电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先...电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先,该方法从多个维度选取电商财务风险相关指标,以全面反映电商企业的财务健康状况。随后,对选取的指标数据进行预处理,确保数据质量和模型训练的准确性。接下来,建立一个改进的BP神经网络模型,用于电商财务风险的预测。在模型建立过程中,特别关注学习速率的调整,通过改变学习率来平衡模型的训练速度和稳定性,从而实现财务风险预测。实验结果表明:基于改进BP神经网络的电商财务风险智能预测方法实现了每2 min更新一次的高频率,其平均更新时间仅为1 s左右,更新成功率稳定在99%以上,在更新能力方面表现优秀,可为电商企业的财务风险预测提供新的解决方案。The financial data of e-commerce enterprises usually involve a large number of transactions and complex business logic, and the collection, cleaning and sorting of data require a lot of time and manpower, resulting in a low frequency of data update, which limits the accuracy of financial risk prediction model. Therefore, this paper studies the intelligent prediction method of financial risk of e-commerce based on improved BP neural network. First, the method selects indicators related to e-commerce financial risks from multiple dimensions to comprehensively reflect the financial health of e-commerce enterprises. Then, the selected index data is preprocessed to ensure the data quality and the accuracy of model training. Next, an improved BP neural network model is established to predict the financial risk of e-commerce. In the process of model building, we pay special attention to the adjustment of learning rate, and balance the training speed and stability of the model by changing the learning rate, so as to realize the financial risk prediction. The experimental results show that the e-commerce financial risk intelligent prediction method based on improved BP neural network can achieve a high frequency of update every 2 min, the average update time is only about 1 s, and the success rate of update is stable at more than 99%, which has excellent performance in updating ability, and can provide a new solution for the financial risk prediction of e-commerce enterprises.展开更多
粉煤瓦斯解吸实验是研究粉煤瓦斯解吸动力学特征的常用手段之一,其结果是揭示粉煤放散瓦斯能力的重要参数。传统实验方法在煤样罐泄压后开始测量瓦斯解吸数据,存在较大误差。利用甲烷与氦气的粉煤吸附特性差异性提出了改进的粉煤瓦斯解...粉煤瓦斯解吸实验是研究粉煤瓦斯解吸动力学特征的常用手段之一,其结果是揭示粉煤放散瓦斯能力的重要参数。传统实验方法在煤样罐泄压后开始测量瓦斯解吸数据,存在较大误差。利用甲烷与氦气的粉煤吸附特性差异性提出了改进的粉煤瓦斯解吸实验方法,并建立了初始瓦斯粉煤快速解吸模型,从而揭示了煤体粉化后瓦斯快速解吸的内在机制。研究结果表明:解吸开始的前5 s,0.075~0.150 mm JG71煤样解吸的瓦斯量是1.00~2.36 mm煤样的2.05倍,而0.075~0.150 mm JG82煤样解析的瓦斯量是1.00~2.30煤样的10.29倍;煤样粉化程度越高,吸附平衡压力越大,初始瓦斯解吸速度越大,传统实验方法得到的数据误差越大。研究结果为突出粉化煤体快速解吸瓦斯、提供瓦斯膨胀能、促进煤与瓦斯突出传播的研究提供了数据支撑,同时为完善煤与瓦斯致灾机理奠定基础。展开更多
文摘电商企业的财务数据通常涉及大量的交易和复杂的业务逻辑,数据的收集、清洗和整理需要耗费大量的时间和人力,导致数据更新的频率较低,从而限制了财务风险预测模型的准确性,为此研究基于改进BP神经网络的电商财务风险智能预测方法。首先,该方法从多个维度选取电商财务风险相关指标,以全面反映电商企业的财务健康状况。随后,对选取的指标数据进行预处理,确保数据质量和模型训练的准确性。接下来,建立一个改进的BP神经网络模型,用于电商财务风险的预测。在模型建立过程中,特别关注学习速率的调整,通过改变学习率来平衡模型的训练速度和稳定性,从而实现财务风险预测。实验结果表明:基于改进BP神经网络的电商财务风险智能预测方法实现了每2 min更新一次的高频率,其平均更新时间仅为1 s左右,更新成功率稳定在99%以上,在更新能力方面表现优秀,可为电商企业的财务风险预测提供新的解决方案。The financial data of e-commerce enterprises usually involve a large number of transactions and complex business logic, and the collection, cleaning and sorting of data require a lot of time and manpower, resulting in a low frequency of data update, which limits the accuracy of financial risk prediction model. Therefore, this paper studies the intelligent prediction method of financial risk of e-commerce based on improved BP neural network. First, the method selects indicators related to e-commerce financial risks from multiple dimensions to comprehensively reflect the financial health of e-commerce enterprises. Then, the selected index data is preprocessed to ensure the data quality and the accuracy of model training. Next, an improved BP neural network model is established to predict the financial risk of e-commerce. In the process of model building, we pay special attention to the adjustment of learning rate, and balance the training speed and stability of the model by changing the learning rate, so as to realize the financial risk prediction. The experimental results show that the e-commerce financial risk intelligent prediction method based on improved BP neural network can achieve a high frequency of update every 2 min, the average update time is only about 1 s, and the success rate of update is stable at more than 99%, which has excellent performance in updating ability, and can provide a new solution for the financial risk prediction of e-commerce enterprises.
文摘粉煤瓦斯解吸实验是研究粉煤瓦斯解吸动力学特征的常用手段之一,其结果是揭示粉煤放散瓦斯能力的重要参数。传统实验方法在煤样罐泄压后开始测量瓦斯解吸数据,存在较大误差。利用甲烷与氦气的粉煤吸附特性差异性提出了改进的粉煤瓦斯解吸实验方法,并建立了初始瓦斯粉煤快速解吸模型,从而揭示了煤体粉化后瓦斯快速解吸的内在机制。研究结果表明:解吸开始的前5 s,0.075~0.150 mm JG71煤样解吸的瓦斯量是1.00~2.36 mm煤样的2.05倍,而0.075~0.150 mm JG82煤样解析的瓦斯量是1.00~2.30煤样的10.29倍;煤样粉化程度越高,吸附平衡压力越大,初始瓦斯解吸速度越大,传统实验方法得到的数据误差越大。研究结果为突出粉化煤体快速解吸瓦斯、提供瓦斯膨胀能、促进煤与瓦斯突出传播的研究提供了数据支撑,同时为完善煤与瓦斯致灾机理奠定基础。
