Pipeline online monitoring system based on microwave photon radar

Abstract

The invention discloses a pipeline online monitoring system based on a microwave photon radar, and relates to the technical field of distributed optical fiber sensing, and the pipeline online monitoring system comprises the microwave photon radar, a signal acquisition module, a signal processing module, a signal extraction module, a data analysis module, a display module, an upper computer and a remote monitoring module, the signal sending module is located at the top of a pipeline and used for sending signals to the signal collection module, the signal collection module is located at the top of the pipeline and used for receiving microwave photon radar signals and transmitting the received signals to the signal processing module, and the signal processing module processes the collected signals and then inputs the signals into the signal extraction module; according to the pipeline online monitoring system, the microwave photon radar is used for constructing the pipeline online monitoring system, a large amount of labor cost needed by manual inspection and high cost of traditional monitoring equipment are reduced, the operation data of the pipeline can be collected in real time, the state of the pipeline can be continuously monitored, and the safety of pipeline operation is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of distributed optical fiber sensing technology, and more specifically, to an online pipeline monitoring system based on microwave photon radar. Background Art

[0002] In modern industry and life, pipelines are widely used in many fields such as petrochemicals, natural gas, environmental engineering, water treatment, food processing, pharmaceuticals, national defense and military. However, pipeline failure will lead to a series of serious consequences, not only causing damage to infrastructure and environmental pollution, but also huge economic losses and casualties. Pipeline defects, as a direct factor leading to pipeline failure, will cause local distortion stress, resulting in reduced pipeline strength and leakage risks. Therefore, safety monitoring of pipelines is the key to ensuring safe and reliable operation of pipelines, and it is also an inevitable requirement for the sustainable development of my country's oil and gas pipeline industry.

[0003] At present, conventional methods for pipeline safety detection, such as monitoring methods based on vision, ultrasound, eddy current, stress, acoustic emission and radar, have many limitations. Most of these methods are not real-time and require manual regular inspection and operation, and cannot achieve all-weather uninterrupted monitoring; the detection cost is high and it is difficult to promote and apply on a large scale; and in electromagnetically sensitive places, traditional methods have electromagnetic blind spots and cannot work properly. Therefore, existing monitoring technologies are difficult to meet the requirements of comprehensive, real-time and efficient monitoring of pipelines.

[0004] With the development of technology, microwave photon radar has emerged due to its unique advantages. It has the characteristics of large microwave detection depth and little influence by environmental humidity and temperature. It can work stably in severe weather and strong clutter environment, can also monitor large airspace, and has anti-multipath reflection and high distance resolution. Compared with traditional radar ranging methods, microwave photon radar performs better in terms of system response time, spatial resolution, anti-interference ability, equipment volume and weight, cost, and networking implementation. These advantages make it show great application potential in the field of pipeline monitoring. As the core means to realize intelligent management and safety detection of pipelines, the pipeline online monitoring system is becoming a key issue that needs to be solved in the field of pipeline application engineering. In view of this, we propose a pipeline online monitoring system based on microwave photon radar. Summary of the invention

[0005] The purpose of the present invention is to provide a pipeline online monitoring system based on microwave photon radar to solve the above technical problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a pipeline online monitoring system based on microwave photon radar, comprising a microwave photon radar, a signal acquisition module, a signal processing module, a signal extraction module, a data analysis module, a display module, a host computer and a remote monitoring module; The microwave photon radar is located at the top of the pipeline and is used to send signals to the signal acquisition module; The signal acquisition module is located at the top of the pipeline, and is used to receive microwave photon radar signals and transmit the received signals to the signal processing module. The signal processing module processes the collected signals and then inputs them into the signal extraction module. The signal extraction module extracts signal characteristic parameters and inputs them into the data analysis module. The data analysis module includes a damage identification unit and a damage assessment unit. The damage identification unit inputs the pipeline damage identification result into the damage assessment unit. The damage assessment unit inputs the damage assessment result into the display module, the host computer and the remote monitoring module. The display module is connected to the host computer. The signal extraction module is provided with a locking unit, and the locking unit controls the signal transmission and signal shutoff of the microwave photon radar through a feedback signal matching the microwave photon radar.

[0007] The present invention uses the real-time working characteristics of microwave photon radar to build an online pipeline monitoring system, which completely changes the mode of traditional monitoring methods that rely on regular manual inspections. Its cost is relatively low, reducing the large amount of manpower costs required for manual inspections and the high cost of traditional monitoring equipment. This allows the monitoring system to be widely used in various pipelines, and can carry out comprehensive and effective monitoring, solving the problem of high cost and difficulty in large-scale promotion of traditional monitoring methods. It can collect pipeline operation data in real time and continuously monitor the pipeline status, effectively solving the problems of non-real-time and inability to monitor all-weather in traditional methods, and can promptly detect abnormal conditions in pipelines, greatly improving the safety of pipeline operation.

[0008] Preferably, the signal acquisition module includes an optical transmission module, an optical fiber coupler, a beam splitter, an optical fiber delay module, an optical fiber circulator, an optical receiving module, a phase-locked amplifier, a power integrator and an electrical bandpass filter; The output end of the optical transmission module is connected to the first input end of the beam splitter, the first port of the optical fiber coupler is connected to the second input end of the beam splitter, the output end of the beam splitter is coupled to the pipeline through a section of optical fiber, the input end of the optical fiber delay module is connected to the second port of the optical fiber coupler, the output end is connected to the first port of the optical fiber circulator, the second port of the optical fiber circulator is coupled to the pipeline through a section of optical fiber, the third port of the optical fiber circulator is connected to the first input end of the beam splitter, the input end of the electrical bandpass filter is connected to the second output end of the beam splitter, and the output end is connected to the input end of the optical receiving module.

[0009] Preferably, the input end of the phase-locked amplifier is connected to the second output end of the electric bandpass filter, and the output end is connected to the input end of the power integrator, and the output end of the power integrator is connected to the signal processing module via analog-to-digital conversion.

[0010] Preferably, the signal processing module includes a first IQ demodulation unit and a first digital sampling unit, and the signal extraction module includes a first bandpass filtering unit, a second IQ demodulation unit, a second digital sampling unit, a second bandpass filtering unit and a third digital sampling unit. The input end of the first IQ demodulation unit is connected to the output end of the signal processing module, the output end of the first IQ demodulation unit is connected to the first input end of the signal extraction module, the input end of the first bandpass filtering unit is connected to the first output end of the signal extraction module, the input end of the second IQ demodulation unit is connected to the output end of the first bandpass filtering unit, the output end of the second IQ demodulation unit is connected to the second input end of the signal extraction module, and the output end of the power integrator is connected to the input end of the signal processing module through analog-to-digital conversion, the second digital sampling unit, the second bandpass filtering unit, and the third digital sampling unit in sequence.

[0011] Preferably, the bandwidth of the second bandpass filtering unit is not less than 1500 MHz or not more than 20 GHz.

[0012] Preferably, the insertion loss of the optical fiber coupler is less than or equal to 0.5 dB, and the optical fiber length of the optical fiber coupler is not less than 2 meters.

[0013] Preferably, the damage identification unit is used for pipeline damage identification, and the identification process is as follows: Extract the frequency domain peak of the time domain signal and use the normalized signal peak as the spectrum feature. Suppose the time domain signal is , after Fourier transform, we get the frequency domain signal , frequency domain peak , the normalized spectrum characteristics: ; Normalize the spectral characteristics of two adjacent time domain signals and calculate the difference. Suppose the spectral characteristics of two adjacent time domain signals are and , difference ; Compare the difference with the threshold: If Greater than the threshold , is an alarm signal; if Less than threshold , which is a normal signal.

[0014] Preferably, the damage assessment unit is used for pipeline damage assessment, and the assessment process of the damage assessment unit is: Extracting the damage signal amplitude , spectrum peak and damage frequency Get feature parameters , the pipeline damage location and pipeline damage type are evaluated through the damage location identification model and the damage type identification model; The damage location identification model is: When , the pipeline damage location is the top of the pipeline; when or When , the pipeline damage location is on the left side of the pipeline; when or When , the pipeline damage location is on the right side of the pipeline; The damage type identification model is: When , the pipeline damage type is large-area damage; when When , the pipeline damage type is point damage.

[0015] A pipeline online monitoring signal extraction method based on microwave photon radar comprises the following steps: S1: Sending a signal to a signal acquisition module through a microwave photon radar, the signal acquisition module transmits the acquired signal to a signal processing module, and the signal processing module performs IQ demodulation and digital-to-analog conversion on the acquired signal to obtain a first digital signal; S2: extracting the power spectrum density value of the first digital signal and using the locking unit to extract and store the reference point, generating a feedback signal to the microwave photon radar according to the reference point, and controlling the microwave photon radar signal transmission and signal shutdown; Assume the first digital signal is , its power spectral density: , where represents the first digital signal, wherein is the index of the discrete time series, Indicates the length of the signal, It is a signal Perform discrete Fourier transform operations, is a complex exponential function, is an imaginary unit. This operation converts the signal from the time domain to the frequency domain. is the normalization factor, What is reflected is the power distribution of the first digital signal on different frequency components.

[0016] S3: Performing power integration on the first digital signal in step S1 to obtain an integrated signal, and selecting a reflected signal through a bandpass filter; S4: performing IQ demodulation and digital-to-analog conversion on the signal after bandpass filtering to obtain a second digital signal; S5: extracting a power spectrum density value of the second digital signal; Assume the second digital signal is , its power spectral density: , where It reflects the power distribution of the second digital signal on different frequency components; S6: Calculate the standard deviation of the second digital signal extracted in step S4 and use the locking unit to extract the first peak value and the second peak value, and obtain the characteristic parameter of the kurtosis through the first peak value and the second peak value; Set the second digital signal Standard Deviation ,in The second digital signal The average value of , The square of the deviation between each sample and the mean is calculated. is the average of the squares of these deviations, which is the variance of the signal, It is the standard deviation obtained by taking the square root of the variance, which measures the degree of dispersion of the second digital signal.

[0017] Kurtosis , where is the fourth power of the standard deviation, is the kurtosis, which reflects the sharpness or flatness of the signal distribution. When the value is larger, it means that the signal distribution is sharper and there are more extreme values. When the value is smaller, the signal distribution is flatter; S7: Calculate the average value of the second digital signal extracted in step S4 and use the locking unit to extract the third peak value, and obtain the characteristic parameter of the skewness according to the third peak value; , where skewness It measures the degree of asymmetry of the signal distribution. When , the signal distribution is right-skewed, that is, there is a longer tail on the right; when When , the signal distribution is left-skewed, that is, there is a longer tail on the left; when When , the signal distribution is approximately symmetrical; S8: Processing the second digital signal extracted in step S4, and storing the kurtosis characteristic parameter, the skewness characteristic parameter and the peak value through a locking unit to obtain a third digital signal; S9: Calculate the difference between the kurtosis characteristic parameter, the skewness characteristic parameter and their respective threshold values. If all the differences are less than 0, the signal is normal and the process ends. If the difference is too small, the signal is an alarm and the process goes to step S10. Set the kurtosis threshold value to , the skewness threshold is , the judgment condition is and ; S10: Perform power integration on the third digital signal in step S8 and extract the integral signal, calculate the kurtosis characteristic parameter and the skewness characteristic parameter of the integral signal by using the locking unit, and compare the calculated kurtosis characteristic parameter and the skewness characteristic parameter with the respective threshold values, if all the differences are greater than 0, the signal is normal; if any difference is less than 0, the signal is an alarm; S11: Generate a feedback signal according to the alarm signal and input it into the microwave photon radar, control the microwave photon radar to turn off the transmission signal, and store the alarm signal.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses the real-time working characteristics of microwave photon radar to build an online pipeline monitoring system, which completely changes the mode of traditional monitoring methods that rely on regular manual inspections. Its cost is relatively low, reducing the large amount of manpower costs required for manual inspections and the high cost of traditional monitoring equipment. This allows the monitoring system to be widely used in various pipelines, and can carry out comprehensive and effective monitoring, solving the problem of high cost and difficulty in large-scale promotion of traditional monitoring methods. It can collect pipeline operation data in real time and continuously monitor the pipeline status, effectively solving the problems of non-real-time and inability to monitor all-weather in traditional methods, and can timely detect abnormal conditions in pipelines, greatly improving the safety of pipeline operation.

[0019] 2. On the basis of solving the real-time monitoring problem, the microwave photon radar of the present invention has the capability of high-frequency waveform agile frequency transmission, which can achieve high speed and distance measurement accuracy. At the same time, the high spatial resolution and anti-interference ability of the system can more accurately locate pipeline defects and obtain more accurate pipeline status information, further improving the monitoring accuracy and reliability and ensuring the safety of pipeline operation.

[0020] 3. The system of the present invention has the characteristic of being easy to realize networking. After realizing large-scale application and complex environment monitoring, each monitoring node can be conveniently connected into a network to realize centralized management and analysis of data, so that managers can fully grasp the overall operation status of the pipeline, make decisions in time, and improve the level of intelligent management of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall structure of the system in the present invention; Figure 2 It is a structural schematic diagram of the signal acquisition module of the system in the present invention. DETAILED DESCRIPTION

[0022] Embodiment 1: Figure 1 , Figure 2As shown, the present invention relates to an online pipeline monitoring system based on microwave photon radar, comprising a microwave photon radar, a signal acquisition module, a signal processing module, a signal extraction module, a data analysis module, a display module, a host computer and a remote monitoring module; The microwave photon radar is located at the top of the pipeline and is used to send signals to the signal acquisition module; On the basis of solving the real-time monitoring problem, the microwave photon radar of the present invention has the capability of high-frequency waveform agile frequency transmission, which can achieve high speed and distance measurement accuracy. At the same time, the high spatial resolution and anti-interference ability of the system can more accurately locate pipeline defects and obtain more accurate pipeline status information, further improving the monitoring accuracy and reliability and ensuring the safety of pipeline operation.

[0023] The signal acquisition module is located at the top of the pipeline and is used to receive microwave photon radar signals and transmit the received signals to the signal processing module. The signal processing module processes the collected signals and then inputs them into the signal extraction module. The signal extraction module extracts signal characteristic parameters and inputs them into the data analysis module. The data analysis module includes a damage identification unit and a damage assessment unit. The damage identification unit inputs the pipeline damage identification result into the damage assessment unit. The damage assessment unit inputs the damage assessment result into the display module, the host computer and the remote monitoring module. The display module is connected to the host computer. In an embodiment of the present invention, in order to achieve locking of the signal source, the signal extraction module is provided with a locking unit, and the locking unit controls the signal emission and signal shutoff of the microwave photon radar through a feedback signal matching the microwave photon radar.

[0024] In an embodiment of the present invention, the signal acquisition module includes an optical transmission module, an optical fiber coupler, a beam splitter, an optical fiber delay module, an optical fiber circulator, an optical receiving module, a phase-locked amplifier, a power integrator and an electrical bandpass filter.

[0025] In an embodiment of the present invention, the output end of the optical transmission module is connected to the first input end of the beam splitter; the first port of the optical fiber coupler is connected to the second input end of the beam splitter; the output end of the beam splitter is coupled to the pipeline through a section of optical fiber; the input end of the optical fiber delay module is connected to the second port of the optical fiber coupler, and the output end is connected to the first port of the optical fiber circulator; the second port of the optical fiber circulator is coupled to the pipeline through a section of optical fiber; the third port of the optical fiber circulator is connected to the first input end of the beam splitter; the input end of the electrical bandpass filter is connected to the second output end of the beam splitter, and the output end is connected to the input end of the optical receiving module.

[0026] In an embodiment of the present invention, in order to realize signal acquisition, the input end of the phase-locked amplifier is connected to the second output end of the electric bandpass filter, and the output end is connected to the input end of the power integrator; the output end of the power integrator is connected to the signal processing module via analog-to-digital conversion.

[0027] In an embodiment of the present invention, the insertion loss of the optical fiber coupler is less than or equal to 0.5 dB, and the optical fiber length of the optical fiber coupler is not less than 2 meters.

[0028] In an embodiment of the present invention, the signal processing module includes a first IQ demodulation unit and a first digital sampling unit; In an embodiment of the present invention, the signal extraction module includes a first bandpass filtering unit, a second IQ demodulation unit, a second digital sampling unit, a second bandpass filtering unit and a third digital sampling unit; In an embodiment of the present invention, an input end of the first IQ demodulation unit is connected to an output end of the signal processing module, and an output end of the first IQ demodulation unit is connected to a first input end of the signal extraction module; an input end of the first band-pass filtering unit is connected to a first output end of the signal extraction module, an input end of the second IQ demodulation unit is connected to an output end of the first band-pass filtering unit, and an output end of the second IQ demodulation unit is connected to a second input end of the signal extraction module; and an output end of the power integrator is connected to an input end of the signal processing module through analog-to-digital conversion, a second digital sampling unit, a second band-pass filtering unit, and a third digital sampling unit in sequence.

[0029] The present invention uses the real-time working characteristics of microwave photon radar to build an online pipeline monitoring system, which completely changes the mode of traditional monitoring methods that rely on regular manual inspections. Its cost is relatively low, reducing the large amount of manpower costs required for manual inspections and the high cost of traditional monitoring equipment. This allows the monitoring system to be widely used in various pipelines, and can carry out comprehensive and effective monitoring, solving the problem of high cost and difficulty in large-scale promotion of traditional monitoring methods. It can collect pipeline operation data in real time and continuously monitor the pipeline status, effectively solving the problems of non-real-time and inability to monitor all-weather in traditional methods, and can promptly detect abnormal conditions in pipelines, greatly improving the safety of pipeline operation.

[0030] In an embodiment of the present invention, the bandwidth of the second bandpass filtering unit is not less than 1500 MHz or not more than 20 GHz.

[0031] In an embodiment of the present invention, the data analysis module includes a damage identification unit and a damage assessment unit.

[0032] In an embodiment of the present invention, the damage identification unit is used for pipeline damage identification, and its identification process is as follows: Extract the frequency domain peak of the time domain signal and use the normalized signal peak as the spectrum feature. Suppose the time domain signal is , after Fourier transform, we get the frequency domain signal , frequency domain peak , the normalized spectrum characteristics: ; Normalize the spectral characteristics of two adjacent time domain signals and calculate the difference. Suppose the spectral characteristics of two adjacent time domain signals are and , difference ; Compare the difference with the threshold: If Greater than the threshold , is an alarm signal; if Less than threshold , is a normal signal; In an embodiment of the present invention, the damage assessment unit is used for pipeline damage assessment, and the assessment process of the damage assessment unit is: Extracting the damage signal amplitude , spectrum peak and damage frequency Get feature parameters , the pipeline damage location and pipeline damage type are evaluated through the damage location identification model and the damage type identification model; The damage location identification model is: When , the pipeline damage location is the top of the pipeline; when or When , the pipeline damage location is on the left side of the pipeline; when or When , the pipeline damage location is on the right side of the pipeline; The damage type identification model is: When , the pipeline damage type is large-area damage; when When , the pipeline damage type is point damage.

[0033] The system of the present invention has the characteristic of being easy to realize networking. After realizing large-scale application and complex environment monitoring, various monitoring nodes can be conveniently connected into a network to realize centralized management and analysis of data, so that managers can fully grasp the overall operation status of the pipeline, make decisions in time, and improve the level of intelligent management of the pipeline.

[0034] Embodiment 2: A pipeline online monitoring signal extraction method based on microwave photon radar comprises the following steps: S1: Sending a signal to a signal acquisition module through a microwave photon radar, the signal acquisition module transmits the acquired signal to a signal processing module, and the signal processing module performs IQ demodulation and digital-to-analog conversion on the acquired signal to obtain a first digital signal; S2: extracting the power spectrum density value of the first digital signal and using the locking unit to extract and store the reference point, generating a feedback signal to the microwave photon radar according to the reference point, and controlling the microwave photon radar signal transmission and signal shutdown; As another embodiment of the present invention, the first digital signal is assumed to be , its power spectral density: , where Word signal, where is the index of the discrete time series, Indicates the length of the signal, It is a signal Perform discrete Fourier transform operations, is a complex exponential function, is an imaginary unit. This operation converts the signal from the time domain to the frequency domain. is the normalization factor, What is reflected is the power distribution of the first digital signal on different frequency components.

[0035] S3: Performing power integration on the first digital signal in step S1 to obtain an integrated signal, and selecting a reflected signal through a bandpass filter; S4: performing IQ demodulation and digital-to-analog conversion on the signal after bandpass filtering to obtain a second digital signal; S5: extracting a power spectrum density value of the second digital signal; As another embodiment of the present invention, the second digital signal is assumed to be , its power spectral density: , where It reflects the power distribution of the second digital signal on different frequency components; S6: Calculate the standard deviation of the second digital signal extracted in step S4 and use the locking unit to extract the first peak value and the second peak value, and obtain the characteristic parameter of the kurtosis through the first peak value and the second peak value; As another embodiment of the present invention, the second digital signal Standard Deviation ,in The second digital signal The average value of , The square of the deviation between each sample and the mean is calculated. is the average of the squares of these deviations, which is the variance of the signal, is the standard deviation obtained by taking the square root of the variance, which measures the degree of dispersion of the second digital signal; As another embodiment of the present invention, the kurtosis , where is the fourth power of the standard deviation, is the kurtosis, which reflects the sharpness or flatness of the signal distribution. When the value is larger, it means that the signal distribution is sharper and there are more extreme values. When the value is smaller, the signal distribution is flatter; S7: calculating the average value of the second digital signal extracted in step S4 and extracting the third peak value by using the locking unit, and obtaining the characteristic parameter of the skewness according to the third peak value; As another embodiment of the present invention, the skewness , where skewness It measures the degree of asymmetry of the signal distribution. When , the signal distribution is right-skewed, that is, there is a longer tail on the right; when When , the signal distribution is left-skewed, that is, there is a longer tail on the left; when When , the signal distribution is approximately symmetrical; S8: Processing the second digital signal extracted in step S4, and storing the kurtosis characteristic parameter, the skewness characteristic parameter and the peak value through a locking unit to obtain a third digital signal; S9: Calculate the difference between the kurtosis characteristic parameter, the skewness characteristic parameter and their respective threshold values. If all the differences are less than 0, the signal is normal and the process ends. If the difference is too small, the signal is an alarm and the process goes to step S10. As another embodiment of the present invention, the kurtosis threshold is set to , the skewness threshold is , the judgment condition is and ; S10: Perform power integration on the third digital signal in step S8 and extract the integral signal, calculate the kurtosis characteristic parameter and the skewness characteristic parameter of the integral signal by using the locking unit, and compare the calculated kurtosis characteristic parameter and the skewness characteristic parameter with the respective threshold values, if all the differences are greater than 0, the signal is normal; if any difference is less than 0, the signal is an alarm; S11: Generate a feedback signal according to the alarm signal and input it into the microwave photon radar, control the microwave photon radar to turn off the transmission signal, and store the alarm signal.

[0036] Example 3: Construct test data of pipeline online monitoring system based on microwave photon radar, and conduct the test in a natural gas transmission pipeline scenario. The pipeline is 1000 meters long, 1 meter in diameter, and made of alloy steel. Pipeline monitoring scenario in actual operating environment. Set the microwave photon radar transmission frequency to 10GHz and the transmission power to 25dBm. The following is the test data divided in detail according to each module of the system: In this test, the microwave photon radar transmits a 10GHz, 25dBm signal. The components of the signal acquisition module work together, and the optical transmission module emits a 1550nm, 10mW optical signal, which is processed by the beam splitter and other components and received by the optical receiving module. The phase-locked amplifier and power integrator further process the signal and transmit it to the signal processing module. The first IQ demodulation unit and demodulation accuracy of the signal processing module are ±0.003, and the first digital sampling unit samples at a sampling rate of 1.5GS / s and a quantization bit of 12 bits. Each unit of the signal extraction module filters, demodulates, and samples the signal according to the set parameters.

[0037] For the damage identification unit, the normal time domain signal After Fourier transform, the frequency domain peak is calculated and normalized to obtain the spectrum characteristics. When there is a small crack in the pipeline, the time domain signal becomes , calculate the spectrum characteristics again and find the difference with the spectrum characteristics during normal time. If the difference is greater than the threshold value of 0.06, it is determined as an alarm signal. The damage assessment unit substitutes the signal amplitude of 0.3, the spectrum peak value of 0.4, and the damage frequency of 1.05MHz at the time of damage into the damage location and type identification model to determine the damage location and type. The locking unit controls the emission and shutdown of the microwave photon radar signal according to the reference point power spectrum density value of 0.1 and the 6ns feedback signal delay, thereby completing the test process of the entire pipeline monitoring system. The embodiments of the present invention disclose preferred embodiments, but are not limited to them. Ordinary technicians in this field can easily understand the spirit of the present invention based on the above embodiments, and make different extensions and changes, but as long as they do not deviate from the spirit of the present invention, they are within the scope of protection of the present invention.

Claims

1. A pipeline online monitoring system based on microwave photon radar, characterized in that: It includes microwave photon radar, signal acquisition module, signal processing module, signal extraction module, data analysis module, display module, host computer and remote monitoring module; The microwave photon radar is located at the top of the pipeline and is used to send signals to the signal acquisition module; The signal acquisition module is located at the top of the pipeline and is used to receive microwave photon radar signals and transmit the received signals to the signal processing module. The signal processing module processes the collected signals and then inputs them into the signal extraction module. The signal extraction module extracts signal characteristic parameters and inputs them into the data analysis module. The data analysis module includes: A damage identification unit, used for inputting pipeline damage identification results into a damage assessment unit; A damage assessment unit, used to input the damage assessment result into a display module, a host computer and a remote monitoring module, wherein the display module is connected to the host computer; The signal extraction module is provided with a locking unit, and the locking unit controls the signal transmission and signal shutoff of the microwave photon radar through a feedback signal matching the microwave photon radar.

2. According to claim 1, a pipeline online monitoring system based on microwave photon radar is characterized in that: The signal acquisition module includes an optical transmission module, an optical fiber coupler, a beam splitter, an optical fiber delay module, an optical fiber circulator, an optical receiving module, a phase-locked amplifier, a power integrator and an electrical bandpass filter; The output end of the optical transmission module is connected to the first input end of the beam splitter, the first port of the optical fiber coupler is connected to the second input end of the beam splitter, the output end of the beam splitter is coupled to the pipeline through a section of optical fiber, the input end of the optical fiber delay module is connected to the second port of the optical fiber coupler, the output end is connected to the first port of the optical fiber circulator, the second port of the optical fiber circulator is coupled to the pipeline through a section of optical fiber, the third port of the optical fiber circulator is connected to the first input end of the beam splitter, the input end of the electrical bandpass filter is connected to the second output end of the beam splitter, and the output end is connected to the input end of the optical receiving module.

3. The pipeline online monitoring system based on microwave photon radar according to claim 2 is characterized in that: The input end of the phase-locked amplifier is connected to the second output end of the electric bandpass filter, and the output end is connected to the input end of the power integrator. The output end of the power integrator is connected to the signal processing module via analog-to-digital conversion.

4. According to claim 1, a pipeline online monitoring system based on microwave photon radar is characterized in that: The signal processing module includes a first IQ demodulation unit and a first digital sampling unit; The signal extraction module includes a first bandpass filtering unit, a second IQ demodulation unit, a second digital sampling unit, a second bandpass filtering unit and a third digital sampling unit.

5. The pipeline online monitoring system based on microwave photon radar according to claim 4 is characterized in that: The input end of the first IQ demodulation unit is connected to the output end of the signal processing module, the output end of the first IQ demodulation unit is connected to the first input end of the signal extraction module, the input end of the first band-pass filtering unit is connected to the first output end of the signal extraction module, the input end of the second IQ demodulation unit is connected to the output end of the first band-pass filtering unit, the output end of the second IQ demodulation unit is connected to the second input end of the signal extraction module, and the output end of the power integrator is connected to the input end of the signal processing module through analog-to-digital conversion, a second digital sampling unit, a second band-pass filtering unit, and a third digital sampling unit in sequence.

6. The pipeline online monitoring system based on microwave photon radar according to claim 5 is characterized in that: The bandwidth of the second bandpass filtering unit is not less than 1500 MHz or not more than 20 GHz.

7. The pipeline online monitoring system based on microwave photon radar according to claim 2 is characterized in that: The insertion loss of the optical fiber coupler is less than or equal to 0.5 dB, and the optical fiber length of the optical fiber coupler is not less than 2 meters.

8. The pipeline online monitoring system based on microwave photon radar according to claim 1 is characterized in that: The damage identification unit is used for pipeline damage identification, and its identification process is as follows: Extract the frequency domain peak of the time domain signal and use the normalized signal peak as the spectrum feature. Suppose the time domain signal is , after Fourier transform, we get the frequency domain signal , frequency domain peak , the normalized spectrum characteristics: ； Normalize the spectral characteristics of two adjacent time domain signals and calculate the difference. Suppose the spectral characteristics of two adjacent time domain signals are and , difference ; Compare the difference with the threshold: If Greater than threshold , is an alarm signal; if Less than threshold , which is a normal signal.

9. The pipeline online monitoring system based on microwave photon radar according to claim 1 is characterized in that: The damage assessment unit is used for pipeline damage assessment. The assessment process of the damage assessment unit is as follows: Extracting the damage signal amplitude , spectrum peak and damage frequency Get feature parameters , the pipeline damage location and pipeline damage type are evaluated through the damage location identification model and the damage type identification model; The damage location identification model is: When , the pipeline damage location is the top of the pipeline; when or When , the pipeline damage location is on the left side of the pipeline; when or When , the pipeline damage location is on the right side of the pipeline; The damage type identification model is: When , the pipeline damage type is large-area damage; when When , the pipeline damage type is point damage.

10. A pipeline online monitoring signal extraction method based on microwave photon radar, which is used in a pipeline online monitoring system based on microwave photon radar according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: Sending a signal to a signal acquisition module through a microwave photon radar, the signal acquisition module transmits the acquired signal to a signal processing module, and the signal processing module performs IQ demodulation and digital-to-analog conversion on the acquired signal to obtain a first digital signal; S2: extracting the power spectrum density value of the first digital signal and using the locking unit to extract and store the reference point, generating a feedback signal to the microwave photon radar according to the reference point, and controlling the microwave photon radar signal transmission and signal shutdown; S3: Performing power integration on the first digital signal in step S1 to obtain an integrated signal, and selecting a reflected signal through a bandpass filter; S4: performing IQ demodulation and digital-to-analog conversion on the signal after bandpass filtering to obtain a second digital signal; S5: extracting a power spectrum density value of the second digital signal; S6: Calculate the standard deviation of the second digital signal extracted in step S4 and use the locking unit to extract the first peak value and the second peak value, and obtain the characteristic parameter of the kurtosis through the first peak value and the second peak value; S7: calculating the average value of the second digital signal extracted in step S4 and extracting the third peak value by using the locking unit, and obtaining the characteristic parameter of the skewness according to the third peak value; S8: Processing the second digital signal extracted in step S4, and storing the kurtosis characteristic parameter, the skewness characteristic parameter and the peak value through a locking unit to obtain a third digital signal; S9: Calculate the difference between the kurtosis characteristic parameter, the skewness characteristic parameter and their respective threshold values. If all the differences are less than 0, the signal is normal and the process ends. If the difference is too small, the signal is an alarm and the process goes to step S10. S10: Perform power integration on the third digital signal in step S8 and extract the integral signal, calculate the kurtosis characteristic parameter and the skewness characteristic parameter of the integral signal by using the locking unit, and compare the calculated kurtosis characteristic parameter and the skewness characteristic parameter with the respective threshold values, if all the differences are greater than 0, the signal is normal; if any difference is less than 0, the signal is an alarm; S11: Generate a feedback signal according to the alarm signal and input it into the microwave photon radar, control the microwave photon radar to turn off the transmission signal, and store the alarm signal.

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