Air pressure feedback method and equipment based on high-precision pressure sensing

Abstract

The invention relates to the technical field of air pressure sensing detection, in particular to an air pressure feedback method and equipment based on high-precision pressure sensing. The method comprises the following steps: carrying out pressure sensing element space positioning calculation on air pressure feedback equipment, carrying out topological distribution fitting, and constructing an element topological distribution diagram; acquiring a historical pressure sensing sample of the high-precision pressure sensing element; performing dynamic pressure deformation rendering on the element topology distribution map based on a historical pressure sensing sample, thereby constructing a dynamic pressure deformation visual network; performing environment barometric disturbance mapping on the dynamic pressure deformation visual network according to the barometric feedback equipment to obtain a barometric disturbance visual network; and carrying out real-time operation monitoring based on the barometric disturbance visual network, and carrying out pressure visual mapping to obtain a real-time pressure deformation visual network. According to the invention, efficient and accurate element air pressure feedback is realized.

Description

Technical Field

[0001] The present invention relates to the field of air pressure sensing detection technology, and in particular to an air pressure feedback method and device based on high-precision pressure sensing. Background Art

[0002] With the continuous development of industrial automation and intelligent manufacturing, high-precision pressure sensors play a vital role in various precision instruments, equipment and production lines, especially in the field of air pressure control and monitoring. High-precision pressure sensing elements are usually used to accurately measure gas pressure to ensure the safe and stable operation of equipment. With the diversification of application scenarios, especially in aerospace, medical equipment and high-precision processing, higher requirements are placed on the accuracy and response speed of pressure measurement. However, the existing air pressure feedback methods are often subject to multiple interferences such as environmental factors, temperature changes, and gas flow, resulting in measurement errors and feedback instability, affecting the overall system performance. Traditional air pressure feedback methods often rely on mechanical or electrical sensors for data acquisition, which has certain limitations in accuracy and response speed. Therefore, in order to achieve more accurate and reliable air pressure feedback, it is necessary to develop a new high-precision air pressure feedback method to improve the measurement accuracy and feedback stability of air pressure sensors to meet the needs of modern industry for high-precision pressure control. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a pressure feedback method and device based on high-precision pressure sensing to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides an air pressure feedback method based on high-precision pressure sensing, comprising the following steps: Step S1: performing spatial positioning calculation of the pressure sensing element of the air pressure feedback device, and performing topological distribution fitting to construct a component topological distribution map; Step S2: obtaining historical pressure sensing samples of high-precision pressure sensing elements; performing dynamic pressure deformation rendering on the element topology distribution map based on the historical pressure sensing samples, thereby constructing a dynamic pressure deformation visualization network; Step S3: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain an air pressure disturbance visualization network; Step S4: performing real-time operation monitoring based on the air pressure disturbance visualization network and performing pressure visualization mapping to obtain a real-time pressure deformation visualization network; Step S5: identifying the pressure deformation state of each component node of the real-time pressure deformation visualization network, and performing dynamic adaptive air pressure compensation, thereby generating air pressure compensation deformation parameters of each component node; Step S6: Perform global pressure feedback fitting on the pressure compensation deformation parameters of each component node, and perform migration fine-tuning learning to build a real-time pressure feedback fine-tuning model to perform pressure feedback operations.

[0005] The present invention helps to accurately describe the position and interconnection relationship of components in space through spatial positioning calculation and topological distribution fitting of pressure sensing elements. Constructing a component topological distribution map can provide an intuitive understanding of the component layout and provide a basis for subsequent analysis and operation. Dynamic pressure deformation rendering based on historical pressure sensing samples can realize a visual display of the deformation of the pressure sensing element. Constructing a dynamic pressure deformation visualization network helps to monitor the pressure state changes of the component in real time, help to find abnormal situations and take corresponding measures. Obtaining an air pressure disturbance visualization network through environmental air pressure disturbance mapping can show the impact of air pressure on the component and provide a more comprehensive data display. The air pressure disturbance visualization network helps to understand the impact of air pressure changes on component pressure and guide subsequent air pressure feedback operations. Real-time operation monitoring and pressure visualization mapping can display the pressure deformation of the component in real time and help monitor the operating status of the system. Obtaining a real-time pressure deformation visualization network helps to find problems and take measures in time to ensure the normal operation of the system. Identifying the pressure deformation state of each component node and performing dynamic adaptive air pressure compensation can accurately compensate the component according to real-time data. Generating the pressure compensation deformation parameters of each component node helps optimize the component response and improve the stability and accuracy of the system. Performing global pressure feedback fitting and migration fine-tuning learning on the pressure compensation deformation parameters of each component node can establish a real-time pressure feedback fine-tuning model. Building a real-time pressure feedback fine-tuning model helps optimize the pressure feedback operation, improve the response speed and accuracy of the system, and ensure the stable operation of the component.

[0006] Preferably, step S1 comprises the following steps: Step S11: identifying a high-precision pressure sensing element of an air pressure feedback device and marking a plurality of pressure sensing element nodes; Step S12: performing spatial positioning calculation on multiple pressure sensing element nodes to obtain element node position coordinates; Step S13: performing component spatial distribution analysis on the component node position coordinates to extract component spatial distribution data; Step S14: performing topological connection analysis on the component node position coordinates to obtain component topological structure data; Step S15: performing topological distribution fitting on the component topological structure data based on the component spatial distribution data to construct a component topological distribution map.

[0007] The present invention helps to determine the key components that need to be monitored by identifying high-precision pressure sensing elements and marking nodes. Marking multiple pressure sensing element nodes can provide specific targets for subsequent spatial positioning and analysis. Performing spatial positioning calculations helps to determine the exact position of each element node in the system. Obtaining the coordinates of the element node position can provide basic data support for subsequent analysis and operation. Analyzing the spatial distribution data of the element node position can reveal the distribution law and characteristics of the element in the system. Extracting the element spatial distribution data helps to understand the relative position relationship between the elements and provide a reference for system design and optimization. Performing topological connection analysis on the element node position can determine the connection mode and correlation between the elements. Obtaining the element topological structure data helps to establish the logical relationship between the elements and provide a basis for system operation and optimization. Performing topological distribution fitting on the element topological structure based on the element spatial distribution data can show the association relationship and layout between the elements. Constructing an element topological distribution diagram helps to intuitively understand the layout and connection of the elements in the system and provide visual support for system management and maintenance.

[0008] Preferably, the specific steps of step S2 are: Step S21: Acquire historical pressure sensing samples of the high-precision pressure sensing element; Step S22: performing high-pass filtering and noise reduction processing on the historical pressure sensing samples to obtain filtered noise reduction sensing samples; Step S23: performing outlier detection on the filtered and denoised sensor samples to extract outliers of the sensor samples; Step S24: performing outlier filtering based on the outliers of the sensing samples, thereby obtaining filtered optimized sensing samples; Step S25: Perform dynamic pressure deformation rendering on the component topology distribution map based on the filtered optimized sensing samples, thereby constructing a dynamic pressure deformation visualization network.

[0009] The present invention helps to analyze the pressure changes of components in different time periods by obtaining historical pressure sensing samples. Historical samples provide a reference for component performance and system operation status, and provide a data basis for subsequent analysis and optimization. High-pass filtering and denoising of historical samples can effectively remove noise interference and improve the accuracy and reliability of data. Obtaining filtered denoised sensing samples helps to accurately reflect the actual pressure state of the component and provide clear data for subsequent analysis and processing. Performing outlier detection on filtered denoised sensing samples can identify and extract abnormal data points. Extracting outliers helps to discover existing abnormal conditions or faults, and provides a basis for subsequent data processing and correction. Filtering sensing samples based on outliers can eliminate the interference of abnormal data points and improve data quality. Obtaining filtered optimized sensing samples helps to accurately describe the actual state of the component and provide a reliable data basis for subsequent analysis and application. Dynamic pressure deformation rendering based on filtered optimized sensing samples can realize an intuitive display of pressure deformation conditions. Building a dynamic pressure deformation visualization network helps to monitor the pressure changes of components in real time, help understand the system operation status and make corresponding adjustments.

[0010] Preferably, the specific steps of step S24 are: extracting pressure parameters of the sensing element of the filtered optimized sensing sample; Performing element-by-element pressure sensing accuracy learning on the pressure parameters of the sensing elements, thereby obtaining a pressure sensing accuracy value of each element; Based on the pressure sensing accuracy value of each component, dynamic sensing parameter mapping is performed on the component topology distribution map to build a component sensing network; Performing end-to-end perceptual learning on the filter-optimized sensing samples to generate component sensing perceptual learning data; Perform pressure sensing deformation visualization design on component sensing perception learning data to obtain pressure sensing visualization data; The pressure sensing visualization data is used to render the dynamic pressure deformation of the component sensing network, thereby constructing a dynamic pressure deformation visualization network.

[0011] The present invention helps to obtain the pressure data of each component by extracting the pressure parameters of the sensing element. This provides basic information for subsequent precision learning and data processing. Pressure sensing precision learning for each component can understand the sensing performance of each component. Obtaining the pressure sensing precision value of each component helps to evaluate the measurement accuracy and stability of the component. Dynamic sensing parameter mapping of the topological distribution diagram based on the pressure sensing precision value of the component can show the distribution of the sensing performance of the component. Building a component sensing network helps to intuitively understand the sensing capabilities of each component in the system and provide guidance for system optimization. End-to-end sensing learning of filtered optimized sensing samples can fully understand the characteristics and associations of sensing data. Generating component sensing perception learning data helps to improve the system's perception and judgment capabilities of the component state. Pressure sensing deformation visualization design of component sensing perception learning data can display the learned information in an intuitive way. Obtaining pressure sensing visualization data helps to better understand the pressure state and change trend of the component. Using pressure sensing visualization data to perform dynamic pressure deformation rendering of the component sensing network can achieve real-time display of component pressure changes. Building a dynamic pressure-deformation visualization network helps monitor the real-time status of components in the system, helping to identify problems in a timely manner and take corresponding measures.

[0012] Preferably, step S3 specifically comprises the following steps: Step S31: monitoring the non-operating static state of the air pressure feedback device and extracting the non-operating static air pressure feedback parameters; Step S32: analyzing the ambient air pressure change of the non-operating static air pressure feedback parameter, thereby obtaining the non-operating ambient air pressure change data; Step S33: calculating the air pressure fluctuation amplitude of the non-operating ambient air pressure change data to generate the ambient air pressure fluctuation amplitude; Step S34: mining dynamic air pressure characteristics of the ambient air pressure fluctuation amplitude to generate dynamic ambient air pressure characteristics; Step S35: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the dynamic ambient air pressure characteristics to obtain an air pressure disturbance visualization network.

[0013] The present invention helps to understand the air pressure parameters of the device in a non-operating state by monitoring the non-operating static state of the air pressure feedback device. Extracting the non-operating static air pressure feedback parameters can provide basic data for subsequent analysis and processing. Performing an environmental air pressure change analysis on the non-operating static air pressure feedback parameters helps to understand the fluctuation of the environmental air pressure. Obtaining the non-operating environmental air pressure change data can provide a basis for the subsequent calculation of the air pressure fluctuation amplitude. Calculating the environmental air pressure fluctuation amplitude can quantify the degree of air pressure fluctuation. Generating the environmental air pressure fluctuation amplitude helps to evaluate the stability and range of change of the environmental air pressure. Dynamic feature mining of the environmental air pressure fluctuation amplitude can discover the laws and characteristics of air pressure fluctuations. Generating dynamic environmental air pressure features helps to more deeply understand the dynamic characteristics of air pressure changes. Mapping the environmental air pressure disturbance to the dynamic pressure deformation visualization network according to the dynamic environmental air pressure features can intuitively display the air pressure disturbance information. Obtaining the air pressure disturbance visualization network helps to monitor the influence of the environmental air pressure on the pressure sensing element in real time, and helps to make corresponding adjustments when the system is running.

[0014] Preferably, the specific steps of step S4 are: Step S41: performing ambient air pressure deformation analysis on the air pressure disturbance visualization network to obtain network deformation data under ambient air pressure; Step S42: performing instant disturbance pressure feedback adjustment on the network deformation data under ambient pressure, thereby obtaining an instant adjustment deformation visualization network; Step S43: performing real-time operation monitoring on the air pressure feedback device and detecting the air pressure sensor signal; Step S44: performing nonlinear pressure sensing analysis on the air pressure sensing signal to generate nonlinear pressure sensing signal characteristics; Step S45: Visually map the instant adjustment deformation visualization network based on the nonlinear pressure sensing signal characteristics to obtain a real-time pressure deformation visualization network.

[0015] The present invention helps to understand the influence of ambient air pressure on network structure by performing ambient air pressure deformation analysis on the air pressure disturbance visualization network. Obtaining network deformation data under ambient air pressure can help evaluate the performance of the system under different air pressure conditions. Performing instant disturbance pressure feedback adjustment on the network deformation data under ambient air pressure can achieve timely adjustment of the system according to the change of ambient air pressure. Obtaining an instant adjustment deformation visualization network helps to monitor the deformation of the system under different air pressures in real time. Real-time monitoring of the air pressure feedback device can ensure the normal collection of air pressure sensor signals during the operation of the system. Detecting air pressure sensor signals helps to timely discover any problems that affect system performance. Performing nonlinear pressure sensing analysis on air pressure sensor signals can deeply understand the nonlinear characteristics of pressure sensor elements. Generating nonlinear pressure sensor signal characteristics helps to improve the system's perception and response capabilities to pressure changes. Visual mapping of the instant adjustment deformation visualization network based on nonlinear pressure sensor signal characteristics can intuitively display the real-time pressure deformation situation. Obtaining a real-time pressure deformation visualization network helps to monitor the deformation state of the system under different pressure conditions in real time, and helps the system achieve high-precision air pressure feedback.

[0016] Preferably, the specific steps of step S5 are: Step S51: performing real-time pressure topology deformation monitoring on the real-time pressure deformation visualization network to generate pressure topology deformation data; Step S52: identifying the pressure deformation state of each component node of the pressure topology deformation data, and extracting the pressure deformation state of each component node; Step S53: performing time-series deformation fluctuation tracking on the pressure deformation state of each component node to generate the time-series deformation fluctuation state of each component node; Step S54: dynamically and adaptively perform air pressure compensation on the time-series deformation fluctuation state of each component node, thereby generating air pressure compensation deformation parameters of each component node.

[0017] The present invention helps to fully understand the dynamic changes of the pressure topology structure by performing real-time pressure topology deformation monitoring on the real-time pressure deformation visualization network. Generating pressure topology deformation data can provide the deformation of the system under different pressure conditions, providing a basis for subsequent analysis. Identifying the pressure deformation state of each component node one by one on the pressure topology deformation data can accurately determine the pressure deformation of each component node. Extracting the pressure deformation state of each component node helps to finely monitor the operating state of each component node in the system. Tracking the temporal deformation fluctuation of the pressure deformation state of each component node can track the deformation change trend of each node. Generating the temporal deformation fluctuation state of each component node helps to understand the dynamic change law of deformation and provide a basis for system adjustment. Dynamic adaptive air pressure compensation for the temporal deformation fluctuation state of each component node can adjust the air pressure according to the real-time deformation situation. Generating the air pressure compensation deformation parameters of each component node helps to achieve high-precision air pressure feedback and improve the adaptability and stability of the system to air pressure changes.

[0018] Preferably, the specific steps of step S6 are: Step S61: predicting the ambient air pressure deviation of the air pressure compensation deformation parameter of each component node to obtain the ambient air pressure deviation prediction value of each component node; Step S62: optimizing the ambient air pressure error according to the ambient air pressure deviation prediction value of each component node to obtain the air pressure error optimization value of each component node; Step S63: performing global air pressure feedback fitting on the air pressure error optimization value of each element node, thereby constructing real-time air pressure feedback deformation data; Step S64: performing migration fine-tuning learning on the real-time air pressure feedback deformation data, and constructing a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

[0019] The present invention can predict the ambient air pressure deviation of each node by predicting the ambient air pressure deviation of the air pressure compensation deformation parameters of each component node. Obtaining the ambient air pressure deviation prediction value of each component node helps the system to perform air pressure compensation more accurately and improves the feedback accuracy. Optimizing the ambient air pressure error according to the ambient air pressure deviation prediction value of each component node can optimize the air pressure feedback performance of each node. Obtaining the air pressure error optimization value of each component node helps to improve the system's responsiveness to ambient air pressure changes and improve the feedback effect. Performing global air pressure feedback fitting on the air pressure error optimization value of each component node can comprehensively consider the air pressure error of each node. Constructing real-time air pressure feedback deformation data helps to optimize the overall performance of the system and improve the stability and accuracy of the system. Performing migration and fine-tuning learning on the real-time air pressure feedback deformation data can continuously optimize the air pressure feedback model according to actual conditions. Constructing a real-time air pressure feedback fine-tuning model helps to achieve higher-precision air pressure feedback, improve the performance and stability of the system, and ensure the accuracy of the air pressure feedback operation.

[0020] In this specification, a high-precision pressure sensing-based air pressure feedback device is provided, which is used to perform the high-precision pressure sensing-based air pressure feedback method as described above, including: The topological distribution module is used to calculate the spatial positioning of the pressure sensing element of the air pressure feedback device, perform topological distribution fitting, and construct a topological distribution map of the element; The pressure deformation rendering module is used to obtain historical pressure sensing samples of high-precision pressure sensing elements; based on the historical pressure sensing samples, the component topology distribution map is dynamically rendered to construct a dynamic pressure deformation visualization network; An ambient air pressure disturbance module is used to map the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain the air pressure disturbance visualization network; The real-time pressure deformation module is used to perform real-time operation monitoring based on the air pressure disturbance visualization network and perform pressure visualization mapping to obtain a real-time pressure deformation visualization network; The adaptive air pressure compensation module is used to identify the pressure deformation state of each component node of the real-time pressure deformation visualization network and perform dynamic adaptive air pressure compensation, thereby generating the air pressure compensation deformation parameters of each component node; The air pressure feedback fine-tuning module is used to perform global air pressure feedback fitting on the air pressure compensation deformation parameters of each component node, and to perform migration fine-tuning learning to build a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

[0021] The present invention helps to accurately locate the position of the component by performing spatial positioning calculation on the pressure sensing element of the air pressure feedback device, improves the positioning accuracy of the system, and constructs a component topological distribution map to help understand the relationship between the components, optimize the system layout, and improve the overall efficiency of the system. Acquiring historical pressure sensing samples can provide data support for analyzing and optimizing system performance. Dynamic pressure deformation rendering helps to monitor the pressure deformation in real time, improves the perception and reaction speed to pressure changes, and performs ambient air pressure disturbance mapping to help identify the situation in which the system is affected by ambient air pressure changes, improves the stability of the system, and obtains an air pressure disturbance visualization network to help intuitively understand the impact range of the air pressure disturbance, which is useful for subsequent adjustments. Provide reference, real-time operation monitoring can timely discover problems and abnormal conditions in the system and ensure the normal operation of the system. Pressure visualization mapping helps to intuitively present the pressure deformation and improve monitoring efficiency and accuracy. The pressure deformation state identification of each component node can accurately grasp the state of each node and realize personalized air pressure compensation. Dynamic adaptive air pressure compensation can be flexibly adjusted according to real-time conditions to improve the adaptability and stability of the system. Global air pressure feedback fitting helps to comprehensively consider the conditions of each node, optimize the overall performance of the system, and improve feedback accuracy. Transfer fine-tuning learning can continuously improve the air pressure feedback model, ensure the accurate response of the system to air pressure changes, and improve the efficiency and accuracy of feedback operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the steps of an air pressure feedback method based on high-precision pressure sensing of the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0024] The present application example provides a method and device for air pressure feedback based on high-precision pressure sensing. The execution subject of the method and device for air pressure feedback based on high-precision pressure sensing includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.

[0025] See also Figures 1 to 4The present invention provides an air pressure feedback method based on high-precision pressure sensing, and the air pressure feedback method based on high-precision pressure sensing comprises the following steps: Step S1: performing spatial positioning calculation of the pressure sensing element of the air pressure feedback device, and performing topological distribution fitting to construct a component topological distribution map; Step S2: obtaining historical pressure sensing samples of high-precision pressure sensing elements; performing dynamic pressure deformation rendering on the element topology distribution map based on the historical pressure sensing samples, thereby constructing a dynamic pressure deformation visualization network; Step S3: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain an air pressure disturbance visualization network; Step S4: performing real-time operation monitoring based on the air pressure disturbance visualization network and performing pressure visualization mapping to obtain a real-time pressure deformation visualization network; Step S5: identifying the pressure deformation state of each component node of the real-time pressure deformation visualization network, and performing dynamic adaptive air pressure compensation, thereby generating air pressure compensation deformation parameters of each component node; Step S6: Perform global pressure feedback fitting on the pressure compensation deformation parameters of each component node, and perform migration fine-tuning learning to build a real-time pressure feedback fine-tuning model to perform pressure feedback operations.

[0026] The present invention helps to accurately describe the position and interconnection relationship of components in space through spatial positioning calculation and topological distribution fitting of pressure sensing elements. Constructing a component topological distribution map can provide an intuitive understanding of the component layout and provide a basis for subsequent analysis and operation. Dynamic pressure deformation rendering based on historical pressure sensing samples can realize a visual display of the deformation of the pressure sensing element. Constructing a dynamic pressure deformation visualization network helps to monitor the pressure state changes of the component in real time, help to find abnormal situations and take corresponding measures. Obtaining an air pressure disturbance visualization network through environmental air pressure disturbance mapping can show the impact of air pressure on the component and provide a more comprehensive data display. The air pressure disturbance visualization network helps to understand the impact of air pressure changes on component pressure and guide subsequent air pressure feedback operations. Real-time operation monitoring and pressure visualization mapping can display the pressure deformation of the component in real time and help monitor the operating status of the system. Obtaining a real-time pressure deformation visualization network helps to find problems and take measures in time to ensure the normal operation of the system. Identifying the pressure deformation state of each component node and performing dynamic adaptive air pressure compensation can accurately compensate the component according to real-time data. Generating the pressure compensation deformation parameters of each component node helps optimize the component response and improve the stability and accuracy of the system. Performing global pressure feedback fitting and migration fine-tuning learning on the pressure compensation deformation parameters of each component node can establish a real-time pressure feedback fine-tuning model. Building a real-time pressure feedback fine-tuning model helps optimize the pressure feedback operation, improve the response speed and accuracy of the system, and ensure the stable operation of the component.

[0027] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of a method for air pressure feedback based on high-precision pressure sensing of the present invention. In this example, the method comprises the following steps: Step S1: performing spatial positioning calculation of the pressure sensing element of the air pressure feedback device, and performing topological distribution fitting to construct a component topological distribution map; In this embodiment, first, ensure that the pressure sensor is correctly installed on the air pressure feedback device and can accurately measure the pressure of each component. Use a positioning device (such as a laser rangefinder, GPS or a three-dimensional scanner) to obtain the spatial position data of each sensor element, including three-dimensional coordinates (x, y, z), record the pressure data of each sensor element, and ensure that the timestamp of the data is consistent with the spatial position data.

[0028] Furthermore, the spatial position data and pressure data collected from each sensor element are cleaned to remove outliers and noise to ensure the accuracy of the data. The spatial position data of each sensor element is paired with the corresponding pressure data to form a structured data set for subsequent visual analysis. If different sensor elements use data in different coordinate systems, the spatial position data of different sensor elements are converted to ensure that all spatial position data are unified in the same coordinate system.

[0029] Furthermore, the spatial position data of the sensor elements are visualized using a three-dimensional visualization tool (such as MATLAB, Python's Matplotlib, or Mayavi) to check the rationality of the position distribution of the sensor elements.

[0030] Specifically, according to the relationship between the sensor elements, a suitable topological model is selected, and the spatial position data and pressure data of the sensor elements are analyzed by applying the fitting algorithm corresponding to the selected topological model to generate a topological distribution model. For example, the point cloud model can describe the spatial distribution of the sensor elements, the graph model can regard the sensor elements as nodes in the graph and use the pressure sensor data as the weight of the edge, the least squares method can be used to calculate the optimal fitting curve or surface, and K-means clustering can be used to identify the group distribution characteristics of the sensor elements, thereby fitting the topological distribution model.

[0031] Use the topological distribution model and select appropriate visualization tools (such as D3.js, Plotly, Gephi, etc.) to construct the component topological distribution map: convert the fitted topological distribution model into a visualization graph, draw the position distribution of the sensor elements in space, use different colors or shapes to represent the pressure, and thus complete the construction of the component topological distribution map. Then, the constructed topological distribution map can be verified to ensure that it can accurately reflect the spatial layout and pressure distribution characteristics of the sensor elements.

[0032] When each new sensor element is used for the first time, the operation of this step is completed once first, so as to ensure that it can be called multiple times in subsequent uses.

[0033] Step S2: obtaining historical pressure sensing samples of high-precision pressure sensing elements, and performing dynamic pressure deformation rendering on the element topology distribution map based on the historical pressure sensing samples, thereby constructing a dynamic pressure deformation visualization network; In this embodiment, firstly, a suitable high-precision pressure sensor is selected and the data source of the historical pressure sensing sample is determined to ensure that it can provide accurate pressure readings within the required range. The data source includes: extracting historical pressure samples from an existing database to construct a historical database, or saving the real-time monitored data in the real-time monitoring record as historical data. When obtaining historical pressure sensing samples through API or database query, it is necessary to ensure that the timestamp, location, pressure value and other information of the data are complete.

[0034] The acquired historical pressure sensing samples are cleaned to remove outliers and missing values ​​to ensure the accuracy and completeness of the data. The pressure readings are standardized to ensure that the data are in the same dimension to facilitate subsequent analysis and rendering, and to determine the dynamic deformation model used for rendering.

[0035] The dynamic deformation model parameters are set according to the historical pressure samples to ensure that the dynamic deformation model can accurately reflect the deformation caused by pressure changes. For example, physical models based on elasticity theory or fluid dynamics are used to simulate the impact of pressure changes on the structure, or mathematical functions (such as polynomials or curve fitting) are used to describe the relationship between pressure and deformation to determine the dynamic deformation model parameters.

[0036] Furthermore, suitable visualization tools (such as Three.js, D3.js, Matplotlib, etc.) are selected to dynamically render the dynamic deformation model, and the results of dynamic rendering are integrated into a visualization network to form a complete dynamic pressure deformation visualization network. Specifically, the historical pressure samples are mapped to the component nodes in the topological distribution diagram, and the position, size and color of the nodes are dynamically adjusted according to the changes in the pressure value. For example, the node size is adjusted according to the intensity of the pressure value. The greater the pressure, the larger the node. The node color is represented by a gradient color according to the change in the pressure value to intuitively reflect the change in pressure. The selected visualization tool is used to achieve dynamic rendering effects to ensure the real-time display of the pressure deformation process. Animation effects can be used to show how pressure changes affect the shape and position of the node, thereby ensuring that each node and edge in the dynamic pressure deformation visualization network can reflect the actual pressure changes and deformation characteristics. Interactive functions are designed for the visualization network, such as: hovering the mouse to display detailed information (such as the current pressure value and historical records), allowing users to select a time period to view the pressure deformation status at a specific time.

[0037] Step S3: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain an air pressure disturbance visualization network; In this embodiment, real-time ambient air pressure data is obtained from an air pressure feedback device or related meteorological sensors, including the current air pressure, historical air pressure records, and air pressure change trends, to ensure that the existing dynamic pressure deformation visualization network contains the pressure data and corresponding spatial position of each component. These data will serve as the basis for mapping to determine the definition of ambient air pressure disturbances.

[0038] In some specific implementation schemes, the change in ambient air pressure is calculated by disturbance amplitude and disturbance frequency, and the change in ambient air pressure is compared with the pressure data in the dynamic pressure deformation visualization network to identify the disturbance area and the degree of impact, and determine a suitable mapping method, wherein the disturbance amplitude refers to the change in ambient air pressure relative to the baseline air pressure, and the disturbance frequency refers to the frequency characteristics of the pressure change.

[0039] For example, linear mapping can be used to directly map air pressure changes to the pressure values ​​of network nodes, or more complex models (such as neural networks or interpolation methods) can be used to capture the nonlinear relationship between air pressure changes and deformation. If nonlinear mapping is used, in order to more accurately reflect the impact of disturbances on the network, a model can be trained with historical data, and the ambient air pressure data can be fused with the data of the dynamic pressure deformation visualization network to generate a new data set that includes the impact of disturbances.

[0040] Use programming tools (such as Python, MATLAB, etc.) to apply the calculated disturbance data to the visualization network, update the pressure value and status of each node to reflect the influence of ambient air pressure, and adjust the color, size or shape of the node according to the mapping results to intuitively display the disturbance impact.

[0041] Furthermore, the updated dynamic pressure deformation visualization network is rendered using visualization tools (such as D3.js, Three.js, Plotly, etc.) to ensure that the visualization effect can clearly show the impact of the ambient air pressure disturbance on each component, and add interactive elements, such as users can select different time periods or conditions to view the corresponding air pressure disturbance effects.

[0042] Step S4: performing real-time operation monitoring based on the air pressure disturbance visualization network and performing pressure visualization mapping to obtain a real-time pressure deformation visualization network; In this embodiment, ensure that the sensors (such as pressure sensors, temperature sensors, etc.) installed on the air pressure feedback device work properly and can collect data in real time. Use a data acquisition system (such as LabVIEW, Arduino or Python script) to set the sampling frequency, regularly collect real-time pressure data, and ensure that the real-time data is transmitted to the data processing and visualization platform through the network or other communication methods (such as MQTT, HTTP). The collected real-time data needs to be cleaned and processed to remove noise and outliers to ensure data quality. Filters (such as Kalman filters) can be used to smooth the data and reduce the impact of sudden fluctuations. Monitor the status of each sensor in real time, check whether the data is within the normal range, and identify potential faults or abnormalities of each sensor.

[0043] Furthermore, determine how to map the real-time pressure data to the visualization network. First, select a suitable mapping method. For example, use node attribute mapping to update the attributes of each node (such as size, color, and transparency) according to the real-time pressure value, or use edge attribute mapping to associate the weight of the edge in the graph model with the pressure value of the node to reflect the connection strength and implement a dynamic update mechanism so that the visualization network can reflect pressure changes in real time. Specifically, WebSocket or other real-time data streaming technologies can be used to ensure that the visualization network can receive the latest pressure data and update it.

[0044] After mapping the real-time pressure data to the visualization network, use the selected visualization tool to dynamically draw the pressure deformation so that the color of the node can change according to the pressure value (for example, red for high pressure, blue for low pressure), and the size of the node can be adjusted according to the intensity of the pressure, providing interactive functions for users. For example: hovering the mouse to display detailed information (such as current pressure value, historical data), users can select different time periods to view the pressure deformation status at a specific time, add dynamic effects, such as the node slowly changing size and color when the pressure changes, to enhance the intuitiveness and attractiveness of the visualization, regularly check the performance of the visualization system to ensure that the data is updated in a timely and accurate manner, monitor whether the system has delays or data loss, and collect user feedback on the real-time pressure deformation visualization network for improvement and optimization.

[0045] Step S5: identifying the pressure deformation state of each component node of the real-time pressure deformation visualization network, and performing dynamic adaptive air pressure compensation, thereby generating air pressure compensation deformation parameters of each component node; In this embodiment, the current pressure value and deformation state data of each component node are obtained from the real-time pressure deformation visualization network. Specifically, a suitable state recognition algorithm is selected to analyze the pressure value of each node, identify the pressure deformation state of each component node, and record the results to design a dynamic adaptive air pressure compensation model. For example: a threshold method is used to set a reasonable pressure threshold, and the pressure state of each node (such as normal, overload, low pressure, etc.) is judged, and a machine learning method is used to use a classification algorithm (such as a support vector machine, a decision tree) to identify the pressure state.

[0046] Considering the impact of pressure change rate and environmental factors on the pressure compensation response, for example, the greater the pressure change rate, the faster the compensation response algorithm should be selected, and the appropriate pressure compensation algorithm should be selected for external factors such as temperature and humidity that affect the air pressure. For linear systems, for example, the PID control algorithm can be selected to achieve adaptive compensation through proportional, integral and differential control; for nonlinear systems, the fuzzy control algorithm can be selected to perform dynamic adjustments based on fuzzy logic, analyze the pressure state of each component node, and calculate the required pressure compensation value.

[0047] Specifically, dynamic adaptive air pressure compensation can be achieved by the following steps: (1) Calculation error: Determine the error between the current pressure value and the target pressure value; (2) Apply the compensation model: Use the compensation model to generate the air pressure compensation value and output the air pressure compensation deformation parameters. The calculated air pressure compensation deformation parameters of each component node are recorded in the database to ensure the traceability of the data. The air pressure compensation deformation parameters are applied to the real-time pressure deformation visualization network, and the node status and appearance are dynamically updated to reflect the effect after compensation.

[0048] Step S6: Perform global pressure feedback fitting on the pressure compensation deformation parameters of each component node, and perform migration fine-tuning learning to build a real-time pressure feedback fine-tuning model to perform pressure feedback operations.

[0049] In this embodiment, a suitable global fitting method is determined, and a global fitting model is trained using a well-organized training data set to fit the relationship between the air pressure compensation deformation parameter and the actual pressure value. For example: multiple linear regression is used to establish a linear relationship between pressure and compensation parameters, or support vector regression (SVR) is used to build a complex model based on the nonlinear relationship between pressure and compensation parameters. The performance of the model is evaluated through cross-validation to ensure the robustness and generalization ability of the model. The fitting effect is evaluated using indicators (such as mean square error, R²) to ensure that the model can accurately predict air pressure feedback. A suitable transfer learning framework (such as TensorFlow, PyTorch) is selected to fine-tune the existing global fitting model.

[0050] Furthermore, new real-time data is collected and a fine-tuning dataset is constructed to ensure that the new data is consistent with the input features of the existing model. The existing global fitting model is used as a basis and new real-time data is input for fine-tuning. During the training process, some layers are fixed to prevent overfitting while optimizing other parts. Appropriate learning rates and optimization algorithms (such as Adam or SGD) are used to improve the convergence speed and accuracy of the model.

[0051] Save the fine-tuned model in a deployable format for use in actual pressure feedback operations, ensure that the model can be quickly loaded and executed in a real-time environment, process real-time input data, integrate the fine-tuned model into the pressure feedback system, ensure that it can respond to pressure changes in real time, and perform pressure feedback operations. Validate the fine-tuned model to ensure prediction accuracy in real-time operations, monitor the performance of the model in actual operation, and ensure that it can effectively adjust pressure compensation.

[0052] The inventive point of the present application is to construct a dynamic pressure deformation visualization network by combining the spatial position information of multiple air pressure sensor elements in the air pressure feedback device and the historical pressure sensor data, and then use the real-time environmental air pressure disturbance and the air pressure adaptive compensation algorithm to determine the air pressure compensation deformation parameters of each air pressure sensor element node in the visualization network, and fit the air pressure feedback fine-tuning model based on the relationship between the air pressure compensation deformation parameters and the actual pressure values. Therefore, in practical applications, the real-time pressure deformation visualization network can not only dynamically update the state and appearance of the node to reflect the effect after air pressure compensation in real time, but also enable the above-mentioned air pressure feedback fine-tuning model to realize real-time response to air pressure changes for air pressure feedback.

[0053] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: identifying a high-precision pressure sensing element of an air pressure feedback device and marking a plurality of pressure sensing element nodes; Step S12: performing spatial positioning calculation on multiple pressure sensing element nodes to obtain element node position coordinates; Step S13: performing component spatial distribution analysis on the component node position coordinates to extract component spatial distribution data; Step S14: performing topological connection analysis on the component node position coordinates to obtain component topological structure data; Step S15: performing topological distribution fitting on the component topological structure data based on the component spatial distribution data to construct a component topological distribution map.

[0054] In this embodiment, ensure that the air pressure feedback device works normally and has the ability to read the pressure sensor data. Use high-precision measuring instruments (such as a multimeter or a pressure calibration device) to identify the pressure sensing elements in the device, record the technical specifications of each sensing element, including the range, sensitivity and output signal type, mark the node position of each pressure sensing element in the device document or drawing, and ensure that each node has a unique identifier for subsequent analysis.

[0055] Use a laser rangefinder, 3D scanner or other measuring equipment to obtain the spatial position data of each sensor element, and convert the measured distance and angle data into three-dimensional spatial coordinates. Commonly used coordinate systems include Cartesian coordinate system and polar coordinate system. Use appropriate mathematical models (such as triangulation) for calculation to ensure the accuracy of each node position, and record the three-dimensional coordinates (x, y, z) of each sensor element in a database or spreadsheet to ensure a unified data format. Specifically, spatial analysis software (such as MATLAB, NumPy and SciPy libraries in Python) can be used to process node position coordinate data and construct a component spatial distribution model.

[0056] The spatial distribution of sensor elements is displayed through 3D visualization tools (such as Matplotlib, ParaView, etc.), which helps to identify the relative position relationship between different sensors, calculate the distance and density distribution between each sensor element, extract the spatial distribution data of the element, record the distribution characteristics (such as clustering, uniformity), determine the connection relationship between elements, use the nodes (elements) and edges (connection relationships) in graph theory to define the topological structure, and use the adjacency matrix or adjacency table to represent the connection relationship between elements.

[0057] Apply graph theory algorithms (such as depth-first search and breadth-first search) to perform topological analysis on sensor components, identify the paths and hierarchical structures of connections, and generate topological structure data, including the connection information of each node, as well as the connectivity and path characteristics of the overall network. Select a suitable topological distribution fitting model, such as a random graph model, a small-world network model, or a scale-free network model, to adapt to the actual connection of the sensor components. Use the extracted spatial distribution data and topological structure data, and adjust the model parameters through methods such as least squares or maximum likelihood estimation to make the model fit the actual component topology as much as possible.

[0058] The fitted topological structure data is plotted into a topological distribution map through visualization software (such as Gephi, Cytoscape, etc.) to display the connection relationship between the sensor elements and their spatial distribution characteristics, analyze the accuracy and effectiveness of the fitting model, and evaluate whether the connection characteristics of the sensor elements reflected in the topological distribution map are consistent with the actual situation.

[0059] In this embodiment, refer to Figure 3, is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Acquire historical pressure sensing samples of the high-precision pressure sensing element; Step S22: performing high-pass filtering and noise reduction processing on the historical pressure sensing samples to obtain filtered noise reduction sensing samples; Step S23: performing outlier detection on the filtered and denoised sensor samples to extract outliers of the sensor samples; Step S24: performing outlier filtering based on the outliers of the sensing samples, thereby obtaining filtered optimized sensing samples; Step S25: Perform dynamic pressure deformation rendering on the component topology distribution map based on the filtered optimized sensing samples, thereby constructing a dynamic pressure deformation visualization network.

[0060] In this embodiment, historical pressure data is extracted from a high-precision pressure sensor. This data can be obtained through the sensor's interface (such as a serial port, I2C, SPI, etc.), and the relevant timestamp is recorded so that the time and pressure data can be associated in subsequent analysis. The obtained pressure sensing samples are stored in a database or file system to ensure that the data format is unified (such as CSV, JSON, etc.) to facilitate subsequent processing.

[0061] Select a suitable high-pass filter design method, such as Butterworth filter, Chebyshev filter, etc. The selected cutoff frequency should be optimized according to the operating characteristics and noise characteristics of the sensor.

[0062] In some specific implementation schemes, a signal processing library (such as Python's SciPy or MATLAB) is used to implement high-pass filtering. The specific steps include: inputting historical pressure samples into the filter for processing, removing low-frequency noise, retaining high-frequency signals, determining a suitable outlier detection algorithm, such as Z-score detection, IQR (interquartile range) method, or Isolation Forest (IsolationForest) based on machine learning, etc., inputting the filtered and denoised sensor samples into the selected outlier detection algorithm, and identifying outliers. For example: using the Z-score method, the Z-score value of each sample is calculated, and a threshold value (such as 3) is set. Samples above this value are identified as outliers, and the detected outliers are recorded to form an outlier list for subsequent filtering processing.

[0063] Based on the outliers detected previously, use a filtering algorithm (such as simple conditional filtering) to delete these outliers from the original sensor samples to obtain filtered and optimized sensor samples. Verify the integrity and consistency of the filtered data set to ensure that valid data is not mistakenly deleted. Save the filtered and optimized sensor samples to a database or file to ensure that the data format is consistent for subsequent use.

[0064] Select appropriate visualization tools (such as Matplotlib, D3.js, Plotly or dedicated visualization software) for dynamic rendering. According to the filtered and optimized sensor samples, a dynamic pressure deformation model is established. The specific steps include: mapping the pressure value to the nodes in the topological distribution diagram so that the pressure value of each node reflects the degree of deformation in the diagram, setting the visualization rules of the deformation (such as node size, color change, etc.) to reflect the high and low pressure, using time series data for dynamic rendering, and visualizing the pressure change as a dynamic network diagram. You can create animations or interactive charts to show the process of pressure change over time.

[0065] In this embodiment, the specific steps of step S24 are: extracting pressure parameters of the sensing element of the filtered optimized sensing sample; Performing element-by-element pressure sensing accuracy learning on the pressure parameters of the sensing elements, thereby obtaining a pressure sensing accuracy value of each element; Based on the pressure sensing accuracy value of each component, dynamic sensing parameter mapping is performed on the component topology distribution map to build a component sensing network; Performing end-to-end perceptual learning on the filter-optimized sensing samples to generate component sensing perceptual learning data; Perform pressure sensing deformation visualization design on component sensing perception learning data to obtain pressure sensing visualization data; The pressure sensing visualization data is used to render the dynamic pressure deformation of the component sensing network, thereby constructing a dynamic pressure deformation visualization network.

[0066] In this embodiment, relevant pressure parameters, such as pressure readings, timestamps, and temperatures of each component, are extracted from the filtered and optimized sensor samples, and the extracted pressure parameters are stored in a structured data format (such as PandasDataFrame) to facilitate subsequent analysis and processing, verify the integrity and consistency of the extracted pressure parameters, and ensure that there are no missing or errors in the data.

[0067] Select a suitable learning algorithm to evaluate the pressure sensing accuracy of each sensing element. Optional algorithms include: Linear regression: used to establish a linear relationship between pressure readings and actual pressure values, Support Vector Machine (SVM): used to model more complex nonlinear relationships, prepare a training set, including the historical pressure readings of each element and its true pressure reference value (such as obtained through standard pressure equipment), input the training set into the selected learning algorithm for training, and obtain the pressure sensing accuracy value of each element. Evaluate the accuracy of the model through methods such as cross-validation to ensure the reliability of the accuracy value.

[0068] Design a dynamic parameter mapping scheme, combine the pressure sensing accuracy value of each component with the node attributes in the topological distribution diagram, set mapping rules, for example: the higher the pressure accuracy value, the darker the node color, and the node size is proportional to the pressure value. Use visualization tools (such as D3.js, Matplotlib, etc.) to map the pressure sensing accuracy value to the topological distribution diagram to generate a preliminary component sensing network.

[0069] Select a suitable deep learning structure, such as a convolutional neural network (CNN) or a recurrent neural network (RNN), for end-to-end perception learning, prepare the input data set, including the filtered and optimized pressure samples and corresponding labels (such as the target pressure state), train the perception learning model on the prepared data set, optimize the parameters to improve the learning effect of the model, use the trained model to infer new pressure samples, and generate the sensor perception learning data of the component.

[0070] Select appropriate visualization software (such as Tableau, Plotly, etc.) to generate deformation visualization data, set basic visualization elements such as nodes, edges, colors, sizes, etc. to reflect pressure changes and sensing accuracy, input the generated sensor perception learning data into the visualization tool, create visualization charts of pressure sensing deformation, and design dynamic rendering effects to ensure that the visualization charts can reflect pressure changes in real time, for example, the color and size of the nodes change with pressure. Specifically, dynamic visualization libraries (such as D3.js, Three.js) can be used to achieve dynamic rendering effects to ensure that users can observe the pressure deformation process in real time, and embed the dynamic pressure deformation visualization network into applications or reports so that relevant personnel can intuitively understand the pressure status of the system.

[0071] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: monitoring the non-operating static state of the air pressure feedback device and extracting the non-operating static air pressure feedback parameters; Step S32: analyzing the ambient air pressure change of the non-operating static air pressure feedback parameter, thereby obtaining the non-operating ambient air pressure change data; Step S33: calculating the air pressure fluctuation amplitude of the non-operating ambient air pressure change data to generate the ambient air pressure fluctuation amplitude; Step S34: mining dynamic air pressure characteristics of the ambient air pressure fluctuation amplitude to generate dynamic ambient air pressure characteristics; Step S35: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the dynamic ambient air pressure characteristics to obtain an air pressure disturbance visualization network.

[0072] In this embodiment, ensure that the air pressure feedback device is in a non-operating state, disconnect all external loads and interferences, ensure the measurement accuracy of the device in a static state, use a pressure sensor to record the air pressure feedback parameters in a non-operating state, record the timestamp when collecting data for subsequent analysis, store the collected air pressure data in a database or file, and ensure that the data format is unified (such as CSV or JSON) for subsequent processing and analysis.

[0073] Clean and organize the non-operating static air pressure feedback parameter data to ensure that there are no missing data and outliers. Determine the air pressure data over a period of time as a baseline (for example, take the average value of the samples) for subsequent analysis of ambient air pressure changes. Analyze the changes in air pressure data through time series analysis methods (such as moving average or sliding window) to identify significant ambient air pressure changes.

[0074] Record and analyze the ambient air pressure change data, including key indicators such as the change amplitude and change rate. Specifically, select an appropriate calculation method to calculate the pressure fluctuation amplitude, for example, calculate the standard deviation of the sample data to reflect the pressure fluctuation amplitude, or calculate the difference between the maximum and minimum values ​​of the ambient air pressure data as another indicator of the fluctuation amplitude. Use a programming language (such as the NumPy library in Python) to calculate and obtain the pressure fluctuation amplitude, and record the calculated ambient air pressure fluctuation amplitude for subsequent analysis and reporting.

[0075] Select a suitable feature mining method, perform frequency domain analysis and autocorrelation analysis on the ambient air pressure fluctuation amplitude data, extract key dynamic air pressure features, and record the extracted dynamic air pressure features (such as frequency, amplitude, periodicity, etc.) in the data set to form a feature data table. For example, the mining method may include: frequency domain analysis method, that is, using Fourier transform to extract the frequency characteristics of air pressure fluctuations, and may also include autocorrelation analysis method, analyzing the autocorrelation of air pressure changes and identifying periodic features.

[0076] Select appropriate visualization tools (such as D3.js, Matplotlib, or Plotly) to build a pressure disturbance visualization network. Specifically, design a mapping scheme to map dynamic pressure features to nodes or lines in the topological distribution diagram. For example, the color of the node reflects the degree of change in ambient pressure, and the thickness of the line indicates the intensity of the pressure change. Use visualization tools to implement dynamic pressure disturbance mapping, dynamically present the pressure change, form a visualization network, and embed the generated pressure disturbance visualization network into applications or reports so that relevant personnel can intuitively understand the impact of ambient pressure.

[0077] In this embodiment, step S4 includes the following steps: Step S41: performing ambient air pressure deformation analysis on the air pressure disturbance visualization network to obtain network deformation data under ambient air pressure; Step S42: performing instant disturbance pressure feedback adjustment on the network deformation data under ambient pressure, thereby obtaining an instant adjustment deformation visualization network; Step S43: performing real-time operation monitoring on the air pressure feedback device and detecting the air pressure sensor signal; Step S44: performing nonlinear pressure sensing analysis on the air pressure sensing signal to generate nonlinear pressure sensing signal characteristics; Step S45: Visually map the instant adjustment deformation visualization network based on the nonlinear pressure sensing signal characteristics to obtain a real-time pressure deformation visualization network.

[0078] In this embodiment, the current state data of the pressure disturbance visualization network is collected, including node positions, connection relationships, and ambient pressure data, and a suitable analysis method is selected. The pressure disturbance visualization network is subjected to deformation simulation using analysis software (such as ANSYS, MATLAB, etc.), and network deformation data under ambient pressure is obtained. The deformation data (such as the new position of the node, the change in connection length, etc.) is recorded in a data set for subsequent use. Among them, the analysis method may include a finite element analysis (FEA) method for simulating and analyzing the deformation of the network under different air pressure conditions, and may also include a geometric transformation method for calculating the geometric shape change of the network under ambient pressure changes based on a mathematical model.

[0079] Design an instant feedback adjustment mechanism to ensure that the network can automatically adjust its deformation when the air pressure changes. For example, set a threshold value. When the air pressure changes beyond a certain range, the deformation adjustment is triggered. According to the deformation data, the position and connection relationship of the network nodes are adjusted to ensure the stability of the network structure. Use a programming language (such as Python or MATLAB) to implement the deformation adjustment algorithm, record the adjusted deformation data, and form a data set for the instant deformation adjustment visualization network.

[0080] Ensure that the air pressure feedback device is operating normally, connect the sensor to the monitoring system, use the data acquisition system (such as LabVIEW, Arduino, etc.) to monitor the air pressure sensor signal in real time, record the pressure readings and timestamps, and store the real-time collected air pressure sensor signals in a database or real-time data stream to ensure that the data format is unified.

[0081] Select a suitable nonlinear analysis method, input the real-time air pressure sensor signal into the selected nonlinear analysis model, generate signal features and model output, and record the generated nonlinear pressure sensor signal features (such as the slope of the curve, inflection point, fluctuation amplitude, etc.) in the data set for subsequent use. Exemplarily, the nonlinear analysis method includes curve fitting method, that is, using polynomial regression, spline interpolation and other methods to analyze nonlinear features, and also includes machine learning methods, such as using neural networks to identify nonlinear patterns in real-time air pressure sensor signals.

[0082] Design a visualization mapping scheme to map the nonlinear pressure sensing signal characteristics to the instant deformation visualization network, for example, adjust the color, size and shape of the node according to the change of nonlinear characteristics. Specifically, use visualization tools (such as D3.js, Matplotlib, Plotly, etc.) to dynamically map the nonlinear signal characteristics to the visualization network to display the real-time pressure deformation. Furthermore, the generated real-time pressure deformation visualization network can be embedded in the application or report, so that relevant personnel can intuitively understand the impact of pressure changes on network deformation.

[0083] In this embodiment, step S5 includes the following steps: Step S51: performing real-time pressure topology deformation monitoring on the real-time pressure deformation visualization network to generate pressure topology deformation data; Step S52: identifying the pressure deformation state of each component node of the pressure topology deformation data, and extracting the pressure deformation state of each component node; Step S53: performing time-series deformation fluctuation tracking on the pressure deformation state of each component node to generate the time-series deformation fluctuation state of each component node; Step S54: dynamically and adaptively perform air pressure compensation on the time-series deformation fluctuation state of each component node, thereby generating air pressure compensation deformation parameters of each component node.

[0084] In this embodiment, the normal operation of the monitoring system of the real-time pressure deformation visualization network is ensured. The monitoring system should include data acquisition equipment, sensors and computing platforms. Sensors are used to monitor the status of the pressure deformation visualization network in real time, record the pressure readings, position changes and other information of each node, and store the collected pressure topology deformation data in a database to ensure the integrity and accessibility of the data.

[0085] Use data processing tools (such as Python's Pandas library) to preprocess real-time data, remove noise and outliers, calculate and record the deformation of each node, and generate a pressure topology deformation data set, including the pressure value of the node, position change and other information. Select a suitable algorithm for state recognition, for example: use the threshold method to set the threshold of pressure change and deformation to identify state changes, or use machine learning algorithms (such as KNN, decision trees) based on classification algorithms to classify states, analyze the pressure topology deformation data of each node one by one, and identify the pressure deformation state of each component node (such as normal, abnormal, overload, etc.).

[0086] Collect the historical pressure deformation state data of each component node, ensure that the data is arranged in chronological order, select a suitable time series analysis method, and track the fluctuation of the pressure deformation state of each node through the time series analysis algorithm to generate the time series deformation fluctuation state of each node, including information such as fluctuation amplitude and period. Exemplarily, the time series analysis method includes using a sliding window method to analyze the pressure deformation fluctuation within a certain period of time, and may also include using an autoregressive model (AR) to model and predict changes in time series data.

[0087] Design an air pressure compensation model, determine the compensation mechanism and algorithm, analyze the time-series deformation fluctuation state of each component node, apply the compensation model to generate air pressure compensation deformation parameters, calculate the air pressure compensation value required for each node, and implement compensation measures. In some implementation schemes, optional compensation algorithms include: PID control algorithm, that is, a feedback mechanism based on proportional, integral, and differential control, and also include an adaptive filtering algorithm, that is, dynamically adjusting the compensation parameters according to real-time data changes. In other implementation schemes, the generated air pressure compensation deformation parameters for each component node are recorded in a database to ensure that the compensation results are traceable.

[0088] In this embodiment, step S6 includes the following steps: Step S61: predicting the ambient air pressure deviation of the air pressure compensation deformation parameter of each component node to obtain the ambient air pressure deviation prediction value of each component node; Step S62: optimizing the ambient air pressure error according to the ambient air pressure deviation prediction value of each component node to obtain the air pressure error optimization value of each component node; Step S63: performing global air pressure feedback fitting on the air pressure error optimization value of each element node, thereby constructing real-time air pressure feedback deformation data; Step S64: performing migration fine-tuning learning on the real-time air pressure feedback deformation data, and constructing a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

[0089] In this embodiment, the pressure compensation deformation parameters and historical ambient air pressure data of each component node are collected to ensure the integrity and consistency of the data. A suitable prediction model is selected, and the pressure compensation deformation parameters and ambient air pressure data are input into the selected prediction model for training and prediction, and the ambient air pressure deviation prediction value of each component node is generated, and the prediction value is recorded in the database to form a list of ambient air pressure deviation prediction values ​​for each component node. Exemplarily, the prediction model may include a time series model, such as ARIMA (autoregressive integrated moving average model), and may also include a machine learning model, such as linear regression, random forest, or long short-term memory network (LSTM).

[0090] Further, a suitable optimization algorithm is determined, and the predicted value of the ambient air pressure deviation of each component node is optimized and calculated to obtain the optimized value of the air pressure error. According to the result of the optimization algorithm, the air pressure parameters are adjusted to ensure that the air pressure error of each node is minimized, and the optimized value of the air pressure error of each component node is saved in the database for subsequent use. Exemplarily, the optimization algorithm may include a least squares method for optimizing the predicted error of the ambient air pressure, and may also include a genetic algorithm for global optimization problems to find the optimal air pressure error value.

[0091] Select a suitable fitting model, input the optimized value of the air pressure error of each component node into the selected fitting model, perform global fitting, generate real-time air pressure feedback deformation data through the fitting process, ensure that the data can reflect the state of the overall system, and record the constructed real-time air pressure feedback deformation data in a database to form a data set that can be used for subsequent analysis. Exemplarily, the fitting model includes a polynomial regression model suitable for nonlinear relationship fitting and a support vector regression (SVR) model for processing complex high-dimensional data.

[0092] Select a suitable transfer learning model, which can be a deep learning-based model (such as a convolutional neural network or a recurrent neural network), and prepare relevant training data. Use real-time air pressure feedback deformation data as the training set, and fine-tune the training in combination with historical data to adapt to the real-time environment. Fine-tune the model and train it with new real-time data to improve the model's predictive ability and accuracy in the current environment. Evaluate the performance of the fine-tuned model to ensure that it can effectively perform air pressure feedback operations, and deploy the fine-tuned model to the actual air pressure feedback system to ensure real-time monitoring and adjustment of air pressure.

[0093] In this embodiment, a high-precision pressure sensing-based air pressure feedback device is provided, which is used to execute the high-precision pressure sensing-based air pressure feedback method as described above, including: The topological distribution module is used to calculate the spatial positioning of the pressure sensing element of the air pressure feedback device, perform topological distribution fitting, and construct a topological distribution map of the element; The pressure deformation rendering module is used to obtain historical pressure sensing samples of high-precision pressure sensing elements; based on the historical pressure sensing samples, the component topology distribution map is dynamically rendered to construct a dynamic pressure deformation visualization network; An ambient air pressure disturbance module is used to map the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain the air pressure disturbance visualization network; The real-time pressure deformation module is used to perform real-time operation monitoring based on the air pressure disturbance visualization network and perform pressure visualization mapping to obtain a real-time pressure deformation visualization network; The adaptive air pressure compensation module is used to identify the pressure deformation state of each component node of the real-time pressure deformation visualization network and perform dynamic adaptive air pressure compensation, thereby generating the air pressure compensation deformation parameters of each component node; The air pressure feedback fine-tuning module is used to perform global air pressure feedback fitting on the air pressure compensation deformation parameters of each component node, and to perform migration fine-tuning learning to build a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

[0094] The present invention helps to accurately locate the position of the component by performing spatial positioning calculation on the pressure sensing element of the air pressure feedback device, thereby improving the positioning accuracy of the system. Constructing a component topological distribution map can help understand the relationship between the components, optimize the system layout, and improve the overall efficiency of the system. Acquiring historical pressure sensing samples can provide data support for analyzing and optimizing system performance. Dynamic pressure deformation rendering helps to monitor the pressure deformation in real time and improve the perception and reaction speed to pressure changes. Performing ambient pressure disturbance mapping can help identify the situation in which the system is affected by ambient pressure changes, thereby improving the stability of the system. Obtaining a pressure disturbance visualization network helps to intuitively understand the impact range of the pressure disturbance, which is useful for subsequent adjustments. Provide reference; real-time operation monitoring can timely discover problems and abnormal conditions in the system and ensure the normal operation of the system. Pressure visualization mapping helps to intuitively present the pressure deformation and improve monitoring efficiency and accuracy; the pressure deformation state identification of each component node can accurately grasp the state of each node and realize personalized air pressure compensation; dynamic adaptive air pressure compensation can be flexibly adjusted according to real-time conditions to improve the adaptability and stability of the system; global air pressure feedback fitting helps to comprehensively consider the conditions of each node, optimize the overall performance of the system, and improve feedback accuracy; transfer fine-tuning learning can continuously improve the air pressure feedback model, ensure the accurate response of the system to air pressure changes, and improve the efficiency and accuracy of feedback operations.

[0095] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0096] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A pressure feedback method based on high-precision pressure sensing, characterized in that: The following steps are involved: Step S1: performing spatial positioning calculation of the pressure sensing element of the air pressure feedback device, and performing topological distribution fitting to construct a component topological distribution map; Step S2: obtaining historical pressure sensing samples of high-precision pressure sensing elements; performing dynamic pressure deformation rendering on the element topology distribution map based on the historical pressure sensing samples, thereby constructing a dynamic pressure deformation visualization network; Step S3: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain an air pressure disturbance visualization network; Step S4: performing real-time operation monitoring based on the air pressure disturbance visualization network and performing pressure visualization mapping to obtain a real-time pressure deformation visualization network; Step S5: identifying the pressure deformation state of each component node of the real-time pressure deformation visualization network, and performing dynamic adaptive air pressure compensation, thereby generating air pressure compensation deformation parameters of each component node; Step S6: Perform global pressure feedback fitting on the pressure compensation deformation parameters of each component node, and perform migration fine-tuning learning to build a real-time pressure feedback fine-tuning model to perform pressure feedback operations.

2. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: identifying a high-precision pressure sensing element of an air pressure feedback device and marking a plurality of pressure sensing element nodes; Step S12: performing spatial positioning calculation on multiple pressure sensing element nodes to obtain element node position coordinates; Step S13: performing component spatial distribution analysis on the component node position coordinates to extract component spatial distribution data; Step S14: performing topological connection analysis on the component node position coordinates to obtain component topological structure data; Step S15: performing topological distribution fitting on the component topological structure data based on the component spatial distribution data to construct a component topological distribution map.

3. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Acquire historical pressure sensing samples of the high-precision pressure sensing element; Step S22: performing high-pass filtering and noise reduction processing on the historical pressure sensing samples to obtain filtered noise reduction sensing samples; Step S23: performing outlier detection on the filtered and denoised sensor samples to extract outliers of the sensor samples; Step S24: performing outlier filtering based on the outliers of the sensing samples, thereby obtaining filtered optimized sensing samples; Step S25: Perform dynamic pressure deformation rendering on the component topology distribution map based on the filtered optimized sensing samples, thereby constructing a dynamic pressure deformation visualization network.

4. The air pressure feedback method based on high-precision pressure sensing according to claim 3 is characterized in that: The specific steps of step S25 are: extracting pressure parameters of the sensing element of the filtered optimized sensing sample; Performing element-by-element pressure sensing accuracy learning on the pressure parameters of the sensing elements, thereby obtaining a pressure sensing accuracy value of each element; Based on the pressure sensing accuracy value of each component, dynamic sensing parameter mapping is performed on the component topology distribution map to build a component sensing network; Performing end-to-end perceptual learning on the filter-optimized sensing samples to generate component sensing perceptual learning data; Perform pressure sensing deformation visualization design on component sensing perception learning data to obtain pressure sensing visualization data; The pressure sensing visualization data is used to render the dynamic pressure deformation of the component sensing network, thereby constructing a dynamic pressure deformation visualization network.

5. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: monitoring the non-operating static state of the air pressure feedback device and extracting the non-operating static air pressure feedback parameters; Step S32: analyzing the ambient air pressure change of the non-operating static air pressure feedback parameter, thereby obtaining the non-operating ambient air pressure change data; Step S33: calculating the air pressure fluctuation amplitude of the non-operating ambient air pressure change data to generate the ambient air pressure fluctuation amplitude; Step S34: mining dynamic air pressure characteristics of the ambient air pressure fluctuation amplitude to generate dynamic ambient air pressure characteristics; Step S35: mapping the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the dynamic ambient air pressure characteristics to obtain an air pressure disturbance visualization network.

6. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing ambient air pressure deformation analysis on the air pressure disturbance visualization network to obtain network deformation data under ambient air pressure; Step S42: performing instant disturbance pressure feedback adjustment on the network deformation data under ambient pressure, thereby obtaining an instant adjustment deformation visualization network; Step S43: performing real-time operation monitoring on the air pressure feedback device and detecting the air pressure sensor signal; Step S44: performing nonlinear pressure sensing analysis on the air pressure sensing signal to generate nonlinear pressure sensing signal characteristics; Step S45: Visually map the instant adjustment deformation visualization network based on the nonlinear pressure sensing signal characteristics to obtain a real-time pressure deformation visualization network.

7. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing real-time pressure topology deformation monitoring on the real-time pressure deformation visualization network to generate pressure topology deformation data; Step S52: identifying the pressure deformation state of each component node of the pressure topology deformation data, and extracting the pressure deformation state of each component node; Step S53: performing time-series deformation fluctuation tracking on the pressure deformation state of each component node to generate the time-series deformation fluctuation state of each component node; Step S54: dynamically and adaptively perform air pressure compensation on the time-series deformation fluctuation state of each component node, thereby generating air pressure compensation deformation parameters of each component node.

8. The air pressure feedback method based on high-precision pressure sensing according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: predicting the ambient air pressure deviation of the air pressure compensation deformation parameter of each component node to obtain the ambient air pressure deviation prediction value of each component node; Step S62: Optimizing the ambient air pressure error according to the ambient air pressure deviation prediction value of each component node to obtain the air pressure error optimization value of each component node; Step S63: performing global air pressure feedback fitting on the air pressure error optimization value of each element node, thereby constructing real-time air pressure feedback deformation data; Step S64: performing migration fine-tuning learning on the real-time air pressure feedback deformation data, and constructing a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

9. A high-precision pressure sensor-based air pressure feedback device, characterized in that: The method for performing the air pressure feedback method based on high-precision pressure sensing as claimed in claim 1 comprises: The topological distribution module is used to calculate the spatial positioning of the pressure sensing element of the air pressure feedback device, perform topological distribution fitting, and construct a topological distribution map of the element; The pressure deformation rendering module is used to obtain historical pressure sensing samples of high-precision pressure sensing elements; based on the historical pressure sensing samples, the component topology distribution map is dynamically rendered to construct a dynamic pressure deformation visualization network; An ambient air pressure disturbance module is used to map the ambient air pressure disturbance to the dynamic pressure deformation visualization network according to the air pressure feedback device to obtain the air pressure disturbance visualization network; The real-time pressure deformation module is used to perform real-time operation monitoring based on the air pressure disturbance visualization network and perform pressure visualization mapping to obtain a real-time pressure deformation visualization network; The adaptive air pressure compensation module is used to identify the pressure deformation state of each component node of the real-time pressure deformation visualization network and perform dynamic adaptive air pressure compensation, thereby generating the air pressure compensation deformation parameters of each component node; The air pressure feedback fine-tuning module is used to perform global air pressure feedback fitting on the air pressure compensation deformation parameters of each component node, and to perform migration fine-tuning learning to build a real-time air pressure feedback fine-tuning model to perform air pressure feedback operations.

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