A method and system for leak diagnosis of a water supply network

Abstract

The present application relates to the field of data processing, in particular to a kind of leakage diagnosis method and system for water supply network, method includes: collecting water supply pipeline normal and leakage signal, Fourier transform is converted into frequency domain, according to low, middle, high frequency division area, calculate the frequency difference of each area to determine target frequency;According to target frequency, signal is divided into sliding window, calculate the frequency domain similarity of real-time signal and historical signal, determine the possibility of leakage signal, exceed threshold value, determine leakage;Combining with the time of first picking up leakage signal of multiple sensors locates leakage position.The present application realizes the accurate, fast detection and positioning of water supply pipeline leakage by comprehensively using signal processing technology and real-time data analysis, provides strong guarantee for the safe, efficient operation of pipeline system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing. More particularly, the present application relates to a method and system for diagnosing leaks in a water supply network. BACKGROUND

[0002] As an important part of the city lifeline, the safe operation of the water supply network is a basic guarantee for urban construction and people's life. However, due to various factors, water supply network leakage problems occur from time to time, causing inconvenience to urban management and people's life. With the development of science and technology, modern water supply network monitoring system can monitor the operation state and water quality of water supply network in real time by using new generation information technology such as artificial intelligence Internet of Things, big data, mobile Internet, industrial Internet and hydraulic model. Through these technical means, the leakage point can be found and located in time, so that corresponding measures can be taken for repair. At the same time, the monitoring system can also analyze historical data to predict future leakage risks and issue early warnings, providing strong guarantee for the safety of urban water supply.

[0003] At present, the existing technology such as the patent application file with publication number CN117823830A discloses a leakage detection method and system of intelligent detection ball for water supply pipeline, which comprises the following steps: setting an ultrasonic transmitter on the pipeline section to be detected and putting a detection ball into the pipeline to collect acoustic information; decomposing the collected acoustic signal into a plurality of decomposition signals according to different decomposition parameters, and obtaining the best decomposition parameter and the best decomposition signal corresponding to the parameter; converting the signal from time domain to frequency domain by Fourier transform to obtain the ultrasonic characteristic frequency domain component of the best decomposition signal; identifying the ultrasonic characteristic frequency domain component by using a pattern recognition method to obtain the signal corresponding to the pipeline leakage and the time of leakage occurrence; and determining the specific position of leakage occurrence by using the time of leakage occurrence combined with the corresponding relationship between position and time, so as to realize the detection of leakage.

[0004] However, when dealing with non-stationary signals such as leakage signals, Fourier transform has certain limitations, for example, the frequency components of water supply pipeline leakage signals change with time, there are signal noises inside the pipeline, etc., which makes it difficult to accurately capture the time-varying characteristics of leakage signals, thereby affecting the accuracy of leakage detection. SUMMARY

[0005] To solve the above technical problems of processing non-stationary signals such as leakage signals, which have certain limitations and are difficult to accurately capture the time-varying characteristics of leakage signals, thereby affecting the accuracy of leakage detection, the present application provides solutions in the following aspects.

[0006] In a first aspect, a method for diagnosing leaks in a water supply network, comprising:

[0007] Collect normal signals and leakage signals of water supply pipeline history after pretreatment;

[0008] Convert the historical normal signals and leakage signals from time domain to frequency domain by Fourier transform, divide the signals according to the frequency coverage range, and the division result is low, medium and high frequency regions; in each region, calculate the frequency difference of the historical normal signals and leakage signals to determine the target frequency of each region;

[0009] The collected historical normal signals and leakage signals are divided by sliding window according to the target frequency of each region, the frequency domain similarity of the real-time signal at each time in each region window with the historical normal signals and leakage signals is calculated, and based on the frequency domain similarity, the possibility of the real-time signal at this time being a leakage signal is calculated, if the possibility is greater than a preset threshold, it is determined as a leakage signal;

[0010] According to the time when the plurality of sensors first pick up the leakage signal, the leakage position is located, wherein the time when the plurality of sensors first pick up the leakage signal is obtained when the possibility of the calculated real-time signal being a leakage signal is greater than a preset threshold.

[0011] The present application converts the signal from time domain to frequency domain by Fourier transform, divides low, medium and high frequency regions, can more clearly identify the frequency difference between normal signals and leakage signals, determines the target frequency in each frequency region, further enhances the capture ability of the leakage signal characteristics, then through sliding window division and frequency domain similarity calculation, the change of the signal can be monitored in real time, and the characteristics similar to the leakage signal can be quickly identified in the frequency domain, when the frequency domain similarity of the real-time signal at a certain time exceeds the preset threshold, it is determined as a leakage signal, and then the signal whether it is a leakage signal can be more accurately judged, finally, by using the time when the plurality of sensors first pick up the leakage signal, the leakage position can be located by time difference calculation and other methods, thereby improving the sensitivity and accuracy of the leakage detection, realizing accurate positioning and optimizing the signal processing process, which helps to improve the safety and reliability of the whole water supply system.

[0012] Preferably, the frequency difference of the historical normal signals and leakage signals satisfies the relationship:

[0013] ; in the formula, is the frequency domain difference of the historical normal signals and leakage signals at frequency , is the number of historical normal signals, is the number of historical leakage signals, represents the spectral amplitude of the first historical normal signal at frequency , represents the spectral amplitude of the first A historical leakage signal at frequency The spectral amplitude below.

[0014] By comparing different frequencies The value can reveal which frequency components of the leaked signal differ most from the normal signal, thus allowing for the extraction of representative frequency domain features.

[0015] Preferably, determining the target frequency for each region includes:

[0016] The frequency corresponding to the maximum frequency domain difference in each region is taken as the target frequency for each region.

[0017] By selecting the frequency corresponding to the maximum frequency domain difference as the target frequency, the most distinctive frequency components in the signal can be accurately located. These frequency components often contain the core information of signal state changes (such as normal and fault states).

[0018] Preferably, determining the target frequency for each region further includes:

[0019] For each region, calculate the relative change between the frequency domain difference of the historical normal signal and leakage signal at the target frequency and the mean of the frequency domain difference in the region where the target frequency is located, and label it as the degree of frequency domain difference between the historical normal signal and leakage signal at the target frequency.

[0020] The optimal partitioning result is the region with the largest ratio between the frequency domain difference between the historical normal signal and the leakage signal at the target frequency in the low-frequency region and the frequency domain difference between the historical normal signal and the leakage signal at the target frequency in the high-frequency region. The target frequency of each region in the optimal partitioning result is then taken as the optimal target frequency.

[0021] By comparing the ratio of the frequency domain differences between historical normal signals and leakage signals at the target frequency in the low-frequency and high-frequency regions, the region division with the largest ratio is selected as the optimal division. This means that when selecting regions, priority is given to those frequency bands where the frequency domain differences between normal signals and leakage signals are most significant and easiest to distinguish. Based on the optimal division results, the target frequency of each region is determined as the optimal target frequency, so that in practical applications, these frequencies can be monitored and analyzed more specifically, improving the accuracy and efficiency of signal processing.

[0022] Preferably, the frequency domain similarity between the real-time signal and the historical leaked signal within the low-frequency region window at each moment satisfies the following relationship:

[0023] In the formula, The frequency domain similarity between the real-time signal and the historical leakage signal within a window divided based on the target frequency in the low-frequency region is calculated. a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0024] a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0025] a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0026] a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0027] a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0028] a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0029] a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0030] a spectrum amplitude of the real-time signal in a first window in a low frequency region

[0031] The beneficial effects of the present application are:

[0032] The present application effectively deals with the problem of processing the non-stationary signal of the leakage signal through frequency domain analysis, frequency region division, sliding window technology, quantitative determination and position positioning, etc. The accuracy of the leakage detection is improved by focusing on the frequency characteristics that can best distinguish the normal signal and the leakage signal, and capturing the time-varying characteristics of the signal in real time. BRIEF DESCRIPTION OF DRAWINGS

[0033] The above and other objects, features and advantages of the exemplary embodiments of the present application will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein like or corresponding elements refer to like or corresponding parts throughout the several embodiments, and in which:

[0034] Figure 1 is a method flowchart of steps S1-S4 in a leakage diagnosis method for a water supply network according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0036] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0037] Referring to Figure 1 A leakage diagnosis method for a water supply network includes steps S1-S4, and specifically as follows:

[0038] S1: Collecting historical preprocessed normal signals and leakage signals of the water supply pipeline.

[0039] Specifically, acoustic sensors are installed at the starting and ending positions of each pipeline of the water supply network, and historical normal signals and leakage signals in the pipeline and real-time signals of the water supply network to be diagnosed are collected.

[0040] Further, a high-pass filter is used to remove environmental noise such as fluid flow and mechanical noise, to obtain the preprocessed historical normal signals and leakage signals of the water supply network.

[0041] Among them, the sampling frequency of the sensor is set to 10 kHz, and the collection time is 10 minutes.

[0042] S2: The historical normal signal and leakage signal are transformed from the time domain to the frequency domain through Fourier transform. The signal is divided into low, medium and high frequency regions according to the frequency coverage. In each region, the frequency difference between the historical normal signal and leakage signal is calculated to determine the target frequency of each region.

[0043] In water supply pipeline systems, leakage signals are often quite weak, while the noise signals from normal fluid flow are much stronger. In the time domain, it is difficult to directly identify leaks simply by observing the amplitude, slope, and other characteristics of the signal. Therefore, Fourier transform is used to convert the signal to the frequency domain for analysis.

[0044] Specifically, Fourier transform is used to convert both the historical normal signal and the leakage signal acquired in S1 into frequency domain signals. Since leakage signals typically exhibit enhanced high-frequency components or abrupt changes in characteristic frequency bands in the frequency domain, while environmental noise is usually concentrated in low frequencies and low-frequency components attenuate less during signal propagation, the signal can be initially divided into three frequency regions: low, medium, and high, based on the frequency coverage. For example, one division result is given: the low-frequency region is (30 Hz, 300 kHz), the medium-frequency region is (300 kHz, 3 MHz), and the high-frequency region is (3 MHz, 30 MHz). Various other division results can be given in other embodiments.

[0045] Furthermore, in order to find the significant differences between historical leakage signals and normal signals in the frequency domain, the frequency domain differences between historical normal signals and leakage signals at each frequency are calculated by comparing the spectral amplitudes of normal signals and leakage signals at the same frequency within each frequency domain region.

[0046] For example, any frequency within the low, medium, and high frequency domains can be selected. Calculate historical normal signals and leakage signals at frequencies The frequency domain difference, i.e., satisfying the following relationship:

[0047]

[0048] In the formula, For historical normal signals and leakage signals at frequency Frequency domain differences The number of normal signals in history. The number of historical leak signals, Indicates the first A historical normal signal at frequency The spectral amplitude below, Indicates the first A historical leakage signal at frequency The spectral amplitude below.

[0049] By calculating at frequency The average difference in spectral amplitude between all historical normal signals and all historical leakage signals is used. The greater the difference, the more obvious the characteristics of the leakage signal at that frequency.

[0050] Based on the above historical normal signals and leakage signals at frequency The frequency domain difference can be calculated similarly for all other frequencies, including historical normal signals and leakage signals.

[0051] Furthermore, for the low, medium, and high frequency regions, each region will be further divided into... The frequency corresponding to the maximum value is marked as the target frequency. This means that within each region, the difference between the normal signal and the leakage signal at the target frequency is the most significant.

[0052] Furthermore, for a target frequency within a frequency range, the relative differences between historical normal signals and leakage signals at the target frequency are calculated, satisfying the following relationship:

[0053]

[0054] In the formula, To be at the target frequency The degree of frequency domain difference between the normal signal and the leaked signal. For historical normal signals and leakage signals at the target frequency Frequency domain differences For historical normal signals and leakage signals at the target frequency The mean of the frequency domain differences across all frequencies within the region.

[0055] The above The larger the value, the higher the target frequency. The more significant the frequency domain difference between normal signals and leaked signals within the target frequency, the better.

[0056] Based on the degree of frequency domain difference between different frequency regions at the target frequency, the three frequency regions of low, medium and high in the above preliminary division results are evaluated in order to find a region division scheme that can maximize the difference between the leaked signal and the normal signal.

[0057] To evaluate the above-mentioned preliminary division of low, medium and high frequency regions, the degree of frequency domain difference of different regions at the target frequency is considered. In particular, the degree of frequency domain difference at the target frequency of the low frequency region and the high frequency region is focused on, and then the ratio of the degree of frequency domain difference of the historical normal signal and the leakage signal at the target frequency in the low frequency region to the degree of frequency domain difference of the historical normal signal and the leakage signal at the target frequency in the high frequency region is selected. The maximum region division result is the optimal division result, that is, under this division, the signal difference of the low frequency region and the high frequency region is the most significant, which may be more conducive to subsequent signal processing or analysis.

[0058] Further, the optimal target frequency of each frequency domain range in the optimal division result is found according to the above-mentioned calculation step according to the relevant frequency domain difference in the preliminary division result.

[0059] S3: The collected historical normal signal and leakage signal are divided into sliding windows according to the target frequency of each region, the frequency domain similarity of the real-time signal at each time in each region window with the historical normal signal and the leakage signal is calculated, and based on the frequency domain similarity, the possibility that the real-time signal at this time is a leakage signal is calculated. If the possibility is greater than a preset threshold, it is determined to be a leakage signal.

[0060] Firstly, the historical normal signal and leakage signal collected in the above S1 are divided into sliding windows according to the target frequency of the low, medium and high frequency regions in the optimal division result. The length of each window is the inverse of the corresponding target frequency, and the number of windows depends on the length of the collected signal. Then, for each time of the real-time signal, the frequency domain similarity of the real-time signal in the window divided based on the target frequency of the low, medium and high frequency regions at this time with all historical leakage signals is calculated, and the general steps are as follows:

[0061] For each historical leakage signal, the difference in frequency domain between it and the real-time signal, i.e. the absolute value of the difference of the spectral amplitude, is calculated; for each window, the average value of the absolute value of the difference of the spectral amplitude corresponding to all frequency points is calculated, and for all windows, the exponential function of the average value of the absolute value of the difference of the spectral amplitude corresponding to all frequency points is calculated, and then the frequency domain similarity of the real-time signal in the window divided based on the target frequency of the low, medium and high frequency regions at the corresponding time with all historical leakage signals is obtained.

[0062] Wherein, taking the low frequency region as an example, for each historical leakage signal (total number of windows), the historical leakage signal is divided into multiple windows (total number of windows), and for each window , the sum of the absolute values of the spectral amplitude difference values of the real-time signal and the historical leakage signal at all frequencies in the window is calculated; for each historical leakage signal The sum of the absolute values ​​of the differences calculated above is divided by the total number of windows corresponding to the historical leakage signals to obtain the average difference of each historical leakage signal. The average of all historical signals is summed and then divided by the number of historical leakage signals to obtain the overall average difference. The overall average difference is negative and then used as the exponent of the exponential function to calculate the frequency domain similarity between the real-time signal and the historical leakage signal within the window based on the target frequency division of the low-frequency region.

[0063] For each moment of the real-time signal, the frequency domain similarity between the real-time signal and the historical leaked signal within the window divided based on the target frequency of the low-frequency region satisfies the following relationship:

[0064]

[0065] In the formula, The frequency domain similarity between the real-time signal and the historical leakage signal within a window divided based on the target frequency in the low-frequency region is calculated. For real-time signals in the low-frequency region Frequency within a window The spectral amplitude below, Historical leak signal In the low frequency region Frequency within a window The spectral amplitude below, This indicates the number of historical leakage signals. This represents the total number of windows divided based on the target frequency in the low-frequency region. This represents an exponential function.

[0066] The calculation methods for frequency domain similarity within the mid-frequency region window and the high-frequency region window are the same as those for frequency domain similarity within the low-frequency region window, and will not be elaborated further here.

[0067] Similarly, for each moment of the real-time signal, the calculation methods for the frequency domain similarity between the real-time signal and the historical normal signal within the window divided by the target frequency of the low-frequency region and the frequency domain similarity between the real-time signal and the historical leaked signal within the window divided by the target frequency of the low-frequency region are the same, and will not be elaborated on here.

[0068] Furthermore, the probability that the real-time signal at each moment is a leaked signal is calculated based on the frequency similarity corresponding to the low, medium, and high frequency regions. Taking any moment of the real-time signal as an example, the following relationship is satisfied:

[0069]

[0070] In the formula, The possibility that the real-time signal is a leaked signal. The normalized weights For the first Frequency similarity between real-time signals and historical leaked signals within a frequency region For the first Frequency similarity between real-time signals and historical normal signals within a frequency region.

[0071] Among them, the normalized weights The relation is satisfied as follows:

[0072]

[0073] In the formula, In the first The degree of frequency domain difference between normal and leaked signals at their target frequency within a frequency region.

[0074] Based on the above calculations Similarly, the probability that the real-time signal is a leaking signal at all times can be calculated. Then, the real-time signal at the corresponding time with a probability greater than 1 is marked as a leaking signal, and the real-time signal at the corresponding time with a probability less than or equal to 1 is marked as a normal signal.

[0075] S4: Locate the leak location based on the time when multiple sensors first pick up the leak signal, wherein the time when the sensor first picks up the leak signal is obtained when the probability that the calculated real-time signal is a leak signal is greater than a preset threshold.

[0076] Specifically, assume there are three sensors located at coordinates (x1, y1), (x2, y2), and (x3, y3). When a leak occurs, the leak signal propagates along the pipe at a certain speed v and is captured by the three sensors at different times. Let t1, t2, and t3 be the times when the three sensors first capture the leak signal. According to the principle of leak signal propagation, the relationship between the distance from the leak location (x, y) to the first sensor and the signal propagation speed and time is expressed as: The relationship between the distance from the leak location (x, y) to the second and third sensors and the signal propagation speed and time is consistent with the expression for the first sensor mentioned above.

[0077] By solving the three relationships above, the exact location (x, y) of the leak can be found.

[0078] The system includes a processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the leak diagnosis method for a water supply network according to the first aspect of the present invention.

[0079] The system also includes other components known to those skilled in the art such as a communication bus and communication interfaces, the arrangement and function of which are known in the art and thus will not be described here.

[0080] In this disclosure, a "storage" can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random-Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), and the like, or any other medium that can be used to store the desired information and that can be accessed by a computer.

[0081] In the description of the present specification, the meaning of "a plurality of" or "several" is at least two, for example, two, three, or more, unless explicitly specifically defined otherwise.

[0082] While the present specification has shown and described a number of embodiments of the present application, it is to be understood that such embodiments are merely illustrative of and not restrictive on the scope of the application. Those skilled in the art will readily devise numerous alterations, modifications and improvements of the embodiments of the present application that fall within the spirit and scope of the present application. It will be understood that in the process of practicing the present application, various alternatives, modifications and equivalents can be employed.

Claims

1. A method for leak diagnosis of a water supply network, characterized in that, The method comprises: collecting historical normal signals and leakage signals of a water supply pipeline; converting the historical normal signals and leakage signals from time domain to frequency domain through Fourier transform, dividing the signals according to frequency coverage, and obtaining a division result of low, medium and high frequency regions; calculating frequency differences of the historical normal signals and leakage signals in each region, and determining target frequencies of each region, which comprises: for each region, calculating a relative change of a frequency domain difference of the historical normal signals and leakage signals at the target frequency and a mean value of the frequency domain difference in the region where the target frequency is located, and marking the relative change as a frequency domain difference degree of the historical normal signals and leakage signals at the target frequency; considering the frequency domain difference degrees of different regions at the target frequencies, focusing on the frequency domain difference degrees at the target frequencies of the low frequency region and the high frequency region, so that the signal differences of the low frequency region and the high frequency region are most significant, selecting a region with the largest ratio of the frequency domain difference degree of the historical normal signals and leakage signals at the target frequency of the low frequency region to the frequency domain difference degree of the historical normal signals and leakage signals at the target frequency of the high frequency region as an optimal division result, and taking the target frequencies of the regions in the optimal division result as optimal target frequencies; performing sliding window division on the collected historical normal signals and leakage signals according to the target frequencies of the regions, calculating a frequency domain similarity between a real-time signal at each time and the historical normal signals and leakage signals in each region window, calculating a possibility that the real-time signal at the time is a leakage signal based on the frequency domain similarity, and determining that the real-time signal is a leakage signal if the possibility is greater than a preset threshold; locating a leakage position according to a time when a sensor first picks up a leakage signal, wherein the time when the sensor first picks up the leakage signal is obtained when the possibility that the real-time signal is a leakage signal is greater than the preset threshold.

2. A method for leak diagnosis of a water supply network according to claim 1, characterized in that, The frequency differences of the historical normal signals and leakage signals satisfy a relationship formula: wherein is a historical normal signal and a leakage signal difference in the frequency domain at frequency is a number of historical normal signals, is a number of historical leakage signals, denotes a spectral amplitude of the th historical normal signal at frequency denotes a spectral amplitude of the th historical leakage signal at frequency .​​ 3. A method for leak diagnosis of a water supply network according to claim 2, characterized in that, The determination of the target frequencies of each region comprises: taking frequencies corresponding to all maximum frequency domain differences in each region as the target frequencies of the regions.

4. A method for leak diagnosis of a water supply network according to claim 3, characterized in that, The frequency domain similarity between the real-time signal at each time and the historical leakage signals in the low frequency region window satisfies a relationship formula: ; wherein is a frequency domain similarity of a real-time signal within a window based on a target frequency division of a low frequency region and a historical leakage signal, is a spectral amplitude of the real-time signal at a frequency within the first window of the low frequency region, is a spectral amplitude of the historical leakage signal at a frequency within the first window of the low frequency region, denotes a number of historical leakage signals, denotes a total number of windows based on a target frequency division of a low frequency region, denotes an exponential function; the frequency domain similarity within the mid frequency region windows and within the high frequency region windows is calculated in the same way as the frequency domain similarity within the low frequency region windows.​ 5. A method for leak diagnosis of a water supply network according to claim 4, characterized in that, The possibility that the real-time signal is a leakage signal satisfies a relationship formula: wherein, is the possibility that the real-time signal is a leakage signal, is the normalized weight, is the frequency similarity between the real-time signal and the historical leakage signal in the th frequency region, is the frequency similarity between the real-time signal and the historical normal signal in the th frequency region.

6. A method for leak diagnosis of a water supply network according to claim 5, characterized in that, The normalized weight satisfies a relationship formula: wherein is the normalized weight, is the normalized weight, is the degree of difference in the frequency domain between the normal signal and the leakage signal at the target frequency in the i-th frequency region.

7. A leak diagnosis system for a water supply network, characterized in that The method comprises: a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, realizing the leakage diagnosis method for a water supply pipeline network according to any one of claims 1-6.

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