[28] highlight the complexity and necessity of tailored NILM solutions in energy-intensive industrial settings, addressing the unique challenges of industrial load disaggregation. Recent NILM research has advanced towards adaptive and intelligent energy management systems, integrating machine learning [29] and IoT technologies [30] to enhance system accuracy and expand use in residential and industrial settings. Methods like electrical quantities [31; 32; 33], power theories [34; 35; 36], and harmonic decomposition [37; 38] are widely used. This paves the way for more sustainable energy use. However, existing methods focusing on current, voltage, and power analysis are limited and not fully representative. Traditional threshold-based load identification methods, although straightforward, face challenges in typical households due to the difficulty in collecting device data and high false-positive rates for new or similar devices. In addressing these challenges, a novel power load identification method is proposed that integrates clustering algorithms with probabilistic graph models. This method begins with the acquisition of electrical behavior characteristics of equipment via NILM, followed by load decomposition to obtain electrical signal parameters under operational states. Subsequently, an electrical behavior event division method is employed to categorize the collected electrical signal parameters into events, identifying independent parameter events within each signal. An improved K-means method is then utilized to cluster the power consumption behavior characteristics under various parameters, determining the feature center. Building upon this, a Bayesian network model is established, including Noisy-Max nodes and leak nodes, with distinct node characteristics set to facilitate the identification of electrical equipment operating states through probabilistic reasoning. A two-layer identification model is thus formulated. The upper layer represents the operating states of the equipment, with each node signifying a specific load equipment state. The lower layer comprises cluster nodes, each denoting the center of a cluster. The proximity of data to the cluster center determines its contribution rate to the cluster, which can be translated into probabilities. Data closer to the cluster center are assigned a higher probability value, indicating a greater likelihood of that data state's existence. The feasibility of this research method is substantiated through practical experiments conducted in an electrical behavior analysis laboratory. This paper is organized into four distinct sections. Section one introduces the NILM technology and delves into its application in analyzing the operational status of household appliances, outlining the relevance, significance, and research developments in this area. The second section presents the proposed inferential algorithms, including advanced clustering techniques, the establishment and configuration of Bayesian networks, and the probability inference calculation based on these networks. Section three details the validation of these methods through simulation experiments, using microwaves as the test subject across various scenarios. The final section synthesizes the outcomes of these experiments, offering a conclusive overview of the results.