Financial risk real-time management method and system based on multi-source data and block chain

Abstract

The invention provides a financial risk real-time management method and system based on multi-source data and a block chain, and the method comprises the steps: carrying out the real-time collection of library table data, webpage data and log data, endowing different data sources with dynamic weights according to the importance and real-time performance of the data sources, and adjusting the weight values through an adaptive algorithm; performing right confirmation operation on each piece of data by using a block chain technology, and endowing the data with a block chain certificate; establishing a risk assessment model, and continuously optimizing and dynamically adjusting the risk assessment model by combining a reinforcement learning algorithm with a traditional machine learning algorithm; financial risks are monitored in real time through a built-in multi-dimensional early warning index system; and in combination with an AIGC technology, carrying out deep analysis on a risk prediction result, and automatically generating a detailed risk analysis report. According to the invention, through the dynamically adjusted and optimized risk assessment model, the real-time and accurate financial risk management and prediction capability is provided, and the security and transparency of data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a real-time financial risk management method and system based on multi-source data and blockchain. Background Art

[0002] Existing financial risk management systems (such as patent document CN201910875528.6) generally lack the ability to dynamically process data sources, have rigid model parameters, and lag behind in risk assessment results when facing rapid market changes. In addition, although blockchain has been used for data rights confirmation, it still has certain limitations in processing efficiency and on-chain storage. In contrast, the present invention significantly improves the flexibility and performance of the system by introducing adaptive algorithms and dynamic storage mechanisms. Summary of the invention

[0003] In view of the defects in the prior art, the purpose of the present invention is to provide a real-time financial risk management method and system based on multi-source data and blockchain.

[0004] The real-time financial risk management method based on multi-source data and blockchain provided by the present invention includes:

[0005] Multi-source data collection and dynamic weighting steps: collect database table data, web page data and log data in real time, assign dynamic weights to different data sources based on the importance and real-time nature of the data source, and adjust the weight value through an adaptive algorithm;

[0006] Data confirmation and dynamic on-chain storage steps: Use blockchain technology to confirm the ownership of each piece of data and give it blockchain credentials. At the same time, a dynamic data tiered storage mechanism is adopted to store frequently accessed data on high-performance nodes first, and historical data is archived and managed through tiered storage;

[0007] Adaptive intelligent risk control and real-time prediction steps: Establish a risk assessment model and use the reinforcement learning algorithm combined with the traditional machine learning algorithm to continuously optimize and dynamically adjust the risk assessment model;

[0008] Efficient early warning and response steps: Through the built-in multi-dimensional early warning indicator system, financial risks are monitored in real time. When potential risks are detected, smart contracts are automatically triggered to execute pre-set response strategies. At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels;

[0009] Intelligent decision-making steps: Combined with AIGC technology, in-depth analysis of risk prediction results is carried out, and a detailed risk analysis report is automatically generated.

[0010] Preferably, an Apache Kafka cluster is used as a message queue system to receive data streams from different data sources and distribute these data streams to multiple consumers;

[0011] Read streaming data from Apache Kafka through the Flink framework and perform data cleaning, transformation, and aggregation operations;

[0012] Crawl web page data through Python scripts and send the processed data to Apache Kafka;

[0013] Log data is collected and transmitted through the Apache Flume transmission system and directly imported into the Apache Kafka system.

[0014] Preferably, assigning dynamic weights to different data sources and adjusting the weight values ​​by an adaptive algorithm includes:

[0015] For each data source, calculate its importance and real-time performance, and then calculate the dynamic weight. The expression is:

[0016] ω i (t) = α·R i (t)+β·I i (t)

[0017] Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which are adjusted according to business needs; the higher the real-time performance and the greater the importance, the higher the weight ω i (t) the higher;

[0018] Based on new data and previous performance, the weights are updated using a weighted moving average formula:

[0019] ω i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1))

[0020] Where: i (t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed.

[0021] Preferably, a multivariate linear regression model is used to combine the features and Q values ​​to establish a risk assessment model and give a risk score, which is expressed as:

[0022]

[0023] Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the expected reward value provided by the reinforcement learning algorithm under the current state s and action a, which is used to adjust the risk score; n is the number of features.

[0024] Preferably, the reinforcement learning algorithm used is the deep Q network DQN, which continuously optimizes and dynamically adjusts the risk assessment model, and the expression is:

[0025]

[0026] Among them: α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action a; γ is the discount factor, which measures the impact of future rewards; s' is the next state after performing action a; max a’ Q(s',a') is the maximum expected reward for all actions a' in state s'.

[0027] The real-time financial risk management system based on multi-source data and blockchain provided by the present invention includes:

[0028] Multi-source data collection and dynamic weighting module: collects database table data, web page data and log data in real time, assigns dynamic weights to different data sources according to their importance and real-time nature, and adjusts the weight values ​​through adaptive algorithms;

[0029] Data confirmation and dynamic on-chain storage module: Use blockchain technology to confirm the ownership of each piece of data and give it blockchain credentials. At the same time, a dynamic data tiered storage mechanism is adopted to store frequently accessed data on high-performance nodes first, and historical data is archived and managed through tiered storage;

[0030] Adaptive intelligent risk control and real-time prediction module: establish a risk assessment model and use the reinforcement learning algorithm combined with the traditional machine learning algorithm to continuously optimize and dynamically adjust the risk assessment model;

[0031] Efficient early warning and response module: Through the built-in multi-dimensional early warning indicator system, financial risks are monitored in real time. When potential risks are detected, smart contracts are automatically triggered to execute pre-set response strategies. At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels;

[0032] Intelligent decision-making module: Combined with AIGC technology, it conducts in-depth analysis of risk prediction results and automatically generates detailed risk analysis reports.

[0033] Preferably, an Apache Kafka cluster is used as a message queue system to receive data streams from different data sources and distribute these data streams to multiple consumers;

[0034] Read streaming data from Apache Kafka through the Flink framework and perform data cleaning, transformation, and aggregation operations;

[0035] Crawl web page data through Python scripts and send the processed data to Apache Kafka;

[0036] Log data is collected and transmitted through the Apache Flume transmission system and directly imported into the Apache Kafka system.

[0037] Preferably, assigning dynamic weights to different data sources and adjusting the weight values ​​by an adaptive algorithm includes:

[0038] For each data source, calculate its importance and real-time performance, and then calculate the dynamic weight. The expression is:

[0039] ω i (t) = α·R i (t)+β·I i (t)

[0040] Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which are adjusted according to business needs; the higher the real-time performance and the greater the importance, the higher the weight ω i (t) the higher;

[0041] Based on new data and previous performance, the weights are updated using a weighted moving average formula:

[0042] ω i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1))

[0043] Where: i (t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed.

[0044] Preferably, a multivariate linear regression model is used to combine the features and Q values ​​to establish a risk assessment model and give a risk score, which is expressed as:

[0045]

[0046] Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the expected reward value provided by the reinforcement learning algorithm under the current state s and action a, which is used to adjust the risk score; n is the number of features.

[0047] Preferably, the reinforcement learning algorithm used is the deep Q network DQN, which continuously optimizes and dynamically adjusts the risk assessment model, and the expression is:

[0048]

[0049] Among them: α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action a; γ is the discount factor, which measures the impact of future rewards; s' is the next state after performing action a; max a’ Q(s',a') is the maximum expected reward for all actions a' in state s'.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) The present invention solves the problems of single data, poor real-time performance, rigid model, and slow risk control response in traditional financial risk management through dynamic weighted data collection, adaptive learning and prediction algorithms, and blockchain dynamic storage and smart contract technology;

[0052] (2) The system can not only accurately and in real time score and predict financial risks, but also automatically execute preset risk control measures, thus improving the risk control efficiency and decision-making ability of financial institutions;

[0053] (3) With the continuous development of artificial intelligence technology, the system's intelligent risk control and real-time decision support modules enable financial institutions to respond quickly in a complex and changing market environment, adjust risk control strategies in real time, and reduce potential financial risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0055] Figure 1 This is a structural diagram of the real-time financial risk management system based on multi-source data and blockchain in the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0057] Example 1

[0058] The present invention provides a real-time financial risk management method based on multi-source data and blockchain, comprising:

[0059] Multi-source data collection and dynamic weighting steps: collect database table data, web page data and log data in real time, assign dynamic weights to different data sources based on the importance and real-time nature of the data source, and adjust the weight value through an adaptive algorithm;

[0060] Data confirmation and dynamic on-chain storage steps: Use blockchain technology to confirm the ownership of each piece of data and give it blockchain credentials. At the same time, a dynamic data tiered storage mechanism is adopted to store frequently accessed data on high-performance nodes first, and historical data is archived and managed through tiered storage;

[0061] Adaptive intelligent risk control and real-time prediction steps: Establish a risk assessment model and use the reinforcement learning algorithm combined with the traditional machine learning algorithm to continuously optimize and dynamically adjust the risk assessment model;

[0062] Efficient early warning and response steps: Through the built-in multi-dimensional early warning indicator system, financial risks are monitored in real time. When potential risks are detected, smart contracts are automatically triggered to execute pre-set response strategies. At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels;

[0063] Intelligent decision-making steps: Combined with AIGC technology, in-depth analysis of risk prediction results is carried out, and a detailed risk analysis report is automatically generated.

[0064] Use Apache Kafka cluster as a message queue system to receive data streams from different data sources and distribute these data streams to multiple consumers;

[0065] Read streaming data from Apache Kafka through the Flink framework and perform data cleaning, transformation, and aggregation operations;

[0066] Crawl web page data through Python scripts and send the processed data to Apache Kafka;

[0067] Log data is collected and transmitted through the Apache Flume transmission system and directly imported into the Apache Kafka system.

[0068] Dynamic weights are assigned to different data sources, and weight values ​​are adjusted through adaptive algorithms, including:

[0069] For each data source, calculate its importance and real-time performance, and then calculate the dynamic weight. The expression is:

[0070] ω i (t) = α·R i (t)+β·I i (t)

[0071] Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which are adjusted according to business needs; the higher the real-time performance and the greater the importance, the higher the weight ω i (t) the higher;

[0072] Based on new data and previous performance, the weights are updated using a weighted moving average formula:

[0073] ω i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1))

[0074] Where: i (t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed.

[0075] A multivariate linear regression model is used to combine features and Q values ​​to establish a risk assessment model and give a risk score, which is expressed as:

[0076]

[0077] Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the expected reward value provided by the reinforcement learning algorithm under the current state s and action a, which is used to adjust the risk score; n is the number of features.

[0078] The reinforcement learning algorithm used is the deep Q network DQN, which continuously optimizes and dynamically adjusts the risk assessment model. The expression is:

[0079]

[0080] Among them: α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action a; γ is the discount factor, which measures the impact of future rewards; s' is the next state after performing action a; max a’ Q(s',a') is the maximum expected reward for all actions a' in state s'.

[0081] Example 2

[0082] The present invention also provides a real-time financial risk management system based on multi-source data and blockchain. The real-time financial risk management system based on multi-source data and blockchain can be implemented by executing the process steps of the real-time financial risk management method based on multi-source data and blockchain, that is, those skilled in the art can understand the real-time financial risk management method based on multi-source data and blockchain as a preferred implementation of the real-time financial risk management system based on multi-source data and blockchain.

[0083] like Figure 1 , the real-time financial risk management system based on multi-source data and blockchain includes:

[0084] Multi-source data collection and dynamic weighting module: Apache Flink, Python, Apache Flume and other technologies are used to collect database table data, web page data and log data in real time. The system assigns dynamic weights to different data sources according to the importance and real-time nature of the data source, and adjusts the weight value through an adaptive algorithm to ensure the accuracy of risk assessment. Using the Apache Kafka cluster as a message queue system, Kafka is used to receive data streams from different data sources and distribute these data streams to multiple consumers; Flink can read stream data from Kafka and clean, transform and aggregate the data; Python scripts can use libraries such as BeautifulSoup or Scrapy to crawl web page data and send the processed data to Kafka for further processing; Apache Flume can collect and transmit large amounts of log data and import it directly into the Kafka system.

[0085] The adaptive algorithm mentioned in the document is used to adjust the weight value according to the importance and real-time nature of the data source. This algorithm may include the following steps:

[0086] 1. Initialize weights: Assign an initial weight to each data source (possibly based on historical data performance or expert knowledge).

[0087] 2. Dynamically adjust weights: As time goes by and new data arrives, the system will continuously evaluate the contribution and performance of each data source. For example, if a data source provides highly relevant or important information over a period of time, its weight will be increased.

[0088] 3. Adaptive learning: Use machine learning algorithms (such as reinforcement learning, weighted moving average, etc.) to adjust weights. Reinforcement learning can adjust decision strategies based on past experience, while weighted moving average can smooth changes in data source weights and avoid excessive fluctuations.

[0089] The process of dynamic weight assignment and adjustment is generally as follows:

[0090] Data importance and real-time evaluation: For each data source, calculate its importance (such as information gain, relevance, etc.) and real-time (such as data timeliness and frequency). These evaluation indicators can be calculated using statistical methods or machine learning algorithms.

[0091] Calculate dynamic weight: Dynamic weight can be expressed by the following formula:

[0092] ω i (t) = α·R i (t)+β·I i (t)

[0093] Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which can be adjusted according to business needs. This formula indicates that the weight of a data source at time t is dynamically adjusted based on the two factors of real-time performance and importance. The higher the real-time performance and the greater the importance, the higher the weight ω. i The higher (t).

[0094] Update weights: Based on new data and previous performance, the weights are updated using a formula similar to a weighted moving average:

[0095] ω i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1))

[0096] Where: i(t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed. This formula indicates that the new weight is the weighted average of the previous weight and the newly calculated weight. Through this smoothing process, large fluctuations in weights can be avoided, making the model more adaptable to emergencies.

[0097] Data confirmation and dynamic on-chain storage module: Blockchain technology is used to confirm the ownership of each piece of data, giving it blockchain credentials to ensure data transparency and traceability. At the same time, a dynamic data tiered storage mechanism is designed to address the efficiency issues of on-chain storage. Frequently accessed data will be stored on high-performance nodes first, while historical data will be archived and managed through tiered storage, improving the overall performance of the system.

[0098] Adaptive intelligent risk control and real-time prediction module: Based on data storage and processing, the system uses reinforcement learning algorithms (such as deep Q network DQN) combined with traditional machine learning algorithms to continuously optimize and dynamically adjust the risk assessment model.

[0099] The core update formula of DQN (Bellman equation) is:

[0100]

[0101] Where: Q(s,a) is the expected reward value (Q value) for performing action α in state s; α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action α; γ is the discount factor, which measures the impact of future rewards; s' is the next state after performing action α; max a’ Q(s',a') is the maximum Q value of all possible actions in state s'.

[0102] We can predict the probability of market fluctuations based on traditional machine learning algorithms, and then use reinforcement learning algorithms to make decisions based on the predictions.

[0103] Risk assessment models can usually calculate risk scores (RiskScore) by combining different data features and algorithms. Assuming that a multivariate linear regression model is used to combine features and Q values, the expression of the risk assessment model can be expressed as:

[0104]

[0105] Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the Q value provided by the reinforcement learning algorithm under the current state and action, which is used to adjust the risk score.

[0106] In order to continuously optimize and dynamically adjust the risk assessment model, real-time data updates, model combinations and switching can be adopted. As the market and environment change, new data sources and features can be continuously introduced, traditional machine learning models can be retrained, and the strategies of reinforcement learning models can be adjusted.

[0107] This module can automatically adjust risk assessment parameters according to market changes, predict possible risk events, and give corresponding handling suggestions.

[0108] Efficient early warning and response module: The system monitors financial risks in real time through a built-in multi-dimensional early warning indicator system. When potential risks are detected, the system can automatically trigger smart contracts and execute pre-set response strategies (such as automatic freezing, notifying relevant parties, etc.). At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels to ensure timely response.

[0109] Enhanced intelligent decision support system: Combined with AIGC technology, the system can conduct in-depth analysis of risk prediction results and automatically generate detailed risk analysis reports.

[0110] Mainly reflected in:

[0111] Data preprocessing: It can realize the processing of unstructured data including some existing market information, macroeconomic indicators, financial statements, etc.

[0112] Model training and optimization: Model training, optimization and use can be completed through the processes of feature extraction, model selection, training process, model verification and model integration.

[0113] Content generation: Based on the output of the risk prediction model, the AIGC system generates a detailed report that includes possible sources of risk, degree of impact, possible countermeasures, etc.

[0114] The process of in-depth analysis of risk prediction results mainly includes the following steps:

[0115] The result output of the risk prediction model: It can realize the processing of unstructured data including some existing market information, macroeconomic indicators, financial statements, etc.

[0116] In-depth analysis process: The model training, optimization and use can be completed through the processes of feature extraction, model selection, training process, model verification and model integration.

[0117] Decision support and report generation: Based on the output of the risk prediction model, the AIGC system generates a detailed report that includes possible risk sources, impact levels, possible countermeasures, etc.

[0118] Through the intelligent analysis engine, the system can also provide executable decision recommendations, Merkel-based blockchain storage and audit tools to ensure that the decision-making process of financial institutions is highly transparent and traceable.

[0119] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0120] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A real-time financial risk management method based on multi-source data and blockchain, characterized in that: include: Multi-source data collection and dynamic weighting steps: collect database table data, web page data and log data in real time, assign dynamic weights to different data sources based on the importance and real-time nature of the data source, and adjust the weight value through an adaptive algorithm; Data confirmation and dynamic on-chain storage steps: Use blockchain technology to confirm the ownership of each piece of data and give it blockchain credentials. At the same time, a dynamic data tiered storage mechanism is adopted to store frequently accessed data on high-performance nodes first, and historical data is archived and managed through tiered storage; Adaptive intelligent risk control and real-time prediction steps: Establish a risk assessment model and use the reinforcement learning algorithm combined with the traditional machine learning algorithm to continuously optimize and dynamically adjust the risk assessment model; Efficient early warning and response steps: Through the built-in multi-dimensional early warning indicator system, financial risks are monitored in real time. When potential risks are detected, smart contracts are automatically triggered to execute pre-set response strategies. At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels; Intelligent decision-making steps: Combined with AIGC technology, in-depth analysis of risk prediction results is carried out, and a detailed risk analysis report is automatically generated.

2. The real-time financial risk management method based on multi-source data and blockchain according to claim 1 is characterized in that: Use Apache Kafka cluster as a message queue system to receive data streams from different data sources and distribute these data streams to multiple consumers; Read streaming data from Apache Kafka through the Flink framework and perform data cleaning, transformation, and aggregation operations; Crawl web page data through Python scripts and send the processed data to Apache Kafka; Log data is collected and transmitted through the Apache Flume transmission system and directly imported into the Apache Kafka system.

3. The real-time financial risk management method based on multi-source data and blockchain according to claim 1 is characterized in that: Dynamic weights are assigned to different data sources, and weight values ​​are adjusted through adaptive algorithms, including: For each data source, calculate its importance and real-time performance, and then calculate the dynamic weight. The expression is: oh i (t)=α·R i (t)+β·I i (t) Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which are adjusted according to business needs; the higher the real-time performance and the greater the importance, the higher the weight ω i (t) the higher; Based on new data and previous performance, the weights are updated using a weighted moving average formula: oh i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1)) Where: i (t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed.

4. The real-time financial risk management method based on multi-source data and blockchain according to claim 1 is characterized in that: A multivariate linear regression model is used to combine features and Q values ​​to establish a risk assessment model and give a risk score, which is expressed as: Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the expected reward value provided by the reinforcement learning algorithm under the current state s and action a, which is used to adjust the risk score; n is the number of features.

5. The real-time financial risk management method based on multi-source data and blockchain according to claim 4 is characterized in that: The reinforcement learning algorithm used is the deep Q network DQN, which continuously optimizes and dynamically adjusts the risk assessment model. The expression is: Among them: α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action a; γ is the discount factor, which measures the impact of future rewards; s, is the next state after performing action a; max a, Q(s,,a,) is the maximum expected reward for all actions,a,in state,s,.

6. A real-time financial risk management system based on multi-source data and blockchain, characterized in that: include: Multi-source data collection and dynamic weighting module: collects database table data, web page data and log data in real time, assigns dynamic weights to different data sources according to their importance and real-time nature, and adjusts the weight values ​​through adaptive algorithms; Data confirmation and dynamic on-chain storage module: Use blockchain technology to confirm the ownership of each piece of data and give it blockchain credentials. At the same time, a dynamic data tiered storage mechanism is adopted to store frequently accessed data on high-performance nodes first, and historical data is archived and managed through tiered storage; Adaptive intelligent risk control and real-time prediction module: establish a risk assessment model and use the reinforcement learning algorithm combined with the traditional machine learning algorithm to continuously optimize and dynamically adjust the risk assessment model; Efficient early warning and response module: Through the built-in multi-dimensional early warning indicator system, financial risks are monitored in real time. When potential risks are detected, smart contracts are automatically triggered to execute pre-set response strategies. At the same time, early warning information is transmitted to relevant personnel in real time through encrypted channels; Intelligent decision-making module: Combined with AIGC technology, it conducts in-depth analysis of risk prediction results and automatically generates detailed risk analysis reports.

7. The real-time financial risk management system based on multi-source data and blockchain according to claim 6 is characterized in that: Use Apache Kafka cluster as a message queue system to receive data streams from different data sources and distribute these data streams to multiple consumers; Read streaming data from Apache Kafka through the Flink framework and perform data cleaning, transformation, and aggregation operations; Crawl web page data through Python scripts and send the processed data to Apache Kafka; Log data is collected and transmitted through the Apache Flume transmission system and directly imported into the Apache Kafka system.

8. The real-time financial risk management system based on multi-source data and blockchain according to claim 6 is characterized in that: Dynamic weights are assigned to different data sources, and weight values ​​are adjusted through adaptive algorithms, including: For each data source, calculate its importance and real-time performance, and then calculate the dynamic weight. The expression is: oh i (t)=α·R i (t)+β·I i (t) Where: i (t) is the dynamic weight of data source i at time t; R i (t) represents the real-time index of data source i; i (t) represents the importance index of data source i; α and β are hyperparameters used to balance the relationship between real-time performance and importance, which are adjusted according to business needs; the higher the real-time performance and the greater the importance, the higher the weight ω i (t) the higher; Based on new data and previous performance, the weights are updated using a weighted moving average formula: oh i (t+1)=γ·ω i (t)+(1-γ)·(α·R i (t+1)+β·I i (t+1)) Where: i (t+1) is the new weight of data source i at time t+1; γ is a smoothing factor between 0 and 1, which is used to control the update speed.

9. The real-time financial risk management system based on multi-source data and blockchain according to claim 6 is characterized in that: A multivariate linear regression model is used to combine features and Q values ​​to establish a risk assessment model and give a risk score, which is expressed as: Where: R is the risk score; β0 is the intercept of the model; β i is each feature x i The coefficient of ; Q(s,a) is the expected reward value provided by the reinforcement learning algorithm under the current state s and action a, which is used to adjust the risk score; n is the number of features.

10. The real-time financial risk management system based on multi-source data and blockchain according to claim 9 is characterized in that: The reinforcement learning algorithm used is the deep Q network DQN, which continuously optimizes and dynamically adjusts the risk assessment model. The expression is: Among them: α is the learning rate, which controls the update step size; r is the immediate reward obtained after performing action a; γ is the discount factor, which measures the impact of future rewards; s, is the next state after performing action a; max a, Q(s,,a,) is the maximum expected reward for all actions,a,in state,s,.

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