In modern relational databases, data security and integrity are crucial, particularly in large-scale, unevenly distributed big data environments. This paper proposes a lossless relational data watermarking scheme based on uneven partitioning, addressing the non-uniformity of data by managing data partitioning within the database. By selecting key attributes and setting appropriate thresholds, data is allocated to partitions of varying sizes and volumes. Different watermark intensities are embedded based on these partitions. Traditional robust watermarking schemes modify carriers to maintain data quality but often lack robustness. To improve this, the proposed scheme uses watermark encoding algorithms to integrate data bits with watermark information, storing the generated auxiliary data. During watermark extraction, the watermark is recovered from this auxiliary data through decoding algorithms. This method enhances watermark capacity by encoding at least one attribute of each data tuple, avoiding original data modifications and common distortion issues. Experimental results show that this scheme, combining uneven partitioning with high-capacity lossless watermarks, offers superior robustness and security compared to traditional methods. It provides an efficient and reliable option for data protection in relational databases, particularly suitable for large-scale and dynamically changing data environments.