Optical Neural Network DAS Integration Without Photoelectric Conversion
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Solution Overview
Problem
Traditional distributed acoustic sensing systems face issues with signal quality degradation due to multiple conversions between light and electricity, slow operation speed, complex system design, susceptibility to electromagnetic interference, and high maintenance costs, which are unsuitable for real-time accurate responses.
Innovation Solution
A distributed acoustic sensing system utilizing an optical neural network and an all-optical integration method, incorporating components like a narrow-linewidth laser, intensity modulator, optical amplifiers, circulators, and optical couplers, to process signals optically without electronic components, enhancing parallel processing and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional photoelectric conversion and digital signal processing modes are used, then signal processing can be completed, but signal quality degrades due to multiple light-electricity conversions and noise introduction
Solution Approach 1:
The patent replaces the traditional photoelectric conversion and digital signal processing system with an all-optical signal processing system. Optical neural networks perform signal processing directly in the optical domain using phenomena such as interference, diffraction, and nonlinear optical effects, eliminating the need for photoelectric detectors, analog-to-digital converters, and electronic processing components. This substitution maintains signal quality by avoiding multiple light-electricity conversions while simplifying the overall system architecture.
Solution Approach 2:
The patent extracts and removes the photoelectric conversion stage from the traditional DAS system architecture. By eliminating photoelectric detectors and subsequent electronic processing components, the system processes signals entirely in the optical domain, thereby preventing noise introduction from conversion processes and reducing system complexity.
2Productivity
If traditional electronic components are introduced for signal processing, then signal demodulation can be achieved, but operation speed becomes slow when large amounts of computing are required
Solution Approach 1:
The patent substitutes electronic computing components with optical neural network components that perform computations using optical phenomena. Optical neural networks leverage parallel processing capabilities of light, performing multiple operations simultaneously through interference patterns and optical path differences, achieving high-speed data processing with lower energy consumption compared to traditional electronic digital signal processing.
Solution Approach 2:
The patent implements continuous optical signal processing without interruption for conversion or discrete computational steps. Optical neural networks process signals continuously through optical paths, maintaining the signal in its native optical form throughout processing, which enables high-speed operation without the latency introduced by sampling, conversion, and discrete electronic computation cycles.
3Reliability
If photoelectric conversion components are introduced, then signal processing capability is enhanced, but system becomes susceptible to electromagnetic interference and maintenance cost increases
Solution Approach 1:
The patent replaces photoelectric conversion components and electronic processing components with all-optical neural network components. Since optical signals and optical processing components are immune to electromagnetic interference, the system achieves high reliability and stability without susceptibility to electromagnetic disturbances. The optical neural network performs signal processing using optical phenomena, completely eliminating the interface between optical and electrical domains where interference could occur.
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
The system improves signal stability, simplifies architecture, increases data processing speed and capacity, reduces system volume and weight, and enhances durability and reliability by eliminating photoelectric conversions and leveraging pure optical neural networks.
Implementation Method 1
a narrow-linewidth laser, configured to output continuous narrow-linewidth high coherence laser
Implementation Method 2
an intensity modulator, modulated by the photoelectric conversion and control module, modulates continuous probe light input by the narrow-linewidth laser into pulsed probe light
Implementation Method 3
analyzes a Rayleigh backscattering light (RBS) signal generated by a sensing fiber
Implementation Method 4
a first optical amplifier... a second optical amplifier, amplifies the RBS signal
Implementation Method 5
The optical neural network performs weighting and activating function operations by utilizing optical phenomena such as interference, diffraction, and nonlinear effects
Implementation Method 6
The optical neural network performs weighting and activating function operations by utilizing optical phenomena such as interference, diffraction, and nonlinear effects
Data Source
AI summary
A distributed acoustic sensing system based on an optical neural network and an all-optical integration method are provided. The distributed acoustic sensing system based on an optical neural network includes: a distributed acoustic sensing (DAS) optical path integrated part, an external connection part, and an integrated signal processing chip. The provided combines an integrated optical delay line, a tri-port analogous detection structure and a pure optical neural network module to achieve all-optical integration of the DAS sensing system and signal processing by a pure optical method, which has the advantages of reducing photoelectric conversion, enhancing parallel processing capabilities, increasing processing speed, reducing energy consumption, improving system stability, and simplifying system architecture.