Safety valve operation state intelligent monitoring method and system based on wireless communication

Abstract

The invention discloses an intelligent monitoring method for the running state of a safety valve based on wireless communication, and belongs to the field of mechanical state detection.The intelligent monitoring method for the running state of the safety valve based on wireless communication comprises the following steps that friction signals generated by process medium residues of a valve element of the safety valve are captured in real time through an acoustic emission sensor; meanwhile, millimeter-level displacement changes of a valve rod of the safety valve and valve inlet pressure dynamic states are collected; compared with the prior art, the method has the advantages that operation data of the safety valve are collected, friction signals generated by residual process media (such as syrup / fermentation liquor) are specially analyzed, and a data packet transmission strategy is mainly triggered by events and is supplemented by timed reporting; the risk can be assessed in time through the medium viscosity-jamming risk model when the safety valve operates abnormally, the analysis result is obtained, and it is not needed to check the safety valve regularly to affect the production efficiency like the prior art.

Description

Technical Field

[0001] This invention belongs to the field of mechanical condition detection, and in particular relates to an intelligent monitoring method and system for the operating status of a safety valve based on wireless communication. Background Technology

[0002] Safety valve failure (such as jamming, inability to close leaks, or pressure drift) is one of the main causes of overpressure explosions in pressure equipment. This is especially true in the pharmaceutical and food processing industries, where the high viscosity of process media (such as syrups / fermentation broths) makes safety valve cores more prone to jamming, leading to delayed or failed responses during emergency pressure relief and directly increasing the risk of explosion.

[0003] Traditional calibration and maintenance involve periodic shutdowns and disassembly. However, in actual operation, most valves may not require maintenance. This excessive maintenance can negatively impact production efficiency, but the consequences of an explosion due to lack of maintenance are unbearable. Therefore, improvements are needed. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for intelligent monitoring of the operating status of safety valves based on wireless communication to address the above-mentioned problems.

[0005] The present invention is implemented as follows: a method for intelligent monitoring of the operating status of a safety valve based on wireless communication includes the following steps: The system uses an acoustic emission sensor to capture in real time the friction signals (often appearing as intermittent pulse groups) generated by residual process media (such as syrup / fermentation broth) in the safety valve spool due to the high frequency range of 200kHz-3.5MHz. Simultaneously, it collects millimeter-level displacement changes of the safety valve stem and dynamic pressure at the valve inlet. Data is generated every N seconds (e.g., 5 seconds), including pressure peak value, acoustic emission energy value, and displacement hysteresis curve (early jamming is identified through data, such as a displacement response delay >0.5 seconds when the pressure reaches a threshold).

[0006] Utilizing a LoRa+BLE dual-mode communication module, encrypted data packets are transmitted via Frequency Hopping Spread Spectrum (FHSS) technology. Adaptive power control (dynamically adjustable from -4dBm to +20dBm) enhances signal penetration, ensuring reliable transmission of encrypted data packets in the electromagnetic environment of a cleanroom (with interference from dense metal equipment). The transmission strategy primarily relies on event triggering (e.g., immediately sending a critical jamming signal upon detecting a 30% increase in frictional energy), supplemented by timed reporting (e.g., sending data packets every hour).

[0007] Based on the collected data, the risk was assessed using a medium viscosity-sticking risk model (e.g., the friction coefficient threshold was lowered when the syrup concentration was >40°Brix), and the analysis results were obtained.

[0008] In one embodiment, the present invention provides an intelligent monitoring method for the operating status of a safety valve based on wireless communication, further comprising: When analysis indicates that the safety valve exhibits signs of failure, including a reseating seal delay exceeding 15% of the baseline value and a pressure drift exceeding 2% after three consecutive operations, a disassembly and cleaning work order is automatically generated. Simultaneously, by analyzing the high-frequency (200kHz-3.5MHz) acoustic emission energy values ​​collected by the acoustic emission sensor (e.g., protein scaling manifests as a narrow-band spike at 3.2MHz, while sugar crystal residue manifests as broadband noise), the type of residue inside the safety valve is intelligently determined. This is then linked to the CIP (On-Pipeline Cleaning) system, which automatically pushes and executes a customized cleaning formula (e.g., using protease solution to treat protein scaling) based on the identified residue type (e.g., protein scaling or sugar crystal residue).

[0009] In one embodiment, the present invention provides an intelligent monitoring method for the operating status of a safety valve based on wireless communication, further comprising: During the safety valve cleaning process, the temperature, flow rate, and vibration data of the cleaning medium and the safety valve are collected in real time to verify whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the acoustic emission energy value in the high-frequency band (200kHz-3.5MHz) collected by the acoustic emission sensor is analyzed again. If the acoustic emission energy value in the high-frequency band (200kHz-3.5MHz) has not dropped to the factory standard level (i.e. the baseline value in the initial health state of the equipment), it indicates that there is still process medium (such as syrup / fermentation liquid) remaining inside the safety valve, and a secondary cleaning alarm is automatically triggered to ensure that the cleaning is thorough and effective.

[0010] In one embodiment, the present invention provides an intelligent monitoring method for the operating status of a safety valve based on wireless communication, further comprising: Under the same equipment and process medium conditions (producing the same drug or food in the same factory, where the process medium composition causing the safety valve core to stick is stable), record the high-frequency (200kHz-3.5MHz) acoustic emission energy value collected by the acoustic emission sensor after each cleaning when the safety valve is at zero pressure and room temperature. Set an initial number of continuous monitoring cycles M (e.g., M=5). Based on the high-frequency acoustic emission energy value after standard cleaning (the high-frequency acoustic emission energy value recorded after secondary cleaning is not included in the statistics as it would interfere with the statistical results), if the high-frequency acoustic emission energy value recorded for M consecutive times shows a statistically increasing trend, immediately trigger an alarm, indicating that the current cleaning formula is causing progressive damage (e.g., corrosion, micro-scratches) to the internal components of the safety valve (e.g., sealing surfaces, springs) or has failed to effectively remove specific residues and instead accelerated their accumulation, requiring evaluation and optimization of the cleaning formula. If a secondary cleaning alarm is triggered during the cleaning process, the value of M is reduced by one each time it is triggered (e.g., from 5 to 4).

[0011] In one embodiment, the present invention provides an intelligent monitoring method for the operating status of a safety valve based on wireless communication. The method involves capturing, in real time, the friction signal generated by residual process media in the valve core of the safety valve using an acoustic emission sensor, while simultaneously acquiring millimeter-level displacement changes of the valve stem and dynamic valve inlet pressure. Data is generated every N seconds, including pressure peak values, acoustic emission energy values, and displacement hysteresis curves. The method further includes: The safety valve is tested for (minor) internal leakage by using a dual-sensor differential pressure method (comparing the valve inlet and outlet pressures). The pressure values ​​at the inlet and outlet of the safety valve are compared in real time (as new data) to accurately identify whether the safety valve has (minor) internal leakage.

[0012] In one embodiment, the present invention provides an intelligent monitoring system for the operating status of a safety valve based on wireless communication, comprising: The data acquisition unit is used to capture in real time (200kHz-3.5MHz high-frequency band) the friction signal (often manifested as intermittent pulse groups) generated by the residual process medium (such as syrup / fermentation broth) in the safety valve core due to the acoustic emission sensor. At the same time, it also acquires the millimeter-level displacement change of the safety valve stem and the dynamics of the valve inlet pressure. Data is generated every N seconds (e.g., 5 seconds), including pressure peak value, acoustic emission energy value, and displacement hysteresis curve (early jamming is identified through data, such as displacement response delay >0.5 seconds when the pressure reaches the threshold).

[0013] The communication transmission unit utilizes a LoRa+BLE dual-mode communication module to transmit encrypted data packets via frequency hopping spread spectrum (FHSS) technology. Adaptive power control (dynamically adjustable from -4dBm to +20dBm) enhances signal penetration, ensuring reliable transmission of encrypted data packets in the electromagnetic environment of a cleanroom (with interference from dense metal equipment). The transmission strategy primarily relies on event triggering (e.g., immediately sending a critical jamming signal upon detecting a 30% increase in frictional energy), supplemented by timed reporting (e.g., sending data packets every hour).

[0014] The data analysis unit is used to assess risks based on the collected data using a medium viscosity-sticking risk model (e.g., adjusting the friction coefficient threshold when the syrup concentration is >40°Brix) and obtain analysis results.

[0015] In one embodiment, the present invention provides an intelligent monitoring system for the operating status of a safety valve based on wireless communication, further comprising: The cleaning execution unit automatically generates a disassembly and cleaning work order when analysis results indicate that the safety valve exhibits signs of failure, including a reseating seal delay exceeding a reference value by more than 15% and a pressure drift exceeding 2% after three consecutive operations. Simultaneously, it intelligently determines the type of residue inside the safety valve by analyzing high-frequency (200kHz-3.5MHz) acoustic emission energy values ​​collected by acoustic emission sensors (e.g., protein scaling manifests as a narrow-band spike at 3.2MHz, while sugar crystal residue manifests as broadband noise). This is then linked to the CIP (On-Pipeline Cleaning) system, which automatically pushes and executes customized cleaning formulas (e.g., using protease solution to treat protein scaling) based on the identified residue type (e.g., protein scaling or sugar crystal residue).

[0016] In one embodiment, the present invention provides an intelligent monitoring system for the operating status of a safety valve based on wireless communication, further comprising: The cleaning effectiveness detection unit is used to collect real-time data on the temperature and flow rate of the cleaning medium and the vibration of the safety valve during the cleaning process. This verifies whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the high-frequency (200kHz-3.5MHz) acoustic emission energy value collected by the acoustic emission sensor is analyzed again. If the high-frequency (200kHz-3.5MHz) acoustic emission energy value has not dropped to the factory standard level (i.e., the baseline value under the initial health condition of the equipment), it indicates that there is still process medium (such as syrup / fermentation liquid) remaining inside the safety valve, and automatically triggers a secondary cleaning alarm to ensure thorough and effective cleaning.

[0017] In one embodiment, the present invention provides an intelligent monitoring system for the operating status of a safety valve based on wireless communication, further comprising: The cumulative alarm unit is used to record the high-frequency (200kHz-3.5MHz) acoustic emission energy value collected by the acoustic emission sensor after each cleaning, under the same equipment and process medium conditions (producing the same drug or food in the same factory, where the process medium composition causing the safety valve core to stick is stable). The initial number of consecutive monitoring cycles, M (e.g., M=5), is set. Based on the high-frequency acoustic emission energy value after standard cleaning (the high-frequency acoustic emission energy value recorded after secondary cleaning is not included in the statistics as it would interfere with the statistical results), if the high-frequency acoustic emission energy value recorded for M consecutive cycles shows a statistically increasing trend, an alarm is immediately triggered, indicating that the current cleaning formula is causing progressive damage (e.g., corrosion, micro-scratches) to the internal components of the safety valve (e.g., sealing surfaces, springs) or has failed to effectively remove specific residues, thus accelerating their accumulation. The cleaning formula needs to be evaluated and optimized. If a secondary cleaning alarm is triggered during the cleaning process, the value of M is decreased by one (e.g., from 5 to 4) for each trigger.

[0018] In one embodiment, the present invention provides an intelligent monitoring system for the operating status of a safety valve based on wireless communication, wherein the data acquisition unit further includes: The internal leakage detection subunit is used to detect (minor) internal leakage of the safety valve by using the dual-sensor differential pressure method (comparing the valve inlet and outlet pressures). It compares the pressure values ​​at the inlet and outlet of the safety valve in real time (as new data content) to accurately identify whether the safety valve has (minor) internal leakage.

[0019] Compared with the prior art, the beneficial effects of the present invention are: the present invention collects the operating data of the safety valve and specifically analyzes the friction signal generated by the residue of the process medium (such as syrup / fermentation broth). The data packet transmission strategy is mainly event-triggered and supplemented by timed reporting, which ensures that when the safety valve operates abnormally, the risk can be assessed in a timely manner through the medium viscosity-sticking risk model and the analysis results can be obtained, without the need for regular inspection of the safety valve as required by the prior art, which affects production efficiency. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the first part of a method for intelligent monitoring of the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the second part of a method for intelligent monitoring of the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the third part of a method for intelligent monitoring of the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0023] Figure 4 This is a schematic flowchart of the fourth part of a method for intelligent monitoring of the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the process for detecting internal leakage in a safety valve according to an embodiment of the present invention.

[0025] Figure 6 This is a schematic diagram of the first part of an intelligent monitoring system for the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0026] Figure 7 This is a schematic diagram of the second part of an intelligent monitoring system for the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0027] Figure 8 This is a schematic diagram of the third part of an intelligent monitoring system for the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0028] Figure 9 This is a schematic diagram of the fourth part of an intelligent monitoring system for the operating status of a safety valve based on wireless communication, provided in an embodiment of the present invention.

[0029] Figure 10 This is a schematic diagram of a subunit of the data acquisition unit provided in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] In one embodiment, such as Figure 1 As shown, a method for intelligent monitoring of the operating status of a safety valve based on wireless communication includes the following steps: Step S1: Real-time capture of friction signals (often manifested as intermittent pulse groups) generated by residual process media (such as syrup / fermentation broth) in the safety valve core due to acoustic emission sensors (high frequency band of 200kHz-3.5MHz). Simultaneously, millimeter-level displacement changes of the safety valve stem and dynamic pressure at the valve inlet are collected. Data is generated every N seconds (e.g., 5 seconds), including pressure peak value, acoustic emission energy value, and displacement hysteresis curve (early jamming is identified through data, such as displacement response delay >0.5 seconds when the pressure reaches the threshold).

[0032] Step S2: Using a LoRa+BLE dual-mode communication module, the encrypted data packet is transmitted via Frequency Hopping Spread Spectrum (FHSS) technology. Adaptive power control (dynamically adjustable from -4dBm to +20dBm) enhances signal penetration, ensuring reliable transmission of the encrypted data packet in the electromagnetic environment of a cleanroom (interference from dense metal equipment). The transmission strategy primarily relies on event triggering (e.g., immediately sending a critical jamming signal upon detecting a 30% increase in frictional energy), supplemented by timed reporting (e.g., sending a data packet every hour).

[0033] Step S3: Based on the collected data, assess the risk using a medium viscosity-sticking risk model (e.g., lower the friction coefficient threshold when syrup concentration > 40°Brix) and obtain the analysis results.

[0034] The construction of the medium viscosity-sticking risk model first involves analyzing the adhesion characteristics of process media with different viscosities (e.g., °Brix value of syrup, solid content of fermentation broth) and compositions (e.g., sugar, protein, particulate matter) on the critical moving parts (valve core / seat) of the safety valve, and the resulting changes in frictional resistance, through laboratory simulations and historical field data collection. A quantitative correlation database is established between key physicochemical parameters of the medium (viscosity, concentration, composition) and monitoring characteristic values ​​(e.g., acoustic emission energy amplitude at a specific frequency band, displacement response time, static friction threshold). Based on this database, machine learning algorithms (e.g., regression analysis) are applied to train the model, dynamically setting risk judgment thresholds under different medium conditions (e.g., when the syrup concentration is detected to be >40°Brix in real time, the model automatically lowers the friction coefficient threshold or acoustic emission energy threshold to trigger the alarm), thereby achieving accurate assessment of the safety valve sticking risk level based on the current process medium state.

[0035] Before step S1, sensors are deployed. While meeting FDA / 3A hygiene standards, externally mounted acoustic emission sensors (monitoring valve core friction acoustic emission) and magnetic induction valve stem displacement sensors (non-contact type) are selected to avoid contact with viscous media such as syrup / fermentation broth. A pressure sensor is installed at the inlet. All equipment uses a 316L stainless steel polished housing (Ra≤0.4μm) to prevent microbial growth and provide a hardware foundation for subsequent data acquisition.

[0036] In one embodiment, such as Figure 2 As shown, a method for intelligent monitoring of the operating status of a safety valve based on wireless communication also includes: Step S4: When the analysis results indicate that the safety valve exhibits signs of failure, including a reseating seal delay exceeding 15% of the baseline value and a pressure drift exceeding 2% after three consecutive operations, a disassembly and cleaning work order is automatically generated. Simultaneously, by analyzing the high-frequency (200kHz-3.5MHz) acoustic emission energy values ​​collected by the acoustic emission sensor (e.g., protein scaling manifests as a narrow-band spike at 3.2MHz, while sugar crystal residue manifests as broadband noise), the type of residue inside the safety valve is intelligently determined. The system is then linked to the CIP (On-Pipeline Cleaning) system, which automatically pushes and executes a customized cleaning formula (e.g., using protease solution to treat protein scaling) based on the identified residue type (e.g., protein scaling or sugar crystal residue).

[0037] Sterilization can also be added to assist in cleaning. Furthermore, monitoring of the concentration (conductivity) of the cleaning and sterilization media, peak pressure fluctuations, and the number of thermal cycles can be added. Real-time warnings are issued when parameters exceed the design limits of the safety valve (to be confirmed with the valve manufacturer) to prevent over-cleaning and damage to the safety valve.

[0038] In one embodiment, such as Figure 3As shown, a method for intelligent monitoring of the operating status of a safety valve based on wireless communication also includes: Step S5: During the safety valve cleaning process, the temperature, flow rate, and vibration data of the cleaning medium and the safety valve are collected in real time to verify whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the acoustic emission energy value in the high-frequency band (200kHz-3.5MHz) collected by the acoustic emission sensor is analyzed again. If the acoustic emission energy value in the high-frequency band (200kHz-3.5MHz) has not dropped to the factory standard level (i.e., the baseline value in the initial healthy state of the equipment), it indicates that there is still process medium (such as syrup / fermentation liquid) remaining inside the safety valve, and a secondary cleaning alarm is automatically triggered to ensure that the cleaning is thorough and effective.

[0039] To verify whether the cleaning agent adequately covers and acts on the inside of the safety valve, a temperature sensor can be installed on the cleaning agent circulation line to monitor the injected liquid temperature in real time (ensuring the thermodynamic conditions required to dissolve residues are met). A flow meter integrated into the inlet pipe can dynamically monitor the flow rate (reflecting whether the flushing intensity is sufficient to cover the dead zones inside the safety valve). Simultaneously, a vibration sensor fixed to the valve body can capture high-frequency micro-vibration signals (20kHz-100kHz frequency band). When the cleaning agent sufficiently impacts internal components (such as the valve core and valve seat annular gap), characteristic cavitation or turbulent vibration spectra will be generated. Real-time coupled data of temperature, flow rate, and vibration are analyzed simultaneously (e.g., a continuous high-frequency vibration energy band appears when the temperature is within the acceptable range and the flow rate is stable). A comprehensive assessment is made to determine whether the cleaning agent effectively contacts and acts on key areas where residues accumulate. Abnormal data (e.g., sufficient flow but weak vibration) indicates the presence of coverage blind spots or a risk of blockage.

[0040] In one embodiment, such as Figure 4 As shown, a method for intelligent monitoring of the operating status of a safety valve based on wireless communication also includes: Step S6: Under the same equipment and process medium conditions (producing the same drug or food in the same factory, resulting in a stable process medium composition that causes the safety valve core to stick), record the high-frequency (200kHz-3.5MHz) acoustic emission energy value collected by the acoustic emission sensor after each cleaning when the safety valve is at zero pressure and room temperature. Set the initial number of continuous monitoring cycles M (e.g., M=5). Based on the high-frequency acoustic emission energy value after standard cleaning (the high-frequency acoustic emission energy value recorded after secondary cleaning is not counted as it would interfere with the statistics), if the high-frequency acoustic emission energy value recorded for M consecutive times shows a statistically increasing trend, immediately trigger an alarm, indicating that the current cleaning formula has caused progressive damage (e.g., corrosion, micro-scratches) to the internal components of the safety valve (e.g., sealing surface, spring) or has failed to effectively remove specific residues and has instead accelerated their accumulation, requiring evaluation and optimization of the cleaning formula. If a secondary cleaning alarm is triggered during the cleaning process, the value of M is reduced by one each time it is triggered (e.g., from 5 to 4).

[0041] Emphasis is placed on using the same equipment and process medium to eliminate interference caused by changes in production materials, focusing on the impact of the cleaning formula. A zero-pressure, ambient temperature setting is implemented to avoid interference during equipment operation, ensuring that the measured acoustic emission energy value only reflects the condition of the safety valve itself (residue after cleaning or structural damage).

[0042] In one embodiment, such as Figure 5 As shown, a method for intelligent monitoring of the operating status of a safety valve based on wireless communication is described. In step S1, a friction signal generated by residual process medium in the valve core of the safety valve is captured in real time using an acoustic emission sensor. Simultaneously, millimeter-level displacement changes of the valve stem and dynamic valve inlet pressure are collected. Data is generated every N seconds, including pressure peak value, acoustic emission energy value, and displacement hysteresis curve. The step also includes: Step S11: Detect (minor) internal leakage of the safety valve using the dual-sensor differential pressure method (comparing the valve inlet and outlet pressures), and compare the pressure values ​​at the inlet and outlet of the safety valve in real time (as new data content) to accurately identify whether the safety valve has (minor) internal leakage.

[0043] For safety valves of bioreactors such as fermenters, additional monitoring of humidity and temperature changes at the discharge outlet is conducted to detect live microorganisms escaping through leak points and polluting the environment.

[0044] In one embodiment, such as Figure 6 As shown, a smart monitoring system for the operating status of a safety valve based on wireless communication includes: Data acquisition unit 1 is used to capture in real time (200kHz-3.5MHz high-frequency band) the friction signal (often manifested as intermittent pulse groups) generated by the residual process medium (such as syrup / fermentation broth) in the valve core of the safety valve due to the acoustic emission sensor. At the same time, it also acquires the millimeter-level displacement change of the safety valve stem and the dynamics of the valve inlet pressure. Data is generated every N seconds (e.g., 5 seconds), including pressure peak value, acoustic emission energy value, and displacement hysteresis curve (early jamming is identified through data, such as displacement response delay >0.5 seconds when the pressure reaches the threshold).

[0045] Communication transmission unit 2 utilizes a LoRa+BLE dual-mode communication module to transmit encrypted data packets via frequency hopping spread spectrum (FHSS) technology. Adaptive power control (dynamically adjustable from -4dBm to +20dBm) enhances signal penetration, ensuring reliable transmission of encrypted data packets in the electromagnetic environment of a cleanroom (interference from dense metal equipment). The transmission strategy primarily relies on event triggering (e.g., immediately sending a critical jamming signal upon detecting a 30% increase in frictional energy), supplemented by timed reporting (e.g., sending data packets every hour).

[0046] Data analysis unit 3 is used to assess the risk based on the collected data using a medium viscosity-sticking risk model (e.g., the friction coefficient threshold is lowered when the syrup concentration is >40°Brix) and obtain analysis results.

[0047] Data acquisition unit 1 uses a high-frequency acoustic emission sensor (to capture intermittent pulse groups caused by friction in viscous media), a valve stem displacement sensor (to detect millimeter-level motion delays, such as response times exceeding 0.5 seconds), and an inlet pressure sensor to perform multi-dimensional real-time synchronous data acquisition. This aims to accurately capture the most direct and sensitive physical signs (microscopic friction, mechanical response delay) of early jamming in safety valves and generate a comprehensive dataset containing pressure peaks, acoustic emission energy values, and displacement hysteresis curves. This lays the foundation for subsequent data-driven failure prediction and diagnosis, solving the pain point that traditional methods cannot perceive changes in the internal microscopic state of safety valves in real time.

[0048] Communication transmission unit 2 employs LoRa (long-range low-power) and BLE (short-range configuration) dual-mode communication, combined with frequency hopping spread spectrum (FHSS) technology and adaptive power control (-4dBm to +20dBm), ensuring reliable penetration and stable transmission of encrypted data packets even in complex environments filled with metal equipment and electromagnetic interference, such as pharmaceutical or food cleanrooms. The transmission strategy, primarily based on event triggering (e.g., immediate transmission upon a 30% increase in frictional energy) and supplemented by timed reporting, prioritizes the real-time performance and low latency of critical jamming warning signals while also considering the data integrity and system energy consumption balance of routine status monitoring.

[0049] Data analysis unit 3 introduces a medium viscosity-jamming risk model, which dynamically links real-time collected sensor data (acoustic emission, displacement, pressure) with specific process medium characteristics (viscosity, concentration) to achieve quantitative risk assessment. This allows the judgment criteria to be automatically adjusted according to production conditions (such as changes in material concentration), improving the accuracy and adaptability of jamming risk warnings for different adhesive media and avoiding misjudgments or omissions by fixed threshold models.

[0050] In one embodiment, such as Figure 7 As shown, a smart monitoring system for the operating status of a safety valve based on wireless communication also includes: The cleaning execution unit 4 automatically generates a disassembly and cleaning work order when analysis results indicate that the safety valve exhibits signs of failure, including a reseating seal delay exceeding a reference value by more than 15% and a pressure drift exceeding 2% after three consecutive operations. Simultaneously, it intelligently determines the type of residue inside the safety valve by analyzing high-frequency (200kHz-3.5MHz) acoustic emission energy values ​​collected by the acoustic emission sensor (e.g., protein scaling manifests as a narrow-band spike at 3.2MHz, while sugar crystal residue manifests as broadband noise). It then links with the CIP (On-Pipeline Injection) system to automatically push and execute a customized cleaning formula (e.g., using protease solution to treat protein scaling) based on the identified residue type (e.g., protein scaling or sugar crystal residue).

[0051] The cleaning execution unit 4 automatically generates disassembly work orders based on risk analysis results, intelligently determines the type of residue, and pushes customized cleaning formulas accordingly. This aims to achieve precise on-demand maintenance (avoiding excessive downtime) and specifically remove the root causes of specific residues that cause jamming, thereby improving cleaning efficiency and effectiveness and reducing the risk of failure due to improper or delayed cleaning from the source.

[0052] In one embodiment, such as Figure 8 As shown, a smart monitoring system for the operating status of a safety valve based on wireless communication also includes: The effective cleaning detection unit 5 is used to collect the temperature, flow rate and vibration data of the cleaning medium and the safety valve in real time during the cleaning process to verify whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the acoustic emission energy value of the high-frequency band (200kHz-3.5MHz) collected by the acoustic emission sensor is analyzed again. If the acoustic emission energy value of the high-frequency band (200kHz-3.5MHz) has not dropped to the factory standard level (i.e. the baseline value of the equipment in the initial healthy state), it indicates that there is still process medium (such as syrup / fermentation liquid) inside the safety valve, and automatically triggers a secondary cleaning alarm to ensure that the cleaning is thorough and effective.

[0053] The cleaning effectiveness detection unit 5 collects real-time data on the temperature and flow rate of the cleaning medium and the vibration of the safety valve during the cleaning process to verify whether the cleaning agent effectively covers and acts on the key internal parts of the valve. After cleaning, the high-frequency acoustic emission energy value is measured again and compared with the factory baseline value to establish an objective standard for verifying the cleaning effect. If the standard is not met, a secondary cleaning alarm is automatically triggered to ensure that the cleaning is thorough and removes residues, preventing potential hazards caused by incomplete cleaning that could lead to valve jamming or failure in subsequent operation.

[0054] In one embodiment, such as Figure 9 As shown, a smart monitoring system for the operating status of a safety valve based on wireless communication also includes: The cumulative alarm unit 6 is used to record the high-frequency (200kHz-3.5MHz) acoustic emission energy value collected by the acoustic emission sensor after each cleaning, under the same equipment and process medium conditions (producing the same drug or food in the same factory, where the process medium composition causing the safety valve core to stick is stable). The initial number of consecutive monitoring cycles, M (e.g., M=5), is set based on the high-frequency acoustic emission energy value after standard cleaning (the high-frequency acoustic emission energy value recorded after secondary cleaning is not included in the statistics as it would interfere with the statistical results). If the high-frequency acoustic emission energy value recorded for M consecutive cycles shows a statistically increasing trend, an alarm is immediately triggered, indicating that the current cleaning formula is causing progressive damage (e.g., corrosion, micro-scratches) to the internal components of the safety valve (e.g., sealing surfaces, springs) or has failed to effectively remove specific residues, thus accelerating their accumulation. The cleaning formula needs to be evaluated and optimized. If a secondary cleaning alarm is triggered during the cleaning process, the value of M is decreased by one (e.g., from 5 to 4) for each trigger.

[0055] Taking the safety valve of a pharmaceutical syrup production line as an example: The initial continuous monitoring count is set to M=5. After each standard cleaning, the high-frequency acoustic emission energy baseline value (e.g., the factory baseline value is 18μV) is recorded under zero pressure and normal temperature conditions when the equipment is shut down. After five consecutive cleanings, the recorded values ​​are 15.2μV, 15.5μV, 16.1μV, 16.8μV, and 17.5μV, respectively. Linear regression analysis reveals a significant upward trend in the energy value (slope > 0.3μV / cycle), immediately triggering an alarm. This indicates that the current alkaline cleaning agent may be corroding the valve core sealing surface or that sugar crystal residue has not been completely removed, leading to cumulative aggravation. If the factory baseline value is 16μV, and a second cleaning is triggered after the third cleaning due to the acoustic emission energy not meeting the standard (16.1μV > 16μV), the M value is automatically reduced to 4. Therefore, after four checks at 15.2μV, 15.5μV, 16.1μV, and 16.8μV, an alarm is triggered if an upward trend is observed.

[0056] This example illustrates how the M value can be reduced by 1 after a secondary cleaning is triggered. In actual use, the M value can also be reduced by 2, which can accelerate the exposure of progressive damage risks. Here, we do not limit the size of the M value or the extent to which the M value decreases after the secondary cleaning alarm is triggered.

[0057] In one embodiment, such as Figure 10 As shown, a smart monitoring system for the operating status of a safety valve based on wireless communication includes a data acquisition unit 1 that further comprises: The internal leakage detection subunit 11 is used to detect (minor) internal leakage of the safety valve by using the dual-sensor differential pressure method (comparing the valve inlet and outlet pressures), and to compare the pressure values ​​of the safety valve inlet and outlet in real time (as new data content) to accurately identify whether the safety valve has (minor) internal leakage.

[0058] By comparing the pressure values ​​at the inlet and outlet of the safety valve in real time using dual sensors (differential pressure method), this method aims to detect minute internal leaks (an important failure mode) in the safety valve itself with high precision. It is one of the most direct and sensitive technical means to identify continuous leakage caused by valve not closing tightly. It further supplements the monitoring of jamming (opening failure) and realizes comprehensive status perception of the two main failure modes of the safety valve (cannot open, cannot close tightly).

[0059] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0063] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for intelligent monitoring of the operating status of a safety valve based on wireless communication, characterized in that, The intelligent monitoring method for the operating status of safety valves based on wireless communication includes the following steps: The system uses an acoustic emission sensor to capture the friction signal generated by the residual process medium in the valve core of the safety valve in real time, and simultaneously collects the millimeter-level displacement change of the valve stem and the dynamics of the valve inlet pressure; data is generated every N seconds, including pressure peak value, acoustic emission energy value and displacement hysteresis curve; The encrypted data packets are transmitted using frequency hopping spread spectrum technology via a LoRa+BLE dual-mode communication module; adaptive power control enhances signal penetration to ensure reliable transmission of encrypted data packets in the electromagnetic environment of the cleanroom; the transmission strategy is primarily event-triggered, supplemented by timed reporting. Based on the collected data, the risk is assessed using a medium viscosity-sticking risk model, and the analysis results are obtained.

2. The intelligent monitoring method for the operating status of a safety valve based on wireless communication according to claim 1, characterized in that, Also includes: When the analysis results indicate that the safety valve has signs of failure, such as a reseating seal delay exceeding 15% of the reference value and a pressure drift exceeding 2% after three consecutive actions, a disassembly and cleaning work order is automatically generated. At the same time, the type of residue inside the safety valve is intelligently determined by analyzing the high-frequency acoustic emission energy value collected by the acoustic emission sensor. The CIP system is then linked to automatically push and execute a customized cleaning formula based on the identified residue type.

3. The intelligent monitoring method for the operating status of a safety valve based on wireless communication according to claim 1 or 2, characterized in that, Also includes: During the safety valve cleaning process, the temperature, flow rate, and vibration data of the cleaning medium are collected in real time to verify whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the high-frequency acoustic emission energy value collected by the acoustic emission sensor is analyzed again. If the high-frequency acoustic emission energy value has not dropped to the factory standard level, it indicates that there is still process medium residue inside the safety valve, and a secondary cleaning alarm is automatically triggered to ensure that the cleaning is thorough and effective.

4. The intelligent monitoring method for the operating status of a safety valve based on wireless communication according to claim 3, characterized in that, Also includes: Under the same equipment and process medium conditions, record the high-frequency acoustic emission energy value collected by the acoustic emission sensor after each cleaning when the safety valve is in a zero-pressure and normal temperature state. Set the initial number of continuous monitoring M. Based on the high-frequency acoustic emission energy value after standard cleaning, if the high-frequency acoustic emission energy value recorded for M consecutive times shows a statistical upward trend, an alarm is immediately triggered, indicating that the current cleaning formula has caused progressive damage to the internal components of the safety valve or has failed to effectively remove specific residues and has instead accelerated their accumulation. The cleaning formula needs to be evaluated and optimized. If a secondary cleaning alarm is triggered during the cleaning process, the M value will be decreased by one for each trigger.

5. The intelligent monitoring method for the operating status of a safety valve based on wireless communication according to claim 1, characterized in that, The step of capturing friction signals from the safety valve core due to residual process media in real time using an acoustic emission sensor, while simultaneously acquiring millimeter-level displacement changes of the safety valve stem and dynamic valve inlet pressure; generating data every N seconds, including pressure peak value, acoustic emission energy value, and displacement hysteresis curve, also includes: The internal leakage of the safety valve is detected by using a dual-sensor differential pressure method, which compares the pressure values ​​at the inlet and outlet of the safety valve in real time to accurately identify whether there is internal leakage in the safety valve.

6. A smart monitoring system for the operating status of a safety valve based on wireless communication, characterized in that, The intelligent monitoring system for the operating status of safety valves based on wireless communication includes: The data acquisition unit is used to capture the friction signal generated by the residual process medium in the valve core of the safety valve in real time through the acoustic emission sensor, and at the same time to collect the millimeter-level displacement change of the valve stem and the dynamic of the valve inlet pressure; it generates data every N seconds, including pressure peak value, acoustic emission energy value and displacement hysteresis curve; The communication transmission unit is used to transmit encrypted data packets via frequency hopping spread spectrum technology using a LoRa+BLE dual-mode communication module; it enhances signal penetration through adaptive power control to ensure reliable transmission of encrypted data packets in the electromagnetic environment of the cleanroom; the transmission strategy is primarily event-triggered, supplemented by timed reporting. The data analysis unit is used to assess risks based on the collected data using a medium viscosity-sticking risk model and obtain analysis results.

7. The intelligent monitoring system for the operating status of a safety valve based on wireless communication according to claim 6, characterized in that, Also includes: The cleaning execution unit is used to automatically generate a disassembly and cleaning work order when the analysis results indicate that the safety valve has failure symptoms, such as a reseating seal delay exceeding the reference value by more than 15% and a pressure drift exceeding 2% after three consecutive actions. At the same time, it intelligently determines the type of residue inside the safety valve by analyzing the high-frequency acoustic emission energy value collected by the acoustic emission sensor; and links with the CIP system to automatically push and execute a customized cleaning formula based on the identified residue type.

8. The intelligent monitoring system for the operating status of a safety valve based on wireless communication according to claim 6 or 7, characterized in that, Also includes: The cleaning effectiveness detection unit is used to collect real-time data on the temperature and flow rate of the cleaning medium and the vibration of the safety valve during the cleaning process. This verifies whether the cleaning agent fully covers and acts on the inside of the safety valve. After cleaning, the high-frequency acoustic emission energy value collected by the acoustic emission sensor is analyzed again. If the high-frequency acoustic emission energy value has not dropped to the factory standard level, it indicates that there is still process medium residue inside the safety valve and automatically triggers a secondary cleaning alarm to ensure thorough and effective cleaning.

9. The intelligent monitoring system for the operating status of a safety valve based on wireless communication according to claim 8, characterized in that, Also includes: The cumulative alarm unit is used to record the high-frequency acoustic emission energy value collected by the acoustic emission sensor after each cleaning when the safety valve is in a zero-pressure and normal-temperature state under the same equipment and process medium conditions. The initial number of continuous monitoring is set to M. Based on the high-frequency acoustic emission energy value after standard cleaning, if the high-frequency acoustic emission energy value recorded for M consecutive times shows a statistical upward trend, an alarm is immediately triggered, indicating that the current cleaning formula has caused progressive damage to the internal components of the safety valve or has failed to effectively remove specific residues and has instead accelerated their accumulation. The cleaning formula needs to be evaluated and optimized. If a secondary cleaning alarm is triggered during the cleaning process, the M value will be decreased by one for each trigger.

10. The intelligent monitoring system for the operating status of a safety valve based on wireless communication according to claim 6, characterized in that, The data acquisition unit also includes: The internal leakage detection subunit is used to detect internal leakage of the safety valve by using a dual-sensor differential pressure method. It compares the pressure values ​​at the inlet and outlet of the safety valve in real time to accurately identify whether there is internal leakage in the safety valve.

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