Method for monitoring and adjusting the sealing integrity of modular construction of clean heating and ventilation workshops

Abstract

This application provides a method for monitoring and adjusting the sealing integrity of modular construction in cleanroom HVAC workshops, comprising: acquiring real-time data of sealing materials from a sensor array to generate sealing status information characterizing sealing integrity; analyzing the performance degradation trend of the sealing materials based on the sealing status information to generate adjustment signal data for adjustment; acquiring environmentally relevant information through the adjustment signal data and integrating it into a dynamic adjustment model to obtain an optimized sealing parameter configuration; determining the air leakage risk based on the optimized sealing parameter configuration and updating the connection model of the modular components; extracting airtightness assessment indicators from the connection model to obtain intervention priority data; adjusting the dynamic adjustment parameters based on the intervention priority data to generate final sealing reinforcement instruction information; updating the data stream of the real-time monitoring system through the sealing reinforcement instruction information to determine the stable output of the cleanliness level and feed it back to the production reliability assessment.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops. Background Technology

[0002] Cleanroom HVAC systems serve as crucial production environments for high-tech industries such as electronics manufacturing and biomedicine. The precise control of environmental parameters, such as air cleanliness, temperature, humidity, and pressure differential, directly impacts product quality and production safety. The importance of this field is self-evident; it is not only the cornerstone of ensuring high-end manufacturing quality but also a key support for driving industrial upgrading.

[0003] However, the construction and maintenance of cleanrooms still face numerous challenges, urgently requiring technological innovation to overcome bottlenecks. The problems exposed by existing methods during construction and operation cannot be ignored. Traditional construction relies heavily on on-site operations, which are cumbersome and time-consuming, making it difficult to uniformly control quality, especially in terms of sealing and environmental stability. Often, the inability to dynamically respond to external changes leads to poor results. A deeper problem lies in the lack of real-time response capabilities to environmental changes and the absence of mechanisms to predict and adjust for potential performance degradation during long-term operation. This often leaves cleanrooms struggling to cope with complex operating conditions. Focusing on the technical difficulties, a core issue in cleanroom construction is ensuring the long-term airtightness of connections between modular components. Unstable sealing directly affects the cleanliness level of the cleanroom, and this instability often stems from the continuous interference of environmental pressure differences and temperature changes on the connection points, causing the sealing materials to gradually fail, creating micro-gaps, and ultimately leading to air leakage. Even more serious is that this failure often intensifies over time, yet there is a lack of effective real-time monitoring and adjustment methods, ultimately causing the cleanroom environmental parameters to deviate from standards and impacting production. For example, in some high-cleanliness electronic manufacturing workshops, even minor leaks at module connections can lead to particulate matter intrusion, directly causing an increase in product defect rates.

[0004] Therefore, how to achieve dynamic adaptation of the sealing performance of joints to environmental changes and timely intervention for potential failures based on modular construction has become a key issue in improving the construction quality and operational stability of cleanrooms. Solving this problem is not only related to improving construction efficiency but also directly affects the long-term reliability of the production environment, urgently requiring innovative technologies to fill this gap. Summary of the Invention

[0005] This invention provides a method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops, mainly including:

[0006] Real-time data of the sealing material is acquired from a sensor array to generate sealing status information characterizing the seal integrity. Based on this sealing status information, the performance degradation trend of the sealing material is analyzed, generating adjustment signal data for regulation. Environmentally relevant information is obtained from the adjustment signal data and integrated into a dynamic adjustment model to obtain optimized sealing parameter configurations. The optimized sealing parameter configurations are used to assess air leakage risk and update the connection model of modular components. Air tightness assessment indicators are extracted from the connection model, potential failure points are analyzed, and intervention priority data is obtained. Based on the intervention priority data, dynamic adjustment parameters are adjusted to generate final sealing reinforcement command information. The sealing reinforcement command information is used to update the data stream of the real-time monitoring system, determine the stable output of the cleanliness level, and feed it back into the production reliability assessment. Furthermore, the step of acquiring real-time data of the sealing material from the sensor array to generate sealing status information characterizing the seal integrity includes: acquiring stress data and environmental pressure difference data of the sealing material at the connection from the sensor array; determining whether the stress data exceeds the normal range by using a preset threshold to obtain an initial anomaly index; integrating the stress data and the environmental pressure difference data using a data fusion processing method for the initial anomaly index to determine the fused deviation value; obtaining the deformation trend of the sealing material based on the fused deviation value, determining whether the deformation trend indicates a potential leakage risk, and obtaining a risk level; generating sealing status information characterizing the seal integrity based on the risk level, and triggering an anomaly alarm mechanism. Furthermore, the step of analyzing the performance degradation trend of the sealing material based on the sealing state information and generating adjustment signal data for adjustment includes: extracting the performance degradation trend from the sealing state information; analyzing the performance degradation trend through a classification algorithm to obtain a degradation degree index; determining whether the degradation degree index exceeds a preset threshold and determining the adjustment demand level; acquiring material performance monitoring data from the sensor array and fusing the material performance monitoring data according to the adjustment demand level to generate adjustment signal data; and triggering adaptive calibration of the sealing material through the adjustment signal data to obtain the final adjustment signal data for adjustment. Furthermore, the step of obtaining environmentally relevant information through the adjustment signal data and integrating it into the dynamic adjustment model to obtain the optimized sealing parameter configuration includes: extracting sealing environmental factors from the adjustment signal data; acquiring environmental temperature monitoring data through sensor acquisition methods to obtain temperature change information; using the temperature change information, combining the temperature data with the existing parameters of the model into the dynamic adjustment model using a multi-source data integration method to determine the application of model fusion technology; incorporating a preset adaptive adjustment mechanism into the dynamic adjustment model, adjusting parameters if the temperature change information exceeds a preset threshold, determining whether it affects the sealing parameter configuration, and obtaining parameter optimization output; generating an optimized configuration scheme based on the parameter optimization output, determining the adjustment level of the sealing environmental factors, and updating the model parameter values.Furthermore, the step of determining the air leakage risk based on the optimized sealing parameter configuration and updating the connection model of the modular component includes: obtaining pressure distribution data from the optimized sealing parameter configuration; determining whether the air leakage risk corresponding to the pressure distribution data is higher than a preset threshold; if the risk is higher than the preset threshold, activating an airtightness enhancement protocol and determining an additional scheme for auxiliary sealing measures; integrating the pressure distribution data through the additional scheme and updating the connection model of the modular component; and incorporating leakage prevention fusion technology into the connection model to obtain a component adjustment strategy for improving sealing performance. Furthermore, the step of extracting airtightness assessment indicators from the connection model, analyzing potential failure points, and obtaining intervention priority data includes: obtaining airtightness assessment indicators from the connection model; integrating material aging data through temperature effect simulation to determine a set of potential failure points; analyzing the set of potential failure points using a regression algorithm to obtain a failure probability distribution and determine the level of intervention demand; integrating pressure distribution data into the failure probability distribution to obtain a priority ranking matrix; and updating the processing order through the priority ranking matrix to generate intervention priority data for subsequent adjustment. Furthermore, the step of adjusting the dynamic adjustment parameters based on the intervention priority data to generate the final sealing reinforcement instruction information includes: obtaining dynamic adjustment parameters from the intervention priority data; adjusting the dynamic adjustment parameters using an optimization algorithm, processing the parameter values ​​through iterative operations to obtain an adjusted parameter set; integrating vibration load simulation for the adjusted parameter set, extracting load change curves from preset vibration data, determining the load influence distribution, and judging the sealing reinforcement requirements; obtaining the reinforcement strategy distribution by combining the load influence distribution with airtightness evaluation indicators, calculating the strategy weights, and obtaining a strategy optimization matrix; and extracting the priority sequence from the strategy optimization matrix to generate the final sealing reinforcement instruction information. Furthermore, the step of updating the data stream of the real-time monitoring system through the sealing enhancement command information to determine the stable output of the cleanliness level and feed it back to the production reliability assessment includes: obtaining environmental noise filtering parameters through the sealing enhancement command information; extracting filtering coefficients from noise data to determine the initial adjustment value of the data stream; integrating production process monitoring based on the initial adjustment value of the data stream, extracting the noise impact distribution from a preset threshold, analyzing noise changes through the distribution curve, and obtaining an optimized sequence for the monitoring system; integrating the optimized sequence of the monitoring system with the command information to determine the stable threshold of the output value, comparing output fluctuations through the threshold to obtain cleanliness level assessment indicators; and updating the feedback loop through the cleanliness level assessment indicators to transmit the stable output value to the production reliability assessment.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention addresses the performance degradation and airtightness stability issues of sealing materials in complex environments. It acquires stress and differential pressure data from a sensor array in real time, fuses and processes this data to generate sealing status information, analyzes performance degradation trends, and generates adjustment signals if thresholds are exceeded. Sealing parameter configuration is optimized in conjunction with temperature changes. When the risk of air leakage exceeds limits, an airtightness enhancement protocol is activated, the connection model is updated, evaluation indicators are extracted, and failure points are analyzed using regression algorithms. Intervention priorities are ranked, dynamic adjustment parameters are optimized, and finally, a sealing reinforcement command is generated, updating the monitoring data stream to ensure stable cleanliness level output and feedback to reliability assessment. This invention, through the synergistic effect of data fusion, classification and regression algorithms, and dynamic adjustment models, achieves precise monitoring and optimized intervention of seal integrity, significantly improving long-term seal stability and production reliability, especially in scenarios with drastic environmental changes, ensuring the system's airtightness and safety. Attached Figure Description

[0009] Figure 1 This is a flowchart of the method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops according to the present invention.

[0010] Figure 2 This is a schematic diagram of the module framework for step S102 in the modular construction sealing integrity monitoring and adjustment method for clean HVAC workshops of the present invention;

[0011] Figure 3 This is a schematic diagram of the module framework for step S103 in the modular construction sealing integrity monitoring and adjustment method for clean HVAC workshops of the present invention;

[0012] Figure 4 This is a schematic diagram of the module framework for step S104 in the modular construction sealing integrity monitoring and adjustment method for clean HVAC workshops of the present invention;

[0013] Figure 5 This is a schematic diagram of the module framework for step S105 in the modular construction sealing integrity monitoring and adjustment method for clean HVAC workshops of the present invention. Detailed Implementation

[0014] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0015] like Figures 1-5 The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops in this embodiment may specifically include:

[0016] Step S101: Obtain real-time stress data and environmental pressure difference data of the sealing material at the connection from the sensor array, and integrate the data through a data fusion processing method to obtain sealing status information for characterizing the integrity of the seal.

[0017] Real-time stress data and ambient pressure difference data of the sealing material at the connection point are acquired from a sensor array. A preset threshold is used to determine whether the stress data exceeds the normal range, thus obtaining an initial anomaly index. For this initial anomaly index, a data fusion processing method is used to integrate the stress data and the ambient pressure difference data to determine the fused deviation value. Based on the fused deviation value, the deformation trend of the sealing material is obtained, and it is determined whether the deformation trend indicates a potential leakage risk, thus obtaining a risk level. Based on the risk level, sealing status information characterizing the seal integrity is generated, and an anomaly alarm mechanism is triggered.

[0018] In one implementation, acquiring real-time stress data and ambient pressure differential data of the sealing material at the joint from a sensor array can be achieved using multiple types of sensors deployed in the industrial piping system. These sensors, including stress sensors and differential pressure sensors, are arranged at the sealing joint, such as a pipe flange interface. The stress sensors monitor the deformation data of the sealing material under mechanical loads, while the ambient pressure differential sensors capture data on the difference between internal and external pressures. This data acquisition process ensures real-time performance, transmitting the data to a central processing unit via a wireless transmission module.

[0019] For example, in the scenario of monitoring the seals of oil pipelines, a sensor array collects data once per second to capture dynamic changes. Furthermore, integrating this data through data fusion processing involves the application of multi-source data preprocessing and fusion algorithms. First, the collected stress and differential pressure data undergo noise filtering and normalization to eliminate environmental interference.

[0020] Specifically, stress data is represented as the tensile or compressive values ​​within the material, while pressure difference data is quantified as numerical differences. The fusion method employs a weighted average algorithm, assigning higher weight to stress data because it directly reflects material integrity, while pressure difference data serves as an auxiliary indicator.

[0021] In one possible implementation, the fusion process calculates a comprehensive index, such as using the product of stress and pressure difference as the fusion feature, thereby generating a unified vector representation. This method, when applied to pipeline systems, can handle data heterogeneity and ensure the reliability of the fusion results.

[0022] It should be noted that the specific steps of data fusion processing include data alignment and model calculation. First, it is ensured that the stress data and differential pressure data are synchronized in terms of timestamps to avoid time series deviations. Then, a Kalman filter is applied as a fusion tool, which integrates multi-sensor inputs by iteratively updating the state estimate.

[0023] For example, in natural gas pipeline seal monitoring, stress data may indicate material fatigue, while differential pressure data indicates leakage risk; after fusion, the filter outputs a smooth state vector characterizing the overall seal condition. This detailed fusion process helps improve monitoring accuracy and reduce false alarms in industrial environments.

[0024] Preferably, the sealing status information used to characterize the sealing integrity is obtained based on the fused data to generate a threshold judgment or probability model.

[0025] For example, the fusion index is compared with a preset threshold, and if it exceeds the threshold, it is marked as an integrity-impaired state.

[0026] In one embodiment, the status information is output as a vector, including an integrity score and a risk level, for system display or alarm purposes. This generation method is applicable to various scenarios within the same domain, such as seal monitoring in chemical pipelines or water treatment systems. In another embodiment, for high-pressure vessel seals, the sensor array can be expanded to include temperature sensors as an auxiliary, but the core focus remains on the fusion of stress and differential pressure data. The fusion method is adjusted to a simple neural network-based model, where the input layer receives raw data, the hidden layer extracts features, and the output layer generates status information. In this way, the system achieves continuous assessment of seal integrity, enabling timely identification of potential faults in practical operation, thereby supporting maintenance decisions.

[0027] Understandably, the aforementioned data fusion processing emphasizes a modular structure in its implementation: the acquisition module is responsible for data acquisition, the fusion module executes the integration algorithm, and the output module generates status information. This structure ensures the flexibility of the technical solution, covering different pipe sizes or material types in the field of industrial seal monitoring.

[0028] For example, in low-pressure drainage pipeline scenarios, stress data may be low, but differential pressure changes are significant; the fusion process generates accurate state information by adjusting weight parameters, demonstrating the method's adaptability. Furthermore, this technical solution achieves effective characterization of seal integrity through real-time data integration, which can reduce leakage risks and extend equipment life in practical applications.

[0029] Step S102: Based on the sealing status information, a classification algorithm is used to analyze the performance degradation trend of the sealing material. If the degradation trend exceeds a preset threshold, adjustment signal data for adjustment is generated.

[0030] The performance degradation trend is obtained from the sealing status information, and analyzed using a classification algorithm to obtain a degradation degree index. For this degradation degree index, it is determined whether it exceeds a preset threshold, thus identifying the adjustment requirement level. Based on the adjustment requirement level, material performance monitoring data is acquired from the sensor array and fused to generate adjustment signal data. This adjustment signal data triggers adaptive calibration of the sealing material, yielding adjustment signal data for further adjustment.

[0031] In one implementation, the process of analyzing the performance degradation trend of the sealing material using a classification algorithm based on the sealing status information first involves input processing of the status information. The sealing status information is typically presented in vector form, including integrity scores and risk levels, which are derived from the aforementioned data fusion step.

[0032] Specifically, the classification algorithm can choose the support vector machine model, which constructs a hyperplane to classify the input data into different categories in order to identify patterns of performance degradation.

[0033] It's important to note that the role of Support Vector Machines (SVMs) here is to analyze time-series data, such as mapping continuous sealing state vectors to decay trend categories like "stable," "slight decay," or "severe decay." In industrial pipeline systems, this analysis ensures long-term monitoring of sealing materials. For example, in oil pipeline scenarios, the algorithm processes hourly collected state information and calculates the trend slope to quantify the decay rate. Furthermore, the implementation of the classification algorithm includes two key stages: feature extraction and model training. Feature extraction extracts key indicators from the sealing state information, such as the rate of stress change and the amplitude of pressure differential fluctuations. These indicators reflect the dynamic changes in material properties. Model training uses historical data samples, such as labeled datasets selected from natural gas pipeline monitoring databases, for supervised learning to optimize the classification boundary.

[0034] For example.

[0035] In one possible implementation, the algorithm takes a state vector containing 10 time points as input and outputs a probability distribution of the degradation trend, thereby predicting future performance degradation. When applied to chemical pipeline sealing monitoring, this method can handle data noise and adjust the model's sensitivity to nonlinear trends through a kernel function, ensuring the accuracy of the analysis.

[0036] Preferably, if the attenuation trend exceeds a preset threshold, adjustment signal data for regulation is generated. This process is based on a threshold comparison, for example, comparing the trend slope with a threshold of 0.05; if it exceeds the threshold, signal generation is triggered. The adjustment signal data can be a digital instruction, such as a coded value for "increase sealing pressure" or "replace material," sent to the control system. In water treatment pipeline scenarios, this signal helps to automatically regulate valves and maintain seal integrity.

[0037] Understandably, the threshold setting is derived from empirical data; for example, a threshold of 0.1 might be set in a high-pressure pipeline environment to accommodate higher risks. In another embodiment, the classification algorithm can be extended to a decision tree model, which constructs a tree structure by recursively splitting the data to analyze multiple dimensions of sealing status information. The advantage of decision trees lies in their strong interpretability; for example, the root node branches based on stress data, child nodes consider pressure difference trends, and the final leaf nodes output the attenuation category. In drainage pipeline sealing monitoring, this model processes low-frequency data, generates trend reports, and immediately generates an alarm signal if the trend shows accelerated attenuation. This implementation emphasizes the modularity of the algorithm, facilitating integration into existing industrial systems.

[0038] Specifically, the steps for generating adjustment signal data include signal encoding and transmission. The signal data is encapsulated in JSON format and contains adjustment type and intensity parameters; for example, in an oil pipeline failure scenario, the signal indicates "reduce flow rate" to mitigate attenuation.

[0039] It should be noted that this process ensures real-time response, with signals transmitted to the actuator via a wireless network, supporting preventative maintenance.

[0040] For example, in continuous monitoring of natural gas pipelines, a classification algorithm analyzes several weeks' worth of status information. Once a decay trend exceeds a threshold, it generates signal data for automatic adjustment of sealing fasteners. This application demonstrates the versatility of the technology within the same field, effectively addressing decay variations caused by different pipeline materials. Furthermore, the analysis and signal generation process employs a parallel computing framework to handle large-scale data. In chemical pipeline environments, the framework distributes tasks across multiple processors, accelerating trend analysis and ensuring timely signal generation. This architecture enhances the system's robustness, supporting dynamic management of sealing performance in practical operations.

[0041] Step S103: Obtain temperature change information related to the sealing environment through the adjustment signal data, and integrate the temperature change information into the dynamic adjustment model to obtain the optimized sealing parameter configuration.

[0042] Sealing environmental factors are extracted from the adjustment signal data. Ambient temperature monitoring data is acquired using sensor acquisition methods to obtain temperature change information. Based on this temperature change information, a multi-source data integration approach is used to combine the temperature data with existing model parameters into the dynamic adjustment model, determining the application of model fusion technology. For the dynamic adjustment model, a preset adaptive adjustment mechanism is incorporated. If the temperature change information exceeds a preset threshold, parameters are adjusted. It is determined whether the temperature change information affects the sealing parameter configuration, resulting in optimized parameter output. Based on the optimized parameter output, an optimized configuration scheme incorporating environmental factor adjustments is generated. The environmental factor results extracted from the adjustment signal data are then integrated to determine the adjustment level of the sealing environmental factors. Based on the adjustment level, the parameter values ​​in the dynamic adjustment model are updated to obtain the optimized sealing parameter configuration.

[0043] In one implementation, the process of acquiring temperature change information related to the sealed environment through the adjustment signal data first involves parsing the signal data. The adjustment signal data is typically in a digital format, such as a coded sequence containing adjustment instructions embedded with environmental parameter indicators.

[0044] Specifically, in oil pipeline systems, after signal data is received via wireless transmission, temperature-related fields, such as the current temperature value and rate of change, are extracted using analytical algorithms.

[0045] It should be noted that this parsing is based on a predefined protocol, such as mapping binary bits in the signal to temperature data, to ensure accurate acquisition.

[0046] For example, in a natural gas pipeline monitoring scenario, when signal data indicates "increased sealing pressure," it simultaneously carries temperature sensor readings. The algorithm calculates changes such as temperature gradients to reflect environmental impacts. This method supports thermal stress analysis of the sealing material, processes data collected every minute, and achieves real-time response. Furthermore, the step of integrating the temperature change information into a dynamic adjustment model emphasizes the model's update mechanism. The dynamic adjustment model can employ a feedback-based control framework that adjusts internal parameters based on input temperature change information.

[0047] Preferably, in a chemical pipeline environment, the fusion process includes combining the temperature change vector with the existing model state, calculating new parameters using a weighted averaging method, such as multiplying the temperature change rate by a weighting factor and then incorporating them into the model equations.

[0048] Understandably, this fusion ensures the model's adaptability to environmental variations. For example, in water treatment pipelines, the initial model parameters are based on historical data, and the sealing tightness threshold is updated after fusion.

[0049] It should be noted that the principle of dynamically adjusting the model lies in iterative optimization, where temperature information serves as an input variable, influencing the computational path of the output configuration and supporting continuous management of sealing performance. In another embodiment, acquiring temperature change information can be extended to a multi-sensor fusion approach.

[0050] Specifically, adjusting the signal data triggers the sensor array to collect temperature data. For example, in the monitoring of drainage pipe seals, after the signal is generated, the system queries the distributed temperature probes and calculates the changing trend, such as the rate of temperature rise.

[0051] For example, this approach processes noisy data, smoothing out changes through filtering algorithms to ensure the reliability of the fusion. In common industrial pipeline scenarios, this process demonstrates the adaptability of the technology, emphasizing its response to extreme temperature fluctuations, such as when applied to high-pressure environments. Based on the above fusion, the optimized sealing parameter configuration is generated from the model's output. Further...

[0052] In one possible implementation, the model is dynamically adjusted to process the fused information and then output configuration data such as pressure values ​​and material thickness recommendations.

[0053] Specifically, in oil pipeline fault prevention, model calculations optimize parameters to ensure seal integrity. For example, by incorporating temperature changes, configurations are adjusted to reduce flow rates to mitigate thermal expansion. This output supports automated control systems and can be directly applied to actuators.

[0054] Preferably, when this technical solution is applied to continuous monitoring of natural gas pipelines, the fusion process processes several days of temperature data to generate a configuration to maintain long-term stability.

[0055] For example, in chemical pipeline scenarios, optimizing parameter configuration helps adjust valve positions to adapt to seasonal temperature changes and ensure the durability of sealing materials.

[0056] In one embodiment, the dynamic adjustment model may employ a neural network structure that fuses temperature information through hierarchical processing.

[0057] It should be noted that the network input layer receives the change vector, the hidden layer calculates the interaction effects, and the output layer generates the parameter configuration. In a water treatment pipeline environment, this model is trained using simulated data, and the fused data optimizes the configuration to reduce the risk of leakage. Furthermore, the acquisition of temperature change information emphasizes synchronization with signal data.

[0058] For example, in drainage pipeline systems, signals are triggered for real-time acquisition, which is then integrated into the model to generate configurations, supporting preventative maintenance.

[0059] Understandably, this process is common in the industrial pipeline sector, such as in high-pressure natural gas scenarios, where optimized configurations improve system robustness.

[0060] Step S104: For the optimized sealing parameter configuration, determine the risk of air leakage. If the risk is higher than a preset threshold, activate the airtightness enhancement protocol and update the connection model of the modular component by adding auxiliary sealing measures.

[0061] Pressure distribution data is obtained from the optimized sealing parameter configuration to assess the risk of air leakage. If the risk exceeds a preset threshold, an airtightness enhancement protocol is activated to determine an additional auxiliary sealing measure. The pressure distribution data is then integrated using this additional measure to update the connection model of the modular components. Leakage prevention fusion is incorporated into the connection model to obtain a component adjustment strategy.

[0062] In one implementation, the process of assessing the risk of air leakage for an optimized sealing parameter configuration first involves establishing a risk assessment mechanism. This mechanism calculates the probability of potential leakage based on sealing parameters such as pressure values ​​and material thickness.

[0063] Specifically, in oil pipeline systems, risk assessment is achieved by comparing the deviation between the current configuration and historical leakage data. For example, optimization parameters are input into a risk function that takes into account environmental factors such as temperature fluctuations and outputs a risk score.

[0064] It should be noted that the risk score uses a quantitative indicator, for example, a score range from 0 to 100, where a score above 50 indicates a potential problem. This method ensures an objective assessment of seal integrity and supports subsequent decision-making. Furthermore, if the risk exceeds a preset threshold, an airtightness enhancement protocol is activated. This protocol is a predefined response framework designed to improve sealing performance through system commands.

[0065] Preferably, in the natural gas pipeline monitoring scenario, the threshold is set to be triggered when the risk score exceeds 70, and the protocol activation involves sending control signals to the execution device.

[0066] Understandably, the protocol's principle lies in its rapid intervention mechanism. For example, the protocol includes steps such as alarm notifications and parameter adjustments to ensure the system switches from normal mode to enhanced mode. This activation process emphasizes real-time performance, processing hourly risk data to achieve timely responses to potential leaks.

[0067] For example, upon activation of the airtightness enhancement protocol, the connection model of the modular components is updated by adding auxiliary sealing measures. This connection model is a structured framework representing the relationships between components, including nodes and edges. Nodes represent sealing modules such as flanges and gaskets, and edges represent connection strengths.

[0068] Specifically, in chemical pipeline environments, the update process first identifies high-risk components and then adds auxiliary measures such as additional tape or clamps, which are incorporated into the model as new edges.

[0069] It should be noted that the model update uses an iterative algorithm. For example, the initial model is based on a standard configuration, and after adding measures, the connection strength is recalculated to ensure that the model reflects the enhanced state. This method supports dynamic management of pipeline systems.

[0070] In one possible implementation, the addition of auxiliary sealing measures emphasizes typological diversity; for example, in water treatment pipelines, rubber sealing rings or metal clamps could be selected based on the level of risk. Furthermore, the process of updating the connection model involves data fusion, combining measure parameters such as thickness values ​​with the existing model and adjusting edge weights using a weighted method.

[0071] Understandably, this fusion ensures the adaptability of the model; for example, in a drainage pipe scenario, the model is updated to generate a new configuration, maintaining system stability.

[0072] Preferably, the entire judgment and update process demonstrates technological continuity when applied in general industrial pipeline scenarios.

[0073] For example, in high-pressure natural gas pipelines, risk assessment, combined with sensor data, activates protocols and updates models to reduce the occurrence of leaks. This process, a chain from risk assessment to model optimization, ensures the integrity of seal management. In another embodiment, air leak risk assessment can be extended to multi-factor analysis.

[0074] Specifically, the judgment process integrates pressure and temperature data to calculate a comprehensive risk index, for example, by representing each factor as a vector and taking a weighted sum as the final score.

[0075] It should be noted that the principle behind this analysis lies in identifying the causes of leaks, such as increased gaps due to thermal expansion, ensuring the accuracy of the judgment. In oil pipeline fault prevention, this method processes real-time data and supports the accuracy of threshold comparisons. Furthermore, the framework design of the airtightness enhancement protocol includes backup path activation; for example, the protocol defines multiple enhancement levels, selecting appropriate measures based on the degree of risk. This design provides flexible response when applied to continuous monitoring of chemical pipelines.

[0076] For example, updating the connection model of modular components emphasizes the advantages of modularity, with components such as detachable joints allowing for the rapid addition of measures.

[0077] Specifically, the model is viewed as a graph structure, and during updates, nodes representing auxiliary components are added, and path strength is recalculated. In a water treatment pipeline environment, this update process handles seasonally varying data to generate a reliable configuration.

[0078] Understandably, the seamless integration of the above steps ensures a smooth logical progression from risk assessment to model updates. For example, protocol activation directly triggers the addition of measures, supporting overall sealing optimization. In drainage pipeline systems, this technology achieves the goal of preventative maintenance.

[0079] Step S105: Extract airtightness assessment indicators for measuring long-term sealing stability from the updated connection model, analyze potential failure points using a regression algorithm, and obtain intervention priority data for sorting the processing order.

[0080] Air tightness assessment indicators are obtained from the connection model. Material aging data is integrated through temperature effect simulation to determine a set of potential failure points. A regression algorithm is used to analyze this set of potential failure points to obtain a failure probability distribution and determine the intervention requirement level. Pressure distribution data is integrated with the failure probability distribution to obtain a priority ranking matrix. The processing order is updated using this priority ranking matrix to obtain intervention priority data.

[0081] In one implementation, the process of extracting airtightness assessment metrics from the updated connection model first involves quantifying the sealing stability of the industrial piping system. This connection model is a structured framework comprising nodes and edges, where nodes represent sealing components such as flanges and gaskets, and edges represent connection strength relationships.

[0082] Specifically, the extraction process involves scanning the edge weight data in the model and calculating a long-term stability index, such as generating a composite score by integrating historical pressure data and material durability parameters. This index measures the reliability of the seal under long-term environmental changes; for example, in oil pipelines, the index considers the impact of temperature fluctuations on the connection, ensuring the objectivity of the assessment.

[0083] It should be noted that this extraction emphasizes the principle of data fusion, that is, combining model parameters with real-time sensor inputs to form a traceable stability trend, supporting an accurate foundation for subsequent analysis. The process begins with reading model data, gradually constructing indicator values ​​to achieve long-term monitoring of pipeline sealing. Furthermore, when the extraction of airtightness assessment indicators is applied in natural gas pipeline scenarios, it focuses on multi-dimensional quantification.

[0084] For example, metrics include the leakage probability index and the durability degradation rate, which are calculated from the attribute values ​​of nodes in the model.

[0085] Specifically, the extraction step first identifies high-risk edges, then aggregates relevant parameters such as pressure threshold and material thickness to generate index scores ranging from 0 to 1, where a score close to 1 indicates high stability.

[0086] Understandably, this method is based on the principle of statistical aggregation to ensure that the indicators reflect actual business needs, such as preventing long-term leakage risks. In chemical pipeline environments, this extraction process processes corrosion factor data and outputs indicators to guide maintenance strategies.

[0087] Preferably, the step of using a regression algorithm to analyze potential failure points involves constructing a predictive model to identify weaknesses in the connectivity model. A regression algorithm is a statistical method that predicts the likelihood of failure by fitting the relationship between input variables and output responses.

[0088] Specifically, in water treatment pipeline systems, the algorithm inputs include extracted airtightness assessment indicators and environmental variables such as humidity levels, and the outputs include the location and probability of potential failure points.

[0089] It's important to note that the analysis process begins with preparing the dataset. Then, linear regression or similar variations are applied to calculate the failure risk coefficient for each node. For example, parameters are optimized using the least squares method to identify failure points such as loose gaskets or deformed flanges. The algorithm's principle lies in capturing linear or nonlinear relationships between variables, ensuring accurate location of sealing failures. In drainage pipeline scenarios, this analysis handles seasonal data variations, generating a list of failure points to support preventative interventions.

[0090] For example, the analysis of regression algorithms is extended to multi-factor scenarios in oil pipeline fault diagnosis.

[0091] Specifically, the algorithm process is divided into data preprocessing, model training, and prediction stages. First, the indicator data is cleaned to remove noise. Then, historical failure records are used to train the model to predict future failure points, such as the location of cracks in connections. Further, the analysis emphasizes iterative optimization, such as adjusting weights through gradient descent, to output a risk vector for the failure points. These detailed steps ensure the algorithm's practicality in business applications, such as reducing the occurrence of pipeline leaks.

[0092] In one possible implementation, the analysis integrates temperature and pressure data to calculate the overall failure probability, supporting the system's switch from normal monitoring to alarm mode.

[0093] In one embodiment, intervention priority data for sorting the processing order is obtained through processing the analysis results.

[0094] Specifically, risk scores are extracted from the failure point data output by the regression algorithm, and then sorted according to the scores to form a priority list.

[0095] For example, in natural gas pipeline maintenance, high-risk failure points such as flanges in high-pressure areas are prioritized, and the generated data includes the intervention sequence and recommended measures such as adding auxiliary seals. The principle behind this data lies in a quantitative prioritization mechanism to ensure the efficiency of resource allocation.

[0096] Understandably, this prioritization supports real-time decision-making, such as directly using the output list to dispatch maintenance teams. Furthermore, the generation of intervention priority data emphasizes dynamic adjustment in chemical pipeline environments.

[0097] Specifically, the data is calculated based on a weighted average of the probability and impact of failure points, and the sorting process uses a simple mechanism such as the bubble sort algorithm to output an ordered list.

[0098] It should be noted that this step connects with the preceding analysis, ensuring a smooth logical flow from metric extraction to priority generation, and supporting the continuity of overall sealing management. In water treatment pipeline scenarios, this data helps prioritize critical components and maintain system stability.

[0099] Step S106: Based on the intervention priority data, an optimization algorithm is used to adjust the dynamic adjustment parameters and generate the final sealing reinforcement instruction information.

[0100] Dynamic adjustment parameters are obtained from the intervention priority data. A genetic algorithm is used to adjust these parameters, and the parameter values ​​are processed through iterative selection and crossover operations to obtain an adjusted parameter set. Vibration load simulation is integrated into the adjusted parameter set, and load change curves are extracted from preset vibration data to determine the load influence distribution and assess sealing reinforcement requirements. The reinforcement strategy distribution is obtained by combining the load influence distribution with airtightness evaluation indicators. Strategy weights are calculated from the distribution values ​​to obtain a strategy optimization matrix. The processing order is updated based on the strategy optimization matrix, and a priority sequence is extracted from the matrix to generate the final sealing reinforcement instruction information.

[0101] In one implementation, the maintenance needs of the industrial piping system are first assessed based on the intervention priority data. This priority data includes ranked failure point risk information to guide parameter adjustments.

[0102] Specifically, the process begins by reading a priority list, identifying high-risk components such as flange connections, and then extracting relevant parameter values, such as the current seal strength and environmental pressure threshold.

[0103] It should be noted that this data is based on the aforementioned regression analysis output, ensuring the reliability of the input. In the oil pipeline scenario, this step processes historical maintenance records, providing a data foundation for subsequent optimization. In this way, preliminary preparation of the sealing system is achieved, forming the input set for adjustment parameters. Further, the step of adjusting the dynamically adjustable parameters using optimization algorithms involves selecting a suitable algorithm to optimize the sealing-related parameters. An optimization algorithm is a mathematical method that achieves optimal parameter configuration by iteratively calculating and minimizing or maximizing an objective function.

[0104] Specifically, in natural gas pipeline systems, the algorithm inputs include risk scores from priority data and dynamic variables such as the rate of temperature change, and outputs adjusted parameter values, such as updates to the sealing pressure threshold.

[0105] For example, the algorithm can adopt the principle of genetic algorithm, first initialize the parameter population, and then gradually optimize the parameter combination through selection, crossover and mutation operations.

[0106] It should be noted that the adjustment process emphasizes dynamism, meaning that parameters are updated as real-time data changes. For example, in high-pressure areas, the algorithm calculates new thresholds to enhance seal durability. Furthermore, the principle of this algorithm lies in searching the optimal solution space to ensure that parameters adapt to environmental fluctuations. In chemical pipeline environments, this step integrates corrosion data and adjusts parameters such as gasket thickness to support long-term stability. The entire adjustment logic, from data input to iterative optimization, forms a closed-loop mechanism to ensure parameter accuracy. Preferably...

[0107] In one possible implementation, the optimization algorithm is adjusted to be adapted for water treatment pipeline scenarios.

[0108] Specifically, the algorithm process is divided into initialization, evaluation and update phases. First, initial parameters such as connection torque values ​​are set. Then, the fitness function is evaluated based on priority data and iteratively adjusted until convergence.

[0109] Understandably, this method is based on the principle of evolutionary computation, avoids local optima traps, and provides robust parameter output.

[0110] For example, when dealing with seasonal humidity changes, the algorithm dynamically modifies durability parameters to generate highly adaptive adjustments. The detailed implementation of this step ensures the responsiveness of the sealing system.

[0111] In one embodiment, the step of generating the final sealing reinforcement instruction information is achieved by processing the adjusted parameters.

[0112] Specifically, parameter values ​​are extracted from the optimized output, and then instruction information is constructed, including specific measures such as "replacing the high-voltage gasket" or "adding an auxiliary sealing layer".

[0113] For example, in oil pipeline maintenance, instructions are output in list format, including execution order and resource allocation suggestions. Furthermore, this generation emphasizes information completeness, ensuring instructions can be directly used in field operations. In natural gas pipeline scenarios, these instructions integrate alarm mechanisms, supporting a seamless transition from monitoring to reinforcement.

[0114] For example, when the generation of sealing reinforcement instructions is applied in a chemical pipeline environment, the focus is on risk mitigation.

[0115] Specifically, based on the adjusted parameters, the instructions include preventative measures such as the application of corrosion coatings, and the output format is structured data, supporting rapid response from the maintenance team.

[0116] Understandably, this generation process connects with the aforementioned adjustments to ensure the continuity of the overall process. In water treatment piping systems, this step generates instructions to maintain continuous system operation, providing guidance such as periodic inspections.

[0117] Step S107: Update the data stream of the real-time monitoring system through the sealing reinforcement instruction information, determine the stable output of the cleanliness level, and feed the output back to the production reliability assessment.

[0118] By using the sealing enhancement command information, environmental noise filtering parameters are obtained, and filtering coefficients are extracted from the noise data to determine the initial adjustment value of the data stream. Based on the initial adjustment value of the data stream, production process monitoring is integrated, and noise impact distribution is extracted from preset thresholds. Noise changes are analyzed using distribution curves to obtain an optimized monitoring system sequence. This optimized sequence, combined with command information, is used to determine a stable output value threshold. Output fluctuations are compared using this threshold to obtain cleanliness level assessment indicators. A cleanliness level assessment indicator update feedback loop is established, transmitting stable output values ​​to the production reliability assessment, and integrating the output feedback from the assessment.

[0119] In one implementation, the process of updating the data stream of the real-time monitoring system through the sealing reinforcement command information first involves extracting key parameters, such as sealing pressure thresholds and component replacement recommendations, from the command information. These parameters are directly integrated into the input of the real-time monitoring system to ensure continuous data stream updates.

[0120] Specifically, a real-time monitoring system is a framework integrating sensors and data processing units to continuously collect internal pipeline status data, such as pressure and temperature readings. In the oil pipeline scenario, this update process involves converting command information into data packets, which are then transmitted to the monitoring system via a network interface to avoid data delays.

[0121] It's important to note that this update emphasizes synchronicity; once the instruction information is generated, the threshold settings in the data stream are immediately modified to support the system's immediate response to potential leaks. This method enables dynamic adjustment of the data stream, forming a reliable monitoring foundation. Furthermore, the steps for determining the stable output of the cleanliness level are based on analysis of the updated data stream. The cleanliness level refers to a classification index of the level of contaminants inside the pipeline, and its stable output is typically determined through particle counting and chemical analysis.

[0122] Specifically, the process extracts pollutant concentration data from the data stream and then applies a threshold comparison method to generate stable output values.

[0123] For example, in natural gas pipeline systems, cleanliness level determination involves the number of particles detected by sensors; if these numbers fall below a preset threshold, a high cleanliness level is output. This step relies on data filtering and averaging to ensure the output is unaffected by short-term fluctuations. In chemical pipeline environments, this determination process integrates corrosion residue data, providing a stable cleanliness assessment to support maintenance decisions.

[0124] Understandably, stable output is achieved through multiple sampling and averaging to avoid errors caused by a single data point, thus forming a reliable grade indicator. Preferably.

[0125] In one possible implementation, feeding the output back into the production reliability assessment involves transferring the cleanliness class value to the assessment module. Production reliability assessment is a systematic process used to quantify the stability and potential risks of pipeline operation.

[0126] Specifically, the feedback process starts with the output value and is injected into the evaluation framework through the interface module, such as updating the parameters of the risk model. In the water treatment pipeline scenario, this feedback adjusts the reliability score, reducing the predicted failure probability if the cleanliness level is high.

[0127] For example, this feedback forms a closed-loop mechanism to ensure that the assessment results reflect the latest sealing status.

[0128] It should be noted that the feedback emphasizes real-time performance, meaning that once the output is determined, it immediately affects the evaluation calculation and provides dynamic production guidance.

[0129] In one embodiment, when the entire process is applied in oil pipeline maintenance, after updating the data stream, the determination of the cleanliness level focuses on the analysis of oil residue.

[0130] Specifically, the system collects data from sensors, calculates an average cleanliness level, and then feeds this data back to the reliability assessment to adjust maintenance schedules. This implementation demonstrates the versatility of the technology, supporting operation under varying pipeline pressures. Furthermore, for natural gas pipelines, this feedback process integrates flow data to ensure that the reliability assessment covers leakage risks.

[0131] For example, when the cleanliness level is low, a feedback triggers an alarm mechanism to improve the overall system response.

[0132] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops, characterized in that, include: Real-time data of the sealing material is acquired from the sensor array to generate sealing status information that characterizes the integrity of the seal; Based on the sealing status information, analyze the performance degradation trend of the sealing material and generate adjustment signal data for adjustment; Environmental information is obtained by adjusting the signal data and integrated into the dynamic adjustment model to obtain an optimized sealing parameter configuration. The optimized sealing parameter configuration is used to assess air leakage risk and update the connection model of the modular components. Air tightness assessment indicators are extracted from the connection model, potential failure points are analyzed, and intervention priority data is obtained. The dynamic adjustment parameters are adjusted based on the intervention priority data to generate the final sealing reinforcement instruction information. The sealing reinforcement instruction information is used to update the data stream of the real-time monitoring system, determine the stable output of the cleanliness level, and feed it back into the production reliability assessment.

2. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The step of acquiring real-time data of the sealing material from the sensor array and generating sealing status information to characterize the seal integrity includes: acquiring stress data and environmental pressure difference data of the sealing material at the connection from the sensor array; determining whether the stress data exceeds the normal range by using a preset threshold to obtain an initial anomaly index; integrating the stress data and the environmental pressure difference data using a data fusion processing method for the initial anomaly index to determine the fused deviation value; obtaining the deformation trend of the sealing material based on the fused deviation value, determining whether the deformation trend indicates a potential leakage risk, and obtaining a risk level; generating sealing status information to characterize the seal integrity based on the risk level, and triggering an anomaly alarm mechanism.

3. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The step of analyzing the performance degradation trend of the sealing material based on the sealing state information and generating adjustment signal data for adjustment includes: extracting the performance degradation trend from the sealing state information; analyzing the performance degradation trend through a classification algorithm to obtain a degradation degree index; determining whether the degradation degree index exceeds a preset threshold and determining the adjustment demand level; acquiring material performance monitoring data from a sensor array and fusing the material performance monitoring data according to the adjustment demand level to generate adjustment signal data; and triggering adaptive calibration of the sealing material through the adjustment signal data to obtain the final adjustment signal data for adjustment.

4. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The process of acquiring environmental information through the adjustment signal data and integrating it into the dynamic adjustment model to obtain optimized sealing parameter configuration includes: extracting sealing environmental factors from the adjustment signal data; acquiring ambient temperature monitoring data through sensor acquisition methods to obtain temperature change information; combining the temperature data with existing model parameters using a multi-source data integration method to determine the application of model fusion technology; incorporating a preset adaptive adjustment mechanism into the dynamic adjustment model, adjusting parameters if the temperature change information exceeds a preset threshold, determining whether it affects the sealing parameter configuration, and obtaining parameter optimization output; generating an optimized configuration scheme based on the parameter optimization output, determining the adjustment level of sealing environmental factors, and updating the model parameter values.

5. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The step of determining the air leakage risk based on the optimized sealing parameter configuration and updating the connection model of the modular component includes: obtaining pressure distribution data from the optimized sealing parameter configuration; determining whether the air leakage risk corresponding to the pressure distribution data is higher than a preset threshold; if the risk is higher than the preset threshold, activating an airtightness enhancement protocol and determining an additional scheme for auxiliary sealing measures; integrating the pressure distribution data through the additional scheme and updating the connection model of the modular component; and incorporating leakage prevention fusion technology into the connection model to obtain a component adjustment strategy for improving sealing performance.

6. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The step of extracting airtightness assessment indicators from the connection model, analyzing potential failure points, and obtaining intervention priority data includes: obtaining airtightness assessment indicators from the connection model; integrating material aging data through temperature effect simulation to determine a set of potential failure points; analyzing the set of potential failure points using a regression algorithm to obtain a failure probability distribution and determine the level of intervention demand; integrating pressure distribution data based on the failure probability distribution to obtain a priority ranking matrix; and updating the processing order through the priority ranking matrix to generate intervention priority data for subsequent adjustment.

7. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The step of adjusting the dynamic adjustment parameters based on the intervention priority data to generate the final sealing reinforcement instruction information includes: obtaining dynamic adjustment parameters from the intervention priority data; adjusting the dynamic adjustment parameters using an optimization algorithm, processing the parameter values ​​through iterative operations to obtain an adjusted parameter set; integrating vibration load simulation for the adjusted parameter set, extracting load change curves from preset vibration data, determining the load influence distribution, and judging the sealing reinforcement requirements; obtaining the reinforcement strategy distribution by combining the load influence distribution with airtightness evaluation indicators, calculating the strategy weights, and obtaining a strategy optimization matrix; and extracting the priority sequence from the strategy optimization matrix to generate the final sealing reinforcement instruction information.

8. The method for monitoring and adjusting the sealing integrity of modular construction in clean HVAC workshops as described in claim 1, characterized in that, The process of updating the data stream of the real-time monitoring system through the sealing enhancement command information to determine the stable output of the cleanliness level and feed it back to the production reliability assessment includes: obtaining environmental noise filtering parameters through the sealing enhancement command information; extracting filtering coefficients from noise data to determine the initial adjustment value of the data stream; integrating production process monitoring based on the initial adjustment value of the data stream, extracting the noise impact distribution from a preset threshold, analyzing noise changes through the distribution curve, and obtaining an optimized sequence for the monitoring system; integrating the optimized sequence of the monitoring system with the command information to determine the stable threshold of the output value, comparing output fluctuations through the threshold to obtain cleanliness level assessment indicators; and updating the feedback loop through the cleanliness level assessment indicators to transmit the stable output value to the production reliability assessment.

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