CN114110443B - Intelligent detection method for odd point characteristics of flow transmission pipeline - Google Patents

Abstract

The invention discloses a method for intelligent detection of singular point characteristics of a flow pipeline, which comprises the following steps: firstly, a background parameter set is formed; secondly, a vibration wave parameter test micro-nano device is installed at each node position of the flow pipeline to be tested, and the The wave parameter test micro-nano device collects the global information of the pipeline transmitted between any two nodes on the pipeline to be tested; sets the pipeline singularity rupture threshold. The present invention utilizes the physical characteristics of the water hammer vibration wave that exists at all times during the operation of the pipeline to transmit along the pipeline, tests the transmission parameters of the vibration wave at the adjacent detection node, and uses the vibration wave sensor and machine learning algorithm firmware to realize on-chip integration, from the vibration wave transmission The parameter set is based on deep learning, data fusion and other methods to obtain the material change singularity of the flow pipeline, the change of the contact boundary around the pipe, etc., and realize the proactive prediction and fixed-point maintenance before the singular point rupture threshold of the flow pipeline. Construct a new intelligent detection method for fluid pipelines and optimize fluid loss control.

Description

An Intelligent Detection Method for Singularity Features of Stream Pipeline

technical field

The invention relates to the technical field of flow pipeline detection, in particular to an intelligent detection method for singular point characteristics of a flow pipeline.

Background technique

At present, industrial pipeline transportation began in the 19th century, starting with the first crude oil pipeline built in Pennsylvania, USA in 1865. At the same time, the water supply and diversion project also involves many pipelines. It can be traced back to 2500 BC, when the Sumerians built aqueducts and tunnels in southern Mesopotamia. Pipeline transportation has gradually developed into one of the five major modes of human material transportation. The general transportation objects are fluids, mainly including water, crude oil, natural gas, etc., and a small amount of solid particles are also transported by wind. The characteristics of pipeline transportation mainly include the huge transportation volume, the transportation time is not bound by the weather and other ground facilities, the continuous transportation time is long, the transportation cost per unit mass is low, the pipeline is used for a long time, and the occupied area is small (3 times that of the road area). % and 10% of the land occupied by the railway), less construction consumables, short construction time, safe and reliable, low energy consumption for transportation (unit energy consumption is less than 1/7 of railway transportation), etc.

According to public data, there are more than 8,000 kilometers of underground wading pipelines in Zhengzhou City, including 4,049 kilometers of tap water supply pipelines, 1,700 kilometers of heating water pipelines, and 1,000 kilometers of gas pipelines. The country currently has 27,000 kilometers of crude oil pipelines, 21,000 kilometers of refined oil pipelines, and 64,000 kilometers of main natural gas pipelines. It is estimated that by 2025, the scale of my country's oil and gas pipelines will reach 240,000 kilometers. At the same time, the data of relevant pipelines in the United States is 2-3 times that of my country. At present, there are more than 3,800 oil, natural gas, and resource water transfer pipelines in stable operation in the world, with a mileage of more than 1.96 million kilometers. There are also many pipelines under construction, such as China-Russia, China-Kazakhstan, China- The planned oil pipelines between Myanmar, Beixi-2, the US-Mexico line, etc. The oil pipelines, natural gas pipelines, municipal wading pipelines, long-distance resource water transfer pipelines, auxiliary pipelines for fluid storage tanks, and pipelines for large and medium-sized equipment built by humans on the earth are already a very large number. , forming a huge pipeline technology market.

The material of the flow pipeline is generally ductile iron, galvanized steel, reinforced concrete (PCCP), polymer (PVC, PPR, PPP, PE), stainless steel, ceramics, copper, etc. During use, due to corrosion, stress, water hammer effect vibration, construction defects, material failure and ground subsidence, etc., the pipeline will form stress concentration, rupture, leakage and dripping, which will easily cause the loss of transport fluid resources (>20%) and further It pollutes the environment, interrupts national production and residents' lives, and even causes secondary disasters such as fire and biological toxicity. Municipal transportation pipelines mainly include water supply, sewage pipelines, hot water supply pipelines, residential natural gas pipelines, etc.; long-distance fluid transportation pipelines are mostly oil, natural gas, and resource fresh water transportation pipelines. The boundary of this part of the flow pipeline is flexible media such as soil, air, and thermal insulation cotton, supported by cement piers or metal supports. There are many water supply, sewage, fire-fighting water pipelines and natural gas pipelines in the walls of large buildings, and the contact interfaces of the pipelines are mostly concrete, air, metal supports, etc.; large fluid storage and transportation equipment, large aircraft, and giant ships There are also many flow pipelines to complete the fluid transportation to maintain the operation of the equipment, such as fuel oil, special gas, upper and lower water, etc., and the contact interface is mostly metal brackets.

In order to ensure the stable operation of fluid transportation, the detection technology of fluid transportation pipelines has developed rapidly. People have designed rich pipeline detection schemes based on various physical effects, such as pressure, sound waves, optical fibers, infrared, fluid characteristics, etc., and gradually formed devices. , The automation and systematization of the device, the market application is broad. Ground-penetrating radar is often used to locate and detect leaks in fluid transportation pipelines. It is necessary to manually push the detector to detect along the distribution path of the pipeline, which is time-consuming, laborious and less accurate; to detect the leakage fluid content in the medium contacting the pipe wall manually or Sensor monitoring, such as noise recorder and corrosion monitoring equipment. The noise recorder can detect the sound of water droplets. If a water leak occurs, the location of the leak can be located by combining the flow parameters; the corrosion monitoring equipment analyzes the soil composition around the pipeline and monitors the corrosion rate of the pipeline. After the safety limit, the alarm is sent to the background center of the pipeline monitoring system; optical fiber or cable distributed sensors are also widely used, with high detection accuracy and accurate positioning. They are applied in long-distance pipeline networks, and optical cables need to be laid synchronously in the early stage of pipeline construction. Or cables, it is relatively difficult to repair or replace, and the cost of repairing cables for old and old fluid pipelines is too high. Pigging (PIG), infrared imaging, acoustic wave method, negative pressure wave method, support vector machine method, magnetic leakage method, adaptive wireless sensor method, sparse Matrix measurement method, transient pressure wave oscillation, gray correlation analysis, genetic algorithm combined with inverse transient wave, continuous linear random estimation method, analysis method along the pressure point, mass/volume balance method, digital signal processing method, etc.

At present, there are many leak detection methods for pipelines, and many of them have been applied in long-distance and resource material transportation pipelines. However, there are still problems such as detection after leakage, low detection accuracy and positioning accuracy, real-time performance, and lack of intelligent detection/monitoring methods. If the location, assessment, and maintenance can be realized before the actual leakage point occurs in the pipeline, better results will be obtained in controlling fluid loss and reducing the cost of fluid transportation, that is, the pre-leakage monitoring that people expect. By analyzing the physical characteristics of the flow pipeline, the singularity of the flow pipeline can be used as an important parameter in the study of pipeline physical properties. The singularity is regarded as the abnormal state area that occurs during the use of the pipeline, including the generation of leaks, the distribution of leaks, the shape of leaks, the dimension of leaks, the corrosion and thinning area of the pipeline wall during the long-term use of the pipeline, Areas of uneven stress applied to the pipe wall caused by changes in the supporting environment around the pipe or water hammer vibrations, straight pipes and curved pipes and connecting transition joints, fluid impurities gradually accumulate and silt in areas with high friction coefficients on the pipe wall, resulting in pipe diameter reduction areas wait. The definition of singularity can technically clarify the potential risk points of the pipeline, which has more engineering value than detecting/monitoring the actual leakage point of the pipeline, such as realizing pre-leakage monitoring, active detection, intelligent positioning, real-time detection, etc.

The application of the present invention aims at the real-time detection before the leakage of the pipeline, and proposes an active, real-time and intelligent pipeline detection technology. A detection method for fluid pipeline water hammer vibration wave transmission parameter fingerprints associated with the physical characteristics of the fluid pipeline is proposed; combined with wireless transmission technology and self-organizing network technology, an intelligent detection scheme for fluid pipelines is formed in the detection principles, devices, and system integration of fluid pipelines. It is expected to change the current status of late detection, high cost, fluid loss, easy to pollute the environment, many auxiliary structures, and low singularity positioning accuracy in the field of pipeline inspection.

Contents of the invention

The purpose of the present invention is to provide an intelligent detection method for the singularity feature of the flow pipeline, which can give an early warning before the pipeline is damaged due to the rupture of the singularity of the flow pipeline and the change of the perimeter. Realize high-precision positioning cut-off maintenance, thereby eliminating the impact on national production and residents' lives caused by the loss of transport fluid and the interruption of the operation of the transport pipeline without warning.

The technical scheme adopted in the present invention is:

An intelligent detection method for a singular point feature of a flow pipeline, comprising the following steps:

A: The micro-nano device array is used to test the vibration wave parameters to collect data on the flow characteristics of standard pipelines to form a background parameter set;

B: Install the vibration wave parameter test micro-nano device array to each node position of the flow pipeline to be tested, and collect the pipeline global information transmitted between two nodes on the flow pipeline to be tested through the vibration wave parameter test micro-nano device array. The above-mentioned pipeline global information includes vibration wave amplitude, frequency, phase, modulation ratio, and frequency deviation;

C: Set the sampling time length of vibration wave parameter data and the start time point, and write all the singular point characteristics and perimeter characteristics of the flow pipeline collected before this point into the pipeline detection background parameter set to realize all background parameter sets in the early stage fusion, and participate in the comparative analysis of the data set for the next detection as a background parameter set;

D: Compare and detect the background parameter set with the data set collected after the set time point. The two data sets are based on deep machine learning to obtain the characteristics of the singularity of the pipeline and the change of the perimeter characteristics;

E: Set the pipeline singularity rupture threshold according to the flow transmission pipeline experiment and theoretical simulation analysis results, and set a specific ratio below the pipeline rupture threshold, that is, the singularity characteristic value will form a wireless signal and transmit it to the regional control center;

F: The information obtained by the node is processed and filtered to form a transmission data packet, and an adaptive network based on the node ID is established to transmit the pipeline information and concentrate it in the master control center; the processing and filtering refers to the spectrum distribution from the spectrum data set, Spectrum range, amplitude change, and frequency modulation coefficient are calculated to obtain the singularity position and physical properties of the singularity.

The node locations include array inspection wells, switch stations, pressure regulating stations, and valves.

The vibration wave parameter testing micro-nano device system includes a sensing array composed of sensing micro-nano devices, a control IC, data processing firmware, a memory, and a data transmission unit, and the output end of the sensing array is connected to a data processing unit. The input terminal of the firmware and the output terminal of the data processing firmware are connected to the master control center through the data transmission and transmission unit, and the output terminal of the control IC is connected to the control input terminal of the sensing array and the control input terminal of the data transmission and transmission unit.

The data processing firmware and control IC, storage unit, and data transmission unit are integrated and laid out on the detection node unit.

In the step D, the acquired data after the time division point is compared with the background parameter set, and when there is no specific change, the acquired data continues to be fused to form a new pipeline background parameter set.

A certain proportion in the described step E is 90%-95%.

It also includes an elastic base, the elastic base is an annular gasket type and an annular sleeve type rubber pad, the elastic base is arrayed with grooves, and the vibration wave test micro-nano devices are embedded in the grooves Inside; the characteristic size of the vibration wave test micro-nano device matches the national standard pipe connector.

The singular point characteristics of the flow pipeline include the shape, depth, and distribution of cracks on the pipe wall; the thickness, distribution, and shape of the corrosion-thinned area; Distribution shape, stress bearing size.

Changes in the perimeter of the flow pipeline include changes in the physical properties of the contact medium, changes in the magnitude of the contact stress, changes in the rigidity of the fixture support, and changes in the amplitude of environmental vibrations.

Based on the fusion of micro-nano manufacturing technology, MEMS device integration and big data learning and mining, the present invention builds an intelligent detection method for the flow pipeline, collects and defines singularity parameters, and detects the physical properties of the pipeline singularity before the singularity of the flow pipeline breaks The process of change and the state of the surrounding environment change, fixed-point cut-off maintenance, fundamentally suppress the loss of fluid transmission, different from the current passive detection after the pipeline rupture, it is an active detection method; further from the transmission parameters of the pipeline water hammer vibration wave Based on big data fusion and deep learning, the real-time evolution of the singularity of the pipeline is obtained, and the monitoring effect is optimized through intelligent learning; finally, the present invention is based on the micro-device integration of the micro-nano manufacturing process, integrating sensing, data processing firmware, The control IC, storage, and information transmission are integrated on the elastic substrate, which improves the detection and processing speed, has little change to the pipeline, and has low industrial application cost.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a flowchart of the present invention;

Fig. 2 is the block diagram of circuit principle of the present invention;

Fig. 3 is the relationship diagram between the change of Young's modulus and the change of vibration wave amplitude according to the present invention;

Fig. 4 is the change diagram that the flow velocity of the fluid in the transport pipeline of the present invention produces to the vibration wave parameter;

Fig. 5 is a graph showing changes in vibration wave amplitude emission caused by changes in the Young's modulus of the contact material around the flow pipeline of the present invention;

Fig. 6 is the influence diagram of the Poisson's ratio of the material of the transport pipeline of the present invention on the amplitude of the vibration wave;

Fig. 7 is a schematic diagram of the variation of the amplitude of the vibration wave when the cracks at different positions and depths on the fluid delivery pipeline of the present invention do not cause fluid leakage.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

As shown in Figures 1, 2 and 3, the present invention comprises the following steps:

A: Collect data on the flow characteristics of standard pipelines by testing micro-nano devices through vibration wave parameters to form a background parameter set;

B: Install the vibration wave parameter test micro-nano device to each node position of the flow pipeline to be tested, and collect the pipeline global information transmitted between any two nodes on the flow pipeline to be tested through the vibration wave parameter test micro-nano device, the said The global pipeline information includes vibration amplitude, frequency, phase, modulation ratio, and frequency offset; these frequency domain information can be related to the physical properties of the pipeline based on specific expressions, such as f=(k/m)1/2.

C: Set the sampling time length of the vibration wave parameter data and the start time point, and write all the singular points and perimeter characteristics of the flow transmission pipeline collected before the time point into the pipeline detection background parameter set, so as to realize the integration of all background parameter sets in the early stage Fusion, and as the next detection background parameter set; singularity characteristics include singularity position, singularity geometry, singularity spatial distribution, singularity physical characteristics. Perimeter characteristics include: contact stress around the tube, contact material around the tube, and supporting devices around the tube.

D: Compare and detect the background parameter set with the data set collected after the set time point, and the two data sets are based on deep machine learning to obtain pipeline singularity and perimeter characteristic changes. For example, new spectral components are generated, the spectral range is narrowed, and the amplitude is increased or decreased.

E: Set the pipe singularity rupture threshold based on a large number of experimental and theoretical analysis results. The lowest value of the pressure flow), below the pipeline rupture threshold (95%), that is, the singularity characteristic forms a wireless signal and transmits it to the regional control center;

F: After the information obtained by the node is processed and filtered (the singularity position and physical properties of the singularity are obtained from the spectrum distribution, spectrum range, amplitude change, and frequency modulation coefficient calculation of the spectrum data set), the adaptive construction network based on the node ID will transfer the pipeline information Transmission, concentrated in the master control center.

The said node positions include array manholes, switch stations, pressure regulating stations, valves and other control devices to ensure the operation of the flow transmission pipeline.

The vibration wave parameter test micro-nano device includes a sensing array composed of sensing micro-nano devices, a control IC, a memory, and a data transmission unit, and the output end of the sensing micro-nano device array is connected to the input end of the control IC , the output end of the control IC is connected to the master control center through the data transmission unit.

The sensing micro-nano device includes an amplitude sensor, a phase sensor, a spectrum sensor, and a modulation sensor. In subsequent experimental research, other sensors can also be set according to actual needs.

The detection node device includes firmware integrating deep learning, data fusion and other algorithms, as well as micro-nano sensor arrays for collecting vibration wave parameter sets, control system system hardware, and signal transmission system hardware;

It also includes an elastic base, the elastic base is an annular gasket type and an annular sleeve type rubber pad, the elastic base is arrayed with grooves, and the vibration wave test micro-nano devices are embedded in the grooves Inside; the characteristic size of the vibration wave test micro-nano device matches the national standard pipe connector. The micro-nano node device is embedded in the elastic substrate, and the geometric size of the relevant detection unit is constructed according to the national standard pipeline characteristic size; it is convenient for a stable fit with the node, and does not cause additional structural changes to the node.

Its test data set is input into the machine learning network, and the data is processed in real time and on the spot to form the background parameter set of the pipeline between two nodes and the singular point characteristic parameter set; the data processing firmware, control IC, storage unit, and data transmission unit are integrated in the detection Unit device; The vibration wave transmission background parameter set of the newly-built initial flow pipeline is derived from the fusion experiment results of the simulation results of the characteristics of the flow pipeline in the standard laboratory and the environmental detection; based on the time axis segmentation, the data obtained before the segmentation point and the initial data set Data fusion is formed to form a new characteristic parameter background of the flow pipeline; the background parameter set of the vibration wave transmission of the flow pipeline, wherein the data obtained after the time division point is compared with the background parameter set, and when there is no specific change, the data continues to be fused to form A new background parameter set of the flow pipeline; the background parameter set of the flow pipeline, wherein the data obtained after the time division point is compared with the background parameter set, and when there is a specific change, the background parameter set and the new test vibration wave are transmitted based on the vibration wave The differential calculation of the parameter set obtains the characteristics of the singular point and the circumference of the pipeline; the characteristics of the singularity of the pipeline and the characteristics of the circumference of the pipeline form a transmission data packet, which is transmitted to the master control center based on the adaptive network of the node ID;

Set the singularity threshold based on the physical properties of the pipeline material and the surrounding environment characteristics experimental results and simulation results. When the test data value reaches 95% of the threshold, an alarm signal is sent to notify the cut-off maintenance;

The singular point characteristics of the flow pipeline include the shape, depth, and distribution of cracks on the pipe wall; the thickness, distribution, and shape of the corrosion-thinned area; Distribution shape, stress bearing size. Changes in the perimeter of the flow pipeline include changes in the contact medium (soil, cement, water, air, metal), changes in the magnitude of the contact stress, changes in the rigidity of the fixture support, and changes in the amplitude of environmental vibrations.

Specifically, as shown in Figure 3, changes in the Young's modulus of the surface flow pipeline will lead to changes in the amplitude of the vibration wave, and the amplitude of the wave parameter is related to the Young's modulus of the flow pipeline. During the operating conditions of the flow pipeline, the Young's modulus of the pipe wall at the singularity position changes due to corrosion, cracks, and thickening, which can be tested from the characteristics of the vibration wave amplitude.

Fig. 4 is the change of the vibration wave parameter caused by the flow velocity of the fluid in the flow pipeline, which shows that there is a relationship between the vibration wave amplitude and the change of the fluid flow velocity. Due to the accumulation of impurities in the pipeline, the diameter of the pipe changes and then affects the flow rate of the flow. The change of the amplitude of the vibration wave obtained from the test node can form a test.

Figure 5 shows that the change of Young's modulus of the contact material around the flow pipeline leads to the change of vibration wave amplitude emission. The contact materials in the working conditions around the flow pipe are generally soil, cement, metal (ship), water and air. Judging from the influence of the contact interface of soil (water content, sand content) with different Young's modulus on the vibration wave amplitude, the impact is obvious. Correspondingly, the impact of cement and metal materials with higher Young's modulus on the vibration wave transmission must be more obvious, and the impact of water and air with lower Young's modulus must also exist. From the change of different physical materials, such as changing from soil to water or air, from metal to water or air, the vibration wave parameter test can test and distinguish this change.

Figure 6 shows the influence of the Poisson's ratio of the flow pipeline material on the amplitude of the vibration wave. The results show that the material at the singular point of the flow pipeline has been modified, and the Poisson's ratio has changed, which can be reflected in the change of the vibration wave amplitude.

Figure 7 shows cracks appearing at different positions and depths on the flow pipeline. Although they have not yet caused fluid leakage, they also have an impact on the amplitude of the vibration wave. By testing the amplitude variation of the vibration wave, it is also possible to detect part of the crack-like singularity information on the flow pipeline.

From the test results in Fig. 3-Fig. 7, it can be confirmed that the singular point characteristics of the flow pipeline are related to the vibration wave amplitude. Therefore, the multi-parameter, multi-data based big data mining method can obtain the singularity characteristics of the flow pipeline from the change of wave parameters.

When the present invention is actually used, firstly, the vibration wave test micro-nano device array is installed at the node positions of the flow pipeline inspection well and the switch station, and the detection node monitors in real time and detects the water hammer vibration signal transmitted from both sides of the node;

Then the micro-nano array is tested to obtain the overall information of the pipeline, such as the amplitude, frequency, phase, modulation ratio, and frequency offset of the vibration wave transmitted between the two nodes. As shown in Figure 2, the sensor array collects various information transmitted by the water hammer vibration wave. , which is processed by the processing center through deep machine learning algorithms and data fusion algorithms to obtain background information and evolution information such as singularities and perimeter changes of the pipeline between two nodes;

The re-detection node integrates algorithm firmware such as deep learning and data fusion, and forms an on-chip system with the detection node detection micro-nano device array, control IC, storage, and data transmission unit; the data set divided by calculation processing is transmitted by the wireless transmission unit;

The system-on-chip is embedded in the elastic substrate, and the geometric dimensions of the detection nodes are constructed according to the national standard pipeline characteristic dimensions. Construct the detection nodes, which are gasket type and sleeve type respectively. The use of elastic base is mainly to facilitate the installation of various flow pipeline nodes and reduce the cost of laying test nodes;

The characteristic data of the flow pipeline obtained through laboratory simulation and experiment are used as the background parameter set of pipeline physical properties before the detection time point of the new pipeline singularity. In view of the fact that there is no background parameter set source for the first use of the pipeline network, the standard laboratory's flow pipeline test, simulated physical properties, and perimeter characteristics are used as the initial background;

Set the length of the interval time, and write the singular point and perimeter characteristics of the pipeline collected before this time point into the pipeline detection background parameter set to realize the fusion of all background parameter sets in the early stage. The data processing flow is shown in Figure 1;

After the time point, the data collected and compared with the detection background parameters are based on machine learning to obtain the singularity of the pipeline and the change of the perimeter characteristics. This step is the key to the intelligent detection of the pipeline. The neural network is used to obtain, Stripping the characteristic parameters of the singularity and the characteristic parameters of the perimeter change, and comparing and analyzing them with the corresponding parameters in the background parameter set, to realize the characteristic analysis and high-precision positioning of the singularity and the perimeter of the pipeline, as shown in Figure 1;

The pipeline singularity threshold is set based on the basis of experiments and theoretical analysis. Below the pipeline rupture threshold, parameters such as the singularity position and physical properties of the singularity are collected in the control center, and the flow is cut off for maintenance. Different materials, different topological structures, different contact interfaces and surrounding environment parameters have different thresholds for rupture singularity of the flow pipeline. Through a large number of experiments and simulation analysis, the stress bearing strength, corrosion rate, stress concentration, etc. of the specific environment and specific material flow pipelines are set as the corresponding thresholds. The system analyzes and judges according to the test results. When it reaches 95% of the threshold, it means There is a risk of rupture at the point of singularity, which requires cut-off maintenance;

After the information obtained by the nodes is processed and filtered, a network is adaptively established based on the node ID to transmit the pipeline information. The node automatically communicates with the surrounding nodes, and the data set carries the ID information, as shown in Figure 6. The data is centralized in the master control center. In order to achieve centralized control, the singular point mutation information of the inter-node flow transmission pipeline obtained by the nodes is relayed and transmitted by each node, and finally returned to the master control center for processing. An intelligent processing chain is formed to reduce the cost of pipeline testing.

Singularity can be regarded as an abnormal state area that occurs during the use of the pipeline, including crack shape; crack spatial distribution; crack geometric size; corrosion and thinning area of the pipeline wall during long-term use of the pipeline; changes in the supporting environment around the pipeline Or the area of uneven stress applied to the pipe wall caused by water hammer vibration; the conversion joint between straight pipe and curved pipe and straight pipe; fluid impurities gradually accumulate at the high friction point of the pipe wall to block the pipe diameter reduction area, etc. The definition of singularity can technically clarify the potential risk points of the pipeline, which has more engineering value than detecting/monitoring the actual leakage point of the pipeline, such as realizing pre-leakage monitoring, active detection, intelligent positioning, and real-time monitoring.

In the description of the present invention, it should be noted that for the orientation words, such as the term "center", "horizontal", "longitudinal", "length", "width", "thickness", "upper", "lower" , "Front", "Back", "Left", "Right", "Vertical", "Horizontal", "Top", "Bottom", "Inner", "Outer", "Clockwise", "Counterclockwise" The indicated orientation and positional relationship are based on the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation or be constructed in a specific orientation. and operation, should not be construed as limiting the specific protection scope of the present invention.

It should be noted that the terms "first" and "second" in the specification and claims of the present application are used to distinguish similar objects, and not necessarily used to describe a specific order or sequence. It should be understood that the data so used may be interchanged under appropriate circumstances for the embodiments of the application described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus comprising a series of steps or elements need not be limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

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

Claims (7)

1. A method for intelligent detection of singular point characteristics of a flow pipeline, characterized in that: comprises the following steps: A: The micro-nano device array is used to test the vibration wave parameters to collect data on the flow characteristics of standard pipelines to form a background parameter set; B: Install the vibration wave parameter test micro-nano device array to each node position of the flow pipeline to be tested, and collect the pipeline global information transmitted between two nodes on the flow pipeline to be tested through the vibration wave parameter test micro-nano device array. The above pipeline global information includes vibration wave amplitude, frequency, phase, modulation ratio, and frequency offset; the vibration wave parameter test micro-nano device array includes a sensing array composed of sensor micro-nano devices, a control IC, and a data processing firmware. , a memory and a data transmitting and transmitting unit, the output of the sensor array is connected to the input of the data processing firmware, the output of the data processing firmware is connected to the master control center through the data transmitting and transmitting unit, and the output of the control IC is connected to the sensing array The control input end of the control input end and the control input end of the data transmission unit; C: Set the sampling time length of vibration wave parameter data and the start time point, and write all the singular point characteristics and perimeter characteristics of the flow pipeline collected before this point into the pipeline detection background parameter set to realize all background parameter sets in the early stage fusion, and participate in the comparative analysis of the data set for the next detection as a background parameter set; D: Compare and detect the background parameter set with the data set collected after the set time point. The two data sets are based on deep machine learning to obtain the characteristics of the singularity of the pipeline and the change of the perimeter characteristics; E: Set the pipeline singularity rupture threshold according to the results of the flow transmission pipeline experiment and theoretical simulation analysis, and set a specific ratio below the pipeline rupture threshold, that is, the singularity characteristic value will form a wireless signal and transmit it to the regional control center; the said A certain proportion in the step E is 90%-95%; F: The information obtained by the node is processed and filtered to form a transmission data packet, and an adaptive network based on the node ID is established to transmit the pipeline information and concentrate it in the master control center; the processing and filtering refers to the spectrum distribution from the spectrum data set, Spectrum range, amplitude change, and frequency modulation coefficient are calculated to obtain the singularity position and physical properties of the singularity. 2. The intelligent detection method for singular point characteristics of a flow pipeline according to claim 1, wherein the node locations include array inspection wells, switch stations, pressure regulating stations, and valves. 3. The method for intelligent detection of singularity features of a flow pipeline according to claim 1, characterized in that: the data processing firmware, control IC, storage unit, and data transmission unit are integrated and laid out on the detection node unit. 4. The method for intelligent detection of singular point features of the transport pipeline according to claim 1, characterized in that: in the step D, the data obtained after the time division point is compared with the background parameter set, and when there is no specific change, the obtained data The data continues to be fused to form a new pipeline background parameter set. 5. The intelligent detection method for singular point characteristics of the flow pipeline according to any one of claims 1-4, characterized in that: it also includes an elastic base, and the elastic base is an annular gasket type and an annular sleeve The sleeve-type rubber pad, the elastic base is provided with an array of grooves, and the vibration wave test micro-nano devices are embedded in the grooves; the characteristic dimensions of the vibration wave test micro-nano devices match the national standard pipe connectors. 6. The intelligent detection method for singular point characteristics of flow pipeline according to claim 1, wherein: the singular point characteristics of flow pipeline include pipe wall crack shape, crack depth, and crack distribution; thickness, distribution, and shape of corrosion thinning area ; The size, distribution range, and distribution shape of the area where the diameter of the deposition impurity accumulation becomes smaller; the area size, distribution shape, and stress bearing size of the stress concentration area. 7. The intelligent detection method for singular point characteristics of the flow pipeline according to claim 1, characterized in that: changes in the perimeter of the flow pipeline include changes in the physical properties of the contact medium, changes in the magnitude of contact stress, changes in the rigidity of fixture supports, and changes in the amplitude of environmental vibrations.

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