技术领域Technical Field
本发明涉及金融风险管理和深度学习技术领域，具体应用于钢铁行业供应链金融风险的评估和管理。通过结合卷积神经网络（CNN）和长短期记忆网络（LSTM），本发明提供了一种基于多变量时间序列数据的深度学习方法，以实现供应链金融风险的精准预测和动态管理。The present invention relates to the field of financial risk management and deep learning technology, and is specifically applied to the assessment and management of supply chain financial risks in the steel industry. By combining convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), the present invention provides a deep learning method based on multivariate time series data to achieve accurate prediction and dynamic management of supply chain financial risks.
背景技术Background Art
钢铁行业作为国民经济的重要基础产业，其产品广泛应用于建筑、机械、汽车、船舶、家电各个领域。As an important basic industry of the national economy, the steel industry's products are widely used in various fields such as construction, machinery, automobiles, ships, and home appliances.
当前，已有部分研究将深度学习应用于金融风险管理领域，但多集中于股票市场、信用评分等方面，对供应链金融风险管理的研究相对较少。尤其是针对钢铁行业，尚缺乏系统性的解决方案。因此，提出一种基于深度学习的钢铁行业供应链金融风险管理方法，通过卷积神经网络和长短期记忆网络的结合，深度分析多变量时间序列数据，捕捉时间序列中的非线性模式和复杂特征，具有重要的现实意义。At present, some studies have applied deep learning to the field of financial risk management, but most of them focus on stock markets, credit scores, etc., and there are relatively few studies on supply chain financial risk management. Especially for the steel industry, there is still a lack of systematic solutions. Therefore, a deep learning-based steel industry supply chain financial risk management method is proposed. Through the combination of convolutional neural networks and long short-term memory networks, multivariate time series data is deeply analyzed to capture nonlinear patterns and complex features in time series, which has important practical significance.
钢铁行业的供应链涉及原材料的采购、生产加工、库存管理、物流运输以及最终产品的销售。准确预测和有效管理这些风险对于钢铁企业的稳健运营至关重要。传统的供应链金融风险管理方法主要依赖于专家经验和简单的统计模型，如时间序列分析模型（ARIMA）、回归分析模型等。这些方法在处理线性和简单关系时表现尚可，但面对钢铁行业复杂多变的市场环境和高度非线性的时间序列数据，往往显得力不从心。The supply chain of the steel industry involves the procurement of raw materials, production and processing, inventory management, logistics and transportation, and the sale of final products. Accurately predicting and effectively managing these risks is crucial to the sound operation of steel companies. Traditional supply chain financial risk management methods mainly rely on expert experience and simple statistical models, such as time series analysis models (ARIMA) and regression analysis models. These methods perform well when dealing with linear and simple relationships, but they often seem to be unable to cope with the complex and changing market environment and highly nonlinear time series data of the steel industry.
近年来，随着深度学习技术的发展，特别是卷积神经网络（CNN）和长短期记忆网络（LSTM）的广泛应用，为解决这些问题提供了新的思路。CNN在图像处理和特征提取方面表现出色，通过卷积和池化操作，能够从多维度数据中提取出重要特征，适用于复杂模式的识别。而LSTM作为一种特殊的递归神经网络（RNN），通过引入记忆单元和门控机制，有效解决了传统RNN在长序列数据处理时的梯度消失和梯度爆炸问题，适用于捕捉时间序列中的长期依赖关系。将CNN和LSTM结合应用于钢铁行业供应链金融风险管理中，可以充分利用两者的优势。具体而言，CNN可以用于对多变量时间序列数据进行特征提取，识别出隐含的模式和趋势，而LSTM则用于对这些提取的特征进行时序建模，捕捉数据中的长期依赖关系和非线性特征。通过这种方式，可以对钢铁行业的供应链金融风险进行更加准确的预测。然而，仅仅依靠CNN和LSTM的简单结合还不足以完全应对钢铁行业供应链的复杂性。因此，在深度学习模型中引入注意力机制（Attention Mechanism）成为一种有效的改进措施。注意力机制通过动态调整模型对不同时间步长特征的关注度，进一步增强了模型对关键特征的捕捉能力，提升了风险预测的准确性。In recent years, with the development of deep learning technology, especially the widespread application of convolutional neural networks (CNN) and long short-term memory networks (LSTM), new ideas have been provided to solve these problems. CNN performs well in image processing and feature extraction. Through convolution and pooling operations, it can extract important features from multi-dimensional data and is suitable for complex pattern recognition. As a special recurrent neural network (RNN), LSTM effectively solves the gradient vanishing and gradient exploding problems of traditional RNN in long sequence data processing by introducing memory units and gating mechanisms, and is suitable for capturing long-term dependencies in time series. Combining CNN and LSTM in the supply chain financial risk management of the steel industry can make full use of the advantages of both. Specifically, CNN can be used to extract features from multivariate time series data and identify implicit patterns and trends, while LSTM is used to perform time series modeling on these extracted features to capture long-term dependencies and nonlinear features in the data. In this way, more accurate predictions of supply chain financial risks in the steel industry can be made. However, relying solely on the simple combination of CNN and LSTM is not enough to fully cope with the complexity of the steel industry supply chain. Therefore, introducing the attention mechanism into the deep learning model becomes an effective improvement measure. The attention mechanism further enhances the model's ability to capture key features and improves the accuracy of risk prediction by dynamically adjusting the model's attention to features at different time steps.
随着物联网（IoT）技术的发展，钢铁企业可以实时采集和监控供应链各环节的运行数据，如原材料的库存水平、生产设备的运行状态、物流运输的路径和时间等。这些数据的实时性和准确性为深度学习模型的训练和应用提供了坚实的基础。通过将IoT数据与深度学习模型相结合，可以实现对供应链金融风险的实时监控和预警，进一步提高风险管理的智能化水平。With the development of Internet of Things (IoT) technology, steel companies can collect and monitor the operation data of each link of the supply chain in real time, such as the inventory level of raw materials, the operating status of production equipment, the path and time of logistics transportation, etc. The real-time and accuracy of these data provide a solid foundation for the training and application of deep learning models. By combining IoT data with deep learning models, real-time monitoring and early warning of supply chain financial risks can be achieved, further improving the intelligent level of risk management.
发明内容Summary of the invention
本发明提出了一种基于深度学习的钢铁行业供应链金融风险管理方法，旨在通过多变量时间序列数据的深度分析，实现对供应链金融风险的精准预测和实时管理。系统采用了卷积神经网络（CNN）和长短期记忆网络（LSTM）相结合的深度学习模型，并引入注意力机制（Attention Mechanism），以提高模型的预测精度和实时性。This paper proposes a steel industry supply chain financial risk management method based on deep learning, aiming to achieve accurate prediction and real-time management of supply chain financial risks through in-depth analysis of multivariate time series data. The system adopts a deep learning model that combines convolutional neural network (CNN) and long short-term memory network (LSTM), and introduces the attention mechanism to improve the prediction accuracy and real-time performance of the model.
在数据采集方面，本发明利用多种数据源，包括市场交易平台、企业ERP系统、企业MES系统和国家统计局等，实时收集市场价格、库存水平、生产效率和宏观经济指标等多变量时间序列数据。数据采集模块通过高精度传感器和物联网（IoT）设备，确保数据的实时性和准确性。为了处理这些海量数据，系统采用了分布式存储HDFS和计算技术Spark，实现数据的快速存储和处理。In terms of data collection, the present invention uses a variety of data sources, including market trading platforms, enterprise ERP systems, enterprise MES systems, and the National Bureau of Statistics, to collect multivariate time series data such as market prices, inventory levels, production efficiency, and macroeconomic indicators in real time. The data collection module ensures the real-time and accuracy of data through high-precision sensors and Internet of Things (IoT) devices. In order to process these massive data, the system uses distributed storage HDFS and computing technology Spark to achieve fast data storage and processing.
数据预处理是本发明的重要环节，主要包括数据清洗、数据归一化和数据增强。首先，数据清洗采用K近邻插值法处理缺失数据，并剔除异常值，确保数据的完整性和一致性。其次，数据归一化采用Min-Max归一化方法，将数据归一化到[0, 1]区间，消除量纲差异，提升模型的训练效果。最后，数据增强通过时间窗口滑动和随机采样技术，增加训练样本，提升模型的泛化能力。Data preprocessing is an important part of the present invention, which mainly includes data cleaning, data normalization and data enhancement. First, data cleaning uses the K nearest neighbor interpolation method to process missing data and remove outliers to ensure the integrity and consistency of the data. Secondly, data normalization uses the Min-Max normalization method to normalize the data to the [0, 1] interval, eliminate dimensional differences, and improve the training effect of the model. Finally, data enhancement increases training samples and improves the generalization ability of the model through time window sliding and random sampling techniques.
在特征提取方面，本发明采用卷积神经网络（CNN）对多变量时间序列数据进行多层特征提取。CNN的卷积层通过卷积操作提取局部特征，池化层通过下采样操作减少数据维度，提升模型的计算效率。具体的CNN结构设计如下：In terms of feature extraction, the present invention uses convolutional neural network (CNN) to perform multi-layer feature extraction on multivariate time series data. The convolution layer of CNN extracts local features through convolution operation, and the pooling layer reduces the data dimension through downsampling operation to improve the computational efficiency of the model. The specific CNN structure design is as follows:
·Conv1：卷积核大小3×3，数量64，步长1，激活函数ReLUConv1: convolution kernel size 3×3, number 64, stride 1, activation function ReLU
·Pool1：最大池化层，池化窗口大小2×2，步长2Pool1: max pooling layer, pooling window size 2×2, stride 2
·Conv2：卷积核大小5×5，数量128，步长1，激活函数ReLUConv2: convolution kernel size 5×5, number 128, stride 1, activation function ReLU
·Pool2：最大池化层，池化窗口大小2×2，步长2Pool2: max pooling layer, pooling window size 2×2, stride 2
·Conv3：卷积核大小7×7，数量256，步长1，激活函数ReLUConv3: convolution kernel size 7×7, number 256, stride 1, activation function ReLU
·Pool3：最大池化层，池化窗口大小2×2，步长2Pool3: max pooling layer, pooling window size 2×2, stride 2
·Flatten：展平层Flatten: Flatten layer
时序建模是本发明的核心部分，通过长短期记忆网络（LSTM）对CNN提取的特征进行时序建模。LSTM的结构设计如下：Time series modeling is the core part of this invention. The features extracted by CNN are modeled by long short-term memory network (LSTM). The structure of LSTM is designed as follows:
·LSTM1：隐藏层单元128，双向LSTMLSTM1: 128 hidden units, bidirectional LSTM
·LSTM2：隐藏层单元128，双向LSTMLSTM2: 128 hidden units, bidirectional LSTM
·Dropout：0.5Dropout: 0.5
·Dense：全连接层，单元数量1，激活函数SigmoidDense: fully connected layer, number of units 1, activation function Sigmoid
为了进一步提高模型的预测精度，本发明在LSTM网络中引入了注意力机制（Attention Mechanism）。注意力机制通过动态调整模型对不同时间步长特征的关注度，增强了模型对关键特征的捕捉能力。具体实现上，注意力权重通过训练动态调整，计算公式如下：In order to further improve the prediction accuracy of the model, the present invention introduces the attention mechanism in the LSTM network. The attention mechanism dynamically adjusts the model's attention to features at different time steps, thereby enhancing the model's ability to capture key features. In specific implementation, the attention weight is dynamically adjusted through training, and the calculation formula is as follows:
其中，为时间步长t的注意力权重，为注意力得分，是时间步k对应的注意力得分，和为可训练权重矩阵，和分别为LSTM的隐状态和输入特征。in, is the attention weight at time step t, Score for attention, is the attention score corresponding to time step k, and is the trainable weight matrix, and are the hidden state and input features of LSTM respectively.
风险评估模块基于LSTM的输出结果，通过全连接层生成风险评估值。评估公式如下：The risk assessment module generates a risk assessment value based on the output of LSTM through a fully connected layer. The assessment formula is as follows:
其中，为Sigmoid激活函数，和为全连接层的权重和偏置，Attentionoutput是通过注意力机制计算的输出结果。风险评估值用于实时监控和管理供应链金融风险，一旦超过设定的阈值，系统将自动触发预警并采取相应的风险管理措施。in, is the Sigmoid activation function, and is the weight and bias of the fully connected layer, and Attentionoutput is the output result calculated by the attention mechanism. The risk assessment value is used to monitor and manage supply chain financial risks in real time. Once the set threshold is exceeded, the system will automatically trigger an early warning and take corresponding risk management measures.
本发明通过结合卷积神经网络（CNN）和长短期记忆网络（LSTM），实现对钢铁行业供应链中的多变量时间序列数据进行深度分析与处理。The present invention combines convolutional neural networks (CNN) and long short-term memory networks (LSTM) to achieve in-depth analysis and processing of multivariate time series data in the steel industry supply chain.
首先，在特征提取方面，卷积神经网络（CNN）在本发明中起到了至关重要的作用。CNN通过多层卷积操作，能够有效提取时间序列数据中的高维特征。具体的卷积操作公式如下：First, in terms of feature extraction, convolutional neural network (CNN) plays a crucial role in the present invention. CNN can effectively extract high-dimensional features from time series data through multi-layer convolution operations. The specific convolution operation formula is as follows:
其中，表示第层卷积输出的第i,j个单元，为第层的卷积核权重，为第层的输入特征，为偏置，ReLU为激活函数。in, Indicates The i,jth unit of the layer convolution output, For the The convolution kernel weights of the layer, For the The input features of the layer, is the bias and ReLU is the activation function.
为了进一步提高特征提取的效率，CNN结构设计中包括多个卷积层和池化层的组合。卷积层通过不同大小的卷积核（3x3, 5x5, 7x7）提取局部特征，池化层则通过最大池化操作减少数据维度，增强模型的平移不变性。池化操作公式如下：In order to further improve the efficiency of feature extraction, the CNN structure design includes a combination of multiple convolutional layers and pooling layers. The convolutional layer extracts local features through convolution kernels of different sizes (3x3, 5x5, 7x7), and the pooling layer reduces the data dimension through the maximum pooling operation to enhance the translation invariance of the model. The pooling operation formula is as follows:
其中，表示第层池化输出的第i,j个单元，表示池化窗口内的最大值操作。in, Indicates The i,jth unit of the layer pooling output, Represents the maximum value operation within the pooling window.
在时序建模方面，本发明采用长短期记忆网络（LSTM）对CNN提取的高维特征进行建模。LSTM通过其记忆单元和门控机制，有效捕捉时间序列数据中的长期依赖关系。LSTM的核心计算公式如下：In terms of time series modeling, the present invention uses the long short-term memory network (LSTM) to model the high-dimensional features extracted by CNN. LSTM effectively captures the long-term dependencies in time series data through its memory units and gating mechanism. The core calculation formula of LSTM is as follows:
其中，、、分别为输入门、遗忘门和输出门的激活值，为候选记忆单元，为记忆单元状态，为隐状态，为Sigmoid激活函数，为双曲正切激活函数，、、、和、、、为LSTM的权重和偏置。in, , , are the activation values of the input gate, forget gate, and output gate, respectively. is a candidate memory unit, is the memory unit state, is the hidden state, is the Sigmoid activation function, is the hyperbolic tangent activation function, , , , and , , , are the weights and biases of LSTM.
在具体实现中，本发明采用双向LSTM，通过前向和后向两个方向的LSTM网络，共同捕捉时间序列数据中的前后依赖关系。双向LSTM的输出公式如下：In the specific implementation, the present invention adopts bidirectional LSTM, and captures the forward and backward dependencies in the time series data through LSTM networks in both the forward and backward directions. The output formula of the bidirectional LSTM is as follows:
其中，和分别为前向和后向LSTM的隐状态，[;]表示向量的连接操作。in, and are the hidden states of the forward and backward LSTMs respectively, and [;] represents the connection operation of the vectors.
本发明在LSTM网络中引入的注意力机制（Attention Mechanism），用于进一步提高预测精度，通过动态调整模型对不同时间步长特征的关注度，增强了模型对关键特征的捕捉能力。注意力机制的计算公式如下：The present invention introduces the attention mechanism in the LSTM network to further improve the prediction accuracy. By dynamically adjusting the model's attention to features at different time steps, the model's ability to capture key features is enhanced. The calculation formula of the attention mechanism is as follows:
其中，为注意力得分，为注意力权重，和为可训练权重和偏置，是时间步k对应的注意力得分，Attention output为注意力机制的输出。in, Score for attention, is the attention weight, and are trainable weights and biases, is the attention score corresponding to time step k, and Attention output is the output of the attention mechanism.
本发明通过高效的深度学习模型和实时数据处理技术，实现对钢铁行业供应链金融风险的精准评估和动态管理。在风险评估方面，本发明设计了一种综合考虑多维度特征的评估模型。通过对市场价格、库存水平、生产效率和宏观经济指标等数据进行深度特征提取和时序建模，评估模型能够准确捕捉供应链中的潜在风险因素。模型结合卷积神经网络（CNN）和长短期记忆网络（LSTM）的优点，利用注意力机制（Attention Mechanism）对不同时间步长特征的关注度进行动态调整，生成风险评估值。核心公式如下：The present invention realizes accurate assessment and dynamic management of financial risks in the steel industry supply chain through efficient deep learning models and real-time data processing technology. In terms of risk assessment, the present invention designs an assessment model that comprehensively considers multi-dimensional features. By performing deep feature extraction and time series modeling on data such as market prices, inventory levels, production efficiency, and macroeconomic indicators, the assessment model can accurately capture potential risk factors in the supply chain. The model combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM), and uses the attention mechanism to dynamically adjust the attention to features at different time steps to generate a risk assessment value. The core formula is as follows:
其中，σ为Sigmoid激活函数，和为全连接层的权重和偏置，Attention output是通过注意力机制计算的输出结果。Among them, σ is the Sigmoid activation function, and is the weight and bias of the fully connected layer, and Attention output is the output result calculated by the attention mechanism.
为了实现实时监控，本发明采用了高频率的数据采集和处理机制。系统通过物联网（IoT）设备和高精度传感器，实时获取市场价格、库存水平、生产效率等关键数据，并利用分布式存储（HDFS）和计算技术（Spark）进行快速处理。这些数据被预处理后输入深度学习模型，包括数据清洗、归一化和特征提取，确保数据的高质量和一致性。实时监控模块结合分布式计算技术，支持大规模数据的并行处理和分析。系统通过设置多个监控节点，分布式采集和处理供应链各环节的运行数据。每个监控节点都配置有高性能计算单元，能够独立完成数据的采集、处理和初步分析，确保数据处理的实时性和高效性。在具体实现中，本发明采用时序数据库（TimescaleDB）进行数据存储，支持高频率、高并发的数据写入和查询。时序数据库的索引机制和压缩算法，能够显著提升数据的存储效率和访问速度，为实时监控提供坚实的数据基础。In order to achieve real-time monitoring, the present invention adopts a high-frequency data collection and processing mechanism. The system uses Internet of Things (IoT) devices and high-precision sensors to obtain key data such as market prices, inventory levels, and production efficiency in real time, and uses distributed storage (HDFS) and computing technology (Spark) for rapid processing. These data are pre-processed and input into the deep learning model, including data cleaning, normalization, and feature extraction to ensure high quality and consistency of the data. The real-time monitoring module combines distributed computing technology to support parallel processing and analysis of large-scale data. The system collects and processes operating data of various links in the supply chain in a distributed manner by setting up multiple monitoring nodes. Each monitoring node is equipped with a high-performance computing unit, which can independently complete data collection, processing, and preliminary analysis to ensure the real-time and high efficiency of data processing. In the specific implementation, the present invention uses a time series database (TimescaleDB) for data storage, which supports high-frequency and high-concurrency data writing and query. The indexing mechanism and compression algorithm of the time series database can significantly improve the storage efficiency and access speed of data, providing a solid data foundation for real-time monitoring.
风险评估结果通过风险评估模块实时计算，并与设定的风险阈值进行比较。一旦发现风险评估值超过设定阈值，系统将自动触发预警机制，并采取相应的风险管理措施。这些措施包括但不限于调整质押率、增加担保、冻结高风险质押物等。预警机制的设计采用了基于规则的专家系统，结合机器学习模型的预测结果，提供多层次的风险预警和管理方案。The risk assessment results are calculated in real time by the risk assessment module and compared with the set risk threshold. Once the risk assessment value exceeds the set threshold, the system will automatically trigger the early warning mechanism and take corresponding risk management measures. These measures include but are not limited to adjusting the pledge rate, increasing guarantees, freezing high-risk pledges, etc. The early warning mechanism is designed using a rule-based expert system, combined with the prediction results of the machine learning model, to provide a multi-level risk warning and management solution.
为了进一步提升系统的灵活性和适应性，本发明引入了自适应学习机制。通过持续监控供应链运行数据和风险评估结果，系统能够动态调整模型参数和风险阈值，适应市场环境的变化。自适应学习机制采用在线学习算法，结合批量学习和增量学习的优点，实现模型的持续优化和性能提升。In order to further improve the flexibility and adaptability of the system, the present invention introduces an adaptive learning mechanism. By continuously monitoring the supply chain operation data and risk assessment results, the system can dynamically adjust the model parameters and risk thresholds to adapt to changes in the market environment. The adaptive learning mechanism adopts an online learning algorithm, combining the advantages of batch learning and incremental learning to achieve continuous optimization and performance improvement of the model.
决策支持系统利用深度学习模型的风险评估结果，结合历史数据分析和实时监控信息，生成详细的风险评估报告。风险评估报告包括但不限于风险评估值、历史数据分析、趋势预测、风险预警等内容。这些报告通过数据可视化工具（Tableau）进行展示，帮助企业管理者直观了解供应链的风险状况和潜在问题。在历史数据分析方面，本发明采用了时序分析方法，包括自回归积分滑动平均模型（ARIMA）、季节性分解方法（STL）等。通过对历史数据的深入分析，系统能够识别出供应链中潜在的周期性和趋势性变化，提供长期的风险预测和管理建议。历史数据分析结果与深度学习模型的预测结果进行综合对比，提升风险评估的准确性和可靠性。趋势预测是决策支持系统的核心功能之一。本发明通过卷积神经网络（CNN）和长短期记忆网络（LSTM）的结合，准确预测供应链金融风险的未来趋势。趋势预测模型考虑了多种影响因素，包括市场价格波动、库存变化、生产效率波动等，通过对多变量时间序列数据的深度分析，生成未来的风险趋势预测结果。趋势预测结果以图表和报告的形式展示，帮助企业提前应对潜在风险。风险预警机制是决策支持系统的重要组成部分。当风险评估值超过设定的阈值，系统将自动生成预警通知，并提供详细的风险管理建议。预警通知包括风险源、风险等级、建议措施等信息，帮助企业快速响应和处理潜在风险。风险管理建议包括调整质押率、增加担保、冻结高风险质押物、实时平仓等具体措施。这些建议基于深度学习模型的风险评估结果和专家系统的规则制定，确保措施的有效性和针对性。决策支持系统还包括模拟仿真功能，通过模拟不同的市场情景和供应链运行状态，评估各种管理措施的效果和风险。模拟仿真功能采用蒙特卡洛模拟（Monte Carlo Simulation）和离散事件仿真（Discrete Event Simulation）方法，结合实际数据和模型预测，生成多种情景下的风险评估结果和管理建议。企业管理者可以根据仿真结果，制定和优化供应链金融风险管理策略，提升决策的科学性和有效性。在具体实现中，决策支持系统集成了大数据平台和人工智能技术，支持大规模数据的并行处理和分析。系统采用分布式计算架构，通过多节点协同工作，提升数据处理的速度和效率。数据存储方面，采用高性能的分布式数据库（Cassandra），确保数据的高效存储和快速访问。The decision support system uses the risk assessment results of the deep learning model, combined with historical data analysis and real-time monitoring information, to generate a detailed risk assessment report. The risk assessment report includes but is not limited to risk assessment values, historical data analysis, trend forecasting, risk warning and other contents. These reports are displayed through a data visualization tool (Tableau) to help enterprise managers intuitively understand the risk status and potential problems of the supply chain. In terms of historical data analysis, the present invention adopts time series analysis methods, including autoregressive integrated moving average model (ARIMA), seasonal decomposition method (STL), etc. Through in-depth analysis of historical data, the system can identify potential cyclical and trend changes in the supply chain and provide long-term risk forecasting and management suggestions. The historical data analysis results are comprehensively compared with the prediction results of the deep learning model to improve the accuracy and reliability of risk assessment. Trend forecasting is one of the core functions of the decision support system. The present invention accurately predicts the future trend of supply chain financial risks by combining convolutional neural network (CNN) and long short-term memory network (LSTM). The trend forecasting model takes into account a variety of influencing factors, including market price fluctuations, inventory changes, production efficiency fluctuations, etc., and generates future risk trend forecasting results through in-depth analysis of multivariate time series data. Trend forecast results are presented in the form of charts and reports to help enterprises deal with potential risks in advance. The risk warning mechanism is an important part of the decision support system. When the risk assessment value exceeds the set threshold, the system will automatically generate a warning notification and provide detailed risk management suggestions. The warning notification includes information such as risk source, risk level, and recommended measures to help enterprises quickly respond to and deal with potential risks. Risk management suggestions include specific measures such as adjusting the pledge rate, increasing guarantees, freezing high-risk pledges, and real-time liquidation. These suggestions are based on the risk assessment results of the deep learning model and the rule formulation of the expert system to ensure the effectiveness and pertinence of the measures. The decision support system also includes simulation functions to evaluate the effects and risks of various management measures by simulating different market scenarios and supply chain operation status. The simulation function uses Monte Carlo simulation and discrete event simulation methods, combined with actual data and model prediction, to generate risk assessment results and management suggestions under various scenarios. Enterprise managers can formulate and optimize supply chain financial risk management strategies based on simulation results to improve the scientificity and effectiveness of decision-making. In the specific implementation, the decision support system integrates big data platforms and artificial intelligence technologies to support parallel processing and analysis of large-scale data. The system adopts a distributed computing architecture, which improves the speed and efficiency of data processing through multi-node collaboration. In terms of data storage, a high-performance distributed database (Cassandra) is used to ensure efficient storage and fast access to data.
决策支持系统的用户界面友好，提供交互式的数据查询和分析功能。企业管理者可以通过界面自定义风险评估报告、趋势预测图表和模拟仿真场景，获得个性化的风险管理建议。用户界面采用响应式设计，支持多设备访问，包括PC、平板电脑和移动设备，方便企业管理者随时随地获取风险评估和管理信息。The decision support system has a user-friendly interface and provides interactive data query and analysis functions. Enterprise managers can customize risk assessment reports, trend forecast charts and simulation scenarios through the interface to obtain personalized risk management suggestions. The user interface adopts a responsive design and supports multi-device access, including PC, tablet and mobile devices, making it convenient for enterprise managers to obtain risk assessment and management information anytime and anywhere.
通过本发明的风险管理决策支持系统，钢铁企业能够实现供应链金融风险的全面监控和科学管理。系统提供的数据支持和决策建议，帮助企业在复杂多变的市场环境中保持稳健运营，提高供应链金融风险管理的智能化水平，增强企业的竞争力和抗风险能力。通过持续监控供应链运行数据和风险评估结果，本发明的系统能够动态调整模型参数和风险阈值，以适应市场环境的变化，确保风险管理的灵活性和精准性。自适应风险管理机制基于在线学习算法，通过实时更新模型参数，增强模型的适应性和预测能力。具体来说，当新数据被采集并预处理后，系统会将这些数据用于模型的增量训练，实时更新模型的权重和偏置。增量训练算法如下：Through the risk management decision support system of the present invention, steel enterprises can achieve comprehensive monitoring and scientific management of supply chain financial risks. The data support and decision-making recommendations provided by the system help enterprises maintain stable operations in a complex and changing market environment, improve the level of intelligence in supply chain financial risk management, and enhance the competitiveness and risk resistance of enterprises. By continuously monitoring supply chain operation data and risk assessment results, the system of the present invention can dynamically adjust model parameters and risk thresholds to adapt to changes in the market environment and ensure the flexibility and accuracy of risk management. The adaptive risk management mechanism is based on an online learning algorithm, which enhances the adaptability and predictive ability of the model by updating model parameters in real time. Specifically, after new data is collected and preprocessed, the system will use this data for incremental training of the model and update the weights and biases of the model in real time. The incremental training algorithm is as follows:
其中，为当前模型参数，为学习率，为损失函数，和分别为输入数据和实际输出。通过不断调整模型参数，系统能够持续优化预测性能。in, are the current model parameters, is the learning rate, is the loss function, and are input data and actual output respectively. By continuously adjusting the model parameters, the system can continuously optimize the prediction performance.
动态调整机制不仅包括模型参数的调整，还包括风险阈值的动态设置。为了确保风险管理的灵活性，系统根据市场环境和企业运营状况，自动调整风险阈值。例如，在市场波动较大的情况下，系统可以降低风险阈值，提高预警的敏感度；在市场较为稳定的情况下，系统可以适当提高风险阈值，减少误报和过度反应。系统采用贝叶斯优化（BayesianOptimization）方法对模型参数和风险阈值进行优化。贝叶斯优化通过构建代理模型，预测不同参数组合的效果，并选择最优的参数设置。优化过程如下：The dynamic adjustment mechanism includes not only the adjustment of model parameters, but also the dynamic setting of risk thresholds. In order to ensure the flexibility of risk management, the system automatically adjusts the risk threshold according to the market environment and the company's operating conditions. For example, when the market fluctuates greatly, the system can lower the risk threshold and increase the sensitivity of the early warning; when the market is relatively stable, the system can appropriately increase the risk threshold to reduce false alarms and overreactions. The system uses the Bayesian Optimization method to optimize the model parameters and risk thresholds. Bayesian optimization predicts the effects of different parameter combinations by constructing a proxy model and selects the optimal parameter settings. The optimization process is as follows:
其中，为最优参数，为损失函数的期望值。通过贝叶斯优化，系统能够在不进行大规模试验的情况下，高效找到最优参数组合。in, is the optimal parameter, is the expected value of the loss function. Through Bayesian optimization, the system can efficiently find the optimal parameter combination without large-scale experiments.
为了提高系统的响应速度和处理效率，本发明在数据处理和模型训练中引入了并行计算技术。通过分布式计算框架（Apache Spark），系统能够将大规模数据处理任务分解到多个计算节点并行执行，大幅提升数据处理速度和模型训练效率。系统还采用GPU加速技术，通过并行计算加速深度学习模型的训练和预测过程。In order to improve the response speed and processing efficiency of the system, the present invention introduces parallel computing technology in data processing and model training. Through the distributed computing framework (Apache Spark), the system can decompose large-scale data processing tasks into multiple computing nodes for parallel execution, greatly improving the data processing speed and model training efficiency. The system also uses GPU acceleration technology to accelerate the training and prediction process of deep learning models through parallel computing.
在数据安全和隐私保护方面，本发明采用多层次的安全措施，确保数据在采集、传输、存储和处理过程中的安全性。传输过程中，系统采用SSL/TLS协议进行数据加密，防止数据在网络传输过程中被窃取和篡改。在数据存储方面，系统采用高级加密标准（AES）对数据进行加密存储，确保数据在静态状态下的安全性。同时，系统还引入基于角色的访问控制（RBAC）机制，确保只有授权用户才能访问敏感数据，防止数据泄露和滥用。系统还支持数据审计和追踪功能，通过记录和监控数据的访问和操作日志，确保数据操作的可追溯性。数据审计功能能够检测和记录所有数据访问和操作行为，包括数据查询、修改、删除等，提供详细的操作日志和审计报告。企业管理者可以通过审计报告，监控数据使用情况，发现和防范潜在的安全威胁。本发明还设计了一套完善的容错和恢复机制，确保系统在故障和异常情况下能够快速恢复正常运行。系统采用多副本数据存储和容灾备份技术，确保数据在任何节点发生故障时不会丢失。故障检测和自动恢复机制能够实时监控系统运行状态，发现故障时自动切换到备用节点，保证系统的高可用性和连续性。通过自适应风险管理和动态调整机制，本发明能够实现对供应链金融风险的持续优化和精准管理。系统的高效数据处理能力和灵活的风险调整机制，确保企业能够在复杂多变的市场环境中快速响应和有效管理风险，提高供应链金融风险管理的智能化水平和抗风险能力，为企业的稳健运营提供有力支持。In terms of data security and privacy protection, the present invention adopts multi-level security measures to ensure the security of data during collection, transmission, storage and processing. During the transmission process, the system uses the SSL/TLS protocol to encrypt data to prevent data from being stolen and tampered with during network transmission. In terms of data storage, the system uses the Advanced Encryption Standard (AES) to encrypt and store data to ensure the security of data in a static state. At the same time, the system also introduces a role-based access control (RBAC) mechanism to ensure that only authorized users can access sensitive data to prevent data leakage and abuse. The system also supports data auditing and tracking functions to ensure the traceability of data operations by recording and monitoring data access and operation logs. The data audit function can detect and record all data access and operation behaviors, including data query, modification, deletion, etc., and provide detailed operation logs and audit reports. Enterprise managers can monitor data usage through audit reports, discover and prevent potential security threats. The present invention also designs a complete fault tolerance and recovery mechanism to ensure that the system can quickly resume normal operation in the event of failure and abnormal conditions. The system uses multi-copy data storage and disaster recovery backup technology to ensure that data will not be lost when any node fails. The fault detection and automatic recovery mechanism can monitor the system operation status in real time, and automatically switch to the backup node when a fault is found, ensuring the high availability and continuity of the system. Through adaptive risk management and dynamic adjustment mechanisms, the present invention can achieve continuous optimization and precise management of supply chain financial risks. The system's efficient data processing capabilities and flexible risk adjustment mechanisms ensure that enterprises can quickly respond to and effectively manage risks in a complex and changing market environment, improve the intelligence level and risk resistance of supply chain financial risk management, and provide strong support for the stable operation of enterprises.
本发明的系统可进行良好的集成与扩展，旨在通过模块化设计和开放接口，实现系统的灵活部署与扩展应用，以适应不同企业的需求和未来技术的发展。系统采用模块化设计，各功能模块独立开发，便于维护和升级。主要功能模块包括数据采集模块、数据预处理模块、特征提取模块、时序建模模块、风险评估模块、决策支持模块、自适应风险管理模块和动态调整模块等。模块化设计不仅提高了系统的可靠性和可维护性，还为系统的功能扩展提供了便利。为了实现系统的灵活部署和扩展应用，本发明设计了多种开放接口（API），支持与外部系统的集成。通过这些接口，企业可以将系统集成到现有的ERP、MES、SCM等管理系统中，实现数据的无缝对接和功能的互操作。开放接口采用RESTful架构，支持JSON和XML格式的数据交换，确保接口的通用性和易用性。系统的部署方式多样化，支持本地部署和云端部署。对于数据量大且对数据安全性要求高的企业，建议采用本地部署方式，通过企业内部服务器和网络实现系统的安全运行。对于希望降低IT基础设施成本和提高系统灵活性的企业，可以选择云端部署方式，通过云计算平台（如AWS、Azure、Alibaba Cloud等）实现系统的快速部署和弹性扩展。在本地部署方案中，系统采用分布式架构，通过多台服务器协同工作，实现高性能计算和高可用性。分布式存储（HDFS）和计算技术（Spark）确保系统在处理大规模数据时的效率和稳定性。云端部署方案则利用云平台的资源弹性和服务高可用性，支持按需扩展计算资源和存储空间，满足企业在不同阶段的需求。本发明还支持定制化开发和二次开发，企业可以根据自身需求，对系统进行定制化调整和功能扩展。开发工具包（SDK）和详细的开发文档，帮助企业快速上手进行二次开发，实现个性化需求。通过插件机制，企业可以增加新的数据源、特征提取方法、模型算法和风险管理策略，进一步提升系统的灵活性和适应性。The system of the present invention can be well integrated and expanded, and aims to realize flexible deployment and extended application of the system through modular design and open interfaces to adapt to the needs of different enterprises and the development of future technologies. The system adopts modular design, and each functional module is independently developed, which is convenient for maintenance and upgrading. The main functional modules include data acquisition module, data preprocessing module, feature extraction module, time series modeling module, risk assessment module, decision support module, adaptive risk management module and dynamic adjustment module. The modular design not only improves the reliability and maintainability of the system, but also provides convenience for the functional expansion of the system. In order to realize the flexible deployment and extended application of the system, the present invention designs a variety of open interfaces (APIs) to support integration with external systems. Through these interfaces, enterprises can integrate the system into existing management systems such as ERP, MES, SCM, etc. to achieve seamless docking of data and interoperability of functions. The open interface adopts RESTful architecture, supports data exchange in JSON and XML formats, and ensures the versatility and ease of use of the interface. The system has diversified deployment methods, supporting local deployment and cloud deployment. For enterprises with large data volumes and high requirements for data security, it is recommended to adopt a local deployment method to achieve safe operation of the system through the internal server and network of the enterprise. For enterprises that want to reduce IT infrastructure costs and improve system flexibility, they can choose cloud deployment to achieve rapid deployment and elastic expansion of the system through cloud computing platforms (such as AWS, Azure, Alibaba Cloud, etc.). In the local deployment solution, the system adopts a distributed architecture, and achieves high-performance computing and high availability through the collaboration of multiple servers. Distributed storage (HDFS) and computing technology (Spark) ensure the efficiency and stability of the system when processing large-scale data. The cloud deployment solution uses the resource elasticity and high service availability of the cloud platform to support on-demand expansion of computing resources and storage space to meet the needs of enterprises at different stages. The present invention also supports customized development and secondary development. Enterprises can customize and expand the system according to their own needs. The development kit (SDK) and detailed development documents help enterprises quickly get started with secondary development and realize personalized needs. Through the plug-in mechanism, enterprises can add new data sources, feature extraction methods, model algorithms and risk management strategies to further enhance the flexibility and adaptability of the system.
为了适应未来技术的发展，本发明设计了可扩展的数据架构和计算框架。数据架构采用基于消息队列的实时数据处理框架（Kafka、RabbitMQ），支持高吞吐量的数据采集和传输。计算框架采用微服务架构，通过Docker和Kubernetes实现服务的容器化和编排，确保系统的高可用性和可扩展性。系统还支持与区块链技术的集成，通过区块链的分布式账本和智能合约技术，进一步提升供应链金融风险管理的透明度和可信度。通过将关键数据和交易记录上链，企业可以实现数据的不可篡改和可追溯，增强数据的公信力和安全性。智能合约技术则可以自动执行预定的风险管理策略和措施，减少人为干预，提高风险管理的自动化水平。在未来的发展中，本发明可以进一步集成更多的前沿技术，如物联网（IoT）、边缘计算（Edge Computing）、5G通信等，提升系统的实时性和智能化水平。通过IoT设备和5G网络，系统可以实现更广泛和实时的数据采集和传输；通过边缘计算，系统可以在数据源附近进行初步数据处理和分析，减少数据传输的延迟，提高实时响应能力。In order to adapt to the development of future technologies, the present invention designs an extensible data architecture and computing framework. The data architecture adopts a real-time data processing framework (Kafka, RabbitMQ) based on message queues to support high-throughput data collection and transmission. The computing framework adopts a microservice architecture, and implements containerization and orchestration of services through Docker and Kubernetes to ensure the high availability and scalability of the system. The system also supports integration with blockchain technology, and further improves the transparency and credibility of supply chain financial risk management through the distributed ledger and smart contract technology of blockchain. By putting key data and transaction records on the chain, enterprises can achieve data immutability and traceability, and enhance the credibility and security of data. Smart contract technology can automatically execute predetermined risk management strategies and measures, reduce human intervention, and improve the automation level of risk management. In future development, the present invention can further integrate more cutting-edge technologies, such as the Internet of Things (IoT), edge computing, 5G communication, etc., to improve the real-time and intelligent level of the system. Through IoT devices and 5G networks, the system can achieve more extensive and real-time data collection and transmission; through edge computing, the system can perform preliminary data processing and analysis near the data source, reduce data transmission delays, and improve real-time response capabilities.
综上所述，本发明通过模块化设计和开放接口，实现了系统的灵活部署与扩展应用。系统不仅能够满足当前钢铁行业供应链金融风险管理的需求，还具备良好的扩展性和适应性，能够适应未来技术的发展和企业需求的变化。通过集成更多的前沿技术，系统将进一步提升风险管理的智能化水平，为企业的稳健运营提供全面、智能的技术支持。In summary, the present invention realizes flexible deployment and extended application of the system through modular design and open interfaces. The system can not only meet the current needs of supply chain financial risk management in the steel industry, but also has good scalability and adaptability, and can adapt to future technological development and changes in corporate needs. By integrating more cutting-edge technologies, the system will further enhance the intelligent level of risk management and provide comprehensive and intelligent technical support for the stable operation of enterprises.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
附图1展示了基于深度学习的钢铁行业供应链金融风险管理系统的整体流程图。Figure 1 shows the overall flow chart of the steel industry supply chain financial risk management system based on deep learning.
附图2展示了数据采集与预处理的详细流程。Figure 2 shows the detailed process of data collection and preprocessing.
附图3展示了风险评估模型的详细流程。Figure 3 shows the detailed process of the risk assessment model.
附图4展示了实时监控与动态调整的详细流程。Figure 4 shows the detailed process of real-time monitoring and dynamic adjustment.
附图5展示了基于智能合约的自动化风险管理流程。Figure 5 shows the automated risk management process based on smart contracts.
具体实施方式DETAILED DESCRIPTION
在本实施例中，结合附图1（S101-S107），描述了如何通过数据的多层次融合与实时决策机制，进一步提升供应链金融风险管理系统的准确性和响应能力。该系统利用多源数据的深度分析和自适应模型更新，实现了供应链金融风险的精准预测、实时监控以及动态管理。In this embodiment, combined with Figure 1 (S101-S107), it is described how to further improve the accuracy and responsiveness of the supply chain financial risk management system through multi-level data integration and real-time decision-making mechanism. The system uses in-depth analysis of multi-source data and adaptive model updates to achieve accurate prediction, real-time monitoring and dynamic management of supply chain financial risks.
S101：数据采集S101: Data Collection
在该步骤中，系统从市场交易平台、企业ERP系统、企业MES系统和国家统计局采集到的多维度数据，融合了市场价格、库存水平、生产效率、经济指标等多种数据源。这些数据通过高精度的传感器和物联网（IoT）设备进行实时采集，确保了数据的准确性和时效性。In this step, the system collects multi-dimensional data from market trading platforms, enterprise ERP systems, enterprise MES systems and the National Bureau of Statistics, integrating multiple data sources such as market prices, inventory levels, production efficiency, economic indicators, etc. These data are collected in real time through high-precision sensors and Internet of Things (IoT) devices to ensure the accuracy and timeliness of the data.
本实施例中引入了一种动态数据加权机制，依据数据源的可靠性和实时性为每个数据源分配权重。通过这种动态加权方法，系统能够根据实时的市场波动或传感器反馈，及时调整各数据源在模型中的影响程度，进一步提高数据采集的灵活性与精准性。This embodiment introduces a dynamic data weighting mechanism, which assigns weights to each data source based on its reliability and real-time performance. Through this dynamic weighting method, the system can timely adjust the influence of each data source in the model according to real-time market fluctuations or sensor feedback, further improving the flexibility and accuracy of data collection.
S102：数据预处理S102: Data preprocessing
在数据预处理阶段，系统对采集到的数据进行数据清洗、归一化处理以及特征提取。与其他实施例不同，本实施例引入了基于机器学习的异常值检测算法。在数据清洗过程中，系统首先利用支持向量机（SVM）分类器对异常数据进行识别，剔除潜在的干扰数据。缺失数据通过插值方法进行补全。系统结合了K近邻插值法与时间序列自回归方法，将缺失数据的填补更加精细化，适用于复杂时间序列数据的处理。In the data preprocessing stage, the system performs data cleaning, normalization and feature extraction on the collected data. Unlike other embodiments, this embodiment introduces an outlier detection algorithm based on machine learning. In the data cleaning process, the system first uses a support vector machine (SVM) classifier to identify abnormal data and eliminate potential interference data. Missing data is supplemented by interpolation methods. The system combines the K nearest neighbor interpolation method with the time series autoregression method to make the missing data filling more refined, which is suitable for the processing of complex time series data.
本实施例中，通过引入自适应归一化技术，根据数据波动幅度和不同数据源的特征值，动态调整归一化范围。与传统的固定归一化区间不同，这种方法可以在市场剧烈波动时调整模型输入的幅度，增强模型的鲁棒性。In this embodiment, adaptive normalization technology is introduced to dynamically adjust the normalization range according to the data fluctuation range and the characteristic values of different data sources. Different from the traditional fixed normalization interval, this method can adjust the amplitude of the model input when the market fluctuates violently, thereby enhancing the robustness of the model.
S103：风险评估建模S103: Risk Assessment Modeling
风险评估建模是整个系统的核心。本实施例采用卷积神经网络（CNN）结合长短期记忆网络（LSTM）进行多变量时间序列建模。CNN用于从多维度数据中提取关键特征，LSTM则用于捕捉时间序列数据中的长期依赖性和非线性变化。在卷积层的设计中，本实施例新增了多尺度卷积核的组合应用，不同大小的卷积核（3×3、5×5、7×7）能够同时捕捉数据中的短期、长期特征。此外，系统引入了层次化注意力机制（Hierarchical AttentionMechanism），进一步提升了模型对不同时间序列步长的关注度，使得系统能更加精细地关注到供应链风险的变化趋势。Risk assessment modeling is the core of the entire system. This embodiment uses a convolutional neural network (CNN) combined with a long short-term memory network (LSTM) for multivariate time series modeling. CNN is used to extract key features from multi-dimensional data, while LSTM is used to capture long-term dependencies and nonlinear changes in time series data. In the design of the convolution layer, this embodiment adds a combined application of multi-scale convolution kernels, and convolution kernels of different sizes (3×3, 5×5, 7×7) can simultaneously capture short-term and long-term features in the data. In addition, the system introduces a hierarchical attention mechanism, which further enhances the model's attention to different time series steps, allowing the system to pay more attention to the changing trends of supply chain risks.
本实施例中的风险评估模型增加了外部事件因子的引入，如重大政策变化、国际市场波动等。这些外部因子通过一个单独的外部因子分析层，融入到CNN-LSTM模型中，能够更好地模拟真实市场环境中的风险波动。The risk assessment model in this embodiment adds the introduction of external event factors, such as major policy changes, international market fluctuations, etc. These external factors are integrated into the CNN-LSTM model through a separate external factor analysis layer, which can better simulate the risk fluctuations in the real market environment.
S104：实时监控与动态调整S104: Real-time monitoring and dynamic adjustment
系统通过高频率的数据采集，结合物联网设备实现了对供应链金融风险的实时监控。本实施例引入了基于贝叶斯优化的在线学习算法（Bayesian Optimization OnlineLearning Algorithm），可以根据实时变化的供应链数据自动调整模型参数。在线学习算法的引入保证了系统能够在面对不确定市场环境时，不断更新风险预测模型的权重，确保模型始终保持最佳的预测性能。此外，系统利用自适应调整机制，能够动态设置不同的风险阈值。例如，在市场波动剧烈时系统会自动降低风险阈值，以提高预警的敏感度；而在市场较为平稳时，系统会适当提高风险阈值，避免误报和过度反应。The system realizes real-time monitoring of supply chain financial risks through high-frequency data collection combined with IoT devices. This embodiment introduces an online learning algorithm based on Bayesian optimization (Bayesian Optimization Online Learning Algorithm), which can automatically adjust model parameters according to real-time changing supply chain data. The introduction of the online learning algorithm ensures that the system can continuously update the weights of the risk prediction model in the face of an uncertain market environment, ensuring that the model always maintains the best prediction performance. In addition, the system uses an adaptive adjustment mechanism to dynamically set different risk thresholds. For example, when the market fluctuates violently, the system will automatically lower the risk threshold to increase the sensitivity of the warning; when the market is relatively stable, the system will appropriately increase the risk threshold to avoid false alarms and overreactions.
本实施例增加了对“突发事件”（如自然灾害、重大政策调整）的实时反应机制，通过快速捕捉外部突发因素的异常信号，触发应急响应模型。这种机制提高了系统对复杂事件的灵活应对能力。This embodiment adds a real-time response mechanism for "emergencies" (such as natural disasters and major policy adjustments), which triggers the emergency response model by quickly capturing abnormal signals of external emergencies. This mechanism improves the system's ability to flexibly respond to complex events.
S105：风险预警与管理S105: Risk Warning and Management
当系统检测到风险评估值超过设定阈值时，会自动触发预警机制。本实施例不仅发出预警通知，还会结合智能合约（Smart Contract）自动执行部分风险控制措施，包括调整质押率、增加担保、冻结高风险质押物等。为了确保系统的风险应对措施更加高效，风险预警模块基于历史数据和当前市场条件，生成多种情景下的风险管理建议。例如，在市场供需严重失衡时，系统会优先执行冻结高风险质押物的策略，避免重大损失。When the system detects that the risk assessment value exceeds the set threshold, the early warning mechanism will be automatically triggered. This embodiment not only issues an early warning notification, but also automatically executes some risk control measures in combination with smart contracts, including adjusting the pledge rate, increasing guarantees, freezing high-risk pledges, etc. In order to ensure that the system's risk response measures are more efficient, the risk early warning module generates risk management suggestions for various scenarios based on historical data and current market conditions. For example, when there is a serious imbalance between market supply and demand, the system will prioritize the strategy of freezing high-risk pledges to avoid significant losses.
本实施例中预警与管理模块新增了“事件驱动的风险管理”功能。通过监测供应链中的异常事件（如关键设备故障、物流中断等），系统能够快速反应，自动调整质押率和担保措施，避免潜在风险的进一步扩大。In this embodiment, the early warning and management module has added an "event-driven risk management" function. By monitoring abnormal events in the supply chain (such as key equipment failures, logistics interruptions, etc.), the system can respond quickly and automatically adjust the pledge rate and guarantee measures to avoid further expansion of potential risks.
S106：决策支持系统S106: Decision Support System
决策支持系统基于风险评估值和趋势预测，为企业管理者提供详细的风险评估报告和管理建议。本实施例采用了更加复杂的情景模拟技术，包括基于蒙特卡洛模拟和多维度回归分析的趋势预测，帮助企业提前预判未来市场走势。系统不仅提供风险报告，还为用户提供交互式决策工具，管理者可以通过界面模拟不同决策下的潜在风险情况，并根据模拟结果优化决策方案。The decision support system provides detailed risk assessment reports and management suggestions for enterprise managers based on risk assessment values and trend forecasts. This embodiment uses more complex scenario simulation technology, including trend forecasts based on Monte Carlo simulation and multi-dimensional regression analysis, to help enterprises predict future market trends in advance. The system not only provides risk reports, but also provides users with interactive decision-making tools. Managers can simulate the potential risk situations under different decisions through the interface and optimize decision-making plans based on simulation results.
本实施例中的决策支持系统增加了“风险优化建议”，通过大数据分析自动推荐不同市场条件下的最佳策略组合，帮助企业高效应对复杂市场环境。The decision support system in this embodiment adds "risk optimization suggestions" to automatically recommend the best strategy combination under different market conditions through big data analysis, helping enterprises to efficiently cope with complex market environments.
S107：数据存储与安全S107: Data Storage and Security
在数据存储与安全层面，本实施例进一步优化了系统的分布式存储架构，采用了多副本存储和容灾备份技术，确保数据在任何情况下不会丢失。此外，系统引入了区块链技术，增强了数据的可追溯性与不可篡改性，确保数据传输和存储过程中的安全性。In terms of data storage and security, this embodiment further optimizes the distributed storage architecture of the system, adopts multi-copy storage and disaster recovery backup technology to ensure that data will not be lost under any circumstances. In addition, the system introduces blockchain technology to enhance the traceability and non-tamperability of data, ensuring the security of data transmission and storage.
本实施例中的数据安全策略进一步提升，通过集成区块链智能合约技术，确保所有数据的访问操作都能被记录和追踪，提供全面的数据审计与追溯功能。The data security strategy in this embodiment is further improved by integrating blockchain smart contract technology to ensure that all data access operations can be recorded and tracked, providing comprehensive data auditing and tracing functions.
在本实施例中，描述了基于深度学习的供应链金融风险评估系统的实现过程，系统旨在通过卷积神经网络（CNN）和长短期记忆网络（LSTM）的结合，实现对钢铁行业供应链金融风险的精准评估与管理。In this embodiment, the implementation process of a supply chain finance risk assessment system based on deep learning is described. The system aims to achieve accurate assessment and management of supply chain finance risks in the steel industry through the combination of convolutional neural networks (CNN) and long short-term memory networks (LSTM).
首先，数据采集模块从多个数据源实时采集数据，包括市场交易平台提供的钢材价格数据、企业ERP系统中的库存水平数据、MES系统中的生产效率数据以及国家统计局发布的宏观经济指标数据。这些数据通过物联网（IoT）设备和高精度传感器进行实时监控和采集，确保数据的实时性和准确性。采集到的数据被传输至数据预处理模块进行处理。数据预处理模块首先进行数据清洗，包括处理缺失数据和剔除异常值。缺失数据通过K近邻插值法进行填补，确保数据的完整性。对于异常值，系统采用基于统计分析的方法进行检测和剔除。接下来，预处理模块对数据进行归一化处理，将数据缩放到[0, 1]区间，消除不同量纲之间的差异，提高模型的训练效果。在特征提取阶段，卷积神经网络（CNN）通过多层卷积操作，提取时间序列数据中的高维特征。每一层卷积操作后紧跟池化操作，以减少数据维度和计算复杂度。提取到的高维特征被传递到长短期记忆网络（LSTM）进行时序建模，LSTM通过其记忆单元和门控机制，有效捕捉数据中的长期依赖关系和复杂非线性模式。在LSTM网络中引入注意力机制，通过动态调整模型对不同时间步长特征的关注度，进一步增强了模型对关键特征的捕捉能力。注意力机制通过计算注意力权重，将更多的注意力放在关键时间步长的特征上，从而提高模型的预测精度。风险评估模块基于LSTM网络的输出结果，生成风险评估值。风险评估值用于实时监控和管理供应链金融风险。系统通过设定风险阈值，实时比较评估值与阈值，当评估值超过设定阈值时，系统自动触发预警机制，提示相关人员采取相应的风险管理措施。First, the data acquisition module collects data in real time from multiple data sources, including steel price data provided by the market trading platform, inventory level data in the enterprise ERP system, production efficiency data in the MES system, and macroeconomic indicator data released by the National Bureau of Statistics. These data are monitored and collected in real time through Internet of Things (IoT) devices and high-precision sensors to ensure the real-time and accuracy of the data. The collected data is transmitted to the data preprocessing module for processing. The data preprocessing module first performs data cleaning, including processing missing data and removing outliers. Missing data is filled by K-nearest neighbor interpolation to ensure data integrity. For outliers, the system uses a statistical analysis-based method to detect and remove them. Next, the preprocessing module normalizes the data and scales the data to the [0, 1] interval to eliminate the differences between different dimensions and improve the training effect of the model. In the feature extraction stage, the convolutional neural network (CNN) extracts high-dimensional features from time series data through multi-layer convolution operations. Each layer of convolution operation is followed by a pooling operation to reduce data dimensions and computational complexity. The extracted high-dimensional features are passed to the long short-term memory network (LSTM) for time series modeling. LSTM effectively captures long-term dependencies and complex nonlinear patterns in the data through its memory units and gating mechanisms. The attention mechanism is introduced into the LSTM network, and the model's ability to capture key features is further enhanced by dynamically adjusting the model's attention to features of different time steps. The attention mechanism pays more attention to the features of key time steps by calculating attention weights, thereby improving the prediction accuracy of the model. The risk assessment module generates a risk assessment value based on the output results of the LSTM network. The risk assessment value is used to monitor and manage supply chain financial risks in real time. The system sets a risk threshold and compares the assessment value with the threshold in real time. When the assessment value exceeds the set threshold, the system automatically triggers the early warning mechanism to prompt relevant personnel to take corresponding risk management measures.
系统的实现采用分布式计算和存储技术，支持大规模数据的并行处理和实时分析。通过分布式存储系统（HDFS）和计算框架（Spark），实现数据的快速存储和处理，保证系统在处理大规模数据时的效率和稳定性。系统还支持GPU加速技术，通过并行计算加速深度学习模型的训练和预测过程，提高系统的实时响应能力。系统的用户界面设计友好，提供直观的数据可视化和交互式的风险评估报告。企业管理者可以通过界面实时查看供应链的风险状况、历史数据分析和未来趋势预测，获取全面的风险管理信息。用户界面支持多设备访问，方便企业管理者随时随地获取风险评估和管理信息。The system is implemented using distributed computing and storage technology to support parallel processing and real-time analysis of large-scale data. Through the distributed storage system (HDFS) and computing framework (Spark), fast data storage and processing are achieved to ensure the efficiency and stability of the system when processing large-scale data. The system also supports GPU acceleration technology to accelerate the training and prediction process of deep learning models through parallel computing, and improve the real-time response capability of the system. The system's user interface is user-friendly and provides intuitive data visualization and interactive risk assessment reports. Enterprise managers can view the risk status of the supply chain, historical data analysis, and future trend forecasts in real time through the interface to obtain comprehensive risk management information. The user interface supports multi-device access, making it convenient for enterprise managers to obtain risk assessment and management information anytime and anywhere.
本实施例展示了一个集成了数据采集、预处理、特征提取、时序建模、风险评估和管理的完整系统，通过深度学习技术和实时数据处理方法，实现了对钢铁行业供应链金融风险的精准评估与管理。系统不仅提高了风险预测的准确性和实时性，还为企业的决策提供了科学的数据支持和创新的技术手段。This example demonstrates a complete system that integrates data collection, preprocessing, feature extraction, time series modeling, risk assessment and management. Through deep learning technology and real-time data processing methods, it realizes accurate assessment and management of supply chain financial risks in the steel industry. The system not only improves the accuracy and real-time nature of risk prediction, but also provides scientific data support and innovative technical means for corporate decision-making.
本实施例介绍了基于深度学习的供应链金融风险管理系统中的实时监控与动态调整机制的实现，系统通过高频率的数据采集、实时监控和动态调整，提高了供应链金融风险管理的灵活性和响应速度。This embodiment introduces the implementation of real-time monitoring and dynamic adjustment mechanisms in a supply chain financial risk management system based on deep learning. The system improves the flexibility and response speed of supply chain financial risk management through high-frequency data collection, real-time monitoring and dynamic adjustment.
首先，实时监控模块通过物联网（IoT）设备和高精度传感器，实时采集市场价格、库存水平、生产效率等关键数据。数据采集频率根据企业需求和市场变化进行调整，通常设置为每小时或更高频率，以确保数据的及时性和准确性。采集到的数据通过高带宽网络传输至数据处理中心。数据处理中心采用分布式计算和存储技术（Apache Kafka和ApacheSpark），对实时数据进行预处理和分析。数据预处理包括数据清洗、归一化和特征提取，确保数据质量和一致性。处理后的数据输入到深度学习模型进行风险评估。通过分布式计算框架，系统能够处理大规模数据，提供快速的风险评估结果。风险评估模块通过卷积神经网络（CNN）和长短期记忆网络（LSTM），结合注意力机制（Attention Mechanism），对多变量时间序列数据进行深度分析和建模。系统根据模型的输出，实时计算供应链金融风险评估值，并将评估结果与设定的风险阈值进行比较。一旦风险评估值超过设定阈值，系统自动触发预警机制。预警机制包括发送预警通知、生成风险管理建议和自动执行部分风险控制措施。例如，当库存水平显著增加且市场价格波动剧烈时，系统可能建议企业调整质押率或增加担保措施。预警通知通过电子邮件、短信或企业内部消息系统发送给相关负责人，确保信息的及时传递。为了提高系统的灵活性和适应性，本实施例引入了动态调整机制。动态调整机制通过持续监控市场环境和供应链运行状况，实时调整模型参数和风险阈值。系统采用在线学习算法，根据新采集的数据，实时更新模型权重和偏置，提升模型的预测性能。动态调整机制还包括风险阈值的自适应设置，根据市场波动情况自动调整预警阈值，提高风险管理的精准度。First, the real-time monitoring module collects key data such as market prices, inventory levels, and production efficiency in real time through Internet of Things (IoT) devices and high-precision sensors. The frequency of data collection is adjusted according to enterprise needs and market changes, and is usually set to every hour or higher to ensure the timeliness and accuracy of the data. The collected data is transmitted to the data processing center through a high-bandwidth network. The data processing center uses distributed computing and storage technologies (Apache Kafka and Apache Spark) to pre-process and analyze real-time data. Data preprocessing includes data cleaning, normalization, and feature extraction to ensure data quality and consistency. The processed data is input into the deep learning model for risk assessment. Through the distributed computing framework, the system is able to process large-scale data and provide fast risk assessment results. The risk assessment module uses convolutional neural networks (CNN) and long short-term memory networks (LSTM) combined with attention mechanisms to conduct in-depth analysis and modeling of multivariate time series data. Based on the output of the model, the system calculates the supply chain financial risk assessment value in real time and compares the assessment result with the set risk threshold. Once the risk assessment value exceeds the set threshold, the system automatically triggers the early warning mechanism. The early warning mechanism includes sending early warning notifications, generating risk management recommendations, and automatically executing some risk control measures. For example, when inventory levels increase significantly and market prices fluctuate violently, the system may recommend that the company adjust the pledge rate or increase collateral measures. Early warning notifications are sent to relevant persons in charge via email, text messages, or the company's internal messaging system to ensure timely delivery of information. In order to improve the flexibility and adaptability of the system, this embodiment introduces a dynamic adjustment mechanism. The dynamic adjustment mechanism adjusts model parameters and risk thresholds in real time by continuously monitoring the market environment and supply chain operation status. The system uses an online learning algorithm to update model weights and biases in real time based on newly collected data to improve the model's predictive performance. The dynamic adjustment mechanism also includes adaptive setting of risk thresholds, which automatically adjusts the early warning thresholds according to market fluctuations to improve the accuracy of risk management.
系统的实现过程中，采用了时序数据库（TimescaleDB）进行数据存储，支持高频率的数据写入和查询。时序数据库通过优化的索引机制和压缩算法，提高了数据存储和访问的效率，确保系统在高负载情况下的稳定运行。系统还集成了贝叶斯优化（BayesianOptimization）方法，用于模型参数和风险阈值的优化。贝叶斯优化通过构建代理模型，预测不同参数组合的效果，并选择最优的参数设置，确保系统在不同市场环境下的最佳性能。在数据安全和隐私保护方面，系统采用了多层次的安全措施，包括SSL/TLS协议的数据加密传输、AES加密的数据存储和基于角色的访问控制（RBAC）。这些措施确保数据在采集、传输、存储和处理过程中的安全性，防止数据泄露和滥用。During the implementation of the system, a time series database (TimescaleDB) was used for data storage, which supports high-frequency data writing and querying. The time series database improves the efficiency of data storage and access through optimized indexing mechanisms and compression algorithms, ensuring the stable operation of the system under high load. The system also integrates the Bayesian Optimization method for optimizing model parameters and risk thresholds. Bayesian optimization predicts the effects of different parameter combinations by building proxy models and selects the optimal parameter settings to ensure the best performance of the system under different market environments. In terms of data security and privacy protection, the system adopts multi-level security measures, including data encryption transmission of SSL/TLS protocol, AES encrypted data storage and role-based access control (RBAC). These measures ensure the security of data during collection, transmission, storage and processing, and prevent data leakage and abuse.
本实施例通过实时监控与动态调整机制，实现了对供应链金融风险的灵活管理和快速响应。系统能够实时监控市场变化，动态调整风险管理策略，提供精准的风险评估和管理建议，确保企业在复杂多变的市场环境中保持稳健运营。This embodiment achieves flexible management and rapid response to supply chain financial risks through real-time monitoring and dynamic adjustment mechanisms. The system can monitor market changes in real time, dynamically adjust risk management strategies, provide accurate risk assessment and management suggestions, and ensure that enterprises maintain stable operations in a complex and changing market environment.
本实施例介绍了基于智能合约的自动化风险管理系统的实现，通过区块链技术和智能合约，实现对供应链金融风险管理的自动化和透明化，提高系统的可靠性和操作效率。This embodiment introduces the implementation of an automated risk management system based on smart contracts. Through blockchain technology and smart contracts, supply chain financial risk management is automated and transparent, improving the reliability and operational efficiency of the system.
首先，数据采集模块实时收集钢铁行业的市场价格、库存水平、生产效率以及宏观经济指标等数据。这些数据通过物联网（IoT）设备和高精度传感器采集，并通过区块链网络进行传输和存储，确保数据的真实性和不可篡改性。在数据预处理阶段，系统对采集到的数据进行清洗、归一化和特征提取，确保数据的质量和一致性。清洗过程中，系统检测并处理缺失数据和异常值，归一化处理则将数据缩放到同一量纲范围内，提高后续模型训练的效果。特征提取则利用深度学习模型，从原始数据中提取高维度、有意义的特征，为风险评估提供输入。风险评估模块采用卷积神经网络（CNN）和长短期记忆网络（LSTM）的结合，结合注意力机制（Attention Mechanism），对多变量时间序列数据进行深度分析。通过模型的训练和预测，系统生成风险评估值，并根据评估值的变化，实时监控供应链金融风险。系统通过智能合约实现自动化风险管理。智能合约是一种在区块链上执行的自执行代码，根据预定义的规则自动执行操作。系统在区块链上部署智能合约，定义供应链金融风险管理的规则和措施。智能合约的主要功能包括实时监控风险评估值、触发预警和执行风险控制措施。当风险评估值超过设定阈值时，智能合约自动触发预警机制，发送预警通知并执行相应的风险管理措施。例如，当市场价格剧烈波动且库存水平显著增加时，智能合约可以自动调整质押率、增加担保或冻结高风险质押物，确保供应链的稳定性和安全性。智能合约的执行过程透明、不可篡改，所有操作记录均在区块链上公开可查，确保风险管理过程的透明度和可信度。企业管理者和相关利益方可以通过区块链浏览器查看智能合约的执行记录，了解风险管理的具体措施和效果。系统还支持多方协作和信息共享，通过区块链网络实现供应链上下游企业、金融机构和监管机构的信息互通和协作管理。各方可以根据智能合约的执行结果，实时获取风险评估和管理信息，协同制定和执行风险控制策略，提升供应链金融风险管理的整体效率和效果。First, the data collection module collects data such as market prices, inventory levels, production efficiency, and macroeconomic indicators of the steel industry in real time. These data are collected through Internet of Things (IoT) devices and high-precision sensors, and transmitted and stored through the blockchain network to ensure the authenticity and immutability of the data. In the data preprocessing stage, the system cleans, normalizes, and extracts features from the collected data to ensure data quality and consistency. During the cleaning process, the system detects and processes missing data and outliers, and normalization scales the data to the same dimension range to improve the effect of subsequent model training. Feature extraction uses deep learning models to extract high-dimensional and meaningful features from raw data to provide input for risk assessment. The risk assessment module uses a combination of convolutional neural networks (CNN) and long short-term memory networks (LSTM), combined with the attention mechanism, to conduct in-depth analysis of multivariate time series data. Through model training and prediction, the system generates risk assessment values and monitors supply chain financial risks in real time based on changes in the assessment values. The system implements automated risk management through smart contracts. Smart contracts are self-executing codes executed on blockchains that automatically perform operations according to predefined rules. The system deploys smart contracts on the blockchain to define the rules and measures for supply chain financial risk management. The main functions of smart contracts include real-time monitoring of risk assessment values, triggering warnings, and executing risk control measures. When the risk assessment value exceeds the set threshold, the smart contract automatically triggers the warning mechanism, sends a warning notification, and executes the corresponding risk management measures. For example, when market prices fluctuate violently and inventory levels increase significantly, smart contracts can automatically adjust the pledge rate, increase guarantees, or freeze high-risk pledges to ensure the stability and security of the supply chain. The execution process of smart contracts is transparent and cannot be tampered with. All operation records are publicly available on the blockchain to ensure the transparency and credibility of the risk management process. Enterprise managers and relevant stakeholders can view the execution records of smart contracts through the blockchain browser to understand the specific measures and effects of risk management. The system also supports multi-party collaboration and information sharing, and realizes information exchange and collaborative management among upstream and downstream enterprises, financial institutions, and regulatory agencies in the supply chain through the blockchain network. All parties can obtain risk assessment and management information in real time based on the execution results of smart contracts, jointly formulate and implement risk control strategies, and improve the overall efficiency and effectiveness of supply chain financial risk management.
在具体实现中，系统采用以太坊（Ethereum）或Hyperledger Fabric等区块链平台，部署智能合约和实现数据共享。智能合约通过Solidity等编程语言编写，并在区块链上进行部署和执行。区块链平台的选择和配置根据企业的具体需求和技术环境进行调整，确保系统的高效运行和安全性。In the specific implementation, the system uses blockchain platforms such as Ethereum or Hyperledger Fabric to deploy smart contracts and realize data sharing. Smart contracts are written in programming languages such as Solidity and deployed and executed on the blockchain. The selection and configuration of the blockchain platform are adjusted according to the specific needs and technical environment of the enterprise to ensure the efficient operation and security of the system.
本实施例展示了基于智能合约的自动化风险管理系统的实现过程，通过区块链技术和智能合约，系统实现了供应链金融风险管理的自动化和透明化。系统不仅提高了风险管理的效率和可靠性，还增强了数据的公信力和安全性，为企业在复杂多变的市场环境中提供了全面、智能的风险管理解决方案。This example demonstrates the implementation process of an automated risk management system based on smart contracts. Through blockchain technology and smart contracts, the system realizes the automation and transparency of supply chain financial risk management. The system not only improves the efficiency and reliability of risk management, but also enhances the credibility and security of data, providing enterprises with a comprehensive and intelligent risk management solution in a complex and changing market environment.
（1）深度学习（Deep Learning）：深度学习是一种基于人工神经网络的机器学习方法，通过多层结构模型从大规模数据中自动提取特征。深度学习模型通常包含多层感知器、卷积神经网络（CNN）、循环神经网络（RNN）等。深度学习在处理复杂非线性问题和大规模数据分析方面表现出色，是本发明中用于供应链金融风险评估的核心技术。(1) Deep Learning: Deep learning is a machine learning method based on artificial neural networks that automatically extracts features from large-scale data through a multi-layer structure model. Deep learning models usually include multi-layer perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc. Deep learning performs well in dealing with complex nonlinear problems and large-scale data analysis, and is the core technology used in the present invention for supply chain finance risk assessment.
（2）卷积神经网络（Convolutional Neural Network, CNN）：卷积神经网络是一种特殊的神经网络，广泛应用于图像处理和时序数据分析。CNN通过卷积层、池化层和全连接层的组合，从输入数据中提取高维特征。本发明中，CNN用于提取多变量时间序列数据中的重要特征，增强风险评估模型的精度。(2) Convolutional Neural Network (CNN): Convolutional Neural Network is a special type of neural network that is widely used in image processing and time series data analysis. CNN extracts high-dimensional features from input data through a combination of convolutional layers, pooling layers, and fully connected layers. In the present invention, CNN is used to extract important features from multivariate time series data and enhance the accuracy of the risk assessment model.
（3）长短期记忆网络（Long Short-Term Memory, LSTM）：长短期记忆网络是一种特殊的循环神经网络（RNN），通过引入记忆单元和门控机制，有效解决了传统RNN在处理长时间序列数据时的梯度消失和梯度爆炸问题。LSTM适用于捕捉时间序列数据中的长期依赖关系和复杂非线性模式，是本发明中进行时序建模的关键组件。(3) Long Short-Term Memory (LSTM): LSTM is a special type of recurrent neural network (RNN). By introducing memory units and gating mechanisms, it effectively solves the gradient vanishing and gradient exploding problems of traditional RNN when processing long time series data. LSTM is suitable for capturing long-term dependencies and complex nonlinear patterns in time series data and is a key component for time series modeling in the present invention.
（4）注意力机制（Attention Mechanism）：注意力机制是一种增强神经网络模型能力的方法，通过动态调整模型对不同时间步长特征的关注度，提升模型对关键特征的捕捉能力。本发明中，注意力机制用于提高LSTM网络的预测精度，增强风险评估的效果。(4) Attention Mechanism: The attention mechanism is a method to enhance the capabilities of neural network models. It dynamically adjusts the model's attention to features at different time steps to improve the model's ability to capture key features. In the present invention, the attention mechanism is used to improve the prediction accuracy of the LSTM network and enhance the effect of risk assessment.
（5）物联网（Internet of Things, IoT）：物联网是一种通过互联网将各种物理设备和传感器连接起来，实现数据的采集、传输和管理的技术。IoT设备和高精度传感器在本发明中用于实时监控和采集钢铁行业的市场价格、库存水平和生产效率等关键数据，确保数据的实时性和准确性。(5) Internet of Things (IoT): IoT is a technology that connects various physical devices and sensors through the Internet to achieve data collection, transmission and management. IoT devices and high-precision sensors are used in the present invention to monitor and collect key data such as market prices, inventory levels and production efficiency of the steel industry in real time to ensure the real-time and accuracy of the data.
（6）分布式计算（Distributed Computing）：分布式计算是一种将计算任务分配到多个计算节点上并行执行的技术，适用于处理大规模数据和复杂计算任务。本发明中，分布式计算技术（如Apache Spark）用于加速数据处理和模型训练，提高系统的效率和响应速度。(6) Distributed Computing: Distributed computing is a technology that distributes computing tasks to multiple computing nodes for parallel execution. It is suitable for processing large-scale data and complex computing tasks. In the present invention, distributed computing technology (such as Apache Spark) is used to accelerate data processing and model training, and improve the efficiency and response speed of the system.
（7）分布式存储（Distributed Storage）：分布式存储是一种将数据分散存储在多个存储节点上的技术，提供高效的数据存储和访问能力。本发明中，分布式存储系统（如HDFS）用于存储大规模时间序列数据，确保数据的高可用性和可靠性。(7) Distributed Storage: Distributed storage is a technology that stores data in multiple storage nodes, providing efficient data storage and access capabilities. In the present invention, a distributed storage system (such as HDFS) is used to store large-scale time series data to ensure high availability and reliability of the data.
（8）时序数据库（Time Series Database, TSDB）：时序数据库是一种专门用于处理和存储时间序列数据的数据库，支持高频率的数据写入和查询。本发明中，时序数据库（如TimescaleDB）用于存储和查询高频率采集的数据，优化数据的存储和访问效率。(8) Time Series Database (TSDB): A time series database is a database specifically used to process and store time series data, supporting high-frequency data writing and querying. In the present invention, a time series database (such as TimescaleDB) is used to store and query data collected at a high frequency, optimizing the storage and access efficiency of data.
（9）在线学习算法（Online Learning Algorithm）：在线学习算法是一种能够在数据流到达时实时更新模型参数的机器学习方法，适用于动态变化的数据环境。本发明中，在线学习算法用于实时调整模型参数和风险阈值，提高风险评估模型的适应性和预测性能。(9) Online Learning Algorithm: Online learning algorithm is a machine learning method that can update model parameters in real time when data streams arrive, and is suitable for dynamically changing data environments. In the present invention, the online learning algorithm is used to adjust model parameters and risk thresholds in real time to improve the adaptability and predictive performance of the risk assessment model.
（10）智能合约（Smart Contract）：智能合约是一种在区块链上自动执行的代码，根据预定义的规则自动触发和执行操作。本发明中，智能合约用于实现自动化风险管理，通过区块链技术确保操作的透明性和可信度。(10) Smart Contract: A smart contract is a code that is automatically executed on the blockchain and automatically triggers and executes operations according to predefined rules. In the present invention, smart contracts are used to achieve automated risk management and ensure the transparency and credibility of operations through blockchain technology.
（11）区块链（Blockchain）：区块链是一种分布式账本技术，通过去中心化和加密算法确保数据的不可篡改和可追溯性。本发明中，区块链用于存储和管理关键数据，增强数据的公信力和安全性。(11) Blockchain: Blockchain is a distributed ledger technology that ensures the immutability and traceability of data through decentralization and encryption algorithms. In the present invention, blockchain is used to store and manage key data to enhance the credibility and security of data.
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