Word segmentation stands as a cornerstone of Natural Language Processing (NLP).Based on the concept of "comprehend first, segment later", we propose a new framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs) and evaluate the semantic understanding capabilities of LLMs based on word segmentation.We employ current mainstream LLMs to perform word segmentation across multiple languages to assess LLMs' "comprehension".Our findings reveal that LLMs are capable of following simple prompts to segment raw text into words.There is a trend suggesting that models with more parameters tend to perform better on multiple languages.Additionally, we introduce a novel unsupervised method, termed LLACA (Large Language Model-Inspired Aho-Corasick Automaton).Leveraging the advanced pattern recognition capabilities of Aho-Corasick automata, LLACA innovatively combines these with the deep insights of well-pretrained LLMs.This approach not only enables the construction of a dynamic n-gram model that adjusts based on contextual information but also integrates the nuanced understanding of LLMs, offering significant improvements over traditional methods.

Fresh grape has the characteristics of seasonal, not easy to transport and perishable. In order to reduce product corruption during transportation, cold chain transportation is often used. However, cold chain transportation can't prevent the fruit from spoiling, only delay the spoiling time. Therefore, this paper uses correlation analysis and quality dynamics model methods to build a fitted prediction model of grape temperature, hardness and water loss during the sales period, and the hardness parameters and water loss rate of fresh grape are constructed, and use it as a coupled temperature-quality prediction model for the cold chain process. The results showed that the absolute relative errors between the predicted hardness and the predicted water loss parameters during the shelf life of fresh grapes are 3.45% and 7.14% respectively, which met the actual transportation requirements, and the prediction results were good.

Firstly, this paper analyzes the technology system and characteristics of the Internet of things. Secondly, the paper mainly studies the intelligent algorithm of data transmission in the Agricultural Internet of things. According to the different energy efficiency and the limited battery capacity of rechargeable sensor nodes, a new clustering routing protocol is proposed. Meanwhile, in order to solve the problem of fault-tolerant routing repair and maintain the quality of fault-tolerant routing, the paper proposes a new routing strategy (CR-WSN). Compared with the CRAS algorithm, they have the same point that a node with more remaining energy is selected as the cluster head node, but the energy consumption ratio of CR-WSN is significantly better than CRAS. Therefore, CREW can achieve the purpose of reducing energy consumption and improving link fault tolerance.