Land Surveying Data Prioritization for Real-Time 3D Mapping
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Solution Overview
Problem
Existing land surveying technologies face issues with latency, erroneous synchronization, and inefficient data processing, particularly in real-time data management and collaborative visualization across users, leading to inaccurate and bandwidth-intensive rendering of survey data.
Innovation Solution
A computer-implemented method and system that utilizes machine learning algorithms to segment and prioritize surveying data based on quality, geolocation, and site conditions, integrating it into a cloud-based platform for real-time 3D mapping and collaborative interaction, using VR/AR technologies to optimize data rendering and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voluminous sensor data is continuously processed in real-time with multiple algorithms applied, then comprehensive 3D mapping and collaborative visualization are achieved, but latency and erroneous synchronization issues arise
Solution Approach 1:
The patent segments surveying data into multiple data structures with different levels of detail and processing requirements. High-priority data structures contain critical information that requires immediate processing, while lower-priority structures can be processed asynchronously. This segmentation allows the system to maintain measurement precision for critical data while reducing overall processing latency through prioritized handling.
Solution Approach 2:
The system dynamically changes processing parameters based on data priority and system state. By adjusting processing algorithms, resolution levels, and synchronization thresholds according to data importance, the system maintains high accuracy for critical survey data while optimizing processing speed for less time-sensitive information, thereby reducing latency without sacrificing necessary precision.
2Loss of information
If multiple algorithms including 3D point cloud generation and machine learning techniques are applied to surveying data, then comprehensive analysis and visualization are achieved, but computational resources and processing time increase
Solution Approach 1:
The patent divides complex surveying data into segmented data structures that can be processed by different algorithms independently. This segmentation allows machine learning models, 3D point cloud generation, and other computational algorithms to operate on specific data portions without requiring all algorithms to process the entire dataset simultaneously, thereby reducing computational complexity while maintaining comprehensive analysis through coordinated processing of segmented data.
Solution Approach 2:
The system introduces intermediary data structures that serve as buffers between raw sensor data and final visualization outputs. These intermediary structures pre-process and organize data in ways that reduce the computational burden on downstream algorithms, allowing comprehensive analysis to be achieved with reduced processing complexity by eliminating redundant computations and optimizing data flow between algorithmic stages.
3Ease of operation
If real-time collaborative visualization is enabled across multiple users on the cloud, then collaborative interaction is improved, but data synchronization and bandwidth requirements increase
Solution Approach 1:
The patent implements local quality optimization where different users receive customized data subsets based on their specific needs, roles, and current interactions. Instead of transmitting complete survey datasets to all users, the system delivers only the relevant portions required for each user's collaborative tasks. This approach maintains ease of operation by enabling seamless collaborative visualization while significantly reducing bandwidth consumption through targeted, efficient data transmission.
4Measurement precision
If high-resolution 3D mapping is rendered in real-time, then visualization quality is improved, but rendering time and computational resources increase
Solution Approach 1:
The system dynamically adjusts rendering parameters and data detail levels based on user interaction, camera position, and current operational context. High-resolution 3D mapping is rendered only in regions of interest or when users require detailed inspection, while other areas use lower resolution representations. This dynamic approach maintains measurement precision where needed while improving overall rendering speed and productivity by avoiding unnecessary high-resolution rendering across the entire dataset.
Data Source
AI summary
A method, computer system, and a computer program for optimizing land surveying is provided. An associated method includes receiving a plurality of sensor data; receiving a plurality of commands associated with at least a site pertaining to the surveying data; analyzing the surveying data based on the plurality of commands; and rendering a 3D-mapping of the site based on the analysis.