CN111540150B - Multi-core distributed optical fiber-based pipeline construction machine early warning system and method - Google Patents

Abstract

The invention discloses an early warning system and method for pipeline construction machines based on multi-core distributed optical fibers, which relates to a pipeline construction machine early warning method based on multi-core distributed optical fibers, comprising the steps of: S11. Receive multi-core distributed optical fibers The first data information of several distributed optical fibers obtained by the sensor; S12. The second data information at the interference source collected by the data acquisition device is received; S13. The first data information obtained from the received multi-core distributed optical fiber sensor and The second data information collected by the data collection device is processed to obtain an early warning analysis result. The present invention is based on the multi-core distributed optical fiber pipeline early warning method, which reduces the influence of external interference factors on the early warning system through the comparison of estimated disturbance and actual defense zone parameters, and through the multi-core distributed optical fiber group decision-making mechanism.

Description

An early warning system and method for pipeline construction machines based on multi-core distributed optical fibers

technical field

The invention relates to the field of communication technology, and in particular to a pipeline construction machine early warning system and method based on multi-core distributed optical fibers.

Background technique

Natural gas pipelines are buried underground and are easily damaged by the construction team's excavators, pile drivers and other machines. However, natural gas pipelines often run through provinces and cities, and the distance is long, making it difficult to accurately warn.

Since any position of the optical fiber is a sensing unit in the distributed optical fiber sensing system, it can obtain the spatial distribution state and time-varying information of the measured parameters over the entire length of the optical fiber. Distributed optical fiber sensing system can realize large-scale monitoring, and has a very important position in many optical fiber sensors. It is the most mature technology and the most widely used category, showing a good application prospect.

In the prior art, the natural gas pipeline early warning method is as follows: laying distributed optical fiber sensors in the natural gas pipeline, if the peak value of the waveform exceeds a certain threshold, an alarm is issued, or a certain feature extraction is performed on the waveform, and a certain model is used for model training. Alert based on model predictions. However, the surrounding highways, factories and other interference factors have a great influence, and the statistics such as frequency and amplitude caused by the interference sources are similar to those of construction machines, so it is difficult to distinguish. In the prior art, the influence of external interference factors is often filtered out from the perspective of an algorithm, but at the same time, changes caused by construction machines are also filtered out.

For example, the patent with publication number CN106197904A discloses a distributed optical fiber pipeline safety monitoring device for simultaneous monitoring of temperature and vibration of optical fiber, which reflects the situation of pipeline damage and leakage in the environment around long-distance oil and gas pipelines, including a light source, a light splitter 1×2 beam splitter with 90:10 ratio, modulator, optical amplifier, circulator, polarization controller, 3 1×2 beam splitters with 50:50 split ratio, sensing cable, frequency shifter, 3 Compared with the prior art, the present invention has significant advantages such as accurate monitoring and reliable operation, including 2×2 optical splitters, 3 balanced detectors, polarization beam splitters, multi-channel high-speed collectors, control cards and industrial computers. Although it can monitor the distributed fiber optic pipeline, it is still difficult to distinguish whether it is caused by an interference source or a construction machine intrusion, and it is difficult to give an accurate early warning.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a pipeline construction machine early warning system and method based on multi-core distributed optical fibers in view of the defects of the prior art.

In order to achieve the above purpose, the present invention adopts the following technical solutions:

A method for early warning of pipeline construction machines based on multi-core distributed optical fibers, comprising the steps of:

S1. Receive the first data information of several distributed optical fibers obtained by the multi-core distributed optical fiber sensor;

S2. Receive the second data information at the interference source collected by the data collection device;

S3. Process the first data information acquired by the multi-core distributed optical fiber sensor and the second data information acquired by the data acquisition device to obtain an early warning analysis result.

Further, in the step S1, each of the distributed optical fibers is divided into multiple defense zones, and each defense zone in the multiple defense zones and each segment of the waveform formed by the multi-core distributed optical fiber sensor. data corresponds.

Further, the step S3 specifically includes:

S31. Calculate time series data of each defense zone in each distributed optical fiber, where the time series data include average amplitude, variance, covariance, and frequency range;

S32. Determine whether the calculated average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, if so, execute step S33; if not, execute step S31;

S33. Count the number of defense zones whose average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, and determine whether the counted number of defense zones reaches the second preset threshold, and if so, execute step S34 ; If not, execute step S31;

S34. Record the distributed optical fibers corresponding to each defense area in which the number of defense zones reaches the second preset threshold, and count the number of recorded distributed optical fibers;

S35. Determine whether the counted number of distributed optical fibers exceeds a third preset threshold, if not, perform step S31; if so, perform an early warning analysis.

Further, the early warning analysis in the step S35 is specifically:

A1. Input the time series data of each defense zone in the distributed optical fiber into the pre-established detection model to determine whether there is construction machine intrusion, if so, record the number of distributed optical fibers with construction machine intrusion, and execute step A2;

A2. Determine whether the number of distributed optical fibers invaded by construction machines exceeds the fourth preset threshold, if so, execute step A3; if not, execute step A1;

A3. Output the interference parameters of each defense zone according to the pre-established neural network interference model, where the interference includes average amplitude, variance, covariance, and frequency range;

A4. Compare the output interference parameters of each defense zone with the pre-stored actual interference parameters of each defense zone, and determine whether the comparison result reaches the fifth preset threshold and the interference reaches the minimum value, and if so, record the interference the number of parameters;

A5. Determine whether the number of recorded interference parameters exceeds the sixth preset threshold, if not, execute step A1; if so, alarm.

Further, in the described A4, the comparison result reaches the fifth preset threshold and the interference reaches the minimum value, which is expressed as:

表示实际干扰参数数值；P3表示第五预设阈 值；B表示干扰最小值。 Among them, m represents the output interference parameter value;

represents the actual interference parameter value; P 3 represents the fifth preset threshold; B represents the minimum value of the interference.

Further, the number of interference parameters recorded in the A5 is expressed as:

Wherein, D represents the number of distributed optical fibers counted in step S34, N represents the number of defense zones divided by each distributed optical fiber, and X represents the number of interference parameters of the defense zone.

Correspondingly, an early warning system for pipeline construction machines based on multi-core distributed optical fibers is also provided, including: several data acquisition devices, Ethernet, remote servers, and multi-core distributed optical fiber sensors; the several data acquisition devices are respectively set At several interference sources, the multi-core distributed optical fiber sensor is arranged at the natural gas pipeline;

The multi-core distributed optical fiber sensor is used for acquiring first data information of several distributed optical fibers, and sending the acquired first data information to a remote server through Ethernet;

The data collection device is used to collect the second data information at the interference source, and send the collected second data information to the remote server through the Ethernet;

The remote server is used to receive and process the first data information sent by the multi-core distributed optical fiber sensor and the second data information sent by the data acquisition device, and obtain an early warning analysis result.

Further, each distributed optical fiber in the several distributed optical fibers is divided into multiple defense zones, and each defense zone in the multiple defense zones corresponds to each segment of data in the waveform formed by the multi-core distributed optical fiber sensor. .

Further, the remote server specifically includes:

a calculation module for calculating time series data of each defense zone in each distributed optical fiber, the time series data including average amplitude, variance, covariance, and frequency range;

a first judging module for judging whether the calculated average amplitude of each defense zone in each distributed optical fiber exceeds a first preset threshold;

a second judging module, configured to count the number of defense zones where the average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, and determine whether the counted number of defense zones reaches the second preset threshold;

The recording module is used to record the distributed optical fibers corresponding to each defense zone in which the number of defense zones reaches the second preset threshold, and count the number of recorded distributed optical fibers;

A third judging module, configured to judge whether the counted number of distributed optical fibers exceeds a third preset threshold.

Further, if the third judgment module exceeds the third preset threshold, an early warning analysis is performed, specifically:

The fourth judgment module is used to input the time series data of each defense zone in the distributed optical fiber into the pre-established detection model to judge whether there is a construction machine intrusion;

a fifth judgment module, used for judging whether the number of distributed optical fibers invaded by construction machines exceeds a fourth preset threshold;

an output module, configured to output the interference parameters of each defense zone according to a pre-established neural network interference model, where the interference includes average amplitude, variance, covariance, and frequency range;

The sixth judgment module is used to compare the output interference parameter of each defense zone with the pre-stored actual interference parameter of each defense zone, and judge whether the comparison result reaches the fifth preset threshold and the interference reaches the minimum value;

The seventh judgment module is used for judging whether the number of recorded interference parameters exceeds the sixth preset threshold.

Compared with the prior art, the present invention has the following advantages:

1. By comparing the estimated disturbance and the actual defense zone parameters, the influence of external disturbance factors on the early warning system is reduced.

2. The group decision-making mechanism of multi-core distributed optical fiber is used to reduce the false alarm rate caused by optical fiber damage and external interference.

Description of drawings

1 is a flowchart of a method for early warning of pipeline construction machines based on multi-core distributed optical fibers provided by Embodiment 1;

2 is a system schematic diagram of a distributed optical fiber and an interference source provided by Embodiment 1;

3 is a schematic diagram of a multi-core distributed optical fiber provided in Embodiment 1;

FIG. 4 is a schematic diagram of defense zone segmentation provided by Embodiment 1;

FIG. 5 is a heat map of time series data of each defense zone provided by each distributed optical fiber provided in the first embodiment;

FIG. 6 is a schematic diagram of the time series data of sub-defense zones provided by Embodiment 1;

7 is a comparison diagram of the average amplitude and threshold value of the distributed optical fiber sub-defense zone in the time period provided by Embodiment 1;

FIG. 8 is a schematic diagram of the interference source and the neural network model of the interference provided in the first embodiment.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict.

The purpose of the present invention is to provide a method and system for early warning of pipeline construction machines based on multi-core distributed optical fibers, aiming at the defects of the prior art.

Example 1

This embodiment provides an early warning method for pipeline construction machines based on multi-core distributed optical fibers, as shown in FIG. 1 , including steps;

S11. Receive the first data information of several distributed optical fibers obtained by the multi-core distributed optical fiber sensor;

S12. Receive the second data information at the interference source collected by the data collection device;

S13. Process the first data information acquired by the multi-core distributed optical fiber sensor and the second data information acquired by the data acquisition device to obtain an early warning analysis result.

In this embodiment, as shown in FIG. 2 is a schematic structural diagram of distributed optical fibers and interference sources, including several data acquisition devices, Ethernet, remote servers, and multi-core distributed optical fiber sensors; data acquisition devices are divided into various types , which are installed at the interference sources such as highways and factories around the pipeline, and collect data for different interference sources. The data acquisition equipment sends the data to the remote server in real time through Ethernet for analysis and storage; multi-core distributed The optical fiber sensor is installed at the natural gas pipeline, and sends the data to the remote server through the Ethernet; the remote server uses the pipeline early warning method based on multi-core distributed optical fiber to estimate the interference of the data of the interference source, and combines the data of the distributed optical fiber sensor to carry out the interference estimation. Warning.

In step S11, the first data information of several distributed optical fibers obtained by the multi-core distributed optical fiber sensor is received.

The remote server receives the first data information of several distributed optical fibers obtained by the multi-core distributed optical fiber sensor.

Distributed fiber optic sensors mainly include: ultra-narrow linewidth lasers, acousto-optic modulators, circulators, photodetectors, sensing fibers, preamplifier circuits, data acquisition cards and hosts. In practical engineering applications, ultra-narrow linewidth lasers, acousto-optic modulators, circulators, photodetectors, and other corresponding power supplies, driving, detection circuits and communication interfaces are usually integrated in the sensor host; the sensing fiber arrangement In the sensing cable of the field. The laser emitted by the ultra-narrow linewidth laser as a light source is modulated into an optical pulse by an acousto-optic modulator. The optical pulse is injected into the sensing fiber through a circulator, and the back Rayleigh scattered light in the sensing fiber coherently interferes within the pulse width. The strong pass through the circulator is detected by the detector, and after being amplified, it enters the host computer through the data acquisition card for data processing and result display.

When a disturbance acts on the sensing fiber, due to the elastic light effect, the phase of the light at the disturbed position changes, causing the phase of the backscattered light at the corresponding position to change, and the interference light intensity of the scattered light within the pulse width will also change accordingly. Therefore, the distributed optical fiber sensor obtains the corresponding data information of the distributed optical fiber.

Figure 3 is a schematic diagram of a multi-core distributed optical fiber. The multi-core distributed optical fiber includes multiple optical fiber cores, and data can be measured at the same time.

The waveform formed by each distributed optical fiber in several distributed optical fibers is divided into multiple defense zones, and each defense zone in the multiple defense zones can receive the data returned when the multi-core distributed optical fiber sensor sends laser light. Figure 4 is a schematic diagram of defense zone division, which means that in a certain test, the distributed optical fiber is divided into N defense zones, and the later data processing and early warning work are based on the defense zone.

In this embodiment, after the laser is sent, it will return to the place where the laser is emitted. And the longer the distance, the longer the return time. Then a continuous waveform is formed. Although this is a waveform graph on the time axis, since the return time and distance are linearly proportional, each zone will correspond to a certain segment of data in this waveform graph.

In step S12, the second data information at the interference source collected by the data collection device is received.

The remote server receives the second data information at the interference source collected by the data collection device.

The data of the interference source includes the weight of the car, the speed of the car, the position of the car, the power consumption of the factory equipment, etc. The interference caused by the distributed optical fiber sensor includes the average amplitude, variance, covariance, frequency range, etc.

In this embodiment, the relationship model between the interference source and the interference is established in advance based on the deep neural network, that is, the neural network interference model (it needs to be explained that the establishment of the neural network interference model can be established according to the existing technology, which is not much in this embodiment. repeat). In the neural network interference model, the time series data of the interference source is input, and the interference of each defense zone in the distributed optical fiber is output, that is, the average amplitude, variance, covariance, frequency range, etc.

Figure 8 shows the neural network model of the interference source and interference (ie, the neural network interference model). The neural network interference model inputs the defense zone parameters of each distributed optical fiber, that is, a matrix (see Figure 5); the neural network model outputs whether there is construction machine intrusion. Before being put into use, all kinds of data are collected and labeled for neural network training.

In step S13, the first data information acquired by the multi-core distributed optical fiber sensor and the second data information acquired by the data acquisition device are processed to obtain an early warning analysis result.

The remote server processes the first data information acquired by the multi-core distributed optical fiber sensor and the second data information acquired by the data acquisition device to obtain an early warning analysis result. Specifically include:

S131. Calculate the time series data of each defense zone in each distributed optical fiber, wherein the time series data include average amplitude, variance, covariance, and frequency range;

First select the time series data of each distributed optical fiber in the recent time, such as the recent time is [T1, T2]; then each defense zone in multiple defense zones can receive the data returned when the multi-core distributed optical fiber sensor sends laser light , and calculate the average value of the data in each defense zone; finally, calculate the statistics of the time series data of each defense zone in each distributed optical fiber.

As shown in Figure 5, each distributed optical fiber is a heat map of time series data in each defense zone, where the abscissa represents time, and the ordinate represents multiple data points in each defense zone. There are 5 data points in the defense zone in the figure, and the data of 14 time points in total. The five data points can be averaged to obtain the average value of the defense zone data at this time point.

S132. Determine whether the calculated average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, if so, execute step S133; if not, execute step S131;

S133. Count the number of defense zones in which the average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, and determine whether the counted number of defense zones reaches the second preset threshold U, and if so, perform step S134; If not, go to step S131;

S134. Record the distributed optical fibers corresponding to each defense area in which the number of defense zones reaches the second preset threshold, and count the number of recorded distributed optical fibers;

S135. Determine whether the counted number of distributed optical fibers exceeds a third preset threshold (the third preset threshold is the percentage of all distributed optical fibers, represented by P1), if not, it is considered that there is no construction machine intrusion, and the steps are executed S131; if yes, perform an early warning analysis.

As shown in Figure 7, the average amplitude and threshold value of the distributed optical fiber sub-defense zone in the time period are compared. Among them, the average amplitude value of each defense zone in the time period indicates that a certain time period is selected. First, the average test result for each defense zone (considered as a time point) is firstly averaged, and then the average value for each time point is calculated. Multiple averages over a time period are averaged to obtain the average magnitude. Because of the need to calculate the variance, covariance, frequency range and other statistics within the time period, the two averaging operations are separated. The first average can be used to calculate variance, covariance, frequency range, etc. over time periods. When the number of defense zones (3 in the figure) whose average amplitude exceeds the threshold (the threshold in the figure is 50) reaches U (U is set by itself), it is preliminarily considered that the distributed optical fiber has detected an obvious external vibration signal. It may be a source of interference such as a factory, highway, or a construction machine.

In step S135, the early warning analysis is performed on the distributed optical fibers whose average amplitude exceeds the first preset threshold and reaches the second preset threshold U. Specifically:

A1. Input the time series data of each defense zone in the distributed optical fiber into the pre-established detection model to determine whether there is construction machine intrusion, if so, record the number of distributed optical fibers with construction machine intrusion, and execute step A2;

Input the time series data of the distributed optical fibers whose average amplitude exceeds the first preset threshold and reach the second preset threshold U into the neural network model, and then judge whether there is a construction machine intrusion, and if so, record the construction The number of distributed fibers hacked by machines.

It should be noted that the detection model is a construction machine intrusion detection model, which is used to find out whether there is a construction machine intrusion.

A2. Judging whether the number of distributed optical fibers invaded by construction machines exceeds a fourth preset threshold (the fourth preset threshold is the number of distributed fibers whose average amplitude exceeds the first preset threshold and reaches the second preset threshold U The total number of optical fibers, represented by P2), if yes, go to step A3; if not, it is considered that there is no construction machine intrusion, go to step A1;

A3. The pre-established neural network interference model outputs the interference parameters of each defense zone, where the interference includes average amplitude, variance, covariance, and frequency range;

Figure 6 shows the time-series data of the sub-zones. The figure includes time series data of about 30 time points in 5 defense zones. Each value of a zone is an average of multiple data points in a single time point (see Figure 4), and the zone's average amplitude, variance, covariance, and frequency range are derived on this basis.

A4. Compare the output interference parameters (average amplitude, variance, covariance, frequency range) of each defense zone with the pre-stored actual interference parameters (average amplitude, variance, covariance, frequency range) of each defense zone , and judge whether the comparison result reaches the fifth preset threshold P3 and the interference reaches the minimum value, if so, record the number of the interference parameters;

In this embodiment, the comparison result reaches the fifth preset threshold and the interference reaches the minimum value, which is expressed as:

表示实际干扰参数数值；P3表示第五预设阈 值；B表示干扰最小值。 Among them, m represents the output interference parameter value;

represents the actual interference parameter value; P 3 represents the fifth preset threshold; B represents the minimum value of the interference.

A5. Determine whether the number of recorded interference parameters exceeds the sixth preset threshold (the sixth preset threshold is the percentage of the number of all interference parameters, represented by P4), if not, execute step A1; if so, alarm . Among them, all parameter records include all distributed fibers whose average amplitude exceeds the threshold and reaches U. The number of recorded interference parameters is expressed as:

Among them, D represents the number of distributed optical fibers counted in step S134, N represents the number of defense zones divided by each distributed optical fiber, and X represents the number of interference parameters in the defense zone, such as average amplitude, variance, covariance, frequency range , then X is 4.

It should be noted that, the detection model and the neural network interference model in this embodiment are two different models, and the establishment methods thereof can be realized by the prior art, and details are not described here in this embodiment.

This embodiment is based on the multi-core distributed optical fiber pipeline early warning method. The method compares the estimated disturbance with the actual defense zone parameters, and uses the multi-core distributed optical fiber group decision-making mechanism to reduce the impact of external interference factors on the early warning system.

Embodiment 2

This embodiment also provides an early warning system for pipeline construction machines based on multi-core distributed optical fibers, including: several data acquisition devices, Ethernet, remote servers, and multi-core distributed optical fiber sensors; the several data acquisition devices are respectively set At several interference sources, the multi-core distributed optical fiber sensor is arranged at the natural gas pipeline;

The multi-core distributed optical fiber sensor is used for acquiring first data information of several distributed optical fibers, and sending the acquired first data information to a remote server through Ethernet;

The data collection device is used to collect the second data information at the interference source, and send the collected second data information to the remote server through the Ethernet;

The remote server is used to receive and process the first data information sent by the multi-core distributed optical fiber sensor and the second data information sent by the data acquisition device, and obtain an early warning analysis result.

Further, each distributed optical fiber in the several distributed optical fibers is divided into multiple defense zones, and each defense zone in the multiple defense zones corresponds to each segment of data in the waveform formed by the multi-core distributed optical fiber sensor. .

Further, the remote server specifically includes:

a calculation module for calculating time series data of each defense zone in each distributed optical fiber, the time series data including average amplitude, variance, covariance, and frequency range;

a first judging module for judging whether the calculated average amplitude of each defense zone in each distributed optical fiber exceeds a first preset threshold;

a second judging module, configured to count the number of defense zones where the average amplitude of each defense zone in each distributed optical fiber exceeds the first preset threshold, and determine whether the counted number of defense zones reaches the second preset threshold;

The recording module is used to record the distributed optical fibers corresponding to each defense zone in which the number of defense zones reaches the second preset threshold, and count the number of recorded distributed optical fibers;

A third judging module, configured to judge whether the counted number of distributed optical fibers exceeds a third preset threshold.

Further, if the third judgment module exceeds the third preset threshold, an early warning analysis is performed, specifically:

The fourth judgment module is used to input the time series data of each defense zone in the distributed optical fiber into the pre-established detection model to judge whether there is a construction machine intrusion;

a fifth judgment module, used for judging whether the number of distributed optical fibers invaded by construction machines exceeds a fourth preset threshold;

The output module is used for the pre-established neural network interference model to output the interference parameters of each defense zone, and the interference includes average amplitude, variance, covariance, and frequency range;

The sixth judgment module is used to compare the output interference parameter of each defense zone with the pre-stored actual interference parameter of each defense zone, and judge whether the comparison result reaches the fifth preset threshold and the interference reaches the minimum value;

The seventh judgment module is used for judging whether the number of recorded interference parameters exceeds the sixth preset threshold.

It should be noted that the early warning system for a pipeline construction machine based on a multi-core distributed optical fiber provided in this embodiment is similar to that of the first embodiment, and details are not described here.

Compared with the prior art, the present invention has the following advantages:

1. By comparing the estimated disturbance and the actual defense zone parameters, the influence of external disturbance factors on the early warning system is reduced.

2. The group decision-making mechanism of multi-core distributed optical fiber is used to reduce the false alarm rate caused by optical fiber damage and external interference.

Note that the above are only preferred embodiments of the present invention and applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present invention. The scope is determined by the scope of the appended claims.

Claims (4)

1. A pipeline construction machine early warning method based on a multi-core distributed optical fiber is characterized by comprising the following steps:

s1, receiving first data information of a plurality of distributed optical fibers acquired by a multi-core distributed optical fiber sensor;

s2, receiving second data information at the interference source acquired by the data acquisition equipment;

s3, processing the received first data information acquired by the multi-core distributed optical fiber sensor and the second data information acquired by the data acquisition equipment to obtain an early warning analysis result;

dividing each of the plurality of distributed optical fibers into a plurality of defense areas in the step S1, each of the plurality of defense areas corresponding to each piece of data in the waveform formed by the multicore distributed optical fiber sensor;

the step S3 specifically includes:

s31, calculating time sequence data of each defense area in each distributed optical fiber, wherein the time sequence data comprises an average amplitude, a variance, a covariance and a frequency range;

s32, judging whether the calculated average amplitude of each defense area in each distributed optical fiber exceeds a first preset threshold value, if so, executing a step S33; if not, go to step S31;

s33, counting the number of defense areas of which the average amplitude of each defense area in each distributed optical fiber exceeds a first preset threshold value, judging whether the counted number of defense areas reaches a second preset threshold value, and if so, executing a step S34; if not, go to step S31;

s34, recording distributed optical fibers corresponding to each defense area when the number of the defense areas reaches a second preset threshold value, and counting the number of the recorded distributed optical fibers;

s35, judging whether the counted number of the distributed optical fibers exceeds a third preset threshold value or not, and if not, executing a step S31; if yes, performing early warning analysis;

the early warning analysis in the step S35 specifically includes:

A1. inputting the time sequence data of each defense area in the distributed optical fiber into a pre-established detection model to judge whether a construction machine invades, if so, recording the number of the distributed optical fibers invaded by the construction machine, and executing the step A2;

A2. judging whether the number of the distributed optical fibers invaded by the construction machine exceeds a fourth preset threshold value or not, if so, executing the step A3; if not, executing the step A1;

A3. outputting interference parameters of each defense area according to a pre-established neural network interference model, wherein the interference comprises an average amplitude, a variance, a covariance and a frequency range;

A4. comparing the output interference parameters of each defense area with prestored actual interference parameters of each defense area, judging whether the comparison result reaches a fifth preset threshold value and the interference reaches a minimum value, and if so, recording the number of the interference parameters;

A5. judging whether the number of the recorded interference parameters exceeds a sixth preset threshold value, if not, executing the step A1; if yes, alarming.

2. The multi-core distributed optical fiber-based pipeline construction machine early warning method as claimed in claim 1, wherein the comparison result in the a4 reaches a fifth preset threshold and the interference reaches a minimum value, which is expressed as:

wherein m represents the output interference parameter value;

representing the actual interference parameter value; p3 denotes a fifth preset threshold; b denotes the interference minimum.

3. The multi-core distributed optical fiber-based pipeline construction machine early warning method as claimed in claim 1, wherein the number of the interference parameters recorded in the a5 is represented as:

D*N*X

where D denotes the number of distributed optical fibers counted in step S34, N denotes the number of zones into which each distributed optical fiber is divided, and X denotes the number of interference parameters of the zones.

4. The utility model provides a pipeline construction machine early warning system based on multicore distributed optical fiber which characterized in that includes: the system comprises a plurality of data acquisition devices, an Ethernet, a remote server and a multi-core distributed optical fiber sensor; the multi-core distributed optical fiber sensor is arranged at a natural gas pipeline;

the multi-core distributed optical fiber sensor is used for acquiring first data information of a plurality of distributed optical fibers and sending the acquired first data information to a remote server through the Ethernet;

the data acquisition equipment is used for acquiring second data information at an interference source and sending the acquired second data information to a remote server through the Ethernet;

the remote server is used for receiving and processing first data information sent by the multi-core distributed optical fiber sensor and second data information sent by the data acquisition equipment to obtain an early warning analysis result;

each distributed optical fiber is divided into a plurality of defense areas in the plurality of distributed optical fibers, and each defense area in the plurality of defense areas corresponds to each section of data in a waveform formed by the multi-core distributed optical fiber sensor;

the remote server specifically includes:

the calculation module is used for calculating time sequence data of each defense area in each distributed optical fiber, and the time sequence data comprises average amplitude, variance, covariance and frequency range;

the first judgment module is used for judging whether the calculated average amplitude of each defense area in each distributed optical fiber exceeds a first preset threshold value or not;

the second judgment module is used for counting the number of defense areas of which the average amplitude of each defense area in each distributed optical fiber exceeds a first preset threshold value and judging whether the counted number of defense areas reaches a second preset threshold value;

the recording module is used for recording the distributed optical fibers corresponding to each defense area when the number of the defense areas reaches a second preset threshold value, and counting the number of the recorded distributed optical fibers;

the third judging module is used for judging whether the counted number of the distributed optical fibers exceeds a third preset threshold value;

if the third judgment module exceeds a third preset threshold, performing early warning analysis, specifically:

the fourth judgment module is used for inputting the time sequence data of each defense area in the distributed optical fiber into a pre-established detection model to judge whether a construction machine invades;

the fifth judgment module is used for judging whether the number of the distributed optical fibers invaded by the construction machine exceeds a fourth preset threshold value or not;

the output module is used for outputting the interference parameters of each defense area according to a pre-established neural network interference model, wherein the interference comprises an average amplitude, a variance, a covariance and a frequency range;

the sixth judging module is used for comparing the output interference parameters of each defense area with the prestored actual interference parameters of each defense area and judging whether the comparison result reaches a fifth preset threshold value and the interference reaches a minimum value;

and the seventh judging module is used for judging whether the number of the recorded interference parameters exceeds a sixth preset threshold value.

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