Geometric Multi-Modal Sensor Fusion for Infrastructure Monitoring
A system and method for monitoring critical infrastructure combines multiple types of sensor data, including acoustic, vibration, and video sensors, into a unified geometric framework for enhanced threat detection. The system represents sensor information as geometric structures within a curved mathematical space that preserves the timing relationships between different types of sensor events. By computing optimal paths through this geometric space, the system can reason across different sensor types to identify patterns that indicate potential threats or equipment failures. The geometric representation naturally handles data compression while maintaining the relationships between different sensor modalities. When anomalies are detected through analysis of geometric patterns and information density, the system can generate alerts and provide explanations by traversing the geometric space. This approach enables earlier, and more accurate threat detection compared to traditional systems that analyze each sensor type separately before combining results.
1 . A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
maintain a latent manifold as a geometric substrate for cognitive operations, wherein the latent manifold has a pseudo-Riemannian metric that distinguishes temporal and spatial directions to preserve causality, and wherein the latent manifold evolves through use;
receive multi-modal sensor data comprising heterogeneous sensor inputs from infrastructure monitoring systems;
encode the multi-modal sensor data into geometric structures within the latent manifold, wherein semantic relationships between sensor modalities are represented through geometric properties including distance and curvature;
compress the multi-modal sensor data using compression methods that preserve geometric relationships;
compute geodesic paths through the latent manifold for cross-modal reasoning, wherein the paths connect related phenomena across different sensor modalities and are influenced by compression pressure derived from manifold curvature;
store persistent multi-modal representations as geometric regions within the latent manifold, wherein frequently accessed sensor fusion patterns develop characteristic geometric properties;
detect anomalies by identifying deviations from normal geometric patterns through compression pressure analysis and cross-modal geodesic correlation;
modify the geometric structure of the latent manifold based on cognitive operations, wherein successful detection patterns create persistent modifications to the manifold geometry; and
generate outputs by traversing the latent manifold and decoding geometric information into user-interpretable responses.
2 . The computer system of claim 1 , wherein the pseudo-Riemannian metric constrains geodesic paths to preserve temporal causality by maintaining time-forward progression through the manifold.
3 . The computer system of claim 1 , wherein the multi-modal sensor data comprises distributed acoustic sensing data, vibration sensor data, and video surveillance data from critical infrastructure monitoring.
4 . The computer system of claim 1 , wherein computing geodesic paths comprises calculating trajectories that maintain causal ordering of sensor events across different modalities.
5 . The computer system of claim 1 , wherein the software instructions further cause the computer system to perform multi-scale navigation by continuously traversing the latent manifold across different levels of spatial and temporal resolution.
6 . The computer system of claim 1 , wherein detecting anomalies comprises identifying regions of high compression pressure that exceed predetermined thresholds indicating areas requiring detailed analysis.
7 . The computer system of claim 1 , wherein the software instructions further cause the computer system to generate alternative scenarios by modifying stored geodesic paths and computing resulting trajectory variations.
8 . The computer system of claim 1 , wherein the compression methods comprise homomorphic encryption that enables computation on encrypted sensor data while preserving the geometric relationships in the latent manifold.
9 . The computer system of claim 1 , wherein the software instructions further cause the computer system to enhance degraded sensor data using correlation-based reconstruction that leverages learned spatiotemporal patterns from multiple sensor modalities.
10 . The computer system of claim 1 , wherein the software instructions further cause the computer system to synchronize multi-modal sensor streams by aligning temporal sequences according to causal ordering constraints derived from the pseudo-Riemannian metric.
11 . A computer-implemented method for multi-modal sensor fusion, comprising:
maintaining a latent manifold as a geometric substrate for cognitive operations, wherein the latent manifold has a pseudo-Riemannian metric that distinguishes temporal and spatial directions to preserve causality, and wherein the latent manifold evolves through use;
receiving multi-modal sensor data comprising heterogeneous sensor inputs from infrastructure monitoring systems; encoding the multi-modal sensor data into geometric structures within the latent manifold, wherein semantic relationships between sensor modalities are represented through geometric properties including distance and curvature;
compressing the multi-modal sensor data using compression methods that preserve geometric relationships;
computing geodesic paths through the latent manifold for cross-modal reasoning, wherein the paths connect related phenomena across different sensor modalities and are influenced by compression pressure derived from manifold curvature;
storing persistent multi-modal representations as geometric regions within the latent manifold, wherein frequently accessed sensor fusion patterns develop characteristic geometric properties;
detecting anomalies by identifying deviations from normal geometric patterns through compression pressure analysis and cross-modal geodesic correlation;
modifying the geometric structure of the latent manifold based on cognitive operations, wherein successful detection patterns create persistent modifications to the manifold geometry; and
generating outputs by traversing the latent manifold and decoding geometric information into user-interpretable responses.
12 . The method of claim 11 , wherein the pseudo-Riemannian metric constrains geodesic paths to preserve temporal causality by maintaining time-forward progression through the manifold.
13 . The method of claim 11 , wherein the multi-modal sensor data comprises distributed acoustic sensing data, vibration sensor data, and video surveillance data from critical infrastructure monitoring.
14 . The method of claim 11 , wherein computing geodesic paths comprises calculating trajectories that maintain causal ordering of sensor events across different modalities.
15 . The method of claim 11 , further comprising performing multi-scale navigation by continuously traversing the latent manifold across different levels of spatial and temporal resolution.
16 . The method of claim 11 , wherein detecting anomalies comprises identifying regions of high compression pressure that exceed predetermined thresholds indicating areas requiring detailed analysis.
17 . The method of claim 11 , further comprising generating alternative scenarios by modifying stored geodesic paths and computing resulting trajectory variations.
18 . The method of claim 11 , wherein the compression methods comprise homomorphic encryption that enables computation on encrypted sensor data while preserving the geometric relationships in the latent manifold.
19 . The method of claim 11 , further comprising enhancing degraded sensor data using correlation-based reconstruction that leverages learned spatiotemporal patterns from multiple sensor modalities.
20 . The method of claim 11 , further comprising synchronizing multi-modal sensor streams by aligning temporal sequences according to causal ordering constraints derived from the pseudo-Riemannian metric.