CN120316329A - A QAR data storage and fast query method based on time drift - Google Patents
A QAR data storage and fast query method based on time driftInfo
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- CN120316329A CN120316329A CN202510768655.1A CN202510768655A CN120316329A CN 120316329 A CN120316329 A CN 120316329A CN 202510768655 A CN202510768655 A CN 202510768655A CN 120316329 A CN120316329 A CN 120316329A
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Abstract
本发明公开了一种基于时间漂移的QAR数据存储和快速查询方法，属于飞机制造技术领域，包括以下步骤：所述方法包括存储步骤和查询步骤两个流程,存储步骤如下：S101、计算QAR数据最大采样率,S102、填充原始时间字段,S103、对齐不同采样率的数据,S104、将数据写入时序数据库IotDB。自动化组装，数模自动化组装可以大大加快生产速度，相较于传统的手工组装，自动化组装可以在更短的时间内完成更多的组装任务，从而提高整体生产效率，持久化数据，通过持久化EBOM数据和飞机数模数据，确保数据的可追溯性和一致性，设定坐标系，设定坐标系提供了统一、精确的空间参考，有助于提高组装的准确性和一致性。
The present invention discloses a QAR data storage and fast query method based on time drift, which belongs to the aircraft manufacturing technology field, and comprises the following steps: the method comprises two processes, namely, a storage step and a query step, and the storage step is as follows: S101, calculating the maximum sampling rate of QAR data, S102, filling the original time field, S103, aligning data with different sampling rates, S104, writing the data into the time series database IotDB. Automated assembly, digital model automated assembly can greatly speed up the production speed. Compared with traditional manual assembly, automated assembly can complete more assembly tasks in a shorter time, thereby improving the overall production efficiency, persisting data, and ensuring the traceability and consistency of data by persisting EBOM data and aircraft digital model data, setting the coordinate system, and setting the coordinate system provides a unified and accurate spatial reference, which helps to improve the accuracy and consistency of assembly.
Description
Technical Field
The invention relates to the technical field of aircraft manufacturing, in particular to a QAR data storage and quick query method based on time drift.
Background
The EBOM data is a list used during the product design phase, which contains product design, assembly, and material information. It describes the engineering structure of the product, including the individual components, sub-components, and relationships between them. Such a list is very important for the manufacturing and assembly process of the product, in particular in the field of aircraft manufacturing.
In the upstream and downstream processes of aircraft manufacturing, the EBOM data typically includes various details of the design stage, such as the names, specifications, dimensions, materials, and associated design drawings of the components. This information is critical throughout the production process, from raw material procurement to assembly and final delivery.
The aircraft digital model is a model file generated during the product design phase, which contains digitized information about the three-dimensional model of the aircraft, and which contains the geometry, structure and other relevant information of the aircraft. In aircraft design and manufacture, these documents play a critical role in visual design, engineering analysis, virtual testing, and production.
With the continuous complexity of aircraft manufacturing processes, these complexities increase the compactness and interdependence between parts, manual assembly is difficult to meet high requirements for precision and integrity, which can lead to inaccurate alignment and assembly problems between components, and traditional manual assembly is difficult to flexibly cope with such highly customized requirements, which can require long time reconfiguration and adjustment, and significant labor costs. Therefore, there is a need for an efficient and accurate automated assembly method and system to improve production efficiency and ensure quality of aircraft digital assembly.
Disclosure of Invention
The invention aims to provide a QAR data storage and quick query method based on time drift. According to the QAR data storage and quick query method based on time drift, the QAR data storage and quick query method based on time drift is automatic, digital-analog automatic assembly can greatly accelerate production speed, compared with traditional manual assembly, automatic assembly can complete more assembly tasks in a shorter time, therefore, overall production efficiency is improved, data are durable, traceability and consistency of the data are ensured through the durable EBOM data and aircraft digital-analog data, a coordinate system is set, unified and accurate spatial reference is provided for the coordinate system, and the accuracy and consistency of assembly are improved.
In order to achieve the above effects, the invention provides a QAR data storage and quick query method based on time drift, which comprises the following steps:
the method comprises two flows of a storage step and a query step;
The storage steps are as follows:
s101, calculating the maximum sampling rate of QAR data;
s102, filling an original time field;
s103, aligning data with different sampling rates;
S104, writing data into a time sequence database IotDB;
the query steps are as follows:
s201, mapping original time and storage time;
S202, acquiring the number of data points of the maximum sampling rate query parameter;
s203, calculating an adaptive downsampling time interval;
S204, executing the query in the time sequence database IotDB.
Further, the step S101 needs to read metadata of all QAR data and calculate a maximum sampling rate by usingIndicating, in units of times/second;
n is the number of parameters, wherein Sample rates of the n parameters are shown in items 1, 2.
Further, the step S102 requires that the original time field is sampled a maximum number of timesFilling is carried out, and the number of time points after filling becomesAnd a time point, wherein m is the QAR data acquisition seconds, and the original time value is the data writing millisecond time.
Further, the original time of the QAR data is recorded to the second level only, the acquisition frequency is 1Hz, the partial parameter acquisition frequency is higher than 1Hz, namely a plurality of sampling points per second, the QAR data is filled in sequence from the left to the right, and the sampling times per second of the QAR data are as followsThe time field will follow 1 secondFilling is performed, and k is the kth sampling point in 1 second.
Further, the step S103 requires the QAR parameter to be sampled a maximum number of times per secondFilling is performed, and the filling in step 102 is cyclically performedThe data at each time point is filled.
Further, according to the operation step in S103, the firstData at various time points, if=0, Then fill the original dataData ofPadding using null values, whereinRepresenting the sampling rate of the current parameter.
Further, the step S201 is to calculate the storage time according to the original timeFor use in subsequent downsampling queries,Is a range of original time searched by using a time sequence database according to a query condition input by a user,Indicating the time at which the storage is to begin,Indicating the end of the storage time.
Further, the step S202 is to obtain the maximum sampling rate parameter of the query parameters according to the metadata of the query parameters, and to obtain the maximum sampling rate parameter of the query parameters according to the metadata of the query parametersCalculating the maximum data point number of the current query parameter。
Further, the step S203 is to calculate the number of down-sampling target data first according to the time interval of down-sampling adaptively calculated according to the number of down-sampling target points specified by the userWhereinTo reduce the number of data points after the sampling,Representing the number of target data points entered by the user,Representing the number of query data points (adaptively determined based on screen pixels).
Further, according to the operation step in S203, a downsampling time interval is calculated,The calculation logic divides the difference between the stored start time and the end time by the sampling time interval, and rounds the result, and finally according toConducting aggregated queriesObtaining downsampled data in whichThe function used for sampling in the finger interval comprises,,,,,. Wherein the method comprises the steps ofRepresenting an average sampling function, and taking the average value of data in a sampling interval; Representing a maximum sampling function, and taking the maximum value of data in a sampling interval; Representing a minimum sampling function, and taking the minimum value of data in a sampling interval; Representing a summation sampling function, taking the sum of data in a sampling interval; Representing an extremum sampling function, and taking extremum of data in a sampling interval; representing a variance sampling function, taking the variance of the data over the sampling interval.
The invention provides a QAR data storage and quick query method based on time drift, which has the advantages that the QAR data storage and quick query method based on time drift is automatic in assembly, the production speed can be greatly accelerated in digital-analog automatic assembly, and compared with the traditional manual assembly, the automatic assembly can complete more assembly tasks in a shorter time, so that the overall production efficiency is improved, the data is durable, the traceability and consistency of the data are ensured through the durable EBOM data and the airplane digital-analog data, a coordinate system is set, the coordinate system is set to provide uniform and accurate spatial reference, and the accuracy and consistency of the assembly are improved.
Drawings
FIG. 1 is a flowchart of a QAR data storage and query process for a time drift based QAR data storage and quick query method of the present invention;
FIG. 2 is a schematic view of QAR initial data of a QAR data storage and fast query method based on time drift of the present invention;
FIG. 3 is a schematic view of QAR processed data of a QAR data storage and fast query method based on time drift according to the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
The embodiment provides a method for storing and quickly inquiring time sequence data of a multi-sampling rate airplane QAR aiming at time drift, which can be applied to the field of visual analysis of the QAR data, and a specific flow of the method is shown in a figure 1. Taking the result of decoding of certain airplane part QAR as an example, the method comprises PRESELECT ALTITUDE with a sampling rate of 1Hz, PITCH ANG ISI with a sampling rate of 2Hz, AILR POSN B LT with a sampling rate of 4Hz and A/P ENGED LT with a sampling rate of 0.5Hz, wherein the original Time field is Time, and the data of 7214 seconds are contained in total, as shown in table 1.
Table 1 aircraft QAR raw data example
A QAR data storage and quick query method based on time drift comprises the following steps
The specific embodiment process comprises two steps of storing and inquiring:
The storage steps are as follows:
step 1. Calculate the maximum sampling rate of the QAR data, in the example the maximum sampling rate of 4 parameters is 4Hz, i.e. 4 times/second, Then the maximum sampling rate4;
step 2, filling the original time field, and 1 second of the original time is processed according to the maximum sampling rate The split times were on the order of 4 milliseconds, 2:27:04.000, 2:27:04.250, 2:27:04.500, 2:27:04.750, and so on, with the filling results shown in table 2;
Step 3, aligning the data with different sampling rates to the maximum sampling rate parameter data, such as parameter PITCH ANG ISI, with sampling rate of 2, for the time point after filling, time point 0 Then it needs to be filledData-0.6152, time 1Null needs to be filled;
and 4, respectively taking the filled original time field and the aircraft parameter data field as the writing time sequence database IotDB.
The query steps are as follows:
step 5, mapping the original time and the storage time, using the service time of the user query as a range condition, constructing IotDB the storage time corresponding to the query IotDB, for example, the service time of the user query is Then the mapped storage time is;
Step 6, obtaining the number of data points of the query parameter with the maximum sampling rate, and obtaining the maximum sampling rate of the query parameter through metadata, for example, the parameters of the query have A/P ENGED LT and PITCH ANG ISI, the maximum sampling rate parameter is PITCH ANG ISI, and IotDB query PITCH ANG ISI fields are constructed in the following fieldsThe number of data points in the range is 14 data points;
step 7, calculating an adaptive downsampling time interval, and assuming that the resolution of the current screen is 10, downsampling target data number is Then the downsampling time interval isI.e. once every 3ms, splicing the downsampling time interval into IotDB aggregate query sentences;
and 8, submitting the query to a time sequence database IotDB to execute the query, wherein the query result is shown in a table 3.
Table 2 example of data after alignment of an aircraft QAR
| IotDB Time | Time | PRESELECT ALTITUDE | AILR POSN B LT | A/P ENGED LT | PITCH ANG ISI |
| 1705025048000 | 2:27:04.000 | 5888 | -0.22 | NOT-ENGAGD | -0.6152 |
| 1705025048001 | 2:27:04.250 | -0.22 | |||
| 1705025048002 | 2:27:04.500 | -0.22 | -0.6152 | ||
| 1705025048003 | 2:27:04.750 | -0.22 | |||
| 1705025048004 | 2:27:05.000 | 5888 | -0.22 | -0.6152 | |
| 1705025048005 | 2:27:05.250 | -0.22 | |||
| 1705025048006 | 2:27:05.500 | -0.22 | -0.6152 | ||
| 1705025048007 | 2:27:05.750 | -0.22 | |||
| 1705025048008 | 2:27:06.000 | 5888 | -0.22 | NOT-ENGAGD | -0.6152 |
| 1705025048009 | 2:27:06.250 | -0.22 | |||
| 1705025048010 | 2:27:06.500 | -0.22 | -0.6152 | ||
| 1705025048011 | 2:27:06.750 | -0.22 | |||
| 1705025048012 | 2:27:07.000 | 5888 | -0.22 | -0.6152 | |
| 1705025048013 | 2:27:07.250 | -0.22 | |||
| 1705025048014 | 2:27:07.500 | -0.22 | -0.6152 | ||
| 1705025048015 | 2:27:07.750 | -0.22 | |||
| 1705025048016 | 2:27:08.000 | 5888 | -0.22 | NOT-ENGAGD | -0.6152 |
| 1705025048017 | 2:27:08.250 | -0.22 | |||
| 1705025048018 | 2:27:08.500 | -0.22 | -0.6152 | ||
| 1705025048019 | 2:27:08.750 | -0.22 | |||
| 1705025048020 | 2:27:08.000 | 5888 | -0.22 | -0.6152 | |
| 1705025048021 | 2:27:08.250 | -0.22 | |||
| 1705025048022 | 2:27:08.500 | -0.22 | -0.6152 | ||
| 1705025048023 | 2:27:08.750 | -0.22 | |||
| 1705025048024 | 2:27:10.000 | 5888 | -0.22 | NOT-ENGAGD | -0.6152 |
| 1705025048025 | 2:27:10.250 | -0.22 | |||
| 1705025048026 | 2:27:10.500 | -0.22 | -0.6152 | ||
| 1705025048027 | 2:27:10.750 | -0.22 | |||
| 1705025048028 | 2:27:11.000 | 5888 | -0.22 | -0.6152 | |
| 1705025048029 | 2:27:11.250 | -0.22 | |||
| 1705025048030 | 2:27:11.500 | -0.22 | -0.6152 | ||
| 1705025048031 | 2:27:11.750 | -0.22 | |||
| ...... |
Table 3 aircraft QAR downsampled query result data examples
| IotDB Time | Time | A/P ENGED LT | PITCH ANG ISI |
| 1705025048000 | 2:27:04.000 | NOT-ENGAGD | -0.6152 |
| 1705025048003 | 2:27:04.750 | ||
| 1705025048006 | 2:27:05.500 | -0.6152 | |
| 1705025048009 | 2:27:06.250 | ||
| 1705025048012 | 2:27:07.000 | -0.6152 | |
| 1705025048015 | 2:27:07.750 | ||
| 1705025048018 | 2:27:08.500 | -0.6152 | |
| 1705025048021 | 2:27:08.250 | ||
| 1705025048024 | 2:27:10.000 | NOT-ENGAGD | -0.6152 |
| 1705025048027 | 2:27:10.750 | ||
| 1705025048030 | 2:27:11.500 | -0.6152 | |
| ...... |
The QAR data records data of the aircraft during its flight. In the conventional QAR data processing and query overcharging, the data is written into and stored after the time drift condition of the QAR data is processed, the processing mode is to discard the data with repeated time, and estimate and fill the data with missing time, but the cleaning and correction of the data may cause the airline to miss critical problem points, the method writes the original data into the storage as one physical quantity, aligns a plurality of parameters, writes the parameters into the storage, fully retains the characteristics of QAR data, is convenient for an analyst to analyze the data with sufficient information quantity, supports an adaptive downsampling algorithm, is convenient for quickly loading the data, and can improve the data query performance when the number of the parameters is large.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A method for QAR data storage and fast interrogation based on time drift comprising the steps of:
the method comprises two flows of a storage step and a query step;
The storage steps are as follows:
s101, calculating the maximum sampling rate of QAR data;
s102, filling an original time field;
s103, aligning data with different sampling rates;
S104, writing data into a time sequence database IotDB;
the query steps are as follows:
s201, mapping original time and storage time;
S202, acquiring the number of data points of the maximum sampling rate query parameter;
s203, calculating an adaptive downsampling time interval;
S204, executing the query in the time sequence database IotDB.
2. The method for storing and rapidly searching QAR data based on time drift as claimed in claim 1, wherein said step S101 requires reading metadata of all QAR data and calculating a maximum sampling rate by employingIndicating, in units of times/second; n is the number of parameters, wherein Sample rates of the n parameters are shown in items 1, 2.
3. The method for storing and rapidly querying QAR data based on time drift as claimed in claim 1, wherein said step S102 requires the original time field to be sampled a maximum number of timesFilling is carried out, and the number of time points after filling becomesAnd a time point, wherein m is the QAR data acquisition seconds, and the original time value is the data writing millisecond time.
4. The method for storing and rapidly inquiring QAR data based on time drift as claimed in claim 2, wherein the initial time of said QAR data is recorded only to the second level, representing the acquisition frequency of 1Hz, the partial parameter acquisition frequency is higher than 1Hz, i.e. a plurality of sampling points per second, while the QAR data is sequentially filled from the left to the right, and the sampling times per second of the QAR data is as followsThe time field will follow 1 secondFilling is performed, and k is the kth sampling point in 1 second.
5. A time-drift-based QAR data storage and fast query method as claimed in claim 3, wherein said step S103 requires the QAR parameter to be set to a maximum number of samples per secondFilling is performed, and the filling in step 102 is cyclically performedThe data at each time point is filled.
6. The method for storing and rapidly querying QAR data based on time drift as claimed in claim 2, wherein according to the operation step in S103, the first step isData at various time points, if=0, Then fill the original dataData ofPadding using null values, whereinRepresenting the sampling rate of the current parameter.
7. The method for storing and rapidly querying QAR data based on time drift as claimed in claim 1, wherein said step S201 requires calculating a storage time based on an original timeFor use in subsequent downsampling queries,Is a range of original time searched by using a time sequence database according to a query condition input by a user,Indicating the time at which the storage is to begin,Indicating the end of the storage time.
8. The method for storing and rapidly querying QAR data based on time drift as claimed in claim 7, wherein said step S202 comprises obtaining the maximum sampling rate parameter of the query parameters according to the metadata of the query parameters, andCalculating the maximum data point number of the current query parameter。
9. The method for QAR data storage and fast query based on time drift as claimed in claim 7, wherein said step S203 requires adaptively calculating the number of downsampled target data according to the user-specified time interval for downsampling target point number
WhereinTo reduce the number of data points after the sampling,Representing the number of target data points entered by the user,Representing the number of query data points.
10. The method for storing and rapidly querying QAR data based on time drift as claimed in claim 9, wherein the downsampling time interval is calculated according to the operation step in S203,
The calculation logic divides the difference between the stored start time and the end time by the sampling time interval, and rounds the result, and finally according toConducting aggregated queriesObtaining downsampled data in whichThe function used for sampling in the finger interval comprises,,,,,。
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