Pressure stability control method and system for air pressure test chamber

Abstract

The present invention relates to the field of air pressure control technology, and in particular to a pressure stability control method and system for an air pressure test chamber. The method comprises the following steps: monitoring the current non-operating state of the air pressure test chamber, thereby obtaining multi-dimensional non-operating state monitoring parameters of the air pressure test chamber; performing non-operating state time series evolution of the multi-dimensional non-operating state monitoring parameters, thereby generating non-operating state time series features; obtaining an internal structure diagram of the air pressure test chamber; performing spatial topological distribution analysis on the internal structure diagram of the air pressure test chamber based on the non-operating state time series features, and performing three-dimensional point cloud modeling to construct a digital twin model of the test chamber; obtaining a target test scene based on a test log; performing target pressure state analysis on the target test scene, and performing final test scene state mining to obtain final test scene state data; the present invention realizes highly controllable and high-precision pressure control of the air pressure test chamber.

Description

Technical Field

[0001] The present invention relates to the technical field of air pressure control, and in particular to a method and system for controlling the pressure stability of an air pressure test chamber. Background Art

[0002] With the continuous improvement of industrial automation level, air pressure test chambers have become key equipment in many industrial production and testing processes. Air pressure test chambers are mainly used to simulate various complex environmental conditions and perform performance tests on products or components such as pressure resistance, vibration resistance, and temperature shock. In these high-pressure and high-temperature environments, the stability of the internal pressure of the air pressure test chamber is crucial, which directly affects the accuracy of the test results and the reliability of the test process.

[0003] Traditional pressure control methods for air pressure test chambers often rely on manual monitoring and manual adjustment, which has the problems of low air pressure control accuracy and poor controllability. As the pace of industrial production becomes faster and faster, higher requirements are placed on the pressure stability and control accuracy of air pressure test chambers. Therefore, there is an urgent need for an intelligent air pressure test chamber pressure stability control method that can automatically monitor and analyze pressure change characteristics, make corresponding adjustments in time, and ensure the smooth progress of the test process. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a pressure stability control method and system for an air pressure test chamber to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for controlling pressure stability of an air pressure test chamber, comprising the following steps:

[0006] Step S1: obtaining multi-dimensional non-operating state monitoring parameters of the air pressure test box; performing non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters, thereby generating non-operating state time series features;

[0007] Step S2: Obtain an internal structure diagram of the air pressure test chamber; perform spatial topological distribution analysis on the internal structure diagram of the air pressure test chamber based on the non-operating state time series characteristics, and construct a digital twin model of the test chamber;

[0008] Step S3: obtaining a target test scenario based on the test log; performing a target pressure state analysis on the target test scenario to obtain final test scenario state data;

[0009] Step S4: performing a spatial air pressure stability compensation calculation on the digital twin model of the test chamber according to the final test scene state data to obtain a spatial air pressure stability compensation value;

[0010] Step S5: performing an operating state air pressure stability test on the air pressure test box based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data;

[0011] Step S6: performing dynamic pressure deviation fine-tuning on the operating state pressure stability data to generate a dynamic pressure deviation fine-tuning strategy to execute the pressure stability control operation of the pressure test chamber.

[0012] The present invention can timely discover potential problems and improve safety by monitoring the non-operating state of the air pressure test box. Generating non-operating state time series characteristics is helpful to understand the characteristic changes of the air pressure test box in different states, providing a basis for subsequent analysis. Constructing a digital twin model of the test box can realize virtual modeling of the internal structure of the air pressure test box, helping to understand the structure and layout inside the box. Through three-dimensional point cloud modeling, the spatial topological distribution of the test box can be more intuitively presented, providing visualization support for subsequent simulation and analysis. Analyzing the target test scene state is helpful to understand the pressure state that the test needs to achieve, guiding the subsequent experimental process, and by mining the final test scene state data, the experimental process and results can be optimized, the test efficiency and quality can be improved, and the air pressure Filling simulation and stability compensation calculation help predict the air pressure response of the air pressure test chamber and ensure the safety and accuracy of the test. Through the spatial air pressure stability compensation value, the air pressure control of the air pressure test chamber can be optimized, and the air pressure stability and experimental reliability can be improved. Real-time monitoring of the operating air pressure stability data helps to ensure the stability and accuracy of the air pressure during the test. The operating air pressure stability data can be used to adjust the air pressure control in time to ensure that the test chamber operates within the normal range. Through air pressure stability delay error analysis and dynamic air pressure deviation fine-tuning, the pressure stability of the air pressure test chamber can be optimized, and the controllability and accuracy of the test can be improved. Generating a dynamic air pressure deviation fine-tuning strategy helps to achieve stability control of the air pressure test chamber and ensure the accuracy and reliability of the experimental data.

[0013] Preferably, step S1 includes the following steps:

[0014] Step S11: monitoring the current non-operating state of the pressure test chamber, thereby obtaining multi-dimensional non-operating state monitoring parameters of the pressure test chamber, wherein the multi-dimensional non-operating state monitoring parameters include infrared thermal imaging temperature monitoring data, chamber pressure state parameters, and chamber humidity parameters;

[0015] Step S12: performing heat distribution analysis on the infrared thermal imaging temperature monitoring data of the test chamber to extract the temperature distribution data of the test chamber;

[0016] Step S13: performing temperature distribution fluctuation fitting on the test chamber temperature distribution data to obtain a temperature distribution fluctuation curve;

[0017] Step S14: performing spatial pressure gradient calculation on the test chamber air pressure state parameters to generate spatial pressure gradient characteristics;

[0018] Step S15: analyzing the pressure balance state characteristics of the test chamber based on the spatial pressure gradient characteristics to obtain the pressure balance state characteristics;

[0019] Step S16: performing a non-operating state time series evolution on the temperature distribution fluctuation curve, the air pressure equilibrium state characteristics and the test chamber humidity parameters, thereby generating a non-operating state time series characteristic.

[0020] By analyzing infrared thermal imaging temperature monitoring data, the present invention can understand the heat distribution inside the test chamber, which helps to discover potential thermal problems. Extracting the test chamber temperature distribution data can provide a basis for subsequent temperature control and adjustment. By performing fluctuation fitting on the test chamber temperature distribution data, the temperature fluctuation can be more clearly understood, providing a reference for stabilizing the temperature. The temperature distribution fluctuation curve helps to analyze the temperature change law and provide support for formulating temperature control strategies. Generating spatial pressure gradient characteristics can help understand the pressure distribution inside the test chamber and provide a basis for air pressure control. By calculating the pressure gradient, the change of air pressure can be evaluated and the stability of the air pressure test chamber can be further improved. By analyzing the air pressure balance state characteristics, the balance of air pressure inside the test chamber can be evaluated, providing a reference for formulating air pressure control strategies. The analysis of air pressure balance state characteristics helps to discover pressure imbalance, thereby improving the air pressure adjustment method of the air pressure test chamber. By generating non-operating state time series characteristics, the changes in parameters such as temperature, pressure and humidity can be comprehensively considered, providing a comprehensive reference for the stability control of the air pressure test chamber. The generation of non-operational state time series features helps to establish a more complete state model of the pressure test chamber and improve the understanding and control of the test chamber performance.

[0021] Preferably, the specific steps of step S2 are:

[0022] Step S21: Obtaining an internal structure diagram of the air pressure test chamber;

[0023] Step S22: performing component structure visual recognition on the internal structure diagram of the air pressure test chamber, and marking multiple component nodes inside the test chamber;

[0024] Step S23: performing spatial topological distribution analysis on the internal component nodes of the multiple test boxes to generate internal component spatial topological distribution data;

[0025] Step S24: performing geometric morphological feature recognition on the internal structure diagram of the air pressure test box to obtain morphological features of the test box;

[0026] Step S25: performing three-dimensional point cloud modeling on the morphological features of the test box based on the spatial topological distribution data of the internal components to generate a three-dimensional structural model of the test box;

[0027] Step S26: Perform dynamic time series feature mapping on the three-dimensional structure model of the test box according to the time series features of the non-operating state, and construct a digital twin model of the test box.

[0028] The present invention helps to gain a deep understanding of the composition and component distribution of the air pressure test chamber by obtaining the internal structure diagram, and provides basic data for subsequent analysis and control. Through component structure visual recognition and node marking, the various components inside the test chamber can be accurately identified, providing specific component location information for subsequent spatial analysis. Analyzing the spatial topological distribution of components helps to understand the relationship between the components inside the test chamber, and provides a basis for building a three-dimensional structural model. Generating spatial topological distribution data can help optimize the component layout and improve the performance and stability of the test chamber. Identifying the geometric morphological features of the test chamber can help understand the overall structure of the test chamber and provide important information for subsequent modeling. Obtaining morphological features helps to evaluate the relationship between the external morphology and internal structure of the test chamber, and provides a reference for model construction. Three-dimensional point cloud modeling based on spatial topological distribution data can more intuitively present the internal structure of the test chamber and provide visual support for subsequent model construction. Generating a three-dimensional structural model helps to more comprehensively understand the internal layout and component relationships of the test chamber. Through dynamic timing feature mapping, the non-operating state timing features can be combined with the three-dimensional structural model to establish a digital twin model of the test chamber. Building a digital twin model helps simulate the behavior of the test chamber under different working conditions and provides real-time simulation and prediction capabilities for pressure stability control.

[0029] Preferably, the specific steps of step S3 are:

[0030] Step S31: obtaining a target test scenario based on the test log;

[0031] Step S32: performing target pressure state analysis on the target test scene and extracting target scene pressure state characteristics;

[0032] Step S33: performing temperature change demand rate analysis on the target test scenario to extract temperature change demand rate parameters of the target scenario;

[0033] Step S34: performing temperature spatiotemporal feature evolution on the temperature change demand rate parameter of the target scene to extract the temperature spatiotemporal variation law of the target scene;

[0034] Step S35: performing final test scene state mining on the target scene pressure state characteristics according to the temperature spatiotemporal variation law of the target scene to obtain final test scene state data.

[0035] The present invention captures the target test scenario through the test log, accurately recording the environmental conditions and parameter changes during the test, providing a realistic data foundation for subsequent analysis. Analyzing the pressure state of the target test scenario helps understand the target pressure requirement and provides a target reference for pressure stability control of the test chamber. Extracting pressure state characteristics can help establish a mathematical model of the target pressure state, supporting the formulation of subsequent control strategies. Analyzing the target scenario's required temperature change rate helps understand the speed of temperature change and provides a reference for formulating temperature control strategies. Extracting the required temperature change rate parameter can help quantify the target scenario's temperature change requirements and provide guidance for subsequent control optimization. Analyzing the target scenario's spatiotemporal temperature variation patterns helps understand the spatial and temporal temperature variation patterns and provides a basis for optimizing temperature control strategies. Extracting the spatiotemporal temperature variation patterns can help establish a temperature evolution model for the target scenario and provide support for achieving target temperature control. Through a comprehensive analysis of the target scenario's spatiotemporal temperature variation patterns and pressure state characteristics, final test scenario state data can be mined, providing the ultimate basis for achieving pressure stability control of the target scenario. Acquiring final test scenario state data helps establish a comprehensive scenario model, providing an important reference for control system design and optimization.

[0036] Preferably, the specific steps of step S32 are:

[0037] Calculate the spatial average pressure of the target test scene to obtain the spatial average pressure parameters of the target scene;

[0038] Predicting the scene pressure peak based on the target scene spatial average pressure parameter to obtain the predicted peak pressure of the target scene;

[0039] Perform pressure filling time series variation analysis on the target test scene to obtain target scene pressure filling time series variation data;

[0040] Perform pressure fluctuation amplitude analysis on the target scene pressure filling time series change data to generate pressure fluctuation amplitude;

[0041] Perform multi-period change pattern recognition on the pressure fluctuation amplitude to generate multi-period pressure change characteristics;

[0042] Perform pressure stability assessment on the pressure change characteristics of multiple periods to obtain the pressure stability assessment value of the target scene;

[0043] The target demand pressure state analysis is performed on the target scenario pressure prediction peak and target scenario pressure stability assessment value to extract the target scenario pressure state characteristics.

[0044] The present invention helps to understand the overall pressure distribution by calculating the spatial average pressure parameters of the target test scene, and provides basic data for subsequent pressure control. Predicting the pressure peak of the target scene can help to identify the extreme value of pressure change in advance, and provide a reference for the formulation of pressure protection and control strategies. Analyzing the pressure filling timing changes of the target test scene helps to understand the pressure change trend over time, and provides a basis for the formulation of pressure stability control strategies. Analyzing the pressure fluctuation amplitude of the target scene can quantify the degree of pressure fluctuation and provide an important indicator for evaluating pressure stability. Identifying the multi-period change pattern of pressure fluctuation helps to understand the pressure change law under different time scales, and provides support for the formulation of targeted control strategies. Evaluating the pressure stability of the target scene can help to judge the effectiveness and stability of pressure control, and provide feedback and improvement direction for adjusting the control strategy. Extracting the pressure state characteristics of the target scene helps to deeply understand the target pressure requirements and scene characteristics, and provide a reference for optimizing pressure stability control.

[0045] Preferably, the specific steps of step S4 are:

[0046] Step S41: performing a rapid air pressure filling simulation on the digital twin model of the test chamber, and collecting the air pressure simulation response data of the test chamber;

[0047] Step S42: identifying the air pressure spatial distribution state of the test chamber air pressure simulation response data to generate air pressure spatial distribution state features;

[0048] Step S43: performing state deviation analysis on the air pressure spatial distribution state characteristics based on the final test scene state data, thereby generating a target air pressure state deviation value;

[0049] Step S44: performing a space pressure stability compensation calculation on the target air pressure state deviation value to obtain a space pressure stability compensation value.

[0050] The present invention simulates the actual air pressure filling process by performing rapid air pressure filling simulation on the digital twin model of the test chamber, providing a simulation data basis for subsequent analysis. Collecting the test chamber air pressure simulation response data helps to understand the test chamber air pressure response characteristics and provides important data support for the pressure stability control of the air pressure test chamber. Identifying the spatial distribution state of the test chamber air pressure simulation response data helps to understand the distribution of air pressure in the test chamber and provides a basis for formulating pressure control strategies. Generating air pressure spatial distribution state characteristics can help quantify the characteristics of air pressure distribution and provide a reference for subsequent pressure stability compensation calculations. Analyzing the state deviation of the air pressure spatial distribution state characteristics helps to understand the difference between the actual air pressure distribution and the target scene and provides a basis for adjusting pressure stability control. Generating a target air pressure state deviation value can quantify the difference between the actual air pressure and the target air pressure and provide a specific numerical basis for subsequent compensation calculations. Performing spatial air pressure stability compensation calculations helps to formulate compensation strategies based on the target air pressure state deviation value, thereby improving the pressure stability of the air pressure test chamber. Obtaining the spatial air pressure stability compensation value can help adjust the air pressure control parameters so that the air pressure in the test chamber reaches the target value more stably, thereby improving the performance and reliability of the test chamber.

[0051] Preferably, the specific steps of step S5 are:

[0052] Step S51: performing air pressure balance optimization on the air pressure spatial distribution state characteristics based on the spatial air pressure stability compensation value to obtain air pressure simulation balance optimization parameters;

[0053] Step S52: operating the air pressure test box in real time according to the air pressure simulation balance optimization parameters, and collecting air pressure state parameters at multiple time points;

[0054] Step S53: calculating the air pressure fluctuation amplitudes at adjacent points on the air pressure state parameters at multiple time points to obtain the air pressure fluctuation amplitudes between the multiple time points;

[0055] Step S54: performing an operating state air pressure stability test based on the air pressure fluctuation amplitudes between multiple time points, thereby obtaining operating state air pressure stability data.

[0056] The present invention helps to adjust the air pressure distribution by optimizing the air pressure balance based on the spatial air pressure stability compensation value, making the air pressure more balanced and stable, and improving the air pressure control accuracy and stability of the test box. In real-time operation, the air pressure test box can be operated according to the air pressure simulation balance optimization parameters to verify the air pressure balance optimization effect and ensure the air pressure stability in actual operation. The collection of air pressure state parameters at multiple time points is helpful to understand the dynamic change trend of air pressure and provide data support for subsequent analysis. The calculation of the air pressure fluctuation amplitude of adjacent points can quantify the degree of change of air pressure between different time points, help evaluate air pressure stability and fluctuation, and provide a reference for stability control. The operation state air pressure stability detection based on the air pressure fluctuation amplitude between multiple time points is helpful to monitor the stability of air pressure in real time, discover abnormal air pressure fluctuation in time, and ensure that the test box works in a stable state. The operation state air pressure stability data can help evaluate the actual operation state of the air pressure test box, guide subsequent adjustment and optimization, and ensure that the air pressure test box meets the expected pressure stability requirements.

[0057] Preferably, the specific steps of step S6 are:

[0058] Step S61: performing air pressure stabilization delay error analysis on the operating air pressure stabilization data to generate an air pressure stabilization delay error value;

[0059] Step S62: performing error optimization based on the air pressure stabilization delay error value to generate error-optimized air pressure stabilization data;

[0060] Step S63: performing secondary pressure deviation calculation on the error-optimized pressure stabilization data based on the final test scene state data to generate a secondary pressure deviation parameter;

[0061] Step S64: fine-tuning the dynamic pressure deviation of the pressure test box based on the secondary pressure deviation parameter to generate a dynamic pressure deviation fine-tuning strategy to perform a pressure stability control operation of the pressure test box.

[0062] The present invention helps to identify the delay situation of air pressure stability by analyzing the delay error of the operating air pressure stability data, and provides a basis for optimizing the control strategy. Generating an air pressure stability delay error value can quantify the degree of delay error and help understand the difference between the actual air pressure response and the expected response. Error optimization based on the air pressure stability delay error value helps to improve the stability of the air pressure test box, reduce the impact of the error on air pressure control, and improve control accuracy. Generating error-optimized air pressure stability data can optimize the air pressure control algorithm, making the air pressure response of the air pressure test box more stable and accurate. Performing secondary air pressure deviation calculation based on error-optimized air pressure stability data helps to further adjust the air pressure control parameters of the air pressure test box and improve air pressure stability. Generating secondary air pressure deviation parameters can quantify the difference between the actual air pressure of the air pressure test box and the expected air pressure, and provide a basis for further fine-tuning. Performing dynamic air pressure deviation fine-tuning based on the secondary air pressure deviation parameters helps to dynamically adjust the air pressure control strategy according to actual conditions, and ensure the stability of the air pressure test box under different working conditions. Generating a dynamic pressure deviation fine-tuning strategy can guide the real-time control of the pressure test chamber, enabling continuous optimization and improvement of pressure stability, thereby enhancing the performance and reliability of the test chamber.

[0063] In this specification, a pressure stability control system for an air pressure test chamber is provided, which is used to execute the pressure stability control method for an air pressure test chamber as described above, comprising:

[0064] A non-operating state module is used to obtain multi-dimensional non-operating state monitoring parameters of the air pressure test box; perform non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters to generate non-operating state time series features;

[0065] The spatial topology module is used to obtain the internal structure diagram of the air pressure test chamber; based on the non-operating state time series characteristics, the spatial topology distribution of the internal structure diagram of the air pressure test chamber is analyzed to build a digital twin model of the test chamber;

[0066] The test scenario module is used to obtain the target test scenario based on the test log; perform target pressure state analysis on the target test scenario to obtain the final test scenario state data;

[0067] The air pressure stability compensation module is used to calculate the space air pressure stability compensation of the digital twin model of the test chamber according to the final test scene state data to obtain the space air pressure stability compensation value;

[0068] An operating state module is used to perform operating state air pressure stability detection on the air pressure test chamber based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data;

[0069] The dynamic air pressure fine-tuning module is used to perform dynamic air pressure deviation fine-tuning on the operating air pressure stability data to generate a dynamic air pressure deviation fine-tuning strategy to perform the pressure stability control operation of the air pressure test chamber.

[0070] The present invention helps to understand the state changes of the test box in the shutdown state by monitoring the multi-dimensional non-operating state parameters of the air pressure test box, and provides basic data for subsequent stability control. Generating non-operating state time series characteristics can reveal the characteristic changes of the air pressure test box in different non-operating states, and provide a basis for subsequent analysis and optimization. Obtaining the internal structure diagram of the air pressure test box helps to understand the spatial layout and structural characteristics of the test box, and provides basic data for subsequent spatial analysis. Spatial topological distribution analysis and three-dimensional point cloud modeling based on non-operating state time series characteristics can construct a digital twin model of the test box, and provide a basis for subsequent simulation and optimization. Analyzing the pressure state of the target test scene helps to understand the working state of the test box in different scenes, and provides a reference for subsequent pressure control. Mining the final test scene state data can provide support for determining the final test conditions and pressure requirements, and provide support for subsequent control strategies. It provides a basis for the formulation of the strategy, and conducting rapid air pressure filling simulation and calculating the space air pressure stability compensation value helps to optimize the air pressure distribution and improve the air pressure stability of the air pressure test chamber. Compensation calculation based on the final test scene state data can adjust the air pressure parameters according to actual needs to ensure the stability of the test chamber in different scenarios. Real-time operation and air pressure stability detection help to verify the stability of the air pressure test chamber, timely discover and deal with abnormal air pressure fluctuations, and obtain operating air pressure stability data. It can provide real-time feedback for adjusting the control strategy and parameters to ensure the stability of the test chamber during operation. Delay error analysis and dynamic air pressure deviation fine-tuning of the operating air pressure stability data help to optimize the air pressure control strategy, improve air pressure stability and control accuracy, and generate a dynamic air pressure deviation fine-tuning strategy to achieve fine control of the pressure stability of the air pressure test chamber, ensuring the stability and reliability of the test chamber under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of the steps of a method for controlling pressure stability of an air pressure test chamber according to the present invention;

[0072] Figure 2 Detailed implementation flow chart of step S1;

[0073] Figure 3 Detailed implementation flow chart of step S2;

[0074] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0076] This application provides a method and system for controlling the pressure stability of a pneumatic test chamber. The system includes, but is not limited to, the following entities: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0077] See also Figures 1 to 4 The present invention provides a pressure stability control method for an air pressure test chamber, the pressure stability control method for an air pressure test chamber comprising the following steps:

[0078] Step S1: obtaining multi-dimensional non-operating state monitoring parameters of the air pressure test box; performing non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters, thereby generating non-operating state time series features;

[0079] Step S2: Obtain an internal structure diagram of the air pressure test chamber; perform spatial topological distribution analysis on the internal structure diagram of the air pressure test chamber based on the non-operating state time series characteristics, and construct a digital twin model of the test chamber;

[0080] Step S3: obtaining a target test scenario based on the test log; performing a target pressure state analysis on the target test scenario to obtain final test scenario state data;

[0081] Step S4: performing a spatial air pressure stability compensation calculation on the digital twin model of the test chamber according to the final test scene state data to obtain a spatial air pressure stability compensation value;

[0082] Step S5: performing an operating state air pressure stability test on the air pressure test box based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data;

[0083] Step S6: performing dynamic pressure deviation fine-tuning on the operating state pressure stability data to generate a dynamic pressure deviation fine-tuning strategy to execute the pressure stability control operation of the pressure test chamber.

[0084] The present invention can timely discover potential problems and improve safety by monitoring the non-operating state of the air pressure test box. Generating non-operating state time series characteristics is helpful to understand the characteristic changes of the air pressure test box in different states, providing a basis for subsequent analysis. Constructing a digital twin model of the test box can realize virtual modeling of the internal structure of the air pressure test box, helping to understand the structure and layout inside the box. Through three-dimensional point cloud modeling, the spatial topological distribution of the test box can be more intuitively presented, providing visualization support for subsequent simulation and analysis. Analyzing the target test scene state is helpful to understand the pressure state that the test needs to achieve, guiding the subsequent experimental process, and by mining the final test scene state data, the experimental process and results can be optimized, the test efficiency and quality can be improved, and the air pressure Filling simulation and stability compensation calculation help predict the air pressure response of the air pressure test chamber and ensure the safety and accuracy of the test. Through the spatial air pressure stability compensation value, the air pressure control of the air pressure test chamber can be optimized, and the air pressure stability and experimental reliability can be improved. Real-time monitoring of the operating air pressure stability data helps to ensure the stability and accuracy of the air pressure during the test. The operating air pressure stability data can be used to adjust the air pressure control in time to ensure that the test chamber operates within the normal range. Through air pressure stability delay error analysis and dynamic air pressure deviation fine-tuning, the pressure stability of the air pressure test chamber can be optimized, and the controllability and accuracy of the test can be improved. Generating a dynamic air pressure deviation fine-tuning strategy helps to achieve stability control of the air pressure test chamber and ensure the accuracy and reliability of the experimental data.

[0085] In the embodiment of the present invention, see Figure 1 , is a schematic flow chart of the steps of a pressure stability control method for an air pressure test chamber according to the present invention. In this example, the steps of the pressure stability control method for an air pressure test chamber include:

[0086] Step S1: monitoring the current non-operating state of the air pressure test box to obtain multi-dimensional non-operating state monitoring parameters of the air pressure test box; performing non-operating state time series evolution of the multi-dimensional non-operating state monitoring parameters to generate non-operating state time series features;

[0087] In this embodiment, the monitoring objective is primarily to obtain various parameters of the pressure test chamber in its non-operating state, such as ambient temperature, humidity, air pressure, device status, and vibration. These parameters will be used to understand the stability and potential issues of the device when it is off. Various sensors are configured to collect the required non-operating state monitoring parameters: A pressure sensor monitors the air pressure inside the chamber. A temperature and humidity sensor monitors the ambient temperature and humidity. When the pressure test chamber is in its non-operating state, the monitoring system is activated to begin real-time data collection of various parameters. An appropriate data collection frequency (e.g., every second, every minute) is set to ensure sufficient data is collected for non-operating state analysis. The collected multi-dimensional non-operating state monitoring parameters are saved to a database or local storage device to ensure data integrity and traceability. Appropriate time series analysis methods (e.g., moving average, exponential smoothing, Fourier transform, etc.) are selected to analyze the changing trends of various parameters in the non-operating state. The calculated statistical features are aggregated to form a comprehensive non-operating state time series feature dataset. This dataset may include timestamps, mean, standard deviation, maximum, and minimum values ​​of each parameter. Save the non-operational state time series characteristics to a database and generate a report documenting the monitoring results and analysis conclusions for subsequent reference and decision-making. Use data visualization tools (such as Matplotlib, Tableau, or PowerBI) to visualize the non-operational state monitoring parameters and their time series characteristics. Graph the parameter changes over time to facilitate analysis and understanding of the evolution trends of each monitoring parameter in the non-operational state.

[0088] Step S2: Obtaining an internal structure diagram of the air pressure test chamber; performing spatial topological distribution analysis on the internal structure diagram of the air pressure test chamber based on the non-operating state time series characteristics, and performing three-dimensional point cloud modeling to construct a digital twin model of the test chamber;

[0089] In this embodiment, the goal of spatial topology analysis is determined, such as identifying the spatial relationship, functional area division and mutual influence of the components inside the air pressure test chamber, and selecting an appropriate topology analysis method, such as graph theory, Voronoi diagram or Delaunay triangulation. These methods help to understand the spatial structure and the relationship between components. The three-dimensional model is exported to a format suitable for analysis (such as STL or OBJ), and relevant non-operational state time series characteristic data (such as temperature, pressure distribution, etc.) is prepared. The three-dimensional model is topologically analyzed using analysis software (such as MATLAB, Python's SciPy library, Blender or MeshLab), and the spatial relationship and interaction between the components are calculated. The collected point cloud data is cleaned and processed using point cloud processing software (such as Cloud Compare, PCL library) to remove noise and unnecessary data points. The point cloud reconstruction algorithm (such as Poisson Surface Reconstruction or Alpha 3D) is used to reconstruct the point cloud data. Use Modeling Software (such as Blender or MeshLab) to convert the cleaned point cloud data into a 3D surface model. Optimize the generated 3D model to reduce unnecessary faces and improve rendering efficiency. Use modeling software (such as Blender or MeshLab) for post-processing to integrate the 3D internal structure model with the point cloud model to ensure data consistency and integrity between the two. Select a suitable digital twin platform (such as Siemens Mind Sphere, PTC Thing Worx, or other industrial IoT platforms) to build and deploy the digital twin model. Upload the integrated 3D model to the digital twin platform and configure relevant parameters (such as sensor data streams and real-time monitoring indicators). Connect the real-time monitoring data to the digital twin model through an API or data communication protocol to ensure that the model can reflect the actual operating status of the equipment. Verify the digital twin model to ensure that it accurately reflects the physical characteristics and behavior of the air pressure test chamber. Adjust and optimize it when necessary to improve the accuracy and practicality of the model.

[0090] Step S3: obtaining a target test scenario based on the test log; performing target pressure state analysis on the target test scenario, and performing final test scenario state mining to obtain final test scenario state data;

[0091] In this embodiment, the sources of test logs are identified, typically including the test chamber's control system, sensor records, user input, and any automatically generated log files. Collected test logs are organized into a unified format (such as CSV or JSON), ensuring that each record contains key information such as timestamp, test type, target pressure, actual pressure, and environmental parameters. Based on the test logs, the definition of the target test scenario is determined. The target test scenario typically includes parameters such as set pressure, temperature, and humidity, as well as corresponding operating conditions. Data analysis tools (such as Pandas, R, or SQL) are used to filter records related to the target test scenario from the test logs. Conditions can be set, such as selecting test records within a specific time period. Data is filtered based on a set target pressure range. The filtered data is aggregated to form a dataset for the target test scenario, including all relevant parameters and status information. Appropriate analysis methods, such as statistical analysis, regression analysis, and time series analysis, are selected to assess the stability and changing trends of the target pressure state. The pressure data for the target test scenario is analyzed to extract the following features: mean: the average value of the target pressure; standard deviation: an indicator reflecting the degree of pressure fluctuation; maximum and minimum values: indicators of the pressure range. Select appropriate data mining tools and algorithms (such as K-means clustering, decision trees, or neural networks) to mine the final test scenario state. Perform feature selection and processing on the target test scenario data to ensure that the data input into the data mining model effectively reflects the test scenario state. Train the selected data mining model using historical data and validate the model to ensure its accuracy and reliability. Evaluate model performance using methods such as cross-validation.

[0092] Step S4: Perform a rapid air pressure filling simulation on the digital twin model of the test chamber and collect the test chamber air pressure simulation response data; perform a spatial air pressure stability compensation calculation on the test chamber air pressure simulation response data based on the final test scene state data to obtain a spatial air pressure stability compensation value;

[0093] In this embodiment, the initial and boundary conditions for the pressure filling process are set based on the final test scenario state data, including the target pressure, environmental conditions (e.g., temperature and humidity), and filling rate. An appropriate pressure filling simulation algorithm is selected. Common methods include: Finite Element Analysis (FEA): suitable for complex airflow dynamics simulations. Computational Fluid Dynamics (CFD): used to simulate the flow and pressure distribution of gas within the test chamber. The pressure filling simulation is initiated and the pressure changes within the chamber are monitored. The pressure response data is recorded at each moment during the simulation. During the simulation, the pressure response data is recorded in real time, including the pressure values ​​at each measurement point and their changing trends. Ensure that the data is stored in a consistent format (e.g., CSV or JSON). Use data analysis tools (e.g., Pandas or MATLAB) to perform preliminary analysis on the collected pressure response data to identify key response characteristics (e.g., peak value, steady-state value, response time). Select an appropriate compensation calculation method, using either linear or nonlinear compensation algorithms, such as PID control algorithms for dynamic pressure regulation. Kalman filtering: used to estimate and compensate for system uncertainties. Prepare the final test scenario state data and the pressure simulation response data, ensuring consistent data formats for ease of calculation. Based on the selected compensation algorithm, the spatial pressure stability compensation calculation is performed on the pressure simulation response data. The calculated spatial pressure stability compensation value is recorded in the database to form a compensation value data set for subsequent analysis and application.

[0094] Step S5: operating the air pressure test box in real time based on the space air pressure stability compensation value, and performing an operating air pressure stability test to obtain operating air pressure stability data;

[0095] In this embodiment, the operating program of the air pressure test chamber is started to ensure that the system enters the real-time monitoring mode. At this time, the control system will dynamically adjust the air pressure according to the set target air pressure and compensation value, and use the sensor to monitor the air pressure status in real time. The air pressure sensor will continuously feedback the current air pressure value. The system should be able to update and display this data in real time. During the operation of the test chamber, the operating air pressure data is regularly collected, including the pressure value of each measuring point and related environmental parameters (such as temperature and humidity). The appropriate sampling frequency is set according to the experimental requirements, such as collecting data once per second or once per minute, so as to obtain sufficient samples for subsequent analysis. The collected air pressure data is imported into the data analysis software (such as Python, MATLAB or Excel) and organized into a structured data format. Formula, to facilitate subsequent analysis, stability detection indicator selection: select appropriate stability detection indicators, such as: mean: reflects the average value of air pressure, standard deviation: reflects the degree of fluctuation of air pressure, maximum and minimum values: reflect the range of change of air pressure, fast Fourier transform (FFT): used for frequency domain analysis, identify possible periodic fluctuations, perform stability calculations on operating air pressure data, use statistical methods to evaluate the stability of air pressure, if the standard deviation exceeds the preset threshold, further analysis of the cause may be required, store the operating air pressure stability data and analysis results in the database to ensure data integrity and traceability, generate a detailed analysis report based on the results of the stability test, and record the operating air pressure stability data and its characteristics, including mean, standard deviation, fluctuation, etc.

[0096] Step S6: performing air pressure stabilization delay error analysis on the operating air pressure stabilization data and performing dynamic air pressure deviation fine-tuning to generate a dynamic air pressure deviation fine-tuning strategy to execute the air pressure test chamber pressure stability control operation.

[0097] In this embodiment, after the air pressure test box reaches a stable operating state, the pressure data sequence is continuously collected to ensure that the data collection frequency is high enough to reflect the dynamic characteristics of pressure changes. The collected pressure data sequence is subjected to time series analysis, the deviation between the pressure value and the expected pressure value is calculated, the trend of the deviation over time is analyzed, and the delay error characteristics under the stable state are identified. Based on the above delay error analysis results, a dynamic model of the pressure deviation is established. A time series analysis method such as the autoregressive moving average (ARIMA) model can be used. The model can describe the dynamic characteristics of the pressure deviation over time. Using the dynamic deviation model, a feedback control strategy is designed to fine-tune the pressure. Control algorithms such as PID and Model Predictive Control can be used.

[0098] Optimize the control parameters so that the pressure can quickly converge to the desired value. Apply the above optimized control strategy to the real-time pressure control of the air pressure test chamber, continuously monitor the pressure data, and perform dynamic deviation fine-tuning in real time to ensure that the pressure is stable near the desired value and meet the test requirements.

[0099] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0100] Step S11: monitoring the current non-operating state of the pressure test chamber, thereby obtaining multi-dimensional non-operating state monitoring parameters of the pressure test chamber, wherein the multi-dimensional non-operating state monitoring parameters include infrared thermal imaging temperature monitoring data, chamber pressure state parameters, and chamber humidity parameters;

[0101] Step S12: performing heat distribution analysis on the infrared thermal imaging temperature monitoring data of the test chamber to extract the temperature distribution data of the test chamber;

[0102] Step S13: performing temperature distribution fluctuation fitting on the test chamber temperature distribution data to obtain a temperature distribution fluctuation curve;

[0103] Step S14: performing spatial pressure gradient calculation on the test chamber air pressure state parameters to generate spatial pressure gradient characteristics;

[0104] Step S15: analyzing the pressure balance state characteristics of the test chamber based on the spatial pressure gradient characteristics to obtain the pressure balance state characteristics;

[0105] Step S16: performing a non-operating state time series evolution on the temperature distribution fluctuation curve, the air pressure equilibrium state characteristics and the test chamber humidity parameters, thereby generating a non-operating state time series characteristic.

[0106] In this embodiment, an infrared thermal imager is installed in the air pressure test chamber to monitor the temperature distribution in the test chamber in real time, a high-precision air pressure sensor is installed to continuously monitor the air pressure changes in the test chamber, and a humidity sensor is installed to obtain humidity data in the test chamber. When the test chamber is not in operation, all sensors are started and a data acquisition system is configured to regularly record various parameters. The recorded data includes temperature data from infrared thermal imaging, readings from air pressure sensors, and outputs from humidity sensors. These data will be stored in the database of the data acquisition system for subsequent analysis. During the monitoring process, the sensors are ensured to work normally and are calibrated regularly to maintain data accuracy. Through data monitoring, reliable basic data can be provided for subsequent analysis. The temperature data acquired by the imager is organized into a two-dimensional matrix to represent the temperature at different points inside the test chamber. This ensures data integrity and handles missing and outliers. The temperature data is analyzed using thermal imaging analysis software. The temperature distribution inside the test chamber is displayed through a heat map, and hot and cold areas in the temperature distribution are identified to evaluate the heat distribution of the equipment when it is not in operation. Based on the temperature distribution results, the heat uniformity and possible heat accumulation problems are analyzed to provide recommendations for the design and use of the test chamber. By performing time series analysis on the temperature data, the temperature changes over time are calculated, and significant fluctuation patterns are identified. Statistical methods (such as polynomial fitting or spline interpolation) are used to fit the temperature fluctuations, generate a temperature distribution fluctuation curve, and evaluate the fit. The accuracy and applicability of the curve are to ensure that it can reflect the actual temperature change trend. The data of the pressure sensor is organized into a format suitable for analysis, usually including a timestamp and the corresponding pressure value. The pressure values ​​at different positions in the test chamber are calculated, the pressure distribution is analyzed and the spatial pressure gradient is calculated. This can be achieved by comparing the pressure readings at different positions, identifying the trend of pressure changes, and drawing a pressure distribution diagram to more clearly see the spatial changes in pressure. The pressure gradient obtained by analysis can be used to evaluate the sealing and pressure control capabilities of the test chamber to ensure its safety and stability in operation. By analyzing the pressure gradient data, it is determined whether the pressure is in a balanced state. Usually, the pressure equilibrium state characteristics can be determined by comparing the pressure mean and the standard The pressure difference is used to evaluate. If the pressure change is small and uniform, the air pressure can be considered to be in a balanced state. A threshold is defined to analyze the air pressure stability. If the standard deviation is lower than this value, the air pressure is considered to be in a balanced state. This analysis helps to understand the air pressure stability of the test chamber in a non-operating state. According to the analysis results of the air pressure equilibrium state characteristics, corresponding maintenance strategies are formulated to ensure the safety and stability of the test chamber during operation. The temperature distribution fluctuation curve, air pressure equilibrium state characteristics and humidity parameters are comprehensively integrated to form a complete time series data set to ensure that these data can be correlated with each other for subsequent analysis. The time series analysis method is used to evaluate the change trend of different parameters over time in a non-operating state. This can be achieved by observing the change chart of each parameter.To identify potential trends and periodic changes, the analysis results can reveal the performance changes of the test chamber under different environmental conditions and provide data support for optimized design and maintenance.

[0107] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0108] Step S21: Obtaining an internal structure diagram of the air pressure test chamber;

[0109] Step S22: performing component structure visual recognition on the internal structure diagram of the air pressure test chamber, and marking multiple component nodes inside the test chamber;

[0110] Step S23: performing spatial topological distribution analysis on the internal component nodes of the multiple test boxes to generate internal component spatial topological distribution data;

[0111] Step S24: performing geometric morphological feature recognition on the internal structure diagram of the air pressure test box to obtain morphological features of the test box;

[0112] Step S25: performing three-dimensional point cloud modeling on the morphological features of the test box based on the spatial topological distribution data of the internal components to generate a three-dimensional structural model of the test box;

[0113] Step S26: Perform dynamic time series feature mapping on the three-dimensional structure model of the test box according to the time series features of the non-operating state, and construct a digital twin model of the test box.

[0114] In this embodiment, an internal structure diagram of the air pressure test chamber is obtained from the design department, engineers or relevant documents to ensure that the drawing accurately shows the positions and connection relationships of all internal components. This can be a CAD file, PDF file or scanned drawing. The format of the drawing is checked to ensure that it can be recognized by subsequent image processing or CAD software. If necessary, the drawing is converted into a suitable format (such as DWG or JPG). The drawing is cleaned and optimized to remove any unnecessary marks or noise to improve the accuracy of subsequent processing. Suitable image processing software, such as OpenCV, MATLAB or CAD software, is selected for visual recognition. Edges in the image are identified using an edge detection algorithm (such as Canny edge detection). This step It will help to distinguish the boundaries of each component and use template matching technology to identify specific components. You can use pre-trained machine learning models to identify components of specific shapes or labels, mark the identified components, and record the coordinates and type of each component node to form a dataset containing all component information. The dataset should include the component ID, type, coordinate position, etc. The marked component nodes are organized into a structured data format, including node ID, coordinate position (x, y, z) and component type. Select a suitable topology analysis tool or library (such as NetworkX or MATLAB) to perform spatial topology analysis, and use spatial distance and connection relationship algorithms (such as Delaunay triangulation) to analyze the spatial relationship between components. The generated topology map should be able to reflect the connectivity and relative positions between components, output topology distribution data, form a topology map, which includes the connection information of each component node, and use CAD software (such as AutoCAD, SolidWorks) or geometric analysis library (such as OpenSCAD) to extract geometric features. Extract the outer contour of the air pressure test box and the geometric features of the internal components from the internal structure diagram, identify the main geometric shapes, such as rectangles, cylinders, pipes, etc., record the geometric parameters of each component, including length, width, height, diameter, curvature, etc., to form a geometric feature data set, and use point cloud processing software (such as PCL, Blender or Rhino) to prepare for 3D modeling. According to the spatial topology distribution data and geometric The three-dimensional coordinates of each node correspond to the position of the component to form a complete three-dimensional structure. The generated point cloud is optimized to remove redundant points and noise to ensure the accuracy and quality of the model. The three-dimensional structural model of the test box is constructed using point cloud data to ensure that the model accurately reflects the design intent. The generated non-operating state timing characteristics (such as temperature, humidity, air pressure, etc.) are integrated into a data set to ensure data integrity. A suitable dynamic mapping tool (such as Unity, UnrealEngine or dedicated visualization software) is selected to map the timing characteristics to the three-dimensional model. The integrated timing feature data is applied to the three-dimensional model to generate a digital twin model. The model should be able to reflect changes in timing characteristics in real time.For example, the temperature and humidity changes in different time periods can be used to verify the accuracy of the digital twin model and ensure that it can effectively reflect the state changes of the pressure test chamber. The digital twin model can be displayed using visualization tools to facilitate analysis and decision support. An interactive interface can be created to allow users to view and analyze the state and dynamic changes of the test chamber in real time.

[0115] In this embodiment, refer to Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0116] Step S31: obtaining a target test scenario based on the test log;

[0117] Step S32: performing target pressure state analysis on the target test scene and extracting target scene pressure state characteristics;

[0118] Step S33: performing temperature change demand rate analysis on the target test scenario to extract temperature change demand rate parameters of the target scenario;

[0119] Step S34: performing temperature spatiotemporal feature evolution on the temperature change demand rate parameter of the target scene to extract the temperature spatiotemporal variation law of the target scene;

[0120] Step S35: performing final test scene state mining on the target scene pressure state characteristics according to the temperature spatiotemporal variation law of the target scene to obtain final test scene state data.

[0121] In this embodiment, the source of the test log is confirmed, which usually includes an experimental record database, a test management system or a real-time monitoring system, to ensure that all logs are the latest and complete versions. SQL queries or data extraction tools are used to extract relevant test logs from the database, which should include fields such as timestamp, test type, environmental conditions (temperature, humidity, etc.), set pressure, real-time records, etc. The extracted log data is converted into a unified format (such as CSV or JSON) to ensure that the data structure is consistent to facilitate subsequent analysis. Missing values ​​or outliers in the data are checked, and they are filled, deleted or retained as appropriate. Mean filling or interpolation methods can be used to handle missing data. According to the test requirements, screening conditions are set, such as specific pressure ranges, temperature conditions and test types (such as startup, shutdown), and records that meet the conditions are screened to extract relevant parameters of the target scenario to form a data set for the target test scenario. The records should include key information such as pressure, temperature, humidity, and test number. Data related to the target test scenario are extracted from the data set. All pressure-related data, including timestamps, set pressure, and real-time pressure, are processed and analyzed using data analysis tools such as the Pandas library in Python, R language, or MATLAB. Descriptive statistical indicators of the target scenario pressure data are calculated, including: mean: indicating the central trend of pressure, standard deviation: reflecting the degree of pressure fluctuation, maximum and minimum values: identifying extreme pressure values, peak and valley values: identifying peaks and valleys of pressure changes. By drawing a trend graph of pressure changes over time, the changing patterns of pressure states, such as stability, volatility, and anomalies, are identified. Key features are recorded. Temperature data is extracted from the target test scenario. Ensure that the timestamps of the temperature data are aligned with the pressure data for synchronous analysis. Select an appropriate calculation method, such as the difference method, to calculate the temperature change demand rate. For consecutive time points t and t+1, calculate the temperature difference: ΔT=T(t+1)−T(t), calculate the time interval: Δt=t+1−t, and calculate the temperature change rate: temperature change rate = , record the temperature change rate at each time point, extract the main features, such as the maximum rate, minimum rate and average rate, combine the temperature change rate data with time and space parameters to form a spatiotemporal data set. The data set should contain timestamps, spatial locations (such as sensor locations) and corresponding temperature change rates. Select appropriate spatiotemporal analysis methods, such as time series analysis (ARIMA model), spatial interpolation (Kriging interpolation), etc., analyze the evolution law of the temperature change demand rate, identify the evolution law of the temperature change rate under different time and space conditions, and extract key spatiotemporal features, such as periodic changes, significant fluctuations, etc. You can use visualization tools (such as Matplotlib, Seaborn) to display the results for easy understanding. The state characteristics (such as stability and volatility) are integrated with the spatiotemporal temperature variation patterns extracted in step S34 to form a comprehensive data set. The data set should include pressure characteristics, temperature change rate, timestamp, and other relevant parameters. An appropriate data mining algorithm (such as cluster analysis, association rule learning, decision tree, or random forest) is selected to analyze the comprehensive data set, identify potential patterns and relationships, and apply the selected mining algorithm to perform data analysis to reveal the final state characteristics of the target scenario. For example, the relationship between the temperature change rate and the pressure state is analyzed, and the pressure change pattern under specific conditions is identified. The final test scenario state data, including key state parameters and feature descriptions, is generated. The results are recorded in a database, and an analysis report is generated to support subsequent decision-making and optimization.

[0122] In this embodiment, the specific steps of step S32 are:

[0123] Calculate the spatial average pressure of the target test scene to obtain the spatial average pressure parameters of the target scene;

[0124] Predicting the scene pressure peak based on the target scene spatial average pressure parameter to obtain the predicted peak pressure of the target scene;

[0125] Perform pressure filling time series variation analysis on the target test scene to obtain target scene pressure filling time series variation data;

[0126] Perform pressure fluctuation amplitude analysis on the target scene pressure filling time series change data to generate pressure fluctuation amplitude;

[0127] Perform multi-period change pattern recognition on the pressure fluctuation amplitude to generate multi-period pressure change characteristics;

[0128] Perform pressure stability assessment on the pressure change characteristics of multiple periods to obtain the pressure stability assessment value of the target scene;

[0129] The target demand pressure state analysis is performed on the target scenario pressure prediction peak and target scenario pressure stability assessment value to extract the target scenario pressure state characteristics.

[0130] In this embodiment, all relevant pressure sensor data are extracted from the target test scene, including the real-time pressure value of each sensor and its corresponding spatial position (such as coordinates), and the spatial average pressure is calculated using a statistical method. Code is written (such as Python using the NumPy library) to calculate the average pressure value of all sensors and record the results. The spatial average pressure data is organized into a time series format for predictive analysis. Time series prediction methods such as ARIMA, SARIMA or LSTM (long short-term memory network) can be used. The time series data is input into a selected model for training. Historical data is used to predict future pressure peaks. The prediction results are recorded, the predicted pressure peaks are extracted, and the relevant confidence intervals are output to ensure the integrity of the pressure data. Interpolation methods (such as linear interpolation or spline interpolation) are used to fill in missing pressure values ​​to form continuous pressure time series data. Trend analysis methods (such as moving average and exponential smoothing) are used to identify the time series change trend of pressure. A chart of pressure changes over time can be drawn to intuitively display the changes in data. The analysis results are recorded in a database to generate a pressure-filled time series change data set, and the pressure fluctuation amplitude (such as the maximum and minimum) is calculated. The difference in pressure values ​​is calculated as follows: A = Pmax − Pmin. Statistical analysis tools are used to calculate the fluctuation amplitude in the pressure filling time series data and record the results. The pressure data is divided into multiple time periods (such as hours, days, or weeks) to prepare for subsequent analysis. Cluster analysis (such as K-means clustering) or pattern recognition algorithms (such as dynamic time warping (DTW)) are used to identify the changing patterns of pressure fluctuations. The pressure fluctuation amplitude in each time period is analyzed to identify similar fluctuation patterns and generate a multi-time period pressure change characteristic report. Stability assessment indicators, such as standard deviation and coefficient of variation (CV), are defined to reflect pressure stability. The previously calculated pressure fluctuation amplitude data is used to calculate the stability indicators for each time period. The assessment values ​​are recorded in a database to form a pressure stability assessment report for subsequent analysis. The predicted pressure peak value in step 2 is integrated with the pressure stability assessment value in step 6 to form a comprehensive data set. Appropriate analysis methods (such as decision trees and regression analysis) are selected to evaluate the target demand pressure status. The selected analysis methods are applied to conduct in-depth analysis of the integrated data to extract the pressure status characteristics of the target scenario, including the recommended pressure setting range and potential safety risks.

[0131] In this embodiment, step S4 includes the following steps:

[0132] Step S41: performing a rapid air pressure filling simulation on the digital twin model of the test chamber, and collecting the air pressure simulation response data of the test chamber;

[0133] Step S42: identifying the air pressure spatial distribution state of the test chamber air pressure simulation response data to generate air pressure spatial distribution state features;

[0134] Step S43: performing state deviation analysis on the air pressure spatial distribution state characteristics based on the final test scene state data, thereby generating a target air pressure state deviation value;

[0135] Step S44: performing a space pressure stability compensation calculation on the target air pressure state deviation value to obtain a space pressure stability compensation value.

[0136] In this embodiment, computational fluid dynamics (CFD) software (such as ANSYS Fluent or OpenFOAM) is used to build a digital twin model of the test box to ensure that the model can accurately reflect the geometric structure and air pressure conditions of the test box. The model parameters, including gas type, initial air pressure, temperature, and boundary conditions, are configured to ensure that the simulation environment is consistent with the actual test conditions. The air pressure filling simulation is run to observe the change process of the air pressure in the test box. During the simulation, dynamic grid or time step control can be used to improve the calculation efficiency. During the simulation, the air pressure response data of each point in the test box is collected in real time, including the time series of pressure changes. The collected air pressure simulation response data is formatted and saved in an appropriate file format (such as CSV or HDF5) for subsequent processing and analysis. The collected air pressure response data is denoised to remove outliers and noise to ensure data quality. The discrete air pressure data is converted into a continuous air pressure spatial distribution map using interpolation methods (such as Kriging interpolation or inverse distance weighted method) to generate an air pressure data map. The spatial distribution state characteristic diagram of air pressure, including the high and low points, uniformity and gradient changes of air pressure distribution, is displayed graphically using visualization tools (such as Matplotlib or ParaView) to facilitate intuitive understanding and analysis of air pressure distribution characteristics. The state data of the final test scene is collected and integrated, including the actually measured air pressure values ​​and the air pressure spatial distribution state characteristics generated by simulation. The deviation values ​​are statistically analyzed, and the average deviation, standard deviation and maximum deviation are calculated to evaluate the accuracy of the air pressure distribution. The calculated air pressure state deviation values ​​are recorded in the database, and relevant reports are generated for subsequent analysis. The air pressure state deviation is calculated using a compensation algorithm (such as PID control or model predictive control (MPC)) to achieve stable compensation of the air pressure. Based on the calculated air pressure deviation values, the air pressure stable compensation calculation is performed, the results of the compensation calculation are recorded, and a data set of air pressure stable compensation values ​​is formed. By comparing the air pressure states before and after compensation, the compensation effect is evaluated to ensure that the target air pressure state meets the expected stability requirements.

[0137] In this embodiment, step S5 includes the following steps:

[0138] Step S51: performing air pressure balance optimization on the air pressure spatial distribution state characteristics based on the spatial air pressure stability compensation value to obtain air pressure simulation balance optimization parameters;

[0139] Step S52: operating the air pressure test box in real time according to the air pressure simulation balance optimization parameters, and collecting air pressure state parameters at multiple time points;

[0140] Step S53: calculating the air pressure fluctuation amplitudes at adjacent points on the air pressure state parameters at multiple time points to obtain the air pressure fluctuation amplitudes between the multiple time points;

[0141] Step S54: performing an operating state air pressure stability test based on the air pressure fluctuation amplitudes between multiple time points, thereby obtaining operating state air pressure stability data.

[0142] In this embodiment, the pressure stabilization compensation value calculated in step S44 is collected and combined with the characteristics of the air pressure spatial distribution (such as the pressure distribution map and uniformity index) to prepare for pressure balance optimization. Based on the characteristics of the pressure distribution, an appropriate optimization algorithm is selected, such as a genetic algorithm, particle swarm optimization, or simulated annealing. The goal is to optimize the pressure distribution to make it more uniform and reduce fluctuations. An optimization objective function is defined, which can be the uniformity or fluctuation amplitude of the pressure distribution. The pressure stabilization compensation value and the current pressure distribution characteristics are input, and the selected optimization algorithm is run to calculate the optimized pressure simulation balance parameters. This process may require multiple iterations to ensure optimal results. The optimized pressure simulation balance parameters (such as the set pressure target and adjustment strategy) are recorded in a database for subsequent use and verification. Based on the pressure simulation balance optimization parameters obtained in step S51, the test chamber's control system (e.g., a PLC or SCADA system) is configured to ensure real-time response and pressure adjustment. The test chamber's pressure system is activated to perform real-time pressure adjustment. The system automatically adjusts the pressure according to the optimized parameters to ensure that the pressure meets the preset target during operation. Set the sampling frequency (such as every second, every minute) according to the experimental requirements, and collect air pressure state parameters at multiple time points. The monitoring system should record the air pressure value, timestamp and related environmental parameters (such as temperature and humidity) at each time point. Monitor the air pressure state in real time through sensors to ensure the accuracy and timeliness of the data. Extract the air pressure state parameters at multiple time points from the database to ensure that the data format is unified (such as timestamp, air pressure value) for subsequent calculations. For any two adjacent time points t and t+1, calculate the air pressure fluctuation amplitude. The calculation formula is: =|P(t+1)−P(t)|, is the fluctuation amplitude at the i-th time point, and P(t) and P(t+1) are the air pressure values ​​at two adjacent time points respectively. Traverse all time points, calculate the air pressure fluctuation amplitude between all adjacent points, and store the results in a list or array for subsequent analysis. Select appropriate indicators to evaluate the stability of the air pressure, such as the standard deviation, mean, maximum and minimum values ​​of the fluctuation amplitude. Perform statistical analysis on the air pressure fluctuation amplitude data obtained in step S53, and calculate the mean, standard deviation and maximum value of the fluctuation amplitude to evaluate the stability of the air pressure. Set the air pressure stability threshold according to industry standards or experimental requirements (for example, if the standard deviation exceeds a certain value, the air pressure is considered unstable). Record the results of the stability test in the database, and generate a relevant report, including the operating air pressure stability data and evaluation conclusions. This will provide a basis for subsequent decision-making and optimization.

[0143] In this embodiment, step S6 includes the following steps:

[0144] Step S61: performing air pressure stabilization delay error analysis on the operating air pressure stabilization data to generate an air pressure stabilization delay error value;

[0145] Step S62: performing error optimization based on the air pressure stabilization delay error value to generate error-optimized air pressure stabilization data;

[0146] Step S63: performing secondary pressure deviation calculation on the error-optimized pressure stabilization data based on the final test scene state data to generate a secondary pressure deviation parameter;

[0147] Step S64: fine-tuning the dynamic pressure deviation of the pressure test box based on the secondary pressure deviation parameter to generate a dynamic pressure deviation fine-tuning strategy to perform a pressure stability control operation of the pressure test box.

[0148] In this embodiment, operating state air pressure stability data, including timestamps, air pressure values, and any relevant environmental parameters, is extracted from the database to ensure data integrity and accuracy. The delay difference between the target value of air pressure stability (such as the set stable air pressure) and the actual measured air pressure is determined. All time data are traversed, the delay error at each time is calculated, and recorded in a list. The Pandas library in Python can be used to process the data to facilitate calculation and analysis. Statistical analysis is performed on the delay error data to calculate the mean, standard deviation, and maximum value, and identify the distribution characteristics of the error, which helps to understand the overall performance and fluctuation of the error. Based on the characteristics and distribution of the error, an appropriate optimization method, such as gradient descent method, least squares method, etc., is selected to reduce the delay error. The optimization goal is set to minimize the error value. The delay error data is input, and the selected optimization algorithm is run to generate optimized air pressure stability data. This may include adjusting the air pressure control strategy or optimizing the control parameters. The generated error-optimized air pressure stability data is recorded in the database, and a relevant report is generated for subsequent verification and use. The final test scene state data, including the final set air pressure target and the optimized air pressure stability data, is collected. The secondary air pressure deviation is calculated using the following formula: Deviation value = − , is the optimized air pressure stability parameter, For the target air pressure set for the test, all relevant data points are traversed, the secondary deviation of each point is calculated, and the results are recorded. According to the secondary air pressure deviation parameters, a dynamic air pressure deviation fine-tuning strategy is formulated. The PID control or fuzzy control method in control theory can be used to achieve fine-tuning of the air pressure. A suitable algorithm is selected to perform dynamic fine-tuning, such as the PID control algorithm, to ensure that the air pressure can respond quickly and stabilize within the set range. The fine-tuning strategy is applied to the control system of the air pressure test chamber, and the air pressure is adjusted in real time to eliminate the deviation. The monitoring system needs to feedback the air pressure status in real time to ensure the adjustment effect. During the adjustment process, the air pressure status is continuously monitored to ensure that it is within the preset stable range. The air pressure status after fine-tuning is recorded and compared with the status before fine-tuning to evaluate the effect of the adjustment. A pressure change chart can be drawn to intuitively display the impact of fine-tuning. The results and evaluation conclusions of the dynamic air pressure deviation fine-tuning are recorded in the database, and relevant reports are generated to support subsequent decision-making and operations.

[0149] In this embodiment, a pressure stability control system for an air pressure test chamber is provided, which is used to execute the pressure stability control method for an air pressure test chamber as described above, including:

[0150] A non-operating state module is used to obtain multi-dimensional non-operating state monitoring parameters of the air pressure test box; perform non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters to generate non-operating state time series features;

[0151] The spatial topology module is used to obtain the internal structure diagram of the air pressure test chamber; based on the non-operating state time series characteristics, the spatial topology distribution of the internal structure diagram of the air pressure test chamber is analyzed to build a digital twin model of the test chamber;

[0152] The test scenario module is used to obtain the target test scenario based on the test log; perform target pressure state analysis on the target test scenario to obtain the final test scenario state data;

[0153] The air pressure stability compensation module is used to calculate the space air pressure stability compensation of the digital twin model of the test chamber according to the final test scene state data to obtain the space air pressure stability compensation value;

[0154] An operating state module is used to perform operating state air pressure stability detection on the air pressure test chamber based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data;

[0155] The dynamic air pressure fine-tuning module is used to perform dynamic air pressure deviation fine-tuning on the operating air pressure stability data to generate a dynamic air pressure deviation fine-tuning strategy to perform the pressure stability control operation of the air pressure test chamber.

[0156] The present invention helps to understand the state changes of the test box in the shutdown state by monitoring the multi-dimensional non-operating state parameters of the air pressure test box, and provides basic data for subsequent stability control. Generating non-operating state time series characteristics can reveal the characteristic changes of the air pressure test box in different non-operating states, and provide a basis for subsequent analysis and optimization. Obtaining the internal structure diagram of the air pressure test box helps to understand the spatial layout and structural characteristics of the test box, and provides basic data for subsequent spatial analysis. Spatial topological distribution analysis and three-dimensional point cloud modeling based on non-operating state time series characteristics can construct a digital twin model of the test box, and provide a basis for subsequent simulation and optimization. Analyzing the pressure state of the target test scene helps to understand the working state of the test box in different scenes, and provides a reference for subsequent pressure control. Mining the final test scene state data can provide support for determining the final test conditions and pressure requirements, and provide support for subsequent control strategies. It provides a basis for the formulation of the strategy, and conducting rapid air pressure filling simulation and calculating the space air pressure stability compensation value helps to optimize the air pressure distribution and improve the air pressure stability of the air pressure test chamber. Compensation calculation based on the final test scene state data can adjust the air pressure parameters according to actual needs to ensure the stability of the test chamber in different scenarios. Real-time operation and air pressure stability detection help to verify the stability of the air pressure test chamber, timely discover and deal with abnormal air pressure fluctuations, and obtain operating air pressure stability data. It can provide real-time feedback for adjusting the control strategy and parameters to ensure the stability of the test chamber during operation. Delay error analysis and dynamic air pressure deviation fine-tuning of the operating air pressure stability data help to optimize the air pressure control strategy, improve air pressure stability and control accuracy, and generate a dynamic air pressure deviation fine-tuning strategy to achieve fine control of the pressure stability of the air pressure test chamber, ensuring the stability and reliability of the test chamber under various working conditions.

[0157] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0158] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A pressure test chamber pressure stability control method, characterized in that: The following steps are involved: Step S1: obtaining multi-dimensional non-operational state monitoring parameters of the air pressure test chamber; Performing non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters, thereby generating non-operating state time series features; Step S2: Obtain an internal structure diagram of the air pressure test chamber; perform spatial topological distribution analysis on the internal structure diagram of the air pressure test chamber based on the non-operating state time series characteristics, and construct a digital twin model of the test chamber; Step S3: Obtain the target test scenario based on the test log; Perform target pressure state analysis on the target test scenario to obtain the final test scenario state data; Step S4: performing a spatial air pressure stability compensation calculation on the digital twin model of the test chamber according to the final test scene state data to obtain a spatial air pressure stability compensation value; Step S5: performing an operating state air pressure stability test on the air pressure test box based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data; Step S6: performing dynamic pressure deviation fine-tuning on the operating state air pressure stability data to generate a dynamic pressure deviation fine-tuning strategy to execute the pressure stability control operation of the air pressure test chamber; Among them, the specific steps of step S2 are: Step S21: Obtaining an internal structure diagram of the air pressure test chamber; Step S22: performing component structure visual recognition on the internal structure diagram of the air pressure test chamber, and marking multiple component nodes inside the test chamber; Step S23: performing spatial topological distribution analysis on the internal component nodes of the multiple test boxes to generate internal component spatial topological distribution data; Step S24: performing geometric morphological feature recognition on the internal structure diagram of the air pressure test box to obtain morphological features of the test box; Step S25: performing three-dimensional point cloud modeling on the morphological features of the test box based on the spatial topological distribution data of the internal components to generate a three-dimensional structural model of the test box; Step S26: Perform dynamic time series feature mapping on the three-dimensional structure model of the test box according to the time series features of the non-operating state, and construct a digital twin model of the test box.

2. The pressure stability control method of the air pressure test chamber according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: monitoring the current non-operating state of the pressure test chamber, thereby obtaining multi-dimensional non-operating state monitoring parameters of the pressure test chamber, wherein the multi-dimensional non-operating state monitoring parameters include infrared thermal imaging temperature monitoring data, chamber pressure state parameters, and chamber humidity parameters; Step S12: performing heat distribution analysis on the infrared thermal imaging temperature monitoring data of the test chamber to extract the temperature distribution data of the test chamber; Step S13: performing temperature distribution fluctuation fitting on the test chamber temperature distribution data to obtain a temperature distribution fluctuation curve; Step S14: performing spatial pressure gradient calculation on the test chamber air pressure state parameters to generate spatial pressure gradient characteristics; Step S15: analyzing the pressure balance state characteristics of the test chamber based on the spatial pressure gradient characteristics to obtain the pressure balance state characteristics; Step S16: performing a non-operating state time series evolution on the temperature distribution fluctuation curve, the air pressure equilibrium state characteristics and the test chamber humidity parameters, thereby generating a non-operating state time series characteristic.

3. The pressure stability control method of the air pressure test chamber according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: obtaining a target test scenario based on the test log; Step S32: performing target pressure state analysis on the target test scene and extracting target scene pressure state characteristics; Step S33: performing temperature change demand rate analysis on the target test scenario to extract temperature change demand rate parameters of the target scenario; Step S34: performing temperature spatiotemporal feature evolution on the temperature change demand rate parameter of the target scene to extract the temperature spatiotemporal variation law of the target scene; Step S35: performing final test scene state mining on the target scene pressure state characteristics according to the temperature spatiotemporal variation law of the target scene to obtain final test scene state data.

4. The pressure stability control method of the air pressure test chamber according to claim 3, characterized in that: The specific steps of step S32 are: Calculate the spatial average pressure of the target test scene to obtain the spatial average pressure parameters of the target scene; Predicting the scene pressure peak based on the target scene spatial average pressure parameter to obtain the predicted peak pressure of the target scene; Perform pressure filling time series variation analysis on the target test scene to obtain target scene pressure filling time series variation data; Perform pressure fluctuation amplitude analysis on the target scene pressure filling time series change data to generate pressure fluctuation amplitude; Perform multi-period change pattern recognition on the pressure fluctuation amplitude to generate multi-period pressure change characteristics; Perform pressure stability assessment on the pressure variation characteristics over multiple periods to obtain the pressure stability assessment value of the target scene; The target demand pressure state analysis is performed on the target scenario pressure prediction peak and target scenario pressure stability assessment value to extract the target scenario pressure state characteristics.

5. The pressure stability control method of the air pressure test chamber according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing a rapid air pressure filling simulation on the digital twin model of the test chamber, and collecting the air pressure simulation response data of the test chamber; Step S42: identifying the air pressure spatial distribution state of the test chamber air pressure simulation response data to generate air pressure spatial distribution state features; Step S43: performing state deviation analysis on the air pressure spatial distribution state characteristics based on the final test scene state data, thereby generating a target air pressure state deviation value; Step S44: performing a space pressure stability compensation calculation on the target air pressure state deviation value to obtain a space pressure stability compensation value.

6. The pressure stability control method of the air pressure test chamber according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing air pressure balance optimization on the air pressure spatial distribution state characteristics based on the spatial air pressure stability compensation value to obtain air pressure simulation balance optimization parameters; Step S52: operating the air pressure test box in real time according to the air pressure simulation balance optimization parameters, and collecting air pressure state parameters at multiple time points; Step S53: calculating the air pressure fluctuation amplitudes of adjacent points on the air pressure state parameters at multiple time points to obtain the air pressure fluctuation amplitudes between the multiple time points; Step S54: performing an operating state air pressure stability test based on the air pressure fluctuation amplitudes between multiple time points, thereby obtaining operating state air pressure stability data.

7. The pressure stability control method of the air pressure test chamber according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing air pressure stabilization delay error analysis on the operating air pressure stabilization data to generate an air pressure stabilization delay error value; Step S62: performing error optimization based on the air pressure stabilization delay error value to generate error-optimized air pressure stabilization data; Step S63: performing secondary pressure deviation calculation on the error-optimized pressure stabilization data based on the final test scene state data to generate a secondary pressure deviation parameter; Step S64: fine-tuning the dynamic pressure deviation of the pressure test box based on the secondary pressure deviation parameter to generate a dynamic pressure deviation fine-tuning strategy to perform a pressure stability control operation of the pressure test box.

8. A pressure stability control system for an air pressure test chamber, characterized in that: The method for controlling the pressure stability of the air pressure test chamber according to claim 1 comprises: A non-operating state module is used to obtain multi-dimensional non-operating state monitoring parameters of the air pressure test box; perform non-operating state time series evolution on the multi-dimensional non-operating state monitoring parameters to generate non-operating state time series features; The spatial topology module is used to obtain the internal structure diagram of the air pressure test chamber; based on the non-operating state time series characteristics, the spatial topology distribution of the internal structure diagram of the air pressure test chamber is analyzed to build a digital twin model of the test chamber; The test scenario module is used to obtain the target test scenario based on the test log; perform target pressure state analysis on the target test scenario to obtain the final test scenario state data; The air pressure stability compensation module is used to calculate the space air pressure stability compensation of the digital twin model of the test chamber according to the final test scene state data to obtain the space air pressure stability compensation value; An operating state module is used to perform operating state air pressure stability detection on the air pressure test chamber based on the space air pressure stability compensation value, thereby obtaining operating state air pressure stability data; The dynamic air pressure fine-tuning module is used to perform dynamic air pressure deviation fine-tuning on the operating air pressure stability data to generate a dynamic air pressure deviation fine-tuning strategy to perform the pressure stability control operation of the air pressure test chamber.

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