Research on Intelligent Big Data Anomaly Detection and Fault Diagnosis Algorithm Based on Machine Learning

In the epoch of voluminous data, to proficiently discern and prognosticate atypical conduct and nascent anomalies within expansive and intricate architectures, this discourse advances a conjoined computational learning framework, amalgamating the K-Means agglomeration procedure with the Particle Swarm Optimization (PSO) heuristic. The K-Means methodology is employed to preliminarily classify the data, thereby identifying intrinsic conglomerates within the data ensemble. Nonetheless, the K-Means technique is exceedingly responsive to the determination of incipient agglomeration nuclei, which might culminate in a parochial zenithal resolution as opposed to a universal zenithal solution. Via the renovation of the individual acme and the collective acme, the zenithal resolution was incrementally approximated, thereby enhancing the precision and resilience of the agglomeration. Moreover, the PSO algorithm could dynamically calibrate the quantity of conglomerates, circumventing the quandary of subjectively stipulating the quantity of conglomerates, rendering the model more adaptable and acclimatizable. This conjunctural strategy exploits the virtues of both methodologies to attain proficient and exact anomaly detection and malfunction prognosis.