Semantic Parsing of Control Speech for Earlier Aircraft Conflict Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing aircraft air conflict detection methods rely on real-time flight data, leading to insufficient response time and are prone to errors due to speech identification issues, which affect the accuracy and efficiency of conflict detection.
Innovation Solution
A method for detecting aircraft air conflict based on semantic parsing of control speech, utilizing real-time speech-to-text processing, intention identification, and keyword extraction to enhance conflict detection accuracy and response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time flight data is used for conflict detection, then the detection can be performed continuously, but the response time is insufficient and conflicts cannot be predicted in advance
Solution Approach 1:
The system performs preliminary action by predicting potential conflict situations before they actually occur. The speech prediction module anticipates future control instructions that may lead to conflicts, allowing the system to prepare and respond in advance rather than merely detecting conflicts after they arise from real-time data alone.
Solution Approach 2:
The invention inverts the conventional approach by not only analyzing past and present flight data but also predicting future control speech and potential conflicts. This bidirectional analysis (combining real-time data with predicted future states) resolves the time response limitation by proactively identifying conflicts before they materialize.
2Ease of manufacture
If template matching method is used to extract control instructions from speech, then the process is straightforward, but speech identification errors significantly affect extraction accuracy
Solution Approach 1:
The system applies parameter changes by transforming the speech processing approach from direct template matching to a multi-stage process involving speech prediction, key information extraction, and semantic analysis. This changes the parameters of processing (adding prediction component, modifying extraction methodology) to improve reliability while maintaining ease of implementation through modular design.
Solution Approach 2:
The invention introduces an intermediary mechanism by adding a speech prediction module and key information extraction layer between the raw speech input and the final conflict detection. This intermediary processing stage filters and validates information, reducing the impact of speech identification errors on the overall system reliability.
3Loss of information
If all control speech texts are processed to extract instructions, then no information is missed, but the detection time increases significantly
Solution Approach 1:
The system applies the extraction principle by selectively extracting only the key control instructions and critical flight parameters from speech, rather than processing all speech text uniformly. The key information extraction module identifies and extracts only the essential elements needed for conflict detection, significantly reducing processing time while maintaining completeness of critical information.
Solution Approach 2:
The invention applies partial action by focusing processing resources on extracting and analyzing only the most critical control instructions and flight parameters necessary for conflict detection, rather than uniformly processing all speech content. This selective partial processing maintains detection effectiveness while reducing overall processing time.
4Speed
If speed formula inference is used for conflict detection, then the calculation is direct, but the computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the conflict detection process into distinct modular stages: speech collection, speech-to-text conversion, speech prediction, key information extraction, and conflict detection. This segmentation of the computational process reduces overall complexity by allowing each module to be optimized independently and processed in sequence rather than as a monolithic complex calculation.
Data Source
AI summary
A method for detecting aircraft flight conflict based on semantic parsing of control speech is provided. Firstly, the control speech is collected in real time, and the speech is converted into text through the control speech identification system combined with real-time radar data; inputting the identified control text into the control text intention identification model, further analyzing the intention, and judging whether the identified control text intention needs to change the flight state of the aircraft, if not, terminating the detection, and if necessary, extracting control instructions; extracting the key data needed for conflict detection from the identified control text; according to the key data and real-time radar data, performing the conflict detection algorithm to judge whether there is flight conflict; if it exists, a conflict alarm mechanism is triggered.