Optimization and query method and system for flight parameter data storage based on configurable label library
By introducing a configurable tag library to dynamically manage the indexing strategy of the time-series database, the problem of low efficiency in traditional databases and the shortcomings of static tag design in time-series databases are solved, realizing efficient and flexible data storage and querying of flight parameter data, and adapting to the ever-changing needs of flight safety analysis.
Patent Information
- Application Number
- CN202610381320.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-23
AI Technical Summary
In existing technologies, traditional relational databases are inefficient in storing and querying flight parameter data and cannot meet the needs of interactive analysis. The static label design of time-series databases is difficult to adapt to the flexible and ever-changing business filtering needs in flight parameter data analysis, resulting in reduced query efficiency.
A configurable tag library is introduced as an intelligent control layer to dynamically manage the indexing strategy of the time-series database. The tag library can be configured through a graphical or file-based interface, and index tag fields can be dynamically added or removed according to the frequency of query requirements for flight parameters, thereby achieving efficient data storage and querying.
It enables flexible and diverse business filtering requirements in flight parameter data analysis, improves query efficiency, avoids database structure changes and underlying code modifications, lowers the technical threshold, and enhances system scalability and query performance.
Smart Images
Figure CN122262141A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of flight data processing technology, and in particular to a method, system, device, and medium for optimizing and querying flight parameter data storage based on a configurable tag library. Background Technology
[0002] Flight parameters are core data that record the status and operation of an aircraft, and they are crucial for flight safety analysis, fault prediction and performance evaluation. Flight parameter data has typical time-series data characteristics such as large data volume, many parameters and high generation frequency.
[0003] Early systems may have used traditional relational databases (such as MySQL and Oracle) to store flight parameter data. Their row-based storage and B+ tree index model suffers from low I / O efficiency when inserting massive amounts of data with time as the primary key, which can easily create a performance bottleneck. More importantly, when performing conditional queries based on parameter values (such as "high-pressure rotor speed > 98%)", the lack of effective index support forces the system to perform a full table scan. The query response time increases exponentially with the amount of data, which cannot meet the needs of interactive analysis.
[0004] To address the problems existing in early systems, time-series databases (such as InfluxDB and TDengine) have gained attention due to their high performance in time-series data storage and querying. Their data models typically include timestamps, tags, and metric values, with tags used for indexing and being key to achieving efficient queries. However, existing flight parameter systems based on time-series databases have static and fixed tag designs. Developers pre-define a few fixed fields as tags, while treating the vast majority of flight parameters as unindexed metric values. This configuration requires a full scan of all metric values when queries are needed based on business metrics that are not set as tags. This implies cumbersome database structure changes, data migrations, or modifications to the underlying software implementation. Therefore, the current static tag design is difficult to adapt to the flexible and ever-changing business filtering requirements in flight parameter data analysis. Summary of the Invention
[0005] This invention provides a method and system for optimizing and querying flight parameter data storage based on a configurable tag library, which can solve the problems existing in the prior art.
[0006] This invention provides a method for optimizing and querying flight parameter data storage based on a configurable tag library, comprising the following steps: Acquire various flight parameter data of the aircraft during its historical flight process; Build a configurable tag library: Pre-set an initial tag library. Based on the frequency of flight parameter data query needs of the aircraft during historical flights, register the specified flight parameter data fields during historical flights as index tag fields, and store the configuration information of the index tag fields in an independent tag rule table within the tag library. At the same time, store other flight parameter data except for the specified flight parameter data as ordinary fields in the tag library. And add or remove any flight parameters from the independent tag rule table according to changes in actual analysis needs, forming a dynamically updated configurable tag library. The system acquires real-time flight parameters of the aircraft during flight, queries an independent tag rule table in the configurable tag library based on the real-time flight parameters, identifies which fields in the real-time flight parameters have been configured as index tag fields, sets the identified tag fields and their corresponding values as tags for time series database data points, sets other tag fields as index values for time series database data points, and stores them in the time series database. During the query, all configured tag fields in the independent tag rule table are retrieved. These tag fields are combined and converted into a query logic tree. The query logic tree is then converted into a query statement for the time series database tags for querying.
[0007] Preferably, the construction of the configurable tag library includes: Configure the interface through a graphical or file-based interface, and receive tag configuration instructions provided by the system administrator based on the configuration interface; Based on various flight parameter data collected during the aircraft's historical flight process, the system administrator analyzes the high-frequency query requirements among these flight parameter data, registers the specified flight parameter data fields as index tag fields, and stores the configuration information of these index tag fields in an independent tag rule table within the tag library. Other flight parameter data, excluding the specified flight parameter data, are stored as ordinary fields in the tag library, forming a dynamically updated configurable tag library. Specifically, based on the actual analysis needs of the aircraft's historical flight history from the previous week or month, any flight parameter can be added to or removed from an independent label rule table; the configuration information of the index label field includes the field name and label description.
[0008] Preferably, the storage of the flight parameter data includes: The system acquires the raw data stream from the onboard recorder during real-time flight, decodes the data, aligns the timestamps, and removes outliers to form a structured data record. Based on structured data records, query the independent tag rule table within the configurable tag library, identify which fields in the structured data records have been configured as index tag fields, set the identified tag fields and their corresponding values as Tags for time series database data points, set other tag fields as Fields for time series database data points, retain the timestamps when set as Tags and Fields, and batch store the set data points into the time series database.
[0009] Preferably, the query of the flight parameters includes: The query interface is dynamically linked with the independent tag rule table in the tag library, and all configured tag fields are loaded as optional filtering dimensions. Users initiate queries by combining these optional filter dimensions. The combined and configured label fields are converted into a query logic tree, and the query logic tree is then converted into a query statement for the time series database labels. The query is then performed in the time series database based on the query statement.
[0010] Preferably, after the flight parameters are queried, the data points queried from the time-series database are aggregated and visualized through trend charts or statistical reports.
[0011] This invention also provides a flight parameter data storage optimization and query system based on a configurable tag library, including: The data module is used to acquire various flight parameter data of the aircraft during its historical flight process; The tag library module is used to build a configurable tag library: an initial tag library is preset, and based on the frequency of flight parameter data query needs of the aircraft during historical flights, specified flight parameter data fields during historical flights are registered as index tag fields, and the configuration information of the index tag fields is stored in an independent tag rule table within the tag library. At the same time, other flight parameter data besides the specified flight parameter data are stored as ordinary fields in the tag library, and any flight parameter can be added to or removed from the independent tag rule table according to changes in actual analysis needs, forming a dynamically updated configurable tag library. The storage and query module is used to obtain real-time flight parameters of the aircraft during flight, query independent tag rule tables in the configurable tag library based on real-time flight parameters, identify which fields in the real-time flight parameters have been configured as index tag fields, set the identified tag fields and their corresponding values as tags of time series database data points, set other tag fields as index values of time series database data points, and store them in the time series database. During the query, all configured tag fields in the independent tag rule table are retrieved. These tag fields are combined and converted into a query logic tree. The query logic tree is then converted into a query statement for the time series database tags for querying.
[0012] This invention also provides an electronic device, including a memory and a processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the flight parameter data storage optimization and query method based on the configurable tag library as described above.
[0013] This invention also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the flight parameter data storage optimization and query method based on a configurable tag library as described above.
[0014] This invention provides a method and system for optimizing and querying flight parameter data storage based on a configurable tag library. Compared with the prior art, its advantages are as follows: This invention first predefines which high-frequency key flight parameters should be created as database indexes and stores this tag configuration information in a separate tag rule table within the tag library. Simultaneously, other flight parameter data, excluding the specified flight parameter data, are stored as ordinary fields in the tag library. Furthermore, based on changes in actual analysis needs within a week or month, any flight parameter can be added to or removed from the separate tag rule table, forming a dynamically updated, configurable tag library as an intelligent control layer. When data is stored in the database, the field values of specified flight parameters are automatically promoted to index tags in the time-series database, while other flight parameters are stored as ordinary fields. During queries, these pre-built tag indexes can be used directly, requiring a full scan of all indicator values to perform the query. This configuration allows for easy addition or removal of any flight parameter from the tag library through a configuration interface, eliminating the need for cumbersome database table structure changes, data migration, or modifications to the underlying application code. It abandons the traditional static indexing strategy, enabling flexible online tag configuration to adapt to the ever-changing business filtering needs in flight parameter data analysis. Attached Figure Description
[0015] Figure 1 A schematic diagram of the system architecture provided for an embodiment of the present invention; Figure 2 This is a schematic diagram of the overall process provided for an embodiment of the present invention. Detailed Implementation
[0016] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0017] Flight parameters are core data recording aircraft status and operations, crucial for flight safety analysis, fault prediction, and performance evaluation. Flight parameter data exhibits typical time-series data characteristics such as large data volume, numerous parameters, and high generation frequency. Currently, the storage, retrieval, and analysis of flight parameter data mainly face the following technical bottlenecks:
[0018] 1. Performance bottlenecks and model mismatch in traditional databases.
[0019] Early systems may have used traditional relational databases (such as MySQL and Oracle) to store flight parameter data. Their row-based storage and B+ tree index model suffers from low I / O efficiency when inserting massive amounts of data with time as the primary key, which can easily create a performance bottleneck. More importantly, when performing conditional queries based on parameter values (such as "high-pressure rotor speed > 98%)", the lack of effective index support forces the system to perform a full table scan. The query response time increases exponentially with the amount of data, which cannot meet the needs of interactive analysis.
[0020] 2. The application of time-series databases is superficial.
[0021] In recent years, time-series databases (such as InfluxDB and TDengine) have attracted attention for their high performance in storing and querying time-series data. Their data models typically include timestamps, tags, and metric values, with tags used for indexing and being key to achieving efficient queries. However, existing flight parameter systems based on time-series databases have static and fixed tag designs. Typically, during the system design phase, developers pre-specify a few fields (such as aircraft ID and flight number) as tags, while treating the vast majority of flight parameters (such as RPM, temperature, and pressure) as unindexed metric values.
[0022] This static tag design pattern leads to new contradictions. When users need to query by predefined tags (such as flight numbers), the efficiency is extremely high; however, when the analysis requirements change and filtering is needed based on business indicators that are not set with tags (such as hydraulic pressure, afterburner status), the query efficiency drops sharply because the database still needs to perform a full scan of all indicator values. Dynamically adjusting tags for new query requirements means, in existing technologies, cumbersome database structure changes, data migration, and even modifications to the underlying software implementation, making online and flexible configuration impossible. Therefore, existing technologies fail to fully realize the potential of time-series databases, and their indexing capabilities are rigid and cannot adapt to the flexible and ever-changing business filtering requirements in flight parameter data analysis.
[0023] Therefore, a system is needed that can dynamically and flexibly manage index tags in time-series databases, so that flight parameter data systems can maintain the high performance of time-series databases while supporting efficient filtering and retrieval based on any key business indicators.
[0024] To address the problems existing in current methods, this invention provides a configurable tag library-based method for optimizing and querying flight parameter data, such as... Figure 1 As shown, its principle lies in introducing a configurable tag library as an intelligent control layer to dynamically optimize the storage structure of flight parameter data in the time series database, thereby achieving efficient querying based on business indicators. First, through a dynamically manageable tag library, it predefines which key flight parameters (such as high-pressure rotor speed n1) should be created as database indexes. When data is entered into the database, the storage engine will automatically promote the field values of the specified parameters to index tags of the time series database according to this configuration, while the remaining parameters are stored as ordinary fields.
[0025] The system administrator can use a graphical or document-based interface to add or remove any flight parameters from an independent tag rule table based on the actual analysis needs of the aircraft's historical flight data from the previous week or month, thus forming a dynamically manageable tag library.
[0026] like Figure 2 The diagram illustrates the complete technical process of the flight parameter data storage optimization and query method based on a configurable tag library according to the present invention, specifically including the following steps: Step 1: Building and managing the configurable tag library.
[0027] (1) Provide configuration interface: Provide system administrators with a graphical or file-based interface for defining and managing the tag library.
[0028] (2) Specifying Tag Fields: Based on the high-frequency needs of business queries, the administrator registers specific flight parameter data fields (such as n1, flight_phase) into the tag library and designates them as "index tag fields".
[0029] (3) Persistent storage configuration: The system stores this tag configuration information (field name, description, etc.) in the database or configuration file to form a dynamically updatable "tag rule base".
[0030] Step 2: Data preprocessing and label rule matching.
[0031] (1) Access raw flight parameter data: Receive raw data stream from the airborne recorder.
[0032] (2) Data parsing and cleaning: Standardize the data by decoding, aligning timestamps, removing outliers, etc., to form structured data records.
[0033] (3) Rule matching: The data storage module queries the "tag rule library" in real time to identify which fields in the current data record have been configured as tags.
[0034] Step 3: Dynamic tagging and storage optimization based on the tag library.
[0035] (1) Data structure encapsulation: Based on the matching results of step two, the data records are encapsulated into a time-series database data model; the fields and their values specified in the rule base are set as Tags of the data points, and the remaining ordinary fields are set as Fields of the data points, while retaining the precise timestamp.
[0036] (2) Efficient writing: Write the encapsulated data points in batches to the time series database.
[0037] Step 4: Semantic queries oriented towards business needs.
[0038] (1) Generate query interface: The query interface is dynamically linked with the tag library to show users all configured and business-understandable tag fields as filtering dimensions.
[0039] (2) Receiving and parsing query requests: Users initiate queries by selecting and combining these dimensions (e.g., n1>98 AND flight_phase = 'takeoff'); after receiving the query, the system parses it into an abstract query logic tree.
[0040] (3) Query transformation and execution: The query engine transforms the abstract query logic into native, efficient query statements for time-series database tags (such as Flux statements in InfluxDB) and executes the query.
[0041] Step 5: Results aggregation and presentation.
[0042] The query engine displays the returned data points to the user in the form of trend charts, reports, etc.
[0043] The row-based storage and B+ tree index model of traditional relational databases are severely incompatible with the essential requirements of high-speed writing of flight parameter data in chronological order and fast querying by time range, resulting in low I / O efficiency. This invention creates a storage architecture based on time-series databases, specifically by fundamentally reconstructing the architecture at the data model level. By adopting time-series databases, their columnar storage and time-series-oriented compression algorithms solve the cost and efficiency problems of massive data storage from the bottom up. More importantly, its tag-metric-timestamp data model is natively adapted to the multi-parameter and multi-dimensional characteristics of flight parameter data, laying a solid physical foundation for subsequent query optimization.
[0044] This invention introduces a central nervous system layer—a "configurable tag library"—to decouple the indexing strategy from physical storage; this brings about fundamental changes on two levels: Dynamic nature: The indexing strategy is no longer hard-coded into the code, but is dynamically configured based on the configured tag library. The system automatically builds the index according to the new rules when flight parameter data flows in, realizing the dynamic construction of database indexes.
[0045] Intelligence: The system encapsulates the low-level index optimization technology that originally relied on DBAs (database administrators) into configuration operations that are understandable to business personnel and geared towards the aviation industry; it transforms a profound technical challenge into a simple management problem, greatly reducing the technical threshold and cost of achieving high-performance queries.
[0046] In this invention, when users need to filter by business metrics (such as "n1>98%), the query engine can directly utilize these pre-built tag indexes to achieve millisecond-level accurate positioning in massive amounts of data, completely avoiding full table scans in traditional solutions. Furthermore, it is the first to realize online and flexible configuration and management of flight parameter database index strategies, fundamentally solving the technical contradiction between query performance and flexibility, and making interactive data exploration based on any key metric a reality.
[0047] This invention achieves logical layering and optimized organization of data at the storage level by using high-frequency query fields as tags and other parameters as fields. The inherent advantages of time-series databases in efficient tag compression and retrieval are maximized, reducing original storage space usage while further improving I / O efficiency to match query patterns, achieving an optimal balance between storage cost and query performance. When a new flight parameter needs to be added or the query pattern needs to be adjusted, the entire system can seamlessly adapt without downtime, simply by updating the tag library. This loosely coupled architecture enhances system scalability, facilitating future integration of more data sources or adaptation to other types of time-series data.
[0048] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.
Claims
1. A method for optimizing and querying flight parameter data storage based on a configurable tag library, characterized in that, Includes the following steps: Acquire various flight parameter data of the aircraft during its historical flight process; Build a configurable tag library: Pre-set an initial tag library. Based on the frequency of flight parameter data query needs of the aircraft during historical flights, register the specified flight parameter data fields during historical flights as index tag fields, and store the configuration information of the index tag fields in an independent tag rule table within the tag library. At the same time, store other flight parameter data except for the specified flight parameter data as ordinary fields in the tag library. And add or remove any flight parameters from the independent tag rule table according to changes in actual analysis needs, forming a dynamically updated configurable tag library. The system acquires real-time flight parameters of the aircraft during flight, queries an independent tag rule table in the configurable tag library based on the real-time flight parameters, identifies which fields in the real-time flight parameters have been configured as index tag fields, sets the identified tag fields and their corresponding values as tags for time series database data points, sets other tag fields as index values for time series database data points, and stores them in the time series database. During the query, all configured tag fields in the independent tag rule table are retrieved. These tag fields are combined and converted into a query logic tree. The query logic tree is then converted into a query statement for the time series database tags for querying.
2. The method for optimizing and querying flight parameter data storage based on a configurable tag library according to claim 1, characterized in that, The construction of the configurable tag library includes: Configure the interface through a graphical or file-based interface, and receive tag configuration instructions provided by the system administrator based on the configuration interface; Based on various flight parameter data collected during the aircraft's historical flight process, the system administrator analyzes the high-frequency query requirements among these flight parameter data, registers the specified flight parameter data fields as index tag fields, and stores the configuration information of these index tag fields in an independent tag rule table within the tag library. Other flight parameter data, excluding the specified flight parameter data, are stored as ordinary fields in the tag library, forming a dynamically updated configurable tag library. Specifically, based on the actual analysis needs of the aircraft's historical flight history from the previous week or month, any flight parameter can be added to or removed from an independent label rule table; the configuration information of the index label field includes the field name and label description.
3. The method for optimizing and querying flight parameter data storage based on a configurable tag library according to claim 2, characterized in that, The storage of the flight parameter data includes: The system acquires the raw data stream from the onboard recorder during real-time flight, decodes the data, aligns the timestamps, and removes outliers to form a structured data record. Based on structured data records, query the independent tag rule table within the configurable tag library, identify which fields in the structured data records have been configured as index tag fields, set the identified tag fields and their corresponding values as Tags for time series database data points, set other tag fields as Fields for time series database data points, retain the timestamps when set as Tags and Fields, and batch store the set data points into the time series database.
4. The method for optimizing and querying flight parameter data storage based on a configurable tag library according to claim 3, characterized in that, The query of flight parameters includes: The query interface is dynamically linked with the independent tag rule table in the tag library, and all configured tag fields are loaded as optional filtering dimensions. Users initiate queries by combining these optional filter dimensions. The combined and configured label fields are converted into a query logic tree, and the query logic tree is then converted into a query statement for the time series database labels. The query is then performed in the time series database based on the query statement.
5. The method for optimizing and querying flight parameter data storage based on a configurable tag library according to claim 1, characterized in that, After the flight parameters are queried, the data points queried from the time-series database are aggregated and visualized through trend charts or statistical reports.
6. A flight parameter data storage optimization and query system based on a configurable tag library, characterized in that: include: The data module is used to acquire various flight parameter data of the aircraft during its historical flight process; The tag library module is used to build a configurable tag library: an initial tag library is preset, and based on the frequency of flight parameter data query needs of the aircraft during historical flights, specified flight parameter data fields during historical flights are registered as index tag fields, and the configuration information of the index tag fields is stored in an independent tag rule table within the tag library. At the same time, other flight parameter data besides the specified flight parameter data are stored as ordinary fields in the tag library, and any flight parameter can be added to or removed from the independent tag rule table according to changes in actual analysis needs, forming a dynamically updated configurable tag library. The storage and query module is used to obtain real-time flight parameters of the aircraft during flight, query independent tag rule tables in the configurable tag library based on real-time flight parameters, identify which fields in the real-time flight parameters have been configured as index tag fields, set the identified tag fields and their corresponding values as tags of time series database data points, set other tag fields as index values of time series database data points, and store them in the time series database. During the query, all configured tag fields in the independent tag rule table are retrieved. These tag fields are combined and converted into a query logic tree. The query logic tree is then converted into a query statement for the time series database tags for querying.
7. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the flight parameter data storage optimization and query method based on a configurable tag library as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that, Used to store computer programs, which, when executed by a processor, implement the steps of the flight parameter data storage optimization and query method based on a configurable tag library as described in any one of claims 1 to 5.