WO2023142424A1 - Procédé et système de commande de risque de service financier puissant basés sur un réseau neuronal gru-lstm - Google Patents

Procédé et système de commande de risque de service financier puissant basés sur un réseau neuronal gru-lstm Download PDF

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WO2023142424A1
WO2023142424A1 PCT/CN2022/110593 CN2022110593W WO2023142424A1 WO 2023142424 A1 WO2023142424 A1 WO 2023142424A1 CN 2022110593 W CN2022110593 W CN 2022110593W WO 2023142424 A1 WO2023142424 A1 WO 2023142424A1
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risk
data
gru
neural network
financial
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PCT/CN2022/110593
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Chinese (zh)
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郑祥华
毕晓蓉
张�杰
李�杰
王惠
王俊康
徐中中
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国网江苏省电力有限公司南京供电分公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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  • the invention belongs to the technical field of data analysis, relates to risk analysis of financial business, and is a method and system for risk control of electric power financial business based on GRU-LSTM neural network.
  • Deep learning technology is the process of digging out potential and valuable information from massive raw data. It is a data processing technology. It can be known from its definition: raw data must be real, objective and noisy, and finally mining The information and knowledge that come out are interesting, acceptable and easy to understand for users, and can also be used in real life.
  • an effective analysis tool or algorithm is selected, and after being decomposed into a multivariate data set, an in-depth mining analysis is performed to provide users with potentially valuable information and knowledge. How to apply deep learning technology to power financial business and improve the ability to analyze business data is a problem to be solved.
  • the purpose of the present invention is to aim at the risk control problem of financial business, analyze the data through deep learning technology, combine the advantages of short prediction time of GRU and high prediction accuracy of LSTM, combine the two, thus propose a neural network based on GRU-LSTM Effectively control the risk control system of the electric power financial business.
  • the technical solution of the present invention is: based on the GRU-LSTM neural network risk control method for electric power financial business, data analysis is performed on the historical data of electric power financial business through deep learning, the risk factors of electric power financial business are obtained, and the risk early warning index system is established according to the risk factors , build a risk early warning model;
  • deep learning is GRU-LSTM network combination prediction: first analyze the financial business data through the GRU network, and then input the LSTM neural network, use the LSTM neural network to simulate risk control and predict risk factors, according to Risk early warning indicators determine the corresponding risk level; use historical data to train the GRU-LSTM network to obtain a risk early warning model, use the risk early warning model to predict the risk level of real-time financial business data, and realize financial business risk control.
  • the present invention comprises the following steps:
  • Step 1 data collection: by monitoring the financial business data, recording the financial data, and collecting the required target financial business historical data from the relevant financial project business according to the financial business risk to be analyzed by deep learning, and training the GRU network for data analysis;
  • Step 2 comprehensive analysis, risk assessment: According to the data analyzed by GRU, the LSTM neural network is used to simulate risk control, and the LSTM neural network is trained to predict and judge the risk factors and risk levels corresponding to financial data through historical data;
  • Step 3 real-time detection, risk prevention and control: real-time monitoring of financial business data, input into the trained GRU-LSTM network, output predicted risk factors and risk levels, and realize financial business risk control.
  • the electric power financial business is collected, and information is randomly extracted from the financial database, business database and human database.
  • the present invention also proposes a wind control system for electric power financial business based on GRU-LSTM neural network, which has a computer-readable storage medium, and a computer program is configured in the computer-readable storage medium, and the above-mentioned risk early warning model is realized when the computer program is executed .
  • the invention can assist the power company to monitor some financial business risk problems when entering the financial market, effectively carry out risk control, and make reasonable business allocation decisions.
  • the method of the present invention uses deep learning technology to learn and analyze complex financial problem data, and analyzes related business information to analyze the relationship between business and problem data, which is conducive to avoiding risks and helping to improve decision-making management capabilities.
  • the GRU network structure is simple so that the parameters are easier to converge, greatly reducing the training time, but the accuracy of GRU is not as good as LSTM, and then through LSTM, the prediction accuracy is greatly improved.
  • the proposed GRU -LSTM combination forecasting can be applied to the risk analysis of power financial business, explore data processing and solutions, and provide a practical method for the prevention of financial risks.
  • Fig. 1 is a schematic structural diagram of the wind control system for electric power financial business of the present invention.
  • Fig. 2 is a schematic diagram of the deep learning process of the present invention.
  • Fig. 3 is a schematic flow chart of the LSTM neural network prediction model adopted in the present invention.
  • the present invention aims at the financial business risks faced by current electric power companies when they go deep into the financial market, combines deep learning technology to carry out risk control, takes the application of deep learning technology in financial business as the core, and conducts in-depth research on complex financial models, thereby helping The extension of power finance business.
  • Use deep learning technology to analyze relevant business information, analyze the stakes, improve internal decision-making management, and provide a scientific and powerful foundation for the efficient development of power financial business by mining valuable data.
  • the theoretical basis of the risk control system of the present invention includes absolute income theory and life cycle theory, etc., which are mainly used to help judge the risk of the electric power financial business, assist the company in making decisions, and when the revenue reaches the maximum value, it is the optimal business performance. Selection enables reasonable allocation of business and provides directions for finding potential benefits.
  • the present invention proposes a power financial business risk control system based on the GRU-LSTM neural network.
  • GRU-LSTM neural network As shown in Figure 1, it is first necessary to randomly extract information from the financial database, business database, and human database, and obtain relevant financial and financial information through the business database.
  • Manpower information, etc., data processing using the advantages of short prediction time of GRU and high precision of LSTM, the combination of the two is the most appropriate for processing.
  • the simple network structure of GRU makes the parameters easier to converge, greatly reducing the training time.
  • the accuracy of GRU is not as good as that of LSTM.
  • After LSTM the prediction accuracy is greatly improved.
  • the processed data can be stored and continue to provide the basis for future training. Diagnose the problem after processing the data, and judge the output results.
  • a first-level alarm will be issued and specific abnormal data will be reported; if 3 or 4 parameters are abnormal, then Issue a second-level alarm and report specific abnormal data; if 5 or more parameters are abnormal, issue a third-level alarm and report specific abnormal data.
  • the computer performs two or more simulations to ensure that there will be no second data abnormality, and to a certain extent achieve the effect of risk control.
  • the process of deep learning of power financial business data by the power financial business risk control system based on GRU-LSTM neural network is shown in Figure 2.
  • Data collection is collected from financial business, and risk feature description is obtained through data mining.
  • Financial business defines risk, combines risk definition with risk characteristic description, and comprehensively determines actual risk factors.
  • the present invention realizes following steps:
  • Step 1 data collection: through the data monitoring of financial business, record the financial data, and according to the theory of deep learning, collect the required target financial data from the relevant financial project business, so as to understand it more accurately, Master and use the financial operation rules to prevent and control risks and crises, making the collected data more accurate and effective.
  • the present invention first needs to randomly extract information from the financial database, business database, and manpower database, and obtain relevant financial and manpower information through the business database, and perform data processing on it.
  • the GRU network is first trained for data analysis.
  • Step 2 comprehensive analysis, risk assessment: According to the data analyzed by GRU, the LSTM neural network is used to simulate risk control, and the LSTM neural network is trained to predict and judge the risk factors and risk levels corresponding to financial data through historical data;
  • Step 3 real-time detection, risk prevention and control: real-time monitoring of financial business data, input into the trained GRU-LSTM network, output predicted risk factors and risk levels, and realize financial business risk control.
  • the data that has been analyzed and processed by the deep learning of the GRU network is put into the LSTM neural network for simulation analysis, the risk level is divided, and the final risk level is judged according to the simulation analysis results, providing managers with scientific and effective judgment basis, so as to advance Risk avoidance, some preventive suggestions for the assessment and control of power financial business risks, and has certain reliability.
  • FIG. 3 it is a schematic flow chart of the LSTM predictive analysis of the present invention.
  • an index that reflects financial risks such as time, number of people, progress, finance, etc.
  • the number of nodes is 12, and the data needs to be normalized before inputting parameters to become a standard value between 0 and 1.
  • the normalization equation is as follows:
  • x max and x min are the maximum and minimum values of the input sample parameters respectively
  • x t and x′ t are the real initial input sample parameters and normalized processing results respectively.
  • n the number of indicators, that is, the input parameters
  • n the number of indicators, that is, the input parameters
  • the number of neurons contained in the hidden layer is assumed to be a j
  • the weight from the input layer to the hidden layer is w i
  • the number of neurons contained in the hidden layer is j
  • the transfer function in the risk prediction model is as follows:
  • the input unit ⁇ i of the hidden layer can be described as:
  • the data After calculation and screening, the data reaches the output layer, and the input item s t and output item ⁇ t of the neurons in the output layer are respectively:
  • v jt is the weight from the hidden layer to the output layer
  • ⁇ t is the threshold of the output layer.
  • 12 parameters are input, including time, number of people, workload, progress, etc., and judged by the results output by the model. If one or two parameters are abnormal, a first-level alarm will be issued, and the specific Abnormal data; if 3 or 4 parameters are abnormal, a second-level alarm will be issued, and specific abnormal data will be reported; if 5 or more parameters are abnormal, a third-level alarm will be issued, specific abnormal data will be reported, and the administrator Perform a second simulation after modifying the business data, so as to ensure that the business data will not have a second abnormality.
  • the present invention proposes a power financial business risk control system based on GRU-LSTM neural network, which has a computer-readable storage medium, and a computer program is configured in the computer-readable storage medium.
  • the computer program is executed, the above-mentioned risk warning model.

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Abstract

Procédé et système de commande de risque de service financier puissant basés sur un réseau neuronal GRU-LSTM. Une analyse de données est effectuée sur des données passées de service financier puissant au moyen d'un apprentissage profond pour obtenir un facteur de risque du service financier puissant, un système d'indice d'avertissement précoce de risque est établi selon le facteur de risque, et un modèle d'avertissement précoce de risque est construit, l'apprentissage profond concernant la prédiction combinée GRU-LSTM : l'analyse de données de service financier au moyen d'un réseau GRU et ensuite la mise en place des données de service financier dans un réseau LSTM pour une analyse de simulation. Selon le procédé de la présente invention, la technologie d'apprentissage profond est utilisée pour apprendre et analyser des données de problèmes financiers complexes, des informations de service associées sont analysées, et l'association entre des données de service et de problèmes est analysée, ce qui permet d'éviter le risque et de faciliter l'amélioration de la capacité de gestion de décision.
PCT/CN2022/110593 2022-01-25 2022-08-05 Procédé et système de commande de risque de service financier puissant basés sur un réseau neuronal gru-lstm WO2023142424A1 (fr)

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CN117314424A (zh) * 2023-09-18 2023-12-29 纬创软件(武汉)有限公司 面向金融大数据的区块链交易***及方法
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CN117829787A (zh) * 2024-03-05 2024-04-05 中亿丰数字科技集团有限公司 一种基于bim和ai大模型的工程安全智能管理方法及***

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CN114580706A (zh) * 2022-01-25 2022-06-03 国网江苏省电力有限公司南京供电分公司 基于gru-lstm神经网络的电力金融业务风控方法及***
CN116863481A (zh) * 2023-06-07 2023-10-10 北京东方海达网络科技有限责任公司 一种基于深度学习的业务会话风险处理方法

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CN117314424B (zh) * 2023-09-18 2024-03-29 纬创软件(武汉)有限公司 面向金融大数据的区块链交易***及方法
CN117314424A (zh) * 2023-09-18 2023-12-29 纬创软件(武汉)有限公司 面向金融大数据的区块链交易***及方法
CN117118745A (zh) * 2023-10-20 2023-11-24 山东慧贝行信息技术有限公司 一种基于深度学习的网络安全动态预警***
CN117595231B (zh) * 2023-10-20 2024-05-31 国网安徽省电力有限公司六安供电公司 一种智能电网配网管理***及其方法
CN117118745B (zh) * 2023-10-20 2024-01-05 山东慧贝行信息技术有限公司 一种基于深度学习的网络安全动态预警***
CN117595231A (zh) * 2023-10-20 2024-02-23 国网安徽省电力有限公司六安供电公司 一种智能电网配网管理***及其方法
CN117172721B (zh) * 2023-10-31 2024-02-20 深圳薪汇科技有限公司 用于融资业务的数据流转监管预警方法及***
CN117172721A (zh) * 2023-10-31 2023-12-05 深圳薪汇科技有限公司 用于融资业务的数据流转监管预警方法及***
CN117235063B (zh) * 2023-11-10 2024-03-29 广州汇通国信科技有限公司 一种基于人工智能技术的数据质量管理方法
CN117235063A (zh) * 2023-11-10 2023-12-15 广州汇通国信科技有限公司 一种基于人工智能技术的数据质量管理方法
CN117374977B (zh) * 2023-12-07 2024-02-20 能拓能源股份有限公司 一种储能***负荷预测及风险分析方法
CN117374977A (zh) * 2023-12-07 2024-01-09 能拓能源股份有限公司 一种储能***负荷预测及风险分析方法
CN117829787A (zh) * 2024-03-05 2024-04-05 中亿丰数字科技集团有限公司 一种基于bim和ai大模型的工程安全智能管理方法及***

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