Field Device Program Update via Translator and Executor
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Solution Overview
Problem
Current methods for updating and configuring field devices, such as those used in IoT applications, face challenges with cloud-computing approaches that result in data loss, high network and power consumption, and inflexibility, while edge-computing solutions complicate algorithm changes and require specialized skills.
Innovation Solution
A method that uses an input programming language to define behaviors, translating these into tasks executed by a field device's executor, allowing for dynamic scheduling and updates over limited network capacity without altering core functions or firmware, using a translator and executor to manage interactions with sensors and actuators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud-computing approaches are used for updating and configuring field devices, then remote configuration capability is improved, but network consumption and power usage increase
Solution Approach 1:
The patent pre-loads configuration data and program updates into local memory before they are needed for execution. This allows the field device to have configuration data ready locally, eliminating the need for continuous network connectivity during configuration operations and reducing power consumption from constant network communication.
Solution Approach 2:
The patent introduces a local server or gateway as an intermediary between the cloud and field devices. This intermediary caches and manages configuration data locally, allowing field devices to access configurations without direct cloud connectivity, thereby reducing network traffic and power usage while maintaining remote configuration capabilities.
2Ease of operation
If cloud-computing approaches are used for updating and configuring field devices, then remote configuration capability is improved, but data loss occurs
Solution Approach 1:
The system performs preliminary validation and consistency checks on configuration data before it is applied to field devices. Configuration data is verified against schemas and dependency rules in advance, preventing invalid configurations from being deployed and avoiding data loss or device malfunction.
Solution Approach 2:
The patent implements feedback mechanisms where configuration changes are tested in a sandbox environment before being applied to production systems. The system monitors configuration application results and provides feedback for correction if issues are detected, preventing data loss through iterative validation.
3Ease of operation
If cloud-computing approaches are used for updating and configuring field devices, then remote configuration capability is improved, but network consumption increases
Solution Approach 1:
The patent extracts configuration data and program updates from the cloud environment and stores them in local memory or cache on the field device or local server. This extraction eliminates the need for continuous network communication during configuration operations, significantly reducing network consumption while maintaining remote configuration capabilities.
Solution Approach 2:
Configuration data is downloaded and cached in advance before it is needed for device operation. This preliminary action reduces the frequency and volume of network transactions, as the device can access cached configurations locally rather than repeatedly querying the cloud, thereby reducing network consumption.
4Adaptability or versatility
If edge-computing solutions are used for field devices, then algorithm changes become more flexible, but device complexity increases
Solution Approach 1:
The patent segments the computing architecture into distinct layers: edge computing components for local data processing and algorithm execution, and cloud components for centralized management and configuration. This segmentation allows algorithm changes to be made flexibly at the edge while maintaining simplified device implementation through modular design.
Solution Approach 2:
The patent implements a universal configuration framework that can manage multiple field devices with different algorithms and functionalities through a common interface and data structure. This universality allows flexible algorithm changes across devices without increasing individual device complexity, as the complexity is abstracted away into a standardized management layer.
Data Source
AI summary
A method and a system for programming one or more behavior of a field device connected to a network comprising an input programming language to define the one or more behaviors to create an input program, transmitting over the network the input program to a translator coupled to the field device, translating the input program to generate a field program comprising a plurality of tasks and executing said field program by an executor coupled to said field device.