Intelligent aircraft skin monitoring system and method with damage assessment capability

Abstract

The invention provides an intelligent aircraft skin monitoring system and method with a damage assessment capability. A data operation unit comprises a damage prediction threshold calculation model, an actual damage index calculation model and a sensitivity grade adjustment model; the distributed sensing array comprises a plurality of sensor nodes, is arranged on the surface of an aircraft skin and is used for collecting multi-dimensional sensing data such as stress, temperature and vibration; data interaction is carried out between the sensor nodes through communication links, and collected multi-dimensional sensing data are transmitted to the central processing module; the central processing module is used for receiving, processing and analyzing data from the distributed sensing array; the edge computing unit receives a sensitivity level adjusting instruction from the central processing module and issues the instruction to a corresponding monitoring node; the data processing module is also used for preprocessing data; and the edge calculation unit performs sensitivity grade adjustment on the corresponding monitoring nodes according to the adjusted parameters so as to ensure that the system can accurately capture the damage development process.

Description

Technical Field

[0001] This invention belongs to the field of aircraft structural health monitoring and smart materials technology, specifically an intelligent aircraft skin monitoring system and method with damage assessment capabilities. Background Technology

[0002] With the continuous development of aircraft manufacturing and maintenance technologies, the demand for real-time damage assessment and intelligent monitoring of aircraft skin is increasing. Existing aircraft skin-related technologies mainly focus on manufacturing, inspection, repair, and assembly, but they still have significant shortcomings in real-time monitoring and damage assessment, making it difficult to meet the modern aviation industry's demand for efficient and intelligent skin monitoring systems.

[0003] A search revealed a device and method for monitoring aircraft skin valves, publication number CN110531683B, published on June 21, 2024. This patent achieves real-time monitoring of the status of aircraft skin valves through trend monitoring and can effectively predict changes in status before a failure. However, this technical solution only monitors the status of specific components (such as valves) and fails to extend to real-time damage assessment of the overall aircraft skin structure. Furthermore, its monitoring range is limited by the sensor placement and type, limiting its ability to cover various types of damage that may occur on the skin surface (such as cracks, corrosion, or fatigue damage), and it does not involve quantitative analysis of the degree of damage, thus exhibiting certain limitations in monitoring capabilities.

[0004] A search revealed a method for online detection of aircraft skin bonding thickness, publication number CN117213365B, published on June 11, 2024. This patent achieves rapid and accurate measurement of aircraft skin bonding thickness in a free state by combining photogrammetry and ultrasonic thickness measurement, thus reflecting the manufacturing quality of the skin. However, this technical solution mainly focuses on deformation detection of the skin during the manufacturing process and fails to cover the assessment of dynamic damage that may occur during the aircraft's service life. Furthermore, this method relies on specific tooling and equipment, making real-time monitoring of the entire lifecycle of the aircraft skin difficult, and it lacks intelligent analysis capabilities for the location and severity of damage.

[0005] The aforementioned problems indicate that existing aircraft skin-related technologies still have significant shortcomings in real-time damage assessment and intelligent monitoring, particularly in comprehensively covering skin surface damage, dynamically monitoring the damage development process, and providing quantitative analysis results. Therefore, this invention provides an intelligent aircraft skin monitoring system and method with damage assessment capabilities. It aims to achieve comprehensive monitoring, precise location, and quantitative assessment of aircraft skin damage by integrating multi-source sensor data, artificial intelligence algorithms, and real-time communication technology, thereby improving aircraft operational safety and maintenance efficiency. Summary of the Invention

[0006] This invention provides an intelligent aircraft skin monitoring system and method with damage assessment capabilities, which solves the problems of limited coverage and low data processing efficiency of traditional monitoring methods. The distributed sensor array design enables the system to cover key areas of the skin surface, while the introduction of edge computing units improves data processing efficiency. The specific technical solution is as follows: In a first aspect, this application provides an intelligent aircraft skin monitoring system with damage assessment capabilities, comprising a distributed sensor array (1), a central processing module (2), an edge computing unit (3), and a communication link (4), wherein: The central processing module (2) includes a data receiving unit (5) and a data processing unit (6); the data processing unit (6) includes a damage prediction threshold calculation model (7), an actual damage index calculation model (8), and a sensitivity level adjustment model (9). The distributed sensor array (1) includes multiple sensor nodes arranged on the surface of the aircraft skin for collecting multi-dimensional sensing data such as stress, temperature, and vibration. The sensor nodes interact with each other through a communication link (4) and transmit the collected multi-dimensional sensing data to the central processing module (2). The central processing module (2) is used to receive, process, and analyze the data from the distributed sensor array (1). The edge computing unit (3) receives sensitivity level adjustment instructions from the central processing module (2) and sends the instructions to the corresponding monitoring nodes. It is also used to preprocess the data. The edge computing unit (3) adjusts the sensitivity level of the corresponding monitoring nodes according to the adjusted parameters to ensure that the system can accurately capture the damage development process.

[0007] Specifically, each sensor node of the distributed sensor array (1) is equipped with an independent data acquisition module and signal transmission module; these sensor nodes are evenly distributed on the skin surface in a grid pattern, and their installation positions are precisely designed to ensure that they can cover the key stress areas and vulnerable parts of the skin.

[0008] Specifically, the data receiving unit (5) is used to receive the raw data transmitted by the distributed sensor array (1) and perform preliminary processing on the data, such as noise reduction filtering and format conversion; the processed data is then transmitted to the data processing unit (6) for further processing.

[0009] Specifically, the data processing unit (6) embeds three key calculation models, namely the damage prediction threshold calculation model (7), the actual damage index calculation model (8), and the sensitivity level adjustment model (9). Secondly, this application provides a method for monitoring intelligent aircraft skin with damage assessment capabilities, the method comprising: Step 1: Distributed sensor arrays deployed on the aircraft skin surface collect multi-dimensional sensor data such as stress, temperature, and vibration in real time, and transmit the collected data to the data receiving unit of the central processing module. Step 2: After receiving the multidimensional sensing data, the data receiving unit of the central processing module notifies the data processing unit of the central processing module to call the actual damage index calculation model to complete the calculation of the actual damage index. Step 3: Compare the calculated actual damage index with the damage prediction threshold generated by the damage prediction threshold calculation model; if the difference between the damage prediction threshold and the actual damage index is greater than the preset tolerance range, proceed to step 4; if the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, proceed to step 5. Step 4: The sensitivity level adjustment model completes the sensitivity level adjustment calculation, and the central processing module sends the adjusted sensitivity level parameters to the edge computing unit; the edge computing unit sends instructions to the corresponding monitoring nodes according to the sent parameters to complete the sensitivity level adjustment. Step 5: If the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, then the adjustment ends and the monitoring node continues to operate according to the current sensitivity level.

[0010] Specifically, step 2, the actual damage index calculation model, calculates the actual damage index including: Generate actual damage indicators based on current multidimensional sensor data. :

[0011] in: For the first Actual damage indicators of each monitoring node; For the first Current stress value of each monitoring node, in MPa; For the first Current stress value of each monitoring node, in MPa; This is the reference stress value, in MPa.

[0012] Specifically, step 3, the damage prediction threshold calculation model, generates the damage prediction threshold by including: The damage prediction threshold is calculated based on stress distribution, ambient temperature, and material fatigue characteristics. :

[0013] in, For the first Damage prediction threshold for each monitoring node, in MPa; The basic damage threshold has a value range of 1.0 ≤ ≤5.0; For the first The stress distribution influence coefficient of each monitoring node, with a value range of 0.8 ≤ ≤1.2; The environmental temperature influence coefficient; This is the coefficient affecting the fatigue properties of the material.

[0014] Specifically, step 3 includes: generating the environmental temperature influence coefficient from the damage prediction threshold calculation model. :

[0015] in: The ambient temperature is in °C. 、 and These are regression coefficients, each taking values ​​ranging from 0.1 to... ≤0.3, 0.01≤ ≤0.05, 0.8≤ ≤1.2.

[0016] Specifically, step 3 includes: the influence coefficient of material fatigue properties. :

[0017] in: The fatigue life cycle number of the material, with a value ranging from 10^4 to 10^4. ≤10^6; and These are regression coefficients, each taking values ​​ranging from 0.05 to... ≤0.1, 0.5≤ ≤1.0.

[0018] Specifically, step 4, the sensitivity level adjustment model, generates sensitivity levels by including: Based on damage prediction threshold Set the sensitivity level of the monitoring nodes. :

[0019] in: For the first Sensitivity levels of each monitoring node; The basic sensitivity level has a value range of 1.0 ≤ ≤3.0; This is the sensitivity compensation factor, expressed as a percentage.

[0020] In summary, this invention provides an intelligent aircraft skin monitoring system and method with damage assessment capabilities. By real-time acquisition of multi-dimensional sensor data from the aircraft skin surface and comparison with a damage prediction threshold constructed based on factors such as stress distribution, ambient temperature, and material fatigue characteristics, the sensitivity level of the monitoring nodes is adjusted according to the difference, achieving dynamic monitoring and precise quantification of damage to the aircraft skin surface. The distributed sensor array design solves the problem of limited coverage in traditional monitoring methods, while the introduction of edge computing units improves data processing efficiency. Furthermore, the modeling and prediction of the damage development process using artificial intelligence algorithms further enhances the system's intelligence level, thereby effectively improving aircraft operational safety and maintenance efficiency. Attached Figure Description

[0021] Figure 1 This is a structural block diagram of an intelligent aircraft skin monitoring system with damage assessment capabilities according to the present invention.

[0022] The attached figures are labeled as follows: 1. Distributed sensor array; 2. Central processing module; 3. Edge computing unit; 4. Communication link; 5. Data receiving unit; 6. Data processing unit; 7. Damage prediction threshold calculation model; 8. Actual damage index calculation model; 9. Sensitivity level adjustment model. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0024] Example 1 The present invention provides an intelligent aircraft skin monitoring system with damage assessment capabilities, which achieves dynamic monitoring and quantitative analysis of damage to the aircraft skin surface through the collaborative work of hardware modules and computing models. Figure 1 This is a structural block diagram of the present invention, in which the specific components and connections of each module are marked. The specific implementation process of the system will be described in detail below with reference to the accompanying drawings.

[0025] like Figure 1 As shown, this application provides an intelligent aircraft skin monitoring system with damage assessment capabilities, including a distributed sensor array (1), a central processing module (2), an edge computing unit (3), and a communication link (4), wherein: The central processing module (2) includes a data receiving unit (5) and a data processing unit (6); the data processing unit (6) includes a damage prediction threshold calculation model (7), an actual damage index calculation model (8), and a sensitivity level adjustment model (9). The distributed sensor array (1) includes multiple sensor nodes arranged on the aircraft skin surface to collect multi-dimensional sensing data such as stress, temperature, and vibration. The sensor nodes interact with each other via a communication link (4) and transmit the collected multi-dimensional sensing data to the central processing module (2). The central processing module (2) receives, processes, and analyzes the data from the distributed sensor array (1). The edge computing unit (3) receives sensitivity level adjustment instructions from the central processing module (2) and sends the instructions to the corresponding monitoring nodes; it is also used to preprocess the data; the edge computing unit (3) adjusts the sensitivity level of the corresponding monitoring nodes according to the adjusted parameters to ensure that the system can accurately capture the damage development process.

[0026] Specifically, each sensor node of the distributed sensor array (1) is equipped with an independent data acquisition module and signal transmission module. These sensor nodes are evenly distributed on the skin surface in a grid pattern, and their installation positions are precisely designed to ensure that they can cover the key stress areas and vulnerable parts of the skin.

[0027] Specifically, the communication link (4) uses wireless communication technology to reduce wiring complexity and improve system flexibility.

[0028] Specifically, the data receiving unit (5) is used to receive the raw data transmitted by the distributed sensor array (1) and perform preliminary processing on the data, such as noise reduction filtering and format conversion. The processed data is then transmitted to the data processing unit (6) for further processing.

[0029] Specifically, the data processing unit (6) embeds three key calculation models, namely the damage prediction threshold calculation model (7), the actual damage index calculation model (8), and the sensitivity level adjustment model (9).

[0030] It should be noted that the damage prediction threshold calculation model (7), the actual damage index calculation model (8), and the sensitivity level adjustment model (9) work together to complete the assessment of the skin damage status and the dynamic adjustment of monitoring parameters.

[0031] It should be noted that the edge computing unit (3) is located between the distributed sensor array (1) and the central processing module (2), playing a crucial role in connecting the two. This distributed computing architecture significantly improves the system's response speed and data processing efficiency.

[0032] In practical applications, for example, when an aircraft is flying at high altitude, the skin surface may be affected by airflow impact and temperature changes. At this time, the distributed sensor array (1) will collect stress, temperature and vibration data of the skin surface in real time and transmit the data to the central processing module (2) through the communication link (4). The data receiving unit (5) performs preliminary processing on the received data and then transmits it to the data processing unit (6). The data processing unit (6) calls the damage prediction threshold calculation model (7) and the actual damage index calculation model (8) to generate the damage prediction threshold and the actual damage index, respectively. If the actual damage index of a certain monitoring node is found to exceed the preset tolerance range, the sensitivity level adjustment process is immediately initiated. The edge computing unit (3) adjusts the sensitivity level of the corresponding monitoring node according to the adjusted parameters to ensure that the system can accurately capture the damage development process.

[0033] Furthermore, this invention also models and predicts the damage development process using artificial intelligence algorithms. The data processing unit (6) constructs a damage development model using historical and real-time data and predicts potential future damage trends based on this model. This function not only improves the intelligence level of the system but also provides a scientific basis for aircraft maintenance.

[0034] The collaborative relationship between the modules is crucial during system operation. The distributed sensor array (1) is responsible for data acquisition, the central processing module (2) is responsible for data analysis and decision-making, the edge computing unit (3) is responsible for command issuance and local data processing, and the communication link (4) serves as a bridge for data transmission to ensure smooth information flow between the modules. Through this clearly defined architecture, the system achieves comprehensive monitoring and precise quantification of damage to the aircraft skin surface.

[0035] This invention addresses the limitations of traditional monitoring methods, namely limited coverage and low data processing efficiency, through the aforementioned embodiments. The distributed sensor array design enables the system to cover critical areas of the skin surface, while the introduction of edge computing units improves data processing efficiency. Furthermore, the application of artificial intelligence algorithms enhances the system's intelligence level, effectively improving aircraft operational safety and maintenance efficiency.

[0036] Example 2 This application provides a method for monitoring intelligent aircraft skin with damage assessment capabilities, applied to an intelligent aircraft skin monitoring system with damage assessment capabilities provided in the above embodiments. By integrating multi-source sensor data, distributed computing units, and artificial intelligence algorithms, it achieves comprehensive monitoring, precise location, and quantitative analysis of damage to the aircraft skin surface. The method includes: Step 1: Distributed sensor arrays deployed on the aircraft skin surface collect multi-dimensional sensor data such as stress, temperature, and vibration in real time, and transmit the collected data to the data receiving unit of the central processing module. Step 2: After receiving the multidimensional sensing data, the data receiving unit of the central processing module notifies the data processing unit of the central processing module to call the actual damage index calculation model to complete the calculation of the actual damage index. Specifically, step 2, the actual damage index calculation model, calculates the actual damage index including: Generate actual damage indicators based on current multidimensional sensor data. :

[0037] in: For the first Actual damage indicators of each monitoring node; For the first Current stress value of each monitoring node, in MPa; For the first Current stress value of each monitoring node, in MPa; This is the reference stress value, in MPa.

[0038] Step 3: Compare the calculated actual damage index with the damage prediction threshold generated by the damage prediction threshold calculation model; if the difference between the damage prediction threshold and the actual damage index is greater than the preset tolerance range, proceed to step 4; if the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, proceed to step 5. Specifically, step 3, the damage prediction threshold calculation model, generates the damage prediction threshold by including: The damage prediction threshold is calculated based on stress distribution, ambient temperature, and material fatigue characteristics. :

[0039] in, For the first Damage prediction threshold for each monitoring node, in MPa; The basic damage threshold has a value range of 1.0 ≤ ≤5.0; For the first The stress distribution influence coefficient of each monitoring node, with a value range of 0.8 ≤ ≤1.2; The environmental temperature influence coefficient; This is the coefficient affecting the fatigue properties of the material.

[0040] Specifically, calculate the influence coefficient of ambient temperature. :

[0041] in: The ambient temperature is in °C. 、 and These are regression coefficients, each taking values ​​ranging from 0.1 to... ≤0.3, 0.01≤ ≤0.05, 0.8≤ ≤1.2.

[0042] Specifically, the influence coefficient of material fatigue characteristics :

[0043] in: The fatigue life cycle number of the material, with a value ranging from 10^4 to 10^4. ≤10^6; and These are regression coefficients, each taking values ​​ranging from 0.05 to... ≤0.1, 0.5≤ ≤1.0.

[0044] Step 4: The sensitivity level adjustment model completes the sensitivity level adjustment calculation, and the central processing module sends the adjusted sensitivity level parameters to the edge computing unit; the edge computing unit sends instructions to the corresponding monitoring nodes according to the sent parameters to complete the sensitivity level adjustment. Specifically, step 4, the sensitivity level adjustment model, generates sensitivity levels by including: Based on damage prediction threshold Set the sensitivity level of the monitoring nodes. :

[0045] in: For the first Sensitivity levels of each monitoring node; The basic sensitivity level has a value range of 1.0 ≤ ≤3.0; This is the sensitivity compensation factor, expressed as a percentage.

[0046] Step 5: If the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, then the adjustment ends and the monitoring node continues to operate according to the current sensitivity level.

[0047] In summary, this application provides an intelligent aircraft skin monitoring method with damage assessment capabilities. Based on a damage mechanics model, a damage prediction threshold is established, taking stress distribution, ambient temperature, and material fatigue characteristics as factors. The sensitivity level of the monitoring node is then set according to this damage prediction threshold. Multidimensional sensor data from the aircraft skin surface is collected in real time, and actual damage indicators are generated based on the current data. The damage prediction threshold is compared with the actual damage indicators. If the difference is less than or equal to a preset tolerance range, the current operating state of the monitoring node is maintained. If the difference is greater than the preset tolerance range, the sensitivity level of the monitoring node is adjusted, and the data acquisition frequency is recalibrated. The monitoring is completed collaboratively by a distributed sensor array deployed on the aircraft skin surface, a central processing module, an edge computing unit, and a communication link.

[0048] In summary, this application provides an intelligent aircraft skin monitoring system and method with damage assessment capabilities. By integrating multi-source sensor data, distributed computing units, and artificial intelligence algorithms, it achieves comprehensive monitoring, precise location, and quantitative analysis of damage to the aircraft skin surface. Its features include the following aspects: establishing a damage prediction threshold based on a damage mechanics model, taking stress distribution, ambient temperature, and material fatigue characteristics as factors, and setting the sensitivity level of monitoring nodes according to this damage prediction threshold; real-time acquisition of multi-dimensional sensor data from the aircraft skin surface, and generating actual damage indicators based on the current data; comparing the damage prediction threshold with the actual damage indicators; if the difference is less than or equal to a preset tolerance range, maintaining the current working state of the monitoring node; if the difference is greater than the preset tolerance range, adjusting the sensitivity level of the monitoring node and recalibrating the data acquisition frequency. The monitoring is completed collaboratively by a distributed sensor array deployed on the aircraft skin surface, a central processing module, an edge computing unit, and a communication link.

[0049] Example 3 To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0050] During takeoff, sensor nodes in a distributed sensor array are evenly distributed across the aircraft skin surface in a pre-defined grid pattern. These sensor nodes acquire stress, temperature, and vibration data of the skin surface in real time through independent data acquisition modules, and transmit the acquired information to the central processing module via wireless communication links. During this process, edge computing units perform preliminary processing on some of the received data, such as filtering out noise and outliers, thereby reducing the computational burden on the central processing module.

[0051] Once the central processing module receives the data, its internal data receiving unit first performs format conversion and noise reduction on the raw data, and then transmits the processed data to the data processing unit. The data processing unit invokes the damage prediction threshold calculation model and, based on the aircraft skin material characteristics and the current operating environment, calculates the damage prediction threshold for each monitoring node. Specifically, the damage prediction threshold is calculated based on the formula... ,in Based on the damage threshold, The stress distribution influence coefficient is... The environmental temperature influence coefficient. These are the coefficients affecting the fatigue properties of the material. The values ​​for these coefficients have all been experimentally verified to ensure the accuracy of the calculation results.

[0052] At the same time, the data processing unit calls the actual damage index calculation model, according to the formula Generate actual damage indicators for each monitoring node. .in, This represents the stress value at the current monitoring node. The stress values ​​are those of adjacent monitoring nodes. This serves as the baseline stress value. Using this formula, the system can quantify the actual damage level of each monitoring node and compare it with the damage prediction threshold. If the difference is less than or equal to the preset tolerance range, the current operating state of the monitoring node is maintained; if the difference exceeds the tolerance range, the sensitivity level adjustment model is invoked to recalculate the sensitivity level. The sensitivity level is calculated based on the formula. ,in Based on the basic sensitivity level, This is the sensitivity compensation factor. The central processing module sends the adjusted parameters to the edge computing unit via the communication link, and the edge computing unit sends instructions to the corresponding monitoring nodes to complete the dynamic adjustment of the sensitivity level.

[0053] During the aircraft's ascent to cruising altitude, the skin surface may experience stress concentration or micro-cracks due to airflow impact and temperature changes. At this time, sensor nodes in the distributed sensor array can capture these changes in real time and transmit the data to the central processing module. The data processing unit combines historical and real-time data and uses artificial intelligence algorithms to construct a damage development model, predicting potential future damage trends. This process not only improves the system's intelligence level but also provides a scientific basis for aircraft maintenance.

[0054] Furthermore, during high-altitude flight, if the actual damage index of a monitoring node exceeds the preset tolerance range, the system will immediately initiate a sensitivity level adjustment process. The edge computing unit optimizes the sensitivity level of the corresponding monitoring node based on the adjustment parameters issued by the central processing module, thereby ensuring that the system can accurately capture the damage development process. Simultaneously, the wireless communication technology of the communication link ensures smooth information flow between modules, avoiding the complexity and limitations of traditional wiring methods.

[0055] During aircraft landing, the skin surface may be subjected to significant stress again. At this time, the distributed sensor array continues to collect multi-dimensional sensor data in real time and transmits the data to the central processing module. The data processing unit generates the latest damage assessment results by calling the damage prediction threshold calculation model and the actual damage index calculation model. If the actual damage index of a certain area is found to be significantly higher than that of other areas, the system will mark that area as a potentially high-risk area and recommend that it be given special attention during subsequent maintenance.

[0056] Through the steps described above, this invention achieves comprehensive monitoring and precise quantification of damage to aircraft skin surfaces. The distributed sensor array design ensures that the system can cover all critical areas of the skin surface, while the introduction of edge computing units significantly improves data processing efficiency. Furthermore, the application of artificial intelligence algorithms further enhances the system's intelligence level, thereby effectively improving aircraft operational safety and maintenance efficiency.

[0057] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. An intelligent aircraft skin monitoring system with damage assessment capabilities, characterized in that, It includes a distributed sensor array (1), a central processing module (2), an edge computing unit (3), and a communication link (4), wherein: The central processing module (2) includes a data receiving unit (5) and a data processing unit (6); the data processing unit (6) includes a damage prediction threshold calculation model (7), an actual damage index calculation model (8), and a sensitivity level adjustment model (9). The distributed sensor array (1) includes multiple sensor nodes arranged on the surface of the aircraft skin for collecting multi-dimensional sensing data such as stress, temperature, and vibration. The sensor nodes interact with each other through a communication link (4) and transmit the collected multi-dimensional sensing data to the central processing module (2). The central processing module (2) is used to receive, process, and analyze the data from the distributed sensor array (1). The edge computing unit (3) receives sensitivity level adjustment instructions from the central processing module (2) and sends the instructions to the corresponding monitoring nodes. It is also used to preprocess the data. The edge computing unit (3) adjusts the sensitivity level of the corresponding monitoring nodes according to the adjusted parameters to ensure that the system can accurately capture the damage development process.

2. The aircraft intelligent skin monitoring system according to claim 1, characterized in that, Each sensor node of the distributed sensor array (1) is equipped with an independent data acquisition module and signal transmission module; these sensor nodes are evenly distributed on the skin surface in a grid pattern, and their installation positions are precisely designed to ensure that they can cover the key stress areas and vulnerable parts of the skin.

3. The aircraft intelligent skin monitoring system according to claim 1, characterized in that, The data receiving unit (5) is used to receive the raw data transmitted by the distributed sensor array (1) and perform preliminary processing on the data, such as noise reduction filtering and format conversion; the processed data is then transmitted to the data processing unit (6) for further processing.

4. The aircraft intelligent skin monitoring system according to claim 1, characterized in that, The data processing unit (6) embeds three key calculation models, namely the damage prediction threshold calculation model (7), the actual damage index calculation model (8), and the sensitivity level adjustment model (9).

5. A method for monitoring intelligent aircraft skin with damage assessment capabilities, characterized in that, The method applied to the aircraft intelligent skin monitoring system with damage assessment capability as described in any one of claims 1 to 4 includes: Step 1: Distributed sensor arrays deployed on the aircraft skin surface collect multi-dimensional sensor data such as stress, temperature, and vibration in real time, and transmit the collected data to the data receiving unit of the central processing module. Step 2: After receiving the multidimensional sensing data, the data receiving unit of the central processing module notifies the data processing unit of the central processing module to call the actual damage index calculation model to complete the calculation of the actual damage index. Step 3: Compare the calculated actual damage index with the damage prediction threshold generated by the damage prediction threshold calculation model; if the difference between the damage prediction threshold and the actual damage index is greater than the preset tolerance range, proceed to step 4; if the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, proceed to step 5. Step 4: The sensitivity level adjustment model completes the sensitivity level adjustment calculation, and the central processing module sends the adjusted sensitivity level parameters to the edge computing unit; the edge computing unit sends instructions to the corresponding monitoring nodes according to the sent parameters to complete the sensitivity level adjustment. Step 5: If the difference between the damage prediction threshold and the actual damage index is less than or equal to the preset tolerance range, then the adjustment ends and the monitoring node continues to operate according to the current sensitivity level.

6. The aircraft intelligent skin monitoring method according to claim 5, characterized in that, Step 2: The actual damage index calculation model calculates the actual damage index, including: Generate actual damage indicators based on current multidimensional sensor data. : in: For the first Actual damage indicators of each monitoring node; For the first Current stress value of each monitoring node, in MPa; For the first Current stress value of each monitoring node, in MPa; This is the reference stress value, in MPa.

7. The aircraft intelligent skin monitoring method according to claim 5, characterized in that, Step 3, the damage prediction threshold calculation model generates damage prediction thresholds including: The damage prediction threshold is calculated based on stress distribution, ambient temperature, and material fatigue characteristics. : in, For the first Damage prediction threshold for each monitoring node, in MPa; The basic damage threshold has a value range of 1.0 ≤ ≤5.0; For the first The stress distribution influence coefficient of each monitoring node, with a value range of 0.8 ≤ ≤1.2; The environmental temperature influence coefficient; This is the coefficient affecting the fatigue properties of the material.

8. The aircraft intelligent skin monitoring method according to claim 5, characterized in that, Step 3 includes: generating the environmental temperature influence coefficient from the damage prediction threshold calculation model. : in: The ambient temperature is in °C. 、 and These are regression coefficients, each taking values ​​ranging from 0.1 to... ≤0.3, 0.01≤ ≤0.05, 0.8≤ ≤1.

2.

9. The aircraft intelligent skin monitoring method according to claim 5, characterized in that, Step 3 includes: the influence coefficient of material fatigue properties. : in: The fatigue life cycle number of the material, with a value ranging from 10^4 to 10^4. ≤10^6; and These are regression coefficients, each taking values ​​ranging from 0.05 to... ≤0.1, 0.5≤ ≤1.

0.

10. The aircraft intelligent skin monitoring method according to claim 5, characterized in that, Step 4, the sensitivity level adjustment model, generates sensitivity levels including: Based on damage prediction threshold Set the sensitivity level of the monitoring nodes. : in: For the first Sensitivity levels of each monitoring node; The basic sensitivity level has a value range of 1.0 ≤ ≤3.0; This is the sensitivity compensation factor, expressed as a percentage.

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