Water supply network pipe burst alarm positioning system based on real-time pressure monitoring data

Abstract

The invention relates to the technical field of pipe network monitoring, in particular to a water supply pipe network pipe burst alarm positioning system based on real-time pressure monitoring data, and the system comprises a pressure data collection module which collects pressure data from a plurality of pressure monitoring points of a water supply pipe network; the pressure drop threshold calculation module is used for determining an alarm pressure drop threshold of each pressure monitoring point based on historical pressure data; the voltage drop alarm module is used for calculating a current time window voltage drop value and comparing the current time window voltage drop value with a voltage drop threshold value; the association alarm module is used for clustering alarm information of adjacent pressure monitoring points and combining a plurality of alarm points which are close in space into a single alarm event; the alarm positioning module is used for matching a pipe explosion node corresponding to the current alarm information by utilizing a mode recognition algorithm and quickly positioning the pipe explosion node; according to the method, the system calculates the aggregation characteristics, the description capability and the credibility of the alarm event are enhanced, and the identification and positioning efficiency of the system on the pipe explosion event in the complex pipe network environment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe network monitoring, and in particular to a pipe burst alarm positioning system for a water supply pipe network based on real-time pressure monitoring data. Background Art

[0002] With the rapid development of urbanization, water supply pipelines, as an important part of urban infrastructure, undertake the key task of ensuring residents' water supply and the normal operation of the city. However, due to the long-term operation of the water supply pipelines, aging of pipelines, poor construction quality, frequent water pressure fluctuations and external force damage, pipe burst accidents occur from time to time. Pipe burst accidents not only lead to a large amount of water resource loss, but may also cause road collapse, traffic paralysis, etc., affecting the safe operation of the city and the quality of life of residents.

[0003] Existing pipe burst detection and location technologies mainly rely on flow monitoring and online hydraulic model analysis. Among them, flow changes in the pipe network are monitored by flow meters, and the location of the pipe burst is inferred by combining hydraulic models. However, this method has the following problems: Dependence on flow meter deployment: The deployment cost of flow monitoring points is high, and some urban water supply networks are mainly deployed with pressure monitoring points, resulting in the inability to effectively apply flow monitoring methods.

[0004] Limited accuracy: If the pipe network is not divided sufficiently or the density of monitoring equipment is not high, the traditional method will have poor accuracy in locating pipe burst nodes, which may lead to misjudgment or ambiguity in positioning.

[0005] Delayed response: Traditional methods usually require a large amount of data analysis, resulting in slow response to pipe burst alarms and positioning, and unable to respond quickly in the early stages of an accident. Summary of the invention

[0006] The invention provides a water supply pipe network burst alarm positioning system based on real-time pressure monitoring data.

[0007] A water supply pipe burst alarm positioning system based on real-time pressure monitoring data, comprising: Pressure data acquisition module: collects pressure data from multiple pressure monitoring points in the water supply network and transmits the data to the data cleaning module; Data cleaning module: cleans the collected pressure data, stores it after cleaning, and forms historical pressure data to ensure the accuracy and effectiveness of the data; Pressure drop threshold calculation module: Based on historical pressure data, determine the alarm pressure drop threshold for each pressure monitoring point, including: a): Identify the periods of continuous pressure drop in the historical pressure data and calculate the corresponding pressure drop values; b): Determine the pressure drop threshold for triggering the alarm based on the order of the pressure drop values ​​and the alarm sensitivity parameter; Pressure drop alarm module: extracts the pressure data in the current time window through the set time window, calculates the pressure drop value in the current time window, and compares the pressure drop value in the current time window with the pressure drop threshold. If the pressure drop value in the current time window exceeds the pressure drop threshold, an alarm is triggered; Associated alarm module: clusters the alarm information of adjacent pressure monitoring points and combines multiple alarm points close in space into a single alarm event; Alarm location module: Based on the pre-built historical pipe burst event database, the pattern recognition algorithm is used to match the pipe burst node corresponding to the current alarm information and quickly locate the pipe burst node; Result output module: used to output alarm status, related alarm events and pipe burst node positioning results.

[0008] Optionally, the data cleaning module specifically includes: Delete data outside the normal range: Screen the collected pressure data and remove data that exceeds the predetermined normal pressure range (0-1 MPa) to ensure that valid data is included in subsequent analysis; Identify and delete pulse data: By setting the threshold, the pulse data caused by instantaneous fluctuations, equipment failure or signal interference in the pressure data can be identified and deleted from the pressure data to prevent false alarms; Storing cleaned data: Store the cleaned pressure data to form complete historical pressure data, providing reliable basic data for subsequent pressure drop threshold calculation, alarm judgment and other modules.

[0009] Optionally, the voltage drop threshold calculation module specifically includes: Continuous pressure drop value recording unit: Identify continuous pressure drop periods from historical pressure data , and calculate the pressure change amplitude in each period , that is, the change in pressure value during this period: ,in, is the pressure at the beginning of the period, The pressure at the end of the period is recorded, and the pressure drop values ​​of all consecutive decreasing periods are recorded; Pressure drop threshold determination unit: Determine the pressure drop threshold for alarm triggering according to the sorting of pressure drop values ​​and alarm sensitivity parameters, and sort the calculated pressure drop values ​​in descending order , and combined with the alarm sensitivity coefficient , calculate the alarm voltage drop threshold by the following formula : ,in, The position in the sorted pressure drop value sequence is The pressure drop threshold is used to trigger an alarm, indicating that when the actual pressure drop exceeds this threshold, the system should trigger an alarm.

[0010] Optionally, the alarm sensitivity coefficient It is obtained based on the fluctuation range and distribution of historical pressure data and by analyzing the standard deviation of historical pressure data. The larger the standard deviation or fluctuation range, the more sensitive it is to pressure fluctuations, and the alarm sensitivity coefficient increases accordingly, and vice versa.

[0011] Optionally, the pressure drop value of the current time window in the pressure drop alarm module is expressed as , and the current time window pressure drop value The calculated voltage drop threshold If the voltage drop value in the current time window exceeds the voltage drop threshold, an alarm is triggered, including: Extract pressure data within the current time window: Set the time window to , extract the pressure data in the current time window through this time window , that is, from the moment To the current time All pressure data of the system are extracted, and the pressure drop value of the current time window is calculated based on the extracted pressure data. : ,in, is the pressure value at the beginning of the time window, is the pressure value at the current moment.

[0012] Optionally, the associated alarm module specifically includes: Extract alarm point information: Get the alarm information of each pressure monitoring point at the current moment ,in Indicates Monitoring points; Calculate the spatial distance between alarm points: Calculate the distance between each alarm point And its adjacent alarm points The space distance between ; Set clustering threshold: Set a spatial distance threshold , if the spatial distance between two alarm points is less than this threshold , then it is considered that these two alarm points belong to the same alarm event; Clustering adjacent alarm points: Clustering all the alarm points that are close in space Aggregate into a single alarm event , indicating that the alarm event occurs in the area adjacent to the alarm point, extracting the aggregate features of the alarm event, including the average pressure drop And the alarm time.

[0013] Output clustering results: cluster the alarm events The output represents a complete single alarm event, which is convenient for subsequent pipe burst location and fault handling.

[0014] Optionally, each alarm point And its adjacent alarm points The space distance between Calculated as: ,in, and Alarm points and The position in the plane coordinate system.

[0015] Optionally, the alarm positioning module specifically includes: Get clustering results: Receive the clustering results of alarm information output by the associated alarm module , including the coordinate set of cluster alarm points and the average pressure drop ; Extract historical event features: Extract historical event feature sets from a pre-built historical pipe burst event database ,in, Average pressure drop including historical pipe burst events , adjacent alarm point coordinate set and specific pipe bursting nodes; Calculate the similarity between the current cluster alarm and historical events: based on the clustering results and historical pipe burst event feature set , using pattern recognition algorithms to calculate the current alarm event and historical events Similarity between ; Match the best bursting node: Based on the calculated similarity , select the historical event with the highest similarity Corresponding pipe burst node The pipe burst node that serves as the current alarm event.

[0016] Optionally, the similarity calculation formula is: ; in, Indicates the similarity between the coordinate set of the clustered alarm point and the coordinate set of the adjacent alarm point in the historical pipe burst event node. Indicates that the difference between the current average pressure drop and the historical pressure drop is calculated based on the similarity of the average pressure drop. and is the weight coefficient of pressure drop similarity and coordinate set similarity.

[0017] Beneficial effects of the present invention: The present invention realizes accurate alarm and positioning of pipe burst in water supply network by combining real-time pressure monitoring data with historical pipe burst event database. Firstly, through pressure drop threshold calculation, historical pressure data is used to dynamically calculate the alarm pressure drop threshold of each monitoring point, thereby avoiding the error caused by manually setting the threshold. Secondly, the pressure drop alarm module dynamically calculates the current pressure drop value based on real-time data in the time window and compares it with the pressure drop threshold to trigger the alarm, thereby effectively filtering out false alarms and missed alarms, significantly improving the accuracy of the alarm, and ensuring that the risk of pipe burst can be identified in time.

[0018] In the present invention, the associated alarm module clusters the alarm information of adjacent pressure monitoring points through a spatial clustering algorithm, and integrates multiple alarm points that are close in space into a single alarm event, which can effectively distinguish local pressure fluctuations from real pipe burst accidents and reduce false alarms caused by data fluctuations or noise. At the same time, the system calculates aggregate features (average pressure drop range), which enhances the description ability and credibility of alarm events and improves the system's efficiency in identifying and locating pipe burst events in complex pipe network environments.

[0019] The present invention, through an alarm positioning module, relies on a pre-built historical pipe burst event database, combines clustered alarm information, uses a pattern recognition algorithm to quickly match current alarm events with historical event features, determines the optimal pipe burst node position, and can quickly locate the historical pipe burst node closest to the current alarm by calculating the pressure drop pattern similarity and the coordinate set similarity, thereby shortening the calculation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of various modules of an alarm positioning system according to an embodiment of the present invention; Figure 2 Schematic diagram of a voltage drop threshold calculation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0023] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0024] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0025] like Figure 1-Figure 2 As shown, a water supply pipe network burst alarm positioning system based on real-time pressure monitoring data includes: Pressure data acquisition module: collects pressure data from multiple pressure monitoring points in the water supply network and transmits the data to the data cleaning module; Data cleaning module: cleans the collected pressure data, stores it after cleaning, and forms historical pressure data to ensure the accuracy and effectiveness of the data; Pressure drop threshold calculation module: Based on historical pressure data, determine the alarm pressure drop threshold for each pressure monitoring point, including: a): Identify the periods of continuous pressure drop in the historical pressure data and calculate the corresponding pressure drop values; b): Determine the pressure drop threshold for triggering the alarm based on the order of the pressure drop values ​​and the alarm sensitivity parameter; Pressure drop alarm module: extracts the pressure data in the current time window through the set time window, calculates the pressure drop value in the current time window, and compares the pressure drop value in the current time window with the pressure drop threshold. If the pressure drop value in the current time window exceeds the pressure drop threshold, an alarm is triggered; Associated alarm module: clusters the alarm information of adjacent pressure monitoring points and combines multiple alarm points close in space into a single alarm event; Alarm location module: Based on the pre-built historical pipe burst event database, the pattern recognition algorithm is used to match the pipe burst node corresponding to the current alarm information and quickly locate the pipe burst node; Result output module: used to output alarm status, related alarm events and pipe burst node positioning results.

[0026] The layout of pressure monitoring points is as follows: Pipeline network inlet and outlet: Install pressure monitoring points at the inlet (such as the water plant outlet) and outlet (such as the water supply terminal in the urban area) of the water supply network to monitor the overall water pressure changes and ensure the stability of water supply.

[0027] Cross-pipes and branch pipes: Pressure monitoring points are arranged at important intersections or branch points in the pipeline network to ensure that pressure changes in local areas can be monitored and potential pressure anomalies can be discovered in a timely manner.

[0028] Water supply areas far away from water plants: Install pressure monitoring points in areas far away from water sources, at the end of pipelines and on the user side to check pressure changes in the end water supply area and ensure that the water pressure can cover the entire water supply area.

[0029] Regularly spaced arrangement based on pipe length: Regular interval arrangement: According to the length of the water supply network and the specifications of the pipes, select appropriate distance intervals (every 500 meters to 1 kilometer) to install pressure monitoring points to ensure that the entire network is covered and possible pressure fluctuations are captured.

[0030] Key layout in high-risk areas: Increase the density of pressure monitoring points for key monitoring in areas with a history of pipeline bursts, failures, or large pressure fluctuations. Install more pressure monitoring points in crowded areas or commercial and industrial areas when water demand is high or the network load is heavy, so as to monitor pressure changes in a timely manner and prevent the risk of water supply interruptions.

[0031] Geographical factors and pipeline structures: In areas with large changes in terrain, especially around mountains, slopes and water towers, pressure monitoring points should be installed to cope with possible water pressure fluctuations; Water tanks, pumping stations and regulating valve control points: Monitoring points should be set up near these equipment to monitor the impact of the equipment's operating status on water pressure.

[0032] The data cleaning module specifically includes: Delete data outside the normal range: Screen the collected pressure data and remove data that exceeds the predetermined normal pressure range (0-1 MPa) to ensure that valid data is included in subsequent analysis; Identify and delete pulse data: By setting the threshold, the pulse data caused by instantaneous fluctuations, equipment failure or signal interference in the pressure data can be identified and deleted from the pressure data to prevent false alarms; Pulse data refers to the sharp fluctuation of pressure due to instantaneous interference or sensor failure. In order to identify these pulse data, the pressure value change rate can be used to To determine whether the fluctuation range of pressure data exceeds a certain threshold , the calculation expression is ,in, , It is the current moment Pressure data, It was the moment before Pressure data, is the pressure difference between the current moment and the previous moment, indicating the magnitude of pressure change. It is the pressure fluctuation threshold. If the change amplitude exceeds this threshold, it is considered as pulse data. This threshold is set according to the actual situation. (According to the error range of the sensor and the requirements of the system); Storing cleaned data: Store the cleaned pressure data to form complete historical pressure data, providing reliable basic data for subsequent pressure drop threshold calculation, alarm judgment and other modules.

[0033] The voltage drop threshold calculation module specifically includes: Continuous pressure drop value recording unit: Identify continuous pressure drop periods from historical pressure data , and calculate the pressure change amplitude in each period , that is, the change in pressure value during this period: ,in, is the pressure at the beginning of the period, The pressure at the end of the period is recorded, and the pressure drop values ​​of all consecutive decreasing periods are recorded; Pressure drop threshold determination unit: Determine the pressure drop threshold for alarm triggering according to the sorting of pressure drop values ​​and alarm sensitivity parameters, and sort the calculated pressure drop values ​​in descending order , and combined with the alarm sensitivity coefficient , calculate the alarm voltage drop threshold by the following formula : ,in, The position in the sorted pressure drop value sequence is The value of (i.e., according to the sensitivity coefficient Determine the voltage drop threshold, is the number of historical data points), the pressure drop threshold is used to trigger an alarm, indicating that when the actual pressure drop exceeds this threshold, the system should trigger an alarm.

[0034] Alarm sensitivity coefficient According to the fluctuation range and distribution of historical pressure data, and by analyzing the standard deviation of historical pressure data, the larger the standard deviation or fluctuation range, the more sensitive it is to pressure fluctuations, and the alarm sensitivity coefficient increases accordingly, and vice versa; Calculation method: First, collect pressure data over a period of historical time , calculate its standard deviation , and then use the following formula to set the sensitivity coefficient : ,in, is the standard deviation of the pressure data, indicating the degree of fluctuation of pressure changes, is the maximum value in the historical pressure data. It is the minimum value in the historical pressure data.

[0035] If the pressure data is highly volatile (i.e. higher), the sensitivity coefficient will increase accordingly, indicating that the system is more sensitive to small pressure changes; if the pressure fluctuation is small, will be lower, indicating that the system is more tolerant to changes.

[0036] The pressure drop value of the current time window in the pressure drop alarm module is expressed as , and the current time window pressure drop value The calculated voltage drop threshold If the voltage drop value in the current time window exceeds the voltage drop threshold, an alarm is triggered, including: Extract pressure data within the current time window: Set the time window to , extract the pressure data in the current time window through this time window , that is, from the moment To the current time All pressure data of the system are extracted, and the pressure drop value of the current time window is calculated based on the extracted pressure data. : ,in, is the pressure value at the beginning of the time window, is the pressure value at the current moment.

[0037] The time window is The setting is based on the response time of the network: different types of water supply networks have different response times. Faster response networks (shorter water supply pipes or small networks) are suitable for shorter time windows, while slower response networks (water supply networks in large cities or long-distance water supply pipes) are suitable for longer time windows. Therefore: Short pipe network (such as a small-scale water supply network in an urban area): the time window can be set to 5-15 minutes.

[0038] Long pipe network (long-distance water supply pipeline or peak area water supply system): The time window can be set to 30 minutes-1 hour, or even longer.

[0039] The associated alarm modules specifically include: Extract alarm point information: Get the alarm information of each pressure monitoring point at the current moment ,in Indicates Monitoring points; Calculate the spatial distance between alarm points: Calculate the distance between each alarm point And its adjacent alarm points The space distance between ; Set clustering threshold: Set a spatial distance threshold , if the spatial distance between two alarm points is less than this threshold , then it is considered that these two alarm points belong to the same alarm event; Clustering adjacent alarm points: Clustering all the alarm points that are close in space Aggregate into a single alarm event , indicating that the alarm event occurs in the area adjacent to the alarm point, extracting the aggregate features of the alarm event, including the average pressure drop And alarm time; by summing up the pressure drop amplitudes of all alarm points and taking the average value, it reflects the overall pressure change of the alarm event. The larger the value, the more drastic the pressure drop in the pipe network area involved in the event, which means a more serious failure or pipe burst risk. The alarm time includes the earliest alarm time and the alarm period. The earliest occurrence time of the alarm event is calculated, which is usually used as the representative time of the event to determine the urgency of the event. The alarm period calculates the time span from the beginning to the end of the event to help evaluate the continuity and impact range of the event. A long duration indicates that the problem is more complicated or the affected area is larger.

[0040] Output clustering results: cluster the alarm events The output represents a complete single alarm event, which is convenient for subsequent pipe burst location and fault handling.

[0041] Each alarm point And its adjacent alarm points The space distance between Calculated as: ,in, and Alarm points and The position in the plane coordinate system.

[0042] The alarm positioning module specifically includes: Get clustering results: Receive the clustering results of alarm information output by the associated alarm module , including the coordinate set of cluster alarm points and the average pressure drop ; Extract historical event features: Extract historical event feature sets from a pre-built historical pipe burst event database ,in, Average pressure drop including historical pipe burst events , adjacent alarm point coordinate set and specific pipe bursting nodes; Calculate the similarity between the current cluster alarm and historical events: based on the clustering results and historical pipe burst event feature set , using pattern recognition algorithms to calculate the current alarm event and historical events The similarity between ; Match the best bursting node: Based on the calculated similarity , select the historical event with the highest similarity Corresponding pipe burst node As the pipe burst node of the current alarm event; Output the coordinate position of the current bursting pipe node and its corresponding similarity score, which is used for rapid location and handling of pipe burst events.

[0043] The similarity calculation formula is: ; in, Indicates the similarity between the coordinate set of the clustered alarm point and the coordinate set of the adjacent alarm point in the historical pipe burst event node. Indicates that the difference between the current average pressure drop and the historical pressure drop is calculated based on the similarity of the average pressure drop. and is the weight coefficient of pressure drop similarity and coordinate set similarity.

[0044] The pressure changes caused by pipe bursts have spatial correlation. Pipe bursts usually affect multiple pressure monitoring points in the surrounding area. These monitoring points show a certain distribution pattern in space. Pipe bursts at different locations will lead to different alarm point distributions. This distribution feature is an important basis for judging the location of the pipe burst.

[0045] The constructed historical pipe burst event database stores the coordinate sets of adjacent alarm points of each historical event. These sets represent the spatial distribution pattern of the affected monitoring points when the pipe burst event occurs. By comparing the clustering result (i.e., coordinate set) of the current alarm event with the coordinate set of the historical pipe burst event, it can be determined which historical event the current event is most similar to, thereby quickly inferring the pipe burst node. The spatial similarity can be quantified by the Euclidean distance mathematical method, which has a clear calculation standard. If the coordinate set of the current clustered alarm point and the coordinate set of the historical event are highly overlapped or close in space, it indicates that the current pipe burst node is likely to be in the same position as the pipe burst node in the historical event.

[0046] Steps to build the historical pipe burst event database: 1. Data Collection: Collect monitoring data on historical pipe burst events in the water supply network, including: Pressure data of monitoring points (pressure changes before and after pipe burst); The time, location and corresponding node coordinates of historical pipe burst events; The coordinate set of adjacent pressure monitoring points and the magnitude of pressure drop when the event occurs.

[0047] 2. Data cleaning: The collected raw monitoring data is cleaned to remove invalid data, pulse data and over-limit data to ensure the accuracy of the data.

[0048] Feature extraction: Extract key features of each pipe burst event, including: Average pressure drop; Coordinate set of adjacent alarm points; Specific location of pipe burst nodes; These features will serve as descriptive attributes for each historical pipe burst event in the database.

[0049] 3. Event Storage The processed historical pipe burst events and their characteristic data are stored in the database. Each record includes: A unique identifier for the pipe burst event; Coordinates of the bursting node; Average pressure drop; Coordinate set of adjacent alarm points; Description of the time and area where the pipe burst occurred; 4. Database optimization and update: The database is updated regularly to add new pipe burst incident data.

[0050] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0051] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A water supply pipe burst alarm positioning system based on real-time pressure monitoring data, characterized in that: include: Pressure data acquisition module: collects pressure data from multiple pressure monitoring points in the water supply network and transmits the data to the data cleaning module; Data cleaning module: cleans the collected pressure data and stores it after cleaning to form historical pressure data; Pressure drop threshold calculation module: Based on historical pressure data, determine the alarm pressure drop threshold for each pressure monitoring point, including: a): Identify the periods of continuous pressure drop in the historical pressure data and calculate the corresponding pressure drop values; b): Determine the pressure drop threshold for triggering the alarm based on the order of the pressure drop values ​​and the alarm sensitivity parameter; Pressure drop alarm module: extracts the pressure data in the current time window through the set time window, calculates the pressure drop value in the current time window, and compares the pressure drop value in the current time window with the pressure drop threshold. If the pressure drop value in the current time window exceeds the pressure drop threshold, an alarm is triggered; Associated alarm module: clusters the alarm information of adjacent pressure monitoring points and combines multiple alarm points close in space into a single alarm event; Alarm location module: Based on the pre-built historical pipe burst event database, the pattern recognition algorithm is used to match the pipe burst node corresponding to the current alarm information and quickly locate the pipe burst node; Result output module: used to output alarm status, related alarm events and pipe burst node positioning results.

2. According to claim 1, a water supply network burst alarm positioning system based on real-time pressure monitoring data is characterized in that: The data cleaning module specifically includes: Delete data outside the normal range: Screen the collected pressure data and remove data that exceeds the predetermined normal pressure range to ensure that valid data is included in subsequent analysis; Identify and delete pulse data: By setting the threshold, the pulse data caused by instantaneous fluctuations, equipment failure or signal interference in the pressure data can be identified and deleted from the pressure data to prevent false alarms; Storing cleaned data: storing cleaned pressure data to form complete historical pressure data.

3. According to claim 1, a water supply network burst alarm positioning system based on real-time pressure monitoring data is characterized in that: The voltage drop threshold calculation module specifically includes: Continuous pressure drop value recording unit: Identify continuous pressure drop periods from historical pressure data , and calculate the pressure change amplitude in each period , that is, the change in pressure value during this period: ,in, is the pressure at the beginning of the period, The pressure at the end of the period is recorded, and the pressure drop values ​​of all consecutive decreasing periods are recorded; Pressure drop threshold determination unit: Determine the pressure drop threshold for alarm triggering according to the sorting of pressure drop values ​​and alarm sensitivity parameters, and sort the calculated pressure drop values ​​in descending order , and combined with the alarm sensitivity coefficient , calculate the alarm voltage drop threshold by the following formula : ,in, The position in the sorted pressure drop value sequence is The pressure drop threshold is used to trigger an alarm, indicating that when the actual pressure drop exceeds this threshold, the system should trigger an alarm.

4. A water supply pipe burst alarm positioning system based on real-time pressure monitoring data according to claim 3, characterized in that: The alarm sensitivity coefficient It is obtained based on the fluctuation range and distribution of historical pressure data and by analyzing the standard deviation of historical pressure data. The larger the standard deviation or fluctuation range, the more sensitive it is to pressure fluctuations, and the alarm sensitivity coefficient increases accordingly, and vice versa.

5. The water supply network burst alarm positioning system based on real-time pressure monitoring data according to claim 3 is characterized in that: The pressure drop value of the current time window in the pressure drop alarm module is expressed as , and the current time window pressure drop value The calculated voltage drop threshold If the voltage drop value in the current time window exceeds the voltage drop threshold, an alarm is triggered, including: Extract pressure data within the current time window: Set the time window to , extract the pressure data in the current time window through this time window , that is, from the moment To the current time All pressure data of the system are extracted, and the pressure drop value of the current time window is calculated based on the extracted pressure data. : ,in, is the pressure value at the beginning of the time window, is the pressure value at the current moment.

6. A water supply pipe burst alarm positioning system based on real-time pressure monitoring data according to claim 1, characterized in that: The associated alarm module specifically includes: Extract alarm point information: Get the alarm information of each pressure monitoring point at the current moment ,in Indicates Monitoring points; Calculate the spatial distance between alarm points: Calculate the distance between each alarm point And its adjacent alarm points The space distance between ; Set clustering threshold: Set a spatial distance threshold , if the spatial distance between two alarm points is less than this threshold , then it is considered that these two alarm points belong to the same alarm event; Cluster adjacent alarm points: All spatially close alarm points are clustered into a single alarm event. , indicating that the alarm event occurs in the area adjacent to the alarm point, extracting the aggregate features of the alarm event, including the average pressure drop and the time of alarm; Output clustering results: cluster the alarm events Output, representing a complete single alarm event.

7. A water supply pipe burst alarm positioning system based on real-time pressure monitoring data according to claim 6, characterized in that: Each alarm point And its adjacent alarm points The space distance between Calculated as: ,in, and Alarm points and The position in the plane coordinate system.

8. The water supply network burst alarm positioning system based on real-time pressure monitoring data according to claim 6 is characterized in that: The alarm positioning module specifically includes: Get clustering results: Receive the clustering results of alarm information output by the associated alarm module , including the coordinate set of cluster alarm points and the average pressure drop ; Extract historical event features: Extract historical event feature sets from a pre-built historical pipe burst event database ,in, Average pressure drop including historical pipe burst events , adjacent alarm point coordinate set and specific pipe bursting nodes; Calculate the similarity between the current cluster alarm and historical events: based on the clustering results and historical pipe burst event feature set , using pattern recognition algorithms to calculate the current alarm event and historical events Similarity between ; Match the best bursting node: Based on the calculated similarity , select the historical event with the highest similarity Corresponding pipe burst node The pipe burst node that serves as the current alarm event.

9. A water supply pipe burst alarm positioning system based on real-time pressure monitoring data according to claim 8, characterized in that: The calculation formula of the similarity is: ； in, Indicates the similarity between the coordinate set of the clustered alarm point and the coordinate set of the adjacent alarm point in the historical pipe burst event node. Indicates that the difference between the current average pressure drop and the historical pressure drop is calculated based on the similarity of the average pressure drop. and is the weight coefficient of pressure drop similarity and coordinate set similarity.

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