An online monitoring system for municipal pipe network health based on big data

Abstract

An online monitoring system for the health of municipal pipe networks based on big data, the system includes sensors for real-time collection of data on water flow, pressure, water quality, and pipe vibration, the sensors being installed at key nodes of the municipal pipe network; an integrated system for integrating data from multiple sources including satellite data, geographic information system GIS, and municipal maintenance records; machine learning and data mining techniques are used to build a baseline model of the health of the pipe network based on historical data; artificial intelligence algorithms are used to analyze the collected real-time data to predict potential problems and maintenance needs of the pipe network; a real-time monitoring platform is used to display the current status of the pipe network and evaluate its health, and when an abnormal state is detected, the system automatically sends an early warning message to the maintenance department. The present invention can accurately identify potential problems and issue alarms in a timely manner. With detailed reports and recommended measures generated by natural language processing technology, managers can quickly understand the problem and take appropriate actions.

Description

Technical Field

[0001] The present invention relates to a municipal pipe network health online monitoring system based on big data. Background Art

[0002] The current technology of online monitoring system for the health of municipal pipe networks has brought improvements to the management and maintenance of urban infrastructure in many aspects, but there are still a series of drawbacks and shortcomings. First, data quality and integrity issues significantly affect the accuracy of the system. Inconsistent data collection methods, sensor failures, communication interruptions or data processing errors lead to missing or inaccurate data. Secondly, technical limitations and cost issues are also important challenges. The installation and maintenance of advanced sensor networks and data processing facilities require significant initial investment and ongoing operating costs, and the complexity of the technology may exceed the technical capabilities or budget range of some cities. In addition, real-time and delay issues affect the response speed and effectiveness of the system. The collection and processing of large amounts of data may cause delays, and the delay between detecting a problem and taking action may cause greater damage.

[0003] The accuracy and adaptability of models and analyses are also key challenges. Predictive models may not accurately reflect complex real-world conditions, especially in extreme or unforeseen situations, while the system may not be adaptable and flexible enough to adapt to new data types, emergencies, or environmental changes. Security and privacy issues should not be ignored. Collecting and transmitting large amounts of sensitive data may increase the risk of data leakage or malicious attacks, while processing data involving personal or sensitive information requires ensuring compliance with privacy protection standards and regulations. Maintenance and sustainability issues are reflected in the fact that continuous technical support and system updates are necessary to ensure long-term effectiveness, but they also bring additional costs and management burdens. The system needs to constantly adapt to new technologies, regulations, and city needs, which requires continuous investment and updates. User acceptance and training issues should not be ignored. The complexity of the system may lead to operational difficulties, requiring detailed training for relevant personnel. A culture of resistance to change and suspicion of new technologies may affect the adoption and effective use of the system. Finally, regulatory and standard issues, including the lack of unified standards and regulatory compliance challenges, may lead to system incompatibility and inefficiency.

[0004] In summary, while some current online monitoring systems for the health of municipal pipe networks offer significant benefits, these drawbacks and shortcomings must be addressed to fully realize their potential. This requires technological innovation, cost-benefit analysis, personnel training, policy development, and ongoing maintenance and evaluation. By taking these factors into consideration, cities can more effectively monitor and maintain their infrastructure to ensure long-term health and sustainability. Summary of the invention

[0005] The purpose of the present invention is to provide an online monitoring system for the health of municipal pipe networks based on big data, so as to solve some of the drawbacks and shortcomings pointed out in the background technology.

[0006] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: an online monitoring system for the health of a municipal pipe network based on big data, the system comprising sensors for collecting real-time data on water flow, pressure, water quality, and pipeline vibration, the sensors being installed at key nodes of the municipal pipe network; an integrated system for integrating data from multiple sources including satellite data, geographic information system GIS, and municipal maintenance records; using machine learning and data mining techniques to build a baseline model of the health status of the pipe network based on historical data; applying artificial intelligence algorithms to analyze the collected real-time data to predict potential problems and maintenance needs of the pipe network; a real-time monitoring platform to display the current status of the pipe network and evaluate its health, and when an abnormal status is detected, the system automatically sends an early warning message to the maintenance department.

[0007] Furthermore, the step of constructing a baseline model of the pipeline network health status includes:

[0008] S1. First, data collection and preprocessing are carried out, including the collection of physical data, operation data, environmental data and maintenance records of the pipeline network, as well as data cleaning and standardization;

[0009] S2. Perform feature engineering to identify key features and create new features: extract trends from time series data or build composite indicators; then select a machine learning model and determine the model based on data characteristics and prediction goals;

[0010] S3, train and verify the model, divide the data set into a training set and a test set, adjust the parameters to optimize the model, and use the test set to verify the accuracy and generalization ability of the model;

[0011] S4. Conduct performance evaluation, analyze the performance of the model under different conditions, and optimize the model according to the evaluation results until the required performance is achieved, ensuring that the constructed pipeline health status benchmark model meets the standard performance in terms of accuracy and practicality.

[0012] Furthermore, the data collection and preprocessing method includes:

[0013] First, use the multi-source data fusion function:

[0014]

[0015] Where X and Y represent sets of variables from different data sources, Θ is a weight parameter, and h is a high-order cross polynomial function used to mix and evaluate the interaction between different data sources;

[0016] A dynamic sensor error correction model is then employed:

[0017]

[0018] Where d is the detection value, t is the time variable, and ω is the adjustment factor to dynamically adjust the sensor error;

[0019] Further application of composite function anomaly detection integral:

[0020]

[0021] To detect abnormal patterns in data streams, g is a matrix containing data points d(t) and their time derivatives. A composite function of that detects unusual patterns in a data stream.

[0022] Then perform data preprocessing complex conversion algorithms:

[0023]

[0024] where d i is the original data point, α and β are the parameters for adjusting the data distribution, and this logistic function is used to convert the original data point d i Convert to a unified standard format;

[0025] Finally, the optimized data storage space mapping function is used:

[0026]

[0027] Where d is the data point, P = (p1, p2, ..., p m ) is the storage optimization parameter, γ j and φ j It is to adjust the cycle and phase parameters of storage mapping to optimize data storage and access efficiency.

[0028] Furthermore, the method for determining the model of data characteristics and prediction targets includes:

[0029] First, use a custom time series analysis function:

[0030]

[0031] Used to extract key trends from time series data X(t), where λ k and ω k are coefficient and frequency parameters;

[0032] Then define the multidimensional data fusion function:

[0033]

[0034] Used to construct a new composite index, where D j Indicates different data sources, f j and g j is the conversion function, α and β adjust the fusion weight;

[0035] Then the model selection optimization algorithm is used:

[0036]

[0037] Select the optimal model from the model set M according to the data characteristics and prediction objectives, where S is the model adaptability scoring function and φ(d) is the data characteristic weight function;

[0038] Then apply the model performance evaluation integral

[0039] E(m)=∫ D p(m,d)·γ(d)dd

[0040] Among them, p(m, d) represents the performance of model m on data point d, and γ(d) is a weight function of the importance of the data point, which is used to calculate the overall performance of the model on the entire dataset.

[0041] Finally, use the dynamic model parameter adjustment function: Adjust model parameters in real time to optimize model performance, where δ is the learning rate, is the gradient of the model loss function.

[0042] Furthermore, the training and verification method of the model includes:

[0043] First, use the dynamic data splitting function:

[0044]

[0045] Dynamically adjust the size of the training set and test set according to time changes, where N is the total amount of data, t is the time parameter, and ξ is the parameter that controls the segmentation ratio;

[0046] Then, a multi-objective optimization function O(p, λ, μ) = λ·A(p) + μ·B(p) is defined to balance prediction accuracy and model complexity, where p is a model parameter, A(p) and B(p) represent quantitative indicators of prediction accuracy and model complexity, respectively, and λ and μ adjust the weights of the two objectives.

[0047] Then, the incremental learning update rule U(p, δp, α) = p + α·δp is used to guide the model to update parameters according to the new data, where δp is the parameter change calculated based on the new data and α is the learning rate.

[0048] The integration method is further verified using the model:

[0049]

[0050] In-depth evaluation of the model's performance on the entire dataset, where m(x) is the model's predicted value, y is the actual value, and σ is the error standard deviation;

[0051] Finally, an adaptive hyperparameter tuning strategy is applied To optimize the model parameters, where L(p) is the loss function, and · are the first and second order derivatives of the loss function, respectively, and β and γ are the rates at which the parameters are adjusted.

[0052] Furthermore, the performance evaluation and optimization method of the benchmark model includes:

[0053] First, define a multidimensional performance evaluation function:

[0054]

[0055] Comprehensively evaluate the multi-faceted performance of model m, where is the evaluation dimension weight vector, φ i is the evaluation function of each dimension;

[0056] Then use the conditional variability analysis formula:

[0057] C(m,θ)=∫ Ω ψ(m,ω,θ)dω

[0058] Evaluate the performance of the model under different environmental conditions, where Ω is the set of environmental conditions, ψ is the performance description function, and θ is the environmental parameter;

[0059] Then, an automated model optimization algorithm O(m, P) = m′ is applied to automatically adjust the model structure or parameters according to the performance evaluation results to achieve better performance, where P is the performance evaluation function and m′ is the optimized model;

[0060] Further develop rules for continuous learning and updating Continuously update the model based on new data, where m is the current model, d is the new data, and η is the learning rate. is the gradient of the loss function Q with respect to the model parameters μ;

[0061] Finally, the model performance standardization process is established Ensure that all models meet predetermined performance criteria, where Λ is a set of performance criteria, Φ is the evaluation function corresponding to each criterion λ, and τ λ is the threshold of the criterion, and m is the set of models that meet all the criteria.

[0062] Furthermore, the real-time health monitoring platform comprises the following three parts:

[0063] P1. Integrate advanced data visualization technology, using interactive maps, real-time data flow diagrams and augmented reality displays to provide intuitive and user-friendly representation of the network status;

[0064] P2. Implement real-time data analysis and health assessment, using stream data processing and complex event processing technologies combined with machine learning models to continuously analyze the pipeline network data stream to assess the health of the pipeline network;

[0065] P3. The model predicts potential problems based on historical data and current real-time data; implements an intelligent anomaly detection and early warning system based on machine learning.

[0066] Furthermore, the advanced data visualization technology: uses advanced GIS technology combined with real-time data analysis functions:

[0067] G(P, T) = ∫ P f(p,t)dt

[0068] Where P represents the location set of the pipeline network, T is the time variable, and f(p, t) is the state function of the pipeline network at location p at time t, in order to create an interactive map showing the physical location and real-time data of the pipeline network;

[0069] Then apply the data flow visualization formula:

[0070]

[0071] Where D represents the data flow, τ is the time window length, and d(t) is the data flow at time point t to generate a dynamic, real-time updated data flow graph; further using augmented reality technology, combined with the actual location and data coverage formula:

[0072]

[0073] Where l represents the physical location, D = {d1, d2, ..., d n} is a real-time dataset, α i is the weight parameter, h(l,d i ) is the data d at position l i The overlay function provides an augmented reality view for field maintenance personnel.

[0074] Furthermore, the real-time data analysis and evaluation method includes: firstly using a real-time stream data processing framework:

[0075]

[0076] Where D represents the real-time data stream, λ is the data processing rate, and d(x, t) is the data point at time t and location x to handle large-scale, high-speed, multi-source data streams;

[0077] Combined with event processing technology, use event identification function:

[0078]

[0079] where e is an event instance, Θ = {θ1, θ2, ..., θ n} is a set of event signature functions to identify and respond to key events and patterns in data streams;

[0080] Integrated machine learning model, through the health assessment function:

[0081]

[0082] Where m is the set of machine learning models, D is the input data, and w i is the model weight, m i (D) is the score of model i on data D to analyze and interpret the data stream to achieve health assessment;

[0083] Introducing adaptive learning mechanism Where p is the model parameter, α and β are the learning rates, and They are the first and second order derivatives of the loss function J, respectively, to continuously adjust and optimize the model performance based on the latest data;

[0084] And implement data fusion strategy and use fusion function:

[0085] C(D s , D o ,γ)=γ·D s +(1-γ)·∫D o

[0086] Where D s is the sensor data, D o are other data sources and γ is the fusion coefficient to provide a more comprehensive assessment of the health of the pipeline network.

[0087] Furthermore, the method for realizing intelligent anomaly detection and early warning of municipal pipe networks includes: firstly creating a hybrid data model, using time series analysis and machine learning technology, specifically using a long short-term memory network or a recurrent neural network, expressed as:

[0088]

[0089] Where X t is the data at time point t, α iis the weight parameter, f is the long short-term memory network or recurrent neural network model, and Θ is the model parameter, which is used to predict potential problems by integrating historical and real-time data;

[0090] An adaptive learning mechanism is introduced, specifically using online learning or incremental learning technology, and the function is expressed as:

[0091] where p t is the model parameter at time t, d t is the new data, η is the learning rate, is the gradient of the loss function, allowing the model to continuously update its parameters and structure based on the latest data; using the anomaly detection algorithm, the function is expressed as:

[0092]

[0093] where d is a data point, Φ = {φ1, φ2, ..., φ m} is a set of algorithm-based anomaly detection functions, θ i is a related parameter, τ is the abnormality judgment threshold; and an intelligent early warning system is developed, combining natural language processing to automatically generate early warning reports and recommended measures, expressed as R(e, V)=generate_report(e)+recommend_action(V(e)), where \e is the detected abnormal event and V is the problem diagnosis and solution generation function.

[0094] After adopting the measurement system of the present invention, city managers can enjoy significant beneficial effects, which improve the monitoring, analysis, prediction and maintenance capabilities of municipal pipe networks in many aspects. The following is a detailed description of these beneficial effects:

[0095] 1. Enhanced real-time monitoring capabilities:

[0096] Through real-time streaming data processing and advanced GIS technology, the system can provide continuous pipe network status monitoring to ensure that any abnormalities can be discovered in a timely manner. This not only reduces the risk of potential damage, but also improves the speed and effectiveness of response to emergencies.

[0097] 2. Improved data processing capabilities:

[0098] Using big data technology, the system can process and analyze large-scale, high-speed, multi-source data streams to provide deeper and more comprehensive insights. This ensures the integrity and consistency of the data and improves the accuracy and reliability of the analysis results.

[0099] 3. Advanced analytical and predictive capabilities:

[0100] Combining machine learning and time series analysis techniques, the system is able to learn patterns from historical and real-time data and predict potential problems. This predictive capability enables city managers to take preventive measures to avoid problems before they occur, saving maintenance costs and reducing unexpected outages.

[0101] 4. Adaptive and flexible learning mechanism:

[0102] Through adaptive learning mechanisms, the system is able to continuously adjust and optimize model performance to adapt to new data and environmental changes. This ensures that the system remains efficient and accurate over time, maintaining its performance even in the face of unknown or changing situations.

[0103] 5. Comprehensive health assessment:

[0104] Using multi-dimensional performance evaluation and conditional variability analysis, the system can comprehensively evaluate the health of the pipeline network and provide in-depth analysis in multiple dimensions. This helps city managers fully understand the status of the pipeline network and make more comprehensive and informed decisions.

[0105] 6. Intelligent anomaly detection and early warning:

[0106] Combined with advanced anomaly detection algorithms and intelligent early warning systems, the system can accurately identify potential problems and issue alerts in a timely manner. Through detailed reports and recommended measures generated by natural language processing technology, managers can quickly understand the problem and take appropriate actions. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 This is a core structure diagram of an online monitoring system for the health of a municipal pipe network based on big data according to the present invention.

[0108] Figure 2 The overall flow chart of the benchmark model for constructing the health status of the pipeline network in the present invention. DETAILED DESCRIPTION

[0109] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0110] A specific scheme of an online monitoring system for the health of a municipal pipe network based on big data involves the use of multiple sensors to collect data on water flow, pressure, water quality, and pipe vibration in real time. These sensors are installed at key nodes of the municipal pipe network, such as at major intersections, bends, water supply sources, and important user access points. Water flow sensors can monitor the amount of water passing through a specific pipe section, usually calculating the flow rate by measuring the flow rate. Pressure sensors are used to monitor the water pressure in the pipe, which is critical for detecting leaks, blockages, or other pipe network integrity issues. Water quality sensors detect parameters such as pH, turbidity, chemical pollutants, and biological pollutants to ensure the safety of water supply. Pipeline vibration sensors can detect vibrations caused by earthquakes, traffic, or nearby construction activities, which may damage pipes and cause leaks or ruptures. By installing these sensors at key nodes, the system is able to provide comprehensive real-time data, which is sent to a central processing system for analysis to assess the overall health of the pipe network and issue alarms when potential problems are detected. This system uses big data technology to process and analyze large amounts of sensor data, using advanced algorithms and models to identify abnormal patterns, predict potential problems, and provide decision support, thereby achieving efficient and real-time monitoring of the health of the municipal pipe network.

[0111] The present invention also has an integrated system that integrates data from multiple data sources to provide a comprehensive view of the health status of the pipeline network. These data sources include satellite data, geographic information systems (GIS), and municipal maintenance records. Satellite data can provide important information about surface changes, soil moisture, groundwater levels, and other important information related to climate change, which is crucial for predicting and understanding external impacts on the pipeline network. GIS is a powerful tool for managing, analyzing, and visualizing data related to geographic locations. Through GIS, the system can more accurately understand and display the geographical layout, surrounding environment, and potential risk areas of the pipeline network. In addition, integrating municipal maintenance records can provide valuable information about historical maintenance activities, repairs, and failure records of the pipeline network, which is extremely important for analyzing the current status of the pipeline network and predicting future health. The integrated system uses big data technology to process and analyze these multi-source data, and uses advanced data fusion algorithms and analysis models to not only ensure the consistency and accuracy of the data, but also reveal a comprehensive view of the health of the pipeline network from multiple dimensions. In this way, the system can provide more accurate and comprehensive pipeline network health analysis, provide strong data support for municipal maintenance decisions, optimize maintenance plans, and improve the efficiency and safety of pipeline network operations.

[0112] The case also involved the use of machine learning and data mining techniques to build a baseline model of the health of the pipeline network based on historical data. The model learns and understands the behavior and status of a normally operating pipeline network by analyzing patterns and trends in historical data, such as flow changes, pressure anomalies, maintenance records, and water quality changes. Machine learning algorithms may include decision trees, support vector machines, neural networks, or deep learning models, which can process complex data relationships and extract useful features and patterns from them. Data mining techniques are used to explore hidden patterns and knowledge in the data, such as identifying problem areas in the pipeline network through cluster analysis, or discovering potential connections between different factors through association rule analysis. The baseline model built based on these technologies can integrate multi-dimensional information from historical data to provide a reference standard for the current and future status of the pipeline network. In subsequent operations, the monitoring system will collect data from the pipeline network in real time and compare it with the baseline model to identify signs of deviation from normal status. Once an anomaly or potential problem is detected, the system can promptly notify maintenance personnel to take appropriate measures.

[0113] The present invention applies artificial intelligence algorithms to analyze the collected real-time data to predict potential problems and maintenance needs of the pipe network. The system uses various sensors to collect key information about the pipe network in real time, such as water flow, pressure, water quality, and pipe vibration data. The collected data is then sent to the central processing system, where advanced artificial intelligence algorithms are running, including but not limited to deep learning, neural networks, decision trees, and ensemble learning. These algorithms are trained to identify complex patterns and trends in the data to evaluate the current state of the pipe network. More importantly, through the comprehensive analysis of historical data and real-time data streams, the artificial intelligence model can not only diagnose current problems, but also predict potential problems and maintenance needs in the future. For example, if the model predicts that the pressure in a certain area is continuously abnormal, which may indicate a future leak or rupture, the system will notify the maintenance team in advance. Similarly, the model can also predict the best time to clean and replace filters based on changes in water quality and usage patterns.

[0114] The present invention also includes a real-time monitoring platform that can display the current status of the pipeline network and assess its health. The system collects data in real time, such as water flow, pressure, water quality, and pipeline vibration, by installing various sensors at key nodes of the pipeline network, and these data are transmitted to a central processing system. Here, using advanced data analysis techniques and machine learning algorithms, the system analyzes the collected data, assesses the immediate health of the pipeline network, and presents it to users in an easy-to-understand form on the interface, such as through dashboards, charts, and maps. This real-time information includes not only the current readings of various parameters, but also analysis of these data, such as trends, patterns, and any indication of deviation from the normal range. In addition, the system has an intelligent anomaly detection function that can identify abnormal patterns in the data, which may indicate potential pipeline network problems, such as leaks, abnormal pressure, or water quality problems. Once such an abnormal state is detected, the system will not only display a warning on the monitoring platform, but also automatically send an early warning message to the maintenance department. These early warning messages include a detailed description of the anomaly, possible causes, scope of impact, and recommended response measures. This instant notification mechanism enables the maintenance team to respond quickly to potential problems and take preventive or remedial measures, thereby reducing downtime, reducing maintenance costs, and improving the overall reliability and safety of the pipeline network. Through this real-time monitoring platform, the big data-based municipal pipeline health online monitoring system provides a powerful tool for urban water management, ensuring the efficient, stable and safe operation of the water supply system.

[0115] Example 1: City A decides to implement an online monitoring system for the health of a municipal pipe network to improve the efficiency and safety of its water management. The following are example steps for the system to build a benchmark model for the health status of the pipe network:

[0116] S1: Data collection and preprocessing

[0117] The water bureau of City A began to collect physical data such as pressure, flow, operational data such as valve switch status, environmental data: temperature, humidity, and maintenance records through sensors installed at key nodes of the pipeline network. For example, in one data collection cycle, sensors collected flow data from 100 key nodes, with values ​​ranging from 200-800 cubic meters per hour. The data was then cleaned to remove five obviously abnormal readings, such as the flow suddenly jumping to a negative number, and converted through standardized methods to ensure that data from different sources and formats can be processed uniformly.

[0118] S2: Feature Engineering and Model Selection

[0119] The analyst team performed feature engineering on the collected data and extracted trends from the time series data, such as the gradual decline in flow at a certain node for three consecutive days. At the same time, they created composite indicators, such as the ratio of pressure to flow, as potential indicators of the health of the pipeline network. The team decided to choose the long short-term memory network (LSTM) as the machine learning model based on the characteristics of the data because it performs well in processing time series data.

[0120] S3: Model training and validation

[0121] The team divided the collected data set into 80% training set and 20% test set. For example, if there are 1,000 records, 800 are used for training and 200 are used for testing. By adjusting parameters such as the number of network layers and the number of neurons, the model's accuracy on the training set reached 90%. Then, the model was verified on the test set and found to be about 88% accurate, showing good generalization ability.

[0122] S4: Performance evaluation and optimization

[0123] To ensure the effectiveness of the model in practical applications, the team tested the model under different weather conditions and pipe network loads. For example, in a dry summer, the model successfully predicted water quality problems due to high temperatures. By comparing the performance of different models, such as replacing different types of neural networks, the team found that LSTM performed best in most cases. To further improve the performance of the model, the team continues to adjust parameters and consider introducing more data sources, such as residents' water use reports.

[0124] Example 2: A system designer of a city B defines a multi-source data fusion function:

[0125]

[0126] For example, X may represent data from a flow sensor, while Y represents data from a pressure sensor, which collects a series of data points such as [200, 210, 220] and [30, 31, 29.5], respectively. By setting the weight parameter θ and defining a suitable high-order cross polynomial function h, the system can effectively fuse the two data sources and extract composite features that reflect the status of the pipeline network.

[0127] A dynamic sensor error correction model is then employed:

[0128] In order to correct possible sensor errors, City B adopted a dynamic sensor error correction model:

[0129]

[0130] Assume that at a certain time t, the flow sensor shows a flow rate d of 205 cubic meters per hour, but it is known that the average error μ(t) of the sensor is 5 cubic meters per hour and the standard deviation σ(t) is 1.5 cubic meters per hour. By applying this model, the system can dynamically adjust the reading to more accurately reflect the actual flow rate.

[0131] Further application of composite function anomaly detection integral:

[0132] To detect anomalous patterns in data streams, the system implements the composite function Anomaly Detection Integral:

[0133]

[0134] Here g is a composite function that takes into account the data point d(t) and its time derivative If, within an hour, the system records a sharp drop in traffic, the model can identify this anomaly by analyzing the speed and pattern of the traffic drop and trigger further analysis.

[0135] Then perform data preprocessing complex conversion algorithms:

[0136] In order to standardize the raw data, City B applied a complex transformation algorithm for data preprocessing:

[0137]

[0138] For example, if the original flow reading is 205, and this embodiment hopes to standardize the data between 0 and 1, by properly selecting parameters α and β, this logic function can convert data of various ranges and distributions into a unified format for subsequent analysis.

[0139] Finally, the optimized data storage space mapping function is used:

[0140] In order to optimize data storage and access efficiency, the system uses the following functions:

[0141]

[0142] Assume that there is a data point \d=0.8\, which is normalized, and this embodiment has a set of storage optimization parameters P=(0.5, 0.7), and the period parameter γ j is (2π, π), the phase parameter φ j is (0, π / 4). Through this function, the system can dynamically adjust the storage method according to the characteristics of the data to improve retrieval efficiency and reduce storage costs.

[0143] Through this series of steps, City B successfully achieved efficient collection, preprocessing and optimization of municipal pipeline network data, providing strong data support for subsequent analysis, monitoring and maintenance.

[0144] Example 3: City D decides to implement an online monitoring system for the health of its municipal pipe network to improve the maintenance and prediction capabilities of its water pipe network. The following is a detailed description of the technical implementation process:

[0145] P1. Application of time series analysis functions:

[0146] The water flow data is assumed to be the average value of daily measurements for 30 consecutive days, and the value increases linearly from 210 cubic meters / hour to 235 cubic meters / hour, increasing by about 0.862 cubic meters / hour every day. The goal of this embodiment is to use a custom time series analysis function T(X, λ) to identify key trends and periodic changes in these data.

[0147] The function is defined as:

[0148]

[0149] in:

[0150] X(t): time series data, which is the actual measured value of water flow rate in the example of this embodiment.

[0151] λ k : Coefficient parameter, which determines the weight of each periodic component.

[0152] ω k : Frequency parameter, which determines the period of each periodic component.

[0153] t: Time, usually a sequence of consecutive or equally spaced time points.

[0154] In order to apply this function, the present embodiment first determines the values ​​of the parameters λ and ω. In this example, the present embodiment selects λ = [0.5, 0.3], indicating that the present embodiment considers two periodic components, the weight of the first component is 0.5, and the weight of the second component is 0.3. Similarly, the present embodiment selects ω = [1, 3], indicating that the period of the first periodic component is longer than the second.

[0155] Substituting these parameters into the function, this embodiment can calculate a trend value for each time point t. This trend value is the result obtained after the original water flow data is analyzed through the time series, reflecting the water flow change trend after considering the periodic component. For example, for the first time point, if the original water flow is 210 cubic meters / hour, this embodiment will calculate an adjusted water flow value after considering the periodicity and trend.

[0156] P2. Application of multidimensional data fusion function:

[0157] It is assumed that there are two sets of data in this embodiment: one set is the average water flow rate measured every day for 30 consecutive days, and the value increases linearly from 210 cubic meters / hour to 235 cubic meters / hour; the other set is the pressure data collected from another sensor in the same time period, and the value is [30, 31, 29.5, ..., 32] psi.

[0158] This embodiment defines the multidimensional data fusion function as:

[0159]

[0160] in:

[0161] D1 and D2 represent flow and pressure data respectively.

[0162] α and β are parameters used to adjust the weights of the two fusion methods. In this example, α=0.6 and β=0.4 are selected.

[0163] f j and g j It is a transformation function used to process and transform data, which may include normalization, standardization or other mathematical transformations so that data from different sources can be effectively integrated.

[0164] In this example, the present embodiment may first use the normalization function f j and g j To convert the flow and pressure data to the same scale. For example, this embodiment can convert both the flow and pressure data to values ​​between 0 and 1. Then, this embodiment calculates the comprehensive index at each time point according to the defined fusion function. This embodiment captures the interaction between data by summing This embodiment retains the independent information of each data.

[0165] Thus, the comprehensive index obtained in this embodiment not only reflects the changes in flow and pressure, but also reflects the possible relationship between them. Through further analysis of this comprehensive index, this embodiment can more deeply understand the relationship between flow and pressure, and how they jointly affect the health of the pipe network.

[0166] P3. Model selection optimization algorithm:

[0167] First, given the time series characteristics and multivariate relationships of pipeline network data, a model that can capture and learn these complex relationships is needed. LSTM, a special type of recurrent neural network (RNN), is well suited for processing time series data. It can remember long-term dependency information, which is critical for understanding and predicting the health of the pipeline network, especially when today's status may be affected by the situation in the past few days.

[0168] In order to select the best model, this example uses a model selection optimization algorithm:

[0169]

[0170] Here:

[0171] S(m, d) is the model fitness score obtained based on cross-validation, which measures the performance of model m on data point d. This score may include precision, recall or other related indicators.

[0172] φ(d) is a data feature weight function that assigns different weights to different data points, which may vary according to their time, importance, or other features.

[0173] M is the set of models considered, which may include LSTM, traditional RNN, random forest, support vector machine, etc.

[0174] During implementation, this example scores the LSTM model and other potential models. This involves splitting the data into a training set and a test set, then training each model on the training set and evaluating its performance on the test set. The score of each model is calculated by combining its performance on all test set data points.

[0175] In the preliminary comparison, this embodiment found that the score of the LSTM model was indeed the lowest among all models, indicating that it had the best average performance on the test set. Therefore, this embodiment confirmed the selection of LSTM as the optimal model. Using this method to select models, this embodiment can ensure that the selected model is most suitable for the specific data and prediction goals of this embodiment. By considering the time series characteristics and multivariate relationships of the data, and using a comprehensive score to evaluate the adaptability of different models, this embodiment can make more informed and quantitative decisions. This will ultimately help this embodiment more accurately predict and identify potential problems in the municipal pipeline network, thereby improving maintenance efficiency and the overall operational safety of the pipeline network.

[0176] P4. Model performance evaluation points:

[0177] After applying the LSTM model to the test set, this example achieved an accuracy of 88%. This means that the model correctly predicted 88% of the cases in the test data. This accuracy is an overall metric that gives this example a quick way to understand the performance of the model, but it does not take into account that different data points may have different importance.

[0178] To get a more detailed performance evaluation, this example uses integration:

[0179] E(m)=∫ D p(m,d)·γ(d)dd

[0180] Here:

[0181] p(m, d) is the accuracy of model m on data point d. In the example of this embodiment, the overall accuracy is 88%, but the accuracy of each data point may be slightly different.

[0182] γ(d) is the importance weight of data point d. This weight reflects the importance of each data point to the health of the system. For example, if a data point corresponds to a key node in the network, such as the outlet of a water plant, it may be more important than other points and should be given a higher weight.

[0183] D represents all data points in the test dataset.

[0184] The importance weights γ(d) can be determined based on various criteria, such as the geographic location of the data point, historical failure records, or correlation with other critical infrastructure. In practice, these weights may need to be set based on expert knowledge or historical data analysis.

[0185] By calculating E(m), this embodiment obtains a comprehensive performance score that not only considers the accuracy of the model, but also the importance of different data points. This comprehensive score provides a more comprehensive view of the model's performance on the entire data set, ensuring that the model's performance on key data points is given enough attention. This is critical for a pipeline health monitoring system because it ensures that the system can identify and prioritize the issues that are most critical to the operation and safety of the pipeline network.

[0186] P5. Dynamic model parameter adjustment:

[0187] After the model is deployed, environmental and system conditions may change, such as weather, seasons, urban water use patterns, etc. These changes may affect the model's prediction accuracy. Dynamic model parameter adjustment is designed to solve this problem by updating model parameters in real time to adapt to new data and conditions, ensuring that the model maintains optimal performance.

[0188] The dynamic adjustment function used is

[0189] in:

[0190] m represents the current model parameters.

[0191] t represents time, reflecting that model adjustment is carried out in a continuous time process.

[0192] θ represents the model parameters.

[0193] δ is the learning rate, a positive number that determines the speed and magnitude of the model parameter update. A higher learning rate means that the model parameters change faster, and a lower learning rate means slower changes.

[0194] is the gradient of the loss function L with respect to the model parameters θ. The loss function measures the difference between the model's predicted value and the actual value, and the gradient indicates the direction of parameter adjustment to reduce this difference.

[0195] Consider a real scenario. In a certain week, due to continuous high temperature weather, the city's water consumption increases, which in turn affects the pressure distribution of the pipe network. This sudden change may cause the prediction performance of the original model to decline. The system monitors these changes in real time and calculates the gradient of the loss function through the collected new data. If the pressure predicted by the model is found to deviate significantly from the actual measured value, the gradient of the loss function will indicate the direction of adjusting the parameters. The system will adjust the model parameter m based on this gradient and the set learning rate δ.

[0196] Through this dynamic adjustment, the model can adapt to new data and environmental changes, improving the accuracy and timeliness of its predictions. This is critical to maintaining the healthy operation of municipal pipe networks and can help identify and respond to potential problems in advance, thereby reducing maintenance costs and improving service quality.

[0197] Example 4: A city E decides to deploy an online monitoring system for the health of a municipal pipe network to improve the efficiency and safety of its pipe network operation. The following is the implementation process of the training and verification method of the model in the system:

[0198] In order to adapt to the changes in the operation status of the pipeline network over time, City E adopts a dynamic data segmentation function:

[0199]

[0200] To adjust the size of the training set and test set. Assume that the system has collected 1000 data (N=1000), and city E wants to adjust the model in the summer, which lasts for 180 days, to adapt to the peak water consumption. If ξ=180 is set, before summer, more data is used for training, and at the beginning of summer, the system will increase the proportion of the test set to verify the adaptability of the model.

[0201] In order to balance the prediction accuracy and complexity of the model, City E defined a multi-objective optimization function O(p, λ, μ) = λ·A(p) + μ·B(p). In one optimization, the prediction accuracy A(p) was set to 88% and the model complexity B(p) was set to 12. In terms of the number of parameters, City E wanted to pay more attention to accuracy, so it set λ = 0.7 and μ = 0.3. Through this function, City E was able to find the model parameters that achieved the best balance between accuracy and complexity.

[0202] As new data continues to arrive, City E hopes that the model can continue to learn and adapt. Use the incremental learning rule U(p, δp, α) = p + α·δp, where the learning rate α is set to 0.05. For example, if the model's performance on new data deteriorates, the system will calculate the required parameter change δp and update the model accordingly.

[0203] In order to fully evaluate the performance of the model, City E adopted the model validation integration method:

[0204]

[0205] Setting the error σ on the test set to 5%, City E calculated the comprehensive performance of the model on the entire test set to ensure that the model can maintain stable prediction effects under various conditions.

[0206] To further improve the model’s predictive power, City E implemented an adaptive hyperparameter tuning strategy In one adjustment, the learning rate β is set to 0.01 and γ is set to 0.001. By calculating the first and second order derivatives of the loss function L(p), Urban E is able to intelligently adjust the model's hyperparameters to respond to the latest data trends and anomalies.

[0207] Through these detailed steps and calculation examples, City E was able to effectively train and validate the model in its big data-based municipal pipe network health online monitoring system. This not only ensured the high accuracy of the model, but also improved the adaptability and long-term performance of the model through dynamic adjustment and incremental learning, thereby better serving the city's water pipe network monitoring and maintenance needs.

[0208] Example 5: A city F decides to implement an online monitoring system for the health of its municipal pipe network, with the goal of improving the understanding of the health of the pipe network and optimizing maintenance plans. The following is a detailed implementation of the performance evaluation and optimization method of the benchmark model in the system:

[0209] First, define a multidimensional performance evaluation function:

[0210] To comprehensively evaluate the performance of the benchmark model, data scientists at City F defined a multi-dimensional performance evaluation function:

[0211]

[0212] For example, if the performance dimensions considered include accuracy, response time, and resource consumption, then the vector It may be set to [0.5, 0.3, 0.2], reflecting the importance attached to these dimensions. The evaluation function φ for each dimension i The score is calculated based on the model's performance in this dimension. Through this function, City F can comprehensively evaluate the overall performance of the model.

[0213] Then use the conditional variability analysis formula:

[0214] Since the operating conditions of the pipeline network may vary with factors such as seasons and weather, City F uses the conditional variability analysis formula

[0215] C(m,θ)=∫ Ω ψ(m,ω,θ)dω

[0216] To evaluate the performance of the model under different environmental conditions. For example, considering the impact of high summer temperatures and low winter temperatures on pipe network pressure, Ω can represent these different temperature conditions, while ψ represents the performance of the model at a specific temperature. In this way, City F is able to ensure that the model maintains good performance under a variety of conditions.

[0217] Then apply the automated model optimization algorithm:

[0218] Based on the results of the multidimensional performance evaluation and conditional variability analysis, City F uses an automated model optimization algorithm O(m, P) = m′ to adjust the structure or parameters of the model. For example, if the performance of the model is found to deteriorate under high temperature conditions, some parameters of the model may be adjusted to accommodate this change. m′ is the optimized model, which is expected to perform better under a variety of conditions.

[0219] Further develop continuous learning and updating rules:

[0220] In order to enable the model to adapt to data that changes over time, City F formulated a continuous learning update rule Set the loss function gradient of the model in the new data cycle It shows that the parameters need to be adjusted to reduce the error. Through this rule, the model will be updated accordingly based on the new data and the learning rate η, for example, set to 0.01.

[0221] Finally, the model performance standardization process is established:

[0222] To ensure the stability and reliability of the model, City F established a model performance standardization process For example, the minimum standards for accuracy, response time and resource consumption are set to 85%, 1 second and a certain computing resource limit respectively. Through this process, only when the threshold τ is reached on all standards, λ The model will be adopted.

[0223] Through these detailed steps and calculation examples, City F was able to effectively evaluate, optimize, and continuously update the benchmark model in its big data-based municipal pipe network health online monitoring system. This ensures that the model not only performs well at initial deployment, but also continues to provide accurate predictions over time and as the environment changes, helping City F to more effectively manage and maintain its municipal pipe network.

[0224] Example 6: A city G is committed to improving the operating efficiency and safety of its municipal pipe network and decides to implement an online monitoring system for the health of the municipal pipe network based on big data. The following is a detailed explanation of the components of the real-time health monitoring platform and its implementation steps:

[0225] P1. Integrate advanced data visualization technology:

[0226] In order to enable managers and engineers to intuitively understand the status of the pipeline network, City G developed a monitoring platform that integrates advanced data visualization technology. This platform includes:

[0227] Interactive map: Displays the network layout and the geographic locations of key nodes, as well as real-time water flow and pressure data. For example, if the flow at a node suddenly increases from an average of 250 cubic meters per hour to 300 cubic meters per hour, the node will be highlighted on the map with a marker of a different color or size.

[0228] Real-time data flow chart: Displays the changes of key indicators such as flow and pressure over time in the form of dynamic charts. For example, a line chart shows the pressure changes of a main pipeline in the past 24 hours, helping users track trends and sudden changes.

[0229] Augmented reality display: Provides on-site maintenance personnel with a view of real-time pipeline network data seen through mobile phones or AR glasses superimposed on the actual environment. For example, when inspecting underground pipelines, maintenance personnel can see the real-time pressure and historical maintenance records of the pipeline in the AR view.

[0230] P2. Implement real-time data analysis and health assessment:

[0231] City G has implemented a series of real-time data analysis and health assessment mechanisms, including: Stream data processing technology: such as Apache Kafka or Apache Flink, real-time collection and processing of streaming data from various nodes in the pipeline network. Complex event processing technology: Analyze patterns and events in data streams, such as abnormal flow increases and decreases, pressure mutations, etc. Machine learning models: For example, using a random forest model to analyze flow and pressure data, the model has an accuracy of 90% on the training set and 88% on the test set, continuously analyzing the pipeline network data stream, assessing the health of the pipeline network, and updating the model in real time to reflect the latest data trends.

[0232] P3. The model predicts potential problems based on historical and real-time data:

[0233] Ultimately, City G’s system is able to predict potential pipe network problems based on historical and real-time data, and implement an intelligent anomaly detection and early warning system: Combination of historical and real-time data: The system predicts potential problems by analyzing long-term trends and patterns in historical data, combined with immediate changes in real-time data, such as predicting possible future leak areas. Intelligent anomaly detection: Uses machine learning algorithms, such as isolation forests, to identify abnormal behavior in the data. For example, if the flow at a certain node increases abnormally in a short period of time, the system identifies this as a potential leak.

[0234] Early warning system: Once a potential problem is detected, the system automatically sends an early warning message to the maintenance department, including a detailed description of the problem, possible causes, and recommended response measures. For example, if a leak is predicted in a certain area, the system will send an early warning containing a map of the area, recent flow and pressure data, and a recommended inspection time.

[0235] Through these detailed steps and the application of actual data, City G was able to effectively implement its big data-based online monitoring system for the health of municipal pipeline networks, achieving real-time monitoring, in-depth analysis, and timely warnings, thereby improving the operational efficiency and safety of the pipeline network.

[0236] Example 7: A city H is committed to improving the operation monitoring and maintenance efficiency of its municipal pipe network and decides to implement an online monitoring system for the health of the municipal pipe network based on big data and advanced data visualization technology. The following are the implementation steps of the advanced data visualization technology in the system:

[0237] Leverage advanced GIS technology combined with real-time data analysis functions:

[0238] City H’s technical team first used advanced geographic information system (GIS) technology to create an interactive map showing the physical location of the pipe network. They defined real-time data analysis functions:

[0239] G(P, T) = ∫ P f(p,t)dt

[0240] Where P represents the location set of the pipe network, T is the time variable, and f(p, t) is the state function of the pipe network at location p at time t. For example, if the pipe network has 100 key nodes and each node collects data once an hour, the function can calculate the average water flow or pressure at each node in the past 24 hours. This information will be displayed on the interactive map with markers of different colors or sizes, providing an intuitive view of the real-time status of the pipe network.

[0241] Apply the data flow visualization formula:

[0242] In order to dynamically display the flow and changes of pipeline network data, City H applies the data flow visualization formula:

[0243]

[0244] Where D represents the data flow, τ is the time window length, and d(t) is the data flow at time point t. For example, if a 2-hour time window is selected, that is, τ = 2 hours, the formula can calculate the average water flow or pressure of each node during this period and display it in the form of a dynamic chart, such as a real-time updated line chart or heat map, allowing users to track real-time changes in the status of the pipe network.

[0245] Using augmented reality technology:

[0246] Finally, to provide more intuitive on-site maintenance support, City H adopted augmented reality (AR) technology and defined a data coverage formula:

[0247]

[0248] Where l represents the physical location, D = {d1, d2, ..., d n} is a real-time dataset, α i is the weight parameter, h(l,d i ) is the data d at position l i For example, when maintenance personnel are inspecting a node, they can use AR devices to see real-time data of the node and its surrounding area superimposed on the real environment, such as flow, pressure, and historical maintenance records, helping them locate problems and make decisions faster.

[0249] Through these detailed steps and calculation examples, City H was able to effectively implement its big data-based online monitoring system for the health of its municipal pipe network and provide an intuitive, dynamic, and practical representation of the pipe network status through advanced data visualization techniques, thereby improving the efficiency of monitoring, analysis, and maintenance of the pipe network.

[0250] Example 8: A city I decides to use an online monitoring system for the health of its municipal pipe network to improve the monitoring and maintenance efficiency of its pipe network. The system aims to analyze and evaluate pipe network data in real time to ensure that problems are discovered and resolved in a timely manner. The following are the detailed steps for implementing the system:

[0251] First, use the real-time stream data processing framework:

[0252] City I uses a real-time streaming data processing framework:

[0253]

[0254] To handle large-scale, high-speed, multi-source data streams from various nodes in the pipe network. For example, if the pipe network has 1,000 sensor nodes and each node sends data once a minute, the framework can process this data in real time and calculate the instantaneous water flow or pressure at each node. By setting an appropriate data processing rate λ, City I can ensure that the system can process and respond to all incoming data in a timely manner.

[0255] Combined with event processing technology:

[0256] To identify key events and patterns in data streams, City I uses the event recognition function:

[0257]

[0258] For example, if the system detects a sudden drop in water pressure at a node, this may indicate a leak. The event feature function set Θ = {θ1, θ2, ..., θ n} is used to analyze various characteristics of these events and trigger appropriate responses.

[0259] Ensemble Machine Learning Models:

[0260] City I integrates multiple machine learning models and defines a health assessment function:

[0261]

[0262] The system assumes that three models (k=3) are used, and in a health assessment, these models give scores to the data at a specific time point. Through this function, City I can obtain a comprehensive health index that reflects the overall status of the pipeline network.

[0263] Introducing adaptive learning mechanism:

[0264] To enable the model to adapt to new data and changes, City I implemented an adaptive learning mechanism For example, if new data show that water use patterns in a region have changed, the system adjusts the model parameters to reflect these changes based on the first and second derivatives of the loss function.

[0265] Implementing a data fusion strategy:

[0266] Finally, city I adopts a data fusion strategy, through the fusion function C(D s , D o ,γ)=γ·D s +(1-γ)·∫D o To integrate data from sensors and other data sources. For example, in addition to sensor data, city I may also have maintenance records, meteorological data, and other information. By appropriately selecting the fusion coefficient γ, the system can provide a more comprehensive and accurate assessment of the health of the pipeline network.

[0267] Through these detailed steps and the introduction of actual data, City I was able to effectively implement its big data-based online monitoring system for the health of its municipal pipe network. This system not only monitors the status of the pipe network in real time, but also analyzes and interprets data streams, predicts potential problems, and continuously adjusts and optimizes based on the latest data, thereby improving the efficiency and effectiveness of pipe network monitoring, maintenance, and preventive measures.

[0268] Example 9: City J is facing challenges in municipal pipe network management, including frequent leaks, abnormal pressure, and inefficient maintenance. To address these issues, City J decided to implement an online monitoring system for the health of the municipal pipe network, especially integrating intelligent anomaly detection and early warning functions. The following are the implementation steps of the system:

[0269] First create a hybrid data model:

[0270] To predict potential pipe network problems, City J’s technical team decided to create a hybrid data model using time series analysis and machine learning techniques:

[0271]

[0272] For example, assuming that the daily water flow data for the past 30 days is [210, 215, 220, ..., 235] cubic meters per hour, the team decided to use a long short-term memory network (LSTM) as model f. By training this model, it is possible to predict future water flow and issue warnings when abnormal fluctuations occur.

[0273] Introducing adaptive learning mechanism:

[0274] To ensure that the model can adapt to the latest data and changes in pipe network conditions, City J implemented an adaptive learning mechanism. For example, if new data show that pressure in a certain area is persistently abnormal, the model will adjust the parameter p t To adapt to this change, the learning rate η is set to 0.01.

[0275] Adopting anomaly detection algorithm:

[0276] CityJ uses anomaly detection algorithms to identify unusual patterns in data. Function:

[0277]

[0278] Based on the Isolation Forest algorithm or other suitable method, for example, if the traffic to a certain node suddenly increases to an abnormally high level, the algorithm will identify this and determine it as an anomaly.

[0279] Developing intelligent early warning systems:

[0280] In order to respond to detected anomalies in a timely and effective manner, City J developed an intelligent early warning system. The system combines natural language processing technology to automatically generate early warning reports containing detailed information, possible causes, and recommended measures. For example, when a possible leak is detected in a certain area, the early warning system will generate a report and recommend that the area be inspected immediately.

[0281] Through these steps, City J was able to establish a powerful real-time monitoring and early warning system that can not only analyze and evaluate the health of the pipeline network in real time, but also respond quickly when potential problems are detected. This will greatly improve the efficiency and safety of City J's pipeline network operation and reduce accidents and maintenance costs.

[0282] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A municipal pipe network health online monitoring system based on big data, characterized by: The system includes sensors for collecting real-time data on water flow, pressure, water quality, and pipe vibration, which are installed at key nodes of the municipal pipe network; an integrated system for integrating data from multiple sources such as satellite data, geographic information system (GIS), and municipal maintenance records; and the use of machine learning and data mining techniques to build a baseline model of the health of the pipe network based on historical data; Apply artificial intelligence algorithms to analyze the collected real-time data to predict potential problems and maintenance needs of the pipeline network; Real-time monitoring platform, showing the current status of the pipeline network and evaluating its health. When abnormal conditions are detected, the system automatically sends warning information to the maintenance department. The steps of constructing a baseline model of the pipeline network health status include: S1. First, data collection and preprocessing are carried out, including the collection of physical data, operation data, environmental data and maintenance records of the pipeline network, as well as data cleaning and standardization; S2. Perform feature engineering to identify key features and create new features: extract trends from time series data or build composite indicators; then select a machine learning model and determine the model based on data characteristics and prediction goals; S3, train and verify the model, divide the data set into a training set and a test set, adjust the parameters to optimize the model, and use the test set to verify the accuracy and generalization ability of the model; S4. Conduct performance evaluation, analyze the performance of the model under different conditions, and optimize the model according to the evaluation results until the required performance is achieved, ensuring that the constructed pipeline health status benchmark model meets the standard performance in terms of accuracy and practicality; The data collection and preprocessing method includes: First, use the multi-source data fusion function: Where X, Y represent sets of variables from different data sources, θ is a weight parameter, and h is a high-order cross polynomial function used to mix and evaluate the interaction between different data sources; A dynamic sensor error correction model is then employed: Where d is the detection value, t is the time variable, and ω is the adjustment factor to dynamically adjust the sensor error; Further application of composite function anomaly detection integral: To detect abnormal patterns in data streams, where g is a matrix containing data points d(t) and their time derivatives Composite functions of , which are used to detect anomalous patterns in data streams; Then perform data preprocessing complex conversion algorithms: in is the original data point, α and β are parameters for adjusting the data distribution, and the logistic function is used to convert the original data point Convert to a unified standard format; Finally, the optimized data storage space mapping function is used: where d is the data point, is the storage optimization parameter, γ j and φ j It is to adjust the cycle and phase parameters of storage mapping to optimize the storage and access efficiency of data; The data characteristics and prediction target determination model method include: First, use a custom time series analysis function: Used to extract key trends from time series data X(t), where λ k and ω′ k are coefficient and frequency parameters; Then define the multidimensional data fusion function: Used to construct a new composite indicator, where Represents different data sources, and is the transfer function, α a and β a Adjust fusion weights; Then the model selection optimization algorithm is used: Select the optimal model from the model set M according to the data characteristics and prediction objectives, where S is the model adaptability scoring function and φ(d) is the data characteristic weight function; Then apply the model performance evaluation integration: E(m)=∫ D p(m,d)·γ(d)dd Where p(m, d) represents the performance of model m on data point d, and γ(d) is a weight function of the importance of the data point, which is used to calculate the overall performance of the model on the entire dataset. Finally, use the dynamic model parameter adjustment function: Adjust model parameters in real time to optimize model performance, where δ is the learning rate, is the gradient of the model loss function; The training and validation methods of the model include: First, use the dynamic data splitting function: Dynamically adjust the size of the training set and test set according to time changes, where N is the total amount of data, t is the time parameter, and ξ is the parameter that controls the segmentation ratio; Then, a multi-objective optimization function O(p, λ, μ) = λ·A(p) + μ·B(p) is defined to balance prediction accuracy and model complexity, where p is a model parameter, A(p) and B(p) represent quantitative indicators of prediction accuracy and model complexity, respectively, and λ and μ adjust the weights of the two objectives. Then, the incremental learning update rule U(p, δp, α′)=p+α′·δp is used to guide the model to update parameters according to the new data, where δp is the parameter change calculated based on the new data and α′ is the learning rate. The model is further used to verify the integration method: In-depth evaluation of the model's performance on the entire dataset, where m(x) is the model's predicted value, y is the actual value, and σ is the error standard deviation; Finally, an adaptive hyperparameter tuning strategy is applied To optimize the model parameters, where L(p) is the loss function, and are the first and second derivatives of the loss function, β″ and γ are the rates of adjusting the parameters; The performance evaluation and optimization method of the benchmark model includes: First, define a multidimensional performance evaluation function: Comprehensively evaluate the multi-faceted performance of model m, where is the evaluation dimension weight vector, is the evaluation function of each dimension; Then use the conditional variability analysis formula: C(m,θ c )=∫ Ω ψ(m,ω a ,the c )dω a Evaluate the performance of the model under different environmental conditions, where Ω is the set of environmental conditions, ψ is the performance description function, and θ c is the environmental parameter; Then, an automated model optimization algorithm O(m, P) = m′ is applied to automatically adjust the model structure or parameters according to the performance evaluation results to achieve better performance, where P is the performance evaluation function and m′ is the optimized model; Further develop continuous learning and updating rules Continuously update the model based on new data, where m″ is the current model, d is the new data, η is the learning rate, is the gradient of the loss function Q with respect to the model parameters μ; Finally, the model performance standardization process is established Ensure that all models meet predetermined performance criteria, where Λ is a set of performance criteria, Φ is the evaluation function corresponding to each criterion λ, is the threshold of the criterion, and m is the set of models that meet all the criteria.

2. According to claim 1, a municipal pipe network health online monitoring system based on big data is characterized in that The health real-time monitoring platform consists of the following three parts: P1. Integrate advanced data visualization technology, using interactive maps, real-time data flow diagrams and augmented reality displays to provide intuitive and user-friendly representation of the network status; P2. Implement real-time data analysis and health assessment, using stream data processing and complex event processing technologies combined with machine learning models to continuously analyze the pipeline network data stream to assess the health of the pipeline network; P3. The model predicts potential problems based on historical data and current real-time data; implements an intelligent anomaly detection and early warning system based on machine learning.

3. According to claim 2, a municipal pipe network health online monitoring system based on big data is characterized in that The advanced data visualization technology described: using advanced GIS technology combined with real-time data analysis functions: G(P′,t)=∫ P′ f(P′,t)dt Where P′ represents the location set of the pipe network, t is the time variable, and f(P′, t) is the state function of the pipe network at location P′ at time t, in order to create an interactive map showing the physical location and real-time data of the pipe network; Then apply the data flow visualization formula: Where D represents the data flow, τ is the time window length, and d(t) is the data flow at time point t to generate a dynamic, real-time updated data flow graph; further using augmented reality technology, combined with the actual location and data coverage formula: Where l represents the physical location, is a real-time dataset. is the weight parameter, The data at position l The overlay function provides an augmented reality view for field maintenance personnel.

4. According to claim 2, a municipal pipe network health online monitoring system based on big data is characterized in that The real-time data analysis and evaluation method includes: firstly using a real-time stream data processing framework: Where D represents the real-time data stream, λ′ is the data processing rate, d(x a , t) is at time t and position x a data points to handle large-scale, high-speed, multi-source data streams; Combined with event processing technology, use event identification function: where e is the event instance, It is a set of event signature functions to identify and respond to key events and patterns in data streams; Integrated machine learning model, through the health assessment function: Where m is the set of machine learning models, D is the input data, and w i ′ is the model weight, m i ′(D) is the score of model i′ on data D to analyze and interpret the data stream to achieve health assessment; Introducing adaptive learning mechanism Where p′ is the model parameter, α′ and β′ are the learning rates, and They are the first and second order derivatives of the loss function J, respectively, to continuously adjust and optimize the model performance based on the latest data; And implement data fusion strategy and use fusion function: CD s ,D o , c) = c·D s +(1-γ)∫D o Where D s is the sensor data, D o are other data sources and γ is the fusion coefficient to provide a more comprehensive assessment of the health of the pipeline network.

5. According to claim 2, a municipal pipe network health online monitoring system based on big data is characterized in that The method for implementing an intelligent anomaly detection and early warning system based on machine learning includes: firstly creating a hybrid data model, using time series analysis and machine learning techniques, specifically using a long short-term memory network or a recurrent neural network, expressed as: Where X t is the data at time point t, is the weight parameter, f is the long short-term memory network or recurrent neural network model, and θ″ is the model parameter, which is used to predict potential problems by integrating historical and real-time data; An adaptive learning mechanism is introduced, specifically using online learning or incremental learning technology, and the function is expressed as: where p″ t is the model parameter at time t, d t is the new data, η′ is the learning rate, is the gradient of the loss function, allowing the model to continuously update its parameters and structure based on the latest data; the method for implementing an intelligent anomaly detection and early warning system based on machine learning adopts an anomaly detection algorithm, and the function is expressed as: where d is the data point, is a collection of algorithm-based anomaly detection functions, is a related parameter, τ is the abnormality judgment threshold; and an intelligent early warning system is developed, combining natural language processing to automatically generate early warning reports and recommended measures, expressed as R(e, V)=generate_report(e)+recommend_action(V(e)), where e is the detected abnormal event and V is the problem diagnosis and solution generation function.

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