Method, system and medium for positioning optimization of water supply network leakage noise

Abstract

The present application relates to the technical field of pipe network acoustic detection, and in particular to a positioning optimization method and system for water supply pipe network leakage noise and a medium. The method comprises: deploying at least one sensor group on a water supply pipeline, each sensor group comprising three noise recorders for synchronously collecting leakage noise signals; using an adaptive fractional time delay estimation strategy for each sensor group to respectively calculate a first time delay difference between the leakage noise signals collected by a first noise recorder and a second noise recorder and a second time delay difference between the leakage noise signals collected by the first noise recorder and a third noise recorder; determining a first leakage position based on the time delay differences and the distances between the noise recorders; and using a Kalman filtering algorithm to dynamically and recursively correct the first leakage position to obtain the final leakage noise positioning of the water supply pipe network, thereby fundamentally solving the technical defects of speed dependence, error coupling and open-loop architecture and effectively improving the leakage noise positioning accuracy of the water supply pipe network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipe network acoustic detection, in particular to a positioning optimization method and system for water supply pipe network leakage noise and a medium. BACKGROUND

[0002] Water supply pipe network leakage detection is a key link to ensure the sustainable use of urban water resources, directly related to infrastructure safety. Precise detection of water supply pipe network leakage noise positioning helps to prevent secondary disasters such as foundation collapse and pipe network burst, reduces energy consumption of pump invalid pressurization, and avoids water waste through rapid repair. It is a key technical support for smart water to achieve water saving and emission reduction and improve urban resilience.

[0003] In order to accurately locate the water supply pipe network leakage noise collected by the noise recorder, the existing technology usually actively emits sound waves to measure the propagation speed (active calibration), and then locates the leakage point through correlation detection, such as Chinese patent CN108194841A, an active calibration type water supply pipe leakage correlation detection method and device. However, the existing technology still has the following technical problems, resulting in insufficient detection accuracy of small defects and poor robustness:

[0004] 1. Strong dependence on core parameters, and parameters are easily disturbed by the environment: existing technologies usually require prior parameters (threshold value / sound speed), but actual scene parameters change dynamically, making it difficult to eliminate system error. For example, the active calibration of CN108194841A only measures the initial sound speed, and the sound speed is fixed in the subsequent positioning process, but the sound speed propagation has a large dynamic uncertainty and is easily affected by pipe materials (steel pipe / cast iron / PVC), pressure, temperature, fluid medium and other factors. Once the sound speed cannot adapt to the dynamic change in the positioning process, the sound speed error will inevitably amplify the positioning deviation, and the longer the time delay, the greater the error.

[0005] 2. Multiple error overlaps, positioning accuracy drops sharply: error sources are not independent, and existing technologies cannot decouple and suppress them. For example, the sensor distance error in CN108194841A will directly affect the positioning result. This measurement error cannot be corrected, and the cross-spectral method has a correlation peak shift under low signal-to-noise ratio, and the sound speed is easily affected by environmental dynamic changes. Multiple error coupling amplification leads to decimeter-level positioning deviation.

[0006] 3. Robustness collapse in low signal-to-noise ratio scenarios: for example, the cross-spectral method in CN108194841A relies on the signal cross-correlation peak value, but environmental noise causes the correlation peak to be fuzzy, and the sampling level precision is not enough to support high-precision positioning, resulting in an exponential increase in time delay error when the signal-to-noise ratio is low.

[0007] 4. The open-loop processing architecture lacks dynamic optimization: the existing technology generally adopts an "single measurement-direct calculation" open-loop architecture. For example, the active calibration of CN108194841A is only used for initial sound velocity measurement, and there is no closed-loop correction for the measured sound velocity and positioning results in the subsequent positioning process, which will undoubtedly lead to detection result drift under variable working condition pipelines (such as pressure sudden change). SUMMARY

[0008] In view of the above technical problems, the present application provides a positioning optimization method, system and medium for water supply pipeline leakage noise, aiming to fundamentally solve the technical defects of sound velocity dependence, error coupling and open-loop architecture, and effectively improve the leakage noise positioning accuracy of water supply pipeline.

[0009] In a first aspect, the present application provides a positioning optimization method for water supply pipeline leakage noise, comprising the following steps:

[0010] Deploy at least one sensor group in the water supply pipeline, each sensor group comprising three noise recorders for synchronously collecting leakage noise signals;

[0011] Adopt an adaptive fractional time delay estimation strategy for each sensor group to calculate a first time delay difference between the leakage noise signals collected by the first noise recorder and the second noise recorder and a second time delay difference between the leakage noise signals collected by the first noise recorder and the third noise recorder;

[0012] Based on the first time delay difference, the second time delay difference and the distance between the first noise recorder and the second noise recorder and the third noise recorder, determine a first leakage position;

[0013] Adopt a Kalman filtering algorithm to dynamically and recursively correct the first leakage position to obtain a second leakage position as the final leakage noise positioning of the water supply pipeline.

[0014] In some embodiments, the calculation process of the first time delay difference or the second time delay difference comprises:

[0015] Adopt a cross-correlation algorithm to determine the integer time delay between the leakage noise signals collected by the noise recorders;

[0016] Based on the determined integer time delay, calculate the peak signal-to-noise ratio;

[0017] Based on the peak signal-to-noise ratio, dynamically select an interpolation algorithm to calculate the fractional offset between the two signals;

[0018] Fuse the integer time delay and the fractional offset to obtain the first time delay difference or the second time delay difference at the sub-sampling level.

[0019] In some embodiments, the cross-correlation algorithm is adopted to determine the integer time delay between the leakage noise signals collected by the noise recorders, comprising:

[0020] Based on the leakage noise signals collected by the two noise recorders, the cross-correlation algorithm is used to calculate the cross-correlation sequence of the two signals under different time delay offsets;

[0021] The peak position in the cross-correlation sequence is taken as an integer time delay.

[0022] In some embodiments, based on the peak signal-to-noise ratio, the fractional offset between the two signals is dynamically selected by an interpolation algorithm, including:

[0023] When the peak signal-to-noise ratio is greater than or equal to the preset upper threshold, the parabolic interpolation method is used to calculate the fractional offset between the two signals;

[0024] When the peak signal-to-noise ratio is less than the preset upper threshold and greater than the preset lower threshold, the cubic spline interpolation method is used to calculate the fractional offset between the two signals;

[0025] When the peak signal-to-noise ratio is less than or equal to the preset lower threshold, the long window Sinc interpolation method is used to calculate the fractional offset between the two signals.

[0026] In some embodiments, the integer time delay and the fractional offset are fused to obtain a first time delay difference or a second time delay difference at a sub-sampling level, which is represented as:

[0027] ,

[0028] Wherein, represents the first time delay difference or the second time delay difference at the sub-sampling level, represents the integer time delay between the two signals, represents the fractional offset between the two signals, represents the fixed sampling rate.

[0029] In some embodiments, based on the first time delay difference, the second time delay difference, and the distances between the first noise recorder and the second noise recorder and the third noise recorder, the first leakage position is determined, which is represented as:

[0030] ,

[0031] Wherein, z represents the first leakage position, D represents the distance between the first noise recorder and the second noise recorder, and L represents the distance between the first noise recorder and the third noise recorder.

[0032] In some embodiments, the Kalman filtering algorithm is used to dynamically and recursively correct the first leakage position to obtain a second leakage position, including:

[0033] Step 301, taking the leakage noise positioning as a state variable, and initializing the optimal state estimation and the prediction error covariance;

[0034] Step 302, predicting the state at the current time based on the optimal state estimation at the previous time, denoted as a predicted value, and incrementing the prediction error covariance to model the dynamic changes of the pipeline;

[0035] Step 303, calculating the Kalman gain based on the incremented prediction error covariance;

[0036] Step 304, fusing the first leakage location at the current time with the predicted value based on the Kalman gain to correct the state, obtaining the optimal state estimation at the current time, denoted as the second leakage location;

[0037] Step 305, updating the incremented prediction error covariance based on the Kalman gain, obtaining the updated prediction error covariance.

[0038] In some embodiments, further comprising:

[0039] Step 306, repeating steps 302-305 for each new time of the first leakage location to obtain a position sequence of the second leakage location;

[0040] Step 307, performing convergence judgment on the position sequence of the second leakage location, and when the judgment converges, calculating the average value of the position sequence of the second leakage location as the final leakage noise positioning of the water supply network.

[0041] In a second aspect, the present application provides a positioning optimization system for leakage noise of a water supply network, comprising:

[0042] A data acquisition module is configured to deploy at least one sensor group on the water supply pipeline, each sensor group comprising three noise recorders, and configured to synchronously acquire leakage noise signals;

[0043] A time delay difference calculation module is configured to calculate a first time delay difference between the leakage noise signals acquired by the first noise recorder and the second noise recorder and a second time delay difference between the leakage noise signals acquired by the first noise recorder and the third noise recorder for each sensor group using an adaptive fractional time delay estimation strategy;

[0044] A first leakage location determination module is configured to determine the first leakage location based on the first time delay difference, the second time delay difference, and the distances between the first noise recorder and the second noise recorder and the third noise recorder, respectively;

[0045] A second leakage location output module is configured to dynamically and recursively correct the first leakage location using a Kalman filtering algorithm to obtain the second leakage location as the final leakage noise positioning of the water supply network.

[0046] In a third aspect, a computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the positioning optimization method for water supply network leakage noise as described above.

[0047] The beneficial technical effects of the present application include at least:

[0048] 1. The positioning optimization method, system and medium for water supply network leakage noise creatively solve the core problems of the prior art by synergistically integrating adaptive fractional time delay estimation strategy, sound speed-free positioning algorithm and Kalman filter closed-loop architecture, in view of the inherent limitations (multiple error coupling amplification) of the relevant detection method. Specifically, first, the adaptive fractional time delay estimation strategy improves the time delay accuracy to the sub-sampling level, significantly suppresses the correlation peak ambiguity problem under low signal-to-noise ratio, and provides high-precision time delay difference input for the subsequent steps; second, the first time delay difference, the second time delay difference and the distance between the first noise recorder and the second noise recorder and the third noise recorder are designed to determine the first leakage position (i.e. sound speed-free positioning algorithm), which mathematically eliminates the sound speed variable and avoids parameter errors caused by dynamic factors such as pipe material and temperature, thereby fundamentally solving the positioning drift problem in dynamic environment; finally, the Kalman filter takes the positioning results output from the front end as the observation value, and realizes stable output at the centimeter level through dynamic recursive correction. The three form a closed-loop optimization link of "high-precision time delay input → parameter error elimination → dynamic noise suppression": adaptive time delay estimation ensures the reliability of the input of the sound speed-free positioning algorithm, the sound speed-free positioning algorithm reduces the error transmission source of the Kalman filter, and the Kalman filter further compensates for the remaining errors through closed-loop feedback, and the stability of the output of the Kalman filter in turn reduces the sensitivity requirement of the time delay estimation accuracy, realizes true "error decoupling", and finally achieves the technical effect of "1+1>2" - compared with the prior art, the leakage noise positioning accuracy of the water supply network is effectively improved, the small leakage detection rate is improved, and the fluctuation amplitude under variable working conditions is reduced, thereby fundamentally solving the technical defects of sound speed dependence, error coupling and open-loop architecture.

[0049] 2. For the time delay estimation link in the positioning of water supply network leakage noise, the present application creatively proposes a signal quality perception-algorithm dynamic adaptation architecture, which dynamically optimizes through cross-correlation function calculation, peak quality evaluation and interpolation strategy, balances the calculation efficiency and accuracy, effectively reduces the mean square error of time delay estimation while ensuring real-time, significantly improves the time delay accuracy compared with the traditional scheme of cross-spectrum method, solves the problem of insufficient time delay accuracy under low signal-to-noise ratio, and the adaptive interpolation strategy ensures robustness under all working conditions, thereby significantly improving the quality of time delay data and making the subsequent steps more effective.

[0050] 3、Prior art usually measures initial sound velocity only based on "sound velocity-time delay" physical model, such as active calibration in CN108194841A, and sound velocity is fixed in subsequent positioning process, but sound velocity propagation has large dynamic uncertainty, which is easily affected by pipe material (steel pipe / cast iron / PVC), pressure, temperature, fluid medium and other factors. Once the sound velocity cannot adapt to the dynamic change in the positioning process, the sound velocity error will inevitably amplify the positioning deviation, and the longer the time delay, the greater the error. To solve this technical problem, the prior art usually uses sound velocity compensation method, such as estimating sound velocity based on temperature / pressure sensor in real time, which reduces sound velocity error to some extent, but it inevitably increases hardware cost and calculation complexity, such as CN118346932A also considers that multi-directional sensors need to train models independently, which undoubtedly increases the calculation load. However, the present application converts the multi-sensor redundant observation into the subsequent error elimination advantage, decouples the sound velocity variable through the space-time relationship of three sensors, constructs a "time delay difference-distance ratio" sound velocity-free positioning algorithm, eliminates the dependence on the "sound velocity" core parameter, mathematically avoids the uncertainty of sound velocity, and directly calculates the position through multi-sensor redundant observation (without preset threshold). In the prior art, the sound velocity error masks the weak signal, resulting in small leakage positioning failure, while the sound velocity-free positioning algorithm eliminates the "sound velocity" parameter to some extent, thereby improving the system sensitivity and enabling small leakage positioning. At the same time, since the sound velocity-free positioning algorithm reduces parameters and simplifies calculation, it is helpful to realize real-time water supply pipeline leakage noise detection.

[0051] 4、Traditional open-loop architecture is easily affected by random errors (such as constant bias or time delay noise caused by distance error ΔD), resulting in fluctuation of positioning results. Therefore, the present application models the state space by regarding the leakage noise positioning as a dynamic system state, iteratively corrects the historical data, separates the process noise (system dynamics) and observation noise (measurement error), absorbs the system dynamics (such as pressure surge) through Q in the prediction process to avoid model rigidity, and dynamically weights the new observation (i.e. the first leakage position) in the update process using Kalman gain to suppress transient anomalies (such as time delay jump under low signal-to-noise ratio), corrects the posteriori estimation based on the new observation and Kalman gain, and outputs high-precision, low-fluctuation leakage noise positioning at centimeter level, thereby completely solving the single calculation fluctuation problem of traditional open-loop architecture and realizing high-robustness positioning of pipeline leakage under varying working conditions.

[0052] Other features and advantages of the present application will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0053] The present application will be further described below in conjunction with the drawings:

[0054] Figure 1 The present application is used for positioning optimization method flowchart for water supply pipeline leakage noise.

[0055] Figure 2 Figure 1 is a schematic diagram of a structure of a positioning optimization system for water supply pipe network leakage noise according to an embodiment of the present application. DETAILED DESCRIPTION

[0056] The technical solutions of the embodiments of the present application will be explained and described below in combination with the drawings of the embodiments of the present application. However, the following embodiments are only preferred embodiments of the present application, and not all. Based on the embodiments in the embodiments, other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0057] In the following description, the appearance of terms such as "inner", "outer", "upper", "lower", "left", "right", etc. only indicates the orientation or positional relationship for the convenience of describing the embodiments and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0058] Embodiment one:

[0059] Please refer to the drawings of the embodiments of the present application Figure 1 , Figure 1 Figure 1 shows a flowchart of a positioning optimization method for water supply pipe network leakage noise provided by an embodiment of the present application.

[0060] As shown in the figure, the positioning optimization method for water supply pipe network leakage noise can at least include the following steps: Figure 1

[0061] Step 101, at least one sensor group is deployed in the water supply pipeline, each sensor group including three noise recorders, for synchronously collecting leakage noise signals.

[0062] It can be understood that the "three noise recorders" in the present embodiment are the minimum hardware requirement for the subsequent sound velocity positioning algorithm, and less than three will not be able to realize the core innovation of the technical solution of the present embodiment. On the one hand, when the length of the water supply pipe network is relatively long, more sensor groups (such as one group every 50m, and three noise recorders in each group) can be designed according to the actual pipe network scene for segmented positioning, to avoid the decline in time delay accuracy caused by signal attenuation, on the other hand, more sensors can also be deployed to form multiple sensor groups according to the actual pipe network scene, such as combination 1 "first noise recorder-second noise recorder-third noise recorder", combination 2 "first noise recorder-second noise recorder-fourth noise recorder"…… The present embodiment does not limit this.

[0063] ​Wherein, the first noise recorder is a reference sensor (position reference), which is deployed at the starting end or key node of the pipeline. The second noise recorder is an auxiliary sensor, which is spaced apart from the first noise recorder by D (unit: meter). The third noise recorder is an auxiliary sensor, which is spaced apart from the first noise recorder by L (unit: meter), and D < L. The sensor spacing is a known distance parameter, which is measured in advance by a tape measure or a laser range finder, and the measurement error cannot be corrected.

[0064] For example, taking three noise recorders as an example, the embodiment can use a high sampling rate analog-to-digital converter (ADC) to synchronously collect time domain signals s1(t), s2(t), and s3(t) of the three sensors. The synchronization mechanism can use GPS or a wireless synchronization module (such as Zigbee) to ensure that the sampling times are aligned as much as possible. It can be understood that if the collection is not synchronized, there is a fixed offset in the time stamp of each noise recorder signal, and the subsequent time delay difference calculation introduces an additional fixed offset, thereby destroying the physical authenticity of the time delay difference, i.e., neglecting the synchronization will cause the entire optimization method proposed in the embodiment to fail.

[0065] It can be understood that the prior art (such as CN118346932A) considers that multi-directional sensors need to be independently trained, which undoubtedly increases the computational load, but the embodiment converts the multi-sensor redundant observation into the subsequent error elimination advantage, and builds a sound speed positioning algorithm based on this to eliminate the dependence on the core parameter "sound speed" and directly solve the leaky noise positioning through multi-sensor redundant observation.

[0066] Step 102, for each sensor group, an adaptive fractional time delay estimation strategy is used to calculate a first time delay difference between the leaky noise signals collected by the first noise recorder and the second noise recorder and a second time delay difference between the leaky noise signals collected by the first noise recorder and the third noise recorder.

[0067] It can be understood that the time delay difference reflects the signal propagation delay. The embodiment improves the time delay accuracy to the sub-sampling level by adaptively estimating the time delay difference between the sensors, so as to solve the time delay error problem caused by the limited sampling rate.

[0068] Specifically, in the embodiment, the calculation process of the first time delay difference or the second time delay difference includes:

[0069] Step 201, an autocorrelation algorithm is used to determine the integer time delay between the leaky noise signals collected by the noise recorder pair.

[0070] Taking the first noise recorder S1 and the second noise recorder S2 as an example, the specific implementation manner of step 201 is:

[0071] First, based on the leakage noise signals collected by two noise recorders, the cross-correlation algorithm is used to calculate the cross-correlation sequence of the two signals under different time delay offsets, which can be expressed as:

[0072] ,

[0073] Wherein, s1[n] represents the discrete sampling signal (length N points) of the first noise recorder, s2[n] represents the discrete sampling signal of the second noise recorder, and n represents the sampling point index , represents the time delay offset, that is, the integer sampling point number, and the delay range is , represents the maximum delay;

[0074] Then, the peak position in the cross-correlation sequence As an integer delay, it can be expressed as:

[0075] ,

[0076] It can be understood that the peak position corresponds to the most similar time offset of the two signals. In the ideal case without noise, Directly equal to the integer part of the real time delay. The target of the cross-correlation function calculation in this embodiment is to extract the time delay coarse estimation of the noise recorder pair from the discrete sampling signal.

[0077] Step 202, based on the determined integer time delay, the peak signal-to-noise ratio is calculated, which provides a decision basis for subsequent adaptive interpolation, which can be expressed as:

[0078] ,

[0079] Wherein, represents the standard deviation of the background noise, , and Δ represents the preset half-width of the peak protection zone, and the background noise is counted after excluding the peak neighborhood.

[0080] Step 203, based on the peak signal-to-noise ratio, dynamically select the interpolation algorithm to calculate the fractional offset between the two signals.

[0081] Wherein, the interpolation algorithm in this embodiment includes parabolic interpolation method, cubic spline interpolation method and long window Sinc interpolation method. The fractional offset between the two signals is the sub-sampling correction value.

[0082] Specifically, in this embodiment, based on the peak signal-to-noise ratio, the interpolation algorithm is dynamically selected to calculate the fractional offset between the two signals, including:

[0083] (a) When the peak signal-to-noise ratio is greater than or equal to the preset upper limit threshold, the parabolic interpolation method is used to calculate the fractional offset between the two signals.

[0084] It can be understood that in the embodiment, the parabolic interpolation method is applied to a scene with sharp peak and low background noise (for example, a laboratory environment). In a high peak signal-to-noise ratio scene, the parabolic interpolation method uses the peak point and the amplitude values of the left and right adjacent points of the peak point for quadratic fitting to calculate the fractional offset between the two signals It can be understood that in the embodiment, the parabolic interpolation method is applied to a scene with sharp peak and low background noise (for example, a laboratory environment). In a high peak signal-to-noise ratio scene, the parabolic interpolation method uses the peak point and the amplitude values of the left and right adjacent points of the peak point for quadratic fitting to calculate the fractional offset between the two signals It can be understood that in the embodiment, the parabolic interpolation method is applied to a scene with sharp peak and low background noise (for example, a laboratory environment). In a high peak signal-to-noise ratio scene, the parabolic interpolation method uses the peak point and the amplitude values of the left and right adjacent points of the peak point for quadratic fitting to calculate the fractional offset between the two signals

[0085] ,

[0086] It can be understood that in the embodiment, in a high peak signal-to-noise ratio scene, the parabolic interpolation method can greatly reduce the calculation amount (3-point operation), optimize the operation speed, and improve the real-time performance.

[0087] (b) When the peak signal-to-noise ratio is less than the preset upper threshold and greater than the preset lower threshold, a cubic spline interpolation method is used to calculate the fractional offset between the two signals.

[0088] It can be understood that in the embodiment, the cubic spline interpolation method is applied to a scene with a slightly blurred peak (such as pipeline background water flow noise). The implementation manner of the cubic spline interpolation method for calculating the fractional offset between the two signals is as follows:

[0089] First, an interpolation point set is constructed, for example four points;

[0090] Next, a cubic spline function is generated, which is continuous and differentiable in the interval .

[0091] Finally, the fractional offset is searched, and the maximum value is searched, that is, the corresponding δ. It can be expressed as:

[0092] ,

[0093] It can be understood that in the embodiment, in a medium peak signal-to-noise ratio scene, the cubic spline interpolation method can use more points to suppress local fluctuations, thereby improving the accuracy.

[0094] (c) When the peak signal-to-noise ratio is less than or equal to the preset lower threshold, a long window Sinc interpolation method is used to calculate the fractional offset between the two signals.

[0095] It can be understood that a low peak signal-to-noise ratio indicates that environmental noise causes the correlation peak to be flat or pseudo-peak. In the embodiment, the long window Sinc interpolation method is applied to a scene where strong noise causes the peak to be severely distorted (such as nearby road vibration, mechanical vibration interference). In a low peak signal-to-noise ratio scene, the long window Sinc interpolation method performs a long window Sinc interpolation on the original waveform or the cross-correlation function of the two signals nearby The long-window Sinc interpolation is expressed as:

[0096]

[0097] where d represents the fractional offset (step size 0.01 sample point) to be searched, represents an ideal interpolation kernel function, w[n] represents a window function (such as a Hamming window) for suppressing spectral leakage, and N represents a window length half-width (for example, N = 5, and a total of 11 points participate in interpolation).

[0098] Then traverse Take The maximum position is the corresponding delta.

[0099] It can be understood that in the embodiment, in a low peak signal-to-noise ratio scene, the long-window Sinc interpolation method can effectively prompt the frequency domain anti-aliasing capability, but the calculation amount is significantly increased.

[0100] It can be understood that in the embodiment, in a low peak signal-to-noise ratio scene, the long-window Sinc interpolation method can effectively prompt the frequency domain anti-aliasing capability, but the calculation amount is significantly increased.

[0101] Step 204, fusing the integer time delay and the fractional offset to obtain the first time delay difference or the second time delay difference of the sub-sampling stage.

[0102] Specifically, in the embodiment, the integer time delay and the fractional offset are fused to obtain the first time delay difference or the second time delay difference of the sub-sampling stage, which is expressed as:

[0103]

[0104] wherein, represents the first time delay difference or the second time delay difference of the sub-sampling stage, represents the integer time delay between two signals, represents the fractional offset between two signals, represents a fixed sampling rate.

[0105] It can be understood that the noise recorders are two pairs of noise recorders, i.e., the first noise recorder and the second noise recorder, or the first noise recorder and the third noise recorder. The embodiment only describes the calculation method of the time delay difference between one pair of noise recorders (for example, the first time delay difference between the first noise recorder and the second noise recorder), and the calculation method of the time delay difference between the other pair of noise recorders (for example, the second time delay difference between the first noise recorder and the third noise recorder) can be referred to the embodiment, and details are not described herein.

[0106] ​It can be understood that for the time delay estimation link in the leakage noise positioning of the water supply network, the embodiment creatively proposes a signal quality perception-algorithm dynamic adaptation architecture. Through cross-correlation function calculation, peak quality evaluation and interpolation strategy dynamic optimization, the calculation efficiency and accuracy are balanced, the mean square error of time delay estimation is effectively reduced while ensuring real-time, the time delay accuracy is greatly improved compared with the cross-spectrum method of the traditional scheme, the problem of insufficient time delay accuracy under low signal-to-noise ratio is solved, and the adaptive interpolation strategy guarantees the robustness of all working conditions, thereby significantly improving the time delay data quality and making the subsequent steps more effective.

[0107] In step 103, based on the first time delay difference, the second time delay difference, and the distance between the first noise recorder and the second noise recorder and the third noise recorder, the first leakage position is determined, which can be represented as:

[0108] ,

[0109] Wherein z represents the first leakage position, that is, one-dimensional leakage noise positioning after eliminating the sound velocity error, D represents the distance between the first noise recorder and the second noise recorder, represents the first time delay difference between the leakage noise signals collected by the first noise recorder and the second noise recorder, L represents the distance between the first noise recorder and the third noise recorder, represents the second time delay difference between the leakage noise signals collected by the first noise recorder and the third noise recorder, Used to suppress the long-distance error in the denominator (the error decreases as the denominator increases).

[0110] It can be understood that the prior art is usually based on the "sound velocity-time delay" physical model. For example, the active calibration in CN108194841A only measures the initial sound velocity, and the sound velocity is fixed in the subsequent positioning process. However, the sound velocity propagation has great dynamic uncertainty and is easily affected by multiple factors such as pipe material (steel pipe / cast iron / PVC), pressure, temperature, and fluid medium. Once the sound velocity cannot adapt to the dynamic change in the positioning process, the sound velocity error will inevitably amplify the positioning deviation, and the longer the time delay, the greater the error. To solve this technical problem, the embodiment converts the multi-sensor redundant observation into the subsequent error elimination advantage, decouples the sound velocity variable through the space-time relationship of the three sensors, constructs a sound velocity-free positioning algorithm based on "time delay difference-distance ratio", eliminates the dependence on the core parameter "sound velocity", mathematically avoids the uncertainty of sound velocity, directly calculates the position through multi-sensor redundant observation (without preset threshold), and effectively detects small leakage positioning. At the same time, since the sound velocity-free positioning algorithm reduces the parameters and simplifies the calculation, it is helpful to realize real-time water supply pipeline leakage noise detection.

[0111] Step 104: The Kalman filter algorithm is used to dynamically and recursively correct the first leakage location to obtain the second leakage location, which is used as the final leakage noise location of the water supply network.

[0112] Understandably, in this embodiment, the Kalman filter algorithm achieves dynamic optimization of the location of the missing point through recursive state estimation, thereby solving the error accumulation problem of the open-loop architecture. Its core is to suppress random fluctuations through the "prediction-update" closed-loop mechanism, transforming the instantaneous location output by the soundless positioning algorithm into a smooth and stable position estimation sequence, thereby improving the robustness of the positioning.

[0113] Specifically, in this embodiment, the Kalman filter algorithm is used to dynamically and recursively correct the first leakage location to obtain the second leakage location, including:

[0114] Step 301: Locate the leakage noise as a state variable. And initializing the optimal state estimate and prediction error covariance, can be expressed as:

[0115] ,

[0116] in, This represents the initial optimal state estimate, which can be the initial first leakage location. , This represents the initial prediction error covariance, which can be preset based on the actual water supply network conditions.

[0117] Step 302: Based on the optimal state estimate of the previous time step, predict the state of the current time step, denoted as the predicted value, and increment the prediction error covariance to model the dynamic changes of the pipeline, which can be expressed as:

[0118] ,

[0119] ,

[0120] in, This represents the predicted state (location prior estimate) at time k. express The optimal state estimate at time (posterior). This represents the prediction error covariance, which reflects the uncertainty of the state estimate. A larger value indicates that the predicted state is less reliable, and a smaller value indicates that the predicted state is more reliable. express The updated prediction error covariance at the moment, Q is the process noise variance, indicating the strength of the system dynamic change, which can be preset or dynamically adjusted according to the actual application scene (such as online adjustment according to pressure sensor data, increasing when the pressure suddenly changes to accelerate tracking), the value is 0.01-0.1, a small value indicates that the leakage point position changes slowly (such as a stable pipeline), and a large value adapts to the pressure change to accelerate tracking and improve the dynamic response speed, which is not limited in the embodiment. The increment (+Q) reflects the accumulation of prediction uncertainty, which provides a correction basis for subsequent updates.

[0121] Step 303, calculate the Kalman gain based on the increment of the prediction error covariance, which can be represented as:

[0122] ,

[0123] Wherein, K represents the Kalman gain, that is, the observation weight coefficient, ranging from 0 to 1, Close to 1 indicates trust in the new observation value (I.e. the first leakage position at time k), close to 0 indicates trust in the predicted value . R represents the observation noise variance, which is used to reflect the measurement reliability and can be determined by time delay estimation error analysis, such as increasing R when the signal-to-noise ratio is low, which is not limited in the embodiment.

[0124] It can be understood that when Large, indicating that the predicted state is unreliable, at this time Close to 1, then prefer to adopt the new observation value (I.e. the first leakage position at time k); when R is large, indicating that the observation noise is large, at this time Close to 0, then prefer to adopt the predicted value , depending on the history prediction to suppress the observation noise amplification, thereby effectively realizing the adaptive correction of the leakage noise positioning and improving the stability of the small leakage detection of the water supply network.

[0125] Step 304, fuse the first leakage position at the current moment with the predicted value based on the Kalman gain to correct the state, and obtain the optimal state estimation at the current moment, denoted as the second leakage position, which can be represented as:

[0126] ,

[0127] Step 305, update the increment of the prediction error covariance based on the Kalman gain, and obtain the updated prediction error covariance, which can be represented as:

[0128] ,

[0129] It can be understood that, Recursive decrease in the update process forces the estimation to converge.

[0130] It can be understood that the traditional open-loop architecture is susceptible to random errors (such as constant bias or delay noise caused by distance error ΔD) in single calculation, resulting in fluctuation of positioning results. Therefore, the present embodiment regards the leak-noise positioning as a dynamic system state, models the state space, iteratively corrects through historical data, simultaneously separates process noise (system dynamics) and observation noise (measurement error), absorbs system dynamics (such as pressure mutation) through Q in the prediction process, avoids model rigidity, and utilizes Kalman gain Dynamic weighting of new observations (i.e. the first leak location at time k), suppresses transient anomalies (such as delay jump under low signal-to-noise ratio), and is based on observations and Kalman gain corrects the posteriori estimation, and outputs high-precision, low-fluctuation leak-noise positioning at centimeter level, thereby completely solving the fluctuation problem of single calculation of the traditional open-loop architecture, and realizing high-robustness positioning of pipeline leaks under varying working conditions.

[0131] In summary, the present embodiment creatively solves the core problems of the prior art by synergistically fusing adaptive fractional delay estimation strategy, sound-speed-free positioning algorithm, and Kalman filter closed-loop architecture, in view of the inherent limitations (amplification of multiple error coupling) of the correlation detection method. Specifically, first, the adaptive fractional delay estimation strategy (based on dynamic selection of parabolic / third-order spline / long-window Sinc interpolation algorithm based on cross-correlation peak signal-to-noise ratio) improves the delay accuracy to sub-sampling level, significantly suppresses the correlation peak ambiguity problem under low signal-to-noise ratio, and provides high-precision delay difference input for subsequent steps; second, the first leak location (i.e. sound-speed-free positioning algorithm) is determined based on the distance between the first delay difference, the second delay difference, and the first noise recorder, and the second noise recorder and the third noise recorder, respectively, utilizes the spatio-temporal relationship of the three sensors to mathematically eliminate the sound speed variable, avoids parameter errors caused by dynamic factors such as pipe material and temperature, and simultaneously suppresses the amplification of long-distance positioning error in the denominator term ); finally, the Kalman filter takes the positioning results output from the front end as the observation value, dynamically separates the process noise (Q parameter absorbs system dynamics such as pressure mutation) and the observation noise (R parameter suppresses delay fluctuation) through the state space model (prediction-update recursion), and utilizes the Kalman gain The adaptive weighted fusion of historical estimates and real-time observations is dynamically recursively corrected to realize a centimeter-level stable output. The three cooperate to form a closed-loop optimized link of "high-precision time delay input → parameter error elimination → dynamic noise suppression": the adaptive time delay estimation ensures the reliability of the input of the sound speed-free positioning algorithm, the sound speed-free positioning algorithm reduces the error propagation source of the Kalman filter, and the Kalman filter further compensates for the remaining error through closed-loop feedback, and the stability of the output of the Kalman filter in turn reduces the sensitivity requirement for the accuracy of the time delay estimation, realizes the true "error decoupling", and finally achieves the technical effect of "1+1>2" - compared with the prior art, the leakage noise positioning accuracy of the water supply network is effectively improved, the small leakage detection rate is improved, and the fluctuation amplitude under variable working conditions is reduced, thereby fundamentally solving the technical defects of sound speed dependence, error coupling and open-loop architecture.

[0132] Embodiment Two:

[0133] This embodiment is only compared with Figure 1 The corresponding embodiment is only described in the part of the difference, and the technical concept of the remaining design is similar to that of Embodiment One. This embodiment will not be described here. In order to further reduce the randomness of single positioning and improve the robustness of leakage noise positioning, in this embodiment, after step 305, the following steps are further included:

[0134] Step 306, steps 302-305 are repeatedly performed for each new time of the first leakage position to obtain a position sequence of the second leakage position M represents the number of consecutive new observations;

[0135] Step 307, the position sequence of the second leakage position is judged for convergence, when the convergence is judged, the average value of the position sequence of the second leakage position is calculated as the final leakage noise positioning of the water supply network.

[0136] Specifically, the variance of the position sequence is calculated to judge the convergence, which can be expressed as:

[0137]

[0138] Wherein, represents the mean value of the position sequence, represents a preset convergence threshold (for example, 0.01 m²), which is not limited in this embodiment.

[0139] It can be understood that if , it is determined that the position sequence converges, otherwise new frames are continuously collected. When the convergence is judged, the average value of the position sequence of the second leakage position can be calculated, which can be expressed as:

[0140] ​​

[0141] wherein P represents the window size, which can be adaptively adjusted according to the signal-to-noise ratio; for example, when the peak signal-to-noise ratio is greater than or equal to a preset upper threshold, P is selected as 30 frames, so as to quickly respond and avoid excessive smoothing of small leakage changes; when the peak signal-to-noise ratio is less than the preset upper threshold and greater than a preset lower threshold, P is selected as 50 frames, so as to balance noise suppression and real-time performance; when the peak signal-to-noise ratio is less than or equal to the preset lower threshold, P is selected as 100 frames, so as to sufficiently average in strong noise and improve stability. The final leakage noise positioning x of the water supply network needs to satisfy 0 < x < L (wherein L represents the distance between the first noise recorder and the third noise recorder).

[0142] It can be understood that the prior art usually relies on a single measurement to directly output the positioning result, without considering random fluctuations caused by environmental noise, pressure surges and other factors, resulting in large errors in the actual measurement positioning result and low detection rate of small leakage points. At the same time, the prior art is difficult to distinguish between random jumps of the positioning result and real leakage signals, and is prone to misjudgment of noise interference as a leakage point, resulting in a high false alarm rate in the pressure fluctuation scenario. Even if some technologies may use a fixed frame number averaging scheme, they do not dynamically adjust according to the signal-to-noise ratio, resulting in poor averaging effect and high error in low signal-to-noise ratio, and response delay in high signal-to-noise ratio. Therefore, the present embodiment breaks through the rigid mode of traditional fixed frame number averaging, and judges the result stability in real time by calculating the variance of the position sequence, distinguishes between real leakage and noise fluctuations, filters transient jumps, and avoids invalid averaging, thereby effectively reducing the false alarm rate in the pressure fluctuation scenario. At the same time, by dynamically binding the window size P and the signal-to-noise ratio, small window is given priority in high signal-to-noise ratio, thereby shortening the processing time and avoiding resource waste, and large window is used for averaging in low signal-to-noise ratio to enhance weak signals, combined with geometric constraints to exclude illegal values, to ensure that the result conforms to the physical structure of the pipeline, thereby effectively improving the small leakage detection rate. The dynamic output process designed in the present embodiment converts the "single calculation result fluctuation" of the traditional open-loop output into "steady and reliable" of the closed-loop protection through the three-level optimization of convergence detection, resource adaptation and physical verification, which is especially suitable for complex detection requirements of variable working condition water supply networks.

[0143] Embodiment Three:

[0144] Please refer to the attached Figure 2 , Figure 2 The positioning optimization system structure schematic diagram for leakage noise of water supply network provided by an embodiment of the present specification.

[0145] As Figure 2 shown, the positioning optimization system for leakage noise of water supply network can at least include:

[0146] A data acquisition module 1 is configured to deploy at least one sensor group on the water supply pipeline, each sensor group including three noise recorders for synchronously collecting leakage noise signals;

[0147] a time delay difference calculation module 2 configured to calculate a first time delay difference between the leak noise signals collected by the first noise recorder and the second noise recorder and a second time delay difference between the leak noise signals collected by the first noise recorder and the third noise recorder for each sensor group by using an adaptive fractional time delay estimation strategy respectively;

[0148] a first leak location determination module 3 configured to determine a first leak location based on the first time delay difference, the second time delay difference, and the distances between the first noise recorder and the second noise recorder and the third noise recorder respectively;

[0149] a second leak location output module 4 configured to obtain a second leak location by using a Kalman filtering algorithm to dynamically and recursively correct the first leak location, and the second leak location is used as the final leak noise positioning of the water supply network.

[0150] It can be understood that the technical concept of the positioning optimization system for leak noise of the water supply network provided by the embodiment is similar to the technical concept of the positioning optimization method for leak noise of the water supply network, and the embodiment will not be described here.

[0151] Embodiment Four

[0152] Yet another embodiment of the present specification provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer or a processor, cause the computer or the processor to perform the steps of one or more of the above-mentioned embodiments. Each component module of the above-mentioned electronic device, if realized in the form of a software function unit and used as an independent downstream task prediction or use, can be stored in the computer-readable storage medium.

[0153] In the embodiments described above, all or some of the operations can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or some of the operations can be implemented in the form of one or more computer programs. The computer program is stored in a computer readable medium, which can be any data storage device that can store data which can be accessed by a computer. The computer readable medium includes one or more of a floppy disk, a compact disk, a DVD, a Blu-ray disk, a hard disk drive, a solid state drive, a magnetic tape, a RAM, a ROM, a PROM, an EPROM, a FLASH, a digital versatile disk (DVD), a memory stick, a memory card, a register, and the like. The computer program is downloaded to the computer from an external computer or websites. Alternatively, the computer program can be stored in a computer readable medium in the computer or can be read by the computer from the external computer or websites. The computer program is executed by the computer. The computer program instructs the computer to perform the operations described above. The computer program can be implemented in any programming language, such as assembly language, machine language, a high-level programming language, etc. The computer program can be implemented in the form of a stand-alone program or a plug-in. The computer program can be implemented in the form of an applet. The computer program can be implemented in the form of a thread of execution. The computer program can be implemented in the form of a computer program product.

[0154] The above description merely provides exemplary embodiments of the present disclosure and a principle of applied technology. It should be understood by those skilled in the art that the scope of protection of the present disclosure is not limited to the above-described technical solutions consisting of a specific combination of technical features, and also covers other technical solutions formed by any combination of the above technical features or equivalent features thereof without departing from the concept of the present disclosure. For example, technical solutions formed by replacing the above-described features with technical features disclosed in the present disclosure (but not limited to) having similar functions.

[0155] Furthermore, although the operations are depicted in a particular order, this should not be understood as requiring the operations to be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing can be advantageous. Likewise, the various embodiments can be described in the context of a single embodiment, but this is not intended to limit the scope of the present disclosure to a single embodiment. Certain features that are described in the context of a single embodiment can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented separately or in any suitable subcombination in multiple embodiments.

Claims

1. A method for positioning optimization of leakage noise in a water distribution network, characterized in that, The method comprises the following steps: deploying at least one sensor group on the water supply pipeline, each sensor group comprising three noise recorders for synchronously collecting leakage noise signals; calculating, for each sensor group, a first time delay difference between the leakage noise signals collected by the first noise recorder and the second noise recorder and a second time delay difference between the leakage noise signals collected by the first noise recorder and the third noise recorder by using an adaptive fractional time delay estimation strategy; determining a first leakage position based on the first time delay difference, the second time delay difference and the distance between the first noise recorder and the second noise recorder and the third noise recorder; dynamically and recursively correcting the first leakage position by using a Kalman filtering algorithm to obtain a second leakage position as the final leakage noise positioning of the water supply pipeline network; wherein the calculation process of the first time delay difference or the second time delay difference comprises: determining an integer time delay between the leakage noise signals collected by the noise recorders by using a cross-correlation algorithm; calculating a peak signal-to-noise ratio based on the determined integer time delay; dynamically selecting an interpolation algorithm to calculate a fractional offset between the two signals based on the peak signal-to-noise ratio; fusing the integer time delay and the fractional offset to obtain the first time delay difference or the second time delay difference at the sub-sampling level.

2. The positioning optimization method for leakage noise of a water supply pipeline network according to claim 1, wherein determining an integer time delay between the leakage noise signals collected by the noise recorders by using a cross-correlation algorithm comprises: calculating a cross-correlation sequence of the two signals at different time delay offsets by using a cross-correlation algorithm based on the leakage noise signals collected by the two noise recorders; taking the peak position in the cross-correlation sequence as the integer time delay.

3. The positioning optimization method for leakage noise of a water supply pipeline network according to claim 1, wherein dynamically selecting an interpolation algorithm to calculate a fractional offset between the two signals based on the peak signal-to-noise ratio comprises: when the peak signal-to-noise ratio is greater than or equal to a preset upper threshold, calculating the fractional offset between the two signals by using a parabolic interpolation method; when the peak signal-to-noise ratio is less than the preset upper threshold and greater than a preset lower threshold, calculating the fractional offset between the two signals by using a cubic spline interpolation method; when the peak signal-to-noise ratio is less than or equal to the preset lower threshold, calculating the fractional offset between the two signals by using a long-window Sinc interpolation method.

4. The positioning optimization method for leakage noise of a water supply pipeline network according to claim 1, wherein fusing the integer time delay and the fractional offset to obtain the first time delay difference or the second time delay difference at the sub-sampling level is represented as: ， wherein, denotes a first or second latency difference of a subsampling stage, denotes an integer latency between two signals, denotes a fractional offset between two signals, denotes a fixed sampling rate.

5. The positioning optimization method for leakage noise of a water supply pipeline network according to claim 1, wherein determining a first leakage position based on the first time delay difference, the second time delay difference and the distance between the first noise recorder and the second noise recorder and the third noise recorder is represented as: ， wherein z represents the first leak location, D represents the distance between the first noise recorder and the second noise recorder, represents the first time delay difference between the leak noise signals collected by the first noise recorder and the second noise recorder, represents the second time delay difference between the leak noise signals collected by the first noise recorder and the third noise recorder, and L represents the distance between the first noise recorder and the third noise recorder.

6. The positioning optimization method for leakage noise of a water supply pipeline network according to claim 1, wherein dynamically and recursively correcting the first leakage position by using a Kalman filtering algorithm to obtain a second leakage position comprises: step 301, taking the leakage noise positioning as a state variable and initializing an optimal state estimation and a prediction error covariance. Step 302, predicting the state at the current time based on the optimal state estimation at the previous time, denoted as a predicted value, and incrementing the prediction error covariance to model the dynamic changes of the pipeline; Step 303, calculating the Kalman gain based on the incremented prediction error covariance; Step 304, fusing the first leakage location at the current time with the predicted value based on the Kalman gain to correct the state, obtaining the optimal state estimation at the current time, denoted as the second leakage location; Step 305, updating the incremented prediction error covariance based on the Kalman gain, obtaining the updated prediction error covariance.

7. The positioning optimization method for water supply pipeline leakage noise according to claim 6, further comprising: Step 306, repeating steps 302-305 for each new time of the first leakage location to obtain a position sequence of the second leakage location; Step 307, performing convergence judgment on the position sequence of the second leakage location, and when the judgment converges, calculating the average value of the position sequence of the second leakage location as the final leakage noise positioning of the water supply pipeline. comprising:

8. A system for the positioning optimization of leak noise in a water distribution network, characterized by, a data acquisition module configured to deploy at least one sensor group on the water supply pipeline, each sensor group comprising three noise recorders configured to synchronously acquire leakage noise signals; a time delay difference calculation module configured to calculate a first time delay difference between the leakage noise signals acquired by the first noise recorder and the second noise recorder and a second time delay difference between the leakage noise signals acquired by the first noise recorder and the third noise recorder for each sensor group using an adaptive fractional time delay estimation strategy; a first leakage location determination module configured to determine the first leakage location based on the first time delay difference, the second time delay difference, and the distance between the first noise recorder and the second noise recorder and the third noise recorder, respectively; a second leakage location output module configured to dynamically and recursively correct the first leakage location using a Kalman filtering algorithm to obtain the second leakage location as the final leakage noise positioning of the water supply pipeline; wherein the time delay difference calculation module is configured to perform the following steps: determining the integer time delay between the leakage noise signals acquired by the noise recorders using a cross-correlation algorithm; calculating the peak signal-to-noise ratio based on the determined integer time delay; dynamically selecting an interpolation algorithm to calculate the fractional offset between the two signals based on the peak signal-to-noise ratio; fusing the integer time delay and the fractional offset to obtain the first time delay difference or the second time delay difference at the sub-sampling level. The computer program is executed by the processor to implement the positioning optimization method for water supply pipeline leakage noise according to any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, ​

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