CN113761666B - Automatic processing method for aircraft quality characteristic data - Google Patents
Automatic processing method for aircraft quality characteristic data Download PDFInfo
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- CN113761666B CN113761666B CN202111102406.7A CN202111102406A CN113761666B CN 113761666 B CN113761666 B CN 113761666B CN 202111102406 A CN202111102406 A CN 202111102406A CN 113761666 B CN113761666 B CN 113761666B
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Abstract
The application belongs to the technical field of data processing, and particularly relates to an automatic processing method for aircraft quality characteristic data. The method comprises the steps of S1, obtaining original airplane quality characteristic data to be processed; step S2, determining each level of data lines through a greedy algorithm, and marking each level of data lines according to a preset first marking rule; step S3, calculating layer by layer from bottom to top by adopting a tower-building type quality characteristic calculation method, determining actual data of each level of data row, and outputting new airplane quality characteristic data; and S4, performing data comparison on the original aircraft quality characteristic data and the new aircraft quality characteristic data, and outputting an error data point. According to the method and the device, the quality characteristic data of each system and structure of the airplane can be quickly and accurately layered and sorted, then the quality characteristic data of the whole airplane is calculated, the relative and absolute errors between the original data of the quality characteristics of all levels of parts of the whole airplane and the calculation results are provided, and an error tree is output to allow designers to trace back the error sources.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to an automatic processing method for aircraft quality characteristic data.
Background
At present, when the quality characteristic data of the airplane is processed domestically, the assembly hierarchical relation among all parts is mainly checked and combed manually by engineers, and the quality characteristic data of each hierarchy is calculated by adopting a simple Excel template, so that time and labor are consumed when the huge quality characteristic data containing errors provided by each specialty are processed, the accuracy of a calculation result is difficult to guarantee, and a simple and efficient calculation method is lacked.
Disclosure of Invention
In order to solve the technical problem, the application provides an automatic processing method of aircraft quality characteristic data, which mainly comprises the following steps:
step S1, obtaining original aircraft quality characteristic data to be processed, wherein the original aircraft quality characteristic data comprises a plurality of 1-n-level data rows from top to bottom, each row at least comprises weight data, the weight data of the 1-level data row is approximately the sum of the weight data of a plurality of 2-level data rows below the 1-level data row, the weight data of any 2-level data row is approximately the sum of a plurality of 3-level data rows between the 2-level data row below the 2-level data row and the next 2-level data row, the weight data of any 3-level data row is approximately the sum of a plurality of 4-level data rows between the 3-level data row below the 3-level data row until the weight data of the n-1-level data row is approximately the sum of a plurality of n-level data rows between the n-1-level data row below the 3-level data row;
step S2, determining each level of data lines through a greedy algorithm, and marking each level of data lines according to a preset first marking rule;
step S3, calculating layer by layer from bottom to top by adopting a tower-building type quality characteristic calculation method, determining actual data of each level of data row, and outputting new airplane quality characteristic data;
and S4, performing data comparison on the original aircraft quality characteristic data and the new aircraft quality characteristic data, and outputting error data.
Preferably, step S1 is preceded by:
and S0, acquiring a constraint range of the aircraft quality characteristic, and verifying the original aircraft quality characteristic data according to the constraint range.
Preferably, each row of the aircraft mass characteristic data further comprises a center of gravity, a moment of inertia thereof, and a product of inertia.
Preferably, the step S2 further includes:
step S21, acquiring a weight error tolerance strict coefficient S and a gravity error range w;
step S22, acquiring the line number of the last line of non-hierarchical data as a starting line;
step S23, acquiring a row of non-layered data from bottom to top;
step S24, calculating an objective function value corresponding to the line of data and satisfying the gravity error range w:
step S25, traversing all data lines, determining an optimal solution, and defining the corresponding line number and the subsequent continuous k data lines as the subset of the ith line;
and step S26, repeating the method to layer the original airplane quality characteristic data.
Preferably, in step S2, the predetermined first marking rule includes colors, and data line data of different levels are marked with different colors.
Preferably, step S3 further includes marking, in the new aircraft quality characteristic data, data of the difference between the new aircraft quality characteristic data and the original aircraft quality characteristic data, which is greater than a threshold value, according to a second marking rule.
Preferably, the second marking rule is font bolding and/or tilting.
Preferably, step S4 further includes marking data exceeding the threshold value in the error data according to a third marking rule.
Preferably, the third marking rule is font bolding and/or tilting.
According to the method and the device, the quality characteristic data of each system and structure of the airplane can be quickly and accurately layered and sorted, then the quality characteristic data of the whole airplane is calculated, errors in the provided data are corrected, relative and absolute errors between the quality characteristic original data of each level of parts of the whole airplane and the calculation result are provided, and an error tree is output to allow designers to trace back error sources.
Drawings
Fig. 1 is a flow chart of an automatic processing method for aircraft quality characteristic data according to the present application.
FIG. 2 is a flowchart of the validation verification and automatic hierarchical combing of data according to the embodiment of FIG. 1 of the present application.
FIG. 3 is a flow chart of the automatic data calculation and error analysis of the embodiment of the present application shown in FIG. 1.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are implementations that are part of this application and not all implementations. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application, and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application. Embodiments of the present application will be described in detail below with reference to the drawings.
The application provides an automatic processing method for aircraft quality characteristic data. Aiming at the problem of layering arrangement of error aircraft quality characteristic data, an aircraft component assembly level representation method and an aircraft quality characteristic data layering quality evaluation standard are defined, a greedy algorithm is introduced into the aircraft quality characteristic data layering arrangement for the first time, and automation of the arrangement of the error aircraft quality characteristic data is realized; aiming at the problems of quality characteristic data calculation and error analysis of the aircraft parts, a tower-built quality characteristic data calculation method for calculating layer by layer from bottom to top and an error tree model error analysis method capable of comprehensively evaluating data error transmission and accumulation processes are developed, so that automation of quality characteristic data calculation, error analysis and error tracing of the aircraft parts is realized. The data processing method comprises the following steps as shown in the attached figures 1-3, and the specific steps are as follows:
step S1, obtaining original aircraft quality characteristic data to be processed, wherein the original aircraft quality characteristic data comprises a plurality of 1-n-level data rows from top to bottom, each row at least comprises weight data, the weight data of the 1-level data row is approximately the sum of the weight data of a plurality of 2-level data rows below the 1-level data row, the weight data of any 2-level data row is approximately the sum of a plurality of 3-level data rows between the 2-level data row below the 2-level data row and the next 2-level data row, the weight data of any 3-level data row is approximately the sum of a plurality of 4-level data rows between the 3-level data row below the 3-level data row until the weight data of the n-1-level data row is approximately the sum of a plurality of n-level data rows between the n-1-level data row below the 3-level data row;
step S2, determining each level of data lines through a greedy algorithm, and marking each level of data lines according to a preset first marking rule;
step S3, calculating layer by layer from bottom to top by adopting a tower-building type quality characteristic calculation method, determining actual data of each level of data row, and outputting new airplane quality characteristic data;
and step S4, comparing the original aircraft quality characteristic data with the new aircraft quality characteristic data, and outputting error data.
In some optional embodiments, step S1 is preceded by:
and S0, acquiring a constraint range of the aircraft quality characteristic, and verifying the original aircraft quality characteristic data according to the constraint range.
In the step, the validity of the aircraft quality characteristic data is checked, the aircraft boundary is set to be the foremost, the rearmost, the leftmost, the rightmost, the uppermost and the lowermost coordinates of the current model size, the left and right attributes of the current calculation component are set to be empty, the error data including zero weight, negative moment of inertia, boundary appearance of the center of gravity, reverse installation of the left and right components and the like are located through traversing data, and the error data are marked with red and thickened and then placed in the center of a screen. In an alternative embodiment, the method further comprises the step of manually changing the error data, and communicating the error data with the data providing unit and changing the error data.
In some alternative embodiments, each row of the aircraft mass characteristic data further comprises a center of gravity, a moment of inertia of itself, and a product of inertia, as shown in table 1.
TABLE 1 original aircraft quality characteristics data (part)
One of the purposes of the present application is to layer the upper table, which is processed into table 2 based on step S2.
TABLE 2 processed aircraft quality characteristic data sheet
Table 1 differs from table 2 in that in table 2 the PartOrAssem01 data lines are bolded, the PartOrAssem02, PartOrAssem07, PartOrAssem11, PartOrAssem15, PartOrAssem19, PartOrAssem23 data lines are underlined, thereby indicating that the PartOrAssem02, PartOrAssem07, PartOrAssem11, PartOrAssem15, PartOrAssem19, PartOrAssem23 data lines are a subset of the PartOrAssem01 data lines, the PartOrAssem04, PartOrAssem04, PartOrAssem05, PartOrAssem06 data lines are a subset of the PartOrAssem02 data lines, etc., which makes the data lines easier to trace back.
In an alternative embodiment, the predetermined first marking rules comprise colors, data lines of different levels are marked with different colors, and the effect is more pronounced with color marking table 2, for example, the entire line of data of PartOrAssem01 is marked pink, the entire line of data of PartOrAssem02, PartOrAssem07, PartOrAssem11, PartOrAssem15, PartOrAssem19, PartOrAssem23 is marked yellow, and the entire line of the other lines is marked green. The following table gives the first marking rules given in engineering practice.
TABLE 3 first labeling rules Table
| Data hierarchy | RGB value | Data hierarchy | RGBValue of |
| Level 1 | (255，153，18) | Stage 7 | (128，100，162) |
| Stage 2 | (255,255,0) | Stage 8 | (255,192,203) |
| Grade 3 | (30,144,255) | Grade 9 | (197,217,241) |
| 4 stage | (146,208,80) | Grade 10 | (238,236,225) |
| Grade 5 | (3,168,158) | Data has errors | (255,0,0) |
| Grade 6 | (255,102,255) |
In some alternative embodiments, step S2 further includes:
step 1: setting a strict coefficient s and a gravity error w;
step 2: acquiring the line number of the last line of non-hierarchical data;
step 3: acquiring a line of non-layered quality characteristic data from bottom to top;
step 4: calculating an objective function value Tar (i, k) corresponding to the line data and meeting the gravity center error;
step 5: judging whether one data traversal is finished, if so, continuing the next Step, otherwise, repeating Step 2;
step 6: acquiring a line number i corresponding to the optimal solution and the number k of subsets contained in the optimal solution;
step 7: defining the rows i +1 to i + k as a subset of the ith row;
step 8: and judging whether a termination condition is met, if so, continuing the next Step, otherwise, repeating Step1, wherein the termination condition is that layering of all data is completed or the current optimal solution is increased compared with the optimal solution obtained by traversing the data last time.
Step 9: and outputting a layering result according to an aircraft part assembly level representation method.
In Step4, automatically layering the aircraft mass characteristic data, setting the currently calculated gravity center error range w, and taking an objective function expression as follows:
wherein, i is the number of the currently traversed data line;
k is the number of elements contained in the current data line;
tar (i, k) is an objective function value when the ith row of data contains k elements;
s is a strict coefficient of the target function for tolerance of data errors, and the larger the value is, the stricter the data errors are;
m is the parent weight provided by the data providing unit;
u is the parent weight calculated by the program according to the summation of all the sub-primary elements;
n is the number of non-layered data lines;
and automatically combing all quality characteristic data assembly level relations of the airplane through a greedy algorithm, identifying the data assembly level through the cell ground color, identifying the subordination relation of the data position, and outputting a layering result.
In some optional embodiments, step S3 further includes, in the new aircraft quality characteristic data, marking the data of the new aircraft quality characteristic data, which has a difference value greater than a threshold value from the original aircraft quality characteristic data, according to a second marking rule. In some alternative embodiments, the second marking rule is font bolding and/or tilting.
Fig. 3 shows the specific steps of step S3, which are as follows:
step 1: c, traversing the data output in the step c to obtain all data without subsets;
step 2: acquiring a data level identifier to judge a data level according to an aircraft part assembly level representation method;
step 3: calculating upper-layer quality characteristic data from bottom to top in a tower building mode;
step 4: judging whether the top graph is calculated or not, if so, continuing the next Step, and otherwise, repeating Step 2;
step 5: and outputting a calculation result according to the tower structure.
The aircraft mass characteristic data obtained by step S3 is shown in table 4.
Table 4 aircraft mass characteristic data second label schematic.
In step S4, data error analysis is performed, the calculation results in step S3 are compared and analyzed, and an error tree is output according to the assembly hierarchical relationship of the components and used for error analysis and error tracing. The error tree model is a tree-shaped visual error analysis and tracing model containing absolute values and relative values of errors of each link in the calculation process and an error transfer relationship, and in some optional embodiments, errors are marked by adopting a third marking rule, such as font thickening and/or inclination. As shown in table 5.
TABLE 5 third label schematic of error data
The application provides a method for representing the assembly level of airplane parts, which lays a foundation for a computer to automatically process quality characteristic data; the application provides a layering quality evaluation method for aircraft quality characteristic data, and evaluation standards are provided for automatically combing the quality characteristic data by a computer; the automation of component assembly hierarchical relation combing and the automation of quality characteristic data calculation are realized, and the working efficiency and the calculation precision are greatly improved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. An automatic processing method for aircraft quality characteristic data is characterized by comprising the following steps:
step S1, obtaining original aircraft quality characteristic data to be processed, wherein the original aircraft quality characteristic data comprises a plurality of 1-n-level data rows from top to bottom, each row at least comprises weight data, the weight data of the 1-level data row is approximately the sum of the weight data of a plurality of 2-level data rows below the 1-level data row, the weight data of any 2-level data row is approximately the sum of a plurality of 3-level data rows between the 2-level data row below the 2-level data row and the next 2-level data row, the weight data of any 3-level data row is approximately the sum of a plurality of 4-level data rows between the 3-level data row below the 3-level data row until the weight data of the n-1-level data row is approximately the sum of a plurality of n-level data rows between the n-1-level data row below the 3-level data row;
step S2, determining each level of data lines through a greedy algorithm, and marking each level of data lines according to a preset first marking rule;
step S3, calculating layer by layer from bottom to top by adopting a tower-building type quality characteristic calculation method, determining actual data of each level of data row, and outputting new airplane quality characteristic data;
step S4, comparing the original aircraft quality characteristic data with the new aircraft quality characteristic data, and outputting error data;
wherein, the step S2 further includes:
step S21, acquiring a weight error tolerance strict coefficient S and a gravity error range w;
step S22, acquiring the line number of the last line of non-hierarchical data as a starting line;
step S23, acquiring a row of non-layered data from bottom to top;
step S24, calculating an objective function value corresponding to the line of data and satisfying the gravity error range w:
step S25, traversing all data lines, determining an optimal solution, and defining the corresponding line number and the subsequent continuous k data lines as the subset of the ith line;
and step S26, repeating the method to layer the original airplane quality characteristic data.
2. The method for automatically processing aircraft quality characteristic data according to claim 1, wherein step S1 is preceded by the further step of:
and S0, acquiring a constraint range of the aircraft quality characteristic, and verifying the original aircraft quality characteristic data according to the constraint range.
3. The method of automatically processing aircraft mass characteristic data according to claim 1, wherein each row of the aircraft mass characteristic data further comprises a center of gravity, a moment of inertia of itself, and a product of inertia.
4. The method of automatically processing aircraft quality characteristic data according to claim 1, wherein in step S2, the predetermined first marking rule includes colors, and data line data of different levels are marked with different colors.
5. The method as claimed in claim 1, wherein step S3 further includes marking, in the new aircraft quality characteristic data, data having a difference greater than a threshold value between the new aircraft quality characteristic data and the original aircraft quality characteristic data according to a second marking rule.
6. The method of automatically processing aircraft quality attribute data of claim 5 wherein the second marking rule is font bolding and/or tilting.
7. The method as claimed in claim 1, wherein step S4 further comprises labeling the error data, which exceeds the threshold value, according to a third labeling rule.
8. The method of automatically processing aircraft quality attribute data of claim 1 wherein the third marking rule is font bolding and/or tilting.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105468851A (en) * | 2015-11-26 | 2016-04-06 | 中国航空工业集团公司沈阳飞机设计研究所 | Method for determining aircraft dynamic weight characteristic |
| CN105488267A (en) * | 2015-11-26 | 2016-04-13 | 中国航空工业集团公司沈阳飞机设计研究所 | Aircraft fuel weight processing method |
| CN107589668A (en) * | 2017-08-31 | 2018-01-16 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of vertically taking off and landing flyer mass property measurement method of parameters |
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| US6633259B1 (en) * | 1999-03-05 | 2003-10-14 | Rannuch Corporation | Method and apparatus for improving utility of automatic dependent surveillance |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105468851A (en) * | 2015-11-26 | 2016-04-06 | 中国航空工业集团公司沈阳飞机设计研究所 | Method for determining aircraft dynamic weight characteristic |
| CN105488267A (en) * | 2015-11-26 | 2016-04-13 | 中国航空工业集团公司沈阳飞机设计研究所 | Aircraft fuel weight processing method |
| CN107589668A (en) * | 2017-08-31 | 2018-01-16 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of vertically taking off and landing flyer mass property measurement method of parameters |
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