Method and device for determining arrangement of aircraft structure health monitoring sensors
By screening and optimizing the monitoring points in aircraft structure health monitoring, the problem of difficulty in achieving global high perception in the prior art is solved, and efficient health monitoring of aircraft structures is achieved.
Patent Information
- Application Number
- CN202411863457.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-04-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art is difficult to achieve global high perception in aircraft structure health monitoring, and unreasonable sensor arrangement will affect data validity and monitoring accuracy.
By determining the original monitoring point, obtaining strain data under different working conditions, screening initial monitoring points with less correlation, building a monitoring plan and calculating the root mean square of Pearson's correlation coefficient, and optimizing the monitoring point combination to achieve final structural health monitoring.
It realizes global high perception of the aircraft structure, optimizes the number of sensors and layout location, and improves the accuracy and effectiveness of monitoring data.
Smart Images
Figure CN119783457A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the technical field of aircraft health monitoring, and in particular, relates to a method and device for determining the arrangement of aircraft structural health monitoring sensors. Background Art
[0002] The rational selection and optimal layout of sensor networks are the key basic issues that should be solved first when establishing a structural health monitoring system. On the one hand, the more sensors are placed on the structure, the more accurate the structural status information will be. However, due to economic factors, additional weight factors, structural characteristics and other reasons, it is unrealistic to arrange a large number of state perception sensors on the aircraft structure at this stage. In addition, too many sensors will also introduce a lot of redundant data, which will make it difficult to effectively analyze the aircraft structure response information. On the other hand, if the location of the sensor arrangement in the actual engineering structure is not appropriate, it may also affect the effectiveness of the sensor data and reduce the accuracy of the monitoring system.
[0003] Existing sensor layout optimization mainly includes effective independence method, sequence method, correlation coefficient method and optimization algorithms based on machine learning, such as genetic algorithm, particle swarm algorithm, LASSO and principal component analysis. The above methods have good effects on specific model examples and can solve some sensor optimization problems, but for complex structural configurations such as aircraft structures, using a single method may only obtain local optimal solutions, but not global optimal solutions. Summary of the invention
[0004] In order to solve the above problems, the present application provides a method and device for determining the layout of aircraft structural health monitoring sensors to obtain real and accurate structural status data and achieve global high perception of the structure.
[0005] The first aspect of the present application provides a method for determining the arrangement of aircraft structural health monitoring sensors, which mainly includes:
[0006] Step S1, determining the original monitoring point;
[0007] Step S2, obtaining the strain of each original monitoring point under different working conditions;
[0008] Step S3, determining n initial monitoring points with smaller correlation according to the correlation of strains of the original monitoring points;
[0009] Step S4, taking each initial monitoring point as the first monitoring point, constructing n monitoring schemes, for each monitoring scheme, calculating the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme, and adding the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient to the monitoring scheme in order;
[0010] Step S5: for each monitoring scheme, select a monitoring point combination scheme in order from few to many. When the error between the measured value of the selected monitoring point and the actual value is less than the set value, determine the final monitoring point combination scheme.
[0011] Preferably, in step S1, the number of the original monitoring points is not less than 500.
[0012] Preferably, in step S2, the number of the working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
[0013] Preferably, the strain vector matrix is constructed by constructing a finite element model and performing virtual load loading to obtain strain values of each original monitoring point.
[0014] Preferably, in step S4, the root mean square c' of the Pearson correlation coefficient is determined by the following formula:
[0015]
[0016] Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
[0017] The second aspect of the present application provides a device for determining the arrangement of aircraft structural health monitoring sensors, which mainly includes:
[0018] An original monitoring point determination module is used to determine the original monitoring point;
[0019] The strain determination module is used to obtain the strain of each original monitoring point under different working conditions;
[0020] The initial monitoring point screening module is used to determine n initial monitoring points with smaller correlation according to the correlation of strains of each original monitoring point;
[0021] A monitoring scheme construction module is used to construct n monitoring schemes with each initial monitoring point as the first monitoring point. For each monitoring scheme, the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme is calculated, and the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient are added to the monitoring scheme in order.
[0022] The monitoring point combination scheme determination module is used to select monitoring point combination schemes for each monitoring scheme in order from few to many, and determine the final monitoring point combination scheme when the error between the measured value of the selected monitoring point and the actual value is less than the set value.
[0023] Preferably, in the original monitoring point determination module, the number of the original monitoring points is not less than 500.
[0024] Preferably, in the strain determination module, the number of working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
[0025] Preferably, the strain determination module includes a finite element calculation unit, which is used to construct the strain vector matrix by constructing a finite element model and obtaining the strain value of each original monitoring point after virtual load loading.
[0026] Preferably, in the monitoring scheme building module, the root mean square c' of the Pearson correlation coefficient is determined by the following formula:
[0027]
[0028] Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
[0029] A third aspect of the present application provides a computer device, characterized in that it includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the above-mentioned method for determining the arrangement of aircraft structural health monitoring sensors.
[0030] A fourth aspect of the present application provides a readable storage medium storing a computer program, wherein the computer program is used to implement the above-mentioned method for determining the arrangement of aircraft structural health monitoring sensors when executed by a processor.
[0031] This application inverts the aircraft load through a small number of monitoring points, optimizing the number of configured sensors and their layout locations. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 The flowchart of a preferred embodiment of the method for determining the arrangement of aircraft structural health monitoring sensors of the present application is shown in FIG.
[0033] Figure 2 It is a schematic diagram of the load loading of the wing box section.
[0034] Figure 3 It is a schematic diagram of the original monitoring point distribution of the wing box section.
[0035] Figure 4 It is a schematic diagram of the initial monitoring point distribution of the wing box section.
[0036] Figure 5 It is a schematic diagram of the structure of a computer device suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION
[0037] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the implementation of this application will be described in more detail in combination with the drawings in the implementation of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described implementation is a part of the implementation of this application, not all of the implementations. The implementation described below with reference to the drawings is exemplary and is intended to be used to explain this application, and cannot be understood as a limitation on this application. Based on the implementation in this application, all other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The implementation of this application is described in detail below in combination with the drawings.
[0038] The present application provides a method for determining the arrangement of aircraft structural health monitoring sensors, such as Figure 1 As shown, it mainly includes:
[0039] Step S1, determining the original monitoring point;
[0040] Step S2, obtaining the strain of each original monitoring point under different working conditions;
[0041] Step S3, determining n initial monitoring points with smaller correlation according to the correlation of strains of the original monitoring points;
[0042] Step S4, taking each initial monitoring point as the first monitoring point, constructing n monitoring schemes, for each monitoring scheme, calculating the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme, and adding the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient to the monitoring scheme in order;
[0043] Step S5: for each monitoring scheme, select a monitoring point combination scheme in order from few to many. When the error between the measured value of the selected monitoring point and the actual value is less than the set value, determine the final monitoring point combination scheme.
[0044] In step S1 of the present application, for different structures to be monitored on the aircraft, the distribution of the aircraft structure under typical working conditions is obtained based on the finite element model, and combined with engineering experience, p monitoring points (p is usually much larger than the actual monitoring requirement) are arranged at relatively important positions of the structure to be monitored as original monitoring points. The original monitoring point positions are generally where the distribution is larger and the position is relatively flat (the stress gradient cannot be too large). For the sake of conservatism, as many positions that meet the requirements as possible can be selected as the original monitoring point positions. In some optional implementations, in step S1, the number of the original monitoring points is not less than 500. Figure 2 Taking the wing box section shown in FIG. 1 as an example, the finite element model of the wing box section adopts the above principle to determine p = 996 monitoring units as the original monitoring points. Figure 3 The strain values of the two actuator loads fed back from these original monitoring points are given.
[0045] Step S2 is used to generate a load matrix based on the actual loading conditions of the aircraft structure to form a training set and a test set. The training set should reflect the loading characteristics of the structure as comprehensively as possible. Ideally, a combination of working conditions under various situations can be applied to the real aircraft, and monitoring points can be arranged at as many parts of the real aircraft as possible to obtain vectors under various combined working conditions. However, this requires a lot of cost. Therefore, in some optional implementations, a finite element model is constructed, and after virtual load loading, the strain value of each original monitoring point is obtained to construct the strain vector matrix.
[0046] In some optional embodiments, in step S2, the number of working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
[0047] In this embodiment, in step S2, q kinds of combined working conditions are applied, and the values of p original monitoring points under q kinds of combined working conditions are calculated, and then the obtained value data are normalized. The normalization method is to set the maximum and minimum of all original monitoring points under each working condition to 1 and -1, and the values of the remaining original monitoring points under the working condition are scaled proportionally. After normalization, each original monitoring point obtains a vector with q elements. Part of the data is selected as a training set and part of the data is selected as a test set in the q working conditions. Generally, the number of training sets is larger than the test set. For example, 90 working conditions are constructed based on two loading actuators of the wing box section, of which 80 groups are randomly selected as training working conditions and 10 groups are randomly selected as test working conditions. The values of 996 monitoring points under 90 working conditions are shown in Table 1.
[0048] Table 1 Working conditions and strain matrix
[0049]
[0050] Afterwards, in step S3, the correlation coefficient method is used as a preliminary screening method for a large amount of original data to find out the mutual relationship between the monitoring point characteristics, and the monitoring points with high correlation coefficients (redundant) are removed to obtain 13 initial monitoring points. Specifically, the monitoring points are preliminarily sorted based on the numerical values of each original monitoring point under the action of the loading matrix of the finite element model of the monitored structure, and the largest monitoring point is used as the starting monitoring point. The column vector in Table 1 is selected to calculate the correlation coefficients of different monitoring points, and the monitoring points with high correlation coefficients (redundant) are removed to obtain 13 preliminarily screened monitoring points, as shown in FIG. Figure 4 shown.
[0051] Then, in step S4, 13 monitoring schemes are constructed for the 13 initial monitoring points. Each monitoring scheme is a monitoring point sequence. The first value in the monitoring point sequence is the monitoring point corresponding to the sequence number of the current monitoring scheme. As shown in Table 2 below, the first monitoring point of the first monitoring scheme is monitoring point No. 1, the first monitoring point of the second monitoring scheme is monitoring point No. 2, etc. Then, for each monitoring scheme, other candidate monitoring points are gradually added to the monitoring point sequence. Specifically, in some optional implementations, in step S4, the root mean square c' of the Pearson correlation coefficient is determined by the following formula:
[0052]
[0053] Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
[0054] For example, for the first monitoring scheme, the root mean square of the Pearson correlation coefficients of the other 12 alternative monitoring points and the first monitoring point, i.e., monitoring point No. 1, is calculated respectively, and it is found that the root mean square of the correlation coefficient of monitoring point No. 10 is the smallest. Therefore, monitoring point No. 10 is added as a newly selected monitoring point to the monitoring point sequence of the first monitoring scheme, and then the root mean square of the Pearson correlation coefficients of the other 11 alternative monitoring points and monitoring points No. 1 and No. 10 are calculated. It is found that the root mean square of the correlation coefficient of monitoring point No. 12 is the smallest. Therefore, monitoring point No. 12 is added as a newly selected monitoring point to the monitoring point sequence of the first monitoring scheme, and the process is repeated, and finally the 13 monitoring schemes shown in Table 2 are obtained.
[0055] Table 2 Monitoring point ranking scheme
[0056] Serial number The first monitoring point Other monitoring points (arranged in order) 1 1 10，12，6，13，2，7，3，8，5，11，4，9 2 2 7，11，4，6，12，8，5，10，1，9，3，13 3 3 10，6，12，11，4，8，5，2，9，1，13，7 4 4 8，13，2，12，6，9，1，10，3，11，5，7 5 5 8，1，10，4，11，3，9，6，12，2，13，7 6 6 12，10，3，11，4，8，5，2，9，1，13，7 7 7 2，11，4，6，12，8，5，10，1，9，3，13 8 8 4，13，2，12，6，9，1，10，3，11，5，7 9 9 1，8，4，11，3，13，2，7，6，5，10，12 10 10 3，6，12，11，4，8，5，2，9，1，13，7 11 11 4，2.，7，6，12，8，5，10，1，9，3，13 12 12 6，10，3，11，4，8，5，2，9，1，13，7 13 13 2，4，8，12，6，9，1，10，3，11，5，7
[0057] Finally, in step S5, the error between the load inversion result and the actual load is compared. When the two monitoring points No. 1 and No. 10 of Scheme 1 are selected, the number of monitoring points used is the least. The maximum error of the 10 test conditions of the No. 1 actuator is 3%, and the maximum error of the 10 test conditions of the No. 2 actuator is 4%. The errors meet the use requirements. Therefore, the final selected monitoring point combination scheme is monitoring point No. 1 and monitoring point No. 10.
[0058] This application optimizes a large number of original monitoring points, inverts the aircraft load through a small number of monitoring points, and optimizes the number of sensor configurations and layout locations.
[0059] The second aspect of the present application provides a device for determining the arrangement of aircraft structural health monitoring sensors corresponding to the above method, mainly comprising:
[0060] An original monitoring point determination module is used to determine the original monitoring point;
[0061] The strain determination module is used to obtain the strain of each original monitoring point under different working conditions;
[0062] The initial monitoring point screening module is used to determine n initial monitoring points with smaller correlation according to the correlation of strains of each original monitoring point;
[0063] A monitoring scheme construction module is used to construct n monitoring schemes with each initial monitoring point as the first monitoring point. For each monitoring scheme, the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme is calculated, and the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient are added to the monitoring scheme in order.
[0064] The monitoring point combination scheme determination module is used to select monitoring point combination schemes for each monitoring scheme in order from few to many, and determine the final monitoring point combination scheme when the error between the measured value of the selected monitoring point and the actual value is less than the set value.
[0065] In some optional implementations, in the original monitoring point determination module, the number of the original monitoring points is not less than 500.
[0066] In some optional embodiments, in the strain determination module, the number of working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
[0067] In some optional embodiments, the strain determination module includes a finite element calculation unit, which is used to construct the strain vector matrix by constructing a finite element model and obtaining the strain value of each original monitoring point after virtual load loading.
[0068] In some optional implementations, in the monitoring scheme building module, the root mean square c' of the Pearson correlation coefficient is determined by the following formula:
[0069]
[0070] Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
[0071] According to a third aspect of the present application, a computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for determining the arrangement of aircraft structural health monitoring sensors.
[0072] In a fourth aspect of the present application, a readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the above-mentioned method for determining the arrangement of aircraft structural health monitoring sensors. The computer-readable storage medium may be included in the device described in the above-mentioned embodiment; or it may exist independently without being assembled into the device. The above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed by the device, the data is processed according to the above-mentioned method.
[0073] Reference below Figure 5 , which shows a schematic structural diagram of a computer device 400 suitable for implementing the embodiments of the present application. Figure 5 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0074] like Figure 5 As shown, the computer device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.
[0075] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
[0076] In particular, according to the embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the above functions defined in the method of the present application are executed. It should be noted that the computer storage medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus or a device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus or a device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0077] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0078] The modules or units involved in the embodiments described in this application may be implemented by software or hardware. The modules or units described may also be set in a processor, and the names of these modules or units do not constitute limitations on the modules or units themselves in some cases.
[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A method for determining the arrangement of aircraft structural health monitoring sensors, characterized in that: include: Step S1, determining the original monitoring point; Step S2, obtaining the strain of each original monitoring point under different working conditions; Step S3, determining n initial monitoring points with smaller correlation according to the correlation of strains of the original monitoring points; Step S4, taking each initial monitoring point as the first monitoring point, constructing n monitoring schemes, for each monitoring scheme, calculating the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme, and adding the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient to the monitoring scheme in order; Step S5: for each monitoring scheme, select a monitoring point combination scheme in order from few to many. When the error between the measured value of the selected monitoring point and the actual value is less than the set value, determine the final monitoring point combination scheme.
2. The method for determining the arrangement of aircraft structural health monitoring sensors according to claim 1, characterized in that: In step S1, the number of the original monitoring points is not less than 500.
3. The method for determining the arrangement of aircraft structural health monitoring sensors according to claim 1, characterized in that: In step S2, the number of the working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
4. The method for determining the arrangement of aircraft structural health monitoring sensors according to claim 3, characterized in that: The strain vector matrix is constructed by constructing a finite element model and performing virtual load loading to obtain the strain value of each original monitoring point.
5. The method for determining the arrangement of aircraft structural health monitoring sensors according to claim 1, characterized in that: In step S4, the root mean square c' of the Pearson correlation coefficient is determined by the following formula: Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
6. A device for determining the arrangement of aircraft structural health monitoring sensors, characterized in that: include: An original monitoring point determination module is used to determine the original monitoring point; The strain determination module is used to obtain the strain of each original monitoring point under different working conditions; The initial monitoring point screening module is used to determine n initial monitoring points with smaller correlation according to the correlation of strains of each original monitoring point; A monitoring scheme construction module is used to construct n monitoring schemes with each initial monitoring point as the first monitoring point. For each monitoring scheme, the root mean square of the Pearson correlation coefficient of each other candidate monitoring point and all monitoring points in the monitoring scheme is calculated, and the candidate monitoring points with the smallest root mean square of the Pearson correlation coefficient are added to the monitoring scheme in order. The monitoring point combination scheme determination module is used to select monitoring point combination schemes for each monitoring scheme in order from few to many, and determine the final monitoring point combination scheme when the error between the measured value of the selected monitoring point and the actual value is less than the set value.
7. The arrangement determination device for aircraft structural health monitoring sensors according to claim 6, characterized in that: In the original monitoring point determination module, the number of the original monitoring points is not less than 500.
8. The arrangement determination device for aircraft structural health monitoring sensors according to claim 6, characterized in that: In the strain determination module, the number of working conditions is not less than 50, and at least 10% of the working conditions are used as test sets, and the other number of working conditions are used as training sets. The strain vector matrix of each working condition is normalized when calculating the correlation.
9. The arrangement determination device for aircraft structural health monitoring sensors according to claim 8, characterized in that: The strain determination module includes a finite element calculation unit, which is used to construct the strain vector matrix by constructing a finite element model and obtaining the strain value of each original monitoring point after virtual load loading.
10. The arrangement determination device for aircraft structural health monitoring sensors according to claim 6, characterized in that: In the monitoring scheme building module, the root mean square c' of the Pearson correlation coefficient is determined by the following formula: Among them, c x1 , c x2… c xm is the Pearson correlation coefficient between the strain vector of the candidate monitoring point x and the m monitoring points added in the monitoring scheme.
11. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining the arrangement of aircraft structural health monitoring sensors according to any one of claims 1 to 5.
12. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the method for determining the arrangement of aircraft structural health monitoring sensors according to any one of claims 1 to 5.