# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 3 Data Compression and Decompression
### Compression and Decompression Model
The probability prediction model is obtained through the abovementioned deep learning algorithm, and then combined with arithmetic coding to achieve lossless compression. Among them, MHSA-MLP adopts multi-head self-attention combined with absolute position encoding as the preliminary probability prediction model, and it introduces an MLP to realize block compression while training, which is in contrast to the preliminary probability prediction model (BiLSTM-MHSA) and the probability prediction model (BiLSTM-MHSA-MLP) presented in this paper. The compression ratio and compression time are shown in Figures 15 and 16. The average compression ratios of the traditional deep learning compression algorithms (BiGRU, GRU_Multi, BiLSTM, LSTM_Multi, Transformer, and MHSA-MLP) are 32.1%, 21.26%, 20.54%, 21.91%, 16.97%, and 13.78%, respectively, and the average compression times are 8.8 s, 8.3 s, 9.9 s, 6.8 s, 15.8 s, and 4.5 s, respectively. Compared with traditional compression algorithms, the average compression ratio is significantly lower, indicating that deep learning compression algorithms are superior to traditional compression algorithms. Compared with MHSA-MLP, the compression algorithm in this paper reduces the average compression ratio by 1.67%, and the average compression time is almost the same. This shows that introducing the BiLSTM layer before the MHSA layer can provide richer context information and improve the model's modeling ability for profile data. In addition, compared with the traditional deep learning compression algorithm, the compression algorithm in this paper reduces the average compression ratio by 10.45% and the average compression time by 5.5 s. This shows that the compression algorithm in this paper has better performance. Compared with those of the previous four RNN algorithms, the compression ratio of the Transformer algorithm is lower, but the compression time is increased. Figure 15: Comparison of the compression ratio with traditional deep learning compression algorithms. Compared to traditional deep learning compression algorithms, the proposed compression algorithm is more suitable for Argo buoy profile data. Argo buoy data exhibits clear time-series characteristics and temporal dependencies, which BiLSTM can effectively capture by processing the input sequence in both directions, helping to analyze and compress the temporal patterns within the data. The MHSA layer further enhances the processing of relationships between different parts of the data by focusing on key time steps, thereby capturing richer contextual information and improving compression performance. The MLP optimizes the compression process by dynamically adjusting the strategy, enhancing compression efficiency. Therefore, BiLSTM-MHSA-MLP is more advantageous than traditional algorithms in handling buoy data with temporal dependencies and dynamic variations. Finally, the compression algorithm in this paper is compared with popular compressors (ZIP, ZZ, TGZ, and GZ), and the compression ratio results are shown in Figure 17.

As shown in Figure 17, the performance of the compression algorithm in this paper is significantly better than that of popular compressors. The average compression ratios of the four compressors are 38.96%, 28.88%, 37.45%, and 33.55%, all with compression times of less than 1 s. Compared to the proposed algorithm, the average compression

Figure 10:0 Comparison of the compression time with traditional deep learning compression algorithms. Figure 10:1 Compression ratio comparison with popular compressors. ratio is improved by 22.6%. This indicates that the proposed compression algorithm is more targeted and demonstrates better performance in compressing 4000 m profile Argo buoy data.