A method and device for diagnosing pipe burst of a water supply network

Abstract

The present application relates to the technical field of pipe network analysis, and discloses a water supply pipe network burst diagnosis method and device, the method comprising: obtaining water sound monitoring equipment collected sound wave information in the water supply pipe network to be diagnosed; analyzing the sound wave information to be diagnosed, determining the installation position of the first water sound monitoring equipment detecting the burst event under the condition that the burst event exists in the water supply pipe network to be diagnosed; obtaining prediction information generated by a water hammer model, the prediction information comprising a burst-prone position in the water supply pipe network to be diagnosed; determining the burst position of the water supply pipe network to be diagnosed based on the installation position and the burst-prone position. The present application combines water sound monitoring technology with MOC analysis of a water hammer model, which can not only determine whether a burst event occurs in the water supply pipe network, but also improve the accuracy of burst position determination, and can more accurately diagnose and warn the burst event in the water supply pipe network.

Description

Technical Field

[0001] This invention relates to the field of pipeline analysis technology, specifically to a method and device for diagnosing pipe bursts in water supply networks. Background Technology

[0002] Water supply networks are pipeline systems that deliver and distribute water to users in water supply projects. They are an indispensable part of urban infrastructure and have a significant impact on the normal operation of cities and residents' lives. However, due to the complexity of water supply network systems and factors such as aging, pipe bursts occur frequently, seriously affecting the normal operation of water supply and the quality of life of citizens.

[0003] In some technologies, pressure monitoring or water quality monitoring is often used to diagnose burst pipes in water supply networks. However, these technologies not only cannot accurately locate the burst pipe, but are also prone to misdiagnosis. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and apparatus for diagnosing burst pipes in water supply networks, in order to solve the problem of low accuracy in diagnosing burst pipes in water supply networks.

[0005] In a first aspect, embodiments of the present invention provide a method for diagnosing burst pipes in a water supply network, including...

[0006] Acquire the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring device in the water supply network to be diagnosed, wherein the underwater acoustic monitoring device is set at at least one preset position in the water supply network to be diagnosed, and the underwater acoustic monitoring device corresponds one-to-one with the acoustic wave information to be diagnosed.

[0007] Analyze the acoustic information to be diagnosed, and if it is determined that there is a pipe burst event in the water supply network to be diagnosed, determine the installation location of the first underwater acoustic monitoring device that detected the pipe burst event;

[0008] Obtain prediction information generated by the water hammer model, wherein the water hammer model is an analysis model established based on the water supply network to be diagnosed to simulate the water flow conditions within the water supply network to be diagnosed, and the prediction information includes the explosive locations in the water supply network to be diagnosed.

[0009] Based on the installation location and the location of potential explosion, the location of the burst pipe in the water supply network to be diagnosed is determined.

[0010] In conjunction with the first aspect, in one implementation, determining the location of the burst pipe in the water supply network to be diagnosed based on the installation location and the potentially explosive location includes:

[0011] Acquire the acoustic wave information to be diagnosed collected by the first underwater acoustic monitoring device;

[0012] Based on the acoustic wave information to be diagnosed, signal analysis is performed to determine the pressure change information within the acquisition range of the first underwater acoustic monitoring device.

[0013] Based on the pressure change information and the water hammer model, the initial leak location is determined;

[0014] Based on the initial leak location, the installation location, and the location prone to explosion, the location of the burst pipe in the water supply network to be diagnosed is determined.

[0015] In conjunction with the first aspect or its corresponding implementation, in one implementation, determining the initial leakage location based on the pressure change information and the hammer model includes:

[0016] Obtain the acquisition range of the underwater acoustic monitoring equipment;

[0017] Based on the installation location, determine the location information of the installation location in the water hammer model;

[0018] Based on the location information and the acquisition range, the pipeline to be simulated in the water hammer model is determined;

[0019] Identify multiple initial leak points within the pipeline to be simulated;

[0020] Based on each initial leak point, the water flow state of the pipeline to be simulated is simulated, and the simulated pressure information of the water flow is determined;

[0021] When the difference between the pressure change information and the simulated pressure information is minimized, the initial leak point corresponding to the simulated pressure information is taken as the initial leak location.

[0022] In conjunction with the first aspect or its corresponding implementation, in one implementation, the prediction information is generated through the following steps:

[0023] Obtain the structural information of the water supply network to be diagnosed and establish a simulation model;

[0024] Obtain the water flow data of the water supply network to be diagnosed, and establish a water hammer model based on the simulation model;

[0025] Control the pump structure in the simulation model to cause a water hammer event, and determine the water flow change information of each pipe in the water hammer model. The water flow change information includes at least one of the following: flow velocity change data, water pressure change data, and flow rate change data.

[0026] Based on the water flow change information and the structural information, the explosive location is determined and used as the prediction information.

[0027] In conjunction with the first aspect, in one implementation, the presence of the pipe burst event in the water supply network to be diagnosed is analyzed through the following steps:

[0028] The acoustic wave information to be diagnosed is subjected to signal preprocessing;

[0029] Extract diagnostic information features from the preprocessed acoustic wave information to be diagnosed, the diagnostic information features including peak value and trough value;

[0030] If the peak value exceeds a first preset threshold and / or the trough value is lower than a second preset threshold, it is determined that the pipe burst event exists in the water supply network to be diagnosed.

[0031] In conjunction with the first aspect or its corresponding implementation, in one implementation, determining the location of the burst pipe in the water supply network to be diagnosed based on the installation location and the potentially explosive location includes:

[0032] Determine the target explosive location within the range of the location information collection;

[0033] Based on the installation location and the target explosive location, the location of the burst pipe in the water supply network to be diagnosed is determined.

[0034] In conjunction with the first aspect or its corresponding implementation, in one implementation, after determining the explosive location, the method further includes: setting the underwater acoustic monitoring device within a preset range of the explosive location.

[0035] Secondly, embodiments of the present invention provide a water pipe network burst diagnosis device, the device comprising:

[0036] The first acquisition module is used to acquire the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring device in the water supply network to be diagnosed. The underwater acoustic monitoring device is set at at least one preset position in the water supply network to be diagnosed, and the underwater acoustic monitoring device corresponds one-to-one with the acoustic wave information to be diagnosed.

[0037] The analysis module is used to analyze the acoustic information to be diagnosed, and if it is determined that there is a pipe burst event in the water supply network to be diagnosed, it determines the installation location of the first underwater acoustic monitoring device that detected the pipe burst event.

[0038] The second acquisition module is used to acquire the prediction information generated by the water hammer model. The water hammer model is an analysis model established based on the water supply network to be diagnosed to simulate the water flow conditions in the water supply network to be diagnosed. The prediction information includes the explosive locations in the water supply network to be diagnosed.

[0039] The determination module is used to determine the location of the burst pipe in the water supply network to be diagnosed based on the installation location and the explosive location.

[0040] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the water supply network burst diagnosis method described in the first aspect or any corresponding embodiment.

[0041] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing a computer to execute the water supply network burst diagnosis method described in the first aspect or any corresponding embodiment.

[0042] The technical solution of this invention has the following advantages:

[0043] The water supply network burst diagnosis method provided by this invention combines the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring equipment with the prediction information obtained from the MOC analysis of the water hammer model to comprehensively diagnose the location of the burst in the water supply network. This not only determines whether a burst event has occurred in the water supply network, but also improves the accuracy of burst location judgment. It enables more accurate diagnosis and early warning of burst events in the water supply network, improves the efficiency of maintenance personnel in maintaining the water supply network, and thus improves the safety and reliability of the water supply network. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for diagnosing burst pipes in a water supply network according to some embodiments of the present invention.

[0046] Figure 2 This is a schematic diagram illustrating a structure for determining the location of a pipeline prone to explosion, as exemplified by an embodiment of the present invention.

[0047] Figure 3 This is a flowchart illustrating another method for diagnosing burst pipes in a water supply network according to some embodiments of the present invention;

[0048] Figure 4 This is another structural schematic diagram illustrating the determination of a pipeline's explosive location, as exemplified by an embodiment of the present invention;

[0049] Figure 5 This is a schematic flowchart illustrating the process of determining the initial leak location according to some embodiments of the present invention;

[0050] Figure 6 This is a schematic diagram of the process for generating prediction information according to some embodiments of the present invention;

[0051] Figure 7 This is a structural block diagram of a water supply network burst pipe diagnosis device according to an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In some application scenarios, water supply network bursts occur frequently. Pressure monitoring technology is commonly used to monitor the internal pressure of the network. When excessive pressure fluctuations are detected, a burst is considered a possible event. However, pressure monitoring data can only indicate a wide range of potential burst locations, making it difficult to pinpoint the exact location. Furthermore, if the pressure change is due to changes in pipe pressure caused by residential water use, false alarms can occur, and the sensitivity of pressure monitoring technology is relatively low.

[0055] In view of this, the present invention provides an embodiment of a method for diagnosing burst pipes in a water supply network. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0056] This embodiment provides a method for diagnosing burst pipes in water supply networks, which can be used on servers, mobile terminals such as mobile phones and tablets. Figure 1 This is a flowchart of a water supply network burst pipe diagnosis method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0057] Step S101: Obtain the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring device in the water supply network to be diagnosed. The underwater acoustic monitoring device is set at at least one preset position in the water supply network to be diagnosed, and the underwater acoustic monitoring device corresponds one-to-one with the acoustic wave information to be diagnosed.

[0058] Underwater acoustic monitoring equipment can be hydrophones, which receive acoustic signals in water and convert them into electrical signals. Hydrophones can be placed at multiple preset locations within the water supply network to be diagnosed, based on factors such as the network's structural distribution and pipe structure information. Structural distribution information includes bends, merging points, branching points, and pump inlets / outlets; pipe structure information includes pipe diameter, material, and connection methods. For example, suitable monitoring points can be selected at the water source inlet, water treatment plant inlet / outlet, main pipeline bends, and water supply wellheads. The underwater acoustic monitoring equipment should cover the entire water supply network to obtain comprehensive monitoring data. The location of the equipment should be convenient for installation and maintenance; it should not be too high or too inaccessible.

[0059] Each underwater acoustic monitoring device can continuously acquire underwater acoustic signals within a certain monitoring range. In this embodiment, one underwater acoustic monitoring device can acquire the acoustic wave information to be diagnosed within a certain monitoring range. Taking a hydrophone as an example, one hydrophone can acquire underwater acoustic signals within a range of approximately 2 km. Based on the monitoring range of the hydrophone, the hydrophones can be evenly distributed at multiple nodes of the water supply network to be diagnosed, thereby enabling monitoring of the entire water supply network.

[0060] Step S102: Analyze the acoustic information to be diagnosed. If it is determined that there is a pipe burst event in the water supply network to be diagnosed, determine the installation location of the first underwater acoustic monitoring device that detected the pipe burst event.

[0061] As described above, the server can acquire the acoustic wave information to be diagnosed sent by underwater acoustic monitoring devices installed at multiple preset locations in the water supply network to be diagnosed, and can analyze all the acoustic wave information to be diagnosed. When an abnormal signal is found in the acoustic wave information to be diagnosed, it is determined that a pipe burst event has occurred in the water supply network to be diagnosed. Then, it is determined which underwater acoustic monitoring device collected the acoustic wave information with the abnormal signal, the first underwater acoustic monitoring device that detected the pipe burst event is identified, and the installation location of the first underwater acoustic monitoring device in the water supply network to be diagnosed is determined.

[0062] Step S103: Obtain the prediction information generated by the water hammer model. The water hammer model is an analysis model established based on the water supply network to be diagnosed to simulate the water flow conditions in the water supply network to be diagnosed. The prediction information includes the explosive locations in the water supply network to be diagnosed.

[0063] A water hammer model is a water hammer analysis model built upon a hydraulic model. It can be used to simulate the water flow conditions within a water supply network to be diagnosed, analyze pressure changes in the fluid within the pipes, and determine flow parameters and pipe parameters. Water hammer refers to the pressure fluctuations and oscillations that occur in a pipe due to a sudden change in flow velocity or the closure of a valve.

[0064] In this embodiment, the water hammer model can analyze and predict multiple potentially explosive locations in the water supply network to be diagnosed based on the parameters and topology of the components such as pipes, valves, and pumps of the water supply network, as well as the characteristic parameters of each pipe, such as inner diameter, wall thickness, material, length, elastic modulus, mass, inertia, and damping.

[0065] Step S104: Based on the installation location and the explosive location, determine the location of the burst pipe in the water supply network to be diagnosed.

[0066] After determining the installation location of the first underwater acoustic monitoring device that detected the pipe burst event, the location of the burst pipe in the water supply network to be diagnosed can be further determined by combining the burst-prone locations predicted based on the water hammer model. For example: Figure 2 As shown, assuming the installation location of the first underwater acoustic monitoring device that detects the pipe burst event is location Q, and the water hammer model predicts that the burst locations include locations A and B, then by combining the installation location Q, the burst locations A and B, and the acquisition range of the hydrophone, the burst location of the water supply network to be diagnosed can be determined to be burst location A or the area near burst location A.

[0067] In this embodiment, the method of combining the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring equipment with the predicted information obtained from the MOC analysis of the water hammer model is used to comprehensively diagnose the location of the burst pipe in the water supply network. This not only determines whether a burst pipe event has occurred in the water supply network, but also improves the accuracy of burst pipe location judgment. It enables more accurate diagnosis and early warning of burst pipe events in the water supply network, improves the efficiency of maintenance personnel in maintaining the water supply network, and thus improves the safety and reliability of the water supply network.

[0068] Furthermore, in this embodiment, the pipe burst diagnosis is performed by combining hydrophone monitoring technology and MOC analysis method of water hammer model. Combining the two takes into account both the real-time and high accuracy of monitoring data and makes full use of the theoretical advantages of hydraulic model, thereby realizing accurate diagnosis and location of pipe burst in water supply network.

[0069] Furthermore, in this embodiment, the above steps can be repeated to determine the duration of the pipe burst event, allowing for appropriate maintenance or repair measures to be taken. Moreover, the water supply network burst diagnosis method provided in this embodiment is feasible and effective for different types and sizes of pipe networks, applicable to various practical application scenarios, and possesses good versatility and applicability.

[0070] like Figure 3 As shown, in some optional embodiments, step S104 above includes:

[0071] Step S1041: Obtain the acoustic wave information to be diagnosed collected by the first underwater acoustic monitoring device. In this embodiment, after obtaining the acoustic wave information to be diagnosed, preprocessing can be performed on the acoustic wave information to be diagnosed through filtering, noise reduction, and other operations to improve the accuracy and stability of the signal.

[0072] Step S1042: Based on the acoustic wave information to be diagnosed, signal analysis is performed to determine the pressure change information within the acquisition range of the first underwater acoustic monitoring device. In this embodiment, a water hammer model can be used to perform MOC analysis on the acoustic wave information to be diagnosed to obtain the pressure change of the water flow inside the pipe, which will be described in detail below. The pressure change information can be the pressure change value between two moments determined according to a preset step size.

[0073] Step S1043: Based on the pressure change information and the water hammer model, determine the initial leakage location;

[0074] Step S1044: Based on the initial leak location, the installation location, and the burst location, determine the burst location of the water supply network to be diagnosed.

[0075] In some application scenarios, multiple explosive locations predicted by water hammer models may appear within the acquisition range of a single underwater acoustic monitoring device. In order to further improve the accuracy of pipe burst location and narrow down the range of pipe burst location determination, this implementation method can be used to determine the pipe burst location.

[0076] When a pipe leaks, the water flow is obstructed by the leak, causing changes in both speed and pressure. This change creates a water hammer effect inside the pipe, resulting in instantaneous pressure fluctuations. The water hammer effect propagates within the pipe, creating a series of fluctuations accompanied by specific acoustic signals. When these acoustic signals reach the pipe surface, they cause vibrations that can be captured and recorded by hydrophones or other sensors.

[0077] In this implementation, the correlation between noise and pressure signals can be used to analyze the pressure change information within the acquisition range of the first underwater acoustic monitoring device based on the acoustic wave information to be diagnosed. Wavelet transform and Fourier transform are used to analyze and process the noise and pressure signals to extract feature information from the acoustic wave information to be diagnosed, thus determining the relationship between the noise and pressure signals. Since changes in water flow velocity and pressure around the leak point can cause water hammer inside the pipe, generating specific acoustic signals, the noise signal can indirectly reflect the pressure changes inside the pipe. Through correlation analysis of the noise and pressure signals, the correspondence between noise and pressure can be determined.

[0078] Furthermore, the pressure change information is used as the target pressure change information of the water hammer model. The water hammer model is used for reverse simulation to calculate the possible leak locations. One or more possible leak locations are used as the initial leak locations in this embodiment.

[0079] like Figure 4 As shown, by simulating the initial leak location C, installation location Q, and potential burst locations A and B using the water hammer model, the possible burst location of the water supply network to be diagnosed can be determined as location B or the area near location B.

[0080] In this embodiment, the method of combining the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring equipment, the predicted information from the MOC analysis of the water hammer model, and the initial leak location simulated based on the pressure change information, can comprehensively diagnose the location of the burst pipe in the water supply network. This can further improve the accuracy of the burst pipe location judgment, narrow the range of actual burst pipe location search by maintenance personnel, and improve the maintenance efficiency of the water supply network.

[0081] like Figure 5 As shown, in some optional embodiments, step S1043 includes:

[0082] Step a1: Obtain the acquisition range of the underwater acoustic monitoring device;

[0083] Step a2: Based on the installation location, determine the location information of the installation location in the water hammer model. In this embodiment, the water hammer model is a simulation model established based on the water supply network structure to be diagnosed. The location information of the installation location in the water hammer model can be determined according to the actual installation location of the water acoustic monitoring equipment in the water supply network to be diagnosed.

[0084] Step a3: Based on the location information and the acquisition range, determine the pipeline to be simulated in the water hammer model. After determining the location of the installation position within the water hammer model, determine the pipeline to be simulated based on the acquisition range of the underwater acoustic monitoring equipment. Modeling only the pipeline to be simulated improves simulation efficiency, and simulation using the water hammer model does not reduce simulation accuracy.

[0085] Step a4: Determine multiple initial leakage points within the pipeline to be simulated;

[0086] Step a5: Based on each of the initial leakage points, simulate the water flow state of the pipeline to be simulated, and determine the simulated pressure information of the water flow;

[0087] Step a6: When the difference between the pressure change information and the simulated pressure information is minimized, the initial leak point corresponding to the simulated pressure information is taken as the initial leak location.

[0088] Based on the structure and parameters of the pipeline to be simulated, multiple initial leakage points can be preliminarily set within the pipeline to improve simulation efficiency. A water hammer model is used to simulate each initial leakage point, deriving the initial state of the flow field inside the pipeline and determining the simulated pressure information. The simulated pressure information can also be the pressure change value between two time points determined according to a pre-set step size. The simulated pressure information is then compared with the pressure change information determined based on the acoustic wave information to be diagnosed. When the simulated pressure information and the pressure change information are infinitesimally close, that is, when the difference between the pressure change value of the pressure change information and the simulated pressure change value is minimal, the initial leakage point corresponding to that simulated pressure information is taken as the initial leakage location.

[0089] In this embodiment, the location closest to the pressure change information can be calculated in reverse using the MOC backtracking method in the water hammer model, thereby determining the initial leak location and further improving the accuracy of determining the burst location of the water supply network to be diagnosed.

[0090] Furthermore, after determining the initial leak location, a forward simulation of the pipeline to be simulated can be performed to determine the pressure change information and further verify whether the leak location is accurate.

[0091] like Figure 6 As shown, in some optional implementations, the prediction information is generated through the following steps:

[0092] Step b1: Obtain the structural information of the water supply network to be diagnosed and establish a simulation model;

[0093] Step b2: Obtain the water flow data of the water supply network to be diagnosed, and establish a water hammer model based on the simulation model;

[0094] Step b3: Control the water pump structure in the simulation model to cause a water hammer event, and determine the water flow change information of each pipe in the water hammer model. The water flow change information includes at least one of the following: flow velocity change data, water pressure change data, and flow rate change data.

[0095] Step b4: Based on the water flow change information and the structural information, determine the explosive location and use the explosive location as the prediction information.

[0096] First, a hydraulic model, also known as a simulation model, of the water supply network to be diagnosed needs to be established. This model includes the parameters and topology of the network's components, such as pipes, valves, and pumps. Second, the characteristics of the pipes in the network need to be collected: for each pipe, its inner diameter, wall thickness, material, length, elastic modulus, and other characteristic parameters need to be determined, and its friction coefficient needs to be calculated. For each pipe, water flow data of the network needs to be collected, and a corresponding water hammer model needs to be established. The pump structure in the simulation model is controlled to simulate a water hammer event. When water hammer occurs in the pipe, it can cause instantaneous changes in pressure and flow velocity. The pressure, velocity, and flow rate changes in the pipe are analyzed, and the water hammer impact force is calculated. Finally, the vulnerable locations of the water supply network to be diagnosed can be determined based on the water hammer impact force and the structural information of the network. In this embodiment, the structural information may include the pipe's inner diameter, wall thickness, and material.

[0097] In this embodiment, by analyzing the effect of water hammer, not only can the location of the water supply network to be diagnosed be determined, but also an optimized design scheme for the water supply network to be diagnosed can be obtained, such as adding pressure reducing valves or installing buffers, to reduce the impact force of water hammer and improve the safety and stability of the system.

[0098] In some alternative implementations, the presence of the pipe burst event in the water supply network to be diagnosed can be analyzed through the following steps:

[0099] The acoustic wave information to be diagnosed undergoes signal preprocessing, which may include noise filtering, denoising, and signal enhancement to ensure data quality. Noise filtering removes unnecessary interference signals, while signal enhancement improves the signal-to-noise ratio.

[0100] The diagnostic information features, including peak value and trough value, are extracted from the preprocessed acoustic wave information. Frequency and time domain analyses can be performed on the preprocessed data to extract the diagnostic information features, including peak value, trough value, and amplitude.

[0101] If the peak value exceeds a first preset threshold and / or the trough value is lower than a second preset threshold, it is determined that a pipe burst event exists in the water supply network to be diagnosed. That is, when the peak value and / or trough value exceed the set threshold, it is determined that there is an abnormal signal in the acoustic information to be diagnosed, and a pipe burst event is confirmed in the water supply network to be diagnosed.

[0102] Specifically, the method for analyzing the acoustic information to be diagnosed can employ machine learning algorithms to classify normal and abnormal water hammer waves, and use a threshold method to compare the peak and trough values ​​to determine whether a pipe burst event has occurred.

[0103] In this embodiment, through steps such as signal preprocessing, data reconstruction, feature extraction, and classification recognition, various problems in traditional methods, such as noise interference, signal acquisition and processing efficiency, are effectively solved, significantly improving the accuracy and practicality of diagnosis. Furthermore, the waveform characteristics in the acoustic information to be diagnosed can be accurately analyzed to determine whether a pipe burst event exists in the water supply network, thus facilitating the location of the burst.

[0104] In some optional implementations, step S104 above includes:

[0105] Determine the target explosive location within the range of the location information collection;

[0106] Based on the installation location and the target explosive location, the location of the burst pipe in the water supply network to be diagnosed is determined.

[0107] After determining the location of the installation site within the water hammer model, the target flammable location within the acquisition range of the underwater acoustic monitoring equipment and the flammable locations predicted by the water hammer model are identified. Finally, the location of the burst pipe in the water supply network to be diagnosed is determined based on the selected target flammable locations and the installation site. In this embodiment, the actual search area for the burst pipe location by maintenance personnel can be narrowed, improving the maintenance efficiency of the water supply network.

[0108] In some optional implementations, after determining the explosive location, the method further includes: installing the underwater acoustic monitoring device within a preset range of the explosive location. In this embodiment, the explosive location can be predicted in advance based on the water hammer model, and then the specific location of the underwater acoustic monitoring device to be installed in the water supply network to be diagnosed can be determined based on the explosive location. This allows for targeted monitoring of key areas in the water supply network to be diagnosed, improving diagnostic accuracy.

[0109] In some optional embodiments, the method further includes: determining the severity of the pipe rupture at the rupture location based on the pressure change information. The severity of the rupture can be assessed based on the degree of pressure change near the rupture location; the greater the pressure change, the higher the severity of the rupture.

[0110] This embodiment also provides a water supply network burst pipe diagnosis device, which is used to implement the above embodiments and implementation methods, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] This embodiment provides a device for diagnosing burst pipes in water supply networks, such as... Figure 7 As shown, it includes:

[0112] The first acquisition module 201 is used to acquire the acoustic wave information to be diagnosed collected by the underwater acoustic monitoring device in the water supply network to be diagnosed. The underwater acoustic monitoring device is set at at least one preset position in the water supply network to be diagnosed, and the underwater acoustic monitoring device corresponds one-to-one with the acoustic wave information to be diagnosed.

[0113] Analysis module 202 is used to analyze the acoustic information to be diagnosed, and if it is determined that there is a pipe burst event in the water supply network to be diagnosed, determine the installation location of the first underwater acoustic monitoring device that detected the pipe burst event.

[0114] The second acquisition module 203 is used to acquire the prediction information generated by the water hammer model. The water hammer model is an analysis model established based on the water supply network to be diagnosed to simulate the water flow conditions in the water supply network to be diagnosed. The prediction information includes the explosive locations in the water supply network to be diagnosed.

[0115] The determination module 204 is used to determine the location of the burst pipe in the water supply network to be diagnosed based on the installation location and the explosive location.

[0116] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0117] This invention also provides a computer device; please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0118] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0119] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0120] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0121] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0122] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0123] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0124] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method of burst diagnosis for a water distribution network, characterized in that, The method comprises: acquiring water sound monitoring equipment collected water sound information to be diagnosed in a water supply network to be diagnosed, the water sound monitoring equipment being arranged in at least one preset position in the water supply network to be diagnosed, the water sound monitoring equipment corresponding to the water sound information to be diagnosed; signal preprocessing is performed on the water sound information to be diagnosed, and information features of the water sound information to be diagnosed after preprocessing are extracted, the information features including a wave peak value and a wave trough value; in a case where the wave peak value exceeds a first preset threshold value and / or the wave trough value is lower than a second preset threshold value, it is determined that the pipe burst event exists in the water supply network to be diagnosed, and an installation position of a first water sound monitoring equipment detecting the pipe burst event is determined; acquiring prediction information generated by a water hammer model, the water hammer model being an analysis model for simulating water flow conditions in the water supply network to be diagnosed and being established based on the water supply network to be diagnosed, the prediction information including an easy-burst position in the water supply network to be diagnosed; acquiring the water sound information to be diagnosed collected by the first water sound monitoring equipment; performing signal analysis based on the water sound information to be diagnosed to determine pressure change information in a collection range of the first water sound monitoring equipment; determining an initial water leakage position based on the pressure change information, the water hammer model; determining a pipe burst position of the water supply network to be diagnosed based on the initial water leakage position, the installation position and the easy-burst position.

2. The method of claim 1, wherein, The pressure change information is used as target pressure change information of the water hammer model, and the water hammer model is used for reverse simulation calculation to obtain the initial water leakage position, comprising: acquiring a collection range of the water sound monitoring equipment; determining position information of the installation position in the water hammer model based on the installation position; determining a to-be-simulated pipeline of the water hammer model based on the position information and the collection range; determining a plurality of initial water leakage points in the to-be-simulated pipeline; simulating water flow states of the to-be-simulated pipeline based on each initial water leakage point and determining simulation pressure information of water flow; when a difference between the pressure change information and the simulation pressure information is the smallest, the initial water leakage point corresponding to the simulation pressure information is used as the initial water leakage position.

3. The method of claim 1, wherein, The prediction information is generated by the following steps: acquiring structure information of the water supply network to be diagnosed to establish a simulation model; acquiring water flow data of the water supply network to be diagnosed, and establishing a water hammer model based on the simulation model; controlling a water pump structure in the simulation model to cause a water hammer event, and determining water flow change information of each pipeline in the water hammer model, the water flow change information including at least one of flow rate change data, water pressure change data and flow change data; determining the easy-burst position based on the water flow change information and the structure information, and using the easy-burst position as the prediction information.

4. The method of claim 2, wherein, The determination of the pipe burst position of the water supply network to be diagnosed based on the installation position and the easy-burst position comprises: determining a target easy-burst position in the collection range of the position information; determining the pipe burst position of the water supply network to be diagnosed based on the installation position and the target easy-burst position.

5. The method of claim 3, wherein, After the explosive position is determined, further comprising: setting the underwater acoustic monitoring device within a preset range of the explosive position.

6. A water distribution network pipe burst diagnosis apparatus characterized by comprising: The device comprises: A first obtaining module is configured to obtain water sound monitoring device collected sound wave information to be diagnosed in a water supply network to be diagnosed, the water sound monitoring device is arranged in at least one preset position in the water supply network to be diagnosed, and the water sound monitoring device corresponds to the sound wave information to be diagnosed one by one. An analysis module is configured to perform signal preprocessing on the sound wave information to be diagnosed, extract information features of the sound wave information to be diagnosed after preprocessing, the information features include peak value and trough value, determine that the pipe explosion event exists in the water supply network to be diagnosed when the peak value exceeds a first preset threshold and / or the trough value is lower than a second preset threshold, and determine the installation position of the first water sound monitoring device detecting the pipe explosion event. A second obtaining module is configured to obtain prediction information generated by a water hammer model, the water hammer model is an analysis model for simulating water flow conditions in the water supply network to be diagnosed based on the water supply network to be diagnosed, and the prediction information includes an explosive position in the water supply network to be diagnosed. A determining module is configured to obtain the sound wave information to be diagnosed collected by the first water sound monitoring device, perform signal analysis based on the sound wave information to be diagnosed, determine pressure change information in the collection range of the first water sound monitoring device, determine an initial leakage position based on the pressure change information and the water hammer model, and determine the pipe explosion position of the water supply network to be diagnosed based on the initial leakage position, the installation position and the explosive position.

7. A computer device, comprising: Comprise: A memory and a processor are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the water supply network pipe explosion diagnosis method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the water supply network pipe explosion diagnosis method of any one of claims 1-5.

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