Automated Solar Site Modeling with Stereoscopic 3D Reconstruction
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Solution Overview
Problem
Existing methods for generating solar energy system proposals are time-consuming, error-prone, and lack accurate, dynamic, and interactive features, particularly in rural areas, due to reliance on limited 3D data and human involvement, leading to elongated sales cycles and potential loss of sales.
Innovation Solution
An automated system and method that generates highly accurate, interactive solar energy system proposals using stereoscopic imagery and neural networks to analyze site features, providing real-time, dynamic proposals and educational Preposal™, eliminating the need for LiDAR instruments and human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems with stereoscopic imagery and neural networks are used, then productivity and accuracy improve, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical surveying methods with automated stereoscopic imagery capture and neural network-based 3D reconstruction. The system uses pairs of images taken from different angles to automatically generate accurate 3D models of sites, eliminating the need for manual LiDAR operations and human measurement while significantly increasing productivity and reducing errors.
2Measurement precision
If LiDAR instruments and manual surveying are used, then measurement precision can be achieved, but loss of time and productivity decrease
Solution Approach 1:
The system performs preliminary 3D site modeling automatically using pre-captured stereoscopic imagery and neural networks before the sales process begins. This preliminary action creates accurate site models in advance, eliminating the need for time-consuming manual surveys during sales cycles while maintaining high measurement precision through automated 3D reconstruction algorithms.
3Reliability
If manual involvement and traditional methods are used, then adaptability to complex situations can be achieved, but reliability and consistency deteriorate due to human error
Solution Approach 1:
The system performs self-service through automated neural network processing of stereoscopic imagery to generate 3D site models and solar proposals without human intervention. The neural networks automatically identify site features, calculate solar potential, and generate accurate proposals consistently, eliminating human error while maintaining high reliability through algorithmic precision.
Data Source
AI summary
Systems and methods are disclosed herein for generating and/or providing a model, such as may be utilized for designing and/or presenting a proposal for a project for a structure or site. The disclosed embodiments can include identifying installation surface faces, installation surface face benefit metrics, obstructions, obstacles affecting placement of elements of the project, and for providing an educational experience, comprising general information and at least one structure-specific installation proposal. A selected plan can be implemented from within a proposal platform of the disclosed systems and methods.