A data compression method and system for aircraft onboard data

Abstract

The present invention proposes a data compression method for aircraft onboard data, the method comprising performing principal component analysis on original signal data to select a plurality of principal components that together constitute a significant proportion, screening signals based on the loads of signals acquired within a time period on the selected principal components and the ranking of these signals in an importance table to distinguish between data that must be stored and data that are not required to be stored, performing status monitoring on the data that are not required to be stored, reclassifying data that triggers an event as data that must be stored and maintaining data that does not trigger an event as data that is not required to be stored, and performing lossless compression on the data that must be stored and lossy compression on the data that are not required to be stored.

Description

Technical Field

[0001] The present invention relates to the field of on-board data compression. Background Art

[0002] In an aircraft's onboard maintenance system, the aircraft health management unit (AHMU) needs to capture a large amount of maintenance data from various member systems. This data is essential for the AHMU to accurately assess the aircraft's health. However, storing all this maintenance data would consume significant onboard storage resources.

[0003] In the prior art, the following airborne data compression methods are generally used: (1) Data compression is performed in a unified manner according to the data type. This method may cause some important data to be compressed in an inappropriate manner, resulting in data loss; (2) Data preprocessing is performed before compression. This method cannot guarantee the complete retention of important data and there is a risk of important data loss; (3) The calculation method of principal component analysis is used and iterative calculation is performed. This method does not further process the volatility of the data in the time series. The data compression ratio has certain defects, and the fluctuation abnormal information of the reduced-dimensional data will be lost, which will have a certain impact on the reliability of data compression.

[0004] Therefore, there is a need for a method that can improve the deficiencies in the prior art. Summary of the Invention

[0005] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In view of the defects of the prior art, the present invention proposes a data compression method for aircraft onboard data, which can achieve reliable compression of onboard data and reasonably allocate onboard storage space.

[0007] Specifically, the present invention provides a data compression method for aircraft onboard data. The method includes performing principal component analysis on raw signal data to select several principal components that collectively constitute a significant proportion, screening signals acquired within a time period based on their loadings on the selected principal components and their rankings in an importance table to distinguish between essential and non-essential data, monitoring the status of the non-essential data, reclassifying data that triggers an event as essential data and maintaining data that does not trigger an event as non-essential data, and performing lossless compression on the essential data and lossy compression on the non-essential data.

[0008] In one embodiment of the present invention, a data compression method for aircraft onboard data is provided, the method comprising:

[0009] Performing principal component analysis on the original signal data of the collected signal to determine the selected principal component and obtain dimension-reduced signal data of the signal;

[0010] determining the loading of the signal on the selected principal component;

[0011] determining an importance ranking of the signal;

[0012] classifying the dimension-reduced signal data of the signal into necessary storage data and non-necessary storage data based on the load and the importance ranking of the signal;

[0013] Performing status monitoring on the non-essentially stored data;

[0014] reclassifying the non-essential storage data that triggers an abnormal event as the essential storage data based on the status monitoring; and

[0015] The necessary storage data is losslessly compressed and the non-essential storage data is losslessly compressed to facilitate data storage.

[0016] In one embodiment of the present invention, the cumulative contribution rate of the selected principal components reaches a threshold ratio.

[0017] In one embodiment of the present invention, the load is the correlation coefficient between the original variable of the signal and the selected principal component.

[0018] In one embodiment of the present invention, the importance ranking is determined by a signal importance query table, and the signal importance query table is constructed based on a system related to the signal.

[0019] In this embodiment of the present invention, the dimension-reduced signal data of the threshold number of signals with the highest ranking in the signal importance query table are classified as the necessary storage data, and the dimension-reduced signal data of the remaining signals are classified as the non-essential storage data.

[0020] In this embodiment of the present invention, classifying the reduced-dimensionality signal data of the signal into necessary storage data and non-necessary storage data based on the load and the importance ranking of the signal includes querying the signal importance query table for signals whose load proportion on the selected principal component reaches a threshold value to determine whether the reduced-dimensionality signal data is classified as the necessary storage data or the non-necessary storage data.

[0021] In one embodiment of the present invention, the state monitoring includes monitoring state quantities of the signal, and the state quantities include peak value, peak-to-peak value, average value and variance of the signal.

[0022] In one embodiment of the present invention, the abnormal event includes an over-limit event.

[0023] In one embodiment of the present invention, the lossless compression includes Huffman compression, and the lossy compression includes downsampling compression.

[0024] In another embodiment of the present invention, a data compression system for aircraft onboard data is provided, the system comprising:

[0025] The principal component analysis module is configured to:

[0026] Performing principal component analysis on the original signal data of the collected signal to determine the selected principal component and obtain dimension-reduced signal data of the signal; and

[0027] determining the loading of the signal on the selected principal component;

[0028] A signal filtering module configured to:

[0029] determining an importance ranking of the signal;

[0030] classifying the dimension-reduced signal data of the signal into necessary storage data and non-necessary storage data based on the load and the importance ranking of the signal;

[0031] Performing status monitoring on the non-essentially stored data; and

[0032] reclassifying the non-essential storage data that triggers an abnormal event as the essential storage data based on the status monitoring; and

[0033] The data compression and storage module is configured to perform lossless compression on the necessary storage data and lossy compression on the non-essential storage data to facilitate data storage.

[0034] In one embodiment of the present invention, the principal component analysis module is further configured to use the principal component whose cumulative contribution rate reaches a threshold ratio as the selected principal component.

[0035] In one embodiment of the present invention, the signal screening module is further configured to construct a signal importance query table based on a system related to the signal and determine the importance ranking by using the signal importance query table.

[0036] In this embodiment of the present invention, the signal screening module is further configured to classify the reduced-dimensionality signal data of the highest-ranked threshold number of signals in the signal importance query table as the necessary storage data, and classify the reduced-dimensionality signal data of the remaining signals as the non-necessary storage data.

[0037] In this embodiment of the present invention, the signal screening module is further configured to query the signal importance query table for signals whose load ratio on the selected principal component reaches a threshold value to determine whether its dimensionality reduction signal data is classified as the necessary storage data or the non-necessary storage data.

[0038] In one embodiment of the present invention, the signal screening module is further configured to perform the state monitoring by monitoring state quantities of the signal, wherein the state quantities include the peak value, peak-to-peak value, average value and variance of the signal.

[0039] In yet another embodiment of the present invention, a computer-readable medium storing computer-executable instructions is provided, the instructions comprising:

[0040] Instructions for performing principal component analysis on the original signal data of the collected signal to determine the selected principal component and obtain dimensionality-reduced signal data of the signal;

[0041] instructions for determining a loading of the signal on the selected principal component;

[0042] instructions for determining an importance ranking of the signal;

[0043] instructions for classifying the dimension-reduced signal data of the signal into data that must be stored and data that is not necessary to store based on the load and the importance ranking of the signal;

[0044] An instruction for monitoring the status of the non-essentially stored data;

[0045] instructions for reclassifying the non-essential storage data that triggers an abnormal event as the essential storage data based on the status monitoring; and

[0046] Instructions for performing lossless compression on the data that must be stored and lossy compression on the data that is not necessary to be stored to facilitate data storage.

[0047] After studying the description of specific exemplary embodiments of the present invention below in conjunction with the accompanying drawings, other aspects, features and embodiments of the present invention will be apparent to those of ordinary skill in the art. Although features of the present invention may be discussed below with respect to certain embodiments and drawings, all embodiments of the present invention may include one or more of the advantageous features discussed herein. In other words, although one or more embodiments may be discussed as having certain advantageous features, one or more of such features may also be used according to the various embodiments of the present invention discussed herein. In a similar manner, although exemplary embodiments may be discussed below as device, system or method embodiments, it should be appreciated that such exemplary embodiments may be implemented in various devices, systems, and methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order that the manner in which the above-recited features of the present disclosure may be understood in detail, a more particular description of the content briefly summarized above may be obtained by reference to various aspects, some of which are illustrated in the accompanying drawings. It should be noted, however, that the drawings illustrate only certain typical aspects of the disclosure and are therefore not to be considered limiting of its scope, as the description may admit to other equally effective aspects.

[0049] Figure 1 FIG2 is a schematic block diagram of a data compression system for aircraft onboard data according to an embodiment of the present disclosure.

[0050] Figure 2 A schematic block diagram of a signal screening module according to an embodiment of the present disclosure is shown.

[0051] Figure 3 A logic diagram of a data compression method for aircraft onboard data according to one embodiment of the present disclosure is shown.

[0052] Figure 4 A flow chart of a data compression method for aircraft onboard data according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0053] The various embodiments will be described in more detail below with reference to the accompanying drawings that form a part of the present invention and illustrate specific exemplary embodiments. However, the embodiments may be implemented in many different forms and should not be construed as limiting the embodiments set forth herein; rather, these embodiments are provided so that this disclosure is thorough and complete and the scope of these embodiments is fully conveyed to those skilled in the art. The embodiments may be implemented as a method, system, or device. Accordingly, the embodiments may be implemented in hardware, in full software, or in a combination of software and hardware. Therefore, the following detailed description is not intended to be limiting.

[0054] The steps in each flowchart may be performed by hardware (e.g., a processor, an engine, a memory, a circuit), software (e.g., an operating system, an application, a driver, a machine / processor executable instruction), or a combination thereof. As will be understood by those skilled in the art, the methods involved in each embodiment may include more or fewer steps than shown.

[0055] To address the shortcomings of the existing technology, a data compression method for aircraft onboard data will determine a signal importance lookup table based on the system from which the signal is collected, combine it with principal component analysis that can fully utilize the data characteristics and status monitoring based on signal eigenvalues ​​(peak value, peak-to-peak value, average value and variance) to form accurate data classification. At the same time, combined with a hybrid compression method of lossy and lossless compression, the compression ratio of the onboard data is dynamically allocated, and the onboard data is reasonably stored, thereby ensuring subsequent data analysis.

[0056] Various aspects of the present disclosure will be described in detail below through block diagrams, data flow diagrams, and method flow diagrams.

[0057] Figure 1 FIG. 1 is a schematic block diagram of a data compression system 100 for aircraft onboard data according to an embodiment of the present disclosure.

[0058] like Figure 1 As shown, the data compression system 100 includes a principal component analysis module 102, a signal screening module 104, and a data compression and storage module 106. Each module in the data compression system 100 will be described in detail below.

[0059] The principal component analysis module 102 is configured to perform principal component analysis on the original signal data of the collected signals to determine the selected principal components and obtain dimension-reduced signal data of the signals, and determine the loads of the signals on the selected principal components.

[0060] Specifically, the principal component analysis module 102 is configured to use the principal component analysis method to reduce the dimensionality of the original signal data of the airborne signal to achieve preliminary compression of the data, and obtain the load of each signal on each principal component after the dimensionality reduction. In one embodiment of the present invention, with respect to the principal component, a set of variables that may have correlations is converted into a set of linearly uncorrelated variables through orthogonal transformation using the principal component analysis method. This set of linearly uncorrelated variables after conversion is called the principal component. Since the principal component analysis method is well known in the art, it will not be described in detail in this specification. In one embodiment of the present invention, with respect to the load, it refers to the correlation coefficient between the principal component obtained by the principal component analysis method and the original variable of the collected signal. The load shows the correlation between the original variables to a certain extent. As can be understood by those skilled in the art, in other embodiments of the present invention, any suitable principal component analysis method can be used to reduce the dimensionality of the original signal data, and any other suitable method can also be used to express the correlation between the determined principal component and the original variable of the signal.

[0061] In one embodiment of the present invention, as an example and not a limitation, taking the measured signals of a certain flight as an example, a total of 576 signals were collected by the entire aircraft, and the system storage interval was 1 second (as an example and not a limitation, any other suitable storage interval time, such as 0.5 seconds, 1.5 seconds, 2 seconds, etc., can also be selected). Therefore, 1 second of data is selected for principal component analysis, and the contribution rate (proportion) of each principal component obtained is shown in the following Table 1.

[0062]

[0063]

[0064] Table 1 Principal component contribution rate

[0065] In one embodiment of the present invention, as an example and not a limitation, the cumulative contribution rate of the top ten principal components in terms of contribution rate exceeds 85%, and the influence of the following principal components on the variance contribution rate is not significant, so the top ten principal components can be directly used as the basis for subsequent result analysis. In other embodiments of the present invention, other cumulative contribution rate thresholds (such as 75%, 80%, 90%, 95%, etc.) can also be used, and the principal components constituting the cumulative threshold contribution rate are determined as the selected principal components (for example, if the first 15 principal components constitute 90% of the cumulative contribution rate, then these 15 principal components are determined as the principal components to be selected).

[0066] After determining which principal components to select (such as the top ten principal components in terms of contribution in the above example), the loads of all collected signals (such as the 576 signals in the above example) on each of the selected principal components are determined. For example, based on the principal component analysis results in the above example, the analysis continues to determine the loads of each signal on principal component 1. Table 2 below shows the load distribution of the signals on principal component 1. Signals 3 and 572 have the highest load proportions on principal component 1.

[0067] signal name Loading ratio on the principal component signal name Loading ratio on the principal component Signal 1 0 ...... Signal 2 0 Signal 572 0.242 Signal 3 0.256 Signal 573 0 Signal 4 0.002 Signal 574 0 Signal 5 0 Signal 575 0.038 Signal 6 0 Signal 576 0

[0068] Table 2 Signal load distribution on principal component 1

[0069] According to the above principal component 1, the signal load analysis for all the selected principal components is completed. As an example and not a limitation, in the above example, the load ratio analysis of the signal 1-576 on the principal component 1-10 is completed.

[0070] The signal screening module 104 is configured to classify the reduced-dimensional signal data of the signal into necessary storage data and non-necessary storage data based on the signal's load and importance ranking on the selected principal component and whether the signal triggers an abnormal event.

[0071] Specifically, the signal screening module 104 determines the importance ranking of the signals based on the constructed signal importance query table. The signal data of the higher-ranked signals after dimensionality reduction (through principal component analysis) can be classified as necessary storage data. In addition, signals with a higher load ratio on the selected principal component can also be considered as potentially important signals (importance is at the necessary storage level). These signals need to be queried one by one in the above-mentioned signal importance query table to determine whether they should be classified as necessary storage data. In addition, if some non-essential storage data triggers an abnormal event, these non-essential storage data will also be reclassified as necessary storage data.

[0072] Further details of the signal filtering module 104 will be described below in conjunction with Figure 2 Describe in more detail.

[0073] The data compression and storage module 106 is configured to perform lossless compression on data that must be stored and to perform lossy compression on data that is not necessary to be stored in order to facilitate data storage.

[0074] Specifically, lossless compression can be Huffman compression or any other suitable lossless compression method, and lossy compression can be frequency reduction compression (such as setting a lower data acquisition frequency for the acquisition function of the onboard maintenance system to reduce the frequency, and the stored data is also frequency-reduced compression) or any other suitable lossy compression method. In various embodiments of the present invention, "must store" means that the signal data must be compressed in a lossless manner to ensure that all data components are completely stored, and "non-must store" means that the signal data can be compressed in a lossy manner. Although it cannot guarantee that all data components are completely stored, it can greatly improve the compression ratio and reduce storage space consumption, so that the onboard storage space can be allocated more flexibly to achieve dynamic allocation of storage space.

[0075] Figure 2 FIG2 shows a schematic block diagram of a signal screening module 200 according to an embodiment of the present disclosure.

[0076] like Figure 2 As shown, the signal screening module 200 includes a signal importance determination component 202 , a signal classification component 204 , and a status monitoring component 206 .

[0077] Signal importance determination component 202 is configured to construct a signal importance query table and determine signal importance rankings / levels based on the table. For aircraft safety reasons, signals representing critical aircraft states and attributes are assigned different importance levels than redundant signals and signals representing less important states and attributes. The signal importance query table can be determined by the onboard health management system through prior research and based on the systems associated with each signal, thereby ranking the importance of each collected signal.

[0078] In another embodiment of the present invention, in addition to pre-research and confirmation with other systems by the onboard maintenance system designer, signal importance can also be positively correlated with the development assurance level of each aircraft system. By way of example and not limitation, development assurance levels are ABCDE. Level A systems store all data, Level B 90%, Level C 80%, and Levels D and E are provided by the system itself, with no more than 20% of storage allocated. Based on this data, the onboard maintenance system can pre-assess its storage requirements and then determine the appropriate data collection and compression methods.

[0079] In the present invention, the highest-ranked threshold number of signals can be considered a mandatory storage level. This threshold number is not limited to a specific number and can be set by those skilled in the art. Furthermore, the onboard health management system needs to support real-time updates of this table and pre-load it into data processing to assist in signal screening. Thus, the raw signals of each load on the principal component obtained by principal component analysis can be compressed (lossless compression / lossy compression) according to their importance.

[0080] The signal classification component 204 is configured to classify the signal into data that must be stored and data that does not need to be stored based on the load of the collected signal on the selected principal component and the importance level of the signal. As an example and not a limitation, taking the above Table 2 as an example, the load ratio of signal 3 and signal 572 on principal component 1 is the highest, which means that signal 3 and signal 572 are of great importance to principal component 1. It is necessary to query the signal importance of signal 3 and signal 572 in the signal importance query table. If the result shows that signal 3 is at the "must be stored" level (ranked higher in the query table), the lossless compression process is directly entered, that is, the signal data of signal 3 is classified as data that must be stored; and signal 572 is at the "not necessary to store" level (not ranked high in the query table), the signal data of the signal is classified as data that does not need to be stored.

[0081] In one embodiment of the present invention, for each principal component analyzed, signals with a load ratio higher than a threshold ratio on the principal component (as an example and not a limitation, signals with a load ratio higher than 10% are selected) can be selected for query in the signal importance query table. The threshold can be set by those skilled in the art as needed, and if the threshold is higher than a specific value (such as 85%), the signal is directly classified as data that must be stored without going through the signal importance query table. In another embodiment of the present invention, for each principal component analyzed, the top several signals that constitute the cumulative threshold load ratio on the principal component can also be selected (as an example and not a limitation, the top several signals that constitute the cumulative ratio of 80% can be selected (for example, if the top twenty signals ranked from largest to smallest in load ratio constitute a cumulative load ratio of 80%, then these twenty signals are selected for query in the signal importance query table)). In another embodiment of the present invention, for each principal component analyzed, all signals with a non-zero load ratio on the principal component can also be selected. As those skilled in the art will appreciate, for each selected principal component, a signal for query in the signal importance query table may be selected in any suitable manner based on the load proportion without departing from the spirit and scope of the present invention.

[0082] Continuing with the above example, in another embodiment of the present invention, although signal 572 is classified as non-essential storage data, if the signal's load and signal importance are ranked high among all "non-essential storage" signals, then the signal will also enter the lossless compression process, that is, it will also be classified as essential storage data. In various embodiments of the present invention, the comprehensive calculation of signal load and signal importance can be performed in any suitable manner, without limitation to any specific manner. As an example and not limitation, for example, for all signals classified as non-essential storage data, they can be ranked according to the load ratio on the principal component currently analyzed, and then the ranking is added to their ranking in the signal importance query table. Finally, the signals with the smallest score before the threshold number (such as 5, etc.) will be classified as essential storage data, rather than the original non-essential storage data. As will be understood by those skilled in the art, the signal load and signal importance can also be comprehensively calculated in any other suitable manner without departing from the spirit and scope of the present invention.

[0083] In the example of Table 2 above, signals such as Signal 1 and Signal 2 whose load ratio on principal component 1 is zero need to be judged for lossy / lossless compression in other principal component analyses until all 576 signals are screened based on the signal loads on all selected principal components and the signal importance, and finally a list of signals that need to be compressed is obtained.

[0084] After the signals are classified as data that must be stored and data that does not need to be stored in the above manner, the aircraft status is continued to be detected, captured and recorded, and abnormal events that have an impact on aircraft safety are pre-set in the system. These events mainly include the over-limit, trend and other states of the aircraft system. Specifically, the state quantity of the signal classified as data that does not need to be stored is monitored by the state monitoring component 206 to detect whether an abnormal event is triggered. The monitored state quantity includes the peak value, peak-to-peak value, average value and variance of the signal. This makes it possible to take into account the volatility of the signal data in the time series when making a lossy / lossless compression judgment. In the example of Table 2 above, the data within the 1-second interval (storage interval) of the signal 572 classified as data that does not need to be stored is state-monitored. When there is no abnormality in its state monitoring, lossy compression is continued to be selected. When an abnormal event occurs, the signal is reclassified as data that must be stored, that is, lossless compression is selected.

[0085] Figure 3 A logic diagram of a data compression method for aircraft onboard data according to one embodiment of the present disclosure is shown.

[0086] like Figure 3As shown, in the data compression logic 300, the original signal data of the signal is first subjected to principal component analysis to determine the selected principal components and the signal load proportions on these principal components. Figure 3 ), when the load ratio of a signal on a principal component of the current analysis is higher than the threshold ratio (such as Figure 3 When the signal weight exceeds 85%, which is only for example and not for limitation, the dimensionality reduction signal data of the signal is directly classified as the data that must be stored and losslessly compressed. For the signal data of the signal that does not exceed the threshold weight, the signal weight is combined with the signal importance query table (i.e. Figure 3 The importance table in the table is used to classify the signal data of these signals into necessary storage data and non-necessary storage data. The specific classification method is as described above. Figure 2 Descriptive.

[0087] Then, the signal data of the signals classified as non-essential storage data within the current storage interval is continuously monitored to detect whether an abnormal event has been triggered. If an abnormal event has been triggered, the signal that triggered the abnormal event is reclassified as essential storage data and losslessly compressed. The signal that did not trigger the abnormal event remains classified as non-essential storage data and losslessly compressed. Finally, the compressed data is stored to implement dynamic storage space allocation.

[0088] Figure 4 A flow chart of a data compression method 400 for aircraft onboard data according to an embodiment of the present disclosure is shown.

[0089] Method 400 begins at step 402, where principal component analysis is performed on the original signal data of the collected signal to determine the selected principal components and obtain the reduced-dimensional signal data of the signal. In one embodiment of the present invention, the cumulative contribution rate of the selected principal components reaches a threshold ratio.

[0090] In step 404, the load of the signal on the selected principal component is determined. In one embodiment of the present invention, the load is the correlation coefficient between the original variable of the signal and the selected principal component.

[0091] In step 406, the importance ranking of the signal is determined. In one embodiment of the present invention, the importance ranking is determined by a signal importance query table, and the signal importance query table is constructed based on a system related to the signal.

[0092] In step 408, the reduced-dimensionality signal data of the signal is classified into data that must be stored and data that does not need to be stored based on the signal's load and importance ranking. In one embodiment of the present invention, the reduced-dimensionality signal data of the signal with the highest threshold number of rankings in the signal importance query table are classified as data that must be stored, and the reduced-dimensionality signal data of the remaining signals are classified as data that does not need to be stored. In this embodiment of the present invention, classifying the reduced-dimensionality signal data of the signal into data that must be stored and data that does not need to be stored based on the signal's load and importance ranking includes querying the signal importance query table for signals whose load ratio on the selected principal component reaches a threshold (in other embodiments of the present invention, it may also be a signal whose load ratio is not zero, or a number of signals whose cumulative load ratio reaches a threshold, or any other suitable method of selecting signals for query in the signal importance query table based on the load ratio) to determine whether its reduced-dimensionality signal data is classified as data that must be stored or data that does not need to be stored (determining whether it is a data that must be stored or a data that does not need to be stored based on the importance ranking in the signal importance query table).

[0093] In step 410, status monitoring is performed on the non-essentially stored data. In one embodiment of the present invention, the status monitoring includes monitoring status quantities of the signal, and the status quantities include peak value, peak-to-peak value, average value and variance of the signal.

[0094] In step 412, the non-essential storage data that triggers the abnormal event is reclassified as essential storage data based on the status monitoring. In one embodiment of the present invention, the abnormal event includes an over-limit event.

[0095] In step 414, the data that must be stored is losslessly compressed and the data that is not necessary to be stored is lossily compressed to facilitate data storage. In one embodiment of the present invention, the lossless compression includes Huffman compression, and the lossy compression includes downsampling compression.

[0096] In summary, the present invention provides a data compression method and system for aircraft onboard data. A signal importance lookup table is determined based on the system from which the signal is collected. This is combined with principal component analysis that can fully utilize the data characteristics and status monitoring based on signal characteristic values ​​(peak value, peak-to-peak value, average value and variance) to form accurate data classification. The volatility of non-essentially stored signals is taken into account through continuous status monitoring. At the same time, a hybrid compression method of lossy and lossless compression is combined to dynamically allocate the compression ratio of the onboard data and reasonably store the onboard data, thereby ensuring subsequent data analysis.

[0097] Embodiments of the present invention have been described above with reference to the block diagrams and / or operational descriptions of methods, systems, and computer program products according to embodiments of the present invention. The functions / actions indicated in the blocks may appear in an order different from that shown in any flow chart. For example, depending on the functions / actions involved, two blocks shown in succession may actually be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order.

[0098] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A data compression method for aircraft onboard data, the method comprising: Performing principal component analysis on the original signal data of the collected signal to determine the selected principal component and obtain dimension-reduced signal data of the signal; determining the loading of the signal on the selected principal component; Determining an importance ranking of the signal, wherein the importance ranking is determined by a signal importance lookup table constructed based on a system related to the signal; Classifying the dimension-reduced signal data of the signal into necessary storage data and non-essential storage data based on the load and the importance ranking of the signal includes: Classifying the dimension-reduced signal data of a threshold number of signals with the highest rankings in the signal importance query table as the data that must be stored and classifying the dimension-reduced signal data of the remaining signals as the data that does not need to be stored; and Searching the signal importance query table for signals whose load ratio on the selected principal component reaches a threshold value to determine whether the reduced-dimensionality signal data thereof is classified as the necessary storage data or the non-necessary storage data; Performing status monitoring on the non-essentially stored data; reclassifying the non-essential storage data that triggers an abnormal event as the essential storage data based on the status monitoring; and The necessary storage data is losslessly compressed and the non-essential storage data is losslessly compressed to facilitate data storage.

2. The method of claim 1, wherein the cumulative contribution rate of the selected principal components reaches a threshold ratio. 3 . The method of claim 1 , wherein the loading is a correlation coefficient between the original variable of the signal and the selected principal component. 4 . The method according to claim 1 , wherein the state monitoring comprises monitoring state quantities of the signal, the state quantities comprising peak value, peak-to-peak value, average value and variance of the signal. The method of claim 1 , wherein the abnormal event comprises an out-of-limit event.

6. The method of claim 1, wherein the lossless compression comprises Huffman compression and the lossy compression comprises downsampling compression.

7. A data compression system for aircraft onboard data, the system comprising: The principal component analysis module is configured to: Performing principal component analysis on the original signal data of the collected signal to determine the selected principal component and obtain dimension-reduced signal data of the signal; and determining the loading of the signal on the selected principal component; A signal filtering module configured to: Determining the importance ranking of the signal, including constructing a signal importance query table based on a system related to the signal and determining the importance ranking using the signal importance query table; classifying the dimension-reduced signal data of the signal into necessary storage data and non-necessary storage data based on the load and the importance ranking of the signal, wherein the signal screening module is further configured to: Classifying the dimension-reduced signal data of a threshold number of signals with the highest rankings in the signal importance query table as the data that must be stored and classifying the dimension-reduced signal data of the remaining signals as the data that does not need to be stored; and Searching the signal importance query table for signals whose load ratio on the selected principal component reaches a threshold value to determine whether the reduced-dimensionality signal data thereof is classified as the necessary storage data or the non-necessary storage data; Performing status monitoring on the non-essentially stored data; as well as reclassifying the non-essential storage data that triggers an abnormal event as the essential storage data based on the status monitoring; as well as The data compression and storage module is configured to perform lossless compression on the necessary storage data and lossy compression on the non-essential storage data to facilitate data storage.

8. The system of claim 7, wherein the principal component analysis module is further configured to use the principal component whose cumulative contribution rate reaches a threshold ratio as the selected principal component.

9. The system of claim 7, wherein the signal screening module is further configured to perform the state monitoring by monitoring state quantities of the signal, the state quantities comprising peak value, peak-to-peak value, average value and variance of the signal.

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