*.csv; *.json;

The application of large language models (LLMs) in urban planning has gained momentum, with prior research demonstrating their value in participatory planning, process streamlining, and event forecasting. This study focuses on further enhancing urban planning through the integration of more comprehensive datasets. We introduce a newly developed instruction dataset that amalgamates crucial information from several prominent urban datasets, including highD, NGSIM, the Road Networks dataset, TLC Trip data, and the Urban Flow Prediction Survey dataset.

Partial dataset of CHVM-1K dataset for illustration purposes.

    {

        "question": "What stages can be divided into in the development history of ancient Chinese bronzes? Why?",

        "answer": "The development history of ancient Chinese bronzes can be divided into several stages: Xia (2100-1600 BCE), Shang (1600-1046 BCE), Early Western Zhou (1046-771 BCE), Middle Western Zhou (771-720 BCE), Late Western Zhou (720-256 BCE), and Eastern Zhou (256-256 BCE). These stages are marked by technological advancements, stylistic evolution, and cultural significance.",

We organized and collected two years' worth of complete fault work orders from a wind farm, and structured these work orders into a fault diagnosis event knowledge graph using the proposed algorithm. This graph includes fault modes, fault impacts, fault symptoms, inspection schemes, root cause identification, and maintenance strategies, covering all potential fault information and handling methods for wind turbines. This dataset records the head entity-relation-tail entity information in the form of triples using JSON format.

This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and potential applications of these models in promoting energy efficiency.