Heating two-network intelligent valve control device and system based on multi-objective optimization

Abstract

The application discloses a kind of based on multi-objective optimization's heating two networks intelligent valve control device and system, belong to heating pipeline valve automatic control technical field, comprising: transverse installation in water supply pipeline's valve body;Vertical installation in the top of valve body's executor;Water outlet end's pressure sensor, flow sensor and control unit;Control unit constructs multi-objective optimization function with the absolute value minimum of real-time room temperature and room temperature target value, circulating water pump power is not greater than energy consumption threshold and pressure difference is in pipe network pressure difference stable threshold interval as target, obtains optimal solution set using multi-objective optimization algorithm, determines optimal opening based on optimal solution set and opening-flow mapping table and issues opening adjustment instruction to executor, executor drives valve core rotation adjustment to optimal opening.Based on multi-objective optimization algorithm, room temperature, energy consumption and pressure difference are optimized and optimal opening instruction is generated, break through the limitation that existing single-target regulation cannot balance multiple demands.

Description

Technical Field

[0001] This application belongs to the field of automatic control technology for heating pipeline valves, and specifically relates to an intelligent valve control device and system for a secondary heating network based on multi-objective optimization. Background Technology

[0002] In centralized heating systems, intelligent valves connecting heat exchange stations and the secondary heating network for end users are core equipment for achieving "precise heating and energy saving." By adjusting their opening degree, they control the flow of hot water at the user end, directly affecting indoor temperature and system energy consumption. With the continuous rise in energy prices, heating companies' demands for intelligent valves in the secondary heating network have evolved from traditional "single temperature control" to a multi-objective synergistic approach encompassing "temperature compliance, energy reduction, and stable operation." On the one hand, it is necessary to strictly ensure that the room temperature of end users is maintained within the national standard range of 18-22℃; on the other hand, it is necessary to minimize the power consumption of circulating water pumps and heat source losses, while also effectively avoiding hydraulic imbalances caused by fluctuations in pipeline pressure, ensuring the stable and efficient operation of the entire secondary heating network system.

[0003] However, existing technical solutions are usually single-objective closed-loop control smart valves based on flow rate or temperature, which are difficult to meet the multi-objective collaborative needs of heating companies. Summary of the Invention

[0004] To address the aforementioned issues, this application provides an intelligent valve control device and system for secondary heating networks based on multi-objective optimization.

[0005] This application provides a smart valve control device for a secondary heating network based on multi-objective optimization, comprising: a valve body horizontally installed on the water supply pipeline; an actuator vertically installed on the top of the valve body; a pressure sensor installed at the outlet end of the valve body; a flow sensor installed downstream of the pressure sensor at the outlet end; and a control unit fixed to the side of the actuator and having an edge computing chip integrated on its internal PCB board; the control unit is connected to the actuator, pressure sensor, and flow sensor respectively via wires. The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the control target value. Based on the received pressure, it calculates the pressure difference before and after the valve. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring the circulating water pump power does not exceed the energy consumption threshold, and maintaining the pressure difference within the stable threshold range of the pipeline pressure difference. A multi-objective optimization algorithm is used to solve for the optimal solution set. Based on the optimal solution set and the opening-flow mapping table, the optimal opening is determined, and an opening adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The circulating water pump power is determined based on the flow rate and the pipeline characteristic coefficient. The actuator is used to receive the opening adjustment command and drive the valve core to rotate and adjust to the optimal opening.

[0006] Furthermore, the control unit is also used to calculate the real-time resistance value of the valve using the fluid dynamics formula S=ΔP / Q², compare the calculated real-time resistance value with the factory preset resistance value, and if the deviation is greater than the preset deviation, correct the opening-flow mapping table, where S represents the resistance value, Q represents the flow rate, and ΔP represents the pressure difference.

[0007] Furthermore, the multi-objective optimization algorithm is either the elitist strategy non-dominated sorting genetic algorithm NSGA-II or the multi-objective particle swarm optimization algorithm MOPSO.

[0008] Furthermore, the actuator is either an electric actuator or an electromagnetic actuator.

[0009] Furthermore, the control unit is also used to receive the real-time flow rate collected after the opening adjustment, monitor the pressure difference before and after the valve, and if the pressure difference exceeds the stable threshold range of the pipeline pressure difference, the adjusted actual flow rate is compared with the target flow rate. If the deviation is greater than the preset flow rate threshold, the opening is finely adjusted according to the set fine adjustment value until the deviation between the adjusted actual flow rate and the target flow rate is not greater than the preset flow rate threshold.

[0010] Furthermore, the intelligent valve control device also includes: a pressure sensor installed at the water inlet end of the valve body; The control unit is also used to calculate the pressure difference before and after the valve using the pressure received from the pressure sensor installed at the inlet end of the valve body and the pressure sensor installed at the outlet end of the valve body.

[0011] Furthermore, the control unit is also used to receive heat exchange station outlet pressure data and calculate the pressure difference before and after the valve using the received heat exchange station outlet pressure data and the pressure sensor installed at the outlet end of the valve body.

[0012] Furthermore, it also includes a multi-protocol communication module supporting Modbus-RTU and BACnetMS / TP protocols, installed at the communication interface on the side of the control unit.

[0013] Furthermore, the control unit is also used to monitor the continuity of data from actuator current, pressure sensors, and flow sensors, identify fault types, and generate and send warning signals containing fault types and valve numbers to the multi-protocol communication module. This allows the multi-protocol communication module to send warning signals to the external data acquisition and monitoring system SCADA, while simultaneously illuminating a red indicator light on the valve's local indicator to remind maintenance personnel to handle the situation promptly.

[0014] This application also provides a multi-objective optimization-based intelligent valve control method for secondary heating networks, used to control the aforementioned multi-objective optimization-based intelligent valve control device for secondary heating networks, comprising: The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the control target value. Based on the received pressure, it calculates the pressure difference before and after the valve. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring the circulating water pump power does not exceed the energy consumption threshold, and maintaining the pressure difference within the stable threshold range of the pipeline pressure difference. A multi-objective optimization algorithm is used to solve for the optimal solution set. Based on the optimal solution set and the opening-flow mapping table, the optimal opening is determined, and an opening adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The circulating water pump power is determined based on the flow rate and the pipeline characteristic coefficient. The actuator receives the opening adjustment command and drives the valve core to rotate and adjust to the optimal opening.

[0015] Compared with the prior art, this application has the following advantages: 1. Based on the NSGA-II algorithm or MOPSO algorithm, multi-objective optimization is performed to simultaneously optimize the three major objectives of "room temperature, heating energy consumption and pressure difference" and generate the optimal opening command, which breaks through the limitation of existing single-objective control that cannot balance multiple demands; 2. Based on the pressure difference and flow data before and after the valve, the resistance number is dynamically calculated and the "opening degree-flow rate" mapping table is corrected, reducing the control accuracy error from the current 15%-20% to within 5%, thus solving the mapping distortion problem caused by scaling and blockage in the pipeline network; 3. By monitoring actuator current and sensor data continuity, real-time identification of anomalies such as "valve jamming, sensor failure, and motor damage" can be achieved, enabling fault early warning and location, and shortening the operation and maintenance response cycle; 4. Supports mainstream protocols such as Modbus-RTU and BACnet MS / TP, and can be seamlessly connected to the existing external data acquisition and monitoring system SCADA of heating companies without the need for additional protocol converters, thus reducing access costs.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a multi-objective optimization-based intelligent valve control device for a secondary heating network according to an embodiment of this application is shown. Figure 2 A flowchart of a smart valve control method for a secondary heating network based on multi-objective optimization, according to an embodiment of this application, is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This application provides a smart valve control device for a secondary heating network based on multi-objective optimization, such as... Figure 1 As shown, it is an integrated device installed on the water supply pipe of the user unit of the heating network. It includes: a valve body (1) installed horizontally on the water supply pipe; an actuator (2) installed vertically on the top of the valve body; pressure sensor interfaces are welded to the inlet and outlet of the valve body respectively, and pressure sensors (3) are connected to it; a flow sensor (4) is installed downstream of the pressure sensor at the outlet; a control unit is fixed on the side of the actuator and has an edge computing chip integrated on the internal PCB board; the side of the box is provided with an RS485 interface and a power interface of a multi-protocol communication module (8); the control unit is connected to the actuator, pressure sensor and flow sensor respectively through wires to realize signal transmission and command control. The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the control target value. Based on the received pressure, it calculates the pressure difference before and after the valve. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring the circulating water pump power does not exceed the energy consumption threshold, and maintaining the pressure difference within the stable threshold range of the pipeline pressure difference. A multi-objective optimization algorithm is used to solve for the optimal solution set. Based on the optimal solution set and the opening-flow mapping table, the optimal opening is determined, and an opening adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The circulating water pump power is determined based on the flow rate and the pipeline characteristic coefficient. The actuator is used to receive the opening adjustment command and drive the valve core to rotate and adjust to the optimal opening.

[0021] Furthermore, the control unit is also used to calculate the real-time resistance value of the valve using the fluid dynamics formula S=ΔP / Q², compare the calculated real-time resistance value with the factory preset resistance value, and if the deviation is greater than the preset deviation, correct the opening-flow mapping table, where S represents the resistance value, Q represents the flow rate, and ΔP represents the pressure difference.

[0022] Furthermore, the multi-objective optimization algorithm is either the elitist strategy non-dominated sorting genetic algorithm NSGA-II or the multi-objective particle swarm optimization algorithm MOPSO.

[0023] The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is adopted. MOPSO is based on the iterative search for the optimal solution by particle swarm optimization. It has a faster convergence speed than NSGA-II and is suitable for scenarios with higher requirements for control response speed (such as old residential areas with frequent fluctuations in pipeline load).

[0024] Furthermore, the actuator is either an electric actuator or an electromagnetic actuator.

[0025] Electromagnetic actuators have a response time of ≤1 second, which is faster than that of electric actuators (≤3 seconds). They are suitable for scenarios with high requirements for flow adjustment speed (such as sudden load changes in commercial buildings). Electric actuators have a longer rated lifespan and should be selected based on actual needs.

[0026] Specifically, this application provides a component, installation location / integration location and core function of a smart valve control device for a secondary heating network based on multi-objective optimization, as shown in Table (1).

[0027] Table (1)

[0028] Furthermore, the control unit is also used to receive the real-time flow rate collected after the opening adjustment, monitor the pressure difference before and after the valve, and if the pressure difference exceeds the stable threshold range of the pipeline pressure difference, the adjusted actual flow rate is compared with the target flow rate. If the deviation is greater than the preset flow rate threshold, the opening is finely adjusted according to the set fine adjustment value until the deviation between the adjusted actual flow rate and the target flow rate is not greater than the preset flow rate threshold.

[0029] Furthermore, the intelligent valve control device also includes: a pressure sensor installed at the water inlet end of the valve body; The control unit is also used to calculate the pressure difference before and after the valve using the pressure received from the pressure sensor installed at the inlet end of the valve body and the pressure sensor installed at the outlet end of the valve body.

[0030] Furthermore, the control unit is also used to receive heat exchange station outlet pressure data and calculate the pressure difference before and after the valve using the received heat exchange station outlet pressure data and the pressure sensor installed at the outlet end of the valve body.

[0031] The above method, which deploys only one pressure sensor at the outlet end and calculates the pressure difference across the valve (ΔP = heat exchange station outlet pressure - outlet pressure) by combining the outlet pressure data of the heat exchange station (obtained through the external data acquisition and monitoring system SCADA), reduces the number of pressure sensors by one compared to deploying pressure sensors at both the inlet and outlet ends of the valve body. This reduces hardware costs by approximately [amount missing]. Furthermore, it relies on the stability of the pressure data transmission from the external data acquisition and monitoring system SCADA. When the transmission delay is less than 1 second, it does not affect the accuracy of the pressure difference calculation.

[0032] Furthermore, it also includes a multi-protocol communication module supporting Modbus-RTU and BACnetMS / TP protocols, installed at the communication interface on the side of the control unit.

[0033] Furthermore, the control unit is also used to monitor the continuity of data from actuator current, pressure sensors, and flow sensors, identify fault types, and generate and send warning signals containing fault types and valve numbers to the multi-protocol communication module. This allows the multi-protocol communication module to send warning signals to the external data acquisition and monitoring system SCADA, while simultaneously illuminating a red indicator light on the valve's local indicator to remind maintenance personnel to handle the situation promptly.

[0034] This application also provides a multi-objective optimization-based intelligent valve control method for secondary heating networks, used to control the aforementioned multi-objective optimization-based intelligent valve control device for secondary heating networks, comprising: The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the control target value. Based on the received pressure, it calculates the pressure difference before and after the valve. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring the circulating water pump power does not exceed the energy consumption threshold, and maintaining the pressure difference within the stable threshold range of the pipeline pressure difference. A multi-objective optimization algorithm is used to solve for the optimal solution set. Based on the optimal solution set and the opening-flow mapping table, the optimal opening is determined, and an opening adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The circulating water pump power is determined based on the flow rate and the pipeline characteristic coefficient. The actuator receives the opening adjustment command and drives the valve core to rotate and adjust to the optimal opening.

[0035] The following uses a 1-minute adjustment cycle, combined with... Figure 2 The dynamic control process of the specific implementation of this application is described, which includes the following steps: Step 1: The control unit performs multi-source data acquisition.

[0036] The pressure sensor (3) collects the real-time pressure difference (ΔP) before and after the valve, such as 0.4MPa at the inlet and 0.35MPa at the outlet, ΔP=0.05MPa. The flow sensor (4) collects real-time flow rate (Q, e.g., 2.5 m³ / h); The external data acquisition and monitoring system SCADA of the heating company sends out "control target parameters" through the multi-protocol communication module (8): user room temperature target value (e.g., 20℃), circulating water pump energy consumption threshold (e.g., 5kW), and pipeline pressure difference stability threshold range (e.g., 0.03-0.08MPa). The user's indoor temperature sensor (external, connected to the smart valve via wireless communication) uploads the real-time room temperature (e.g., 19°C).

[0037] Step 2: The control unit identifies the resistance number and corrects the "opening degree-flow rate" mapping table.

[0038] Specifically, the edge computing chip (5) calls the resistance number identification algorithm to calculate the current resistance number (S) of the valve based on the fluid dynamics formula S=ΔP / Q²: for example, when ΔP=0.05MPa and Q=2.5m³ / h, S=0.05 / (2.5)²=0.008MPa h² / m 6 The calculated real-time S value is compared with the factory preset S0 value (e.g., 0.006 MPa). h² / m 6 If the deviation is greater than 5%, the "opening degree-flow rate" mapping table is corrected (e.g., the original opening degree of 50% corresponds to Q=3m³ / h, and the corrected value corresponds to Q=2.8m³ / h) to eliminate the mapping distortion caused by scaling and blockage in the pipeline network.

[0039] Step 3: The control unit performs multi-objective optimization calculations.

[0040] Specifically, the multi-objective optimization control module (6) loads the NSGA-II algorithm to construct an optimization model based on the "three major objectives": Objective 1 (Room temperature meets target): Minimize the absolute value of "real-time room temperature - target room temperature" (e.g., 19℃ - 20℃ = 1℃, the objective is to make this value ≤ 0.5℃). Objective 2 (Energy Consumption Reduction): Ensure that the power of the circulating water pump is ≤ the energy consumption threshold (e.g., 5kW, by using the formula P=kQ³ related to the flow rate Q, where k is the pipeline characteristic coefficient, and control Q to make P≤5kW). Objective 3 (stable pressure difference): Ensure that the pressure difference ΔP before and after the valve is within the threshold range (e.g., 0.03-0.08 MPa, to avoid insufficient flow due to ΔP being too small and the risk of pressure on the pipeline network being too large). The multi-objective optimization algorithm generates the Pareto optimal solution set through population iteration (50 iterations, population size 100), selects the solution set with "minimum room temperature deviation, energy consumption closest to the threshold, and stable pressure difference", and determines the optimal opening value (e.g., 55%) based on the modified "opening degree-flow rate" mapping table.

[0041] Step 4: The control unit performs dual closed-loop control based on flow rate and pressure difference.

[0042] Specifically, the edge computing chip (5) sends the optimal opening command to the electric actuator (2) to drive the valve core to adjust to 55% opening. Pressure differential closed loop: The control unit monitors the pressure difference ΔP before and after the valve (e.g., ΔP = 0.06MPa after adjustment). If ΔP exceeds the threshold (0.03-0.08MPa), ΔP is indirectly adjusted through the flow closed loop (e.g., if ΔP is too large, the opening is appropriately reduced to decrease the flow rate Q, so that ΔP drops to 0.07MPa) to ensure the pressure difference is stable. Flow closed loop: The flow sensor (4) synchronously collects the adjusted actual flow (e.g., 2.9 m³ / h) and compares it with the target flow (e.g., 3.0 m³ / h). If the deviation is >2%, the opening is finely adjusted (e.g., increased to 56%) until the flow meets the standard. The dual closed-loop coordinated control in step 4 achieves "precise flow and stable pressure difference", thereby ensuring that the room temperature meets the standard and energy consumption is controllable.

[0043] Step 5: The control unit performs fault self-diagnosis and early warning.

[0044] Specifically, the fault self-diagnosis module (7) monitors the system status in real time: if the current of the electric actuator (2) is greater than 120% of the rated current (e.g., rated current 1A, actual current 1.3A), it is determined to be "valve jamming"; if the pressure sensor (3) does not upload data for the first set time period (e.g., within 10 seconds), it is determined to be "sensor failure"; if the opening degree of the electric actuator does not change for the second set time period (e.g., within 5 seconds) after receiving the command, it is determined to be "motor damage".

[0045] After identifying the fault, a warning signal (including fault type and valve number) is sent to the external data acquisition and monitoring system SCADA through the multi-protocol communication module (8). At the same time, the local indicator light on the valve turns red to remind the maintenance personnel to handle the problem in a timely manner.

[0046] The intelligent valve control device of this application was piloted in a heating community (600 households) for 4 months. The test results showed that: ① the room temperature compliance rate (18-22℃) of users was 97.6%, which is 7.6 percentage points higher than the existing average level (90%); ② the power consumption of the corresponding heat exchange station was reduced by 30%; ③ the total heat consumption of the area was reduced by 3.2%.

[0047] This application is applicable to two core scenarios: ① Residential communities: The optimization priority is "meeting room temperature standards > reducing energy consumption > stabilizing pressure difference" to ensure user comfort; ② Commercial buildings (such as office buildings): The optimization priority is "reducing energy consumption > meeting room temperature standards > stabilizing pressure difference" to balance room temperature requirements during office hours and energy saving during non-office hours.

[0048] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A smart valve control device for a secondary heating network based on multi-objective optimization, characterized in that, include: A valve body installed horizontally in a water supply pipeline; An actuator is vertically mounted on the top of the valve body; a pressure sensor is installed at the outlet end of the valve body; a flow sensor is installed downstream of the pressure sensor at the outlet end; a control unit is fixed to the side of the actuator and has an edge computing chip integrated on its internal PCB board; the control unit is connected to the actuator, pressure sensor and flow sensor respectively via wires. The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the target adjustment value; it calculates the pressure difference before and after the valve based on the received pressure. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring that the power of the circulating water pump does not exceed the energy consumption threshold, and ensuring that the pressure difference is within the stable threshold range of the pipeline pressure difference. The optimal solution set is obtained by using a multi-objective optimization algorithm. Based on the optimal solution set and the opening degree-flow mapping table, the optimal opening degree is determined and the opening degree adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The power of the circulating water pump is determined based on the flow rate and the pipeline characteristic coefficient. The actuator is used to receive the opening adjustment command and drive the valve core to rotate and adjust to the optimal opening.

2. The intelligent valve control device according to claim 1, characterized in that, The control unit is also used to calculate the real-time resistance value of the valve using the fluid dynamics formula S=ΔP / Q², compare the calculated real-time resistance value with the factory preset resistance value, and if the deviation is greater than the preset deviation, correct the opening-flow mapping table, where S represents the resistance value, Q represents the flow rate, and ΔP represents the pressure difference.

3. The intelligent valve control device according to claim 1, characterized in that, The multi-objective optimization algorithm is either the non-dominated sorting genetic algorithm NSGA-II or the multi-objective particle swarm optimization algorithm MOPSO, which employs an elite strategy.

4. The intelligent valve control device according to claim 1, characterized in that, The actuator is an electric actuator or an electromagnetic actuator.

5. The intelligent valve control device according to claim 1, characterized in that, The control unit is also used to receive the real-time flow rate collected after the opening is adjusted, monitor the pressure difference before and after the valve, and if the pressure difference exceeds the stable threshold range of the pipeline pressure difference, compare the adjusted actual flow rate with the target flow rate. If the deviation is greater than the preset flow rate threshold, fine-tune the opening according to the set fine-tuning value until the deviation between the adjusted actual flow rate and the target flow rate is not greater than the preset flow rate threshold.

6. The intelligent valve control device according to claim 1, characterized in that, The intelligent valve control device also includes: a pressure sensor installed at the water inlet end of the valve body; The control unit is also used to calculate the pressure difference before and after the valve using the pressure received from the pressure sensor installed at the inlet end of the valve body and the pressure sensor installed at the outlet end of the valve body.

7. The intelligent valve control device according to claim 1, characterized in that, The control unit is also used to receive heat exchange station outlet pressure data and calculate the pressure difference before and after the valve using the received heat exchange station outlet pressure data and the pressure of the pressure sensor installed at the outlet end of the valve body.

8. The intelligent valve control device according to claim 1, characterized in that, Also includes: A multi-protocol communication module supporting Modbus-RTU and BACnet MS / TP protocols is installed at the communication interface on the side of the control unit.

9. The intelligent valve control device according to claim 8, characterized in that, The control unit is also used to monitor the continuity of data from actuator current, pressure sensors, and flow sensors, identify fault types, and generate and send a warning signal containing the fault type and valve number to the multi-protocol communication module. This allows the multi-protocol communication module to send the warning signal to the external data acquisition and monitoring system SCADA, while simultaneously illuminating a red indicator light on the valve to remind maintenance personnel to handle the issue promptly.

10. A method for intelligent valve control in a secondary heating network based on multi-objective optimization, characterized in that, The intelligent valve control device for a secondary heating network based on multi-objective optimization as described in any one of claims 1-9 includes: The control unit receives real-time pressure from a pressure sensor, real-time flow from a flow sensor, and real-time room temperature from a user's indoor temperature sensor, as well as the control target value. Based on the received pressure, it calculates the pressure difference before and after the valve. A multi-objective optimization function is constructed with the objectives of minimizing the absolute value of the real-time room temperature and the target room temperature, ensuring the circulating water pump power does not exceed the energy consumption threshold, and maintaining the pressure difference within the stable threshold range of the pipeline pressure difference. A multi-objective optimization algorithm is used to solve for the optimal solution set. Based on the optimal solution set and the opening-flow mapping table, the optimal opening is determined, and an opening adjustment command is issued to the actuator. The control target values ​​include: the target room temperature value, the energy consumption threshold, and the pipeline pressure difference threshold range. The circulating water pump power is determined based on the flow rate and the pipeline characteristic coefficient. The actuator receives the opening adjustment command and drives the valve core to rotate and adjust to the optimal opening.

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