IoT Device Model Testing via Networked Physical Output Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The testing of device models in an IoT environment is not scalable or user-friendly, particularly for line of business (LOB), leading to inaccuracies in modeling due to increased complexity and demand for accurate modeling.
Innovation Solution
A device model testing tool (DMT tool) that enables testing of device models via a network connection, allowing access to physical devices, running tests, comparing outputs, and presenting results on a display, supporting various device models like CAPI, Modbus, and OPC-DA, and providing detailed reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device models are tested manually without automated tools, then testing can be performed with simple processes, but testing scalability and accuracy deteriorate due to increased complexity and demand for accurate modeling
Solution Approach 1:
The patent creates virtual copies of physical devices through device models that can be tested independently. The testing tool uses these model copies to simulate and validate device behavior without requiring complex physical testing setups, thereby improving modeling accuracy while reducing testing complexity.
Solution Approach 2:
The patent introduces a device model testing tool as an intermediary between the physical device and the validation process. This intermediary automates the comparison between expected model behavior and actual device performance, resolving the contradiction by providing accurate validation through automated mediation rather than complex manual testing.
2Productivity
If device models are tested with automated tools, then testing scalability and accuracy improve, but device complexity increases due to the need for network connections and software applications
Solution Approach 1:
The patent designs the device model testing tool with universal functionality that can test multiple device models across different domains (CAPI, Modbus, OPC-DA). This multi-functional approach improves testing scalability by using a single automated system for diverse testing needs, rather than requiring separate complex systems for each device type.
Solution Approach 2:
The testing tool performs self-service automation by automatically executing tests, comparing results, and generating reports without requiring extensive manual intervention. This self-automating capability improves scalability while managing system complexity through automated self-management of the testing process.
3Measurement precision
If comprehensive testing is performed on device models, then modeling accuracy improves, but time consumption increases due to the need to access physical devices and run multiple tests
Solution Approach 1:
The patent performs preliminary actions by pre-configuring device models and test cases before actual testing. The testing tool prepares virtual models and test scenarios in advance, allowing comprehensive accuracy validation to be achieved more efficiently by having all testing components ready beforehand rather than setting them up during the testing process.
Solution Approach 2:
The patent enables continuous testing through automated processes that can run tests continuously without manual interruption. The testing tool maintains continuous operation by automatically accessing physical devices, executing test sequences, and processing results without breaks, thereby achieving comprehensive modeling accuracy while reducing total testing time through uninterrupted continuous action.
Data Source
AI summary
An application or software tool is used for testing a device model with a physical device. The tool runs a test on the physical device, via the network connection, using an input of test data. The test data includes the input and a corresponding output value of the device model. The tool compares an output value of the physical device generated by the test with the output value of the device model to generate a result of the test. The results of the test are displayed on a display of a computing device.