To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms.

In this paper, for the failure of spraying workshop door switch, exhaust fan failure and other faults in the spraying workshop, a fault diagnosis algorithm based on the BP (Back Propagation) neural network is proposed to solve the fault diagnosis problem in the spraying workshop. First, the sensor network of the spraying workshop was built. Multi-source sensors were used to realize the dynamic perception of the on-site environment and equipment status. Through the preprocessing of multi-source heterogeneous data, a unified data template and interface were established. Secondly, the BP neural network algorithm is used to simulate and diagnose the possible faults in the spraying process. Finally, MATLAB test simulation proves its high accuracy of fault diagnosis.

Introduction: Rapid advances in smart agriculture have highlighted the potential of large language models (LLMs), while practical applications remain limited by privacy risks, high training costs, and hallucinations. Focusing on garlic cultivation, this study proposes a knowledge-graph-enhanced framework that improves domain-specific LLMs through graph-based retrieval. Methods: A hybrid clustering algorithm with intra-cluster multidimensional ranking is employed to identify ten core entity types from garlic-related corpora. Few-shot learning and chain-of-thought prompting are further integrated to optimize entity and relation extraction using Qwen 2.5:32B, leading to the construction of a structured garlic cultivation knowledge graph. The knowledge graph is integrated with LLMs via GraphRAG, while prompt reconstruction strategies are adopted to enrich contextual information and constrain generation, thereby improving domain grounding and reducing hallucinations. Results: Experimental results demonstrate absolute improvements of 13.0, 37.0, and 28.2 percentage points in precision, recall, and F1-score, respectively, for triple extraction compared with the baseline LLM, along with a 37.5 percentage point increase in retrieval-based question-answering accuracy. Discussion: The proposed approach is interpretable and locally deployable without dependence on commercial APIs, offering a transferable methodological reference for domain-specific knowledge graph construction and intelligent question answering across diverse crop systems.