Random Sub-Map Processing for Large-Scale Map Creation
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Solution Overview
Problem
Current methods for creating maps from large datasets collected by mobile sensors are impractical due to the enormous quantity of data and the need for centralized processing, which is costly and inefficient, especially when covering large areas like the entire planet.
Innovation Solution
A method that uses a random statistical combination of sub-maps created within a distributed computer network, allowing for the creation of maps without the need for deterministic processing or centralized data repatriation, leveraging 'cloud' computing to manage dispersed data and facilitate efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized processing methods are used to create maps from large datasets, then map accuracy can be maintained, but processing costs and time become prohibitively expensive and impractical
Solution Approach 1:
The patent divides the large dataset into multiple smaller sub-maps that can be processed independently and in parallel. Each sub-map is generated by a separate computational unit, allowing the overall mapping task to be distributed across many processors simultaneously, thus maintaining accuracy while dramatically improving processing efficiency.
Solution Approach 2:
The patent creates multiple copies of the mapping process running in parallel across different computational units. Each unit independently generates sub-maps from the same source data, and these sub-maps are then combined to form the complete map, enabling scalable processing without sacrificing map quality.
2Loss of information
If all measurement data is centralized to a single location for processing, then complete map coverage can be achieved, but data transmission costs and network bandwidth requirements become unmanageable
Solution Approach 1:
Instead of centralizing all data, the patent segments both the data and the processing tasks. Each computational unit processes a portion of the data locally to generate sub-maps, eliminating the need to transmit entire datasets across the network while still achieving complete map coverage through combination of sub-maps.
Solution Approach 2:
The patent transitions from a centralized single-point processing model to a distributed multi-point processing model, adding the dimension of spatial distribution to the processing architecture. This allows data to be processed wherever it is received, eliminating the need for centralized data aggregation.
3Stability of the object's composition
If deterministic processing methods are used with known partitioning, then data can be systematically organized, but the system becomes rigid and cannot adapt to dynamic cloud computing environments
Solution Approach 1:
The patent replaces deterministic partitioning with dynamic, random partitioning where computational units independently generate sub-maps based on random sampling of the data. This dynamic approach allows the system to adapt to changing cloud computing resources while still producing systematically organized output through the statistical combination of sub-maps.
Solution Approach 2:
The patent changes the fundamental parameter of data partitioning from fixed and deterministic to random and flexible. By using random partitioning, the system can dynamically adapt to available computational resources in cloud environments while maintaining data organization through the statistical aggregation of results.
4Area of stationary object
If the entire globe is mapped with high resolution renewal, then comprehensive geographic coverage is achieved, but the data volume becomes absolutely unmanageable by conventional computing methods
Solution Approach 1:
The patent segments the enormous task of global mapping into countless small sub-map generation tasks that can be distributed across cloud computing resources. Each computational unit generates a small sub-map through random sampling, and the collective output of many such units produces the complete high-resolution global map, making the data volume manageable through parallel processing.
Solution Approach 2:
The patent uses random sampling to generate sub-maps, which inherently processes only a partial subset of the total data at each computational unit. This partial action approach allows the system to handle massive data volumes by processing representative samples in parallel, with the statistical combination of these samples providing complete coverage without requiring processing of every single data point centrally.
Data Source
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AI summary
The invention concerns a method for creating maps in an automatic manner using sub-maps created randomly and combined by statistical accumulation. Said measurements from information sensors, positioned on mobile platforms, are sent to reception means (71, 72, 73, 74, 75) disposed randomly and independent of each other and transmitting the received information to a random computer processing layer. The random processing layer (200), creates within same random sub-maps as the information received by the computer network becomes available, the random sub-maps (90, 91, 92, 93, 94) being dispersed arbitrarily in the random processing layer, access pointers being associated with the random sub-maps in order to make it possible to find them. An organising layer (100), using the access pointers, carries out the statistical recombination (110, 111) of the random sub-maps dispersed in the random processing layer with a view to reconstructing the desired final map or maps (21, 22). The method according to the invention, in one of the applications of same, is particularly useful for the economical and swift creation of complex maps with very large coverage, as required to monitor a country, the resources of same or the economic activity thereof.