石油化工品交易市场企业状态预警数据

通过对石油化工品交易市场引进入驻企业成立至今月份数、登记车辆出入市场次数、企业租金缴纳月份数、企业物业费缴纳月份数等4项指标进行计算分析，判断企业目前经营状态，根据不同预警状态作出相应跟进处理，了解企业困难，协调处理相关事宜，在当前企业经营普遍困难的经济形势下，为政府稳经济工作提供参考，争取优惠政策，营造更优专业市场营商环境。一是数据采集由本公司市场招商记录、道闸管理系统、物业管理系统综合汇总而成，每日更新、每月汇总；二是数据处理由公司业务管理部门对收集到的数据进行汇总、整理等处理，对企业名称、负责人、车牌号等进行关键字脱敏处理；三是数据分析，根据市场企业入驻至今得出月份数A，根据各公司预登记车牌号统计出近30天内进出市场天数B，根据预缴的租金和物业费计算企业租金缴纳月份数C和企业物业费缴纳月份数D，如房屋为买入的则租金缴纳月份数C算12个月，如已欠费则计负数；企业状态预警值S=企业入驻至今月份数A×系数0.1+企业登记车辆近30天内进出市场天数B×系数2+企业租金缴纳月份数C+企业物业费缴纳月份数D，如S值在20分以上则判断为正常状态，如S值在10分（不含）至20分之间则判断为关注状态，如S值在0分至10分之间则判断为临界状态，每月联系确认企业经营情况，如S值在0分以下则判断为异常状态，即时联系确认状态；四是数据应用，通过对石油化工品交易市场企业状态预警数据进行分析，判断企业目前经营状态，根据不同预警状态作出相应跟进处理，了解企业困难，协调处理相关事宜，营造更优专业市场营商环境。

This dataset is developed to evaluate the operational status of enterprises in the petrochemical trading market by calculating and analyzing four indicators: the number of months since the settled-in enterprises settled in the market, the number of days registered vehicles enter and exit the market, the number of months of rent payments, and the number of months of property fee payments. Corresponding follow-up measures will be taken based on different early warning statuses to understand enterprises' difficulties and coordinate relevant matters. Against the current economic backdrop where most enterprises face operational hardships, this dataset provides references for the government's economic stabilization work, strives for preferential policies, and fosters a more favorable business environment for professional markets.

First, data collection: The data is comprehensively aggregated from the company's market investment promotion records, barrier gate management system, and property management system, with daily updates and monthly summaries.
Second, data processing: The company's business management department conducts aggregation and organization of the collected data, and performs keyword desensitization on information such as enterprise names, responsible persons, and license plate numbers.
Third, data analysis: 1. Derive the number of months since the enterprise settled in the market, denoted as A; 2. Count the number of days that registered vehicles enter and exit the market within the past 30 days, denoted as B; 3. Calculate the number of months of rent payments, denoted as C: if the enterprise's premises are purchased, C is set to 12; if there are outstanding arrears, C is recorded as a negative number; 4. Calculate the number of months of property fee payments, denoted as D; 5. The enterprise status early warning score S is calculated as: $S = 0.1 imes A + 2 imes B + C + D$. The operational status of the enterprise is judged according to the value of S: - Normal status: S ≥ 20; - Attention status: 10 < S < 20; - Critical status: 0 ≤ S ≤ 10: the enterprise shall be contacted monthly to confirm its operational status; - Abnormal status: S < 0: the enterprise shall be contacted immediately to confirm its status.
Fourth, data application: By analyzing the early warning data of enterprise status in the petrochemical trading market, the current operational status of enterprises is judged, corresponding follow-up measures are taken based on different early warning statuses, enterprises' difficulties are understood, relevant matters are coordinated, and a more favorable business environment for professional markets is created.