Secondary water supply optimal scheduling method and device for building

Abstract

The invention relates to a secondary water supply optimal scheduling method and device for a building, and aims to realize efficient, stable and intelligent operation of a secondary water supply system through water supply demand prediction, multi-objective optimal scheduling and 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-objective optimization model containing energy consumption, water supply pressure and pump set life, and solving an optimal scheduling scheme by adopting a genetic algorithm. By monitoring water supply pressure and flow in real time and dynamically adjusting start and stop and frequency conversion speed regulation of the water pump, energy-saving operation of the system is ensured, and the service life of equipment is prolonged. The system is suitable for secondary water supply systems of high-rise buildings and large building groups.

Description

Technical Field

[0001] The invention relates to the technical field of building water supply and drainage engineering, and in particular to a secondary water supply optimization scheduling method and device for buildings. Background Art

[0002] In high-rise buildings and large building complexes in modern cities, the water supply system is one of the important infrastructures to ensure the normal operation of residents and various water-using facilities. Since the vertical height of high-rise buildings far exceeds the pressure bearing capacity of the municipal water supply system, it is often difficult to meet the water supply needs of high-rise buildings directly relying on the municipal water supply network. Therefore, a secondary water supply system is generally used to lift the municipal water supply to high-rise users of the building through pressurized equipment. However, the existing secondary water supply system still faces many technical difficulties and optimization space in practical applications, mainly manifested in high energy consumption, unstable water supply pressure, short equipment life and other problems.

[0003] First, energy consumption is one of the most prominent problems in the existing secondary water supply system. Traditional water supply systems usually rely on fixed-frequency water pumps for pressurization. The operating status of the water pump (such as start and stop, speed) is often preset, and lacks the ability to respond to actual water demand and water fluctuations in real time. This operating mode usually leads to the following two situations: First, the water pump still runs at a higher frequency when the water demand is low, resulting in energy waste; second, when the water demand suddenly increases, the system cannot respond quickly, resulting in overload of the water pump, further increasing energy consumption. In addition, when setting the operating parameters of the water pump, the traditional system usually adopts a conservative strategy, that is, setting a higher operating water pressure and a longer operating time to cope with possible water supply fluctuations, which to a certain extent aggravates the energy consumption problem. According to statistics, the energy consumption of the secondary water supply system accounts for a high proportion of the total energy consumption of the building, especially in large-scale building complexes, the energy consumption of the water supply system can even reach more than 30%. Therefore, how to reduce the energy consumption of the secondary water supply system has become an important research direction for energy conservation in urban buildings.

[0004] Secondly, unstable water supply pressure is also an important problem faced by the existing secondary water supply system. The instability of water supply pressure is mainly reflected in two aspects: on the one hand, due to the different water use conditions on each floor of the building, the water supply pressure often fluctuates significantly, resulting in excessive water pressure on some floors and insufficient water pressure on some floors, affecting the user's water use experience; on the other hand, when the water supply system is started or stopped, the sudden change in water flow can easily cause water hammer, resulting in impact and damage to pipes and related equipment. The existing system usually controls the water supply pressure by setting fixed water pump start and stop time and speed, but this method is often not ideal when facing actual complex water demand fluctuations. For example, during peak water use, a fixed water pump may not be able to provide enough pressure, resulting in water difficulties for high-rise residents; while during low water use, the water pump may still be running at high load, resulting in excessive water pressure for low-rise residents, causing unnecessary waste of water resources and risk of pipe network damage.

[0005] Third, the short life of the pump set is also a significant problem facing the current secondary water supply system. As the core equipment of the secondary water supply system, the operating status of the water pump directly affects the stability and life of the entire system. Since the traditional secondary water supply system lacks dynamic adjustment of the operating status of the water pump, the water pump often operates in a non-optimal state, resulting in increased equipment wear. For example, the frequent start and stop of the water pump will not only increase the mechanical wear of the motor and related components, but may also cause overheating and failure of the electrical system. At the same time, the long-term operation of the water pump under high load is prone to cause motor overload and overheating, further shortening the service life of the equipment. Especially in large-scale building complexes or super-high-rise buildings, the water pump has a greater workload and more severe wear, resulting in a significant increase in the maintenance frequency and replacement cost of the pump set. The short life of the equipment not only increases the operating and maintenance costs, but may also cause unplanned shutdown of the water supply system, causing inconvenience and safety hazards to the daily operation of the building and the lives of residents.

[0006] In addition, with the increasing demand for building intelligence and energy conservation and emission reduction, the management mode of traditional secondary water supply systems is becoming less and less suitable for the requirements of modern buildings. Existing water supply systems usually rely on manual operation and experience judgment for management, lacking systematic optimization scheduling and intelligent control methods. This management method is not only inefficient, but also easily leads to unstable operation of the water supply system. For example, in actual operation, the start and stop of the water supply system is often judged by experienced operators based on water use conditions. This human factor makes it difficult to maintain consistent operating efficiency of the system. In addition, as the scale of the water supply system expands, the complexity and difficulty of manual management are also increasing, and the traditional management model is difficult to adapt to the needs of large-scale building complexes.

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

[0008] In summary, the current secondary water supply system still has significant room for improvement in energy consumption control, water supply pressure stability and equipment life management. Therefore, an optimization scheduling method based on advanced algorithms and real-time monitoring technology is proposed, in order to significantly reduce energy consumption, extend equipment life, and realize intelligent management of the system while ensuring the stable operation of the water supply system, which has important practical significance and application prospects. Summary of the invention

[0009] The object of the present invention is to provide a method and device for optimizing the scheduling of secondary water supply for buildings, so as to solve the problems raised by the above-mentioned background technology.

[0010] To achieve the above object, the present invention provides the following technical solution: a secondary water supply optimization scheduling method for a building, comprising the following steps:

[0011] Water supply demand forecast: Based on the historical water use data of the building, weather forecast, and holiday factors, the water supply demand in a specific time period in the future (such as within 24 hours) is predicted through the long short-term memory (LSTM) neural network or time series analysis method to obtain the future water supply demand curve D(t), where t is time and D(t) represents the predicted water demand at time t;

[0012] Construction of multi-objective optimization scheduling model: Based on the predicted water supply demand curve D(t), combined with the operating cost of the water supply system, water supply pressure, pump unit service life and other factors, a multi-objective optimization scheduling model is constructed. The objective function of the model includes the following:

[0013]

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

[0015] E is the system energy consumption, expressed as Where P i (t) is the power of the i-th pump at time t, S i (t) is the start / stop status 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 targetThe target water supply pressure;

[0017] L is the service life of the pump set, which is inversely proportional to the number of starts and stops and the operating time of the pump set. 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 service life of the pump set;

[0018] Optimization solution: Genetic algorithm or other optimization algorithms are used to solve the multi-objective optimization scheduling model to obtain the optimal water supply scheduling plan, including the start and stop time t of the water pump. on ,t off and running state S(t), the key parameters of the genetic algorithm include population size, crossover probability, mutation probability and number of iterations;

[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 (t) and the actual water supply Q(t), and dynamically adjust the water supply scheduling plan according to the actual water supply status through fuzzy control algorithm or other feedback mechanism to ensure the stable operation of the system;

[0020] Scheduling execution: According to the optimized water supply scheduling plan, the start and stop operations of the water pump are controlled, and the speed of the water pump is adjusted 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 supply 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 obtained water supply 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, in the multi-objective optimization scheduling model, the objective function of water supply pressure stability can be expressed as:

[0023]

[0024] Among them, by minimizing the sum of squares of water supply pressure deviations, the water supply system is ensured to maintain a stable water supply pressure during the entire scheduling period.

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

[0026]

[0027] in:

[0028] L0 is the initial life of the pump set; N startis the number of starts and stops of the pump group, H oper is the accumulated running time of the pump set;

[0029] k and m are the coefficients of the number of starts and stops and the running time of the pump group on its life.

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

[0031] Data acquisition module: including pressure sensor, flow meter and temperature sensor, used to collect water supply pressure, water supply flow, water supply temperature and other parameters 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, analyzes the operating status of the water supply system by receiving data transmitted by the data acquisition module, and generates an optimal water supply scheduling plan. 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 the dispatching instructions sent by the central control unit, control the start and stop and speed regulation of the water pump, and ensure that the water pump runs according to the optimal dispatching plan;

[0034] Human-computer interaction interface: includes a display screen and input device, which is used to display the operating status, energy consumption, historical data of the water supply system, and allow users to manually adjust the 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 the key node of the water supply network, the flow meter is installed on the water supply main road, 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 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 needs 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 operating status of the water supply system, energy consumption change trends and start and stop frequency of the water pump over a period of time in the past, 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 solution is obtained.

[0040] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a secondary water supply optimization scheduling method and device for buildings, which has the following significant beneficial effects compared with the prior art:

[0041] Significantly reduce energy consumption: By introducing the long short-term memory (LSTM) neural network and the multi-objective optimization scheduling algorithm, the present invention can accurately predict the water demand of the building and dynamically adjust the start and stop time and speed of the water pump according to the prediction results. The optimized water supply scheduling scheme enables the water pump to operate under the most reasonable load, avoiding unnecessary energy consumption. According to experimental verification, the application of the present invention can reduce the system energy consumption by about 15%.

[0042] Improve the stability of water supply pressure: This invention monitors the pressure and flow of the water supply system in real time and dynamically adjusts the water supply pressure in combination with the fuzzy control algorithm, effectively reducing the fluctuation of water supply pressure and avoiding the water hammer effect. The optimized system can maintain the long-term stability of water supply pressure, ensuring that users can get a consistent water experience during peak and trough periods.

[0043] Extend the service life of the pump group: By optimizing the scheduling, the frequent start and stop of the water pump and the long-term high-load operation are reduced, and the wear rate of the pump group is slowed down. The service life optimization model can balance the load distribution of the water pump, so that each pump group operates under its optimal working condition, 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 the present invention integrates a multi-objective optimization scheduling algorithm and a real-time monitoring module, which can automatically execute the optimal scheduling plan and dynamically adjust it according to the actual operation situation. The system also provides a human-computer interaction interface, through which users can monitor the system operation status in real time, analyze historical data and manually adjust scheduling parameters. The overall system realizes efficient intelligent management and reduces the necessity of manual intervention.

[0045] Significant economic benefits: By reducing energy consumption and extending equipment life, the present invention can effectively reduce the operating costs of the water supply system and improve the overall energy efficiency of the building. At the same time, the efficient operation and stable water supply of the system also improve user satisfaction and bring higher economic benefits to the building operator.

[0046] In summary, the present invention achieves multiple goals of energy saving, stability, long life and intelligence while optimizing the secondary water supply system, and has broad application prospects and significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a method flow chart of a secondary water supply optimization scheduling method for buildings proposed by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1: Water supply demand prediction based on LSTM neural network

[0050] In a specific embodiment, the secondary water supply system of a building needs to be scheduled according to the water demand in the next 24 hours. To this end, a long short-term memory (LSTM) neural network is used to predict the future water demand. The specific process is as follows:

[0051] Data collection and preprocessing: Collect the building’s water usage data for the past year, including features such as timestamp, water usage, outdoor temperature, weekdays or holidays, etc. Standardize this data to eliminate dimensional differences.

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

[0053] Predict water demand: Use the trained LSTM model to predict water demand for the next 24 hours and generate a water demand curve D(t), which will serve as the basic data for subsequent optimization and 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: According to the objective function formula in claim 1, the optimization objectives are constructed, including energy consumption E, water supply pressure stability P(t) and pump unit service life L. The objective function is:

[0058]

[0059] The energy consumption E is calculated by the power consumption of all water pumps in the system in each time period. The water supply pressure P(t) is calculated by the real-time pressure monitoring data of the water supply network. The service life L of the pump group is estimated based on the number of starts and stops and the cumulative operating time of the pump group.

[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 water pump start and stop times and operating status S(t) schemes.

[0061] Optimization solution: The genetic algorithm evolves the individuals in the population (i.e. different scheduling schemes) through operations such as selection, crossover, and mutation, and selects the appropriate scheduling scheme based on the value of the objective function. After multiple iterations, the algorithm converges to the optimal solution and outputs the optimal pump start and stop time and variable frequency speed regulation strategy.

[0062] Example 3: Application of real-time monitoring and feedback mechanism

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

[0064] The feedback control algorithm adopts fuzzy control algorithm to adjust the operation state of the water pump according to the real-time collected data. The fuzzy control rule is based on the water supply pressure deviation ΔP=P real (t)-P target and flow deviation ΔQ=Q(t)-Q target , by controlling the start and stop of the water pump and frequency conversion speed regulation, the system fluctuations can be reduced and the water supply can be kept stable.

[0065] Real-time scheduling adjustment: During the operation of the system, if it is detected that the water supply pressure or flow deviates from the set target, the central control unit will adjust the scheduling plan to control the operating status of the water pump in real time to ensure efficient and stable operation of the system.

[0066] Example 4: Design and implementation of a central control unit

[0067] This embodiment describes a design of a central control unit for realizing secondary water supply optimization scheduling, which is described in detail as follows:

[0068] Hardware configuration: The central control unit uses an embedded controller based on the ARM architecture, which contains a processor, memory, communication module and I / O interface. The processor is responsible for executing the optimization 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 to exchange data with the data acquisition module and the control execution module, and the I / O interface connects the human-computer interaction interface and external sensors.

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

[0070] Communication protocol: The central control unit communicates with the control execution module through the Modbus protocol to transmit instructions for starting and stopping the water pump and adjusting the speed of the inverter. The real-time collected data is transmitted to the central control unit through the CAN bus or RS485 interface.

[0071] Example 5: Design of human-computer interaction interface

[0072] This embodiment describes the design and implementation of a human-computer interaction interface in a water supply optimization scheduling device, which specifically includes the following contents:

[0073] Display and control interface: The human-computer interaction interface displays the current operating status of the water supply system through the LCD touch screen, including water supply pressure, flow, temperature, energy consumption and other parameters. Users can view historical data, scheduling plan execution status and system alarm information through the touch screen.

[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 time of the water pump, the speed regulation range of the inverter, etc. The system provides a simulated operation function after parameter adjustment, so that users can view the expected operation effect of the system after adjustment.

[0075] Data analysis function: The interface provides historical data analysis tools, allowing users to view the operation status of the water supply system over a period of time, including water supply 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] In this embodiment, the focus is on how to extend the service life of the pump set by optimizing the start and stop frequency and running time of the water pump.

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

[0079]

[0080] Among them, by optimizing the scheduling plan, the number of starts and stops of the water pump N is controlled start and the cumulative running time H oper , thereby extending the actual service life of the pump unit.

[0081] Scheduling strategy adjustment: During the optimization process, the genetic algorithm pays special attention to the optimization of the start and stop frequency of the pump group, reducing unnecessary start and stop operations and optimizing the load distribution of the pump group to reduce equipment wear.

[0082] Real-time monitoring and maintenance recommendations: The system estimates the current life status of the pump group based on real-time monitoring data, and provides pump group maintenance recommendations on the interface, such as regular inspections or replacement of worn parts, to ensure long-term and stable operation of the system.

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

[0084] In order 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 object to compare the energy consumption before and after the optimization scheduling was applied. The experiment lasted for one month, and the data of water pump energy consumption, water supply pressure, flow rate, etc. were recorded.

[0086] Result analysis: The experimental results show that after applying the optimized scheduling method, the system energy consumption is reduced by about 15%, the water supply pressure fluctuation is reduced to 20% of the original, and the average start and stop times of the pump group are reduced by about 30%. These data verify the energy-saving effect and stability improvement of the present invention.

[0087] Through the above multiple embodiments, the application effect of the present invention in the secondary water supply system of the building is further demonstrated. The method and device of the present invention can not only effectively improve the operating efficiency of the system and reduce energy consumption, but also extend the life of the equipment, and has a wide range of application prospects.

[0088] The above embodiments are specific implementations of the present invention, and those skilled in the relevant technical field 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 in the protection scope of the present invention.

[0089] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A secondary water supply optimization scheduling method for a building, characterized in that: The following steps are involved: Water supply demand forecast: Based on the historical water use data of the building, weather forecast, and holiday factors, the water supply demand in a specific time period in the future is predicted through long short-term memory neural network or time series analysis method to obtain the future water supply demand curve D(t), where t is time and D(t) represents the predicted water demand at time t; Construction of multi-objective optimization scheduling model: Based on the predicted water supply demand curve D(t), combined with the operating cost of the water supply system, water supply pressure, and pump unit service life factors, a multi-objective optimization scheduling model is constructed. The objective function of the model is: Where: Z is the optimization target value; E is the system energy consumption, expressed as Where P i (t) is the power of the i-th pump at time t, S i (t) is the start / stop status of the i-th water pump at time t, and N is the total number of water pumps; P(t) is the water supply pressure at time t, P target The target water supply pressure; L is the service life of the pump set, which is inversely proportional to the number of starts and stops and the operating time of the pump set. w1, w2, and w3 are the weight coefficients of the objective function; Optimization solution: Genetic algorithm or other optimization algorithms are used to solve the multi-objective optimization scheduling model to obtain the optimal water supply scheduling plan, including the start and stop time t of the water pump. on ,t off and running state S(t), the key parameters of the genetic algorithm include population size, crossover probability, mutation probability and number of iterations; Real-time monitoring and feedback: The pressure sensors and flow meters in the water supply system are used to monitor the equipment in real time to obtain the actual water supply pressure P. real (t) and the actual water supply Q(t), and dynamically adjust the water supply scheduling plan according to the actual water supply status through fuzzy control algorithm; Scheduling execution: According to the optimized water supply scheduling plan, the start and stop operations of the water pump are controlled, and the speed of the water pump is adjusted 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 is characterized in that: In the water supply demand prediction step, a long short-term memory 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 obtained water supply demand curve D(t) is used to predict the water demand in the next 24 hours.

3. The secondary water supply optimization scheduling method according to claim 1 is characterized in that: In the multi-objective optimization scheduling model, the objective function of water supply pressure stability can be expressed as: Among them, by minimizing the sum of squares of water supply pressure deviations, the water supply system is ensured to maintain a stable water supply pressure during the entire scheduling period.

4. The secondary water supply optimization scheduling method according to claim 1 is characterized in that: The calculation formula for the service life L of the pump group is: in: L0 is the initial life of the pump set; N start is the number of starts and stops of the pump group, H oper is the accumulated running time of the pump set; k and m are the coefficients of the number of starts and stops and the running time of the pump group on its life.

5. A secondary water supply optimization scheduling device for a building, characterized in that: include: Data acquisition module: including pressure sensor, flow meter and temperature sensor, used to collect parameters such as water supply pressure, water supply flow, water supply temperature in the building, and transmit the collected data to the central control unit; Central control unit: embedded with a multi-objective optimization scheduling algorithm, analyzes the operating status of the water supply system by receiving data transmitted by the data acquisition module, and generates an optimal water supply scheduling plan. The central control unit includes a processor, a memory and a communication module; Control execution module: including frequency converter and relay, used to receive the dispatching instructions sent by the central control unit, control the start and stop and speed regulation of the water pump, and ensure that the water pump runs according to the optimal dispatching plan; Human-computer interaction interface: includes a display screen and input device, which is used to display the operating status, energy consumption, historical data of the water supply system, and allow users to manually adjust the scheduling parameters.

6. The secondary water supply optimization scheduling device according to claim 5, characterized in that: The central control unit adopts an embedded controller of 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.

7. The secondary water supply optimization scheduling device according to claim 5, characterized in that: The pressure sensor of the data acquisition module is installed at the key node of the water supply network, the flow meter is installed on the water supply main road, 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 provide basic data for scheduling optimization.

8. The secondary water supply optimization scheduling device according to claim 5, 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 demand while reducing the energy consumption of the pump group.

9. The secondary water supply optimization scheduling device according to claim 5, characterized in that: The human-computer interaction interface also includes a historical data analysis function, which can display the operating status of the water supply system, energy consumption change trends and the start and stop frequency of the water pump over a period of time in the past, and allow users to adjust future scheduling plans based on historical data.

10. 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 to balance the computational complexity and the globality of the solution, the optimal water supply scheduling solution is obtained.

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