A secondary water supply optimization scheduling method and device for a building

Abstract

The application relates to a secondary water supply optimization scheduling method and device for buildings, aiming at realizing efficient, stable and intelligent operation of a secondary water supply system through water supply demand prediction, multi-target optimization scheduling, real-time monitoring and feedback. The method comprises the following steps: predicting future water demand by using a long short-term memory neural network; constructing a multi-target optimization model comprising energy consumption, water supply pressure and pump set service life; and solving an optimal scheduling scheme by using a genetic algorithm. By monitoring water supply pressure and flow in real time, the start and stop and variable frequency speed regulation of water pumps are dynamically adjusted, so that the system can be operated in an energy-saving mode and the service life of equipment is prolonged. The application is suitable for secondary water supply systems of high-rise buildings and large building groups.

Description

Technical Field

[0001] This invention relates to the field of building water supply and drainage engineering technology, specifically to a method and apparatus for optimizing the scheduling of secondary water supply in buildings. Background Technology

[0002] In modern cities, water supply systems are crucial infrastructure for ensuring the normal operation of residents and various water facilities in high-rise buildings and large building complexes. Because the vertical height of high-rise buildings far exceeds the pressure capacity of municipal water supply systems, directly relying on the municipal water supply network often fails to meet their water needs. Therefore, secondary water supply systems are commonly used, using pressurization equipment to boost municipal water supply to upper-floor users. However, existing secondary water supply systems still face many technical challenges and room for improvement in practical applications, mainly manifested in high energy consumption, unstable water pressure, and short equipment lifespan.

[0003] First, energy consumption is one of the most prominent issues in existing secondary water supply systems. Traditional water supply systems typically rely on pumps operating at a fixed frequency for pressurization. The pump's operating status (such as start / stop and speed) is often preset, lacking real-time responsiveness to actual water demand and fluctuations. This operating mode usually leads to two situations: first, pumps continue to operate at a higher frequency during periods of low water demand, resulting in energy waste; second, when water demand suddenly increases, the system cannot respond quickly, causing pumps to work overloaded, further increasing energy consumption. Furthermore, traditional systems often adopt a conservative strategy when setting pump operating parameters, i.e., setting higher operating water pressure and longer operating times to cope with potential water supply fluctuations, which exacerbates the energy consumption problem to some extent. Statistics show that the energy consumption of secondary water supply systems accounts for a high proportion of total building energy consumption, especially in large building complexes, where the energy consumption of water supply systems can even reach over 30%. Therefore, how to reduce the energy consumption of secondary water supply systems has become an important research direction for urban building energy conservation.

[0004] Secondly, unstable water supply pressure is also a significant problem faced by existing secondary water supply systems. This instability manifests in two main ways: firstly, due to varying water usage patterns on different floors within a building, water pressure often fluctuates significantly, resulting in excessively high pressure on some floors and insufficient pressure on others, impacting the user experience; secondly, sudden changes in water flow during system startup or shutdown can easily trigger water hammer, causing impact and damage to pipes and related equipment. Existing systems typically control water pressure by setting fixed pump start / stop times and speeds, but this method is often ineffective when faced with complex fluctuations in actual water demand. For example, during peak water usage periods, the fixed pump settings may not provide sufficient pressure, leading to water shortages for residents on higher floors; conversely, during off-peak periods, the pumps may still be operating at high load, resulting in excessively high pressure for residents on lower floors, causing unnecessary water waste and the risk of pipeline damage.

[0005] Third, short pump lifespan is also a significant problem facing current secondary water supply systems. As the core equipment of a secondary water supply system, the operating status of the pump directly affects the stability and lifespan of the entire system. Because traditional secondary water supply systems lack dynamic adjustment of pump operating conditions, pumps often operate under suboptimal conditions, leading to accelerated equipment wear. For example, frequent pump start-ups and shutdowns not only increase mechanical wear on the motor and related components but can also cause overheating and malfunctions in the electrical system. Simultaneously, prolonged operation under high loads can easily cause motor overload and overheating, further shortening the equipment's lifespan. This is especially true in large building complexes or high-rise buildings, where the pump workload is even greater and wear is more severe, leading to a significant increase in pump maintenance frequency and replacement costs. Short equipment lifespan not only increases operating and maintenance costs but can also cause unplanned shutdowns of the water supply system, causing inconvenience and safety hazards to the daily operation of the building and the lives of residents.

[0006] Furthermore, with the increasing demand for building intelligence and energy conservation and emission reduction, the traditional management model of secondary water supply systems is becoming increasingly inadequate for the requirements of modern buildings. Existing water supply systems typically rely on manual operation and experience-based judgment for management, lacking systematic optimization scheduling and intelligent control methods. This management approach is not only inefficient but also prone to causing instability in the water supply system's operation. For example, in practice, the start and stop of the water supply system are often determined by experienced operators based on water usage, and this human factor makes it difficult to maintain consistent system efficiency. Moreover, as the scale of water supply systems expands, the complexity and difficulty of manual management also increase, making traditional management models unsuitable for the needs of large building complexes.

[0007] To address these challenges, researchers and engineers have proposed various optimization solutions in recent years, such as variable frequency speed control technology, water demand prediction models, and intelligent control systems. However, most of these solutions are independent and lack system integration and coordination, making it difficult to improve the overall efficiency and stability of secondary water supply systems. Especially in high-rise buildings with complex and variable water supply demands, existing technologies struggle to provide a comprehensive solution that can significantly reduce energy consumption while ensuring water supply stability and equipment lifespan.

[0008] In summary, current secondary water supply systems still have significant room for improvement in areas such as energy consumption control, water supply pressure stability, and equipment lifespan management. Therefore, this paper proposes an optimized scheduling method based on advanced algorithms and real-time monitoring technology. This method aims to significantly reduce energy consumption, extend equipment lifespan, and achieve intelligent system management while ensuring stable operation of the water supply system. This approach has significant practical implications and promising application prospects. Summary of the Invention

[0009] The purpose of this invention is to provide a method and apparatus for optimizing the scheduling of secondary water supply in buildings, so as to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the scheduling of secondary water supply in buildings, comprising the following steps:

[0011] Water demand forecasting: Based on historical water usage data of buildings, weather forecasts, and holiday factors, the water demand for a specific time period (such as within 24 hours) is predicted using long short-term memory (LSTM) neural networks or time series analysis methods, resulting in the future water demand curve D(t), where t is time and D(t) represents the predicted water demand at time t.

[0012] Construction of a multi-objective optimization scheduling model: Based on the predicted water demand curve D(t), and considering factors such as the operating cost of the water supply system, water supply pressure, and pump lifespan, a multi-objective optimization scheduling model is constructed. The objective function of this model includes the following:

[0013]

[0014] Where: Z is the optimization objective value;

[0015] E represents the system energy consumption, expressed as: Where P i (t) represents the power of the i-th pump at time t, S i (t) represents the start / stop state of the i-th water pump at time t, and N is the total number of water pumps;

[0016] P(t) is the water supply pressure at time t, P targetTarget water supply pressure;

[0017] L is the service life of the pump set, which is inversely proportional to the number of pump start-ups and shutdowns and the running time. w1, w2, and w3 are the weight coefficients of the objective function, which are used to adjust the balance between energy consumption, water supply pressure stability and pump set service life, respectively.

[0018] Optimization Solution: The multi-objective optimization scheduling model is solved using a genetic algorithm or other optimization algorithms to obtain the optimal water supply scheduling scheme, including the pump start-up and shutdown times t. on t off The key parameters of the genetic algorithm, including population size, crossover probability, mutation probability, and number of iterations, are the population size and the running state S(t).

[0019] Real-time monitoring and feedback: The actual water supply pressure P is obtained through real-time monitoring equipment such as pressure sensors and flow meters in the water supply system. real The system calculates the actual water supply (t) and the actual water supply Q(t), and uses fuzzy control algorithms or other feedback mechanisms to dynamically adjust the water supply scheduling scheme according to the actual water supply status to ensure the stable operation of the system.

[0020] Dispatch execution: Based on the optimized water supply dispatch plan, control the start and stop of the water pumps and adjust the pump speed through the frequency converter to achieve precise control of the water supply pressure and ensure that the water supply system operates according to the optimized plan.

[0021] As a preferred technical solution of the present invention, in the water demand prediction step, a long short-term memory (LSTM) neural network model is used to train historical water use data. The specific training process includes data preprocessing, network structure design, loss function setting and gradient descent algorithm optimization. The resulting water demand curve D(t) is used to predict water demand in the next 24 hours.

[0022] As a preferred technical solution of the present invention, the objective function for water supply pressure stability in the multi-objective optimization scheduling model can be expressed as:

[0023]

[0024] Specifically, by minimizing the sum of squares of water supply pressure deviations, the system ensures that the water supply pressure remains stable throughout the entire scheduling period.

[0025] As a preferred technical solution of the present invention, the formula for calculating the service life L of the pump set is:

[0026]

[0027] in:

[0028] L0 represents the initial lifespan of the pump unit; N startH represents the number of times the pump unit starts and stops. oper This refers to the cumulative operating time of the pump unit.

[0029] k and m are coefficients representing the impact of the number of pump start-ups and shutdowns and the operating time on the pump's lifespan.

[0030] The present invention also provides a secondary water supply optimization and scheduling device for buildings, comprising:

[0031] Data acquisition module: including pressure sensor, flow meter and temperature sensor, used to collect parameters such as water supply pressure, water supply flow and water supply temperature in the building, and transmit the collected data to the central control unit;

[0032] Central control unit: Embedded with a multi-objective optimization scheduling algorithm, it analyzes the operating status of the water supply system by receiving data transmitted from the data acquisition module and generates the optimal water supply scheduling scheme. The central control unit includes a processor, a memory and a communication module.

[0033] Control execution module: including frequency converter and relay, used to receive scheduling instructions sent by central control unit, control the start and stop and speed adjustment of water pump, and ensure that water pump operates according to the optimal scheduling scheme;

[0034] Human-machine interface: including display screen and input device, used to display the operating status of water supply system, energy consumption, historical data, and allow users to manually adjust scheduling parameters.

[0035] As a preferred technical solution of the present invention, the central control unit adopts an embedded controller with an ARM architecture and integrates a multi-objective optimization scheduling algorithm module. The algorithm module includes a data processing submodule, an optimization calculation submodule, and a real-time monitoring submodule. The controller communicates with the control execution module through the Modbus protocol.

[0036] As a preferred technical solution of the present invention, the pressure sensor of the data acquisition module is installed at a key node of the water supply network, the flow meter is installed on the main water supply line, and the temperature sensor is installed at the water supply inlet. The collected data is used to monitor the operating status of the water supply system in real time and to provide basic data for scheduling optimization.

[0037] As a preferred technical solution of the present invention, the frequency converter in the control execution module can adjust the speed of the water pump in real time according to the instructions of the central control unit to ensure that the water supply pressure and flow meet the predicted requirements, while reducing the energy consumption of the pump group.

[0038] As a preferred technical solution of the present invention, the human-computer interaction interface also includes a historical data analysis function, which can display the operation status of the water supply system, the trend of energy consumption changes and the start-stop frequency of water pumps over a period of time, and allow users to adjust future scheduling plans based on historical data.

[0039] As a preferred technical solution of the present invention, in the optimization solution step, the population size of the genetic algorithm is set to 100, the number of iterations is 500, the crossover probability is 0.8, and the mutation probability is 0.01. By adjusting these parameters to balance the computational complexity and the globality of the solution, the optimal water supply scheduling scheme is obtained.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method and apparatus for optimizing the scheduling of secondary water supply in buildings, which has the following significant advantages compared with the prior art:

[0041] Significantly Reduced Energy Consumption: By introducing a Long Short-Term Memory (LSTM) neural network and a multi-objective optimization scheduling algorithm, this invention can accurately predict the water demand of buildings and dynamically adjust the start-up and shutdown times and speeds of water pumps based on the prediction results. The optimized water supply scheduling scheme enables water pumps to operate under the most reasonable load, avoiding unnecessary energy consumption. Experimental verification shows that the application of this invention can reduce system energy consumption by approximately 15%.

[0042] Improving water supply pressure stability: This invention dynamically adjusts the water supply pressure by real-time monitoring of the system's pressure and flow rate, combined with a fuzzy control algorithm. This effectively reduces pressure fluctuations and prevents water hammer. The optimized system maintains long-term stable water supply pressure, ensuring users receive a consistent water experience during peak and off-peak hours.

[0043] Extending pump set lifespan: By optimizing scheduling, frequent pump start-ups and shutdowns and prolonged high-load operation are reduced, slowing down the wear rate of the pump sets. The lifespan optimization model can balance the load distribution of the pumps, allowing each pump set to operate under its optimal conditions, thereby significantly extending the service life of the equipment and reducing maintenance and replacement costs.

[0044] System Intelligence and Automation: The central control unit of this invention integrates a multi-objective optimization scheduling algorithm and a real-time monitoring module, enabling it to automatically execute the optimal scheduling scheme and dynamically adjust it based on actual operating conditions. The system also provides a human-machine interface, allowing users to monitor the system's operating status in real time, analyze historical data, and manually adjust scheduling parameters. The overall system achieves efficient intelligent management, reducing the need for manual intervention.

[0045] Significant economic benefits: By reducing energy consumption and extending equipment lifespan, this invention effectively reduces the operating costs of water supply systems and improves the overall energy efficiency of buildings. Simultaneously, the system's efficient operation and stable water supply enhance user satisfaction, bringing greater economic benefits to building operators.

[0046] In summary, this invention optimizes the secondary water supply system while achieving multiple objectives such as energy saving, stability, long service life, and intelligence, and has broad application prospects and significant economic and social benefits. Attached Figure Description

[0047] Figure 1 This is a flowchart of a method for optimizing the scheduling of secondary water supply in buildings, as proposed in this invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1: Water Demand Forecasting Based on LSTM Neural Network

[0050] In one specific embodiment, the building's secondary water supply system needs to be scheduled based on the water demand for the next 24 hours. Therefore, a Long Short-Term Memory (LSTM) neural network is used to predict future water demand. The specific process is as follows:

[0051] Data Acquisition and Preprocessing: Collect water usage data for the building over the past year, including timestamps, water consumption, outdoor temperature, and weekday or holiday characteristics. Standardize this data to eliminate dimensional differences.

[0052] Model Training: The processed data is divided into training and test sets. The training set is used to train the LSTM neural network model. The network structure includes an input layer, LSTM hidden layers, and an output layer. The mean squared error (MSE) loss function is used, and the Adam optimization algorithm is employed.

[0053] Predicting water demand: Using a trained LSTM model, predict the water demand for the next 24 hours and generate a water demand curve D(t), which will serve as the basis for subsequent optimized scheduling.

[0054] Example 2: Construction and Solution of Multi-Objective Optimization Scheduling Model

[0055] This embodiment constructs a multi-objective optimization scheduling model based on the water supply demand curve D(t) predicted in Embodiment 1, and uses a genetic algorithm to solve it.

[0056] The specific process is as follows:

[0057] Objective function design: Based on the objective function formula in claim 1, an optimization objective is constructed, including energy consumption E, water supply pressure stability P(t), and pump set service life L. The objective function is:

[0058]

[0059] Among them, energy consumption E is calculated by the power consumption of all water pumps in the system in each time period, water supply pressure P(t) is calculated by real-time pressure monitoring data of water supply network, and pump set service life L is estimated based on the number of pump set start-ups and shutdowns and cumulative running time.

[0060] Genetic algorithm parameter settings: During the solution process, the population size of the genetic algorithm is set to 100, the number of iterations is set to 500, the crossover probability is 0.8, and the mutation probability is 0.01. The initial population is obtained by randomly generating different pump start-up and shutdown times and operating states S(t).

[0061] Optimization Solution: The genetic algorithm evolves individuals (i.e., different scheduling schemes) in the population through operations such as selection, crossover, and mutation, and selects a suitable scheduling scheme based on the merits of the objective function value. After multiple iterations, the algorithm converges to the optimal solution, outputting the optimal pump start-up and shutdown times and the variable frequency speed control strategy.

[0062] Example 3: Application of Real-time Monitoring and Feedback Mechanism

[0063] This embodiment further expands the optimized scheduling scheme in Embodiment 2, ensuring the stability of the water supply system through a real-time monitoring and feedback mechanism. The specific process is as follows: Real-time monitoring equipment deployment: Pressure sensors, flow meters, and temperature sensors are installed at key nodes of the water supply system to collect real-time operating data, including water supply pressure P. real (t), flow rate Q(t), and temperature T(t).

[0064] The feedback control algorithm employs a fuzzy control algorithm to adjust the water pump's operating state based on real-time collected data. The fuzzy control rule is based on the water supply pressure deviation ΔP = P real (t)-P target The flow deviation ΔQ = Q(t) - Q target By controlling the start and stop of the water pump and using frequency conversion speed regulation, system fluctuations are reduced and a stable water supply is maintained.

[0065] Real-time scheduling and adjustment: During system operation, if the water supply pressure or flow rate deviates from the set target, the central control unit adjusts the scheduling scheme to control the operation status of the water pump in real time, ensuring the efficient and stable operation of the system.

[0066] Example 4: Design and Implementation of the Central Control Unit

[0067] This embodiment describes the design of a central control unit for optimizing the scheduling of secondary water supply, as detailed below:

[0068] Hardware configuration: The central control unit adopts an embedded controller based on the ARM architecture, containing a processor, memory, communication module, and I / O interface. The processor is responsible for executing the optimized scheduling algorithm and real-time control logic, the memory is used to store historical data, model parameters, and scheduling schemes, the communication module is used for data exchange with the data acquisition module and the control execution module, and the I / O interface connects to the human-machine interface and external sensors.

[0069] Software Architecture: The embedded controller runs on a software architecture based on a real-time operating system (RTOS). The software is divided into a data acquisition module, an optimization and scheduling module, a real-time monitoring module, and a user interface module. The data acquisition module is responsible for acquiring real-time data from external sensors, the optimization and scheduling module executes multi-objective optimization algorithms, the real-time monitoring module implements feedback-based dynamic adjustments, and the user interface module provides status display and manual adjustment functions for the water supply system.

[0070] Communication Protocol: The central control unit and the control execution module communicate via the Modbus protocol to transmit commands for pump start / stop and inverter speed regulation. Real-time acquired data is transmitted to the central control unit via CAN bus or RS485 interface.

[0071] Example 5: Design of Human-Computer Interaction Interface

[0072] This embodiment describes the design and implementation of the human-machine interface in a water supply optimization scheduling device, specifically including the following:

[0073] Display and Control Interface: The human-machine interface displays the current operating status of the water supply system via an LCD touchscreen, including parameters such as water pressure, flow rate, temperature, and energy consumption. Users can view historical data, scheduling plan execution status, and system alarm information through the touchscreen.

[0074] Parameter adjustment function: Users can manually adjust the scheduling parameters of the water supply system on the interface, such as the start and stop times of the water pumps and the speed range of the frequency converter. The system provides a simulation operation function after parameter adjustment, so that users can view the expected operating effect of the system after adjustment.

[0075] Data analysis function: The interface provides historical data analysis tools, allowing users to view the operation of the water supply system over a period of time, including water pressure change curves, water consumption trends, energy consumption statistics, etc., and generate reports for energy saving analysis and system optimization.

[0076] Example 6: Application of Pump Unit Service Life Optimization

[0077] This embodiment focuses on how to extend the service life of the pump set by optimizing the pump start-up and shutdown frequency and running time.

[0078] Pump set life model: The pump set life calculation formula in claim 4 is adopted:

[0079]

[0080] Among these measures, the number of times the water pumps start and stop is controlled by optimizing the scheduling scheme. start and cumulative running time H oper This extends the actual service life of the pump set.

[0081] Scheduling strategy adjustment: During the optimization process, the genetic algorithm pays special attention to optimizing the pump start-up and shutdown frequency. By reducing unnecessary start-up and shutdown operations and optimizing the load distribution of the pumps, it reduces equipment wear.

[0082] Real-time monitoring and maintenance recommendations: Based on real-time monitoring data, the system estimates the current lifespan of the pump set and provides maintenance recommendations on the interface, such as regular inspection or replacement of worn parts, to ensure long-term stable operation of the system.

[0083] Example 7: Verification of the system's energy-saving effect

[0084] To verify the energy-saving effect of the secondary water supply optimization scheduling method and device, the following experiments and analyses were conducted in this embodiment:

[0085] Experimental setup: A high-rise building was selected as the experimental subject to compare energy consumption before and after the application of optimized scheduling. The experiment lasted for one month, and data such as water pump energy consumption, water supply pressure, and flow rate were recorded.

[0086] Results Analysis: Experimental results show that after applying the optimized scheduling method, system energy consumption is reduced by approximately 15%, water supply pressure fluctuation is reduced to 20% of the original level, and the average number of pump start-ups and shutdowns is reduced by approximately 30%. These data verify the energy-saving effect and stability improvement of the present invention.

[0087] The above embodiments further demonstrate the application effects of the present invention in building secondary water supply systems. The method and apparatus of the present invention can not only effectively improve system operating efficiency and reduce energy consumption, but also extend equipment lifespan, and have broad application prospects.

[0088] The above embodiments are specific implementations of the present invention. Those skilled in the art can make different changes and modifications based on the content of the present invention without departing from the spirit and scope of the present invention. Therefore, all equivalent implementations that do not depart from the spirit and scope of the present invention should be included within the protection scope of the present invention.

[0089] The contents not described in detail in this description are existing technologies known to those skilled in the art. 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 make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of secondary water supply in buildings, characterized in that, Includes the following steps: Water demand forecasting: Based on historical water usage data of buildings, weather forecasts, and holiday factors, water demand for a specific future period is predicted using long short-term memory neural networks or time series analysis methods, resulting in a future water demand curve. ,in For time, Indicates time Forecasted water demand; Construction of a multi-objective optimization scheduling model: Based on the predicted water supply demand curve Taking into account factors such as the operating cost of the water supply system, water supply pressure, and pump lifespan, a multi-objective optimization scheduling model is constructed. The objective function of this model is: in: To optimize the target value; The system energy consumption is expressed as... ,in For the first Taiwan water pump in time power, For the first Taiwan water pump in time Start-stop status, This represents the total number of water pumps. For time Water supply pressure, Target water supply pressure; The service life of a pump set is inversely proportional to the number of pump start-ups and shutdowns and the operating time. , , These are the weighting coefficients of the objective function; Optimization Solution: The multi-objective optimization scheduling model is solved using a genetic algorithm or other optimization algorithms to obtain the optimal water supply scheduling scheme, including the start-up and shutdown times of the water pumps. , and running status The key parameters of the genetic algorithm include population size, crossover probability, mutation probability, and number of iterations. Real-time monitoring and feedback: The actual water supply pressure is obtained through real-time monitoring equipment such as pressure sensors and flow meters in the water supply system. and actual water supply Furthermore, the water supply scheduling scheme is dynamically adjusted based on the actual water supply status through a fuzzy control algorithm. Dispatch execution: Based on the optimized water supply dispatch plan, control the start and stop of the water pumps, and adjust the speed of the water pumps through the frequency converter to achieve precise control of the water supply pressure.

2. The secondary water supply optimization scheduling method according to claim 1, characterized in that, In the water demand forecasting step, a long short-term memory neural network model is used to train historical water usage data. The specific training process includes data preprocessing, network structure design, loss function setting, and gradient descent algorithm optimization to obtain the water demand curve. Used to predict water demand in the next 24 hours.

3. The secondary water supply optimization scheduling method according to claim 1, characterized in that, In the multi-objective optimization scheduling model, the objective function for water supply pressure stability can be expressed as: Specifically, by minimizing the sum of squares of water supply pressure deviations, the system ensures that the water supply pressure remains stable throughout the entire scheduling period.

4. The secondary water supply optimization scheduling method according to claim 1, characterized in that, The service life of the pump set The calculation formula is: in: This refers to the initial lifespan of the pump unit. The number of times the pump unit is started and stopped. This refers to the cumulative operating time of the pump unit. and This is a coefficient representing the impact of the number of pump start-ups and shutdowns and operating time on the pump's lifespan.

5. The secondary water supply optimization scheduling method according to claim 1, characterized in that, In the optimization solution step, the population size of the genetic algorithm is set to 100, the number of iterations is 500, the crossover probability is 0.8, and the mutation probability is 0.

01. By adjusting these parameters, the computational complexity and the globality of the solution are balanced, thereby obtaining the optimal water supply scheduling scheme.

6. A secondary water supply optimization and scheduling device for buildings, used to implement the method described in any one of claims 1-5, characterized in that, include: Data acquisition module: including pressure sensor, flow meter and temperature sensor, used to collect water supply pressure, water supply flow and water supply temperature parameters in the building, and transmit the collected data to the central control unit; Central control unit: Embedded with a multi-objective optimization scheduling algorithm, it analyzes the operating status of the water supply system by receiving data transmitted from the data acquisition module and generates the optimal water supply scheduling scheme. The central control unit includes a processor, a memory and a communication module. Control execution module: including frequency converter and relay, used to receive scheduling instructions sent by central control unit, control the start and stop and speed adjustment of water pump, and ensure that water pump operates according to the optimal scheduling scheme; Human-machine interface: including display screen and input device, used to display the operating status of water supply system, energy consumption, historical data, and allow users to manually adjust scheduling parameters.

7. The secondary water supply optimization and scheduling device according to claim 6, characterized in that, The central control unit adopts an ARM-based embedded controller and integrates a multi-objective optimization scheduling algorithm module. The algorithm module includes a data processing submodule, an optimization calculation submodule, and a real-time monitoring submodule. The controller communicates with the control execution module via the Modbus protocol.

8. The secondary water supply optimization and scheduling device according to claim 6, characterized in that, The pressure sensor of the data acquisition module is installed at a key node of the water supply network, the flow meter is installed on the main water supply line, and the temperature sensor is installed at the water supply inlet. The collected data is used to monitor the operating status of the water supply system in real time and to provide basic data for scheduling optimization.

9. The secondary water supply optimization and scheduling device according to claim 6, characterized in that, The frequency converter in the control execution module can adjust the speed of the water pump in real time according to the instructions of the central control unit to ensure that the water supply pressure and flow meet the predicted requirements, while reducing the energy consumption of the pump set.

10. The secondary water supply optimization and scheduling device according to claim 6, characterized in that, The human-computer interface also includes a historical data analysis function, which can display the operating status of the water supply system, energy consumption trends, and pump start-stop frequency over a period of time, and allow users to adjust future scheduling plans based on historical data.

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