An intelligent reverse flow valve based on fuzzy control

Abstract

The present invention provides an intelligent reverse flow valve based on fuzzy control, comprising a valve body, a movable valve core, a pressure sensor, a flow sensor, an execution motor and a controller; the pressure sensor and the flow sensor are respectively arranged at the inlet and outlet ends of the valve body, and are used to monitor the fluid pressure and flow state in real time; the execution motor is connected to the movable valve core through a screw mechanism to drive the valve core to adjust the valve opening; the controller comprises a fuzzy control module, a self-learning module, a reverse flow judgment module and an execution control module; the fuzzy control module calculates the valve core adjustment parameters based on a preset fuzzy rule library, and the self-learning module optimizes the fuzzy rules to improve the response speed and control accuracy; the execution control module drives the execution motor according to the optimized fuzzy rules to realize the intelligent adjustment of the valve; the present invention improves the dynamic adjustment capability of the reverse flow valve and enhances the stability and adaptability of the system through fuzzy control and self-learning algorithms, and is suitable for a variety of fluid control scenarios.

Description

Technical Field

[0001] The invention relates to the technical field of reverse flow valve control, and in particular to an intelligent reverse flow valve based on fuzzy control. Background Art

[0002] In fluid control systems, reverse flow valves are a key component used to prevent fluid backflow. Traditional reverse flow valves usually use spring reset or hydraulic control to open and close to achieve one-way control of flow direction. These valves have a simple structure and are suitable for different fluid systems. They are widely used in water supply, chemical industry, petroleum, natural gas and industrial fluid transportation. With the improvement of industrial automation, intelligent reverse flow valves have gradually attracted attention. Through electronic control, real-time monitoring and adjustment of fluid flow status can be achieved to improve control accuracy and system response speed.

[0003] However, the existing reverse flow valve control method still has certain limitations. On the one hand, the traditional reverse flow valve based on mechanical structure usually cannot respond accurately to complex fluid conditions, resulting in valve switching lag, affecting fluid delivery efficiency. On the other hand, most of the existing electronically controlled reverse flow valves use fixed control logic, which is difficult to adapt to different working conditions and is prone to misjudgment or control failure due to changes in working conditions. In addition, reverse flow valves that lack intelligent learning and adaptive adjustment capabilities are prone to wear or fluctuations in working conditions during long-term operation, which can affect the control effect.

[0004] Therefore, it is necessary to develop an intelligent reverse flow valve based on fuzzy control. Summary of the invention

[0005] The present application provides an intelligent reverse flow valve based on fuzzy control to improve user experience.

[0006] The present application provides an intelligent reverse flow valve based on fuzzy control, comprising:

[0007] A valve body, wherein a flow channel is provided in the valve body;

[0008] A movable valve core is arranged in the flow channel and is used to control the opening of the flow channel;

[0009] Pressure sensors are respectively arranged at the inlet and outlet ends of the valve body and are used to detect the inlet and outlet pressures;

[0010] Flow sensors are respectively arranged at the inlet and outlet ends of the valve body and are used to detect inlet and outlet flows;

[0011] An actuator motor is connected to the movable valve core through a lead screw mechanism and is used to drive the movable valve core to move;

[0012] The controller is electrically connected to the pressure sensor, flow sensor and execution motor respectively, and the controller includes: a fuzzy control module, which is used to set the fuzzy rule base based on the initial working condition, and calculate the valve core position adjustment parameters according to the detection data of the pressure sensor and the flow sensor; a self-learning module, which is used to optimize the membership function parameters of the fuzzy rule base to improve the response speed and control accuracy of the valve; a backflow judgment module, which is used to detect whether the outlet pressure is greater than the inlet pressure, or whether the outlet flow is greater than the inlet flow, judge whether the backflow occurs, and trigger the closing instruction when the threshold is exceeded; an execution control module, which is used to output a control signal according to the optimized fuzzy rules, drive the execution motor to adjust the movable valve core position, and realize the intelligent control of the fluid.

[0013] This application has the following beneficial technical effects:

[0014] (1) Dynamically adjust the valve core position based on the fuzzy control algorithm to improve the accuracy of fluid flow control and reduce false triggering and delay. (2) Optimize the fuzzy rule base through the self-learning module, so that the system can be adaptively adjusted according to different working conditions and is suitable for a variety of fluid environments. (3) Use pressure and flow dual parameter monitoring, combined with the backflow judgment module, to achieve rapid detection and precise control of backflow, thereby improving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of an intelligent reverse flow valve based on fuzzy control provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0016] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0017] The first embodiment of the present application provides an intelligent reverse flow valve based on fuzzy control. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 The first embodiment of the present application provides an intelligent reverse flow valve based on fuzzy control and is described in detail.

[0018] The intelligent reverse flow valve based on fuzzy control includes a valve body 101 , a movable valve core 102 , a pressure sensor 103 , a flow sensor 104 , an actuator motor 105 and a controller 106 .

[0019] A valve body 101 is provided with a flow channel.

[0020] The valve body 101 is the main structure of the intelligent reverse flow valve of the present invention, and is provided with a flow channel for fluid to pass through, which can carry, guide and control the flow direction of the fluid. The valve body is made of metal or high-strength composite materials to ensure that it can operate stably under high pressure or high temperature environment, and has strong corrosion resistance to be suitable for different types of fluid media, such as water, oil, gas or other industrial liquids.

[0021] The structural design of the valve body 101 optimizes the shape of the fluid channel to reduce flow resistance and eddy currents and improve the stability of the fluid passing through. The interior of the flow channel adopts a smooth surface treatment process, such as electroplating or special coating, to reduce fluid friction and improve circulation efficiency. In addition, in order to adapt to different working conditions, the valve body 101 can adopt a modular design so that its inlet and outlet calibers can be adjusted according to specific applications to adapt to pipeline systems with different flow requirements.

[0022] A valve seat for mounting a movable valve core 102 is provided on the valve body 101, and a guide structure is provided inside the valve seat, so that the movable valve core moves more smoothly in the flow channel to avoid stagnation or wear caused by fluid impact. The valve body 101 is also provided with a sensor mounting hole for fixing a pressure sensor 103 and a flow sensor 104 to ensure that they can accurately sense the pressure and flow data of the inlet and outlet, and transmit the data to the controller 106.

[0023] In addition, the inlet and outlet ends of the valve body 101 can be equipped with standard flanges or threaded connection structures to facilitate docking with the pipeline system to ensure sealing and ease of installation. For specific application scenarios, the valve body 101 can also integrate a buffer structure to reduce the impact of the water hammer effect on the valve and pipeline, thereby improving the overall stability of the system.

[0024] In summary, the valve body 101 is not only the basic supporting structure of the entire intelligent reverse flow valve, but also combines optimized flow channel design, precise sensor arrangement, reliable sealing and connection methods, ensuring that the present invention can achieve efficient and stable operation in a complex fluid control environment.

[0025] Furthermore, the valve body adopts a split modular design, including a main valve body and a detachable sensor installation cavity, and the sensor installation cavity is used to accommodate a pressure sensor and a flow sensor to facilitate replacement and maintenance of the sensor, optimize its measurement position, and improve the accuracy of data collection.

[0026] The valve body adopts a split modular design, including a main valve body and a detachable sensor installation cavity to improve the system's maintenance convenience, sensor installation accuracy, and fluid measurement stability. The main valve body, as the basic structure of the entire fluid control system, is provided with a flow channel inside to guide the normal flow of the fluid and provide a stable installation base to ensure that each component can maintain a reliable mechanical connection under high pressure, high temperature or corrosive fluid environments. The material of the main valve body can be selected from high-strength stainless steel, aluminum alloy, engineering ceramics or corrosion-resistant composite materials according to different use environments to meet the requirements of different working conditions, and fine polishing or surface coating treatment is performed inside the flow channel to reduce fluid friction resistance, improve flow stability, and reduce turbulence and pressure loss.

[0027] The removable sensor mounting cavity is independent of the main valve body structure and is fixed to the main valve body by threaded connection, snap connection or flange sealing connection. This design allows the pressure sensor and flow sensor to be installed, maintained and replaced independently of the main valve body without disassembling the entire valve body or affecting the normal operation of the fluid system. The size, shape and internal structure of the mounting cavity are optimized to ensure that the sensor can be in the optimal measurement position, so that it can accurately collect dynamic data of the fluid while avoiding the impact of fluid impact, bubble interference or external vibration on the measurement accuracy. The inner wall of the mounting cavity can be streamlined and provided with a guide groove to reduce the turbulent effect of the fluid, optimize the measurement environment of the sensor, and improve the stability and accuracy of the measurement data.

[0028] The sensor installation cavity is equipped with standardized sensor interfaces, including threaded interfaces, quick-plug connections or flange sealing structures, to be compatible with different types of pressure sensors and flow sensors, and to ensure that the sensor does not need to be recalibrated or the measurement parameters adjusted when it is replaced. In order to improve the long-term reliability of the sensor, the sealing structure of the installation cavity adopts high-temperature and corrosion-resistant rubber sealing rings or metal sealing gaskets to prevent fluid leakage and reduce the impact of environmental humidity or temperature changes on sensor performance. In addition, the installation cavity can be equipped with an isolation cavity or a buffer device to reduce the impact of fluid pressure pulsation on the sensor and improve the stability and measurement life of the sensor when the working conditions change drastically.

[0029] Another advantage of this modular design is that it can adapt to sensors of different specifications and measurement ranges, allowing users to flexibly replace pressure sensors or flow sensors according to specific working conditions without changing the main valve body structure. For example, in a high-pressure fluid environment, a high-range pressure sensor can be selected, while in a low-flow measurement scenario, a higher-sensitivity flow sensor can be replaced to improve the system's adaptability and measurement accuracy. In addition, the sensor installation cavity can be designed as a dual-cavity or multi-cavity structure, allowing multiple sensors to work in parallel to form a redundant measurement mechanism to improve the reliability of data acquisition, and automatically switch to the backup sensor when the main sensor fails to ensure the continuous operation of the system.

[0030] Through the split modular design, the present invention provides a high-precision, easy-to-maintain, and highly adaptable sensor installation solution, which enables the intelligent reverse flow valve to maintain accurate fluid measurement capabilities under different working conditions and improve the overall stability and reliability of the system.

[0031] The movable valve core 102 is disposed in the flow channel and is used to control the opening of the flow channel.

[0032] The movable valve core 102 is the core control component of the intelligent reverse flow valve of the present invention, which is responsible for adjusting the opening of the flow channel inside the valve body 101 to achieve precise control of the fluid flow and respond quickly when a reverse flow trend is detected. The structural design of the valve core directly affects the sealing performance, response speed and service life of the valve, so its material selection, movement mode and sealing structure must be accurately optimized to ensure long-term stable operation.

[0033] The main body of the movable valve core 102 can be made of wear-resistant and corrosion-resistant materials, such as stainless steel, alloy steel or high-strength engineering plastics, to adapt to the working environment of different fluid media. For high-temperature, high-pressure or corrosive media applications, the valve core surface can be treated with special coatings, such as nitriding, ceramic coating or PTFE coating, to improve corrosion resistance and wear resistance, while reducing the scouring effect of the fluid on the valve core surface, thereby extending the service life.

[0034] The movement of the valve core in the valve body 101 can adopt a linear movement or a rotation structure to adapt to different fluid control requirements. In the linear movement mode, the movable valve core moves axially along the guide structure of the valve body through the screw mechanism driven by the actuator motor 105 to realize the switching or adjustment function of the valve. This type of linear drive mechanism is suitable for scenarios where precise flow control is required, such as precision metering systems or high-precision fluid control devices. In the rotational movement mode, the movable valve core rotates around a specific axis through a gear set or connecting rod mechanism driven by the actuator motor to control the flow channel opening. The advantage of this rotary structure is that it has a fast switching speed and is suitable for systems with high flow requirements.

[0035] In order to improve the sealing performance, the movable valve core 102 may be provided with an elastic sealing structure at the part where it contacts the valve seat, such as a rubber sealing ring, a metal elastic sealing member or a composite material sealing member, so that it can effectively prevent fluid leakage in the closed state. In addition, in order to reduce the wear generated during the opening and closing process, a low-friction coating, such as polytetrafluoroethylene (PTFE) or silicon nitride coating, may be provided on the surface of the valve core, thereby reducing the movement resistance and improving the response speed.

[0036] In terms of intelligent control, the motion state of the movable valve core is adjusted in real time by the controller 106 according to the data of the pressure sensor 103 and the flow sensor 104. When the fluid pressure difference or abnormal flow change is detected, the controller can calculate the optimal valve core adjustment amount through the fuzzy control algorithm, and drive the valve core to move through the execution motor 105 to keep it at the optimal opening position, so as to take into account the fluid stability and backflow protection capability.

[0037] In summary, the movable valve core 102 combines precise structural design, optimized material selection, low-friction sealing technology and intelligent control algorithm, so that the present invention can operate efficiently under various complex working conditions.

[0038] Furthermore, the surface of the movable valve core is provided with a low-friction self-lubricating layer, which is composed of polytetrafluoroethylene, silicon nitride or ceramic coating, and is fixed to the surface of the movable valve core by plasma spraying, electrochemical deposition or high-temperature sintering process to reduce the friction of the valve core when it moves in the flow channel.

[0039] The surface of the movable valve core is provided with a low-friction self-lubricating layer. The main function of the self-lubricating layer is to reduce the friction resistance of the valve core when it moves in the flow channel, improve the response speed and durability of the valve, and reduce the wear and sticking caused by long-term operation. The coating is composed of polytetrafluoroethylene (PTFE), silicon nitride ( ) or ceramic materials and is fixed to the valve core surface using advanced surface treatment technology to ensure stable performance under different fluid media and working conditions.

[0040] Due to its extremely low friction coefficient and good chemical corrosion resistance, the polytetrafluoroethylene coating allows the movable valve core to maintain low friction characteristics in high-flow and highly corrosive fluid environments, avoiding erosion and wear of the valve core surface by solid particles in the fluid medium. At the same time, polytetrafluoroethylene also has good hydrophobicity and oleophobicity, which can reduce the adhesion of dirt and impurities on the valve core surface and improve the long-term operation stability of the valve. For applications requiring higher hardness and wear resistance, silicon nitride coating can provide stronger anti-erosion ability. Its high hardness characteristics enable the valve core to maintain good shape stability in high-pressure and high-speed fluid environments and reduce surface damage caused by particle erosion. In addition, ceramic coatings are suitable for extremely high temperature or extremely chemically corrosive environments. Its high temperature stability and acid and alkali corrosion resistance make it an ideal choice for high-demand fluid control systems.

[0041] The preparation of the low-friction self-lubricating layer adopts plasma spraying, electrochemical deposition or high-temperature sintering process to ensure that the coating can firmly adhere to the surface of the movable valve core while maintaining sufficient thickness uniformity and surface smoothness. The plasma spraying process can form a high-density coating structure on the surface of the valve core, improve wear resistance and enhance the bonding force with the substrate, so that the coating is not easy to peel off under high mechanical stress. Electrochemical deposition technology is suitable for fine control of coating thickness and can achieve uniform coating on the surface of complex-shaped valve cores, especially suitable for small precision fluid control valves. The high-temperature sintering process is used for the preparation of ceramic coatings. Through high-temperature treatment, the ceramic particles are strongly bonded to the surface of the valve core, improving corrosion resistance and erosion resistance, while ensuring that the coating can still maintain low friction characteristics in high temperature environments.

[0042] In practical applications, in order to further improve the movement stability of the valve core, nano-lubricating particles or solid lubricants can be combined with the low-friction self-lubricating layer to further reduce the static friction and dynamic friction coefficients, reduce the starting torque, and improve the small displacement response capability of the movable valve core. Especially in intelligent fluid control systems, low-friction coatings can reduce the nonlinear hysteresis effect caused by friction during the adjustment process of the valve core, so that the system can more accurately execute the opening adjustment instructions calculated by the fuzzy control algorithm and improve the control accuracy.

[0043] In addition, the use of low-friction self-lubricating layer can also reduce the load of the actuator motor, allowing the motor to drive the valve core for rapid adjustment with lower energy consumption, thereby improving the energy efficiency of the overall system and reducing the performance degradation caused by motor heating. During long-term use, the coating can significantly reduce the mechanical wear between the valve core and the flow channel, reduce maintenance frequency and replacement costs, and increase the service life of the entire reverse flow valve.

[0044] The pressure sensors 103 are respectively arranged at the inlet and outlet ends of the valve body and are used to detect the inlet and outlet pressures.

[0045] The pressure sensor 103 is an important component of the intelligent reverse flow valve of the present invention. Its main function is to monitor the pressure changes at the inlet and outlet of the valve body 101 in real time and provide key pressure data to the controller 106 to achieve accurate analysis and intelligent control of the fluid state. The selection, installation location, signal processing method and environmental adaptability of the pressure sensor directly affect the response speed, control accuracy and overall operation stability of the system.

[0046] The pressure sensor can be of different types such as strain gauge, piezoresistive, electrostatic capacitance or optical fiber, and can be selected according to the use environment and accuracy requirements. For application scenarios with high precision and high dynamic response requirements, such as fine flow control of liquid or gas pipelines, it is recommended to use piezoresistive pressure sensors, which have the characteristics of high sensitivity and low drift, and can accurately reflect small pressure changes. For applications that need to work under harsh working conditions, such as high temperature, high pressure, and highly corrosive fluid environments, high temperature resistant and corrosion resistant electrostatic capacitance or optical fiber sensors can be selected to ensure long-term stable operation.

[0047] In order to ensure the accuracy of the monitoring data, the pressure sensor 103 is respectively arranged at the inlet and outlet of the valve body 101, so that it can collect the pressure values ​​of the inlet and outlet respectively, and ensure the real-time monitoring of the fluid state. The installation position of the sensor is usually selected in the front and rear areas where the pipeline is connected to the valve to reduce the influence of factors such as fluid turbulence and pulsation on the measurement accuracy. The installation method can be flange connection, threaded connection or welding fixation, and a protective structure such as a metal filter or a diaphragm sealing layer is added to the part where the sensor contacts the fluid to prevent impurities from clogging or corroding the sensor.

[0048] The signal output by the pressure sensor is usually an analog voltage signal, a current signal (such as 4-20mA) or a digital signal (such as I²C, SPI, RS485). The appropriate signal interface can be selected according to the system requirements. After amplification, filtering and digital processing, the signal is received by the controller 106 and calculated in real time. The controller analyzes the fluid flow state based on the pressure difference data between the inlet and outlet, combined with the fuzzy control algorithm, and determines whether the opening of the valve core 102 needs to be adjusted. When the outlet pressure is higher than the inlet pressure or the pressure change trend is abnormal, the controller can further combine the flow data to comprehensively determine whether backflow occurs and take corresponding adjustment measures.

[0049] In addition, in order to adapt to different working conditions, the pressure sensor 103 can have a temperature compensation function to reduce the impact of temperature changes on measurement accuracy and ensure that accurate and reliable data can be provided under different environmental conditions. In some special scenarios, a dual sensor redundancy design can also be selected, that is, two pressure sensors are installed at the same measuring point. On the one hand, the fault tolerance of the system can be improved, and on the other hand, stable data acquisition can be ensured when one sensor fails, thereby improving the reliability and safety of the entire reverse flow valve system.

[0050] In summary, the pressure sensor 103 plays a key role in data collection in the present invention, and its accuracy, installation method, signal processing capability and environmental adaptability directly affect the intelligent control effect of the system.

[0051] Furthermore, the detection method of the pressure sensor is combined with dynamic pressure compensation technology, and by integrating a micro pressure buffer cavity and a micro channel pressure relief structure in the sensor diaphragm structure, the pressure sensor can automatically adjust the pressure shock response speed to reduce the impact of transient pressure fluctuations on measurement accuracy; the signal acquisition circuit of the pressure sensor adopts a multi-stage adaptive filtering algorithm, including low-pass filtering, median filtering and Kalman filtering, to optimize signal stability, reduce measurement errors, and automatically adjust weights in the fuzzy control module, so that the controller can calculate the optimal valve core position adjustment parameters based on accurate pressure data.

[0052] The pressure sensor combines dynamic pressure compensation technology to improve the stability and accuracy of pressure measurement and ensure reliable data input in an environment with large fluid pressure fluctuations. The sensor adopts a micro pressure buffer chamber and a micro channel pressure relief structure to achieve adaptive adjustment of transient pressure shocks. The micro pressure buffer chamber is arranged inside the sensor diaphragm or in its adjacent area. The buffer chamber can absorb part of the pressure energy when the pressure suddenly changes, so that the deformation of the sensor diaphragm is more uniform, thereby reducing the measurement error caused by the pressure shock. The micro channel pressure relief structure is used to form a dynamic pressure balance between the inside of the buffer chamber and the sensing element of the sensor, so that the sensor can adapt to the rapid changes in fluid pressure in a short time and avoid erroneous readings caused by transient pulse pressure.

[0053] This dynamic pressure compensation technology can effectively reduce the mismeasurement or overshoot caused by pressure pulses in the process of unstable fluid flow or rapid start-stop, so that the system can obtain more accurate pressure data. At the same time, this buffer structure can be optimized according to different fluid working conditions. For example, in a high-pressure system, the volume of the buffer cavity and the size of the microchannel can be appropriately adjusted to ensure a fast pressure response speed, while in a low-pressure system, the buffer cavity can adopt a flexible membrane structure to further improve the sensitivity of pressure measurement.

[0054] In addition, in order to enhance the stability of pressure data and improve signal processing capabilities, the signal acquisition circuit of the pressure sensor adopts a multi-stage adaptive filtering algorithm, including low-pass filtering, median filtering and Kalman filtering. Low-pass filtering is mainly used to eliminate high-frequency noise signals, ensure the smoothness of measurement data, and reduce the impact of external electromagnetic interference on the sensor output signal. Median filtering can effectively remove sudden abnormal signals, especially when the fluid flow state changes suddenly or there is transient pressure interference, and can avoid single abnormal data from misleading the control system. The Kalman filter algorithm combines historical measurement data and current measurement data, and optimizes the measurement value of the sensor through recursive calculation, so that the sensor can still provide high-precision pressure measurement results in the presence of random errors.

[0055] Through multi-level adaptive filtering, the sensor can not only provide more stable data under harsh working conditions, but also dynamically adjust the filtering parameters according to the characteristics of fluid pressure changes. For example, in the early stage of system operation, when the pressure fluctuates greatly, the filtering algorithm can enhance the ability to suppress abnormal data, and after the pressure tends to stabilize, the filtering intensity can be reduced to improve the real-time performance of the pressure data, thereby ensuring that the controller performs calculations based on the optimal pressure data.

[0056] After receiving the pressure data after dynamic compensation and filtering, the fuzzy control module will conduct a comprehensive analysis of the measured data under different working conditions and automatically adjust the weights to optimize the control decision of the valve core position. The core of the weight adjustment is to dynamically optimize the importance of the input variables according to the stability of the pressure measurement, the transient fluctuation amplitude, the historical data trend and the coordinated changes of the flow data, so that the controller can more accurately calculate the optimal opening of the valve core, thereby improving the accuracy of fluid regulation and the sensitivity of system response.

[0057] When the system detects large fluctuations in pressure data, such as pulsating flow or sudden flow changes in the fluid, the fuzzy control module will conduct a comprehensive evaluation of the current measurement data. If the short-term change in the data exceeds the preset threshold, the weight of the data in the control calculation will be reduced to avoid misadjustment of the valve core due to transient anomalies. At the same time, the module will increase the proportion of historical data in the decision-making process to ensure that the controller can still maintain a stable valve core adjustment strategy when the pressure fluctuates greatly, avoiding system oscillation caused by frequent valve core adjustments.

[0058] On the contrary, when the pressure data is stable for a long time and has small fluctuations, the fuzzy control module will increase the weight of the current measurement data, so that the controller can quickly respond to small changes in fluid pressure and achieve more accurate flow regulation. When the fluid state is in a transitional stage, such as when the flow rate is gradually increasing or decreasing, the system will balance the contribution ratio of historical data and real-time data, so that the control decision can take into account the dynamic trend of the system without causing over-response due to short-term small disturbances, thereby ensuring the stability of regulation.

[0059] In addition, during the self-learning process of the fuzzy control module, the controller optimizes the response strategy of different weight adjustment parameters based on long-term operating data. For example, when certain specific pressure change patterns are detected, such as slowly increasing pressure gradients or periodic fluctuations, the system can identify these trends and automatically adjust the calculation logic of the weights to match different fluid control scenarios and improve the adaptability of valve core adjustment. For long-term accumulated data deviations, the system can also identify the source of measurement errors through statistical analysis and adjust the weight of filtered data in control decisions accordingly to reduce the impact of measurement errors on system adjustment accuracy.

[0060] Through such a dynamic weight adjustment mechanism, the fuzzy control module can use the data of the pressure sensor more accurately to achieve intelligent control of the valve core position, so that the intelligent reverse flow valve can adapt to more complex fluid conditions and maintain efficient and stable regulation capabilities under various flow and pressure conditions.

[0061] The flow sensors 104 are respectively arranged at the inlet and outlet ends of the valve body and are used to detect the inlet and outlet flows.

[0062] The flow sensor 104 plays a vital role in the control system of the intelligent reverse flow valve of the present invention. Its main function is to monitor the flow change of the fluid in real time and provide accurate data input to the controller 106, so that the system can accurately determine the flow state of the fluid and adjust the position of the valve core 102 according to the flow change to achieve intelligent control of the fluid. The measurement accuracy, installation method, signal processing and environmental adaptability of the sensor directly affect the response speed, stability and control accuracy of the system.

[0063] The flow sensor uses high-precision flow measurement technology. Different types such as turbine, electromagnetic, ultrasonic or Coriolis mass flowmeter can be selected to match according to the fluid characteristics and use environment. For conductive liquids such as water or certain chemical solutions, electromagnetic flow sensors have good measurement accuracy and stability. For gases or high-viscosity liquids, ultrasonic flow sensors or Coriolis mass flowmeters can provide more reliable measurement results. For application scenarios that require lower cost and higher durability, turbine flow sensors can be a suitable choice.

[0064] In order to ensure comprehensive perception of the fluid flow state, the flow sensor 104 is installed at the inlet and outlet ends of the valve body 101 respectively to monitor the inlet and outlet flow in real time, and calculate the actual flow of the fluid in combination with the detection data of the pressure sensor 103. The installation position of the flow sensor needs to be optimized to reduce the measurement error caused by fluid turbulence or changes in flow channel curvature. Generally, the flow sensor should be installed in a position where the flow is relatively stable, such as away from bends, valve openings or other parts that may affect the flow characteristics. In addition, in order to prevent the influence of fluid impurities on the sensor measuring element, a filter device or a fluid rectifier can be set at the front end of the sensor to improve the measurement accuracy and reduce wear during long-term use.

[0065] The flow sensor can output various types of signals, including analog voltage signals, current signals (such as 4-20mA) or digital signals (such as I²C, SPI, RS485) to meet different control system requirements. After receiving the signal from the flow sensor, the controller 106 analyzes the actual flow state of the fluid through the fuzzy control module in combination with the pressure data, and adjusts the position of the valve core 102 when necessary to optimize fluid control. If an abnormal increase in the outlet flow or a sudden drop in the inlet flow is detected, the system can determine that there may be a risk of backflow, and then trigger the execution control module to make the execution motor 105 respond quickly, adjust the valve core opening or completely close the flow channel to prevent backflow.

[0066] Considering the reliability and long-term operation requirements of the system, the flow sensor 104 may have a self-diagnosis function to detect its own working status and ensure the stability of data acquisition. If the sensor fails or the measurement deviation exceeds the set range, the controller 106 can correct the error through a redundant algorithm or based on the auxiliary data of the pressure sensor 103, and even send an alarm signal when necessary to prompt maintenance personnel to perform maintenance. In addition, for environments with large temperature and pressure changes, a flow sensor with temperature and pressure compensation functions can be selected to reduce the impact of environmental factors on measurement accuracy.

[0067] The present invention uses flow sensor 104 to independently measure the inlet and outlet flow rates, so that the system can calculate the difference between the inlet and outlet flow rates in real time, and optimize the valve core adjustment strategy in combination with the fuzzy control algorithm. This dual flow monitoring solution improves the reliability and response speed of the system, makes the backflow detection more accurate, and reduces the misjudgment caused by pressure fluctuations or transient fluid disturbances.

[0068] Furthermore, the flow sensor adopts a dual-mode measurement technology, including an ultrasonic measurement mode and a thermal measurement mode, wherein the ultrasonic measurement mode is based on the time difference method or the Doppler effect, and is used to measure fluids with larger flow rates to ensure high-precision detection of fluid flow rate and flow direction; the thermal measurement mode is used to detect low flow rates or trace fluid flows to ensure high-sensitivity measurements under micro-flow conditions; the signal processing unit of the flow sensor is combined with an intelligent switching mechanism to automatically switch between the two measurement modes according to the flow range.

[0069] The flow sensor adopts dual-mode measurement technology to improve the measurement accuracy and adaptability in different flow ranges, so that the system can accurately detect the fluid flow state and ensure that the reverse flow valve can maintain stable and reliable control under various working conditions. The sensor combines ultrasonic measurement mode and thermal measurement mode, and through an intelligent switching mechanism, the control system can achieve accurate measurement in a wide flow range, and automatically select the optimal measurement method according to real-time flow changes to optimize the system response speed and measurement accuracy.

[0070] The ultrasonic measurement mode uses the time difference method or the Doppler effect for flow detection, which is mainly suitable for medium and high flow rate conditions to ensure accurate measurement of the fluid in a large flow rate range. In the time difference method measurement, the ultrasonic sensor is arranged at different positions of the pipeline, and the flow velocity is calculated by measuring the time difference of the ultrasonic signal propagating in the downstream and upstream directions of the fluid. It is suitable for uniform flow fluid environments and can provide high-precision velocity measurement data. The Doppler effect measurement method calculates the flow velocity by monitoring the frequency offset of the ultrasonic signal caused by the change in flow velocity during the flow of the fluid. It is suitable for working conditions containing tiny particles or bubbles in the fluid, and can provide stable and reliable measurement results in complex fluid environments. Regardless of which method is used, the ultrasonic measurement mode can maintain the advantages of non-contact measurement under high flow conditions, reduce direct contact between the sensor and the fluid medium, reduce sensor contamination or loss caused by long-term operation, and increase service life.

[0071] The thermal measurement mode is suitable for low flow rate or trace fluid measurement to ensure that it can still provide high-sensitivity detection capabilities under low flow conditions. This mode is based on the principle of heat diffusion. A heating element and a temperature detection unit are set in the measurement area of ​​the flow sensor to calculate the flow rate by measuring the change in heat carried away by the fluid. When the fluid flow rate is low, the traditional measurement method may cause measurement errors due to weak flow rate signals, while the thermal measurement mode can accurately capture tiny flow changes, allowing the system to maintain measurement accuracy under low flow or static boundary conditions. It is particularly suitable for precision fluid transportation, gas flow measurement or low-flow chemical process.

[0072] The signal processing unit, combined with the intelligent switching mechanism, can analyze the current flow status in real time and automatically select the most appropriate measurement mode according to the flow range. The control logic is based on the preset flow threshold. When the flow is detected to be higher than the set threshold, the system automatically activates the ultrasonic measurement mode to ensure stability and measurement accuracy at high-speed flow; when the flow drops below the set value, the system switches to the thermal measurement mode to ensure high sensitivity and accuracy under low flow conditions. The intelligent switching mechanism can also combine historical measurement data and fluid trend analysis to avoid data discontinuity caused by frequent switching, and perform signal smoothing during the mode conversion process to ensure the stability of flow data.

[0073] In addition, in order to improve measurement accuracy and adapt to different working conditions, the flow sensor can be equipped with temperature and pressure compensation functions to reduce the impact of ambient temperature changes or pressure fluctuations on the measurement results. When switching modes, the intelligent switching mechanism can also combine temperature, pressure and other multi-parameter data for comprehensive analysis, thereby optimizing the switching point and improving the reliability of the measurement. In this way, the flow sensor can not only provide accurate measurement results within a wide flow range, but also improve the system's adaptability in complex fluid environments, ensuring that the reverse flow valve can maintain stable fluid control capabilities under different working conditions.

[0074] The actuator motor 105 is connected to the movable valve core through a screw mechanism and is used to drive the movable valve core to move.

[0075] The actuator motor 105 is the key driving component of the intelligent reverse flow valve of the present invention. Its main function is to receive the command of the controller 106 and drive the movement of the movable valve core 102 through the screw mechanism to adjust the opening of the flow channel, thereby accurately controlling the flow rate and pressure of the fluid. The performance of this component directly affects the response speed, control accuracy and long-term operation stability of the valve. Therefore, it is necessary to optimize the selection, driving mode, installation structure and signal processing to ensure that the system can adapt to different fluid control requirements and quickly execute corresponding adjustment measures when the risk of reverse flow is detected.

[0076] The actuator motor can be a stepper motor, servo motor or DC motor, and can be matched according to the control requirements of different application scenarios. Stepper motors are suitable for working conditions with high precision requirements for valve core position. Due to their open-loop control characteristics, they can achieve high positioning accuracy without a complex feedback system. Servo motors have higher dynamic response capabilities and are suitable for occasions that require rapid adjustment and closed-loop precise control, especially in systems where fluid pressure and flow change rapidly, and can provide smoother control effects. For systems with lower power requirements, DC motors combined with position feedback devices can also be a viable option, with the advantages of simple structure and easy integration.

[0077] The actuator motor is connected to the movable valve core through a screw mechanism. The function of the screw mechanism is to convert the rotational motion of the motor into linear motion, so that the movable valve core can move smoothly in the flow channel of the valve body 101, thereby adjusting the amount of fluid passing through. The screw mechanism can adopt a ball screw or a trapezoidal screw. The ball screw is suitable for high-precision and high-response speed application scenarios due to its low friction and high transmission efficiency. Although the trapezoidal screw has a large friction loss, it has a better self-locking characteristic. Under certain working conditions, it can reduce energy consumption and prevent the valve core from accidentally moving due to external forces. In addition, in order to reduce the wear of the screw mechanism and increase its service life, a low-friction coating can be applied to the contact part or a self-lubricating material such as a polytetrafluoroethylene coating or an oil-containing bearing can be used.

[0078] The control method of the actuator motor 105 can adopt PWM (pulse width modulation), PID (proportional-integral-differential) control or fuzzy control algorithm to achieve accurate adjustment of the moving speed and position of the valve core 102. The controller 106 calculates the optimal valve core position based on the real-time detection data of the pressure sensor 103 and the flow sensor 104, and sends a control signal to the actuator motor to drive the motor to adjust the position of the screw mechanism to keep the valve core at the optimal opening. When the system detects abnormal flow, excessive pressure or reverse flow trend, the actuator motor can respond quickly and move the valve core quickly to a preset safe position to prevent reverse flow from affecting the stability of the system.

[0079] In order to improve the reliability of the actuator motor, a position feedback device such as a photoelectric encoder, Hall sensor or potentiometer can be equipped to provide real-time feedback data of the valve core position, thereby forming a closed-loop control and improving the regulation accuracy of the system. If the motor performance deteriorates due to external interference, overload or long-term use, the controller can automatically compensate by monitoring the deviation between the feedback data and the expected value, and even trigger a protection mechanism when the deviation exceeds the limit to avoid damage to the motor. In addition, in high-pressure, high-temperature or highly corrosive environments, a sealing structure with a higher protection level, such as a waterproof and dustproof housing of IP67 or above, can be used to ensure long-term and stable operation of the motor.

[0080] In summary, as the core component for driving the movable valve core, the actuator motor 105 has been optimized in terms of selection, driving mode, feedback control and combination design with the screw mechanism to ensure that the present invention can achieve accurate, stable and fast response in various fluid control environments.

[0081] Furthermore, the actuator motor adopts a high-precision servo motor or a stepper motor and integrates a position feedback device, which includes a photoelectric encoder or a Hall effect sensor for detecting the rotation angle and movement position of the actuator motor; the position feedback device is also used to transmit the detected position signal to the controller, and the controller performs closed-loop control based on the signal, and dynamically adjusts the driving force and response speed of the actuator motor in combination with a fuzzy control algorithm.

[0082] The actuator motor adopts a high-precision servo motor or a stepper motor to ensure the precise adjustment of the movable valve core in the flow channel and provide rapid response capabilities, so that the reverse flow valve can adapt to the fluid control requirements under different working conditions. With its closed-loop control characteristics, the servo motor can provide precise position control in a high dynamic environment and automatically adjust the power when the load changes to ensure stable operation. The stepper motor has good open-loop positioning capabilities and can maintain high control accuracy without relying on complex sensors. It is suitable for application scenarios of flow fine-tuning and precise opening control. No matter which motor is selected, it can ensure that the actuator has good repeatability during the valve core position adjustment process and avoid error accumulation due to mechanical inertia or load changes.

[0083] To further improve control accuracy, the actuator motor is integrated with a position feedback device, which includes a photoelectric encoder or Hall effect sensor to achieve real-time detection of the motor's rotation angle and movement position. The photoelectric encoder detects the motor's rotational displacement through a high-precision optical grid, which can provide high-resolution position information and ensure accurate positioning during rapid system start-up and stop or minor adjustments. The Hall effect sensor detects the rotor position based on magnetic field changes, can maintain stable operation in harsh environments, and provides vibration-resistant and pollution-resistant detection capabilities, allowing the system to maintain stable positioning accuracy during long-term operation. These feedback devices achieve high-precision measurement of the valve core position by directly detecting the motor's motion state, and reduce adjustment misalignment caused by cumulative errors.

[0084] The position feedback device is not only used to detect the position information of the actuator motor in real time, but also can transmit the detected position signal to the controller to achieve closed-loop control. After receiving the feedback signal, the controller will compare the current valve core position with the target position, and calculate the optimal motor driving force and response speed based on the error. Combined with the fuzzy control algorithm, the system can dynamically adjust the driving parameters of the actuator motor to ensure sufficient adjustment accuracy and maintain a smooth response under different fluid conditions, avoiding overshoot or oscillation caused by motor inertia.

[0085] Under the closed-loop control strategy, the controller can optimize the driving force of the actuator motor based on real-time feedback data to ensure that it still has sufficient control stability during low-speed fine-tuning, and can provide sufficient thrust during high-speed response, so that the valve core can quickly move to the optimal position. When a load change is detected, such as a sudden increase in fluid pressure or a sudden change in flow, the control system can immediately adjust the motor driving force to compensate for the impact of external factors on the valve core position, thereby ensuring the accuracy of fluid regulation. In addition, the fuzzy control algorithm can optimize the operating mode of the actuator motor under different working conditions, such as increasing the response speed when adjusting a large range of openings, and reducing the adjustment step when fine-tuning small amplitudes, to avoid system oscillations and improve overall stability.

[0086] The control system of the actuator motor can also be adaptively optimized in combination with historical operating data, so that the fuzzy control algorithm can continuously adjust the response characteristics of the motor to adapt to different fluid environments. For example, when the system detects a certain fluid flow pattern, such as periodic pressure fluctuations or continuous load changes, the controller can dynamically adjust the motor's gain parameters to match the current operating conditions, reduce unnecessary frequent adjustments, and increase the life of the actuator.

[0087] By introducing the position feedback device, the actuator motor can always maintain high-precision opening control and ensure stable adjustment of the valve core under different flow and pressure conditions.

[0088] The controller 106 is electrically connected to the pressure sensor, the flow sensor and the execution motor respectively, and the controller includes: a fuzzy control module, which is used to set the fuzzy rule base based on the initial working condition, and calculate the valve core position adjustment parameters according to the detection data of the pressure sensor and the flow sensor; a self-learning module, which is used to optimize the membership function parameters of the fuzzy rule base to improve the response speed and control accuracy of the valve; a backflow judgment module, which is used to detect whether the outlet pressure is greater than the inlet pressure, or whether the outlet flow is greater than the inlet flow, judge whether the backflow occurs, and trigger the closing instruction when the threshold is exceeded; an execution control module, which is used to output a control signal according to the optimized fuzzy rules, drive the execution motor to adjust the movable valve core position, and realize the intelligent control of the fluid.

[0089] The controller 106 is the core control unit of the intelligent reverse flow valve of the present invention, which is responsible for receiving and processing the real-time detection data of the pressure sensor 103 and the flow sensor 104, executing the fuzzy control algorithm, adaptively adjusting the position of the valve core 102, and responding quickly when the reverse flow risk is detected to ensure the safety and stability of the fluid system. The design of the controller involves multiple key links such as data acquisition, signal processing, intelligent algorithm, self-learning optimization and actuator control, and its performance directly determines the response speed, adjustment accuracy and adaptability of the system.

[0090] The controller uses a high-performance microprocessor or embedded control chip, and integrates an analog-to-digital conversion unit, a data storage unit, a computing unit, and a communication interface to support high-precision acquisition and rapid calculation of sensor data. The pressure sensor 103 and the flow sensor 104 are respectively installed at the inlet and outlet of the valve body 101, and their detection data are input to the controller in the form of analog signals or digital signals. In order to improve the stability and accuracy of the data, signal filtering and noise suppression algorithms are set inside the controller, such as low-pass filtering, Kalman filtering, or median filtering, to eliminate data fluctuations caused by external environmental interference and ensure the reliability of data input.

[0091] The fuzzy control module is the core algorithm unit of the controller, which is used to establish a fuzzy rule base based on the initial working conditions and calculate the optimal valve core position adjustment parameters in combination with real-time detection data. This module uses the fuzzy logic reasoning method to take the pressure difference, flow change trend and valve core position as input variables, and uses the membership function and rule base to fuzzify the system state and calculate the appropriate output variables, thereby adjusting the drive signal of the actuator motor 105 to stabilize the movable valve core 102 at the optimal opening position. Since the traditional fixed valve control strategy is difficult to adapt to complex fluid working conditions, the introduction of the fuzzy control algorithm has greatly improved the adaptability of the system, enabling it to maintain precise control under different fluid characteristics, pipeline pressure fluctuations or external interference.

[0092] The self-learning module is used to optimize the membership function parameters of the fuzzy rule base, so that the system can automatically adjust the control strategy according to long-term operation data to improve response speed and control accuracy. Based on the principle of machine learning, this module combines historical operation data and real-time feedback information to dynamically adjust the weight coefficients, threshold settings and output mappings in the fuzzy control algorithm. For example, when the fluid flow pattern changes, the system can adaptively adjust the fuzzy rules to make the control logic more consistent with the current fluid environment, improve the dynamic response capability of the valve, and reduce false triggering or control lag caused by improper parameter settings.

[0093] The working process of the fuzzy control module starts with data acquisition. First, it receives real-time detection data from the pressure sensor and flow sensor, including inlet pressure, outlet pressure, inlet and outlet flow, and the current position of the valve core. Since these data are continuously changing, the fuzzy control module needs to normalize them and convert them into standardized inputs that can adapt to fuzzy reasoning. For example, the pressure difference can be divided into "low", "medium", and "high", the flow ratio can be defined as "normal", "reduced", or "sharply reduced", and the position of the valve core can be described as "fully open", "partially open", or "nearly closed". This fuzzy processing enables the system to understand complex fluid conditions in a more flexible way, without being limited to fixed numerical thresholds in traditional control methods.

[0094] After obtaining the input data and completing the fuzzification process, the fuzzy control module will perform reasoning calculations based on the preset fuzzy rule base. For example, if the system detects that the inlet pressure is significantly higher than the outlet pressure and the flow rate decreases, the system will determine that there may be a backflow trend based on the fuzzy rules. At this time, the valve core opening should be reduced to avoid the impact of backflow on the system. In this process, the fuzzy control module will calculate the applicability of all relevant rules, and weigh the impact of each rule based on the characteristics of the input data, and finally come up with a comprehensive adjustment plan.

[0095] In order to make the adjustment smoother, the fuzzy control module does not directly output a fixed valve core position, but calculates the step size and direction of the valve core adjustment to ensure that the adjustment process meets the dynamic change requirements of the fluid. For example, when the flow rate decreases but the pressure change is small, the system may choose a smaller adjustment step size to slowly shrink the valve core, and when the system detects a large pressure mutation, it will choose a faster adjustment strategy to restore the normal fluid flow state as soon as possible. After completing this inference calculation, the system will transmit the adjustment signal to the actuator motor to drive the valve core to move, and monitor the effect of the adjustment in real time for the next round of calculations.

[0096] On the basis of the fuzzy control module, the self-learning module is used to optimize the control strategy, so that the system can continuously adjust the fuzzy rules according to the long-term operation data, improve the adaptability and control accuracy. The working mode of this module is based on the analysis of historical data, and the effectiveness of the fuzzy rules is evaluated by recording the changes in the fluid state after the valve core position is adjusted. For example, during the operation of the system, if it is found that some adjustment strategies are less effective under specific working conditions, such as too fast adjustment resulting in excessive pressure fluctuations, or too slow adjustment failing to suppress the reverse flow trend in time, the self-learning module will adjust the applicable conditions of the fuzzy rules based on these data, so that more reasonable adjustment strategies can be selected under similar working conditions in the future.

[0097] The optimization process of the self-learning module is mainly reflected in two aspects. First, it will continuously adjust the membership function so that the fuzzy control module can more accurately match the input data under different fluid conditions. For example, the system can find that a certain pressure change pattern is more common than the originally set rules, and then optimize the membership function so that the system can make more accurate judgments in this specific mode. Secondly, the self-learning module will optimize the weight distribution of the fuzzy rule base, increase the influence of efficient rules, and weaken the weight of rules with poor performance in historical data, and even replace some rules when they are invalid for a long time. This optimization method enables the fuzzy control system to continuously evolve and adapt to long-term changing fluid conditions without manual intervention to adjust the control logic.

[0098] In actual operation, the self-learning module will also make rapid adjustments based on the short-term change trend of the fluid. Especially in the case of drastic changes in flow and pressure, the module can identify deviations in the system's operating status and dynamically adjust the decision logic of the fuzzy control module. For example, when it is detected that the fluid flow rate is slow but the pressure changes suddenly, the system can infer whether there is an external disturbance and adjust the adjustment range of the valve core accordingly to avoid unnecessary overshoot or oscillation.

[0099] The combination of the fuzzy control module and the self-learning module enables the reverse flow valve to not only make intelligent adjustments based on real-time data, but also continuously optimize its own control strategy over time to improve long-term operational reliability.

[0100] The backflow judgment module is specifically used to identify whether there is a backflow risk in the system. Based on the principles of fluid mechanics, this module determines whether an abnormal situation occurs by monitoring the pressure and flow data of the inlet and outlet. Usually, during normal flow, the inlet pressure is greater than the outlet pressure, and the inlet flow rate and the outlet flow rate are close to or maintain a certain flow ratio. When it is detected that the outlet pressure is higher than the inlet pressure, or the outlet flow rate increases abnormally, the system can determine that backflow may occur. In order to improve the detection accuracy, the module can be combined with multi-parameter analysis strategies, such as considering the viscosity, temperature changes or transient flow characteristics of the fluid, to reduce the misjudgment rate. If the backflow situation continues to exceed the set time threshold, the system immediately triggers a shutdown command and executes corresponding safety measures to prevent the backflow from damaging the pipeline system or equipment.

[0101] The execution control module is responsible for converting the optimized fuzzy rule calculation results into specific control signals, driving the execution motor 105 to adjust the position of the movable valve core 102, and realizing intelligent control of the fluid. The module generates corresponding motor drive signals according to the calculated target valve core opening through PWM (pulse width modulation), PID (proportional-integral-differential) or fuzzy PID hybrid control strategy, so that the execution motor can accurately control the movement speed and final position of the valve core. For scenarios that require fast response, the controller can calculate the predicted position in advance according to the fluid change trend, and dynamically adjust the acceleration and deceleration of the motor to improve the stability and response speed of the control system, and avoid water hammer effect or fluid disturbance caused by too fast valve movement.

[0102] The controller has a variety of communication interfaces, such as I²C, SPI, RS485 or CAN bus, to facilitate integration with external monitoring systems or other intelligent devices to achieve remote monitoring and control. In addition, in order to improve the reliability of the system, the controller supports self-diagnosis function, which can monitor sensor data anomalies, motor drive anomalies or communication failures in real time, and trigger alarm signals or automatically execute backup control strategies when necessary to ensure stable operation of the system under various complex working conditions.

[0103] Through the optimized design of the controller, the present invention can realize intelligent reverse flow control with high precision and high response speed, so that the valve can adapt to different fluid flow conditions and respond quickly when a reverse flow trend is detected.

[0104] Furthermore, the fuzzy control module adopts an adaptive fuzzy reasoning method and combines a variable weight dynamic adjustment mechanism to optimize the valve core position adjustment parameters, so that the controller can adaptively adjust the control strategy under different fluid flow conditions, thereby improving the accuracy of flow control and the stability of the system;

[0105] The input variables of the fuzzy control module include the inlet and outlet pressure difference , import and export flow ratio And the current opening of the valve core , the output variable is the valve core adjustment step .

[0106] The core of the fuzzy control module lies in the adaptive fuzzy reasoning method and variable weight dynamic adjustment mechanism, which aims to optimize the position adjustment parameters of the valve core so that the system can automatically adjust the control strategy according to different fluid conditions, thereby improving the accuracy and overall stability of flow control. The input variables of this module include the inlet and outlet pressure difference , import and export flow ratio And the current valve core opening The final output variable is the valve core adjustment step length The entire reasoning process is based on the fuzzy rule base. By calculating the membership of the input variables and combining the dynamic weight coefficient to calculate the final adjustment step, the system can adapt to different working conditions and improve the response speed and control accuracy.

[0107] The model rule base is adjusted in real time based on the following inference formula 1:

[0108] ;

[0109] in, It is used to describe the input variable inlet and outlet pressure difference The number of fuzzy sets, for example, can be Categorized into three sets of "small", "medium" and "large", or expanded to five or more sets depending on system complexity for greater control accuracy.

[0110] It is used to describe the ratio of import and export flow of input variables. The number of fuzzy sets, and its division method is the same as Similarly, it can be divided into three categories according to the flow state: "low", "normal" and "high", and can be further refined to improve the adaptability to different fluid states.

[0111] For input variables The membership function of For input variables The membership function of

[0112] The membership function is used to describe the degree to which an input variable belongs to a specific fuzzy set. It is usually calculated using a triangular membership function or a Gaussian membership function. For example, for an input variable , the triangular membership function is usually defined as:

[0113] ;

[0114] in is the definition interval of the fuzzy set, and the Gaussian membership function is usually used In order to ensure smooth function changes, different membership functions affect the calculation accuracy of fuzzy reasoning. The appropriate function type can be selected according to the application scenario.

[0115] Indicates the current valve core opening; is the variable weight coefficient; function Used for nonlinear compensation to optimize the dynamic response characteristics of the valve core at different openings. This function is implemented using the following formula 2:

[0116] ;

[0117] in, The optimal opening position can be set based on system optimization experiments or empirical data. For example, in a typical fluid system, if the full opening of the valve core is 100%, Set to 50%, indicating the opening range for optimal flow control.

[0118] It is the adjustment coefficient used to control the amplitude of nonlinear compensation. The larger its value, the more concentrated the compensation effect is. Nearby, generally recommended The value is between 0.01 and 1, and the specific value can be adjusted through system response testing. The purpose of this compensation function is to ensure that the adjustment step size is small when the valve core is near the optimal position to avoid over-adjustment, and to avoid over-adjustment when the valve core deviates from the optimal position. Provides a larger adjustment range to accelerate the system's recovery to a stable state.

[0119] The variable weight dynamic adjustment mechanism uses the following formula 3 to adjust :

[0120] ;

[0121] in, It is a preset dynamic pressure threshold, which represents the reference point for the system to judge the pressure status. For example, in some liquid delivery applications, this value can be set to 80% or 90% of the normal working pressure to ensure a quick response to abnormal pressure changes.

[0122] It is a preset dynamic flow threshold, which indicates the reference point for the system to determine the flow state. This value is usually the median of the normal flow range or an empirically set value. For example, in some industrial pipeline systems, it can be set to 1.0 (i.e., the inlet and outlet flow ratio should be close to 1 under normal conditions). If it is significantly higher than 1, it may indicate a significant fluid reflux trend.

[0123] Through the combination of the above-mentioned fuzzy reasoning method and variable weight adjustment mechanism, the fuzzy control module can adapt to different working conditions and dynamically optimize the valve core adjustment step to improve the accuracy and stability of fluid control.

[0124] The main purpose of the fuzzy control module combined with the self-learning module is to continuously optimize the membership function parameters in the fuzzy rule base through historical data feedback to improve the valve's response speed and control accuracy. The optimization process dynamically adjusts the center point and spread parameters of the membership function based on historical data, so that the system can maintain high control accuracy under different fluid conditions and have adaptive capabilities, thereby improving overall stability and intelligence.

[0125] The optimization process of the self-learning module includes the adjustment of the center point of the membership function and the optimization of the broadening parameters. The adjustment of the center point is used to ensure that the fuzzy rule base can accurately reflect the current fluid state, while the adjustment of the broadening parameters is used to optimize the coverage of the fuzzy set, so that the system can respond to fluid changes more sensitively and reduce misjudgment.

[0126] The fuzzy control module is combined with the self-learning module to optimize the membership function parameters of the fuzzy rule base through historical data feedback to improve the response speed and control accuracy of the valve;

[0127] The self-learning module dynamically adjusts the membership function parameters of the input variables in the fuzzy rule base based on historical data, including adjusting the center point of the membership function according to the following formulas 4 and 5: and the broadening parameters :

[0128] ;

[0129] ;

[0130] in, is the center point of the membership function after optimization, indicating the optimal fuzzy set center obtained by the system through historical data optimization, so that the description of the membership function is more in line with the current fluid working conditions.

[0131] It is the center point of the membership function before optimization, that is, the center of the fuzzy set initially set by the system. This value is usually set based on experimental data or experience.

[0132] is the learning rate. This parameter controls the step size of the center point update. The recommended value range is 0.01 to 0.2. When the value is larger, the system will be more sensitive to the latest data changes, but it may cause large control fluctuations. When the value is smaller, the update process is smoother and is suitable for systems with slowly changing fluid conditions.

[0133] is the mean of historical data, which indicates the average value of the input variables of the fluid system in the past specified time period, reflecting the long-term trend of fluid changes, so that the optimized membership function can accurately match the current fluid state. For example, within a time window, if the system collects multiple fluid pressure data, then It is calculated as the arithmetic mean of all measured values.

[0134] is the optimized broadening parameter, which indicates the optimal fuzzy set width calculated by the system based on historical data, so that the fuzzy rule is neither too sensitive to cause misjudgment nor too broad to affect control accuracy.

[0135] is the broadening parameter of the membership function before optimization, which indicates the coverage of the fuzzy set. This value is usually based on experimental settings. For example, in the initial stage, the standard deviation obtained by empirical values ​​or offline data training may be used.

[0136] The learning rate controls the update amplitude of the broadening parameters. The recommended value range is 0.01 to 0.3. It enables the system to quickly adapt to changes in the fluid environment, but may lead to over-adjustment and unstable control. It is suitable for slowly changing systems and can smoothly optimize fuzzy rules.

[0137] The number of samples indicates the number of samples used to calculate the statistical characteristics of historical data. The selection of this value requires a balance between the real-time and stability of the system. The recommended The value range is 10 to 1000, with smaller values Suitable for high dynamic fluid control, while the larger Suitable for fluid environments with strong stability.

[0138] For the Historical data points, indicating the past The actual measured value within a time step. This data comes directly from the sensor measurement results, such as the historical data of the pressure sensor and flow sensor. The calculation method of this variable usually adopts the rolling window method, that is, each time it is updated, the latest The system calculates the optimal value based on the measured values ​​and removes the oldest data point to ensure that the system always uses the latest historical data for optimization.

[0139] The core idea of ​​the entire optimization process is to enable the membership function of the fuzzy rule base to be dynamically adjusted as the fluid conditions change, rather than using fixed rules, so as to improve the adaptability and control accuracy of the system. By adjusting the center point of the membership function, the system can more accurately match the current fluid state, and by optimizing the broadening parameters, the system can more reasonably divide the fuzzy set range, so that the accuracy and robustness of fuzzy reasoning are improved.

[0140] Furthermore, the backflow judgment module realizes the backflow state recognition through a multi-stage judgment strategy based on the real-time detection data of the pressure sensor and the flow sensor, and executes the corresponding control instructions when the trigger conditions are met, so as to ensure the system stability and reduce misjudgment;

[0141] The reverse flow judgment module first receives the data of the inlet pressure, outlet pressure, inlet flow rate and outlet flow rate, and calculates the inlet and outlet pressure difference and the inlet and outlet flow rate ratio to judge whether the outlet pressure is continuously higher than the inlet pressure, or whether the outlet flow rate is continuously greater than the inlet flow rate; if the pressure difference or the flow rate ratio exceeds the set dynamic threshold, it is judged that reverse flow may occur, and enters the trend analysis stage;

[0142] During the trend analysis phase, the system monitors the pressure change rate and flow change rate. If the outlet pressure continues to increase or the ratio of the outlet flow to the inlet flow continues to increase within the set time window, the reverse flow state is confirmed and the hysteresis confirmation phase is entered; the hysteresis confirmation phase is used to avoid misjudgment caused by short-term fluctuations. The system calculates the time the reverse flow state is maintained. If the reverse flow state continues to exceed the set time threshold, a shutdown command is triggered, otherwise normal monitoring is resumed.

[0143] The backflow judgment module is used to identify the backflow state and execute the corresponding control instructions when the trigger conditions are met to ensure the stability of the system and reduce misjudgment. This module relies on real-time data input from pressure sensors and flow sensors. It first receives the inlet pressure, outlet pressure, inlet flow and outlet flow, and determines whether there is a backflow trend by calculating the inlet and outlet pressure difference and the inlet and outlet flow ratio. When the system detects that the outlet pressure is continuously higher than the inlet pressure, or the outlet flow continues to increase relative to the inlet flow, it indicates that there may be a backflow risk. At this time, the system will perform dynamic threshold judgment on these data to distinguish normal fluctuations from abnormal flow conditions.

[0144] In order to avoid misjudgment caused by short-term pressure or flow fluctuations, the system will not immediately trigger a shutdown command after detecting that the pressure difference or flow ratio exceeds the set threshold, but will enter the trend analysis stage. In this stage, the system not only detects the current pressure and flow status, but also calculates their rate of change to determine whether the reverse flow trend persists. By analyzing the pressure change rate and flow change rate at multiple time points, it can be determined whether the outlet pressure continues to increase in a short period of time, or the ratio of the outlet flow to the inlet flow continues to increase within the set time window. If the trend analysis shows that the reverse flow state is not an instantaneous fluctuation, but a continuous development phenomenon, the system confirms the reverse flow state and enters the delayed confirmation stage.

[0145] The main function of the delayed confirmation stage is to further reduce misjudgment and ensure that only stable backflow phenomena will trigger the closing command. At this stage, the system will calculate the duration of the backflow state and compare it with the set time threshold. If the backflow state is only a short-term instantaneous fluctuation, such as a short-term change in pressure or flow caused by fluid pulsation or temporary disturbances in the pipeline, the system will not take immediate closing measures, but will continue to monitor and automatically exit the abnormal judgment state after the system returns to normal. However, if the backflow state lasts for more than the set time threshold, the system determines that the backflow has become a stable state, and the closing command is triggered at this time, and the valve core position is adjusted to prevent the further development of the backflow phenomenon.

[0146] The entire judgment process is implemented through a multi-stage strategy, which enables the system to not only respond quickly to sudden backflow situations, but also effectively filter out false alarms caused by short-term disturbances. In addition, the backflow judgment module can combine historical operating data to optimize the dynamic threshold so that it can adapt to different fluid conditions. For example, under certain conditions, the pressure difference during normal operation may be large. The system can adjust the judgment threshold based on long-term data analysis to avoid misjudgment. At the same time, for scenarios with periodic pressure fluctuations, the system can optimize the trend analysis algorithm so that it can more accurately distinguish between true backflow and normal fluid changes when facing periodic disturbances, thereby improving the intelligence and reliability of the entire system.

[0147] Through this method, the backflow judgment module can realize accurate backflow identification in various complex fluid environments, and reduce false triggering through a multi-stage judgment strategy, thereby improving the stability and adaptability of the intelligent backflow valve.

[0148] Furthermore, the execution control module dynamically adjusts the driving signal of the execution motor based on the valve core adjustment step calculated by the fuzzy control module to optimize the valve core movement process;

[0149] The execution control module first receives the target position of the valve core, calculates the difference between the current valve core opening and the target position, determines the required adjustment step, and calculates the optimal driving torque in combination with the changes in fluid pressure and flow rate;

[0150] During the movement of the valve core, the system monitors the current changes of the actuator motor, the valve core displacement speed and the fluid state in real time, and dynamically adjusts the motor drive parameters based on the feedback data to ensure sufficient driving force under high pressure difference or large flow conditions, and reduces the motor power output at low flow or small adjustments to reduce energy consumption and extend equipment life.

[0151] The execution control module is responsible for optimizing the drive signal of the execution motor according to the valve core adjustment step calculated by the fuzzy control module to ensure that the movement of the valve core is smooth and accurate and adapts to the changes in different fluid working conditions. First, the execution control module receives the target valve core position calculated by the fuzzy control module and obtains the real-time opening of the current valve core. The system calculates the difference between the current opening and the target opening, determines the step size required for the valve core to be adjusted, and based on the current fluid state, comprehensively considers the inlet and outlet pressures, flow change trends, and historical adjustment data to calculate the optimal driving torque required for the execution motor.

[0152] When calculating the driving torque, the system not only takes into account the deviation between the target valve core position and the current opening, but also combines the data of the pressure sensor and the flow sensor to determine the impact of the fluid flow state on the movement of the valve core. When the inlet and outlet pressure difference is large, the movement of the valve core may be subject to greater resistance. At this time, the system will increase the driving force of the actuator motor to ensure that the valve core can respond quickly to the control command without causing movement hysteresis due to the pressure reaction force. In the case of small adjustments or low fluid flow rates, the system will reduce the power output of the actuator motor to avoid overshoot or oscillation caused by inertia, while reducing energy consumption and extending the service life of the actuator motor.

[0153] During the movement of the valve core, the execution control module continuously monitors the operating status of the execution motor, including the change of motor current, the real-time displacement speed of the valve core, and the dynamic change of the fluid state, and makes adaptive adjustments based on the feedback data. If an abnormal increase in motor current is detected, it may mean that the valve core is blocked or the fluid pressure has changed suddenly. The system will immediately adjust the drive signal, increase the output torque or change the acceleration curve to ensure that the valve core can reach the target position smoothly. If it is detected that the valve core movement speed exceeds expectations, the system will appropriately reduce the motor power or adopt a segmented adjustment strategy to reduce repeated adjustments caused by overshoot, thereby improving control stability.

[0154] In order to optimize the entire adjustment process, the execution control module also combines historical data analysis to evaluate the effects of different adjustment modes under different working conditions, and optimizes the drive strategy accordingly. For example, the system can analyze the valve core response characteristics under different pressure conditions through long-term operation data, and dynamically adjust the gain parameters of the execution motor so that it can achieve precise control in different fluid environments. At the same time, if the system detects that the adjustment error of a certain adjustment strategy under specific working conditions is large, the execution control module can combine the self-learning algorithm to fine-tune the control curve, so that the valve core adjustment is smoother, reduce unnecessary oscillations during the adjustment process, and improve the long-term stability of the system.

[0155] In addition, in order to further improve the working efficiency of the actuator motor, the actuator control module can intelligently select different drive modes according to the size of the valve core adjustment step. When the adjustment step is large, the system adopts the fast drive mode to make the valve core quickly approach the target position. When it is close to the target position, it switches to the fine adjustment mode to reduce the driving force and speed to avoid overshoot caused by inertia. Through this intelligent drive control strategy, the actuator control module can always maintain the optimal control accuracy under different fluid environments and valve core opening adjustment requirements, and ensure stable and reliable system operation.

[0156] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. An intelligent reverse flow valve based on fuzzy control, characterized in that: include: A valve body, wherein a flow channel is provided in the valve body; A movable valve core is arranged in the flow channel and is used to control the opening of the flow channel; Pressure sensors are respectively arranged at the inlet and outlet ends of the valve body and are used to detect the inlet and outlet pressures; Flow sensors are respectively arranged at the inlet and outlet ends of the valve body and are used to detect inlet and outlet flows; An actuator motor is connected to the movable valve core through a lead screw mechanism and is used to drive the movable valve core to move; A controller is electrically connected to the pressure sensor, the flow sensor and the execution motor respectively, and the controller includes: a fuzzy control module, which is used to set a fuzzy rule base based on an initial working condition, and calculate the valve core position adjustment parameters according to the detection data of the pressure sensor and the flow sensor; a self-learning module, which is used to optimize the membership function parameters of the fuzzy rule base to improve the response speed and control accuracy of the valve; a backflow judgment module, which is used to detect whether the outlet pressure is greater than the inlet pressure, or whether the outlet flow is greater than the inlet flow, judge whether the backflow occurs, and trigger a closing instruction when the threshold is exceeded; an execution control module, which is used to output a control signal according to the optimized fuzzy rule to drive the execution motor to adjust the movable valve core position; The fuzzy control module adopts an adaptive fuzzy reasoning method and combines it with a variable weight dynamic adjustment mechanism to optimize the valve core position adjustment parameters, so that the controller can adaptively adjust the control strategy under different fluid flow conditions, thereby improving the accuracy of flow control and the stability of the system. The input variables of the fuzzy control module include the inlet and outlet pressure difference , import and export flow ratio And the current opening of the valve core , the output variable is the valve core adjustment step , the model rule base is adjusted in real time based on the following inference formula (1): ； in, It is used to describe the input variable inlet and outlet pressure difference The number of fuzzy sets; It is used to describe the ratio of import and export flow of input variables. The number of fuzzy sets; For input variables The membership function of For input variables The membership function of Indicates the current valve core opening; is the variable weight coefficient; function It is used for nonlinear compensation to optimize the dynamic response characteristics of the valve core at different openings. The function is implemented using the following formula (2): ； in, is the best opening position; is the adjustment coefficient; The variable weight dynamic adjustment mechanism uses the following formula (3) to adjust : ； in, is a preset dynamic pressure threshold; It is a preset dynamic flow threshold.

2. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The valve body adopts a split modular design, including a main valve body and a detachable sensor installation cavity, and the sensor installation cavity is used to accommodate a pressure sensor and a flow sensor.

3. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The surface of the movable valve core is provided with a low-friction self-lubricating layer, which is composed of polytetrafluoroethylene, silicon nitride or ceramic coating and is fixed to the surface of the movable valve core by plasma spraying, electrochemical deposition or high-temperature sintering process to reduce the friction of the valve core when it moves in the flow channel.

4. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The detection method of the pressure sensor is combined with dynamic pressure compensation technology. By integrating a micro pressure buffer cavity and a micro channel pressure relief structure in the sensor diaphragm structure, the pressure sensor can automatically adjust the pressure shock response speed to reduce the impact of transient pressure fluctuations on measurement accuracy. The signal acquisition circuit of the pressure sensor adopts a multi-stage adaptive filtering algorithm, including low-pass filtering, median filtering and Kalman filtering, to optimize signal stability, reduce measurement errors, and automatically adjust weights in the fuzzy control module, so that the controller can calculate the optimal valve core position adjustment parameters based on accurate pressure data.

5. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The flow sensor adopts dual-mode measurement technology, including an ultrasonic measurement mode and a thermal measurement mode, wherein the ultrasonic measurement mode is based on the time difference method or the Doppler effect, and is used to measure fluids with larger flow rates to ensure high-precision detection of fluid flow rate and flow direction; the thermal measurement mode is used to detect low flow rates or trace fluid flows to ensure high-sensitivity measurements under micro-flow conditions; the signal processing unit of the flow sensor is combined with an intelligent switching mechanism to automatically switch between the two measurement modes according to the flow range.

6. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The actuator motor adopts a high-precision servo motor or a stepper motor and integrates a position feedback device. The position feedback device includes a photoelectric encoder or a Hall effect sensor for detecting the rotation angle and movement position of the actuator motor; the position feedback device is also used to transmit the detected position signal to the controller, and the controller performs closed-loop control based on the signal, and dynamically adjusts the driving force and response speed of the actuator motor in combination with a fuzzy control algorithm.

7. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The fuzzy control module is combined with the self-learning module to optimize the membership function parameters of the fuzzy rule base through historical data feedback to improve the response speed and control accuracy of the valve; The self-learning module dynamically adjusts the membership function parameters of the input variables in the fuzzy rule base based on historical data, including adjusting the center point of the membership function according to the following formulas (4) and (5): and the broadening parameters : ； ； in, is the center point of the membership function after optimization; is the center point of the membership function before optimization; is the learning rate; It is the mean of historical data, which indicates the average value of input variables of the fluid system in the past specified time period, reflecting the long-term trend of fluid changes; is the optimized broadening parameter; is the expansion parameter of the membership function before optimization, which indicates the coverage of the fuzzy set; is the learning rate; is the sample size, which indicates the number of samples used to calculate the statistical characteristics of historical data; For the Historical data points, indicating the past The actual measured value within the time step.

8. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The backflow judgment module realizes the backflow state recognition through a multi-stage judgment strategy based on the real-time detection data of the pressure sensor and the flow sensor, and executes the corresponding control instructions when the trigger conditions are met to ensure the system stability and reduce misjudgment; The backflow judgment module first receives the data of the inlet pressure, outlet pressure, inlet flow rate and outlet flow rate, and calculates the inlet and outlet pressure difference and the inlet and outlet flow rate ratio to judge whether the outlet pressure is continuously higher than the inlet pressure, or whether the outlet flow rate is continuously greater than the inlet flow rate; If the pressure difference or flow ratio exceeds the set dynamic threshold, it is determined that reverse flow may occur and the trend analysis stage is entered; During the trend analysis phase, the system monitors the pressure change rate and flow change rate. If the outlet pressure continues to increase or the ratio of the outlet flow to the inlet flow continues to increase within the set time window, the reverse flow state is confirmed and the hysteresis confirmation phase is entered; the hysteresis confirmation phase is used to avoid misjudgment caused by short-term fluctuations. The system calculates the time the reverse flow state is maintained. If the reverse flow state continues to exceed the set time threshold, a shutdown command is triggered, otherwise normal monitoring is resumed.

9. The intelligent reverse flow valve based on fuzzy control according to claim 1 is characterized in that: The execution control module dynamically adjusts the driving signal of the execution motor based on the valve core adjustment step calculated by the fuzzy control module to optimize the valve core movement process; The execution control module first receives the target position of the valve core, calculates the difference between the current valve core opening and the target position, determines the required adjustment step, and calculates the optimal driving torque in combination with the changes in fluid pressure and flow rate; During the movement of the valve core, the system monitors the current changes of the actuator motor, the valve core displacement speed and the fluid state in real time, and dynamically adjusts the motor drive parameters based on the feedback data to ensure sufficient driving force under high pressure difference or large flow conditions.

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