Wall Diagnostic Measuring Device With Automatic Object Recognition
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Solution Overview
Problem
Existing wall diagnostic devices require manual user input for determining wall type, which can lead to inaccurate object recognition and diagnostics.
Innovation Solution
A computer-implemented method for operating a wall diagnostic device that automatically classifies wall type and recognizes objects using radar data, incorporating a diagnostic module for wall type classification and object recognition, and provides diagnostic results including object position and type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input is used for determining wall type, then device complexity is reduced, but measurement precision deteriorates due to potential user errors
Solution Approach 1:
The measuring device automatically determines wall type by processing radar data through a diagnostic module, eliminating the need for manual user input. The system performs self-service by autonomously classifying wall types based on measured data, thereby improving measurement precision while accepting increased device complexity through automated intelligence.
Solution Approach 2:
The patent replaces manual mechanical input (user selecting wall type) with an automated electronic/radar-based system. The diagnostic module uses radar data processing and classification algorithms to substitute human judgment with automated technical analysis, improving accuracy by eliminating human error in wall type determination.
2Measurement precision
If automatic wall type classification is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The diagnostic module serves multiple functions: it processes radar data, performs wall type classification, identifies objects within walls, and provides comprehensive diagnostic results. By making the diagnostic module universal and multi-functional, the patent improves object recognition accuracy while consolidating complexity into a single integrated system rather than multiple separate components.
Solution Approach 2:
The system performs preliminary wall type classification before conducting detailed object recognition. This preliminary action prepares the data and establishes the context needed for more accurate subsequent object identification, improving overall measurement precision while organizing the complex diagnostic process into manageable sequential steps.
3Reliability
If wall type is determined manually before object recognition, then device complexity is minimized, but reliability deteriorates due to wrong wall type settings
Solution Approach 1:
The diagnostic module continuously processes radar data and uses the results to refine wall type classification. The system incorporates feedback loops where measurement results inform subsequent classifications, ensuring reliable and accurate diagnostic outcomes by automatically adjusting to the actual wall conditions rather than relying on potentially incorrect manual settings.
Solution Approach 2:
The system performs self-service by automatically determining wall type without human intervention. This autonomous capability eliminates the reliability issue of wrong manual settings while accepting the necessary device complexity to implement the automated determination system.
4Productivity
If radar data is analyzed in smaller windows, then device complexity is reduced, but productivity deteriorates due to slower processing
Solution Approach 1:
The patent segments radar data into multiple windows for parallel or sequential processing. By dividing the large dataset into smaller manageable windows, the system can process data more efficiently and quickly, improving productivity. The segmentation approach organizes complex data processing into structured units that can be handled systematically.
Solution Approach 2:
The patent processes radar data by introducing a spatial or temporal window dimension, analyzing data in structured segments across different dimensions. This dimensional organization of data processing enables faster computation and improves productivity while managing device complexity through structured multi-dimensional analysis rather than monolithic processing.
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 precise and accurate wall diagnostics by automatically determining wall type and object recognition, reducing errors and enhancing the quality of diagnostic results.
Implementation Method 1
receiving radar data from a radar sensor unit of the measuring device
Implementation Method 2
the received radar data comprises a plurality of radar signals of different frequencies reflected by the wall
Data Source
AI summary
A computer-implemented method for operating a measuring device, in particular a wall diagnostic device, includes (i) receiving radar data of a radar sensor unit of the measuring device, (ii) performing wall diagnostics by performing an analysis of the radar data and generating diagnostic results using a diagnostic module of the measuring device, (iii) performing an object recognition of an object disposed in the wall using the diagnostic module based on the radar data and taking into account the wall type classification results of the wall type, and (iv) providing the diagnostic results of the diagnostic module to a display unit of the measuring device for displaying the diagnostic results to a user of the measuring device.