An underground pipeline location system based on active source HV detection method

Abstract

This invention discloses an underground pipeline location system based on the active source HV detection method, comprising: a signal generation unit, a signal acquisition unit, a data processing unit, and a pipeline location analysis unit. The signal generation unit applies a vibration signal at the pipeline outcrop location. The signal acquisition unit is a matrix observation structure composed of several detection nodes, used to acquire micro-motion data. The data processing unit performs windowing processing on the acquired micro-motion data from each node in the time domain and calculates the HV curve corresponding to each node after windowing processing. The pipeline location analysis unit performs curve feature clustering based on the HV curves corresponding to each node and analyzes the clustering results to obtain the pipeline direction. This system provides non-destructive location of pipelines with outcrops, solving the long-standing problem of the lack of effective pipeline detection methods in the field of pipeline detection.

Description

Technical Field

[0001] This invention belongs to the field of relationship detection technology, and particularly relates to an underground pipeline location system based on the active source HV detection method. Background Technology

[0002] With the acceleration of urban modernization, the number of buildings above ground is increasing, the types of underground pipelines are constantly expanding, and their distribution is becoming denser. Underground pipelines, as the "neural network" of a city, play a vital role in the normal operation of modern cities and the smooth functioning of people's lives. Currently, with the increasing popularity of "urban utility tunnel" construction, many large and medium-sized cities are paying close attention to the construction and management of underground pipelines.

[0003] Underground pipelines are diverse in type, with numerous branches of varying sizes, and are distributed in a chaotic and dense manner. Many urban residential areas were built a long time ago, and the pipelines, after years of construction, suffer from problems such as aging pipelines, coexistence of pipelines from different eras, coexistence of effective and abandoned pipelines, coexistence of pipelines made of different materials, lack of pipeline data, and discrepancies between theoretical and actual data. The main problems are as follows:

[0004] (1) Diversity and complexity of pipeline routes

[0005] While urban underground pipeline construction sometimes involves design plans, it is frequently constrained by various practical conditions during construction, such as slope and manholes. This gives pipeline builders considerable autonomy over the specific location of pipelines, resulting in discrepancies between the actual location and depth of the pipelines and the nominal location and depth indicated on the drawings. The routing and depth of underground pipelines are highly flexible, exhibiting diverse routing characteristics. This phenomenon is particularly common in older residential areas, where different types of pipelines are not constructed simultaneously, further increasing the complexity between them. This diversity in pipeline routing leads to significant uncertainty in the distribution, burial depth, and spatial intersections of pipelines, making pipeline combinations more complex and greatly increasing the difficulty of detection.

[0006] (2) Complex detection environment

[0007] Due to the complex urban environment, such as vehicles frequently parking on roads and obstructing pipeline maintenance wells, the detection work cannot be carried out normally; some wells have not been opened for many years and are severely corroded, while others are buried by asphalt pavement, making them very difficult to open, leaving safety hazards during and after the detection; there are many pedestrians in the community, especially children and the elderly, which poses a great safety hazard during the detection process.

[0008] On the other hand, the variability of geological conditions also brings many challenges to pipeline detection, such as pipeline detection in karst areas. Moreover, these factors can affect detection efficiency and must be taken into account in the detection process.

[0009] The aforementioned problems all have a serious impact on the detection of underground pipelines. During the construction of urban subways, and the construction, renovation, and expansion of residential areas, major accidents such as power outages, water outages, heating outages, and communication disruptions frequently occur. This can threaten people's lives, disrupt residents' normal lives, and hinder modern urban development. Therefore, strengthening the detection of underground pipelines has become the most pressing issue in urban underground pipeline construction.

[0010] Traditional pipeline detection methods have undergone decades of development and have achieved considerable engineering application scale. From technical principles to operational procedures, they are relatively mature and largely standardized, facilitating practical operation and management. However, we must also recognize their shortcomings. On the one hand, the development and updating speed of most traditional pipeline detection technologies is slow; hardware equipment and data processing and analysis methods have not been improved for decades. With the rapid development of urban construction and increasing environmental protection requirements, relying solely on traditional pipeline detection methods is no longer sufficient to meet the current needs of urban underground pipeline detection. Their defects and shortcomings have gradually become apparent, generally manifested in the following aspects:

[0011] (1) Limited detection depth: The burial depth of urban underground pipelines is widely distributed, ranging from 0.5m to 30m. Traditional detection methods such as ground penetrating radar have limited detection depth and cannot meet the detection requirements well.

[0012] (2) Poor detection accuracy: The data processing and data imaging technologies relied upon by traditional detection methods are outdated, and some methods are still stuck at the stage of viewing the original waveform, resulting in poor detection accuracy;

[0013] (3) Susceptible to interference: The complex environment of urban areas has many interference factors, such as strong electromagnetic interference, which causes data distortion, insufficient depth and reduced detection accuracy of conventional geophysical exploration methods such as electromagnetic method and gravity method. For example, the signal strength of very low frequency method is greatly affected by radio station.

[0014] (4) Severely limited by site conditions: The pipeline detection area is often located above busy urban main roads, and many pipeline detection methods have high requirements for site conditions. For example, the DC resistivity method requires good grounding conditions, and the reflection wave method requires sufficient working space. Neither of these methods can well meet the detection requirements of urban underground pipelines.

[0015] (5) It is destructive and not environmentally friendly: For example, the transmission wave method requires artificial seismic sources, drilling, etc. The drilling depth of geological boreholes is at least 10 meters, which will damage urban roads and may also damage existing underground pipelines. The resulting water outages, power outages, pipeline leaks, and even gas explosions will seriously affect the quality of life of residents and social security. In addition, continuous drilling operations will also bring a series of problems such as noise pollution and dust.

[0016] (6) Some types of pipelines remain very difficult to detect, and there is no particularly suitable method, such as small-diameter pipelines made of non-metallic materials. For these types of pipelines, on the one hand, various electromagnetic detection techniques are basically ineffective due to the material, and on the other hand, due to the small pipe diameter and usually shallow burial depth, passive source elastic wave methods cannot be located because of the superposition of direct waves and reflected signals. Summary of the Invention

[0017] The purpose of this invention is to overcome the problems of the prior art by disclosing an underground pipeline location system based on the active source HV detection method. This system enables non-destructive location of exposed pipelines and solves the long-standing problem of the lack of an effective pipeline detection method in the field of pipeline detection.

[0018] The objective of this invention is achieved through the following technical solution:

[0019] An underground pipeline location system based on active source HV detection method, the underground pipeline location system comprising: a signal generation unit, a signal acquisition unit, a data processing unit, and a pipeline location analysis unit; wherein,

[0020] The signal generating unit is used to apply a vibration signal at the pipeline outcrop location;

[0021] The signal acquisition unit is a matrix observation structure composed of several detection nodes, used to complete the acquisition of micro-motion data;

[0022] The data processing unit is used to perform windowing processing on the collected micro-motion data of each node in the time domain, and to calculate the HV curve corresponding to the data of each node after windowing processing.

[0023] The pipeline location analysis unit performs curve feature clustering based on the HV curves corresponding to each node, and obtains the pipeline route based on the clustering results.

[0024] According to a preferred embodiment, the signal generating unit includes a vibration generator, and the vibration generator is parallel to the vibration direction of the coupling surface vibration source of the pipeline protrusion, thereby realizing the generation of a shear wave vibration source signal by the vibration generator.

[0025] According to a preferred embodiment, when acquiring signal data, the signal acquisition unit is rotated as a whole with the pipeline outcrop as the origin, covering the expected possible route of the pipeline in each direction, and data acquisition is completed in each direction respectively.

[0026] According to a preferred embodiment, the signal acquisition unit includes several parallel survey lines, each survey line is provided with several detection nodes, and each detection node is provided with a digital seismograph.

[0027] According to a preferred embodiment, the signal acquisition unit includes 5 measuring lines with a spacing of 1m between adjacent measuring lines; each measuring line is provided with 10 detection nodes with a spacing of 1m between adjacent detection nodes.

[0028] According to a preferred embodiment, the digital seismograph has a built-in three-component seismic sensor, a BeiDou and / or GPS timing and positioning module, an electronic compass, an attitude sensor, a Zigbee module, a Bluetooth module, and a rechargeable lithium battery.

[0029] According to a preferred embodiment, the data processing unit performs windowing processing on the micro-motion data of each node in the time domain, specifically including: selecting the time window width based on the signal spectrum, calculating the average signal amplitude STA in each time window and the average value LTA of the entire data segment, obtaining the ratio STA / LTA, and removing signals with a ratio greater than a preset value.

[0030] According to a preferred embodiment, the HV curves corresponding to the data of each node after windowing are obtained by calculating the following formula.

[0031] The formula for calculating the HV spectral ratio is:

[0032]

[0033] Among them, P UD (ω) is the power spectrum of the vertical component of the small motion, P NS (ω) and P EW (ω) represents the power spectra of two mutually orthogonal horizontal components.

[0034] According to a preferred embodiment, based on the calculated HV curves of each node, the peak frequency and peak magnitude of the HV curves are selected as features for feature extraction, and the Kmeans clustering algorithm is used for clustering. The clustering results are analyzed to obtain the pipeline route.

[0035] The aforementioned main solution of the present invention and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed by the present invention. Those skilled in the art, after understanding the solution of the present invention, will realize that there are many combinations based on existing technology and common knowledge, all of which are technical solutions to be protected by the present invention, and will not be exhaustively listed here.

[0036] The beneficial effects of this invention are as follows: This invention's underground pipeline positioning system introduces traditional micro-motion HV detection technology into the field of pipeline detection through improvements, and specifically constructs a new non-destructive underground pipeline routing positioning technology suitable for exposed pipelines, which is applicable to pipelines of various materials. After testing, the technology has shown good results and fills the gap in this type of pipeline detection technology in the field of pipeline detection. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the underground pipeline positioning system of the present invention;

[0038] Figure 2 This is a schematic diagram of the underground pipeline positioning system of the present invention;

[0039] Figure 3 This is a schematic diagram of the arrangement structure of the signal generation unit in the underground pipeline positioning system of the present invention;

[0040] Figure 4 This is a schematic diagram of the signal acquisition unit in the underground pipeline positioning system of the present invention;

[0041] Figure 5 It is a data windowing and noise reduction image;

[0042] Figure 6 This is a partial diagram of the original signal waveforms at certain locations;

[0043] Figure 7 This is a partial HV curve graph;

[0044] Figure 8 This is a clustering result diagram of the HV curves of the array nodes in the test case;

[0045] Figure 9 This is a graph showing the clustering analysis results of the HV curve features in the test cases;

[0046] Figure 10 This is a diagram showing the pipeline location results in the test case. Detailed Implementation

[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0048] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0049] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. In addition, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0050] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0051] Furthermore, it should be noted that, unless otherwise specified, the structures, connections, positions, power sources, etc. involved in this invention are all things that a person skilled in the art can know without creative effort based on the prior art.

[0052] Example 1:

[0053] refer to Figure 1 and Figure 2 As shown in the figure, an underground pipeline location system based on the active source HV detection method is illustrated. The underground pipeline location system includes: a signal generation unit, a signal acquisition unit, a data processing unit, and a pipeline location analysis unit.

[0054] The signal generation unit is used to apply a vibration signal at the pipeline outcrop location; the signal acquisition unit is a matrix observation structure composed of several detection nodes, used to complete the acquisition of micro-motion data; the data processing unit is used to perform windowing processing on the acquired micro-motion data of each node in the time domain, and calculate the HV curve corresponding to the data of each node after windowing processing; the pipeline positioning analysis unit performs curve feature clustering based on the HV curves corresponding to each node, and obtains the pipeline direction based on the clustering results.

[0055] Preferably, the signal generating unit includes a vibration generator, and the vibration generator is parallel to the vibration direction of the coupling surface vibration source of the pipeline protrusion, with reference to... Figure 3As shown, this enables the generation of shear wave source signals by the vibration generator.

[0056] Specifically, the parameters of the vibration generator as an artificial vibration source can be set as shown in the table below.

[0057] Table 1 Vibration source parameters

[0058] Parameter name numerical values signal frequency 20~100Hz Coupling method Cut Sampling frequency 50MHz~200MHz

[0059] Preferably, refer to Figure 4 As shown, the signal acquisition unit includes several parallel survey lines, each survey line is equipped with several detection nodes, and each detection node is equipped with a digital seismograph.

[0060] In this embodiment, the signal acquisition unit includes 5 measurement lines with a spacing of 1m between adjacent measurement lines; each measurement line is equipped with 10 detection nodes with a spacing of 1m between adjacent detection nodes.

[0061] Preferably, the digital seismograph has a built-in three-component seismic sensor, a BeiDou and / or GPS timing and positioning module, an electronic compass, an attitude sensor, a Zigbee module, a Bluetooth module, and a rechargeable lithium battery.

[0062] In this embodiment, the seismograph is an IGU-BD3C-5 integrated, high-precision, wideband digital seismograph. This seismograph can operate continuously for more than 30 days without any external power supply. Its main performance indicators are shown in Table 2. Data is collected independently at each observation point, and time synchronization between instruments is automatically achieved by receiving GPS satellite signals. The instrument sampling frequency is set to 500Hz.

[0063] Table 2 Main performance indicators of the seismograph

[0064]

[0065] When acquiring signal data, the signal acquisition unit is rotated as a whole, with the pipeline outcrop as the origin, to cover the expected possible route of the pipeline in each direction and complete the data acquisition in each direction.

[0066] Specifically, the data acquisition steps performed by the signal acquisition unit may be as follows:

[0067] (1) Before data acquisition, ensure that the seismic recorder has sufficient power and memory card capacity, that the GPS signal is stable, that time calibration is completed, and that the instrument is placed in a horizontal position.

[0068] (2) Before data collection, use a high-precision mapping system to accurately mark the points and strictly follow the placement of the micro-motion array according to the observation system.

[0069] (4) After the instruments at each node of the array are placed and it is ensured that there are no major noise sources in the surrounding environment, the vibration source is started.

[0070] (5) After the artificial vibration source is turned on, wait 30 seconds. After the vibration source-pipeline-stratum system enters steady-state vibration, start data acquisition. The data acquisition time for a single measuring point shall not be less than 20 minutes.

[0071] (6) During the data acquisition process, the data acquisition personnel should monitor the working status (signal lights) of the seismic recorder in real time;

[0072] (7) After the observation is completed, check the collected data and formulate reasonable data processing measures.

[0073] (8) Using the pipeline outcrop as the origin, rotate the array as a whole and repeat the above measurement process until all possible directions of the pipeline are covered.

[0074] Preferably, the data processing unit performs windowing processing on the micro-motion data of each node in the time domain, which includes: selecting the time window width based on the signal spectrum, calculating the average signal amplitude STA in each time window and the average value LTA of the entire data segment, obtaining the ratio STA / LTA, and removing signals with a ratio greater than a preset value.

[0075] Specifically, the field data is windowed in the time domain. The window width is determined based on the signal spectrum, typically 20 to 50 times the main period of the signal. The mean signal amplitude (STA, short-time mean) within each time window and the mean amplitude (LTA, long-time mean) for the entire data segment are calculated. The ratio STA / LTA measures the relative peak level of the signal segment within each time window. Signals with a ratio exceeding 3 are generally considered short-time interference and their data within that window (e.g., ...) need to be removed. Figure 5 As shown, the signal within the blank window is the interference signal that needs to be eliminated. The original waveforms of the measured micro-motion signals at some locations are shown below. Figure 6 As shown, the HV curves for some locations are as follows: Figure 7 As shown.

[0076] Preferably, after data windowing processing, the HV curve corresponding to each node's data is calculated using the following formula. Some measured HV curves are shown below. Figure 7 As shown.

[0077] The formula for calculating the HV spectral ratio is:

[0078]

[0079] Among them, P UD (ω) is the power spectrum of the vertical component of the small motion, P NS (ω) and P EW (ω) represents the power spectra of two mutually orthogonal horizontal components.

[0080] Prior to this, the pipeline location analysis unit selects the peak frequency and peak value of the HV curves as features based on the calculated HV curves of each node for feature extraction, and uses the Kmeans clustering algorithm to perform clustering, and analyzes the clustering results to obtain the pipeline route.

[0081] The basic idea behind using HV curves for pipeline location is as follows: HV curves are calculated using the vibration energy of the medium below the measuring point and are strongly correlated with the geological structure properties. Generally, information such as geological interfaces can be obtained through HV curve characteristic analysis. When an artificial vibration source is placed at the pipeline outcrop, the vibration energy will mainly propagate along the underground pipeline, inevitably affecting the signal received by the entire array. The changes in signal characteristics at the acquisition nodes located above the pipeline in the array will be the most significant. Figure 2 As shown. Therefore, the positional relationship between the node and the pipeline can be obtained from the HV curve features extracted from each acquisition node.

[0082] Specifically, in this embodiment, the pipeline location analysis unit uses clustering methods from artificial intelligence analysis technology to classify and identify the HV curve features of each node. Clustering methods divide a dataset into different classes or clusters according to a specific criterion, maximizing the similarity of data within the same cluster and maximizing the differences between data in different clusters. This ensures that data of the same class are grouped together as much as possible after clustering, while data of different classes are separated as much as possible. The following steps are defined to meet the HV curve classification requirements:

[0083] 1. Data preparation: Calculate the HV curve for each node.

[0084] 2. Feature selection: Select the peak frequency and peak magnitude of the HV curve as features.

[0085] 3. Feature extraction: Extracting features.

[0086] 4. Clustering: Clustering is performed using the K-means clustering algorithm.

[0087] 5. Clustering result evaluation: Analyze the clustering results to obtain the pipeline route.

[0088] This invention's underground pipeline positioning system introduces traditional micro-motion HV detection technology into the field of pipeline detection through improvements, and specifically constructs a new non-destructive underground pipeline routing positioning technology suitable for exposed pipelines. This technology is applicable to pipelines of various materials, and after testing, it has shown good results, filling the gap in this type of pipeline detection technology in the field of pipeline detection.

[0089] Test Cases

[0090] Site information: A PVC drainage pipeline with a known orientation was selected as the detection target.

[0091] Array layout: A matrix observation system is adopted. Due to the limitations of the test area, the number of nodes is reduced to 6*4 with a spacing of 1m. To simplify the test process, the test line C is selected to be laid out along the actual route of the pipeline.

[0092] The HV curve results of the probe node are as follows Figure 8 As shown in the figure. The clustering analysis results of the HV curve features are as follows. Figure 9 The figure shows the separation degree of the two characteristic classes in the parameter space.

[0093] Pipeline location analysis:

[0094] like Figure 10 As shown, based on the clustering results, all node HV curves were divided into two categories according to their morphological characteristics, and they showed good separation in the parameter space. All six points in category one originated from survey line C. Therefore, it was determined that the nodes on survey line C are located directly above the pipeline, and the pipeline direction is the same as that of survey line C. This determination is consistent with the actual setup.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An underground pipeline location system based on active source HV detection method, characterized in that, The underground pipeline positioning system includes: a signal generation unit, a signal acquisition unit, a data processing unit, and a pipeline positioning analysis unit; wherein, The signal generating unit is used to apply a vibration signal at the pipeline outcrop location; The signal acquisition unit is a matrix observation structure composed of several detection nodes, used to complete the acquisition of micro-motion data; The data processing unit is used to perform windowing processing on the collected micro-motion data of each node in the time domain, and to calculate the HV curve corresponding to the data of each node after windowing processing. The pipeline location analysis unit performs curve feature clustering based on the HV curves corresponding to each node, and obtains the pipeline route based on the clustering results.

2. The underground pipeline positioning system as described in claim 1, characterized in that, The signal generation unit includes a vibration generator, and the vibration generator is parallel to the vibration direction of the coupling surface vibration source of the pipeline protrusion, thereby realizing the generation of shear wave vibration source signal by the vibration generator.

3. The underground pipeline positioning system as described in claim 1, characterized in that, When acquiring signal data, the signal acquisition unit is rotated as a whole, with the pipeline outcrop as the origin, to cover the expected possible route of the pipeline in each direction and complete the data acquisition in each direction.

4. The underground pipeline positioning system as described in claim 1, characterized in that, The signal acquisition unit includes several parallel survey lines, each survey line is equipped with several detection nodes, and each detection node is equipped with a digital seismograph.

5. The underground pipeline positioning system as described in claim 4, characterized in that, The signal acquisition unit includes 5 measurement lines with a spacing of 1m between adjacent measurement lines; each measurement line is equipped with 10 detection nodes with a spacing of 1m between adjacent detection nodes.

6. The underground pipeline positioning system as described in claim 4, characterized in that, The digital seismograph is equipped with a three-component seismic sensor, a BeiDou and / or GPS timing and positioning module, an electronic compass, an attitude sensor, a Zigbee module, a Bluetooth module, and a rechargeable lithium battery.

7. The underground pipeline positioning system as described in claim 4, characterized in that, The data processing unit performs windowing processing on the micro-motion data of each node in the time domain, specifically including: The time window width is selected based on the signal spectrum. The mean signal amplitude STA in each time window and the mean LTA of the entire data segment are calculated to obtain the ratio STA / LTA. Signals with a ratio greater than a preset value are then removed.

8. The underground pipeline positioning system as described in claim 7, characterized in that, The HV curves corresponding to the data of each node after windowing are obtained by calculating the following formula. The formula for calculating the HV spectral ratio is: Among them, P UD (ω) is the power spectrum of the vertical component of the small motion, P NS (ω) and P EW (ω) represents the power spectra of two mutually orthogonal horizontal components.

9. The underground pipeline positioning system as described in claim 8, characterized in that, The pipeline location analysis unit extracts features by selecting the peak frequency and peak value of the HV curves as features based on the calculated HV curves of each node, and performs clustering using the Kmeans clustering algorithm. The clustering results are then analyzed to obtain the pipeline route.

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