Corrosion-resistant self-compensation sealing stop valve

Abstract

The invention discloses a corrosion-resistant self-compensation sealing stop valve, and relates to the technical field of valves, the corrosion-resistant self-compensation sealing stop valve comprises a valve body, an elastic sealing seat, a valve core and a self-compensation sealing control assembly, and the control assembly comprises a parameter acquisition module, a signal preprocessing module, a main control decision module, an electric control execution module and a feedback verification module. In the working process, multi-dimensional signals such as sealing abrasion and medium characteristics are collected firstly, after anti-interference and standardization processing is conducted, the main control decision module calculates the sealing compensation amount through an algorithm, correction is conducted in combination with the corrosion working condition, then a driving instruction is generated through abnormal fault-tolerant processing, and the electric control execution module adjusts the sealing gap and pressure through the electric push rod and the micro air pump. And the feedback verification module performs real-time feedback to form a closed loop. The valve achieves sealing self-adaptive compensation, is high in corrosion resistance and interference resistance, accurate and stable in sealing, capable of prolonging the service life of core components, adaptive to complex working conditions and high in practicability.

Description

Technical Field

[0001] This invention relates to the field of valve technology, specifically to a corrosion-resistant, self-compensating, sealing shut-off valve. Background Technology

[0002] As a core component in fluid transport systems that enables medium flow control and flow regulation, gate valves are widely used in various fields such as chemical, petroleum, municipal water supply, energy and power, pharmaceutical and food industries. Their sealing performance and operational stability directly affect the safe and efficient operation of the system.

[0003] However, under complex actual working conditions, traditional gate valves have gradually revealed many technical defects: First, the sealing structure is mostly fixed, and the fit clearance between the elastic sealing seat and the valve core cannot be dynamically adjusted according to the wear of the sealing surface. As the service time increases, the sealing surface is prone to sealing performance degradation due to media erosion, corrosion and mechanical wear, which in turn leads to media leakage. Especially in corrosive media environments, the risk of leakage increases significantly. Second, there is a lack of a comprehensive sealing condition monitoring mechanism, which cannot simultaneously capture key parameters in multiple dimensions such as sealing wear, media characteristics, sealing leakage and external environment, resulting in untimely warning of sealing failure. To address the shortcomings of existing technologies, this invention provides a corrosion-resistant self-compensating sealing shut-off valve to solve one or more of the problems mentioned in the background section. Summary of the Invention

[0004] The purpose of this invention is to provide a corrosion-resistant, self-compensating sealing stop valve to solve one or more of the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a corrosion-resistant self-compensating sealing stop valve, comprising a valve body, an elastic sealing seat installed on the valve seat hole of the valve body, a valve core installed at the bottom of the valve stem that seals with the elastic sealing seat, and a self-compensating sealing control component installed on the outer wall of the valve body; the self-compensating sealing control component includes a parameter acquisition module, a signal preprocessing module, a main control decision module, an electronic control execution module, and a feedback verification module; The parameter acquisition module is used to acquire the sealing wear status signal, medium characteristic signal, sealing leakage signal and environmental status signal of the elastic sealing seat, and generate raw sensing data. The signal preprocessing module is used to perform anti-interference processing and digital conversion on the raw sensing data to generate standardized digital signals. The main control decision module is used to receive the standardized digital signal, calculate the sealing compensation amount through the built-in core algorithm unit, adjust the calculation result by combining the adaptation parameters of the corrosion condition correction unit, and then generate accurate compensation drive instructions through the abnormal fault tolerance unit. The electronic control execution module is used to receive the compensation drive command and adjust the sealing compensation pressure of the elastic sealing seat and the displacement distance of the valve core through the drive device. The feedback verification module is used to collect real-time continuous data signals from the electronic control execution module and transmit the real-time continuous data signals to the main control decision module, providing a basis for the core algorithm unit to iteratively optimize the compensation drive command and form a closed-loop control.

[0006] Preferably, the drive device includes an electric actuator and a micro air pump. The electric actuator is installed inside the valve stem, and a valve core is fixedly provided at the output end of the electric actuator. The micro air pump is installed inside the self-compensating sealing control assembly, and the micro air pump is connected to the inflation chamber of the elastic sealing seat through a corrosion-resistant hose.

[0007] Preferably, the parameter acquisition module includes: a sensor deployment unit, used to fix and deploy wear sensing components, media sensing components, leakage sensing components and environmental sensing components in the circumferential direction of the sealing surface of the elastic sealing seat, the media flow channel inside the valve body and the corresponding area of ​​the outer wall of the valve body, respectively. The sealing wear sensing unit is used to monitor the contact impedance change signal and compression change signal of the sealing surface of the elastic sealing seat through the wear sensing component deployed by the sensor deployment unit, and form the original signal related to sealing wear. The medium characteristic sensing unit is used to capture the chemical characteristic parameter signals and physical state parameter signals of the medium flowing inside the valve body through the medium sensing components deployed by the sensor deployment unit, and form the original signal related to the medium characteristics. The sealing leakage sensing unit is used to detect the medium permeation signal at the gap between the elastic sealing seat and the valve core through the leakage sensing components deployed by the sensor deployment unit, and to generate original signals related to sealing leakage. The environmental condition sensing unit is used to collect temperature and humidity signals and electromagnetic interference signals outside the valve body through the environmental sensing components deployed by the sensor deployment unit, and form raw environmental condition signals. The data integration unit receives various raw signals output from the sealing wear sensing unit, the medium characteristic sensing unit, the sealing leakage sensing unit, and the environmental condition sensing unit, classifies and summarizes them in time stamp order, and generates the raw sensing data required by the parameter acquisition module.

[0008] Preferably, the signal preprocessing module includes: an interference filtering unit, a signal amplification unit, an analog-to-digital conversion unit, and a normalization processing unit; The interference filtering unit is used to filter the original sensing data according to the signal characteristics and environmental interference characteristics of the original sensing data, remove invalid interference signals caused by electromagnetic interference and medium fluctuations, and generate effective signals. The signal amplification unit is used to adjust the amplitude of the effective signal so that the signal strength is adapted to the subsequent conversion requirements and to generate an amplified analog signal. The analog-to-digital conversion unit is used to synchronously record the time stamps during the signal conversion process and convert the amplified analog signal into a preprocessed digital signal in a preset format. The standardization processing unit is used to normalize the preprocessed digital signal, eliminate the dimensional differences between different types of signals, and generate a standardized digital signal.

[0009] Preferably, the core algorithm unit, corrosion condition correction unit, and anomaly tolerance unit of the main control decision module include: The feature extraction unit is used to extract sealing wear feature parameters, medium corrosion feature parameters and sealing state feature parameters from the standardized digital signal to form the first feature calculation parameters. The initial compensation calculation unit is used to calculate the initial values ​​of the sealing gap compensation amount and the sealing pressure compensation amount based on the first feature calculation parameters and through a preset algorithm model. The operating condition identification unit is used to determine the corrosion level and operating condition type of the current medium based on the medium corrosion characteristic parameters, providing an adaptation basis for the corrosion operating condition correction unit; The parameter adaptation unit is used to retrieve preset working condition adaptation parameters based on the corrosion level and working condition type determined by the working condition identification unit. The adaptation parameters include compensation sensitivity parameters and compensation interval parameters. The result correction unit is used to substitute the adaptation parameters output by the parameter adaptation unit into the initial calculation result of the compensation amount calculation unit, dynamically adjust the sealing gap compensation amount and the sealing pressure compensation amount, and generate the corrected first compensation amount data. The anomaly detection unit is used to monitor the transmission status of the standardized digital signal and the logic status of the compensation calculation process in real time, and to identify sensor signal anomalies and calculation logic anomalies. The anomaly handling unit is used to call the corresponding anomaly handling strategy for the sensor signal anomaly and the calculation logic anomaly identified by the anomaly detection unit. When the sensor signal is abnormal, the redundant data completion method is used, and when the calculation logic is abnormal, the historical data derivation method is used to obtain the anomaly correction data. The instruction generation unit is used to receive the first compensation amount data output by the result correction unit or the abnormal correction data output by the abnormality processing unit, and convert it into a sealing gap adjustment instruction and a sealing pressure adjustment instruction according to a preset instruction format, and combine them to form a precise compensation drive instruction.

[0010] Preferably, the anomaly processing unit includes: an anomaly type subdivision subunit, used to receive sensor signal anomalies and computational logic anomalies identified by the anomaly detection unit, further classify sensor signal anomalies into signal missing anomalies and signal fluctuation anomalies, further classify computational logic anomalies into algorithm parameter anomalies and data association anomalies, and clarify the specific type of anomaly. The redundant data retrieval subunit is used to retrieve backup sensor data of the same type of sensor component and historical data of the same monitoring dimension in the parameter acquisition module to form a redundant data set in response to signal loss anomalies and signal fluctuation anomalies. The redundant data completion subunit is used to interpolate and complete the signal missing anomaly using redundant data from adjacent time nodes, and to generate sensor anomaly completion data by weighted fusion of effective data from the redundant data set for the signal fluctuation anomaly. The historical data filtering subunit is used to retrieve historical compensation calculation data under the same working conditions and wear level stored in the main control decision module for cases of abnormal algorithm parameters and abnormal data correlation, and to filter out valid historical data that meets the standards of data integrity and correlation. The historical data derivation subunit is used to deduce the algorithm parameter correction value adapted to the current working condition based on the effective historical data for algorithm parameter anomalies, and to generate logical anomaly derivation data by fitting the reasonable range of the current compensation amount through the changing trend of effective historical data for data correlation anomalies. The anomaly correction data integration subunit is used to receive sensor anomaly completion data output by the redundant data completion subunit or logical anomaly deduction data output by the historical data deduction subunit, and to standardize and integrate them according to a preset data format to obtain anomaly correction data.

[0011] Preferably, the iterative optimization process of the core algorithm unit includes: The feedback data receiving unit is used to receive the real-time continuous data signal transmitted by the feedback verification module, and to determine the actual displacement execution parameters, actual pressure control parameters and actual load operation parameters contained in the signal. The feedback feature extraction unit is used to extract the deviation value, deviation change rate and deviation duration between the actual parameters and the target parameters corresponding to the previous compensation drive command from the real-time continuous data signal, and form feedback feature parameters. The deviation cause analysis unit is used to combine the latest raw sensor data output by the parameter acquisition module with the feedback characteristic parameters to analyze the causes of the deviation. The causes include changes in the seal wear state, fluctuations in the medium corrosion condition, and lag in the response of the actuator. The algorithm parameter dynamic adjustment unit is used to adjust the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model in the core algorithm unit according to the deviation cause analysis results, and generate the adjusted algorithm parameters. The compensation amount iterative calculation unit is used to call the adjusted algorithm parameters, combine them with the latest extracted sealing wear characteristic parameters and medium corrosion characteristic parameters, and recalculate the sealing gap compensation amount and sealing pressure compensation amount to obtain the second compensation amount data after iterative optimization. The optimization result output unit is used to transmit the second compensation quantity data to the instruction generation unit of the main control decision module, providing a basis for updating the compensation driving instruction and realizing the iterative optimization of the core algorithm unit.

[0012] Preferably, the feedback verification module includes: The signal acquisition unit is used to acquire displacement execution signals, pressure control signals, and load operation signals from the electronic control execution module, and synchronously record the acquisition timestamps of each signal. The signal noise reduction unit is used to remove noise from the acquired real-time continuous data signals, eliminate invalid signals caused by actuator vibration and electromagnetic interference, and generate a noise-reduced signal. The data alignment unit is used to align the noise-reduced displacement execution signal, pressure control signal, and load operation signal to the same time dimension according to the acquisition timestamp, forming time-series aligned data; The signal output unit is used to transmit time-aligned data to the main control decision module, providing standardized feedback data for the iterative optimization of the core algorithm unit.

[0013] Preferably, the electronic control execution module includes: The instruction parsing unit is used to receive the sealing gap adjustment instruction and sealing pressure adjustment instruction output by the main control decision module, and parse them to obtain the target displacement of the electric actuator and the target pressure value of the micro air pump. The collaborative control unit is used to determine the control logic based on the wear level and corrosion conditions of the sealing pair. When the wear is slight, only the micro air pump is activated; when the wear is moderate, the electric actuator and the micro air pump are activated simultaneously; when the wear is severe, the electric actuator is activated first and then the micro air pump is finely adjusted. The displacement drive unit is used to control the extension and retraction rate and stroke of the electric actuator according to the target displacement. It collects the actual displacement data in real time through the built-in displacement sensor and compares it with the target displacement. When the deviation exceeds the preset range, it makes real-time correction. The pressure drive unit is used to control the inflation rate of the micro air pump according to the target pressure value. The pressure sensor in the inflation chamber of the elastic sealing seat collects the actual pressure data in real time and compares it with the target pressure value. When the deviation exceeds the preset range, the micro air pump is started or stopped to replenish or release pressure. The execution status feedback unit is used to synchronously transmit the actual displacement data of the displacement drive unit and the actual pressure data of the pressure drive unit to the feedback verification module, forming a signal closed loop between the execution layer and the verification layer.

[0014] Preferably, the initial compensation calculation unit adopts a preset algorithm model that integrates fuzzy PID and multi-parameter coupling, with the sealing wear characteristic parameters as the core input and the medium corrosion characteristic parameters as the correction input; The operating condition identification unit determines the corrosion level and operating condition type of the medium based on the pH value, ion concentration and ambient temperature parameters in the medium corrosion characteristic parameters. The redundant data completion subunit completes signal missing anomalies by interpolating redundant data from adjacent time nodes and completes signal fluctuation anomalies by weighted fusion of redundant data. The signal acquisition unit acquires corresponding signals through the displacement sensor built into the electric actuator, the pressure sensor in the air chamber of the elastic sealing seat, and the torque sensor, respectively. The algorithm parameter dynamic adjustment unit adjusts the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model according to the cause of the deviation. The sensor deployment unit deploys a high-frequency impedance sensor circumferentially on the sealing surface of the elastic sealing seat, a pH sensor and an ion concentration sensor in the medium channel inside the valve body, a conductive rubber sensor on the outside of the sealing gap, and a temperature and humidity sensor and an electromagnetic interference sensor on the outer wall of the valve body.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves dynamic adaptation of sealing gap and pressure through the collaborative work of a self-compensating sealing control component and multiple modules. It precisely adjusts the compensation method according to the seal wear level and media corrosion conditions, effectively resisting corrosion and wear, reducing leakage risk, and extending the service life of core components. Multi-dimensional sensing acquisition and refined signal processing provide comprehensive and reliable data. Combined with fuzzy PID fusion multi-parameter coupling algorithm and precise operating condition identification, it improves the scientific nature and adaptability of compensation calculations. A robust fault-tolerant mechanism specifically handles signal and logic anomalies, ensuring control continuity. Closed-loop iterative optimization ensures that the algorithm and execution actions continuously align with the actual operating state, further improving the accuracy and stability of sealing control. The modular and corrosion-resistant structural design balances reliability and practicality, reduces maintenance costs, adapts to various complex operating conditions, and achieves synergistic optimization of sealing performance, operational stability, and economy. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the vertical cross-sectional structure of the present invention.

[0017] Figure 2 This is a diagram of the architecture of the self-compensating sealing control component of the present invention.

[0018] In the diagram: 1. Valve body; 2. Valve core; 3. Resilient sealing seat; 4. Valve stem; 5. Electric actuator; 6. Self-compensating sealing control assembly. Detailed Implementation

[0019] In this invention, the terms "first," "second," etc., are used for descriptive purposes only and do not specifically refer to any order or sequence, nor are they intended to limit the invention. They are merely used to distinguish protective components or operations described using the same technical terms, and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions and features of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0020] Example 1, please refer to Figures 1-2 The present invention provides a technical means including a valve body 1, an elastic sealing seat 3 installed on the valve seat hole of the valve body 1, a valve core 2 installed at the bottom of the valve stem 4 and sealingly cooperating with the elastic sealing seat 3, and a self-compensating sealing control component 6 installed on the outer wall of the valve body 1; the self-compensating sealing control component 6 includes a parameter acquisition module, a signal preprocessing module, a main control decision module, an electronic control execution module and a feedback verification module. The parameter acquisition module is used to acquire the sealing wear status signal, medium characteristic signal, sealing leakage signal and environmental status signal of the elastic sealing seat 3, and generate raw sensing data; The signal preprocessing module is used to perform anti-interference processing and digital conversion on the raw sensing data to generate standardized digital signals. The main control decision module is used to receive the standardized digital signal, calculate the sealing compensation amount through the built-in core algorithm unit, adjust the calculation result by combining the adaptation parameters of the corrosion condition correction unit, and then generate accurate compensation drive instructions through the abnormal fault tolerance unit. The electronic control execution module is used to receive the compensation drive command and adjust the sealing compensation pressure of the elastic sealing seat 3 and the displacement distance of the valve core 2 through the drive device. The feedback verification module is used to collect real-time continuous data signals from the electronic control execution module (real-time continuous data signals include: displacement execution signals, pressure control signals and load operation signals), and transmit the real-time continuous data signals to the main control decision module to provide a basis for the core algorithm unit to iteratively optimize the compensation drive command, thus forming closed-loop control.

[0021] In this embodiment, the self-compensating sealing control component 6 refers to an integrated component installed on the outer wall of the valve body 1, which integrates signal acquisition, processing, decision-making, execution and verification functions to achieve adaptive compensation control of the sealing state.

[0022] In this embodiment, the raw sensing data refers to the initial unprocessed data generated by collecting signals related to the sealing wear state, medium characteristics, sealing leakage, and environmental conditions of the elastic sealing seat 3.

[0023] In this embodiment, the standardized digital signal refers to a unified format digital signal formed by eliminating the differences in the dimensions of different types of signals after the original sensor data has undergone anti-interference processing and digital conversion.

[0024] In this embodiment, the compensation drive command refers to the command used to control the drive device to adjust the sealing compensation pressure of the elastic sealing seat 3 and the displacement distance of the valve core 2 after the compensation amount calculation, working condition adaptation correction and abnormal handling are completed based on standardized digital signals.

[0025] In this embodiment, the real-time continuous data signal refers to the continuous feedback data signal related to displacement execution, pressure regulation, and load operation collected during the operation of the electronic control execution module.

[0026] The working principle and beneficial effects of the above solution are as follows: This invention integrates multi-dimensional signal acquisition and processing to accurately capture the wear state, medium characteristics, sealing leakage and environmental conditions of the elastic sealing seat 3. It calculates the sealing compensation amount through algorithms and dynamically adapts to corrosion conditions and wear degree, adjusts the sealing compensation pressure of the elastic sealing seat 3 and the displacement distance of the valve core 2. At the same time, it relies on real-time feedback to achieve closed-loop optimization, avoids the problem of over-compensation or under-compensation, significantly improves the sealing reliability and corrosion resistance of the valve body 1, extends the overall service life of the valve, adapts to the sealing requirements under complex media conditions, and has a compact structure and convenient operation, and can operate stably without complex manual intervention.

[0027] Example 2: Please refer to Figures 1-2 Based on Example 1, the driving device includes an electric actuator 5 and a micro air pump. The electric actuator 5 is installed inside the valve stem 4. The output end of the electric actuator 5 is fixedly provided with a valve core 2. The micro air pump is installed inside the self-compensating sealing control assembly 6, and the micro air pump is connected to the inflation chamber of the elastic sealing seat 3 through a corrosion-resistant hose.

[0028] In this embodiment, the driving device refers to the actuating component installed in the valve stem 4 and the self-compensating sealing control assembly 6, used to adjust the displacement of the valve core 2 and the sealing pressure of the elastic sealing seat 3, including the electric actuator 5 and the micro air pump.

[0029] In this embodiment, the electric actuator 5 refers to an electric actuator installed inside the valve stem 4, with its output end fixedly connected to the valve core 2, used to drive the valve core 2 to adjust its displacement.

[0030] In this embodiment, the micro air pump refers to a pneumatic actuator installed in the self-compensating sealing control assembly 6, which is connected to the inflation chamber of the elastic sealing seat 3 via a corrosion-resistant hose, and is used to adjust the pressure of the inflation chamber.

[0031] In this embodiment, the corrosion-resistant hose refers to the air-filled chamber that connects the micro air pump and the elastic sealing seat 3, and is a connecting pipe used to transmit gas and has corrosion resistance.

[0032] In this embodiment, the air chamber of the elastic sealing seat 3 refers to the cavity structure opened inside the elastic sealing seat 3 for receiving gas delivered by a micro air pump and achieving sealing compensation through pressure changes.

[0033] The working principle and beneficial effects of the above scheme are as follows: Based on the closed-loop control of Example 1, after the compensation drive command is issued, the electric actuator 5 drives the valve core 2 to achieve precise displacement adjustment. The micro air pump pressurizes or depressurizes the air chamber of the elastic sealing seat 3 through the anti-corrosion hose. The two work together according to the degree of seal wear and the corrosive conditions of the medium, and dynamically optimize the adjustment range with real-time feedback signals. This ensures both precise matching of the sealing gap and pressure, and resists media erosion through the anti-corrosion design. It eliminates the need for a complex mechanical transmission structure, achieves efficient and stable execution of seal compensation, improves the valve sealing reliability and adaptability to operating conditions, reduces leakage risk, extends the service life of the valve body 1 and core sealing components, and adapts to the long-term operation requirements of multi-media corrosive environments.

[0034] Example 3: Please refer to Figures 1-2 Based on any one of Embodiments 1-2, the parameter acquisition module includes: a sensor deployment unit, used to fix and deploy wear sensing components, medium sensing components, leakage sensing components and environmental sensing components in the circumferential direction of the sealing surface of the elastic sealing seat 3, the internal medium flow channel of the valve body 1 and the corresponding area of ​​the outer wall of the valve body 1, respectively. The sealing wear sensing unit is used to monitor the contact impedance change signal and compression change signal of the sealing surface of the elastic sealing seat 3 through the wear sensing component deployed by the sensor deployment unit, and form the original signal related to sealing wear. The medium characteristic sensing unit is used to capture the chemical characteristic parameter signals and physical state parameter signals of the medium flowing inside the valve body 1 through the medium sensing components deployed by the sensor deployment unit, and form the original signal related to the medium characteristics. The sealing leakage sensing unit is used to detect the medium permeation signal at the mating gap between the elastic sealing seat 3 and the valve core 2 through the leakage sensing component deployed by the sensor deployment unit, and to form the original signal related to sealing leakage. The environmental state sensing unit is used to collect temperature and humidity signals and electromagnetic interference related signals outside the valve body 1 through the environmental sensing components deployed by the sensor deployment unit, and form raw environmental state related signals. The data integration unit receives various raw signals output from the sealing wear sensing unit, the medium characteristic sensing unit, the sealing leakage sensing unit, and the environmental condition sensing unit, classifies and summarizes them in time stamp order, and generates the raw sensing data required by the parameter acquisition module.

[0035] In this embodiment, the parameter acquisition module refers to the functional module used to acquire signals related to the sealing wear state, medium characteristics, sealing leakage and environmental conditions of the elastic sealing seat 3, and generate raw sensing data.

[0036] In this embodiment, the sensor deployment unit refers to a functional unit used to fix and deploy various sensing components in the circumferential direction of the sealing surface of the elastic sealing seat 3, the media flow channel inside the valve body 1, and the corresponding area of ​​the outer wall of the valve body 1.

[0037] In this embodiment, the wear sensing component refers to the sensing element deployed by the sensor deployment unit, used to monitor the changes in contact resistance and compression of the sealing surface of the elastic sealing seat 3.

[0038] In this embodiment, the medium sensing component refers to the sensing element deployed by the sensor deployment unit, which is used to capture the chemical characteristic parameters and physical state parameters of the medium flowing inside the valve body 1.

[0039] In this embodiment, the leakage sensing component refers to the sensing element deployed by the sensor deployment unit, used to detect the medium penetration at the gap between the elastic sealing seat 3 and the valve core 2.

[0040] In this embodiment, the environmental sensing component refers to the sensing element deployed by the sensor deployment unit, which is used to collect signals related to external temperature, humidity and electromagnetic interference of the valve body 1.

[0041] In this embodiment, the original signal related to seal wear refers to the initial signal detected by the wear sensing component, which reflects the change in contact impedance and compression of the sealing surface of the elastic sealing seat 3.

[0042] In this embodiment, the original signal related to medium characteristics refers to the initial signal captured by the medium sensing component, which reflects the chemical characteristics and physical state of the medium flowing inside the valve body 1.

[0043] In this embodiment, the original signal related to sealing leakage refers to the initial signal detected by the leakage sensing component, which reflects the medium penetration at the mating gap between the elastic sealing seat 3 and the valve core 2.

[0044] In this embodiment, the environmental state-related raw signal refers to the initial signal collected by the environmental sensing component, which reflects the external temperature, humidity and electromagnetic interference of the valve body 1.

[0045] In this embodiment, the data integration unit refers to the functional unit used to receive various raw signals, classify and summarize them according to timestamp order, and generate raw sensor data.

[0046] In this embodiment, the raw sensing data refers to a comprehensive data set that includes information related to seal wear, media characteristics, seal leakage, and environmental conditions after being categorized and summarized by the data integration unit.

[0047] The working principle and beneficial effects of the above scheme are as follows: By arranging various sensing components around the sealing surface of the elastic sealing seat 3, in the internal medium flow channel of the valve body 1, and in the corresponding area of ​​the outer wall of the valve body 1, various signals related to sealing surface wear, medium characteristic parameters, sealing gap leakage, and external environmental status are captured. These signals are then categorized and summarized according to timestamps to form complete raw sensor data. This comprehensively covers key influencing factors such as sealing status, medium properties, and external environment, achieving accurate acquisition and orderly integration of multi-dimensional data. It provides comprehensive and reliable data support for subsequent signal processing and sealing compensation decisions, effectively avoiding compensation deviations caused by missing or incomplete data, improving the targeting and accuracy of valve sealing control, and adapting to sealing monitoring needs under complex operating conditions.

[0048] Example 4: Please refer to Figures 1-2 Based on any one of Embodiments 1-3, the signal preprocessing module includes: an interference filtering unit, a signal amplification unit, an analog-to-digital conversion unit, and a normalization processing unit; The interference filtering unit is used to filter the original sensing data according to the signal characteristics and environmental interference characteristics of the original sensing data, remove invalid interference signals caused by electromagnetic interference and medium fluctuations, and generate effective signals. The signal amplification unit is used to adjust the amplitude of the effective signal so that the signal strength is adapted to the subsequent conversion requirements and to generate an amplified analog signal. The analog-to-digital conversion unit is used to synchronously record the time stamps during the signal conversion process and convert the amplified analog signal into a preprocessed digital signal in a preset format. The standardization processing unit is used to normalize the preprocessed digital signal, eliminate the dimensional differences between different types of signals, and generate a standardized digital signal.

[0049] In this embodiment, the signal preprocessing module refers to the functional module that receives the raw sensor data generated by the parameter acquisition module, and generates a standardized digital signal after filtering, amplification, conversion and standardization.

[0050] In this embodiment, the interference filtering unit refers to a functional unit used to filter the original sensing data based on the signal characteristics and environmental interference characteristics of the original sensing data, and to remove invalid interference signals caused by electromagnetic interference and medium fluctuations.

[0051] In this embodiment, an effective signal refers to a signal that can truly reflect the sealing status, medium properties, or environmental conditions after invalid interference signals have been removed by the interference filtering unit.

[0052] In this embodiment, the signal amplification unit refers to a functional unit used to adjust the amplitude of the effective signal so that the signal strength is adapted to the subsequent conversion requirements.

[0053] In this embodiment, the amplified analog signal refers to an effective signal that retains its analog signal properties even after its amplitude has been adjusted by the signal amplification unit.

[0054] In this embodiment, the analog-to-digital conversion unit refers to a functional unit used to synchronously record time stamps during the signal conversion process and convert amplified analog signals into digital signals of a preset format.

[0055] In this embodiment, the time stamp refers to the acquisition or conversion time information corresponding to the signal that is synchronously recorded by the analog-to-digital conversion unit when performing signal conversion.

[0056] In this embodiment, the preprocessed digital signal refers to a digital signal that has been converted by the analog-to-digital converter and has a preset format but has not yet been normalized.

[0057] In this embodiment, the standardization processing unit refers to a functional unit used to normalize the preprocessed digital signal and eliminate the differences in the dimensions of different types of signals.

[0058] In this embodiment, the standardized digital signal refers to a digital signal with a uniform format and no quantitative differences after being normalized by the standardization processing unit, which is used to transmit to the main control decision module for subsequent calculations.

[0059] The working principle and beneficial effects of the above scheme are as follows: First, for invalid signals such as electromagnetic interference and medium fluctuations mixed in the original sensor data, targeted filtering is performed based on the signal characteristics of the data itself and the interference characteristics of the field environment to screen out the effective signals that can truly reflect the sealing state, medium properties and environmental conditions; then, the amplitude of the effective signals is adjusted so that its strength meets the requirements of the subsequent conversion process to form an amplified analog signal; next, based on the synchronous recording of the signal conversion time stamp, the amplified analog signal is converted into a digital signal of a preset format to complete the signal type adaptation; finally, normalization processing is used to eliminate the dimensional differences between different types of signals (such as wear signals and medium characteristic signals) to generate a standardized digital signal with a unified format and accurate data, providing directly usable data input for the subsequent sealing compensation calculation of the main control decision module. Targeted interference filtering effectively eliminates invalid signals caused by electromagnetic interference and media fluctuations under complex operating conditions, significantly improving data purity and authenticity and preventing distorted data from misleading subsequent decisions. Signal amplification ensures the identifiability of weak but effective signals, preventing omissions or errors in conversion due to insufficient signal strength. Analog-to-digital conversion adapts analog signals to digital signals, meeting the signal type requirements of subsequent algorithm calculations, while synchronous recording of time stamps supports time-series data analysis. Standardization resolves the inconsistency of dimensions between signals from different sources and of different types, enabling various types of data to directly participate in comprehensive calculations and avoiding calculation deviations caused by format differences. The overall processing flow forms a complete link from raw data to standardized data, ensuring the accuracy, consistency, and usability of data input to the main control decision module. This lays a solid foundation for the scientific and accurate decision-making of valve sealing compensation. Even in environments with strong interference and complex operating conditions, it can stably output reliable data, significantly improving the anti-interference capability and overall stability of valve sealing control, and indirectly ensuring the precise execution of sealing compensation actions.

[0060] Example 5: Please refer to Figures 1-2 Based on any one of Examples 1-4, the core algorithm unit, corrosion condition correction unit, and anomaly fault tolerance unit of the main control decision module include: The feature extraction unit is used to extract sealing wear feature parameters, medium corrosion feature parameters and sealing state feature parameters from the standardized digital signal to form the first feature calculation parameters. The initial compensation calculation unit is used to calculate the initial values ​​of the sealing gap compensation amount and the sealing pressure compensation amount based on the first feature calculation parameters and through a preset algorithm model. The operating condition identification unit is used to determine the corrosion level and operating condition type of the current medium based on the medium corrosion characteristic parameters, providing an adaptation basis for the corrosion operating condition correction unit; The parameter adaptation unit is used to retrieve preset working condition adaptation parameters based on the corrosion level and working condition type determined by the working condition identification unit. The adaptation parameters include compensation sensitivity parameters and compensation interval parameters. The result correction unit is used to substitute the adaptation parameters output by the parameter adaptation unit into the initial calculation result of the compensation amount calculation unit, dynamically adjust the sealing gap compensation amount and the sealing pressure compensation amount, and generate the corrected first compensation amount data. The anomaly detection unit is used to monitor the transmission status of the standardized digital signal and the logic status of the compensation calculation process in real time, and to identify sensor signal anomalies and calculation logic anomalies. The anomaly handling unit is used to call the corresponding anomaly handling strategy for the sensor signal anomaly and the calculation logic anomaly identified by the anomaly detection unit. When the sensor signal is abnormal, the redundant data completion method is used, and when the calculation logic is abnormal, the historical data derivation method is used to obtain the anomaly correction data. The instruction generation unit is used to receive the first compensation amount data output by the result correction unit or the abnormal correction data output by the abnormality processing unit, and convert it into a sealing gap adjustment instruction and a sealing pressure adjustment instruction according to a preset instruction format, and combine them to form a precise compensation drive instruction.

[0061] In this embodiment, the master control decision module refers to the functional module used to receive standardized digital signals, and generate accurate compensation drive commands after feature extraction, compensation calculation, operating condition correction and anomaly handling.

[0062] In this embodiment, the core algorithm unit refers to the core functional unit that integrates feature extraction, initial calculation of compensation amount, working condition identification, parameter adaptation and result correction functions to complete the calculation of sealing compensation amount and working condition adaptation.

[0063] In this embodiment, the corrosion condition correction unit refers to a functional unit used to retrieve the appropriate parameters and correct the compensation calculation results according to the corrosion level of the medium and the type of operating conditions.

[0064] In this embodiment, the fault tolerance unit refers to a functional unit used to monitor anomalies in the signal transmission and calculation process, process them through corresponding strategies, and generate anomaly correction data.

[0065] In this embodiment, the first feature calculation parameter refers to the set of sealing wear feature parameters, medium corrosion feature parameters, and sealing state feature parameters extracted from the standardized digital signal.

[0066] In this embodiment, the preset algorithm model refers to the algorithm model used to preliminarily calculate the initial values ​​of the sealing gap compensation amount and the sealing pressure compensation amount based on the first feature calculation parameters.

[0067] In this embodiment, the operating condition adaptation parameters refer to the parameters preset based on different corrosion levels and operating condition types, used to correct the compensation calculation results, including compensation sensitivity parameters and compensation interval parameters.

[0068] In this embodiment, the first compensation amount data refers to the sealing gap compensation amount and sealing pressure compensation amount data obtained after correction by the working condition adaptation parameters.

[0069] In this embodiment, abnormal sensing signal refers to situations where the standardized digital signal does not conform to normal transmission rules, such as signal loss or excessive fluctuations, during transmission.

[0070] In this embodiment, computational logic anomaly refers to situations that do not conform to preset computational rules, such as deviations in algorithm parameters or errors in data association, that occur during the compensation calculation process.

[0071] In this embodiment, the redundant data completion method refers to the processing method of calling the same type of backup sensor data or the same monitoring dimension of historical data during the same period to complete the missing or correct the fluctuating signal when the sensor signal is abnormal.

[0072] In this embodiment, the historical data derivation method refers to the process of retrieving historical compensation calculation data under the same working conditions and wear level when the calculation logic is abnormal, and deriving the current reasonable compensation amount.

[0073] In this embodiment, the anomaly correction data refers to the effective data obtained by supplementing redundant data or deducing historical data, which is used to replace the error compensation data in abnormal situations.

[0074] In this embodiment, the compensation drive command refers to a command formed by combining the sealing gap adjustment command and the sealing pressure adjustment command, used to control the operation of the electronic control execution module.

[0075] The working principle and beneficial effects of the above scheme are as follows: This embodiment takes the standardized digital signal output by Embodiment 4 as input, firstly extracts the core feature parameters related to the wear of the elastic sealing seat 3, media corrosion and overall sealing state from the signal, forming the first feature calculation parameter for calculation; based on this parameter, the initial compensation value of the sealing gap and sealing pressure is initially calculated through a preset algorithm model, and at the same time, the corrosion level and working condition type of the current medium are determined according to the media corrosion feature parameters, and the corresponding compensation sensitivity, compensation interval and other adaptation parameters are retrieved and substituted into the initial calculation result for dynamic adjustment, so as to obtain the corrected compensation amount data that is more in line with the actual working condition; in this process, the transmission status of the standardized digital signal and the logic status of the compensation amount calculation are monitored in real time. Once the sensor signal or calculation logic is identified as abnormal, it is processed by redundant data completion or historical data derivation to generate abnormal correction data; finally, the corrected compensation amount data or abnormal correction data is converted into sealing gap adjustment and sealing pressure adjustment related instructions according to a preset format, and combined to form accurate compensation drive instructions, providing a basis for the action of the electronic control execution module. This embodiment ensures comprehensive and core basic data for compensation calculation by accurately extracting multi-dimensional feature parameters, avoiding calculation deviations caused by missing key information. It dynamically adjusts compensation parameters based on media corrosion level and operating condition type, enabling precise adaptation of sealing compensation actions to sealing requirements under different corrosion intensities and operating conditions. This prevents leakage due to insufficient compensation and avoids accelerated wear of sealing components due to overcompensation. The inclusion of anomaly monitoring and handling mechanisms effectively addresses unexpected situations such as signal transmission failures and computational logic anomalies, preventing sealing control failures caused by malfunctions and ensuring valve operation stability. The overall process forms a complete decision-making chain from data input to command output. The decision-making process is scientific, rigorous, and possesses anti-interference and anti-fault capabilities, significantly improving the accuracy, adaptability, and reliability of valve sealing compensation. It can stably adapt to complex media corrosion and fluctuating operating conditions for a long time, extending the service life of core sealing components such as the elastic sealing seat 3 and valve core 2, and reducing valve maintenance costs.

[0076] Example 6: Please refer to Figures 1-2 Based on any one of embodiments 1-5, the anomaly processing unit includes: an anomaly type subdivision subunit, used to receive sensor signal anomalies and computational logic anomalies identified by the anomaly detection unit, further classify sensor signal anomalies into signal missing anomalies and signal fluctuation anomalies, further classify computational logic anomalies into algorithm parameter anomalies and data association anomalies, and clarify the specific type of anomaly. The redundant data retrieval subunit is used to retrieve backup sensor data of the same type of sensor component and historical data of the same monitoring dimension in the parameter acquisition module to form a redundant data set in response to signal loss anomalies and signal fluctuation anomalies. The redundant data completion subunit is used to interpolate and complete the signal missing anomaly using redundant data from adjacent time nodes, and to generate sensor anomaly completion data by weighted fusion of effective data from the redundant data set for the signal fluctuation anomaly. The historical data filtering subunit is used to retrieve historical compensation calculation data under the same working conditions and wear level stored in the main control decision module for cases of abnormal algorithm parameters and abnormal data correlation, and to filter out valid historical data that meets the standards of data integrity and correlation. The historical data derivation subunit is used to deduce the algorithm parameter correction value adapted to the current working condition based on the effective historical data for algorithm parameter anomalies, and to generate logical anomaly derivation data by fitting the reasonable range of the current compensation amount through the changing trend of effective historical data for data correlation anomalies. The anomaly correction data integration subunit is used to receive sensor anomaly completion data output by the redundant data completion subunit or logical anomaly deduction data output by the historical data deduction subunit, and to standardize and integrate them according to a preset data format to obtain anomaly correction data.

[0077] In this embodiment, the anomaly processing unit refers to a functional unit used to receive the anomaly signals identified by the anomaly detection unit, and to ensure the continuous and stable sealing compensation control by subdividing the anomaly type, retrieving the corresponding data, processing it in a targeted manner, and integrating and correcting the data.

[0078] In this embodiment, the anomaly type subdivision subunit refers to a functional unit used to further subdivide sensor signal anomalies and computational logic anomalies to clarify the specific type of anomaly.

[0079] In this embodiment, signal loss anomaly refers to situations such as signal interruption and data blankness that occur when the sensing component collects signals related to wear of the elastic sealing seat 3 and medium characteristics.

[0080] In this embodiment, abnormal signal fluctuation refers to the phenomenon that the amplitude and frequency of the signal collected by the sensing component exceed the normal fluctuation range and cannot truly reflect the actual situation such as the state of the elastic sealing seat 3 and the properties of the medium.

[0081] In this embodiment, abnormal algorithm parameters refer to the situation where the parameters of the preset algorithm model deviate from the adaptation range during the compensation calculation process, resulting in distorted calculation results.

[0082] In this embodiment, data association anomaly refers to a situation where the association logic between different dimensions of data, such as seal wear and medium corrosion, deviates, affecting the rationality of the compensation calculation.

[0083] In this embodiment, the redundant data retrieval subunit refers to a functional unit used to retrieve backup sensor data of the same type and historical data of the same monitoring dimension for sensor-related anomalies, forming a redundant data set.

[0084] In this embodiment, the redundant data set refers to a data set composed of backup sensing data from the same type of sensing components and historical data from the same monitoring dimension, used to process abnormal sensing signals.

[0085] In this embodiment, the redundant data completion subunit refers to a functional unit used to generate sensor anomaly completion data by interpolation completion and weighted fusion methods for different sensor signal anomalies.

[0086] In this embodiment, the sensor anomaly completion data refers to the data that, after being processed by the redundant data completion subunit, replaces the missing or fluctuating abnormal signals and can reflect the true monitoring status.

[0087] In this embodiment, the historical data filtering subunit refers to a functional unit used to retrieve and filter valid historical compensation calculation data under the same working conditions and wear level for logical anomalies.

[0088] In this embodiment, valid historical data refers to historical compensation calculation data that is selected from the historical data stored in the master control decision module, meets the standards for data integrity and correlation, and can be used to correct abnormal calculation logic.

[0089] In this embodiment, the historical data derivation subunit refers to a functional unit used to derive corrective data for different logical anomalies based on valid historical data.

[0090] In this embodiment, logical anomaly derivation data refers to valid data that has been processed by the historical data derivation subunit and is used to correct algorithm parameter anomalies and data association anomalies.

[0091] In this embodiment, the anomaly correction data integration subunit refers to the functional unit used to standardize and integrate sensor anomaly completion data and logic anomaly deduction data to generate anomaly correction data.

[0092] In this embodiment, abnormal correction data refers to effective data that, after standardization and integration, can replace abnormal data in the generation of subsequent compensation driving instructions.

[0093] The working principle and beneficial effects of the above scheme are as follows: Based on the control system constructed in Examples 1-5, this embodiment first precisely subdivides the two types of anomalies—sensor signal anomalies and computational logic anomalies identified by the anomaly detection unit—by further classifying them into signal loss anomalies and signal fluctuation anomalies, and computational logic anomalies into algorithm parameter anomalies and data association anomalies, thus clarifying the specific types of anomalies to adapt to differentiated processing strategies. For sensor-related anomalies (signal loss, fluctuation), backup sensor data of the same type of sensor component and historical data of the same monitoring dimension are retrieved from the parameter acquisition module to form a redundant data set. Then, the missing signal is supplemented by interpolation of redundant data at adjacent time nodes, and the fluctuation signal is corrected by weighted fusion of redundant data to generate a sensor. Abnormal data completion; for logic-related abnormalities (algorithm parameter, data association abnormalities), historical compensation calculation data under the same working conditions and wear level stored in the main control decision module are retrieved, and valid historical data that meets the standards of completeness and correlation are selected. Based on this data, the algorithm parameter correction value adapted to the current working condition is derived in reverse, or the reasonable range of the current compensation amount is fitted by the trend of historical data changes to generate logic abnormality derivation data; finally, the sensor abnormality completion data or logic abnormality derivation data is standardized and integrated according to the preset format to obtain abnormality correction data, which provides reliable data support for the generation of subsequent compensation drive commands, ensuring that even if signal or calculation abnormalities occur, the valve sealing compensation control can still operate continuously and stably without interrupting the precise control of the sealing state of the elastic sealing seat 3 and the valve core 2. This embodiment avoids strategy mismatch caused by generalized handling by finely classifying anomalies, allowing each anomaly to receive a targeted solution, significantly improving the efficiency and effectiveness of anomaly handling. The method of retrieving and processing redundant data effectively compensates for data gaps caused by missing or fluctuating sensor signals, ensuring the continuity of data reflecting the wear of the elastic sealing seat 3, media corrosion, and other conditions, and preventing the control system from paralyzing due to sensor failure. The historical data screening and derivation mechanism provides a reliable basis for correcting anomalies in the calculation logic. By relying on historical experience data under the same operating conditions to calibrate algorithm parameters and fit the compensation range, it ensures that the compensation calculation logic does not deviate from the actual operating conditions. The standardized anomaly correction data integration enables the corrected data to be directly adapted to the subsequent master control decision process, achieving seamless connection between anomaly handling and compensation command generation, and ensuring the closed-loop stability of the sealing control system. The overall anomaly handling process significantly improves the anti-interference and anti-fault capabilities of the valve control system, effectively dealing with anomalies caused by complex media corrosion, electromagnetic interference, algorithm fluctuations, etc., avoiding problems such as sealing leakage and accelerated component wear caused by anomalies, extending the service life of core components such as valve body 1 and elastic sealing seat 3, and ensuring long-term stable operation of the valve under harsh operating conditions.

[0094] Example 7: Please refer to Figures 1-2Based on any one of Examples 1-6, the iterative optimization process of the core algorithm unit includes: The feedback data receiving unit is used to receive the real-time continuous data signal transmitted by the feedback verification module, and to determine the actual displacement execution parameters, actual pressure control parameters and actual load operation parameters contained in the signal. The feedback feature extraction unit is used to extract the deviation value, deviation change rate and deviation duration between the actual parameters and the target parameters corresponding to the previous compensation drive command from the real-time continuous data signal, and form feedback feature parameters. The deviation cause analysis unit is used to combine the latest raw sensor data output by the parameter acquisition module with the feedback characteristic parameters to analyze the causes of the deviation. The causes include changes in the seal wear state, fluctuations in the medium corrosion condition, and lag in the response of the actuator. The algorithm parameter dynamic adjustment unit is used to adjust the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model in the core algorithm unit according to the deviation cause analysis results, and generate the adjusted algorithm parameters. The compensation amount iterative calculation unit is used to call the adjusted algorithm parameters, combine them with the latest extracted sealing wear characteristic parameters and medium corrosion characteristic parameters, and recalculate the sealing gap compensation amount and sealing pressure compensation amount to obtain the second compensation amount data after iterative optimization. The optimization result output unit is used to transmit the second compensation quantity data to the instruction generation unit of the main control decision module, providing a basis for updating the compensation driving instruction and realizing the iterative optimization of the core algorithm unit.

[0095] In this embodiment, the iterative optimization process of the core algorithm unit refers to the cyclic process of continuously optimizing the sealing compensation calculation result and updating the compensation drive command based on the real-time data of the feedback verification module through steps such as deviation analysis, parameter adjustment, and compensation recalculation.

[0096] In this embodiment, the feedback data receiving unit refers to the functional unit used to receive the real-time continuous data signal transmitted by the feedback verification module and to identify the actual displacement execution parameters, actual pressure control parameters and actual load operation parameters therein.

[0097] In this embodiment, the real-time continuous data signal refers to the continuous data signal collected by the feedback verification module that reflects the actual operating status of the electronic control execution module, including the actual displacement execution parameters, the actual pressure control parameters, and the actual load operating parameters.

[0098] In this embodiment, the actual displacement execution parameters refer to the actual displacement-related data generated when the electronic control execution module drives the valve core 2 to move through the electric push rod 5.

[0099] In this embodiment, the actual pressure control parameter refers to the pressure-related data actually achieved when the electronic control execution module adjusts the pressure of the air chamber of the elastic sealing seat 3 through the micro air pump.

[0100] In this embodiment, the actual load operating parameters refer to the load-related data actually generated by the electronic control execution module during the process of driving the electric push rod 5 and the micro air pump.

[0101] In this embodiment, the feedback characteristic parameters refer to a set of parameters extracted from real-time continuous data signals that reflect the deviation between the actual parameters and the target parameters, including the deviation value, the rate of change of the deviation, and the duration of the deviation.

[0102] In this embodiment, the deviation cause analysis unit refers to a functional unit used to analyze the cause of deviation by combining the latest raw sensing data and feedback characteristic parameters from the parameter acquisition module.

[0103] In this embodiment, the algorithm parameter dynamic adjustment unit refers to the functional unit used to adjust the relevant parameters of the preset algorithm model in the core algorithm unit according to the deviation cause analysis results.

[0104] In this embodiment, the working condition adaptation correlation factor refers to the correlation parameter in the preset algorithm model used to correlate the working conditions of media corrosion, the state of seal wear and the calculation of compensation amount.

[0105] In this embodiment, the compensation amount iterative calculation unit refers to the functional unit used to call the adjusted algorithm parameters and recalculate the sealing gap compensation amount and sealing pressure compensation amount in combination with the latest feature parameters.

[0106] In this embodiment, the second compensation amount data refers to the sealing gap compensation amount and sealing pressure compensation amount data that have been recalculated after iterative optimization.

[0107] In this embodiment, the optimization result output unit refers to the functional unit used to transmit the second compensation amount data to the instruction generation unit, providing a basis for updating the compensation driving instruction.

[0108] The working principle and beneficial effects of the above scheme are as follows: The core of this embodiment is to realize the iterative optimization of the core algorithm. Starting with the real-time continuous data signal transmitted by the feedback verification module, the actual displacement execution parameters for adjusting the displacement of valve core 2, the actual pressure control parameters for adjusting the pressure of the air chamber of elastic sealing seat 3, and the actual load operation parameters during the execution process are first identified from the data. Then, the deviation values, deviation change rates, and deviation durations between these actual parameters and the target parameters set by the previous compensation drive command are extracted to form feedback characteristic parameters. Subsequently, the latest original sensor data output by the parameter acquisition module is combined to correlate the feedback characteristic parameters. The process begins by analyzing the causes of deviations to pinpoint whether they are due to changes in seal wear, fluctuations in media corrosion, or lag in actuator response. Based on these causes, the weighting coefficients, compensation thresholds, and operating condition adaptation factors of the preset algorithm model are dynamically adjusted to generate adjusted algorithm parameters. These adjusted parameters are then used in conjunction with the latest extracted seal wear and media corrosion characteristics to recalculate the seal gap and seal pressure compensation, resulting in iteratively optimized second compensation data. Finally, this second compensation data is transmitted to the instruction generation unit to provide a basis for updating compensation-driven instructions, forming a cyclical iterative optimization process. This embodiment accurately captures the deviation between the compensation action and the target requirements by receiving feedback data from the execution end in real time, avoiding the disconnect between the compensation command and the actual execution effect, and solving the problem that fixed algorithms are difficult to adapt to dynamic working conditions. In-depth analysis of the causes of deviation and targeted adjustment of algorithm parameters enable the compensation calculation to dynamically adapt to real-time changes in seal wear, fluctuations in media corrosion, and the response characteristics of the actuator, significantly improving the accuracy of the compensation command. This avoids seal failure caused by accumulated deviations and reduces additional wear on components such as the elastic sealing seat 3 and valve core 2 due to overcompensation. The iterative optimization mode allows the algorithm to continuously self-correct, constantly adapting to the actual operating state of the valve over time, maintaining the stability and reliability of seal control even under long-term complex working conditions. Simultaneously, the accurate location of the causes of deviations provides indirect reference for subsequent maintenance, helping to promptly detect potential problems such as accelerated wear of sealing components and abnormal response of the actuator, extending the overall service life of the valve, reducing maintenance costs, and ensuring that the valve maintains high-efficiency sealing performance in long-term complex media corrosion and fluctuating working conditions.

[0109] Example 8: Please refer to Figures 1-2 Based on any one of Embodiments 1-7, the feedback verification module includes: The signal acquisition unit is used to acquire displacement execution signals, pressure control signals, and load operation signals from the electronic control execution module, and synchronously record the acquisition timestamps of each signal. The signal noise reduction unit is used to remove noise from the acquired real-time continuous data signals, eliminate invalid signals caused by actuator vibration and electromagnetic interference, and generate a noise-reduced signal. The data alignment unit is used to align the noise-reduced displacement execution signal, pressure control signal, and load operation signal to the same time dimension according to the acquisition timestamp, forming time-series aligned data; The signal output unit is used to transmit time-aligned data to the main control decision module, providing standardized feedback data for the iterative optimization of the core algorithm unit.

[0110] In this embodiment, the feedback verification module refers to the functional module used to collect the operating signals of the electronic control execution module, and after noise reduction and alignment processing, transmit standardized feedback data to the main control decision module to support the iterative optimization of the core algorithm.

[0111] In this embodiment, the signal acquisition unit refers to the functional unit used to acquire the displacement execution signal, pressure regulation signal and load operation signal of the electronic control execution module, and to synchronously record the timestamp of each signal acquisition.

[0112] In this embodiment, the displacement execution signal refers to the displacement-related signal generated when the electronic control execution module drives the valve core 2 to adjust the displacement through the electric push rod 5.

[0113] In this embodiment, the pressure control signal refers to the pressure-related signal generated when the electronic control execution module adjusts the pressure of the air chamber of the elastic sealing seat 3 through the micro air pump.

[0114] In this embodiment, the load operation signal refers to the load-related signal generated by the electronic control execution module during the operation of the electric actuator 5 and the micro air pump.

[0115] In this embodiment, the acquisition timestamp refers to the acquisition time information of the corresponding signal that the signal acquisition unit records synchronously when acquiring various types of signals.

[0116] In this embodiment, the real-time continuous data signal refers to the continuous raw data signal acquired by the signal acquisition unit, which includes displacement execution signal, pressure control signal and load operation signal.

[0117] In this embodiment, the signal noise reduction unit refers to a functional unit used to remove noise from real-time continuous data signals and eliminate invalid signals caused by actuator vibration and electromagnetic interference.

[0118] In this embodiment, the noise-reduced signal refers to the signal that, after the signal noise reduction unit removes invalid interference signals, can truly reflect the actual operating status of the electronic control execution module.

[0119] In this embodiment, the data alignment unit refers to a functional unit used to align various noise-reduced signals to the same time dimension based on the acquisition timestamp.

[0120] In this embodiment, time-aligned data refers to the set of displacement execution signals, pressure control signals, and load operation signals that are matched with each other in the same time dimension after being processed by the data alignment unit.

[0121] In this embodiment, the signal output unit refers to the functional unit used to transmit timing-aligned data to the master control decision module, providing standardized feedback data for the iterative optimization of the core algorithm unit.

[0122] In this embodiment, standardized feedback data refers to feedback data that has been processed by noise reduction and alignment, has a unified format, consistent timing, and accurate data, and can be directly used for iterative optimization of the core algorithm unit.

[0123] The working principle and beneficial effects of the above scheme are as follows: In this embodiment, the displacement execution signal of the electric actuator 5 driving the valve core 2, the pressure control signal of the micro air pump regulating the pressure of the air chamber of the elastic sealing seat 3, and the load operation signal during the operation of both are first collected by the signal acquisition component. At the same time, the acquisition timestamp of each type of signal is recorded synchronously to form a real-time continuous data signal. Then, the signal is processed for noise removal to eliminate invalid signals caused by factors such as actuator vibration and external electromagnetic interference, and a clean noise-reduced signal is obtained. Then, according to the previously recorded acquisition timestamp, the noise-reduced displacement execution signal, pressure control signal and load operation signal are aligned to the same time dimension to ensure the consistency of different types of signals in time sequence, forming time-series aligned data. Finally, the time-series aligned data is transmitted to the main control decision module as standardized feedback data for the core algorithm unit to iteratively optimize the compensation drive command, providing a real and synchronous actual operation basis for algorithm parameter adjustment and compensation recalculation, forming a complete closed loop. This embodiment ensures the integrity and time-series traceability of feedback data by comprehensively collecting three types of core signals from the execution end and recording timestamps, providing comprehensive data support for subsequent deviation analysis and avoiding optimization direction deviations caused by missing data. Targeted noise removal effectively filters out irrelevant interference signals, ensuring the authenticity and accuracy of feedback data, preventing distorted data from misleading algorithm adjustments, and ensuring that the core algorithm unit can perform iterative optimization based on real working conditions. Time-stamp-based data alignment processing solves the problem of timing misalignment of different types of signals, enabling the main control decision module to accurately correlate displacement, pressure, and load at the same time point. The system accurately judges the actual effect and cause of deviation of the compensation action under load conditions, improving the targeting of algorithm optimization. The overall feedback process provides stable, reliable and standardized input data for the cyclic iteration of the core algorithm, which is the key support for the realization of closed-loop control. It not only allows the compensation drive command to continuously match the actual operating state of the valve, greatly improving the accuracy and stability of sealing control, but also timely captures abnormal operating signals of the actuator, providing a data basis for potential fault warning, extending the service life of core components such as valve body 1, elastic sealing seat 3, and valve core 2, and ensuring that the valve maintains high-efficiency sealing performance under long-term complex corrosion conditions and operating condition fluctuations.

[0124] Example 9: Please refer to Figures 1-2 Based on any one of Embodiments 1-8, the electronically controlled execution module includes: The instruction parsing unit is used to receive the sealing gap adjustment instruction and sealing pressure adjustment instruction output by the main control decision module, and to parse the target displacement of the electric push rod 5 and the target pressure value of the micro air pump. The collaborative control unit is used to determine the control logic based on the wear level and corrosion conditions of the sealing pair. When there is light wear, only the micro air pump is started; when there is moderate wear, the electric actuator 5 and the micro air pump are started simultaneously; when there is heavy wear, the electric actuator 5 is started first and then the micro air pump is finely adjusted. The displacement drive unit is used to control the extension and retraction rate and stroke of the electric actuator 5 according to the target displacement. It collects the actual displacement data in real time through the built-in displacement sensor and compares it with the target displacement. When the deviation exceeds the preset range, it makes real-time correction. The pressure drive unit is used to control the inflation rate of the micro air pump according to the target pressure value. The pressure sensor in the inflation chamber of the elastic sealing seat 3 collects the actual pressure data in real time and compares it with the target pressure value. When the deviation exceeds the preset range, the micro air pump is started or stopped to replenish or release pressure. The execution status feedback unit is used to synchronously transmit the actual displacement data of the displacement drive unit and the actual pressure data of the pressure drive unit to the feedback verification module, forming a signal closed loop between the execution layer and the verification layer.

[0125] In this embodiment, the electronic control execution module refers to the functional module used to receive compensation drive commands from the main control decision module, realize the displacement adjustment of valve core 2 and the pressure regulation of elastic sealing seat 3 through electric push rod 5 and micro air pump, and provide feedback on the execution status.

[0126] In this embodiment, the instruction parsing unit refers to a functional unit used to receive sealing gap adjustment instructions and sealing pressure adjustment instructions, and to parse the target displacement of the electric push rod 5 and the target pressure value of the micro air pump.

[0127] In this embodiment, the sealing gap adjustment command refers to the command output by the main control decision module, which is used to control the electric actuator 5 to drive the valve core 2 to move, so as to adjust the gap between the valve core 2 and the elastic sealing seat 3.

[0128] In this embodiment, the sealing pressure adjustment command refers to the command output by the main control decision module, which is used to control the micro air pump to adjust the pressure of the air chamber of the elastic sealing seat 3 to enhance the sealing effect.

[0129] In this embodiment, the target displacement refers to the displacement value that the electric actuator 5 needs to drive the valve core 2 to achieve, which is parsed by the instruction parsing unit from the sealing gap adjustment instruction.

[0130] In this embodiment, the target pressure value refers to the pressure value that the air chamber of the elastic sealing seat 3 needs to reach, which is parsed by the command parsing unit from the sealing pressure adjustment command.

[0131] In this embodiment, the collaborative control unit refers to the functional unit used to determine the starting logic of the electric actuator 5 and the micro air pump based on the wear level and corrosion conditions of the sealing pair.

[0132] In this embodiment, the sealing pair refers to the structural combination in which the valve core 2 and the elastic sealing seat 3 cooperate with each other to achieve sealing of the internal medium of the valve body 1.

[0133] In this embodiment, the displacement drive unit refers to a functional unit used to control the extension and retraction rate and stroke of the electric actuator 5 according to the target displacement amount, and to correct the deviation in real time through the built-in displacement sensor.

[0134] In this embodiment, the pressure drive unit refers to a functional unit used to control the inflation rate of the micro air pump according to the target pressure value, and to replenish or release pressure in real time through the pressure sensor in the inflation chamber of the elastic sealing seat 3.

[0135] In this embodiment, the execution status feedback unit refers to the functional unit used to synchronously transmit actual displacement data and actual pressure data to the feedback verification module to form a signal closed loop.

[0136] In this embodiment, the actual displacement data refers to the real displacement information generated by the electric actuator 5 driving the valve core 2, which is collected by the displacement sensor.

[0137] In this embodiment, the actual pressure data refers to the real pressure information inside the cavity collected by the pressure sensor of the air-filled cavity of the elastic sealing seat 3.

[0138] The working principle and beneficial effects of the above scheme are as follows: This embodiment, as the core of the electronic control execution link, is based on the instructions output by the main control decision module. First, it receives the sealing gap adjustment instruction and the sealing pressure adjustment instruction, and analyzes to obtain the target displacement of the electric actuator 5 and the target pressure value of the micro air pump. Then, combined with the wear level of the sealing pair (valve core 2 and elastic sealing seat 3) and the medium corrosion condition, it determines the differentiated control logic—when there is slight wear, only the micro air pump is started to strengthen the pressure; when there is moderate wear, the electric actuator 5 and the micro air pump are started simultaneously to achieve coordinated compensation of displacement and pressure; when there is severe wear, the valve core 2 displacement is first adjusted by the electric actuator 5 to reduce the sealing gap, and then the micro air pump is started to reduce the pressure. The micro air pump pressure is adjusted to achieve precise sealing. Subsequently, the displacement drive unit controls the extension and retraction rate and stroke of the electric actuator 5 according to the target displacement amount. The actual displacement data is collected in real time through its built-in displacement sensor and compared with the target displacement amount. If the deviation exceeds the preset range, it is corrected immediately. The pressure drive unit controls the inflation rate of the micro air pump according to the target pressure value. The actual pressure data is collected through the pressure sensor in the inflation chamber of the elastic sealing seat 3 and compared with the target pressure value. If the deviation exceeds the preset range, the micro air pump is started or stopped to replenish or release pressure. Finally, the execution status feedback unit transmits the actual displacement data and actual pressure data to the feedback verification module simultaneously to ensure that the compensation action is accurately implemented. This embodiment utilizes differentiated control logic to dynamically adjust the compensation method based on wear level and corrosion conditions. This avoids the inadequacy of a single compensation mode for different operating conditions. For minor wear, pressure reinforcement is used to reduce component damage, while for severe wear, displacement is prioritized to ensure sealing performance, balancing sealing performance and component lifespan. The real-time correction mechanism for displacement and pressure effectively offsets external interference and component response deviations during execution, ensuring that the displacement of valve core 2 and the pressure of elastic sealing seat 3 strictly adhere to target requirements, significantly reducing the risk of sealing leakage. Real-time feedback of the execution status provides real and immediate execution data support for subsequent algorithm iteration and optimization, forming a complete closed loop in the entire control system and improving the overall coherence and stability of valve sealing control. Simultaneously, collaborative control and precise correction reduce ineffective wear on core components such as electric actuator 5, micro-pump, elastic sealing seat 3, and valve core 2, extending component lifespan and enabling long-term adaptation to complex media corrosion, operating condition fluctuations, and sealing surface wear evolution scenarios. This ensures long-term stable sealing performance of the valve, reducing maintenance frequency and costs.

[0139] Example 10: Please refer to Figures 1-2 Based on any one of Examples 1-9, the initial calculation unit for compensation adopts a preset algorithm model that integrates fuzzy PID with multi-parameter coupling, with sealing wear characteristic parameters as the core input and medium corrosion characteristic parameters as the correction input. The operating condition identification unit determines the corrosion level and operating condition type of the medium based on the pH value, ion concentration and ambient temperature parameters in the medium corrosion characteristic parameters. The redundant data completion subunit completes signal missing anomalies by interpolating redundant data from adjacent time nodes and completes signal fluctuation anomalies by weighted fusion of redundant data. The signal acquisition unit acquires corresponding signals through the displacement sensor built into the electric push rod 5, the pressure sensor in the air chamber of the elastic sealing seat 3, and the torque sensor, respectively. The algorithm parameter dynamic adjustment unit adjusts the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model according to the cause of the deviation. The sensor deployment unit deploys a high-frequency impedance sensor around the sealing surface of the elastic sealing seat 3, a pH sensor and an ion concentration sensor in the medium channel inside the valve body, a conductive rubber sensor on the outside of the sealing gap, and a temperature and humidity sensor and an electromagnetic interference sensor on the outer wall of the valve body.

[0140] In this embodiment, the preset algorithm model of fuzzy PID fusion multi-parameter coupling refers to an algorithm model that combines the robustness of fuzzy control with the precision of PID control, while incorporating multi-parameter coupling logic such as seal wear and medium corrosion, and is used to calculate the initial values ​​of the seal gap compensation amount and the seal pressure compensation amount.

[0141] In this embodiment, the pH value in the medium corrosion characteristic parameters refers to the parameter that reflects the acidity or alkalinity of the medium flowing inside the valve body 1, and is one of the key indicators for determining the level of medium corrosion.

[0142] In this embodiment, the ion concentration in the medium corrosion characteristic parameters refers to the parameter that reflects the content of corrosive ions in the circulating medium inside the valve body 1, and is one of the key indicators for determining the medium corrosion level.

[0143] In this embodiment, the ambient temperature parameter refers to the parameter that reflects the external ambient temperature of the valve body 1, and is used to help determine the operating condition type and the corrosion level of the medium.

[0144] In this embodiment, redundant data interpolation completion refers to a processing method that uses redundant data from adjacent time nodes to interpolate and complete the missing signal when a sensor signal is missing or abnormal.

[0145] In this embodiment, redundant data weighted fusion refers to the method of correcting the processing of fluctuating signals by assigning corresponding weights to the effective data in the redundant data set and performing fusion calculations when abnormal fluctuations occur in the sensing signal.

[0146] In this embodiment, the torque sensor refers to the sensor used to collect load-related signals during the operation of the electric actuator 5 and the micro air pump, and is one of the core components for signal acquisition.

[0147] In this embodiment, the high-frequency impedance sensor refers to a sensing component that is arranged around the sealing surface of the elastic sealing seat 3 to monitor the change in contact impedance of the sealing surface and thus reflect the wear state of the seal.

[0148] In this embodiment, the pH sensor refers to a sensing component installed in the medium channel inside the valve body 1 to collect the pH value of the medium and reflect the acidity or alkalinity of the medium.

[0149] In this embodiment, the ion concentration sensor refers to a sensing component installed inside the medium channel of the valve body 1 to collect the content of corrosive ions in the medium.

[0150] In this embodiment, the conductive rubber sensor refers to a sensing component that is arranged on the outside of the sealing gap between the valve core 2 and the elastic sealing seat 3 to detect the medium penetration and reflect the state of sealing leakage.

[0151] In this embodiment, the temperature and humidity sensor refers to a sensing component installed on the outer wall of the valve body 1 to collect external ambient temperature and humidity signals.

[0152] In this embodiment, the electromagnetic interference sensor refers to a sensing component installed on the outer wall of the valve body 1 to collect electromagnetic interference signals from the external environment.

[0153] In this embodiment, the working condition adaptation correlation factor refers to the key parameter in the preset algorithm model used to correlate the working conditions of medium corrosion, the state of seal wear and the calculation results of compensation amount, which can be dynamically adjusted according to the cause of deviation.

[0154] The working principle and beneficial effects of the above scheme are as follows: In this embodiment, the compensation calculation stage employs a fuzzy PID fusion multi-parameter coupled algorithm model, using core parameters related to seal wear as the main input, combined with medium corrosion characteristic parameters for correction, ensuring the accuracy of the initial compensation calculation; the operating condition identification process relies on pH value, ion concentration, and ambient temperature parameters among the medium corrosion characteristic parameters to scientifically determine the medium corrosion level and actual operating condition type, providing a clear basis for subsequent parameter adaptation; in the face of abnormal sensor signals, a differentiated processing method is adopted: when a signal is missing, it is filled by interpolation using redundant data from adjacent time nodes; when the signal fluctuates, it is corrected by weighted fusion of redundant data to ensure data integrity; the signal acquisition stage uses an electric actuator 5... Built-in displacement sensors, pressure sensors in the air chamber of the elastic sealing seat 3, and torque sensors accurately collect displacement, pressure, and load-related signals, ensuring the relevance and reliability of data acquisition. When adjusting algorithm parameters, the weight coefficients of the model, the compensation calculation threshold, and the working condition adaptation correlation factors are adjusted according to the causes of deviations, enabling the algorithm to dynamically adapt to changes in actual working conditions. The sensor layout is selectively adapted to different types, with high-frequency impedance sensors arranged circumferentially on the sealing surface of the elastic sealing seat 3, pH sensors and ion concentration sensors arranged in the internal medium channel of the valve body 1, conductive rubber sensors arranged on the outside of the sealing gap, and temperature and humidity sensors and electromagnetic interference sensors arranged on the outer wall of the valve body 1, achieving comprehensive coverage and accurate perception of key monitoring dimensions. This embodiment significantly improves the scientific rigor and adaptability of compensation calculations through precise algorithm models and clear input parameter configurations, avoiding calculation deviations caused by single algorithms or parameter inputs. The operating condition identification method based on specific quantitative parameters makes operating condition judgments more objective and accurate, providing reliable support for subsequent parameter retrieval and ensuring precise matching of compensation actions with actual operating conditions. Differentiated signal anomaly handling methods specifically address signal loss and fluctuation issues, ensuring data validity and continuity, and providing stable data input for decision-making and optimization. Precise deployment and signal acquisition of dedicated sensors enable accurate capture of key physical quantities, avoiding monitoring deviations caused by incompatible sensor types or unreasonable placement. Dynamic adjustment of algorithm parameters based on the causes of deviations allows the algorithm to quickly respond to changes in operating conditions and execution deviations, improving the dynamic adaptability of the control system. Comprehensive and targeted sensor deployment achieves all-round monitoring of key factors such as sealing status, media properties, and environmental interference, with no monitoring blind spots, providing comprehensive data support for the entire control process. The overall technical solution clarifies the specific implementation logic of each core component, making the entire sealing compensation control system more operable and reliable. It significantly improves the valve's sealing accuracy, anti-interference ability, and adaptability to operating conditions, effectively resists corrosion from complex media and environmental interference, extends the service life of core components, and ensures the long-term stable operation of the valve.

[0155] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A corrosion-resistant, self-compensating sealing stop valve, characterized in that, The valve body (1) is equipped with an elastic sealing seat (3) on the valve seat hole of the valve body (1), and a valve core (2) that seals with the elastic sealing seat (3) is installed at the bottom of the valve stem (4). A self-compensating sealing control assembly (6) is installed on the outer wall of the valve body (1). The self-compensating sealing control assembly (6) includes a parameter acquisition module, a signal preprocessing module, a main control decision module, an electronic control execution module, and a feedback verification module. The parameter acquisition module is used to acquire the sealing wear status signal, medium characteristic signal, sealing leakage signal and environmental status signal of the elastic sealing seat (3) and generate raw sensing data. The signal preprocessing module is used to perform anti-interference processing and digital conversion on the raw sensing data to generate standardized digital signals. The main control decision module is used to receive the standardized digital signal, calculate the sealing compensation amount through the built-in core algorithm unit, adjust the calculation result by combining the adaptation parameters of the corrosion condition correction unit, and then generate accurate compensation drive instructions through the abnormal fault tolerance unit. The electronic control execution module is used to receive the compensation drive command and adjust the sealing compensation pressure of the elastic sealing seat (3) and the displacement distance of the valve core (2) through the drive device; The feedback verification module is used to collect real-time continuous data signals from the electronic control execution module and transmit the real-time continuous data signals to the main control decision module, providing a basis for the core algorithm unit to iteratively optimize the compensation drive command and form a closed-loop control.

2. The corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The drive unit includes an electric actuator (5) and a micro air pump. The electric actuator (5) is installed inside the valve stem (4). The output end of the electric actuator (5) is fixedly provided with a valve core (2). The micro air pump is installed inside the self-compensating sealing control assembly (6), and the micro air pump is connected to the air chamber of the elastic sealing seat (3) through a corrosion-resistant hose.

3. The corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The parameter acquisition module includes: a sensor deployment unit, which is used to fix and deploy wear sensing components, medium sensing components, leakage sensing components and environmental sensing components in the circumferential direction of the sealing surface of the elastic sealing seat (3), the internal medium flow channel of the valve body (1) and the corresponding area of ​​the outer wall of the valve body (1); The sealing wear sensing unit is used to monitor the contact impedance change signal and compression change signal of the sealing surface of the elastic sealing seat (3) through the wear sensing component deployed by the sensor deployment unit, and form the original signal related to sealing wear. The medium characteristic sensing unit is used to capture the chemical characteristic parameter signal and physical state parameter signal of the medium flowing inside the valve body (1) through the medium sensing component deployed by the sensor deployment unit, and form the original signal related to the medium characteristics. The sealing leakage sensing unit is used to detect the medium permeation signal at the gap between the elastic sealing seat (3) and the valve core (2) through the leakage sensing component deployed by the sensor deployment unit, and to form the original signal related to sealing leakage. The environmental state sensing unit is used to collect temperature and humidity signals and electromagnetic interference related signals outside the valve body (1) through the environmental sensing components deployed by the sensor deployment unit, and form environmental state related raw signals. The data integration unit receives various raw signals output from the sealing wear sensing unit, the medium characteristic sensing unit, the sealing leakage sensing unit, and the environmental condition sensing unit, classifies and summarizes them in time stamp order, and generates the raw sensing data required by the parameter acquisition module.

4. The corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The signal preprocessing module includes: an interference filtering unit, a signal amplification unit, an analog-to-digital conversion unit, and a normalization processing unit; The interference filtering unit is used to filter the original sensing data according to the signal characteristics and environmental interference characteristics of the original sensing data, remove invalid interference signals caused by electromagnetic interference and medium fluctuations, and generate valid signals. The signal amplification unit is used to adjust the amplitude of the effective signal so that the signal strength is adapted to the subsequent conversion requirements and to generate an amplified analog signal. The analog-to-digital conversion unit is used to synchronously record the time stamps during the signal conversion process and convert the amplified analog signal into a preprocessed digital signal in a preset format. The standardization processing unit is used to normalize the preprocessed digital signal, eliminate the dimensional differences between different types of signals, and generate a standardized digital signal.

5. The corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The core algorithm unit, corrosion condition correction unit, and anomaly tolerance unit of the main control decision module include: The feature extraction unit is used to extract sealing wear feature parameters, medium corrosion feature parameters and sealing state feature parameters from the standardized digital signal to form the first feature calculation parameters. The initial compensation calculation unit is used to calculate the initial values ​​of the sealing gap compensation amount and the sealing pressure compensation amount based on the first feature calculation parameters and through a preset algorithm model. The operating condition identification unit is used to determine the corrosion level and operating condition type of the current medium based on the medium corrosion characteristic parameters, providing an adaptation basis for the corrosion operating condition correction unit; The parameter adaptation unit is used to retrieve preset working condition adaptation parameters based on the corrosion level and working condition type determined by the working condition identification unit. The adaptation parameters include compensation sensitivity parameters and compensation interval parameters. The result correction unit is used to substitute the adaptation parameters output by the parameter adaptation unit into the initial calculation result of the compensation amount calculation unit, dynamically adjust the sealing gap compensation amount and the sealing pressure compensation amount, and generate the corrected first compensation amount data. The anomaly detection unit is used to monitor the transmission status of the standardized digital signal and the logic status of the compensation calculation process in real time, and to identify sensor signal anomalies and calculation logic anomalies. The anomaly handling unit is used to call the corresponding anomaly handling strategy for the sensor signal anomaly and the calculation logic anomaly identified by the anomaly detection unit. When the sensor signal is abnormal, the redundant data completion method is used, and when the calculation logic is abnormal, the historical data derivation method is used to obtain the anomaly correction data. The instruction generation unit is used to receive the first compensation amount data output by the result correction unit or the abnormal correction data output by the abnormal processing unit, and convert it into a sealing gap adjustment instruction and a sealing pressure adjustment instruction according to a preset instruction format, and combine them to form a precise compensation drive instruction.

6. The corrosion-resistant self-compensating sealing stop valve according to claim 5, characterized in that, The anomaly processing unit includes an anomaly type subdivision subunit, which is used to receive sensor signal anomalies and computational logic anomalies identified by the anomaly detection unit, further classify sensor signal anomalies into signal missing anomalies and signal fluctuation anomalies, and further classify computational logic anomalies into algorithm parameter anomalies and data association anomalies, thereby clarifying the specific type of anomaly. The redundant data retrieval subunit is used to retrieve backup sensor data of the same type of sensor component and historical data of the same monitoring dimension in the parameter acquisition module to form a redundant data set in response to signal loss anomalies and signal fluctuation anomalies. The redundant data completion subunit is used to interpolate and complete the signal missing anomaly using redundant data from adjacent time nodes, and to generate sensor anomaly completion data by weighted fusion of effective data from the redundant data set for the signal fluctuation anomaly. The historical data filtering subunit is used to retrieve historical compensation calculation data under the same working conditions and wear level stored in the main control decision module for cases of abnormal algorithm parameters and abnormal data correlation, and to filter out valid historical data that meets the standards of data integrity and correlation. The historical data derivation subunit is used to deduce the algorithm parameter correction value adapted to the current working condition based on the effective historical data for algorithm parameter anomalies, and to generate logical anomaly derivation data by fitting the reasonable range of the current compensation amount through the changing trend of effective historical data for data correlation anomalies. The anomaly correction data integration subunit is used to receive sensor anomaly completion data output by the redundant data completion subunit or logical anomaly deduction data output by the historical data deduction subunit, and to standardize and integrate them according to a preset data format to obtain anomaly correction data.

7. A corrosion-resistant self-compensating sealing stop valve according to claim 5, characterized in that, The iterative optimization process of the core algorithm unit includes: The feedback data receiving unit is used to receive the real-time continuous data signal transmitted by the feedback verification module, and to determine the actual displacement execution parameters, actual pressure control parameters and actual load operation parameters contained in the signal. The feedback feature extraction unit is used to extract the deviation value, deviation change rate and deviation duration between the actual parameters and the target parameters corresponding to the previous compensation drive command from the real-time continuous data signal, and form feedback feature parameters. The deviation cause analysis unit is used to combine the latest raw sensor data output by the parameter acquisition module, correlate the feedback characteristic parameters, and analyze the causes of the deviation. The causes include changes in the seal wear state, fluctuations in the medium corrosion condition, and lag in the response of the actuator. The algorithm parameter dynamic adjustment unit is used to adjust the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model in the core algorithm unit according to the deviation cause analysis results, and generate the adjusted algorithm parameters. The compensation amount iterative calculation unit is used to call the adjusted algorithm parameters, combine them with the latest extracted sealing wear characteristic parameters and medium corrosion characteristic parameters, and recalculate the sealing gap compensation amount and sealing pressure compensation amount to obtain the second compensation amount data after iterative optimization. The optimization result output unit is used to transmit the second compensation quantity data to the instruction generation unit of the main control decision module, providing a basis for updating the compensation driving instruction and realizing the iterative optimization of the core algorithm unit.

8. The corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The feedback verification module includes: The signal acquisition unit is used to acquire displacement execution signals, pressure control signals, and load operation signals from the electronic control execution module, and synchronously record the acquisition timestamps of each signal. The signal noise reduction unit is used to remove noise from the acquired real-time continuous data signals, eliminate invalid signals caused by actuator vibration and electromagnetic interference, and generate a noise-reduced signal. The data alignment unit is used to align the noise-reduced displacement execution signal, pressure control signal, and load operation signal to the same time dimension according to the acquisition timestamp, forming time-series aligned data; The signal output unit is used to transmit time-aligned data to the main control decision module, providing standardized feedback data for the iterative optimization of the core algorithm unit.

9. A corrosion-resistant self-compensating sealing stop valve according to claim 1, characterized in that, The electronic control execution module includes: The instruction parsing unit is used to receive the sealing gap adjustment instruction and sealing pressure adjustment instruction output by the main control decision module, and to parse the target displacement of the electric push rod (5) and the target pressure value of the micro air pump. The collaborative control unit is used to determine the control logic according to the wear level and corrosion conditions of the sealing pair. When the wear is mild, only the micro air pump is started. When the wear is moderate, the electric actuator (5) and the micro air pump are started simultaneously. When the wear is severe, the electric actuator (5) is started first and then the micro air pump is finely adjusted. The displacement drive unit is used to control the extension and retraction rate and stroke of the electric push rod (5) according to the target displacement amount. It collects the actual displacement data in real time through the built-in displacement sensor and compares it with the target displacement amount. When the deviation exceeds the preset range, it makes real-time correction. The pressure drive unit is used to control the inflation rate of the micro air pump according to the target pressure value. The pressure sensor in the inflation chamber of the elastic sealing seat (3) collects the actual pressure data in real time and compares it with the target pressure value. When the deviation exceeds the preset range, the micro air pump is started or stopped to replenish or release pressure. The execution status feedback unit is used to synchronously transmit the actual displacement data of the displacement drive unit and the actual pressure data of the pressure drive unit to the feedback verification module, forming a signal closed loop between the execution layer and the verification layer.

10. A corrosion-resistant self-compensating sealing stop valve according to any one of claims 1, 2, 3, 5, 6 to 8, characterized in that, The initial calculation unit for the compensation amount adopts a preset algorithm model that integrates fuzzy PID and multi-parameter coupling, with the sealing wear characteristic parameters as the core input and the medium corrosion characteristic parameters as the correction input. The operating condition identification unit determines the corrosion level and operating condition type of the medium based on the pH value, ion concentration and ambient temperature parameters in the medium corrosion characteristic parameters. The redundant data completion subunit completes signal missing anomalies by interpolating redundant data from adjacent time nodes and completes signal fluctuation anomalies by weighted fusion of redundant data. The signal acquisition unit acquires corresponding signals through the displacement sensor built into the electric push rod (5), the pressure sensor and torque sensor in the air chamber of the elastic sealing seat (3); The algorithm parameter dynamic adjustment unit adjusts the weight coefficients, compensation calculation thresholds, and working condition adaptation correlation factors of the preset algorithm model according to the cause of the deviation. The sensor deployment unit deploys a high-frequency impedance sensor around the sealing surface of the elastic sealing seat (3), a pH sensor and an ion concentration sensor in the internal medium channel of the valve body (1), a conductive rubber sensor on the outside of the sealing gap, and a temperature and humidity sensor and an electromagnetic interference sensor on the outer wall of the valve body (1).

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