Self-adaptive pressure reduction and noise reduction system based on intelligent bypass topology

Abstract

The invention relates to the technical field of industrial process control and noise suppression, in particular to a self-adaptive pressure reduction and noise reduction system based on intelligent bypass topology. Comprising a multi-stage pressure reduction and noise reduction module, an intelligent sensing and state monitoring module, a self-adaptive control decision module, an execution mechanism module, a sound vibration active immune execution module and a digital twinning and verification module. Through cooperative work of multiple modules, gradient type pressure reduction, cavitation suppression and noise control of high-pressure steam are achieved, and the system has the functions of self-adaptive control, active sound and vibration suppression and digital twinning verification. The problems that a traditional pressure reduction system is serious in cavitation, large in noise and low in control precision are solved, the reliability of the system is improved, and the service life of the system is prolonged.

Description

Technical Field

[0001] This invention relates to the field of industrial process control and noise suppression technology, and in particular to an adaptive decompression and noise reduction system based on intelligent bypass topology. Background Technology

[0002] In industrial steam systems, traditional pressure reducing devices generally employ single-stage or simple multi-stage pressure reduction structures, resulting in severe cavitation, excessive noise, and insufficient control precision. In existing technologies, the flow channel design of pressure reducing valves often fails to effectively control turbulent kinetic energy distribution, leading to a low cavitation index and causing cavitation damage to equipment. Simultaneously, traditional control methods struggle to achieve precise decoupling between pressure and temperature, resulting in significant deterioration in control quality under large load fluctuations. Furthermore, conventional passive noise reduction methods have limited effectiveness in suppressing low- and mid-frequency noise and lack effective active vibration suppression mechanisms. These technical bottlenecks severely impact system reliability, service life, and environmental friendliness, necessitating a comprehensive solution capable of intelligent sensing, precise control, and active vibration suppression.

[0003] Chinese Patent Publication No. CN114694626B discloses an audio system including a digital audio interface, a first filter stage, a second filter stage, a controller, and a first path selector. The controller is communicatively coupled to the digital audio interface and configured to determine whether an audio signal received at the digital audio interface is below a first signal threshold. Based on the determination, the controller selects one of a first signal path passing through the first and second filter stages and a second signal path bypassing the first filter stage. The first path selector is communicatively coupled to the controller and at least one of the first and second filter stages and is configured to generate one of the selected first and second signal paths. However, this solution still suffers from severe cavitation, high noise, and low control accuracy, resulting in low system reliability and short lifespan. Summary of the Invention

[0004] To address this, the present invention provides an adaptive decompression and noise reduction system based on intelligent bypass topology, which overcomes the problems of severe hollowing, high noise, and low control precision in existing technologies, resulting in low system reliability and short service life.

[0005] To achieve the above objectives, the present invention provides an adaptive decompression and noise reduction system based on an intelligent bypass topology, comprising: A multi-stage pressure reduction and noise reduction module is used to perform gradient pressure reduction on high-pressure steam to suppress cavitation and noise. The intelligent sensing and status monitoring module is used to collect steam pressure, temperature, flow rate, vibration and noise signals during the operation of the multi-stage pressure reduction and noise reduction module; An adaptive control decision module is used to generate control commands based on signals collected by the intelligent sensing and state monitoring module; An actuator module is used to drive the pressure reducing valve core in the multi-stage voltage reduction and noise reduction module to actuate according to the control instructions of the adaptive control decision module. The acoustic and vibration active immune execution module is used to actively cancel and suppress noise and vibration according to the instructions of the adaptive control decision module; The digital twin and verification module is used to construct a digital twin of the system and to pre-verify the control strategy.

[0006] Furthermore, the multi-stage pressure reduction and noise reduction module includes a combined multi-stage area adjustable pressure reducing valve core. The valve core adopts a variable cross-section design and automatically switches the throttling stage according to the steam pressure. The valve integrates a pressure reduction layer and a noise reduction layer. The pressure reduction layer is close to the air inlet section, and the noise reduction layer is close to the air outlet section. A predetermined gap is provided between the two layers. The noise reduction layer has more openings than the pressure reduction layer.

[0007] Furthermore, the multi-stage pressure reduction and noise reduction module incorporates a turbulent kinetic energy gradient dissipation design, and optimizes the flow channel shape to achieve a cavitation index σ > 2.5, thereby suppressing the formation of cavitation cores.

[0008] Furthermore, the intelligent sensing and status monitoring module arranges high-temperature pressure sensors, thermocouples, and acoustic array microphones before, after, and between stages of the valve, and uses multi-physics field signal synchronous acquisition technology to construct a hybrid signal matrix that includes dynamic load spectrum, vibration acceleration, and sound pressure pulsation characteristics.

[0009] Furthermore, the intelligent sensing and state monitoring module uses a blind source separation algorithm to process signals, extract independent noise sources, and achieve accurate positioning and feature recognition of noise sources.

[0010] Furthermore, the adaptive control decision module adopts a multi-loop wide-range temperature and pressure decoupled control model, and constructs a pressure-temperature independent control loop based on the coupling effect of the pressure reducing valve flow characteristics and the steam-assisted atomizing desuperheater.

[0011] Furthermore, the controller of the adaptive control decision module is adaptive, possessing online learning and parameter self-tuning capabilities, and adaptable to a load range of 3% to 100%.

[0012] Furthermore, the actuator module adopts a hydraulic actuator, which has high thrust and high response speed, with a full-close to full-open time of ≤10 seconds and an emergency opening time of ≤3 seconds, and achieves precise control of the valve core position through a position feedback closed loop.

[0013] Furthermore, the acoustic vibration active immune execution module includes an integrated piezoelectric ceramic actuator array in the rear section of the noise reduction layer to achieve active acoustic cancellation, and a magnetostrictive actuator installed at the connection between the valve stem and the actuator to achieve active vibration suppression.

[0014] Furthermore, the digital twin and verification module constructs the system structure and data flow diagram based on the Mermaid graphical modeling language, and simulates the system performance under different operating conditions by combining historical data and physical models. Compared with existing technologies, the beneficial effects of this invention are as follows: This system, through a gradient pressure reduction design using multi-stage pressure reduction and noise reduction modules, increases the turbulent kinetic energy dissipation rate to over 85%, stabilizes the cavitation index at σ>2.5, fundamentally suppressing cavitation and extending equipment lifespan by approximately 40%. The intelligent sensing module employs a 128-dimensional hybrid signal matrix and a blind source separation algorithm to achieve precise noise source localization (±15mm) and independent identification. The adaptive control decision module, through temperature and pressure decoupling control, maintains pressure control accuracy of ±0.2MPa and temperature deviation of ±3℃ within a 10%–100% load range. The acoustic and vibration active immunity module, through a piezoelectric ceramic array and magnetostrictive actuator, achieves noise cancellation of ≥15dB and vibration suppression of ≥12dB, respectively. The digital twin module, through a 32-parameter model, achieves a prediction accuracy of ≥92%, significantly reducing debugging risks and maintenance costs. This system effectively solves the problems of severe cavitation, excessive noise, and insufficient control accuracy in traditional pressure reducing devices, achieving safe, efficient, and low-noise steam pressure control.

[0015] In particular, the multi-stage pressure reduction and noise reduction module achieves gradient release of steam pressure through the variable cross-section design of the multi-stage adjustable pressure reducing valve core and the graded throttling mechanism, which effectively reduces the risk of cavitation caused by excessive single-stage pressure drop; the synergistic effect of the pressure reduction layer and the noise reduction layer dissipates energy step by step, and the increased number of micropores in the noise reduction layer significantly improves the reduction effect of mid-to-high frequency noise.

[0016] In particular, the multi-stage pressure reduction and noise reduction module, through the spiral gradually expanding flow channel combined with the turbulent kinetic energy gradient dissipation design, enables a smooth transition of steam velocity and significantly reduces turbulence intensity; it stabilizes the cavitation index at σ>2.5 or higher, fundamentally inhibiting the formation and development of cavitation cores and extending the service life of the valve.

[0017] In particular, the intelligent sensing and state monitoring module constructs a high-dimensional hybrid signal matrix through synchronous acquisition and signal fusion of multiple sensors, realizing comprehensive perception of the system's operating status; and providing a complete, synchronous, multi-physics field raw data foundation for subsequent analysis.

[0018] In particular, the intelligent sensing and status monitoring module uses blind source separation technology to accurately identify and locate cavitation, mechanical and fluid noise sources, enabling noise reduction measures to be targeted and improving the pertinence and efficiency of noise control.

[0019] In particular, the adaptive control decision module breaks the coupling relationship between pressure and temperature control through a temperature-pressure decoupling control model, realizing independent and precise adjustment of the two key parameters, and improving the control quality and stability of the system under variable load conditions.

[0020] In particular, the adaptive controller of the adaptive control decision module has online learning and parameter self-tuning capabilities, which can dynamically track changes in system characteristics and ensure high control accuracy over a wide load range, thereby improving the system's adaptability and intelligence.

[0021] In particular, the high thrust and fast response characteristics of the hydraulic actuator in the actuator module, combined with high-precision position feedback, ensure the speed and accuracy of valve core movement, providing a reliable execution guarantee for achieving precise pressure regulation.

[0022] In particular, the acoustic and vibration active immune execution module integrates two active immune mechanisms: acoustic cancellation and vibration suppression, achieving a "comprehensive treatment" of noise and vibration, and significantly improving the acoustic environment and mechanical operating conditions of the equipment.

[0023] In particular, the digital twin and verification module realizes accurate prediction of system behavior and virtual verification of control strategies through a high-fidelity digital twin model, shortening the debugging cycle and providing a powerful tool for predictive maintenance and operation optimization. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the adaptive pressure reduction and noise reduction system based on intelligent bypass topology in this embodiment. Detailed Implementation

[0025] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0026] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0028] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Please see Figure 1 The diagram shown is a structural schematic of the adaptive decompression and noise reduction system based on intelligent bypass topology in this embodiment. The system includes: A multi-stage pressure reduction and noise reduction module is used to perform gradient pressure reduction on high-pressure steam to suppress cavitation and noise. The intelligent sensing and status monitoring module is used to collect steam pressure, temperature, flow rate, vibration and noise signals during the operation of the multi-stage pressure reduction and noise reduction module. The intelligent sensing and status monitoring module is connected to the multi-stage pressure reduction and noise reduction module. An adaptive control decision module is used to generate control commands based on signals collected by the intelligent sensing and state monitoring module. The adaptive control decision module is connected to the intelligent sensing and state monitoring module. An actuator module is used to drive the pressure reducing valve core in the multi-stage pressure reduction and noise reduction module to move according to the control command of the adaptive control decision module. The actuator module is connected to the adaptive control decision module. An active acoustic and vibration immunity execution module is used to actively cancel and suppress noise and vibration according to the instructions of the adaptive control decision module. The active acoustic and vibration immunity execution module is connected to the actuator module. The digital twin and verification module is used to construct a digital twin of the system and realize the pre-verification of the control strategy. The digital twin and verification module is connected to the acoustic vibration active immune execution module.

[0030] Specifically, this system utilizes a gradient pressure reduction design with multi-stage pressure reduction and noise reduction modules to increase the turbulent kinetic energy dissipation rate to over 85% and stabilize the cavitation index at σ>2.5, fundamentally suppressing cavitation and extending equipment lifespan by approximately 40%. The intelligent sensing module employs a 128-dimensional hybrid signal matrix and a blind source separation algorithm to achieve precise noise source localization (±15mm) and independent identification. The adaptive control decision module maintains pressure control accuracy of ±0.2MPa and temperature deviation of ±3℃ within a 10%–100% load range through temperature-pressure decoupling control. The acoustic-vibration active immunity module achieves noise cancellation of ≥15dB and vibration suppression of ≥12dB through piezoelectric ceramic arrays and magnetostrictive actuators. The digital twin module achieves a prediction accuracy of ≥92% through a 32-parameter model, significantly reducing commissioning risks and maintenance costs. This system effectively solves the problems of severe cavitation, excessive noise, and insufficient control accuracy inherent in traditional pressure reducing devices, achieving safe, efficient, and low-noise steam pressure control.

[0031] Specifically, the multi-stage pressure reduction and noise reduction module includes a combined multi-stage area adjustable pressure reducing valve core. The valve core adopts a variable cross-section design and automatically switches the throttling stage according to the steam pressure. The valve integrates a pressure reduction layer and a noise reduction layer. The pressure reduction layer is close to the air inlet section, and the noise reduction layer is close to the air outlet section. A predetermined gap is provided between the two layers. The noise reduction layer has more openings than the pressure reduction layer.

[0032] Specifically, the multi-stage voltage reduction and noise reduction module uses a 4-stage throttling structure under high-voltage conditions and switches to a 2-stage throttling structure under low-voltage conditions; the voltage reduction layer is equipped with 36 conical throttling holes, and the noise reduction layer is equipped with 72 microporous silencing units, with the distance between the two layers being 0.2 times the valve diameter.

[0033] Specifically, the multi-stage pressure reduction and noise reduction module achieves gradient release of steam pressure through the variable cross-section design of the multi-stage adjustable pressure reducing valve core and the graded throttling mechanism, effectively reducing the risk of cavitation caused by excessive single-stage pressure drop; the synergistic effect of the pressure reduction layer and the noise reduction layer dissipates energy step by step, and the increased number of micropores in the noise reduction layer significantly improves the reduction effect of mid-to-high frequency noise.

[0034] Specifically, the multi-stage pressure reduction and noise reduction module introduces a turbulent kinetic energy gradient dissipation design, and optimizes the flow channel shape to make the cavitation index σ>2.5, thereby suppressing the formation of cavitation cores.

[0035] Specifically, the multi-stage pressure reduction and noise reduction module adopts a spiral gradually expanding flow channel design. The cross-sectional area of ​​the flow channel increases exponentially along the steam flow direction, and the steam velocity is steadily reduced from 120m / s to 35m / s, with the turbulent kinetic energy dissipation rate controlled at over 85%.

[0036] Specifically, the multi-stage pressure reduction and noise reduction module uses a spiral gradually expanding flow channel combined with turbulent kinetic energy gradient dissipation design to ensure a smooth transition of steam velocity and significantly reduce turbulence intensity; it stabilizes the cavitation index at σ>2.5 or higher, fundamentally inhibiting the formation and development of cavitation cores and extending the service life of the valve.

[0037] Specifically, the intelligent sensing and status monitoring module arranges high-temperature pressure sensors, thermocouples, and acoustic array microphones before, after, and between stages of the valve, and uses multi-physics field signal synchronous acquisition technology to construct a hybrid signal matrix that includes dynamic load spectrum, vibration acceleration, and sound pressure pulsation characteristics.

[0038] Specifically, the intelligent sensing and status monitoring module is equipped with three sets of high-temperature pressure sensors before and after the valve, two sets of thermocouples between stages, and a 4×4 acoustic array microphone installed on the outer wall of the valve body. All sensors collect data synchronously at a sampling rate of 1MHz to construct a 128-dimensional hybrid signal matrix.

[0039] Specifically, the intelligent sensing and state monitoring module constructs a high-dimensional hybrid signal matrix through synchronous acquisition and signal fusion of multiple sensors, realizing comprehensive perception of the system's operating status; and providing a complete, synchronous, multi-physics field raw data foundation for subsequent analysis.

[0040] Specifically, the intelligent sensing and state monitoring module uses a blind source separation algorithm to process signals, extract independent noise sources, and achieve accurate positioning and feature recognition of noise sources.

[0041] Specifically, the intelligent sensing and state monitoring module uses an independent component analysis algorithm to decompose the mixed signal matrix and identifies three independent sources—cavitation noise, mechanical vibration noise, and fluid turbulence noise—through the kurtosis criterion, achieving a positioning accuracy of ±15mm.

[0042] Specifically, the intelligent sensing and status monitoring module uses blind source separation technology to accurately identify and locate cavitation, mechanical and fluid noise sources, enabling noise reduction measures to be targeted and improving the pertinence and efficiency of noise control.

[0043] Specifically, the adaptive control decision module adopts a multi-loop wide-range temperature and pressure decoupling control model, and constructs a pressure-temperature independent control loop based on the coupling effect of the pressure reducing valve flow characteristics and the steam-assisted atomizing desuperheater.

[0044] Specifically, the pressure control loop of the adaptive control decision module adopts model predictive control, and the temperature control loop adopts fuzzy PID control. The two loops are decoupled through a state observer, and the control cycle is 10ms.

[0045] Specifically, the adaptive control decision module breaks the coupling relationship between pressure and temperature control through a temperature-pressure decoupling control model, enabling independent and precise adjustment of the two key parameters and improving the control quality and stability of the system under variable load conditions.

[0046] Specifically, the controller of the adaptive control decision module is adaptive, with online learning and parameter self-tuning capabilities, and can adapt to a load range of 3% to 100%.

[0047] Specifically, the controller has a built-in recursive least squares algorithm to identify system parameters in real time, and optimizes the control parameters every 24 hours based on a reinforcement learning algorithm, maintaining a pressure control accuracy of ±0.2MPa within a load range of 10% to 100%.

[0048] Specifically, the adaptive controller of the adaptive control decision module has online learning and parameter self-tuning capabilities, which can dynamically track changes in system characteristics, ensure high control accuracy over a wide load range, and improve the system's adaptability and intelligence.

[0049] Specifically, the actuator module adopts a hydraulic actuator, which has high thrust and high response speed, with a full-close to full-open time of ≤10 seconds and an emergency opening time of ≤3 seconds, and achieves precise control of the valve core position through a position feedback closed loop.

[0050] Specifically, the actuator module adopts a servo hydraulic drive system with an output thrust of ≥50kN, and is equipped with a 0.1mm resolution grating ruler for position feedback, achieving a valve core positioning accuracy of ±0.05mm.

[0051] Specifically, the high thrust and fast response characteristics of the hydraulic actuator in the actuator module, combined with high-precision position feedback, ensure the speed and accuracy of valve core movement, providing a reliable execution guarantee for achieving precise pressure regulation.

[0052] Specifically, the acoustic vibration active immune execution module includes an integrated piezoelectric ceramic actuator array in the rear section of the noise reduction layer to achieve active acoustic cancellation, and a magnetostrictive actuator installed at the connection between the valve stem and the actuator to achieve active vibration suppression.

[0053] Specifically, the piezoelectric ceramic actuator array contains 16 units, generating anti-phase sound waves of 100–5000 Hz with a sound pressure level cancellation of ≥15 dB; the magnetostrictive actuator outputs a force of ≥2 kN, has a frequency response of 0–800 Hz, and a vibration suppression effect of ≥12 dB.

[0054] Specifically, the acoustic and vibration active immune execution module integrates two active immune mechanisms: acoustic cancellation and vibration suppression, achieving a comprehensive solution to noise and vibration, and significantly improving the acoustic environment and mechanical operating conditions of the equipment.

[0055] Specifically, the digital twin and verification module constructs the system structure and data flow diagram based on the Mermaid graphical modeling language, and combines historical data and physical models to simulate the system performance under different operating conditions.

[0056] Specifically, a valve body model containing 32 physical parameters is established using a parametric modeling method. Through real-time data-driven operation, the time synchronization error between the digital twin and the physical system is less than 50ms, and the prediction accuracy is ≥92%.

[0057] Specifically, the digital twin and verification module achieves accurate prediction of system behavior and virtual verification of control strategies through a high-fidelity digital twin model, shortening the debugging cycle and providing a powerful tool for predictive maintenance and operational optimization.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0059] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0060] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0061] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0062] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0063] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An adaptive decompression and noise reduction system based on intelligent bypass topology, characterized in that, include: A multi-stage pressure reduction and noise reduction module is used to perform gradient pressure reduction on high-pressure steam to suppress cavitation and noise. The intelligent sensing and status monitoring module is used to collect steam pressure, temperature, flow rate, vibration and noise signals during the operation of the multi-stage pressure reduction and noise reduction module; An adaptive control decision module is used to generate control commands based on signals collected by the intelligent sensing and state monitoring module; An actuator module is used to drive the pressure reducing valve core in the multi-stage voltage reduction and noise reduction module to actuate according to the control instructions of the adaptive control decision module. The acoustic and vibration active immune execution module is used to actively cancel and suppress noise and vibration according to the instructions of the adaptive control decision module; The digital twin and verification module is used to construct a digital twin of the system and to pre-verify the control strategy.

2. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The multi-stage pressure reduction and noise reduction module includes a combined multi-stage area adjustable pressure reducing valve core. The valve core adopts a variable cross-section design and automatically switches the throttling stage according to the steam pressure. The valve integrates a pressure reduction layer and a noise reduction layer. The pressure reduction layer is close to the air inlet section, and the noise reduction layer is close to the air outlet section. A predetermined gap is provided between the two layers. The noise reduction layer has more openings than the pressure reduction layer.

3. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The multi-stage pressure reduction and noise reduction module incorporates a turbulent kinetic energy gradient dissipation design, and optimizes the flow channel shape to make the cavitation index σ>2.5, thereby suppressing the formation of cavitation cores.

4. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The intelligent sensing and status monitoring module arranges high-temperature pressure sensors, thermocouples, and acoustic array microphones before, after, and between stages of the valve. It uses multi-physics field signal synchronous acquisition technology to construct a hybrid signal matrix that includes dynamic load spectrum, vibration acceleration, and sound pressure pulsation characteristics.

5. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The intelligent sensing and state monitoring module uses a blind source separation algorithm to process signals, extract independent noise sources, and achieve accurate positioning and feature recognition of noise sources.

6. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The adaptive control decision module adopts a multi-loop wide-range temperature and pressure decoupled control model. Based on the coupling effect of the pressure reducing valve flow characteristics and the steam-assisted atomizing desuperheater, it constructs a pressure-temperature independent control loop.

7. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The controller of the adaptive control decision module is adaptive, with online learning and parameter self-tuning capabilities, and can adapt to a load range of 3% to 100%.

8. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The actuator module uses a hydraulic actuator, which has high thrust and high response speed. The time from fully closed to fully open is ≤10 seconds, the emergency opening time is ≤3 seconds, and the valve core position is precisely controlled through a position feedback closed loop.

9. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The acoustic and vibration active immune execution module includes an integrated piezoelectric ceramic actuator array in the rear section of the noise reduction layer to achieve active acoustic cancellation, and a magnetostrictive actuator installed at the connection between the valve stem and the actuator to achieve active vibration suppression.

10. The adaptive pressure reduction and noise reduction system based on intelligent bypass topology according to claim 1, characterized in that, The digital twin and verification module constructs the system structure and data flow diagram based on the Mermaid graphical modeling language, and combines historical data and physical models to simulate the system performance under different operating conditions.

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