Full-automatic high-precision cross-system test data clock alignment method
An automated data clock alignment method addresses inefficiencies in cross-system data alignment by aligning parameters through discrete time sequences and optimal adjustments, enhancing precision and reducing costs in aircraft engine testing.
Patent Information
- Application Number
- CN202510394802.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-15
AI Technical Summary
The prior art has long operation time, high manual occupancy, low efficiency and human factors in the process of data alignment across systems, making it difficult to meet the high-precision needs of modern aero engine testing.
Using a fully automatic method, by determining the common test parameters of each system as a reference, a sequence of inflection points and characteristic time points is constructed, and a sequence of characteristic time points is calculated using the equivalent area conversion method, and combined with the optimal solution judgment, the time axis of each system is automatically corrected to achieve high-precision alignment.
It greatly improves computing speed and accuracy, reduces human resource usage, reduces costs, and is suitable for large-scale test data analysis to meet the needs of aircraft engine testing.
Smart Images
Figure CN120315535A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the technical field of electrical data processing, and particularly relates to a fully automatic and high-precision cross-system test data clock alignment method. Background Art
[0002] In the process of modern aero-engine testing, due to different requirements, numerous test parameters are usually stored in multiple test devices, resulting in differences in their sampling methods. In the processes of performance analysis, model troubleshooting, etc., it is often necessary to collect multiple parameters from different systems for comparative analysis. In this process, how to achieve clock alignment among multiple parameters will become the key to the accuracy of data analysis.
[0003] Currently, to achieve cross-system data alignment, generally an artificial alignment method is adopted, that is, taking a certain test parameter stored in each system as a reference parameter, and selecting a point with an obvious change in the value of this parameter as a reference point to manually align the data of each system.
[0004] The present invention proposes a fully automatic data alignment method, which is not only far higher than ordinary methods in terms of accuracy, but also can achieve great progress in terms of cost and efficiency compared with the current methods.
[0005] Although the existing technical solutions for cross-system data alignment are diverse in form, their basic operation cores are the same, that is, under the participation of humans, cross-system data alignment is performed through obvious features of common parameters. Its disadvantages are also obvious. This process not only takes a long time to operate and has a high occupancy rate of humans, resulting in a sharp increase in research and development costs; but also in the case of processing a large amount of test data, this solution is inefficient and has a high error rate, making it difficult to be popularized on a large scale. From another perspective, the addition of human factors also has more influence on the accuracy of data alignment, thus greatly increasing the uncertainty of data analysis. Summary of the Invention
[0006] To solve the above problems, this application provides a fully automatic and high-precision cross-system test data clock alignment method, including:
[0007] Step S1: Generate discrete time series for the test parameters measured by each system through different sampling methods, and use the test parameters common to each system as the reference parameters for aligning the time axis;
[0008] Step S2: Determine the inflection point sequence of the reference parameters of each system;
[0009] Step S3: Determine the characteristic time point sequence of the reference parameters of each system;
[0010] Step S4: Align each system's reference parameters one by one with the aligned inflection point sequence and characteristic time point sequence, and equally spaced points are taken at equal time intervals centered on the coincidence point T within the overlapping range of the time axes of each system's reference parameters, and multiple equidistant parameter value series are obtained on each system's reference parameters;
[0011] Step S5: Calculate the calculation difference sequence from the parameter value series obtained with the aligned inflection point sequence, and calculate the calculation characteristic difference sequence from the parameter value series obtained with the aligned characteristic time point sequence;
[0012] Step S6: Select the minimum values of the difference sequence and the characteristic difference sequence, and calculate the relative time difference combinations of each system under the corresponding time axis combination modes, which are the optimal solution 1 and the optimal solution 2 respectively;
[0013] Step S7: When the optimal solution 1 and the optimal solution 2 are the same, combine the two as the final solution; when the optimal solution 1 and the optimal solution 2 are different, calculate the equivalent time difference, and select the corresponding processing method according to the threshold to determine the final solution;
[0014] Step S8: Select the system time axis with the largest data volume or the highest time progress as the reference, and use the time difference sequence of the final solution to correct the time axes of other systems, so that each system is aligned with the time axis of the reference system.
[0015] Preferably, determining the inflection point sequence of each system's reference parameters includes the following sub-steps:
[0016] Fit the function curve of the reference parameter within the range of the preset window;
[0017] Perform first-order and second-order difference solutions on the fitted function curve;
[0018] Eliminate adjacent inflection points and poles to generate the inflection point sequence of each system's reference parameters.
[0019] Preferably, determining the characteristic time point sequence of each system's reference parameters includes the following sub-steps:
[0020] Based on the given characteristic parameter value, determine the normalization processing range of the reference parameter at the characteristic parameter value;
[0021] Perform normalization processing on the reference parameter values within the range;
[0022] Calculate the actual area through the equivalent area conversion algorithm, and generate the characteristic time point sequence of each system.
[0023] Preferably, the processing method in step S7 includes: when the equivalent time difference is less than the preset threshold, select the solution corresponding to the minimum value of the calculation difference or the characteristic difference as the final solution; when the equivalent time difference is greater than the preset threshold, re-select the characteristic parameter value or check the data consistency.
[0024] Preferably, the determination of the inflection point sequence includes: fitting a reference parameter curve within a window using a polynomial or spline function; solving for the positions of the inflection points by differentiation, and removing redundant inflection points based on an adjacent inflection point spacing threshold.
[0025] Preferably, the normalization formula is:
[0026] f(i) = F(i) - F0
[0027] f(i) is the normalization result of the i-th reference parameter, F(i) is the i-th reference parameter within the normalization range, and F0 is the characteristic parameter value.
[0028] Preferably, the actual area S includes an area S' where two adjacent points are on the same side of the characteristic parameter value F0 and an area S'' where they are on different sides, which are calculated respectively by the following formulas:
[0029]
[0030] Preferably, the calculation formula for the relative characteristic time point C is:
[0031]
[0032] Preferably, the calculation formula for the characteristic difference sequence is as follows:
[0033]
[0034] where N is the number of extended calculation points; X is the number of systems; and k is the parameter value sequence.
[0035] Preferably, the calculation formula for the equivalent time difference Δt is:
[0036]
[0037] where t i -t i ' is the difference between the relative time differences of the optimal solution 1 and the optimal solution 2.
[0038] The advantages of this application include: This application greatly liberates human resources, and both the calculation speed and efficiency are greatly improved. It not only saves a large amount of costs, but also eliminates the influence of human factors on the calculation accuracy as much as possible, improving the calculation precision. At the same time, the present invention can be applied to the analysis task of large-scale test data and can better meet the test requirements of modern aero-engines. Description of the Drawings
[0039] Figure 1 is a flowchart of a fully automatic and high-precision cross-system test data clock alignment method according to a preferred embodiment of this application.
[0040] Figure 2 These are the curve graphs of two reference parameters in a preferred embodiment of the present application. Specific Embodiment
[0041] To make the technical solutions and their advantages of the present application clearer, the technical solutions of the present application will be further described clearly and completely below in conjunction with the accompanying drawings. It can be understood that the specific embodiments described herein are only partial embodiments of the present application, which are only used to explain the present application and are not intended to limit the present application. It should be noted that for the convenience of description, only the parts related to the present application are shown in the drawings, and other related parts can refer to the normal design. Without conflict, the embodiments in the present application and the technical features in the embodiments can be combined with each other to obtain new embodiments.
[0042] In addition, it should also be noted that, unless otherwise clearly specified and limited, the similar terms such as "installation", "connection", and "coupling" used in the description of the present application should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can also be the communication inside two components. Those skilled in the art can understand their specific meanings in the present application according to the specific circumstances.
[0043] As Figure 1 - Figure 2 shown, the present invention proposes a fully automatic and high-precision cross-system test data clock alignment method to achieve the high-precision automatic clock alignment function of test data under different systems and different sampling methods.
[0044] Basic idea: Construct two main characteristic parameters through certain rules, and on the basis of the reference parameters (that is, the common test parameters measured by each system used to align the time axis, hereinafter referred to as reference parameters), calculate the eigenvalue sequences of different characteristic parameters. Through certain comparison rules, obtain the combination of eigenvalue sequences with the highest degree of coincidence, and calculate the time differences of each system under this combination. Finally, with the principle of minimizing the workload, taking the time series of the test parameters of a certain system as the reference, correct the time series of the parameters of other systems so that the time axes of the parameters measured by each system completely coincide.
[0045] To improve the accuracy of time alignment, the construction of the two main characteristic parameters needs to start from different perspectives. Among them, the parameter "inflection point" starts from the perspective of curve characteristics, and the parameter "characteristic point" starts from the perspective of numerical magnitude, and has a certain degree of randomness.
[0046] The basic principle block diagram is shown in the following figure. The specific design steps are as follows:
[0047] Step 1: Create a timeline. According to the different sampling methods of each test system, such as equidistant sampling, passive sampling, etc., create discrete time series of each parameter.
[0048] Step 2: Determine the inflection points. The key here is to determine the inflection points of the reference parameter in the form of discrete data (i.e., the common test parameter measured by each system used to align the timeline, hereinafter referred to as the reference parameter). Due to the discreteness and randomness of the test data, the effect of calculating the difference at the i-th point by the method of weighted summation is not very ideal. The present invention uses a new method. Within a window of 2k + 1, fit these 2k + 1 points into a function, and then perform first-order and second-order difference solutions on this function. After removing adjacent inflection points and extreme points, obtain the inflection point sequences R1, R2…R r 。
[0049] Step 3: Calculate the characteristic points. At a given characteristic parameter value (F0), determine the sequence of characteristic time points of the reference parameter of each system at the position of the given characteristic parameter value. Due to the certain fluctuation characteristics of the test data, especially the data of the dynamic test system, it often fluctuates multiple times above and below the characteristic speed before it can completely "cross" the characteristic parameter value. In order to accurately locate the time point of the reference curve at the characteristic parameter value, the general linear averaging method is obviously not applicable. The present invention uses an equivalent area conversion algorithm for calculation, and its basic calculation process is as follows.
[0050] a) Determine the calculation range: from the last point that is closest to but has not "crossed" the characteristic parameter value to the first point that has completely "crossed" the characteristic parameter value, with a total of n points, n = 1, 2, 3…
[0051] b) Normalize the reference parameter values within the range:
[0052] f(i) = F(i) - F0
[0053] c) Calculate the actual area (S), including S + and S - in two parts. The data points above the characteristic parameter value form S + , and the data points below the characteristic parameter value form S - . And in the process of calculating the actual area, it can be divided into two cases where two adjacent points are on the same side (S′) and different sides (S") of F0, and different formulas are used for calculation.
[0054]
[0055] d) Equivalent calculation to obtain the relative characteristic time points C1, C1…C c :
[0056]
[0057] Step 4: Expand calculation points. After the above work is completed, the next step is to align the inflection points and characteristic points of each system's reference parameters one by one with time, and then within the overlapping range U of the time axes of each system, take points at equal time intervals before and after with the coincidence point T0 as the center, so as to obtain a series of N equidistantly distributed parameter values k on each system's reference parameters.
[0058] Step 5: Calculate differences. For the calculation of characteristic differences, use the series of parameter values k1, k2, k3... obtained by aligning inflection points to calculate the difference sequences D1, D2... D r , and use the series of parameter values k′1, k′2, k′3... obtained by aligning characteristic points to calculate the characteristic difference sequences D′1, D′2... D′ c . The calculation formulas are as follows.
[0059]
[0060] In the formula, N is the number of expanded calculation points;
[0061] X is the number of systems;
[0062] k is the sequence of parameter values.
[0063] Step 6: Comparative judgment. Through the calculations in Step 5, a set of difference sequences and a set of characteristic difference sequences can be obtained. Select the minimum value of each sequence, and calculate the relative time difference combinations (t1, t2... t X-1 ) of each system under the corresponding time axis combination methods, which are the optimal solutions 1 and 2. What needs to be compared in this step is whether the optimal solutions 1 and 2 are the same. If the two are the same, then their combination is the actual optimal solution.
[0064] Step 7: Secondary judgment. If the optimal solutions 1 and 2 are different, then the actual optimal solution cannot be generated, and further calculations and judgments are required. Define a parameter: equivalent time difference Δt, which reflects the degree of difference between the two solutions. In this step, corresponding choices are made based on the degree of difference between the two solutions. The threshold selection of Δt is determined according to the average value and pulsation value of the reference parameters, and a practical and reasonable threshold range can only be formed after multiple experimental trainings.
[0065]
[0066] According to different values of Δt, the secondary judgment can trigger three different processing methods. When Δt is relatively small, it can be considered that the time axis correction values represented by the two solutions are the same, and the solution with the smaller mathematical value among the two solutions is selected as the final solution, that is, the comparison optimal solution; when Δt is very large, it indicates that the time correction targets constructed by the two main characteristic parameters are very different, and it is necessary to recheck the data and calculation process, etc.; when the value of Δt falls within a certain finite threshold, the difference between the two solutions may be due to data randomness or inappropriate selection of characteristic parameter values. After reselecting the characteristic parameter values, the optimal solution 2 can be recalculated for verification.
[0067] Step 8: Reconstruct the time axis. Select the system time axis with the largest data volume or the highest time progress as the reference, and use the time difference sequence of the final solution (the actual optimal solution or the comparison optimal solution) to correct the time axes of other systems, so that each system is aligned with the time axis of the reference system, creating conditions for subsequent data analysis.
[0068] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. A fully automatic and high-precision cross-system test data clock alignment method, characterized in that, Including: Step S1: Generate discrete time series from the test parameters measured by each system through different sampling methods, and use the test parameters common to each system as the reference parameters for aligning the time axes. Step S2: Determine the inflection point sequences of the reference parameters of each system. Step S3: Determine the characteristic time point sequences of the reference parameters of each system. Step S4: Align the reference parameters of each system one by one with the aligned inflection point sequences and characteristic time point sequences, and take points at equal time intervals centered on the coincidence point T0 within the overlapping range of the time axes of the reference parameters of each system, and obtain multiple equidistant parameter value series on the reference parameters of each system. Step S5: Calculate the calculation difference series from the parameter value series obtained by aligning the inflection point sequences, and calculate the calculation characteristic difference series from the parameter value series obtained by aligning the characteristic time point sequences. Step S6: Select the minimum values of the difference series and the characteristic difference series, and calculate the relative time difference combinations of each system under the corresponding time axis combination methods respectively, which are the optimal solution 1 and the optimal solution 2. Step S7: When the optimal solution 1 and the optimal solution 2 are the same, combine the two as the final solution. When the optimal solution 1 and the optimal solution 2 are different, calculate the equivalent time difference, and select the corresponding processing method according to the threshold to determine the final solution. Step S8: Select the system time axis with the largest data volume or the highest time progress as the reference, and use the time difference series of the final solution to correct the time axes of other systems, so that each system is aligned with the time axis of the reference system.
2. The fully automatic and high-precision cross-system test data clock alignment method according to claim 1, characterized in that, Determining the inflection point sequences of the reference parameters of each system includes the following sub-steps: Fit the function curve of the reference parameter within the range of the preset window. Perform first-order and second-order difference solutions on the fitted function curve. Eliminate the adjacent inflection points and poles to generate the inflection point sequences of the reference parameters of each system.
3. The fully automatic and high-precision cross-system test data clock alignment method according to claim 1, characterized in that Determining the characteristic time point sequences of the reference parameters of each system includes the following sub-steps: Based on the given characteristic parameter value, determine the normalization processing range of the reference parameter at the characteristic parameter value. Perform normalization processing on the reference parameter values within the range. Calculate the actual area through the equivalent area conversion algorithm, and generate the characteristic time point sequences of each system.
4. The fully automatic high-precision cross-system test data clock alignment method according to claim 1, characterized in that The processing methods described in Step S7 include: when the equivalent time difference is less than the preset threshold, select the solution corresponding to the minimum value of the calculation difference or the characteristic difference as the final solution; when the equivalent time difference is greater than the preset threshold, re-select the characteristic parameter value or check the data consistency.
5. The fully automatic and high-precision cross-system test data clock alignment method according to claim 1, characterized in that The determination of the inflection point sequence includes: fitting the reference parameter curve within the window using a polynomial or spline function; solving the position of the inflection point through difference, and eliminating redundant inflection points based on the adjacent inflection point spacing threshold.
6. The fully automatic and high-precision cross-system test data clock alignment method according to claim 1, characterized in that, The normalization processing formula is: f(i) = F(i) - F0 f(i) is the normalization result of the i-th reference parameter, F(i) is the i-th reference parameter within the normalization processing range, and F0 is the characteristic parameter value.
7. The fully automatic high-precision cross-system test data clock alignment method according to claim 6, wherein The actual area S includes the area S' on the same side of the characteristic parameter value F0 between two adjacent points and the area S" on the opposite side, and is calculated respectively through the following formulas:
8. The fully automatic and high-precision cross-system test data clock alignment method according to claim 7, characterized in that The calculation formula for the relative characteristic time point C is:
9. The fully automatic and high-precision cross-system test data clock alignment method according to claim 8, wherein The calculation formula for the characteristic difference series is as follows: Where, N is the number of extended calculation points; X is the number of systems; k is the parameter value sequence.
10. The fully automatic and high-precision cross-system test data clock alignment method according to claim 7, wherein, The calculation formula for the equivalent time difference Δt is as follows: Among them, t i -t i ′ is the difference between the relative time differences of the optimal solution 1 and the optimal solution 2.