A fatigue life prediction system for composite materials

Abstract

The present invention relates to the technical field of fatigue prediction of composite materials, and in particular to a system for predicting fatigue life of composite materials, comprising a material fatigue analysis platform, a material information unit, an appearance image unit, a defect impact unit, a real-time analysis unit, an information tracking unit and a response display unit; the present invention analyzes the composite material from two points of appearance and interior, so as to consider the prediction error factor coefficient S of the composite material for detection analysis before predicting the fatigue life of the composite material, so as to ensure the accuracy of subsequent analysis results and rationalize detection settings, and to perform analysis on the interference risk of analysis results of actual detection information of the composite material by means of information feedback, so as to reduce the interference of detection parameter information on the composite material detection result, and further perform information validity supervision and evaluation analysis on the fatigue detection information of the composite material, which helps to ensure the credibility of the output composite material fatigue life prediction result.

Description

Technical Field

[0001] The invention relates to the technical field of composite material fatigue prediction, and in particular to a composite material fatigue life prediction system. Background Art

[0002] In recent years, composite materials have been widely used in aerospace and other fields due to their excellent mechanical properties such as high specific strength and specific stiffness, and their development prospects are very broad. Fatigue damage is a common damage of composite materials during service, mainly including three forms of damage: matrix cracks, delamination, and fiber fracture. These damages interact with each other, accumulate and expand continuously, and eventually lead to structural failure. Therefore, the fatigue life prediction of composite materials is very important;

[0003] However, in the prior art, it is impossible to monitor and analyze the internal and external interference factors of the fatigue life prediction of composite materials, which leads to large deviations in the prediction results, affecting the credibility of the analysis results. It is also impossible to supervise the actual values ​​of the parameters set for the fatigue life prediction test of composite materials, which leads to large deviations in the actual parameter information, resulting in inaccurate prediction results. It is also impossible to supervise the collection and processing of the data collected for the fatigue life prediction of composite materials, which leads to reduced reliability of the output prediction results.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a fatigue life prediction system for composite materials to solve the technical defects mentioned above. The present invention analyzes the composite material from two points of view, namely, the appearance and the interior, so as to consider the prediction error factor coefficient S of the composite material for detection and analysis before predicting the fatigue life of the composite material, so as to ensure the accuracy of the subsequent analysis results and the rationalization of the detection settings. The actual detection information of the composite material is subjected to analysis of the interference risk supervision of the analysis results by means of information feedback, so as to adjust the detection parameter information set for the composite material detection, so as to reduce the interference of the detection parameter information on the composite material detection results, and further conduct information effectiveness supervision and evaluation analysis on the fatigue detection information of the composite material. On the one hand, it helps to ensure the credibility of the output composite material fatigue life prediction results, and on the other hand, it helps to ensure the validity and reliability of the composite material fatigue life prediction analysis data.

[0006] The object of the present invention can be achieved by the following technical solutions: A system for predicting fatigue life of composite materials, comprising a material fatigue analysis platform, a material information unit, an appearance image unit, a defect impact unit, a real-time analysis unit, an information tracking unit, and a response display unit;

[0007] The material information unit is used to collect appearance feature data and ultrasonic detection data of the composite material, and send the appearance feature data and ultrasonic detection data to the appearance image unit and the defect impact unit respectively;

[0008] After receiving the appearance feature data, the appearance image unit immediately performs appearance image interference analysis on the appearance feature data, performs one-to-one comparison analysis on the obtained recognition character strings, and obtains an appearance deviation index;

[0009] After receiving the ultrasonic detection data, the defect impact unit immediately performs internal detection impact feedback analysis on the ultrasonic detection data, performs in-depth analysis on the obtained internal interference index, obtains the prediction error factor coefficient S, and performs comparison analysis on the prediction error factor coefficient S to obtain a normal life signal and a remaining life signal;

[0010] The real-time analysis unit is used to respond to the normal life signal and the remaining life signal to collect the actual detection information of the composite material, and to perform the analysis result interference risk supervision analysis on the actual detection information, and to compare and analyze the obtained result deviation index to obtain the stability signal and the alarm signal;

[0011] The information tracking unit is used to respond to the stability signal and collect fatigue detection information of the composite material, perform information validity supervision evaluation analysis on the fatigue detection information, compare and analyze the obtained front-end timeliness value and processing deviation coefficient, and obtain output signals and correction signals.

[0012] Preferably, the appearance image interference analysis process of the appearance image unit is as follows:

[0013] A test period is set, and the test period is set as a time threshold, and appearance feature data of the composite material within the time threshold is obtained, the appearance feature data represents an appearance feature image, and the appearance feature image is preprocessed, the preprocessing includes scaling and cropping, and the preprocessed appearance feature image is divided into i sub-region blocks, i is a natural number greater than zero, and each sub-region block is grayed, and then the maximum value and minimum value of the gray value of each sub-region block are obtained, and the maximum value and minimum value of the gray value of the sub-region block are digitally extracted, and the character string composed of the digital extraction of the maximum value and minimum value of the gray value of the sub-region block is set as the recognition character string, and the recognition character string of each sub-region block is compared and analyzed with the stored standard recognition character string one by one:

[0014] If the identification string corresponds to the standard identification string one by one, a normal signal is generated;

[0015] If the one-to-one correspondence between the identification string and the standard identification string is not satisfied, a defect signal is generated, and the ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is obtained, and the ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is set as the appearance deviation index.

[0016] Preferably, the internal detection impact feedback analysis process of the defect impact unit is as follows:

[0017] g ultrasonic detection points are set on the composite material, where g is a natural number greater than zero, and ultrasonic detection data of each ultrasonic detection point on the composite material within a time threshold is obtained, the ultrasonic detection data including real-time amplitude and real-time flight time, and the product value obtained by multiplying the real-time amplitude and the real-time flight time after data normalization is set as the deviation evaluation index. It should be noted that the randomly set detection points can be used to understand the influence of the defects of the composite material itself on the lifespan;

[0018] A rectangular coordinate system is established with the number of ultrasonic detection points as the X-axis and the deviation evaluation index as the Y-axis. The deviation evaluation index curve is drawn by plotting points, the maximum peak value and the minimum trough value in the deviation evaluation index curve are obtained, and the difference between the maximum peak value and the minimum trough value in the deviation evaluation index curve is set as the internal interference index.

[0019] Preferably, the appearance deviation index and the internal interference index are labeled WG and NG respectively, and the appearance deviation index WG and the internal interference index NG are substituted into the formula The prediction error factor coefficient is obtained, where f1 and f2 are the preset weight factor coefficients of the appearance deviation index and the internal interference index respectively, f3 is the preset fault tolerance factor coefficient, and S is the prediction error factor coefficient. The prediction error factor coefficient S is compared with the preset prediction error factor coefficient threshold value stored in the defect impact unit for analysis:

[0020] If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is less than or equal to 1, a normal life signal is generated;

[0021] If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is greater than 1, a remaining life signal is generated.

[0022] Preferably, the analysis result of the real-time analysis unit interferes with the risk supervision analysis process as follows:

[0023] The actual detection information of the composite material within the time threshold is obtained, and the actual detection information includes the cyclic load index, the loading assessment index and the actual environmental value. The cyclic load index, the loading assessment index and the actual environmental value are labeled as XH, JZ and HJ respectively, and the result deviation risk coefficient J is calculated according to the formula;

[0024] Then, a change curve of the result deviation risk coefficient J of the composite material within the time threshold is obtained, and the change curve of the result deviation risk coefficient J of the composite material is set as the result deviation risk coefficient characteristic curve, the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is obtained, and the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is set as the result deviation index, and the result deviation index is compared and analyzed with the stored preset result deviation index threshold:

[0025] If the result deviation index is less than or equal to the preset result deviation index threshold, a stable signal is generated;

[0026] If the result deviation index is greater than the preset result deviation index threshold, an alarm signal is generated.

[0027] Preferably, the cyclic load index represents the portion of the actual cyclic load amplitude that exceeds the preset cyclic load amplitude threshold; the loading assessment index represents the portion of the loading frequency that exceeds the preset loading frequency threshold; the actual environmental value represents the number of values ​​corresponding to environmental parameters that exceed the preset threshold, and the environmental parameters include temperature and humidity.

[0028] Preferably, the information effectiveness supervision evaluation and analysis process of the information tracking unit is as follows:

[0029] The fatigue detection information of the composite material within the time threshold is obtained, and the fatigue detection information includes the front-end aging value and the processing deviation coefficient, and the front-end aging value and the processing deviation coefficient are compared and analyzed with the preset front-end aging value threshold and the preset processing deviation coefficient threshold recorded and stored in the information tracking unit:

[0030] If the front-end aging value is less than the preset front-end aging value threshold, and the processing deviation coefficient is less than the preset processing deviation coefficient threshold, an output signal is generated;

[0031] If the front-end aging value is greater than or equal to a preset front-end aging value threshold, or the processing deviation coefficient is greater than or equal to a preset processing deviation coefficient threshold, a correction signal is generated.

[0032] Preferably, the front-end aging value indicates the number of collection times corresponding to the difference between the collection time corresponding to the collection time of the life-related information and the set collection time, and the life-related information includes yield strength and tensile strength; the processing deviation coefficient indicates the product value obtained by multiplying the difference between the processing time in the processing item corresponding to the life-related information exceeding the preset processing time and the processing number and the preset processing number after data normalization.

[0033] The beneficial effects of the present invention are as follows:

[0034] The present invention analyzes the composite material from two points, namely, performing appearance image interference analysis on the appearance feature data and performing internal detection impact feedback analysis on the ultrasonic detection data, so as to understand the influence of the composite material's own appearance and internal defects on fatigue life prediction by means of image and ultrasonic analysis, so as to consider the prediction error factor coefficient S of the composite material for detection and analysis before predicting the fatigue life of the composite material, so as to ensure the accuracy of subsequent analysis results and rationalize detection settings;

[0035] The present invention conducts risk supervision analysis on the interference of analysis results of actual detection information of composite materials by means of information feedback, so as to adjust the detection parameter information set for composite material detection to reduce the interference of the detection parameter information on the composite material detection results, thereby helping to improve the accuracy of composite material fatigue life prediction results, and further conducts information effectiveness supervision, evaluation and analysis on the fatigue detection information of composite materials. On the one hand, it helps to ensure the credibility of the output composite material fatigue life prediction results, and on the other hand, it helps to ensure the validity and reliability of composite material fatigue life prediction analysis data. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings;

[0037] Figure 1 It is a flowchart of the system of the present invention;

[0038] Figure 2 It is a local analysis reference diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] See also Figure 1 to Figure 2 As shown, the present invention is a fatigue life prediction system for composite materials, including a material fatigue analysis platform, a material information unit, an appearance image unit, a defect impact unit, a real-time analysis unit, an information tracking unit and a response display unit, the material fatigue analysis platform is connected to the material information unit in a one-way communication, the material information unit is connected to the appearance image unit and the defect impact unit in a one-way communication, the appearance image unit and the defect impact unit are connected to the real-time analysis unit in a one-way communication, the real-time analysis unit is connected to the information tracking unit and the response display unit in a one-way communication, and the information tracking unit is connected to the response display unit in a one-way communication;

[0042] When the material fatigue analysis platform generates a detection instruction, the detection instruction is sent to the material information unit. When the material information unit receives the detection instruction, it immediately collects the appearance feature data and ultrasonic detection data of the composite material, and sends the appearance feature data and ultrasonic detection data to the appearance image unit and the defect impact unit respectively. After receiving the appearance feature data, the appearance image unit immediately performs appearance image interference analysis on the appearance feature data, so as to understand the influence of the composite material's own appearance on fatigue life prediction through image analysis, so as to provide data support for subsequent analysis. The specific appearance image interference analysis process is as follows:

[0043] A test period is set, and the test period is set as a time threshold, and appearance feature data of the composite material within the time threshold is obtained, the appearance feature data represents an appearance feature image, and the appearance feature image is preprocessed, the preprocessing includes scaling, cropping, etc., the preprocessed appearance feature image is divided into i sub-region blocks, i is a natural number greater than zero, each sub-region block is grayed, and then the maximum value and minimum value of the gray value of each sub-region block are obtained, the maximum value and minimum value of the gray value of the sub-region block are digitally extracted, and the character string composed of the digital extraction of the maximum value and minimum value of the gray value of the sub-region block is set as the recognition character string, and the recognition character string of each sub-region block is compared and analyzed with the stored standard recognition character string one by one:

[0044] If the identification string corresponds to the standard identification string one by one, a normal signal is generated;

[0045] If the one-to-one correspondence between the identification string and the standard identification string is not satisfied, a defect signal is generated, a ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is obtained, and the ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is set as an appearance deviation index;

[0046] In the embodiments of the present invention, the composite material includes, but is not limited to: a fiber composite material, a sandwich composite material, and a fine-grained composite material;

[0047] In the embodiment of the present invention, the appearance feature image is obtained by photographing and collecting by an image acquisition device, and the image acquisition device includes but is not limited to: an SK line array camera;

[0048] After receiving the ultrasonic test data, the defect impact unit immediately performs internal test impact feedback analysis on the ultrasonic test data, so as to understand the impact of the composite material's internal structure on fatigue life prediction through ultrasound, so as to provide data support for subsequent analysis. The specific internal test impact feedback analysis process is as follows:

[0049] g ultrasonic detection points are set on the composite material, where g is a natural number greater than zero, and ultrasonic detection data of each ultrasonic detection point on the composite material within a time threshold is obtained. The ultrasonic detection data includes real-time amplitude and real-time flight time, and the product value obtained by multiplying the real-time amplitude and the real-time flight time after data normalization is set as the deviation evaluation index. It should be noted that the influence of the defects of the composite material itself on the lifespan can be understood through the randomly set detection points. The real-time amplitude represents the amount of amplitude drop of the ultrasonic wave during the detection process, and the real-time flight time represents the time between the sound wave emission time and the detection point time.

[0050] A rectangular coordinate system is established with the number of ultrasonic detection points as the X-axis and the deviation evaluation index as the Y-axis, a deviation evaluation index curve is drawn by plotting points, the maximum peak value and the minimum trough value in the deviation evaluation index curve are obtained, and the difference between the maximum peak value and the minimum trough value in the deviation evaluation index curve is set as the internal interference index;

[0051] The appearance deviation index and the internal interference index are labeled WG and NG respectively. Substitute the appearance deviation index WG and the internal interference index NG into the formula The prediction error factor coefficient is obtained, where f1 and f2 are the preset weight factor coefficients of the appearance deviation index and the internal interference index respectively, f3 is the preset fault tolerance factor coefficient, and S is the prediction error factor coefficient. The prediction error factor coefficient S is compared with the preset prediction error factor coefficient threshold value stored in the defect impact unit for analysis:

[0052] If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is less than or equal to 1, a normal life signal is generated;

[0053] If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is greater than 1, a remaining life signal is generated, and the normal life signal and the remaining life signal are sent to the response display unit. After receiving the normal life signal and the remaining life signal, the response display unit immediately displays the preset warning text corresponding to the normal life signal and the remaining life signal, so as to perform reasonable fatigue life prediction operations according to the composite material's own analysis feedback information, and help understand the prediction error interference of the composite material itself on the fatigue life prediction, so as to consider the prediction error factor coefficient S of the composite material for detection and analysis before predicting the fatigue life of the composite material, so as to ensure the accuracy of subsequent analysis results.

[0054] Embodiment 2:

[0055] After the normal life signal or the remaining life signal is generated, the real-time analysis unit is used to collect the actual detection information of the composite material, and perform the analysis result interference risk supervision analysis on the actual detection information, so as to adjust the detection parameter information set for the composite material detection, so as to reduce the interference of the detection parameter information on the composite material detection result, thereby helping to improve the accuracy of the fatigue life prediction result of the composite material. The specific analysis result interference risk supervision analysis process is as follows:

[0056] The actual detection information of the composite material within the time threshold is obtained. The actual detection information includes the cyclic load index, the loading evaluation index and the actual environmental value. The cyclic load index, the loading evaluation index and the actual environmental value are labeled as XH, JZ and HJ respectively. According to the formula The result deviation risk coefficient is obtained, where a1, a2 and a3 are preset proportional factor coefficients of the cyclic load index, the loading assessment index and the actual environmental value respectively. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. a4 is the preset correction factor coefficient. a1, a2, a3 and a4 are all greater than zero. J is the result deviation risk coefficient;

[0057] Then, a change curve of the result deviation risk coefficient J of the composite material within the time threshold is obtained, and the change curve of the result deviation risk coefficient J of the composite material is set as the result deviation risk coefficient characteristic curve, the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is obtained, and the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is set as the result deviation index, and the result deviation index is compared and analyzed with the stored preset result deviation index threshold:

[0058] If the result deviation index is less than or equal to the preset result deviation index threshold, a stable signal is generated;

[0059] If the result deviation index is greater than the preset result deviation index threshold, an alarm signal is generated and sent to the response display unit. After receiving the alarm signal, the response display unit immediately displays the preset warning text corresponding to the alarm signal, so as to adjust the detection parameter information set for the composite material detection, so as to reduce the interference of the detection parameter information on the composite material detection result, thereby helping to improve the accuracy of the composite material fatigue life prediction result;

[0060] In the embodiment of the present invention, the cyclic load index represents the portion of the actual cyclic load amplitude exceeding the preset cyclic load amplitude threshold. It should be noted that the larger the value of the cyclic load index is, the greater the deviation of the fatigue life prediction result of the composite material is.

[0061] In the embodiment of the present invention, the loading evaluation index represents the portion of the loading frequency that exceeds the preset loading frequency threshold. It should be noted that the larger the value of the loading evaluation index is, the larger the deviation of the fatigue life prediction result of the composite material is, and the loading evaluation index is one of the influencing parameters of the fatigue life prediction result of the composite material;

[0062] In the embodiment of the present invention, the actual environmental value indicates the number of times the corresponding value of the environmental parameter exceeds the corresponding value of the preset threshold value, and the environmental parameter includes temperature, humidity, etc. It should be noted that the actual environmental value is one of the influencing parameters of the fatigue life prediction result of the composite material;

[0063] When a stable signal is generated, the information tracking unit is used to respond to the stable signal, collect fatigue detection information of the composite material, and perform information validity supervision, evaluation and analysis on the fatigue detection information to understand the validity of the data collected for the fatigue life prediction of the composite material, so as to ensure the accuracy of the output of the fatigue life prediction results of the composite material. The specific information validity supervision, evaluation and analysis process is as follows:

[0064] The fatigue detection information of the composite material within the time threshold is obtained, and the fatigue detection information includes the front-end aging value and the processing deviation coefficient, and the front-end aging value and the processing deviation coefficient are compared and analyzed with the preset front-end aging value threshold and the preset processing deviation coefficient threshold recorded and stored in the information tracking unit:

[0065] If the front-end aging value is less than the preset front-end aging value threshold, and the processing deviation coefficient is less than the preset processing deviation coefficient threshold, an output signal is generated;

[0066] If the front-end aging value is greater than or equal to the preset front-end aging value threshold, or the processing deviation coefficient is greater than or equal to the preset processing deviation coefficient threshold, a correction signal is generated, and the output signal and the correction signal are sent to the response display unit. After receiving the output signal and the correction signal, the response display unit immediately displays the preset warning text corresponding to the output signal and the correction signal, which helps to ensure the credibility of the output composite material fatigue life prediction results on the one hand, and on the other hand, helps to ensure the validity and accuracy of the composite material fatigue life prediction analysis data;

[0067] In the embodiment of the present invention, the front-end aging value indicates the number of collection times corresponding to the difference between the collection time corresponding to the collection time of the life-related information and the set collection time. The life-related information includes yield strength, tensile strength, etc. It should be noted that the analysis is performed from the perspective of the accuracy of the information collection time to ensure the accuracy and effectiveness of the data collection time to reduce the impact of the collection on the result analysis. The yield strength indicates the stress at which the composite material produces the maximum uniform plastic deformation; the tensile strength indicates the maximum stress value that the composite material withstands before breaking;

[0068] In the embodiment of the present invention, the processing deviation coefficient represents the product value obtained by multiplying the difference between the processing time of the processing item corresponding to the life-related information exceeding the preset processing time and the processing times and the preset processing times after data normalization. It should be noted that the analysis is performed from the perspective of information processing to reduce the influence of information processing on result analysis;

[0069] In summary, the present invention analyzes the composite material from two points of appearance and interior, that is, performs appearance image interference analysis on the appearance feature data and performs internal detection influence feedback analysis on the ultrasonic detection data, so as to understand the influence of the appearance and internal defects of the composite material itself on the fatigue life prediction by means of image and ultrasonic analysis, so as to consider the prediction error factor coefficient S of the composite material for detection analysis before predicting the fatigue life of the composite material, so as to ensure the accuracy of the subsequent analysis results and rationalize the detection settings, and perform analysis on the analysis result interference risk supervision of the actual detection information of the composite material by means of information feedback, so as to adjust the detection parameter information set for the composite material detection, so as to reduce the interference of the detection parameter information on the composite material detection result, thereby helping to improve the accuracy of the composite material fatigue life prediction result, and further perform information validity supervision and evaluation analysis on the fatigue detection information of the composite material, which helps to ensure the credibility of the output composite material fatigue life prediction result on the one hand, and helps to ensure the validity and reliability of the composite material fatigue life prediction analysis data on the other hand.

[0070] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0071] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A system for predicting fatigue life of composite materials, characterized in that: It includes material fatigue analysis platform, material information unit, appearance image unit, defect impact unit, real-time analysis unit, information tracking unit and response display unit; The material information unit is used to collect appearance feature data and ultrasonic detection data of the composite material, and send the appearance feature data and ultrasonic detection data to the appearance image unit and the defect impact unit respectively; After receiving the appearance feature data, the appearance image unit immediately performs appearance image interference analysis on the appearance feature data, performs one-to-one comparison analysis on the obtained recognition character strings, and obtains an appearance deviation index; After receiving the ultrasonic detection data, the defect impact unit immediately performs internal detection impact feedback analysis on the ultrasonic detection data, performs in-depth analysis on the obtained internal interference index, obtains the prediction error factor coefficient S, and performs comparison analysis on the prediction error factor coefficient S to obtain a normal life signal and a remaining life signal; The appearance deviation index and the internal interference index are labeled WG and NG respectively. Substituting the appearance deviation index WG and the internal interference index NG into the formula The prediction error factor coefficient is obtained, where f1 and f2 are the preset weight factor coefficients of the appearance deviation index and the internal interference index respectively, f3 is the preset fault tolerance factor coefficient, and S is the prediction error factor coefficient. The prediction error factor coefficient S is compared with the preset prediction error factor coefficient threshold value stored in the defect impact unit for analysis: If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is less than or equal to 1, a normal life signal is generated; If the ratio between the prediction error factor coefficient S and the preset prediction error factor coefficient threshold is greater than 1, a remaining life signal is generated; The real-time analysis unit is used to respond to the normal life signal and the remaining life signal to collect the actual detection information of the composite material, and to perform the analysis result interference risk supervision analysis on the actual detection information, and to compare and analyze the obtained result deviation index to obtain the stability signal and the alarm signal; The information tracking unit is used to respond to the stable signal and collect fatigue detection information of the composite material, perform information effectiveness supervision evaluation analysis on the fatigue detection information, compare and analyze the obtained front-end timeliness value and processing deviation coefficient, and obtain an output signal and a correction signal; The appearance image interference analysis process of the appearance image unit is as follows: A test period is set, and the test period is set as a time threshold, and appearance feature data of the composite material within the time threshold is obtained, the appearance feature data represents an appearance feature image, and the appearance feature image is preprocessed, the preprocessing includes scaling and cropping, and the preprocessed appearance feature image is divided into i sub-region blocks, i is a natural number greater than zero, and each sub-region block is grayed, and then the maximum value and minimum value of the gray value of each sub-region block are obtained, and the maximum value and minimum value of the gray value of the sub-region block are digitally extracted, and the character string composed of the digital extraction of the maximum value and minimum value of the gray value of the sub-region block is set as the recognition character string, and the recognition character string of each sub-region block is compared and analyzed with the stored standard recognition character string one by one: If the identification string corresponds to the standard identification string one by one, a normal signal is generated; If the one-to-one correspondence between the identification string and the standard identification string is not satisfied, a defect signal is generated, a ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is obtained, and the ratio between the number of sub-region blocks corresponding to the defect signal and the number of sub-region blocks corresponding to the normal signal is set as the appearance deviation index; The internal detection impact feedback analysis process of the defect impact unit is as follows: g ultrasonic detection points are set on the composite material, where g is a natural number greater than zero, and ultrasonic detection data of each ultrasonic detection point on the composite material within a time threshold is obtained, the ultrasonic detection data including real-time amplitude and real-time flight time, and the product value obtained by multiplying the real-time amplitude and the real-time flight time after data normalization is set as the deviation evaluation index. It should be noted that the randomly set detection points can be used to understand the influence of the defects of the composite material itself on the lifespan; A rectangular coordinate system is established with the number of ultrasonic detection points as the X-axis and the deviation evaluation index as the Y-axis, a deviation evaluation index curve is drawn by plotting points, the maximum peak value and the minimum trough value in the deviation evaluation index curve are obtained, and the difference between the maximum peak value and the minimum trough value in the deviation evaluation index curve is set as the internal interference index; The analysis results of the live analysis unit interfere with the risk supervision analysis process as follows: The actual detection information of the composite material within the time threshold is obtained, and the actual detection information includes the cyclic load index, the loading assessment index and the actual environmental value. The cyclic load index, the loading assessment index and the actual environmental value are labeled as XH, JZ and HJ respectively, and the result deviation risk coefficient J is calculated according to the formula; Then, a change curve of the result deviation risk coefficient J of the composite material within the time threshold is obtained, and the change curve of the result deviation risk coefficient J of the composite material is set as the result deviation risk coefficient characteristic curve, the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is obtained, and the area of ​​the region enclosed by the result deviation risk coefficient characteristic curve and the X-axis is set as the result deviation index, and the result deviation index is compared and analyzed with the stored preset result deviation index threshold: If the result deviation index is less than or equal to the preset result deviation index threshold, a stable signal is generated; If the result deviation index is greater than the preset result deviation index threshold, an alarm signal is generated; The cyclic load index represents the portion of the actual cyclic load amplitude that exceeds the preset cyclic load amplitude threshold; the loading assessment index represents the portion of the loading frequency that exceeds the preset loading frequency threshold; the actual environmental value represents the number of times the corresponding value of the environmental parameter exceeds the preset threshold, and the environmental parameters include temperature and humidity; The information tracking unit's information effectiveness supervision assessment and analysis process is as follows: The fatigue detection information of the composite material within the time threshold is obtained, and the fatigue detection information includes the front-end aging value and the processing deviation coefficient, and the front-end aging value and the processing deviation coefficient are compared and analyzed with the preset front-end aging value threshold and the preset processing deviation coefficient threshold recorded and stored in the information tracking unit: If the front-end aging value is less than the preset front-end aging value threshold, and the processing deviation coefficient is less than the preset processing deviation coefficient threshold, an output signal is generated; If the front-end aging value is greater than or equal to the preset front-end aging value threshold, or the processing deviation coefficient is greater than or equal to the preset processing deviation coefficient threshold, a correction signal is generated; The front-end aging value indicates the number of collection times corresponding to the difference between the collection time corresponding to the collection time of the life-related information and the set collection time, and the life-related information includes yield strength and tensile strength; the processing deviation coefficient indicates the product value obtained by multiplying the difference between the processing time exceeding the preset processing time and the processing times and the preset processing times of the processing items corresponding to the life-related information after data normalization.

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