Modern district heating (DH) systems are complex engineering structures that play an essential role in large city infrastructures. DH networks have many sensors, nodes, and methods for monitoring the status of the DH network. Sensing, processing, analytical actuation (SPA) of incoming information handled by the SPA semantic Computing method can be applied to similar problems. The SPA Semantic Computing method searches for correlations between the sets of incoming data and to identify the correct scenario to respond to events. This article explores the integration of SPA functions to analyze multivariate sensing data, including data from multivariable sensors and infrared images, for creating a monitoring system for DH networks. The focus is to assess whether the SPA approach is a suitable candidate to use to monitor the emergency events of the DH network. Specific target data for the assessment are [1] multi-parameter DH network sensor data, such as water temperature, sweat rate, energy delivered, etc., and [2] infrared image data from a camera mounted on the unmanned aerial vehicle (UAV) for monitoring the location of the underground DH network leaks. A multivariate computational model, a mathematical model of meaning (MMM), and a spatial image filtering method are proposed for integrating SPA semantic computing for emergency leak detection in DH networks.

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