High-pressure cylinder valve pressure dynamic monitoring and self-adaptive adjustment method

Abstract

The application relates to the technical field of high-pressure gas cylinder control, and discloses a high-pressure gas cylinder valve pressure dynamic monitoring and self-adaptive adjustment method. The method first divides the high-pressure gas cylinder valve into a plurality of monitoring units, obtains pressure, temperature and flow data of each unit to form a comprehensive monitoring data set; then establishes a pressure stability evaluation index and an environmental factor influence evaluation index based on the data set, applies a probability early warning mechanism and a self-adaptive prediction model to generate an initial risk value; next, according to the spatial correlation or time sequence dependency of adjacent monitoring units, the initial risk value is dynamically corrected and the corrected risk value is output; finally, the corrected risk value is sorted to generate a priority control instruction list, and a self-adaptive adjustment signal is output in combination with a control execution unit to adjust the valve opening degree. The method realizes comprehensive monitoring and dynamic adjustment of the high-pressure gas cylinder valve, and helps to improve the stability of valve operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-pressure gas cylinder control, in particular to a high-pressure gas cylinder valve pressure dynamic monitoring and self-adaptive adjustment method. BACKGROUND

[0002] In many fields such as industrial production, energy supply, medical equipment, high-pressure gas cylinders as key equipment for storing and transporting high-pressure gas, their safe and stable operation is directly related to production efficiency and operation safety. As the core component of regulating gas flow and pressure, the monitoring and regulation of the working state of high-pressure gas cylinder valve is particularly important.

[0003] Currently, the traditional high-pressure gas cylinder valve monitoring method mainly relies on single sensor to collect pressure data, and the monitoring dimension is limited, which is difficult to fully reflect the actual running state of the valve. For example, only through the pressure sensor to obtain data, it is difficult to take into account the influence of temperature change on gas pressure, when the environmental temperature fluctuates sharply, it is easy to cause the deviation of pressure monitoring result, and then affect the judgment of valve state.

[0004] The existing adjustment method often carries out simple control based on preset threshold value, and lacks dynamic adaptability. In actual application, the working condition of high-pressure gas cylinder is complex and changeable, and the gas flow will change with the use demand, environmental condition and other factors, and the fixed adjustment mode is difficult to cope with these dynamic changes. When the pressure of adjacent monitoring areas influences each other, single local adjustment may cause pressure imbalance of the whole system, increasing the safety hazard.

[0005] The traditional risk assessment mechanism mainly adopts static assessment method, and fails to consider the spatial correlation and time sequence dependence between monitoring units. For example, the pressure anomaly of a monitoring unit may be caused by the pressure change of adjacent unit, if the correlation is ignored, only the independent assessment and adjustment of the unit is carried out, which will lead to inaccurate risk judgment, lack of pertinence of adjustment measures, and unable to fundamentally solve the problem, and even may aggravate the instability of the system. SUMMARY

[0006] The purpose of the present application is to provide a high-pressure gas cylinder valve pressure dynamic monitoring and self-adaptive adjustment method to solve the problems raised in the background.

[0007] To achieve the above purpose, the present application provides a high-pressure gas cylinder valve pressure dynamic monitoring and self-adaptive adjustment method, which comprises:

[0008] The high-pressure gas cylinder valve is divided into several monitoring units, the pressure sensor data, temperature data and flow data of each monitoring unit are obtained, and a comprehensive monitoring data set is formed;

[0009] Based on the comprehensive monitoring data set, pressure stability evaluation index and environmental factor influence evaluation index are established, and probability early warning mechanism and adaptive prediction model are applied to dynamically generate initial risk value;

[0010] According to the spatial correlation or time sequence dependence of adjacent monitoring units, dynamic correction operation is performed on the initial risk value of each monitoring unit, and the corrected risk value is output;

[0011] The corrected risk value is sorted and processed to generate a priority control instruction list, and an adaptive adjustment signal is output in combination with the control execution unit to adjust the valve opening degree.

[0012] Preferably, the specific rules for dividing the monitoring units are as follows:

[0013] The valve area is divided into monitoring sub-units with size difference within the allowed range through the physical structure characteristics of the high-pressure cylinder valve and the preset safety partition boundary, each monitoring sub-unit covers at least one independent pressure sensing point, and the monitoring sub-unit is taken as the monitoring unit.

[0014] Preferably, the specific establishment process of the pressure stability evaluation index is as follows:

[0015] The pressure fluctuation frequency is analyzed based on the pressure sensor data;

[0016] The temperature change trend and temperature stability parameter are extracted from the temperature data, and the pressure change rate parameter is calculated through a preset weight distribution model;

[0017] The pressure safety index is generated according to the comparison result of the pressure change rate parameter and the preset safety threshold;

[0018] The flow stability score is generated according to the number of flow abnormalities and amplitude changes within a preset time window in the flow data;

[0019] The pressure stability evaluation score value is calculated by integrating the pressure safety index and the flow stability score.

[0020] Preferably, the specific establishment process of the environmental factor influence evaluation index is as follows:

[0021] The temperature change rate, humidity influence coefficient and vibration intensity parameter are extracted from the comprehensive monitoring data set and standardized converted;

[0022] The weighted fusion calculation is performed on each parameter after standardization conversion to obtain the environmental factor influence evaluation score value;

[0023] When the vibration intensity parameter exceeds the set threshold, the environmental factor influence evaluation score update is triggered, and the environmental factor influence evaluation score update rule is as follows:

[0024] If the proportion of the continuous monitoring time periods in which the vibration intensity parameter exceeds the set threshold value to the total monitoring time periods exceeds a limit, a compensation proportion threshold value is triggered, and the environmental factor influence evaluation score value is increased accordingly according to the compensation proportion threshold value;

[0025] Otherwise, the vibration intensity parameter is divided into gradient intervals according to the difference between the vibration intensity parameter and the set threshold value, and is increased in a decreasing proportion, and the upper limit is the set compensation proportion threshold value.

[0026] Preferably, the specific generation process of the initial risk value is as follows:

[0027] The pressure stability evaluation index warning threshold value and the environmental factor influence evaluation index warning threshold value are set;

[0028] If any evaluation index of a certain monitoring unit exceeds the set warning threshold value, the initial risk value of the monitoring unit is assigned as the maximum value;

[0029] If there is an index lower than the set warning threshold value, the initial risk value is calculated by a prediction function according to the pressure stability evaluation index and the environmental factor influence evaluation index.

[0030] Preferably, the specific execution steps of the dynamic correction operation include:

[0031] A monitoring unit is randomly selected as a target monitoring unit, and the initial risk values of all adjacent monitoring units thereof are obtained;

[0032] Based on the spatial distance and signal transmission delay time between the target monitoring unit and the adjacent monitoring units, a final spatial correlation index value is calculated;

[0033] By matching the valve function types of the adjacent monitoring units and the target monitoring unit, a function synergy index value is generated;

[0034] According to the final spatial correlation index value and the function synergy index value, a correction weight coefficient is assigned;

[0035] Based on the correction weight coefficient, a weighted correction operation is performed on the initial risk value of the target monitoring unit, and a corrected risk value is output;

[0036] All monitoring units are traversed in turn to complete the correction operation, and the corrected risk values corresponding to all monitoring units are output.

[0037] Preferably, the specific calculation process of the spatial correlation index value includes:

[0038] According to the signal transmission delay time between the target monitoring unit and the adjacent monitoring units, a signal response efficiency is calculated;

[0039] According to the spatial straight-line distance between the target monitoring unit and the adjacent monitoring units, a position proximity is calculated;

[0040] The spatial correlation index value is generated by weighted fusion operation by combining the signal response efficiency and location proximity, and the adjustment coefficient is set according to the connection type of adjacent monitoring units;

[0041] The final spatial correlation index value is obtained by adjusting the coefficients accordingly.

[0042] The adjustment coefficient set according to the connection type shall meet the following rules:

[0043] A high adjustment factor value is assigned if the connection type is a direct physical connection, and a low adjustment factor value is assigned if the connection type is an indirect signal transmission, with the high adjustment factor value being greater than the low adjustment factor value.

[0044] Preferably, the specific process for generating the functional synergy index value includes:

[0045] Match the dominant valve function type of adjacent monitoring units with the valve function type of the target monitoring unit;

[0046] If it belongs to the preset collaborative type combination, the functional collaborative index value is calculated according to the matched valve function type and the corresponding function type preset weight;

[0047] If it does not belong to a collaborative combination, a collaborative index is calculated based on the functional compatibility specification, and the functional collaborative index value is obtained by mapping through a preset transformation function based on the collaborative index.

[0048] Preferably, the specific calculation process of the synergy index is as follows:

[0049] Acquire historical pressure monitoring datasets and then statistically analyze the co-occurrence frequency of valve function types corresponding to the target monitoring unit and adjacent monitoring units;

[0050] Based on the valve function type dependency weight table, query the initial value of the function dependency weight of the valve function type corresponding to the target monitoring unit and the adjacent monitoring units;

[0051] Calculate the compatibility parameters between the target monitoring unit and adjacent monitoring units based on the operational compatibility rules of high-pressure gas cylinder valves;

[0052] The modified functional dependency weight values ​​are obtained by performing constraint operations on the initial values ​​of the functional dependency weights based on the compatibility parameters.

[0053] The comprehensive correction function relies on weight values ​​and co-occurrence frequencies to calculate the synergy index.

[0054] Preferably, the specific allocation process of the corrected weighting coefficients is as follows:

[0055] Obtain the adjustment strategy category of the high-pressure gas cylinder valve, and set spatial correlation weight and functional synergy weight according to the adjustment strategy category;

[0056] The corrected weight coefficients are calculated based on the set weights, the final spatial relevance index value, and the functional synergy index value.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] By dividing the high-pressure gas cylinder valve into several monitoring units, and acquiring pressure, temperature, and flow data from each unit to form a comprehensive monitoring dataset, the limitations of traditional single-data monitoring are overcome. Integrating multi-dimensional data provides a more comprehensive reflection of the operating status of each part of the valve, resulting in a more three-dimensional understanding of the valve's operation and avoiding misjudgments of the valve's condition due to incomplete data.

[0059] Based on a comprehensive monitoring dataset, pressure stability assessment indicators and environmental factor impact assessment indicators are established. A probabilistic early warning mechanism and an adaptive prediction model are applied to dynamically generate initial risk values, freeing risk assessment from reliance on static threshold settings. Combining probabilistic early warning and adaptive prediction enables more sensitive detection of potential risks and early awareness of possible anomalies, facilitating timely intervention at the risk nascent stage and overcoming the lag in traditional risk assessment methods.

[0060] The initial risk value is dynamically corrected based on the spatial correlation or time series dependence of adjacent monitoring units, fully considering the mutual influence between monitoring units. This correction method makes the risk value calculation more in line with actual working conditions. When the state change of one unit is affected by other units, the corrected risk value can accurately reflect this correlation, thus making the risk judgment more objective and accurate, and avoiding the bias caused by isolated assessment.

[0061] The corrected risk values ​​are sorted to generate a priority control instruction list. This list, combined with adaptive adjustment signals output by the control execution unit, adjusts the valve opening, making the adjustment process more targeted and flexible. Adjusting according to risk priority allows for the prioritization of high-risk units, ensuring resources are concentrated on resolving critical issues. Simultaneously, the adaptive adjustment signal can adjust the valve opening in real time according to changes in actual operating conditions, coping with complex and ever-changing operating environments and ensuring the valve remains in a reasonable operating state, reducing system fluctuations caused by improper adjustment. Attached Figure Description

[0062] Figure 1 This is a schematic diagram illustrating the working principle of the high-pressure gas cylinder valve dynamic monitoring and adaptive adjustment method described in this invention.

[0063] Figure 2A flowchart for establishing pressure stability assessment metrics;

[0064] Figure 3 A flowchart for establishing environmental impact assessment indicators;

[0065] Figure 4 A flowchart for the dynamic correction operation execution;

[0066] Figure 5 A flowchart for calculating spatial correlation index values. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Please see Figure 1 This invention provides a method for dynamic monitoring and adaptive adjustment of pressure in high-pressure gas cylinder valves, the method comprising:

[0069] The high-pressure gas cylinder valve is divided into several monitoring units. Pressure sensor data, temperature data and flow data of each monitoring unit are obtained by deploying pressure sensors, temperature sensors and flow meters to form a comprehensive monitoring dataset.

[0070] Based on this dataset, pressure stability assessment indicators and environmental factor impact assessment indicators are established, and initial risk values ​​are dynamically generated through a probabilistic early warning mechanism and an adaptive prediction model.

[0071] Based on the spatial correlation or time series dependence of adjacent monitoring units, a dynamic correction operation is performed on the initial risk value of each monitoring unit, and the corrected risk value is output.

[0072] The corrected risk values ​​are sorted to generate a priority control instruction list, and the control execution unit outputs an adaptive adjustment signal to adjust the valve opening.

[0073] Example 1: See Figure 2The monitoring unit division of high-pressure gas cylinder valves is designed based on their physical structural characteristics and safety zone boundaries. The geometry and functional zoning of the valve area are the main basis for division, and the valve surface is divided into several monitoring sub-units using a meshing algorithm. The size difference of each monitoring sub-unit is controlled within the allowable range to ensure data comparability between units. The boundary of the monitoring sub-unit must coincide with the safety zone boundary of the valve to avoid monitoring blind spots. Each monitoring sub-unit covers at least one independent pressure sensing point to ensure the independence and integrity of data acquisition. During the division process, 3D modeling technology is used to digitally reconstruct the valve structure, and combined with preset safety zone parameters, an initial monitoring sub-unit layout is automatically generated. Subsequently, through manual verification and algorithm optimization, the sub-unit boundaries are adjusted to meet dimensional requirements while minimizing cross-interference. The finally determined monitoring sub-units serve as monitoring units for subsequent data acquisition and analysis.

[0074] The establishment of pressure stability assessment indices involves the comprehensive processing of multi-source data. Pressure sensor data is first preprocessed to remove noise and outliers, and then pressure fluctuation frequency characteristics are extracted using spectral analysis. Temperature data processing employs a sliding window technique to calculate the temperature change trend within the window, and statistical methods are used to generate temperature stability parameters. The calculation of the pressure change rate parameter relies on a pre-defined weighting model. This model sets the weight ratios for different parameters based on historical data and expert experience, integrating pressure fluctuation frequency, temperature change trend, and temperature stability parameters into a unified pressure change rate parameter. This parameter is compared with a pre-defined safety threshold to generate a pressure safety index, used to quantify the stability of the current pressure state.

[0075] The flow data processing employs a time window statistical method, counting the number of flow anomalies and their magnitude changes within a preset time window. The number of anomalies is determined based on the standard deviation and mean of the flow data; flow values ​​exceeding a set range are marked as anomalies. The magnitude change is quantified by calculating the maximum deviation of the flow within the window. The flow stability score is generated by comprehensively considering the impact of both the number of anomalies and the magnitude change, using a weighted summation method to merge them into a single score. The final pressure stability assessment score is a proportional fusion of the pressure safety index and the flow stability score. This proportion is dynamically adjusted according to the valve's operating characteristics and safety requirements to adapt to assessment needs under different operating conditions.

[0076] The division of monitoring units and the establishment of pressure stability assessment indicators are interrelated processes. A well-defined set of monitoring units provides an accurate data foundation, while scientific assessment indicators effectively reflect the valve's operating status. In practical applications, the division of monitoring units must consider the valve's physical limitations and installation conditions to ensure the feasibility of sensor deployment and the reliability of data. The establishment of assessment indicators requires combining the valve's operating history and environmental characteristics, dynamically adjusting parameter weights and threshold settings to adapt to different application scenarios.

[0077] The analysis of pressure fluctuation frequency employs a multi-scale approach, extracting frequency features from different time scales to comprehensively reflect dynamic pressure changes. Temperature data processing not only focuses on overall trends but also analyzes local fluctuations to identify potential hotspots. Anomaly detection for flow data utilizes adaptive thresholding technology, dynamically adjusting anomaly criteria based on the distribution characteristics of the flow data to reduce false alarms and false negatives. The calculation model for the pressure change rate parameter optimizes weight allocation through machine learning methods, using historical data to train model parameters and improve the accuracy of parameter calculations.

[0078] The generation process of the stress safety index incorporates fuzzy logic technology to handle fuzzy regions near the threshold, avoiding misjudgments caused by minor fluctuations. The calculation of the traffic stability score employs a nonlinear fusion method, considering the interactive effects of variations in the number and magnitude of anomalies to improve the robustness of the score. The final stress stability assessment score is normalized to map it to a unified scoring range, facilitating subsequent risk assessment and decision-making.

[0079] The division of monitoring units and the establishment of evaluation indicators are dynamic processes. As valve operating time increases and environmental conditions change, the layout of monitoring units and the setting of evaluation parameters need to be updated regularly. Optimization of the division algorithm and iterative training of the evaluation model can continuously improve the accuracy of monitoring and evaluation. By introducing a real-time data feedback mechanism, the division of monitoring units and the establishment of evaluation indicators can be adaptively adjusted to adapt to the dynamic operating status of the valve.

[0080] The establishment of pressure stability assessment indicators relies not only on real-time data but also on valve operation logs and maintenance records. Analysis of historical data can identify typical and abnormal valve operating modes, providing a reference for setting assessment indicators. Environmental monitoring data is also incorporated into the assessment process to comprehensively reflect the valve's operating status. Visualizing the assessment results helps operators intuitively understand the valve's operating condition and take timely and necessary adjustments.

[0081] The division of monitoring units and the establishment of evaluation indicators are systematic projects that require the integration of knowledge from multiple disciplines. The analysis of mechanical structures ensures the rational layout of monitoring units, data science methods improve the accuracy of evaluation indicators, and engineering experience guides the practical adjustment of parameters.

[0082] Example 2: See Figure 3 The establishment of environmental impact assessment indicators is a dynamic calculation system integrating multiple parameters. This system extracts three key environmental variables from the comprehensive monitoring dataset: temperature change rate, humidity influence coefficient, and vibration intensity parameter. Standardization transformation eliminates dimensional differences, enabling unified calculation of parameters with different physical dimensions. The temperature change rate is calculated using a difference method within a sliding time window, with the window size dynamically adjusted according to the valve's operating cycle to capture temperature change characteristics at different time scales. The humidity influence coefficient is obtained by combining absolute humidity measurements and relative humidity trends, using an environmental sensor array for multi-point synchronous acquisition. The vibration intensity parameter is derived from a triaxial accelerometer installed at key parts of the valve; its sampling frequency must satisfy the Nyquist sampling theorem to fully record the mechanical vibration spectrum.

[0083] The standardization process employs a statistical distribution-based normalization method to map the original parameter values ​​to a unified numerical range. The conversion of the temperature change rate considers its historical distribution characteristics, using a piecewise linear mapping to preserve the resolution of key change ranges. The standardization of the humidity influence coefficient incorporates an environmental compensation mechanism, adjusting the baseline value range according to seasonal characteristics. The conversion of vibration intensity parameters uses logarithmic compression to preserve the characteristics of large-amplitude vibrations while preventing small-amplitude vibration signals from being overwhelmed by noise. The converted parameters all have the same numerical range and distribution characteristics, creating conditions for subsequent weighted fusion calculations.

[0084] The weighted fusion calculation employs a dynamic weight allocation strategy, with weight ratios automatically adjusted based on the valve's current operating state. Under normal operating conditions, the temperature change rate carries the majority of the weight, reflecting the direct impact of thermodynamic factors on valve performance. When abnormal vibration is detected, the system automatically increases the weight of the vibration intensity parameter, enhancing the contribution of mechanical condition to the evaluation results. The weight of the humidity influence coefficient is adjusted according to changes in environmental sealing, with its calculation weight appropriately increased under open operating conditions. A moving average filter is incorporated into the weighted calculation process to eliminate the interference of instantaneous fluctuations on the evaluation results and maintain the stability of the output indicators.

[0085] The handling mechanism for vibration intensity parameters exceeding a set threshold employs a multi-level response strategy. The threshold is set with reference to the fatigue characteristic curve of the valve's structural materials, combined with historical operating data to determine the critical vibration level. When excessive vibration is detected, the system first determines its persistence, calculating the ratio of consecutive periods of exceeding limits to the total monitoring periods. This ratio reflects the persistence characteristics of the vibration anomaly; short-term instantaneous exceedances and long-term continuous vibrations are handled differently. For short-term exceedances, the system divides the processing gradient based on the difference between the vibration intensity and the threshold. Different gradient intervals correspond to different score correction magnitudes, forming a smooth-transition correction curve.

[0086] The handling of long-term continuous vibration employs a cumulative compensation mechanism. When the proportion of time exceeding the limit reaches a preset critical value, a compensation ratio threshold is triggered. This threshold is calculated based on the valve's design life and a vibration-accelerated aging model, ensuring that the evaluation score objectively reflects the actual impact of vibration on the valve's lifespan. The compensation ratio has a non-linear relationship with the vibration duration; the initial compensation amplitude increases rapidly, then levels off, avoiding evaluation distortion caused by over-correction. A hysteresis effect is introduced into the score update process, maintaining a compensation state for a certain period even after the vibration parameters have fallen back to a safe range, simulating the irreversible characteristics of material fatigue.

[0087] The initial risk value is generated using a tiered judgment logic, achieving rapid risk classification through early warning thresholds. The early warning threshold for the pressure stability assessment index is determined based on the valve's design operating pressure and safety margin, while the early warning threshold for the environmental factor impact assessment index is derived from material environmental adaptability test data. When the assessment index of any monitoring unit exceeds the early warning threshold, the system immediately marks that unit as high-risk and assigns it the highest risk level. This approach prioritizes system safety, ensuring that potential risks are identified promptly.

[0088] For monitoring units that have not triggered early warnings, a prediction function model is used to calculate the risk value. This model takes pressure stability assessment indicators and environmental factor impact assessment indicators as inputs and outputs a risk estimate through a nonlinear mapping relationship. The model parameters are trained using historical fault data and can reflect the actual risk probability distribution under different combinations of indicators. The calculation process of the prediction function considers the interaction between indicators; when two indicators are simultaneously in a critical state, the increase in risk value is greater than the result of linear superposition. This approach is more consistent with the risk accumulation effect caused by the coupling of multiple factors in engineering practice.

[0089] The dynamic risk value update mechanism enables the assessment system to be adaptive. As valve operating time increases and environmental conditions change, the warning threshold and prediction function parameters are periodically optimized and adjusted. The adjustment process is based on statistical analysis of newly added monitoring data, allowing the assessment criteria to track the gradual changes in valve performance. Simultaneously, the system retains a manual intervention interface, allowing experienced operators to temporarily adjust the risk judgment logic based on special circumstances, incorporating expert judgment into the automatic assessment.

[0090] The environmental impact assessment system is designed to fully consider the complex operating conditions of industrial sites. Temperature parameter acquisition employs a redundant sensor layout, with a majority voting mechanism to eliminate interference from faulty sensors. Humidity monitoring takes into account the impact of condensation, incorporating anti-condensation designs on the sensor surfaces. Vibration measurement utilizes broadband accelerometers to cover various mechanical vibration frequencies that valves may generate. Signal transmission employs anti-interference design to prevent electromagnetic noise from contaminating weak vibration signals. These engineering measures ensure the reliability of the assessment indicators from the data source.

[0091] The assessment results are output in a standardized format for easy interaction with other system modules. The environmental factor impact assessment score for each monitoring unit is stored in time-series format, supporting historical trend playback and comparative analysis. The risk value generation process is logged in detail, including intermediate parameters and judgment criteria, providing data support for subsequent fault diagnosis and accountability. The system provides a visual display interface, using color coding to intuitively present the risk level distribution of different areas, helping operators quickly grasp the overall operational status.

[0092] This implementation method achieves a comprehensive quantitative assessment of the impact of environmental factors through multi-level parameter processing and dynamic adjustment mechanisms. Standardized data processing workflows ensure comparability between different monitoring units, weighted fusion methods reflect the relative importance of each environmental factor, and threshold triggering mechanisms ensure timely risk identification. The entire system maintains automatic operation while retaining necessary manual intervention channels, balancing assessment efficiency and flexibility, and can adapt to the monitoring needs of high-pressure gas cylinder valves in various complex environments.

[0093] Example 3: See Figure 4 The dynamic correction operation employs a distributed computing architecture. Each monitoring unit independently completes its own correction calculation while maintaining real-time data interaction with neighboring units. Target monitoring units are selected using a round-robin algorithm. The system maintains a dynamically updated unit access sequence to ensure that all monitoring units are fairly selected as target units for processing. The determination of neighboring monitoring units is based on three-dimensional spatial topology. A connection map is established on the valve structure model, recording the physical connections and signal transmission paths between units. Initial risk values ​​are acquired using a caching mechanism. At the beginning of each calculation cycle, the initial risk values ​​of each unit are loaded into a shared memory area for quick retrieval during correction calculations.

[0094] The calculation of spatial correlation index values ​​integrates both geometric distance and signal transmission characteristics. The straight-line distance is measured using Euclidean distance in the valve coordinate system, and the actual physical interval is determined by the three-dimensional coordinate difference between the installation positions of each monitoring unit. The acquisition of signal transmission delay time relies on a time synchronization protocol; the main controller periodically sends timestamp signals, and each monitoring unit records and transmits the received time back, allowing the system to calculate the transmission delay between units. After normalization, the distance and delay parameters are then incorporated into the spatial correlation calculation process.

[0095] Generating functional synergy index values ​​requires analyzing the functional logic relationships of valves. The system maintains a valve function type knowledge base, recording the standard function definitions and interaction rules of various valve components. The functional type matching between the target monitoring unit and adjacent units employs a fuzzy matching algorithm to handle potential functional definition deviations in actual engineering. For known synergy type combinations, the system directly calls a preset weight coefficient table for index calculation; for new combinations, a compatibility analysis process is initiated, generating temporary synergy parameters through a rule reasoning engine.

[0096] The allocation of weighting coefficients employs a dynamic strategy selection mechanism. The system incorporates multiple weighting strategies, each corresponding to different spatial correlation and functional synergy weight ratios. Strategy selection is based on the valve's current operating mode and safety requirements, automatically switching to the most suitable weight combination under different modes such as normal operation, load fluctuation, and emergency response. A smooth transition process is incorporated into the weighting coefficient calculation to avoid abrupt changes in indicators during strategy switching.

[0097] The weighted correction operation employs a sliding window process in the time domain, integrating correction results from multiple calculation periods, both current and historical, to enhance the continuity of risk value changes. The output of the correction calculation results undergoes range limiting processing to constrain the risk values ​​within an effective range, preventing assessment distortion caused by outlier data. After all monitoring units complete the correction calculation, the system generates a global risk distribution map for subsequent prioritization and control decisions.

[0098] The correction process for all monitoring units is optimized using parallel computing. Based on the regional division of the valve structure, monitoring units are grouped and assigned to different processing cores, and the computational load is balanced through a task scheduling algorithm. Necessary data synchronization mechanisms are maintained during parallel computing to ensure that data dependencies between adjacent units are handled correctly. The system monitors the computation progress of each processing core in real time and dynamically adjusts the task allocation strategy to maximize overall computational efficiency.

[0099] The spatial correlation index value is calculated using the following formula:

[0100]

[0101] in, This represents the spatial correlation index value. This represents the three-dimensional Euclidean distance between monitoring units i and j. For signal transmission delay time, and These are the weighting coefficients of the distance factor and the delay factor. This represents the delay attenuation coefficient. The formula uses an exponential attenuation term to reflect the nonlinear impact of signal transmission efficiency on spatial correlation, while the distance term uses an inverse proportional function to characterize the effect of physical distance.

[0102] The calculation of functional synergy index values ​​employs a hierarchical query mechanism. The system first searches the standard synergy relation database for functional combinations of the target unit and adjacent units. If a record exists, the preset index value is directly invoked; otherwise, a compatibility analysis process is initiated, calculating two sub-indicators: functional interface matching degree and operational sequence coordination degree. A temporary synergy index value is generated through weighted summation. Historical maintenance records of the valves are considered during the calculation process, and appropriate index increases are given to unit combinations that have undergone synergistic optimization modifications.

[0103] The dynamic adjustment of the weighting coefficients incorporates a feedback control mechanism. The system records historical adjustment effect data, analyzes the risk prediction accuracy under different weight combinations, and automatically fine-tunes the weight allocation parameters accordingly. The feedback adjustment process employs a gradual optimization strategy, with each adjustment limited in magnitude to avoid system instability caused by drastic changes. The weighting coefficients are stored using version control, retaining records of all adjustments and supporting manual rollback operations when necessary.

[0104] The verification of risk value correction results employs a cross-validation method. The system retains the original risk values ​​of some monitoring units as a reference to compare the reasonableness of the trends before and after correction. For units with abnormal correction magnitudes, a manual review process is triggered, where operators confirm the reliability of the correction results. Feedback information generated during the verification process is used to optimize the correction algorithm parameters, forming a closed-loop learning system.

[0105] The implementation of dynamic correction operations fully considers the real-time requirements of industrial sites. The calculation cycle is adaptively adjusted according to the valve's operating status; a longer cycle is used during stable operation to reduce the computational load, while a shorter cycle is used to improve response speed when operating conditions change. The algorithm implementation employs fixed-point arithmetic optimization to improve processing efficiency while ensuring computational accuracy. The system reserves hardware acceleration interfaces to support further enhancement of computational performance through dedicated processors such as FPGAs.

[0106] The data interaction process employs a lightweight communication protocol, achieving efficient inter-unit information sharing with limited bandwidth resources. Communication messages are designed to include necessary checksum fields to detect and correct data errors during transmission. Updates to key parameters utilize a differential transmission strategy, sending only the changed portion to reduce communication load. The system implements disconnection reconnection and buffered continuation mechanisms to ensure data integrity during brief communication interruptions.

[0107] The visual monitoring interface displays the correction calculation process in real time, using different colors to indicate the processing status of each monitoring unit. Operators can view detailed correction parameters for any unit, including spatial correlation indicators, functional synergy indicators, and final correction weights. The system provides a correction process replay function, supporting visual traceability and analysis of historical operations. The interface design follows ergonomic principles, highlighting key information to reduce the cognitive load on operators.

[0108] The implementation of dynamic correction operations establishes a spatial correlation model for risk assessment, transforming the originally independent unit assessment into a collaborative assessment considering the overall valve status. Spatial correlation calculations capture the impact of physical layout on risk propagation, functional synergy analysis reflects the logical connections between valve components, and a weight allocation mechanism balances the relative importance of different influencing factors. The entire correction process retains necessary human supervision channels while operating automatically, forming a human-machine collaborative intelligent decision-making system.

[0109] The allocation of computing resources employs a flexible management strategy, dynamically adjusting based on valve size and processing demands. Small valve systems can complete all computations on a single processor, while large distributed valve systems utilize multi-node collaborative processing. The system automatically detects available computing resources and optimizes task allocation, improving resource utilization while meeting real-time requirements. The software implementation adopts a modular design, separating core algorithms from hardware interfaces, facilitating customized deployment for valves of different sizes.

[0110] An anomaly handling mechanism ensures the reliability of the correction process. When a monitoring unit failure is detected, the system automatically reconstructs the connectivity graph and estimates the risk value of the failed unit based on the data of the remaining units. For communication anomalies, a nearest neighbor substitution strategy is adopted, using historical data for temporary correction calculations. In the event of a severe fault, the system can switch to a simplified correction mode, retaining only the basic processing flow of key parameters and maintaining a minimum level of risk assessment functionality.

[0111] The implementation of dynamic correction operations transforms risk assessment from static, single-point judgment to dynamic, spatially collaborative analysis. By incorporating the influencing factors of adjacent units, the corrected risk value more comprehensively reflects the actual state of the valve system. Parallel design and communication optimization of the computation process ensure the system's real-time performance in industrial environments, while flexible resource management and anomaly handling mechanisms enhance the system's robustness. This implementation provides a spatial-dimensional intelligent analysis method for the safety monitoring of high-pressure gas cylinder valves.

[0112] Example 4: See Figure 5 The calculation process of the spatial correlation index value is illustrated using a high-pressure hydrogen storage cylinder valve of a certain model as an example. This valve adopts a modular design, containing six monitoring units, which are installed in key areas such as the inlet control area, pressure regulation area, and safety relief area. The three-dimensional coordinate positions and connection relationships of each monitoring unit are shown in the table below:

[0113]

[0114] Signal transmission delay is measured using the IEEE 1588 precision time protocol. The main controller broadcasts synchronization messages every 100ms, and each monitoring unit records the message reception timestamp and calculates the clock deviation. The system statistically analyzes the transmission delay data for the most recent 10 cycles, taking the 90th percentile value as the stable delay parameter. For example, the average delay from MU-01 to MU-03 is 12.3ms, with a 90th percentile value of 15.6ms; while the delay from MU-04 to MU-05, due to the fiber optic connection, is only 2.1ms.

[0115] The calculation of location proximity is based on Euclidean distance in a three-dimensional coordinate system.

[0116] Taking MU-02 as an example, its distance from MU-01 is:

[0117] sqrt((120.5-0.0)^2+(80.2-0.0)^2+(15.3-0.0)^2)=145.7mm,

[0118] The distance to MU-04 is:

[0119] sqrt((150.3-120.5)^2+(-45.6-80.2)^2+(18.9-15.3)^2)=126.3mm.

[0120] These raw distance values ​​are normalized and mapped to the range of 0-1, where the shortest distance corresponds to 1 and the longest distance corresponds to 0.

[0121] The adjustment coefficients are set according to the principle of prioritizing connection type. Unit pairs with direct physical connections (such as MU-01 and MU-02) are assigned an adjustment coefficient of 1.2, reflecting the strong correlation caused by mechanical linkage. Unit pairs with indirect signal transmission (such as MU-03 and MU-05) use an adjustment coefficient of 0.8, characterizing the weaker coupling of purely electrical connections. Mixed connection paths are handled using a coefficient product rule. For example, the path from MU-01 to MU-06 contains both direct and indirect connection segments, and its combined adjustment coefficient is 1.2 × 0.8 = 0.96.

[0122] The allocation strategy for adjusting the weighting coefficients is dynamically selected based on the valve's operating stage. During the normal inflation stage, a "safety first" strategy is adopted, with spatial relevance weighting set at 60% and functional synergy weighting at 40%. During the rapid venting stage, the strategy is switched to "efficiency first," with the weighting ratios adjusted to 40% and 60%, respectively. A 30-second transition period is set during the strategy switching process, during which the weights change gradually according to a linear pattern to avoid abrupt changes in control commands.

[0123] The calculation example of the spatial correlation index is illustrated using MU-04 as the target cell. Its adjacent cells MU-02 and MU-05 have proximity values ​​of 0.68 and 0.72, respectively, and signal response efficiencies of 0.92 and 0.85, respectively. For the directly connected MU-02, an adjustment factor of 1.2 is used, resulting in a spatial correlation index value of (0.6 × 0.68 + 0.4 × 0.92) × 1.2 = 0.89; for the indirectly connected MU-05, an adjustment factor of 0.8 is used, resulting in an index value of (0.6 × 0.72 + 0.4 × 0.85) × 0.8 = 0.62. These values ​​will be used in subsequent weighting calculations.

[0124] The matching process for the functional synergy index value takes into account the valve control logic. MU-04, as an auxiliary intake valve, belongs to a preset synergy combination with the main intake valve MU-01, and the synergy weight obtained from the knowledge base is 0.7. It belongs to a non-standard combination with the flow metering valve MU-05, so a compatibility analysis process is initiated. Historical data shows that these two units often work together during flow regulation, with a co-occurrence frequency of 83%. The initial value of the functional dependency weight is 0.5, which, after correction by compatibility parameters, yields a synergy index of 0.52, ultimately mapping to a functional synergy index value of 0.65.

[0125] The correction calculation employs a sliding window smoothing process. The initial risk value of the current cycle MU-04 is 75, while the risk values ​​of adjacent units MU-02 and MU-05 are 68 and 82, respectively. According to the "safety first" strategy, a spatial correlation weight of 60% produces a correction coefficient of 0.76, and a functional synergy weight of 40% produces a correction coefficient of 0.71. The overall corrected risk value is 75×0.76+(68×0.89+82×0.62) / 2×0.71=73.4, and after range limiting, the final corrected value is output as 74.

[0126] The visualization interface uses a 3D heatmap to display the spatial correlation distribution. The valve model surface is colored according to the index values ​​between each unit: directly connected areas are displayed in dark red, indirectly connected areas in orange, and weakly correlated areas in light yellow. Operators can select any unit to view detailed correction parameters, including the current strategy mode, a list of adjacent units, and intermediate calculation results. The system refreshes the displayed data every 5 seconds to maintain the real-time nature of the monitoring information.

[0127] The handling of abnormal situations is illustrated by communication interruption. When MU-03 loses contact with the main controller, the system automatically removes it from the list of adjacent units of MU-01 and MU-06, and estimates its risk value using historical data from the most recent 10 minutes. During this period, the spatial correlation calculation of MU-01 only considers MU-02, and the adjustment factor is changed to 1.0. After communication is restored, the system gradually reintegrates MU-03 into the calculation, and after a transition period of 3 cycles, the normal processing flow is fully restored.

[0128] Dynamic allocation of computing resources is reflected in multi-valve monitoring scenarios. When the system manages eight similar valves simultaneously, each valve is allocated an independent computing thread, sharing four physical processor cores. The scheduling algorithm automatically adjusts the computing priority based on the operating conditions of each valve, allocating more computing resources to valves in the rapid venting phase. Memory usage employs object pooling technology, reusing data structures of intermediate computation results to reduce the overhead of frequently creating and destroying objects.

[0129] This implementation demonstrates the practical application of spatial correlation analysis through a specific engineering case. Three-dimensional coordinate data provides the foundation for physical distance calculation, a precise time protocol ensures the accuracy of delay measurements, and connection type differentiation reflects the essential differences between various interaction methods. A dynamic weighting strategy enables the system to adapt to multiple operating modes, while a visual interface helps operators understand complex spatial relationships. The entire implementation process transforms the abstract concept of spatial correlation into actionable engineering practices, providing a reliable spatial dimension analysis method for the intelligent monitoring of high-pressure gas cylinder valves.

[0130] Example 5: The generation process of functional synergy index values ​​is based on a deep analysis of the functional logical relationships of valve components. The system maintains a dynamically updated valve function knowledge base. This knowledge base adopts an object-oriented data structure, abstracting each valve function type into an independent class instance, containing attributes such as function definition, operational constraints, and interaction rules. The initial content of the knowledge base comes from valve design documents and engineering specifications, and is continuously supplemented with implicit association rules discovered in actual operation through machine learning during system operation. When the function type of the target monitoring unit matches that of an adjacent unit, the system first retrieves the standardized synergy relationship definition in the knowledge base. This retrieval uses a multi-level caching mechanism to accelerate the query response.

[0131] For preset combinations of collaborative types, the system invokes a predefined weight allocation scheme. The weight scheme is formulated with reference to the valve control flowchart, analyzing the coordination relationships of each functional unit in the normal operating sequence. The collaborative weights of the main control functional unit and auxiliary functional units are typically set higher, reflecting their dominant position in system control; the weights between parallel functional units are relatively balanced, indicating an equal collaborative relationship. The specific values ​​of the weights are normalized to ensure comparability between different combinations. When a standard collaborative relationship is found, the system directly applies the preset weights to calculate the functional collaboration index value; this calculation method is deterministic and repeatable.

[0132] When encountering non-standard functional combinations, the system initiates a compatibility analysis process. This process first retrieves the historical operation database and calculates the co-occurrence frequency of the target unit and adjacent units in past operations. The calculation of co-occurrence frequency takes into account time window factors, with more recent data having a higher weight than earlier records, reflecting potential parameter drift in the system. The statistical process excludes abnormal data from maintenance cycles and fault periods to avoid interfering with the identification of the true operating mode. Historical data analysis not only focuses on the simultaneous activation state of functional units but also records the time sequence relationship between preceding and subsequent operations to capture potential causal relationships.

[0133] The initial value of the functional dependency weight is queried based on the technical parameter table provided by the valve manufacturer. This table details the interdependence of various valve functions, and the numerical ranges have been standardized by the industry. The system considers the specific model and version differences of the functional units during the query and adaptively adjusts the basic parameters. The initial dependency weight reflects the expected strength of functional correlation during the design phase; however, deviations may occur in actual operation due to equipment aging and changes in operating conditions.

[0134] The calculation of compatibility parameters incorporates an operation timing analysis module. This module monitors the timestamps of functional unit activation commands and calculates the intervals and sequence patterns of sequential operations. For functional combinations with strict timing constraints, such as requiring the main valve to be opened before adjusting the auxiliary valve, the compatibility parameters will be increased accordingly; functional groups that allow random sequential operations will obtain baseline parameter values. The timing analysis also detects abnormal operation sequences, and automatically lowers the compatibility rating of the relevant units when a functional activation pattern that violates the standard procedure is detected.

[0135] The correction function employs a gradual adjustment strategy in the generation process of the weight values. The system compares the differences between the current operating parameters and the design baseline, and applies a compensation coefficient based on the initial weights. An upper limit is set for the application of the compensation coefficient to prevent excessively large single adjustments from causing system oscillations. The newly generated correction weights undergo stability verification; only corrections that maintain a consistent direction over multiple consecutive periods are ultimately adopted. This mechanism avoids misadjustments of weights caused by instantaneous operating condition fluctuations, maintaining the stability of the evaluation indicators.

[0136] The synergy index is calculated by integrating two dimensions: adjusted weight values ​​and co-occurrence frequency. The system sets differentiated calculation ratios for different operational phases, emphasizing weight design during startup and increasing the consideration of actual co-occurrence frequency during stable operation. A non-linear mapping relationship is introduced into the calculation process; when both input parameters reach high levels, the synergy index significantly improves, enhancing the identification of strongly correlated functional combinations. The calculation results undergo smoothing filtering to eliminate the impact of random fluctuations on the index values.

[0137] The final determination of functional synergy index values ​​adopts a hierarchical mapping method. The continuously calculated synergy index is discretized into a finite number of levels, each corresponding to a preset range of index values. This approach enhances the relative evaluation capability of different functional combinations and makes it easier for operators to understand the meaning of the indicators. The mapping relationship is dynamically adjusted according to valve type, with more detailed level divisions for critical safety function units and relatively looser level intervals for ordinary regulating functions. The system retains internally calculated values ​​with decimal precision, only using integer conversion during final display.

[0138] The knowledge base's update mechanism enables continuous optimization of its functionality. The system automatically records process data for each collaborative analysis, including query results, calculation parameters, and final indicator values. Regularly running data mining algorithms analyze these records to discover potential new collaborative patterns. Valid patterns confirmed by engineers are added to the standard collaborative relationship database, gradually expanding the coverage of preset combinations. Knowledge base version management retains all update records and supports version rollback operations when necessary.

[0139] An isolation learning strategy is employed to handle abnormal operation patterns. When a non-standard functional activation sequence is detected, the system creates a temporary analysis instance to study the characteristics of the pattern without affecting the main knowledge base. Fully validated abnormal patterns may be identified as new, valid operation methods and, after confirmation, incorporated into the formal collaboration rules; patterns confirmed as faults are added to a blacklist, triggering corresponding early warning mechanisms. This approach maintains system stability while enabling adaptation to new operation patterns.

[0140] The visual interface displays collaborative relationships through a functional topology diagram. Each monitoring unit of the valve is represented as a node, and the strength of functional collaboration is indicated by the thickness and color depth of the connecting lines. Operators can click on nodes to view detailed functional definitions and drag connecting lines to adjust collaboration weights. This interactive design facilitates manual verification and correction of automated analysis results. The layout algorithm of the topology diagram automatically optimizes the display effect, visually clustering strongly related functional units and dispersing weakly related units to form an intuitive functional relationship map.

[0141] Real-time performance of the computation process is ensured through a priority scheduling mechanism. Queries of standard collaborative relationships are set as high-priority tasks to ensure rapid response; complex compatibility analyses are executed as background tasks, allowing for appropriate processing delays. The system dynamically monitors the computational load, automatically simplifying non-critical analysis steps during peak periods to maintain the real-time performance of core evaluation functions. Key parameters are stored using double buffering to ensure that complete data is always available for risk assessment.

[0142] This implementation method transforms abstract concepts of synergy into quantifiable evaluation indicators through systematic functional relationship analysis. Knowledge-based standard queries ensure the reliability of the basic assessment, historical data analysis captures dynamic correlations in actual operation, and compatibility parameters reflect the temporal characteristics of operational constraints. A progressive adjustment strategy balances the differences between design expectations and actual performance, while visualization tools bridge the gap between machine analysis and human judgment. The entire system maintains automated operating efficiency while possessing the ability to continuously learn and adapt to new operating conditions, providing a comprehensive solution for the functional synergy evaluation of high-pressure gas cylinder valves.

[0143] The application of functional synergy indicators not only serves for immediate risk assessment but also provides a basis for optimized valve control. The system records long-term trends in synergy indicators for various functional combinations, identifying gradually weakening functional correlations—changes that may indicate equipment wear or parameter drift. Maintenance personnel can then conduct targeted checks on the status of relevant valve components based on these indicator changes, enabling predictive maintenance. Control strategy optimization also references the distribution of synergy indicators, increasing the frequency of use of efficient functional combinations and avoiding inefficient synergy patterns, thereby improving overall system performance.

[0144] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0145] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic monitoring and adaptive adjustment of pressure in a high-pressure gas cylinder valve, characterized in that, The method includes: The high-pressure gas cylinder valve is divided into several monitoring units, and the pressure sensor data, temperature data and flow data of each monitoring unit are acquired to form a comprehensive monitoring dataset. Based on the comprehensive monitoring dataset, pressure stability assessment indicators and environmental factor impact assessment indicators were established, and initial risk values ​​were dynamically generated by applying a probabilistic early warning mechanism and an adaptive prediction model. Based on the spatial correlation or time series dependence of adjacent monitoring units, the initial risk value of each monitoring unit is dynamically corrected, and the corrected risk value is output. The corrected risk values ​​are sorted to generate a priority control instruction list, and the control execution unit outputs an adaptive adjustment signal to adjust the valve opening. The specific execution steps of the dynamic correction operation include: Randomly select a monitoring unit as the target monitoring unit and obtain the initial risk values ​​of all its neighboring monitoring units; The final spatial correlation index value is calculated based on the spatial distance between the target monitoring unit and adjacent monitoring units and the signal transmission delay time. By matching the valve function types of adjacent monitoring units and the target monitoring unit, a functional synergy index value is generated; Adjusted weighting coefficients are assigned based on the final spatial relevance index value and functional synergy index value; The corrected weighting coefficients include corrected weighting coefficients corresponding to spatial correlation and corrected weighting coefficients corresponding to functional synergy; The initial risk value of the target monitoring unit is weighted and corrected based on the corrected weighting coefficient, and the corrected risk value is output. The weighted correction operation includes multiplying the initial risk value of the target monitoring unit by the correction weight coefficient corresponding to spatial correlation, then multiplying the product of the initial risk value of the adjacent monitoring unit and the corresponding final spatial correlation index value by the average value and the correction weight coefficient corresponding to functional synergy, and finally adding the results of the two multiplications to obtain the corrected risk value. Iterate through all monitoring units, perform the correction operation in sequence, and output the corrected risk value for each monitoring unit.

2. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 1, characterized in that, The specific rules for dividing the monitoring units are as follows: Based on the physical structural characteristics of the high-pressure gas cylinder valve and the preset safety partition boundaries, the valve area is divided into monitoring sub-units with size differences within the allowable range. Each monitoring sub-unit covers at least one independent pressure sensing point, and the monitoring sub-unit is used as a monitoring unit.

3. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 1, characterized in that, The specific process for establishing the pressure stability assessment index is as follows: Analyze pressure fluctuation frequency based on pressure sensor data; Temperature change trend and temperature stability parameters are extracted from temperature data, and pressure change rate parameters are calculated through a preset weight allocation model. A pressure safety index is generated based on the comparison between the pressure change rate parameter and the preset safety threshold. A traffic stability score is generated based on the number and magnitude of traffic anomalies within a preset time window in the traffic data. The pressure stability assessment score is calculated by combining the pressure safety index and the flow stability score.

4. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 1, characterized in that, The specific process for establishing the environmental impact assessment indicators is as follows: Temperature change rate, humidity influence coefficient, and vibration intensity parameters are extracted from the comprehensive monitoring dataset and then standardized. The environmental impact assessment score is obtained by performing a weighted fusion calculation on the standardized parameters. Specifically, when the vibration intensity parameter exceeds a set threshold, the environmental factor impact assessment score is updated. The rules for updating the environmental factor impact assessment score are as follows: If the ratio of the number of consecutive monitoring periods exceeding the set threshold to the total number of monitoring periods exceeds the limit, the setting of the compensation ratio threshold will be triggered, and the environmental factor impact assessment score will be increased accordingly based on the compensation ratio threshold. Otherwise, the gradient interval is divided according to the difference between the vibration intensity parameter and the set threshold, and the gradient is increased in a decreasing proportion, with the upper limit of the increase being the set compensation ratio threshold.

5. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 1, characterized in that, The specific process for generating the initial risk value is as follows: Set early warning thresholds for pressure stability assessment indicators and environmental factor impact assessment indicators; If any evaluation indicator of a monitoring unit exceeds the set early warning threshold, its initial risk value will be assigned the maximum value. If any indicator falls below the set warning threshold, the initial risk value is calculated using a prediction function based on the pressure stability assessment indicator and the environmental factor impact assessment indicator.

6. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 5, characterized in that, The specific calculation process for the spatial correlation index value includes: The signal response efficiency is calculated based on the signal transmission delay time between the target monitoring unit and adjacent monitoring units. The proximity of the target monitoring unit to the adjacent monitoring unit is calculated based on the straight-line spatial distance between the target monitoring unit and the adjacent monitoring unit. The spatial correlation index value is generated by weighted fusion operation by combining the signal response efficiency and location proximity, and the adjustment coefficient is set according to the connection type of adjacent monitoring units; The final spatial correlation index value is obtained by adjusting the coefficients accordingly. The adjustment coefficient set according to the connection type shall meet the following rules: A high adjustment factor value is assigned if the connection type is a direct physical connection, and a low adjustment factor value is assigned if the connection type is an indirect signal transmission, with the high adjustment factor value being greater than the low adjustment factor value.

7. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 6, characterized in that, The specific process for generating the functional synergy index value includes: Match the dominant valve function type of the adjacent monitoring unit with the valve function type of the target monitoring unit; If it belongs to the preset collaborative type combination, the functional collaborative index value is calculated according to the matched valve function type and the corresponding function type preset weight; If it does not belong to a collaborative combination, a collaborative index is calculated based on the functional compatibility specification, and the functional collaborative index value is obtained by mapping through a preset transformation function based on the collaborative index.

8. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 7, characterized in that, The specific calculation process for the synergy index is as follows: Acquire historical pressure monitoring datasets and then statistically analyze the co-occurrence frequency of valve function types corresponding to the target monitoring unit and adjacent monitoring units; Based on the valve function type dependency weight table, query the initial value of the function dependency weight of the valve function type corresponding to the target monitoring unit and the adjacent monitoring units; Calculate the compatibility parameters between the target monitoring unit and adjacent monitoring units based on the operational compatibility rules of high-pressure gas cylinder valves; The modified functional dependency weight values ​​are obtained by performing constraint operations on the initial values ​​of the functional dependency weights based on the compatibility parameters. The comprehensive correction function relies on weight values ​​and co-occurrence frequencies to calculate the synergy index.

9. The method for dynamic monitoring and adaptive adjustment of high-pressure gas cylinder valve pressure according to claim 8, characterized in that, The specific allocation process of the corrected weighting coefficients is as follows: Obtain the adjustment strategy category of the high-pressure gas cylinder valve, and set spatial correlation weight and functional synergy weight according to the adjustment strategy category; The adjustment strategy selection is based on the valve's current operating mode and safety level requirements, and automatically switches the weight combination under different modes such as normal operation, load variation, and emergency response.

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