Fire Control Panel Model Generation for Cloud Integration
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Solution Overview
Problem
Current fire control systems face challenges in integrating third-party fire control panels due to incompatible event text formats with cloud data structures, requiring time-consuming and resource-intensive methods that involve parsing and converting text strings without altering the base code.
Innovation Solution
Generating a model for the control panel based on user input and event text from third-party panels, allowing the cloud engine to translate event text into the fire control system's data structure, facilitating the rapid integration of new brands without modifying the base code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional parsing and conversion methods are used to integrate third-party control panels, then integration capability is achieved, but integration time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and standardizing event text from third-party control panels into a universal format during the integration setup phase. This preliminary conversion creates a standardized data structure that can be directly utilized by the cloud engine without requiring time-consuming parsing operations during actual fire alarm events, thereby resolving the contradiction between integration capability and integration time.
Solution Approach 2:
The patent introduces an intermediary component - a format conversion layer that translates third-party control panel event texts into a standardized universal format. This intermediary acts as a mediator between the diverse third-party panels and the cloud engine, enabling seamless integration without requiring the cloud engine to perform complex parsing operations, thus reducing integration time while maintaining high adaptability.
2Adaptability or versatility
If base code is modified to support new control panel brands, then integration flexibility improves, but system complexity and maintenance difficulty increase
Solution Approach 1:
The system segments the integration functionality into a separate, modular format conversion component that operates independently from the core base code. By dividing the integration task into distinct modules (parsing layer, conversion layer, standardized output layer), the system achieves high integration flexibility without modifying the base code, thereby preventing an increase in overall system complexity.
Solution Approach 2:
The patent implements a universal format conversion layer that can handle multiple third-party control panel brands through a single standardized interface. This multi-functional component serves all integration needs without requiring separate code modifications for each brand, maintaining integration flexibility while avoiding the complexity that would arise from multiple specialized code paths.
3Measurement precision
If comprehensive text parsing is performed on event strings, then data extraction accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs comprehensive text parsing and data extraction as a preliminary action during the format conversion phase, before data is transmitted to the cloud engine. By completing the resource-intensive parsing operations in advance and storing the extracted data in a standardized structure, the system achieves high data extraction accuracy while avoiding repeated computational resource consumption during critical fire alarm response operations.
Data Source
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AI summary
Methods, devices, and systems for generating a model for a control panel of a fire control system, so that the text messages issued can be interpreted by a centralised platform. A user is prompted to identify variables and their positions in a text string message issued by the control panel which is then used for the model. Allows integration of control panels of different brands with the fire system. One computing device includes a memory and a processor configured to execute executable instructions stored in the memory to: receive a test string from a control panel of a fire control system, the text string comprising a number of values; receive a number of locations from a user, wherein each location of the number of locations is a location within the text string of a value of the number of values corresponding to a variable of a number of variables; generate a model for the control panel based on the number of locations and the number of variables; and store the model in the memory.