-time graph; send the generated first, second and third abnormality indexes to the signal generation module; The signal generation module performs numerical analysis after receiving the first, second, and third abnormal indexes and the peak characteristic value PF; for the same distributed prefabricated optical cable number symbol i, if the first abnormal index α1i is greater than the preset threshold α1Max or the second abnormal index α2i is greater than the preset threshold α2Max or the third abnormal index α3i is greater than the preset threshold α3Max, a warning signal is generated and the distributed prefabricated optical cable number symbol i contained in the first, second, or third abnormal index exceeding the threshold is output; For the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the second abnormality index is greater than the preset threshold value α2Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output; For the same distributed prefabricated optical cable number symbol i, if the first abnormality index is greater than the preset threshold value α1Max and the third abnormality index is greater than the preset threshold value α3Max at the same time, a damage alarm signal is generated and the distributed prefabricated optical cable number symbol i is output; After the early warning signal is generated, it indicates that the Rayleigh scattered light detection abnormality occurs on the outputted prefabricated optical cable number symbol i, and it is determined that the optical transmission line of the prefabricated optical cable i is abnormal; Make a numerical judgment on the peak characteristic value PF of the distributed prefabricated optical cable number i that is synchronously output with the early warning signal; If the peak characteristic value exceeds the preset threshold value PFmax, it is determined that for the distributed prefabricated optical cable i, abnormal vibrations including excessive background noise, human attack by oil transportation sound, vibration of electric tools, construction of excavation equipment, vibration of impact equipment and blasting are generated while the optical transmission line is working abnormally; Subsequently, the early warning signal is converted into an attack alarm signal, and the attack alarm signal and the number symbol i of the distributed prefabricated optical cable are output and displayed correspondingly through a display screen.

3. The early warning system based on distributed prefabricated optical cables according to claim 1 is characterized in that: The specific process of grouping and denoising the OTDR test curves generated in each time period by using the sliding equal weight algorithm is as follows; A total of k OTDR test curves generated after k detection cycles are recorded as a detection curve sequence group, where k is a preset curve group capacity; In this group, the earliest generated OTDR test curve is marked as OTDR(1), and the most recently generated OTDR test curve is marked as OTDR(k); Obtain the image analysis formula fj(x)=f1(x),f2(x),f3(x),...,fk(x) of each OTDR test curve OTDR(j)=OTDR(1),OTDR(2),OTDR(3),...,OTDR(k) in the detection curve sequence group; through the formula Calculate the second-order moment Ej=E1, E2, E3, ..., Ek of each image analytical expression; Where L is the total length of the distributed prefabricated optical cable i; By formula Calculate the dynamic weight μj=μ1,μ2,μ3,...,μk; by formula A sliding equalization noise reduction operation is performed to obtain the image analytical expression g(x) of the OTDR test curve after noise reduction and draw its function graph, which is recorded as the noise reduction curve OTDR(0).

4. The early warning system based on distributed prefabricated optical cables according to claim 3 is characterized in that: The specific process of dynamically updating the detection curve sequence group is as follows: Whenever a detection cycle T ends, the detection curve sequence group is dynamically updated, OTDR(1), that is, the earliest generated OTDR test curve, is removed from the detection curve sequence group, and the numbers k of all remaining OTDR test curves are reduced by one; at the same time, the OTDR test curve generated in the previous detection cycle T is marked as the new OTDR(k) and added to the detection curve sequence group.

5. The early warning system based on distributed prefabricated optical cables according to claim 1 is characterized in that: The specific process of obtaining the optical fiber detection characteristic parameters, the skewness factor and the kurtosis factor of the reflection point distribution through calculation is as follows; By formula Calculate the optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i; by formula Calculate the mean value Ei(xp) and variance σi(xp) of the distribution of reflection point xp in the distributed prefabricated optical cable i; By formula Calculate the skewness factor S1i and kurtosis factor S2i of the distribution of reflection point xp; The optical fiber detection characteristic parameter C1i of each distributed prefabricated optical cable i, the skewness factor S1ixp and the kurtosis factor S2i of the distribution of the reflection point xp are sent to the time domain analysis module.

6. The early warning system based on distributed prefabricated optical cables according to claim 1 is characterized in that: The specific process of generating the first, second and third abnormal indexes by extracting the features of the optical fiber detection characteristic parameter-time graph, the skewness factor-time graph and the kurtosis factor-time graph is as follows; In the optical fiber detection characteristic parameter-time diagram, the expression of the image is obtained by C1i=F1i(t), and the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R1 is the preset maximum threshold; through the formula Calculate the first abnormal index α1i, where K1 and K2 are preset weight factors; In the skewness factor-time diagram, the expression of the image is obtained S1i=F2i(t), through the formula Calculate the cumulative anomaly index of the skewness factor and transient anomaly index Where R2 is the preset maximum threshold; through the formula Calculate the second anomaly index α2i, where K3 and K4 are preset circle factors; In the kurtosis factor-time diagram, the expression of the image is obtained, C2i=F3i(t), through the formula Calculate the cumulative anomaly index of fiber optic detection characteristic parameters and transient anomaly index Where R13 is the preset maximum threshold; through the formula The third abnormality index α3i is calculated, where K5 and K6 are preset weight factors.

7. The early warning system based on distributed prefabricated optical cables according to claim 1 is characterized in that: The specific process of the vibration monitoring module obtaining the peak characteristic value through calculation is as follows: The vibration acceleration data is collected by accelerometers arranged in each distributed prefabricated optical cable i; each accelerometer collects the acceleration data of the distributed prefabricated optical cable 1000 times per second, records the collection time t and the specific value of the acceleration data Acc, and records 1000 acceleration data points (t, Acc) per second. t ); every preset monitoring period T, all recorded acceleration data points are input into the discrete Fourier transform formula, the acceleration data points are converted from time domain signals to frequency domain signals, the main frequency components are identified and the characteristic data containing abnormal vibration information is extracted; the discrete Fourier transform formula is: Where N is the total number of acceleration data points and N=1000×T; T is the total number of seconds of the preset monitoring period; where j is an imaginary unit and j×j=-1; where f is the frequency, DFT(f) is the Fourier transform result of the acceleration data point at the frequency f; According to the Fourier transform result, the acceleration frequency spectrum is generated with the frequency f as the horizontal axis and the Fourier transform result FFT(f) as the vertical axis, that is, the f-DFT(f) diagram, and the fitting function DFT(f)=X(f) of the acceleration frequency spectrum is obtained; Vibration signal detection is performed by extracting abnormal feature data, specifically: Extract the highest point in the peak image in the acceleration frequency spectrum, record it as the frequency peak point, number all the frequency peak points, the number symbol is q, q = 1, 2, 3, ..., u; where u is the number of peak images in the acceleration frequency spectrum, obtain the coordinates of all frequency peak points (fq, DFT (fq)) = (f1, DFT (f1)), (f1, DFT (f1)), (f2, DFT (f2)), ..., (fu, DFT (fu)); through the formula The root mean square eigenvalue RMS and the peak eigenvalue PF of the acceleration frequency spectrum are calculated, and the peak eigenvalue PF of each distributed prefabricated optical cable i is output to the signal generation module.

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