Semantic Agent Deployment for Resource-Matched Target Devices
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Solution Overview
Problem
In complex industrial systems, the manual selection and configuration of target devices for deploying analytical agents is error-prone and inefficient, often resulting in suboptimal resource utilization and data delays due to the need for manual intervention and lack of automation in selecting suitable devices for data processing.
Innovation Solution
A deployment system comprising a mapping unit for semantic mapping of agent data models to target device data models, a calculation unit for determining suitability based on resource requirements, and a deployment unit for automatically deploying agents on the most suitable target devices, using conversion mappers to ensure compatibility and optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and configuration of target devices is performed, then deployment accuracy can be ensured, but deployment efficiency and productivity deteriorate due to time-consuming manual intervention
Solution Approach 1:
The system enables self-service deployment by automatically selecting target devices and configuring agents without manual intervention. The automated deployment system evaluates device suitability, performs semantic mapping, and deploys agents autonomously based on resource requirements and data model compatibility, eliminating the need for manual configuration while maintaining deployment accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of device selection and configuration with an automated computational system. The system uses semantic mapping, resource evaluation algorithms, and automated decision-making to substitute human operators, thereby improving deployment efficiency while maintaining accuracy through systematic evaluation criteria
2Reliability
If manual configuration of agents on target devices is performed, then deployment reliability can be maintained, but deployment time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-evaluating target device suitability, pre-mapping data models semantically, and pre-configuring deployment parameters before actual agent deployment. This preliminary preparation ensures reliability by validating compatibility upfront while reducing deployment time by eliminating configuration steps during the actual deployment process
Solution Approach 2:
Manual configuration tasks are replaced with automated computational processes that evaluate device compatibility, map data models, and configure agents programmatically. This substitution maintains reliability through systematic validation while dramatically reducing the time required for configuration compared to manual operations
3Loss of information
If data is concentrated at a central control element, then data availability improves, but data transmission delays and inconsistencies increase
Solution Approach 1:
The system applies local quality by deploying agents directly at the edge devices where data is generated rather than concentrating all data processing at a central control element. Each target device processes its own sensor data locally using deployed agents, ensuring data availability while eliminating transmission delays and inconsistencies associated with centralized data concentration
4Productivity
If agent deployment is automated, then productivity improves, but system complexity increases due to semantic mapping and suitability calculation requirements
Solution Approach 1:
The system achieves universality by creating a general-purpose automated deployment framework that handles multiple device types, data models, and agent configurations through a unified semantic mapping approach. This universal system manages complexity through standardized processes while maintaining high productivity across diverse industrial automation scenarios
Solution Approach 2:
The patent introduces semantic mapping as an intermediary layer between agent data models and target device data models. This intermediary handles the complexity of compatibility checking and transformation automatically, enabling productive automated deployment without requiring direct complex interactions between diverse systems
Data Source
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AI summary
A method and apparatus for deployment of an agent to a target device of a target system A deployment system for deployment of an agent to a target device of a target system, said deployment system (1) comprising: a mapping unit (2) adapted to perform a semantic mapping of an agent data model of the agent to target device data models of target devices of said target system, wherein each target device data model comprises an associated suitability model; calculation unit (3) adapted to calculate the suitability of each matching target device data model for the respective agent on the basis of its suitability model depending on resource requirements of the agent; a deployment unit (4) adapted to deploy an agent on the target device having the maximum calculated suitability.