人造板产品质量风险预测预警数据

人造板产品质量风险预测预警数据是一个创新的量化工具，用于评估当前市场上流通的人造板产品可能出现质量问题的风险程度并进行预警。
1.人造板行业内的生产企业可以通过本数据了解当前市场上人造板产品的整体质量状况，来优化生产流程和提高产品质量。通过对潜在风险因素的分析，企业能够及时调整生产工艺，改进原材料选择，加强质量控制点的监控，从而降低产品缺陷率和召回风险，提高产品在市场上的竞争力。
2.市场监管部门可以利用本数据识别和跟踪人造板产品潜在的质量问题，及时采取监管措施，如发出消费警告、加强检查和执法力度，确保市场上流通的产品符合安全标准。市场监管部门还可以将本数据对外披露公开，体现政府和本区域对人造板产品质量的重视和承诺，有利于增强下游采购商的信任。1.数据采集和预处理：
（1）数据采集：采集人造板产品每次送检结果数据，包括送样地区、检验日期、产品名称、检验结论。（2）数据预处理：对采集的数据进行清洗，将检验结论不合格、合格分别用1、0代替，以便后续分析和建模。
2.数据加工和分析：
（1）计算近30次累计不合格次数及不合格率、最高连续不合格次数及其占比：用SUM函数计算近30次累计不合格次数；用CountIf函数和MAX函数嵌套确定近30次最高连续不合格次数；近30次累计不合格率=近30次累计不合格次数÷30×100%；近30次最高连续不合格次数占比=近30次最高连续不合格次数÷30×100%；
（2）建立预测预警模型：①计算每次检验后的质量风险评分=近30次累计不合格率×100×0.6+近30次最高连续不合格次数占比×100×0.4；②利用AVERAGE函数计算近10次质量风险评分平均得分，并进行风险等级判定：平均得分≤5，为低风险；5＜平均得分≤10，为中风险；10＜平均得分≤15，为高风险；平均得分＞15，为极高风险；④预警等级采用便于目视化管理方式，划分为绿色（低风险）、黄色（中风险）、橙色（高风险）、红色（极高风险）。

The Wood-based Panel Product Quality Risk Prediction and Early Warning Dataset is an innovative quantitative tool for assessing the risk level of potential quality issues of wood-based panel products currently circulating in the market and issuing corresponding early warnings.

1. Manufacturers in the wood-based panel industry can use this dataset to understand the overall quality status of wood-based panel products in the current market, so as to optimize production processes and improve product quality. By analyzing potential risk factors, enterprises can adjust production processes in a timely manner, improve raw material selection, and strengthen monitoring of quality control points, thereby reducing product defect rates and recall risks, and enhancing their market competitiveness.

2. Market supervision departments can use this dataset to identify and track potential quality issues of wood-based panel products, and take regulatory measures promptly, such as issuing consumer warnings, strengthening inspections and law enforcement, to ensure that products circulating in the market comply with safety standards. In addition, market supervision departments can publicly disclose this dataset, demonstrating the government and the region's attention to and commitment to the quality of wood-based panel products, which helps to enhance the trust of downstream purchasers.

1. Data Collection and Preprocessing:
(1) Data Collection: Collect the test result data for each submission of wood-based panel products, including sampling region, inspection date, product name, and inspection conclusion.
(2) Data Preprocessing: Clean the collected data, replace the inspection conclusions of "unqualified" and "qualified" with 1 and 0 respectively, to facilitate subsequent analysis and modeling.

2. Data Processing and Analysis:
(1) Calculate the cumulative number of unqualified tests and unqualified rate in the latest 30 tests, as well as the maximum consecutive unqualified times and its proportion: Use the SUM function to calculate the cumulative number of unqualified tests in the latest 30 tests; Use the nested COUNTIF and MAX functions to determine the maximum consecutive unqualified times in the latest 30 tests; The cumulative unqualified rate in the latest 30 tests = (cumulative number of unqualified tests in the latest 30 tests ÷ 30) × 100%; The proportion of maximum consecutive unqualified times in the latest 30 tests = (maximum consecutive unqualified times in the latest 30 tests ÷ 30) × 100%;
(2) Establish a prediction and early warning model:
① Calculate the quality risk score after each inspection = (cumulative unqualified rate in the latest 30 tests) × 100 × 0.6 + (proportion of maximum consecutive unqualified times in the latest 30 tests) × 100 × 0.4;
② Use the AVERAGE function to calculate the average score of the quality risk scores in the latest 10 tests, and conduct risk level judgment:
- Low risk: Average score ≤ 5;
- Medium risk: 5 < Average score ≤ 10;
- High risk: 10 < Average score ≤ 15;
- Extreme high risk: Average score > 15;
④ The early warning levels are classified for visual management, divided into Green (Low Risk), Yellow (Medium Risk), Orange (High Risk), and Red (Extreme High Risk).