WhaleDI 支持综合能源智能服务

从过去能源领域数字化建设案例的梳理分析，能源行业数字化升级过程中经常遇到的四个问题，包括建设前期数据使用困难、需求多样化、多变施工中期、施工后期、持续运行能力不足、安全要求不匹配等。

• 数据难用：在数据量爆炸式增长的同时，数据烟囱依然存在。据相关机构统计分析，由于数据孤岛、分析能力不足，约80%的数据没有得到有效利用。
• 需求多样化、多变：供需双方、生产环节、业务部门对数据使用的需求不同。业务场景复杂，容错性低，难以打通CPS（信息物理系统）。
• 持续运营能力不足：业务应用和数据建设分离，数据服务无法应对业务的快速变化，导致运营服务缺乏深度和广度，客户满意度大幅下降。
• 安全性能不匹配：综合能源服务需要广泛访问客户数据，难以避免内外部网络之间的数据交互安全问题。

鲸迪专注能源行业数字化转型，针对“大场景小流量”能源服务场景的特殊性和海量业务的复杂性，推出了适合能源行业数字化转型的数据智能解决方案。无法利用的生态场景。具体而言，基于数字智能时代企业转型升级的方法论，鲸迪增强企业数据智能化基础，细化能源行业数据智能中心，致力于构建能源行业新基础设施，从而加速能源行业的数字化。创新。

• [Data Management - Data Access]
  

  Lean energy management: WhaleDI aggregates and integrates multi-industry, multi-enterprise, multi-level and multi-type energy data, forms a smart energy data view through data fusion and orchestration, and uses WhaleDI AI factory to conduct online energy consumption related models, training, management, optimization, subscription, and effectively solve the three major energy difficulties: 1) the difficulty of comprehensive energy consumption index statistics; 2) the difficulty of locating the root cause of excessive energy consumption; 3) the difficulty of active control and prediction of energy consumption, so that statistics can be checked and located.

• [Product Improvement - Scalability]
  

  Smart decision-making on energy use: based on the comprehensive energy consumption index system and customer detailed portraits constructed by WhaleDI, it can help energy companies summarize the energy consumption characteristics of customers in different industries in the actual business process and explore potential customers for comprehensive energy services. , promote the development of targeted and personalized comprehensive energy service business, provide targeted users with accurate and scientific guidance on energy use, increase user stickiness, and facilitate intelligent decision-making on energy use.

• [Data Management - Data Availability]
  

  WhaleDI provides customers with mature digital intelligence platforms, engines and operation services. While realizing integrated digital intelligence operations and improving the operational efficiency of enterprise data intelligence applications, it helps enterprises quickly establish a digital architecture and accelerate digital transformation with limited resources and trial costs.