资产管理软件表单管理功能用户使用强度预测数据
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1.对本公司的用途：1）基于使用强度预测数据，公司可以了解资产管理软件中表单管理功能的当前使用状况和未来可能的变化趋势，有助于指导产品功能的优化和迭代；2）本预测数据还有助于公司在资源分配上做出更明智的决策，例如公司可以根据用户使用强度的未来变化规律，决定服务器容量的增减，不仅能够保证服务的稳定性和响应速度，还能降低公司的运营成本。 2.对其他资产管理软件（或其他类似软件）开发者（同行）的用途：本数据能为同行进一步分析了解资产管理软件中表单管理功能的市场普及度、用户依赖度和受欢迎程度提供基础数据支撑，为同行在类似软件中设置或改进表单管理功能（或类似功能）提供辅助决策依据，推动行业发展。1.数据采集和预处理： （1）从公司自营的资产管理软件的用户行为日志中，提取反映用户每天使用“表单管理功能”情况的数据，包括采集日期、是否为工作日、采集时间、功能名称、当日活跃用户数A、当日使用频次B、当日使用总时长C、当日软件总活跃用户数D；（2）对采集的数据进行清洗，以便后续的加工和建模。 2.建立表单管理功能的用户使用强度计算模型： （1）计算功能当日使用率S：S=A÷D×100%；（2）引入时间加权因子q：若当天是工作日，q为1.2，反之为0.8；（3）计算当日加权平均使用时长Pq：Pq=C÷A×q；（4）计算当日用户活跃度指数H：H=√[A×(B×q)]；（5）建立使用强度W计算模型：W=0.5H+0.25Pq+0.25S；权重系数依据每个指标的重要性并结合行业经验确定。 3.建立使用情况预测模型： （1）用rolling函数统计过去30日和90日的总使用强度，在此基础上分别除以30和90，计算过去30日和90日的日均使用强度Wa、Wb；（2）基于Wa和Wb进行预测：未来7天使用强度预测值=Wa×7，为短期预测；未来30天使用强度预测值=Wb×30，为中期预测。
1. Corporate applications: 1) Based on the usage intensity prediction data, the company can understand the current usage status and future potential change trends of the form management function in its self-operated asset management software, which helps guide product function optimization and iteration; 2) The prediction data also assists the company in making more informed resource allocation decisions. For example, the company can adjust server capacity based on the future change patterns of user usage intensity, ensuring service stability and response speed while reducing operational costs. 2. Applications for developers of other asset management software (or similar software, peers): This data provides basic data support for peers to further analyze and understand the market penetration, user dependency and popularity of the form management function in asset management software, and provides auxiliary decision-making basis for peers to set up or improve the form management function (or similar functions) in similar software, thereby promoting industry development. 1. Data collection and preprocessing: (1) Extract data reflecting users' daily usage of the "form management function" from the user behavior logs of the company's self-operated asset management software, including collection date, whether it is a working day, collection time, function name, daily active users A, daily usage frequency B, total daily usage duration C, and total daily active users of the software D; (2) Clean the collected data to facilitate subsequent processing and modeling. 2. Establishment of user usage intensity calculation model for the form management function: (1) Calculate the daily usage rate S of the function: S = A ÷ D × 100%; (2) Introduce the time-weighting factor q: q is 1.2 if the day is a working day, and 0.8 otherwise; (3) Calculate the daily weighted average usage duration Pq: Pq = C ÷ A × q; (4) Calculate the daily user activity index H: H = √[A × (B × q)]; (5) Establish the usage intensity W calculation model: W = 0.5H + 0.25Pq + 0.25S. The weight coefficients are determined based on the importance of each indicator combined with industry experience. 3. Establishment of usage status prediction model: (1) Use the rolling function to count the total usage intensity of the past 30 days and 90 days, then divide by 30 and 90 respectively to calculate the average daily usage intensity Wa and Wb of the past 30 days and 90 days; (2) Prediction based on Wa and Wb: Short-term prediction: the 7-day future usage intensity prediction value = Wa × 7; Medium-term prediction: the 30-day future usage intensity prediction value = Wb × 30.
