A monitoring system and method for infrasonic wave detection of pipeline pigging operations

Abstract

The present invention provides a monitoring system and method for infrasound detection of pipeline cleaning operations, which relates to the technical field of pipeline monitoring. By acquiring pipeline distribution information, constructing pipeline topology, retrieving pipeline monitoring data in a preset time zone, mining risk features and marking pipeline topology, passive infrasound sensing is performed based on a sensor array to determine a first sound wave signal, active sound wave detection is performed based on the sound wave detection frequency to determine a second sound wave signal, and a sound wave decision module is combined to perform sound wave signal recognition and pipeline abnormality positioning, determine pipeline state characteristics, generate a pipeline monitoring sheet based on the pipeline state characteristics, and perform terminal visual early warning. The technical problem that the existing technology cannot comprehensively and accurately monitor pipeline cleaning operations, resulting in inefficient pipeline maintenance and increased safety hazards is solved. The technical effect of efficiently and comprehensively monitoring the operating status of the pipeline, accurately locating risks, improving pipeline maintenance efficiency, and ensuring safe operation of the pipeline is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline monitoring, and in particular to a monitoring system and method for infrasonic wave detection of pipeline pigging operations. Background Art

[0002] In the pipeline transportation industry, pipeline pigging is a key step to ensure efficient operation and maintenance of pipeline systems. Pigging is designed to remove impurities, sediments and moisture from the pipeline to improve transmission efficiency and extend pipeline life. Traditional pipeline pigging monitoring and anomaly detection rely on manual inspections and regular physical testing, such as ultrasonic testing and magnetic particle testing. However, these methods rely on manual judgment and intervention, and have problems such as low monitoring efficiency, incomplete data collection, and insufficient accuracy of information analysis, which in turn leads to inaccurate pipeline status assessment, inefficient pipeline maintenance, and even increased safety hazards in pipeline operation.

[0003] There is a technical problem in the existing technology that it is unable to comprehensively and accurately monitor the risk characteristics of pipeline cleaning operations, resulting in inefficient pipeline maintenance and increased safety hazards. Summary of the invention

[0004] The present application provides a monitoring system and method for infrasound detection of pipeline pigging operations, which is used to solve the technical problem that the prior art cannot fully and accurately monitor the risk characteristics of pipeline pigging operations, resulting in inefficient pipeline maintenance and increased safety hazards.

[0005] In view of the above problems, the present application provides a monitoring system and method for infrasonic wave detection of pipeline pigging operations.

[0006] In a first aspect of the present application, a monitoring system for infrasonic wave detection of pipeline cleaning operations is provided, the system comprising: a pipeline topology construction module, the pipeline topology construction module is used to obtain pipeline distribution information and construct a pipeline topology, wherein the pipeline topology is a spatial topology and includes multiple pipeline types in a preset space; a pipeline topology marking module, the pipeline topology marking module is used to retrieve pipeline monitoring data in a preset time zone, mine risk features and mark the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship; a sensor configuration module, the sensor configuration module is used to traverse the pipeline topology, configure a sensor array and set the sensor array in combination with the risk features. A fixed sound wave detection frequency, wherein the sensor array is a sound wave sensor type; a passive infrasound wave perception module, wherein the passive infrasound wave perception module is used to perform passive infrasound wave perception based on the sensor array to determine a first sound wave signal; an active sound wave detection module, wherein the active sound wave detection module is used to perform active sound wave detection based on the sound wave detection frequency to determine a second sound wave signal; a pipeline state feature determination module, wherein the pipeline state feature determination module is used to combine the first sound wave signal and the second sound wave signal, and combine with the sound wave decision module to perform sound wave signal recognition and pipeline abnormality positioning to determine the pipeline state feature; a visual early warning module, wherein the visual early warning module is used to generate a pipeline monitoring list based on the pipeline state feature and perform terminal visual early warning.

[0007] The second aspect of the present application provides a monitoring method for infrasound detection pipeline cleaning operations, the method comprising: obtaining pipeline distribution information and constructing a pipeline topology, wherein the pipeline topology is a spatial topology and includes multiple pipeline types in a preset space; retrieving pipeline monitoring data in a preset time zone, mining risk features and marking the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship; traversing the pipeline topology, configuring a sensor array and setting a sound wave detection frequency in combination with the risk features, the sensor array being a sound wave sensor type; performing passive infrasound sensing based on the sensor array to determine a first sound wave signal; performing active sound wave detection based on the sound wave detection frequency to determine a second sound wave signal; combining the first sound wave signal and the second sound wave signal, and combining a sound wave decision module to perform sound wave signal recognition and pipeline anomaly positioning to determine pipeline status features; generating a pipeline monitoring sheet based on the pipeline status features and performing terminal visual early warning.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The system provided by the embodiment of the present application obtains pipeline distribution information, constructs pipeline topology, retrieves pipeline monitoring data of preset time zones, mines risk features and marks the pipeline topology, configures a sensor array and sets the frequency of acoustic wave detection, performs passive infrasound wave perception based on the sensor array, determines a first acoustic wave signal, performs active acoustic wave detection based on the acoustic wave detection frequency, determines a second acoustic wave signal, combines the first acoustic wave signal with the second acoustic wave signal, and performs acoustic wave signal recognition and pipeline abnormality location in combination with an acoustic wave decision module, determines pipeline status features, generates a pipeline monitoring form based on the pipeline status features, and performs terminal visual warning. The technical effect of efficiently and comprehensively monitoring the operating status of the pipeline, accurately locating risks, improving pipeline maintenance efficiency, and ensuring safe operation of the pipeline is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of the structure of a monitoring system for infrasonic wave detection of pipeline pigging operations provided in this application;

[0012] Figure 2 A flow chart of a monitoring method for infrasonic wave detection of pipeline pigging operations provided in this application.

[0013] Explanation of the reference numerals: pipeline topology construction module 11, pipeline topology marking module 12, sensor configuration module 13, passive infrasound wave perception module 14, active sound wave detection module 15, pipeline state characteristic determination module 16, visual early warning module 17. DETAILED DESCRIPTION

[0014] This application provides a monitoring system and method for infrasonic detection of pipeline pigging operations, which is used to solve the technical problem that the risk characteristics of pipeline pigging operations cannot be fully and accurately monitored in the prior art, resulting in inefficient pipeline maintenance and increased safety hazards. The technical effect of efficiently and comprehensively monitoring the operating status of the pipeline, accurately locating risks, improving pipeline maintenance efficiency, and ensuring safe operation of the pipeline is achieved.

[0015] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a monitoring system for infrasound detection of pipeline pigging operations, the system comprising:

[0017] The pipeline topology construction module 11 is used to obtain pipeline distribution information and construct a pipeline topology, wherein the pipeline topology is a spatial topology including multiple pipeline types in a preset space.

[0018] Specifically, the pipeline topology construction module 11 obtains detailed distribution information of multiple pipelines in a preset space, including the length, diameter, material, burial depth, and connection relationship of the pipelines, by integrating the geographic information system (GIS) with the pipeline database. Then, the spatial topology analysis method is used to perform spatial analysis on the acquired pipeline data to generate a spatial topological structure of multiple pipeline types and their relative positions, namely, the pipeline topology. The pipeline topology refers to the relative layout and connection relationship of pipelines in three-dimensional space. Multiple pipeline types include pipelines of different materials, diameters, and uses, such as oil pipelines, natural gas pipelines, and water pipes. Finally, the pipeline topology map generated by the pipeline topology construction module 11 can intuitively display the structure of the pipeline network and provide basic data support for subsequent monitoring and analysis.

[0019] The pipeline topology marking module 12 is used to retrieve pipeline monitoring data in a preset time zone, mine risk features and mark the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship.

[0020] Specifically, the pipeline topology marking module 12 first retrieves the monitoring data of multiple pipelines in a preset time zone from the pipeline monitoring database. The pipeline monitoring data includes various parameter changes of the operating status of the pipeline, such as pipeline pressure, flow, temperature, etc. Then, the monitoring data is used as input data and input into the pre-trained machine learning model. The machine learning model can be an algorithm based on random forest, support vector machine or deep learning. The machine learning model deeply mines the pipeline monitoring data by identifying abnormal patterns and trends in the data, and identifies the risk characteristics that may exist in the pipeline. The risk characteristics include risk location, risk type, risk level and risk frequency. The risk location refers to the specific location where a failure may occur in the pipeline. The risk type refers to the type of failure that may occur in the pipeline, such as corrosion, leakage, blockage, etc. The risk level refers to the severity of the risk, and the risk frequency refers to the frequency of the risk. The risk characteristics have an upstream and downstream relationship in the pipeline. Based on the mined risk characteristics and their upstream and downstream relationships, the pipeline topology is marked, and the risk area of ​​the pipeline is clearly identified, providing an important reference for subsequent monitoring and maintenance.

[0021] In a possible implementation, the pipeline topology marking module 12 is used to: traverse the risk features, divide them into multiple feature groups based on mutual influence, wherein the mutual influence includes the occurrence of synergy and timing influence; based on the pipeline position, associate the multiple feature groups upstream and downstream, and determine the association relationship, and determine the upstream and downstream relationship.

[0022] Specifically, the pipeline topology marking module 12 sequentially traverses each identified risk feature, namely, risk location, risk type, risk level and risk frequency. Then, based on the mutual influence relationship between different risk features, namely, the possible occurrence synergy and timing influence between risk features, the interrelated risk features are divided into multiple feature groups. The occurrence synergy refers to the simultaneous occurrence of multiple risk features at the same time or at a similar time, indicating that there may be a common cause or mutual promotion relationship between them. The timing influence refers to the appearance of a risk feature that may trigger or aggravate another risk feature, forming a temporal sequence and causal relationship. Then, using the pipeline location information in the pipeline topology, multiple feature groups are associated with upstream and downstream. The risk features in the pipeline network often do not exist in isolation, and may propagate along the flow direction or specific path of the pipeline to form an upstream and downstream relationship. For example, the risk of the upstream pipe section may trigger a chain reaction in the downstream pipe section. The pipeline topology marking module 12 determines the upstream and downstream associations between feature groups by comparing the relative positions of the risk positions in the feature groups, combining the flow direction and connection relationship of the pipeline, and further clarifies this association relationship to determine the upstream and downstream relationship. By determining the upstream and downstream relationships of each risk feature group, a basis is provided for a comprehensive assessment of pipeline risks.

[0023] The sensor configuration module 13 is used to traverse the pipeline topology, configure the sensor array and set the acoustic wave detection frequency in combination with the risk characteristics, and the sensor array is an acoustic wave sensor type.

[0024] In a possible implementation, the sensor configuration module 13 is used to traverse the risk characteristics, determine the infrasound wave generation characteristics of the risk event, and the infrasound wave generation characteristics include the propagation direction of the generated infrasound wave along the pipe wall; determine the effective sensing position based on the infrasound wave generation characteristics; traverse the effective sensing positions and configure the sensor array.

[0025] Specifically, the sensor configuration module 13 first traverses the entire pipeline topology and analyzes the location, direction and material information of each pipeline. Then, according to the risk characteristics identified by the pipeline topology marking module 12, the infrasound generation characteristics of the risk event are determined, and the infrasound generation characteristics refer to the small pressure fluctuations and propagation paths generated by the infrasound when the risk event occurs, including the propagation direction of the generated infrasound along the pipe wall. Then, the infrasound generation characteristics are further analyzed to determine the best position where the sensor can efficiently sense the infrasound, that is, the effective sensing position. Then all the determined effective sensing positions are traversed, and sensors are configured at these effective sensing positions to form a sensor array to ensure that the sensor covers all risk areas. The sensor array is an acoustic wave sensor class, such as an infrasound sensor, a broadband acoustic wave sensor, etc., which detects the leakage of the pipeline by sensing the infrasound. At the same time, according to the risk characteristics and the actual situation of the pipeline, the acoustic wave detection frequency is set for each risk feature to ensure that the sensor array can perform acoustic wave detection at a certain time. By rationally configuring the sensor array and setting the frequency through the sensor configuration module 13, it is possible to efficiently and comprehensively monitor the operation status of the pipeline and timely discover and deal with potential risks.

[0026] The passive infrasound wave sensing module 14 is used to perform passive infrasound wave sensing based on the sensor array to determine a first sound wave signal.

[0027] Specifically, the passive infrasound wave sensing module 14 performs passive monitoring without actively emitting sound wave signals through an array of sound wave sensors arranged in the risk area of ​​the pipeline. The passive infrasound wave sensing module 14 can capture and identify low-frequency infrasound waves naturally generated in the pipeline environment through highly sensitive infrasound wave sensors. Low-frequency infrasound waves are sound waves with a frequency lower than 20 Hz, which are small pressure fluctuations caused by risk events such as pipeline leakage and corrosion. When an infrasound wave signal is detected by the sensor array, the passive infrasound wave sensing module 14 immediately starts the analysis and processing mechanism to filter, amplify and extract features of the received sound wave signal to eliminate background noise interference and highlight the features related to the change in pipeline status. Finally, the first sound wave signal is determined and output, which is an infrasound wave signal that is first identified by passive sensing and may indicate pipeline abnormality or potential risks. The first sound wave signal reflects the potential abnormal conditions in the pipeline. Through this process, the passive infrasound wave sensing module 14 can perceive the infrasound changes inside the pipeline in real time and accurately, providing basic data support for subsequent pipeline status assessment.

[0028] The active sound wave detection module 15 is used to perform active sound wave detection based on the sound wave detection frequency to determine a second sound wave signal.

[0029] In a possible implementation, the active acoustic wave detection module 15 is used to: differentiate the acoustic wave detection frequencies of the risk features of the pipeline topology identification; identify the acoustic wave detection frequencies and determine the pre-detection pipeline position; and based on the infrasound wave transmitter, directionally emit active detection acoustic waves to perform infrasound wave detection on the pre-detection pipeline position.

[0030] Specifically, the active acoustic wave detection module 15 sets different acoustic wave detection frequencies according to the risk characteristics of the pipeline topology identification, and there are differences. Identify the acoustic wave detection frequency of each risk characteristic, determine the pre-detection pipeline position that needs to be detected, and the pre-detection pipeline position is determined based on the identified risk characteristics, and there may be potential problems. Then, use the infrasound transmitter to actively and directionally transmit the detection sound wave to the pre-detection pipeline position, and perform infrasound detection on the pre-detection pipeline position. The infrasound transmitter refers to a device that can generate and emit infrasound waves, which is used to actively detect the conditions inside the pipeline. Active detection can improve the pertinence and efficiency of detection. In the process of actively emitting detection sound waves for infrasound detection, after the sensor array receives the reflected signal of the active detection sound wave propagating and reflected in the pipeline, it is filtered, amplified and feature extracted to eliminate background noise interference and highlight the features related to the change of pipeline state, forming a second sound wave signal, that is, the second sound wave signal refers to the infrasound reflection signal obtained by active detection, which can reflect the conditions and potential abnormalities in the pipeline, and provide accurate and reliable basic data support for subsequent pipeline state assessment.

[0031] In a possible implementation, the active acoustic wave detection module 15 is also used to: determine the initialization detection acoustic wave; compensate the initialization detection acoustic wave as an active detection acoustic wave; wherein the acoustic wave compensation method includes: determining multiple frequency wavelengths, compensating and marking the initialization detection acoustic wave as a type of active detection acoustic wave; introducing a tracking medium, combined with the initialization detection acoustic wave, as a second type of active detection acoustic wave.

[0032] Specifically, the active sound wave detection module 15 is not only used to emit infrasound waves for detection, but also to solve the problem of poor infrasound directionality, and to quickly and accurately track and locate sound waves. The active sound wave detection module 15 first determines an initialization detection sound wave, which is an infrasound signal used for preliminary detection and is the starting point of sound wave tracking. Since the directionality of infrasound waves is relatively weak, in order to enhance the accuracy of detection, the initialization detection sound wave is compensated to enhance its effectiveness in different environments and form an active detection sound wave. The sound wave compensation method includes selecting a plurality of different frequency wavelengths within the infrasound wave frequency range, that is, multi-frequency wavelengths. Then, the initialization detection sound wave is adjusted and enhanced in multiple dimensions such as frequency and amplitude according to the multi-frequency wavelength, forming a class of active detection sound waves with specific identification and compensation characteristics. By changing the frequency characteristics of the initialization detection sound wave, it can be better propagated in different media and environments, thereby improving the accuracy of detection.

[0033] Secondly, a tracking medium is introduced. The tracking medium is a material or method used to adjust the characteristics of the sound wave to enhance the traceability of the sound wave signal. For example, the tracking medium can be a material with specific acoustic properties, or a gas or liquid introduced into the pipeline, which can interact with the sound wave and change the propagation path or characteristics of the sound wave. By combining the tracking medium with the initialization detection sound wave, a second type of active detection sound wave is generated. The second type of active detection sound wave not only inherits the basic characteristics of the initialization sound wave, but also obtains stronger directional control capabilities and more accurate detection information due to the intervention of the tracking medium, thereby improving the positioning accuracy. Through the above-mentioned dual strategies of compensation and medium introduction, the active sound wave detection module can significantly improve the tracking and positioning accuracy of infrasound waves, and provide more reliable and efficient technical support for subsequent pipeline monitoring, troubleshooting and safety warning.

[0034] The pipeline state characteristic determination module 16 is used to combine the first sound wave signal and the second sound wave signal with the sound wave decision module to perform sound wave signal recognition and pipeline abnormality positioning to determine the pipeline state characteristics.

[0035] Specifically, the pipeline state feature determination module 16 receives the first sound wave signal determined by the passive infrasound wave sensing module 14 and the second sound wave signal determined by the active sound wave detection module 15, and respectively identifies and analyzes the first sound wave signal and the second sound wave signal according to the active decision area and the passive decision area in the sound wave decision module, accurately distinguishes normal sound wave features from abnormal sound wave features, and then identifies and locates leakage, blockage, vibration exceeding the standard and other abnormalities in the pipeline. According to the analysis results, the pipeline state feature is comprehensively determined, and the pipeline state feature is a comprehensive indicator reflecting the current health status of the pipeline, including the condition of the inner wall of the pipeline, the location and severity of potential risks, etc., which ensures a comprehensive assessment of the pipeline state and improves the accuracy and efficiency of pipeline abnormality identification.

[0036] In a possible implementation, the pipeline state feature determination module 16 is used for: the acoustic wave decision module includes an active decision area and a passive decision area; receiving the first acoustic wave signal, activating the active decision area to perform acoustic wave signal recognition and pipeline abnormality positioning; receiving the second acoustic wave signal, activating the passive decision area to perform acoustic wave signal recognition and pipeline abnormality positioning, wherein the active decision area and the passive decision area are relatively independent.

[0037] Specifically, since the sound wave states obtained by active detection and passive detection are different, the sound wave decision module includes an active decision area and a passive decision area. The active decision area refers to an analysis area that specifically processes passive sensing signals, identifies abnormal features in sound wave signals through a specific algorithm, and locates possible pipeline problems. The passive decision area refers to an analysis area that specifically processes active detection signals, and locates possible pipeline problems by analyzing abnormal features in reflected signals. The pipeline state feature determination module 16 receives the first sound wave signal determined by the passive infrasound wave perception module 14, activates the active decision area, analyzes the first sound wave information using a pre-set algorithm and model, identifies sound wave features related to pipeline abnormalities (such as leakage, cracks, etc.), and locates pipeline abnormalities. The pre-set algorithm and model are, for example, a spectrum analysis algorithm, a waveform matching model, and a machine learning classifier. For example, after receiving the first sound wave signal, the pipeline state feature determination module 16 activates the active decision area, processes the first sound wave signal using a spectrum analysis algorithm, and can identify whether there is a peak that matches the characteristic frequency of pipeline leakage by analyzing the spectrum diagram. If so, it is determined that there may be a leakage problem in the pipeline, and the pipeline abnormality is located.

[0038] Similarly, after the pipeline state characteristic determination module 16 receives the second sound wave signal determined by the active sound wave detection module 15, it activates the passive decision area in the sound wave decision module and inputs the second sound wave signal into the passive decision area for analysis. By analyzing the abnormal characteristics in the reflected signal, potential problems in the pipeline are identified. Using algorithms such as time domain reflection analysis, the time and intensity changes of the infrasound reflected back when encountering anomalies in the pipeline are detected by the time domain reflection analysis algorithm, so as to accurately locate the problem area. Since the sound wave states obtained by active detection and passive detection are different, the active decision area and the passive decision area are relatively independent, and targeted analysis is performed separately to ensure the accuracy and comprehensiveness of the analysis results.

[0039] The visual warning module 17 is used to generate a pipeline monitoring list and perform terminal visual warning based on the pipeline status characteristics.

[0040] Specifically, the visual warning module 17 receives the pipeline status characteristics obtained from the pipeline status characteristic determination module 16, organizes the received pipeline status characteristics into a form, and generates a pipeline monitoring sheet, which is a detailed pipeline monitoring report, including each risk location and its corresponding risk characteristics, for reference by pipeline maintenance personnel. And use data visualization technology to intuitively display the information in the monitoring sheet on the terminal device to perform terminal visual warning. For example, using charts, heat maps, and pipeline topology maps, and using visual elements such as graphics, colors, and animations, pipeline anomalies are highlighted and emergency prompts are given, and the risk distribution of the pipeline is intuitively presented, ensuring that the operation and maintenance personnel can quickly capture key information and respond in a timely manner. The visual warning module 17 uses terminal visual warning methods to not only improve the efficiency and accuracy of pipeline monitoring, but also enhance the timeliness of emergency treatment, ensuring the safe and stable operation of the pipeline.

[0041] In a possible implementation, the visual warning module 17 is used to: prioritize the pipeline monitoring list based on the risk level as the first priority feature and the affected scope as the second priority feature to determine the risk sequence; perform homologous clustering on the pipeline status features to determine the clustering results; based on the clustering results, mark the risk sequence to perform terminal visual warning and pipeline operation and maintenance management.

[0042] Specifically, the visual early warning module 17 first uses the risk level as the first priority feature to sort the pipeline monitoring forms. The risk level refers to the severity of each risk event. The higher the risk level, the higher the priority of its processing, ensuring that high-risk events can be responded to quickly. Then, for situations with the same risk level, the scope of impact is used as the second priority feature to further subdivide and sort the pipeline monitoring forms. The scope of impact refers to the length of the pipeline or the size of the area that may be affected by a certain risk event. The wider the scope of impact, the greater its urgency and importance, to ensure that problems with greater potential impacts can be dealt with first. According to the priority sorting, the risk sequence is determined. The risk sequence refers to the pipeline monitoring forms that have been prioritized, arranged according to risk level and scope of impact.

[0043] After the sorting is completed, the state characteristics of the pipeline are clustered using homologous clustering methods, such as k-means or DBSCAN algorithms. The homologous clustering refers to classifying pipeline risk events with similar or identical sources (such as materials, installation environment, service life, etc.) and performance characteristics into the same category, so as to better identify and handle similar risks. The clustering results are formed by aggregating the same or similar risk events together. Finally, based on the clustering results, the determined risk sequence is marked, and the risk points with the highest operation and maintenance priority will be handled first. Since the homologous clustering results show that the operation and maintenance methods of these risk points are similar, the homologous risk points are marked in the same way, that is, the same identifier or symbol is used for the same type of risk points, which is convenient for rapid identification in the visual interface. In the terminal visual warning interface, different colors or icons are used to represent risk points of different categories and priorities. For example, a red triangle mark indicates an area with high risk and needs to be handled immediately, and a yellow circle mark indicates a medium risk area. Users can quickly understand which areas need to be prioritized for operation and maintenance through the visual interface. The marking and visual warning based on the clustering results improve the operation and maintenance efficiency and reduce the response time, ensuring the safe and stable operation of the pipeline.

[0044] Embodiment 2, as Figure 2 As shown, the present application provides a monitoring method for infrasound detection of pipeline pigging operations, wherein the method comprises:

[0045] Obtain pipeline distribution information and construct a pipeline topology, wherein the pipeline topology is a spatial topology and includes multiple pipeline types in a preset space; retrieve pipeline monitoring data in a preset time zone, mine risk features and mark the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship; traverse the pipeline topology, configure a sensor array and set the sound wave detection frequency in combination with the risk features, and the sensor array is a sound wave sensor type; based on the sensor array, perform passive infrasound wave perception to determine a first sound wave signal; based on the sound wave detection frequency, perform active sound wave detection to determine a second sound wave signal; combine the first sound wave signal and the second sound wave signal, and combine the sound wave decision module to perform sound wave signal recognition and pipeline anomaly positioning to determine pipeline status features; based on the pipeline status features, generate a pipeline monitoring form and perform terminal visual early warning.

[0046] Furthermore, the risk features have an upstream and downstream relationship, including: traversing the risk features, dividing into multiple feature groups based on mutual influence, wherein the mutual influence includes synergy and timing influence; based on the pipeline position, associating the multiple feature groups upstream and downstream, and determining the association relationship, thereby determining the upstream and downstream relationship.

[0047] Furthermore, configuring the sensor array in combination with the risk characteristics includes: traversing the risk characteristics to determine the infrasound wave generation characteristics of the risk event, the infrasound wave generation characteristics including the propagation direction of the generated infrasound wave along the pipe wall; determining effective sensing positions based on the infrasound wave generation characteristics; traversing the effective sensing positions to configure the sensor array.

[0048] Furthermore, based on the acoustic wave detection frequency, active acoustic wave detection is performed, including: there is a difference in the acoustic wave detection frequency of each risk feature of the pipeline topology identification; identifying the acoustic wave detection frequency, and determining the pre-detection pipeline position; based on the infrasound wave transmitter, active detection acoustic waves are directionally emitted to perform infrasound wave detection on the pre-detection pipeline position.

[0049] Furthermore, before the directionally emitting active detection sound waves, it includes: determining an initialization detection sound wave; compensating the initialization detection sound wave as an active detection sound wave; wherein the sound wave compensation method includes: determining a multi-frequency wavelength, compensating and marking the initialization detection sound wave as a type of active detection sound wave; introducing a tracking medium, combined with the initialization detection sound wave, as a type of second active detection sound wave.

[0050] Furthermore, the combination of the acoustic wave decision module for acoustic wave signal recognition and pipeline abnormality location includes: the acoustic wave decision module includes an active decision area and a passive decision area; receiving the first acoustic wave signal, activating the active decision area to perform acoustic wave signal recognition and pipeline abnormality location; receiving the second acoustic wave signal, activating the passive decision area to perform acoustic wave signal recognition and pipeline abnormality location, wherein the active decision area and the passive decision area are relatively independent.

[0051] Furthermore, the generation of pipeline monitoring sheets and terminal visual early warning include: taking risk level as the first priority feature and the affected scope as the second priority feature, prioritizing the pipeline monitoring sheets and determining a risk sequence; performing homologous clustering on the pipeline status features and determining clustering results; and marking the risk sequence based on the clustering results, and performing terminal visual early warning and pipeline operation and maintenance management.

[0052] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0053] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A monitoring system for infrasound detection of pipeline pigging operations, characterized in that: The system comprises: A pipeline topology construction module, the pipeline topology construction module is used to obtain pipeline distribution information and construct a pipeline topology, wherein the pipeline topology is a spatial topology, including multiple pipeline types in a preset space; A pipeline topology marking module, which is used to retrieve pipeline monitoring data in a preset time zone, mine risk features and mark the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship; A sensor configuration module, the sensor configuration module is used to traverse the pipeline topology, configure a sensor array in combination with the risk characteristics, and set the acoustic wave detection frequency according to the risk characteristics and the pipeline topology, the sensor array being an acoustic wave sensor class; A passive infrasound wave sensing module, the passive infrasound wave sensing module is used to perform passive infrasound wave sensing based on the sensor array to determine a first sound wave signal; An active sound wave detection module, the active sound wave detection module is used to perform active sound wave detection based on the sound wave detection frequency to determine a second sound wave signal; A pipeline state feature determination module, the pipeline state feature determination module is used to combine the first acoustic wave signal and the second acoustic wave signal with the acoustic wave decision module to perform acoustic wave signal recognition and pipeline abnormality location to determine pipeline state features; A visual warning module, which is used to generate a pipeline monitoring sheet and perform terminal visual warning based on the pipeline status characteristics; The sensor configuration module is used to: Traversing the risk characteristics to determine the infrasound wave generation characteristics of the risk event, wherein the infrasound wave generation characteristics include the propagation direction of the generated infrasound wave along the pipe wall; Determining an effective sensing position based on the infrasound wave generation characteristics; Traversing the valid sensing positions and configuring the sensor array; The active acoustic wave detection module is used for: There are differences in the acoustic wave detection frequencies of the various risk features identified by the pipeline topology; Identifying the acoustic wave detection frequency and determining the pre-detection pipeline position; Based on the infrasound wave transmitter, active detection sound waves are emitted in a direction to perform infrasound wave detection on the pre-detection pipeline position.

2. A monitoring system for infrasound wave detection of pipeline pigging operations as claimed in claim 1, characterized in that: The pipeline topology marking module is used for: Traversing the risk features, and dividing them into a plurality of feature groups based on mutual influence, wherein the mutual influence includes occurrence synergy and timing influence; Based on the pipeline position, the multiple feature groups are associated with each other upstream and downstream, and the association relationship is determined to determine the upstream and downstream relationship.

3. The monitoring system for infrasound wave detection of pipeline pigging operations according to claim 1, characterized in that: The active acoustic wave detection module is also used for: Determine the initialization detection sound wave; Compensating the initialization detection sound wave as an active detection sound wave; Among them, the acoustic wave compensation methods include: Determine the multi-frequency wavelength, and compensate and mark the initialization detection sound wave as a type of active detection sound wave; A tracking medium is introduced and combined with the initialization detection sound wave as a second type of active detection sound wave.

4. The monitoring system for infrasound wave detection of pipeline pigging operations according to claim 1, characterized in that: The pipeline state feature determination module is used for: The acoustic wave decision module includes an active decision area and a passive decision area; receiving the first acoustic wave signal, and activating the active decision-making area to perform acoustic wave signal recognition and pipeline abnormality location; The second acoustic wave signal is received, and the passive decision area is activated to perform acoustic wave signal recognition and pipeline abnormality positioning, wherein the active decision area and the passive decision area are relatively independent.

5. The monitoring system for infrasound wave detection of pipeline pigging operations according to claim 1, characterized in that: The visual warning module is used for: Taking the risk level as the first priority feature and the affected scope as the second priority feature, the pipeline monitoring list is prioritized to determine the risk sequence; Performing homologous clustering on the pipeline status features to determine a clustering result; Based on the clustering results, the risk sequence is marked, and terminal visual early warning and pipeline operation and maintenance management are performed.

6. A monitoring method for infrasound detection of pipeline pigging operations, characterized in that: The method comprises: Obtain pipeline distribution information and construct a pipeline topology, wherein the pipeline topology is a spatial topology including multiple pipeline types in a preset space; Retrieve pipeline monitoring data of a preset time zone, mine risk features and mark the pipeline topology, wherein the risk features include risk location-risk type-risk level-risk frequency, and the risk features have an upstream and downstream relationship; Traversing the pipeline topology, configuring a sensor array in combination with the risk characteristics, and setting an acoustic wave detection frequency according to the risk characteristics and the pipeline topology, wherein the sensor array is an acoustic wave sensor type; Based on the sensor array, passive infrasound wave sensing is performed to determine a first sound wave signal; Based on the acoustic wave detection frequency, active acoustic wave detection is performed to determine a second acoustic wave signal; Combining the first acoustic wave signal and the second acoustic wave signal with an acoustic wave decision module to perform acoustic wave signal recognition and pipeline abnormality location, and determine pipeline status characteristics; Based on the pipeline status characteristics, a pipeline monitoring sheet is generated and a terminal visual early warning is performed; The configuring of the sensor array in combination with the risk characteristics includes: Traversing the risk characteristics to determine the infrasound wave generation characteristics of the risk event, wherein the infrasound wave generation characteristics include the propagation direction of the generated infrasound wave along the pipe wall; Determining an effective sensing position based on the infrasound wave generation characteristics; Traversing the valid sensing positions and configuring the sensor array; Based on the acoustic wave detection frequency, active acoustic wave detection is performed, including: There are differences in the acoustic wave detection frequencies of the various risk features identified by the pipeline topology; Identifying the acoustic wave detection frequency and determining the pre-detection pipeline position; Based on the infrasound wave transmitter, active detection sound waves are emitted in a direction to perform infrasound wave detection on the pre-detection pipeline position.

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