Leakage analysis method, system and related device

Abstract

The invention provides a leakage analysis method and system and a related device, and relates to the technical field of computers. The method comprises the following steps: determining water consumption related data of each independent metering area in a target area; the water consumption related data is determined based on a data acquisition and monitoring control system, a geographic information system and an intelligent internet-of-things water meter system; inputting the water consumption related data into a leakage analysis model, and determining leakage condition information of each independent metering area; and if it is determined that the leakage condition information of the first independent metering area indicates that sudden leakage exists, performing pipe explosion diagnosis on the first independent metering area based on a pipe explosion diagnosis model to determine a pipe explosion point of the first independent metering area. According to the invention, the abnormal independent metering area is accurately positioned through the leakage analysis model, and the pipe explosion point diagnosis is carried out on the abnormal independent metering area through the pipe explosion diagnosis model, so that the efficiency and accuracy of the pipe explosion point diagnosis are greatly improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a leakage analysis method, system and related apparatus. Background Technology

[0002] Currently, water supply networks generally face common technical challenges in the industry, such as high leakage rates, frequent pipe bursts, difficulty in locating faults, and delayed response and handling.

[0003] In related technologies, the water supply network monitoring and diagnosis methods provided to address the aforementioned problems mainly rely on regular manual inspections, experience-based judgment, and simple threshold alarm systems based on a single indicator. Such methods not only have low leakage detection efficiency but may also result in poor accuracy of leakage results due to insufficient experience. Summary of the Invention

[0004] This disclosure provides a leakage analysis method, leakage analysis device, leakage analysis system, electronic device, and computer program product, which improve the efficiency and accuracy of leakage analysis to a certain extent.

[0005] According to a first aspect of this disclosure, a leakage analysis method is provided, the method comprising: Water consumption data for each independent metering area within the target region is determined; the water consumption data is determined based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system. The water consumption data is input into the leakage analysis model to determine the leakage information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; If the leakage information of the first independent metering area indicates that there is a sudden leakage, then based on the pipe burst diagnosis model, pipe burst diagnosis is performed on the first independent metering area to determine the pipe burst point in the first independent metering area.

[0006] In one possible implementation, the water consumption data is input into a leakage analysis model to determine leakage information for each of the independent metering areas, including: Perform flow balance calculation on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area; and determine whether each independent metering area is an abnormal area based on the obtained leakage amount and a first threshold. If an abnormally independent metering area is determined to be an abnormal area, then based on the minimum nighttime flow analysis algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the first analysis result information; and based on the historical water consumption baseline comparison algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the second analysis result information; the first analysis result information and the second analysis result information are cross-validated to determine the leakage situation information of the abnormally independent metering area.

[0007] In one possible implementation, cross-validation is performed on the first analysis result information and the second analysis result information to determine the leakage information of the abnormal independent metering region, including: If the first analysis result indicates that the current minimum nighttime water consumption is greater than a first preset threshold for the baseline nighttime water consumption, and the second analysis result indicates that the total water supply and the water supply of the first branch exceed historical levels for the same period, then the first sub-verification information used to indicate persistent leakage is determined; or, If the first analysis result indicates that the water consumption at the first moment of the night exceeds the second preset threshold of the baseline nighttime water consumption, and the second analysis result indicates that the current hourly flow curve of the second branch is significantly different from the historical same period, then the second sub-verification information used to indicate sudden leakage is determined.

[0008] In one possible implementation, if leakage information in the first independent metering area indicates a sudden leakage, then based on a pipe burst diagnosis model, a pipe burst diagnosis is performed on the first independent metering area to determine the pipe burst point in the first independent metering area, including: The first water consumption data of the first independent metering area is input into the real-time diagnostic model of the pipe burst diagnosis model to obtain pipe burst anomaly information; the pipe burst anomaly information includes the pipe segment at the anomaly point and the anomaly data. The static risk prediction model in the rupture diagnosis model is invoked to predict the pipe segment at the anomaly point and obtain the basic risk level; and the dynamic analysis model in the rupture diagnosis model is invoked to perform pattern analysis on the abnormal data and obtain rupture feature information. The confidence level of the pipe burst is determined based on the basic risk level and the pipe burst characteristic information; If the confidence level of the burst pipe is greater than the second threshold, it is determined that there is a burst pipe event at the abnormal point, and the abnormal point is designated as the burst pipe point.

[0009] In one possible implementation, the method further includes: Determine the pipeline topology of the first independent metering area; Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the first monitoring point; Based on the first monitoring point, the target pipe segment corresponding to the pipe burst point is determined.

[0010] In one possible implementation, based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the first monitoring point, including: Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the timing and magnitude of pressure drop at each monitoring point. The first monitoring point is determined based on the timing and magnitude of the pressure drop at each monitoring point and the preset screening rules.

[0011] In one possible implementation, the method further includes: From the first water consumption related data, obtain the associated dataset of the target pipe segment. The associated dataset includes the location information of the target pipe segment, the basic data of valves along the pipeline network, and the relationship data between valves, pipe segments, and users. Based on the pipeline topology and hydraulic model, the flow state under different valve closure combinations is simulated on the associated dataset to determine the constraints. The pipeline topology is transformed into a node-edge model. A breadth-first search algorithm is used to search for shut-off valve combinations layer by layer with the target pipe segment as the core. The number of affected users and the number of valves to be shut off for each valve combination are determined to identify a set of candidate valve shut-off schemes. Based on the preset dual objectives and the constraints, the target valve-closing scheme is determined from the set of candidate valve-closing schemes.

[0012] In one possible implementation, the method further includes: The display information of the first analysis result, the second analysis result, the target pipe section, and the target valve shut-off scheme is determined, and the display information is displayed on the human-computer interaction interface. The human-computer interaction interface includes one or more functions from the following: a geographic information map main window, a pipeline operation status layer, a real-time monitoring data panel, an early warning and event list, a valve shut-off scheme preview window, and a comprehensive situation dashboard.

[0013] According to a second aspect of this disclosure, a leakage analysis system is provided, characterized in that the system comprises a sensing layer, a communication layer, a data layer, a service layer, and an application layer, wherein: The sensing layer is used to collect water consumption data related to each independent metering area within the target area; The communication layer is used to send the water consumption-related data collected by the sensing layer to the data layer; The data layer is used to classify, store, manage, and generate preliminary data on the water consumption-related data transmitted by the communication layer. The service layer includes data fusion service, model calculation service, geographic information system service, and message push service. The service layer is used to analyze and process the water consumption-related data to obtain service information. The application layer includes a single-map system, a burst pipe diagnosis and treatment module, a leakage analysis module, a smart IoT water meter system, an engineering operation and maintenance management system, and a water supply and distribution scheduling system. The application layer is used to call the service information to execute preset business processes and obtain execution results.

[0014] According to a third aspect of this disclosure, a leakage analysis apparatus is provided, the apparatus comprising: The first determining unit is used to determine the water consumption-related data for each independent metering area within the target area; the water consumption-related data is determined based on the data acquisition and monitoring control system, geographic information system, and smart IoT water meter system; The second determining unit is used to input the water consumption-related data into the leakage analysis model to determine the leakage information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; The processing unit is configured to perform a pipe burst diagnosis on the first independent metering area based on a pipe burst diagnosis model if the leakage information of the first independent metering area indicates that there is a sudden leakage, so as to determine the pipe burst point in the first independent metering area.

[0015] In one possible implementation, the second determining unit is configured to: Perform flow balance calculation on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area; and determine whether each independent metering area is an abnormal area based on the obtained leakage amount and a first threshold. If an abnormally independent metering area is determined to be an abnormal area, then based on the minimum nighttime flow analysis algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the first analysis result information; and based on the historical water consumption baseline comparison algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the second analysis result information; the first analysis result information and the second analysis result information are cross-validated to determine the leakage situation information of the abnormally independent metering area.

[0016] In one possible implementation, the second determining unit is configured to: If the first analysis result indicates that the current minimum nighttime water consumption is greater than a first preset threshold for the baseline nighttime water consumption, and the second analysis result indicates that the total water supply and the water supply of the first branch exceed historical levels for the same period, then the first sub-verification information used to indicate persistent leakage is determined; or, If the first analysis result indicates that the water consumption at the first moment of the night exceeds the second preset threshold of the baseline nighttime water consumption, and the second analysis result indicates that the current hourly flow curve of the second branch is significantly different from the historical same period, then the second sub-verification information used to indicate sudden leakage is determined.

[0017] In one possible implementation, the processing unit is configured to: The first water consumption data of the first independent metering area is input into the real-time diagnostic model of the pipe burst diagnosis model to obtain pipe burst anomaly information; the pipe burst anomaly information includes the pipe segment at the anomaly point and the anomaly data. The static risk prediction model in the rupture diagnosis model is invoked to predict the pipe segment at the anomaly point and obtain the basic risk level; and the dynamic analysis model in the rupture diagnosis model is invoked to perform pattern analysis on the abnormal data and obtain rupture feature information. The confidence level of the pipe burst is determined based on the basic risk level and the pipe burst characteristic information; If the confidence level of the burst pipe is greater than the second threshold, it is determined that there is a burst pipe event at the abnormal point, and the abnormal point is designated as the burst pipe point.

[0018] In one possible implementation, the processing unit is further configured to: Determine the pipeline topology of the first independent metering area; Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the first monitoring point; Based on the first monitoring point, the target pipe segment corresponding to the pipe burst point is determined.

[0019] In one possible implementation, the processing unit is configured to: Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the timing and magnitude of pressure drop at each monitoring point. The first monitoring point is determined based on the timing and magnitude of the pressure drop at each monitoring point and the preset screening rules.

[0020] In one possible implementation, the processing unit is further configured to: From the first water consumption related data, obtain the associated dataset of the target pipe segment. The associated dataset includes the location information of the target pipe segment, the basic data of valves along the pipeline network, and the relationship data between valves, pipe segments, and users. Based on the pipeline topology and hydraulic model, the flow state under different valve closure combinations is simulated on the associated dataset to determine the constraints. The pipeline topology is transformed into a node-edge model. A breadth-first search algorithm is used to search for shut-off valve combinations layer by layer with the target pipe segment as the core. The number of affected users and the number of valves to be shut off for each valve combination are determined to identify a set of candidate valve shut-off schemes. Based on the preset dual objectives and the constraints, the target valve-closing scheme is determined from the set of candidate valve-closing schemes.

[0021] In one possible implementation, the processing unit is further configured to: The display information of the first analysis result, the second analysis result, the target pipe section, and the target valve shut-off scheme is determined, and the display information is displayed on the human-computer interaction interface. The human-computer interaction interface includes one or more functions from the following: a geographic information map main window, a pipeline operation status layer, a real-time monitoring data panel, an early warning and event list, a valve shut-off scheme preview window, and a comprehensive situation dashboard.

[0022] According to a fourth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method of the first aspect described above and possible implementations thereof.

[0023] According to a fifth aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of the first aspect and possible implementations thereof by executing the executable instructions.

[0024] The technical solution disclosed herein has the following beneficial effects: In this embodiment, water consumption data for each independent metering area within the target region can be automatically determined. This water consumption data is based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system. This involves determining various data, such as pressure, flow rate, water quality, user water consumption, valve execution data, and pipeline geographic location information. This water consumption data is then input into a leakage analysis model to determine the leakage situation for each independent metering area. In other words, the leakage analysis model automatically analyzes various data to determine the leakage situation for each independent metering area. The leakage situation information includes, for example, leakage periods and abnormal branches, as well as continuous or sudden leakage. Furthermore, the leakage analysis model employs a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data. For example, it uses a fusion strategy determined by the minimum nighttime flow analysis algorithm, the historical water consumption baseline comparison algorithm, and the flow balance algorithm. This not only eliminates the need for manual inspection, improving inspection efficiency to some extent, but also improves the accuracy of regional leakage assessment due to the use of a fusion strategy determined by multiple leakage analysis methods. Furthermore, if the leakage information in the first independent metering area indicates a sudden leakage, then based on the pipe burst diagnosis model, a pipe burst diagnosis is performed on the first independent metering area to determine the burst point. In other words, when the leakage information in the first independent metering area indicates a sudden leakage, further diagnosis and determination of the burst point can be performed, further enriching the specific content of the leakage analysis and improving the accuracy of the leakage analysis.

[0025] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments of this disclosure will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This illustration shows a schematic diagram of an application scenario in this exemplary embodiment; Figure 2 A schematic diagram of a leakage analysis system in this exemplary embodiment is shown; Figure 3A flowchart illustrating a leakage analysis method in this exemplary embodiment is shown. Figure 4 This illustration shows a process diagram of a leakage analysis method in this exemplary embodiment; Figure 5 This illustration shows a schematic diagram of a zoned flow balance calculation for an independent metering area in this exemplary embodiment. Figure 6 This diagram illustrates a principle for locating a burst pipe in this exemplary embodiment. Figure 7 This diagram illustrates a process for diagnosing and locating a burst pipe in this exemplary embodiment. Figure 8 This diagram illustrates the structure of a leakage analysis device according to this exemplary embodiment. Figure 9 A schematic diagram of the structure of an electronic device in this exemplary embodiment is shown. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. Unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0029] The term "comprising" and any variations thereof in the specification and claims of this disclosure are intended to cover non-exclusive protection. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0030] In this disclosure, there are one or more embodiments; "multiple" refers to two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following associated objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0031] It should be noted that the terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order, sequence, size, or priority. For example, the first threshold and second threshold in the embodiments of this disclosure are merely used to distinguish different thresholds. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0032] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, which are schematic illustrations of this disclosure and are not necessarily drawn to scale. Some block diagrams shown in the drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in hardware modules or integrated circuits, or in networks, processors, or microcontrollers. Implementations can be carried out in various forms and should not be construed as limited to the examples set forth herein. The features, structures, or characteristics described in this disclosure can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough description of embodiments of this disclosure. However, those skilled in the art will recognize that one or more specific details may be omitted when implementing the technical solutions of this disclosure, or other methods, components, apparatuses, steps, etc., may be used to replace one or more specific details.

[0033] It should be noted that certain software, components, models, and other existing industry solutions may be mentioned in the embodiments disclosed herein. These should be considered exemplary and intended only to illustrate the feasibility of implementing the technical solutions disclosed herein, but do not imply that the applicant has already used or necessarily used such solutions. The collection, dissemination, and use of data in the technical solutions disclosed herein all comply with relevant regulations.

[0034] Currently, water supply networks generally face common technical challenges in the industry, such as high leakage rates, frequent pipe bursts, difficulty in locating faults, and delayed response and handling.

[0035] In related technologies, the water supply network monitoring and diagnosis methods provided to address the aforementioned problems mainly rely on regular manual inspections, empirical judgments, and simple threshold alarm systems based on a single indicator.

[0036] Water network monitoring and diagnosis methods that rely on regular manual inspections and experience-based judgment are highly dependent on the personal experience of dispatchers throughout the entire process, from the occurrence of an anomaly to its discovery, location, and decision-making. Insufficient personal experience can easily lead to abnormal leak detection. Furthermore, simple threshold alarm systems based on a single indicator, such as relying solely on pressure surges to detect pipe bursts, are easily affected by peak water usage periods and normal operating conditions like pump station start-ups and shutdowns, resulting in a persistently high false alarm rate for leak anomalies.

[0037] Furthermore, the diagnostic methods provided in the relevant technologies lack effective early identification capabilities for slowly developing leaks; and for sudden pipe bursts, they are difficult to quickly and accurately locate in complex pipe network structures.

[0038] To address one or more of the above-mentioned problems, an exemplary embodiment of this disclosure provides a leakage analysis method. This method automatically determines water consumption-related data for each independent metering area within a target region. The water consumption-related data is determined based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system. This involves identifying various data points, such as pressure, flow rate, water quality, user water consumption, valve execution data, and pipeline geographical location information. This water consumption-related data is then input into a leakage analysis model to determine the leakage situation information for each independent metering area. In other words, the leakage analysis model automatically analyzes various data points to determine the leakage situation information for each independent metering area. This leakage situation information includes, for example, leakage time periods and abnormal branches, as well as continuous or sudden leakage. Furthermore, the leakage analysis model employs a fusion strategy of multiple leakage analysis methods when analyzing water consumption data. For example, it utilizes a fusion strategy determined by the minimum nighttime flow analysis algorithm, the historical water consumption baseline comparison algorithm, and the flow balance algorithm. This not only eliminates the need for manual inspections, improving inspection efficiency to some extent, but also enhances the accuracy of regional leakage assessment due to the fusion strategy based on multiple leakage analysis methods. Additionally, if the leakage information in the first independent metering area indicates a sudden leakage, a burst pipe diagnosis is performed on the first independent metering area based on the burst pipe diagnosis model to determine the burst point. In other words, when the leakage information in the first independent metering area indicates a sudden leakage, further diagnosis and determination of the burst point can be performed, enriching the specific content of the leakage analysis and improving its accuracy.

[0039] To better understand the technical solutions provided in the embodiments of this disclosure, the following is a brief introduction to the application scenarios applicable to the technical solutions provided in the embodiments of this disclosure. It should be noted that the application scenarios described below are only for illustrating the embodiments of this disclosure and are not intended to limit the scope. In specific implementation, the technical solutions provided in the embodiments of this disclosure can be flexibly applied according to actual needs.

[0040] Please see Figure 1 As shown, Figure 1 This is an application scenario to which the technical solution of the present disclosure embodiment can be applied. The schematic diagram of this scenario includes multiple data acquisition devices 101 and electronic devices 102. The data acquisition devices 101 and electronic devices 102 can be directly or indirectly connected via one or more networks 103.

[0041] In this embodiment, the data acquisition device 101 can acquire multi-source data. This multi-source data includes data from Supervisory Control and Data Acquisition (SCADA) systems, Geographic Information Systems (GIS), and smart IoT water meter systems. In other words, this scenario could include data acquisition devices 101-1 for pressure acquisition, 101-2 for water flow acquisition, 101-3 for water quality acquisition, and 101-4 for water level acquisition, etc., but this embodiment does not limit the scope of the data acquisition device.

[0042] In this embodiment of the disclosure, the acquisition device 101 can send the acquired multi-source data to the electronic device 102, and then the electronic device 102 can determine the water consumption related data of each independent metering area within the target area; then, the water consumption related data is input into the leakage analysis model to determine the leakage information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; if the leakage information of the first independent metering area indicates that there is a sudden leakage, then based on the pipe burst diagnosis model, the pipe burst diagnosis is performed on the first independent metering area to determine the pipe burst point of the first independent metering area.

[0043] In this embodiment of the disclosure, the electronic device 102 can be a server. The server can be a cloud server or cloud server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms, but is not limited to this.

[0044] Of course, the methods provided in this disclosure are not limited to... Figure 1 The application scenarios shown can also be used in other possible application scenarios, such as the application scenario where only the electronic device 102 implements the leakage analysis method. This will be used as an example in the following description, and the embodiments disclosed herein are not intended to limit the scope of the application.

[0045] In this example embodiment, the leakage analysis system includes a perception layer, a communication layer, a data layer, a service layer, and an application layer. Specifically: the perception layer collects water consumption data for each independent metering area within the target region; the communication layer sends the water consumption data collected by the perception layer to the data layer; the data layer classifies, stores, manages, and performs preliminary data processing on the water consumption data transmitted by the communication layer; the service layer includes data fusion services, model calculation services, geographic information system services, and message push services, and is used to analyze and process the water consumption data to obtain service information; the application layer includes a map system, a burst pipe diagnosis and treatment module, a leakage analysis module, a smart IoT water meter system, an engineering operation and maintenance management system, and a water supply and distribution scheduling system, and is used to invoke service information to execute preset business processes and obtain execution results.

[0046] See Figure 2 The diagram shown is a schematic of a leakage analysis system provided in an embodiment of this disclosure.

[0047] exist Figure 2 In the system, the leakage analysis system consists of five layers: perception layer, communication layer, data layer, service layer, and application layer. Each layer works together through data collection, transmission, access, and invocation, and is supported by a standard specification system.

[0048] In this embodiment, the sensing layer includes at least the following components: a pressure sensor, a flow sensor, a water quality sensor, a smart IoT water pump, video surveillance, and a valve actuator. The sensing layer interacts directly with the physical infrastructure of the pipeline network, collecting raw data on network operation, such as pipeline pressure, water flow rate, water quality indicators, pump status, and valve opening / closing actions. It serves as the data entry point for the entire system.

[0049] In this embodiment, the communication layer includes at least the following components: industrial Ethernet, fiber optic private network, MODBUS protocol, 4G / 5G mobile communication technology, and Narrowband Internet of Things (NB-IoT). The communication layer is used to transmit the raw data collected by the sensing layer to the data layer in real time and stably. Different communication methods are adapted to different scenarios. For example, industrial Ethernet / fiber optic private network is used for high-speed wired connections between pipeline control centers and factory areas or large pumping stations; 4G / 5G and NB-IoT are used for wireless data transmission from sensors distributed throughout the city (such as roadside fire hydrant flow meters), balancing bandwidth and low power consumption.

[0050] As can be seen, this embodiment innovatively integrates multiple communication protocols such as MODBUS, NB-IoT, and 4G / 5G to construct a heterogeneous sensing network covering the entire chain of "source, plant, network, and user". This enables minute-level high-frequency acquisition and reliable transmission of multi-dimensional data such as pressure, flow, water quality, water level, and equipment status, providing a comprehensive, real-time, and high-quality data foundation for upper-level analysis.

[0051] In this exemplary embodiment, the data layer includes components such as a real-time monitoring database, a pipeline topology database, a historical event database, a model parameter database, and a business management database. The data layer is used to classify, store, manage, and perform preliminary processing of data transmitted from the communication layer, serving as the system's data warehouse. Specifically, the real-time monitoring database stores dynamic data such as pressure and flow rate transmitted in real-time from the sensing layer; the pipeline topology database stores the physical connection relationships of the water supply network (spatial and logical associations of valves, pipe sections, and users); the historical event database stores the time, location, and processing records of historical events such as pipe bursts, leaks, and equipment failures; the model parameter database stores calculation parameters (such as pipe friction coefficient and pipe section length) for hydraulic models and leakage analysis models; and the business management database stores business-related data such as user information, water fee data, and engineering operation and maintenance records.

[0052] In the disclosed embodiments, the service layer includes components: a data fusion service, a model calculation service, a geographic information system (GIS) service, and a message push service. The service layer processes, analyzes, and encapsulates the raw data from the data layer, transforming it into directly callable services to support the business needs of the application layer. Specifically, the data fusion service integrates multi-source data (such as pressure, flow rate, and user water usage data) to form a unified analysis dataset; the model calculation service calls a model parameter library to run hydraulic models, leakage prediction models, etc., and outputs analysis results (such as leakage probability and pressure distribution); the GIS service provides visualization, query, and analysis services for pipeline network spatial information based on a pipeline network topology database (such as valve location and pipe length measurement); and the message push service pushes abnormal events (such as pipe burst alarms) to the application layer's business systems or administrators.

[0053] In this embodiment, the application layer includes the following components: a single-map system, a burst pipe diagnosis and treatment module, a leakage analysis module, a smart IoT water meter system, an engineering operation and maintenance management system, and a water supply and distribution scheduling system. The application layer directly serves users (e.g., water management personnel, operation and maintenance personnel, and users), implementing various business functions and representing the ultimate value of the system. Specifically, the single-map system integrates GIS services to visualize the pipeline network topology, equipment location, and monitoring data on a map, supporting a comprehensive overview of the pipeline network status; the burst pipe diagnosis and treatment module uses model calculation services and a historical event database to quickly locate burst pipe points and generate valve shut-off solutions; the leakage analysis module analyzes the distribution of pipeline network leakage and calculates leakage volume based on data fusion services and model calculation services; the smart IoT water meter system interfaces with the business management database to meter, calculate, and charge user water fees; the engineering operation and maintenance management system manages the construction, repair, and equipment inspection of pipeline projects; and the water supply and distribution scheduling system optimizes pump and valve scheduling based on real-time monitoring data and hydraulic models to ensure a balance between water supply pressure and volume.

[0054] In this embodiment of the disclosure, the "one-map" system is, for example, a visualization output terminal and human-computer interaction interface for all analysis results of this disclosure, and its core functional areas include: (1) Geographic Information Map Main Window: Based on the GIS engine, the base map is overlaid with administrative divisions, roads, and satellite imagery. Pipeline equipment (pumps, valves, water meters, sensors) are displayed on the map according to their actual geographic coordinates.

[0055] (2) Pipeline operation status layer: The pipeline is rendered in different colors to represent the real-time status.

[0056] (3) Real-time monitoring data panel: Click on any device on the map to pop up a floating window that displays its real-time monitoring data (pressure, flow, water level, etc.) and historical trend curves.

[0057] (4) Warning and Event List: The sidebar scrolls to display the latest warning information (such as "abnormal pressure drop on XX Road") and a list of confirmed events. Clicking on an event will automatically locate it on the map and highlight it.

[0058] (5) Preview window for handling plan: When a pipe bursts, this window will pop up automatically, showing the valves recommended to be closed by the system and the list of affected communities and important users, and providing "one-click execution" or "manual confirmation" buttons.

[0059] (6) Comprehensive situation dashboard: Displays global key performance indicators (KPIs), such as current average pressure of the entire network, instantaneous flow, total leakage today, and statistics on pipe bursts.

[0060] In this embodiment, the leakage analysis system can perform data acquisition through the perception layer and transmit the acquired data to the data layer through the communication layer. The data layer then performs relevant processing and access operations based on the received data, such as accessing data fusion services and message push services in the service layer. Furthermore, when executing services in the service layer—that is, when making service calls or using application programming interfaces (APIs)—it can invoke various systems in the application layer, thereby achieving leakage analysis.

[0061] As can be seen, the leakage analysis system provided in this embodiment is layered, decoupled, and data-driven. The bottom layer focuses on data acquisition, the middle layer on data processing and service provisioning, and the top layer on business implementation. This ensures system flexibility (i.e., upgrades to one layer do not affect other layers) and achieves intelligent management of the pipeline network through end-to-end data flow.

[0062] To further illustrate the technical solutions provided by the embodiments of this disclosure, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this disclosure provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-inventive methods. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided by the embodiments of this disclosure. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0063] The following combination Figure 3 The flowchart shown illustrates the leakage analysis method in the embodiments of this disclosure. Figure 3 The steps shown can be derived from, for example: Figure 1 The illustrated electronic device 102 performs the following actions, wherein the electronic device 102 is deployed with Figure 2 The leakage analysis system shown.

[0064] Step 301: Determine the water consumption data for each independent metering area within the target area; the water consumption data is determined based on the data acquisition and monitoring control system, geographic information system, and smart IoT water meter system.

[0065] In this embodiment of the disclosure, data acquisition can be performed by various sensors in the sensing layer (such as pressure sensors, flow sensors, etc.) at a set frequency (such as once per minute), and data acquisition can be performed by valve actuators and video monitoring to obtain raw multi-source data. The multi-source data is then transmitted to the data layer through the communication layer. The data layer can perform cleaning processing on the raw multi-source data (such as removing jump outliers), filtering processing for smoothing noise, and normalization processing for eliminating dimensions, thereby forming a high-quality time-series data sequence.

[0066] In this embodiment of the disclosure, data related to water use in each independent metering area within the target area can also be obtained from the geographic information system and the smart IoT water meter, such as pipeline location information and user water consumption information within the area.

[0067] In this embodiment of the disclosure, the aforementioned time-series data sequence and the data related to water use in each independent metering area within the target area in the geographic information system and smart IoT water meter system can be unified as water consumption related data, thereby determining the water consumption related data for each independent metering area within the target area.

[0068] Step 302: Input water consumption data into the leakage analysis model to determine the leakage information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on water consumption data.

[0069] In this embodiment, leakage analysis can be performed on water consumption data for each independent metering area based on a leakage analysis model to obtain leakage information for each independent metering area. The leakage analysis model employs a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data. The fusion strategy involves the system periodically (e.g., daily) running a flow balancing algorithm to obtain the overall leakage level for each independent metering area. Furthermore, for areas with excessive leakage, the minimum night flow (MNF) analysis algorithm and the historical water consumption baseline comparison algorithm are automatically invoked for auxiliary verification and preliminary location, thereby determining the leakage information for each independent metering area.

[0070] See Figure 4 As shown, Figure 4 A schematic diagram of a leakage analysis method in this exemplary embodiment is shown.

[0071] Step 410: Perform flow balance calculation on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area.

[0072] Step 420: Based on the obtained leakage amount and the first threshold, determine whether each independent metering area is an abnormal area.

[0073] Step 430: For abnormally independent metering areas, call the minimum nighttime flow analysis algorithm and the historical water consumption baseline comparison algorithm to perform auxiliary verification and preliminary location, and determine the leakage information of abnormally independent metering areas.

[0074] Step 440: Generate a leakage diagnosis report based on the leakage information.

[0075] Step 450: Generate a maintenance work order based on the leakage diagnosis report and send the maintenance work order to the operation and maintenance system.

[0076] As can be seen, in this embodiment, the leakage analysis model is initiated, and then flow balance calculations are performed on the water consumption data of each independent metering area to determine the leakage amount of each independent metering area. Based on whether the leakage amount exceeds the standard, the leakage analysis ends when it is determined not to exceed the standard. When it is determined to exceed the standard, the abnormal leakage in the area exceeding the standard (hereinafter referred to as the abnormal independent metering area) can be precisely located. Specifically, the minimum nighttime flow analysis algorithm and the historical water consumption baseline comparison algorithm can be used for auxiliary verification and preliminary location to determine the leakage information of the abnormal independent metering area. Furthermore, a leakage diagnosis report can be generated based on the leakage information, and this report can be pushed to the operation and maintenance system so that the corresponding staff can perform appropriate processing.

[0077] In step 410, flow balance calculation is performed on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area.

[0078] In this embodiment of the disclosure, flow balance calculation processing can be performed on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area; and based on the obtained leakage amount and a first threshold, it can be determined whether each independent metering area is an abnormal area.

[0079] In an exemplary embodiment, see Figure 5 As shown, for District Metered Area (DMA)-A, water consumption data from the District Metered Area (e.g.) can be obtained. Figure 5 (Flow information corresponding to FT-01), obtain the total inlet metering data of the independent metering area (DMA) (obtain the total inlet water flow, for example) Figure 5 The total inflow rate Qin in the metered area, and the sum of all user water meter readings within the independently metered area (e.g., the total inflow rate Qin). Figure 5 This includes the water used in the independent metering area (such as fire-fighting water) and legally unmetered water use within the metered area. Then, based on the aforementioned data, a balance calculation is performed hourly or daily to directly obtain the leakage and loss (e.g., water loss in the independent metering area) of that independent metering area. Figure 5(Q-leakage in the middle).

[0080] For example, the total leakage in an independent metering area can be determined based on the following formula: Total leakage in the DMA area = Total inlet flow in the DMA area - Sum of water meter readings for all users in the DMA area - Legitimate unmetered water usage. Formula 1.

[0081] In step 420, based on the obtained leakage amount and the first threshold, it is determined whether each independent metering area is an abnormal area.

[0082] In this embodiment of the disclosure, after determining the leakage amount of each independent metering area, a first threshold can be determined. The first threshold can be determined based on the actual implementation, and this embodiment of the disclosure does not limit this. Further, the leakage amount of the independent metering area can be compared with the first threshold. If it is determined that the leakage amount is greater than the first threshold, it is determined that the leakage amount of the independent metering area exceeds the standard, thereby identifying the independent metering area as an abnormal independent metering area.

[0083] In step 430, for the abnormally independent metering area, the minimum nighttime flow analysis algorithm and the historical water consumption baseline comparison algorithm are invoked to perform auxiliary verification and preliminary location, and to determine the leakage information of the abnormally independent metering area.

[0084] In this embodiment of the disclosure, once an abnormal independent metering area is identified, the minimum nighttime flow analysis algorithm and the historical water consumption baseline comparison algorithm can be automatically invoked for the abnormal independent metering area to perform auxiliary verification and preliminary positioning, and to determine the leakage information of the abnormal independent metering area.

[0085] The principle behind the minimum nighttime flow analysis algorithm is as follows: During the period when user water consumption is at its lowest and most stable at night (e.g., 1:00 AM to 5:00 AM), the inflow rate to a certain DMA area should theoretically equal the legal nighttime water consumption of that area plus leakage. Through long-term monitoring, a baseline for normal nighttime water consumption in that area can be established. If the minimum flow rate monitored on a certain night consistently exceeds the baseline by a certain percentage (e.g., 20%), it is determined that there may be new leakage in that area.

[0086] For example, the suspected leakage in an abnormally independent metering area can be determined based on the following formula: Suspected leakage = Measured minimum nighttime flow rate - Baseline nighttime water consumption. Formula 2.

[0087] The principle of the historical water usage baseline comparison algorithm is to compare the current flow meter reading with the historical readings of the same period (e.g., the same day of the same week last year) and the readings of the same type of day (e.g., comparing weekdays with weekdays). Flow meters with abnormal water usage patterns are identified using statistical methods (e.g., Z-score test) or machine learning algorithms (e.g., isolated forest), thereby determining that there may be leakage in the downstream pipe network.

[0088] In this embodiment of the disclosure, if an abnormal independent metering area is determined to be an abnormal area, the following steps are used, but not limited to, to determine the leakage information of the abnormal independent metering area.

[0089] Step A: Based on the minimum nighttime flow analysis algorithm, perform leakage analysis on the water consumption data of abnormally independent metering areas to determine the first analysis result information.

[0090] In this embodiment of the disclosure, determining the first analysis result information includes the following steps: collecting nighttime flow data of the abnormal independent metering area; wherein, the nighttime flow data includes 15-minute / hourly flow data of the water meter at the area's main inlet. Based on historical data of the abnormal independent metering area (e.g., nighttime flow with no leakage records in the past 3 months), a normal minimum nighttime flow baseline is determined, and based on the normal minimum nighttime flow baseline, a baseline threshold (e.g., 1.2-1.5 times the baseline) is determined. Based on the baseline threshold and the current minimum nighttime flow data, the first analysis result information is determined. Optionally, if the current minimum nighttime flow data is greater than the baseline threshold, it indicates that there is additional abnormal loss in the nighttime flow, and it is preliminarily verified that the leakage exceeding the standard in the abnormal independent area is not a misjudgment. Furthermore, if the nighttime flow is consistently high, it indicates that the leakage is continuous (e.g., seepage from a concealed pipe, or damage to a pipe that has not burst); if the nighttime flow suddenly increases and then remains high, it indicates that the leakage is sudden (e.g., a nighttime pipe burst), and the moment of the sudden increase can be accurately identified as the starting period of the exceedance.

[0091] As can be seen, in this embodiment of the disclosure, the minimum nighttime flow analysis algorithm can eliminate interference from user water usage, directly capture pure leakage signals, verify the authenticity and continuity of leakage, and accurately pinpoint the time period exceeding the standard.

[0092] Step B: Based on the historical water usage baseline comparison algorithm, perform leakage analysis on the water usage data of abnormally independent metering areas to determine the second analysis result information.

[0093] In this embodiment of the disclosure, considering that the water use behavior of the pipeline network has a strong periodicity, the flow data of the current independent metering area (e.g., total water supply, branch water use) can be compared with the historical normal baseline data to determine whether the current data deviates from the pattern. If the deviation is significant and cannot be explained by normal factors, the leakage exceeding the standard is verified to be a real situation.

[0094] In this embodiment of the disclosure, determining the second analysis result information may involve, but is not limited to, the following steps: First, determining the historical data without leakage for the abnormally independent metering area as a baseline; wherein the baseline includes a concurrent baseline, a periodic baseline, and a trend baseline. Second, acquiring the current data for the abnormally independent metering area; wherein the current data includes the total daily water supply, the daily water consumption of branch lines, and the hourly flow curve. Third, comparing the current data with the baseline, and determining the second analysis result information based on the comparison result.

[0095] In this disclosed embodiment, the concurrent baseline is, for example, daily / hourly traffic data for the same period last year (e.g., October last year) (excluding the influence of seasonal differences); the periodic baseline is, for example, the average traffic data for the same type of date (e.g., every Monday) in the past 3 months (excluding weekday / weekend differences); and the trend baseline is, for example, the traffic change trend in the past 6 months (e.g., the average monthly leakage rate is stable at 8%, without significant fluctuations).

[0096] For example, if the current daily total water supply is greater than 1.3 times the historical baseline for the same period (this multiple can be customized), and there is no increase in the number of users or water demand in the area, then it can be verified that the abnormal total water supply is caused by leakage.

[0097] For example, a normal flow curve should conform to the pattern of "high flow during the morning peak (7:00-9:00) and evening peak (18:00-20:00), and low flow at night"; if the current curve shows "no low flow at night" and "high flow during off-peak hours", and is significantly different from the historical baseline curve, then it is verified that leakage has caused abnormal flow patterns. For example, if the current water consumption of a certain branch is greater than 1.5 times the historical baseline for the same period (this multiple can be customized), and there is no special change in the water consumption of users on that branch, then it is verified that there is abnormal leakage in that branch.

[0098] As can be seen, in this embodiment of the disclosure, the algorithm based on historical water usage baseline comparison can eliminate temporary interference factors, confirm that the leakage exceeding the standard is a "systemic anomaly" rather than a "random fluctuation", and can accurately locate the abnormal branch.

[0099] Step C: Cross-validate the first and second analysis results to determine the leakage information in the abnormal independent measurement areas.

[0100] In this embodiment of the disclosure, if the first analysis result information indicates that the current minimum nighttime water consumption is greater than the first preset threshold of the baseline nighttime water consumption, and the second analysis result information indicates that the total water supply and the water supply of the first branch exceed the historical average for the same period, then the first sub-verification information for indicating continuous leakage is determined.

[0101] For example, if the first analysis result indicates that the current minimum nighttime water consumption is 1.3 times greater than the baseline nighttime water consumption, and the second analysis result indicates that the total water supply exceeds the historical average for the same period by 1.2 times, and branch A exceeds the historical average for the same period by 1.5 times, then the first sub-verification information is determined to be true leakage exceeding the standard and is a continuous leakage, the period of exceeding the standard is the whole day, and the abnormal branch is branch A.

[0102] In this embodiment of the disclosure, if the first analysis result information indicates that the water consumption at the first moment of the night exceeds the second preset threshold of the baseline nighttime water consumption, and the second analysis result information indicates that the current hourly flow curve of the second branch is significantly different from the historical same period, then the second sub-verification information used to indicate sudden leakage is determined.

[0103] For example, if the first analysis result indicates that the flow rate suddenly increased at 2:30 am, exceeding the baseline nighttime water consumption by 1.8 times, and the second analysis result indicates that the current hourly flow rate curve of branch B is significantly different from the historical data, with the flow rate only reaching a high level after 2:30 am, then it can be determined that the second sub-verification information is true and sudden leakage, with the leakage period starting at 2:30 am; and the abnormal branch is branch B, which experienced a sudden increase in flow rate at night.

[0104] As can be seen, in this embodiment, the minimum nighttime flow analysis algorithm, the historical water usage baseline comparison algorithm, and the flow balance analysis algorithm based on independent metering areas are comprehensively applied to form a complementary leakage analysis model chain. Furthermore, through a confidence-weighted fusion mechanism, the leakage amount at different levels of the pipe network, from the main pipe to the inlet pipe, is accurately quantified and spatially located, significantly improving the comprehensiveness and accuracy of leakage detection.

[0105] In step 440, a leakage diagnosis report is generated based on the leakage information.

[0106] In this embodiment of the disclosure, a leakage diagnosis report can be generated based on the leakage information, including the period of exceeding the standard and the abnormal branches.

[0107] In step 450, a maintenance work order is generated based on the leakage diagnosis report and sent to the operation and maintenance system.

[0108] In one possible implementation, a maintenance work order can be generated based on the leakage diagnosis report and sent to the operation and maintenance system. This allows the corresponding maintenance personnel to handle the issue accordingly.

[0109] In one possible implementation, after determining that the leakage information of the first independent metering area indicates the presence of a sudden leakage, step 303 can be executed: if it is determined that the leakage information of the first independent metering area indicates the presence of a sudden leakage, then based on the pipe burst diagnosis model, pipe burst diagnosis is performed on the first independent metering area to determine the pipe burst point of the first independent metering area.

[0110] In this embodiment of the disclosure, when it is determined that the leakage information of the first independent metering area indicates the existence of a sudden leakage, a pipe burst diagnosis can be performed on the first independent metering area based on the pipe burst diagnosis model to determine the pipe burst point of the first independent metering area.

[0111] In this embodiment, the first water consumption data of the first independent metering area can be input into the real-time diagnostic model of the pipe burst diagnosis model to obtain pipe burst anomaly information. The pipe burst anomaly information includes the pipe segment at the anomaly point and the anomaly data. Then, the static risk prediction model in the pipe burst diagnosis model is called to perform prediction processing on the pipe segment at the anomaly point to obtain the basic risk level. And, the dynamic analysis model in the pipe burst diagnosis model is called to perform pattern analysis processing on the anomaly data to obtain pipe burst feature information. Further, the pipe burst confidence level can be determined based on the basic risk level and the pipe burst feature information. If the pipe burst confidence level is greater than a second threshold, it is determined that there is a pipe burst event at the anomaly point, and the anomaly point is designated as the pipe burst point.

[0112] In this embodiment, the real-time diagnostic model first calculates the residual between the first water consumption-related data and the predicted value trained based on historical data. When the residual exceeds a dynamically adjusted threshold, a pipe burst anomaly is identified. The threshold can be adaptively adjusted based on factors such as time period (e.g., a lower threshold during low-flow periods at night) and season to reduce the false alarm rate.

[0113] In this embodiment of the disclosure, the static risk prediction model is based on machine learning algorithms such as logistic regression and random forest, and integrates multi-dimensional attribute features such as pipe material, pipe age, pipe diameter, soil corrosivity, and historical pipe burst records to conduct long-term risk assessment and classification of the probability of pipe bursts in each pipe section of the pipeline network, and guide the formulation of preventive maintenance plans.

[0114] In this embodiment, the dynamic analysis model is based on real-time monitored flow and pressure data, combined with the pipeline network topology, to construct an anomaly detection algorithm based on Support Vector Machine (SVM) or Convolutional Neural Network (CNN). This model can not only identify significant abrupt pipe bursts, but also keenly capture minute leakage signals, and achieve rapid diagnosis and preliminary location of pipe burst events by analyzing the propagation patterns of anomalies in the pipeline network.

[0115] In one exemplary embodiment, upon determining that a pipe burst event has occurred, a pipe network topology map of a first independent metering area can be determined. Then, based on the principles of mass conservation and pressure propagation, the pipe network topology map is traversed to determine a first monitoring point. Based on this first monitoring point, the target pipe segment corresponding to the pipe burst point can be identified.

[0116] In one exemplary embodiment, see Figure 6 As shown, based on the principles of mass conservation and pressure propagation, the pipeline topology can be traversed to determine each monitoring point (e.g., Figure 6 The pressure information for p1, p2, p3, p4, etc., and the pressure information for each monitoring point are shown in the example below. Figure 6 The real-time pressure monitoring table shown is illustrated. Pressure information includes, for example, the timing of pressure drops (e.g.,...). Figure 6 The pressure at point P2 drops sharply first, followed by a decrease at point P3, and the magnitude of this drop is considered. Based on the timing and magnitude of the pressure decrease at each monitoring point, and according to preset screening rules, the first monitoring point is determined. For example, the preset screening rules may involve finding the monitoring point with the earliest and largest pressure decrease (see [reference]). Figure 6 As shown, the first monitoring point is, for example, P2. Furthermore, the downstream adjacent pipe segment of the first monitoring point can be taken as the target pipe segment, that is, the downstream adjacent pipe segment of P2 (i.e., the pipe segment where the pipe burst occurs) is located.

[0117] In one exemplary embodiment, a related dataset of the target pipe segment can also be obtained from the first water consumption related data. The related dataset includes the location information of the target pipe segment, the basic data of valves along the pipeline network, and the relationship data between valves, pipe segments, and users. Based on the pipeline network topology map and the pipeline network hydraulic model, the water flow state under different valve closure combinations is simulated on the related dataset to determine the constraints. The pipeline network topology map is transformed into a node edge model, and a breadth-first search algorithm is used to search for valve combinations that can be closed layer by layer with the target pipe segment as the core. The number of affected users and the number of valves closed corresponding to each valve combination are determined to determine the candidate valve closure scheme set. The target valve closure scheme is determined from the candidate valve closure scheme set according to the preset dual objectives and constraints.

[0118] In this embodiment, the upstream and downstream connections of the target pipe segment, the location, type (e.g., gate valve / butterfly valve), and status (e.g., normally closable / faulty) of valves along the pipeline can be determined first based on the pipeline network topology diagram, as well as the relationship between valves and pipe segments and users (e.g., the downstream user range controlled by a certain valve). The pressure and flow changes in the pipeline network after valve closure are simulated based on the official website topology diagram and hydraulic model, satisfying two core constraints: the target pipe segment must be completely isolated from the pipeline network after valve closure, i.e., the water flow must be cut off; and the pressure of the remaining pipeline network must be maintained within a safe range (e.g., not lower than the minimum service pressure and not higher than the pipeline network pressure resistance limit). This avoids negative pressure, water hammer, or large-scale water outages.

[0119] In this embodiment, a preset dual objective can be set, such as minimizing the number of affected users and minimizing the number of valves closed. Furthermore, when the two objectives conflict, weighting coefficients can be set. For example, if closing fewer valves leads to more affected users, the weight of user impact (objective one) can be set to 0.7, and the weight of the number of valves (objective two) to 0.3. Thus, a comprehensive optimal solution can be obtained through weighted summation.

[0120] In this embodiment of the disclosure, the pipeline topology can be transformed into a node-edge model, that is, a node-edge model with valves as nodes and pipe segments as edges. Then, the attributes of each node can be labeled, such as the number of controlled users and the difficulty of operation; and the association relationship of each edge can also be labeled, such as whether it is a target pipe segment or an upstream or downstream connecting node.

[0121] In this embodiment, the target pipe segment can be used as the core to search for closable valves layer by layer outwards. The number of affected users and the number of valves closed corresponding to each valve combination are recorded to determine the candidate valve-closing scheme set. Furthermore, the optimal solution can be selected based on the lowest weighted score of the two objectives, while also satisfying hydraulic constraints, thereby determining the target valve-closing scheme.

[0122] In one possible implementation, the display information of the first analysis result, the second analysis result, the target pipe section, and the target valve shut-off scheme can also be determined and displayed on the human-machine interface; wherein, the human-machine interface includes one or more functions from the following: the geographic information map main window, the pipeline network operation status layer, the real-time monitoring data panel, the early warning and event list, the valve shut-off scheme preview window, and the comprehensive situation dashboard.

[0123] In this embodiment, all the aforementioned analysis results, real-time data, early warning information, and response plans (such as target valve shut-off plans) can be seamlessly integrated into a "single map" digital twin management platform based on Building Information Modeling (BIM) and Geographic Information System (GIS), i.e., the aforementioned single map system. This single map system uses 3D visualization technology to achieve real-time mapping of pipeline network operation status, intuitive labeling of abnormal events, and dynamic simulation of the response process, providing managers with an integrated decision support interface for monitoring, diagnosis, and control.

[0124] In this disclosure embodiment, see Figure 7 As shown, Figure 7 This diagram illustrates a process for diagnosing and locating a burst pipe in this exemplary embodiment.

[0125] Step 710: Input the first water consumption related data of the first independent metering area into the real-time diagnostic model in the pipe burst diagnosis model to obtain pipe burst anomaly information; the pipe burst anomaly information includes the pipe segment at the anomaly point and the anomaly data.

[0126] Step 720: Call the static risk prediction model in the pipe burst diagnosis model to predict the pipe segment at the abnormal point and obtain the basic risk level; and call the dynamic analysis model in the pipe burst diagnosis model to perform pattern analysis on the abnormal data and obtain the pipe burst feature information.

[0127] Step 730: Determine the confidence level of the pipe burst based on the basic risk level and pipe burst characteristic information.

[0128] Step 740: If the confidence level of the burst pipe is greater than the second threshold, determine that there is a burst pipe event at the anomaly point and designate the anomaly point as the burst pipe point.

[0129] Step 750: If the confidence level of the burst pipe is not greater than the second threshold, record the burst pipe event as a suspected event and start the monitoring program.

[0130] As can be seen, in this embodiment of the disclosure, when a pipe burst event is determined to be a suspected event, that is, when a pipe burst is suspected to exist, a monitoring program can be started to continuously monitor the target area. In this way, a shift from passive response to proactive early warning can be achieved, and the health status of the pipeline network can be continuously assessed.

[0131] Step 760: Based on the principles of mass conservation and pressure propagation, traverse the pipeline topology map to determine the first monitoring point; and based on the first monitoring point, determine the target pipe segment corresponding to the burst point.

[0132] Step 770: Determine the target valve shut-off scheme corresponding to the target pipe section.

[0133] Step 780: Generate alarm information based on the target valve shut-off scheme, and push the alarm information and the target valve shut-off scheme.

[0134] In one exemplary embodiment, after the pipe burst is accurately located, the system can automatically retrieve pipeline topology data and analyze the affected area using anti-spoofing algorithms (such as Dijkstra's algorithm). With multiple objectives including "minimizing the water outage area," "minimizing the impact on key users," and "reducing the number of handling operations," a genetic algorithm (such as Non-dominated Sorting Genetic Algorithm II, NSGA-II)) is used for optimization to automatically generate the optimal valve shut-off scheduling sequence and emergency water supply plan, and simultaneously generate notification content for affected users.

[0135] Exemplary embodiments of this disclosure also provide a leakage analysis apparatus. (See reference...) Figure 8 As shown, the leakage analysis device 800 includes the following program units: The first determining unit 801 is used to determine water consumption-related data for each independent metering area within the target area; the water consumption-related data is determined based on the data acquisition and monitoring control system, the geographic information system, and the smart IoT water meter system. The second determining unit 802 is used to input the water consumption-related data into the leakage analysis model to determine the leakage information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; The processing unit 803 is configured to perform a pipe burst diagnosis on the first independent metering area based on the pipe burst diagnosis model if the leakage information of the first independent metering area indicates that there is a sudden leakage, so as to determine the pipe burst point of the first independent metering area.

[0136] In one possible implementation, the second determining unit 802 is configured to: Perform flow balance calculation on the water consumption data of each independent metering area within the target area to determine the leakage amount of each independent metering area; and determine whether each independent metering area is an abnormal area based on the obtained leakage amount and a first threshold. If an abnormally independent metering area is determined to be an abnormal area, then based on the minimum nighttime flow analysis algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the first analysis result information; and based on the historical water consumption baseline comparison algorithm, leakage analysis is performed on the water consumption data of the abnormally independent metering area to determine the second analysis result information; the first analysis result information and the second analysis result information are cross-validated to determine the leakage situation information of the abnormally independent metering area.

[0137] In one possible implementation, the second determining unit 802 is configured to: If the first analysis result indicates that the current minimum nighttime water consumption is greater than a first preset threshold for the baseline nighttime water consumption, and the second analysis result indicates that the total water supply and the water supply of the first branch exceed historical levels for the same period, then the first sub-verification information used to indicate persistent leakage is determined; or, If the first analysis result indicates that the water consumption at the first moment of the night exceeds the second preset threshold of the baseline nighttime water consumption, and the second analysis result indicates that the current hourly flow curve of the second branch is significantly different from the historical same period, then the second sub-verification information used to indicate sudden leakage is determined.

[0138] In one possible implementation, the processing unit 803 is configured to: The first water consumption data of the first independent metering area is input into the real-time diagnostic model of the pipe burst diagnosis model to obtain pipe burst anomaly information; the pipe burst anomaly information includes the pipe segment at the anomaly point and the anomaly data. The static risk prediction model in the rupture diagnosis model is invoked to predict the pipe segment at the anomaly point and obtain the basic risk level; and the dynamic analysis model in the rupture diagnosis model is invoked to perform pattern analysis on the abnormal data and obtain rupture feature information. The confidence level of the pipe burst is determined based on the basic risk level and the pipe burst characteristic information; If the confidence level of the burst pipe is greater than the second threshold, it is determined that there is a burst pipe event at the abnormal point, and the abnormal point is designated as the burst pipe point.

[0139] In one possible implementation, the processing unit 803 is further configured to: Determine the pipeline topology of the first independent metering area; Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the first monitoring point; Based on the first monitoring point, the target pipe segment corresponding to the pipe burst point is determined.

[0140] In one possible implementation, the processing unit 803 is configured to: Based on the principles of mass conservation and pressure propagation, the pipeline topology is traversed to determine the timing and magnitude of pressure drop at each monitoring point. The first monitoring point is determined based on the timing and magnitude of the pressure drop at each monitoring point and the preset screening rules.

[0141] In one possible implementation, the processing unit 803 is further configured to: From the first water consumption related data, obtain the associated dataset of the target pipe segment. The associated dataset includes the location information of the target pipe segment, the basic data of valves along the pipeline network, and the relationship data between valves, pipe segments, and users. Based on the pipeline topology and hydraulic model, the flow state under different valve closure combinations is simulated on the associated dataset to determine the constraints. The pipeline topology is transformed into a node-edge model. A breadth-first search algorithm is used to search for shut-off valve combinations layer by layer with the target pipe segment as the core. The number of affected users and the number of valves to be shut off for each valve combination are determined to identify a set of candidate valve shut-off schemes. Based on the preset dual objectives and the constraints, the target valve-closing scheme is determined from the set of candidate valve-closing schemes.

[0142] In one possible implementation, the processing unit 803 is further configured to: The display information of the first analysis result, the second analysis result, the target pipe section, and the target valve shut-off scheme is determined, and the display information is displayed on the human-computer interaction interface. The human-computer interaction interface includes one or more functions from the following: a geographic information map main window, a pipeline operation status layer, a real-time monitoring data panel, an early warning and event list, a valve shut-off scheme preview window, and a comprehensive situation dashboard.

[0143] The specific details of each part of the above-mentioned device have been described in detail in the method section of the implementation plan. For any undisclosed details, please refer to the implementation plan of the method section, and therefore will not be repeated here.

[0144] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0145] Exemplary embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the above-described leakage analysis method.

[0146] In one implementation, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0147] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0148] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0149] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic radiation, and infrared radiation. Electronic devices can convert signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, the processor of the electronic device to execute) the method steps of various exemplary embodiments of this disclosure, such as the above-described leakage analysis method, which includes the following steps: Step 301: Determine the water consumption-related data for each independent metering area within the target area; the water consumption-related data is determined based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system; Step 302: Input the water consumption-related data into a leakage analysis model to determine the leakage information for each independent metering area; the leakage analysis model employs a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; Step 303: If the leakage information of the first independent metering area indicates the existence of a sudden leakage, then based on the burst pipe diagnosis model, perform burst pipe diagnosis on the first independent metering area to determine the burst pipe point in the first independent metering area.

[0150] By implementing the above methods and steps through a computer program, water consumption data for each independent metering area within the target region can be automatically determined. This water consumption data is based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system. This involves identifying various data points, such as pressure, flow rate, water quality, user water consumption, valve execution data, and pipeline geographic location information. This water consumption data is then input into a leakage analysis model to determine the leakage situation for each independent metering area. In other words, the leakage analysis model automatically analyzes various data points to determine the leakage situation for each independent metering area. This leakage situation information includes, for example, leakage periods and abnormal branches, as well as persistent or sudden leakage. Furthermore, the leakage analysis model employs a fusion strategy of multiple leakage analysis methods when performing leakage analysis on water consumption data. For example, it uses a fusion strategy determined by the minimum nighttime flow analysis algorithm, the historical water consumption baseline comparison algorithm, and the flow balance algorithm. This not only eliminates the need for manual inspection, improving inspection efficiency to some extent, but also, because it uses a fusion strategy determined by multiple leakage analysis methods, it can improve the accuracy of regional leakage assessment to a certain extent. Furthermore, if the leakage information in the first independent metering area indicates a sudden leakage, then based on the pipe burst diagnosis model, a pipe burst diagnosis is performed on the first independent metering area to determine the burst point. In other words, when the leakage information in the first independent metering area indicates a sudden leakage, further diagnosis and determination of the burst point can be performed, further enriching the specific content of the leakage analysis and improving the accuracy of the leakage analysis.

[0151] Exemplary embodiments of this disclosure also provide an electronic device, which may include a processor and a memory. The memory stores executable instructions for the processor, such as computer programs. The processor executes the executable instructions to perform the method steps of various exemplary embodiments of this disclosure.

[0152] The following is for reference. Figure 9 The electronic device is illustrated by way of a general-purpose computing device. It should be understood that... Figure 9 The electronic device 102 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0153] like Figure 9 As shown, the electronic device 102 may include: a processor 910, a memory 920, a bus 930, an I / O (input / output) interface 940, and a network adapter 950.

[0154] The memory 920 may include volatile memory, such as RAM 921 and cache unit 922, and may also include non-volatile memory, such as ROM 923. The memory 920 may also include one or more program modules 924, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. For example, program module 924 may include the units described in the above-described apparatus.

[0155] The processor 910 may include one or more processing units, such as an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit).

[0156] The processor 910 can be used to execute executable instructions stored in the memory 920, such as the above-mentioned leakage analysis method, which includes the following steps: Step 301: Determine the water consumption-related data for each independent metering area within the target area; the water consumption-related data is determined based on the data acquisition and monitoring control system, geographic information system, and smart IoT water meter system; Step 302: Input the water consumption-related data into the leakage analysis model to determine the leakage information for each independent metering area; the leakage analysis model uses a fusion strategy of multiple leakage analysis methods when performing leakage analysis on the water consumption data; Step 303: If the leakage information of the first independent metering area indicates the existence of a sudden leakage, then based on the burst pipe diagnosis model, perform burst pipe diagnosis on the first independent metering area to determine the burst pipe point of the first independent metering area.

[0157] By executing the above method steps through processor 910, water consumption-related data for each independent metering area within the target region can be automatically determined. This water consumption-related data is based on a data acquisition and monitoring control system, a geographic information system, and a smart IoT water meter system. This involves determining various data points, such as pressure, flow rate, water quality, user water consumption, valve execution data, and pipeline geographical location information. This water consumption-related data is then input into a leakage analysis model to determine the leakage situation for each independent metering area. In other words, the leakage analysis model automatically analyzes various data points to determine the leakage situation for each independent metering area. This leakage situation information includes, for example, leakage periods and abnormal branches, as well as continuous or sudden leakage. Furthermore, the leakage analysis model employs a fusion strategy of multiple leakage analysis methods when performing leakage analysis on water consumption data. For example, it uses a fusion strategy determined by the minimum nighttime flow analysis algorithm, the historical water consumption baseline comparison algorithm, and the flow balance algorithm. This not only eliminates the need for manual inspection, improving inspection efficiency to some extent, but also, because it uses a fusion strategy determined by multiple leakage analysis methods, it can improve the accuracy of regional leakage assessment to a certain extent. Furthermore, if the leakage information in the first independent metering area indicates a sudden leakage, then based on the pipe burst diagnosis model, a pipe burst diagnosis is performed on the first independent metering area to determine the burst point. In other words, when the leakage information in the first independent metering area indicates a sudden leakage, further diagnosis and determination of the burst point can be performed, further enriching the specific content of the leakage analysis and improving the accuracy of the leakage analysis.

[0158] Bus 930 is used to connect different components of electronic device 102 and may include a data bus, an address bus and a control bus.

[0159] Electronic device 102 can communicate with one or more external devices 1000 (such as keyboard, mouse, external controller, etc.) through I / O interface 940.

[0160] Electronic device 102 can communicate with one or more networks via network adapter 950. For example, network adapter 950 can provide mobile communication solutions such as 3G / 4G / 5G, or wireless communication solutions such as wireless LAN, Bluetooth, and near-field communication. Network adapter 950 can communicate with other modules of electronic device 102 via bus 930.

[0161] although Figure 9 Other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, may also be configured in electronic device 102.

[0162] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which may be referred to as "circuit," "module," or "system," respectively.

[0163] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A leak analysis method characterized by, The method comprises: determining water consumption related data of each independent metering area in a target area; the water consumption related data is determined based on a data acquisition and monitoring control system, a geographic information system, and an intelligent internet of things water meter system; inputting the water consumption related data into a leakage analysis model to determine leakage condition information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on water consumption data; if it is determined that the leakage condition information of a first independent metering area indicates that there is a sudden leakage, performing a burst pipe diagnosis on the first independent metering area based on a burst pipe diagnosis model to determine a burst pipe point of the first independent metering area.

2. The method of claim 1, wherein, inputting the water consumption related data into a leakage analysis model to determine leakage condition information of each independent metering area, comprising: performing flow balance calculation processing on the water consumption related data of each independent metering area in the target area to determine a leakage amount of each independent metering area; and based on the obtained leakage amount and a first threshold value, determining whether each independent metering area is an abnormal area; if it is determined that an abnormal independent metering area is an abnormal area, performing leakage analysis processing on the water consumption related data of the abnormal independent metering area based on a minimum night flow analysis algorithm to determine first analysis result information, and performing leakage analysis processing on the water consumption related data of the abnormal independent metering area based on a historical water consumption baseline comparison algorithm to determine second analysis result information; cross-verification processing is performed on the first analysis result information and the second analysis result information to determine leakage condition information of the abnormal independent metering area.

3. The method according to claim 1 or 2, characterized in that, if it is determined that the leakage condition information of a first independent metering area indicates that there is a sudden leakage, performing a burst pipe diagnosis on the first independent metering area based on a burst pipe diagnosis model to determine a burst pipe point of the first independent metering area, comprising: inputting first water consumption related data of the first independent metering area into a real-time diagnosis model in the burst pipe diagnosis model to obtain burst pipe abnormal information; the burst pipe abnormal information includes an abnormal point pipe segment and abnormal data; calling a static risk prediction model in the burst pipe diagnosis model to perform prediction processing on the abnormal point pipe segment to obtain a basic risk level, and calling a dynamic analysis model in the burst pipe diagnosis model to perform pattern analysis processing on the abnormal data to obtain burst pipe feature information; determining a burst pipe confidence level according to the basic risk level and the burst pipe feature information; if the burst pipe confidence level is greater than a second threshold value, it is determined that the abnormal point has a burst pipe event, and the abnormal point is taken as a burst pipe point.

4. The method of claim 3, wherein, The method further comprises: determining a pipe network topology graph of the first independent metering area; based on the principle of mass conservation and pressure propagation, traversing the pipe network topology graph to determine a first monitoring point; determining a target pipe segment corresponding to a burst pipe point according to the first monitoring point.

5. The method of claim 4, wherein, based on the principle of mass conservation and pressure propagation, traversing the pipe network topology graph to determine a first monitoring point, comprising: Based on the principle of mass conservation and pressure propagation, the pipe network topology graph is traversed to determine the timing and amplitude of pressure drop of each monitoring point; According to the timing and amplitude of pressure drop of each monitoring point and a preset screening rule, a first monitoring point is determined.

6. The method of claim 5, wherein, The method further comprises: From the first water consumption related data, an associated data set of the target pipe section is obtained, the associated data set comprising position information of the target pipe section, basic data of valves along the pipe network, and associated relationship data of valves, pipe sections and users; Based on the pipe network topology graph and the pipe network hydraulic model, the associated data set is simulated to determine the water flow state under different valve combination, and a constraint condition is determined; The pipe network topology graph is converted into a node edge model, and a breadth-first search algorithm is used to search for closable valve combinations layer by layer with the target pipe section as the core to determine the number of affected users and the number of closed valves corresponding to each valve combination, so as to determine a candidate valve combination set; According to a preset double target and the constraint condition, a target valve combination is determined from the candidate valve combination set.

7. A leak analysis system characterized by, The system comprises a perception layer, a communication layer, a data layer, a service layer and an application layer, wherein: The perception layer is used to collect water consumption related data of each independent metering area in a target area; The communication layer is used to send the water consumption related data collected by the perception layer to the data layer; The data layer is used to classify, store, manage and preliminarily analyze the water consumption related data transmitted by the communication layer; The service layer comprises data fusion service, model calculation service, geographic information system service and message pushing service, and is used to analyze and process the water consumption related data to obtain service information; The application layer comprises a one-map system, a pipe burst diagnosis and disposal module, a leakage analysis module, an intelligent internet of things water meter system, an engineering operation and maintenance management system and a water supply and distribution scheduling system, and is used to call the service information to execute a preset business process to obtain an execution result.

8. A leakage analysis device, characterized by comprising: The device comprises: A first determination unit configured to determine water consumption related data of each independent metering area in a target area; the water consumption related data is determined based on a data acquisition and monitoring control system, a geographic information system and an intelligent internet of things water meter system; A second determination unit configured to input the water consumption related data into a leakage analysis model to determine leakage situation information of each independent metering area; the leakage analysis model adopts a fusion strategy of multiple leakage analysis methods when performing leakage analysis on water consumption data; A processing unit configured to, if the leakage situation information of a first independent metering area indicates that there is a sudden leakage, execute pipe burst diagnosis on the first independent metering area based on a pipe burst diagnosis model to determine a pipe burst point of the first independent metering area.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method of any one of claims 1 to 6.

10. An electronic device, comprising: Comprise: A processor; A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the method of any one of claims 1 to 6 by executing the executable instructions.

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