Building water pipe maintenance system based on big data

Abstract

The present invention relates to the field of intelligent building maintenance technology, specifically to a building water pipe maintenance system based on big data, the system includes a real-time monitoring and response module, a water consumption dynamic prediction module, a maintenance strategy intelligent optimization module, a fault diagnosis module, a system performance enhancement module, an intelligent water flow control module, a network optimization and planning module, and a continuous improvement and upgrade module. In the present invention, sensor fusion technology and real-time data flow analysis are used to improve monitoring accuracy and response speed, timely identify and respond to abnormalities, time series and support vector machine models optimize water consumption prediction, help rational allocation of resources, genetic algorithms and multi-objective optimization improve the scientific nature and cost efficiency of maintenance strategies, system simulation and fluid dynamics analysis help improve performance, adaptive control and optimization adjustment algorithms enhance water flow intelligent regulation, network analysis and graph optimization algorithms improve network structure and operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent building maintenance, and in particular to a building water pipe maintenance system based on big data. Background Art

[0002] Smart building maintenance is a technology field that combines automation, information technology, and architecture to improve the efficiency and effectiveness of building maintenance. Within this field, systems and solutions often rely on sensor data, real-time monitoring, and data analysis to predict and solve problems in buildings. This includes energy management, security monitoring, and maintenance of facilities such as plumbing systems. By using big data analysis and intelligent algorithms, smart building maintenance systems can improve the reliability and efficiency of building operations and reduce maintenance costs, while also improving the comfort and safety of the living or working environment.

[0003] Among them, the building water pipe maintenance system based on big data is a solution that uses big data technology to monitor and maintain the water pipe system in the building. The main purpose of this system is to improve the efficiency and effectiveness of water pipe maintenance by collecting and analyzing large amounts of data. For example, real-time monitoring of water pipe flow, pressure, temperature and other parameters, using data analysis to identify potential problems such as leaks, blockages or corrosion. By detecting these problems early, the system aims to reduce the risk of sudden failures, extend the life of water pipes, and reduce maintenance costs. In addition, the system also optimizes the use of water resources and supports sustainable building management practices.

[0004] Traditional water pipe maintenance systems are deficient in many aspects. The lack of data integration and analysis capabilities makes it difficult to accurately monitor the system status in real time, resulting in untimely responses to system anomalies, waste of resources or system failures. Traditional systems generally lack efficient water consumption prediction capabilities, making it difficult to accurately predict future water demand, resulting in uneven resource allocation and high operating costs. In the formulation of maintenance strategies, there is a lack of effective optimization tools, resulting in unscientific maintenance plans and low efficiency. In terms of fault diagnosis, traditional methods cannot accurately identify complex faults, resulting in slow or inaccurate maintenance responses. Traditional systems generally lack a high degree of automation and intelligent support in terms of performance improvement, intelligent regulation and network optimization, resulting in insufficient overall system efficiency and reliability. These deficiencies lead to increased operating costs, reduced service quality, and even more serious system failures. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a building water pipe maintenance system based on big data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: the building water pipe maintenance system based on big data includes a real-time monitoring and response module, a water consumption dynamic prediction module, a maintenance strategy intelligent optimization module, a fault diagnosis module, a system efficiency enhancement module, an intelligent water flow control module, a network optimization and planning module, and a continuous improvement and upgrade module;

[0007] The real-time monitoring and response module uses sensor fusion technology and real-time data stream analysis based on multi-source data sets to monitor system status, instantly identify system deviations through anomaly detection algorithms, and use adaptive control strategies to adjust operating parameters to generate real-time monitoring data sets;

[0008] The water consumption dynamic prediction module uses time series analysis and support vector machine models based on real-time monitoring data sets to perform trend analysis on historical and real-time data, uses machine learning technology to predict future water demand, and adjusts the prediction model in combination with seasonality and consumption pattern changes to generate water consumption trend forecasts;

[0009] The maintenance strategy intelligent optimization module is based on water consumption trend prediction, uses genetic algorithms and multi-objective optimization methods, comprehensively refers to cost, efficiency, and environmental factors, formulates a maintenance plan for the water pipe system, and applies multi-objective optimization to adjust and optimize the strategy to generate an intelligent maintenance strategy;

[0010] The fault diagnosis module is based on intelligent maintenance strategy, uses Bayesian network and fault tree analysis to analyze historical fault data and real-time monitoring data, identify potential fault modes, and uses expert system to perform in-depth diagnosis of fault causes and generate fault identification;

[0011] The system efficiency enhancement module uses system simulation and fluid dynamics analysis based on fault identification to identify potential areas for efficiency improvement, implements energy efficiency improvement measures through optimization and adjustment algorithms, and evaluates the effects of the improvement results to generate an efficiency maximization plan;

[0012] The intelligent water flow control module is based on the efficiency maximization solution, applies adaptive control and optimization adjustment algorithms, intelligently adjusts water flow and pressure according to real-time data, optimizes system operation efficiency and stability, and conducts continuous performance monitoring to generate intelligent water flow management;

[0013] The network optimization and planning module is based on intelligent water flow management, uses network analysis and graph theory optimization algorithms to optimize the structure and operation parameters of the water pipe network, simulates and verifies the optimization effect, and generates a network optimization plan;

[0014] The continuous improvement and upgrade module is based on network optimization planning, uses data-driven algorithms, combines technology updates and market demand indicators, regularly evaluates system performance, identifies upgrade needs, and uses Ansible automated deployment tools to implement system function updates and generate a system continuous upgrade plan.

[0015] As a further solution of the present invention, the real-time monitoring data set includes pipeline pressure readings, flow changes, and temperature fluctuations. The water consumption trend forecast is specifically daily, weekly, and monthly water consumption change trends and peak time period forecasts. The intelligent maintenance strategy includes maintenance cycle adjustment, resource allocation, and cost-benefit analysis. The fault identification includes fault location, fault cause, and impact range. The efficiency maximization plan includes efficiency improvement area identification, energy efficiency improvement measures, and effect evaluation results. The intelligent water flow management includes pressure adjustment strategy, flow control, and emergency response plan. The network optimization planning includes structure optimization plan, parameter adjustment plan, and simulation verification results. The system continuous upgrade plan includes performance evaluation results, upgrade demand identification, and automated deployment plan.

[0016] As a further solution of the present invention, the real-time monitoring and reaction module includes a sensor deployment submodule, a data monitoring submodule, and a real-time response submodule;

[0017] The sensor deployment submodule is based on a multi-source data set and uses a weighted average method to integrate and summarize the data collected by multiple types of sensors, balance the measurement differences between multiple sensors, and apply a Kalman filter algorithm to process the summarized data to reduce noise. Through continuous prediction and update steps, a multi-dimensional monitoring configuration is generated;

[0018] The data monitoring submodule is based on a multi-dimensional monitoring configuration and adopts event stream processing technology to capture and process real-time data streams, sort and classify content items in the data streams, and apply time window technology to divide the data streams into independent time series, provide context for the data in each time period, analyze each time series separately, and generate real-time data analysis results;

[0019] The real-time response submodule uses the support vector machine algorithm to identify abnormal points in the data set based on the real-time data analysis results, separates normal data and abnormal data by constructing an optimal hyperplane, and uses the isolation forest algorithm to analyze the detected anomalies, identify abnormal patterns, and make adjustments in combination with PID control strategy and fuzzy logic control to respond to multiple types of abnormal conditions and generate a real-time monitoring data set.

[0020] As a further solution of the present invention, the water consumption dynamic prediction module includes a trend analysis submodule, a machine learning model submodule, and a prediction result submodule;

[0021] The trend analysis submodule is based on the real-time monitoring data set and uses the autoregressive moving average model to analyze the data. The autoregressive part analyzes the time series dependency of the data, identifies and extracts the long-term trend in the data, and uses the moving average part to eliminate random fluctuations, iteratively refine trend characteristics, capture the change pattern of water consumption over time, and generate trend analysis results.

[0022] The machine learning model submodule applies the random forest algorithm to perform deep learning on the trend based on the trend analysis results, builds batch decision trees, each decision tree analyzes multiple data samples and features, aggregates decisions, learns and simulates the change pattern of water consumption, and generates a machine learning prediction model;

[0023] The prediction result submodule is based on a machine learning prediction model and uses linear regression analysis to predict future water demand. By establishing a relationship model between water consumption and time and seasonal factors, it analyzes the changing trend of water consumption, adjusts model parameters to match current data trends and consumption patterns, and generates a water consumption trend forecast.

[0024] As a further solution of the present invention, the maintenance strategy intelligent optimization module includes a multi-objective analysis submodule, a strategy formulation submodule, and an optimization adjustment submodule;

[0025] The multi-objective analysis submodule is based on water consumption trend prediction, uses genetic algorithms to represent problem parameters as chromosomes through coding schemes, and uses selection, crossover and mutation operations to search for optimal solutions. It combines multi-objective optimization with a multi-dimensional evaluation index system to conduct system performance analysis and generate comprehensive analysis results with reference to cost, efficiency and environmental impact factors.

[0026] The strategy formulation submodule, based on the comprehensive analysis results, applies the decision tree algorithm to construct a tree model, analyzes the estimated results of the decision path from the root node to the leaf node, and adopts the simulated annealing algorithm to avoid the local optimal solution through the temperature drop and energy minimization mechanism in the simulated annealing process, and generates a maintenance strategy plan;

[0027] The optimization and adjustment submodule is based on the maintenance strategy scheme and uses linear programming to mathematically optimize resource allocation by constructing objective functions and constraints. At the same time, a neural network is used to dynamically learn and adjust the strategy by simulating the connection and weight adjustment of neurons to generate an intelligent maintenance strategy.

[0028] As a further solution of the present invention, the fault diagnosis module includes a fault detection submodule, a cause analysis submodule, and a diagnostic tool submodule;

[0029] The fault detection submodule is based on intelligent maintenance strategy and adopts Bayesian network algorithm. It constructs conditional probability model between faults and potential causes, analyzes historical fault data and real-time monitoring data, calculates the probability of occurrence of differentiated fault modes, identifies the fault mode faced by the current system, and then generates fault mode mapping;

[0030] The cause analysis submodule uses the fault tree analysis method based on fault mode mapping to set the top event of the fault tree, analyzes the contribution of each basic event to the occurrence of the top event by constructing a hierarchical structure of downstream logic gates and basic events, and explores the root cause of each potential fault mode to generate a fault cause analysis;

[0031] The diagnostic tool submodule is based on fault cause analysis, applies an expert system, analyzes the identified fault modes through an integrated knowledge base and reasoning rules, formulates fault solutions based on historical maintenance records and expert experience, and then generates fault identification.

[0032] As a further solution of the present invention, the system performance enhancement module includes a performance analysis submodule, a simulation test submodule, and a performance improvement submodule;

[0033] The performance analysis submodule is based on fault identification and adopts Monte Carlo simulation to conduct system simulation by constructing mathematical models and setting various operation scenarios, analyze the performance of the system under different working conditions, and use sensitivity analysis to monitor the response changes of system performance by changing various parameters in the model, select factors affecting performance, and generate performance key area mapping;

[0034] The simulation test submodule is based on the performance key area mapping, applies computational fluid dynamics analysis, constructs the system's flow field model and sets multiple boundary conditions to simulate the flow and interaction of fluids in the system, analyzes the impact of multiple adjustment schemes on flow field characteristics, predicts the improvement effect of differentiated configurations on system performance, and generates an optimization adjustment scheme prediction;

[0035] The performance improvement submodule is based on the prediction of the optimization adjustment plan and uses a multi-objective optimization algorithm to optimize the comprehensive parameters of the proposed improvement plan to achieve the optimal balance between system performance and efficiency. At the same time, by adjusting key parameters, iteratively optimizes the overall performance of the system, and performs system efficiency evaluation and verification, thereby generating an efficiency maximization plan.

[0036] As a further solution of the present invention, the intelligent water flow control module includes a demand analysis submodule, a control algorithm submodule, and a regulation implementation submodule;

[0037] The demand analysis submodule is based on the efficiency maximization scheme, adopts the time series analysis algorithm, collects and analyzes the system historical operation data and real-time monitoring data, uses the autoregressive model to predict the future water flow demand, identifies the current demand peak and valley, adjusts the formulated strategy and provides data support to generate demand forecast results;

[0038] The control algorithm submodule applies an adaptive control algorithm based on the demand forecast results, analyzes the demand changes in the forecast results, dynamically adjusts the control parameters according to the current state of the system and future demand trends, responds to demand changes, and implements a continuous parameter tuning process to generate a water flow regulation strategy;

[0039] The regulation implementation submodule is based on the water flow regulation strategy and adopts a real-time feedback control algorithm to automatically adjust the valve and pump speed. At the same time, it applies sensor data fusion technology to monitor the changes in water flow and pressure in real time, and then uses data analysis methods to continuously collect and analyze system performance data. Combined with the PID control algorithm, it generates intelligent water flow management.

[0040] As a further solution of the present invention, the network optimization and planning module includes a network analysis submodule, an optimization design submodule, and a planning implementation submodule;

[0041] The network analysis submodule is based on intelligent water flow management and adopts a topology analysis algorithm to analyze each node and connection in the water pipe network, evaluate the impact on the overall network performance, and perform network flow analysis to calculate the flow transmission efficiency of differentiated paths in the network, identify flow bottlenecks and optimization points, and generate network performance analysis results;

[0042] The optimization design submodule uses a graph optimization algorithm based on the network performance analysis results to establish a mathematical model of the water pipe network, identifies the shortest path and optimal flow distribution in the network by analyzing the mathematical model, and adjusts and optimizes the layout and operation parameters of the pipe network according to the analysis results, and integrates the optimization measures to generate an optimization design solution;

[0043] The planning implementation submodule uses system simulation technology to perform simulation based on the optimization design solution. By comparing the simulation results with the actual operation data of the current network, it evaluates the degree of improvement of the optimization solution on network performance, and iteratively analyzes and adjusts the overall network performance and stability to generate a network optimization plan.

[0044] As a further solution of the present invention, the continuous improvement and upgrading module includes a performance evaluation submodule, a technology updating submodule, and an upgrading implementation submodule;

[0045] The performance evaluation submodule is based on network optimization planning and uses deep learning algorithms to analyze data traffic patterns, energy efficiency indicators and user feedback in the network. It also uses statistical and predictive models to evaluate the overall performance of the system and uses the analysis results to identify deficiencies and areas for improvement in system performance, thereby generating performance improvement demand results.

[0046] The technology update submodule analyzes the current market trends and technology development directions based on the performance improvement demand results, adopts market analysis and trend prediction algorithms, combines the analysis results, evaluates the changes and development directions of user needs, and performs technology updates with reference to the current status and future development needs of the system, thereby generating a technology update plan;

[0047] The upgrade implementation submodule is based on the technology update plan and uses the Ansible automated deployment tool to implement system updates and optimizations in stages, including new technology integration and performance adjustments, to assist in improving system performance and user experience each iteration and generate a continuous system upgrade plan.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, the sensor fusion technology and real-time data flow analysis used improve the monitoring accuracy and response speed, so that the system can detect and respond to abnormal situations in a timely manner. Time series analysis and support vector machine models make water consumption predictions more accurate, which is conducive to the rational allocation and planning of resources. The application of genetic algorithms and multi-objective optimization methods makes maintenance strategies more scientific and reasonable, reducing costs while improving efficiency. The use of Bayesian networks and fault tree analysis methods improves the accuracy and depth of fault diagnosis. System simulation and fluid dynamics analysis provide a reliable basis for performance improvement. The application of adaptive control and optimization adjustment algorithms enhances the intelligence and flexibility of water flow regulation. Network analysis and graph optimization algorithms optimize the network structure and improve overall operating efficiency. The application of data-driven algorithms and iterative development methods enables the system to continuously adapt to technological advances and market changes and maintain continuous competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a system flow chart of the present invention;

[0051] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0052] Figure 3 It is a flow chart of the real-time monitoring and reaction module of the present invention;

[0053] Figure 4 It is a flow chart of the water consumption dynamic prediction module of the present invention;

[0054] Figure 5This is a flow chart of the maintenance strategy intelligent optimization module of the present invention;

[0055] Figure 6 is a flow chart of a fault diagnosis module of the present invention;

[0056] Figure 7 The system performance enhancement module flow chart of the present invention;

[0057] Figure 8 This is a flow chart of the intelligent water flow control module of the present invention;

[0058] Fig. 9 It is a flow chart of the network optimization and planning module of the present invention;

[0059] Fig.10 This is a flow chart of the continuous improvement and upgrade module of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0062] Example 1: Please refer to Figure 1 to Figure 2 ,The building water pipe maintenance system based on big data includes real-time monitoring and response module, water consumption dynamic prediction module, maintenance strategy intelligent optimization module, fault diagnosis module, system efficiency enhancement module, intelligent water flow control module, network optimization and planning module, and continuous improvement and upgrade module;

[0063] The real-time monitoring and response module uses sensor fusion technology and real-time data stream analysis based on multi-source data sets to monitor system status, instantly identify system deviations through anomaly detection algorithms, and use adaptive control strategies to adjust operating parameters to generate real-time monitoring data sets;

[0064] The water consumption dynamic forecasting module uses time series analysis and support vector machine models based on real-time monitoring data sets to conduct trend analysis of historical and real-time data, use machine learning technology to predict future water demand, and adjust the forecasting model based on seasonality and consumption pattern changes to generate water consumption trend forecasts;

[0065] The maintenance strategy intelligent optimization module uses genetic algorithms and multi-objective optimization methods based on water consumption trend prediction, comprehensively refers to cost, efficiency, and environmental factors, formulates a maintenance plan for the water pipe system, and applies multi-objective optimization to adjust and optimize the strategy to generate an intelligent maintenance strategy;

[0066] The fault diagnosis module is based on intelligent maintenance strategies and uses Bayesian networks and fault tree analysis to analyze historical fault data and real-time monitoring data, identify potential fault modes, and use expert systems to perform in-depth diagnosis of fault causes and generate fault identification;

[0067] The system efficiency enhancement module uses system simulation and fluid dynamics analysis based on fault identification to identify areas with potential for efficiency improvement, implements energy efficiency improvement measures through optimization and adjustment algorithms, and evaluates the effects of the improvement results to generate an efficiency maximization plan;

[0068] The intelligent water flow control module is based on the efficiency maximization solution, applies adaptive control and optimization adjustment algorithms, intelligently adjusts water flow and pressure according to real-time data, optimizes system operation efficiency and stability, and conducts continuous performance monitoring to generate intelligent water flow management;

[0069] The network optimization and planning module is based on intelligent water flow management and uses network analysis and graph optimization algorithms to optimize the structure and operating parameters of the water pipe network, simulate and verify the optimization effect, and generate a network optimization plan;

[0070] The continuous improvement and upgrade module is based on network optimization planning, uses data-driven algorithms, combines technology updates and market demand indicators, regularly evaluates system performance, identifies upgrade requirements, and uses Ansible automated deployment tools to implement system function updates and generate a continuous system upgrade plan.

[0071] The real-time monitoring data set includes pipeline pressure readings, flow changes, and temperature fluctuations. The water consumption trend forecast includes daily, weekly, and monthly water consumption trends and peak time period forecasts. The intelligent maintenance strategy includes maintenance cycle adjustment, resource allocation, and cost-benefit analysis. Fault identification includes fault location, fault cause, and impact range. The efficiency maximization plan includes efficiency improvement area identification, energy efficiency improvement measures, and effect evaluation results. Intelligent water flow management includes pressure adjustment strategy, flow control, and emergency response plan. Network optimization planning includes structural optimization plan, parameter adjustment plan, and simulation verification results. The system continuous upgrade plan includes performance evaluation results, upgrade demand identification, and automated deployment plan.

[0072] In the real-time monitoring and response module, by integrating multi-source data, including sensor data such as temperature, pressure, flow, etc., sensor fusion technology, such as weighted average or Kalman filter algorithm, is used to achieve data accuracy and completeness. Real-time data stream analysis uses a sliding window method to dynamically capture the latest changes in system status. Anomaly detection algorithms, such as isolation forests or autoencoders, are used to identify outliers in the data and instantly discover system deviations. When an anomaly is detected, adaptive control strategies, such as fuzzy logic controllers or neural network controllers, adjust operating parameters based on real-time data to maintain system stability. The real-time monitoring data sets generated by these processes are stored in timestamp, sensor identification and measurement value format to provide basic data for subsequent modules.

[0073] In the water consumption dynamic forecasting module, time series analysis, such as the ARIMA model, is used to analyze the trends of historical and real-time data based on real-time monitoring data sets. Support vector machine models, especially regression support vector machines (SVR), predict future water demand based on historical data patterns. The forecasting model also integrates seasonal and consumption pattern changes, such as using Fourier transforms to process periodic patterns. Machine learning technology ensures the accuracy and adaptability of the forecast here. The generated water consumption trend forecast results are stored in a time series format, and the forecast values ​​include expected water consumption and estimated fluctuation ranges, providing decision support for resource allocation.

[0074] In the maintenance strategy intelligent optimization module, based on the water consumption trend forecast, a genetic algorithm is used to simulate the natural selection process for multi-objective optimization problems such as cost minimization, efficiency maximization and environmental impact minimization. The algorithm iterates selection, crossover and mutation to produce the optimal maintenance strategy. Multi-objective optimization methods use methods such as Pareto front analysis to ensure a balance between multiple objectives. Multi-objective optimization combines expert knowledge and historical data to fine-tune the strategy. The results of the intelligent maintenance strategy are displayed in a planning table or graphical interface, including maintenance time, location, budget and expected results.

[0075] In the fault diagnosis module, Bayesian networks are used to analyze historical fault data and real-time monitoring data to calculate the conditional probabilities of different fault modes. Fault tree analysis rules identify the logical relationships and root causes of faults. Expert systems, combined with domain knowledge and rule bases, perform in-depth diagnosis and point out the specific causes and solutions of faults. Fault diagnosis results are presented in the form of reports, including fault type, estimated cause, and recommended maintenance measures.

[0076] In the system efficiency enhancement module, system simulation tools, such as computational fluid dynamics (CFD) software, are used to analyze the behavior of water flow in the pipe network and identify potential areas for efficiency improvement. Optimization algorithms, such as linear programming or meta-heuristic algorithms, are used to adjust system parameters to improve energy efficiency. The effect evaluation uses performance indicators, such as energy consumption reduction ratio or service quality improvement, to generate an efficiency maximization plan, which is saved in the form of an optimization report, listing improvement measures and expected results.

[0077] In the intelligent water flow control module, the water flow and pressure are adjusted by combining the efficiency maximization scheme, adaptive control and optimization regulation algorithms. Real-time data analysis is used to monitor the water flow status, and intelligent algorithms, such as genetic programming or reinforcement learning, optimize parameter settings based on data feedback. The system operation efficiency and stability are improved, and the performance monitoring report records the improvement process and results.

[0078] In the network optimization and planning module, network analysis tools, such as Graph Theory Algorithms, are used to evaluate the water pipe network structure. Graph theory optimization algorithms, such as the shortest path algorithm or the minimum spanning tree algorithm, are used to optimize the network layout. Simulations are performed to verify the optimization effect, and network optimization plans are generated and saved in the form of network diagrams and performance indicator reports to show the comparison before and after optimization.

[0079] In the continuous improvement and upgrade module, data-driven algorithms analyze technological progress and market demand to evaluate system performance. The Ansible automated deployment tool implements system function updates based on evaluation results and upgrade requirements. The update process is recorded in the system log, including update content, time, and effect. This module ensures that the system continues to adapt to environmental changes and remains competitive.

[0080] See also Figure 3 ,The real-time monitoring and response module includes the sensor deployment submodule, the data monitoring submodule, and the real-time response submodule;

[0081] The sensor deployment submodule uses a weighted average method based on multi-source data sets to integrate and summarize the data collected by multiple types of sensors, balance the measurement differences between multiple sensors, and apply the Kalman filter algorithm to process the summarized data to reduce noise. Through continuous prediction and update steps, a multi-dimensional monitoring configuration is generated;

[0082] The data monitoring submodule is based on multi-dimensional monitoring configuration and adopts event stream processing technology to capture and process real-time data streams, sort and classify content items in the data streams, and apply time window technology to split the data streams into independent time series, provide context for the data in each time period, analyze each time series separately, and generate real-time data analysis results;

[0083] The real-time response submodule uses the support vector machine algorithm to identify abnormal points in the data set based on the real-time data analysis results, separates normal data and abnormal data by constructing the optimal hyperplane, and uses the isolation forest algorithm to analyze the detected anomalies and identify abnormal patterns. It combines PID control strategy and fuzzy logic control for adjustment, responds to multiple types of abnormal conditions, and generates a real-time monitoring data set.

[0084] In the sensor deployment submodule, sensor data is collected and aggregated by integrating multi-source data sets, and the weighted average method and Kalman filter algorithm are used to achieve accurate data fusion. Specifically, the data collected by different types of sensors, such as temperature, pressure, and flow sensors, are first processed by the weighted average method, which considers the accuracy and reliability of each sensor and assigns different weights to them. For example, high-precision sensors are given higher weights, and vice versa. After data fusion, the Kalman filter algorithm further optimizes the data. Kalman filtering is a recursive algorithm that reduces random noise in the data through prediction and update steps. In the prediction step, the algorithm predicts the next state based on the current state and the process model; in the update step, the state estimate is corrected by combining the prediction results and the new measurement data. This method can effectively balance historical data and new data, ensuring the accuracy and real-time nature of the data set. The generated multidimensional monitoring configuration is saved in the form of a data set containing multiple sensor types and timestamps, providing a comprehensive monitoring perspective for the system.

[0085] In the data monitoring submodule, real-time data streams are captured and processed through multi-dimensional monitoring configuration. Event stream processing technology plays a key role here, which can capture, sort and classify various events in the data stream in real time. Data stream content items, such as temperature changes, pressure fluctuations, etc., are sorted and classified according to timestamps to ensure the order and structure of the data. Time window technology divides the data stream into independent time series, provides context for the data in each time period, and makes the analysis of each time series more targeted and accurate. For example, a sliding time window can capture data changes every minute or every hour, providing a more detailed data perspective for the system. Real-time data analysis results are stored in a structured data format, such as a database table or a JSON file, recording key indicators and trends in each time period, providing immediate basis for system decision-making.

[0086] In the real-time response submodule, based on the real-time data analysis results, the support vector machine (SVM) algorithm and the isolation forest algorithm are used to identify and analyze anomalies in the data set. The support vector machine algorithm effectively separates normal and abnormal data by constructing the optimal hyperplane. In this process, the algorithm first selects a kernel function, such as the radial basis function (RBF), and then determines the optimal interval boundary to distinguish different categories of data points. The isolation forest algorithm is used as a supplement to further analyze the detected anomalies and identify abnormal patterns. This algorithm isolates observation points by randomly selecting features and cutoff values, and abnormal points are usually easier to isolate. After the data anomalies are identified, the system uses proportional-integral-derivative (PID) control strategy and fuzzy logic control to make response adjustments. The PID controller adjusts the control variable according to the deviation value, while the fuzzy logic controller handles more complex situations, such as nonlinear or model uncertainty problems. These control strategies ensure that the system can flexibly respond to multiple types of abnormal conditions. The generated real-time monitoring data set is recorded in the form of time series and abnormal event logs, including the time, type, severity and response measures of each event, providing the system with real-time monitoring and rapid response capabilities.

[0087] In a building water pipe maintenance system based on big data, assume that the system collects the following simulated data: the temperature sensor reports 28°C at 9:00 AM, the pressure sensor reports 3.5 bar, and the flow sensor reports 250 L / min. These data are processed by weighted averaging and Kalman filtering algorithms. The weighted averaging method gives the temperature sensor a weight of 0.3, the pressure sensor a weight of 0.4, and the flow sensor a weight of 0.3. The Kalman filtering algorithm further processes these weighted data to reduce noise. The data monitoring submodule captures this data in real time through event stream processing technology and updates the data every 5 minutes using sliding time window technology. The real-time response submodule finds that at 9:05 AM, the temperature rises to 30°C and the pressure drops to 3.0 bar, which is identified as an abnormal pattern by the SVM and isolation forest algorithms. The PID controller and fuzzy logic controller respond to this anomaly and adjust the relevant parameters to stabilize the system. The generated data content includes timestamps, sensor readings, identified abnormal patterns, and response measures. This information constitutes a real-time monitoring data set, which provides key information for the operation and maintenance of the system.

[0088] See also Figure 4 ,The water consumption dynamic prediction module includes a trend analysis submodule, a machine learning model submodule, and a prediction result submodule;

[0089] The trend analysis submodule uses the autoregressive moving average model to analyze the data based on the real-time monitoring data set. The autoregressive part analyzes the time series dependency of the data, identifies and extracts the long-term trend in the data, and uses the moving average part to eliminate random fluctuations, iteratively refine trend characteristics, capture the change pattern of water consumption over time, and generate trend analysis results.

[0090] Based on the trend analysis results, the machine learning model submodule applies the random forest algorithm to conduct deep learning of trends. By building batch decision trees, each decision tree analyzes multiple data samples and features, and aggregates decisions, it learns and simulates the change pattern of water consumption to generate a machine learning prediction model;

[0091] The prediction result submodule is based on a machine learning prediction model and uses linear regression analysis to predict future water demand. By establishing a relationship model between water consumption and time and seasonal factors, it analyzes the changing trend of water consumption, adjusts model parameters to match current data trends and consumption patterns, and generates a water consumption trend forecast.

[0092] In the trend analysis submodule, the autoregressive moving average (ARMA) model is used to analyze the data through real-time monitoring data sets. The ARMA model combines autoregressive (AR) and moving average (MA) parts to effectively capture and analyze the characteristics of time series data. The autoregressive part focuses on analyzing the time series dependence of the data, and captures the impact of historical data on current data by determining the number of lags (p) of the data. For example, when analyzing daily water consumption, if p is determined to be 7, the model will consider the impact of water consumption in the past 7 days on the current forecast. The moving average part focuses on eliminating random fluctuations. By setting the size of the sliding window (q), for example, setting it to 3, the model will use the average water consumption of the past 3 days to smooth the current forecast. This combined method enables the ARMA model to identify and extract long-term trends in the data, eliminate short-term random fluctuations, and enhance the accuracy of the forecast. By iteratively refining trend features, the model can accurately capture the changes in water consumption over time. The generated trend analysis results are displayed in the form of graphics or reports, revealing the historical trends and future trends of water consumption, and providing a basis for forecasting and decision-making.

[0093] In the machine learning model submodule, based on the trend analysis results, the random forest algorithm is used to deeply learn the changing pattern of water consumption. Random forest is an integrated learning model that performs prediction and classification by building multiple decision trees. When processing water consumption data, multiple sample subsets are first randomly selected from the overall data set, and different data samples and features are provided to each decision tree. Each decision tree independently analyzes its assigned data, such as analyzing the difference in water consumption between weekdays and weekends, the impact of seasonal changes on water consumption, etc. After the results of the decision trees are aggregated, the final prediction is obtained by voting or averaging. This method reduces the bias of a single model and enhances the generalization ability of the prediction. The generated machine learning prediction model is stored in the form of an algorithm model file, which contains the model's parameters and training results. It can be adjusted and optimized in real time according to new data, providing a dynamic and accurate reference for future water consumption predictions.

[0094] In the prediction result submodule, linear regression analysis is used to predict future water demand based on the machine learning prediction model. The linear regression model establishes a relationship model between water consumption and time and seasonal factors, and provides accurate prediction results by identifying and analyzing the changing trend of water consumption. The parameter adjustment of the model is based on the current data trend and consumption pattern. For example, by adjusting the slope and intercept in the linear regression model, the model can better fit the recent water consumption data and seasonal changes. The prediction results are presented in the form of graphs, tables or reports, showing the predicted water consumption values ​​for a certain period of time in the future, including the accuracy and confidence interval of the prediction. These prediction results play an important role in resource planning and management, helping decision makers adjust water supply strategies, optimize resource allocation, and prevent resource waste or shortages.

[0095] In a building water pipe maintenance system based on big data, it is assumed that the system collects water consumption data for each day of the week, with values ​​of 1200L, 1150L, ​​1180L, 1230L, 1210L, 1250L, and 1190L. In the trend analysis submodule, the ARMA model analyzes these data, setting the number of lags in the autoregressive part to 3 and the window size of the moving average part to 2. The model identifies the trend of gradually increasing water consumption within a week. In the machine learning model submodule, the random forest algorithm constructs 50 decision trees based on these trend data, each of which analyzes different data subsets and features, such as the difference in water consumption between weekdays and weekends. The final model combines the results of these decision trees to generate a prediction model. In the prediction result submodule, the linear regression model predicts the water consumption for the next week based on this prediction model. The results show that the water consumption for the next week will maintain a steady growth, with a predicted value of about 1220L to 1260L per day. These prediction results provide key decision support for the operation and maintenance of the water pipe system.

[0096] See also Figure 5,The maintenance strategy intelligent optimization module includes a multi-objective analysis submodule, a strategy formulation submodule, and an optimization and adjustment submodule;

[0097] The multi-objective analysis submodule is based on the water consumption trend prediction. It uses genetic algorithms to represent the problem parameters as chromosomes through a coding scheme, and uses selection, crossover and mutation operations to search for the optimal solution. It combines multi-objective optimization to establish a multi-dimensional evaluation index system, refer to cost, efficiency and environmental impact factors, conduct system performance analysis, and generate comprehensive analysis results.

[0098] Based on the comprehensive analysis results, the strategy formulation submodule applies the decision tree algorithm to build a tree model, analyzes the estimated results of the decision path from the root node to the leaf node, and uses the simulated annealing algorithm to avoid the local optimal solution and generate a maintenance strategy plan through the temperature drop and energy minimization mechanism in the simulated annealing process;

[0099] The optimization and adjustment submodule is based on the maintenance strategy plan. It uses linear programming to mathematically optimize resource allocation by constructing objective functions and constraints. At the same time, it uses neural networks to dynamically learn and adjust strategies by simulating the connection and weight adjustment of neurons to generate intelligent maintenance strategies.

[0100] In the multi-objective analysis submodule, the genetic algorithm is used to achieve multi-dimensional evaluation and optimization of system performance through water consumption trend prediction data. Genetic algorithms simulate natural selection and genetics principles to find the optimal solution. First, the problem parameters are encoded into chromosome format, for example, parameters such as water consumption, cost, efficiency and environmental impact factors are converted into a string of digital sequences. In the selection stage of the algorithm, excellent individuals are selected from the current population as parents of the next generation, usually based on the fitness function, which calculates the fitness of each individual according to a multi-dimensional evaluation index system. For example, an individual represents a specific resource allocation scheme, and its fitness reflects the comprehensive performance of the scheme in terms of cost, efficiency and environmental impact. In the crossover stage, the selected individuals exchange part of their chromosomes to produce new individuals, simulating gene recombination in biological genetics, which helps to explore new possibilities in the solution space. In the mutation stage, some genes of some individuals are randomly changed to improve the diversity of the algorithm and avoid local optimality. The whole process is iterated repeatedly until the termination condition is met, such as reaching the preset number of iterations. Finally, the algorithm generates one or more optimal solutions, which, as a comprehensive analysis result, provide the best strategy for balancing various objectives.

[0101] In the strategy formulation submodule, maintenance strategies are formulated based on the comprehensive analysis results by applying decision tree algorithms and simulated annealing algorithms. The decision tree algorithm analyzes the potential results of each decision path from the root node to the leaf node by constructing a tree model. During the construction process, each node represents a decision problem, for example, whether to increase the water supply in a certain area, and the branch represents the answer. The construction of the tree is based on the characteristics of the training data set, such as historical water consumption data, cost, and environmental impact, and features are selected using criteria such as information gain or Gini impurity. Once the tree is built, it can be used to predict the best decision in a new situation. At the same time, the simulated annealing algorithm is used to optimize the decision process and prevent the algorithm from falling into the local optimum. Simulated annealing simulates the process of cooling after heating a substance, allowing the algorithm to accept suboptimal solutions at high temperatures. As the temperature gradually decreases, the algorithm tends to accept better solutions. This method helps the decision tree avoid falling into the local optimal solution and find a more global optimal strategy. The generated maintenance strategy plan is presented in the form of a table or report, providing a clear decision path and expected results, providing a scientific basis for the maintenance plan.

[0102] In the optimization and adjustment submodule, based on the maintenance strategy scheme, linear programming and neural networks are combined to optimize resource allocation and adjust the strategy. Linear programming achieves mathematical optimization of resources by constructing objective functions and constraints. For example, the objective function is to minimize cost or maximize efficiency, while the constraints include budget constraints, resource availability, etc. By solving the linear programming model, the best resource allocation scheme to achieve the goal under given constraints can be found. At the same time, neural networks are used to simulate the dynamic learning and adjustment of strategies. Neural networks are composed of multiple layers of neurons, each of which processes input data through an activation function. During the training process, the network learns data patterns by adjusting the weights of the connections between neurons. For example, a neural network can learn changes in water consumption patterns based on historical data and automatically adjust strategies based on new trends. This method of combining linear programming and neural networks makes optimization and adjustment more precise and adaptable. The generated intelligent maintenance strategy is stored in the form of an algorithm model file and an operation guide, which provides specific optimization suggestions and implementation steps for the current strategy.

[0103] In the building water pipe maintenance system based on big data, it is assumed that the system collects water consumption trend forecast data, which shows that the average daily water consumption will gradually increase from 1200L to 1500L in the next month. The multi-objective analysis submodule uses genetic algorithms to process these data and generate several different resource allocation schemes, taking into account cost, efficiency and environmental impact. In the strategy formulation submodule, decision tree algorithms and simulated annealing algorithms are used to analyze and select the optimal maintenance strategy, such as the decision to increase water pressure or increase water supply. The optimization adjustment submodule further refines these strategies through linear programming and neural networks, such as determining specific water pressure adjustment values ​​or water supply time. Ultimately, the generated intelligent maintenance strategy includes detailed operation steps and expected results, such as increasing water pressure by 5% within a specific time period, which is expected to meet the increased demand without increasing costs.

[0104] See also Figure 6 ,The fault diagnosis module includes a fault detection submodule, a cause analysis submodule, and a diagnostic tool submodule;

[0105] The fault detection submodule is based on intelligent maintenance strategy and adopts Bayesian network algorithm. It constructs conditional probability model between faults and potential causes, analyzes historical fault data and real-time monitoring data, calculates the probability of occurrence of differentiated fault modes, identifies the fault mode faced by the current system, and then generates fault mode mapping;

[0106] The cause analysis submodule uses the fault tree analysis method based on fault mode mapping to set the top event of the fault tree. By constructing a hierarchical structure of downstream logic gates and basic events, it analyzes the contribution of each basic event to the occurrence of the top event, and explores the root cause of each potential fault mode to generate a fault cause analysis.

[0107] The diagnostic tool submodule is based on fault cause analysis and applies an expert system. Through an integrated knowledge base and reasoning rules, it analyzes the identified fault modes, combines historical maintenance records and expert experience, develops fault solutions, and then generates fault identification.

[0108] In the fault detection submodule, the Bayesian network algorithm analyzes historical fault data and real-time monitoring data to identify the fault mode of the current system. Bayesian network is a probabilistic graph model that analyzes data by building a conditional probability model between faults and potential causes. First, define the nodes in the network, each of which represents a factor that affects the system, such as sensor readings, environmental conditions, or equipment status. These nodes are connected by directed edges to represent cause-effect relationships. For example, a node represents pipeline pressure, and the node connected to it represents a fault caused by abnormal pressure. Using historical fault data, the algorithm calculates the conditional probability of fault occurrence under different node combinations. For example, if historical data shows that the probability of abnormal pipeline pressure increases significantly under high temperature conditions, this relationship will be reflected in the network. Real-time monitoring data is input into this network, and the Bayesian algorithm calculates the probability of fault occurrence based on the current observations. For example, if the currently monitored pipeline pressure exceeds the normal range, the network will calculate the probability of the corresponding fault mode. The generated fault mode map is presented in graphical or tabular form, revealing the main fault risks and their probabilities faced by the current system, providing a basis for fault prevention and response.

[0109] In the cause analysis submodule, based on the fault mode mapping, the fault tree analysis method is used to deeply explore the root cause of each potential failure mode. Fault tree analysis is a top-down method that starts from a specific top event (such as a water pipe leak) and gradually decomposes it into multiple causes that cause the event (such as corrosion, excessive pressure, abnormal temperature, etc.). Each cause is further decomposed into more specific events or conditions to form a hierarchical structure. This analysis connects different events through logic gates (such as AND gates and OR gates) to show how different combinations of events lead to the occurrence of top events. The contribution of each basic event is quantitatively analyzed to calculate its probability or severity of occurrence. In this way, the fault tree analysis reveals the root cause of each potential failure mode and the contribution of these causes to the overall failure risk of the system. The generated failure cause exploration report describes each failure mode and its potential causes in detail, providing important information for taking preventive measures and improvement strategies.

[0110] In the diagnostic tool submodule, based on the fault cause analysis, the expert system is applied to conduct in-depth fault diagnosis and solution formulation. The expert system integrates a rich knowledge base and reasoning rules, which contain information such as historical maintenance records, failure cases, and expert experience. When a specific failure mode is identified, the expert system analyzes the cause of the failure through reasoning rules and proposes a solution. For example, if the system identifies a pipeline leak due to excessive pressure, the expert system will recommend decompression measures or replacement of weakened pipe sections based on historical cases and maintenance records. This approach combines data-driven analysis and expert experience to ensure the accuracy of fault diagnosis and the practicality of solutions. The generated fault identification report describes each failure mode, cause analysis, and recommended maintenance measures in detail, providing clear guidance for actual operators to help solve faults quickly and effectively.

[0111] In a building water pipe maintenance system based on big data, the system collects a series of real-time monitoring data, such as the pipe pressure in a certain area exceeded the normal range for three consecutive days. In the fault detection submodule, the Bayesian network analyzed the relationship between this abnormal situation and historical fault data and calculated that the probability of pipeline leakage was 75%. The cause analysis submodule determined that high pressure was the main cause of the leakage through fault tree analysis, and identified related secondary factors, such as aging of pipe materials. In the diagnostic tool submodule, the expert system analyzed this information and made suggestions to reduce water supply pressure and check pipe materials. This series of analysis and diagnosis provides the maintenance team with a clear fault response strategy, helping them to quickly locate problems and take effective measures.

[0112] See also Figure 7 ,The system performance enhancement module includes a performance analysis submodule, a simulation test submodule, and a performance improvement submodule;

[0113] The performance analysis submodule is based on fault identification and adopts Monte Carlo simulation. By constructing mathematical models and setting various operation scenarios, the system simulation is carried out to analyze the performance of the system under different working conditions. At the same time, sensitivity analysis is used to monitor the response changes of system performance by changing various parameters in the model, select the factors affecting performance, and generate performance key area mapping;

[0114] The simulation test submodule is based on the mapping of key performance areas and the application of computational fluid dynamics analysis. By building a flow field model of the system and setting various boundary conditions, it simulates the flow and interaction of fluids in the system, analyzes the impact of various adjustment schemes on flow field characteristics, predicts the improvement effect of differentiated configurations on system performance, and generates predictions for optimized adjustment schemes.

[0115] The performance improvement submodule uses a multi-objective optimization algorithm to optimize the comprehensive parameters of the proposed improvement plan based on the prediction of the optimization adjustment plan, and to achieve the optimal balance between system performance and efficiency. At the same time, by adjusting key parameters, iteratively optimizes the overall performance of the system, and conducts system performance evaluation and verification, thereby generating a plan to maximize efficiency.

[0116] In the performance analysis submodule, the performance of the system is comprehensively evaluated through Monte Carlo simulation and sensitivity analysis. Monte Carlo simulation is a method based on probability and random sampling, which is used to build and analyze the performance of the system under various operating scenarios. First, a mathematical model is established based on the actual situation and requirements of the system, including key parameters such as flow, pressure, temperature, etc. Then, multiple operating scenarios are set, such as different environmental conditions, equipment status and operation modes. In each scenario, Monte Carlo simulation generates a large number of estimated parameter combinations through random sampling technology and calculates the performance of the system under these conditions. For example, the simulation considers the performance of the system under high temperature, high pressure and different flow rates. At the same time, through sensitivity analysis, the key parameters that have a significant impact on the performance of the system are identified. This analysis monitors how these changes affect the overall performance of the system by changing various parameters in the model one by one. For example, analyze how temperature changes affect the flow and pressure of the pipeline. In this way, the factors that have the greatest impact on system performance can be identified and a performance key area map can be generated. This map is presented in the form of a chart or report, revealing the key drivers of system performance and potential risk areas, providing a basis for subsequent optimization.

[0117] In the simulation test submodule, based on the mapping of key efficiency areas, computational fluid dynamics (CFD) analysis is used to deeply explore the impact of different adjustment schemes on the flow field characteristics of the system. CFD analysis numerically simulates the flow and interaction of fluids in the system. First, a flow field model of the system is constructed, including pipeline networks, valves, and other components. Then, multiple boundary conditions, such as inlet pressure, flow rate, and temperature, are set to simulate the behavior of the fluid under these conditions. Using CFD software, flow field simulation is performed to analyze the flow rate, pressure distribution, and temperature changes under different operating conditions. In addition, by simulating multiple adjustment schemes, such as changing the pipeline layout, adjusting the valve opening, or changing the operation strategy of the pump, how these adjustments affect the flow field characteristics is analyzed. For example, the effect of increasing the pump speed on the pressure and flow rate of the pipeline network is tested. The generated prediction of the optimized adjustment scheme is presented in the form of graphics, animations, or reports, predicting the flow field changes and potential performance improvements under different configurations, providing a scientific basis for actual operation.

[0118] In the efficiency improvement submodule, based on the prediction of the optimization adjustment plan, a multi-objective optimization algorithm is used to find the best balance between system performance and efficiency. The multi-objective optimization algorithm finds the solution with the best comprehensive performance by considering multiple goals at the same time, such as minimizing costs, maximizing efficiency, and minimizing environmental impact. In this process, the algorithm evaluates and compares different improvement plans, such as different pump speeds, valve settings, or pipe network layouts. By adjusting the key parameters in these plans and iteratively optimizing, the algorithm gradually finds the solution that performs best in meeting all goals. For example, the algorithm may find that under certain pump speeds and valve settings, the system can achieve the highest energy efficiency while keeping the operating costs within an acceptable range. After the optimization is completed, the system's efficiency evaluation and verification are carried out to ensure that the proposed plan can achieve the expected results in actual operation. The generated efficiency maximization plan is presented in the form of a detailed report, including the optimized operating parameters, expected performance improvement, and implementation steps. This plan provides clear guidance for the long-term operation and maintenance of the system, helping to improve the overall efficiency and reliability of the system.

[0119] In a building water pipe maintenance system based on big data, the system collects a series of real-time monitoring data including pipeline pressure, flow and temperature. The efficiency analysis submodule found that temperature is a key factor affecting system performance through Monte Carlo simulation and sensitivity analysis. The simulation test submodule uses CFD analysis to simulate the flow field characteristics under temperature changes and found that pipeline pressure and flow rate increase significantly under high temperature. The efficiency improvement submodule uses a multi-objective optimization algorithm to propose a series of adjustment plans, such as adjusting the pipeline layout and changing the pump operation strategy to cope with the impact of high temperature. Ultimately, the generated efficiency maximization plan is expected to improve the overall efficiency of the system while keeping the operating cost unchanged.

[0120] See also Figure 8 ,The intelligent water flow control module includes demand analysis submodule, control algorithm submodule, and regulation implementation submodule;

[0121] The demand analysis submodule is based on the efficiency maximization solution and adopts the time series analysis algorithm to collect and analyze the system historical operation data and real-time monitoring data, use the autoregressive model to predict the future water flow demand, identify the current demand peak and valley, adjust the formulated strategy and provide data support to generate demand forecast results;

[0122] Based on the demand forecast results, the control algorithm submodule applies an adaptive control algorithm to analyze the demand changes in the forecast results, dynamically adjusts the control parameters according to the current state of the system and future demand trends, responds to demand changes, and implements a continuous parameter tuning process to generate a water flow regulation strategy;

[0123] The regulation implementation submodule is based on the water flow regulation strategy and adopts a real-time feedback control algorithm to automatically adjust the valve and pump speed. At the same time, it applies sensor data fusion technology to monitor the changes in water flow and pressure in real time, and then uses data analysis methods to continuously collect and analyze system performance data. Combined with the PID control algorithm, it generates intelligent water flow management.

[0124] In the demand analysis submodule, the system's historical operating data and real-time monitoring data are analyzed by time series analysis algorithms to predict future water flow demand. First, historical data such as past water flow, pressure readings, and related environmental parameters, as well as real-time data such as current water flow rate and pressure changes, are collected. These data are formatted as time series, where each data point is timestamped. An autoregressive model (AR model) is used, which predicts future trends by analyzing the time series dependencies between data points. In the AR model, the system's current water flow demand is assumed to be a function of past demand. The key to the model is to determine the lag value, that is, how far back in the historical data is used to predict the current point. The choice of lag value is based on statistical tests such as the partial autocorrelation function (PACF). Through this analysis, the model is able to identify peaks and valleys in demand and predict future demand changes. For example, the model finds that water flow demand in summer is higher than in winter, or that demand on weekdays is higher than on weekends. The generated demand forecast results are presented in the form of charts or reports, detailing the predicted water flow demand trends and providing data support for formulating regulation strategies.

[0125] In the control algorithm submodule, an adaptive control algorithm is applied to dynamically adjust the operating parameters of the system based on the demand forecast results. The adaptive control algorithm is able to adjust the control parameters according to the current state of the system and future demand trends. This algorithm analyzes the demand changes in the forecast results, identifies the trend of increasing or decreasing demand, and adjusts control parameters such as valve opening and pump speed accordingly. For example, if the forecast shows that demand will increase in the next few hours, the control algorithm increases the pump speed in advance to meet the upcoming increase in demand. At the same time, this algorithm also implements a continuous parameter tuning process to ensure that the control strategy is always adapted to the latest demand and system status. The generated water flow regulation strategy is presented in the form of an operation guide or control scheme, which details the specific measures to be taken in response to the predicted demand changes.

[0126] In the regulation implementation submodule, based on the water flow regulation strategy, a real-time feedback control algorithm is used to automatically adjust the operation of the system. The submodule monitors the changes in water flow and pressure in real time and applies sensor data fusion technology to ensure the accuracy and integrity of the monitoring data. Real-time feedback control algorithms, such as PID control algorithms, continuously adjust the valve opening and pump speed according to real-time data to respond to actual water flow and pressure requirements. For example, if the actual water flow monitored is lower than the predicted value, the PID controller will increase the pump speed to increase the flow. At the same time, the submodule also uses data analysis methods to continuously collect and analyze system performance data, identify any deviations from the predetermined performance goals, and optimize the control strategy accordingly. The generated intelligent water flow management is presented in the form of system configuration files and performance reports, including adjusted parameter settings, real-time performance monitoring results, and future adjustment suggestions to ensure that the system can operate efficiently and stably.

[0127] In a building water pipe maintenance system based on big data, the system collects daily water flow data for the past year, ranging from 200 to 800 cubic meters per hour. The demand analysis submodule uses an autoregressive model to analyze this data and predicts that the average daily demand for the next week will be 600-850 cubic meters per hour. Based on this prediction result, the control algorithm submodule automatically adjusts the valve opening and pump speed to ensure that the predicted demand is met. The regulation implementation submodule uses a PID control algorithm to continuously adjust the control parameters according to real-time water flow and pressure data to ensure stable operation of the system. The generated intelligent water flow management report displays real-time monitoring data, adjusted system parameters and performance indicators, providing clear operation guidance and adjustment suggestions for system operators.

[0128] See also Fig. 9 ,The network optimization and planning module includes the network analysis submodule, the ,optimization design submodule, and the planning implementation submodule;

[0129] The network analysis submodule is based on intelligent water flow management and adopts a topology analysis algorithm to analyze each node and connection in the water pipe network, evaluate the impact on the overall network performance, and perform network flow analysis to calculate the flow transmission efficiency of differentiated paths in the network, identify flow bottlenecks and optimization points, and generate network performance analysis results;

[0130] Based on the results of network performance analysis, the optimization design submodule uses graph optimization algorithms to establish a mathematical model of the water pipe network. By analyzing the mathematical model, the shortest path and optimal flow distribution in the network are identified. Based on the analysis results, the layout and operation parameters of the pipe network are adjusted and optimized, and comprehensive optimization measures are taken to generate an optimized design solution.

[0131] The planning and implementation submodule uses system simulation technology to perform simulation based on the optimized design plan. By comparing the simulation results with the actual operation data of the current network, it evaluates the degree of improvement of the optimization plan on network performance, and iteratively analyzes and adjusts the overall performance and stability of the network to generate a network optimization plan.

[0132] In the network analysis submodule, the performance of the water pipe network is evaluated by topological analysis algorithms and network flow analysis. The topological analysis algorithm is mainly used to analyze the structure of each node (such as water pumps, valves, pipe intersections) and connection (such as pipes) in the network, and evaluate the impact on the performance of the entire water pipe network. The topological structure data of the network includes the location of the node, the length and diameter of the connection, the flow direction, etc. The topological analysis identifies the key characteristics of the network structure, such as potential weak links or important distribution centers, by calculating indicators such as the connectivity of each node and the number of edge nodes. At the same time, the network flow analysis focuses on calculating the flow transfer efficiency of different paths in the network. This analysis is based on the principles of flow conservation and fluid mechanics, and calculates the flow transfer from one node to another, identifying flow bottlenecks and optimization points. For example, the analysis found that the flow of some paths exceeded the design capacity, or the water supply efficiency of some areas was inefficient. The generated network performance analysis results are presented in the form of charts and reports, which detail the flow distribution, bottleneck areas and potential optimization points in the network, providing an important basis for network improvement and optimization.

[0133] In the optimization design submodule, based on the results of network performance analysis, the graph theory optimization algorithm is used to mathematically model and optimize the water pipe network. The graph theory optimization algorithm regards the water pipe network as a graph, where nodes represent pumps, valves or connection points, and edges represent pipes. The model includes parameters such as flow, pressure, pipe diameter and length. By analyzing this graphical model, the algorithm identifies the shortest path and the optimal flow distribution scheme in the network. The shortest path analysis helps to determine the most efficient path for water to flow from the source to the destination, while the optimal flow distribution analysis aims to achieve flow balance throughout the network and reduce energy consumption and pressure loss. Based on the analysis results, the layout and operating parameters of the pipe network are adjusted, such as changing the diameter of certain pipes or adjusting the working mode of the pump. The generated optimization design scheme is presented in the form of graphical models, charts and detailed reports, providing specific adjustment and optimization suggestions for the pipe network, helping to improve the overall efficiency and reliability of the network.

[0134] In the planning and implementation submodule, the network is simulated and tested based on the optimized design scheme using system simulation technology. System simulation technology evaluates the impact of the optimization scheme on network performance by creating a virtual model of the network and simulating its operation in a controlled environment. In the simulation, multiple boundary conditions and operating scenarios are set, such as different demand levels, water supply pressures, or environmental changes. The effectiveness of the proposed optimization scheme is evaluated by comparing the simulation results with the current actual operating data of the network. For example, the simulation shows that under the new pipe network layout, the flow distribution is more uniform and the pressure loss is reduced. In addition, the overall performance and stability of the network are iteratively analyzed and adjusted to ensure that the optimization scheme works effectively under various conditions. The generated network optimization plan is presented in the form of a detailed report and operation guide, including the improved network layout diagram, performance improvement forecasts, and implementation steps, providing a clear roadmap for long-term planning and continuous improvement of the network.

[0135] In a building water pipe maintenance system based on big data, the system collects network data including pipe length, diameter, connection type, flow and pressure. The network analysis submodule found that a certain area was inefficient due to the long water transmission path through topological analysis. The optimization design submodule used graph optimization algorithm to redesign the pipe network layout of this area and proposed a solution to add new pipes to reduce the water transmission path. The planning implementation submodule simulated the new pipe network layout through system simulation technology, and the results showed that the new pipes could significantly improve the water supply efficiency of the area. The generated network optimization plan detailed the layout of the new pipes, construction steps and expected performance improvements, providing detailed guidance for future network upgrades.

[0136] See also Fig.10 ,The continuous improvement and upgrade module includes the performance evaluation submodule, the ,technology update submodule, and the upgrade implementation submodule;

[0137] The performance evaluation submodule uses deep learning algorithms to analyze data traffic patterns, energy efficiency indicators, and user feedback in the network based on network optimization planning. It also uses statistical and predictive models to evaluate the overall performance of the system, and uses the analysis results to identify deficiencies and areas for improvement in system performance, thereby generating performance improvement demand results.

[0138] The technology update submodule uses market analysis and trend prediction algorithms to analyze the current market trends and technology development directions based on the performance improvement demand results. Combined with the analysis results, it evaluates the changes and development directions of user needs, and conducts technology updates with reference to the current status and future development needs of the system, thereby generating a technology update plan.

[0139] The upgrade implementation submodule is based on the technology update plan and uses the Ansible automated deployment tool to implement system updates and optimizations in stages, including new technology integration and performance adjustments, to assist in improving system performance and user experience with each iteration and generate a continuous system upgrade plan.

[0140] In the performance evaluation submodule, deep learning algorithms are applied to conduct a comprehensive analysis of data traffic patterns, energy efficiency indicators, and user feedback in the network. This process involves collecting a large amount of data, including historical and real-time network performance data (such as traffic, pressure, energy consumption) and user feedback data (such as satisfaction survey results). The data is formatted into a format suitable for machine learning processing, for example, time series data is used to analyze traffic patterns, and tabular data is used to record energy efficiency indicators and user feedback. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are used to identify patterns and trends in the data. CNNs are suitable for processing data with spatial relationships, such as traffic distribution at each point in the network, while RNNs are suitable for processing time series data, such as historical traffic change trends. At the same time, statistical and predictive models, such as linear regression or ARIMA models, are used to evaluate the overall performance of the system and predict future performance trends. The analysis results reveal the shortcomings and improvement areas of the system performance, and the generated performance improvement requirements are presented in the form of a report, detailing the specific areas that need to be improved and the recommended measures, providing an important basis for subsequent technical updates and system optimization.

[0141] In the technology update submodule, based on the performance improvement demand results, market analysis and trend prediction algorithms are used to analyze the current market trends and technology development directions. Market analysis involves collecting and analyzing data such as industry reports, user surveys, and competitor information to understand changes in market demand and the development of emerging technologies. Trend prediction algorithms, such as time series prediction or machine learning models (such as decision trees or random forests), are used to predict the direction of technology development and the trend of market demand. Combined with these analysis results, evaluate changes in user needs and the direction of technology updates that may be required for the system. For example, the analysis found that in order to improve energy efficiency and reduce operating costs, the market is increasingly inclined to adopt intelligent and automated water pipe maintenance systems. Based on this, a technology update plan is proposed, including the integration of new technologies and the upgrade of existing systems. The generated technology update plan is presented in the form of a report and roadmap, which describes in detail the recommended technology update measures, expected performance improvements, and implementation schedules, providing guidance for the long-term development of the system and adaptation to market changes.

[0142] In the upgrade implementation submodule, based on the technology update plan, the Ansible automated deployment tool is used to implement system updates and optimizations. As a powerful automation tool, Ansible makes system updates and configuration management efficient and reliable. The upgrade process is divided into multiple stages, each of which targets specific technology updates or performance adjustments. For example, the first stage focuses on integrating new traffic monitoring technology, and the second stage focuses on updating optimization algorithms and user interfaces. In each stage, Ansible automatically performs update tasks such as software installation, configuration adjustments, and test verification according to preset scripts. At the same time, through continuous performance monitoring and user feedback collection, each iteration is committed to improving system performance and user experience. The generated system continuous upgrade plan is presented in the form of a detailed operation guide and update report, which includes detailed steps, expected results, and performance evaluation results for each update, ensuring that the system can continuously adapt to new technological changes and market demands and remain competitive.

[0143] In a building water pipe maintenance system based on big data, the performance evaluation submodule collected system operation data including flow, pressure, and energy consumption, as well as user satisfaction feedback. The deep learning model analyzed this data and identified the deficiencies in flow monitoring and pressure control. The technology update submodule found through market analysis that smart sensors and automated control technologies are the future development trend. Based on this, a technology update plan including integrated smart sensors and optimized control algorithms was proposed. The upgrade implementation submodule used the Ansible automated deployment tool to implement these updates in stages, improving the system's monitoring accuracy and response speed. The updated system demonstrated improved performance indicators and user satisfaction, proving the effectiveness of the upgrade measures.

[0144] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Building water pipe maintenance system based on big data, characterized by: The system includes a real-time monitoring and response module, a water consumption dynamic prediction module, a maintenance strategy intelligent optimization module, a fault diagnosis module, a system performance enhancement module, an intelligent water flow control module, a network optimization and planning module, and a continuous improvement and upgrade module; The real-time monitoring and response module uses sensor fusion technology and real-time data stream analysis based on multi-source data sets to monitor system status, instantly identify system deviations through anomaly detection algorithms, and use adaptive control strategies to adjust operating parameters to generate real-time monitoring data sets; The water consumption dynamic prediction module uses time series analysis and support vector machine models based on real-time monitoring data sets to perform trend analysis on historical and real-time data, uses machine learning technology to predict future water demand, and adjusts the prediction model in combination with seasonality and consumption pattern changes to generate water consumption trend forecasts; The maintenance strategy intelligent optimization module is based on water consumption trend prediction, uses genetic algorithms and multi-objective optimization methods, comprehensively refers to cost, efficiency, and environmental factors, formulates a maintenance plan for the water pipe system, and applies multi-objective optimization to adjust and optimize the strategy to generate an intelligent maintenance strategy; The fault diagnosis module is based on intelligent maintenance strategy, uses Bayesian network and fault tree analysis to analyze historical fault data and real-time monitoring data, identify potential fault modes, and uses expert system to perform in-depth diagnosis of fault causes and generate fault identification; The system efficiency enhancement module uses system simulation and fluid dynamics analysis based on fault identification to identify potential areas for efficiency improvement, implements energy efficiency improvement measures through optimization and adjustment algorithms, and evaluates the effects of the improvement results to generate an efficiency maximization plan; The intelligent water flow control module is based on the efficiency maximization solution, applies adaptive control and optimization adjustment algorithms, intelligently adjusts water flow and pressure according to real-time data, optimizes system operation efficiency and stability, and conducts continuous performance monitoring to generate intelligent water flow management; The network optimization and planning module is based on intelligent water flow management, uses network analysis and graph theory optimization algorithms to optimize the structure and operation parameters of the water pipe network, simulates and verifies the optimization effect, and generates a network optimization plan; The continuous improvement and upgrade module is based on network optimization planning, uses data-driven algorithms, combines technology updates and market demand indicators, regularly evaluates system performance, identifies upgrade needs, and uses Ansible automated deployment tools to implement system function updates and generate a system continuous upgrade plan.

2. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The real-time monitoring data set includes pipeline pressure readings, flow changes, and temperature fluctuations. The water consumption trend forecast is specifically the daily, weekly, and monthly water consumption change trends and peak time period forecasts. The intelligent maintenance strategy includes maintenance cycle adjustment, resource allocation, and cost-benefit analysis. The fault identification includes fault location, fault cause, and impact range. The efficiency maximization plan includes efficiency improvement area identification, energy efficiency improvement measures, and effect evaluation results. The intelligent water flow management includes pressure adjustment strategy, flow control, and emergency response plan. The network optimization plan includes structure optimization plan, parameter adjustment plan, and simulation verification results. The system continuous upgrade plan includes performance evaluation results, upgrade demand identification, and automated deployment plan.

3. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The real-time monitoring and response module includes a sensor deployment submodule, a data monitoring submodule, and a real-time response submodule; The sensor deployment submodule is based on a multi-source data set and uses a weighted average method to integrate and summarize the data collected by multiple types of sensors, balance the measurement differences between multiple sensors, and apply a Kalman filter algorithm to process the summarized data to reduce noise. Through continuous prediction and update steps, a multi-dimensional monitoring configuration is generated; The data monitoring submodule is based on a multi-dimensional monitoring configuration and adopts event stream processing technology to capture and process real-time data streams, sort and classify content items in the data streams, and apply time window technology to divide the data streams into independent time series, provide context for the data in each time period, analyze each time series separately, and generate real-time data analysis results; The real-time response submodule uses the support vector machine algorithm to identify abnormal points in the data set based on the real-time data analysis results, separates normal data and abnormal data by constructing an optimal hyperplane, and uses the isolation forest algorithm to analyze the detected anomalies, identify abnormal patterns, and make adjustments in combination with PID control strategy and fuzzy logic control to respond to multiple types of abnormal conditions and generate a real-time monitoring data set.

4. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The water consumption dynamic prediction module includes a trend analysis submodule, a machine learning model submodule, and a prediction result submodule; The trend analysis submodule is based on the real-time monitoring data set and uses the autoregressive moving average model to analyze the data. The autoregressive part analyzes the time series dependency of the data, identifies and extracts the long-term trend in the data, and uses the moving average part to eliminate random fluctuations, iteratively refine trend characteristics, capture the change pattern of water consumption over time, and generate trend analysis results. The machine learning model submodule applies the random forest algorithm to perform deep learning on the trend based on the trend analysis results, builds batch decision trees, each decision tree analyzes multiple data samples and features, aggregates decisions, learns and simulates the change pattern of water consumption, and generates a machine learning prediction model; The prediction result submodule is based on a machine learning prediction model and uses linear regression analysis to predict future water demand. By establishing a relationship model between water consumption and time and seasonal factors, it analyzes the changing trend of water consumption, adjusts model parameters to match current data trends and consumption patterns, and generates a water consumption trend forecast.

5. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The maintenance strategy intelligent optimization module includes a multi-objective analysis submodule, a strategy formulation submodule, and an optimization adjustment submodule; The multi-objective analysis submodule is based on water consumption trend prediction, uses genetic algorithms to represent problem parameters as chromosomes through coding schemes, and uses selection, crossover and mutation operations to search for optimal solutions. It combines multi-objective optimization with a multi-dimensional evaluation index system to conduct system performance analysis and generate comprehensive analysis results with reference to cost, efficiency and environmental impact factors. The strategy formulation submodule, based on the comprehensive analysis results, applies the decision tree algorithm to construct a tree model, analyzes the estimated results of the decision path from the root node to the leaf node, and adopts the simulated annealing algorithm to avoid the local optimal solution through the temperature drop and energy minimization mechanism in the simulated annealing process, and generates a maintenance strategy plan; The optimization and adjustment submodule is based on the maintenance strategy scheme and uses linear programming to mathematically optimize resource allocation by constructing objective functions and constraints. At the same time, a neural network is used to dynamically learn and adjust the strategy by simulating the connection and weight adjustment of neurons to generate an intelligent maintenance strategy.

6. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The fault diagnosis module includes a fault detection submodule, a cause analysis submodule, and a diagnostic tool submodule; The fault detection submodule is based on intelligent maintenance strategy and adopts Bayesian network algorithm. It constructs conditional probability model between faults and potential causes, analyzes historical fault data and real-time monitoring data, calculates the probability of occurrence of differentiated fault modes, identifies the fault mode faced by the current system, and then generates fault mode mapping; The cause analysis submodule uses the fault tree analysis method based on fault mode mapping to set the top event of the fault tree, analyzes the contribution of each basic event to the occurrence of the top event by constructing a hierarchical structure of downstream logic gates and basic events, and explores the root cause of each potential fault mode to generate a fault cause analysis; The diagnostic tool submodule is based on fault cause analysis, applies an expert system, analyzes the identified fault modes through an integrated knowledge base and reasoning rules, formulates fault solutions based on historical maintenance records and expert experience, and then generates fault identification.

7. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The system performance enhancement module includes a performance analysis submodule, a simulation test submodule, and a performance improvement submodule; The performance analysis submodule is based on fault identification and adopts Monte Carlo simulation to conduct system simulation by constructing mathematical models and setting various operation scenarios, analyze the performance of the system under different working conditions, and use sensitivity analysis to monitor the response changes of system performance by changing various parameters in the model, select factors affecting performance, and generate performance key area mapping; The simulation test submodule is based on the performance key area mapping, applies computational fluid dynamics analysis, constructs the system's flow field model and sets multiple boundary conditions to simulate the flow and interaction of fluids in the system, analyzes the impact of multiple adjustment schemes on flow field characteristics, predicts the improvement effect of differentiated configurations on system performance, and generates an optimization adjustment scheme prediction; The performance improvement submodule is based on the prediction of the optimization adjustment plan and uses a multi-objective optimization algorithm to optimize the comprehensive parameters of the proposed improvement plan to achieve the optimal balance between system performance and efficiency. At the same time, by adjusting key parameters, iteratively optimizes the overall performance of the system, and performs system efficiency evaluation and verification, thereby generating an efficiency maximization plan.

8. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The intelligent water flow control module includes a demand analysis submodule, a control algorithm submodule, and a regulation implementation submodule; The demand analysis submodule is based on the efficiency maximization scheme, adopts the time series analysis algorithm, collects and analyzes the system historical operation data and real-time monitoring data, uses the autoregressive model to predict the future water flow demand, identifies the current demand peak and valley, adjusts the formulated strategy and provides data support to generate demand forecast results; The control algorithm submodule applies an adaptive control algorithm based on the demand forecast results, analyzes the demand changes in the forecast results, dynamically adjusts the control parameters according to the current state of the system and future demand trends, responds to demand changes, and implements a continuous parameter tuning process to generate a water flow regulation strategy; The regulation implementation submodule is based on the water flow regulation strategy and adopts a real-time feedback control algorithm to automatically adjust the valve and pump speed. At the same time, it applies sensor data fusion technology to monitor the changes in water flow and pressure in real time, and then uses data analysis methods to continuously collect and analyze system performance data. Combined with the PID control algorithm, it generates intelligent water flow management.

9. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The network optimization and planning module includes a network analysis submodule, an optimization design submodule, and a planning implementation submodule; The network analysis submodule is based on intelligent water flow management and adopts a topology analysis algorithm to analyze each node and connection in the water pipe network, evaluate the impact on the overall network performance, and perform network flow analysis to calculate the flow transmission efficiency of differentiated paths in the network, identify flow bottlenecks and optimization points, and generate network performance analysis results; The optimization design submodule uses a graph optimization algorithm based on the network performance analysis results to establish a mathematical model of the water pipe network, identifies the shortest path and optimal flow distribution in the network by analyzing the mathematical model, and adjusts and optimizes the layout and operation parameters of the pipe network according to the analysis results, and integrates the optimization measures to generate an optimization design solution; The planning implementation submodule uses system simulation technology to perform simulation based on the optimization design solution. By comparing the simulation results with the actual operation data of the current network, it evaluates the degree of improvement of the optimization solution on network performance, and iteratively analyzes and adjusts the overall network performance and stability to generate a network optimization plan.

10. The building water pipe maintenance system based on big data according to claim 1 is characterized by: The continuous improvement and upgrade module includes a performance evaluation submodule, a technology update submodule, and an upgrade implementation submodule; The performance evaluation submodule is based on network optimization planning and uses deep learning algorithms to analyze data traffic patterns, energy efficiency indicators and user feedback in the network. It also uses statistical and predictive models to evaluate the overall performance of the system and uses the analysis results to identify deficiencies and areas for improvement in system performance, thereby generating performance improvement demand results. The technology update submodule analyzes the current market trends and technology development directions based on the performance improvement demand results, adopts market analysis and trend prediction algorithms, combines the analysis results, evaluates the changes and development directions of user needs, and performs technology updates with reference to the current status and future development needs of the system, thereby generating a technology update plan; The upgrade implementation submodule is based on the technology update plan and uses the Ansible automated deployment tool to implement system updates and optimizations in stages, including new technology integration and performance adjustments, to assist in improving system performance and user experience each iteration and generate a continuous system upgrade plan.

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