Composite ML Model for Infrastructure SLA Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack a universal method for assessing and balancing service level agreements (SLAs) for facility infrastructure, leading to unreliable information and inefficient management of infrastructure reliability, particularly in predicting failures and coordinating maintenance across different services and localities.
Innovation Solution
A system and method utilizing a composite machine-learning (ML) model that evaluates facilities infrastructure reliability by training on historical availability, performance, and error rates data, enabling predictive analysis for quality assessment and proactive maintenance planning, including the integration of predictive analysis modules and model orchestrators to coordinate construction and service operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional on-site survey methods are used for CBYD programs, then infrastructure location information can be obtained, but the information is unreliable and outdated
Solution Approach 1:
The system performs preliminary actions by continuously collecting and updating infrastructure location data from multiple sources (GIS systems, utility companies, construction permits, aerial imagery) before CBYD requests are made. This proactive data gathering and validation process ensures that when location information is needed, accurate and current data is already available, eliminating the need for unreliable traditional on-site surveys.
2Loss of information
If manual on-site surveying is performed for each CBYD request, then infrastructure information can be obtained, but the process is time-consuming and inefficient
Solution Approach 1:
The system creates and maintains digital copies of infrastructure location information from multiple authoritative sources (GIS databases, utility company records, construction permit data, aerial imagery). These digital copies are continuously updated and validated, allowing instant retrieval of accurate infrastructure location data without requiring physical on-site surveys for each CBYD request, thus eliminating time losses while ensuring information availability.
3Measurement precision
If comprehensive infrastructure data collection from multiple sources is implemented, then information accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex data collection process into distinct modular components: GIS system integration module, utility company data interface, construction permit processing module, aerial imagery analysis module, and data validation module. Each module independently collects and processes data from its specific source, then passes validated information to the central infrastructure database. This segmentation manages system complexity by creating manageable, independent data collection channels while maintaining comprehensive coverage and high accuracy through multiple sources.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of constructing a composite machine-learning (ML) model for facilities infrastructure from facilities infrastructure data; training the composite ML model with historical availability data, historical performance data, and historical error rates, wherein the composite ML model yields quality of the facilities infrastructure; receiving a query of a facility in an area from a user; predicting a quality of the facility based on recent facilities data using the composite ML model; and providing the quality of the facility responsive to the query. Other embodiments are disclosed.