Self-diagnosis intelligent butterfly valve based on internet of things and control method

Abstract

This invention discloses a self-diagnostic intelligent butterfly valve and its control method based on the Internet of Things (IoT), belonging to the technical field of intelligent control equipment for fluid transportation. It includes a butterfly valve body, an electric actuator drive module, a multi-dimensional sensor array, an edge computing self-diagnostic main control unit, an NB-IoT+LoRa dual-mode IoT communication module, a tri-mode redundant power supply module, a fault emergency control module, and a local storage and indication module. This invention employs the aforementioned self-diagnostic intelligent butterfly valve and its control method based on IoT to achieve full-dimensional perception of the butterfly valve's operating status, local low-latency fault diagnosis, and remote IoT control, solving practical engineering pain points in the field of industrial fluid control, improving the safe operation level of fluid systems, and reducing operation and maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent control equipment for fluid transport, and in particular to a self-diagnostic intelligent butterfly valve and control method based on the Internet of Things. Background Technology

[0002] Butterfly valves, as fluid control valves with simple structure, convenient operation, and excellent flow regulation performance, are widely used in various industrial fluid transportation systems. Their operational stability directly affects the safe and efficient operation of the entire fluid system. However, existing butterfly valves, whether traditional manual / electric or a few so-called "intelligent butterfly valves," all have many technical defects, making it difficult to meet the development needs of industrial intelligence and unattended operation. Specifically: (1) Delayed fault detection and prominent safety hazards: Traditional butterfly valves only have basic switching or flow regulation functions, without any status perception or fault diagnosis capabilities. They rely entirely on manual inspection to detect faults such as leakage, jamming, and overload. The fault detection time is long, which can easily lead to safety accidents such as fluid leakage, pipeline overpressure, and equipment damage. The hidden dangers are even more prominent in the transportation of dangerous media such as chemicals and gas.

[0003] (2) Low diagnostic accuracy and high false alarm rate: A few existing intelligent butterfly valves can only collect a single valve position opening or pipeline pressure signal. The diagnostic logic adopts a simple threshold judgment method, which cannot fully reflect the operating status of the butterfly valve. False alarms and missed alarms are likely to occur, which not only increases the workload of operation and maintenance personnel, but may also lead to unnecessary shutdowns due to false alarms, or miss the best time to deal with faults due to missed alarms.

[0004] (3) Relying on cloud computing, with high response latency and failure when the network is disconnected: The fault diagnosis of existing intelligent butterfly valves mostly relies on cloud servers for data processing and analysis. The data transmission and diagnostic response latency is large, making it impossible to handle faults in real time. At the same time, once the network is interrupted, the system will completely lose its diagnostic and control capabilities, and the butterfly valve will be out of control, which may easily lead to safety risks.

[0005] To address the shortcomings of the existing technologies, there is an urgent need for an intelligent butterfly valve and control method that features full-dimensional self-diagnosis, local real-time handling, fault self-repair, full-scenario adaptation, and strong compatibility. This would solve the pain points of the existing technologies, improve the intelligence level, operational safety and reliability of fluid control systems, and reduce maintenance costs. Summary of the Invention

[0006] The purpose of this invention is to provide a self-diagnostic intelligent butterfly valve and control method based on the Internet of Things (IoT), which realizes full-dimensional perception of the butterfly valve's operating status, local low-latency fault diagnosis, and IoT remote control, thereby solving practical engineering pain points in the field of industrial fluid control, improving the safe operation level of fluid systems, and reducing operation and maintenance costs.

[0007] To achieve the above objectives, the present invention provides a self-diagnostic intelligent butterfly valve based on the Internet of Things, including a butterfly valve body, an electric actuator drive module, a multi-dimensional sensor array, an edge computing self-diagnostic main control unit, an Internet of Things communication module, a redundant power supply module, a fault emergency control module, and a local storage and indication module. The multi-dimensional sensor array is signal-connected to the edge computing self-diagnostic main control unit, used to collect full-dimensional status data of the butterfly valve operation and transmit it to the edge computing self-diagnostic main control unit. The edge computing self-diagnostic main control unit is electrically connected to the electric actuator drive module, the NB-IoT+LoRa dual-mode IoT communication module, the fault emergency control module, and the local storage and indication module, respectively, used to receive sensor data, perform local AI fault diagnosis, generate control commands based on the diagnostic results and send them to the electric actuator drive module and the fault emergency control module. The tri-mode redundant power supply module adopts a multi-power redundancy design to provide stable and redundant power supply for the entire intelligent butterfly valve system, ensuring uninterrupted system operation. It is used to receive the fault classification results output by the edge computing self-diagnostic main control unit and execute corresponding emergency and self-repair actions. The edge computing self-diagnostic main control unit has built-in fault classification judgment rules, which are set based on the feature values ​​of the status data collected by the multi-dimensional sensor array.

[0008] Preferably, the butterfly valve body is made of ductile iron QT450-10 or stainless steel 304 to ensure valve body strength and corrosion resistance; the valve plate has an eccentric structure, which can reduce sealing wear and improve sealing performance; the sealing pair adopts EPDM / PTFE composite seal, the flange standard is compatible with GB / T9113 "Integral Steel Pipe Flange", the structural form is compatible with the wafer type specified in GB / T12221 "Valve Structural Length", and the overall protection level reaches IP67, which can adapt to harsh working conditions such as outdoor and humid conditions.

[0009] Preferably, the electric actuator drive module includes a brushless DC motor, a planetary gear reducer, and an absolute angle encoder, wherein the absolute angle encoder has an opening resolution of ±0.1°, which can accurately feedback the valve opening degree; the electric actuator drive module has a built-in real-time torque sampling circuit, which is used to collect the driving load torque and transmit it to the edge computing self-diagnostic main control unit to provide data support for fault diagnosis; through the cooperation of the brushless DC motor and the planetary gear reducer, it supports switch control, 4~20mA / 0~10V proportional adjustment and forward and reverse reversing escape action, and has an overload automatic shutdown protection function to avoid damage to the drive module.

[0010] Preferably, the multi-dimensional sensor array includes an absolute magnetically coded valve position opening sensor, a diffused silicon inlet pressure sensor, a diffused silicon outlet pressure sensor, a piezoelectric vibration sensor, an NTC / Pt100 medium temperature sensor, a cavity-type gas / liquid level micro-leakage sensor, and a drive torque sensor; wherein, the leakage sensor is arranged in the liquid accumulation cavity below the valve shaft seal, and by monitoring the change in the medium liquid level and the gas concentration in the liquid accumulation cavity, both internal and external leakage can be detected simultaneously. Leakage determination uses a threshold comparison formula: ; when > The leak was determined to be present at that time. For real-time monitoring of concentration / liquid level; Reference concentration / liquid level; The threshold for determining leakage; The pressure sensor has a range of 0~1.6MPa, and the temperature sensor has a measurement range of -40℃~125℃. The parameters of the pressure and temperature sensors are adapted to the parameter detection requirements of industrial pipelines under normal operating conditions. The preprocessing of the acquired data adopts a first-order low-pass filter formula: ; in, This is the filtered data; This is the currently collected data; This is the data from the previous filtering; These are the filter coefficients, with values ​​ranging from 0.1 to 0.3.

[0011] Preferably, the edge computing self-diagnostic main control unit adopts an industrial-grade MCU + edge AI inference chip, with a built-in lightweight CNN fault diagnosis model that can run locally offline. The fault diagnosis response time is <50ms, and it can identify 7 typical faults, including but not limited to valve shaft jamming, valve shaft stalling, seal wear, internal leakage, external leakage, drive overload, and sensor abnormality. The diagnostic logic adopts a triple judgment method of AI model judgment + multi-threshold cross-validation + trend analysis, which can effectively improve the accuracy of fault diagnosis and reduce misjudgment and missed judgment.

[0012] Preferably, the IoT communication module adopts an NB-IoT+LoRa dual-mode structure. The NB-IoT module is used for remote data upload and remote control, realizing wide-coverage remote communication; the LoRa module is used for self-organizing local area networks in the plant / pipeline network and multi-valve linkage, realizing short-range low-power communication. The two work together to improve communication stability. The NB-IoT+LoRa dual-mode IoT communication module supports data caching when the network is down, automatic resume transmission when the network is connected, and adaptive signal strength reporting functions to ensure the continuity of data transmission.

[0013] Preferably, the redundant power supply module includes a 220V AC main power supply, a lithium battery backup power supply with a battery life of ≥72h, and a solar auxiliary power supply module, which together constitute a three-mode redundant power supply. The redundant power supply module is connected to each power consumption module. The three-mode redundant power supply module has a built-in ultra-low power consumption sleep-wake mechanism. Under normal conditions, the system is in sleep mode and wakes up to collect data at a cycle of 5s to 30s. Under abnormal conditions, it immediately wakes up and performs reporting actions to ensure uninterrupted power supply to the system.

[0014] Preferably, the fault emergency control module has a three-level emergency strategy: Level I minor faults (which do not affect the normal operation of the valve and only have minor abnormalities, such as minor leakage or opening deviation) execute local self-calibration, automatic closing compensation, and log recording actions; Level II general faults (which affect some functions of the valve, such as abnormal driving torque or sensor data deviation) execute forward and reverse rotation escape, opening limit, and remote reporting actions; Level III serious faults (which endanger system safety or cause the valve to be unable to operate, such as valve shaft jamming or serious leakage) execute emergency shutdown / micro-opening pressure relief, audible and visual alarm (red light flashing, alarm frequency of 1 time / second), and control permission locking actions.

[0015] This invention also provides a self-diagnostic intelligent butterfly valve control method based on the Internet of Things, comprising the following steps: Step S1: Power on the system and initialize it, complete the self-test of each module, and ensure normal operation; Step S2: The multi-dimensional sensor array collects status data according to a set period; Step S3: The main control unit performs filtering and noise reduction preprocessing on the collected data and extracts fault feature values; Step S4: The edge computing self-diagnosis main control unit performs local real-time fault diagnosis. If no fault is detected, it returns to step S2 to continue collecting data. If a fault is detected, it proceeds to step S5. Step S5: Based on the preset fault classification judgment rules, perform fault classification judgment according to the fault characteristic values. Step S6: The fault emergency control module executes the corresponding emergency and self-repair strategies based on the fault classification results; Step S7: Upload the fault information, emergency operation record and current operating data to the remote platform and simultaneously store them in the local storage module; Step S8, periodic self-maintenance, including sensor calibration, actuator lubrication, local cache clearing, and module status self-check, to ensure long-term stable operation of the system.

[0016] Preferably, the data acquisition cycle in step S2 can be adaptively adjusted. In normal state (no fault, no warning), it is 5s~30s; in warning state (sensor data is close to the preset threshold and the main control unit issues a warning signal), it is 1s; and in fault state, it is 100ms high-frequency sampling. The self-repair actions in step S6 include: closed-loop correction of opening deviation for valve shaft jamming fault, forward and reverse pulse freeing for valve shaft jamming fault, seal wear compensation for valve wear fault, overpressure relief for overpressure fault, and power outage switching to backup power supply for mains power interruption fault.

[0017] Therefore, the present invention employs the above-mentioned self-diagnostic intelligent butterfly valve and control method based on the Internet of Things, and the technical effects are as follows: (1) An integrated intelligent butterfly valve technology solution of multi-dimensional sensor array + edge computing self-diagnosis + three-level fault emergency + dual-mode communication + three-mode power supply is proposed, which is different from the existing butterfly valve products with single sensing, single communication, no local diagnosis and no self-repair; the detection method of cavity micro-leakage sensor and multi-sensor fusion is pioneered to achieve accurate detection of internal and external leakage at the same time; the mode of combining edge AI local diagnosis and cloud collaborative management is adopted to break through the technical limitations of existing cloud diagnosis and has significant novelty.

[0018] (2) Overcoming the technical biases that are common in the industry, such as "reliance on cloud computing, single diagnosis, loss of control due to network and power outages, and lack of proactive emergency protection", it brings significant technological progress; the edge computing self-diagnosis main control unit realizes local low-latency diagnosis (response time < 50ms), solving the problem of high latency in existing cloud diagnosis; the dual-mode communication module adapts to multiple scenarios of remote and local area networks, solving the problem of limited coverage of a single communication method; the triple-mode redundant power supply module realizes dual power supply of mains power and lithium battery + low-power sleep, solving the problems of loss of control due to power outages and poor adaptability; the three-level fault emergency self-repair mechanism realizes proactive closed-loop handling of faults, reduces manual intervention, and can reduce fault handling time by more than 80% and reduce operation and maintenance costs by more than 30% compared with existing technologies; multi-sensor fusion + triple diagnostic logic greatly reduces false alarm rate and false alarm rate, and improves diagnostic accuracy to more than 99%.

[0019] (3) The butterfly valve body adopts the national standard flange / clip structure, which is compatible with the installation size of traditional butterfly valves. It can directly replace the existing traditional butterfly valves without modifying the pipeline. It can be used immediately after installation, with low modification cost and short construction period. The industrial-grade protection design (IP67) is suitable for harsh industrial environments such as outdoor, humid and dusty environments. It can be widely used in various industrial fluid control scenarios such as water, chemical, gas, HVAC, petroleum and mining. It has local independent operation capability. It can still realize fault diagnosis and emergency handling when the network is disconnected or the power is cut off, ensuring the safe and stable operation of the system. Local storage and status indication functions facilitate on-site operation and maintenance and fault diagnosis. Periodic self-maintenance and self-repair functions extend the service life of the valve and reduce operation and maintenance costs. It truly solves the actual engineering pain points of unattended operation, fault delay and difficult operation and maintenance in the field of industrial fluid control. It can be mass-produced industrially and has strong practicality and promotion value.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a structural diagram of an embodiment of a self-diagnostic intelligent butterfly valve based on the Internet of Things according to the present invention; Figure 2 This is a flowchart of an embodiment of a self-diagnostic intelligent butterfly valve control method based on the Internet of Things according to the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in this invention, mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. The terms "connected" or "linked," etc., are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0024] Example 1: Self-diagnostic intelligent butterfly valve for conventional industrial pipeline networks (basic product example). This embodiment describes a self-diagnostic smart butterfly valve based on the Internet of Things for use in municipal water supply and drainage and industrial circulating water pipelines.

[0025] 1. Specific structure and parameters, structural components as follows: Figure 1 As shown; Butterfly valve body: made of ductile iron QT450-10, with eccentric valve plate structure, EPDM / PTFE composite seal, flange connection conforming to GB / T9113, structural length conforming to GB / T12221 wafer type, and protection level IP67.

[0026] Electric actuator drive module: DC brushless motor + planetary reduction mechanism, absolute angle encoder with opening resolution of ±0.1°, built-in torque sampling circuit, supports 4~20mA proportional adjustment, forward and reverse rotation to get out of trouble, and overload shutdown protection.

[0027] Multi-dimensional sensor array: Absolute magnetic coded valve position sensor, 0~1.6MPa diffused silicon inlet and outlet pressure sensor, -40℃~125℃ Pt100 temperature sensor; piezoelectric vibration sensor, drive torque sensor; cavity-type gas / liquid level micro-leakage sensor, installed in the liquid accumulation cavity below the valve shaft seal.

[0028] Edge computing self-diagnostic main control unit: industrial-grade MCU + edge AI chip, built-in lightweight CNN model, fault diagnosis response time <50ms, can identify 7 types of faults such as valve shaft jamming, stalling, seal wear, internal / external leakage, drive overload, and sensor abnormality.

[0029] Communication module: NB-IoT+LoRa dual-mode, NB-IoT remote upload, LoRa reserved interface for factory networking.

[0030] Tri-mode redundant power supply: 220V AC main power supply + 72h lithium battery backup + solar auxiliary power supply, normal sleep wake-up cycle 5~30s.

[0031] Fault emergency control: Three-level emergency strategy: Level I self-calibration, Level II escape current limiting, and Level III emergency shutdown + audible and visual alarm.

[0032] 2. The work process is as follows: 1. System power-on initialization, each module performs self-test; 2. The sensor array collects data in 5-second cycles, which are then filtered by a first-order low-pass filter. , =0.2; 3. Micro-leakage determination: Real-time and leakage threshold contrast; 4. Edge unit offline AI diagnosis; data is uploaded periodically if no fault is found. 5. Slight wear on the seal is detected (Level I fault), and automatic tightening compensation and log storage are executed; 6. If valve shaft jamming (Level II fault) is detected, execute forward and reverse pulses to break free and report remotely via NB-IoT.

[0033] 3. Technical effects: It enables local offline self-diagnosis, self-repair of minor faults, and remote status monitoring of industrial pipeline butterfly valves, eliminating the need for manual on-site inspections and reducing the failure rate by more than 80%.

[0034] Example 2: LoRa self-organizing network smart butterfly valve for multi-valve linkage in the factory area (product optimization example); This embodiment is designed for centralized control of multiple valves in chemical industrial parks and plant pipeline networks, emphasizing dual-mode communication and multi-valve linkage.

[0035] 1. Features that distinguish it from Embodiment 1: Communication configuration: The LoRa module is set to master-slave self-organizing network mode, and a single gateway supports linkage of 32 butterfly valves in this embodiment; Power supply and power consumption: Normal sleep cycle is 10s, wake up immediately in case of abnormality, cache ≥1000 data entries when disconnected from the network, and automatically resume transmission when connected to the network; Linkage logic: When a single valve detects a Level III serious fault (such as pipeline overpressure or serious leakage), it sends synchronous commands to other valves in the same pipeline network via LoRa local area network to perform coordinated pressure relief / shutdown.

[0036] 2. Work process: 1. Multiple butterfly valves form a local area network in the factory via LoRa, and NB-IoT is responsible for remote cloud platform connection; 2. A valve detected that its outlet pressure exceeded the threshold, which was determined to be a Level III fault, and an emergency micro-opening pressure relief was initiated. 3. Synchronously send linkage commands to upstream and downstream valves in the pipeline network via LoRa, and the upstream and downstream valves simultaneously execute the opening limit; 4. All fault and linkage records are stored locally and backed up remotely.

[0037] 3. Technical effects: It enables clustered, networked, and coordinated control of valves in the plant area, avoiding pipeline safety accidents caused by single-point failures, and improving communication reliability by 95%.

[0038] Example 3: Tri-mode redundant intelligent butterfly valve for remote outdoor scenarios without mains power (Extreme operating condition example of the product). This embodiment is designed for scenarios such as field pipelines, remote factory areas, and areas without mains power access, highlighting tri-mode redundant power supply and ultra-low power consumption.

[0039] 1. Specific structure and parameters: Power supply module: The solar panel + lithium battery pack + emergency supercapacitor form a triple redundancy. When there is no mains power, it is powered by solar energy + lithium battery, with a battery life of ≥72 hours. Power consumption strategy: Normal ultra-low power sleep mode, wake-up cycle of 30 seconds, only collecting key data; Fault strategy: Automatically switch to backup power when mains power is interrupted, and report power supply abnormality alarm at the same time.

[0040] 2. Work process: 1. Without mains power access, the system defaults to solar power as the main source and lithium battery as backup; 2. During continuous rainy weather, when solar power is insufficient, the system automatically switches to lithium batteries and reduces the sampling frequency of unnecessary sensors. 3. When the power supply voltage is lower than the threshold, execute the low power mode and only retain the valve position, leakage and power supply key detection; 4. After power is restored, the cached data during the network outage / low power period will be automatically retransmitted.

[0041] 3. Technical effects: It can meet the requirements for long-term stable operation in environments without mains power, remote outdoor areas, and harsh conditions, truly achieving unattended operation.

[0042] Example 4: Intelligent butterfly valve control method with adaptive sampling and three-level emergency response; This embodiment includes an adaptive cycle, fault classification, and a complete self-repair process.

[0043] 1. Specific steps (such as...) Figure 2 (as shown) S1. System power-on initialization: Completes self-test of MCU, sensor, communication and drive modules, and reports any abnormalities directly.

[0044] S2, Adaptive Data Acquisition: The acquisition period is adaptively adjusted based on the data deviation value. ; normal state ( ): 30s / time; Early warning status ( ): 1 second / time; Fault state ( ): 100ms high-frequency sampling; S3. Data preprocessing: First-order low-pass filtering to remove vibration and electromagnetic interference noise.

[0045] S4. Local real-time AI fault diagnosis: lightweight CNN inference + threshold verification + trend analysis triple judgment.

[0046] S5, Fault Classification: Level I: Minor opening deviation, slight leakage; Level II: Abnormal torque, sensor drift; Level III: Valve shaft jamming, severe leakage, overpressure.

[0047] S6. Emergency and Self-Repair: Level I: Opening degree closed-loop correction, sealing tightness compensation; Level II: Forward and reverse pulse escape, opening limitation; Level III: Emergency shutdown / pressure relief, audible and visual alarm, remote access lock.

[0048] S7. Data Upload and Storage: Local Flash storage, NB-IoT remote upload, and offline cache resume transmission.

[0049] S8. Periodic self-maintenance: sensor calibration, actuator self-test, and cache clearing.

[0050] 2. Technical effects: It automates the entire process from data collection to diagnosis, classification, repair, and uploading, with fast diagnostic response, low false positive rate, and self-repair capabilities covering mainstream faults.

[0051] Therefore, the present invention adopts the above-mentioned self-diagnostic intelligent butterfly valve and control method based on the Internet of Things to realize the full-dimensional perception of the butterfly valve's operating status, local low-latency fault diagnosis, and remote control via the Internet of Things, thereby solving practical engineering pain points in the field of industrial fluid control, improving the safe operation level of fluid systems, and reducing operation and maintenance costs.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A self-diagnostic intelligent butterfly valve based on the Internet of Things, characterized in that, It includes the butterfly valve body, electric actuator drive module, multi-dimensional sensor array, edge computing self-diagnostic main control unit and Internet of Things communication module; The multi-dimensional sensor array is signal-connected to the edge computing self-diagnosis main control unit, which is used to collect butterfly valve operating status data and transmit it to the edge computing self-diagnosis main control unit; the edge computing self-diagnosis main control unit is controlled to the electric actuator drive module, the Internet of Things communication module, and the fault emergency control module, respectively, and is used to receive sensor data, perform local fault diagnosis, generate control commands based on the diagnosis results, and send them to the electric actuator drive module. It also includes a fault emergency control module, which is connected to the edge computing self-diagnosis main control unit and is used to receive the fault classification results output by the edge computing self-diagnosis main control unit and execute corresponding emergency and self-repair actions. The edge computing self-diagnostic main control unit has built-in fault classification and determination rules, which are set based on the feature values ​​of the status data collected by the multi-dimensional sensor array.

2. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The butterfly valve body is made of ductile iron or stainless steel; the valve plate has an eccentric structure, and the sealing pair uses elastic sealing material.

3. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The electric actuator drive module includes a DC brushless motor, a planetary gear reducer, and an absolute angle encoder. The absolute angle encoder has an opening resolution of ±0.1°, which can accurately feedback the valve position opening. The electric actuator drive module has a built-in real-time torque sampling circuit, which is used to collect the driving load torque and transmit it to the edge computing self-diagnostic main control unit.

4. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The multi-dimensional sensor array includes a valve position opening sensor, an inlet pressure sensor, an outlet pressure sensor, a vibration sensor, a medium temperature sensor, a leakage sensor, and a drive torque sensor. The leakage sensor is located in the liquid accumulation chamber below the valve shaft seal and is used to detect internal and external leakage in the butterfly valve. The leakage determination is based on a threshold comparison formula: ； when > The leak was determined to be present at that time. For real-time detection of concentration / liquid level; Reference concentration / liquid level; The threshold for determining leakage; The pressure sensor has a range of 0~1.6MPa, and the temperature sensor has a measurement range of -40℃~125℃. Data preprocessing uses a first-order low-pass filter formula. ； in, This is the filtered data; This is the currently collected data; This is the data from the previous filtering; These are the filter coefficients.

5. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The edge computing self-diagnostic main control unit uses an industrial-grade chip, has a built-in fault diagnosis model, and can run locally offline.

6. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The IoT communication module adopts an NB-IoT+LoRa dual-mode structure. The NB-IoT module is used for remote data upload and remote control; the LoRa module is used for local area network self-organizing network and multi-valve linkage control.

7. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, It also includes a redundant power supply module, which is connected to each power consumption module. It adopts a multi-source redundancy design, has a built-in sleep-wake mechanism, and wakes up to collect data according to a set period. In abnormal conditions, it immediately wakes up and performs a reporting action.

8. The self-diagnostic intelligent butterfly valve based on the Internet of Things according to claim 1, characterized in that, The fault classification of the fault emergency control module includes Level I minor fault, Level II general fault and Level III serious fault. Level I minor fault performs local self-calibration and log recording actions. Level II general faults trigger the escape operation, opening degree restriction, and remote reporting actions; Level III serious faults trigger the emergency shutdown or pressure relief, audible and visual alarms, and control authority locking actions.

9. A self-diagnostic intelligent butterfly valve control method based on the Internet of Things, applied to the self-diagnostic intelligent butterfly valve based on the Internet of Things as described in any one of claims 1-8, characterized in that, Includes the following steps: Step S1: Power on the system and initialize it, completing the self-test of each module; Step S2: The multi-dimensional sensor array collects status data according to a set period; Step S3: The main control unit preprocesses the collected data and extracts fault characteristic values; Step S4: The edge computing self-diagnosis main control unit performs local real-time fault diagnosis. If no fault is detected, it returns to step S2; if a fault is detected, it proceeds to step S5. Step S5: Based on the preset fault classification judgment rules, perform fault classification judgment according to the fault characteristic values. Step S6: The fault emergency control module executes the corresponding emergency and self-repair strategies based on the fault classification results; Step S7: Upload the fault information, emergency operation record and current operating data to the remote platform and simultaneously store them in the local storage module; Step S8: Perform system self-maintenance periodically, including sensor calibration and module status self-check, to ensure long-term stable operation of the system.

10. The self-diagnostic intelligent butterfly valve control method based on the Internet of Things according to claim 9, characterized in that, In step S2, the data acquisition cycle can be adaptively adjusted, and different acquisition frequencies can be set according to the system operating status; The self-repair actions in step S6 include: opening closed-loop correction, forward and reverse rotation to get out of trouble, sealing compensation, overpressure relief and backup power switching.

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