A method for analyzing urban pipeline leakage based on LCD

Abstract

The invention discloses a method for analyzing urban pipeline leakage based on LCD, comprising obtaining sample data of infrasound original signal of urban pipeline and corresponding time domain waveform; using LCD to adaptively decompose the obtained infrasound original signal to obtain ISC component and residual component; calculating mutual information entropy between adjacent ISC components, and using each mutual information entropy to determine the high-frequency part combination of the infrasound original signal; calculating the similarity coefficient between the high-frequency part combination and the infrasound original signal, and obtaining an effective characteristic signal and a corresponding effective time domain waveform according to the similarity coefficient; using the effective time domain waveform to determine whether the urban pipeline is leaking, if the urban pipeline is leaking, analyzing the aperture of the urban pipeline leakage according to the average peak value, mean square amplitude and preset aperture function of the effective characteristic signal. The invention can use the effective time domain waveform to determine whether the urban pipeline is leaking, and analyze the aperture of the urban pipeline leakage, so as to provide a data basis for formulating an emergency repair plan.

Description

Technical Field

[0001] The invention relates to an urban pipeline leakage analysis method based on LCD, belonging to the technical field of pressure pipeline leakage detection. Background Art

[0002] As a tool for long-distance transportation of liquid and gas resources, urban pipelines play a vital role in daily production and life. However, as the use time increases, the probability of corrosion, aging and accidental damage gradually increases, making urban pipeline leakage accidents occur from time to time. Leakage of urban pipelines not only affects the transportation of resources, causes huge losses and waste of resources to economic development, but also poses a huge threat to social public safety. Therefore, taking appropriate and effective methods to detect urban pipelines and timely discover and prevent leakage is of great significance to ensuring urban safety and residents' lives.

[0003] Among various pipeline detection methods, infrasound detection has high sensitivity, low false alarm rate, good adaptability, easy installation and maintenance, and good application effect. However, the infrasound signal of pipeline leakage is easily disturbed by factors such as environment and medium during propagation, and carries various noises, thus affecting the accuracy of pipeline leakage detection. Therefore, how to process infrasound signals and extract the infrasound signal of pipeline leakage from the mixed signals becomes the key to pipeline leakage detection.

[0004] Domestic and foreign scholars have done a lot of research on the processing of infrasound leakage signals and proposed a variety of processing methods, such as wavelet analysis, empirical mode decomposition, and variational mode decomposition. However, these methods have certain limitations. For example, wavelet transform can overcome the shortcomings of Fourier transform and effectively distinguish the effective part and the noise part of the signal, but this method is affected by factors such as the number of decomposition layers, wavelet coefficients, and thresholds; empirical mode decomposition is efficient and highly adaptive, but this method will cause mode aliasing due to noise interference when processing signals, which will cause signal distortion; variational mode decomposition can avoid the problems existing in empirical mode decomposition, but it needs to preset some parameters before decomposition, and the value of the parameters affects the effect of variational mode decomposition. The local characteristic scale decomposition (LCD) proposed by Yang Yu et al. in "A New Time-Frequency Analysis Method-Local Characteristic Scale Decomposition" is adaptive. Compared with empirical mode decomposition, it has fewer iterations, faster decomposition speed, and the decomposed signal is more real and accurate, and the endpoint effect is weaker than empirical mode decomposition. In "Extraction of Infrasonic Signal Features Based on Multi-Scale Decomposition", Wang Meng proposed to apply the local feature scale decomposition algorithm to the feature extraction of infrasonic signals. However, when selecting the main signal components after local feature scale decomposition, the article relies on the observation of the local feature scale decomposition diagram to make choices. Through observation, high-frequency signal components and low-frequency signal components with large differences can be distinguished, but there is subjectivity, and it is easy to lose low-frequency signal components containing effective information. And the weaker the leakage signal, the more difficult it is to distinguish between high-frequency signal components and low-frequency signal components. Therefore, selecting the main signal components by observation is likely to affect the accuracy of pipeline infrasonic leakage signal feature extraction, thereby reducing the accuracy of pipeline leakage detection. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an urban pipeline leakage analysis method based on LCD, which can use an effective time domain waveform to determine whether an urban pipeline is leaking and analyze the aperture of the urban pipeline leakage, thereby providing a data basis for formulating an emergency repair plan.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] The present invention provides an urban pipeline leakage analysis method based on LCD, comprising the following steps:

[0008] Obtain the original signal sample data of infrasound waves in urban pipelines and the corresponding time domain waveform;

[0009] The acquired infrasound original signal is adaptively decomposed by using LCD to obtain several ISC components and a residual component;

[0010] The mutual information entropy between adjacent ISC components is calculated respectively, and the high-frequency part of the original infrasound wave signal is determined by using the mutual information entropy to obtain a high-frequency part combination;

[0011] Calculate the similarity coefficient between the high-frequency part combination and the infrasound original signal, and extract the effective characteristic component from the high-frequency part combination according to the similarity coefficient to obtain the effective characteristic signal and the corresponding effective time domain waveform diagram;

[0012] The effective time domain waveform is used to determine whether the urban pipeline is leaking. If the urban pipeline is leaking, the average peak value and mean square amplitude of the effective characteristic signal are extracted, and the aperture of the urban pipeline leakage is analyzed based on the average peak value, mean square amplitude and the preset aperture function.

[0013] Furthermore, the method of using LCD to adaptively decompose the acquired infrasound original signal to obtain a plurality of ISC components and a residual component includes:

[0014] The following steps are iterated cyclically. When the residual is a monotonic function, the iterative cycle process is terminated and the original infrasound signal is updated by the following formula:

[0015] , where X(t) is the original signal of infrasound, ISC i is the i-th ISC component, r n (t) is the residual component;

[0016] Execute iterative step I. When the maximum and minimum values ​​of the original infrasound signal meet the restriction strategy I, the ISC component is obtained by the following formula and the iterative step I is terminated:

[0017] ; In the formula, I i (t) is the i-th residual component;

[0018] Will ISC i The component is stripped from the original infrasound signal, and the original infrasound signal is updated by the following formula:

[0019] , In the formula, U i (t) is the i-th residual amount;

[0020] The iterative step I comprises:

[0021] The baseline signal point is obtained by the following formula:

[0022] , ; In the formula, X K is the Kth extreme point of the original infrasound signal, a is a constant parameter, A k+1 The extreme point X K Corresponding time T k The function value of L k The extreme point X K The corresponding baseline signal point;

[0023] MATLAB software was used to fit all baseline signal points to obtain the baseline signal of the original infrasound signal, and the baseline signal in the original infrasound signal was separated by the following formula to obtain the residual component:

[0024] ;in, L(t) is the baseline signal, X(t) is the original signal of infrasound;

[0025] The restriction strategy I includes:

[0026] In the original infrasound signal, the maximum is positive, the minimum is negative, and there is strict monotonicity between any two adjacent maximum and minimum points. In addition, the straight line connecting any two adjacent maximum or minimum points corresponds to the time T k+1 The function value satisfies the following formula:

[0027] ; In the formula, X K+1 It is the K+1th extreme point of the original infrasound signal.

[0028] Further, the step of respectively calculating the mutual information entropy between adjacent ISC components and determining the high-frequency part of the original infrasound wave signal using the mutual information entropy to obtain a high-frequency part combination includes:

[0029] Calculate each adjacent ISC Mutual information entropy between components:

[0030] ; In the formula, p(ISC i ) and p(ISC i+1 ) For two adjacent ISC The marginal probability distribution of the components, p(ISC i., ISC i+1 ) is the joint probability distribution of two adjacent ISC components;

[0031] The first local minimum value in the mutual information entropy is used as the dividing line between the high-frequency part and the low-frequency part of the original infrasound signal, and the high-frequency part combination is obtained by the following formula:

[0032] ; In the formula, HC Z (t) For the high frequency part combination, Z is a positive integer less than n, and ISC Z-1 and ISC Z The mutual information entropy between is the first local minimum. ISC Z-1 is the Z-1th ISC component, ISC Z is the Zth ISC component.

[0033] Further, the calculating of the similarity coefficient between the high-frequency part combination and the infrasound original signal, and extracting effective characteristic components from the high-frequency part combination according to the similarity coefficient to obtain an effective characteristic signal and a corresponding effective time domain waveform diagram includes:

[0034] The similarity coefficients of each ISC component in the high-frequency combination and the original infrasound signal are calculated one by one by the following formula:

[0035] ; R i =cos i α； In the formula, α is the angle between the high-frequency combination and the original infrasound signal, R i is the similarity coefficient between the high frequency part combination and the original infrasound signal;

[0036] Determine the size of the similarity coefficient: when the similarity coefficient is greater than 0.5, the corresponding ISC component in the high-frequency part combination is recorded as the effective characteristic component;

[0037] Each effective characteristic component is fitted into an effective characteristic signal by the following formula:

[0038] X new (t)= [ HC 1, HC 2，......， HCs ]In the formula, X new (t) is the effective characteristic signal, HC s is the effective characteristic component;

[0039] The corresponding ripples in the time domain waveform are extracted according to the effective characteristic signal to obtain the effective time domain waveform.

[0040] Further, the method of using the effective time domain waveform to determine whether the urban pipeline is leaking, if the urban pipeline is leaking, extracting the average peak value and mean square amplitude of the effective characteristic signal includes:

[0041] Use the effective time domain waveform to determine whether the urban pipeline is leaking: If the ripples of the effective time domain waveform are smooth and regular without abnormal mutations, it is determined that the urban pipeline is not leaking. Otherwise, it is determined that the urban pipeline is leaking. The average peak value and mean square amplitude of the effective characteristic signal are calculated by the following formula:

[0042] , , where N is the length of the effective characteristic signal, is the average value of the effective characteristic signal, X am is the average peak value, X rms is the mean square amplitude.

[0043] Further, the aperture analysis of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes:

[0044] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0045] , where f(x) is the aperture function, x is the aperture, b1-b5 are the aperture function parameters.

[0046] Further, the aperture analysis of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes:

[0047] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0048] , where f(x) is the aperture function, x is the aperture, b 1 - b 5is the aperture function parameter.

[0049] Further, the aperture analysis of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes:

[0050] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0051] , where f(x) is the aperture function, x is the aperture, b 1 -b 5 is the aperture function parameter.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The analysis method of the present invention can quantitatively determine the high-frequency part combination in the original infrasound signal by using LCD and mutual information entropy, so as to prevent the signal containing effective information from being omitted; the present invention uses the similarity coefficient to verify the effectiveness of signal processing by using mutual information entropy from the time perspective, further obtains the effective characteristic signal and the corresponding effective time domain waveform diagram, and improves the effect of denoising the original infrasound signal; the present invention analyzes whether the urban pipeline is leaking by using the effective time domain waveform diagram, and improves the accuracy of the analysis result; for the leaking urban pipeline, the present invention analyzes the aperture of the urban pipeline leakage according to the average peak value, mean square amplitude and preset aperture function of the effective characteristic signal, provides a data basis for formulating an emergency repair plan, and reduces the loss and potential danger caused by the leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The figure is a flow chart of an embodiment of the urban pipeline leakage analysis method based on LCD of the present invention;

[0055] Figure 2 Shown is a schematic diagram of an experimental device for a simulation experiment of the present invention;

[0056] Figure 3 The figure shows the time domain waveform of the urban pipeline with a leakage hole diameter of 4 mm according to the present invention;

[0057] Figure 4 The eight ISC components of the city pipeline with a leakage hole diameter of 4 mm according to the present invention are shown;

[0058] Figure 5 The figure shows the effective time domain waveform of the urban pipeline with a leakage hole diameter of 4 mm according to the present invention;

[0059] Figure 6 Shown is the fitting result of the aperture function parameters. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0061] Embodiment 1:

[0062] This embodiment provides a method for analyzing urban pipeline leakage based on LCD.

[0063] refer to Figure 1 ,The urban pipeline leakage analysis method based on LCD includes the following steps:

[0064] S1 obtains the original signal sample data of the infrasound wave of the urban pipeline and the corresponding time domain waveform diagram;

[0065] S2 uses LCD to adaptively decompose the acquired infrasound original signal to obtain several ISC components and a residual component;

[0066] S3 respectively calculates the mutual information entropy between each adjacent ISC component, and uses each mutual information entropy to determine the high-frequency part of the original infrasound signal to obtain a high-frequency part combination;

[0067] S4 calculates the similarity coefficient between the high-frequency part combination and the infrasound original signal, and extracts effective characteristic components from the high-frequency part combination according to the similarity coefficient to obtain an effective characteristic signal and a corresponding effective time domain waveform diagram;

[0068] S5 uses the effective time domain waveform to determine whether the urban pipeline is leaking. If the urban pipeline is leaking, it extracts the average peak value and mean square amplitude of the effective characteristic signal, and analyzes the aperture of the urban pipeline leakage based on the average peak value, mean square amplitude and the preset aperture function, providing a data basis for formulating emergency repair plans.

[0069] The analysis method of the present invention can quantitatively determine the high-frequency part combination in the original infrasound signal by using LCD and mutual information entropy, so as to prevent the signal containing effective information from being omitted; the present invention uses the similarity coefficient to verify the effectiveness of signal processing by using mutual information entropy from the time perspective, further obtains the effective characteristic signal and the corresponding effective time domain waveform diagram, and improves the effect of denoising the original infrasound signal; the present invention analyzes whether the urban pipeline is leaking by using the effective time domain waveform diagram, and improves the accuracy of the analysis result; for the leaking urban pipeline, the present invention analyzes the aperture of the urban pipeline leakage according to the average peak value, mean square amplitude and preset aperture function of the effective characteristic signal, provides a data basis for formulating an emergency repair plan, and reduces the loss and potential danger caused by the leakage.

[0070] Embodiment 2:

[0071] Based on Example 1, this example introduces in detail the LCD decomposition steps, the method for obtaining the high-frequency part combination, the method for checking the high-frequency part combination, and the method for obtaining the time domain characteristic parameters.

[0072] (I) LCD disassembly steps

[0073] LCD is used to adaptively decompose the acquired infrasound original signal to obtain several ISC components and a residual component including:

[0074] S21 loop iterates the following steps. When the residual is a monotonic function, the iterative loop process is terminated and the original infrasound signal is updated by the following formula:

[0075] ; In the formula, X(t) is the original signal of infrasound, ISC i is the i-th ISC component, r n (t) is the residual component;

[0076] S22 executes iterative step I. When the maximum and minimum values ​​of the original infrasound signal satisfy the restriction strategy I, the ISC component is obtained by the following formula, and the iterative step I is terminated;

[0077] , where I i (t) is the i-th residual component;

[0078] S23 ISC i The component is stripped from the original infrasound signal, and the original infrasound signal is updated by the following formula:

[0079] , , where U i (t) is the i-th residual amount.

[0080] In the application, iterative step I includes:

[0081] S221 obtains the baseline signal point by the following formula:

[0082] , ; In the formula, X K is the Kth extreme point of the original infrasound signal, a is a constant parameter. In actual application, a =0.5, A k+1 The extreme pointX K Corresponding time T k The function value of L k The extreme point X K The corresponding baseline signal point;

[0083] S222 uses MATLAB software to fit all baseline signal points to obtain the mean curve of the original infrasound signal, that is, the baseline signal, and separates the baseline signal in the original infrasound signal by the following formula to obtain the residual component:

[0084]

[0085] in, L(t) is the baseline signal, X(t) is the original infrasound signal.

[0086] In the application, restriction strategy I includes: in the original infrasound signal, the maximum value is positive, the minimum value is negative, any two adjacent maximum values ​​and minimum values ​​are strictly monotonic, and the straight line obtained by connecting any two adjacent maximum points or minimum points corresponds to the time T k+1 The function value satisfies the following formula:

[0087]

[0088] In the formula, X K+1 It is the K+1th extreme point of the original infrasound signal.

[0089] In practical application, a =0.5, A k+1 and X k+1 The ratio remains unchanged.

[0090] (II) Method for obtaining high-frequency combination

[0091] through LCD In the decomposed infrasound original signal, the effective signal is in the high frequency part, and the low frequency part is the noise signal or false signal. Usually, the denoising method is to directly remove the low frequency part. However, in Wang Meng's study "Extraction of Infrasound Signal Features Based on Multi-Scale Decomposition", the high frequency part and the low frequency part are distinguished by empirical observation of the waveform of the ISC component and the corresponding spectrum. This method is highly subjective and has large errors.

[0092] In this embodiment, the mutual information entropy between adjacent ISC components is calculated respectively, and the high-frequency part of the original infrasound wave signal is determined by using the mutual information entropy to obtain the high-frequency part combination including:

[0093] S31 is calculated by the following formula for each adjacent ISC Mutual information entropy between components:

[0094]

[0095] In the formula, p(ISC i ) and p(ISC i+1 ) For two adjacent ISC The marginal probability distribution of the components, p(ISC i. , ISC i+1 ) is the joint probability distribution of two adjacent ISC components;

[0096] In practical application, when two adjacent ISC When the components are related or independent, their mutual information entropy is the smallest and the value is 0, that is, the two ISC There is no identical information between components; on the contrary, when two adjacent ISC The higher the degree of mutual dependence of the components, the greater the value of mutual information entropy. ISC The more common information is contained between components.

[0097] S32 uses LCD to adaptively decompose the acquired infrasound original signal. After n iterations, several ISC components and a residual component are obtained. As the number of iterations increases, ISC The high-frequency signal contained in the component gradually decreases, and the low-frequency signal gradually increases. ISC 1 contains the most high-frequency useful information and does not contain low-frequency noise information. ISC n contains the most low-frequency noise information and does not contain high-frequency useful information. Therefore, when calculating each adjacent ISC When the mutual information entropy between components reaches a local minimum, it indicates that the adjacent ISC The components contain the least amount of identical information, i.e. ISC The component contains high-frequency valid information and the other adjacent ISC The component contains low-frequency useless information.

[0098] This embodiment uses the first local minimum value in the mutual information entropy as the dividing line between the high-frequency part and the low-frequency part of the original infrasound signal, and obtains the high-frequency part combination through the following formula:

[0099] , where HC Z (t) For the high frequency part combination, Z is a positive integer less than n, and ISC Z-1 and ISC Z The mutual information entropy between is the first local minimum. ISC Z-1 is the Z-1th ISC component, ISC Z is the Zth ISC component.

[0100] (III) Method of combined verification of high frequency parts

[0101] Since mutual information entropy is used to screen and process the original infrasound signal from a spatial perspective, and signal processing is generally performed from a temporal perspective, it is necessary to use the similarity method to verify the effectiveness of using mutual information entropy for signal processing from a temporal perspective.

[0102] Since useful information exists in the high-frequency part combination, this embodiment calculates the similarity coefficient between the high-frequency part combination and the original infrasound signal, and extracts effective characteristic components from the high-frequency part combination according to the similarity coefficient to obtain effective characteristic signals and corresponding effective time domain waveforms, including:

[0103] S41 calculates the similarity coefficient between each ISC component in the high-frequency part combination and the original infrasound signal one by one through the following formula:

[0104] , R i =cos i α， In the formula, α is the angle between the high-frequency combination and the original infrasound signal, R i is the similarity coefficient between the high frequency part combination and the original infrasound signal;

[0105] In practical application, when α =0°, R i =1, indicating that the high-frequency part combination is exactly the same as the original infrasound signal; when α =90°, R i =0, indicating that the high-frequency combination is not related to the original infrasound signal;

[0106] S42 stipulates that R iWhen it is close to 1, it means that the correlation between the high-frequency combination and the original infrasound signal is higher; R i When it is [0,0.3], it means that the high-frequency combination is slightly correlated with the original infrasound signal; R i When it is [0.3,0.5], it means that the high-frequency combination is actually correlated with the original infrasound signal; when R i When it is [0.5,0.8], it means that the high-frequency combination is significantly correlated with the original infrasound signal; when R i When it belongs to [0.8,1], it means that the high-frequency combination is highly correlated with the original infrasound signal.

[0107] This embodiment determines the size of the similarity coefficient: when the similarity coefficient is greater than 0.5, the corresponding ISC component in the high-frequency part combination is recorded as a valid feature component;

[0108] S43 fits each effective characteristic component into an effective characteristic signal by the following formula: X new (t)= [ HC 1, HC 2，......， HC s ], where X new (t) is the effective characteristic signal, HC s is the effective characteristic component;

[0109] S44 extracts the corresponding ripples in the time domain waveform according to the effective characteristic signal, obtains the effective time domain waveform, and completes the denoising process of the original infrasound signal.

[0110] (IV) Methods for obtaining time domain characteristic parameters

[0111] Use the effective time domain waveform to determine whether the urban pipeline is leaking. If the urban pipeline is leaking, the average peak value and mean square amplitude of the effective characteristic signal are extracted, including:

[0112] S51 If the urban pipeline is not leaking, the ripples of the effective time domain waveform diagram are relatively smooth and regular without abnormal mutations; if the urban pipeline is leaking, the ripples of the effective time domain waveform diagram have relatively obvious mutations. Therefore, the effective time domain waveform diagram is used to determine whether the urban pipeline is leaking: if the ripples of the effective time domain waveform diagram are smooth and regular without abnormal mutations, it is determined that the urban pipeline is not leaking, otherwise, it is determined that the urban pipeline is leaking.

[0113] S52 When the urban pipeline is under pressure, the aperture of the urban pipeline leakage hole will change. In addition, since the original infrasound signal is a time domain signal arranged in time sequence and related, the amplitude and peak value of the effective characteristic signal will also change accordingly. Therefore, when determining the urban pipeline leakage, the average peak value and mean square amplitude of the effective characteristic signal are calculated by the following formula: , , where N is the length of the effective characteristic signal, is the average value of the effective characteristic signal, X am is the average peak value, X rms is the mean square amplitude.

[0114] Embodiment 3:

[0115] Based on Example 1 or 2, this example introduces in detail a method for analyzing the aperture of urban pipeline leakage.

[0116] The apertures for analyzing urban pipeline leakage based on average peak value, mean square amplitude and preset aperture function include:

[0117] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0118]

[0119] In the formula, f(x) is the aperture function, x is the aperture, b1-b5 are the aperture function parameters.

[0120] In actual application, a more reasonable and effective repair plan can be formulated according to the size of the leakage hole to reduce the losses and potential dangers caused by the leakage.

[0121] Embodiment 4:

[0122] Based on Example 1 or 2, this example introduces in detail a method for analyzing the aperture of urban pipeline leakage.

[0123] The apertures for analyzing urban pipeline leakage based on average peak value, mean square amplitude and preset aperture function include:

[0124] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0125]

[0126] In the formula, f(x) is the aperture function, x is the aperture, b 1-b 5 is the aperture function parameter.

[0127] Embodiment 5:

[0128] Based on Example 1 or 2, this example introduces in detail a method for analyzing the aperture of urban pipeline leakage.

[0129] The apertures for analyzing urban pipeline leakage based on average peak value, mean square amplitude and preset aperture function include:

[0130] Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula:

[0131]

[0132] In the formula, f(x) is the aperture function, x is the aperture, b 1 -b 5 In the application, the parameters of the aperture function recorded in Examples 3-5 were fitted using MATLAB software to calculate the aperture of the leakage hole, and it was found that the MATLAB software had the best fitting effect on the parameters of the aperture function recorded in Example 3.

[0133] Example 6

[0134] Based on Examples 1-3, this example introduces in detail a method for analyzing the aperture of urban pipeline leakage.

[0135] refer to Figure 2 The experimental device used in this embodiment includes buried non-metallic urban pipelines and related instruments and meters. The instruments and meters include pressure sensors, ball valves, air pumps, turbine flowmeters, computers, digital network transmission instruments, and infrasonic wave sampling sensors; the buried non-metallic urban pipeline includes a U-shaped main pipeline and two branch pipelines. Leakage holes are respectively provided on the U-shaped main pipeline and the branch pipelines, and the aperture of the leakage hole is adjustable. In this embodiment, when the pressure condition is 0.3MPa, the leakage holes of the urban pipelines with apertures of 1mm, 2mm, 3mm and 4mm are simulated respectively.

[0136] Figure 3 This is the time domain waveform of a city pipeline with a leakage hole diameter of 4mm. Due to factors such as the noise caused by the experimental instrument and the experimental site, the waveform of the time domain waveform is messy, and no obvious features can be observed from the time domain waveform to determine whether there is a leakage in the city pipeline.

[0137] Therefore, this embodiment analyzes the infrasound original signal sample data and the corresponding time domain waveform diagram of the urban pipeline with a leakage hole diameter of 4 mm through the following steps.

[0138] First, the acquired infrasound original signal is adaptively decomposed using LCD to obtain 8 ISC components and one residual component. Figure 4 .

[0139] Then, the mutual information entropy between each adjacent ISC component is calculated respectively, as shown in Table 1, and the high-frequency part of the original infrasound signal is determined by using each mutual information entropy to obtain a high-frequency part combination.

[0140] When applied, it can be seen from Table 1 that ISC 34 and ISC 56 are all local minimum points, but according to ISC The order of the components, ISC 34 is the first local minimum point, so ISC 4 It is the dividing point between the high and low frequency parts of the original infrasound signal, so the high frequency signal component is retained. ISC 1 , ISC 2 , ISC 3 , ISC 4 , remove low-frequency signal components ISC 5 , ISC 6 , ISC 7 , ISC 8 , then we get the high frequency part combination HC 4 (t) = ISC 1 + ISC 2 + ISC 3 + ISC 4 .

[0141]

[0142] Next, the similarity coefficient between the high-frequency part combination and the infrasound original signal is calculated, and the effective characteristic component is extracted from the high-frequency part combination according to the similarity coefficient to obtain the effective characteristic signal and the corresponding effective time domain waveform diagram.

[0143] In the application, the similarity coefficient is calculated by the following formula:

[0144]

[0145] Since the similarity coefficient is greater than 0.5, the corresponding ISC component in the high-frequency part combination is recorded as the effective characteristic component, then the effective characteristic signal = [ ISC 1, ISC 2， ISC 3， ISC 4 ], the corresponding effective time domain waveform is as follows Figure 5 shown.

[0146] Then, the effective time domain waveform is used to determine whether the urban pipeline is leaking. Figure 5 It can be seen that at around 1800s, the waveform has an obvious mutation, indicating that there is a leakage in the city pipeline. Therefore, the average peak value and mean square amplitude of the effective characteristic signal are calculated by the following formula, see Table 2 for details:

[0147]

[0148] ;

[0149]

[0150] Finally, the aperture of urban pipeline leakage is analyzed based on the average peak value, mean square amplitude and the following aperture function, as shown in Table 3. The parameters of the aperture function are fitted using MATLAB software:

[0151]

[0152]

[0153] In practical applications, the accuracy of the parameters in the aperture function is determined by using the sum variance and goodness of fit. SSE It is the sum of squares of the errors between the fitted data and the original data. The closer the value is to 0, the better the fitting effect. R-square The goodness of fit is characterized by the change in data. The range of goodness of fit is [0,1]. The closer the value is to 1, the better the fit. Figure 6 The sum variance can be obtained SSE is 0.46, goodness of fit R-square It is 0.8892, which shows that the parameter fitting effect of the aperture function is good.

[0154] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0158] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for analyzing urban pipeline leakage based on LCD, characterized in that: The following steps are involved: Obtain the original signal sample data of infrasound waves in urban pipelines and the corresponding time domain waveform; The acquired infrasound original signal is adaptively decomposed by using LCD to obtain several ISC components and a residual component; The mutual information entropy between adjacent ISC components is calculated respectively, and the high-frequency part of the original infrasound wave signal is determined by using the mutual information entropy to obtain a high-frequency part combination; Calculate the similarity coefficient between the high-frequency part combination and the infrasound original signal, and extract the effective characteristic component from the high-frequency part combination according to the similarity coefficient to obtain the effective characteristic signal and the corresponding effective time domain waveform diagram; The effective time domain waveform is used to determine whether the urban pipeline is leaking. If the urban pipeline is leaking, the average peak value and mean square amplitude of the effective characteristic signal are extracted, and the aperture of the urban pipeline leakage is analyzed based on the average peak value, mean square amplitude and the preset aperture function.

2. The urban pipeline leakage analysis method based on LCD according to claim 1 is characterized in that: The method of using LCD to adaptively decompose the acquired infrasound original signal to obtain a plurality of ISC components and a residual component includes: The following steps are iterated cyclically. When the residual is a monotonic function, the iterative cycle process is terminated and the original infrasound signal is updated by the following formula: In the formula, X(t) is the original signal of infrasound, ISC i is the i-th ISC component, r n (t) is the residual component; Execute iterative step I. When the maximum and minimum values ​​of the original infrasound signal meet the restriction strategy I, the ISC component is obtained by the following formula and the iterative step I is terminated: ; In the formula, I i (t) is the i-th residual component; Will ISC i The component is stripped from the original infrasound signal, and the original infrasound signal is updated by the following formula: , In the formula, U i (t) is the i-th residual amount; The iterative step I comprises: The baseline signal point is obtained by the following formula: , ; In the formula, X K is the Kth extreme point of the original infrasound signal, a is a constant parameter, A k+1 The extreme point X K Corresponding time T k The function value of L k The extreme point X K The corresponding baseline signal point; MATLAB software was used to fit all baseline signal points to obtain the baseline signal of the original infrasound signal, and the baseline signal in the original infrasound signal was separated by the following formula to obtain the residual component: ;in, L(t) is the baseline signal, X(t) is the original signal of infrasound; The restriction strategy I includes: In the original infrasound signal, the maximum is positive, the minimum is negative, and there is strict monotonicity between any two adjacent maximum and minimum points. In addition, the straight line connecting any two adjacent maximum or minimum points corresponds to the time T k+1 The function value satisfies the following formula: ; In the formula, X K+1 It is the K+1th extreme point of the original infrasound signal.

3. The urban pipeline leakage analysis method based on LCD according to claim 1 is characterized in that: The step of respectively calculating the mutual information entropy between adjacent ISC components and determining the high-frequency part of the original infrasound wave signal using the mutual information entropy to obtain a high-frequency part combination comprises: Calculate each adjacent ISC Mutual information entropy between components: ; In the formula, p(ISC i ) and p(ISC i+1 ) For two adjacent ISC The marginal probability distribution of the components, p(ISC i. , ISC i+1 ) is the joint probability distribution of two adjacent ISC components; The first local minimum value in the mutual information entropy is used as the dividing line between the high-frequency part and the low-frequency part of the original infrasound signal, and the high-frequency part combination is obtained by the following formula: ; In the formula, HC Z (t) For the high frequency part combination, Z is a positive integer less than n, and ISC Z-1 and ISC Z The mutual information entropy between is the first local minimum. ISC Z-1 is the Z-1th ISC component, ISC Z is the Zth ISC component.

4. The urban pipeline leakage detection method based on improved LCD according to claim 3, characterized in that: The method of calculating the similarity coefficient between the high-frequency part combination and the original infrasound wave signal and extracting effective characteristic components from the high-frequency part combination according to the similarity coefficient to obtain an effective characteristic signal and a corresponding effective time domain waveform diagram includes: The similarity coefficients of each ISC component in the high-frequency combination and the original infrasound signal are calculated one by one by the following formula: ；R i =cos i α ; In the formula, α is the angle between the high-frequency combination and the original infrasound signal, R i is the similarity coefficient between the high frequency part combination and the original infrasound signal; Determine the size of the similarity coefficient: when the similarity coefficient is greater than 0.5, the corresponding ISC component in the high-frequency part combination is recorded as the effective characteristic component; Each effective characteristic component is fitted into an effective characteristic signal by the following formula: X new (t)= [ HC 1, HC 2，......， HC s ], where X new (t) is the effective characteristic signal, HC s is the effective characteristic component; The corresponding ripples in the time domain waveform are extracted according to the effective characteristic signal to obtain the effective time domain waveform.

5. The urban pipeline leakage detection method based on improved LCD according to claim 4, characterized in that: The method of using the effective time domain waveform to determine whether the urban pipeline is leaking, and if the urban pipeline is leaking, extracting the average peak value and mean square amplitude of the effective characteristic signal includes: Use the effective time domain waveform to determine whether the urban pipeline is leaking: If the ripples of the effective time domain waveform are smooth and regular without abnormal mutations, it is determined that the urban pipeline is not leaking. Otherwise, it is determined that the urban pipeline is leaking. The average peak value and mean square amplitude of the effective characteristic signal are calculated by the following formula: , , where N is the length of the effective characteristic signal, is the average value of the effective characteristic signal, X am is the average peak value, X rms is the mean square amplitude.

6. The urban pipeline leakage detection method based on improved LCD according to claim 5, characterized in that: The method of analyzing the aperture of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes: Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula: , where f(x) is the aperture function, x is the aperture, b1-b5 are the aperture function parameters.

7. The urban pipeline leakage detection method based on improved LCD according to claim 6, characterized in that: The method of analyzing the aperture of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes: Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula: , where f(x) is the aperture function, x is the aperture, b 1 -b 5 is the aperture function parameter.

8. The urban pipeline leakage detection method based on improved LCD according to claim 7, characterized in that: The method of analyzing the aperture of urban pipeline leakage according to the average peak value, the mean square amplitude and the preset aperture function includes: Based on the average peak value and mean square amplitude, the aperture of the urban pipeline leakage is calculated by the following formula: , where f(x) is the aperture function, x is the aperture, b 1 -b 5 is the aperture function parameter.

Images