Temporal Knowledge Graphs (TKGs) often suffer from data inconsistencies such as event timestamp conflicts, logical contradictions in entity attributes over time, and relational inconsistencies. These issues undermine the reliability of TKGs and significantly impair the accuracy and stability of downstream tasks, thereby limiting their applicability in real-world scenarios. To address these challenges, this paper proposes a privacy-preserving inconsistency repair method-Temporal Knowledge Graphs Data Repair Solver (TDRSolver). TDRSolver innovatively integrates Temporal Graph Functional Dependencies (TGFDs) with the Ant Colony Optimization (ACO) algorithm, leveraging the expressive power of TGFDs to capture time-sensitive constraints, while utilizing ACO for global optimization. Furthermore, a differential privacy mechanism is incorporated to protect sensitive information during the repair process. TDRSolver consists of five key modules: data preprocessing module, inconsistency detection module, ACO repair module, repair evaluation module, and cyclic validation module, forming a closed-loop detect-repair-validate workflow. Extensive experiments on multiple TKG datasets demonstrate that the proposed method not only achieves high-accuracy inconsistency resolution but also provides strong privacy protection. Additionally, TDRSolver exhibits a degree of adaptiveness under varying experimental settings, effectively balancing semantic consistency, repair cost, and privacy preservation.