CN117372708A - Aircraft tail flame characteristic extraction and correlation analysis method - Google Patents
Aircraft tail flame characteristic extraction and correlation analysis method Download PDFInfo
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Abstract
The embodiment of the disclosure relates to an aircraft tail flame characteristic extraction and correlation analysis method. The method comprises the following steps: acquiring image data and pressure data of the tail flame; extracting pressure curve characteristics of the pressure data to obtain pressure curve characteristic points; preprocessing the tail flame image data to obtain a tail flame region; extracting Mach ring features from the tail flame region to obtain optimal Mach ring features; extracting shape characteristics of the tail flame area to obtain optimal shape characteristics of the tail flame; and carrying out multi-dimensional fusion analysis on the optimal Mach ring characteristics, the optimal tail flame shape characteristics and the pressure curve characteristic points to obtain an analysis result. The embodiment of the disclosure can rapidly and efficiently extract Mach ring features and tail flame shape features in any tail flame image, and rapidly migrate to correlation analysis of tail flame features of different types of aircrafts. Ji Duowei-degree information can be fused and analyzed, and the multidimensional information is aligned to microsecond level, so that the analysis accuracy is high.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of image processing, in particular to an aircraft tail flame characteristic extraction and correlation analysis method.
Background
Technological innovation in the world is a major battlefield in international strategic games, and competition around technological highpoints is unprecedented. An aircraft is an instrument that flies in the atmosphere or in an extra-atmospheric space (space), and includes three general classes: aircraft, spacecraft, rockets, and missiles. The aircraft technology in one country directly reflects the strategic goals, comprehensive national forces, comprehensive technological and industrial forces of the country, and the system mobilization and resource integration capability. With the rapid development of science and technology and aerospace technology, new opportunities for competition and cooperation appear in aspects of manned aerospace, deep space exploration, satellite navigation, space science, commercial emission and the like in the corner by corner of a new round of aerospace, and the China builds an aircraft on occasion when the country is strong.
Aircraft engines are the highest end product in the field of equipment manufacturing, representing the science and technology and comprehensive national forces of a country, and have long been considered as core technologies affecting national air transportation, national defense security, and maintaining national strategic advantages. As the "heart" of an aircraft, the engine is a very complex pneumatic thermodynamic rotating machine, and visual evaluation of the performance and reliability of the engine is critical in the design and development process of the aircraft engine. The working condition of the aircraft engine can be intuitively reflected through the extraction of the engine tail flame characteristics and the correlation analysis, and the method is a key technical means in the performance and reliability test of the aircraft. The main flow analysis methods of the tail flame characteristics of the aircraft mainly comprise two types: one is to analyze the tail flame according to a physical mechanism, verify its reliability by a numerical model, and simulate the combustion process of the tail flame temperature field. And the other is to design different sensors or temperature and radiance detection equipment to perform relevant feature detection on the tail flame area of the aircraft engine, and then automatically analyze the correlation between all data by scientific researchers with expert knowledge. Although these methods can obtain the characteristics of the tail flame of the aircraft, they are limited by factors such as the model of the engine, limited detection capability and limited precision of a detection instrument, and the like, and cannot be more universally applied to the characteristics analysis of most tail flame of the aircraft. And barriers exist among different characteristic data, so that information fusion of different dimensions is difficult to carry out tail flame characteristic analysis.
Disclosure of Invention
In order to avoid the defects of the prior art, the application provides an aircraft tail flame characteristic extraction and correlation analysis method, which is used for solving the problems of poor universality and difficult multidimensional information fusion analysis in the aircraft tail flame characteristic extraction and correlation analysis in the prior art.
According to an embodiment of the present disclosure, there is provided a method for extracting tail flame characteristics and analyzing correlation of an aircraft, the method including:
acquiring tail flame data of the tail flame; wherein the tail flame data includes image data and pressure data;
extracting the pressure curve characteristics of the pressure data to obtain pressure curve characteristic points;
preprocessing the tail flame image data to obtain a tail flame region in the tail flame image data;
extracting Mach ring features from the tail flame region to obtain optimal Mach ring features;
extracting shape characteristics of the tail flame area to obtain optimal shape characteristics of the tail flame;
and carrying out multidimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic points to obtain an analysis result.
The step of acquiring the tail flame data of the tail flame comprises the following steps:
acquiring the tail flame data of the tail flame by using a pressure sensor;
and shooting the complete tail flame area by using a high-speed camera and a thermal imager, and obtaining the tail flame image data.
The step of extracting the pressure curve characteristic of the pressure data to obtain the pressure curve characteristic point comprises the following steps:
adopting a two-end approach type smoothing algorithm to carry out preliminary smoothing treatment on a pressure curve of the pressure data;
acquiring the pressure curve characteristic points in the pressure curve; the pressure curve characteristic points at least comprise an ignition pressure peak, the highest pressure and a pressure oscillation section.
The step of preprocessing the tail flame image data to obtain a tail flame region in the tail flame image data includes:
self-adaptive filtering is adopted for the tail flame image data, and interference noise in the tail flame image data is removed;
and finely extracting the tail flame region in the de-noised tail flame image data.
The step of extracting the mach ring features of the tail flame region to obtain the optimal mach ring features includes:
and carrying out threshold screening on the image intensity value of the thermal imager based on a Mach-Zehnder physical mechanism, and selecting and scoring the optimal Mach-Zehnder characteristics of the tail flame region according to a formula.
The step of extracting the mach ring features of the tail flame region to obtain the optimal mach ring features further includes:
the area, the highest temperature and the average temperature of the area inside and between the Mach rings are extracted.
The step of extracting the shape feature of the tail flame region to obtain the optimal shape feature of the tail flame comprises the following steps:
and carrying out foreground and background replacement operation on the tail flame image data, continuously expanding the tail flame region into a background region, calculating the score of the tail flame region according to a formula after each replacement, and obtaining the optimal tail flame shape characteristic.
The step of extracting the shape feature of the tail flame region to obtain the optimal shape feature of the tail flame further includes:
extracting the area, the highest temperature and the average temperature of the whole tail flame area.
The step of performing multidimensional fusion analysis on the optimal mach ring feature, the optimal tail flame shape feature and the pressure curve feature point to obtain an analysis result includes:
and carrying out multi-dimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic point, and visualizing the analysis result.
The method further comprises the steps of:
determining important focusing time of tail flame combustion according to the pressure curve characteristic points, and extracting a UNIX time stamp corresponding to the important focusing time;
and extracting the tail flame image data corresponding to the time according to the UNIX time stamp and the data shooting offset time.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the embodiment of the disclosure, through the aircraft tail flame characteristic extraction and correlation analysis method, on one hand, mach ring characteristics and tail flame shape characteristics in any tail flame image can be extracted rapidly and efficiently, and the aircraft tail flame characteristics can be rapidly migrated to correlation analysis of tail flame characteristics of different types of aircraft. On the other hand, ji Duowei-degree information can be subjected to fusion analysis, and the multidimensional information is aligned to microsecond level, so that the analysis accuracy is high.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a step diagram of a method of aircraft tail flame feature extraction and correlation analysis in an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a schematic diagram of the collection of tail flame data of an aircraft in an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method of aircraft tail flame feature extraction and correlation analysis in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of embodiments of the disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In the present exemplary embodiment, an aircraft tail flame feature extraction and correlation analysis method is provided first. Referring to fig. 1, the aircraft tail flame feature extraction and correlation analysis method may include: step S101 to step S106.
Step S101: acquiring tail flame data of the tail flame; wherein the tail flame data includes image data and pressure data;
step S102: extracting the pressure curve characteristics of the pressure data to obtain pressure curve characteristic points;
step S103: preprocessing the tail flame image data to obtain a tail flame region in the tail flame image data;
step S104: extracting Mach ring features from the tail flame region to obtain optimal Mach ring features;
step S105: extracting shape characteristics of the tail flame area to obtain optimal shape characteristics of the tail flame;
step S106: and carrying out multidimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic points to obtain an analysis result.
According to the aircraft tail flame characteristic extraction and correlation analysis method, on one hand, mach ring characteristics and tail flame shape characteristics in any tail flame image can be extracted rapidly and efficiently, and the aircraft tail flame characteristics can be rapidly migrated to correlation analysis of tail flame characteristics of different types of aircraft. On the other hand, ji Duowei-degree information can be subjected to fusion analysis, and the multidimensional information is aligned to microsecond level, so that the analysis accuracy is high.
Hereinafter, each step of the above-described aircraft tail flame feature extraction and correlation analysis method in the present exemplary embodiment will be described in more detail with reference to fig. 1 to 3.
In one embodiment, the step of acquiring the tail flame data of the tail flame includes: acquiring the tail flame data of the tail flame by using a pressure sensor; and shooting the complete tail flame area by using a high-speed camera and a thermal imager, and obtaining the tail flame image data.
Specifically, as shown in fig. 2, a pressure sensor is arranged at the tail flame nozzle to acquire pressure data of the tail flame; and simultaneously setting a high-speed camera and a thermal imager on the same side to shoot a complete tail flame area, and obtaining tail flame image data.
In one embodiment, as shown in fig. 3, the step of extracting the pressure data to obtain the pressure curve feature point includes: adopting a two-end approach type smoothing algorithm to carry out preliminary smoothing treatment on a pressure curve of the pressure data; acquiring the pressure curve characteristic points in the pressure curve; the pressure curve characteristic points at least comprise an ignition pressure peak, the highest pressure and a pressure oscillation section.
Specifically, a two-end approach type smoothing algorithm is adopted to carry out preliminary smoothing treatment on the pressure curve. And then, calculating key time positions such as ignition pressure peaks, highest pressure, pressure oscillation sections and the like in the curve.
And adopting a two-end approach type smoothing algorithm to carry out preliminary smoothing treatment on the pressure curve. The formula of the two-end approach type smoothing algorithm is as follows:
data[i]＝max(data[i]，data[i+1])i∈[0，MaxIndex)
data[i]＝max(data[i]，data[i-1])i∈[MaxIndex，len(data))
wherein, maxIndex is the corresponding subscript of the maximum point of the pressure curve, data is the tail flame pressure data, and len represents the length of the obtained data.
And then, according to the front-back slope difference between the data points, the areas of the ignition pressure peak, the highest pressure and the pressure oscillation section in the curve are obtained. The metric function is:
wherein Y is i For the pressure value of the tail flame at the ith node, xi Is that Moment corresponding to ith node, K before Represents the forward slope, K after Indicating the backward slope.
In one embodiment, as shown in fig. 3, the step of preprocessing the tail flame image data to obtain a tail flame region in the tail flame image data includes: self-adaptive filtering is adopted for the tail flame image data, and interference noise in the tail flame image data is removed; and finely extracting the tail flame region in the de-noised tail flame image data.
Specifically, firstly, adaptive median filtering is adopted for the acquired tail flame image data, and interference noise in the tail flame image is removed. And then, carrying out fine extraction on the tail flame region in the image, and detecting and retaining the foreground region where the tail flame is located.
In one embodiment, as shown in fig. 3, the step of extracting the mach ring feature of the tail flame region to obtain the optimal mach ring feature includes: and carrying out threshold screening on the image intensity value of the thermal imager based on a Mach-Zehnder physical mechanism, and selecting and scoring the optimal Mach-Zehnder characteristics of the tail flame region according to a formula.
Specifically, adaptive threshold Mach-Zehnder feature extraction is adopted for the tail flame region in the tail flame image data.
Firstly, based on a Mach-Zehnder physical mechanism, threshold value screening is carried out on the data intensity value of the tail flame image, and the maximum value Max of the temperature intensity of the tail flame area is calculated. And then, selecting an adaptive threshold thre, and counting the data positions with intensity values larger than Max thre in the tail flame image area. And adopting a connected region judgment algorithm to combine adjacent data positions. Finally, according to the following formula:
and obtaining Mach ring characteristic score under the current threshold value. Where n represents the number of currently detected Mach rings, innerDis (x) represents the inner diameter of the currently detected Mach rings, interDis (x) represents the distance between the Mach rings,representing the average distance between the mach rings. And repeating the steps, and selecting the threshold value with the highest Mach ring characteristic score as the final output. And meanwhile, the area, the highest temperature and the average temperature information of the area inside the Mach ring and between the Mach rings are extracted.
In one embodiment, as shown in fig. 3, the step of extracting the shape feature of the tail flame region to obtain the optimal shape feature of the tail flame includes: and carrying out foreground and background replacement operation on the tail flame image data, continuously expanding the tail flame region into a background region, calculating the score of the tail flame region according to a formula after each replacement, and obtaining the optimal tail flame shape characteristic.
Specifically, an adaptive shape feature extraction method is adopted for the tail flame region in the tail flame image data. And performing foreground and background replacement operation on the tail flame image, continuously expanding the tail flame region into a background region, calculating the score of the tail flame region according to the formula after each replacement, and obtaining the tail flame shape characteristic with the optimal score.
Firstly, calculating the maximum value Max and the minimum value Min of the temperature intensity of the tail flame area, and adaptively selecting a threshold value thre. And then setting the data intensity value of which the intensity value is larger than Max thre in the tail flame image data as 1, and setting the area of which the intensity value is smaller than Max thre as 0 to obtain tail flame shape binary data. Then, the binary data is subjected to foreground contour quantity detection, if the contour quantity is detected to be larger than 1, the threshold value is reselected, and otherwise, the method is performed according to the following formula:
and calculating a tail flame region score, and obtaining the tail flame shape characteristics with optimal scores. In the formula, M and N respectively represent the height and width of the image, P (i, j) represents the pixel value of the ith row and the jth column, and mu represents the average value of the image. And repeating the steps, and selecting the threshold value with the highest Mach ring characteristic score as the final output.
And extracting the information of the area, the highest temperature and the average temperature of the whole tail flame.
In one embodiment, as shown in fig. 3, the step of performing multi-dimensional fusion analysis on the optimal mach ring feature, the optimal tail flame shape feature, and the pressure curve feature point to obtain an analysis result includes: and carrying out multi-dimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic point, and visualizing the analysis result.
Specifically, firstly, multi-dimensional data are aligned in a time dimension, the important focusing time of tail flame combustion is determined according to the characteristic points of the pressure curve, and a UNIX time stamp corresponding to the time is extracted. And extracting the tail flame image corresponding to the moment from the high-speed data and the thermal imager data according to the UNIX time stamp and the data shooting offset moment. And finally extracting Mach ring characteristics, shape characteristics and temperature information of different characteristic areas in the area of the tail flame at the moment from the tail flame image data. And carrying out fusion analysis on the multidimensional information and visualizing the result.
The effects of the present application can be further illustrated by the following experiments.
1. Experimental conditions
The application is that the central processing unit isi7-10700F 2.9GHz CPU, memory 16G, WINDOWS, on operating system, experiment was performed using Python。
The data used in the experiments were from the internet.
2. Experimental details
The accuracy of mach ring feature extraction was tested as follows. Table 1 shows a test of the axial position of the front end of the Mach ring, wherein the predicted value is the result extracted by the application method, the true value is the Mach ring characteristic position marked by the true scientific research personnel, and the deviation degree of the predicted value and the actual value is calculated through experiments. As can be seen from the table, the extracted characteristic deviation value is less than 1.3%, and the error value is almost negligible.
TABLE 1
Table 2 shows experimental results of the prediction accuracy of the number of mach rings, the extraction accuracy of the tail flame area feature extraction result and the real result, and the selection accuracy of the pressure curve feature points in the tail flame feature extraction. As can be seen from the table, the method can well extract the relevant characteristics of the tail flame of the aircraft and conduct correlation analysis.
TABLE 2
| Mach ring number accuracy | Tail flame area overlap | Pressure curve feature point accuracy |
| 98％ | 95％ | 96％ |
By the aircraft tail flame characteristic extraction and correlation analysis method, the method is more universal. The method and the device decouple the strong coupling relation between the model of the aircraft and the physical mechanism, and can extract the tail flame characteristic and analyze the correlation of all the aircraft with the observable tail flame. In addition, the method and the device comprehensively utilize the tail flame information of various different dimensions to perform fusion analysis, extract tail flame characteristics more accurately, and extract key frame tail flame information more perfectly.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, one skilled in the art can combine and combine the different embodiments or examples described in this specification.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An aircraft tail flame feature extraction and correlation analysis method is characterized by comprising the following steps:
acquiring tail flame data of the tail flame; wherein the tail flame data includes image data and pressure data;
extracting the pressure curve characteristics of the pressure data to obtain pressure curve characteristic points;
preprocessing the tail flame image data to obtain a tail flame region in the tail flame image data;
extracting Mach ring features from the tail flame region to obtain optimal Mach ring features;
extracting shape characteristics of the tail flame area to obtain optimal shape characteristics of the tail flame;
and carrying out multidimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic points to obtain an analysis result.
2. The method for extracting tail flame characteristics and analyzing correlation of an aircraft according to claim 1, wherein the step of acquiring tail flame data of the tail flame comprises:
acquiring the tail flame data of the tail flame by using a pressure sensor;
and shooting the complete tail flame area by using a high-speed camera and a thermal imager, and obtaining the tail flame image data.
3. The method according to claim 1, wherein the step of extracting the pressure data to obtain the pressure curve feature points comprises:
adopting a two-end approach type smoothing algorithm to carry out preliminary smoothing treatment on a pressure curve of the pressure data;
acquiring the pressure curve characteristic points in the pressure curve; the pressure curve characteristic points at least comprise an ignition pressure peak, the highest pressure and a pressure oscillation section.
4. The method of claim 1, wherein the step of preprocessing the tail flame image data to obtain tail flame regions in the tail flame image data comprises:
self-adaptive filtering is adopted for the tail flame image data, and interference noise in the tail flame image data is removed;
and finely extracting the tail flame region in the de-noised tail flame image data.
5. The method for extracting tail flame features and analyzing the correlation of the aircraft according to claim 1, wherein the step of extracting the mach ring features from the tail flame region to obtain the optimal mach ring features comprises:
and carrying out threshold screening on the image intensity value of the thermal imager based on a Mach-Zehnder physical mechanism, and selecting and scoring the optimal Mach-Zehnder characteristics of the tail flame region according to a formula.
6. The method for extracting tail flame features and analyzing the correlation of the aircraft according to claim 5, wherein the step of extracting the mach ring features from the tail flame region to obtain the optimal mach ring features further comprises:
the area, the highest temperature and the average temperature of the area inside and between the Mach rings are extracted.
7. The method of extracting and analyzing correlation of tail flame features of an aircraft according to claim 1, wherein the step of extracting shape features of the tail flame region to obtain optimal tail flame shape features comprises:
and carrying out foreground and background replacement operation on the tail flame image data, continuously expanding the tail flame region into a background region, calculating the score of the tail flame region according to a formula after each replacement, and obtaining the optimal tail flame shape characteristic.
8. The method of extracting and analyzing correlation of tail flame features of an aircraft according to claim 7, wherein the step of extracting shape features of the tail flame region to obtain optimal tail flame shape features further comprises:
extracting the area, the highest temperature and the average temperature of the whole tail flame area.
9. The method for extracting tail flame characteristics and analyzing correlation of an aircraft according to claim 1, wherein the step of performing multidimensional fusion analysis on the optimal mach ring characteristics, the optimal tail flame shape characteristics and the pressure curve characteristic points to obtain analysis results comprises:
and carrying out multi-dimensional fusion analysis on the optimal Mach ring characteristic, the optimal tail flame shape characteristic and the pressure curve characteristic point, and visualizing the analysis result.
10. The method of aircraft tail flame feature extraction and correlation analysis of claim 1, further comprising:
determining important focusing time of tail flame combustion according to the pressure curve characteristic points, and extracting a UNIX time stamp corresponding to the important focusing time;
and extracting the tail flame image data corresponding to the time according to the UNIX time stamp and the data shooting offset time.
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