A method of non-invasively detecting pipe deposits using acoustic arrays

Abstract

The application discloses a kind of acoustic wave array non-invasive detection pipeline deposit method, belong to acoustic wave detection technical field.The application of a kind of acoustic wave array non-invasive detection pipeline deposit method, comprising step one, sequentially install several sensor groups on the pipeline to be detected;Step two, obtain the historical flow rate data, historical sound velocity data of each detection point of the pipeline to be detected under the condition of no deposition by sensor group;Step three, according to each sensor group real-time monitoring flow rate data of each detection point, and compare real-time monitoring flow rate data with historical flow rate data, judge the deposition state of each detection point.The application adopts the acoustic wave sensor array distributed along the circumferential and axial direction of pipeline, realizes the synchronous, in situ, non-invasive and real-time high-precision measurement of deposition thickness, three-dimensional spatial distribution form and medium flow rate under the premise of not interfering with pipeline operation.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for non-invasively detecting pipeline deposits by using acoustic wave array, and belongs to the technical field of acoustic wave detection. BACKGROUND

[0002] Oil and gas pipelines are the lifelines of national energy transportation, and their operating conditions directly affect the stability and overall safety of energy supply. During long-term service, waxes, hydrates, salts, and sulfides inevitably deposit inside the pipelines. These deposits have multiple hazards: they significantly reduce transportation efficiency, cause pipeline blockage, accelerate pipeline corrosion, increase the risk of maintenance equipment jamming, and in severe cases, can directly lead to medium leakage or even catastrophic explosion accidents. Therefore, implementing regular and effective pipeline deposit detection and scientifically determining the cleaning cycle are essential for preventing accidents and ensuring the efficient and safe operation of pipelines.

[0003] The core challenge of current pipeline deposit detection and evaluation lies not only in the diversity of pipeline operating conditions and the complexity of deposit composition, but also in the deep dynamic coupling relationship between deposit formation and flow velocity distribution in the pipeline. Experience shows that low flow velocity areas, including the bottom of the pipeline, the downstream of the bend, and the expansion section of the pipe diameter, are high-risk areas for deposition. Accurate understanding of the flow velocity distribution inside the pipeline is the fundamental prerequisite for predicting these deposition hotspots. In turn, once the deposits form, they will significantly change the flow field: the deposition layer occupies the effective flow area, increases the pipe wall roughness, causes flow velocity distribution distortion, and significantly increases the flow resistance, i.e., the head loss. Therefore, to accurately assess the current actual transportation capacity of the pipeline and predict future flow velocity trends, accurate measurement of deposit thickness and volume is a fundamental prerequisite.

[0004] There is a significant mutual confirmation relationship between deposit detection results and flow velocity measurement data, which is crucial for optimizing detection strategies. Specifically, when the flow velocity measurement value of a certain section of the pipeline shows abnormal changes, such as deviating from the design expectation or the upstream and downstream values exceeding a certain threshold, it may indicate that there are deposits at that location. At this time, simultaneous deposit detection confirms the presence of significant deposits at that location, which can determine that the deposits are the main cause of the flow velocity anomaly, thereby effectively excluding the interference of instrument failure or valve misoperation and other factors. The effectiveness of this mutual confirmation relationship highly depends on the spatial resolution accuracy of deposit detection.

[0005] In the field of oil and gas pipeline transportation, the accumulation of pipe wall deposits and the resulting reduction of effective flow area and increase of pipe wall roughness are key factors that directly lead to a dramatic increase in flow resistance, accelerate the process of local corrosion, and significantly increase the risk of pipeline blockage and even failure accidents. However, the traditional detection techniques have inherent systemic defects, especially in obtaining real-time images of the spatial distribution of deposits:

[0006] Mainstream technologies such as ultrasonic method, radiographic method, eddy current detection method, and weak magnetic detection method can only perform point or line scanning detection on local deposits and cannot provide real-time, high-precision three-dimensional deposition images of the entire pipeline cross-section. Flow rate measurement requires the installation of special instruments. This functional separation not only significantly increases the overall detection cost, but also isolates the deposition state and flow rate information, making it impossible to analyze them in real time. The industry has long been trapped in the dilemma of not knowing the flow rate when detecting deposits and being unable to detect deposits when measuring flow rate. Many high-precision detection methods, such as radiographic imaging or internal detection, require the pipeline to be shut down or to be subjected to destructive operations, making it impossible to achieve real-time and continuous detection and imaging in the running state. Existing technologies generally cannot accurately analyze complex and variable deposition patterns, especially asymmetric accumulation or thin layer deposition. For example, conventional ultrasonic detection can only obtain limited data from a single point at a time, requiring multiple repeated operations and relying on the experience of the operator to infer the status of the entire pipeline cross-section. In the operation, uneven pressure on the probe can easily introduce human error, and the results cannot truly reflect the three-dimensional distribution of the pipe circumferential deposition. At the same time, ultrasonic signals are easily disturbed by steel pipe wall propagation, severely affecting the signal-to-noise ratio of the detection results. Radiographic detection technology is subject to strict radiation safety restrictions and cannot be used frequently for real-time imaging. Eddy current technology is ineffective for non-conductive deposits, and weak magnetic method is only sensitive to ferromagnetic debris, both of which do not have comprehensive deposition imaging capabilities. These technical defects in existing technologies have led to a lack of real-time and accurate understanding of the spatial distribution of deposits in the industry, forcing the industry to frequently schedule shutdowns for maintenance, resulting in huge economic losses of billions of yuan per year and potential safety hazards. SUMMARY

[0007] The present application aims to solve the problems existing in the prior art and provides a method for non-invasive detection of pipeline deposits using an acoustic wave array. The method uses a distributed acoustic wave sensor array along the circumferential and axial directions of the pipeline to achieve simultaneous, in-situ, non-invasive, and real-time high-precision measurement of deposit thickness, three-dimensional spatial distribution, and medium flow rate without interfering with the operation of the pipeline.

[0008] The technical solution provided by the present application to solve the above technical problems is a method for non-invasive detection of pipeline deposits using an acoustic wave array, comprising:

[0009] Step one, install several sensor groups on the detected pipeline in turn, the sensor group includes several main probes arranged in a circumferential array and installed vertically, a pair of obliquely inserted ultrasonic probes;

[0010] Step two, obtain historical flow rate data and historical sound speed data of each detection point of the detected pipeline under the non-deposition working condition through the sensor group;

[0011] Step three, monitor the flow rate data of each detection point in real time according to each sensor group;

[0012] Step four, compare the real-time monitored sound speed data with the historical sound speed data, locate the deposition position through the coincident path sound speed data change and judge the deposition state of each monitoring point;

[0013] If the flow rate deviation is greater than or equal to 5%, the sound speed data of each ultrasonic sensor is obtained in real time through the ultrasonic sensor, and then the sound speed data of each ultrasonic sensor is compared with the historical sound speed data,

[0014] If each sound speed deviation is less than 5%, the detection point is in a non-deposition state;

[0015] If there is a sound speed deviation greater than or equal to 5%, the deposition layer thickness of the detection point is calculated through the acoustic inversion algorithm, and compared with the preset minimum pigging thickness value;

[0016] If the deposition layer thickness is greater than or equal to the minimum pigging thickness value, the detection point is seriously blocked and needs to be cleaned immediately;

[0017] If the deposition layer thickness is less than the minimum pigging thickness value, the state of the adjacent detection point of the detection point is determined, if the adjacent detection point of the detection point is determined to be slightly deposited or seriously blocked, the detection point is determined to be slightly deposited and pre-alarm observation; otherwise, it is determined to be in a non-deposition state;

[0018] If the flow rate deviation is less than 5% to avoid misjudgment of the detection result, the sound speed data of each ultrasonic sensor is obtained in real time through the ultrasonic sensor, and then the sound speed data of each ultrasonic sensor is compared with the historical sound speed data;

[0019] If each sound speed deviation is less than 5%, the detection point is in a non-deposition state;

[0020] If there is a sound speed deviation greater than or equal to 5%, the detection point is slightly deposited and pre-alarm observation, and the deposition layer thickness of the detection point is calculated through the acoustic inversion algorithm.

[0021] Further technical solutions are that the acquisition process of the sound velocity data is that when the ultrasonic sensor transmits a main sound wave pulse, the symmetric two-side sensors synchronously transmit a reverse signal, the strong interference signal propagating along the steel pipe wall at high speed is actively eliminated by using the coherent cancellation principle, and active cancellation is realized, so that the sound velocity data is obtained.

[0022] Further technical solutions are that the acquisition of the flow velocity data is based on a Doppler frequency shift method, which requires that there are suspended impurities in the fluid medium in the pipeline, which are sufficient to produce scattering; and a pair of oblique insertion ultrasonic probes installed in the axial sensor array are used to implement the method.

[0023] Further technical solutions are that the specific process of calculating the thickness of the deposited layer of the detection point in step four through the acoustic inversion algorithm is that:

[0024] Step 1, the system controls the M ultrasonic sensors in the circumferential array to be used as a transmission source in turn, obtains MxM acoustic measurement data of transmission-reception paths, and constructs an MxM measurement matrix S through the original measurement data, obtains the signal attenuation of each path through feature extraction calculation, and then converts M 2 x1 sound velocity observation vector b t and the attenuation observation vector b s .

[0025] Step 2, discretize the pipeline cross section into N grids, and based on the acoustic measurement data in step 1, construct a sound velocity inversion system A x t = b t and an attenuation coefficient inversion system A x s = b s .

[0026] Step 3, set the initial slowness distribution matrix x t0 and the initial attenuation coefficient distribution matrix x s0 of the pipeline cross section, substitute them into the sound velocity inversion system, obtain the residual Δb between the initial sound velocity observation vector b t0 and the sound velocity observation vector b t , superimpose the slowness correction vector Δx t and the current slowness field x t0 to update the model as x t1 =x t0 +Δx t ; repeat the above iteration process until the residual is less than a set threshold, and the sound velocity matrix can be obtained by using the final slowness field; and the same solving method is used to solve the attenuation coefficient matrix;

[0027] Step 4: Post-process the sound velocity matrix and attenuation coefficient matrix, filter and process the signal to generate the pipe sound velocity distribution and attenuation coefficient image;

[0028] Step 5: Based on the sound velocity distribution and attenuation coefficient image of the pipeline, perform threshold segmentation and morphological analysis on the image to identify the sediment interface and determine the thickness of the sediment layer.

[0029] A further technical solution is that the observation matrix in the sound speed inversion system and the attenuation coefficient inversion system... A It consists of M sub-matrices stacked vertically, with each sub-matrix corresponding to a emission source.

[0030] A further technical solution is that the observation matrix A The specific form is as follows:

[0031]

[0032] In the formula: A The observation matrix; For the source of the emission k Submatrix of time.

[0033] A further technical solution is that the specific form of the sub-matrix is:

[0034]

[0035] In the formula: For the source of the emission k At that time, the first k The sensor to the first i The acoustic wave path of the sensor in the first... j The traversal length in each grid; For the source of the emission k Submatrix of time.

[0036] A further technical solution is that the measurement matrix is ​​in the following specific form:

[0037]

[0038] In the formula: S This is the measurement matrix.

[0039] The beneficial effects of this invention are:

[0040] ① This invention integrates sediment spatial distribution imaging and flow velocity measurement functions into the same acoustic array platform, overcoming the limitations of functional separation and data isolation in traditional technologies.

[0041] ②Adopting active cancellation technology, through the transmission of opposite phase signals by adjacent symmetrical sensors, the strong interference signals propagating along the steel pipe wall are effectively suppressed, and the problem of low signal-to-noise ratio caused by pipe wall interference in traditional ultrasonic detection is overcome. Combined with the influence of deposition on sound velocity and attenuation coefficient, the sound velocity change in the same path is preliminarily judged, the deposition position is quickly located, and the attenuation coefficient distribution of the pipeline cross section or even three-dimensional space is reconstructed with high precision, so as to realize high-resolution imaging of the thickness, shape and position of the deposition.

[0042] ③The whole measurement process is carried out outside the pipe, without the need of pipe shutdown, opening or inserting internal devices, and it is a truly in-situ, non-invasive online monitoring method.

[0043] ④The present application establishes a logical mutual verification mechanism of "deposition-flow rate" data, explores the data relationship between flow rate and deposition, and the high-resolution deposition distribution map and accurate flow rate value output by the system at the same time are no longer isolated information, but key evidence that can be mutually verified and interpreted. This mechanism can effectively distinguish between "deposition blockage", "instrument failure" and other different scenarios, greatly improving the reliability of the diagnostic conclusion, and providing quantitative and visual scientific decision-making basis for the start of pigging operation, priority judgment and pipeline integrity management.

[0044] ⑤Through early and accurate identification of deposition risk, unnecessary preventive shutdown and pigging can be avoided, pigging cycle can be optimized, and operating cost can be significantly reduced. At the same time, through real-time and continuous monitoring of the pipeline state, safety accidents such as blockage, corrosion aggravation and leakage caused by deposition accumulation can be effectively prevented, the stability and safety of energy transportation can be ensured, and the social and economic benefits are huge. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 It is a schematic diagram of circumferential array acoustic wave propagation of the emission source;

[0046] Figure 2 It is a schematic diagram of the effect of active cancellation technology;

[0047] Figure 3 It is a schematic diagram of the installation structure of a pair of obliquely inserted ultrasonic probes;

[0048] Figure 4 It is a three-dimensional view of the installation of the sensor group on the pipeline. DETAILED DESCRIPTION

[0049] The technical solutions of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0050] The present invention provides a non-invasive method for detecting pipe deposits using an acoustic array, comprising the following steps:

[0051] Step 1: Install several sensor groups sequentially on the pipeline to be inspected, such as... Figure 4 As shown, its sensor groups are numbered A0, A... 1....... A （M-1） The sensor group includes several main probes arranged in a circumferential array and installed vertically, and a pair of obliquely inserted ultrasonic probes.

[0052] Among them, such as Figure 1 As shown, several main probes (ultrasonic sensors) are evenly arranged M times along the circumference of the outer wall of the pipe, and are numbered a. i0 a i1....... a i（M-1） The angle between adjacent sensors is 2π / M, used for transmitting signal s(t) and receiving reflected signal;

[0053] As can be seen from the circumferential array layout diagram, when a i0 When the core emission source emits the main acoustic pulse s(t), it will simultaneously generate acoustic waves that radiate into the pipe and acoustic waves that propagate along the pipe wall.

[0054] Through active phase cancellation technology, at the central sensor a i0 When the main acoustic pulse is emitted, a pair of sensors a on its symmetrical sides are controlled. i1 and a i（M-1） Synchronously transmitting inverse signals suppresses interference signals propagating along the steel pipe wall, significantly improving the signal-to-noise ratio. From Figure 2 The actual cancellation result signal can be observed. After cancellation, the amplitude of the interference signal is reduced, which indicates that the acoustic signal transmitted along the pipe wall has been significantly reduced and suppressed, thus creating conditions for subsequent sediment detection.

[0055] like Figure 3 As shown, a pair of angled ultrasonic probes are positioned opposite each other on the upper and lower sides of the pipe, specifically for flow velocity measurement, and the sound waves they generate are incident obliquely at a specific angle.

[0056] Its main probe acquires flow velocity data using the Doppler frequency shift method;

[0057] When using the Doppler frequency shift method, only two angled probes arranged opposite each other are used for measurement, employing a one-to-one transmit / receive mode.

[0058] Step 2: Obtain historical flow velocity and historical sound velocity data at each detection point of the pipeline under non-deposition conditions using the sensor array;

[0059] The historical flow rate data are measured by a pair of oblique ultrasonic probes based on the principle of Doppler shift method; and the historical sound velocity data are obtained by measuring the propagation time of sound waves in the fluid through a vertically installed straight probe and then being calculated and calibrated.

[0060] Step three, select the flow rate detection method and use the corresponding oblique probe pair to monitor the flow rate data in the pipeline in real time; after the ultrasonic signal attenuation disappears, the deposition detection is started, and the system switches different straight probes in the circumferential array as the emission source (from a i0 to a i（M-1） ) in turn and performs active cancellation in step one. The system timing is designed based on the speed difference of different sound wave paths. Since the speed of sound waves propagating along the metal pipe wall is much higher than that of sound waves propagating in the medium inside the pipe, the system control logic is that after performing the active cancellation operation, the probe pair participating in the emission of the reverse signal is immediately converted to the receiving state. This ensures that the suppression of the pipe wall interference signal that arrives first has been completed before the effective target signal reaches the receiving probe, thereby optimizing and isolating the signal acquisition time window.

[0061] Step four, monitor the sound velocity data of each detection point in real time according to each sensor group, compare the real-time monitored sound velocity data with the historical sound velocity data, locate the deposition position through the sound velocity data change of the coincident path, and judge the deposition state of each detection point;

[0062] If the flow rate deviation is greater than or equal to 5% (this threshold value can be adjusted according to the characteristics of the pipeline and the measurement accuracy), the sound velocity data of each ultrasonic sensor are obtained in real time by the ultrasonic sensor, and then the sound velocity data of each ultrasonic sensor are compared with the historical sound velocity data,

[0063] If each sound velocity deviation is less than 5%, the detection point is in the no-deposition state;

[0064] If there is a sound velocity deviation greater than or equal to 5%, the deposition layer thickness of the detection point is calculated by the acoustic inversion algorithm, and compared with the preset minimum pigging thickness value;

[0065] If the deposition layer thickness is greater than or equal to the minimum pigging thickness value, the detection point is in the serious blockage state, and the pipeline cleaning operation needs to be performed immediately;

[0066] If the deposition layer thickness is less than the minimum pigging thickness value, the state of the adjacent detection point of the detection point is determined. If the adjacent detection point of the detection point is determined to be in the slight deposition or serious blockage state, the detection point is determined to be in the slight deposition state and a warning observation is performed; otherwise, the detection point is determined to be in the no-deposition state;

[0067] If the flow rate deviation is less than 5%, the detection result is avoided to be misjudged, then the sound velocity data of each ultrasonic sensor is acquired in real time through the ultrasonic sensor, and the sound velocity data of each ultrasonic sensor is compared with historical sound velocity data;

[0068] If each sound velocity deviation is less than 5%, the detection point is in a non-deposition state;

[0069] If there is a sound velocity deviation greater than or equal to 5%, the detection point is in a secondary deposition and blockage state, and the deposition layer thickness of the detection point is calculated through an acoustic inversion algorithm.

[0070] The specific process of calculating the deposition layer thickness of the detection point through the acoustic inversion algorithm is as follows:

[0071] Step 1, selecting the probe receiving path directly opposite the emission source as the reference data; the system controls the M ultrasonic sensors in the circumferential array to be used as emission sources in turn. When a certain sensor is used as an emission source, all M sensors (including itself) in the array are used as receivers, and the acoustic wave signals are recorded synchronously. Thus, for a complete measurement period, MxM acoustic wave measurement data of the "emission-receiving" path can be obtained. Through all effective path collection, an MxM measurement matrix S is constructed using the original measurement data, the acoustic travel time data and amplitude attenuation data of each path are obtained through feature extraction calculation, and an MxM measurement matrix S is constructed 2 The sound velocity observation vector b t and the attenuation observation vector b s are constructed, and each element of the vector represents the sound velocity measurement and attenuation measurement value of the ith path; the specific form of the measurement matrix is as follows:

[0072]

[0073] Step 2, discretizing the pipeline cross section into N grids, the medium in the grid is uniform, and the signal parameters are the same. Based on the relationship between sound velocity and travel time data, a large number of division operations are avoided, the sound velocity reciprocal slowness is taken to construct a sound velocity inversion system A x t = b t and an attenuation coefficient inversion system A x s = b s is constructed; wherein x t is a slowness distribution matrix; x s is an attenuation coefficient distribution matrix;

[0074] The measurement matrix A in the sound velocity inversion system and the attenuation coefficient inversion system is vertically stacked by M sub-matrices, and each sub-matrix corresponds to an emission source:

[0075]

[0076] submatrix :

[0077] When the k When a sensor is used as a transmission source, its corresponding sub-matrix The specific form is as follows:

[0078]

[0079] Indicates the source of the emission is k At that time, the first k The sensor to the first i The acoustic wave path of the sensor in the first... j The traversal length in each grid; For the source of the emission k Submatrix of time.

[0080] Step 3: Set the initial slowness distribution matrix x of the pipe cross-section t0 Substitute the vectors into the sound speed inversion system to obtain the initial sound speed observation vector b. t0 With the sound speed observation vector b t The residual Δb reflects the difference between the current sound speed model and the actual model on different paths. A residual Δb > 0 indicates that the model predicts the sound speed too fast, or too slow, resulting in too short a computation time, meaning the actual path is curved and the path length increases. A residual Δb < 0 indicates that the model predicts the sound speed too slow, or too slow, resulting in too long a computation time. This situation may occur due to sound speed calibration errors, therefore, it is necessary to accurately measure and determine the reference sound speed value in the pipe and air at the beginning of the detection. If the residual Δb = 0, it indicates that the current sound speed model and the actual model are perfectly matched.

[0081] When the residual is too large, a correction vector is introduced for a second iteration. The slowness correction vector is derived from... A Δx t The solution for Δb is often ill-conditioned because the number of probes is less than the number of pixels, resulting in unstable solutions. Therefore, an iterative inversion algorithm is needed to solve for Δx. t To achieve high-precision inversion, the least squares QR (LSQR) method is used. Its core idea is to "allocate" or "backproject" the residuals Δb on different paths back to these pixels according to the proportion of the ray's length passing through each pixel, serving as the basis for adjusting their slowness. Pixels that pass through longer paths have a greater impact on the ray's trajectory and receive a larger correction.

[0082] The slowness correction vector Δxt with the initial slowness distribution matrix x t0 The superposition is carried out, and the model is updated to x t1 = x t0 + Δx t The iteration process is repeated until the residual is less than a set threshold, and the sound velocity matrix can be obtained by derivation of the final slowness field.

[0083] The attenuation coefficient matrix solving process is the same as above.

[0084] Step 4, post-processing is carried out on the sound velocity matrix and the attenuation coefficient matrix, and after filtering and processing of the signal, a pipeline sound velocity distribution and an attenuation coefficient image are generated;

[0085] Step 5, according to the pipeline sound velocity distribution and the attenuation coefficient image, threshold segmentation and morphological analysis are carried out on the image, and a sediment interface is identified to determine the thickness of the sediment layer.

[0086] The above description does not limit the present application in any form, although the present application has been disclosed by the above examples, however, it is not intended to limit the present application, any skilled person in the art, without departing from the technical solution of the present application, can make some changes or modifications to the above disclosed technical content as equivalent examples, but as long as it does not deviate from the technical solution of the present application, any simple modification, equivalent change and modification made to the above examples according to the technical essence of the present application, are within the scope of the technical solution of the present application.

Claims

1. A method for non-invasive detection of pipe deposits using an acoustic array, characterized in that, Includes the following steps: Step 1: Install several sensor groups sequentially on the pipeline to be inspected. The sensor group includes several main probes arranged in a circumferential array and installed vertically, and a pair of obliquely inserted ultrasonic probes. Step 2: Obtain historical flow velocity and historical sound velocity data at each detection point of the pipeline under non-deposition conditions using the sensor array; Step 3: Monitor the flow rate data at each detection point in real time based on the data from each sensor group; Step 4: Compare the real-time monitored sound velocity data with historical sound velocity data, locate the deposition location by the change in sound velocity data along the overlapping path, and determine the deposition status of each monitoring point. If the flow velocity deviation is greater than or equal to 5%, the sound velocity data of each ultrasonic sensor is acquired in real time through ultrasonic sensors, and then the sound velocity data of each ultrasonic sensor is compared with historical sound velocity data. If the deviation of each sound velocity is less than 5%, then the detection point is in a state of no deposition. If the sound velocity deviation is greater than or equal to 5%, the deposition layer thickness at the detection point is calculated using an acoustic inversion algorithm and compared with the preset minimum cleaning thickness value. If the thickness of the deposit layer is greater than or equal to the minimum cleaning thickness, the detection point is severely blocked and pipeline cleaning operation must be carried out immediately. If the thickness of the deposit layer is less than the minimum cleaning thickness, the determination is made based on the status of the adjacent detection points. If the adjacent detection points are determined to have slight deposits or severe blockages, the detection point is determined to have slight deposits and is placed under early warning observation. Otherwise, it is determined to be a non-depositional state; If the flow velocity deviation is less than 5%, to avoid misjudgment of the detection results, the sound velocity data of each ultrasonic sensor is acquired in real time through ultrasonic sensors, and then the sound velocity data of each ultrasonic sensor is compared with the historical sound velocity data. If the deviation of each sound velocity is less than 5%, then the detection point is in a state of no deposition. If the sound velocity deviation is greater than or equal to 5%, the detection point is considered to have slight deposition. An early warning observation is conducted, and the deposition layer thickness at the detection point is calculated using an acoustic inversion algorithm. The specific process of calculating the deposition layer thickness at the detection point using the acoustic inversion algorithm is as follows: Step 1: The system controls M ultrasonic sensors in the circumferential array to act as transmitters in sequence, obtaining acoustic measurement data for M×M transmit-receive paths. An M×M measurement matrix S is constructed from the raw measurement data. Through feature extraction calculations, the signal attenuation of each path is obtained, and then converted into M... 2 ×1 sound speed observation vector b t and decay observation vector b s ; Step 2: Discretize the pipe cross-section into N grids. Based on the acoustic measurement data from Step 1, construct a sound velocity inversion system using the reciprocal of the sound velocity as the slowness factor. A x t = b t and attenuation coefficient inversion system A x s = b s ;where x t Here is the slowness distribution matrix; x s This is the attenuation coefficient distribution matrix; A The observation matrix; Step 3: Set the initial slowness distribution matrix x of the pipe cross-section t0 and the initial attenuation coefficient distribution matrix x s0 Substitute the vectors into the sound speed inversion system to obtain the initial sound speed observation vector b. t0 With the sound speed observation vector b t The residual Δb between them, and then the slowness correction vector Δx t With the initial slowness distribution matrix x t0 Perform overlay and update the model to x. t1 =x t0 +Δx t Repeat the above iterative process until the residual is less than the set threshold, then use the derivative of the final slow field to obtain the sound speed matrix; and use the same solution method to solve the attenuation coefficient matrix. Step 4: Post-process the sound velocity matrix and attenuation coefficient matrix, filter and process the signal to generate the pipe sound velocity distribution and attenuation coefficient image; Step 5: Based on the sound velocity distribution and attenuation coefficient image of the pipeline, perform threshold segmentation and morphological analysis on the image to identify the sediment interface and determine the thickness of the sediment layer.

2. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 1, characterized in that, The process of acquiring the sound velocity data is as follows: when the ultrasonic sensor emits the main sound wave pulse, it controls the sensors on both sides of its symmetrical sides to emit anti-phase signals synchronously. The strong interference signal propagating at high speed along the steel pipe wall is actively eliminated by using the coherent cancellation principle, thereby achieving active cancellation; thus, the sound velocity data is obtained.

3. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 1, characterized in that, The flow velocity data was obtained using the Doppler frequency shift method.

4. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 1, characterized in that, The observation matrix in the sound speed inversion system and the attenuation coefficient inversion system A It consists of M sub-matrices stacked vertically, with each sub-matrix corresponding to a emission source.

5. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 4, characterized in that, The observation matrix A The specific form is: In the formula: A The observation matrix; For the source of the emission k Submatrix of time.

6. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 4, characterized in that, The specific form of the submatrix is ​​as follows: In the formula: For the source of the emission k At that time, the first k The sensor to the first i The acoustic wave path of the sensor in the first... j The traversal length in each grid; For the source of the emission k Submatrix of time.

7. The method for non-invasive detection of pipe deposits using an acoustic array according to claim 1, characterized in that, The specific form of the measurement matrix is ​​as follows: In the formula: S This is the measurement matrix.

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