基于趋势预测与优化模型的晶硅片企业未来经营策略设计

Future Business Strategy Design for Crystalline Silicon Wafer Enterprises Based on Trend Prediction and Optimization Models

摘要: 随着光伏产业的快速迭代，晶硅片企业面临着成本波动大、市场竞争激烈的双重挑战。为优化企业产销策略，本文基于四型硅片(N型-182、N型-210等)的历史产销数据，构建了一套集利润核算、趋势预测与决策优化于一体的数学模型体系。首先，通过Z-Score标准化与线性回归分析，识别出销售收入与生产变动成本是影响利润的关键因子(回归系数分别为0.919和−1.238)，并构建了高精度的月利润计算模型。其次，利用ARIMA时间序列模型对2024年9月的销量、售价及单晶方棒成本进行预测，结果显示N型硅片价格呈下行趋势，但销量有望回升。在此基础上，建立以利润最大化为目标的非线性规划模型，采用trust-constr算法求解，制定了最优生产销售计划，使预期经营利润(税前)较历史均值提升约163%。最后，提出了一种融合随机森林回归与大语言模型(LLM)的智能决策路径，通过对异常数据的清洗与重构，结合LLM的逻辑推理能力，为企业提供更具适应性的市场策略。本文的研究不仅验证了数学模型在企业经营中的有效性，也为光伏行业的数字化转型提供了新的思路。

Abstract: Amidst the rapid technological iteration in the photovoltaic industry, crystalline silicon wafer enterprises face dual challenges of high cost volatility and intensifying market competition. To optimize production and sales strategies, this paper proposes an integrated decision‑support framework combining profit accounting, trend prediction, and decision optimization, utilizing historical data from four types of silicon wafers. First, through Z-score standardization and linear regression analysis, sales revenue and variable production costs are identified as the primary drivers of profitability (coefficients of 0.919 and −1.238, respectively), forming the basis of a high‑precision monthly profit model. Secondly, the sales volume, selling price, and cost of monocrystalline square rods in September 2024 were predicted using the ARIMA time series model. Results indicate a downward price trend for N‑type wafers, contrasted with a potential rebound in sales volume. Subsequently, a nonlinear programming model targeting profit maximization is constructed and solved via the trust‑constr algorithm. The optimal production plan is projected to increase expected operating profit (pre‑tax) by approximately 163% compared to the historical average. Finally, an intelligent decision‑making paradigm integrating Random Forest Regression with Large Language Models (LLM) is introduced. By leveraging LLMs for logical reasoning on cleaned data, this approach provides adaptive, qualitative strategic insights. This study validates the efficacy of mathematical modeling in enterprise management and offers a novel pathway for the digital transformation of the photovoltaic sector.