Processing method and system of distributed signal array, electronic equipment and storage medium

Abstract

The application discloses a kind of processing method, system, electronic equipment and storage medium of distributed signal array, belong to signal processing technical field.The application utilizes empirical mode decomposition method to process the multiple original signal curves obtained by distributed sensor, the original data curve is carried out empirical mode decomposition, obtains several different intrinsic mode components and a residual steady-state quantity;Determine the optimal trend item in several different intrinsic mode components and a residual steady-state quantity, and the original data curve is subtracted to obtain the data curve after eliminating baseline drift;Multiple data curves after eliminating baseline drift are interpolated to obtain newly encrypted data curves.The method eliminates the error superposition caused by the distributed sensor array structure when interpolating data later, can more clearly locate the pipe wall defect position and accurately depict the defect shape, so as to realize high-resolution in-pipe detection.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, and specifically relates to a method, system, electronic device and storage medium for processing distributed signal arrays. Background Technology

[0002] Currently, pulsed eddy current (PEC) technology is widely used in pipeline inspection. For example, existing technologies for PEC include online multi-channel eddy current testing equipment and methods (CN118050422A), eddy current testing probes (CN220983192U), and dedicated test blocks for pulsed eddy current testing (CN220894212U). To improve the longitudinal resolution of eddy current testing instruments, researchers have proposed a distributed sensor array model, which can overcome the low longitudinal resolution of traditional single-measuring chamber testing. However, the distributed sensor array model, due to the introduction of a distributed multi-sensor array, places each sensor array in a different testing chamber, resulting in different magnetic field environments for each array. This causes the distributed sensor array to be affected by the background magnetic field, leading to baseline drift in the obtained data curves.

[0003] Baseline drift can also occur in single-chamber pipe inspection instruments, but it does not affect the acquisition of pipe wall information. Because the structure of a single inspection chamber does not require interpolation of multiple data curves, it does not affect the subsequent observation of the trend and peak value of a single data curve. Interpolating two data curves essentially involves alternately acquiring data points from both curves to obtain a new, encrypted curve; the operation for encrypting multiple data curves is similar. Therefore, if the baselines of the multiple encrypted data curves are different, the new data curve formed by the alternately acquired data points during interpolation will be jagged, and the errors will accumulate, making it impossible to observe the spikes in the sensor-detected signal data curve at defects. This accumulation of errors will significantly affect the accuracy of defect detection, making it impossible to accurately depict the shape of the defect. Summary of the Invention

[0004] To address the above problems, this invention proposes a method and system for processing distributed signal arrays, the specific technical solution of which is as follows:

[0005] In a first aspect, the present invention provides a method for processing a distributed signal array, the method comprising the following steps:

[0006] The signal data collected by the distributed sensor array is obtained to generate multiple raw data curves containing pipe wall information.

[0007] Empirical mode decomposition is performed on each original data curve to obtain several different intrinsic mode components and a residual steady-state quantity;

[0008] The optimal trend term among the several different intrinsic modal components and a residual steady-state quantity is determined, and the original data curve is subtracted from the optimal trend term to obtain the data curve after eliminating baseline drift.

[0009] Multiple data curves after baseline drift elimination are interpolated to obtain a newly encrypted data curve.

[0010] Furthermore, the empirical mode decomposition of each original data curve includes the following steps:

[0011] S1: Obtain all local maxima and local minima in the original data curve;

[0012] S2: Use cubic spline interpolation to fit the upper envelope of all local maxima and the lower envelope of all local minima in each original data curve;

[0013] S3: Determine the average line function based on the upper and lower envelope lines;

[0014] S4: Subtract the average line function from the original data curve to obtain a new data curve, and determine whether the new data curve satisfies the constraint conditions of the intrinsic modal component. If the new data curve satisfies the constraint conditions of the intrinsic modal component, then the new data curve is taken as a first-order intrinsic modal component.

[0015] S5: Subtract the first-order intrinsic mode component from the original data curve. Repeat steps S1-S4 for the remaining signal to obtain the nth-order intrinsic mode component. Stop empirical mode decomposition when the remaining component is a monotonic function or a constant. n is greater than or equal to 2.

[0016] Furthermore, if the new data curve does not satisfy the constraint conditions of the intrinsic modal component, the new data curve is used as a new original signal, and steps S1-S4 are repeated until an intrinsic modal component that satisfies the constraint conditions of the intrinsic modal component is obtained.

[0017] Furthermore, the optimal trend term is either the residual steady-state quantity or the last-order intrinsic mode component.

[0018] Furthermore, the constraints on the intrinsic modal components include:

[0019] Within the entire data segment, the number of extreme points and the number of zero-crossing points must be equal or differ by no more than one. The extreme points include maximum points and minimum points.

[0020] At any given time, the average of the upper envelope formed by local maxima and the lower envelope formed by local minima is zero.

[0021] Furthermore, if a new data curve obtained after performing empirical mode decomposition on the original data curve satisfies the following formula, then the new data curve is considered to satisfy the constraint conditions of the intrinsic mode components:

[0022]

[0023] In the formula, Let represent the new data curves obtained in the (k-1)th empirical mode decomposition and the new data curves obtained in the kth empirical mode decomposition, respectively, and ε be the screening threshold.

[0024] Furthermore, the value of the filtering threshold ε is between 0.2 and 0.3.

[0025] Secondly, the present invention proposes a processing system for a distributed signal array, the system comprising:

[0026] The raw data curve acquisition unit is used to acquire signal data collected by the distributed sensor array and obtain multiple raw data curves containing pipe wall information.

[0027] The empirical mode decomposition unit performs empirical mode decomposition on each original data curve to obtain several different intrinsic mode components and a residual steady-state quantity;

[0028] The baseline drift elimination unit is used to determine the optimal trend term among the several different intrinsic modal components and a residual steady-state quantity, and to subtract the optimal trend term from the original data curve to obtain the data curve after baseline drift elimination.

[0029] The interpolation unit is used to interpolate multiple data curves after baseline drift elimination to obtain a newly encrypted data curve.

[0030] Thirdly, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0031] Memory, which stores computer programs;

[0032] The processor, when executing the program stored in the memory, implements the processing method of the distributed signal array.

[0033] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when run, executes the processing method of the distributed signal array.

[0034] The beneficial effects of this invention are:

[0035] This invention utilizes the empirical mode decomposition (EMD) method to process multiple raw signal curves acquired by a distributed sensor, decomposing them into several distinct intrinsic mode components and a residual steady-state quantity. Each intrinsic mode component reflects different frequency components of the signal in the time domain, while the residual steady-state quantity reflects the signal's trend or mean, with the trend term representing the signal's baseline position. This processing yields a cluster of data curves without baseline drift. Interpolating these processed data curves then produces encrypted data curves, enabling high-resolution pipe internal detection. This method eliminates the error superposition caused by the distributed sensor array structure during subsequent data interpolation, allowing for clearer location of pipe wall defects and more accurate depiction of defect shapes, thus achieving high-resolution pipe internal detection.

[0036] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart of a method for processing a distributed signal array according to an embodiment of the present invention is shown;

[0039] Figure 2 A schematic diagram of the multi-compartment structure of the distributed sensor array in an embodiment of the present invention is shown;

[0040] Figure 3 This diagram illustrates a curve obtained by directly interpolating unprocessed raw data curves in an embodiment of the present invention.

[0041] Figure 4 A flowchart of the EMD decomposition method in an embodiment of the present invention is shown;

[0042] Figure 5 This illustrates the signal schematic of the distributed signal array obtained using the method of the present invention in an embodiment of the invention;

[0043] Figure 6 A schematic diagram of a distributed signal array processing system proposed in an embodiment of the present invention is shown;

[0044] Figure 7 A schematic diagram of an electronic device proposed in an embodiment of the present invention is shown. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] This invention uses a multi-compartment pipe inspection instrument as an example to illustrate the processing method of a distributed signal array. Leveraging the advantage of Empirical Mode Decomposition (EMD) in separating different frequency signal components, the resulting original signal array is processed to obtain a signal without baseline drift, thereby achieving high-resolution pipe inspection. Figure 1 As shown, the specific steps include:

[0047] S1: Acquire signal data collected by the distributed sensor array to obtain multiple raw data curves containing pipe wall information;

[0048] In one embodiment of the present invention, the multi-compartment structure of the distributed sensor array is as follows: Figure 2 As shown, each sensor array is located in a different detection chamber, and the sensor arrays in each chamber are in different magnetic field environments.

[0049] The data curve obtained by directly interpolating the unprocessed raw data curve is as follows: Figure 3 As shown, Figure 3 The curve at the top represents the raw data acquired by the two sensors. Because the two measuring chambers are some distance apart, the data curves will shift accordingly. During processing, they need to be first shifted to the same position (i.e.,...). Figure 3 The data curve obtained by directly interpolating the corrected displacement diagram in the middle of the curve is jagged.

[0050] S2: Perform empirical mode decomposition on each original data curve to obtain several different intrinsic mode components (imf) and a residual steady-state quantity r(t);

[0051] like Figure 4 As shown, the empirical mode decomposition of each original data curve includes the following steps:

[0052] S2.1: Obtain all local maxima and local minima in the original data curve;

[0053] S2.2: Use cubic spline interpolation to fit the upper envelope of all local maxima and the lower envelope of all local minima of each original data curve;

[0054] Specifically, cubic spline functions can be used to connect all the local maxima points in each original data curve to form the upper envelope of the signal, and cubic spline functions can be used to connect all the local minima points in each original data curve to form the lower envelope of the signal. These two envelopes should be able to completely enclose all the data points of the signal.

[0055] S2.3: Determine the average line function based on the upper and lower envelope lines;

[0056] In an exemplary embodiment of the present invention, the average line function is determined according to the following formula:

[0057]

[0058] Where e1(t) represents the average value of the upper and lower envelopes obtained for the first time at time t, x max (t) represents the value of the maximum envelope at time t, x min (t) represents the value of the minimum envelope at time t.

[0059] S2.4: Subtract the average line function from the original data curve to obtain a new data curve, and determine whether the new data curve satisfies the constraint conditions of the intrinsic modal component; if the new data curve satisfies the constraint conditions of the intrinsic modal component, then the new data curve is taken as the first-order intrinsic modal component (imf1).

[0060] It should be noted that the constraints on the intrinsic modal components are as follows:

[0061] 1) The number of extreme points and the number of zero-crossing points must be equal or differ by no more than one. Extreme points include maximum points and minimum points.

[0062] 2) At any given time, the average value of the upper envelope formed by the local maxima and the lower envelope formed by the local minima is zero, meaning that the upper and lower envelopes are locally symmetrical with respect to the time axis.

[0063] If the new data curve does not meet the constraint conditions of the intrinsic modal component, then the new data curve is used as the new original signal, and steps S2.1-S2.4 are repeated until the obtained data curve meets the constraint conditions of the intrinsic modal component, and the first-order intrinsic modal component imf1 is extracted.

[0064] In an exemplary embodiment of the present invention, the expression for the new data curve is as follows:

[0065]

[0066] in, Let x(t) represent the new data curve, x(t) represent the original data curve, and e1(t) represent the average value of the upper and lower envelopes obtained in the first calculation. If the obtained... If the two constraints of the intrinsic modal components are not satisfied, continue to repeat S2.1-S2.3 with the new data curve. Replacing x(t) in the original data curve, it is denoted as x(t) after satisfying the constraint condition of the intrinsic modal component for the kth time. The expression for the first-order intrinsic mode component of the signal is as follows:

[0067]

[0068] S2.5: Subtract the first-order intrinsic mode component imf1 from the original data curve, and repeat S2.1-S2.4 on the remaining signal to obtain the nth-order intrinsic mode component. Stop the empirical mode decomposition when the remaining component is a monotonic function or a constant.

[0069] The relationship obtained from the empirical mode decomposition of the original data curves is as follows:

[0070]

[0071] Where x(t) represents the original data curve, x i (t) represents the n intrinsic modal components imf1, imf2...imf after EMD decomposition, with frequencies ranging from high to low. n r n (t) represents the steady-state residual quantity, which represents the average value of the signal.

[0072] For example, a new data curve obtained after a decomposition is considered to satisfy the constraints of the intrinsic modal components when it meets the following conditions:

[0073]

[0074] In the formula, These represent the new data curves obtained during the (k-1)th EMD decomposition and the new data curves obtained during the kth EMD decomposition, respectively. ε is the screening threshold, which is generally between 0.2 and 0.3.

[0075] S3: Determine the optimal trend term among the several different intrinsic modal components and a residual steady-state quantity, and subtract the optimal trend term from the original data curve to obtain the data curve after eliminating baseline drift;

[0076] The optimal trend term (i.e. the component that best reflects the trend of the data curve) is generally the residual steady-state quantity or the last intrinsic mode component.

[0077] In one embodiment of the present invention, such as Figure 5 As shown, the steady-state residual is the optimal trend term, at which point:

[0078] r(t)=x(t)-[imf1(t)+imf2(t)+…imf n (t)](6)

[0079] x′(t)=x(t)-r(t))(7)

[0080] Where x′(t) represents the data curve after removing the baseline, r(t) represents the trend term, x(t) represents the original data curve, and imf1(t) + imf2(t) + ... + imf n (t) represents the sum of multiple intrinsic modal components.

[0081] S4: Interpolate multiple data curves after baseline drift elimination to obtain a newly encrypted data curve.

[0082] Based on the same inventive concept, one embodiment of the present invention proposes a processing system for a distributed signal array, such as... Figure 6 As shown, it includes:

[0083] The raw data curve acquisition unit 601 is used to acquire signal data collected by the distributed sensor array and obtain multiple raw data curves containing pipe wall information.

[0084] Empirical mode decomposition unit 602 is used to perform empirical mode decomposition on each original data curve to obtain several different intrinsic mode components and a residual steady-state quantity;

[0085] Baseline drift elimination unit 603 is used to determine the optimal trend term among the several different intrinsic modal components and a residual steady-state quantity, and to subtract the optimal trend term from the original data curve to obtain the data curve after eliminating baseline drift.

[0086] Interpolation unit 604 is used to interpolate multiple data curves after baseline drift elimination to obtain a newly encrypted data curve.

[0087] The execution steps of the original data curve acquisition unit 601, empirical mode decomposition unit 602, baseline drift elimination unit 603, and interpolation unit 604 are similar to the above method and will not be described in detail here.

[0088] Another exemplary embodiment of the present invention provides an electronic device. For example... Figure 7As shown, the electronic device includes at least one processor 701, at least one communication interface 702, at least one memory 703, and at least one communication bus 704; wherein the processor 701, communication interface 702, and memory 703 communicate with each other through the communication bus 704.

[0089] Memory 703 stores computer programs;

[0090] The processor 701 is used to execute the program stored in the memory 703 to implement the processing method of the distributed signal array.

[0091] Optionally, the communication interface can be an interface of a communication module, such as the interface of a GSM module; the processor may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include high-speed RAM and may also include non-volatile memory, such as at least one disk storage device. The memory stores a program, and the processor calls the program stored in the memory to execute some or all of the above-described method embodiments.

[0092] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed, implements some or all of the above-described method embodiments. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0093] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing a distributed signal array, characterized in that, The method includes the following steps: The signal data collected by the distributed sensor array is obtained to generate multiple raw data curves containing pipe wall information. Empirical mode decomposition is performed on each original data curve to obtain several different intrinsic mode components and a residual steady-state quantity; The optimal trend term among the several different intrinsic modal components and a residual steady-state quantity is determined, and the original data curve is subtracted from the optimal trend term to obtain the data curve after eliminating baseline drift. Multiple data curves after baseline drift elimination are interpolated to obtain a newly encrypted data curve.

2. The processing method for a distributed signal array according to claim 1, characterized in that, The empirical mode decomposition of each original data curve includes the following steps: S1: Obtain all local maxima and local minima in the original data curve; S2: Use cubic spline interpolation to fit the upper envelope of all local maxima and the lower envelope of all local minima in each original data curve; S3: Determine the average line function based on the upper and lower envelope lines; S4: Subtract the average line function from the original data curve to obtain a new data curve, and determine whether the new data curve satisfies the constraint conditions of the intrinsic modal component. If the new data curve satisfies the constraint conditions of the intrinsic modal component, then the new data curve is taken as a first-order intrinsic modal component. S5: Subtract the first-order intrinsic mode component from the original data curve. Repeat steps S1-S4 for the remaining signal to obtain the nth-order intrinsic mode component. Stop empirical mode decomposition when the remaining component is a monotonic function or a constant. n is greater than or equal to 2.

3. The processing method for a distributed signal array according to claim 2, characterized in that, If the new data curve does not meet the constraints of the intrinsic modal component, then the new data curve is used as a new original signal, and steps S1-S4 are repeated until an intrinsic modal component that meets the constraints of the intrinsic modal component is obtained.

4. The processing method for a distributed signal array according to any one of claims 1-3, characterized in that, The optimal trend term is either the residual steady-state quantity or the last intrinsic mode component.

5. The processing method for a distributed signal array according to claim 2, characterized in that, The constraints on the intrinsic modal components include: Within the entire data segment, the number of extreme points and the number of zero-crossing points must be equal or differ by no more than one. The extreme points include maximum points and minimum points. At any given time, the average of the upper envelope formed by local maxima and the lower envelope formed by local minima is zero.

6. The processing method for a distributed signal array according to claim 2, characterized in that, When a new data curve obtained after empirical mode decomposition of the original data curve satisfies the following formula, the new data curve is considered to satisfy the constraint conditions of the intrinsic modal components: In the formula, Let represent the new data curves obtained in the (k-1)th empirical mode decomposition and the new data curves obtained in the kth empirical mode decomposition, respectively, and ε be the screening threshold.

7. The method for processing a distributed signal array according to claim 6, characterized in that, The filtering threshold ε is between 0.2 and 0.

3.

8. A processing system for a distributed signal array, characterized in that, The system includes: The raw data curve acquisition unit is used to acquire signal data collected by the distributed sensor array and obtain multiple raw data curves containing pipe wall information. The empirical mode decomposition unit performs empirical mode decomposition on each original data curve to obtain several different intrinsic mode components and a residual steady-state quantity; The baseline drift elimination unit is used to determine the optimal trend term among the several different intrinsic modal components and a residual steady-state quantity, and to subtract the optimal trend term from the original data curve to obtain the data curve after baseline drift elimination. The interpolation unit is used to interpolate multiple data curves after baseline drift elimination to obtain a newly encrypted data curve.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, which stores computer programs; A processor, when executing a program stored in a memory, implements the processing method of the distributed signal array as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run, it performs the processing method of the distributed signal array as described in any one of claims 1-7.

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