Facility Surveillance Using BIM-Based Anomaly Classification
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Solution Overview
Problem
Existing facility surveillance systems struggle to effectively classify and respond to anomalous states due to limitations in data analysis and sensor integration, particularly in complex environments like buildings and industrial plants, leading to inefficiencies and potential safety risks.
Innovation Solution
A facility surveillance system utilizing a central computing unit with a dynamic building information model (BIM) and integrated sensors for continuous data generation, combined with machine learning algorithms for state pattern recognition and classification, enabling real-time anomaly detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surveillance systems are used for facility monitoring, then device complexity is reduced, but measurement precision and anomaly classification accuracy deteriorate
Solution Approach 1:
The system segments the facility into a building information model with hierarchical structure (rooms, floors, building levels), allowing precise localization and classification of anomalies within specific spatial contexts. This segmentation enables the system to process and classify anomalies at appropriate granularities, improving measurement precision without overwhelming the system with undifferentiated data.
Solution Approach 2:
The building information model serves as an intermediary layer between raw sensor data and anomaly classification. It provides topological and spatial context that mediates between simple sensor inputs and complex classification decisions, enabling accurate anomaly evaluation while maintaining manageable system complexity through structured information representation.
2Reliability
If simple surveillance systems are deployed, then ease of operation is improved, but reliability and safety monitoring capability worsen
Solution Approach 1:
The system performs self-service through automated anomaly detection and classification using the building information model and sensor integration. It automatically evaluates surveillance data, identifies anomalies, and classifies them without requiring manual intervention, thereby improving reliability while maintaining ease of operation through autonomous functionality.
Solution Approach 2:
The system implements feedback mechanisms where anomaly detection results are continuously evaluated and used to refine monitoring. The building information model provides feedback loops that allow the system to learn from detected anomalies and improve its classification accuracy over time, enhancing reliability while operating autonomously.
3Measurement precision
If comprehensive sensor integration is implemented, then measurement precision and anomaly detection improve, but device complexity and data processing requirements worsen
Solution Approach 1:
The building information model serves as a universal framework that integrates multiple sensor types (motion detectors, temperature sensors, smoke detectors, etc.) into a unified surveillance system. This multi-functional approach allows diverse sensors to contribute to anomaly detection through a common evaluation mechanism, improving measurement precision while managing integration complexity through standardized processing.
Solution Approach 2:
The system adds spatial and temporal dimensions to sensor data through the building information model's topological representation. By organizing sensor inputs within the hierarchical structure of rooms, floors, and building levels, the system transforms raw sensor data into spatially-contextualized information, improving anomaly detection precision while managing data complexity through dimensional organization.
4Productivity
If real-time anomaly classification is performed, then productivity and response time improve, but use of energy and computational resources worsen
Solution Approach 1:
The system applies partial action by focusing computational resources on evaluating only those surveillance data elements that represent potential anomalies rather than processing all sensor data uniformly. The building information model enables selective evaluation of relevant facility elements, improving productivity by responding quickly to anomalies while reducing energy consumption by avoiding unnecessary processing of normal conditions.
Data Source
AI summary
Systems and methods for surveillance of a facility including facility elements. The system includes a central computing unit providing a digital model of the facility providing topological or logical or functional relationships of the facility elements, surveillance sensors adapted for surveillance of a plurality of the facility elements and for generation of surveillance data, communication means for transmitting data from the surveillance sensors to the central computing unit, and state derivation means configured to analyse the surveillance data and derive a state of a respective facility element. The central computing unit is configured to record a state pattern by combining states of at least one facility element based on at least one relationship of the facility element provided by the facility model, provide a state pattern critical-noncritical classification model which considers relationships provided by the facility model, and perform a criticality-classification based on the relationship.