Laser Radar Wind Speed Field Interpolation
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Solution Overview
Problem
Current laser radar devices face limitations in generating dense wind speed field data efficiently, either requiring costly hardware upgrades or simulations that lack real-time reality.
Innovation Solution
A laser radar device equipped with a signal processor that includes a spectrum conversion processor, integration processor, wind speed field calculator, and algorithm learning AI, which performs FFT processing, integration, and interpolation to generate dense wind speed field data in a shorter time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware performance is improved to increase sampling speed, then the number of observation points increases and wind speed field data becomes dense, but cost increases
Solution Approach 1:
The patent changes the processing parameters and methods by introducing AI-based interpolation algorithms that process existing measurement data more efficiently, rather than increasing hardware sampling speed. This allows dense wind speed field data generation without costly hardware upgrades.
Solution Approach 2:
The patent replaces the mechanical approach of increasing hardware sampling speed with an information processing approach using AI algorithms. Instead of physically measuring more points through faster hardware, the system interpolates unmeasured points based on measured data and structure information, substituting mechanical measurement expansion with computational inference.
2Measurement precision
If fluid simulation is performed to generate dense wind speed field data, then data density improves, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary measurements at strategically selected observation points and uses AI interpolation to predict unmeasured points. This preliminary measurement approach, combined with structure information, allows rapid generation of dense wind speed field data without time-consuming full-domain fluid simulation.
Solution Approach 2:
The patent introduces structure information as an intermediary element that bridges measured and unmeasured points. By incorporating building and environment structure data into the AI interpolation process, the system can accurately predict wind speeds at unmeasured locations without performing comprehensive fluid simulations.
3Reliability
If observation points are increased to cover blind spots of structures, then measurement completeness improves, but hardware complexity and cost increase
Solution Approach 1:
The patent creates virtual copies of measurement data through AI interpolation at unmeasured observation points, particularly at blind spots of structures. Instead of physically placing sensors at every location including hard-to-reach blind spots, the system generates virtual measurement data based on interpolated predictions, maintaining measurement completeness without increasing physical hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the interpolation of observation results and generation of dense wind speed field data faster than fluid simulation, without the need for extensive hardware upgrades or costly calculations.
Implementation Method 1
the spectrum conversion processor performs FFT processing on a beat signal that is a time-series digital signal, and generates spectrum data
Implementation Method 2
a laser radar that measures a moving speed of fine liquid or solid particles (aerosol) floating in the atmosphere using a principal similar to that of a weather radar
Data Source
AI summary
A laser radar device according to the technique of the present disclosure is a laser radar device that scans a laser beam and measures a wind speed field of observation environment, and includes a signal processor including a spectrum conversion processor, an integration processor, a wind speed field calculator, and a algorithm learning AI, and in which the spectrum conversion processor performs FFT processing on a beat signal that is a time-series digital signal, and generates spectrum data, the integration processor performs integration processing on the spectrum data, the wind speed field calculator calculates the wind speed field by referring to information on data processed by the integration processor, the algorithm learning AI includes a trained artificial intelligence, and interpolates an observation result by referring to information on the wind speed field.