## 1 Introduction  
The weak purchasing power of agricultural machinery is China's current condition, and the cost of large agricultural machinery is very high. Due to the small size of farmland and strong seasonal characteristics of crops in China, it is difficult for farmers who have bought agricultural machinery in a short time to make profits. Agricultural machinery idles for a long time, which is a waste of resources. In addition, the function of Chinese agricultural machinery is singular, and many different types of agricultural machinery are needed in the production of a crop. The role of a single type of agricultural machinery is extremely limited, so farmers are extremely unwise to buy large agricultural machinery. Agricultural machinery rental is a new service form that can lighten the burden of buying agricultural machinery. This service improves the utilization rate of agricultural machinery and promotes the development of the agricultural economy. However, most agricultural machinery rental companies are still in the stage of immediate deployment. Staff only considers the time sequence when the agricultural machinery is deployed, so the efficiency is low in the busy season and the needs of farmers are difficult to meet. With the rise of network technology and vigorous development, digital technology has permeated all areas. Big data is starting to be used in agriculture, and cloud services are becoming more popular with agribusiness [1]. Agricultural machinery rental services have also been combined with big data. Many local small agricultural machinery rental enterprises have started to develop cloud platforms in China. However, considering the traveling salesman problem (TSP) and scheduling method, it is still difficult to achieve large-scale agricultural machinery scheduling. At present, there is little research on agricultural machinery scheduling. [PERSON] et al. proposed a modified fuzzy hybrid genetic algorithm for agricultural machinery scheduling [2], but it is limited to genetic algorithms. To achieve an optimal schedule for sequential agricultural operations, [PERSON] et al. used a tabu search method for exploration and verification [3], but its scope of application is relatively small. Therefore, large-scale agricultural machinery scheduling needs to be developed. With the development of artificial intelligence algorithms, various intelligent algorithms have been widely used in various optimization problems, such as genetic algorithms (GAs) [4; 5], ACO algorithms [6; 7], particle swarm optimization (PSO) [8; 9], ABC algorithms [10; 11] and firefly algorithms (FAs) [12; 13]. [PERSON] et al. proposed a generalized ant colony optimizer algorithm (GACO) to solve the cloud resource allocation problem [14]. [PERSON] et al. proposed multi-objective task scheduling optimization (MOTSO) to find the optimal solution [15], which solved the optimization problem well. [PERSON] et al. proposed a depth optimization analysis method based on the GA algorithm [16]. [PERSON] et al. realized the optimization and application of agricultural machinery rental in cloud manufacturing services based on the reliability feedback update strategy and the dynamic coefficient strategy [17]. All the above studies on solving problems with artificial intelligence algorithms have achieved good results, but there are few applications in agriculture. Artificial intelligence algorithms are introduced in agricultural machinery rental optimization by using agricultural big data, so a DABAA is proposed in this paper. First, to enhance the accuracy of the mathematical model, a dynamic coefficient is added to the Quality of Service (QoS) evaluation model. Then, to further improve the global search capability, the mutation operation is added to the ABC algorithm. Additionally, to combine ABC with the max-min ant system (MMAS), a method capable of iterative threshold adjustment and optimal fusion evaluation is adopted.