Cloud Control Templates for Precise Remote Setting Updates
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Solution Overview
Problem
Existing systems struggle to efficiently and precisely adjust variable settings on remotely-controlled systems in response to changing conditions, leading to inefficiencies and resource wastage.
Innovation Solution
A cloud-based control platform that coordinates and iteratively updates settings across multiple systems by leveraging virtually unlimited computing resources, using templates and hyperparameters to adjust settings based on real-time data and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized cloud-based control is implemented to precisely adjust variable settings of remotely-controlled systems, then control precision and efficiency are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
A cloud-based control platform is introduced as an intermediary between users and remotely-controlled systems. The platform receives control inputs, processes them through template-based algorithms, and generates precise control outputs. This intermediary architecture enables sophisticated control precision while keeping individual remote systems simple, as the computational complexity is centralized in the cloud platform rather than distributed across multiple remote devices.
2Adaptability or versatility
If cloud-based control platform processes data for multiple disparate systems, then centralized control capability is improved, but computational resource consumption increases
Solution Approach 1:
The cloud-based control platform implements a universal template-based control architecture that can handle multiple types of remotely-controlled systems through a single unified interface. The platform uses parameterized templates that can be configured for different system types (IoT devices, industrial equipment, vehicles, etc.), allowing one platform to serve multiple functions without requiring separate processing systems for each device category, thereby reducing overall computational resource consumption.
Solution Approach 2:
The control platform utilizes parameterized templates with configurable hyperparameters that can be adjusted based on the specific requirements of different remotely-controlled systems. By changing parameters within the same template structure rather than creating entirely new control logic for each system type, the platform achieves adaptability across diverse systems while minimizing computational overhead through reusable template frameworks.
3Productivity
If iterative updates of system settings are performed based on real-time data, then operational efficiency is improved, but data processing time and complexity increase
Solution Approach 1:
The control platform pre-processes and validates control inputs against template definitions before executing iterative updates. By performing preliminary checks on data format, parameter ranges, and template compatibility upfront, the system avoids time-consuming error handling and validation during the actual iterative update process, thereby reducing overall data processing time while maintaining operational efficiency.
Solution Approach 2:
The platform implements efficient feedback mechanisms where control outputs are monitored and fed back into the template-based processing system for continuous optimization. The feedback loop uses predefined thresholds and update triggers to determine when iterative adjustments are necessary, avoiding unnecessary processing cycles and reducing data processing time while maintaining high operational efficiency through targeted, condition-based updates.
Data Source
AI summary
In a general aspect, a cloud-based control platform remotely controls variable settings on a remotely-controlled system. In some aspects, the platform maintains a database of data objects for assets, and the data objects include static and dynamic hyperparameters associated with the asset and a template that specifies values of a variable associated with the asset for future time points. The platform updates a template for a data object by calculating target values for the variable for the future time points based on a target criterion, communicating with remote computer systems to determine current values of the dynamic hyperparameters, calculating scaled values for the future time points by applying a determined ratio to the target values, adjusting the scaled values based on the static hyperparameters, applying an override value to the adjusted scaled values. The remotely-controlled system is updated according to the updated template at each of the future time points.