  In the rapidly advancing information era, various human behaviors are being
precisely recorded in the form of data, including identity information,
criminal records, and communication data. Law enforcement agencies can
effectively maintain social security and precisely combat criminal activities
by analyzing the aforementioned data. In comparison to traditional data
analysis methods, deep learning models, relying on the robust computational
power in cloud centers, exhibit higher accuracy in extracting data features and
inferring data. However, within the architecture of cloud centers, the
transmission of data from end devices introduces significant latency, hindering
real-time inference of data. Furthermore, low-latency edge computing
architectures face limitations in direct deployment due to relatively weak
computing and storage capacities of nodes. To address these challenges, a
lightweight distributed knowledge graph completion architecture is proposed.
Firstly, we introduce a lightweight distributed knowledge graph completion
architecture that utilizes knowledge graph embedding for data analysis.
Subsequently, to filter out substandard data, a personnel data quality
assessment method named PDQA is proposed. Lastly, we present a model pruning
algorithm that significantly reduces the model size while maximizing
performance, enabling lightweight deployment. In experiments, we compare the
effects of 11 advanced models on completing the knowledge graph of public
security personnel information. The results indicate that the RotatE model
outperforms other models significantly in knowledge graph completion, with the
pruned model size reduced by 70\%, and hits@10 reaching 86.97\%.}
