基于大数据的电力通信网络风险辨识与评估方法研究

为了高效、准确地识别网络庞大、结构复杂电力通信网中所潜在的风险点，从网络结构、承载业务等方面分析其对电力通信的影响，并结合电力事故事件调查规程的评定标准，客观且科学地评估其对电力通信引起的电力安全事件，为电力通信风险管控提供决策支撑。本文结合电力通信风险管控具体内容及特点，分析电力通信网事故发生原因，推理事故演变过程，提出解决思路和方案，并通过实际测试案例，验证电力事故评估模型具有较高的计算效率和准确性，为准确定位事故发生原因和评判风险等级提供科学依据。该模型结合复杂网络理论可靠性因素的抗毁性、生存性、有效性等特征，计算其相关特性的风险因子值；利用通径系数分析技术，验证风险因子的完备性；根据已标注的风险等级样例数据，构建深层卷积神经网络CNN模型，实时评测事故的风险等级。

Research on risk identification and assessment method of electric power communication network based on big data

In order to efficiently and accurately identify potential risk points in a large and complex network of power communication networks, analyze the impact of power communication from the aspects of network structure and bearer services, and combine the assessment criteria of power accident incident investigation procedures, objectively and scientifically evaluate the power safety incidents caused by power communication and provides decision support for risk management and control of power communication. Based on the content and characteristics of risk management and control, analyzes the causes of accidents in power communication networks, infers the process of accident evolution, proposes solutions and solutions, verify model with high computational efficiency and accuracy through actual verification, that provide scientific basis for accurately locating the causes of accidents and evaluating risk levels. The model that combines the invulnerability, survivability and effectiveness of reliability factors of complex network theory calculate the risk factor values of related characteristics. The path coefficient analysis technique is used to verify the completeness of risk factors; The CNN model of deep convolution neural network is constructed based on the labeled sample data of risk level, it assess risk Level of accidents in a real-time manner.