Intelligent water affair scheduling method based on LSTM and multi-objective optimization

Abstract

The invention provides an intelligent water affair scheduling method based on LSTM and multi-objective optimization, and belongs to the technical field of intelligent water affair management and urban water supply system optimization. Comprising the following steps: based on water consumption data, performing analysis through a long short term memory neural network to obtain water consumption demand prediction; based on the water consumption demand prediction, combining pipeline information and pipe network layout data, performing pipe network pressure partition optimization, and obtaining a pipe network layout optimization result; and based on the pipe network layout optimization result, combining the historical operation data of the equipment and the water demand prediction, carrying out efficiency analysis on the pump station equipment to obtain an operation combination scheme of the equipment.

Description

Technical Field

[0001] This application belongs to the technical field of intelligent water management and urban water supply system optimization, and specifically relates to a smart water scheduling method based on LSTM and multi-objective optimization. Background Art

[0002] With the acceleration of urbanization and continued population growth, urban water supply systems are becoming increasingly complex and facing increasing operational pressure. Traditional water supply management methods, which rely primarily on empirical judgment and manual scheduling, are unable to cope with the challenges posed by ever-changing water demand, complex pipe network structures, and aging equipment.

[0003] The inventors found that the existing technology has poor water usage prediction accuracy, pipeline network optimization is mostly based on static data modeling, and equipment scheduling uses fixed logic to control the start and stop of pump groups, resulting in low overall efficiency and poor sustainability of urban water supply systems, and high energy consumption and costs. Summary of the Invention

[0004] In order to solve at least one technical problem existing in the background technology, the present application provides a smart water scheduling method based on LSTM and multi-objective optimization.

[0005] The technical solutions adopted in this application are: The first embodiment of the present application provides a smart water management scheduling method based on LSTM and multi-objective optimization, including: Based on the water consumption data, the water demand forecast is obtained by analyzing it through the long short-term memory neural network; Based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning is optimized to obtain the pipeline network layout optimization result; Based on the pipe network layout optimization results, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan.

[0006] According to one embodiment of the present application, the water demand forecast is obtained by analyzing the water use data through a long short-term memory neural network, specifically: Collecting the water use data, wherein the water use data includes at least one of historical water use data, user information, seasonal factors, and weather factors; Cleaning the water consumption data and extracting relevant features, wherein the relevant features include at least one of time features, weather features, and user features; Use a long short-term memory neural network to build a prediction model, input the relevant features, and perform preliminary training on the prediction model; The preliminarily trained prediction model is retrained using historical water usage data, and the performance of the retrained prediction model is evaluated through cross-validation to obtain a trained prediction model. The actual water usage data is input into the trained prediction model to obtain the water demand prediction.

[0007] According to one embodiment of the present application, based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning optimization is performed to obtain the pipeline network layout optimization result, specifically: Based on the water demand forecast, the network topology is annotated using GIS tools to establish a three-dimensional model of the urban water supply network and the corresponding fluid dynamics equations; Determine the initial conditions and boundary conditions of the fluid dynamics equation, build a physical information neural network to solve it, and obtain simulation results; According to the simulation results and optimization objectives, a multi-objective genetic algorithm is used to optimize the pipe network structure to obtain the pipe network layout optimization result.

[0008] According to one embodiment of the present application, based on the pipe network layout optimization result, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan, specifically: Based on the historical operation data of the equipment, the operation status of the equipment is classified and predicted through the decision tree. Combined with the information in the mechanism rule model library, the health status of the equipment is evaluated to obtain the health assessment result; Based on the historical operation data of the equipment, the least squares method is used to fit the equipment performance fitting results; Based on the water demand forecast, the pipe network layout optimization results, the historical operation data of the equipment, the health assessment results and the equipment performance fitting results, an operation combination plan of the equipment in the pump station is obtained through a water pump unit matching intelligent scheduling model based on offline deep reinforcement learning.

[0009] According to one embodiment of the present application, the simulation results are the water flow, water pressure and water volume distribution in the pipe network under different topological structures.

[0010] According to one embodiment of the present application, the multi-objective genetic algorithm is used to optimize the pipe network structure based on the simulation results and the optimization target to obtain the pipe network layout optimization result, which is specifically: The water flow, water pressure and water volume distribution of the pipe network are encoded into chromosomes, and the initial population is randomly generated; According to the defined optimization goal, the fitness value of each chromosome is calculated, and the fitness value indicates the conformity between the pipe network structure represented by the chromosome and the optimization goal; Perform selection, crossover and mutation operations to generate a new pipe network structure, perform non-dominated sorting on each generation of population, and determine which individuals enter the next generation of population based on crowding comparison; Repeat the above steps until the pipe network layout optimization result is obtained.

[0011] According to one embodiment of the present application, the health assessment result includes a health assessment report and a health score.

[0012] According to one embodiment of the present application, the device performance fitting result includes a performance index curve and an efficient operation range defined based on the performance index curve.

[0013] A second aspect of the present application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the method described in any one of the embodiments of the first aspect.

[0014] An embodiment of the third aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the embodiments of the first aspect when executing the program.

[0015] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows: This application uses the LSTM model to model historical water use data, user behavior characteristics, seasonal factors, and weather information, significantly improving the accuracy of water demand forecasts. It can identify and predict water use peaks and troughs in different regions and time periods, providing a reliable basis for subsequent scheduling decisions. It supports multi-period forecasts (such as daily, weekly, and monthly) to meet management needs at different levels.

[0016] This application achieves accurate simulation of water pressure and water volume distribution by establishing a three-dimensional pipe network model and fluid dynamics simulation; combines a multi-objective genetic algorithm to optimize the topology structure, solving multiple conflicting objectives (such as energy saving, pressure stabilization, and water supply balance) that are difficult to handle with traditional methods; proposes a dynamic pressure zoning strategy to enable the pipe network to maintain the optimal operating state under different water use conditions, reducing the risk of pipe bursts and energy waste; and outputs executable pipe network renovation suggestions to provide a scientific basis for infrastructure upgrades.

[0017] This application introduces an equipment health assessment mechanism, combined with a mechanism rule model library, to accurately identify potential fault hazards and extend the service life of equipment; uses the least squares method to fit the equipment performance curve, define the efficient operation range, and avoid unnecessary energy consumption; constructs an offline deep reinforcement learning scheduling model, comprehensively considers water demand, pipeline network status, and equipment performance, and generates the optimal pump group start and stop strategy; realizes adaptive scheduling of pump station operation, improves system response speed, reduces operating costs, and enhances water supply guarantee capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flowchart of a smart water management scheduling method based on LSTM and multi-objective optimization provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0019] Reference numerals: 810 , processor; 820 , communication interface; 830 , memory; 840 , communication bus. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0021] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0022] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0023] The first embodiment of the present application provides a smart water management scheduling method based on LSTM and multi-objective optimization, including: Based on the water usage data, the water demand forecast is obtained by analyzing it through long short-term memory neural network.

[0024] As mentioned above, a large amount of water use data needs to be collected. This data includes but is not limited to: Time series data from historical water usage data: for example, hourly, daily, or monthly water consumption records.

[0025] Weather conditions: Meteorological data such as temperature and rainfall, as weather conditions have a significant impact on users' water usage habits.

[0026] Seasonal changes and holiday information: Different seasons and holidays may cause changes in water usage patterns.

[0027] Basic user information: Long-term unchanging factors such as family size and living habits help to understand the water use behavior of specific user groups.

[0028] The collected data is usually raw and may contain outliers or missing values. Therefore, the following preprocessing steps are required: Clean data: remove outliers to ensure data accuracy.

[0029] Fill missing values: Use appropriate methods (such as interpolation) to fill missing data points.

[0030] Normalization: Converting data to a uniform scale so that the model can better understand and process it.

[0031] Feature engineering is the process of extracting and selecting key features that are helpful for model training. It mainly includes the following aspects: Time features: Decomposing dates into features such as hours, days of the week, and months helps capture periodic patterns.

[0032] Weather characteristics: Extract weather-related characteristics such as temperature and rainfall, which are often closely related to water consumption.

[0033] Seasonality and holiday characteristics: Mark special time periods, such as holidays or specific seasons, which tend to have different water usage patterns.

[0034] User characteristics: Extract relevant characteristics based on the user's basic information, such as family size, living habits, etc.

[0035] At this stage, we use a long short-term memory (LSTM) neural network to build a prediction model. LSTM is a special type of recurrent neural network (RNN) that is particularly suitable for processing time series data. The specific steps are as follows: The preprocessed and feature-engineered data is used as input to form feature vectors. An LSTM network architecture is designed and constructed that can learn the complex patterns of water consumption over time. The LSTM model is trained using historical data to learn the relationship between water consumption and other factors.

[0036] To ensure model accuracy, it is necessary to validate and tune the model. The dataset is divided into training and validation sets, and cross-validation is used to evaluate model performance. Based on the validation results, the model's hyperparameters are adjusted to improve prediction accuracy. Model performance is evaluated using various metrics (such as mean squared error and mean absolute error) to ensure that it can accurately predict future water demand.

[0037] Once the model is trained and validated, it can be used for real-world predictions: using the trained LSTM model to predict water demand over a period of time. The prediction results are then applied to scheduling decisions for the water supply system, guiding its operation and ensuring efficient resource allocation, avoiding waste, and improving overall efficiency.

[0038] By analyzing water usage data using a long short-term memory (LSTM) neural network (LSTM), we can accurately predict future water demand. This not only helps optimize water supply system scheduling strategies, but also reduces energy consumption, extends equipment life, and ultimately enables intelligent management of urban water supply systems. This process begins with data collection, continues through data preprocessing, feature engineering, model building and training, model validation and tuning, and finally generates prediction results that are applied in real-world operations, forming a complete workflow.

[0039] For example, historical water usage data for the past year was collected from the city water system's database. This data includes: Hourly water usage records to understand peak and low times of the day.

[0040] Weather data: average daily temperature and rainfall, as weather conditions can influence people’s water use habits (for example, on hot days people might use more water to water their gardens or swimming pools).

[0041] Seasonal changes and holiday information: This information helps you understand how different seasons and holidays (such as Spring Festival and National Day) affect water consumption patterns. For example, during Spring Festival, there are more family gatherings, which typically leads to increased water consumption.

[0042] The collected data were preprocessed: We examined and removed outliers, such as extremely high or low water usage records due to sensor failures. For periods with missing records, we interpolated the missing water usage data. We also converted all data to a consistent scale so that the model could better understand and process it.

[0043] To extract key features that aid in model training, the date is broken down into features such as hour, day of the week, and month. For example, we know that water consumption peaks between 7:00 and 9:00 a.m. on Mondays, while water consumption is lower during the same period on weekends. The average daily temperature and rainfall are extracted as additional input features. For example, hot weather may lead to increased outdoor water use, while rainfall may reduce this demand. Special time periods are marked, such as summer, winter, Spring Festival, and National Day. These periods often have different water use patterns. For example, water consumption is generally higher in the summer, while water consumption also increases significantly during the Spring Festival due to increased family gatherings.

[0044] We chose the Long Short-Term Memory (LSTM) neural network to build the forecasting model because it is particularly suitable for processing time series data. The specific steps are as follows: The preprocessed and feature-engineered data was organized into feature vectors that incorporated information about time, weather, seasonality, holidays, and more. A neural network architecture consisting of multiple LSTM layers was designed to learn the complex patterns of water consumption over time.

[0045] To ensure model accuracy, we divided the dataset into training and validation sets, and used cross-validation to evaluate model performance. This ensured that the model not only performed well on the training data but also made accurate predictions on unseen data. Based on the validation results, we adjusted some model hyperparameters, such as the learning rate and batch size, to further improve prediction accuracy.

[0046] Once the model is trained and validated, the trained LSTM model is used to predict daily water consumption for the coming week. For example, the forecast shows that next Monday will be a peak water consumption period, with a projected water consumption of 800,000 cubic meters; while next Wednesday will be a low water consumption period, with a projected water consumption of only 500,000 cubic meters. These forecast results are fed back to the water supply system's dispatch center to guide them in optimizing water supply strategies. For example, during predicted peak periods, backup pumping stations can be activated in advance to ensure sufficient water supply pressure; during low water periods, the pumping station workload can be appropriately reduced to save energy.

[0047] It should be noted that in specific implementation scenarios, the above solution can also be combined with a real-time data stream processing framework (such as Apache Kafka or Apache Flink) to continuously receive the latest water usage data and immediately input it into the trained LSTM model for instant prediction. This enables real-time monitoring and prediction of water demand. Through online learning mechanisms, the LSTM model can continuously update and optimize itself based on the incoming data, thereby improving the accuracy and timeliness of predictions.

[0048] In specific implementation scenarios, building on the above solution, in addition to basic information such as water consumption and weather, more external data can be integrated, such as public event announcements on social media, local economic indicators, and tourist statistics. These factors may indirectly influence water demand. This information can be integrated using big data technologies and added as additional features to the LSTM model. Using multimodal learning methods, different types of data (such as text descriptions and images) are converted into numerical features. These are then combined with traditional numerical data and fed into the LSTM model, enhancing the model's learning capabilities and prediction accuracy.

[0049] In specific implementation scenarios, the above solution can be supplemented with algorithms specifically designed to detect abnormal water usage patterns (such as isolation forests and autoencoders) based on the existing LSTM model. When the system detects abnormally high or low water usage, it automatically generates an alert and notifies relevant personnel. By integrating historical event records (such as pipe ruptures and large-scale water outages), an event correlation database can be established to help understand the causes behind abnormal water usage and provide a basis for rapid response.

[0050] Based on the water demand forecast, combined with pipeline information and pipeline network layout data, pipeline network pressure zoning optimization is performed to obtain a pipeline network layout optimization result.

[0051] As mentioned above, first, based on the water demand forecast results for a certain period of time obtained through the long short-term memory neural network (LSTM), we can understand the expected water consumption in different time periods and different regions. At the same time, it is necessary to collect and integrate existing water supply network information, including but not limited to: Basic information of pipelines: such as diameter, material, length, etc.

[0052] Pipeline network layout data: describes in detail how the various pipelines are connected and their relative positions.

[0053] This data provides a comprehensive understanding of the current water supply system and helps identify potential problem areas, such as areas where water supply may be insufficient or water pressure may be too high.

[0054] Geographic Information System (GIS) tools will be used to build a three-dimensional model of the city's water supply network. This model not only takes into account the location and properties of the physical pipes, but also incorporates the results of water demand forecasts to simulate changes in water flow and water pressure under different water use patterns. Specifically: For simple pipe network structures, one-dimensional fluid dynamics equations can be used to describe the water flow state; however, for complex multi-branch or circulating pipe networks, multiple Navier-Stokes (NS) equations are required to more accurately simulate the conduction process of water flow, water pressure and water volume.

[0055] This modeling approach allows different operating scenarios to be tested in a virtual environment, assessing how the existing network will perform in the face of future water demands.

[0056] After establishing an accurate fluid dynamics model, the next step is to optimize the entire water supply network in order to find the optimal pressure zoning scheme. This usually involves the following aspects: Reduce unnecessary energy consumption by optimizing pumping station operation strategies. For example, reduce pumping station operation intensity during low-demand periods and increase pumping capacity appropriately during peak periods.

[0057] Ensure that all users have access to sufficient water pressure while avoiding the risk of damage to the pipe network due to excessive water pressure.

[0058] Adjust water supply to each area based on predicted water demand to ensure efficient allocation of water resources and reduce waste.

[0059] In order to achieve the above goals, a multi-objective genetic algorithm can be used to optimize the pipe network structure. This method can explore the best solution while meeting multiple optimization goals.

[0060] Once the optimal pressure zone plan has been determined, it needs to be translated into practical and feasible operational guidelines. This includes: Specific improvement measures are proposed for areas identified as inefficient or with bottlenecks, such as replacing old pipelines, adding new branches, or redesigning key nodes.

[0061] Taking into account the possible impact on daily water supply during the renovation process, the schedule and execution order of each step need to be carefully planned to minimize the impact on residents' lives.

[0062] Even after initial optimization is complete, it’s important to continuously monitor the actual operation of the network and adjust the optimization strategy based on feedback. As the city grows and water usage patterns change, the original optimization plan may no longer be applicable, so regular evaluation and updates are essential.

[0063] In this way, optimizing pipe network pressure zones based on water demand forecasts combined with pipeline information and network layout data not only improves the overall efficiency of the water supply system, but also extends equipment life, reduces maintenance costs, and ultimately achieves more intelligent urban water supply management. This process embodies a complete chain from data collection to model building to practical application, with each link closely linked to promote the optimization and upgrading of the water supply system.

[0064] For example, a long short-term memory neural network (LSTM) model was used to analyze historical water consumption data from the past few years and, combined with external factors such as weather forecasts, seasonal changes, and holiday schedules, to predict daily water demand for the next month. The forecast results show: Water usage peaks in the mornings and evenings on weekdays, especially at the junction of commercial and residential areas.

[0065] Daytime water use increases significantly on weekends, mainly due to increased outdoor activities by households, such as watering gardens and washing cars.

[0066] During hot weather, overall water consumption generally increases, especially the demand for cooling water increases significantly.

[0067] Information was collected on the entire water supply network, including: Basic information of the pipeline: diameter, material, length and laying year of each section of the pipeline.

[0068] Pipeline network layout data: A detailed topological diagram of the pipeline network showing how the individual pipelines are connected and their relative positions.

[0069] Pump station distribution: the location of existing pump stations and their design parameters, such as maximum flow rate, head, etc.

[0070] Using a geographic information system (GIS), these data were integrated into a three-dimensional urban water supply network model, which not only displays the location and properties of the physical pipes but also simulates changes in water flow and pressure over time.

[0071] Based on the above water demand forecast and pipe network layout data, the optimization work of pipe network pressure zones begins. The specific steps are as follows: By simulating water demand over different time periods, we found that certain areas experience significant water shortages during peak hours, primarily at the intersection of commercial and residential areas. Furthermore, in areas with a high concentration of old and deteriorating pipes, water pressure is unstable due to the small diameter and severe aging of the pipes.

[0072] At the junction of commercial and residential areas, a new large-diameter pipeline is planned to be added to alleviate water supply pressure during peak hours. This new pipeline will be directly connected to the nearest pumping station to ensure sufficient water supply capacity.

[0073] For areas with a high density of old pipelines, it is recommended to gradually replace them with large-diameter pipelines made of new materials, and install intelligent valve control systems to dynamically adjust the pressure of each branch pipeline according to real-time water demand to ensure stable water supply.

[0074] During high temperature weather, start the backup pump station to increase water supply, and adjust the pressure setting of the main pipeline to give priority to the demand for cooling water.

[0075] A multi-objective genetic algorithm is used to comprehensively consider multiple goals, including minimizing energy consumption, ensuring stable water pressure, and optimizing water flow distribution. For example, by optimizing the operating strategies of pumping stations, unnecessary energy consumption is reduced; ensuring that all users receive sufficient water pressure while avoiding the risk of damage to the pipe network caused by excessive water pressure.

[0076] Based on the above optimization plan, a detailed transformation plan was formulated: Immediately activate backup pump stations and adjust control valves on existing pipelines to prioritize water demand in high-demand areas. At the same time, strengthen routine maintenance of older pipelines, promptly repair leaks, and reduce water waste.

[0077] We will replace old pipes in phases, starting with the most problematic areas and gradually expanding to the entire network. After each section is replaced, it will be immediately put into operation and monitored to ensure that the new pipes are effectively improving water quality.

[0078] Over the next five years, the water supply network will be comprehensively upgraded, including the construction of several new large-diameter pipelines, expansion of pump station capacity, and the introduction of an intelligent monitoring system to achieve real-time monitoring and automated scheduling of the entire water supply system.

[0079] Even after initial optimization is complete, it's crucial to continuously monitor the actual network operation and adjust optimization strategies based on feedback. For example, as a city develops and new residential and commercial areas emerge, water usage patterns will change. Regularly reassessing water demand forecasts and adjusting the network layout and pressure zoning strategies accordingly will ensure the water supply system remains optimal.

[0080] It should be noted that in specific implementation scenarios, building on the above solution, a series of sensors (such as pressure sensors and flow sensors) can be deployed at key nodes to monitor the status of the water supply network in real time. These sensors can provide real-time network operation data, helping us understand the current pressure distribution. Edge computing devices can process this sensor data and respond quickly, such as automatically adjusting the operating status of valves or pump stations to address emergencies or optimize daily operations.

[0081] In specific implementation scenarios, building on the above solutions, in addition to water demand forecasting, specialized machine learning models can be trained to identify potential pipeline network failures. By analyzing historical failure data and real-time monitoring data, potential problems can be predicted in advance and preventive measures can be developed. An anomaly detection system based on statistical methods or deep learning can be established to identify abnormal behavior in the pipeline network, such as sudden pressure drops or flow surges, and promptly issue alerts and notify relevant personnel for inspection.

[0082] In specific implementation scenarios, building on the above solution, a mobile app could be developed to allow users to report any water supply issues (such as insufficient water pressure or water quality issues) and receive water-saving advice or promotional information through the platform. User feedback can directly influence water supply system optimization strategies. Community organizations should be encouraged to participate in water resource management by organizing lectures and workshops to raise public awareness of water conservation and collect residents' opinions and suggestions as a valuable reference for improving water supply services.

[0083] Based on the pipe network layout optimization results, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan.

[0084] As mentioned above, the results of the previous network pressure zone optimization were used. These results include: Optimal pressure zoning scheme: clarifies the optimal water supply strategy for different areas, ensuring stable water pressure and minimized energy consumption.

[0085] Pipeline improvement suggestions: Specific transformation measures were proposed for old or bottleneck pipelines, improving the transmission capacity of the overall pipeline network.

[0086] This information provides a basic framework for pump station equipment performance analysis, helping us understand the current state of the pipeline network and potential areas for improvement.

[0087] It is necessary to collect and analyze historical operating data of pump station equipment. Such data usually includes but is not limited to: Parameters such as flow rate, head, efficiency, etc.: reflect the performance of the equipment under different working conditions.

[0088] Maintenance records: These record the repair and maintenance of the equipment, helping to assess its health.

[0089] Energy consumption record: shows the energy consumption of the equipment under various operating conditions.

[0090] Through in-depth analysis of this data, it is possible to identify which equipment is working efficiently and which may have potential problems and need attention or maintenance. In addition, it is possible to discover synergies between equipment, that is, certain equipment can more effectively meet water supply needs when working together.

[0091] Combined with the latest water demand forecast results, it is possible to foresee the changing trend of water consumption in the future. This includes: Differences in water usage during peak and off-peak hours: For example, mornings and evenings are peak water usage times in residential areas, while commercial areas may have higher water demand during the day.

[0092] Seasonal and holiday impacts: Summer may increase water use due to irrigation and cooling needs, while holidays may lead to increased family activities, which in turn affects water use patterns.

[0093] Understanding this forecast information is crucial for developing reasonable equipment operation plans, as it can help us allocate resources appropriately during different periods of time and avoid overuse or undersupply problems.

[0094] Based on the above information, a comprehensive pumping station equipment performance analysis can now be performed. The specific steps are as follows: Health Assessment: Based on historical operational data, a mechanistic rule model and decision tree algorithm are used to comprehensively assess the health of each pump. This step helps determine which equipment requires immediate maintenance or replacement, thereby avoiding unexpected failures.

[0095] Performance Curve Fitting: We use statistical methods like the least squares method to fit the optimal performance curve for each pump. This allows us to define the efficient operating range of each device and adjust its operating mode accordingly to ensure it always operates at its optimal level.

[0096] Intelligent Scheduling Model Training: Build an intelligent scheduling model for pump unit pairing based on offline deep reinforcement learning. The model's goal is to find the most appropriate pump combination and operating mode to meet the predicted water demand. During this process, a reward function is defined to measure the effectiveness of different scheduling strategies. Through continuous training, the model learns to select the optimal solution.

[0097] Finally, all analysis results are integrated to develop a detailed pump station equipment operation combination plan. This plan should include the following aspects: Equipment selection and configuration: Clearly indicate which pumps should be enabled at different time periods, as well as their specific configuration parameters (such as speed, flow setting, etc.).

[0098] Dynamic Adjustment Mechanism: Design a real-time monitoring system that can dynamically adjust pump operation based on actual water demand. For example, more high-power pumps can be activated during peak water demand periods, while some can be shut down during low water demand periods to save energy.

[0099] Maintenance plan: Based on the health assessment results, a maintenance plan is developed to ensure all equipment remains in good working condition. For equipment nearing the end of its life or with potential hazards, regular inspections and necessary repairs are arranged.

[0100] Even after developing a preliminary equipment operation combination plan, it's necessary to continuously monitor the operation of the entire water supply system and adjust the optimization strategy based on actual conditions. As the city develops and water usage patterns change, the original plan may no longer be suitable. Therefore, it is essential to regularly reassess water demand forecasts, update the network layout optimization results, and adjust the equipment operation combination plan accordingly.

[0101] This comprehensive approach not only improves the overall efficiency of the water supply system, but also extends the life of equipment, reduces unnecessary energy consumption, and achieves more intelligent urban water supply management. This process embodies a complete chain from data collection to model building and practical application, with each link closely linked to promote the optimization and upgrading of the water supply system.

[0102] For example, imagine managing the water supply system for a rapidly growing urban area. With population growth and increased economic activity, water demand is fluctuating, causing the existing water supply network to experience water shortages or unstable water pressure during certain peak hours. To address this issue, a pressure zoning optimization has been completed, resulting in an optimized network layout. Based on these results, combined with historical equipment operating data and the latest water demand forecast, an analysis of pump station equipment performance will be conducted to determine the optimal equipment operation combination.

[0103] Through previous work on optimizing the pressure zones of the pipe network, the following key points have been identified: A new large-diameter pipeline needs to be added at the junction of commercial and residential areas to alleviate water supply pressure during peak hours.

[0104] It is recommended to gradually replace old pipelines in areas with a high density of old pipelines with large-diameter pipelines made of new materials, and install intelligent valve control systems to dynamically adjust the pressure of each branch pipeline according to real-time water demand.

[0105] During high temperature weather, start the backup pump station to increase water supply, and adjust the pressure setting of the main pipeline to give priority to the demand for cooling water.

[0106] These optimization measures provide a framework for improving the water supply system, ensuring the stability and efficiency of water supply.

[0107] Collected and analyzed historical operating data of existing pump station equipment, including: Parameters such as flow rate, head, efficiency, etc.: record the performance of each pump under different working conditions.

[0108] Maintenance records: These provide detailed records of repairs and maintenance to the equipment, helping us assess its health.

[0109] Energy consumption record: shows the energy consumption of the equipment under various operating conditions.

[0110] Through in-depth analysis of these data, we found that: The two old pumps in Pumping Station A were inefficient under high load conditions and frequently broke down.

[0111] Although the several new high-efficiency pumps at Pumping Station B performed well, their capacity was not fully utilized under low-load conditions.

[0112] According to the latest water demand forecast results, the water consumption pattern will change as follows in the next month: Water usage peaks in the mornings and evenings on weekdays, especially at the junction of commercial and residential areas.

[0113] Daytime water use increases significantly on weekends, mainly due to increased outdoor activities by households, such as watering gardens and washing cars.

[0114] During hot weather, overall water consumption generally increases, especially the demand for cooling water increases significantly.

[0115] These forecast information provides an important basis for formulating reasonable equipment operation plans.

[0116] Based on the above information, a comprehensive pumping station equipment performance analysis can now be performed. The specific steps are as follows: Health Assessment: A comprehensive assessment of the health of each pump is performed using a mechanism-based rule model and decision tree algorithm. For example, if two older pumps in Pumping Station A are found to have potential problems, immediate maintenance or replacement is recommended.

[0117] Performance Curve Fitting: We use the least squares method to fit the optimal performance curve for each pump. This allows us to define the most efficient operating range for each device and adjust its operating mode accordingly. For example, the new, high-efficiency pumps at Pumping Station B perform best under moderate loads. Therefore, their operating load can be reduced during off-peak hours to extend their service life.

[0118] Intelligent Scheduling Model Training: Build an intelligent scheduling model for pump unit configuration based on offline deep reinforcement learning. The model aims to find the most appropriate pump combination and operation mode to meet the predicted water demand. For example, it can start more high-power pumps during peak water demand periods and shut down some equipment during off-peak periods to save energy.

[0119] Finally, all analysis results are integrated to develop a detailed pump station equipment operation combination plan. This plan should include the following aspects: Equipment selection and configuration: Clearly specify which pumps should be activated during different time periods, along with their specific configuration parameters (such as speed and flow setting). For example, during peak water usage in the morning and evening, all high-performance pumps at Pumping Station A would be activated, while several new, high-efficiency pumps at Pumping Station B would be set to maximum capacity. During low water usage at night, some equipment would be shut down, leaving only the minimum necessary to maintain basic water supply.

[0120] Dynamic Adjustment Mechanism: A real-time monitoring system was designed to dynamically adjust pump operating conditions based on actual water demand. For example, during weekends when daytime water demand increases, backup pump stations are activated promptly to ensure adequate water supply. During hot weather, cooling water demand is prioritized, and the main pipeline pressure settings are adjusted appropriately.

[0121] Maintenance plan: Based on the health assessment results, a maintenance plan is developed. For example, two older pumps in Pump Station A are scheduled for regular inspections and necessary repairs to ensure they don't fail at critical moments.

[0122] Even after the initial equipment operation combination plan is formulated, it is necessary to continuously monitor the operation of the entire water supply system and continuously adjust the optimization strategy based on the actual situation. For example: Regularly reassess water demand forecasts, update network layout optimization results, and adjust equipment operation combination plans accordingly.

[0123] Monitor the actual operating data of the pumping station, promptly detect and resolve any unexpected problems, and ensure the stability and reliability of the water supply system.

[0124] It should be noted that, in specific implementation scenarios, the above solution can also be used to establish a comprehensive real-time monitoring system, integrating a sensor network (such as pressure sensors and flow sensors) to continuously monitor the status of pumping station equipment and the water supply network. This system can provide immediate data feedback, helping managers understand the current pressure distribution and equipment operation. Leveraging Internet of Things (IoT) technology and edge computing capabilities, automatic control of pumping station equipment can be achieved. For example, if a sudden increase in water consumption in a certain area is detected, the system can automatically adjust the operating status of the relevant pumps to ensure sufficient water supply and stable water pressure.

[0125] In specific implementation scenarios, building on the above approach, specialized machine learning models can be trained to identify potential equipment failures. By analyzing historical equipment operating data and real-time monitoring data, potential problems can be predicted in advance and preventive measures can be developed. For example, if the vibration frequency of a pump exceeds the normal range, the system will issue an alarm and recommend an inspection. Develop anomaly detection systems based on statistical methods or deep learning to identify abnormal behavior in equipment. For example, a sudden drop in pressure or a surge in flow may indicate a leak or other problem, and the system will promptly notify relevant personnel to address it.

[0126] In specific implementation scenarios, building on the above solutions, integrated energy management systems can be developed by combining multi-dimensional data such as electricity and water consumption. Overall energy consumption can be reduced by optimizing the operating parameters of pumping stations and other key equipment. For example, pumping station power can be automatically reduced during low-demand periods, while adopting a more efficient operation mode during peak hours. Exploring how to integrate renewable energy sources such as solar and wind power into the water supply system's power supply system can reduce reliance on the external power grid and reduce carbon emissions. For example, photovoltaic panels can be installed to power pumping stations, or energy storage systems can be used to store excess energy for nighttime use.

[0127] According to one embodiment of the present application, the water demand forecast is obtained by analyzing the water use data through a long short-term memory neural network, specifically: Collecting the water use data, wherein the water use data includes at least one of historical water use data, user information, seasonal factors, and weather factors; Cleaning the water consumption data and extracting relevant features, wherein the relevant features include at least one of time features, weather features, and user features; Use a long short-term memory neural network to build a prediction model, input the relevant features, and perform preliminary training on the prediction model; The preliminarily trained prediction model is retrained using historical water usage data, and the performance of the retrained prediction model is evaluated through cross-validation to obtain a trained prediction model. The actual water usage data is input into the trained prediction model to obtain the water demand prediction.

[0128] As mentioned above, first, a wide range of water use-related data needs to be collected, including at least one or more of the following types: Historical water use data: This typically refers to records of water use over a period of time, at various timescales (e.g. hourly, daily or monthly).

[0129] User information: Specific information about users, such as family size and living habits, helps understand the water usage behavior of specific user groups.

[0130] Seasonal factors: Take into account the impact of different seasons on water use patterns, for example, summer may increase water use due to irrigation and cooling needs.

[0131] Weather factors: Weather conditions have a significant impact on water usage; for example, hot weather may lead to more outdoor water use.

[0132] Once enough data is collected, the next step is data cleaning and feature extraction: Data cleaning: This step involves removing outliers, filling in missing values, and standardizing to ensure data quality and consistency.

[0133] Feature extraction: Extract relevant features from raw data that are helpful for model training. These features include at least: Temporal features: Breaking down dates into hours, days of the week, months, etc. helps capture cyclical patterns.

[0134] Weather characteristics: Weather-related characteristics such as temperature and rainfall, which are often closely related to water use.

[0135] User characteristics: Extract relevant characteristics based on the user's basic information, such as family size, living habits, etc.

[0136] Build a forecasting model using a long short-term memory (LSTM) neural network, a variant of a recurrent neural network (RNN) that is particularly well-suited for processing time series data. The specific steps are as follows: Input related features: The preprocessed and feature-engineered data is used as input to form a feature vector.

[0137] Preliminary training model: The extracted relevant features are used to preliminarily train the LSTM model so that it can learn the complex pattern of water consumption changing over time.

[0138] In order to improve the accuracy and reliability of the model, the model needs to be further optimized: Secondary training: The initially trained prediction model is retrained using historical water usage data to enable the model to better adapt to the nuances in real-world data.

[0139] Cross-validation evaluation: The cross-validation method is used to evaluate the performance of the model after secondary training. This method can effectively test the performance of the model on unseen data, thereby ensuring its generalization ability.

[0140] Once the model is trained and has passed rigorous validation, it can be used for actual predictions: Input actual water usage data: Input the latest actual water usage data into the trained prediction model.

[0141] Generate water demand forecasts: The model uses input data to predict water demand over a period of time. These forecasts can help water supply systems make more accurate scheduling decisions, ensuring efficient resource allocation, avoiding waste, and improving overall efficiency.

[0142] According to one embodiment of the present application, based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning optimization is performed to obtain the pipeline network layout optimization result, specifically: Based on the water demand forecast, the network topology is annotated using GIS tools to establish a three-dimensional model of the urban water supply network and the corresponding fluid dynamics equations; Determine the initial conditions and boundary conditions of the fluid dynamics equation, build a physical information neural network to solve it, and obtain simulation results; According to the simulation results and optimization objectives, a multi-objective genetic algorithm is used to optimize the pipe network structure to obtain the pipe network layout optimization result.

[0143] As mentioned above, the results of the water demand forecast need to be combined with the city's water supply network information. This step includes: The long short-term memory neural network (LSTM) and other technologies are used to accurately predict water demand in the future.

[0144] Use geographic information system (GIS) tools to annotate the topology of the urban water supply network. This means mapping the location, connection methods, and properties (such as diameter, material, length, etc.) of all water supply pipes on a GIS platform to form a detailed 3D model.

[0145] Based on this information, a three-dimensional model of the urban water supply network is constructed. This model not only shows the location and properties of the physical pipelines, but also simulates the changes in water flow and water pressure in different time periods.

[0146] Furthermore, the corresponding fluid dynamics equations must be set for the 3D model. These equations describe the movement of water in the pipe and serve as the basis for subsequent simulation and optimization.

[0147] Next, to accurately simulate and analyze the behavior of the water distribution network, it is necessary to determine the initial and boundary conditions for the fluid dynamics equations and solve them using advanced computational methods: Initial and boundary conditions: For example, the initial state of the fluid, such as velocity, pressure, and temperature, as well as boundary conditions such as its interaction with pipe walls, valves, and pumps, are crucial for accurately modeling the flow in a water distribution network.

[0148] Physically Informed Neural Networks (PINNs): This approach combines the laws of physics with deep learning. It trains by randomly sampling across the entire computational domain, performs inference by collecting sample points, calculates a loss function, and dynamically adjusts the sample points to optimize the loss. This approach is particularly well-suited for solving complex fluid dynamics problems.

[0149] Simulation results: By building and training a physical information neural network, we can obtain simulation results of water flow, water pressure, and water volume distribution in pipe networks under different topological structures.

[0150] Finally, based on the simulation results and the preset optimization goals, a multi-objective genetic algorithm is used to optimize the pipe network structure to find the optimal solution: Simulation results analysis: Carefully analyze the simulation results to understand the performance of the existing pipe network structure under different working conditions and identify potential problem areas or inefficient parts.

[0151] Optimization goal setting: Clearly define the optimization goals, such as minimizing energy consumption, ensuring stable water pressure, optimizing water flow distribution, etc. These goals guide the direction of the optimization process.

[0152] Multi-objective genetic algorithm application: A multi-objective genetic algorithm is a search algorithm used to find the optimal compromise between multiple conflicting objectives. In this process, the various physical parameters of the pipeline network are first encoded into chromosomes, and an initial population is randomly generated. Next, based on the defined optimization objective, the fitness value of each chromosome is calculated, and new pipeline network structures are generated through selection, crossover, and mutation operations. This process is repeated until the optimal pipeline network optimization solution is found.

[0153] Pipeline network layout optimization results: After multiple rounds of iteration, the optimized pipeline network layout results are finally obtained, including specific renovation suggestions (such as replacing old pipes, adding new branches, or redesigning certain key nodes), as well as implementation step plans to ensure that the impact on daily water supply during the renovation process is minimized.

[0154] According to one embodiment of the present application, based on the pipe network layout optimization result, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan, specifically: Based on the historical operation data of the equipment, the operation status of the equipment is classified and predicted through the decision tree. Combined with the information in the mechanism rule model library, the health status of the equipment is evaluated to obtain the health assessment result; Based on the historical operation data of the equipment, the least squares method is used to fit the equipment performance fitting results; Based on the water demand forecast, the pipe network layout optimization results, the historical operation data of the equipment, the health assessment results and the equipment performance fitting results, an operation combination plan of the equipment in the pump station is obtained through a water pump unit matching intelligent scheduling model based on offline deep reinforcement learning.

[0155] As mentioned above, it is necessary to use the historical operating data of the equipment to assess its health status: Historical equipment operation data: including but not limited to the measured values ​​of parameters such as flow rate, head, efficiency under different working conditions, as well as maintenance records and energy consumption records.

[0156] Decision tree model: This is a common machine learning method used to classify and predict the operating status of equipment. By analyzing the performance data of the equipment under different operating conditions, it can identify patterns between normal and abnormal equipment conditions.

[0157] Health Assessment: This step combines information from the mechanism rule model library (such as vibration functions, mathematical functions, model functions, vibration characteristics, process parameters, and judgment rules) to conduct a comprehensive health assessment of the equipment. This step helps determine which equipment is in good condition and which may have problems or require maintenance.

[0158] After completing the above steps, we obtain an assessment result on the health status of each device, usually presented in the form of a score or grade, which provides a basis for subsequent optimization.

[0159] Next, in order to better understand the working performance of the equipment and its efficient operating range, performance curve fitting is required: Least Squares Method: This is a statistical method used to fit the best curve to a given set of data points. In this scenario, it is used to fit the relationship between pump performance indicators (such as flow rate, head, efficiency) and certain operating parameters (such as speed and load).

[0160] Performance Fitting Results: The best-fit curve calculated using the least squares method can help define the equipment's efficient operating range. For example, a pump achieves peak efficiency under specific flow and head conditions.

[0161] This step generates a performance index curve for the equipment, clarifying the operating conditions under which the equipment can achieve maximum efficiency, which is crucial for formulating the optimal operating strategy.

[0162] Finally, all relevant information is integrated to build and train an intelligent scheduling model to determine the optimal equipment operation combination plan: Input data integration: Integrate all key information obtained in the previous steps, including the latest water demand forecast, pipe network layout optimization results, equipment historical operation data, health assessment results, and equipment performance fitting results.

[0163] Intelligent Scheduling Model: Build an intelligent scheduling model for pump unit pairing based on offline deep reinforcement learning. This model aims to find the most appropriate pump combination and operating mode to meet the predicted water demand. Specifically, the model defines a reward function to measure the effectiveness of different scheduling strategies. Through continuous training, the model learns to select the optimal solution.

[0164] Offline deep reinforcement learning: This approach allows the model to conduct numerous experiments in a simulated environment, learning how to make the best decisions in various situations without actually operating the physical device. This learning method helps identify potential optimization opportunities and reduces the cost of trial and error.

[0165] Operational combination plans: Based on the trained intelligent scheduling model, specific equipment operation combination plans are formulated. These plans not only take into account the current demand forecast and pipeline network conditions, but also the specific health status and performance characteristics of the equipment to ensure that each device operates at its optimal state.

[0166] Output: The resulting system generates a detailed operational plan, specifying which pumps should be activated during different time periods, along with their specific configuration parameters (such as speed and flow rate settings). This plan also includes a dynamic adjustment mechanism, enabling the system to automatically adjust pump operating conditions based on real-time water demand changes, ensuring the stability and efficiency of the water supply system.

[0167] According to one embodiment of the present application, the simulation results are the water flow, water pressure and water volume distribution in the pipe network under different topological structures.

[0168] As mentioned above, simulation results are a key basis for evaluating and improving system performance during water supply network optimization. Specifically, "the simulation results are the water flow, water pressure, and water volume distribution in the network under different topologies" refers to the use of computer simulation methods to calculate and display the speed and direction of water flow in the entire water supply network, the water pressure level at each node, and the water volume distribution within each pipe section under given different network layouts (i.e., topologies). The following is a detailed explanation: Water flow distribution refers to the process of how water flows from the source (such as a pumping station or reservoir) to the various user endpoints throughout the water supply network.

[0169] Flow velocity and flow rate refer to the speed and volumetric flow of water within each pipe segment. These parameters directly impact water supply efficiency and service quality. Flow paths describe the paths water takes to reach different user areas, helping to identify key water supply routes and potential bottlenecks. Directionality refers to the direction of water flow. Understanding flow direction, particularly in complex pipe networks, can help predict potential issues such as backflow.

[0170] Water pressure distribution describes the pressure levels at various nodes in a water distribution network, which is crucial to ensure that all users receive adequate water pressure.

[0171] Node pressure refers to the actual water pressure at each connection point (such as valves and branch points). This ensures that neither excessive pressure damages the pipe network nor excessively low pressure affects user performance. Pressure gradient refers to the pressure trend along the water supply line, helping to identify high- and low-pressure areas and guiding subsequent pressure regulation measures. Volatility refers to the fact that water pressure may fluctuate throughout the day due to changes in water demand. Pay particular attention to pressure stability during peak hours.

[0172] Water distribution shows the distribution of water in each section of the water supply network, reflecting the effective utilization of water resources.

[0173] Matching supply with demand involves checking whether the actual water supply in each area meets local needs, and whether there is excess or shortage. Loss analysis involves assessing water losses due to leaks or other causes, identifying problem areas for timely repair. Balance involves ensuring that water is evenly distributed throughout the network, avoiding oversupply in some areas and undersupply in others.

[0174] The design and planning phase involves the construction or renovation of new water supply systems. Simulation results can be used to pre-test different design options and select the optimal pipe network layout to achieve optimal service and economic benefits. Daily operations management involves regular simulations of existing systems, helping managers monitor the system's operating status in real time, identify potential issues in advance, and develop appropriate adjustment strategies. Emergency response involves rapidly simulating the effects of different response plans in the event of an emergency (such as a pipe rupture or extreme weather), enabling swift decision-making and minimizing the impact on residents' lives.

[0175] Detailed analysis of these simulation results not only provides a comprehensive understanding of the current operating status of the water supply network but also provides a scientific basis for future optimization, thereby improving the reliability and efficiency of the entire system. This process embodies a complete chain from theoretical model construction to practical application, with each link closely linked to promote the optimization and upgrading of the water supply system.

[0176] According to one embodiment of the present application, the multi-objective genetic algorithm is used to optimize the pipe network structure based on the simulation results and the optimization target to obtain the pipe network layout optimization result, which is specifically: The water flow, water pressure and water volume distribution of the pipe network are encoded into chromosomes, and the initial population is randomly generated; According to the defined optimization goal, the fitness value of each chromosome is calculated, and the fitness value indicates the conformity between the pipe network structure represented by the chromosome and the optimization goal; Perform selection, crossover and mutation operations to generate a new pipe network structure, perform non-dominated sorting on each generation of population, and determine which individuals enter the next generation of population based on crowding comparison; Repeat the above steps until the pipe network layout optimization result is obtained.

[0177] As mentioned above, a chromosome represents a potential solution in a genetic algorithm. Here, a "chromosome" refers to a numerical representation of a specific pipe network structure and its operating status.

[0178] The encoding method uses key parameters such as simulated flow velocity, water pressure distribution, and water supply at each node as variables. These variables can be combined into a numerical sequence to represent a specific pipe network operating state or topology. For example, a chromosome might contain pressure values ​​at multiple nodes, flow rates at different pipe sections, and suggested pipe diameter changes. In this way, each chromosome represents a possible pipe network configuration.

[0179] In genetic algorithms, a population is a collection of chromosomes, representing a set of candidate solutions at the current stage. Initial population generation involves randomly generating several different pipe network configurations based on historical data or empirical rules to ensure diversity in the search space. Each individual represents a simulated representation of an independent pipe network structure and its operating status.

[0180] The fitness function is the core part of the genetic algorithm, which is used to evaluate the quality of a chromosome (that is, a pipe network scheme).

[0181] Optimization objectives often include multiple conflicting goals, such as: Minimize pump station energy consumption; maintain stable water pressure in the pipe network; ensure sufficient water supply without blind spots; reduce water loss; and improve system robustness and risk resistance (such as responding to emergencies such as pipe bursts and peak water use).

[0182] The fitness value refers to the fitness value of each chromosome, which is scored according to the above multiple goals and comprehensively obtained into one or more dimensions, indicating the degree of match between the solution and the ideal optimization goal.

[0183] The core of the genetic algorithm is to continuously optimize the solution by imitating the biological evolution mechanism in nature. The specific steps are as follows: According to the fitness value, the "parent" individuals with better performance are selected from the current population and enter the next generation reproduction process.

[0184] Commonly used methods include roulette wheel selection and tournament selection to ensure that high-quality individuals have a higher probability of being retained.

[0185] Part of the information of the two "parent" chromosomes is exchanged to generate a new offspring individual.

[0186] For example, the pressure settings of some pipe network nodes, the diameter parameters of certain pipes, etc. can be exchanged to form a new pipe network structure concept.

[0187] In the newly generated chromosome, random changes with small probabilities are introduced, such as adjusting the size of a certain section of the pipeline, changing the control strategy of a certain node, etc.

[0188] The purpose of mutation is to enhance the diversity of the population and prevent the algorithm from falling into a local optimal solution.

[0189] Since this is a multi-objective optimization problem, we cannot simply look at a single fitness value, but need to deal with multiple conflicting objectives. Therefore, the non-dominated sorting and congestion mechanism in the multi-objective genetic algorithm are used: Non-dominated sorting is used to determine which individuals are not inferior to other individuals in all objectives. These individuals constitute the Pareto Front, that is, the set of optimal compromise solutions.

[0190] The population is divided into multiple levels according to the non-dominance relationship, and the individuals with the highest ranking are retained first.

[0191] Crowding comparison refers to further using crowding indicators to measure the distribution density of multiple individuals at the same level in the target space.

[0192] The purpose is to maintain the diversity of the solution set and avoid multiple excellent solutions being concentrated in a small range and losing global representativeness.

[0193] The construction of a new generation population refers to selecting the most representative, well-performing and widely distributed individuals to form a new generation population based on the results of non-dominated sorting and crowding comparison.

[0194] This process is iterative, with each cycle of "selection-crossover-mutation-evaluation" completing the evolution of a generation of the population. As the number of generations increases, the population gradually approaches a more optimal solution. When the preset termination conditions are met (such as reaching the maximum number of iterations, fitness convergence, or the solution becomes stable), the calculation stops and the final optimization result is output.

[0195] After multiple generations of evolution, the algorithm ultimately outputs one or more high-quality pipe network optimization solutions, including: The network topology corresponding to each preferred solution; the pressure distribution and flow distribution at each node; recommended renovation measures, such as adding new pipelines, changing pipe diameters, and adding pressure-regulating valves; and the balance analysis of each solution under multiple optimization objectives help managers make the best choice.

[0196] According to one embodiment of the present application, the health assessment result includes a health assessment report and a health score.

[0197] As mentioned above, a health assessment report is a detailed document that summarizes various information about the health of the device, providing a comprehensive basis for device maintenance and optimization. The report typically includes the following aspects: Basic equipment information: Lists the basic properties of the evaluated equipment, such as model, manufacturer, installation date, and last maintenance time.

[0198] Historical Operation Data Summary: Review the equipment's historical operation records, including trends in key performance indicators such as flow rate, head, efficiency, and energy consumption. This data helps identify long-term equipment performance and patterns of change.

[0199] Fault Recording and Analysis: Record and analyze past equipment failures, including fault type, occurrence time, cause analysis, and repair measures. This helps predict future problems and develop preventive strategies in advance.

[0200] Current Status Assessment: Based on the latest operating data and inspection results, this assessment describes the equipment's current operating status. For example, it identifies any abnormal vibration, noise, or leakage, and the extent to which these issues are impacting equipment performance.

[0201] Maintenance recommendations: Based on the assessment results, specific maintenance recommendations or improvement measures are provided. For example, it may be recommended to replace certain parts that are severely worn, or to adjust certain parameters to improve equipment efficiency.

[0202] Risk warning: If any potential risk factors are found (such as critical components that are about to reach the end of their service life), they should be clearly pointed out in the report and corresponding countermeasures should be recommended.

[0203] The health score is a quantitative representation of the health of a device, typically using a numerical range to reflect the overall health of the device. This score helps managers quickly understand the status of the device and make decisions accordingly. Here are some specific details about the health score: Scoring criteria: The criteria for health scoring can be set based on multiple dimensions. Common dimensions include but are not limited to: Performance indicators: whether the efficiency and output capacity of the equipment meet the design requirements; Reliability: the probability of equipment failure and its severity; Maintenance requirements: Does the equipment require frequent maintenance and what are the maintenance costs? Service life: the remaining effective service life of the equipment; Environmental adaptability: the stability of the equipment under different working conditions.

[0204] Scoring Methodology: A weighted average or other mathematical model can be used to comprehensively consider the performance of each of the above dimensions to calculate the final score. For example, each dimension can be assigned a certain weight, and then a weighted score can be calculated based on the actual measured values, and finally summarized to obtain an overall health score.

[0205] Score range: The health score is usually set within a fixed range, such as 0 to 100. A score close to 100 indicates that the device is in very good condition, while a lower score indicates that the device may have more problems and requires attention or immediate action.

[0206] Application Value: Health scores not only provide a visual representation of the health status of equipment but also serve as a decision-making support tool. For example, when a device's health score falls below a certain threshold, the system can automatically trigger an alert, prompting management personnel to conduct further inspection or maintenance on the device.

[0207] According to one embodiment of the present application, the device performance fitting result includes a performance index curve and an efficient operation range defined based on the performance index curve.

[0208] As mentioned above, a performance index curve is a relationship diagram extracted from historical equipment operating data through mathematical modeling and data analysis techniques. It shows the trend of key performance parameters of the equipment (such as flow rate, head, efficiency, etc.) changing with certain operating variables (such as speed and load). The following are some specific details about the performance index curve: Key performance parameters: Flow rate (Q): refers to the amount of water flowing through the equipment per unit time.

[0209] Head (H): The height or pressure to which the pump lifts water.

[0210] Efficiency (η): The ability of a device to convert input energy into useful output, usually expressed as a percentage.

[0211] Manipulating variables: Speed ​​(n): For variable frequency pumps, the speed can be adjusted, affecting the flow rate and head.

[0212] Load (P): refers to the workload borne by the equipment, usually related to flow rate and head.

[0213] Curve form: QH curve: shows the relationship between flow rate and head. As flow rate increases, head generally decreases.

[0214] Q-η curve: This shows the relationship between flow rate and efficiency. The equipment achieves its highest efficiency within a specific flow rate range.

[0215] QP curve: shows the relationship between traffic flow and power consumption. Generally, power consumption increases with increasing traffic flow.

[0216] These curves provide information about the equipment's performance under different working conditions, helping managers understand the equipment's optimal working state and its ultimate capabilities.

[0217] Based on the above performance index curve, the efficient operation range of the equipment can be defined. The efficient operation range means that when the equipment operates in this range, it can achieve the best energy conversion efficiency while ensuring sufficient flow and head requirements. The following is a detailed explanation of the efficient operation range: Methods for determining efficient operating range: Find the point of highest efficiency: By analyzing the Q-η curve, find the flow rate at which the equipment reaches its highest efficiency. For example, if a pump is most efficient at a flow rate of 500 cubic meters per hour, this point can be used as a reference benchmark.

[0218] Setting a tolerance range: Considering the fluctuations in actual operation, a reasonable tolerance range is usually set around the maximum efficiency point. For example, a range of 10% above or below the maximum efficiency point can be defined as the high-efficiency operating range.

[0219] Consider other factors: In addition to efficiency, other key performance parameters must also be considered to ensure they meet actual needs. For example, within the efficient operating range, flow rate and head should also meet user water needs.

[0220] The significance of efficient operating range: Energy saving and consumption reduction: When the equipment works within the high-efficiency operating range, the energy conversion efficiency is the highest and the energy consumption is the lowest, which helps to reduce operating costs.

[0221] Extend equipment life: Avoid equipment being in a high-load or low-load state for a long time, reduce wear and tear, and extend service life.

[0222] Improve system stability: Ensure the stability and reliability of the water supply system to avoid water supply interruptions or other problems caused by insufficient equipment performance.

[0223] Daily Operation and Maintenance: Regularly monitor the actual operating parameters of the equipment and compare them with the performance indicator curve to ensure that the equipment always operates within the efficient operating range. If deviation from the efficient operating range is found, the operating parameters are adjusted or maintenance is performed in a timely manner.

[0224] Equipment selection and upgrade: When selecting new equipment or upgrading existing equipment, refer to the performance index curve and efficient operating range to select the equipment model and configuration that best suits current needs.

[0225] Optimize scheduling strategy: Combine water demand forecasts and pipe network layout optimization results to develop a reasonable equipment scheduling plan to ensure that all equipment operates within the efficient operating range as much as possible, thereby improving the overall efficiency of the entire water supply system.

[0226] A second aspect of the present application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the method described in any one of the embodiments of the first aspect.

[0227] An embodiment of a third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of the embodiments of the second aspect when executing the program.

[0228] Figure 2 An example of a physical structure diagram of an electronic device is shown below. Figure 2 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the method in any embodiment of the first aspect above, the method including: Based on the water consumption data, the water demand forecast is obtained by analyzing it through the long short-term memory neural network; Based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning is optimized to obtain the pipeline network layout optimization result; Based on the pipe network layout optimization results, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan.

[0229] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0230] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0231] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0232] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present application.

Claims

1. A smart water management scheduling method based on LSTM and multi-objective optimization, characterized by: include: Based on the water consumption data, the water demand forecast is obtained by analyzing it through the long short-term memory neural network; Based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning is optimized to obtain the pipeline network layout optimization result; Based on the pipe network layout optimization results, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan.

2. The method according to claim 1, characterized in that The water demand forecast is obtained by analyzing the water use data through the long short-term memory neural network, specifically: Collecting the water use data, wherein the water use data includes at least one of historical water use data, user information, seasonal factors, and weather factors; Cleaning the water consumption data and extracting relevant features, wherein the relevant features include at least one of time features, weather features, and user features; Use a long short-term memory neural network to build a prediction model, input the relevant features, and perform preliminary training on the prediction model; The preliminarily trained prediction model is retrained using historical water usage data, and the performance of the retrained prediction model is evaluated through cross-validation to obtain a trained prediction model. The actual water usage data is input into the trained prediction model to obtain the water demand prediction.

3. The method according to claim 1, characterized in that Based on the water demand forecast, combined with pipeline information and pipeline network layout data, the pipeline network pressure zoning optimization is performed to obtain the pipeline network layout optimization result, specifically: Based on the water demand forecast, the network topology is annotated using GIS tools to establish a three-dimensional model of the urban water supply network and the corresponding fluid dynamics equations; Determine the initial conditions and boundary conditions of the fluid dynamics equation, build a physical information neural network to solve it, and obtain simulation results; According to the simulation results and optimization objectives, a multi-objective genetic algorithm is used to optimize the pipe network structure to obtain the pipe network layout optimization result.

4. The method according to claim 1, wherein Based on the pipe network layout optimization results, combined with the equipment historical operation data and the water demand forecast, the pump station equipment efficiency analysis is performed to obtain the equipment operation combination plan, specifically: Based on the historical operation data of the equipment, the operation status of the equipment is classified and predicted through the decision tree. Combined with the information in the mechanism rule model library, the health status of the equipment is evaluated to obtain the health assessment result; Based on the historical operation data of the equipment, the least squares method is used to fit the equipment performance fitting results; Based on the water demand forecast, the pipe network layout optimization results, the historical operation data of the equipment, the health assessment results and the equipment performance fitting results, an operation combination plan of the equipment in the pump station is obtained through a water pump unit matching intelligent scheduling model based on offline deep reinforcement learning.

5. The method according to claim 3, characterized in that The simulation results show the water flow, water pressure and water volume distribution in the pipe network under different topological structures.

6. The method according to claim 3, characterized in that According to the simulation results and optimization objectives, a multi-objective genetic algorithm is used to optimize the pipe network structure to obtain the pipe network layout optimization result, which is specifically: The water flow, water pressure and water volume distribution of the pipe network are encoded into chromosomes, and the initial population is randomly generated; According to the defined optimization goal, the fitness value of each chromosome is calculated, and the fitness value indicates the conformity between the pipe network structure represented by the chromosome and the optimization goal; Perform selection, crossover and mutation operations to generate a new pipe network structure, perform non-dominated sorting on each generation of population, and determine which individuals enter the next generation of population based on crowding comparison; Repeat the above steps until the pipe network layout optimization result is obtained.

7. The method according to claim 4, characterized in that The health assessment result includes a health assessment report and a health score.

8. The method according to claim 4, characterized in that The equipment performance fitting result includes a performance index curve and an efficient operation range defined based on the performance index curve.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the method according to any one of claims 1 to 7 are implemented.

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