Water supply network water leakage detection method based on emergence phenomenon and federated learning

Abstract

The invention discloses a water supply network water leakage detection method based on an emergence phenomenon and federal learning, and relates to the technical field of intelligent monitoring and management of water supply networks. The method specifically comprises the steps of data acquisition and data preprocessing, emergence feature extraction, federated learning framework, quantum calculation acceleration, artificial intelligence model construction, real-time monitoring and early warning system, data-driven pipe network optimization, test and evaluation and the like. According to the method, efficient processing of data, accurate extraction of water leakage characteristics, rapid training and optimization of a model and collaborative analysis of multi-source data on the premise of protecting data privacy are realized, so that the precision and efficiency of water leakage detection are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring and management of water supply pipe networks, and in particular relates to a water supply pipe network leakage detection method based on emergence phenomenon and federated learning. Background Art

[0002] With the continuous advancement of urbanization, the safe and stable operation of water supply networks, as an important part of urban infrastructure, is crucial to ensuring the daily life and industrial water needs of urban residents. However, water leakage in water supply networks has always been a major problem plaguing the water supply industry, which not only causes waste of water resources, but also may cause safety hazards such as ground collapse.

[0003] Current technical status:

[0004] 1. Traditional detection methods: The commonly used water leakage detection methods currently include acoustic detection, ground detection radar and other technologies. Most of these methods rely on manual operation, have low detection efficiency, and have limited accuracy in complex environments.

[0005] 2. Sensor monitoring: Some automated monitoring systems collect pipeline network data by installing sensors, but these systems often only provide a limited monitoring range and are unable to detect small leaks.

[0006] 3. Data collection and processing: Although some systems can realize automatic data collection, their data processing capabilities are limited, making it difficult to extract useful leakage features from large amounts of data.

[0007] 4. Technical limitations: Existing technologies have obvious limitations in processing large-scale data, real-time monitoring and precise positioning of leaks, and cannot meet the needs of modern urban water supply systems.

[0008] Technology development trends:

[0009] 1. Intelligence: With the development of artificial intelligence and machine learning technology, intelligent water leakage detection technology has gradually become a research hotspot.

[0010] 2. Data fusion: Multi-source data fusion technology is believed to improve the accuracy and robustness of the detection system.

[0011] 3. Real-time monitoring: Real-time monitoring of the water supply network is the key to improving the efficiency of water leakage detection.

[0012] 4. Privacy protection: In data-driven detection systems, how to protect data privacy becomes an important issue.

[0013] Deficiencies of existing technology:

[0014] 1. Detection accuracy: Traditional methods are difficult to accurately identify tiny or hidden leaks.

[0015] 2. Data processing capabilities: Existing systems have limitations in processing large-scale, high-dimensional pipeline network data.

[0016] 3. Real-time: Lack of effective real-time monitoring and rapid response mechanisms.

[0017] 4. Data privacy: In the process of multi-source data fusion, how to protect data privacy has not been effectively solved. Summary of the invention

[0018] The technical problem to be solved by the present invention is to provide a new type of water supply network leakage detection technology in response to the shortcomings of the background technology. This technology can utilize cutting-edge technologies such as deep learning, federated learning, quantum computing, etc. to achieve efficient data processing, accurate extraction of leakage features, rapid training and optimization of models, and collaborative analysis of multi-source data while protecting data privacy, thereby significantly improving the accuracy and efficiency of leakage detection.

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

[0020] A water supply network leakage detection method based on emergence phenomenon and federated learning, specifically comprising the following steps;

[0021] Step 1: Data collection and data preprocessing: Deploy a high-precision sensor network to collect real-time pressure, flow, and sound data of the water supply network; implement data cleaning, denoising, and standardization; build a database to integrate and store historical and real-time data to provide data support for analysis and model training;

[0022] Step 2: Extraction of emergent features: Use deep learning models to automatically extract high-level features related to water leakage from preprocessed data; design self-supervised learning tasks to learn feature representations of water leakage from data in an unsupervised manner;

[0023] Step 3, Federated Learning Framework: Use the federated learning algorithm to achieve data collaborative training between multiple independent pipe network systems while protecting their respective data privacy; integrate differential privacy and homomorphic encryption technologies to ensure data security during data sharing and model training;

[0024] Step 4, quantum computing acceleration: Identify computationally intensive tasks in model training that can be accelerated by quantum computers, including feature transformation and optimization algorithms; use the high parallelism of quantum computers to increase the speed of model training and reasoning;

[0025] Step 5, AI model construction: Build a water leakage prediction model based on deep learning, use the extracted features for training, and predict water leakage events; combine GIS technology to accurately locate and visualize the leakage location;

[0026] Step 6, system integration and optimization: used to integrate data collection, feature extraction, model training, prediction and positioning modules into a unified system; tune the system to ensure efficient collaboration between modules and adapt to different pipe network environments;

[0027] Step 7, real-time monitoring and early warning system: used to display the status of the pipe network and the prediction results in real time through the real-time data monitoring interface. When the system detects a potential water leakage risk, it can promptly notify the operation and maintenance personnel;

[0028] Step 8, data-driven network optimization: Use the collected data and prediction results to provide decision support for the maintenance, upgrade and optimization of the network, realize intelligent operation and maintenance of the network, and improve the utilization efficiency and economic benefits of water resources;

[0029] Step 9, testing and evaluation: Test the performance of the system in the actual pipe network environment, evaluate the detection accuracy, response speed and user satisfaction, and iteratively optimize the system based on the test results to meet higher performance requirements.

[0030] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention,

[0031] In step 1, data collection and preprocessing specifically include the following steps:

[0032] Step 1.1, sensor deployment: deploy various types of sensors at key nodes of the water supply network, including but not limited to pressure sensors, flow sensors, acoustic sensors, etc., to monitor the status of the network in real time;

[0033] Step 1.2, data acquisition: The data collected by the sensor is transmitted to the central database in real time through the data acquisition system; the data includes pressure readings, flow data, and sound signals;

[0034] Step 1.3, data cleaning: preprocess the collected data to remove invalid or erroneous data points through outlier detection and elimination;

[0035] Step 1.4, denoising: Apply signal processing techniques, including wavelet transform or filter, to remove noise components in the data and improve signal clarity;

[0036] Step 1.5, data standardization: convert data of different dimensions and magnitudes into a unified format for easy subsequent processing and analysis;

[0037] Step 1.6, feature engineering: extract useful features from the raw data, including statistical features and frequency domain features, to provide input for model training;

[0038] Step 1.7, Data storage: Build a data lake to store cleaned and standardized data, including historical data and real-time data streams;

[0039] In the denoising process, the filter formula used is: y(t) = ∫x(τ)h(t-τ)dτ;

[0040] Where x(τ) is the original signal, y(t) is the filtered signal, and h(t-τ) is the impulse response of the filter;

[0041] Data normalization formula: In the formula, x is the original data, z is the standardized data, μ is the mean of the data, and σ is the standard deviation of the data.

[0042] As a further preferred solution of the water supply network leakage detection method based on the emergence phenomenon and federated learning of the present invention, in step 2, the emergence feature extraction specifically includes the following steps:

[0043] Step 2.1, data input: input the preprocessed data into the deep learning model. This data may include time series data, image data, or signal data.

[0044] Step 2.2, deep learning model selection: select a suitable deep learning model based on the data type and feature requirements, including CNN for image data, RNN or LSTM for time series data, and Transformer for sequence or collection data;

[0045] Step 2.3, feature extraction: The model automatically learns high-level features in the data through its multi-layer structure;

[0046] Among them, water leakage related features:

[0047] Abnormal pressure: Water leakage may cause a sudden drop or abnormal fluctuation in pressure in the pipe network;

[0048] Flow rate changes: When a leak occurs, there may be an abnormal increase in flow rate in a specific area;

[0049] Sound signal: Water leakage will produce sound of a specific frequency, which can be captured by acoustic sensors;

[0050] Frequency characteristics: Extract frequency domain characteristics from pressure or flow data through methods such as Fourier transform;

[0051] Time domain characteristics: including the mean, variance, skewness, and kurtosis statistical characteristics of the time series;

[0052] Time-frequency characteristics: Combine time domain and frequency domain analysis to extract the time-frequency distribution characteristics of the signal;

[0053] Signal amplitude changes: Water leakage may cause sudden or periodic changes in signal amplitude;

[0054] Trends and Patterns: Trend lines or recurring patterns extracted from time series;

[0055] Correlation feature: Analyze the correlation between different sensor data and identify water leakage events;

[0056] Machine learning features: features learned from raw data using machine learning algorithms, including principal component analysis features;

[0057] Feature recognition and extraction methods:

[0058] Pressure and flow analysis: Calculate statistical characteristics of pressure and flow, including mean and standard deviation, using a sliding window approach; detect threshold crossings of pressure or flow to identify anomalies;

[0059] Sound signal processing: Apply fast Fourier transform or other frequency domain analysis methods to extract the frequency characteristics of sound signals; use filters to separate specific frequency components related to water leakage;

[0060] Time domain feature extraction: Calculate basic statistics of time series data, including mean, variance, skewness, and kurtosis; use the autoregressive integrated moving average (ARIMA) model to analyze the trend and seasonality of time series;

[0061] Frequency domain feature extraction: Perform Fourier transform on the signal to extract spectrum features; use wavelet transform to analyze the time-frequency characteristics of the signal;

[0062] Time-frequency feature extraction: Apply short-time Fourier transform (STFT) or wavelet transform to extract the time-frequency distribution of the signal; use time-frequency analysis to identify non-stationary components in the signal;

[0063] Amplitude change analysis: detects sudden or periodic changes in signal amplitude, which may indicate a water leak;

[0064] Trend and pattern recognition: Analyze trends in data using linear regression or polynomial fitting; apply clustering algorithms to identify patterns in data;

[0065] Correlation analysis: Calculate the correlation coefficient between different sensor data and identify potential water leakage correlations;

[0066] Machine learning feature extraction: Extract the principal components of data using PCA and t-SNE dimensionality reduction techniques; train deep learning models, including autoencoders, to automatically learn low-dimensional representations of data;

[0067] Deep learning model feature extraction: Use CNN, RNN, and Transformer models to automatically extract high-level features of data; extract feature maps or hidden states through the model's intermediate layer output;

[0068] Step 2.4, self-supervised learning: Design a self-supervised learning task so that the model can learn the intrinsic structure and characteristics of the data without labels;

[0069] Step 2.5, feature representation learning: The data feature representation learned by the model can capture the key information of the water leakage event and provide support for subsequent water leakage detection and location;

[0070] Step 2.6, model training and validation: Use historical water leakage event data to train the model and evaluate the model performance through the validation set;

[0071] Step 2.7, feature selection: select the features that are most helpful for water leakage detection from the features learned by the model;

[0072] Among them, for the convolutional neural network CNN, the convolution operation can be expressed as: (f*g)(i)=∑ m f(m)g(mi);

[0073] In the formula, f is the input signal, g is the convolution kernel, and * represents the convolution operation;

[0074] For the recurrent neural network RNN, the recursive formula is: t =tanh(W hh h t-1 +W xh x t +b h );

[0075] Among them, h t is the hidden state at time step t, x t is the input, W and b are the weight and bias;

[0076] The calculation of the self-attention mechanism in the Transformer model can be expressed as:

[0077] Among them, Q, K, and V are query, key, and value matrices respectively. k is the dimension of the key.

[0078] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 3, the federated learning framework specifically includes the following steps:

[0079] Step 3.1, data localization: Each participant or client keeps its data locally and does not share data directly;

[0080] Step 3.2, model distribution: a global model or initial model is distributed to all participants;

[0081] Step 3.3, local training: Each participant trains the model using its local data and generates model updates;

[0082] Step 3.4, model aggregation: Model updates are sent back to the central server and merged using some aggregation algorithm including federated averaging to update the global model;

[0083] Step 3.5, privacy protection: Differential privacy and homomorphic encryption techniques are used to protect data privacy throughout the process;

[0084] The federal average is: In the formula, θ new is the new global model parameter, θ i is the model parameter of the ith participant, w i is the weight of the ith participant;

[0085] Differential privacy: ∈-DP = max adjacent dataset D, where ∈ is the privacy budget, M is the mechanism, and D and D′ are adjacent datasets.

[0086] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 4, quantum computing acceleration specifically includes:

[0087] Step 4.1, qubit: the basic unit of a quantum computer, representing both 0 and 1 states at the same time;

[0088] Step 4.2, superposition: qubits can exist in multiple states at the same time, allowing quantum computers to process a large number of calculations in parallel;

[0089] Step 4.3, quantum entanglement: a special connection can be established between quantum bits, so that the state of one quantum bit can instantly affect the state of another quantum bit;

[0090] Step 4.4, quantum gate: change the state of the quantum bit through quantum gate operation to perform computing tasks;

[0091] Step 4.5, Quantum Algorithms: Design algorithms specifically for quantum computers to solve specific computational problems;

[0092] Among them, the quantum superposition principle: |ψ>=α|0>+β|1>; where |ψ> represents the state of the quantum bit; a and β are complex coefficients;

[0093] Quantum entanglement: [ψ>=α[00)+β[11) represents the entangled state between two quantum bits;

[0094] Quantum gate operation: U[ψ); where U is the quantum gate and [ψ) is the initial state of the quantum bit.

[0095] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 5, the artificial intelligence model is constructed, specifically including:

[0096] Step 5.1, data preparation: Use the features extracted in the previous step as input to the deep learning model;

[0097] Step 5.2, model selection: choose a suitable deep learning architecture based on the nature of the problem;

[0098] Step 5.3, training process: Use the labeled data to train the model so that it can recognize patterns related to water leakage;

[0099] Step 5.4, model evaluation: evaluate the performance and accuracy of the model through the validation set and test set;

[0100] Step 5.5, GIS integration: Combine the deep learning model with geographic information system (GIS) technology to accurately locate the water leak location;

[0101] Among them, the loss function is: Where L is the loss function, N is the number of samples, and y i is the true label, is the predicted label, l is the loss of a single sample;

[0102] Backward Propagation: Where θ is the model parameter, σ′(z) is the derivative of the activation function, Denotes the Hadamard product.

[0103] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 6, system integration and optimization specifically include the following steps:

[0104] Step 6.1, Requirements Analysis: Clarify the system's goals and requirements, including functions, performance, and user interface;

[0105] Step 6.2, module identification: determine the modules that need to be integrated, such as data acquisition, feature extraction, model training, prediction and positioning;

[0106] Step 6.3, interface design: design appropriate interfaces and communication protocols for interaction between modules;

[0107] Step 6.4, data management: ensure the effective flow of data between different modules, including the unification and conversion of data formats;

[0108] Step 6.5, System Architecture: Design the system's software and hardware architecture to support module integration and expansion;

[0109] Step 6.6, performance evaluation: evaluate the performance of the system, including processing speed, accuracy and stability;

[0110] Step 6.7, optimization strategy: Based on the evaluation results, formulate an optimization strategy to improve system performance;

[0111] Performance evaluation indicators:

[0112] System response time: ResponseTime = processing time + delay time.

[0113] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 7, the real-time monitoring and early warning system specifically includes the following steps:

[0114] Step 7.1. Data stream acquisition: collect data streams from sensors and monitoring points in real time;

[0115] Step 7.2. Real-time data processing: Quickly process and analyze real-time data to identify abnormal patterns;

[0116] Among them, the data update frequency in real-time data processing is:

[0117]

[0118] Step 7.3. User interface: Develop an intuitive user interface to display the network status and analysis results;

[0119] Step 7.4. Early warning algorithm: Implement intelligent early warning algorithm to trigger alarms based on thresholds or abnormal behaviors;

[0120] Warning trigger conditions: Where θ is the monitored parameter, θ threshold is the preset threshold;

[0121] Step 7.5. Notification system: When potential risks are detected, the operation and maintenance personnel are notified in a timely manner through various channels including SMS, email or application notifications.

[0122] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 8, system integration and optimization specifically include the following steps:

[0123] Step 8.1, data collection: collect pipe network operation data, including flow, pressure, water quality, and maintenance records;

[0124] Step 8.2, Data Analysis: Apply statistical analysis and machine learning algorithms to extract valuable information from the data;

[0125] Step 8.3, predictive modeling: build a predictive model to predict the operation status and potential problems of the pipeline network;

[0126] Among them, the prediction model accuracy is:

[0127] Where TP is the number of true positive examples, TN is the number of true negative examples, FP is the number of false positive examples, and FN is the number of false negative examples;

[0128] Step 8.4, Decision Support: Provide data-based decision support, including maintenance planning and resource allocation;

[0129] Step 8.5, Optimization Suggestions: Generate network optimization solutions, including upgrading existing facilities or designing new network structures;

[0130] Among them, resource allocation optimization:

[0131] In the formula, x i is the amount of resources allocated to the ith task, c i is the reward of the task, h i It is the resource cost.

[0132] As a further preferred solution of the water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention, in step 9, testing and evaluation specifically includes the following steps:

[0133] Step 9.1, Test plan development: Develop a detailed test plan based on system requirements and goals;

[0134] Step 9.2, performance indicator definition: clarify the key performance indicators to be evaluated in the test, including accuracy, speed and stability;

[0135] Among them, the detection accuracy is: Where TP is the number of true positives and FP is the number of false positives;

[0136] Recall is sensitivity: Where FN is the number of false negative examples;

[0137] The F1 score is the harmonic average of precision and recall:

[0138] Step 9.3, test environment construction: build a test platform in the actual pipe network environment or in a simulated environment;

[0139] Step 9.4, system test execution: execute the test plan and collect system operation data;

[0140] Step 9.5, data analysis: analyze test data and evaluate system performance;

[0141] Step 9.6, Problem Identification: Identify problems and performance bottlenecks found during testing;

[0142] Step 9.7, Optimization strategy formulation: formulate system optimization strategy based on test results;

[0143] Step 9.8, system iteration: iteratively improve the system to improve performance.

[0144] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0145] 1. The present invention provides a water supply network leakage detection method based on emergence phenomenon and federated learning, which utilizes cutting-edge technologies such as deep learning, federated learning, and quantum computing to achieve efficient data processing, accurate extraction of leakage features, rapid training and optimization of models, and collaborative analysis of multi-source data under the premise of protecting data privacy, thereby significantly improving the accuracy and efficiency of leakage detection;

[0146] 2. High-precision monitoring of the present invention: the use of advanced deep learning models can significantly improve the accuracy of water leakage detection; data privacy protection: federated learning ensures that data does not need to be centralized, thereby enhancing data privacy and security; enhanced computing efficiency: quantum computing technology has greatly improved the speed of model training and data processing; real-time response capability: the real-time monitoring system ensures rapid identification and response to water leakage incidents; intelligent decision-making: the integrated artificial intelligence model provides intelligent decision-making support for pipeline maintenance and optimization; system stability: continuous performance evaluation and iterative optimization ensure the long-term stable operation of the system; sustainable development: by reducing water waste, it supports environmental sustainability and social long-term water supply security. BRIEF DESCRIPTION OF THE DRAWINGS

[0147] 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.

[0148] Figure 1 It is an architecture diagram of a water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention;

[0149] Figure 2 It is a data flow diagram of a water supply network leakage detection method based on emergence phenomenon and federated learning of the present invention;

[0150] Figure 3 It is a flow chart of data collection and data preprocessing of the present invention;

[0151] Figure 4 is a flow chart of emergent feature extraction of the present invention;

[0152] Figure 5 is a flow chart of the federated learning framework of the present invention;

[0153] Figure 6 is a flow chart of quantum computing acceleration of the present invention;

[0154] Figure 7 is a flow chart of the construction of the artificial intelligence model of the present invention;

[0155] Figure 8 is a flow chart of system integration and optimization of the present invention;

[0156] Fig. 9 is a flow chart of the real-time monitoring and early warning system of the present invention;

[0157] Fig.10 is a flow chart of data-driven pipe network optimization of the present invention;

[0158] Fig.11 is a flow chart of the testing and evaluation of the present invention; DETAILED DESCRIPTION

[0159] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0160] 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.

[0161] A water supply network leakage detection method based on emergence phenomenon and federated learning, such as Figure 1 and 2 As shown, the specific steps include the following:

[0162] Step 1: Data collection and data preprocessing: Deploy a high-precision sensor network to collect real-time pressure, flow, and sound data of the water supply network; implement data cleaning, denoising, and standardization; build a database to integrate and store historical and real-time data to provide data support for analysis and model training;

[0163] Step 2: Extraction of emergent features: Use deep learning models to automatically extract high-level features related to water leakage from preprocessed data; design self-supervised learning tasks to learn feature representations of water leakage from data in an unsupervised manner;

[0164] Step 3, Federated Learning Framework: Use the federated learning algorithm to achieve data collaborative training between multiple independent pipe network systems while protecting their respective data privacy; integrate differential privacy and homomorphic encryption technologies to ensure data security during data sharing and model training;

[0165] Step 4, quantum computing acceleration: Identify computationally intensive tasks in model training that can be accelerated by quantum computers, including feature transformation and optimization algorithms; use the high parallelism of quantum computers to increase the speed of model training and reasoning;

[0166] Step 5, AI model construction: Build a water leakage prediction model based on deep learning, use the extracted features for training, and predict water leakage events; combine GIS technology to accurately locate and visualize the leakage location;

[0167] Step 6, system integration and optimization: used to integrate data collection, feature extraction, model training, prediction and positioning modules into a unified system; tune the system to ensure efficient collaboration between modules and adapt to different pipe network environments;

[0168] Step 7, real-time monitoring and early warning system: used to display the status of the pipe network and the prediction results in real time through the real-time data monitoring interface. When the system detects a potential water leakage risk, it can promptly notify the operation and maintenance personnel;

[0169] Step 8, data-driven network optimization: Use the collected data and prediction results to provide decision support for the maintenance, upgrade and optimization of the network, realize intelligent operation and maintenance of the network, and improve the utilization efficiency and economic benefits of water resources;

[0170] Step 9, testing and evaluation: Test the performance of the system in the actual pipe network environment, evaluate the detection accuracy, response speed and user satisfaction, and iteratively optimize the system based on the test results to meet higher performance requirements.

[0171] Data collection and preprocessing are used to deploy high-precision sensor networks to collect real-time data such as pressure, flow, and sound of the water supply network. Data cleaning, denoising, and standardization are implemented to improve data quality. Data lakes are built to integrate and store historical and real-time data to provide data support for analysis and model training. Figure 3 As shown, the specific steps include the following:

[0172] Step 1.1, sensor deployment: deploy various types of sensors at key nodes of the water supply network, including but not limited to pressure sensors, flow sensors, acoustic sensors, etc., to monitor the status of the network in real time;

[0173] Step 1.2, data acquisition: The data collected by the sensor is transmitted to the central database in real time through the data acquisition system; the data includes pressure readings, flow data, and sound signals;

[0174] Step 1.3, data cleaning: preprocess the collected data to remove invalid or erroneous data points through outlier detection and elimination;

[0175] Step 1.4, denoising: Apply signal processing techniques, including wavelet transform or filter, to remove noise components in the data and improve signal clarity;

[0176] Step 1.5, data standardization: convert data of different dimensions and magnitudes into a unified format for easy subsequent processing and analysis;

[0177] Step 1.6, feature engineering: extract useful features from the raw data, including statistical features and frequency domain features, to provide input for model training;

[0178] Step 1.7, Data storage: Build a data lake to store cleaned and standardized data, including historical data and real-time data streams;

[0179] In the denoising process, the filter formula used is: y(t) = ∫x(τ)h(t-τ)dτ;

[0180] Where x(τ) is the original signal, y(t) is the filtered signal, and h(t-τ) is the impulse response of the filter;

[0181] Data normalization formula: In the formula, x is the original data, z is the standardized data, μ is the mean of the data, and σ is the standard deviation of the data.

[0182] Emergent feature extraction is used to automatically extract high-level features related to water leakage from preprocessed data using deep learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN), or Transformer models. Self-supervised learning tasks are designed to learn feature representations of water leakage from data in an unsupervised manner.

[0183] Emergence refers to the phenomenon that in a complex system, individual components interact according to simple rules without central control or guidance, thus generating new patterns, structures or behavioral characteristics. These new characteristics are not directly predicted from the characteristics of individual components, but are spontaneously generated in the process of interaction between multiple components. For example, the flocking flight of birds or the collective swimming of fish are not controlled by a single bird or fish, but are generated by the interaction of simple individual rules (such as following the bird or fish in front, avoiding collisions, etc.). For another example, the evacuation behavior of a crowd in an emergency is not controlled by a single individual, but is a collective behavior generated by individuals following simple rules (such as following others and staying away from danger).

[0184] Emergent phenomena study the behavior of complex systems and provide methods for designing more effective algorithms and systems. In water leakage detection technology, emergent phenomena refer to high-level features that are automatically discovered from a large amount of sensor data and can characterize water leakage behavior. These features may not be directly detectable by a single sensor or simple algorithm.

[0185] like Figure 4 As shown, the specific steps include the following:

[0186] Step 2.1, data input: input the preprocessed data into the deep learning model. This data may include time series data, image data, or signal data.

[0187] Step 2.2, deep learning model selection: select a suitable deep learning model based on the data type and feature requirements, including CNN for image data, RNN or LSTM for time series data, and Transformer for sequence or collection data;

[0188] Step 2.3, feature extraction: The model automatically learns high-level features in the data through its multi-layer structure;

[0189] Among them, water leakage related features:

[0190] Abnormal pressure: Water leakage may cause a sudden drop or abnormal fluctuation in pressure in the pipe network;

[0191] Flow rate changes: When a leak occurs, there may be an abnormal increase in flow rate in a specific area;

[0192] Sound signal: Water leakage will produce sound of a specific frequency, which can be captured by acoustic sensors;

[0193] Frequency characteristics: Extract frequency domain characteristics from pressure or flow data through methods such as Fourier transform;

[0194] Time domain characteristics: including the mean, variance, skewness, and kurtosis statistical characteristics of the time series;

[0195] Time-frequency characteristics: Combine time domain and frequency domain analysis to extract the time-frequency distribution characteristics of the signal;

[0196] Signal amplitude changes: Water leakage may cause sudden or periodic changes in signal amplitude;

[0197] Trends and Patterns: Trend lines or recurring patterns extracted from time series;

[0198] Correlation feature: Analyze the correlation between different sensor data and identify water leakage events;

[0199] Machine learning features: features learned from raw data using machine learning algorithms, including principal component analysis features;

[0200] Feature recognition and extraction methods:

[0201] Pressure and flow analysis: Calculate statistical characteristics of pressure and flow, including mean and standard deviation, using a sliding window approach; detect threshold crossings of pressure or flow to identify anomalies;

[0202] Sound signal processing: Apply fast Fourier transform or other frequency domain analysis methods to extract the frequency characteristics of sound signals; use filters to separate specific frequency components related to water leakage;

[0203] Time domain feature extraction: Calculate basic statistics of time series data, including mean, variance, skewness, and kurtosis; use the autoregressive integrated moving average (ARIMA) model to analyze the trend and seasonality of time series;

[0204] Frequency domain feature extraction: Perform Fourier transform on the signal to extract spectrum features; use wavelet transform to analyze the time-frequency characteristics of the signal;

[0205] Time-frequency feature extraction: Apply short-time Fourier transform (STFT) or wavelet transform to extract the time-frequency distribution of the signal; use time-frequency analysis to identify non-stationary components in the signal;

[0206] Amplitude change analysis: detects sudden or periodic changes in signal amplitude, which may indicate a water leak;

[0207] Trend and pattern recognition: Analyze trends in data using linear regression or polynomial fitting; apply clustering algorithms to identify patterns in data;

[0208] Correlation analysis: Calculate the correlation coefficient between different sensor data and identify potential water leakage correlations;

[0209] Machine learning feature extraction: Extract the principal components of data using PCA and t-SNE dimensionality reduction techniques; train deep learning models, including autoencoders, to automatically learn low-dimensional representations of data;

[0210] Deep learning model feature extraction: Use CNN, RNN, and Transformer models to automatically extract high-level features of data; extract feature maps or hidden states through the model's intermediate layer output;

[0211] Step 2.4, self-supervised learning: Design a self-supervised learning task so that the model can learn the intrinsic structure and characteristics of the data without labels;

[0212] Step 2.5, feature representation learning: The data feature representation learned by the model can capture the key information of the water leakage event and provide support for subsequent water leakage detection and location;

[0213] Step 2.6, model training and validation: Use historical water leakage event data to train the model and evaluate the model performance through the validation set;

[0214] Step 2.7, feature selection: select the features that are most helpful for water leakage detection from the features learned by the model;

[0215] Among them, for the convolutional neural network CNN, the convolution operation can be expressed as: (f*g)(i)=∑ m f(m)g(mi);

[0216] In the formula, f is the input signal, g is the convolution kernel, and * represents the convolution operation;

[0217] For the recurrent neural network RNN, the recursive formula is: t =tanh(W hh h t-1 +W xh x t +b h );

[0218] Among them, h t is the hidden state at time step t, x t is the input, W and b are the weight and bias;

[0219] The calculation of the self-attention mechanism in the Transformer model can be expressed as:

[0220] Among them, Q, K, and V are query, key, and value matrices respectively. k is the dimension of the key.

[0221] The federated learning framework is used to develop federated learning algorithms to achieve data collaborative training between multiple independent pipe network systems while protecting their respective data privacy. It integrates differential privacy and homomorphic encryption technologies to ensure data security during data sharing and model training.

[0222] Federated learning is a distributed machine learning method that allows multiple parties to jointly train models while keeping data local. This method is particularly suitable for scenarios involving privacy protection, including cross-water company collaboration.

[0223] The key features of federated learning include:

[0224] Data privacy protection: Data does not need to leave the client device, thus reducing the risk of data leakage.

[0225] Distributed computing: Model training is performed in a distributed manner, utilizing the computing power of many clients.

[0226] Model parameter aggregation: After each client trains a model on local data, it only sends model updates (such as gradients or weight changes) to the central server instead of the original data.

[0227] Central coordination: The central server is responsible for receiving model updates from clients and using some form of aggregation algorithm (such as federated averaging) to update the global model.

[0228] Model distribution: The updated global model is sent back to the client for the next round of local training.

[0229] like Figure 5 As shown, the specific steps include the following:

[0230] Step 3.1, data localization: Each participant or client keeps its data locally and does not share data directly;

[0231] Step 3.2, model distribution: a global model or initial model is distributed to all participants;

[0232] Step 3.3, local training: Each participant trains the model using its local data and generates model updates;

[0233] Step 3.4, model aggregation: Model updates are sent back to the central server and merged using some aggregation algorithm including federated averaging to update the global model;

[0234] Step 3.5, privacy protection: Differential privacy and homomorphic encryption techniques are used to protect data privacy throughout the process;

[0235] The federal average is: In the formula, θ new is the new global model parameter, θ i is the model parameter of the ith participant, w i is the weight of the ith participant;

[0236] Differential privacy: ∈-DP = max adjacent dataset D, where ∈ is the privacy budget, M is the mechanism, and D and D′ are adjacent datasets.

[0237] Quantum computing acceleration is used to identify computationally intensive tasks in model training that can be accelerated by quantum computers, including feature transformation, optimization algorithms, etc. The high parallelism of quantum computers is used to increase the speed of model training and reasoning.

[0238] Quantum computing acceleration refers to the use of quantum computers to process certain types of computing tasks that may be very time-consuming on traditional computers. Quantum computers achieve extremely high parallelism and speed advantages through quantum bits (qubits) and the principles of quantum mechanics, including superposition and entanglement.

[0239] like Figure 6 As shown, specifically including;

[0240] Step 4.1, qubit: the basic unit of a quantum computer, representing both 0 and 1 states at the same time;

[0241] Step 4.2, superposition: qubits can exist in multiple states at the same time, allowing quantum computers to process a large number of calculations in parallel;

[0242] Step 4.3, quantum entanglement: a special connection can be established between quantum bits, so that the state of one quantum bit can instantly affect the state of another quantum bit;

[0243] Step 4.4, quantum gate: change the state of the quantum bit through quantum gate operation to perform computing tasks;

[0244] Step 4.5, Quantum Algorithms: Design algorithms specifically for quantum computers to solve specific computational problems;

[0245] Among them, the quantum superposition principle: |ψ>=α|0>+β|1>; where |ψ> represents the state of the quantum bit; a and β are complex coefficients;

[0246] Quantum entanglement: [ψ>=α[00)+β[11) represents the entangled state between two quantum bits;

[0247] Quantum gate operation: U[ψ); where U is the quantum gate and [ψ) is the initial state of the quantum bit.

[0248] Artificial intelligence model construction, build a water leakage prediction model based on deep learning, use the extracted features for training, and predict water leakage events. Combined with GIS technology, the precise positioning and visualization of the leakage location can be achieved.

[0249] AI model building refers to the development and training of intelligent systems that can perform specific tasks, including water leak prediction. This typically involves using deep learning algorithms to process and analyze data and build models to identify patterns and predict outcomes.

[0250] like Figure 7 As shown, specifically including;

[0251] Step 5.1, data preparation: Use the features extracted in the previous step as input to the deep learning model;

[0252] Step 5.2, model selection: choose a suitable deep learning architecture based on the nature of the problem;

[0253] Step 5.3, training process: Use the labeled data to train the model so that it can recognize patterns related to water leakage;

[0254] Step 5.4, model evaluation: evaluate the performance and accuracy of the model through the validation set and test set;

[0255] Step 5.5, GIS integration: Combine the deep learning model with geographic information system (GIS) technology to accurately locate the water leak location;

[0256] Among them, the loss function is: Where L is the loss function, N is the number of samples, and y i is the true label, is the predicted label, l is the loss of a single sample;

[0257] Backward Propagation: Where θ is the model parameter, σ′(z) is the derivative of the activation function, Denotes the Hadamard product.

[0258] System integration and optimization is used to integrate data collection, feature extraction, model training, prediction and positioning modules into a unified system. The system is tuned to ensure efficient collaboration between modules and adapt to different pipe network environments.

[0259] System integration and optimization refers to integrating different technical components and modules into a unified system and ensuring that they can work together efficiently. This process includes ensuring that data flow, control flow and interfaces between modules are seamlessly connected, as well as optimizing system performance.

[0260] like Figure 8 As shown, the specific steps include the following:

[0261] Step 6.1, Requirements Analysis: Clarify the system's goals and requirements, including functions, performance, and user interface;

[0262] Step 6.2, module identification: determine the modules that need to be integrated, such as data acquisition, feature extraction, model training, prediction and positioning;

[0263] Step 6.3, interface design: design appropriate interfaces and communication protocols for interaction between modules;

[0264] Step 6.4, data management: ensure the effective flow of data between different modules, including the unification and conversion of data formats;

[0265] Step 6.5, System Architecture: Design the system's software and hardware architecture to support module integration and expansion;

[0266] Step 6.6, performance evaluation: evaluate the performance of the system, including processing speed, accuracy and stability;

[0267] Step 6.7, optimization strategy: Based on the evaluation results, formulate an optimization strategy to improve system performance;

[0268] Performance evaluation indicators:

[0269] System response time: ResponseTime = processing time + delay time.

[0270] Real-time monitoring and early warning system is used to develop real-time data monitoring interface, display the status of pipe network and prediction results in real time. Design intelligent early warning mechanism, when the system detects potential water leakage risk, it can notify the operation and maintenance personnel in time.

[0271] The real-time monitoring and early warning system is a dynamic monitoring solution designed to track and analyze the status of the pipeline network in real time, timely predict and warn of potential water leakage incidents, and ensure rapid response and processing.

[0272] like Fig. 9 As shown, the specific steps include the following:

[0273] Step 7.1. Data stream acquisition: collect data streams from sensors and monitoring points in real time;

[0274] Step 7.2. Real-time data processing: Quickly process and analyze real-time data to identify abnormal patterns;

[0275] Among them, the data update frequency in real-time data processing is:

[0276]

[0277] Step 7.3. User interface: Develop an intuitive user interface to display the network status and analysis results;

[0278] Step 7.4. Early warning algorithm: Implement intelligent early warning algorithm to trigger alarms based on thresholds or abnormal behaviors;

[0279] Warning trigger conditions: Where θ is the monitored parameter, θ threshold is the preset threshold;

[0280] Step 7.5. Notification system: When potential risks are detected, the operation and maintenance personnel are notified in a timely manner through various channels including SMS, email or application notifications.

[0281] System integration and optimization uses the collected data and prediction results to provide decision support for the maintenance, upgrade and optimization of the pipeline network. It realizes the intelligent operation and maintenance of the pipeline network and improves the utilization efficiency and economic benefits of water resources.

[0282] Data-driven network optimization is a method based on data analysis and machine learning technology to improve the operation and maintenance efficiency of water supply networks, reduce costs, extend the life of the network, and ensure water supply safety.

[0283] like Fig.10 As shown, the specific steps include the following:

[0284] Step 8.1, data collection: collect pipe network operation data, including flow, pressure, water quality, and maintenance records;

[0285] Step 8.2, Data Analysis: Apply statistical analysis and machine learning algorithms to extract valuable information from the data;

[0286] Step 8.3, predictive modeling: build a predictive model to predict the operation status and potential problems of the pipeline network;

[0287] Among them, the prediction model accuracy is:

[0288] Where TP is the number of true positive examples, TN is the number of true negative examples, FP is the number of false positive examples, and FN is the number of false negative examples;

[0289] Step 8.4, Decision Support: Provide data-based decision support, including maintenance planning and resource allocation;

[0290] Step 8.5, Optimization Suggestions: Generate network optimization solutions, including upgrading existing facilities or designing new network structures;

[0291] Among them, resource allocation optimization:

[0292] In the formula, x i is the amount of resources allocated to the ith task, c i is the reward of the task, h i It is the resource cost.

[0293] Testing and evaluation: Test the performance of the system in the actual pipe network environment to evaluate the detection accuracy, response speed and user satisfaction. Based on the test results, iteratively optimize the system to meet higher performance requirements.

[0294] Testing and evaluation is a critical step to ensure that the system meets predetermined performance standards and user needs. This process involves subjecting the system to a series of tests, collecting data, evaluating performance, and optimizing based on feedback.

[0295] like Fig.11 As shown, the specific steps include the following:

[0296] Step 9.1, Test plan development: Develop a detailed test plan based on system requirements and goals;

[0297] Step 9.2, performance indicator definition: clarify the key performance indicators to be evaluated in the test, including accuracy, speed and stability;

[0298] Among them, the detection accuracy is: Where TP is the number of true positives and FP is the number of false positives;

[0299] Recall is sensitivity: Where FN is the number of false negative examples;

[0300] The F1 score is the harmonic average of precision and recall:

[0301] Step 9.3, test environment construction: build a test platform in the actual pipe network environment or in a simulated environment;

[0302] Step 9.4, system test execution: execute the test plan and collect system operation data;

[0303] Step 9.5, data analysis: analyze test data and evaluate system performance;

[0304] Step 9.6, Problem Identification: Identify problems and performance bottlenecks found during testing;

[0305] Step 9.7, Optimization strategy formulation: formulate system optimization strategy based on test results;

[0306] Step 9.8, system iteration: iteratively improve the system to improve performance.

[0307] The present invention discloses a water supply network leakage detection method based on emergence phenomenon and federated learning, which utilizes cutting-edge technologies such as deep learning, federated learning, and quantum computing to achieve efficient data processing, accurate extraction of leakage features, rapid training and optimization of models, and collaborative analysis of multi-source data under the premise of protecting data privacy, thereby significantly improving the accuracy and efficiency of leakage detection.

[0308] The invention provides high-precision monitoring: it adopts advanced deep learning models to significantly improve the accuracy of water leakage detection; data privacy protection: federated learning ensures that data does not need to be centralized, thus strengthening data privacy and security; computing efficiency enhancement: quantum computing technology greatly improves the speed of model training and data processing; real-time response capability: the real-time monitoring system ensures rapid identification and response to water leakage incidents; intelligent decision-making: the integrated artificial intelligence model provides intelligent decision-making support for pipeline maintenance and optimization; system stability: continuous performance evaluation and iterative optimization ensure the long-term stable operation of the system; sustainable development: by reducing water waste, it supports environmental sustainability and social long-term water supply security.

[0309] 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.

[0310] 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 leakage detection method based on emergence phenomenon and federated learning, characterized by: The specific steps include: Step 1: Data collection and data preprocessing: Deploy a high-precision sensor network to collect pressure, flow, and sound data of the water supply network in real time; perform data cleaning, denoising, and standardization processing; Build a database to integrate and store historical and real-time data to provide data support for analysis and model training; Step 2, emergent feature extraction: using deep learning models to automatically extract high-level features related to water leakage from preprocessed data; Design a self-supervised learning task to learn leaky feature representations from data in an unsupervised manner; Step 3, Federated Learning Framework: Use the federated learning algorithm to achieve data collaborative training between multiple independent pipe network systems while protecting their respective data privacy; integrate differential privacy and homomorphic encryption technologies to ensure data security during data sharing and model training; Step 4, quantum computing acceleration: Identify computationally intensive tasks in model training that can be accelerated by quantum computers, including feature transformation and optimization algorithms; use the high parallelism of quantum computers to increase the speed of model training and reasoning; Step 5, AI model construction: Build a water leakage prediction model based on deep learning, use the extracted features for training, and predict water leakage events; combine GIS technology to accurately locate and visualize the leakage location; Step 6, system integration and optimization: used to integrate data collection, feature extraction, model training, prediction and positioning modules into a unified system; Optimize the system to ensure efficient collaboration among modules and adapt to different pipe network environments; Step 7, real-time monitoring and early warning system: used to display the status of the pipe network and the prediction results in real time through the real-time data monitoring interface. When the system detects a potential water leakage risk, it can promptly notify the operation and maintenance personnel; Step 8, data-driven network optimization: Use the collected data and prediction results to provide decision support for the maintenance, upgrade and optimization of the network, realize intelligent operation and maintenance of the network, and improve the utilization efficiency and economic benefits of water resources; Step 9, testing and evaluation: Test the performance of the system in the actual pipe network environment, evaluate the detection accuracy, response speed and user satisfaction, and iteratively optimize the system based on the test results to meet higher performance requirements.

2. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 1, data collection and preprocessing specifically include the following steps: Step 1.1, sensor deployment: deploy various types of sensors at key nodes of the water supply network, including but not limited to pressure sensors, flow sensors, acoustic sensors, etc., to monitor the status of the network in real time; Step 1.2, data acquisition: The data collected by the sensor is transmitted to the central database in real time through the data acquisition system; the data includes pressure readings, flow data, and sound signals; Step 1.3, data cleaning: preprocess the collected data to remove invalid or erroneous data points through outlier detection and elimination; Step 1.4, denoising: Apply signal processing techniques, including wavelet transform or filter, to remove noise components in the data and improve signal clarity; Step 1.5, data standardization: convert data of different dimensions and magnitudes into a unified format for easy subsequent processing and analysis; Step 1.6, feature engineering: extract useful features from the raw data, including statistical features and frequency domain features, to provide input for model training; Step 1.7, Data storage: Build a data lake to store cleaned and standardized data, including historical data and real-time data streams; In the denoising process, the filter formula used is: y(t) = ∫x(τ)h(t-τ)dτ; Where x(τ) is the original signal, y(t) is the filtered signal, and h(t-τ) is the impulse response of the filter; Data normalization formula: In the formula, x is the original data, z is the standardized data, μ is the mean of the data, and σ is the standard deviation of the data.

3. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 2, emergent feature extraction is performed, which specifically includes the following steps: Step 2.1, data input: input the preprocessed data into the deep learning model. This data may include time series data, image data, or signal data. Step 2.2, deep learning model selection: select a suitable deep learning model based on the data type and feature requirements, including CNN for image data, RNN or LSTM for time series data, and Transformer for sequence or collection data; Step 2.3, feature extraction: The model automatically learns high-level features in the data through its multi-layer structure; Among them, water leakage related features: Abnormal pressure: Water leakage may cause a sudden drop or abnormal fluctuation in pressure in the pipe network; Flow rate changes: When a leak occurs, there may be an abnormal increase in flow rate in a specific area; Sound signal: Water leakage will produce sound of a specific frequency, which can be captured by acoustic sensors; Frequency characteristics: Extract frequency domain characteristics from pressure or flow data through methods such as Fourier transform; Time domain characteristics: including the mean, variance, skewness, and kurtosis statistical characteristics of the time series; Time-frequency characteristics: Combine time domain and frequency domain analysis to extract the time-frequency distribution characteristics of the signal; Signal amplitude changes: Water leakage may cause sudden or periodic changes in signal amplitude; Trends and Patterns: Trend lines or recurring patterns extracted from time series; Correlation feature: Analyze the correlation between different sensor data and identify water leakage events; Machine learning features: features learned from raw data using machine learning algorithms, including principal component analysis features; Feature recognition and extraction methods: Pressure and flow analysis: Calculate statistical characteristics of pressure and flow, including mean and standard deviation, using a sliding window approach; detect threshold crossings of pressure or flow to identify anomalies; Sound signal processing: Apply fast Fourier transform or other frequency domain analysis methods to extract the frequency characteristics of sound signals; use filters to separate specific frequency components related to water leakage; Time domain feature extraction: Calculate the basic statistics of time series data, including mean, variance, skewness, and kurtosis; use the autoregressive integrated moving average ARIMA model to analyze the trend and seasonality of time series; Frequency domain feature extraction: Perform Fourier transform on the signal to extract spectrum features; use wavelet transform to analyze the time-frequency characteristics of the signal; Time-frequency feature extraction: Apply short-time Fourier transform (STFT) or wavelet transform to extract the time-frequency distribution of the signal; use time-frequency analysis to identify non-stationary components in the signal; Amplitude change analysis: detects sudden or periodic changes in signal amplitude, which may indicate a water leak; Trend and pattern recognition: Analyze trends in data using linear regression or polynomial fitting; apply clustering algorithms to identify patterns in data; Correlation analysis: Calculate the correlation coefficient between different sensor data and identify potential water leakage correlations; Machine learning feature extraction: Extract the principal components of data using PCA and t-SNE dimensionality reduction techniques; train deep learning models, including autoencoders, to automatically learn low-dimensional representations of data; Deep learning model feature extraction: Use CNN, RNN, and Transformer models to automatically extract high-level features of data; extract feature maps or hidden states through the model's intermediate layer output; Step 2.4, self-supervised learning: Design a self-supervised learning task so that the model can learn the intrinsic structure and characteristics of the data without labels; Step 2.5, feature representation learning: The data feature representation learned by the model can capture the key information of the water leakage event and provide support for subsequent water leakage detection and location; Step 2.6, model training and validation: Use historical water leakage event data to train the model and evaluate the model performance through the validation set; Step 2.7, feature selection: select the features that are most helpful for water leakage detection from the features learned by the model; Among them, for the convolutional neural network CNN, the convolution operation can be expressed as: (f*g)(i)=∑ m f(m)g(mi); In the formula, f is the input signal, g is the convolution kernel, and * represents the convolution operation; For the recurrent neural network RNN, the recursive formula is: t =tanh(W hh h t-1 +W xh x t +b h ); Among them, h t is the hidden state at time step t, x t is the input, W and b are the weight and bias; The calculation of the self-attention mechanism in the Transformer model can be expressed as: Among them, Q, K, and V are query, key, and value matrices respectively. k is the dimension of the key.

4. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 3, the federated learning framework specifically includes the following steps: Step 3.1, data localization: Each participant or client keeps its data locally and does not share data directly; Step 3.2, model distribution: a global model or initial model is distributed to all participants; Step 3.3, local training: Each participant trains the model using its local data and generates model updates; Step 3.4, model aggregation: Model updates are sent back to the central server and merged using some aggregation algorithm including federated averaging to update the global model; Step 3.5, privacy protection: Differential privacy and homomorphic encryption techniques are used to protect data privacy throughout the process; The federal average is: In the formula, θ new is the new global model parameter, θ i is the model parameter of the ith participant, w i is the weight of the ith participant; Differential privacy: ∈-DP = max adjacent dataset D, where ∈ is the privacy budget, M is the mechanism, and D and D′ are adjacent datasets.

5. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 4, quantum computing acceleration includes: Step 4.1, qubit: the basic unit of a quantum computer, representing both 0 and 1 states at the same time; Step 4.2, superposition: qubits can exist in multiple states at the same time, allowing quantum computers to process a large number of calculations in parallel; Step 4.3, quantum entanglement: a special connection can be established between quantum bits, so that the state of one quantum bit can instantly affect the state of another quantum bit; Step 4.4, quantum gate: change the state of the quantum bit through quantum gate operation to perform computing tasks; Step 4.5, Quantum Algorithms: Design algorithms specifically for quantum computers to solve specific computational problems; Among them, the quantum superposition principle: |ψ>=α|0>+β|1>; where |ψ> represents the state of the quantum bit; a and β are complex coefficients; Quantum entanglement: [ψ>=α[00)+β[11) represents the entangled state between two quantum bits; Quantum gate operation: U[ψ); where U is the quantum gate and [ψ) is the initial state of the quantum bit.

6. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 5, the artificial intelligence model is constructed, specifically including; Step 5.1, data preparation: Use the features extracted in the previous step as input to the deep learning model; Step 5.2, model selection: choose a suitable deep learning architecture based on the nature of the problem; Step 5.3, training process: Use the labeled data to train the model so that it can recognize patterns related to water leakage; Step 5.4, model evaluation: evaluate the performance and accuracy of the model through the validation set and test set; Step 5.5, GIS integration: Combine the deep learning model with geographic information system (GIS) technology to accurately locate the water leak location; Among them, the loss function is: Where L is the loss function, N is the number of samples, and y i is the true label, is the predicted label, l is the loss of a single sample; Backward Propagation: Where θ is the model parameter, σ′(z) is the derivative of the activation function, Denotes the Hadamard product.

7. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 6, system integration and optimization specifically includes the following steps: Step 6.1, Requirements Analysis: Clarify the system's goals and requirements, including functions, performance, and user interface; Step 6.2, module identification: determine the modules that need to be integrated, such as data acquisition, feature extraction, model training, prediction and positioning; Step 6.3, interface design: design appropriate interfaces and communication protocols for interaction between modules; Step 6.4, data management: ensure the effective flow of data between different modules, including the unification and conversion of data formats; Step 6.5, System Architecture: Design the system's software and hardware architecture to support module integration and expansion; Step 6.6, performance evaluation: evaluate the performance of the system, including processing speed, accuracy and stability; Step 6.7, optimization strategy: Based on the evaluation results, formulate an optimization strategy to improve system performance; Performance evaluation indicators: System response time: ResponseTime = processing time + delay time.

8. According to claim 1, a water supply network leakage detection method based on emergence phenomenon and federated learning is characterized by: In step 7, the real-time monitoring and early warning system specifically includes the following steps: Step 7.

1. Data stream acquisition: collect data streams from sensors and monitoring points in real time; Step 7.

2. Real-time data processing: Quickly process and analyze real-time data to identify abnormal patterns; Among them, the data update frequency in real-time data processing is: Step 7.

3. User interface: Develop an intuitive user interface to display the network status and analysis results; Step 7.

4. Early warning algorithm: Implement intelligent early warning algorithm to trigger alarms based on thresholds or abnormal behaviors; Warning trigger conditions: Where θ is the monitored parameter, θ threshold is the preset threshold; Step 7.

5. Notification system: When potential risks are detected, the operation and maintenance personnel are notified in a timely manner through various channels including SMS, email or application notifications.

9. According to a water supply network leakage detection method based on emergence phenomenon and federated learning according to claim 1, it is characterized by: In step 8, system integration and optimization specifically includes the following steps: Step 8.1, data collection: collect pipe network operation data, including flow, pressure, water quality, and maintenance records; Step 8.2, Data Analysis: Apply statistical analysis and machine learning algorithms to extract valuable information from the data; Step 8.3, predictive modeling: build a predictive model to predict the operation status and potential problems of the pipeline network; Among them, the prediction model accuracy is: Where TP is the number of true positive examples, TN is the number of true negative examples, FP is the number of false positive examples, and FN is the number of false negative examples; Step 8.4, Decision Support: Provide data-based decision support, including maintenance planning and resource allocation; Step 8.5, Optimization Suggestions: Generate network optimization solutions, including upgrading existing facilities or designing new network structures; Among them, resource allocation optimization: In the formula, x i is the amount of resources allocated to the ith task, c i is the reward of the task, h i It is the resource cost.

10. According to the water supply network leakage detection method based on emergence phenomenon and federated learning according to claim 1, it is characterized by: In step 9, testing and evaluation, specifically includes the following steps; Step 9.1, Test plan development: Develop a detailed test plan based on system requirements and goals; Step 9.2, performance indicator definition: clarify the key performance indicators to be evaluated in the test, including accuracy, speed and stability; in, Where TP is the number of true positives and FP is the number of false positives; Recall is sensitivity: Where FN is the number of false negative examples; The F1 score is the harmonic average of precision and recall: Step 9.3, test environment construction: build a test platform in the actual pipe network environment or in a simulated environment; Step 9.4, system test execution: execute the test plan and collect system operation data; Step 9.5, data analysis: analyze test data and evaluate system performance; Step 9.6, Problem Identification: Identify problems and performance bottlenecks found during testing; Step 9.7, Optimization strategy formulation: formulate system optimization strategy based on test results; Step 9.8, system iteration: iteratively improve the system to improve performance.

Images