Predictive maintenance and fault diagnosis system for water supply network

Abstract

The invention discloses a predictive maintenance and fault diagnosis system for a water supply network, belongs to the technical field of intelligent monitoring and fault diagnosis, and realizes real-time monitoring, fault prediction, diagnosis and maintenance decision support for the water supply network by integrating various monitoring devices and applying an advanced data analysis technology. The platform comprises a multi-parameter monitoring equipment integration module, a data fusion and real-time analysis module, a fault diagnosis model, a predictive maintenance planning module, a water quality monitoring and pollution control module, an intelligent early warning and emergency response system, a system integration and communication protocol and a cloud platform and data analysis center. Through the modules, the accuracy of fault detection can be improved, the allocation of maintenance resources can be optimized, the unplanned maintenance and downtime can be reduced, the water supply safety can be enhanced, and the emergency response capability can be improved; the platform also realizes centralized management and efficient processing of data, improves the expansibility and flexibility of the system, and ensures the data security and privacy.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring and fault diagnosis, and in particular relates to a predictive maintenance and fault diagnosis system for a water supply network. Background Art

[0002] With the acceleration of urbanization, the safe, stable and efficient operation of urban water supply networks, as an important part of urban infrastructure, is crucial to the daily life of urban residents and the sustainable development of the urban economy. The health of the water supply network is directly related to the continuity of water supply, the safety of water quality and the ability to respond to emergencies. However, the current monitoring and maintenance of water supply networks faces a series of technical challenges and bottlenecks, which limit the further improvement of the performance of water supply networks.

[0003] 1. Limitations of monitoring technology:

[0004] Traditional water supply network monitoring mainly relies on manual inspections and regular maintenance, which is costly, inefficient, and difficult to achieve real-time monitoring. Existing monitoring equipment can often only provide single parameter measurements and cannot fully reflect the operating status of the network, resulting in limited accuracy and timeliness of fault diagnosis.

[0005] 2. Insufficient data collection and processing:

[0006] The lack of efficient data collection and processing technology makes it impossible to fully utilize the monitoring data for in-depth analysis, and thus cannot timely discover abnormal conditions in the pipe network. The lack of accuracy and stability of data collection equipment limits the reliability and accuracy of monitoring data.

[0007] 3. Fault diagnosis technology bottleneck:

[0008] Most existing fault diagnosis methods rely on empirical judgment and lack systematic data analysis and scientific decision-making support, resulting in low accuracy and efficiency of fault diagnosis. The generalization ability and adaptability of fault diagnosis models are insufficient, making it difficult to cope with complex and changeable pipeline network environments and fault types.

[0009] 4. Maintenance decisions lack scientific basis:

[0010] Maintenance decisions are often based on historical experience and intuition, lacking scientific decision-making support based on data analysis, resulting in irrational allocation of maintenance resources and low maintenance efficiency. The lack of predictive maintenance technology makes it impossible to carry out targeted maintenance based on the actual operating status and failure risk of the pipeline network.

[0011] 5. Inadequate water quality monitoring and pollution control technology:

[0012] The development of water quality monitoring technology lags behind, and it is impossible to monitor and warn of water quality changes in real time, especially in terms of water source pollution and secondary pollution of pipe networks. The emergency response mechanism for water pollution incidents is imperfect, and there is a lack of effective pollution control and purification technology.

[0013] 6. Low level of technology integration and intelligence:

[0014] The monitoring, diagnosis and maintenance technologies of water supply networks are scattered, lacking effective technology integration and intelligent management platforms, resulting in information islands and waste of resources. The intelligence level is low, and it is impossible to achieve automated and intelligent fault warning, diagnosis and maintenance decisions.

[0015] 7. Insufficient ability to respond to emergencies:

[0016] When faced with emergencies such as natural disasters, the water supply network lacks an effective monitoring and emergency response mechanism, making it difficult to quickly restore water supply.

[0017] The current monitoring and maintenance technology of water supply networks faces a series of technical challenges from data collection, processing, fault diagnosis to maintenance decision-making. In order to improve the operating efficiency and safety of water supply networks, reduce maintenance costs, and ultimately improve the reliability of urban water supply systems and the quality of life of residents, it is urgent to develop an intelligent water supply network predictive maintenance and fault diagnosis system that integrates multiple monitoring devices, has real-time data analysis and fault diagnosis capabilities, and can provide scientific maintenance decision support. This patent is proposed in this context, aiming to solve the existing technical bottlenecks through technological innovation and promote the development of water supply network monitoring and maintenance technology. Summary of the invention

[0018] The technical problem to be solved by the present invention is to provide a water supply network predictive maintenance and fault diagnosis system in response to the shortcomings of the background technology. By integrating multiple monitoring devices and applying advanced data analysis technology, real-time monitoring, fault prediction, diagnosis and maintenance decision support for the water supply network can be achieved.

[0019] The present invention adopts the following technical solutions to solve the above technical problems:

[0020] A water supply network predictive maintenance and fault diagnosis system, including a multi-parameter monitoring equipment integration module, a data fusion and real-time analysis module, a fault diagnosis model, a predictive maintenance plan module, a maintenance decision support module, a water quality monitoring and pollution control module, an intelligent early warning and emergency response system, a system integration and communication protocol and a platform and data analysis center;

[0021] Among them, the multi-parameter monitoring equipment integration module is used to use a variety of sensors to monitor the key parameters of the water supply network in real time, and transmit these data to the central processing unit for analysis and processing; to achieve comprehensive monitoring of the status of the water supply network, so as to detect abnormal conditions in time and perform fault diagnosis;

[0022] Data fusion and real-time analysis module, which is used to effectively integrate data from different sensors and conduct in-depth analysis to provide a comprehensive view of the pipeline network status and identify potential abnormal behaviors;

[0023] Fault diagnosis model, which is used to learn historical fault data, automatically identify and classify various fault modes in the water supply network; identify water leakage, pipe burst, abnormal water quality faults, and provide precise positioning of the fault location;

[0024] The predictive maintenance planning module is used to use artificial intelligence technology to predict the maintenance needs and potential failure risks of the pipeline network based on the historical operation data and real-time monitoring data of the pipeline network; and then automatically generate maintenance plans, optimize the allocation of maintenance resources, and reduce unplanned maintenance and downtime;

[0025] Maintenance decision support module, which provides maintenance decision support based on fault diagnosis results and predictive maintenance plans, including suggestions on maintenance priority, time and methods;

[0026] Water quality monitoring and pollution control module: used to monitor water quality parameters in real time, including pH value, turbidity, and residual chlorine; when abnormal water quality is detected, pollution control measures are automatically initiated, including adjusting the amount of disinfectant added or switching the water source;

[0027] Intelligent early warning and emergency response system: When a potential fault or emergency is detected, the system can automatically issue an early warning and initiate an emergency response procedure; the emergency response procedure includes notifying maintenance personnel, automatically adjusting the network operating parameters to mitigate the impact of the fault, and initiating a backup water supply plan;

[0028] System integration and communication protocol: Used to adopt a unified communication protocol to ensure compatibility and data exchange between different monitoring devices and system modules; responsible for coordinating the work of various subsystems to ensure data consistency and system stability;

[0029] The platform and data analysis center are used to utilize cloud computing technology to establish a data analysis center to store and manage large amounts of monitoring data.

[0030] As a further preferred solution of the water supply network predictive maintenance and fault diagnosis system of the present invention, the principle of the data fusion and real-time analysis module specifically includes the following:

[0031] Data preprocessing: cleaning, denoising and standardizing the raw data from each sensor;

[0032] Among them, the signal denoising is calculated as follows:

[0033] y(t)=x(t)*h(t); where x(t) is the original signal, h(t) is the impulse response of the filter, and y(t) is the filtered signal;

[0034] Data standardization: Data standardization is to scale the data so that it falls into a small specific interval [0,1];

[0035] The standardization methods include Min-Max standardization and Z-score standardization;

[0036] Min-Max Normalization: Where x is the original data, min(X) is the minimum value in the data set, max(X) is the maximum value in the data set, and x′ is the standardized data;

[0037] Z-score normalization: Among them, μ is the mean of the data set, σ is the standard deviation of the data set, and z is the standardized Z score;

[0038] Data fusion: Using statistical methods, machine learning or deep learning techniques, the data from different sensors are fused into a comprehensive data set to reflect the overall status of the pipe network;

[0039] Real-time analysis: Using signal processing and data analysis algorithms, the fused data is analyzed in real time to identify abnormal patterns and trends in the network;

[0040] Anomaly Detection: Anomaly detection is the process of identifying data points in the data that do not conform to expected patterns or are significantly different from the majority of the data; specifically:

[0041] Interquartile range IQR anomaly detection:

[0042] Calculate the first quartile Q of the data 1 and the third quartile Q 3 ;

[0043] Calculate the interquartile range IQR: IQR = Q 3 -Q 1 ;

[0044] Define lower and upper bounds: lower bound = Q 1 -1.5xIQR, lower bound = Q 3 +1.5xIQR;

[0045] If the original signal x is less than the lower bound or greater than the upper bound, it is considered an outlier;

[0046] Mahalanobis distance anomaly detection:

[0047] Calculate the covariance matrix S of the data and the mean μ of the data set;

[0048] For a new original signal x, calculate its Mahalanobis distance D:D 2 =(x-μ) T S -1 (x-μ);

[0049] If D 2 If the value exceeds the preset threshold, it is considered an outlier.

[0050] As a further preferred embodiment of a water supply network predictive maintenance and fault diagnosis system of the present invention, the principle of the fault diagnosis model specifically includes the following steps:

[0051] (1) Data collection and preprocessing, including:

[0052] Historical fault data collection: Collect historical fault records of the water supply network, including fault type, occurrence time, duration, impact scope, and maintenance records;

[0053] Real-time monitoring data integration: Integrate real-time monitoring data such as flow, pressure, noise level and water quality parameters;

[0054] Data cleaning: remove outliers and noise, fill in missing values, and ensure data quality;

[0055] Feature engineering: Extract features from raw data that are helpful for fault diagnosis, including sudden pressure changes, abnormal flow, and noise frequency changes;

[0056] (2) Feature selection, including:

[0057] Correlation analysis: Use statistical methods to analyze the correlation between features and fault types and select the most informative features;

[0058] Dimensionality reduction technology: Apply dimensionality reduction technologies such as principal component analysis (PCA) or autoencoders to reduce feature dimensions and improve model training efficiency;

[0059] (3) Model training, including:

[0060] Model selection: Choose the appropriate machine learning algorithm, such as random forest, support vector machine SVM, deep learning network, etc.;

[0061] Training set and test set division: historical fault data is divided into training set and test set for model training and verification;

[0062] Model training: Use the training set data to train the selected machine learning model and adjust the model parameters to optimize performance;

[0063] Model validation: Use the test set data to evaluate the model's accuracy, recall, F1 score and other indicators to ensure the model's generalization ability;

[0064] (4) Fault identification and classification;

[0065] Real-time data input: input real-time monitoring data into the trained fault diagnosis model;

[0066] Fault mode recognition: The model automatically identifies the fault mode corresponding to the input data, including water leakage, pipe burst, and abnormal water quality;

[0067] Among them, water leakage identification: Water leakage identification usually involves analyzing changes in pressure and flow, as well as changes in noise levels;

[0068] Pressure drop analysis: ΔP=P initial -P current ; Among them, among them, P initial is the initial pressure, P current is the current pressure, ΔP is the pressure change;

[0069] Traffic anomaly detection: in, is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size;

[0070] Noise level analysis: Among them, x(t) is the signal in the time domain, X(f) is the signal in the frequency domain, and f is the frequency;

[0071] Exploded pipe identification: Exploded pipe identification involves a sharp drop in pressure and a sharp increase in flow;

[0072] Pressure change rate: Among them, P t1 and P t0 are the pressure values ​​at time t1 and to respectively;

[0073] Flow rate change: Among them, Q t1 and Q t0 are the flow values ​​at time t1 and to respectively;

[0074] Explosive pipe energy release model: Where E is the energy released, p is the fluid density, and A is the cross-sectional area of ​​the pipe;

[0075] Identification of water quality anomalies: Identification of water quality anomalies involves the monitoring and analysis of multiple water quality parameters;

[0076] The rate of change of water quality parameters is the rate of change of turbidity: Among them, C t1 and C t0 are the water quality parameter values ​​at time t1 and to respectively;

[0077] Correlation analysis of water quality parameters: Among them, X i and Y i are the observed values ​​of two water quality parameters, and is their mean;

[0078] Abnormal water quality detection: D 2 =(x-μ) T S -1 (x-μ); where x is the observed water quality parameter vector, μ is the mean vector of normal water quality parameters, and S is the covariance matrix of normal water quality parameters;

[0079] (5) Fault location: Use machine learning technology, combined with monitoring data such as noise, pressure, and flow, to predict the precise location of faults in the water supply network. Based on regression analysis or cluster analysis, by analyzing the relationship between historical fault data and location, train the model to quickly locate new faults.

[0080] Among them, the regression analysis positioning is as follows:

[0081] Linear regression: Position = β 0 +β 1 ×Noise+β 2 ×Pr essure+β 3 ×Flow

[0082] Among them, Position is the predicted position, β 0 , β 1 , β 2 , β 3 are model parameters, Noise, Pressure, and Flow are the characteristic values ​​of noise, pressure, and flow, respectively;

[0083] Support Vector Machine Regression SVR: Where f(x) is the predicted position, a i is the Lagrange multiplier, K(x,x i ) is the kernel function, x i is the support vector, b is the bias term;

[0084] Cluster analysis positioning: K-means clustering: Cluster = arg min c ∑ x∈c ||x-μc || 2 ; where c is the cluster, x is the data point, μ c is the center of the cluster, and the goal is to minimize the distance from the data point to the cluster center;

[0085] Location prediction: Combined with the fault identification results, the location algorithm is used to predict the geographical location where the fault occurs;

[0086] Location prediction is a key step in the fault diagnosis process. It combines the fault identification results and the location algorithm to determine the geographical location of the fault:

[0087] Fault pattern matching: Matching real-time monitoring data with known fault patterns to identify the type of fault currently occurring;

[0088] Feature extraction: Extract key features related to location prediction from monitoring data, such as pressure drop, flow change, noise level, etc.

[0089] Application of location prediction algorithms: Use trained location algorithms, such as regression analysis or cluster analysis, combined with extracted features to predict fault locations;

[0090] Regression analysis position prediction: Longitude, Latium = f(Feature 1 ,Feature 2 ,…,Feature n ), where f is the regression model, Feature is the feature that affects the location prediction, and Longitude, Latitude is the predicted geographic location coordinates;

[0091] Cluster analysis location prediction: N is the number of data points in the cluster, x i ,y i is the longitude and latitude coordinates of the i-th data point in the cluster, and Cluster Center is the center coordinate of the cluster, which represents the predicted value of the fault location.

[0092] As a further preferred solution of the water supply network predictive maintenance and fault diagnosis system of the present invention, the fault risk score is specifically calculated as follows:

[0093] F n is the fault characteristic, is the corresponding weight, and RiskScore is the calculated failure risk score;

[0094] Maintenance Time t =φ 1×Maintenance Time t-1 +φ 2 ×Status 1 +…+φ m ×Status m +∈;

[0095] Among them, Maintenance Time t is the predicted maintenance time, φ m is a model parameter, Status m is the state characteristic of the pipeline network, and ∈ is the error term.

[0096] As a further preferred embodiment of a water supply network predictive maintenance and fault diagnosis system of the present invention, the maintenance priority score is: Priority Score = α×Urgency+β×Im pact+γ×Resource Availability; wherein a, β, γ are weight coefficients, Urgency is the urgency of the fault, Im pact is the impact range of the fault, and Resource Availability is the availability of maintenance resources;

[0097] Maintenance time optimization: Among themMa intenance Time i is the time of the ith maintenance task, Priority i is the corresponding priority;

[0098] As a further preferred embodiment of the water supply network predictive maintenance and fault diagnosis system of the present invention, the water quality parameter change rate is calculated as follows:

[0099] Among them, the current value is the water quality parameter value monitored in real time, and the baseline value is the normal value or historical average value of the parameter;

[0100] Pollution control measures decision-making, the specific calculation is as follows:

[0101]

[0102] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0103] The present invention discloses a predictive maintenance and fault diagnosis system for a water supply network, which realizes real-time monitoring, fault prediction, diagnosis and maintenance decision support for a water supply network by integrating multiple monitoring devices and applying advanced data analysis technology; specifically, it comprises a multi-parameter monitoring device integration module, a data fusion and real-time analysis module, a fault diagnosis model, a predictive maintenance plan module, a water quality monitoring and pollution control module, an intelligent early warning and emergency response system, a system integration and communication protocol, and a cloud platform and a data analysis center; through these modules, the present invention can improve monitoring efficiency, improve the accuracy of fault detection, optimize maintenance resource allocation, enhance predictive maintenance capabilities, reduce maintenance costs, improve water supply safety, reduce unplanned maintenance and downtime, enhance water supply safety, and enhance emergency response capabilities; the platform also realizes centralized management and efficient processing of data, improves the scalability and flexibility of the system, and ensures data security and privacy; it realizes intelligent management of the water supply network, improves management efficiency and level, and provides technical support for the modern management of the water supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the embodiments of the present application or the technical solutions in 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 described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0105] Figure 1 It is a diagram of the integrated module architecture of the multi-parameter monitoring device of the present invention;

[0106] Figure 2 It is a flow chart of the real-time data analysis and early warning module of the present invention;

[0107] Figure 3 It is a structural diagram of the fault diagnosis model of the present invention;

[0108] Figure 4 It is a flow chart of the maintenance decision support module of the present invention;

[0109] Figure 5 is a flow chart of the algorithm of the predictive maintenance planning module of the present invention;

[0110] Figure 6 It is a schematic diagram of the integrated module of the multi-parameter monitoring device of the present invention;

[0111] Figure 7 It is a schematic diagram of the data fusion and real-time analysis module of the present invention;

[0112] Figure 8 It is a schematic diagram of the fault diagnosis model of the present invention;

[0113] Fig. 9 is a schematic diagram of a predictive maintenance planning module of the present invention;

[0114] Fig.10 It is a schematic diagram of the maintenance decision support module of the present invention;

[0115] Fig.11 It is a schematic diagram of the water quality monitoring and pollution control module of the present invention;

[0116] Fig.12 It is a schematic diagram of the intelligent early warning and emergency response system of the present invention;

[0117] Fig.13 It is a schematic diagram of the system integration and communication protocol of the present invention;

[0118] Fig.14 It is a schematic diagram of the cloud platform and data analysis center of the present invention. DETAILED DESCRIPTION

[0119] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0120] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0121] like Figures 1 to 14 As shown, the present invention relates to a smart water supply platform, which aims to achieve real-time monitoring, fault prediction, diagnosis and maintenance decision support for the water supply network by integrating multiple monitoring equipment and applying advanced data analysis technology. The platform includes a multi-parameter monitoring equipment integration module, a data fusion and real-time analysis module, a fault diagnosis model, a predictive maintenance plan module, a water quality monitoring and pollution control module, an intelligent early warning and emergency response system, a system integration and communication protocol, and a cloud platform and a data analysis center. Through these modules, the present invention can improve the accuracy of fault detection, optimize the allocation of maintenance resources, reduce unplanned maintenance and downtime, enhance water supply safety, and improve emergency response capabilities. The platform also realizes centralized management and efficient processing of data, improves the scalability and flexibility of the system, and ensures data security and privacy. The technical solution of the present invention is suitable for the intelligent management and maintenance of urban water supply systems, and has broad application prospects and significant social and economic benefits.

[0122] The system integrates various types of sensors and monitoring equipment, such as flow meters, pressure gauges, noise sensors and water quality monitors, to achieve real-time data collection, analysis and processing of the water supply network. The core of the system is to use advanced data fusion technology, machine learning algorithms, artificial intelligence and big data analysis technology to monitor the operating status of the water supply network in real time, predict potential faults such as water leakage, pipe burst and water pollution, and provide timely warnings and maintenance suggestions.

[0123] Sensor integration and data acquisition technology: involves integrating multiple sensors into the water supply network system to collect key parameters such as flow, pressure, noise and water quality of the network in real time.

[0124] Data fusion and real-time analysis technology: involves fusing and analyzing data collected by different sensors to provide more comprehensive and accurate information on the status of the pipeline network.

[0125] Machine learning and fault diagnosis technology: involves using machine learning algorithms to analyze historical fault data and establish fault diagnosis models to identify and classify various faults in the water supply network.

[0126] Artificial intelligence and predictive maintenance technology: involves using artificial intelligence technology to optimize maintenance plans, reduce unnecessary maintenance activities, reduce maintenance costs, and ensure the normal operation of the pipeline network.

[0127] Early warning and decision support technology: involves automatically issuing early warnings when potential faults are detected, and providing maintenance decision support based on fault diagnosis results, including recommendations on maintenance priority, time and method.

[0128] Water quality monitoring and pollution control technology: involves monitoring water quality changes in the water supply network, timely detecting and responding to water pollution incidents, and ensuring water supply safety.

[0129] System optimization and resource management technology: involves optimizing the allocation of maintenance resources for the water supply network, improving maintenance efficiency, and reducing downtime and economic losses caused by failures.

[0130] Regarding the real-time monitoring and data collection issues: the existing technology cannot achieve comprehensive real-time monitoring of the water supply network, resulting in the inability to timely discover and respond to abnormal conditions in the network. This patent integrates multiple sensors and monitoring equipment to achieve real-time monitoring of key parameters such as flow, water pressure, noise and water quality, thereby improving the comprehensiveness and real-time nature of data collection.

[0131] Regarding data fusion and analysis issues: Due to the lack of effective data fusion technology, the existing monitoring system cannot fully utilize multi-source data for in-depth analysis, and thus cannot accurately evaluate the operating status of the pipeline network. This patent uses advanced data fusion technology to integrate data from different sensors to provide more comprehensive and accurate information on the status of the pipeline network.

[0132] Regarding the problem of fault diagnosis accuracy: Most existing fault diagnosis methods rely on experience and judgment, lack of systematic data analysis and scientific decision-making support, resulting in low accuracy and efficiency of fault diagnosis. This patent uses machine learning and artificial intelligence technology to develop a fault diagnosis model to improve the accuracy of fault identification and classification.

[0133] Regarding the problem of predictive maintenance and decision support: the lack of scientific decision support based on data analysis leads to unreasonable allocation of maintenance resources and low maintenance efficiency. This patent provides maintenance decision support based on the actual operation status and failure risk of the pipeline network through a predictive maintenance planning module, and optimizes the allocation of maintenance resources.

[0134] Regarding water quality monitoring and pollution control issues: the development of water quality monitoring technology lags behind, and it is impossible to monitor and warn of water quality changes in real time, especially in terms of water source pollution and secondary pollution of pipe networks. This patent integrates a water quality monitor to monitor water quality changes in real time, and combines pollution control technology to respond to water pollution incidents in a timely manner.

[0135] Regarding the issue of technology integration and intelligent management: the monitoring, diagnosis and maintenance technologies of water supply networks are scattered, and there is a lack of effective technology integration and intelligent management platforms, which leads to information islands and waste of resources. This patent builds an integrated intelligent management platform to achieve the organic integration of monitoring, diagnosis and maintenance technologies and improve the level of intelligent management.

[0136] Regarding the problem of the ability to respond to emergencies: When facing emergencies such as natural disasters, the water supply network lacks an effective monitoring and emergency response mechanism, making it difficult to quickly restore water supply. This patent improves the ability of the water supply network to respond to emergencies by enhancing the system's early warning and emergency response functions.

[0137] Regarding maintenance cost and efficiency issues: Traditional maintenance methods are costly, inefficient, and difficult to achieve predictive maintenance. This patent uses predictive maintenance technology to reduce unnecessary maintenance activities, lower maintenance costs, and ensure the normal operation of the pipeline network.

[0138] Regarding the issues of system reliability and water supply safety: Due to the lack of effective monitoring and diagnostic technology, the reliability and safety of the water supply network are difficult to guarantee. This patent improves the reliability of the network and ensures water supply safety through real-time monitoring and fault diagnosis technology.

[0139] By solving the above technical problems, this patent aims to provide a comprehensive solution to improve the monitoring capability, fault diagnosis accuracy, maintenance efficiency and water supply safety of the water supply network, thereby improving the overall performance of the urban water supply system and the quality of life of residents. The specific embodiments are as follows:

[0140] like Figure 1 As shown, the multi-parameter monitoring equipment integrated module is used to use multiple sensors to monitor the key parameters of the water supply network in real time, and transmit these data to the central processing unit for analysis and processing; to achieve comprehensive monitoring of the status of the water supply network, so as to detect abnormal conditions in time and perform fault diagnosis;

[0141] like Figure 2 and Figure 6 As shown, the data fusion and real-time analysis module is used to effectively integrate the data from different sensors and conduct in-depth analysis to provide a comprehensive view of the pipeline network status and identify potential abnormal behaviors; the principles of the data fusion and real-time analysis module are specifically as follows:

[0142] Data preprocessing: cleaning, denoising and standardizing the raw data from each sensor;

[0143] Among them, the signal denoising is calculated as follows:

[0144] y(t)=x(t)*h(t); where x(t) is the original signal, h(t) is the impulse response of the filter, and y(t) is the filtered signal;

[0145] Data standardization: Data standardization is to scale the data so that it falls into a small specific interval [0,1];

[0146] The standardization methods include Min-Max standardization and Z-score standardization;

[0147] Min-Max Normalization: Where x is the original data, min(X) is the minimum value in the data set, max(X) is the maximum value in the data set, and x′ is the standardized data;

[0148] Z-score normalization: Among them, μ is the mean of the data set, σ is the standard deviation of the data set, and z is the standardized Z score;

[0149] Data fusion: Using statistical methods, machine learning or deep learning techniques, the data from different sensors are fused into a comprehensive data set to reflect the overall status of the pipe network;

[0150] Real-time analysis: Using signal processing and data analysis algorithms, the fused data is analyzed in real time to identify abnormal patterns and trends in the network;

[0151] Anomaly Detection: Anomaly detection is the process of identifying data points in the data that do not conform to expected patterns or are significantly different from the majority of the data; specifically:

[0152] Interquartile range IQR anomaly detection:

[0153] Calculate the first quartile Q of the data 1 and the third quartile Q 3 ;

[0154] Calculate the interquartile range IQR: IQR = Q 3 -Q 1 ;

[0155] Define lower and upper bounds: lower bound = Q 1 -1.5xIQR, lower bound = Q 3 +1.5xIQR;

[0156] If the original signal x is less than the lower bound or greater than the upper bound, it is considered an outlier;

[0157] Mahalanobis distance anomaly detection:

[0158] Calculate the covariance matrix S of the data and the mean μ of the data set;

[0159] For a new original signal x, calculate its Mahalanobis distance D:D 2 =(x-μ) T S -1 (x-μ);

[0160] If D 2 If the value exceeds the preset threshold, it is considered an outlier.

[0161] like Figure 3 and Figure 8 As shown, the fault diagnosis model is used to learn historical fault data, automatically identify and classify various fault modes in the water supply network; identify water leakage, pipe burst, abnormal water quality faults, and provide accurate positioning of the fault location;

[0162] The principle of the fault diagnosis model specifically includes the following steps:

[0163] (1) Data collection and preprocessing, including:

[0164] Historical fault data collection: Collect historical fault records of the water supply network, including fault type, occurrence time, duration, impact scope, and maintenance records;

[0165] Real-time monitoring data integration: Integrate real-time monitoring data such as flow, pressure, noise level and water quality parameters;

[0166] Data cleaning: remove outliers and noise, fill in missing values, and ensure data quality;

[0167] Feature engineering: Extract features from raw data that are helpful for fault diagnosis, including sudden pressure changes, abnormal flow, and noise frequency changes;

[0168] (2) Feature selection, including:

[0169] Correlation analysis: Use statistical methods to analyze the correlation between features and fault types and select the most informative features;

[0170] Dimensionality reduction technology: Apply dimensionality reduction technologies such as principal component analysis (PCA) or autoencoders to reduce feature dimensions and improve model training efficiency;

[0171] (3) Model training, including:

[0172] Model selection: Choose the appropriate machine learning algorithm, such as random forest, support vector machine SVM, deep learning network, etc.;

[0173] Training set and test set division: historical fault data is divided into training set and test set for model training and verification;

[0174] Model training: Use the training set data to train the selected machine learning model and adjust the model parameters to optimize performance;

[0175] Model validation: Use the test set data to evaluate the model's accuracy, recall, F1 score and other indicators to ensure the model's generalization ability;

[0176] (4) Fault identification and classification;

[0177] Real-time data input: input real-time monitoring data into the trained fault diagnosis model;

[0178] Fault mode recognition: The model automatically identifies the fault mode corresponding to the input data, including water leakage, pipe burst, and abnormal water quality;

[0179] Among them, water leakage identification: Water leakage identification usually involves analyzing changes in pressure and flow, as well as changes in noise levels;

[0180] Pressure drop analysis: ΔP=P initial -P current ; Among them, among them, P initial is the initial pressure, P current is the current pressure, ΔP is the pressure change;

[0181] Traffic anomaly detection: in, is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size;

[0182] Noise level analysis: Among them, x(t) is the signal in the time domain, X(f) is the signal in the frequency domain, and f is the frequency;

[0183] Exploded pipe identification: Exploded pipe identification involves a sharp drop in pressure and a sharp increase in flow;

[0184] Pressure change rate: Among them, P t1 and P t0 are the pressure values ​​at time t1 and to respectively;

[0185] Flow rate change: Among them, Q t1 and Q t0 are the flow values ​​at time t1 and to respectively;

[0186] Explosive pipe energy release model: Where E is the energy released, p is the fluid density, and A is the cross-sectional area of ​​the pipe;

[0187] Identification of water quality anomalies: Identification of water quality anomalies involves the monitoring and analysis of multiple water quality parameters;

[0188] The rate of change of water quality parameters is the rate of change of turbidity: Among them, C t1 and C t0 are the water quality parameter values ​​at time t1 and to respectively;

[0189] Correlation analysis of water quality parameters: Among them, X i and Y i are the observed values ​​of two water quality parameters, and is their mean;

[0190] Abnormal water quality detection: D 2 =(x-μ) T S -1 (x-μ); where x is the observed water quality parameter vector, μ is the mean vector of normal water quality parameters, and S is the covariance matrix of normal water quality parameters;

[0191] (5) Fault location: Use machine learning technology, combined with monitoring data such as noise, pressure, and flow, to predict the precise location of faults in the water supply network. Based on regression analysis or cluster analysis, by analyzing the relationship between historical fault data and location, train the model to quickly locate new faults.

[0192] Among them, the regression analysis positioning is as follows:

[0193] Linear regression: Position = β 0 +β 1 ×Noise+β 2 ×Pressure+β 3 ×Flow

[0194] Among them, Position is the predicted position, β 0 , β 1 , β 2 , β 3 are model parameters, Noise, Pressure, and Flow are the characteristic values ​​of noise, pressure, and flow, respectively;

[0195] Support Vector Machine Regression SVR: Where f(x) is the predicted position, a i is the Lagrange multiplier, K(x,x i ) is the kernel function, x i is the support vector, b is the bias term;

[0196] Cluster analysis positioning: K-means clustering: Cluster = arg min c ∑ x∈c ||x-μ c || 2 ; where c is the cluster, x is the data point, μ c is the center of the cluster, and the goal is to minimize the distance from the data point to the cluster center;

[0197] Location prediction: Combined with the fault identification results, the location algorithm is used to predict the geographical location where the fault occurs;

[0198] Location prediction is a key step in the fault diagnosis process. It combines the fault identification results and the location algorithm to determine the geographical location of the fault:

[0199] Fault pattern matching: Matching real-time monitoring data with known fault patterns to identify the type of fault currently occurring;

[0200] Feature extraction: Extract key features related to location prediction from monitoring data, such as pressure drop, flow change, noise level, etc.

[0201] Application of location prediction algorithms: Use trained location algorithms, such as regression analysis or cluster analysis, combined with extracted features to predict fault locations;

[0202] Regression analysis position prediction: Longitude, Latium = f(Feature 1 ,Feature 2 ,…,Feature n ), where f is the regression model, Feature is the feature that affects the location prediction, and Longitude, Latitude is the predicted geographic location coordinates;

[0203] Cluster analysis location prediction: N is the number of data points in the cluster, x i ,y i is the longitude and latitude coordinates of the i-th data point in the cluster, and Cluster Center is the center coordinate of the cluster, which represents the predicted value of the fault location.

[0204] like Figure 4 and Fig. 9 As shown in the figure, the predictive maintenance planning module is used to use artificial intelligence technology to predict the maintenance needs and potential failure risks of the pipeline network based on the historical operation data and real-time monitoring data of the pipeline network; then automatically generate maintenance plans, optimize the allocation of maintenance resources, and reduce unplanned maintenance and downtime; the failure risk score is specifically calculated as follows:

[0205] F n is the fault characteristic, is the corresponding weight, and RiskScore is the calculated failure risk score;

[0206] Maintenance time required:MaintenanceTime t =φ 1 ×Maintenance Time t-1 +φ 2 ×Status 1 +…+φ m ×Status m +∈;

[0207] Among them, MaintenanceTime t is the predicted maintenance time, φ m is a model parameter, Status m is the state characteristic of the pipeline network, and ∈ is the error term.

[0208] like Figure 5 and Fig.10 As shown, the maintenance decision support module provides maintenance decision support based on fault diagnosis results and predictive maintenance plans, including suggestions on maintenance priority, time and methods;

[0209] The maintenance priority is: Priority Score = α × Urgency + β × Im pact + γ × Resource Availability; where α, β, and γ are weight coefficients, Urgency is the urgency of the fault, Im pact is the impact range of the fault, and Resource Availability is the availability of maintenance resources;

[0210] Maintenance time optimization: Among themMa intenance Time i is the time of the ith maintenance task, Priority i is the corresponding priority;

[0211] like Fig.11 As shown, the water quality monitoring and pollution control module is used to monitor water quality parameters in real time, including pH value, turbidity, and residual chlorine; when abnormal water quality is detected, pollution control measures are automatically initiated, including adjusting the amount of disinfectant added or switching the water source; the water quality parameter change rate is calculated as follows:

[0212] Among them, the current value is the water quality parameter value monitored in real time, and the baseline value is the normal value or historical average value of the parameter;

[0213] Pollution control measures decision-making, the specific calculation is as follows:

[0214]

[0215] Intelligent early warning and emergency response system: When a potential fault or emergency is detected, the system can automatically issue an early warning and initiate an emergency response procedure; the emergency response procedure includes notifying maintenance personnel, automatically adjusting the network operating parameters to mitigate the impact of the fault, and initiating a backup water supply plan; Fig.12 As shown,

[0216] Real-time monitoring and data analysis: The system monitors the operation status of the pipeline network in real time and identifies anomalies through data analysis technology.

[0217] Early warning trigger mechanism: The system automatically triggers an early warning when an anomaly is detected, providing key fault information.

[0218] Emergency response procedure starts: The system automatically starts the emergency response procedure based on the early warning information, including notifying maintenance personnel and adjusting operating parameters.

[0219] Maintenance personnel notification: The system quickly notifies maintenance personnel through a variety of methods to ensure timely response.

[0220] Automatic adjustment of pipeline network operating parameters: The system automatically adjusts the pipeline network operating parameters to reduce the impact of failures.

[0221] Backup water supply plan activated: When necessary, the system activates the backup water supply plan to ensure that water supply is not affected.

[0222] Emergency response effectiveness evaluation: The system evaluates the effectiveness of emergency response and makes adjustments based on feedback.

[0223] System recovery and optimization: After the fault is resolved, the system resumes normal operation and optimizes the early warning and response mechanisms.

[0224] System integration and communication protocol: Used to adopt a unified communication protocol to ensure compatibility and data exchange between different monitoring devices and system modules; responsible for coordinating the work of various subsystems to ensure data consistency and system stability; Fig.13 As shown,

[0225] Communication protocol design: Design a communication protocol suitable for the water supply network monitoring system to ensure compatibility between different devices and modules.

[0226] Equipment compatibility testing: Testing monitoring devices to ensure they can follow unified communication protocols and transmit data smoothly.

[0227] System integration module development: Develop an integration module to coordinate the work between subsystems and manage and schedule data flows.

[0228] Data consistency guarantee: Ensure the consistency of data in the system through transaction management and data synchronization mechanisms.

[0229] System stability test: Ensure the stability and reliability of the system through stress testing and fault recovery testing.

[0230] Communication security measures: Take necessary communication security measures, such as SSL / TLS encryption, to ensure the security of data transmission.

[0231] Monitoring and maintenance: Systematically monitor and maintain communication protocols and integration modules to ensure long-term stable operation of the system.

[0232] The platform and data analysis center are used to use cloud computing technology to establish a data analysis center to store and manage a large amount of monitoring data. Fig.14 As shown,

[0233] Cloud platform selection and deployment: Select appropriate cloud computing services and deploy them based on business needs and compliance requirements.

[0234] Data storage architecture design: Design a highly available and scalable data storage architecture to support the storage and management of large amounts of data.

[0235] Build data processing capabilities: Build strong data processing capabilities, including using data warehouse and data lake technologies provided by cloud services.

[0236] Data analysis tool integration: Integrate advanced data analysis tools and machine learning platforms to support complex data analysis and model training.

[0237] Data security and privacy protection: Take necessary security measures, such as data encryption and access control, to protect data security and privacy.

[0238] System monitoring and optimization: Systematically monitor the performance of the cloud platform and data analysis center, and optimize as needed.

[0239] Disaster recovery plan development: Develop a disaster recovery plan to ensure that data and business can be quickly restored in the event of a failure.

[0240] The specific applications are as follows:

[0241] Yining City Smart Water Supply Platform

[0242] 1. Background

[0243] As a rapidly developing city, Yining City is facing problems such as aging water supply pipes, water pollution and inefficient water resource management. In order to improve the intelligence level of the water supply system, Yining City implemented the Smart Water Supply Platform Project.

[0244] 2. Implementation steps

[0245] 1. Project planning and design:

[0246] Conduct a comprehensive survey of Yining City’s water supply network to identify monitoring points and key nodes.

[0247] Design the overall architecture of the smart water supply platform, including data collection, processing, analysis and decision support systems.

[0248] 2. Multi-parameter monitoring equipment integrated module deployment:

[0249] Flow meters, pressure gauges, noise sensors and water quality monitors are installed in Yining City’s main water supply pipelines, pumping stations, water storage facilities and user ends.

[0250] The data from the monitoring devices are transmitted to the central processing unit via the wireless sensor network.

[0251] 3. Implementation of data fusion and real-time analysis module:

[0252] Data preprocessing and fusion algorithms are deployed in the central processing unit to clean, denoise and standardize the collected data.

[0253] Implement real-time data analytics algorithms, such as machine learning models, to identify abnormal network behavior and potential failures.

[0254] 4. Fault diagnosis model development and training:

[0255] Based on historical fault data, fault diagnosis models are developed, including water leakage detection, pipe burst identification and water quality anomaly analysis.

[0256] Train a model to identify and classify various failure modes in water distribution networks.

[0257] 5. Implementation of predictive maintenance planning module:

[0258] Using machine learning technology, the maintenance needs and potential failure risks of the pipeline network can be predicted based on the historical operation data and real-time monitoring data of the pipeline network.

[0259] Automatically generate maintenance plans to optimize the allocation of maintenance resources.

[0260] 6. Maintenance decision support module development:

[0261] Develop maintenance decision support modules to provide recommendations on maintenance priorities, timing and methods.

[0262] Provide operational guidance to the maintenance team and optimize the maintenance process.

[0263] 7. Implementation of water quality monitoring and pollution control module:

[0264] Install water quality monitors at water sources and key water quality monitoring points to monitor water quality parameters in real time.

[0265] When abnormal water quality is detected, pollution control measures are automatically initiated, such as adjusting the disinfectant dosage or switching the water source.

[0266] 8. Deployment of intelligent early warning and emergency response systems:

[0267] Deploy an intelligent early warning system to automatically issue an early warning once a potential failure or emergency is detected.

[0268] Initiate emergency response procedures, including notifying maintenance personnel, automatically adjusting network operating parameters to mitigate the impact of the failure, and activating backup water supply plans.

[0269] 9. System integration and communication protocol implementation

[0270] Design a unified communication protocol to ensure compatibility and data exchange between different monitoring devices and system modules.

[0271] Coordinate the work of each subsystem to ensure data consistency and system stability.

[0272] 10. Construction of cloud platform and data analysis center

[0273] Select a suitable cloud computing service provider and deploy a cloud platform.

[0274] Design data storage architecture, integrate data analysis tools and machine learning platforms, and support complex data analysis and model training.

[0275] 3. Implementation Effect

[0276] By implementing the smart water supply platform, Yining City has achieved the following results:

[0277] The monitoring and response capabilities of the water supply system have been improved, and water leaks, pipe bursts and abnormal water quality problems in the pipeline network have been discovered and dealt with in a timely manner.

[0278] The allocation of maintenance resources is optimized, unplanned maintenance and downtime are reduced, and maintenance costs are lowered.

[0279] Enhanced water supply security, ensuring water quality through real-time water quality monitoring and pollution control measures.

[0280] It has improved emergency response capabilities, enabling rapid response in emergency situations and reducing the impact on residents’ lives and urban operations.

[0281] It realizes centralized management and efficient processing of data, providing a scientific basis for decision-making in the water supply system.

[0282] This embodiment demonstrates the effect of the smart water supply platform in practical applications and proves the feasibility and effectiveness of the patented technical solution.

[0283] The present invention improves monitoring efficiency: real-time monitoring of key parameters of the water supply network improves the monitoring efficiency and response speed of the network status. Improved fault detection accuracy: advanced data analysis and machine learning technologies are used to improve the accuracy of fault detection and the ability to identify fault types. Enhanced predictive maintenance capabilities: by analyzing historical and real-time data, potential faults and maintenance requirements are predicted, thereby transforming maintenance work from passive response to active prevention. Reduced maintenance costs: optimized maintenance resource allocation, reduced unplanned maintenance and downtime, and effectively reduced maintenance costs. Improved water supply safety: real-time monitoring of water quality parameters, and automatic initiation of pollution control measures when abnormalities are detected to ensure water supply safety. Enhanced emergency response capabilities: rapid initiation of emergency response procedures when an emergency is detected to reduce the impact of faults on the water supply system. Improved data management and analysis capabilities: using cloud platforms and data analysis centers, centralized storage, management and efficient processing of large amounts of monitoring data are achieved. Enhanced system scalability and flexibility: unified communication protocols and system integration modules are designed to improve the scalability and flexibility of the system. Realized intelligent management: the entire system realizes intelligent management of the water supply network, improves management efficiency and level, and provides technical support for the modern management of the water supply system.

[0284] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention should be included in the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0285] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A water supply network predictive maintenance and fault diagnosis system, characterized in that: It includes multi-parameter monitoring equipment integration module, data fusion and real-time analysis module, fault diagnosis model, predictive maintenance plan module, maintenance decision support module, water quality monitoring and pollution control module, intelligent early warning and emergency response system, system integration and communication protocol and platform and data analysis center; Among them, the multi-parameter monitoring equipment integration module is used to use a variety of sensors to monitor the key parameters of the water supply network in real time, and transmit these data to the central processing unit for analysis and processing; to achieve comprehensive monitoring of the status of the water supply network, so as to detect abnormal conditions in time and perform fault diagnosis; Data fusion and real-time analysis module, which is used to effectively integrate data from different sensors and conduct in-depth analysis to provide a comprehensive view of the pipeline network status and identify potential abnormal behaviors; Fault diagnosis model, which is used to learn historical fault data, automatically identify and classify various fault modes in the water supply network; identify water leakage, pipe burst, abnormal water quality faults, and provide precise positioning of the fault location; The predictive maintenance planning module is used to use artificial intelligence technology to predict the maintenance needs and potential failure risks of the pipeline network based on the historical operation data and real-time monitoring data of the pipeline network; and then automatically generate maintenance plans, optimize the allocation of maintenance resources, and reduce unplanned maintenance and downtime; Maintenance decision support module, which provides maintenance decision support based on fault diagnosis results and predictive maintenance plans, including suggestions on maintenance priority, time and methods; Water quality monitoring and pollution control module: used to monitor water quality parameters in real time, including pH value, turbidity, and residual chlorine; when abnormal water quality is detected, pollution control measures are automatically initiated, including adjusting the amount of disinfectant added or switching the water source; Intelligent early warning and emergency response system: When a potential fault or emergency is detected, the system can automatically issue an early warning and initiate an emergency response procedure; the emergency response procedure includes notifying maintenance personnel, automatically adjusting the network operating parameters to mitigate the impact of the fault, and initiating a backup water supply plan; System integration and communication protocol: Used to adopt a unified communication protocol to ensure compatibility and data exchange between different monitoring devices and system modules; responsible for coordinating the work of various subsystems to ensure data consistency and system stability; The platform and data analysis center are used to utilize cloud computing technology to establish a data analysis center to store and manage large amounts of monitoring data.

2. A water supply network predictive maintenance and fault diagnosis system according to claim 1, characterized in that: The principles of the data fusion and real-time analysis module are specifically as follows: Data preprocessing: cleaning, denoising and standardizing the raw data from each sensor; Among them, the signal denoising is calculated as follows: y(t)=x(t)*h(t); where x(t) is the original signal, h(t) is the impulse response of the filter, and y(t) is the filtered signal; Data standardization: Data standardization is to scale the data so that it falls into a small specific interval [0,1]; The standardization methods include Min-Max standardization and Z-score standardization; Min-Max Normalization: Where x is the original data, min(X) is the minimum value in the data set, max(X) is the maximum value in the data set, and x′ is the standardized data; Z-score normalization: Among them, μ is the mean of the data set, σ is the standard deviation of the data set, and z is the standardized Z score; Data fusion: Using statistical methods, machine learning or deep learning techniques, the data from different sensors are fused into a comprehensive data set to reflect the overall status of the pipe network; Real-time analysis: Using signal processing and data analysis algorithms, the fused data is analyzed in real time to identify abnormal patterns and trends in the network; Anomaly Detection: Anomaly detection is the process of identifying data points in the data that do not conform to expected patterns or are significantly different from the majority of the data; specifically: Interquartile range IQR anomaly detection: Calculate the first quartile Q1 and the third quartile Q3 of the data; Calculate the interquartile range IQR: IQR = Q3-Q1; Define lower and upper bounds: lower bound = Q1-1.5xIQR, lower bound = Q3+1.5xIQR; If the original signal x is less than the lower bound or greater than the upper bound, it is considered an outlier; Mahalanobis distance anomaly detection: Calculate the covariance matrix S of the data and the mean μ of the data set; For a new original signal x, calculate its Mahalanobis distance D:D 2 =(x-μ) T S -1 (x-μ); If D 2 If the value exceeds the preset threshold, it is considered an outlier.

3. A water supply network predictive maintenance and fault diagnosis system according to claim 1, characterized in that: The principle of the fault diagnosis model specifically includes the following steps: (1) Data collection and preprocessing, including: Historical fault data collection: Collect historical fault records of the water supply network, including fault type, occurrence time, duration, impact scope, and maintenance records; Real-time monitoring data integration: Integrate real-time monitoring data such as flow, pressure, noise level and water quality parameters; Data cleaning: remove outliers and noise, fill in missing values, and ensure data quality; Feature engineering: Extract features from raw data that are helpful for fault diagnosis, including sudden pressure changes, abnormal flow, and noise frequency changes; (2) Feature selection, including: Correlation analysis: Use statistical methods to analyze the correlation between features and fault types and select the most informative features; Dimensionality reduction technology: Apply dimensionality reduction technologies such as principal component analysis (PCA) or autoencoders to reduce feature dimensions and improve model training efficiency; (3) Model training, including: Model selection: Choose the appropriate machine learning algorithm, such as random forest, support vector machine SVM, deep learning network, etc.; Training set and test set division: historical fault data is divided into training set and test set for model training and verification; Model training: Use the training set data to train the selected machine learning model and adjust the model parameters to optimize performance; Model validation: Use the test set data to evaluate the model's accuracy, recall, F1 score and other indicators to ensure the model's generalization ability; (4) Fault identification and classification; Real-time data input: input real-time monitoring data into the trained fault diagnosis model; Fault mode recognition: The model automatically identifies the fault mode corresponding to the input data, including water leakage, pipe burst, and abnormal water quality; Among them, water leakage identification: Water leakage identification usually involves analyzing changes in pressure and flow, as well as changes in noise levels; Pressure drop analysis: ΔP=P initial -P current ; Among them, among them, P initial is the initial pressure, P current is the current pressure, ΔP is the pressure change; Traffic anomaly detection: in, is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size; Noise level analysis: Among them, x(t) is the signal in the time domain, X(f) is the signal in the frequency domain, and f is the frequency; Exploded pipe identification: Exploded pipe identification involves a sharp drop in pressure and a sharp increase in flow; Pressure change rate: Among them, P t1 and P t0 are the pressure values ​​at time t1 and to respectively; Flow rate change: Among them, Q t1 and Q t0 are the flow values ​​at time t1 and to respectively; Explosive pipe energy release model: Where E is the energy released, p is the fluid density, and A is the cross-sectional area of ​​the pipe; Identification of water quality anomalies: Identification of water quality anomalies involves the monitoring and analysis of multiple water quality parameters; The rate of change of water quality parameters is the rate of change of turbidity: Among them, C t1 and C t0 are the water quality parameter values ​​at time t1 and to respectively; Correlation analysis of water quality parameters: Among them, X i and Y i are the observed values ​​of two water quality parameters, and is their mean; Abnormal water quality detection: D 2 =(x-μ) T S -1 (x-μ); where x is the observed water quality parameter vector, μ is the mean vector of normal water quality parameters, and S is the covariance matrix of normal water quality parameters; (5) Fault location: Use machine learning technology, combined with monitoring data such as noise, pressure, and flow, to predict the precise location of faults in the water supply network. Based on regression analysis or cluster analysis, by analyzing the relationship between historical fault data and location, train the model to quickly locate new faults. Among them, the regression analysis positioning is as follows: Linear regression: Position = β0 + β1 × Noise + β2 × Pressure + β3 × Flow Among them, Position is the predicted position, β0, β1, β2, β3 are model parameters, Noise, Pressure and Flow are the characteristic values ​​of noise, pressure and flow respectively; Support Vector Machine Regression SVR: Where f(x) is the predicted position, a i is the Lagrange multiplier, K(x,x i ) is the kernel function, x i is the support vector, b is the bias term; Cluster analysis positioning: K-means clustering: Cluster = argmin c ∑ x∈c ||x-μ c || 2 ; where c is the cluster, x is the data point, μ c is the center of the cluster, and the goal is to minimize the distance from the data point to the cluster center; Location prediction: Combined with the fault identification results, the location algorithm is used to predict the geographical location where the fault occurs; Location prediction is a key step in the fault diagnosis process. It combines the fault identification results and the location algorithm to determine the geographical location of the fault: Fault pattern matching: Matching real-time monitoring data with known fault patterns to identify the type of fault currently occurring; Feature extraction: Extract key features related to location prediction from monitoring data, such as pressure drop, flow change, noise level, etc. Application of location prediction algorithms: Use trained location algorithms, such as regression analysis or cluster analysis, combined with extracted features to predict fault locations; Regression analysis location prediction: Longitude, Latium = f(Feature1, Feature2,…, Feature n ), where f is the regression model, Feature is the feature that affects the location prediction, and Longitude, Latitude is the predicted geographic location coordinates; Cluster analysis location prediction: N is the number of data points in the cluster, x i ,y i is the longitude and latitude coordinates of the i-th data point in the cluster, and Cluster Center is the center coordinate of the cluster, which represents the predicted value of the fault location.

4. A water supply network predictive maintenance and fault diagnosis system according to claim 1, characterized in that: The predictive maintenance planning module includes a failure risk score, which is specifically calculated as follows: F n is the fault characteristic, is the corresponding weight, and RiskScore is the calculated failure risk score; Maintenance time required:MaintenanceTime t =φ1×MaintenanceTime t-1 +φ2×Status1+…+φ m ×Status m +∈; Among them, MaintenanceTime t is the predicted maintenance time, φ m is a model parameter, Status m is the state characteristic of the pipeline network, and ∈ is the error term.

5. A water supply network predictive maintenance and fault diagnosis system according to claim 1, characterized in that: The maintenance priority score is: Priority Score = α × Urgency + β × Impact + γ × Resource Availability; where α, β, and γ are weight coefficients, Urgency is the urgency of the fault, Impact is the impact range of the fault, and Resource Availability is the availability of maintenance resources; Maintenance time optimization: MaintenanceTime i is the time of the ith maintenance task, Priority i is the corresponding priority.

6. A water supply network predictive maintenance and fault diagnosis system according to claim 1, characterized in that: The water quality parameter change rate is calculated as follows: Among them, the current value is the water quality parameter value monitored in real time, and the baseline value is the normal value or historical average value of the parameter; Pollution control measures decision-making, the specific calculation is as follows:

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