CN109357167B - Gas pipeline leakage point detection device and detection method - Google Patents
Gas pipeline leakage point detection device and detection method Download PDFInfo
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- CN109357167B CN109357167B CN201811276735.1A CN201811276735A CN109357167B CN 109357167 B CN109357167 B CN 109357167B CN 201811276735 A CN201811276735 A CN 201811276735A CN 109357167 B CN109357167 B CN 109357167B
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Abstract
The invention discloses a gas pipeline leakage point detection device and a detection method, and relates to the field of gas pipeline detection, wherein the gas pipeline leakage point detection device comprises a sensor module, a data acquisition module and a data acquisition module, wherein the sensor module is used for acquiring flow, pressure and infrasonic wave data of a gas pipeline; the data transmission module is connected with the sensor module; a fixed IP line connected with the data transmission module; the server is connected with the fixed IP line, and the flow, pressure and infrasonic data are uploaded to the server through the data transmission module and the fixed IP line; the gas pipeline leakage alarm management system installed in the server receives the flow, pressure and infrasonic data, calculates and analyzes the data, and displays and alarms the gas operation state. And the management computer is connected with the server and is used for managing the gas pipeline leakage alarm management system. The invention provides the gas characteristic maps of different road sections, reduces the influence factors such as environment and the like, reduces the false alarm rate of gas alarm, and has high detection precision and alarm efficiency.
Description
Technical Field
The invention relates to the technical field of gas pipeline detection, in particular to a gas pipeline leakage point detection device and a detection method.
Background
Natural gas is the cleanest energy in fossil fuels, has the advantages of high heat, high energy efficiency and the like, and is regarded as the promising new high-efficiency energy at present. The pipeline transmission is the preferred mode of natural gas transportation because of the advantages of low cost, safety, tightness, large transportation amount, guaranteed quality, easy control and management and the like. However, natural gas is flammable and explosive, and once a pipeline leakage accident occurs, a series of disasters such as explosion, fire, poisoning, environmental pollution and the like can be caused, and if the natural gas occurs in a residential area, more serious harm can be caused. The leakage of the natural gas pipeline mainly comprises external force damage, corrosion and aging of material equipment, illegal operation, natural disasters and the like.
Leakage of natural gas during production, processing, transportation, storage and the like is inevitable, thousands of leakage sources exist on pipelines with the length of tens of thousands of kilometers, and pipeline maintenance is a difficult process. At present, natural gas pipeline leakage detection methods are various, and acoustic wave detection methods, negative pressure wave detection methods, pressure gradient methods and the like are more applied at present.
The technical principle of the acoustic wave detection method is as follows:
when the pipeline generates a leak point, the gas is sprayed from the crack or the decayed small hole to form a sound wave source, and then the sound wave source releases energy to the outside of the pipeline through the reaction force with the pipeline to form a transmitting wave. And is mainly focused on low frequency bands, which can be captured with specific sensors (sonic sensors, microphones, etc.). Because the sound wave is transmitted only at the moment when the pipeline pressure fluctuates, whether the potential leakage threat exists can be detected only by detecting the fluctuation of the sound wave curve. Once the natural gas pipeline leaks, the high-pressure natural gas in the pipeline is suddenly released, so that high-pressure shock waves are generated at the leakage hole, and then sound waves are generated, namely the leaked sound waves are generated by gas leakage excitation and are continuous sound wave signals.
The formula for the impact model is as follows:
wherein t is a time value and the unit is second; t is t0Is the time constant corresponding to the initial amplitude e of the sound wave attenuation-1The time of day; t is t1The time when the pipeline leaks is unit of second; p is a radical of0Is t1Sound pressure amplitude corresponding to the moment in unit Pa; p is the sound pressure amplitude corresponding to the time t value in Pa.
Applying a fourier transform to equation (1) results in a spectral function as follows:
wherein p is0Is t1Sound pressure amplitude corresponding to the moment in unit Pa; p (w) is a single sine wave after Fourier transform, in Pa; t is t0Is the time constant corresponding to the initial amplitude e of the sound wave attenuation-1The time of day.
After the leakage is formed, gas is continuously released through the leakage port to form micro vibration of the pipeline, so that a continuous stable sound source which is a continuous sound wave signal is generated. This is one of the reasons why the sonic method is superior to other methods. The frequency band of the sound wave at the leakage is wide, and the vast majority of the sound wave is 175KHz to 750 KHz. In the propagation process of the acoustic wave, high-frequency components in the signal can be gradually attenuated in a pipeline medium, and low-frequency components can be remotely propagated.
The positioning formula of the gas leakage point is as follows:
wherein v is the propagation speed of infrasonic waves and is unit of meter/second; u is the speed of the fuel gas in the pipeline, and the unit is meter/second; delta t is the time difference of the two infrasonic wave sensors, unit second; l is the distance between the two sensors and is unit meter; x is the leak location in meters.
The pressure distribution in the pipeline is formulated as follows:
wherein, PQ、PZThe pressure of the Q monitoring point and the pressure of the Z monitoring point are measured in Pa; l is the distance between the Q monitoring point and the Z monitoring point, and is unit meter; x is the distance between the leakage point and the Q monitoring point, and is unit meter; pxPressure at X in Pa.
Because the environment of the urban underground gas pipeline is complex, the field interference conditions are numerous, the alarms generated by different pipeline materials, different diameters, different pressures and other conditions are different, and if the leakage judgment is carried out by only depending on a set threshold value, the leakage misjudgment rate and the leakage judgment rate are increased greatly.
In recent years, accidents caused by urban pipe network leakage occur, the existing detection technology is difficult to meet the increasing demand of natural gas industry development, a scheme for rapidly detecting gas leakage is urgently needed to be researched, and the method has great social significance for reducing the accidents.
Disclosure of Invention
The invention provides a device and a method for detecting a leakage point of a gas pipeline, which aim to solve the problem of gas leakage.
The technical scheme adopted by the invention for solving the technical problem is as follows:
the invention relates to a gas pipeline leakage point detection device, which comprises:
the sensor module is used for acquiring flow, pressure and infrasonic data of the gas pipeline;
the data transmission module is connected with the sensor module;
a fixed IP line connected with the data transmission module;
the server is connected with the fixed IP line, and the flow, pressure and infrasonic data are uploaded to the server through the data transmission module and the fixed IP line;
the gas pipeline leakage alarm management system installed in the server receives the flow, pressure and infrasonic data, calculates and analyzes the data, and displays and alarms the gas operation state.
And the management computer is connected with the server and is used for managing the gas pipeline leakage alarm management system.
Further, the sensor module comprises a flow sensor, a plurality of pressure sensors and a plurality of infrasonic wave sensors which are all connected with the data transmission module; the flow sensor is installed in the access & exit department of gas pipeline, installs 1 pressure sensor and 1 infrasonic wave sensor at every interval 1Km in the gas pipeline, installs 1 infrasonic wave sensor at every interval 1Km outside the gas pipeline, installs the infrasonic wave sensor in the gas pipeline and installs the one-to-one setting of infrasonic wave sensor outside the gas pipeline, installs the infrasonic wave sensor outside the gas pipeline and is 10-15cm with the perpendicular distance of gas pipeline outer wall.
Furthermore, the data transmission module comprises a shell, and an input module, a multi-channel AD conversion module, a storage module and a transmission module which are arranged in the shell; the input module is connected with the multi-channel AD conversion module, the multi-channel AD conversion module is connected with the storage module, the storage module is connected with the transmission module, and the transmission module is connected with the fixed IP circuit; the input module is a four-way input module; the transmission module is in NB-IoT mode;
the input module receives an analog signal transmitted by the sensor module, the analog signal is converted into a digital signal through the multi-channel AD conversion module and is stored in the storage module, and the stored detection data is transmitted to the gas pipeline leakage alarm management system through the transmission module and the fixed IP circuit every 30 seconds.
Further, the gas pipeline leakage alarm management system includes: the intelligent gas leakage monitoring system comprises a data receiving module connected with a fixed IP line, a gas operation state display module and a mode recognition module which are connected with the data receiving module, an alarm module and a gas leakage intelligent learning module which are connected with the mode recognition module, wherein the gas operation state display module is connected with the mode recognition module; the data receiving module receives detection data transmitted by the sensor module through the data transmission module and the fixed IP line, and transmits the detection data to the gas operation state display module and the mode identification module in a message transmission mode; the gas running state display module displays the detection data of the gas pipeline, the mode identification module calculates the detection data, if the detection data exceed an alarm value, the mode identification module sends an alarm signal to the alarm module and the gas running state display module in a message transmission mode, the alarm module gives an alarm, the gas running state display module displays alarm information, the gas leakage intelligent learning module completes the setting of alarm models of different gas pipeline sections by learning various detection data, and the alarm models are solidified into the mode identification module.
Furthermore, a client program management system is installed in the management computer, the management computer accesses the gas pipeline leakage alarm management system through the web browser and the client program management system, and after the identity verification, the management computer uses the data receiving module, the gas running state display module, the mode identification module, the alarm module and the gas leakage intelligent learning module to complete the gas pipeline leakage alarm management function.
The invention discloses a detection method of a gas pipeline leakage point detection device, which comprises the following steps:
step one, dynamic fingerprint collection
Collecting flow, pressure and infrasonic wave data of the gas pipeline through a sensor module, and uploading the data to a gas pipeline leakage alarm management system through an NB-IoT network and a fixed IP line;
step two, leakage simulation data acquisition
Simulating 100 gas leakage points in a gas pipeline, wherein the simulated leakage diameter range of each gas leakage point is 1-20 cm, and each time of leakage is more than 10 minutes; collecting flow, pressure and infrasonic wave data of the gas pipeline from 1 hour before leakage to 1 hour after leakage, and uploading the data to a gas pipeline leakage alarm management system through an NB-IoT network and a fixed IP line;
step three, identifying characteristic monochromatic audio frequency spectrum
S301: single frequency splitting
Performing single-frequency splitting on infrasonic data, and identifying single-frequency amplitude;
s302: spectral correlation analysis
Respectively calculating the probability of the infrasonic frequency inside the gas pipeline and outside the gas pipeline at the non-leakage point and the leakage point;
s303: spectral noise rejection
Comparing and analyzing infrasonic wave frequencies occurring in three time periods before, during and after leakage in the gas pipeline and outside the gas pipeline, and rejecting the infrasonic wave frequency outside the gas pipeline according to the infrasonic wave frequency in the gas pipeline;
s304: spectrum classification
Sorting infrasonic wave frequencies in the gas pipeline by adopting a wavelet transform method, and calculating the probability of the infrasonic wave frequencies in the gas pipeline appearing in three time periods before leakage, during leakage and after leakage;
s305: spectral temporal culling
Rejecting repeated frequency spectrums of infrasonic wave frequency in the gas pipeline in three time periods before leakage, during leakage and after leakage;
s306: feature spectrum extraction
After the repeated frequency spectrums are removed, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is adjusted to 5%, namely the frequency spectrums with the average audio intensity lower than 5% are removed firstly, then the steps S303 to S305 are repeated, at this time, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is increased by 5%, namely the frequency spectrums with the average audio intensity lower than 10% are removed firstly, then the steps S303 to S305 are repeated until the number of the residual frequency spectrums in the time period of leakage is more than 2, and the residual frequency spectrums are defined as gas leakage characteristic monochromatic frequency spectrums;
step four, amplitude characteristic identification
Identifying the amplitude characteristics by taking the infrasonic wave intensity change rule corresponding to the monochromatic spectrum of the gas leakage characteristics obtained in the step three as a verification map for identifying the gas leakage;
s401: sound intensity clipping
The intensity P of infrasonic waves collected in a gas pipeline0Comparing the intensity of infrasonic wave with the intensity of infrasonic wave collected outside the gas pipeline, and measuring the intensity P of infrasonic wave in the gas pipeline0Cutting a proportion to form P1The value range of a is between 1 and 10;
s402: finding singular points
Will P1Solving a second derivative according to time, and calculating a singular point;
s403: characteristic amplitude extraction
Taking singular point as origin, and taking P as1The wave intensity curves of the first 30 seconds, the last 30 seconds and the last 60 seconds are taken as characteristic wave amplitude spectral lines of gas leakage；
Step five, identifying the pressure disturbance wave of the gas pipeline
Each detection point in the gas pipeline follows a pressure balance formula to make a pressure and time change curve, a second derivative of the pressure and time change curve to time is calculated, and then an extreme value is calculated, wherein the time point of the extreme value is the time point of leakage, namely the pressure disturbance time, so that whether a gas leakage event and the nearest leakage point occur or not is judged according to the time point of the extreme value;
step six, calculating the position of the leakage point
S601: calculating the position of a leakage point X by adopting an infrasonic wave method;
s602: pressure disturbance method for calculating leakage point
Calculating the time points sensed by the two closest pressure sensors according to the pressure disturbance time obtained in the step five, and calculating the distance X of the gas leakage occurrence point according to the distance and the time difference of the two time points1The calculation formula is as follows:
wherein, PQ、PZThe pressure of the Q monitoring point and the pressure of the Z monitoring point are respectively in Pa; l is the distance between the Q monitoring point and the Z monitoring point, and is unit meter; x1The distance between a leakage point X and a Q monitoring point is unit meter;
s603: position calibration parameter calculation
Calculating the obtained X and X1And the actual site of leakage X0And (3) carrying out verification and deviation correction, and calculating the value A and the value B by adopting a formula (6):
X0＝A×X+B×X1(6)
a, B is a position calibration parameter;
seventhly, solidifying detection parameters of leakage points of the gas pipeline
And storing various parameters obtained in the third step to the sixth step into a pattern recognition module.
Further, after the seventh step, the method further comprises an eighth step of: gas pipeline running state display
S801: gas pipeline running state simulation
The gas operation state display module displays an actual gas pipeline operation state and an expected virtual gas pipeline operation state by adopting a digital twinning method, a core driving engine is a fault prediction and health management engine PHM, namely data calculated in the PHM driven by digital twinning data is analog data, and the actual operation state of the gas pipeline is sensed in real time through a pressure sensor, a flow sensor and an infrasonic wave sensor; the gas operation state display module simulates an expected virtual gas pipeline operation state, and the virtual gas pipeline data transmission can synchronously operate with the actual gas pipeline data transmission under the drive of the digital twin data;
s802: gas leak shut-off valve treatment
At any place of the gas pipeline, assuming a leakage event occurs, in the digital twin data driven PHM, the valve to be closed and the affected user are shown in a prominent color;
s803: parameter adjustment
And extracting pressure data, flow data and infrasonic wave data in the gas pipeline within 2-4 hours after the gas leaks to the closed valve, comparing the extracted pressure data, flow data and infrasonic wave data with the analog data calculated in the PHM driven by the digital twin data, and adjusting PHM parameters to make the PHM parameters consistent with the detected actual gas pipeline running state.
Further, the following steps are also included between the third step and the fourth step: identifying a characteristic composite audio frequency spectrum; the specific operation is as follows:
taking the frequency spectrum with the frequency spectrum noise removed in the step S303 as a data source, and respectively taking the time of 10 seconds before the gas leakage and the time of 10 seconds after the gas leakage as data aggregation kernels and calculating by adopting a fuzzy kernel aggregation algorithm to obtain kernel-based possibility clusters; after the likelihood clustering is carried out, each sample correspondingly obtains C membership coefficients which represent the degree of the sample to be affiliated to each class, values are obtained in [0, 1], and the samples are divided into two main classes: the first type of sample is close to the center of a certain type and is far away from other types and is a non-support vector; the second type samples are in the junction positions of different types, can belong to a plurality of types and are support vectors; directly dividing the first class sample into the closest class without considering the relation with other classes and adding the sample into a training set of a support vector machine; and using the second type of sample for training a support vector machine until a set threshold value is met, and obtaining a fuel gas leakage characteristic conforming frequency spectrum.
The invention has the beneficial effects that:
the invention adopts the intelligent learning algorithm of gas leakage to acquire the actual gas pipeline in sections and learn the characteristic wave spectrum of various natural gas leakage in sections to form the natural gas operation network leakage map, thereby solving the problems of missing judgment and erroneous judgment caused by setting a threshold value and improving the alarm precision.
The invention provides the gas characteristic maps of different road sections, reduces the influence factors such as environment and the like, reduces the false alarm rate of gas alarm, improves the detection precision, improves the alarm efficiency and the gas safety management level, improves the level of maintaining the social public safety capability, reduces the frequency of accidents, reduces the casualties and property loss caused after the accidents occur, forms a demonstration driving effect on the safety management of the industry, enhances the core competitiveness of the enterprise, improves the access barrier of the industry and improves the economic benefit of the enterprise.
Drawings
Fig. 1 is a schematic structural diagram of a gas pipeline leak point detection device of the present invention.
Fig. 2 is a schematic structural diagram of a data transmission module in the gas pipeline leakage point detection device of the present invention.
Fig. 3 is a schematic structural diagram of a gas pipeline leakage alarm management system in the gas pipeline leakage point detection device of the present invention.
Fig. 4 is a flowchart of a gas pipeline leak detection method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, the gas pipeline leakage point detection device of the present invention is composed of a sensor module, a data transmission module, a fixed IP line, a server, a management computer, and a gas pipeline leakage alarm management system.
The sensor module is connected with the data transmission module, the data transmission module is connected with the fixed IP circuit, the fixed IP circuit is connected with the server, the management computer is connected with the server through a network, the gas pipeline leakage alarm management system is installed in the server, and the client program management system is installed in the management computer.
The signal that the sensor module gathered is connected to the data transmission module through the wire, and the data transmission module passes through NB-IoT network, fixed IP circuit and transmits the signal of gathering to the gas pipeline of installing in the server and leaks the warning management system, and the management computer manages gas pipeline through client management system and leaks the warning management system.
The sensor module includes: 1 flow sensor L, a plurality of pressure sensors Y, a plurality of infrasonic wave sensors C. And the flow sensor L, the pressure sensor Y and the infrasonic wave sensor C are all connected with the data transmission module T. The flow sensor L is installed at the inlet and the outlet of the gas pipeline. 1 pressure sensor Y and 1 infrasonic wave sensor C are installed in the gas pipeline at intervals of 1 Km. Every interval 1Km installs 1 infrasonic wave sensor C outside the gas pipeline, installs the infrasonic wave sensor C in the gas pipeline and installs the one-to-one setting of infrasonic wave sensor C outside the gas pipeline, and the line segment is perpendicular with the gas pipeline behind the infrasonic wave sensor C who installs in the gas pipeline and the infrasonic wave sensor C who installs outside the gas pipeline that promptly installs in the gas pipeline. The vertical distance between the infrasonic wave sensor C arranged outside the gas pipeline and the outer wall of the gas pipeline is 10-15 cm.
As shown in fig. 2, the data transmission module is composed of a housing, an input module, a multi-channel AD conversion module, a storage module, and a transmission module. The outer shell is a plastic piece of a fuel gas pipeline buried mark with the diameter of 15cm, is placed outside a fuel gas pipeline buried place, and has the waterproof capability of 50 meters. The input module, the multi-channel AD conversion module, the storage module and the transmission module are all installed inside the shell. The input module is connected with the multi-channel AD conversion module, the multi-channel AD conversion module is connected with the storage module, the storage module is connected with the transmission module, and the transmission module is connected with the fixed IP circuit. The input module is a four-way input module; the transmission module is in NB-IoT mode; the input module receives the analog signals transmitted by the sensor module, the analog signals are converted into digital signals through the multi-channel AD conversion module, and the digital signals are stored in the storage module. The sampling frequency of the storage module is 100HZ, and the data storage file format is time, a sensor number and a detection value. And transmitting the stored detection data to a gas pipeline leakage alarm management system through a transmission module and a fixed IP line every 30 seconds.
As shown in fig. 3, the gas pipeline leakage alarm management system is composed of a data receiving module, a gas operation state display module, a mode identification module, an alarm module, and a gas leakage intelligent learning module. The fixed IP circuit is connected with the data receiving module, the data receiving module is connected with the gas running state display module and the mode recognition module, the gas running state display module is connected with the mode recognition module, and the mode recognition module is connected with the alarm module and the gas leakage intelligent learning module. The data receiving module receives detection data transmitted by the sensor module through the data transmission module and the fixed IP line, and transmits the detection data to the gas operation state display module and the mode recognition module in a message transmission mode. The gas operation state display module displays detection data of a gas pipeline on a gas operation diagram, the mode identification module calculates the detection data by adopting a preset calculation method, if the detection data exceed an alarm value, the mode identification module sends an alarm signal to the alarm module and the gas operation state display module in a message transmission mode, the alarm module gives an alarm, and the gas operation state display module displays alarm information on the gas operation diagram. The gas leakage intelligent learning module completes the setting of alarm models of different gas pipeline sections by learning various detection data and solidifies the alarm modules into the pattern recognition module.
The management computer accesses the gas pipeline leakage alarm management system through the network browser and the client program management system, and completes each gas pipeline leakage alarm management function by using the data receiving module, the gas running state display module, the mode identification module, the alarm module and the gas leakage intelligent learning module after the identity authentication.
As shown in fig. 4, the method for detecting a leak point of a gas pipeline of the present invention comprises the following specific steps:
step one, dynamic fingerprint collection
Under the condition that the gas pipeline normally operates, the sensor module collects 7 x 24 hours of flow, pressure and infrasonic wave data of the gas pipeline, and transmits the collected flow, pressure and infrasonic wave data to the gas pipeline leakage alarm management system through the NB-IoT network and the fixed IP circuit every 30 seconds.
Step two, leakage simulation data acquisition
Simulating 100 gas leakage points in a gas pipeline, wherein the simulated leakage diameter range of each gas leakage point is 1-20 cm, and each time of leakage is more than 10 minutes; collecting the flow, pressure and infrasonic data of the gas pipeline from 1 hour before leakage to 1 hour after leakage, and uploading the collected flow, pressure and infrasonic data to a server through an NB-IoT network and a fixed IP line at intervals of 30 seconds.
Step three, identifying characteristic monochromatic audio frequency spectrum
S301: single frequency splitting
And (3) performing single-frequency splitting on infrasonic data (infrasonic data are complex audio signals inside and outside the gas pipeline) in the server at a time interval of 1 minute or 2 minutes, and identifying single-frequency amplitude.
S302: spectral correlation analysis
And respectively calculating the probability of the occurrence of each infrasonic wave frequency inside the gas pipeline and outside the gas pipeline at the non-leakage point (normal point) and the leakage point.
S303: spectral noise rejection
And comparing and analyzing infrasonic wave frequencies occurring in three time periods before, during and after leakage in the gas pipeline and outside the gas pipeline, and rejecting the infrasonic wave frequency outside the gas pipeline according to the infrasonic wave frequency in the gas pipeline so as to reject the influence of the environmental audio frequency on the gas pipeline audio frequency.
S304: spectrum classification
And (3) sorting infrasonic wave frequencies in the gas pipeline by adopting a wavelet transform method, and calculating the probability of the infrasonic wave frequencies in the gas pipeline appearing in three time periods before leakage, during leakage and after leakage.
S305: spectral temporal culling
And eliminating repeated frequency spectrums of infrasonic wave frequency in the gas pipeline in three time periods before leakage, during leakage and after leakage.
S306: feature spectrum extraction
After the repeated frequency spectrums are removed, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is adjusted to 5%, namely the frequency spectrums with the average audio intensity lower than 5% are removed firstly, and then S303 to S305 are repeated, at this time, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is adjusted to be higher by 5%, namely the frequency spectrums with the average audio intensity lower than 10% are removed firstly, and then S303 to S305 are repeated until the number of the residual frequency spectrums in the time period of leakage is greater than 2, and then the residual frequency spectrums are defined as the gas leakage characteristic monochromatic frequency spectrums.
Step four, identifying the characteristic composite audio frequency spectrum
And if the gas leakage characteristic monochromatic spectrum cannot be found after the third step or the effect of the gas leakage characteristic monochromatic spectrum in the later verification is not ideal, performing characteristic composite audio frequency spectrum identification by adopting the third step.
And taking the frequency spectrum with the removed frequency spectrum noise in the step S303 as a data source, adopting a fuzzy kernel clustering algorithm, mapping data which is not easy to cluster in a low-dimensional space to a high-dimensional feature space by utilizing a kernel function to enlarge the difference between the pattern classes, then clustering the data in the high-dimensional space, increasing the optimization of sample characteristics, and nonlinearly mapping samples which are linearly inseparable in an observation space to the high-dimensional feature space by utilizing the kernel function to become linearly separable, so that the sample characteristics can be clustered more accurately after being well distinguished, extracted and amplified, and a better clustering effect can be achieved. By utilizing the thought of a nuclear learning method, the probability cluster based on the kernel can be obtained by respectively taking the 10 seconds before the gas leakage and the 10 seconds after the gas leakage as data aggregation kernels and combining a fuzzy kernel aggregation algorithm.
After clustering, each sample can obtain C (C is the clustering number) membership coefficients, which represent the degree of the sample belonging to each class, and the samples can be divided into two categories, namely: one is that the degree of membership of a sample to a certain class is very large, and the degree of membership to other classes is very small, and such samples are generally very close to the center of a certain class and far from other classes and generally not support vectors; in another case, the samples are not very different in distance from each class, and are located at different boundary positions of different classes, and may belong to multiple classes, and such samples may be support vectors. For the former, the method can be directly classified into the closest class without considering the relationship with other classes and without adding the method into the training set of the support vector machine; for the latter, a support vector machine is required to be used for training until a set threshold value is met, and the obtained gas leakage characteristic conforms to a frequency spectrum.
Step five, wave amplitude characteristic identification
And taking the obtained monochromatic spectrum of the gas leakage characteristic or the infrasonic wave intensity change rule corresponding to the compound spectrum of the gas leakage characteristic obtained in the step four as a verification map for identifying the gas leakage.
S501: sound intensity clipping
Firstly, the intensity P of infrasonic waves collected in a gas pipeline0Comparing the intensity of infrasonic wave with the intensity of infrasonic wave collected outside the gas pipeline, and measuring the intensity P of infrasonic wave in the gas pipeline0Cutting a proportion to form P1And the value range of a is between 1 and 10, and the specific value of a can be determined according to an actual detection field test.
S502: finding singular points
The intensity P of infrasonic wave in the gas pipeline0P formed by cutting according to a proportion1And solving a second derivative according to time, and calculating a singular point.
S503: characteristic amplitude extraction
Taking singular point as origin, and taking P as1And the wave intensity curves of the first 30 seconds, the last 30 seconds and the last 60 seconds are taken as characteristic wave amplitude spectral lines of the gas leakage.
Sixthly, identifying the pressure disturbance wave of the gas pipeline
Because the pressure of the gas pipeline is a dynamic platform under the condition of normal operation of the gas pipeline, each detection point follows a pressure balance formula to make a pressure and time change curve. If a leakage event occurs, the pressure dynamic balance formula is broken through, the second derivative of the pressure time curve to the time is calculated, then the extreme value of the pressure time curve is calculated, the time point of the extreme value is the time point at which leakage is possible, namely the pressure disturbance time, and whether the gas leakage event and the nearest leakage point occur is judged according to the time point.
Step seven, calculating the position of the leakage point
S701: calculation of leakage point by infrasonic wave method
The position of the leakage point is calculated according to infrasonic data, namely the time difference of audio signals, acquired by an infrasonic sensor, and the position of the leakage point X is calculated by specifically adopting a gas leakage point positioning formula (3).
S702: pressure disturbance method for calculating leakage point
When the gas leaks, the pressure of the leakage point is close to 0. Therefore, according to the pressure disturbance time calculated in the step six, the time points sensed by the two closest pressure sensors are calculated, and according to the distance and the time difference of the two time points, the distance X of the gas leakage occurrence point is calculated1. The specific formula is as follows:
wherein, PQ、PZThe pressure of the Q monitoring point and the pressure of the Z monitoring point are respectively in Pa; l is the distance between the Q monitoring point and the Z monitoring point, and is unit meter; x1The distance between the leakage point X and the Q monitoring point is unit meter.
S703: position calibration parameter calculation
Calculating the obtained X and X1And the actual site of leakage X0And (3) carrying out verification and deviation correction, and calculating the value A and the value B by adopting a formula (6):
X0＝A×X+B×X1(6)
a, B is a position calibration parameter, i.e. a deviation correction constant, and the deviation correction constants of different regions are different.
Step eight, solidifying detection parameters of leakage points of gas pipelines
And storing various parameters obtained in the third step to the seventh step into a pattern recognition module.
Ninth step, displaying the running state of the gas pipeline
S901: gas pipeline running state simulation
The gas operation state display module displays an actual gas pipeline operation state and an expected virtual gas pipeline operation state by adopting a digital twin method, and a core driving engine of the gas operation state display module is a fault prediction and health management engine (PHM), namely data calculated in the PHM driven by digital twin data is analog data. Sensing the actual running state of the gas pipeline in real time through a pressure sensor, a flow sensor and an infrasonic wave sensor; the gas operation state display module simulates an expected virtual gas pipeline operation state, and the virtual gas pipeline data transmission can synchronously operate with the actual gas pipeline data transmission under the drive of the digital twin data.
S902: gas leak shut-off valve treatment
During the operation of the system, the gas leakage condition is simulated at least once every year. Anywhere in the gas pipeline, assuming a leak event occurs, in a digital twin data driven PHM, the valves that need to be closed and the affected users are presented in prominent colors.
S903: parameter adjustment
And extracting pressure data, flow data and infrasonic wave data in the gas pipeline within 2-4 hours after the gas leaks to the closed valve, comparing the extracted pressure data, flow data and infrasonic wave data with the analog data calculated in the PHM driven by the digital twin data, and adjusting PHM parameters to make the PHM parameters consistent with the detected actual gas pipeline running state.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the uses set forth in the specification and examples. It is fully applicable to a variety of fields in which the present invention is applicable. Additional modifications will readily occur to those skilled in the art. It is therefore intended that the invention not be limited to the exact details and illustrations described and illustrated herein, but fall within the scope of the appended claims and equivalents thereof.
Claims (8)
1. A gas pipeline leakage point detection method is characterized in that a gas pipeline leakage point detection device is adopted for detection, and the method comprises the following steps:
step one, dynamic fingerprint collection
Collecting flow, pressure and infrasonic wave data of the gas pipeline through a sensor module, and uploading the data to a gas pipeline leakage alarm management system through an NB-IoT network and a fixed IP line;
step two, leakage simulation data acquisition
Simulating 100 gas leakage points in a gas pipeline, wherein the simulated leakage diameter range of each gas leakage point is 1-20 cm, and each time of leakage is more than 10 minutes; collecting flow, pressure and infrasonic wave data of the gas pipeline from 1 hour before leakage to 1 hour after leakage, and uploading the data to a gas pipeline leakage alarm management system through an NB-IoT network and a fixed IP line;
step three, identifying characteristic monochromatic audio frequency spectrum
S301: single frequency splitting
Performing single-frequency splitting on infrasonic data, and identifying single-frequency amplitude;
s302: spectral correlation analysis
Respectively calculating the probability of the infrasonic frequency inside the gas pipeline and outside the gas pipeline at the non-leakage point and the leakage point;
s303: spectral noise rejection
Comparing and analyzing infrasonic wave frequencies occurring in three time periods before, during and after leakage in the gas pipeline and outside the gas pipeline, and rejecting the infrasonic wave frequency outside the gas pipeline according to the infrasonic wave frequency in the gas pipeline;
s304: spectrum classification
Sorting infrasonic wave frequencies in the gas pipeline by adopting a wavelet transform method, and calculating the probability of the infrasonic wave frequencies in the gas pipeline appearing in three time periods before leakage, during leakage and after leakage;
s305: spectral temporal culling
Rejecting repeated frequency spectrums of infrasonic wave frequency in the gas pipeline in three time periods before leakage, during leakage and after leakage;
s306: feature spectrum extraction
After the repeated frequency spectrums are removed, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is adjusted to 5%, namely the frequency spectrums with the average audio intensity lower than 5% are removed firstly, then the steps S303 to S305 are repeated, at this time, if the number of the residual frequency spectrums in the time period of leakage is less than 2, the intensity of the filtered infrasonic wave is increased by 5%, namely the frequency spectrums with the average audio intensity lower than 10% are removed firstly, then the steps S303 to S305 are repeated until the number of the residual frequency spectrums in the time period of leakage is more than 2, and the residual frequency spectrums are defined as gas leakage characteristic monochromatic frequency spectrums;
step four, amplitude characteristic identification
Identifying the amplitude characteristics by taking the infrasonic wave intensity change rule corresponding to the monochromatic spectrum of the gas leakage characteristics obtained in the step three as a verification map for identifying the gas leakage;
s401: sound intensity clipping
The intensity P of infrasonic waves collected in a gas pipeline0Comparing the intensity of infrasonic wave with the intensity of infrasonic wave collected outside the gas pipeline, and measuring the intensity P of infrasonic wave in the gas pipeline0Cutting a proportion to form P1The value range of a is between 1 and 10;
s402: finding singular points
Will P1Solving a second derivative according to time, and calculating a singular point;
s403: characteristic amplitude extraction
Taking singular point as origin, and taking P as1The wave intensity curves of the first 30 seconds, the last 30 seconds and the last 60 seconds are used as characteristic wave amplitude spectral lines of gas leakage;
step five, identifying the pressure disturbance wave of the gas pipeline
Each detection point in the gas pipeline follows a pressure balance formula to make a pressure and time change curve, a second derivative of the pressure and time change curve to time is calculated, and then an extreme value is calculated, wherein the time point of the extreme value is the time point of leakage, namely the pressure disturbance time, so that whether a gas leakage event and the nearest leakage point occur or not is judged according to the time point of the extreme value;
step six, calculating the position of the leakage point
S601: calculating the position of a leakage point X by adopting an infrasonic wave method;
s602: pressure disturbance method for calculating leakage point
Calculating the time points sensed by the two closest pressure sensors according to the pressure disturbance time obtained in the step five, and calculating the distance X of the gas leakage occurrence point according to the distance and the time difference of the two time points1The calculation formula is as follows:
wherein, PQ、PZThe pressure of the Q monitoring point and the pressure of the Z monitoring point are respectively in Pa; l is the distance between the Q monitoring point and the Z monitoring point, and is unit meter; x1The distance between a leakage point X and a Q monitoring point is unit meter;
s603: position calibration parameter calculation
Calculating the obtained X and X1And the actual site of leakage X0And (3) carrying out verification and deviation correction, and calculating the value A and the value B by adopting the following formulas:
X0＝A×X+B×X1
a, B is a position calibration parameter;
seventhly, solidifying detection parameters of leakage points of the gas pipeline
And storing various parameters obtained in the third step to the sixth step into a pattern recognition module.
2. The method for detecting the leakage point of the gas pipeline according to the claim 1, wherein the gas pipeline leakage point detecting device comprises:
the sensor module is used for acquiring flow, pressure and infrasonic data of the gas pipeline;
the data transmission module is connected with the sensor module;
a fixed IP line connected with the data transmission module;
the server is connected with the fixed IP line, and the flow, pressure and infrasonic data are uploaded to the server through the data transmission module and the fixed IP line;
the gas pipeline leakage alarm management system is arranged in the server, receives the flow, pressure and infrasonic data, calculates and analyzes the data, and displays and alarms the gas operation state;
and the management computer is connected with the server and is used for managing the gas pipeline leakage alarm management system.
3. The method of claim 2, wherein the sensor module comprises a flow sensor, a plurality of pressure sensors, and a plurality of infrasonic sensors, all connected to a data transmission module; the flow sensor is installed in the access & exit department of gas pipeline, installs 1 pressure sensor and 1 infrasonic wave sensor at every interval 1Km in the gas pipeline, installs 1 infrasonic wave sensor at every interval 1Km outside the gas pipeline, installs the infrasonic wave sensor in the gas pipeline and installs the one-to-one setting of infrasonic wave sensor outside the gas pipeline, installs the infrasonic wave sensor outside the gas pipeline and is 10-15cm with the perpendicular distance of gas pipeline outer wall.
4. The detection method according to claim 2, wherein the data transmission module comprises a housing and an input module, a multi-channel AD conversion module, a storage module and a transmission module which are arranged in the housing; the input module is connected with the multi-channel AD conversion module, the multi-channel AD conversion module is connected with the storage module, the storage module is connected with the transmission module, and the transmission module is connected with the fixed IP circuit; the input module is a four-way input module; the transmission module is in NB-IoT mode;
the input module receives an analog signal transmitted by the sensor module, the analog signal is converted into a digital signal through the multi-channel AD conversion module and is stored in the storage module, and the stored detection data is transmitted to the gas pipeline leakage alarm management system through the transmission module and the fixed IP circuit every 30 seconds.
5. The detection method according to claim 2, wherein the gas pipeline leakage alarm management system comprises: the intelligent gas leakage monitoring system comprises a data receiving module connected with a fixed IP line, a gas operation state display module and a mode recognition module which are connected with the data receiving module, an alarm module and a gas leakage intelligent learning module which are connected with the mode recognition module, wherein the gas operation state display module is connected with the mode recognition module; the data receiving module receives detection data transmitted by the sensor module through the data transmission module and the fixed IP line, and transmits the detection data to the gas operation state display module and the mode identification module in a message transmission mode; the gas running state display module displays the detection data of the gas pipeline, the mode identification module calculates the detection data, if the detection data exceed an alarm value, the mode identification module sends an alarm signal to the alarm module and the gas running state display module in a message transmission mode, the alarm module gives an alarm, the gas running state display module displays alarm information, the gas leakage intelligent learning module completes the setting of alarm models of different gas pipeline sections by learning various detection data, and the alarm models are solidified into the mode identification module.
6. The detection method according to claim 2, wherein a client program management system is installed in the management computer, the management computer accesses the gas pipeline leakage alarm management system through a web browser and the client program management system, and after identity verification, the management computer uses a data receiving module, a gas operation state display module, a mode identification module, an alarm module and a gas leakage intelligent learning module to complete a gas pipeline leakage alarm management function.
7. The detection method according to claim 1, characterized by further comprising, after step seven, step eight: gas pipeline running state display
S801: gas pipeline running state simulation
The gas operation state display module displays an actual gas pipeline operation state and an expected virtual gas pipeline operation state by adopting a digital twinning method, a core driving engine is a fault prediction and health management engine PHM, namely data calculated in the PHM driven by digital twinning data is analog data, and the actual operation state of the gas pipeline is sensed in real time through a pressure sensor, a flow sensor and an infrasonic wave sensor; the gas operation state display module simulates an expected virtual gas pipeline operation state, and the virtual gas pipeline data transmission can synchronously operate with the actual gas pipeline data transmission under the drive of the digital twin data;
s802: gas leak shut-off valve treatment
At any place of the gas pipeline, assuming a leakage event occurs, in the digital twin data driven PHM, the valve to be closed and the affected user are shown in a prominent color;
s803: parameter adjustment
And extracting pressure data, flow data and infrasonic wave data in the gas pipeline within 2-4 hours after the gas leaks to the closed valve, comparing the extracted pressure data, flow data and infrasonic wave data with the analog data calculated in the PHM driven by the digital twin data, and adjusting PHM parameters to make the PHM parameters consistent with the detected actual gas pipeline running state.
8. The detection method according to claim 1, further comprising the following steps between step three and step four: identifying a characteristic composite audio frequency spectrum; the specific operation is as follows:
taking the frequency spectrum with the frequency spectrum noise removed in the step S303 as a data source, and respectively taking the time of 10 seconds before the gas leakage and the time of 10 seconds after the gas leakage as data aggregation kernels and calculating by adopting a fuzzy kernel aggregation algorithm to obtain kernel-based possibility clusters; after the likelihood clustering is carried out, each sample correspondingly obtains C membership coefficients which represent the degree of the sample to be affiliated to each class, values are obtained in [0, 1], and the samples are divided into two main classes: the first type of sample is close to the center of a certain type and is far away from other types and is a non-support vector; the second type samples are in the junction positions of different types, can belong to a plurality of types and are support vectors; directly dividing the first class sample into the closest class without considering the relation with other classes and adding the sample into a training set of a support vector machine; and using the second type of sample for training the support vector machine until a set threshold value is met, and obtaining a gas leakage characteristic conforming frequency spectrum.
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Denomination of invention: A gas pipeline leak detection device and detection method Effective date of registration: 20230302 Granted publication date: 20200825 Pledgee: Agricultural Bank of China Limited Changchun Chaoyang sub branch Pledgor: CHANGCHUN WHY-E SCIENCE AND TECHNOLOGY Co.,Ltd. Registration number: Y2023220000012 |