## 6 Conclusions  
Due to the small farmland sizes and strong seasonal crops in China, large-scale agricultural machinery scheduling is difficult to realize in the rental industry. To solve this problem, a DABAA is proposed in this paper. First, the dynamic coefficient is introduced to balance the time demand of farmers, so the applicability of the model is enhanced in \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Scale**} & \multicolumn{6}{c}{**The Optimal QoS Fitness Value**} & \multicolumn{6}{c}{**The Average Time Consumption**} \\ \cline{3-10}  & & **DABAA** & **MMAS** & **SLGA** & **ABC-Q** & **DABAA** & **MMAS** & **SLGA** & **ABC-Q** \\ \hline
1 & T(300,10) & 0.782 & 0.779 & 0.763 & 0.767 & 36.11 & 39.85 & 38.59 & 41.89 \\
2 & T(400,10) & 0.794 & 0.783 & 0.775 & 0.762 & 36.28 & 40.33 & 38.74 & 42.16 \\
3 & T(500,10) & 0.803 & 0.794 & 0.781 & 0.756 & 37.84 & 42.13 & 39.07 & 43.59 \\
4 & T(300,20) & 1.455 & 1.443 & 1.409 & 1.368 & 39.45 & 42.71 & 40.58 & 44.12 \\
5 & T(400,20) & 1.464 & 1.455 & 1.397 & 1.311 & 43.97 & 48.13 & 44.57 & 49.55 \\
6 & T(500,20) & 1.511 & 1.502 & 1.359 & 1.308 & 44.18 & 50.39 & 46.72 & 53.34 \\
7 & T(300,30) & 2.085 & 2.063 & 1.983 & 1.945 & 43.28 & 50.75 & 48.02 & 52.58 \\
8 & T(400,30) & 2.165 & 2.144 & 2.034 & 1.897 & 45.74 & 51.26 & 48.39 & 55.96 \\
9 & T(500,30) & 2.201 & 2.175 & 2.108 & 1.993 & 47.31 & 53.79 & 49.44 & 57.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of the running results of the four algorithms. Figure 6: Boxplot of QoS fitness values. Figure 7: Average time consumption. the farming season. Then, the traditional ABC algorithm is modified to obtain the ABC algorithm with mutation, which further improves the global search ability. Additionally, optimal fusion evaluation is performed to combine the ABC with the MMAS to reduce computation time, and the iterative threshold adjustment method improves the accuracy of the optimal solution. Therefore, these improvements enhance the global search capability of the algorithm in the early stage and increase the convergence speed in the later stage. The comparison experiments on the ABC, GA, MMAS and DABAA show that the DABAA is superior to the other algorithms. Our team has been engaged in the agricultural machinery industry for many years and has discovered the realistic requirements of agricultural machinery rental from actual situations. Based on the team's agricultural practical experience, the model in this paper is not much different from the actual situation. Moreover, the research in this paper is based on big data; it is meaningless to experiment with a small range of small data, which does not reflect the advantages of the algorithm. At present, the domestic agricultural machinery environment in China restricts large-scale scheduling experiments, such as the lack of large agricultural machinery rental companies and the differences in agricultural machinery standards in different regions. Carrying out large-scale leasing experiments requires the government to formulate supporting policies and unified standards and the cooperation of agricultural machinery companies and farmers. At present, the research in this paper provides theoretical and model support for future practical applications, and the team is conducting research on the agricultural machinery rental platform. In the future, the team will contact appropriate agricultural machinery companies to jointly develop agricultural machinery rental platforms and negotiate with the government to apply the proposed system of this paper to actual verification. In general, especially in China, the combination of cloud manufacturing and agricultural machinery rental is still immature. There is still much room for improvement in the optimization of agricultural machinery rental in cloud services. Various intelligent algorithms need to be further optimized and improved.