# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 2 System Design and Application
### Overall System Architecture Design
This model organizes the required knowledge and diagnostic processes into two distinct but interconnected layers:

Data Layer: This foundational layer establishes a structured and formalized knowledge base. Its purpose is to represent the complex relationships between BMU components, operational states, potential faults, and associated data points, integrating prior knowledge in a machine-interpretable format. Inference Layer: this layer operates on top of the data layer. It implements the diagnostic logic, utilizing the structured knowledge base and real-time operational data feeds to infer the current system state and identify potential faults. #### 2.2.1 Construction of Data Layer Structure

The construction of the data layer, introduced above, focuses on creating a comprehensive and structured representation of BMU-related knowledge. This involves capturing both concrete semantic descriptions of facts and abstract conceptual attributes [22; 23]. The primary goal is to abstract key concepts (like equipment components, actions, and faults), their attributes, and the relationships between them from the available domain knowledge. This process defines the description scope, hierarchical architecture, and association rules of the global data structure needed for the diagnosis. This study employs a top-down construction methodology. Concepts are identified and then linked via directed edges that represent the logical or physical relationships between them. This methodology results in the BMU data layer structure framework diagram shown in Figure 2.

Figure 2 illustrates the correlation between device structure, action form, fault information, and operational data. Among them, the structure of the device determines the actions and functions that the device can perform, and each action has data points related to it. The data points have attributes such as the type, address, and value, and the action

Figure 2: BMU data layer structure framework diagram. or fault state of the device can be inferred through the value attributes of the data points. The type and address attributes can be used to determine the fault type and locate the fault. Therefore, the core concept of describing the fault diagnosis problem of BMU operations can be divided into basic equipment data, which mainly include the equipment model, the user unit, factory installation information, project information, etc. ; operating data, which mainly include the equipment operating status, operating time, abnormal alarms, operating data values, etc. ; and fault data, which mainly include the fault type, fault description, fault analysis, fault location, and handling suggestions. Finally, the integration of BMU equipment structure, installation location, action form, fault description, fault location, fault analysis and other information was achieved, and the correlation between all concepts was expressed. #### 2.2.2 Design of the Diagnostic Model Based on a Fault Constitutive Relationship

The relationship between general fault phenomena and root causes is based on binary data, which cannot be used to handle more complex multi-source data relationships [24; 25]. Therefore, based on the established data layer structure model and the application of multivariate relationship knowledge expression methods, this article introduces operational data variables into the fault phenomenon-fault cause correlation matrix and establishes a multi-source relationship correlation matrix based on operational data, fault phenomena, and fault causes. Firstly, establish the correlation between operational data and fault phenomena, as shown in \[L_{n}:D_{p}^{\prime}(t)\xrightarrow{\Pi_{\text{TCP/IP},\Pi_{\text{SC/WIFI}}} }D_{dp}(t) \tag{2}\]0

From Figure 3, it can be observed that the real-time operational dataset \(D_{t}\) plays a crucial role in this process, representing different real-time data states or feature sets.