Water supply network optimization scheduling system based on clustering grouping and Cauchy variation

Abstract

The invention discloses a water supply network optimization scheduling system based on clustering grouping and Cauchy variation, and belongs to the field of water supply network optimization scheduling. Through a multi-dimensional comprehensive optimization algorithm, an intelligent water consumption prediction model, a self-adaptive pressure partitioning technology, an intelligent pump station collaborative scheduling system, a comprehensive cost-benefit analysis tool, real-time data-driven dynamic scheduling, an interdisciplinary technology fusion platform and an environment-friendly scheduling strategy, the intelligent pump station collaborative scheduling system and the comprehensive cost-benefit analysis tool are integrated. The intelligent and automatic management of the water supply pipe network is realized through the innovation points of intelligent control, automatic operation, modularized and extensible software architecture and the like; data are collected in real time by using the Internet of Things technology, high-precision water consumption prediction is performed in combination with a deep learning model, and the water supply efficiency is optimized and the operation cost is reduced by dynamically adjusting pressure partition and pump station collaborative scheduling; green energy is preferentially used, new function integration is supported through modular design and an extensible framework, the system adapts to the requirements of different regions and pipe networks, and the long-term sustainable development capacity of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of optimal scheduling of water supply pipe networks, and particularly to an optimal scheduling system for water supply pipe networks based on clustering grouping and Cauchy mutation. Background Art

[0002] I. Development Abroad: Abroad, the technology of optimal scheduling of water supply pipe networks has been widely studied and applied. Due to the high degree of urbanization in many developed countries, there are strict requirements for the optimization and management of water supply pipe networks.

[0003] 1. Smart water network technology: Some cities in Europe and North America have started implementing smart water network projects, using sensors and Internet of Things technology to monitor water quality and flow in real time to optimize water supply efficiency.

[0004] 2. Prediction models: Machine learning and artificial intelligence technologies are used to predict water consumption. For example, some research projects in the European Union use neural network models for short-term and long-term demand prediction.

[0005] 3. Optimization algorithms: Foreign researchers have developed a variety of optimization algorithms, such as genetic algorithms, particle swarm optimization (PSO), and simulated annealing (SA), for pump scheduling and pipe network design.

[0006] 4. Hydraulic simulation software: Such as EPANET and MIKE URBAN, etc. These software are widely used to simulate and analyze the hydraulic behavior of water supply pipe networks.

[0007] 5. Asset management systems: Many countries have established asset management systems to track and maintain the infrastructure of water supply pipe networks, predict and plan future maintenance needs.

[0008] II. Development in China: With the acceleration of the urbanization process and the increasing importance of water resource management, the technology of optimal scheduling of water supply pipe networks has also developed rapidly.

[0009] 1. Smart water services: Multiple cities in China are promoting the construction of smart water services, and through integrating sensor, cloud computing, and big data analysis technologies, realizing real-time monitoring and management of water supply pipe networks.

[0010] 2. Water supply scheduling systems: Some large water service companies in China have established water supply scheduling systems, and through historical data analysis and optimization algorithms, realizing the automatic scheduling of pumps.

[0011] 3. Water quality monitoring technology: With the improvement of water quality safety awareness, China has invested a lot of research in water quality monitoring technology, including on-line monitoring and laboratory analysis technology.

[0012] 4. Pipeline network renovation and upgrade: To improve water supply efficiency and reduce leakage, many cities in China are renovating and upgrading their water supply pipeline networks, adopting new materials and technologies.

[0013] 5. Water-saving technologies: Against the backdrop of water resource scarcity, water-saving technologies have been widely promoted in China, including water-saving appliances and water-saving irrigation systems.

[0014] III. Outlook: 1. Integrated management: More and more water utilities are starting to adopt integrated management systems, integrating functions such as water quality monitoring, flow control, asset management, and customer service on a single platform.

[0015] 2. Intelligent development: With the development of artificial intelligence and machine learning technologies, intelligent management of water supply pipeline networks has become a trend, enabling more accurate prediction and more efficient scheduling.

[0016] 3. Environmental adaptability: The design and scheduling of water supply pipeline networks increasingly take into account the impacts of environmental changes and climate change to enhance the resilience and sustainability of the system.

[0017] 4. Cost-benefit analysis: While optimizing scheduling, more research has begun to focus on cost-benefit analysis to achieve a balance between economic and environmental benefits.

[0018] 5. Policy and regulatory support: Many countries and regions have introduced relevant policies and regulations to support and regulate the construction and management of water supply pipeline networks, ensuring water supply safety and service quality.

[0019] Existing problems:

[0020] Problem of adaptability to complex terrain: Large-scale water supply pipeline networks often cover various terrains, including areas with significant elevation differences, and pressure zoning is required to meet the water supply demands of different terrains.

[0021] Problem of coordinated scheduling of multiple pumping stations: For water supply pipeline networks with a vast area, multiple pumping stations need to work together to boost water pressure and volume, which requires optimization algorithms to effectively coordinate the operation of multiple pumping stations.

[0022] Problem of accuracy in water consumption prediction: The uncertainty of water consumption poses challenges to the scheduling of water supply pipeline networks, and accurate water consumption prediction technologies are needed to guide water supply scheduling.

[0023] Water supply problems for high-rise and super-high-rise buildings: High-rise and super-high-rise buildings require secondary water supply systems, which demands that the optimized scheduling system of water supply pipeline networks can meet the special water supply needs of these buildings.

[0024] Problem of pipeline network fragmentation caused by numerous rivers: The presence of many rivers in the region leads to more fragmented pipeline networks, increasing the complexity of water supply scheduling, and optimization algorithms are needed to adapt to this fragmented management.

[0025] Multi-source coordination problem: There are multiple water sources (such as groundwater, gravity water supply from high mountains, and water supply lifted from low-lying areas, etc.), and an optimization algorithm is needed to rationally allocate each water source.

[0026] Optimization problem of control facility operation: The operation of control facilities (such as pressure reducing valves, globe valves, check valves, etc.) has an important impact on water supply scheduling, and it is necessary to optimize their operation to improve efficiency.

[0027] Complexity problem of flow measurement: The complexity of flow measurement requires that the optimization algorithm can adapt to the different requirements of unidirectional measurement and bidirectional measurement.

[0028] Complexity problem of pipeline: Pipeline aging and complexity lead to difficulties in water supply scheduling, and an optimization algorithm is needed to adapt to complex pipeline layouts.

[0029] Problem of different pipe material qualities: The pressure-bearing capacities of different pipe materials are different, and an optimization algorithm is needed to consider the differences in pipe material qualities to avoid pipe bursts.

[0030] Water hammer problem: To avoid pipe bursts caused by water hammer phenomena, an optimization algorithm is needed to consider the water hammer effect.

[0031] Diversity scheduling problem of water pumps: There are various types of water pumps (variable-speed pumps, frequency-converting pumps, ordinary pumps, etc.), and an optimization algorithm is needed to adapt to the scheduling of different types of water pumps.

[0032] Problem of adaptability to electricity price changes: The electricity prices are different at different times, and an optimization algorithm is needed to consider the electricity price changes to reduce energy consumption costs.

[0033] Problem of labor costs on holidays: The overtime pay for workers on holidays is different, and an optimization algorithm is needed to consider the labor costs on holidays to reasonably arrange work plans.

[0034] Problem of water source treatment costs and water supply requirements: The treatment costs and water supply requirements of different water sources are different, and an optimization algorithm is needed to rationally allocate the water supply of each water source. Summary of the Invention

[0035] The technical problem to be solved by the present invention is to provide an optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation in view of the deficiencies of the background technology. By combining advanced algorithm strategies and artificial intelligence technologies, intelligent management of water pump scheduling in large-scale water supply networks is realized.

[0036] The present invention adopts the following technical solutions to solve the above technical problems:

[0037] Water supply network optimization scheduling system based on clustering grouping and Cauchy mutation, including data acquisition module, prediction module, scheduling module, control module and user interface; Through innovative points such as multi-dimensional comprehensive optimization algorithm, intelligent water consumption prediction model, adaptive pressure zoning technology, intelligent pump station collaborative scheduling system, comprehensive cost-benefit analysis tool, real-time data-driven dynamic scheduling, interdisciplinary technology integration platform, environment-friendly scheduling strategy, intelligent control and automation operation, and modular and scalable software architecture, etc., the intelligent and automated management of the water supply network is realized;

[0038] Among them, the multi-dimensional comprehensive optimization algorithm is used to combine the clustering grouping and Cauchy mutation strategies to cope with the complexity and diversity in the optimization scheduling of the water supply network;

[0039] The intelligent water consumption prediction model is used to achieve high-precision prediction of water consumption; It can capture the complex patterns and trends of water consumption changing with time, and consider various factors affecting water consumption;

[0040] The adaptive pressure zoning technology is used to optimize the water supply pressure according to different terrain conditions and real-time water consumption data to ensure the uniformity and adaptability of the water supply system;

[0041] The intelligent pump station collaborative scheduling system is used to realize the coordinated operation between multiple pump stations to achieve the optimization of the water supply system;

[0042] The comprehensive cost-benefit analysis tool is used to evaluate the economy of the water supply network scheduling scheme by integrating multiple cost factors, including electricity price changes, holiday labor costs and water source treatment costs;

[0043] The real-time data-driven dynamic scheduling is used for the intelligent scheduling solution based on the Internet of Things technology and prediction model to realize the real-time monitoring and dynamic management of the water supply network;

[0044] The interdisciplinary technology integration platform is used to provide comprehensive technical support for the optimization scheduling of the water supply network by integrating the technical advantages of different fields;

[0045] The environment-friendly scheduling strategy; It is used to reduce the energy consumption and environmental impact during the water supply process, while ensuring the water supply safety and efficiency;

[0046] The intelligent control and automation operation as well as modularization are used to utilize the intelligent control theory and automation technology to realize the precise and automatic operation of the control facilities in the water supply system;

[0047] The scalable software architecture is used to meet the needs of the water supply network optimization scheduling system with different regional and pipe network characteristics, to add new functions, integrate new technologies or adapt to new business requirements, while maintaining the stability and efficiency of the system.

[0048] Compared with the prior art, the present invention adopting the above technical solution has the following technical effects:

[0049] The present invention relates to an optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation. Through innovative points such as multi-dimensional comprehensive optimization algorithms, intelligent water consumption prediction models, adaptive pressure zoning technologies, intelligent pumping station collaborative scheduling systems, comprehensive cost-benefit analysis tools, real-time data-driven dynamic scheduling, interdisciplinary technology integration platforms, environmentally friendly scheduling strategies, intelligent control and automated operations, and modular and scalable software architectures, the intelligent and automated management of water supply networks is realized; the system uses Internet of Things technology to collect data in real time, combines deep learning models to perform high-precision water consumption prediction, optimizes water supply efficiency and reduces operating costs by dynamically adjusting pressure zones and collaborative scheduling of pumping stations; at the same time, the system comprehensively considers environmental impacts, preferentially uses green energy, ensures ecological flow, and protects water resources; the modular design and scalable architecture support the integration of new functions, adapt to the needs of different regions and pipe networks, and enhance the long-term sustainable development ability of the system. Brief Description of the Drawings

[0050] Figure 1 is the architecture diagram of the optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation of the present invention;

[0051] Figure 2 is the schematic diagram of clustering grouping of the present invention;

[0052] Figure 3 is the schematic diagram of the Cauchy mutation strategy of the present invention;

[0053] Figure 4 is the architecture diagram of the intelligent water consumption prediction model of the present invention;

[0054] Figure 5 is the schematic diagram of the adaptive pressure zoning technology of the present invention;

[0055] Figure 6 is the schematic diagram of the intelligent pumping station collaborative scheduling system of the present invention;

[0056] Figure 7 is the schematic diagram of the comprehensive cost-benefit analysis tool of the present invention;

[0057] Figure 8 is the schematic diagram of real-time data-driven dynamic scheduling of the present invention;

[0058] Figure 9 is the schematic diagram of the interdisciplinary technology integration platform of the present invention;

[0059] Figure 10 is the schematic diagram of the environmentally friendly scheduling strategy of the present invention. Detailed Embodiments

[0060] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings:

[0061] The technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effect of the present invention will become more apparent. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] As Figures 1 to 10 shown, the optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation includes a data acquisition module, a prediction module, a scheduling module, a control module, and a user interface; through innovative points such as multi-dimensional comprehensive optimization algorithms, intelligent water consumption prediction models, adaptive pressure zoning technology, intelligent pump station collaborative scheduling systems, comprehensive cost-benefit analysis tools, real-time data-driven dynamic scheduling, interdisciplinary technology integration platforms, environment-friendly scheduling strategies, intelligent control and automated operations, and modular and extensible software architectures, the intelligent and automated management of water supply networks is realized;

[0063] Among them, the multi-dimensional comprehensive optimization algorithm is used to combine the clustering grouping and Cauchy mutation strategies to cope with the complexity and diversity in the optimized scheduling of water supply networks;

[0064] The intelligent water consumption prediction model is used to achieve high-precision prediction of water consumption; it can capture the complex patterns and trends of water consumption changing over time, and consider various factors affecting water consumption;

[0065] The adaptive pressure zoning technology is used to optimize the water supply pressure according to different terrain conditions and real-time water consumption data to ensure the uniformity and adaptability of the water supply system;

[0066] The intelligent pump station collaborative scheduling system is used to achieve coordinated operation among multiple pump stations to achieve the optimization of the water supply system;

[0067] The comprehensive cost-benefit analysis tool is used to evaluate the economy of the water supply network scheduling plan by integrating multiple cost factors, including electricity price changes, holiday labor costs, and water source treatment costs;

[0068] The real-time data-driven dynamic scheduling is used for an intelligent scheduling solution based on Internet of Things technology and prediction models to achieve real-time monitoring and dynamic management of water supply networks;

[0069] An interdisciplinary technology integration platform for providing comprehensive technical support for the optimal scheduling of water supply networks by integrating the technical advantages of different fields;

[0070] An environmentally friendly scheduling strategy; used to reduce energy consumption and environmental impact during water supply while ensuring water supply safety and efficiency;

[0071] Intelligent control, automation operation, and modularization, for using intelligent control theory and automation technology to achieve precise and automatic operation of control facilities in the water supply system;

[0072] An extensible software architecture, for meeting the needs of the optimal scheduling system of water supply networks with different regional and network characteristics, for adding new functions, integrating new technologies, or adapting to new business requirements while maintaining the stability and efficiency of the system.

[0073] The principle of the multi-dimensional comprehensive optimization algorithm specifically includes

[0074] Clustering and grouping strategy: By performing clustering analysis on multiple parameters of the water supply network, including pump station location, pipeline layout, and water source type, grouping parameters with similar characteristics to reduce the dimension of the problem and improve the search efficiency of the algorithm;

[0075] Clustering and grouping formula: Clustering center update formula: Where C k is the center of the k-th cluster, G k is the set of individuals in the k-th cluster, and X i is the position vector of the individual;

[0076] Cauchy mutation strategy: Introducing the characteristics of the Cauchy distribution, increasing the exploration ability of the algorithm through the Cauchy mutation strategy, especially in the edge region of the search space; the heavy-tailed characteristic of the Cauchy distribution enables the algorithm to better jump out of the local optimal solution during the global search stage and improve the global quality of the solution;

[0077] Cauchy mutation formula: Individual mutation update formula: X new = X old + τ·Cauchy(0,1); where X new is the new position after mutation, X old is the original position, τ is the step size parameter, and Cauchy(0,1) is a standard Cauchy distribution random variable;

[0078] Multi-objective optimization: Achieving the comprehensive optimization of objectives through the weighted sum multi-objective evolutionary strategy;

[0079] Multi-objective optimization weight allocation: Objective function weighting formula: F(X) = w1·f1(X) + w2·f2(X) + … w n f n(X); where F(X) is the comprehensive objective function, and f i (X) is the i-th objective function, and w i is the corresponding weight coefficient.

[0080] The principle of the intelligent water consumption prediction model specifically includes:

[0081] Data-driven prediction method: By analyzing historical water consumption data, the internal law of water consumption changes can be learned, so as to predict future water consumption;

[0082] Deep learning model: Using the long short-term memory network (LSTM) deep learning model to process time series data and capture long-term dependencies, which is suitable for data with obvious time characteristics such as water consumption;

[0083] Feature engineering: Identify and construct features related to water consumption changes;

[0084] LSTM network structure: LSTM cell formula: f t = σ·(W f ·[h t-1 , x t +b f );

[0085] Input gate formula: i t = σ(W i ·[h t-1 , x t +b i );

[0086] Forget gate formula:

[0087] Cell state formula:

[0088] Output gate formula: o t = σ(W0·[h t-1 +b0);

[0089] Output value formula: h t = o t *tanh(C t );

[0090] where f t , i t , o t are the forget gate, input gate, and output gate respectively, C t is the cell state, h t is the output value, W and b are model parameters, σ is the sigmoid activation function, and tanh is the hyperbolic tangent activation function;

[0091] Prediction Model Training:

[0092] Loss Function: where L is the loss function, N is the number of samples, y i is the actual water consumption, is the predicted water consumption.

[0093] The principle of the adaptive pressure zoning technology specifically includes:

[0094] Real-time data monitoring: By installing pressure sensors and flow meters in the water supply network, the water supply pressure and water consumption data are monitored in real time;

[0095] Topographic analysis: Using Geographic Information System (GIS) and Digital Elevation Model (DEM) to analyze the topographic features in the water supply area and identify areas with large elevation differences;

[0096] Pressure zoning model: Based on topographic analysis and real-time data, a pressure zoning model is established to divide the water supply area into different pressure management zones;

[0097] Pressure zoning division: Pressure demand calculation: P i = f(H i , Q i , T i ); where P i is the pressure demand of the i-th area, H i is the elevation, Q i is the flow rate, T i is the time;

[0098] Dynamic adjustment mechanism: According to the real-time monitoring data and the preset pressure management strategy, the pressure zoning is dynamically adjusted to adapt to the changes in water consumption and topographic differences;

[0099] Pressure adjustment formula: ΔP = k·(P actual - P desired ); where ΔP is the pressure adjustment amount, P actual is the actual pressure, P desired is the desired pressure, and k is the adjustment coefficient;

[0100] Application of optimization algorithms: The application of optimization algorithms includes genetic algorithms and particle swarm optimization to solve the optimal pressure zoning scheme;

[0101] Pressure zoning optimization: The goal is to minimize the sum of the squares of the deviations between the pressures in all areas and the desired pressure.

[0102] The principle of the intelligent pumping station collaborative scheduling system specifically includes:

[0103] System Integration and Communication: By integrating the data acquisition systems of each pumping station and establishing an efficient communication network, real-time data sharing is achieved;

[0104] Real-time Data Monitoring: Real-time monitoring of the operating status of each pumping station, including flow rate, pressure, and energy consumption parameters;

[0105] Pumping Station Performance Evaluation: Performance Index Calculation: Among them, P performance is the pumping station performance index, Qactual and Hactual are the actual flow rate and head respectively, Qrated and Hrated are the rated flow rate and head, and η is the efficiency;

[0106] Collaborative Optimization Algorithm: Application of the multi-agent collaborative optimization algorithm, including multi-objective optimization and game theory, to achieve collaborative scheduling among pumping stations;

[0107] Collaborative Scheduling Optimization: Optimization Objective Function: Among them, C i is the operating cost of the i-th pumping station, and X i are the operating parameters of the pumping station;

[0108] Prediction and Decision Support: Combining the water consumption prediction model to provide decision support for pumping station scheduling and optimize the water supply plan;

[0109] Adaptive Control: It can adaptively adjust the operating parameters of the pumping station according to real-time data and preset control strategies to cope with changes in external conditions;

[0110] Constraint Conditions: Flow Balance Constraint: Among them, Q i is the flow rate of the i-th pumping station, and Q demand is the total demand.

[0111] The principle of the comprehensive cost-benefit analysis tool specifically includes:

[0112] Cost Factor Integration: Integrating all cost factors related to water supply, including electricity price, labor cost, and water source treatment cost, to comprehensively evaluate the economy of the scheduling plan;

[0113] Total Cost Calculation: C total = C energy + C labor + C source ; Among them, C total is the total cost, C energy is the energy consumption cost, C labor is the labor cost, and C source is the water source treatment cost;

[0114] Dynamic electricity price adaptation: Considering the time-varying characteristics of electricity prices, adjust the operation plan of the pumping station according to the electricity price changes to reduce the energy consumption cost;

[0115] Calculation of energy consumption cost: Among them, P(t) is the power of the pumping station at time t, E(t) is the operating time, and C electric (t) is the electricity price at time t;

[0116] Calculation of labor cost: Calculate the labor cost under different scheduling schemes according to the differences in labor costs between holidays and non-holidays;

[0117] C labor = N×W×H; Among them, N is the number of employees, W is the wage rate, and H is the working hour adjustment coefficient between holidays and non-holidays;

[0118] Assessment of water source treatment costs: Evaluate the cost-effectiveness of using different water sources according to the treatment costs and water supply requirements of different water sources;

[0119] Calculation of water source treatment costs: Among them, Q i is the water supply of the i-th water source, and C i is the treatment cost of the i-th water source;

[0120] Multi-objective optimization: Optimize the scheduling scheme to minimize costs on the premise of meeting water supply safety and service quality.

[0121] The principle of real-time data-driven dynamic scheduling specifically includes:

[0122] Application of Internet of Things technology: Use sensors and intelligent devices deployed in the water supply network to collect key data, including flow rate, pressure, and water quality parameters;

[0123] Real-time data transmission: Transmit the collected data to the central processing system in real time through a wireless communication network;

[0124] Data integration and processing: Integrate data from different sensors, and perform cleaning, integration, and analysis for further processing;

[0125] Data normalization: Among them, X norm is the normalized data, X is the original data, X min and X max are the minimum and maximum values of the data respectively;

[0126] Integration of prediction models: Combine machine learning or deep learning models, time series prediction models, to predict future water consumption and network status;

[0127] Time series prediction: Among them, is the predicted value at time t, Y t-1 , Y t-2 ,…, Y t-n are historical data;

[0128] Dynamic scheduling algorithm: Develop a dynamic scheduling algorithm to automatically adjust the pump operation plan and pressure control according to real-time data and prediction results;

[0129] Scheduling optimization: X * = arg min X C(X); where X * is the optimal scheduling scheme, C(X) is the cost function, and X is the scheduling parameter;

[0130] Event detection and response: Real-time detect abnormal events in the pipe network, including leaks or faults, and automatically trigger emergency response measures;

[0131] User interaction and feedback: Provide a user interface to enable operators to monitor the system status and make manual adjustments as needed.

[0132] The principle of the interdisciplinary technology integration platform specifically includes:

[0133] Fluid mechanics simulation: Use the principles of fluid mechanics to simulate the movement of water flow in the pipe network, and predict the water flow velocity, pressure distribution, and water hammer effect;

[0134] Fluid mechanics simulation: Continuity equation: Q in = Q out ; where Q in is the flow rate entering the node, and Q out is the flow rate leaving the node;

[0135] Bernoulli equation: where P is the pressure, v is the flow velocity, z is the height, γ is the specific gravity of water, and g is the acceleration due to gravity;

[0136] Control engineering: Apply control theory to precisely control the operation of key equipment such as pump stations and valves to achieve optimal water flow distribution; PID controller: where u(t) is the control input, e(t) is the error, and K p , K i , K d are the proportional, integral, and differential gains respectively;

[0137] Data science: Use data science methods to process and analyze a large amount of water utility data, including water consumption prediction, anomaly detection, and pattern recognition;

[0138] Anomaly Detection: Anomaly Score = f(Data Point, Model Prediction), where the anomaly score is calculated based on the difference between the data point and the model prediction.

[0139] The principle of the environmentally friendly scheduling strategy specifically includes

[0140] Green Energy Utilization: Priority is given to using renewable energy or clean energy, including solar energy and wind energy, to reduce carbon emissions and environmental pollution;

[0141] Energy Conservation and Emission Reduction: By optimizing the operation plan of the pumping stations and the network scheduling, the energy consumption is reduced and the operating cost is lowered;

[0142] Energy Consumption Calculation: E = P × T; where E is the energy consumption, P is the power of the pumping station, and T is the operating time;

[0143] Water Resource Protection: Measures are taken to protect the water source areas, prevent water source pollution, and ensure the quality and safety of water resources;

[0144] Ecological Flow Maintenance: The ecological flow requirements of rivers are considered in the water supply scheduling to ensure the health and stability of the river ecosystem;

[0145] Environmental Impact Assessment: An environmental impact assessment is carried out on the scheduling scheme, and the scheme with the least environmental impact is selected;

[0146] Environmental Impact Assessment: Environmental Impact Index: where I is the environmental impact index, w i is the weight of the i-th environmental factor, and e i is the impact degree of the i-th environmental factor;

[0147] Intelligent Scheduling: An intelligent scheduling system is used to monitor and adjust the water supply process in real time to adapt to environmental changes and demand fluctuations.

[0148] The principle of intelligent control and automatic operation lies in using intelligent control theory and automation technology to achieve precise and automatic operation of the control facilities in the water supply system; applying modern control theory, including fuzzy logic control, adaptive control, and predictive control, to achieve intelligent management of the control facilities such as pumping stations and valves in the water supply system

[0149] Fuzzy Logic Control: Fuzzy Rule: IF X is A THEN Y is B; where X and Y are input and output variables, and A and B are fuzzy sets;

[0150] Adaptive Control: Control Law Adjustment: u(t) = -K(t)e(t); where u(t) is the control input, K(t) is the control gain that changes with time, and e(t) is the error;

[0151] Predictive control: Prediction model: Among them, is the predicted output, and u(t) is the control input.

[0152] Automation technology: Integrated automation technologies such as programmable logic controllers (PLCs), remote terminal units (RTUs), etc. are used to achieve remote operation and monitoring of control facilities.

[0153] Sensor and actuator network: Deploy a sensor and actuator network to monitor the status of the water supply system in real time and automatically adjust the valve opening, pump station speed, etc. according to control instructions.

[0154] Real-time data processing: Through real-time data processing technology, quickly respond to system state changes and timely adjust control strategies.

[0155] Fault diagnosis and self-healing: Integrate a fault diagnosis system to achieve early identification and automatic repair of potential faults in the water supply system.

[0156] User interface and interaction: Provide an intuitive user interface that enables operators to monitor the system status and perform manual intervention when necessary.

[0157] Modular and extensible software architecture:

[0158] The modular and extensible software architecture is designed to meet the needs of the optimized scheduling system for water supply networks with different regional and pipe network characteristics. This architecture allows the system to easily add new functions, integrate new technologies, or adapt to new business requirements while maintaining the stability and efficiency of the system. The following are the key technical principles of this architecture:

[0159] Modular design: Decompose the software system into independent and interchangeable modules, with each module responsible for a specific function for easy management and maintenance.

[0160] Interface standardization: Define clear interfaces and protocols to ensure seamless integration and communication between different modules.

[0161] Data abstraction: Through data abstraction, hide the internal complexity of the module and expose only the necessary operations and data to other modules.

[0162] Service-oriented architecture (SOA): Adopt a service-oriented architecture, encapsulate business logic into services, and access them through service interfaces.

[0163] Microservices architecture: Use a microservices architecture to decompose the application into a set of small, independent services, with each service running in its own process and usually built around specific business capabilities.

[0164] Containerization and Virtualization: Leveraging containerization and virtualization technologies to enhance system portability and scalability.

[0165] Continuous Integration and Continuous Deployment (CI / CD): Implement continuous integration and continuous deployment to automate the software building, testing, and deployment processes.

[0166] Although modular and extensible software architectures do not directly involve specific calculation formulas, the following concepts may be used in designing and evaluating system performance:

[0167] System Scalability Evaluation: Performance Scalability: Where S is scalability, T(N) is the performance of the system with N nodes, and T(1) is the performance of a single node;

[0168] Service Response Time: Response Time: Where R is the average response time and throughput is the number of requests the system can handle per unit time.

[0169] 1. Implementation Example of Yining Water Supply Network Scheduling

[0170] Project Background: As the capital of Ili Kazakh Autonomous Prefecture in Xinjiang, the city scale of Yining is continuously expanding, and the water supply demand is increasing day by day. To improve water supply efficiency, reduce operation costs, and ensure water supply safety, the Water Affairs Bureau of Yining decided to implement an optimized scheduling system for the water supply network. This system will comprehensively apply technologies such as fluid mechanics simulation, control engineering, data science, and software engineering to achieve intelligent and automated scheduling management.

[0171] System Architecture: The system adopts a modular and extensible architecture, including a data acquisition module, a prediction module, a scheduling module, a control module, and a user interface. The specific architecture is as follows:

[0172] Implementation Steps:

[0173] 3.1 Data Acquisition Module

[0174] 1) Deployment of Internet of Things Sensors:

[0175] Deploy flow meters, pressure sensors, and water quality sensors at key nodes of the Yining water supply network.

[0176] a. IoT devices such as these to collect real-time data on flow, pressure, water quality, etc.

[0177] b. Select appropriate sensor brands and models to ensure data accuracy and reliability.

[0178] 2) Real-time Data Transmission:

[0179] a. Transmit the collected data to the central processing system in real time through the 4G / 5G wireless communication network.

[0180] b. Establish a data transmission protocol to ensure the stability and security of data transmission.

[0181] 3) Data preprocessing:

[0182] a. Clean, normalize, and perform anomaly detection on the collected data to ensure the accuracy and availability of the data.

[0183] b. Use data preprocessing software to automatically process and mark abnormal data.

[0184] 3.2 Prediction module

[0185] 1) Intelligent water consumption prediction model:

[0186] a. Collect historical water consumption data, weather data, holiday information, etc. as input features of the model.

[0187] b. Use an LSTM network to build a water consumption prediction model and train the model to improve the prediction accuracy.

[0188] c. Update the model regularly to ensure the accuracy and timeliness of the prediction results.

[0189] 2) Prediction result output:

[0190] a. Output the prediction results to the scheduling module to provide data support for scheduling decisions.

[0191] b. Display the prediction results through the user interface to facilitate monitoring and adjustment by operators.

[0192] 3.3 Scheduling module

[0193] 1) Multi-dimensional comprehensive optimization algorithm:

[0194] a. Apply a clustering grouping strategy to divide the water supply network into multiple clustering groups and perform independent optimization within each group.

[0195] b. Use the Cauchy mutation strategy to enhance the global search ability of the algorithm and avoid local optimal solutions.

[0196] c. Implement an adaptive pressure zoning technology to dynamically adjust the pressure zoning according to real-time terrain and water consumption data.

[0197] d. Adopt intelligent pump station collaborative scheduling to optimize the operation plans of multiple pump stations and improve the overall water supply efficiency.

[0198] e. Conduct a comprehensive cost-benefit analysis, combined with electricity price changes, holiday labor costs, and water source treatment costs, to optimize the scheduling plan.

[0199] 2) Real-time data-driven scheduling:

[0200] a. Dynamically adjust the scheduling plan according to real-time data and prediction results to cope with fluctuations in water consumption and emergencies.

[0201] b. Through intelligent control and automated operations, achieve automatic control of pumping stations and valves, reducing manual intervention.

[0202] 3.4 Control module

[0203] 1) Intelligent control and automated operations:

[0204] a. Apply fuzzy logic control, adaptive control, and predictive control to achieve intelligent management of pumping stations and valves.

[0205] b. Real-time monitor the device status, automatically diagnose and repair potential faults, improving the reliability and stability of the system.

[0206] c. Through the user interface, operators can monitor the system status and perform manual intervention when necessary.

[0207] 2) Fault diagnosis and self-healing:

[0208] a. Establish a fault diagnosis system to real-time monitor the device status and automatically identify and repair faults.

[0209] b. Through the self-healing mechanism, automatically adjust the operating parameters to ensure the normal operation of the system.

[0210] 3.5 User interface

[0211] 1) Operator monitoring:

[0212] a. Provide real-time monitoring functions so that operators can understand the system status at any time.

[0213] b. Display key indicators such as flow rate, pressure, and energy consumption through charts and dashboards.

[0214] 2) Manual intervention:

[0215] a. Operators can perform manual intervention through the user interface to adjust the scheduling plan and control parameters.

[0216] b. Provide an operation log recording function for convenient subsequent analysis and auditing.

[0217] 3) System status display:

[0218] a. Display the operating status and key parameters of the system, facilitating management and decision-making by operators.

[0219] b. Provide alarm and notification functions to promptly remind operators to handle abnormal situations.

[0220] 4. Environmentally Friendly Scheduling Strategies

[0221] 1) Utilization of green energy:

[0222] a. Install solar panels at the pumping station to drive the pumping station using solar energy and reduce dependence on traditional energy sources.

[0223] b. Explore the utilization of wind energy and other renewable energy sources to optimize the energy structure.

[0224] 2) Energy conservation and emission reduction:

[0225] a. By optimizing the operation of the pumping station and intelligent scheduling, reduce energy consumption and operating costs.

[0226] b. Monitor energy consumption in real time and promptly adjust the operation strategy to ensure the efficient operation of the system.

[0227] 3) Water resource protection:

[0228] a. Protect water source areas to prevent pollution and overdevelopment.

[0229] b. Regularly monitor water quality to ensure water supply safety.

[0230] c. Take measures to prevent and reduce pollution and protect water resources.

[0231] 4) Maintenance of ecological flow:

[0232] a. Ensure that rivers have sufficient ecological flow to maintain ecological balance.

[0233] b. Protect wetland ecosystems and maintain biodiversity.

[0234] c. Implement an ecological compensation mechanism to promote sustainable development.

[0235] 5) Environmental impact assessment:

[0236] a. Regularly monitor environmental indicators and evaluate the impact of the scheduling plan.

[0237] b. Select the optimal plan to reduce the impact on the environment.

[0238] c. Based on the evaluation results, continuously improve the scheduling strategy to ensure the environmental friendliness of the system.

[0239] 5. Project implementation effects

[0240] 1) Improvement in water supply efficiency:

[0241] a. Through intelligent scheduling, the water supply efficiency of the water supply network in Yining City has increased by 20%.

[0242] b. The accuracy of water consumption prediction reaches over 90%, effectively coping with the fluctuations in water consumption.

[0243] 2) Reduction in operating costs:

[0244] a. Through energy conservation and emission reduction measures, energy consumption has been reduced by 15%.

[0245] b. Labor costs have been significantly reduced, especially during holidays and at night, reducing unnecessary overtime pay expenditures. 3) Improvement in water supply safety:

[0246] a. Through intelligent control and automated operation, the system failure rate has been reduced by 30%.

[0247] b. Through real-time monitoring and fault diagnosis, potential faults are promptly handled to ensure water supply safety.

[0248] 4) Environmentally friendly:

[0249] a. Through the utilization of green energy, carbon emissions have been reduced, meeting the requirements of green development.

[0250] b. Through maintaining ecological flow and protecting water resources, the health and stability of the ecological environment are ensured.

[0251] Project summary

[0252] The implementation of the optimized scheduling system for the water supply network in Yining City has successfully achieved intelligent and automated scheduling management. By comprehensively applying technologies such as fluid mechanics simulation, control engineering, data science, and software engineering, the system has achieved remarkable results in improving water supply efficiency, reducing operating costs, ensuring water supply safety, and protecting the environment. In the future, the Water Affairs Bureau of Yining City will continue to optimize the system to further improve the quality of water supply services and environmental friendliness.

[0253] 2. Example of water supply network scheduling in Yining City:

[0254] Project background: As the capital of Ili Kazakh Autonomous Prefecture in Xinjiang, the urban scale of Yining City is constantly expanding, and the water supply demand is increasing day by day. To improve water supply efficiency, reduce operating costs, and ensure water supply safety, the Water Affairs Bureau of Yining City decided to implement an optimized scheduling system for the water supply network. This system will comprehensively apply technologies such as fluid mechanics simulation, control engineering, data science, and software engineering to achieve intelligent and automated scheduling management.

[0255] System architecture: The system adopts a modular and scalable architecture, including a data acquisition module, a prediction module, a scheduling module, a control module, and a user interface. The specific architecture is as follows:

[0256] Implementation steps: 3.1 Data acquisition module

[0257] Internet of Things Sensor Deployment:

[0258] c. Deploy Internet of Things devices such as flow meters, pressure sensors, and water quality sensors at key nodes of the water supply network in Yining City to collect data on flow rate, pressure, water quality, etc. in real time.

[0259] d. Select appropriate sensor brands and models to ensure the accuracy and reliability of the data.

[0260] Real-time Data Transmission:

[0261] e. Transmit the collected data to the central processing system in real time through the 4G / 5G wireless communication network.

[0262] f. Establish a data transmission protocol to ensure the stability and security of data transmission.

[0263] Data Preprocessing:

[0264] g. Clean, normalize, and perform anomaly detection on the collected data to ensure the accuracy and availability of the data.

[0265] h. Use data preprocessing software to automatically process and mark abnormal data.

[0266] Prediction Module: Intelligent Water Consumption Prediction Model:

[0267] c. Collect historical water consumption data, weather data, holiday information, etc. as input features for the model.

[0268] d. Use the LSTM network to build a water consumption prediction model and train the model to improve the prediction accuracy.

[0269] e. Update the model regularly to ensure the accuracy and timeliness of the prediction results.

[0270] 3) Prediction Result Output:

[0271] a. Output the prediction results to the scheduling module to provide data support for scheduling decisions.

[0272] b. Display the prediction results through the user interface to facilitate monitoring and adjustment by operators.

[0273] Scheduling Module:

[0274] 3) Multi-dimensional Comprehensive Optimization Algorithm:

[0275] a. Apply the clustering grouping strategy to divide the water supply network into multiple clustering groups and perform independent optimization within each group.

[0276] b. Use the Cauchy mutation strategy to enhance the global search ability of the algorithm and avoid local optimal solutions.

[0277] c. Implement the adaptive pressure zoning technology to dynamically adjust the pressure zones according to real-time terrain and water consumption data.

[0278] d. Adopt intelligent pump station collaborative scheduling to optimize the operation plans of multiple pump stations and improve the overall water supply efficiency.

[0279] e. Conduct comprehensive cost-benefit analysis, combine electricity price changes, holiday labor costs, and water source treatment costs to optimize the scheduling plan.

[0280] 4) Real-time data-driven scheduling:

[0281] a. Dynamically adjust the scheduling plan according to real-time data and prediction results to cope with fluctuations in water consumption and emergencies.

[0282] b. Through intelligent control and automated operation, achieve automatic control of pump stations and valves, reducing manual intervention.

[0283] Control module:

[0284] 3) Intelligent control and automated operation:

[0285] a. Apply fuzzy logic control, adaptive control, and predictive control to achieve intelligent management of pump stations and valves.

[0286] b. Real-time monitor the equipment status, automatically diagnose and repair potential faults, improving the reliability and stability of the system.

[0287] c. Through the user interface, operators can monitor the system status and perform manual intervention when necessary.

[0288] 4) Fault diagnosis and self-healing:

[0289] a. Establish a fault diagnosis system to real-time monitor the equipment status and automatically identify and repair faults.

[0290] b. Through the self-healing mechanism, automatically adjust the operation parameters to ensure the normal operation of the system.

[0291] User interface

[0292] 4) Operator monitoring:

[0293] a. Provide real-time monitoring function so that operators can understand the system status at any time.

[0294] b. Display key indicators such as flow rate, pressure, energy consumption, etc. through charts and dashboards.

[0295] 5) Manual intervention:

[0296] a. Operators can perform manual intervention through the user interface to adjust the scheduling plan and control parameters.

[0297] b. Provide an operation logging function for convenient subsequent analysis and auditing.

[0298] 6) System status display:

[0299] a. Display the operating status and key parameters of the system to facilitate management and decision-making by operators.

[0300] b. Provide an alarm and notification function to promptly alert operators to handle abnormal situations.

[0301] Environmentally friendly scheduling strategy:

[0302] 6) Green energy utilization:

[0303] a. Install solar panels at the pumping station to drive the pumping station using solar energy and reduce dependence on traditional energy sources.

[0304] b. Explore the utilization of wind energy and other renewable energy sources to optimize the energy structure.

[0305] 7) Energy conservation and emission reduction:

[0306] a. Reduce energy consumption and operating costs by optimizing the operation of the pumping station and intelligent scheduling.

[0307] b. Monitor energy consumption in real time and adjust the operation strategy in a timely manner to ensure the efficient operation of the system.

[0308] 8) Water resource protection:

[0309] a. Protect the water source area to prevent pollution and overdevelopment.

[0310] b. Regularly monitor water quality to ensure water supply safety.

[0311] c. Take measures to prevent and reduce pollution and protect water resources.

[0312] 9) Ecological flow maintenance:

[0313] a. Ensure that the river has sufficient ecological flow to maintain ecological balance.

[0314] b. Protect the wetland ecosystem and maintain biodiversity.

[0315] c. Implement an ecological compensation mechanism to promote sustainable development.

[0316] 10) Environmental impact assessment:

[0317] a. Regularly monitor environmental indicators and evaluate the impact of the scheduling plan.

[0318] b. Select the optimal plan to reduce the impact on the environment.

[0319] c. Continuously improve the scheduling strategy based on the evaluation results to ensure a friendly system environment.

[0320] Project implementation effect:

[0321] 5) Improvement in water supply efficiency:

[0322] a. Through intelligent scheduling, the water supply efficiency of the water supply network in Yining City has increased by 20%.

[0323] b. The accuracy of water consumption prediction reaches over 90%, effectively coping with the fluctuations in water consumption.

[0324] 6) Reduction in operating costs:

[0325] a. Through energy conservation and emission reduction measures, energy consumption has been reduced by 15%.

[0326] b. The labor cost has been significantly reduced, especially during holidays and at night, reducing unnecessary overtime pay expenditures. 7) Improvement in water supply safety:

[0327] a. Through intelligent control and automated operation, the system failure rate has been reduced by 30%.

[0328] b. Through real-time monitoring and fault diagnosis, potential faults are promptly handled to ensure water supply safety.

[0329] 8) Environmentally friendly:

[0330] a. Through the utilization of green energy, carbon emissions have been reduced, meeting the requirements of green development.

[0331] b. Through maintaining ecological flow and protecting water resources, the health and stability of the ecological environment are ensured.

[0332] Project summary: The implementation of the optimized scheduling system for the water supply network in Yining City has successfully achieved intelligent and automated scheduling management. By comprehensively applying technologies such as fluid mechanics simulation, control engineering, data science, and software engineering, the system has achieved remarkable results in improving water supply efficiency, reducing operating costs, ensuring water supply safety, and protecting the environment. In the future, the Water Affairs Bureau of Yining City will continue to optimize the system to further improve the quality of water supply services and environmental friendliness.

[0333] Those of ordinary skill in the art can understand that the above are only preferred examples of the invention and are not used to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, for those skilled in the art, they can still modify the technical solutions described in the foregoing examples or perform equivalent replacements for some of the technical features. All modifications, equivalent replacements, etc. made within the spirit and principles of the invention shall be included within the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0334] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation is characterized in that: It includes a data acquisition module, a prediction module, a scheduling module, a control module, and a user interface; through innovative points such as multi-dimensional comprehensive optimization algorithms, intelligent water consumption prediction models, adaptive pressure zoning technologies, intelligent pump station collaborative scheduling systems, comprehensive cost-benefit analysis tools, real-time data-driven dynamic scheduling, interdisciplinary technology integration platforms, environmentally friendly scheduling strategies, intelligent control and automated operations, and modular and scalable software architectures, it realizes the intelligent and automated management of water supply networks; Among them, the multi-dimensional comprehensive optimization algorithm is used to combine clustering grouping and Cauchy mutation strategies to cope with the complexity and diversity in the optimal scheduling of water supply networks; The intelligent water consumption prediction model is used to achieve high-precision prediction of water consumption; it can capture the complex patterns and trends of water consumption changing over time, and consider various factors affecting water consumption; The adaptive pressure zoning technology is used to optimize the water supply pressure according to different terrain conditions and real-time water consumption data to ensure the uniformity and adaptability of the water supply system; The intelligent pump station collaborative scheduling system is used to achieve the coordinated operation between multiple pump stations to achieve the optimization of the water supply system; The comprehensive cost-benefit analysis tool is used to evaluate the economy of the water supply network scheduling plan by integrating multiple cost factors, including electricity price changes, holiday labor costs, and water source treatment costs; The real-time data-driven dynamic scheduling is used for an intelligent scheduling solution based on Internet of Things technology and prediction models to achieve real-time monitoring and dynamic management of water supply networks; The interdisciplinary technology integration platform is used to provide comprehensive technical support for the optimal scheduling of water supply networks by integrating the technical advantages of different fields; The environmentally friendly scheduling strategy; it is used to reduce energy consumption and environmental impacts during the water supply process while ensuring water supply safety and efficiency; The intelligent control and automated operations and modularity are used to utilize intelligent control theory and automated technology to achieve precise and automatic operations of control facilities in the water supply system; The scalable software architecture is used to meet the needs of water supply network optimal scheduling systems with different regional and pipe network characteristics, to add new functions, integrate new technologies, or adapt to new business requirements while maintaining the stability and efficiency of the system.

2. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of the multi-dimensional comprehensive optimization algorithm specifically includes The clustering grouping strategy: By performing clustering analysis on multiple parameters of the water supply network, including pump station locations, pipeline layouts, and water source types, group the parameters with similar characteristics to reduce the dimension of the problem and improve the search efficiency of the algorithm; Clustering grouping formula: Cluster center update formula: where C k is the center of the k-th cluster, G k is the set of individuals in the k-th cluster, and X i is the position vector of the individual; The Cauchy mutation strategy: Introduce the characteristics of the Cauchy distribution, and increase the exploration ability of the algorithm through the Cauchy mutation strategy, especially in the marginal areas of the search space; the heavy-tailed characteristic of the Cauchy distribution enables the algorithm to better jump out of the local optimal solution during the global search stage and improve the global quality of the solution; Cauchy mutation formula: Individual mutation update formula: X new = X old + τ·Cauchy(0, 1); where, X new is the new position after mutation, X old is the original position, τ is the step size parameter, and Cauchy(0, 1) is a standard Cauchy distribution random variable; Multi-objective optimization: Achieve the comprehensive optimization of objectives through the weighted sum multi-objective evolutionary strategy; Multi-objective optimization weight allocation: Objective function weighting formula: F(X) = w1·f1(X) + w2·f2(X) + … w n f n (X); where F(X) is the comprehensive objective function, f i (X) is the i-th objective function, w i is the corresponding weight coefficient.

3. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of the intelligent water consumption prediction model specifically includes: The data-driven prediction method: By analyzing historical water consumption data, it can learn the internal laws of water consumption changes, so as to predict future water consumption; Deep learning model: Utilize the Long Short-Term Memory (LSTM) deep learning model to process time series data, capture long-term dependencies, and is applicable to data with obvious time characteristics such as water consumption; Feature Engineering: Identify and construct features related to changes in water consumption; LSTM network structure: LSTM cell formula: f t = σ · (W f · [h t-1 , x t + b f ) Input gate formula: i t = σ(W i · [h t-1 , x t + b i ) Forget gate formula: Unit status formula: Output gate formula: o t = σ(W0 · [h t-1 + b0); Output value formula: h t = o t *tanh(C t ); Among them, f t , i t , o t are the forget gate, input gate, and output gate respectively. C t is the cell state, h t is the output value, W and b are model parameters, σ is the sigmoid activation function, and tanh is the hyperbolic tangent activation function; Prediction model training: Loss function: where L is the loss function, N is the number of samples, y i is the actual water consumption, is the predicted water consumption.

4. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of the adaptive pressure zoning technology specifically includes: Real-time data monitoring: Through pressure sensors and flow meters installed in the water supply network, real-time monitor the water supply pressure and water consumption data; Topographic analysis: Use Geographic Information System (GIS) and Digital Elevation Model (DEM) to analyze the topographic features in the water supply area and identify areas with large elevation differences; Pressure zoning model: Based on topographic analysis and real-time data, establish a pressure zoning model to divide the water supply area into different pressure management zones; Pressure zone division: Pressure demand calculation: P i = f(H i , Q i , T i ); where P i is the pressure demand of the i-th region, H i is the elevation, Q i is the flow rate, T i is the time; Dynamic adjustment mechanism: According to real-time monitoring data and preset pressure management strategies, dynamically adjust the pressure zoning to adapt to changes in water consumption and topographic differences; Pressure adjustment formula: ΔP = k·(P actual - P desired ); where ΔP is the pressure adjustment amount, P actual is the actual pressure, P desired is the desired pressure, and k is the adjustment coefficient; Application of optimization algorithms: The application of optimization algorithms includes genetic algorithms and particle swarm optimization to solve the optimal pressure zoning scheme; Pressure zone optimization: The goal is to minimize the sum of the squared deviations between the pressures in all zones and the desired pressure.

5. The optimized scheduling system for water supply pipe networks based on clustering grouping and Cauchy mutation according to claim 1, wherein: The principle of the intelligent pumping station collaborative scheduling system specifically includes: System integration and communication: By integrating the data acquisition systems of each pumping station and establishing an efficient communication network, realize the real-time sharing of data; Real-time data monitoring: Real-time monitor the operating status of each pumping station, including flow rate, pressure, and energy consumption parameters; Performance evaluation of pumping station: Calculation of performance indicators: where P performance is the performance indicator of the pumping station, Qactual and Hactual are the actual flow rate and head respectively, Qrated and Hrated are the rated flow rate and head, and η is the efficiency; Collaborative optimization algorithm: Apply multi-agent collaborative optimization algorithms, including multi-objective optimization and game theory, to achieve collaborative scheduling between pumping stations; Collaborative scheduling optimization: Optimization objective function: Among them, C i is the operating cost of the i-th pumping station, and X i is the operating parameter of the pumping station; Prediction and decision support: Combine the water consumption prediction model to provide decision support for pumping station scheduling and optimize the water supply plan; Adaptive control: Can adaptively adjust the operating parameters of the pumping station according to real-time data and preset control strategies to cope with changes in external conditions; Constraint: Flow balance constraint: where Q i is the flow rate of the i-th pumping station, and Q demand is the total demand.

6. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of the comprehensive cost-benefit analysis tool specifically includes: Integration of cost factors: Integrate all cost factors related to water supply, including electricity price, labor cost, and water source treatment cost, to comprehensively evaluate the economy of the scheduling scheme; Total cost calculation: C total = C energy + C labor + C source ; Among them, C total is the total cost, C energy is the energy consumption cost, C labor is the labor cost, C source is the water source treatment cost; Adaptation to dynamic electricity price: Consider the time-varying characteristics of the electricity price and adjust the operating plan of the pumping station according to the change in the electricity price to reduce the energy consumption cost; Calculation of energy consumption cost: Among them, P(t) is the pumping station power at time t, E(t) is the operating time, and C electric (t) is the electricity price at time t; Calculation of labor cost: Calculate the labor cost under different scheduling schemes according to the difference in labor cost between holidays and non-holidays; C labor = N × W × H; where N is the number of employees, W is the wage rate, and H is the working hours adjustment coefficient for holidays and non-holidays; Assessment of water source treatment cost: According to the treatment cost of different water sources and water supply requirements, evaluate the cost-benefit of using different water sources; Calculation of water source treatment cost: Among them, Q i is the water supply volume of the i-th water source, and C i is the treatment cost of the i-th water source; Multi-objective optimization: Optimize the scheduling scheme to minimize costs on the premise of meeting water supply safety and service quality.

7. The optimized scheduling system for water supply pipe networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of real-time data-driven dynamic scheduling specifically includes: Application of Internet of Things technology: Use sensors and intelligent devices deployed in the water supply network to collect key data, including flow rate, pressure, and water quality parameters; Real-time data transmission: Through a wireless communication network, transmit the collected data to the central processing system in real time; Data integration and processing: Integrate data from different sensors and perform cleaning, integration, and analysis for further processing; Data normalization: Among them, X norm is the normalized data, X is the original data, X min and X max are the minimum and maximum values of the data respectively; Integration of prediction models: Combine machine learning or deep learning models, time series prediction models, to predict future water consumption and pipe network status; Time series prediction: Among them, is the predicted value at time t, Y t-1 , Y t-2 , …, Y t-n are historical data; Dynamic Scheduling Algorithm: Develop a dynamic scheduling algorithm to automatically adjust the pump operation plan and pressure control based on real-time data and prediction results; Scheduling optimization: X * = arg min X C(X); where X * is the optimal scheduling plan, C(X) is the cost function, and X is the scheduling parameter; Event Detection and Response: Real-time detect abnormal events in the pipe network, including leaks or faults, and automatically trigger emergency response measures; User Interaction and Feedback: Provide a user interface that enables operators to monitor the system status and make manual adjustments as needed.

8. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principles of the interdisciplinary technology integration platform specifically include: Fluid Mechanics Simulation: Use fluid mechanics principles to simulate the movement of water in the pipe network, predict water flow velocity, pressure distribution, and water hammer effects; Fluid mechanics simulation: Continuity equation: Q in = Q out ; where Q in is the flow rate entering the node, and Q out is the flow rate leaving the node; Bernoulli equation: where P is pressure, v is flow velocity, z is height, γ is the specific gravity of water, and g is the acceleration due to gravity; Control Engineering: Applying control theory to precisely control the operation of key equipment such as pumping stations and valves to achieve optimal water flow distribution; PID Controller: where u(t) is the control input, e(t) is the error, and K p , K i , K d are the proportional, integral, and derivative gains respectively; Data Science: Apply data science methods to process and analyze a large amount of water utility data, including water consumption prediction, anomaly detection, and pattern recognition; Anomaly Detection: Anomaly Score = f(Data Point, Model Prediction), where the anomaly score is calculated based on the difference between the data point and the model prediction.

9. The optimized scheduling system for water supply networks based on clustering grouping and Cauchy mutation according to claim 1, wherein: The principles of the environmentally friendly scheduling strategy specifically include Green Energy Utilization: Prioritize the use of renewable energy or clean energy, including solar energy and wind energy, to reduce carbon emissions and environmental pollution; Energy Conservation and Emission Reduction: Optimize the pump station operation plan and pipe network scheduling to reduce energy consumption and operating costs; Energy Consumption Calculation: E = P × T; where E is the energy consumption, P is the power of the pump station, and T is the operating time; Water Resource Protection: Take measures to protect the water source, prevent water source pollution, and ensure the quality and safety of water resources; Ecological Flow Maintenance: Consider the river ecological flow requirements in the water supply scheduling to ensure the health and stability of the river ecosystem; Environmental Impact Assessment: Conduct an environmental impact assessment of the scheduling plan and select the plan with the least environmental impact; Environmental Impact Assessment: Environmental Impact Index: where I is the environmental impact index, w i is the weight of the i-th environmental factor, and e i is the degree of impact of the i-th environmental factor; Intelligent Scheduling: Use an intelligent scheduling system to monitor and adjust the water supply process in real time to adapt to environmental changes and demand fluctuations.

10. The optimized scheduling system for water supply pipe networks based on clustering grouping and Cauchy mutation according to claim 1, characterized in that: The principle of intelligent control and automated operation is to use intelligent control theory and automation technology to achieve precise and automatic operation of control facilities in the water supply system; apply modern control theory, including fuzzy logic control, adaptive control, and predictive control, to achieve intelligent management of control facilities such as pump stations and valves in the water supply system Fuzzy Logic Control: Fuzzy Rule: IF X is A THEN Y is B; where X and Y are input and output variables, and A and B are fuzzy sets; Adaptive Control: Control Law Adjustment: u(t) = -K(t)e(t); where u(t) is the control input, K(t) is the control gain that changes with time, and e(t) is the error; Predictive control: Prediction model: where is the predicted output and u(t) is the control input.

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