Interference judgment method and device based on fiber vibration sensing system

Abstract

The application provides an interference judgment method and device based on an optical fiber vibration sensing system, and belongs to the field of optical fiber vibration sensing. The method comprises the following steps: collecting vibration signals of the areas corresponding to each optical fiber monitoring unit based on a preset sampling frequency; performing time-frequency analysis on each vibration signal collected, and forming a time-frequency matrix set by the time-frequency matrices obtained after the time-frequency analysis; calculating the low-frequency band cumulative value and the medium-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and recording them in a first sequence N0 and a second sequence N1 corresponding to each time-frequency matrix, respectively, wherein the time of the first sequence N0 corresponds to the time of the second sequence N1; calculating a fitting coefficient based on the first sequence N0 and the second sequence N1 corresponding to the time-frequency matrix of each optical fiber monitoring unit, and judging the type of the excitation signal according to the size relationship between the fitting coefficient and a preset threshold value.

Description

Technical Field

[0001] This invention relates to the field of fiber optic vibration sensing, and more specifically to an interference judgment method based on a fiber optic vibration sensing system, an interference judgment device based on a fiber optic vibration sensing system, and an electronic device. Background Technology

[0002] During the operation of long-distance pipelines, third-party sabotage, such as construction work above the pipeline or drilling for oil and gas theft, is a significant risk factor jeopardizing the safe and stable operation of oil and gas pipeline networks and has long been a problem for petroleum companies. Therefore, a stable and reliable safety monitoring system is needed to provide timely early warning and location of third-party sabotage above the pipeline, which is of great importance for the safe operation of oil and gas pipelines.

[0003] In existing technologies, safety monitoring systems typically operate at distances of tens or even hundreds of kilometers, detecting numerous vibration signals. However, due to the sensitivity of fiber optic vibration sensing systems, a large number of construction operation signals located tens or even hundreds of meters perpendicular to the pipeline will be detected and identified as valid vibration signals. When pipelines are laid parallel to roads, vehicle interference signals can be relatively easily distinguished using time-domain characteristics. However, when pipelines are laid perpendicular to roads, vehicle passing signals and mechanical operation signals are very similar in the time domain, making them difficult to distinguish. This can cause false alarms and interference in the safety monitoring system, reducing the accuracy of early warnings. Summary of the Invention

[0004] The purpose of this invention is to provide an interference judgment method and device based on an optical fiber vibration sensing system to solve the technical problem of low early warning accuracy mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, embodiments of this application provide an interference judgment method based on an optical fiber vibration sensing system. The optical fiber vibration sensing system includes: a plurality of optical fiber monitoring units for detecting vibration signals and laid along a pipeline; the method includes:

[0007] Vibration signals of the corresponding areas of each fiber optic monitoring unit are collected based on a preset sampling frequency.

[0008] Each collected vibration signal was subjected to time-frequency analysis, and the time-frequency matrices obtained after the analysis were combined into a time-frequency matrix set.

[0009] Calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1 that correspond one-to-one with each time-frequency matrix, where the time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1.

[0010] The fitting coefficients are calculated based on the first sequence N0 and the second sequence N1, which correspond one-to-one with the time-frequency matrix of each fiber optic monitoring unit. The type of excitation signal is then determined based on the relationship between the fitting coefficients and the preset threshold.

[0011] As can be seen from the method described in the first aspect, based on the high sensitivity of the fiber optic vibration sensing system, vibration signals along the pipeline are collected. By performing time-frequency analysis on the collected vibration signals, the energy of specific frequency bands at each moment is calculated, and the changing trend of energy in each frequency band is extracted and analyzed. This allows for the differentiation and judgment of mechanical construction signals and vehicle interference signals, thereby reducing alarms issued due to false alarm interference sources and improving the accuracy of early warning.

[0012] Optionally, time-frequency analysis is performed on each of the acquired vibration signals, and the time-frequency matrices obtained after the analysis are combined into a time-frequency matrix set, including:

[0013] Vibration signals for the corresponding region of each fiber optic monitoring unit are sequentially and slidably captured based on time sequence.

[0014] The vibration signal sequence captured by the sliding intercept is subjected to a fast Fourier transform in sequence to obtain the corresponding spectral vibration signal;

[0015] The spectral vibration signals obtained during the sliding process are spliced ​​together to obtain the time-frequency matrix of the corresponding region of each optical fiber monitoring unit, where the horizontal axis of the time-frequency matrix is ​​frequency and the vertical axis is time.

[0016] Optionally, fitting coefficients are calculated based on a first sequence N0 and a second sequence N1 that correspond one-to-one with the time-frequency matrix of each fiber optic monitoring unit, including:

[0017] Determine whether there are data points in the second sequence N1 that exceed the preset trigger threshold. If so, determine the time T corresponding to each data point that satisfies the judgment condition.

[0018] Extract data points from the first sequence N0 within a first preset duration before each time T to form a first data point set, and extract data points from the first sequence N0 within a first preset duration after each time T to form a second data point set. Record the index value I of the first data point in the first sequence N0 that exceeds a preset trigger threshold. a And the index value I of the last data point that exceeds the preset trigger threshold b ;

[0019] Extract the first data point from the first data point set and the second data point set up to the Ith data point set. a -1 data points form the first array, and the i-th data point is extracted from the first data point set and the second data point set. bAdd 1 data point to the last data point to form the second array;

[0020] Linear fitting is performed on the first array and the second array respectively to obtain the first fitting coefficient and the second fitting coefficient, and the excitation signal type is determined based on the first fitting coefficient and the second fitting coefficient.

[0021] Optionally, linear fitting is performed on the first array and the second array respectively, including:

[0022] Based on the first array and the second array respectively, fit straight lines to obtain the corresponding mean square error;

[0023] The normal equation expression for the corresponding fitted curve is obtained based on the mean square error;

[0024] Solve the corresponding normal equation expressions using either the elimination method or the Cramer method to obtain the first and second fitting coefficients.

[0025] Optionally, the excitation signal type can be determined based on the first and second fitting coefficients, including:

[0026] Determine whether the absolute values ​​of the first and second fitting coefficients are both greater than or equal to a preset threshold.

[0027] If both judgments are yes, then the excitation signal is determined to be an interference signal.

[0028] If at least one of the two judgment results is negative, then the excitation signal for this time will be determined as mechanical excavation excitation.

[0029] Optionally, the method further includes:

[0030] Determine whether the number of times mechanical digging stimuli occur within a second preset time period exceeds a preset number; if so, issue an alarm message, wherein the alarm message includes alarm location information.

[0031] Optionally, the method also includes:

[0032] The alarm information will be pushed to at least one mobile smart terminal.

[0033] Secondly, embodiments of this application provide an interference judgment device based on an optical fiber vibration sensing system, the device comprising:

[0034] The acquisition module is used to collect vibration signals from the corresponding areas of each fiber optic monitoring unit based on a preset sampling frequency.

[0035] The time-frequency analysis module is used to perform time-frequency analysis on each of the acquired vibration signals, and to form a time-frequency matrix set by combining the time-frequency matrices obtained after the analysis.

[0036] The calculation module is used to calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1, which correspond one-to-one with each time-frequency matrix, respectively. The time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1.

[0037] The processing module is used to calculate the fitting coefficients based on the first sequence N0 and the second sequence N1, which correspond one-to-one with the time-frequency matrix of each light monitoring unit, and to determine the type of excitation signal based on the relationship between the fitting coefficients and the preset threshold.

[0038] Thirdly, embodiments of this application provide an electronic device, including: one or more processors; a memory connected to the one or more processors; wherein the memory stores instructions executable by the one or more processors, and the one or more processors implement the interference judgment method based on the fiber optic vibration sensing system as described in the first aspect by executing the instructions stored in the memory.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium storing program code, wherein when the instructions are executed by a processor, the processor performs the interference judgment method based on the fiber optic vibration sensing system as described in the first aspect.

[0040] Compared with existing technologies, the beneficial effects of this invention are as follows: Utilizing the sensitivity of the fiber optic vibration sensing system to detect vibration signals, time-frequency analysis is performed on the vibration signals collected along the fiber optic monitoring unit. Based on the difference in excitation signals caused by the Doppler effect when a vehicle passes a road perpendicular to the pipeline, relative to the sensing fiber optic cable, the vibration signal will first move from far to near and then from near to far, while mechanical operations, due to their relatively fixed excitation position in a short period, will not produce a significant frequency shift, time-frequency analysis of the collected external vibration signals and monitoring the change pattern of the signal spectrum can effectively distinguish between vehicle passing signals and mechanical operation signals, thereby improving the system's alarm accuracy.

[0041] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention;

[0044] Figure 2 A flowchart illustrating an interference judgment method based on an optical fiber vibration sensing system, provided for one embodiment of the present invention;

[0045] Figure 3 A time-domain signal diagram of mechanical construction vibration data provided in one embodiment of the present invention;

[0046] Figure 4 This invention provides a time-frequency spectrum corresponding to mechanical construction vibration data in one embodiment.

[0047] Figure 5 A time-domain signal diagram of vehicle vibration data provided in one embodiment of the present invention;

[0048] Figure 6 This is a time-frequency spectrum corresponding to the vibration data of a vehicle passing by, provided in one embodiment of the present invention;

[0049] Figure 7 This is a schematic diagram of the structure of an interference judgment device based on an optical fiber vibration sensing system provided in one embodiment of the present invention.

[0050] Explanation of reference numerals in the attached figures

[0051] 10-Electronic device, 111-Memory, 112-Communication interface, 113-Communication bus, 114-Processor, 20-Hazard level judgment device based on fiber optic vibration sensing system, 210-Acquisition module, 220-Time and frequency analysis module, 230-Calculation module, 240-Processing module. Detailed Implementation

[0052] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0053] Please see Figure 1 This application provides an electronic device 10 in some possible embodiments. The electronic device 10 can be a personal computer (PC), tablet computer, smartphone, personal digital assistant (PDA), etc., or the electronic device 10 can be a network server, database server, cloud server, or a server integration consisting of multiple sub-servers, etc.

[0054] Furthermore, the electronic device 10 may include a memory 111, a communication interface 112, a communication bus 113, and a processor 114, wherein the processor 114, the communication interface 112, and the memory 111 are connected via the communication bus 113. The processor 114 is used to execute executable modules, such as computer programs, stored in the memory 111. Figure 1 The components and structure of the electronic device 10 shown are merely exemplary and not limiting. The electronic device 10 may also have other components and structures as needed.

[0055] The memory 111 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 111 may be integrated with the processor 114 or may exist independently and be coupled to the processor 114 through the communication interface 112; this embodiment of the application does not specifically limit this.

[0056] The communication bus 113 can be an ISA bus (Industry Standard Architecture), a PCI bus (Peripheral Component Interconnect), or an EISA bus (Extended Industry Standard Architecture), etc. Communication buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 1 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0057] Processor 114 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 114 or by software instructions. Processor 114 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or as execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art.

[0058] The method executed by the device defined in this embodiment of the invention can be applied to or implemented by the processor 114. The processor 114 can cooperate with other modules or components in the electronic device 10 to execute the interference judgment method based on the fiber optic vibration sensing system. The execution of the interference judgment method based on the fiber optic vibration sensing system will be described in detail below.

[0059] Fiber optic vibration sensing systems are detection systems that use optical fibers as the sensing medium. They have advantages such as long detection distance, simple installation, and high sensitivity, and are widely used in railway transportation, engineering structure safety monitoring, oil and gas pipeline transportation maintenance, earthquake monitoring, security and other fields.

[0060] In this embodiment, the fiber optic vibration sensing system includes: several fiber optic monitoring units laid along the pipeline for detecting vibration signals; an acquisition module for acquiring each vibration signal; a low-frequency analysis module for analyzing low-frequency signals in the vibration signals; a high-frequency analysis module for analyzing high-frequency signals in the vibration signals; and an alarm module for issuing an alarm for the mechanical excavation signal based on its hazard level. In this embodiment, the fiber optic monitoring unit is a fiber optic vibration measurement cable; that is, the fiber optic vibration measurement cable is laid along the pipeline as a vibration sensor to acquire vibration signals from the entire detection area.

[0061] It should be noted that the monitoring range of each fiber optic monitoring unit is limited, and each unit detects vibration signals within that range. Therefore, several fiber optic monitoring units correspond one-to-one with the vibration signals detected within their respective monitoring areas.

[0062] The fiber optic monitoring unit can detect various types of vibration. In this embodiment, the vibration signals mainly include excavation vibrations during mechanical construction and vibration signals generated by passing vehicles. Time-frequency analysis is performed on the collected vibration signals to calculate the energy of specific frequency bands at each moment. The changing trends of energy in each frequency band are extracted and analyzed to distinguish and judge mechanical construction signals and vehicle interference signals, thereby reducing alarms issued due to false alarms and improving the accuracy of early warnings.

[0063] Based on the aforementioned fiber optic vibration sensing system, real-time acquisition of vibration signals along the pipeline is achieved, enabling real-time remote monitoring of the pipeline.

[0064] Please see Figure 2 This application provides an interference judgment method based on an optical fiber vibration sensing system, which includes steps S1, S2, S3 and S4.

[0065] Step S1: Collect vibration signals of the corresponding area of ​​each fiber optic monitoring unit based on a preset sampling frequency;

[0066] Step S2: Perform time-frequency analysis on each of the collected vibration signals, and form a time-frequency matrix set from the time-frequency matrices obtained after the analysis.

[0067] Step S3: Calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1 that correspond one-to-one with each time-frequency matrix, wherein the time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1.

[0068] Step S4: Calculate the fitting coefficients based on the first sequence N0 and the second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit, and determine the excitation signal type according to the relationship between the fitting coefficients and the preset threshold.

[0069] The following section will provide a detailed explanation of the execution flow of this interference judgment method based on a fiber optic vibration sensing system.

[0070] Step S1: Collect vibration signals of the corresponding area of ​​each fiber optic monitoring unit based on the preset sampling frequency.

[0071] Each fiber optic monitoring unit has a corresponding monitoring range. Therefore, to monitor a larger area along the oil and gas pipeline, several fiber optic monitoring units need to be laid along the pipeline to detect vibration signals within each monitoring range.

[0072] Each fiber optic monitoring unit detects a corresponding vibration signal. The vibration signal acquisition module in the fiber optic vibration sensing system acquires the vibration signals corresponding to each fiber optic monitoring unit based on a preset sampling frequency, thereby completing the detection and acquisition of vibration signals.

[0073] The preset sampling frequency can be related to the original frequency of the specific vibration signal. In this embodiment, the preset sampling frequency is set to 1000Hz.

[0074] Step S2: Perform time-frequency analysis on each of the collected vibration signals, and form a time-frequency matrix set by combining the time-frequency matrices obtained after the time-frequency analysis.

[0075] Specifically, time-frequency analysis is performed on each of the collected vibration signals, and the time-frequency matrices obtained after the analysis are combined into a time-frequency matrix set. This includes: sequentially sliding and truncating the vibration signals of the corresponding area of ​​each optical fiber monitoring unit based on time order; sequentially performing fast Fourier transform on the sequence of vibration signals obtained by sliding to obtain the corresponding spectral vibration signals; and splicing the spectral vibration signals obtained during the sliding process to obtain the time-frequency matrix of the corresponding area of ​​each optical fiber monitoring unit, where the horizontal axis of the time-frequency matrix is ​​frequency and the vertical axis is time.

[0076] In this embodiment, the vibration signal collected by each optical fiber monitoring unit is truncate in chronological order using a window function of length n1 and overlapping data points n2. For each data sequence truncated in the window, a fast Fourier transform is performed sequentially, and the spectrum data sequence obtained in this calculation is recorded. The spectrum data sequences obtained in each calculation during the window sliding process are spliced ​​together to obtain a time-frequency matrix, where the horizontal axis of the time-frequency analysis matrix is ​​frequency and the vertical axis is time.

[0077] The vibration signals collected by each fiber optic monitoring unit are subjected to the above time-frequency analysis steps to obtain the time-frequency matrix corresponding to each fiber optic monitoring unit. Multiple time-frequency matrices are then combined to form a time-frequency matrix set.

[0078] Step S3: Calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1 that correspond one-to-one with each time-frequency matrix, wherein the time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1.

[0079] Specifically, each time-frequency matrix in the time-frequency matrix set contains frequency domain signals including low-frequency band signals and mid-to-high frequency signals. That is, the low-frequency band and mid-to-high frequency band in the time-frequency matrix are calculated separately, the accumulated value of the low-frequency band is recorded in the first sequence N0, and the accumulated value of the mid-to-high frequency band is recorded in the second sequence N1.

[0080] The frequency domain analysis result at any given moment will be either low frequency or mid-to-high frequency. Therefore, to analyze frequency domain change information, the cumulative value of the frequency band over a continuous period of time must be calculated to obtain the trend of frequency domain energy change. In other words, by analyzing frequency domain information over a period of time, further judgment of the excitation signal can be made based on the changes in the cumulative value of the frequency band. Here, the moments of the first sequence N0 and the second sequence N1 are corresponding.

[0081] It should be noted that the above calculation of the low-frequency band accumulation value and the mid-to-high-frequency band accumulation value of a time-frequency matrix yields the first sequence N0 and the second sequence N1 corresponding to that time-frequency matrix. Calculating the low-frequency band accumulation value and the mid-to-high-frequency band accumulation value for each time-frequency matrix will yield the first sequence N0 and the second sequence N1 corresponding to each time-frequency matrix. In other words, each time-frequency matrix has a corresponding first sequence N0 and second sequence N1.

[0082] Step S4: Calculate the fitting coefficients based on the first sequence N0 and the second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit, and determine the excitation signal type according to the relationship between the fitting coefficients and the preset threshold.

[0083] Specifically, based on the first sequence N0 and the second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit, the energy corresponding to at least one preset frequency band at several times is calculated, including: determining whether there are data points in the second sequence N1 that exceed a preset trigger threshold; if so, determining the time T corresponding to each data point that satisfies the judgment condition.

[0084] Extract data points from the first sequence N0 within a first preset duration before each time T to form a first data point set, and extract data points from the first sequence N0 within a first preset duration after each time T to form a second data point set. Record the index value I of the first data point in the first sequence N0 that exceeds a preset trigger threshold. a And the index value I of the last data point that exceeds the preset trigger threshold b Each data point corresponds to a different time point;

[0085] Extract the first data point from the first data point set and the second data point set up to the Ith data point set. a -1 data points form the first array, and the i-th data point is extracted from the first data point set and the second data point set. b Add 1 data point to the last data point to form the second array;

[0086] Linear fitting is performed on the first array and the second array respectively to obtain the first fitting coefficient and the second fitting coefficient, and the excitation signal type is determined based on the first fitting coefficient and the second fitting coefficient.

[0087] In this embodiment of the application, a preset trigger threshold T is set for the second sequence N1. N If the second sequence N1 exceeds the trigger threshold T N The data points are collected, and the time T corresponding to the acquisition of the data point is recorded; assuming the first preset duration is t, the data of T ± t seconds in the first sequence N0 is extracted, and the first time the trigger threshold T is exceeded is recorded. N Data point index value I a And the last one that exceeds the trigger threshold T N The data point index value Ib.

[0088] It should be noted that in this embodiment of the application, the first trigger threshold T is distinguished. N The data points and the data point corresponding to the last trigger threshold TN are determined based on the time corresponding to the data point. The earlier the time, the higher the ranking of the corresponding data point. For example, in the second sequence N1, exceeding the trigger threshold T... N The data points are A1, A2, and A3. A1 corresponds to time 12:00, A2 to time 12:03, and A3 to time 13:09. Therefore, A1 is identified as the first data point to exceed the trigger threshold TN, and its index value is recorded as I. a A3 is identified as the last data point to exceed the trigger threshold TN, and its index value is recorded as I. b .

[0089] For the data of T±t seconds extracted from the first sequence N0, the data from the 1st to the 1st second are truncated. a-1 data points are stored in the first array C1; the i-th data point is extracted. b +1 to the last data point, and store it in the second array C2. Because the data within the T±t time segment extracted from the first sequence N0 may exceed the trigger threshold T... N There are multiple data points. In this embodiment, it is assumed that the first one exceeds the trigger threshold T. N The data point corresponds to time T1, and the last one exceeds the trigger threshold T. N The data points correspond to time T2. Therefore, the data points in the first array C1 are determined based on the data points included in the time period from time T1-t to time T1, and the data points in the first array C2 are determined based on the data points included in the time period from time T2 to time T2+t.

[0090] After determining the first and second arrays, perform linear fitting on the first and second arrays respectively to obtain the fitting coefficients K. a and K b The specific linear fitting method is as follows:

[0091] Suppose there exists a set of data (x) i ,y i For each i = 0, 1, ..., m-1, a fitted curve is plotted, and the mean square error is:

[0092]

[0093] In calculus, the minimum value of Q(a,b) must satisfy:

[0094]

[0095]

[0096] Rearranging the equations into a matrix, we obtain the normal equation for the fitted curve:

[0097]

[0098] Solve the corresponding normal equation expressions using either the elimination method or the Cramer method:

[0099]

[0100]

[0101] Where b is the fitting coefficient that needs to be solved.

[0102] Based on the above linear fitting method, linear fitting is performed on the first array and the second array respectively to obtain the first fitting coefficient Ka and the second fitting coefficient Kb.

[0103] The type of excitation signal is determined based on the first and second fitting coefficients, including:

[0104] Determine whether the absolute values ​​of the first and second fitting coefficients are both greater than or equal to a preset threshold.

[0105] If both judgments are yes, then the excitation signal is determined to be an interference signal.

[0106] If at least one of the two judgment results is negative, then the excitation signal for this time will be determined as mechanical excavation excitation.

[0107] As one possible implementation, the method further includes:

[0108] Determine whether the number of times mechanical digging stimuli occur within a second preset time period exceeds a preset number; if so, issue an alarm message, wherein the alarm message includes alarm location information.

[0109] Specifically, it is determined whether the number of mechanical excavation excitations within the second preset time period exceeds the preset number. If so, the alarm location information is determined based on the absolute coordinates corresponding to the pipeline mapping and the relative coordinates corresponding to each fiber optic monitoring unit.

[0110] As one possible implementation, the method further includes: pushing alarm information to at least one mobile smart terminal. By promptly pushing alarm information to at least one mobile smart terminal held by a technician, the relevant technician can respond quickly and make appropriate judgments and take appropriate measures.

[0111] Please see Figures 3 to 6 ,Compare Figure 3 and Figure 5 It is evident that the vibration excitation from mechanical construction and the vibration excitation from passing vehicles are difficult to distinguish in the time domain waveform. (Comparison) Figure 4 and Figure 6 It can be seen that the vibration excitation of mechanical construction does not show obvious frequency shift in the low-frequency part, while the vibration excitation of vehicles shows obvious frequency shift in the low-frequency part. This can be used to distinguish between mechanical construction and vehicle interference.

[0112] Please see Figure 7 This application provides an interference judgment device 20 based on an optical fiber vibration sensing system. The device includes:

[0113] The acquisition module 210 is used to acquire vibration signals of the corresponding areas of each optical fiber monitoring unit based on a preset sampling frequency.

[0114] The time-frequency analysis module 220 is used to perform time-frequency analysis on each of the acquired vibration signals, and to form a time-frequency matrix set by combining the time-frequency matrices obtained after the time-frequency analysis.

[0115] The calculation module 230 is used to calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1 that correspond one-to-one with each time-frequency matrix, wherein the time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1.

[0116] The processing module 240 is used to calculate the fitting coefficients based on the first sequence N0 and the second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit, and to determine the excitation signal type according to the relationship between the fitting coefficients and the preset threshold.

[0117] The solution in this application embodiment, by performing time-frequency analysis on the collected external vibration signals and monitoring the changing patterns of the signal spectrum, can effectively distinguish between vehicle passing signals and mechanical operation signals, thereby improving the system alarm accuracy.

[0118] In summary, this application provides an interference judgment method and apparatus based on an optical fiber vibration sensing system. The method includes: acquiring vibration signals of the corresponding areas of each optical fiber monitoring unit based on a preset sampling frequency; performing time-frequency analysis on each acquired vibration signal, and forming a time-frequency matrix set by the time-frequency matrices obtained after the time-frequency analysis; calculating the low-frequency band accumulation value and the mid-to-high frequency band accumulation value of each time-frequency matrix in the time-frequency matrix set, and recording them into a first sequence N0 and a second sequence N1 corresponding to each time-frequency matrix, wherein the time of the first sequence N0 corresponds to the time of the second sequence N1; calculating the fitting coefficient based on the first sequence N0 and the second sequence N1 corresponding to each time-frequency matrix of the optical fiber monitoring unit, and judging the excitation signal type according to the relationship between the fitting coefficient and a preset threshold.

[0119] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0120] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. An interference judgment method based on an optical fiber vibration sensing system, wherein the optical fiber vibration sensing system comprises several optical fiber monitoring units laid along an oil and gas pipeline for detecting vibration signals, characterized in that, The method includes: Vibration signals of the corresponding areas of each fiber optic monitoring unit are collected based on a preset sampling frequency. Each collected vibration signal was subjected to time-frequency analysis, and the time-frequency matrices obtained after the analysis were combined into a time-frequency matrix set. Calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1 that correspond one-to-one with each time-frequency matrix, where the time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1. The fitting coefficients are calculated based on the first sequence N0 and the second sequence N1, which correspond one-to-one with the time-frequency matrix of each fiber optic monitoring unit. The type of excitation signal is then determined based on the relationship between the fitting coefficients and a preset threshold. The calculation of the fitting coefficients based on the first sequence N0 and the second sequence N1, which correspond one-to-one with the time-frequency matrix of each fiber optic monitoring unit, includes: determining whether there are data points in the second sequence N1 that exceed a preset trigger threshold; if so, determining the time T corresponding to each data point that satisfies the determination condition; extracting data points from the first sequence N0 within a first preset duration before each time T to form a first data point set; and extracting data points from the first sequence N0 within a first preset duration after each time T to form a second data point set, and recording the index value I of the first data point in the first sequence N0 that exceeds the preset trigger threshold. a And the index value I of the last data point that exceeds the preset trigger threshold b Extract the first data point from the first data point set and the second data point set up to the Ith data point set. a -1 data points form the first array, and the i-th data point is extracted from the first data point set and the second data point set. b The first array is formed by adding 1 data points to the last data point; the first array and the second array are linearly fitted to obtain the first fitting coefficient and the second fitting coefficient, and the excitation signal type is determined based on the first fitting coefficient and the second fitting coefficient.

2. The interference judgment method based on an optical fiber vibration sensing system according to claim 1, characterized in that, The step involves performing time-frequency analysis on each of the acquired vibration signals, and then forming a time-frequency matrix set from the time-frequency matrices obtained after the analysis, including: Vibration signals for the corresponding region of each fiber optic monitoring unit are sequentially and slidably captured based on time sequence. The vibration signal sequence captured by the sliding intercept is subjected to a fast Fourier transform in sequence to obtain the corresponding spectral vibration signal; The spectral vibration signals obtained during the sliding process are spliced ​​together to obtain the time-frequency matrix of the corresponding region of each optical fiber monitoring unit, where the horizontal axis of the time-frequency matrix is ​​frequency and the vertical axis is time.

3. The interference judgment method based on an optical fiber vibration sensing system according to claim 1, characterized in that, Perform linear fitting on the first and second arrays respectively, including: Based on the first array and the second array respectively, fit straight lines to obtain the corresponding mean square error; The normal equation expression for the corresponding fitted curve is obtained based on the mean square error; Solve the corresponding normal equation expressions using either the elimination method or the Cramer method to obtain the first and second fitting coefficients.

4. The interference judgment method based on an optical fiber vibration sensing system according to claim 3, characterized in that, The step of determining the excitation signal type based on the first fitting coefficient and the second fitting coefficient includes: Determine whether the absolute values ​​of the first and second fitting coefficients are both greater than or equal to a preset threshold. If both judgments are yes, then the excitation signal is determined to be an interference signal. If at least one of the two judgment results is negative, then the excitation signal for this time will be determined as mechanical excavation excitation.

5. The interference judgment method based on an optical fiber vibration sensing system according to claim 4, characterized in that, The method further includes: Determine whether the number of times mechanical digging stimuli occur within a second preset time period exceeds a preset number; if so, issue an alarm message, wherein the alarm message includes alarm location information.

6. The interference judgment method based on an optical fiber vibration sensing system according to claim 5, characterized in that, The method further includes: The alarm information will be pushed to at least one mobile smart terminal.

7. An interference detection device based on an optical fiber vibration sensing system, characterized in that, The device includes: The acquisition module is used to collect vibration signals from the corresponding areas of each fiber optic monitoring unit based on a preset sampling frequency. The time-frequency analysis module is used to perform time-frequency analysis on each of the acquired vibration signals, and to form a time-frequency matrix set by combining the time-frequency matrices obtained after the analysis. The calculation module is used to calculate the low-frequency band cumulative value and the mid-to-high frequency band cumulative value of each time-frequency matrix in the time-frequency matrix set, and record them into the first sequence N0 and the second sequence N1, which correspond one-to-one with each time-frequency matrix, respectively. The time of the first sequence N0 corresponds one-to-one with the time of the second sequence N1. The processing module is used to calculate fitting coefficients based on a first sequence N0 and a second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit, and to determine the excitation signal type based on the relationship between the fitting coefficients and a preset threshold. The calculation of fitting coefficients based on the first sequence N0 and the second sequence N1 that correspond one-to-one with the time-frequency matrix of each optical fiber monitoring unit includes: determining whether there are data points in the second sequence N1 that exceed a preset trigger threshold; if so, determining the time T corresponding to each data point that satisfies the determination condition; extracting data points from the first sequence N0 within a first preset duration before each time T to form a first data point set; and extracting data points from the first sequence N0 within a first preset duration after each time T to form a second data point set, and recording the index value I of the first data point in the first sequence N0 that exceeds the preset trigger threshold. a And the index value I of the last data point that exceeds the preset trigger threshold b Extract the first data point from the first data point set and the second data point set up to the Ith data point set. a -1 data points form the first array, and the i-th data point is extracted from the first data point set and the second data point set. b The first array is formed by adding 1 data points to the last data point; the first array and the second array are linearly fitted to obtain the first fitting coefficient and the second fitting coefficient, and the excitation signal type is determined based on the first fitting coefficient and the second fitting coefficient.

8. An electronic device, characterized in that, include: One or more processors; Memory, connected to the one or more processors; The memory stores instructions that can be executed by the one or more processors, and the one or more processors implement the interference judgment method based on the fiber optic vibration sensing system as described in any one of claims 1 to 6 by executing the instructions stored in the memory.

9. A computer-readable storage medium storing instructions, characterized in that, When the instruction is executed by the processor, the processor performs the interference judgment method based on the fiber optic vibration sensing system as described in any one of claims 1 to 6.

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