Financial industry-oriented data intelligent analysis and optimization system

Abstract

The invention relates to the technical field of financial science and technology, in particular to a financial industry-oriented data intelligent analysis and optimization system, which comprises a multi-level data receiving module, a data cleaning enhancement module, a block chain data verification module, a deep semantic analysis module, a multi-dimensional risk assessment module and an intelligent contract execution module. Wherein the multi-level data receiving module is used for receiving original data from a plurality of financial data sources; the data cleaning and enhancing module is used for cleaning the received original data; the block chain data verification module is used for verifying and storing the data processed by the data cleaning and enhancing module; and the deep semantic analysis module is used for identifying potential market trends and investment opportunities. According to the method, the speed and accuracy of financial data analysis are remarkably improved, meanwhile, the real-time performance and automation of risk management are enhanced, and a financial institution is effectively supported to make accurate and rapid decisions in a rapidly changing market environment.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a data intelligent analysis and optimization system for the financial industry. Background Art

[0002] In the modern financial industry, the role of data is becoming increasingly important, especially in the fields of asset management, investment decision-making and risk management. With the rapid development of financial markets and the surge in data volume, traditional data processing and analysis methods face many challenges, including slow data processing speed, unstable data quality, and difficulty in capturing complex market dynamics. In addition, risk assessment methods often rely on simplified statistical models, which are not sufficient to handle large-scale heterogeneous data and are difficult to provide real-time risk control and investment strategy adjustments.

[0003] The limitations of existing technologies are mainly reflected in two aspects: first, the lack of data processing and analysis. Existing systems are often unable to effectively integrate and analyze large-scale data from multiple sources, resulting in slow response of decision support systems and low information utilization; second, insufficient risk assessment and management. Existing risk management tools often fail to fully consider the multi-dimensional factors of the market and find it difficult to respond quickly to market dynamics.

[0004] Therefore, it is necessary to develop a new solution to improve the efficiency and accuracy of financial data processing, while realizing real-time, dynamic risk assessment and investment strategy adjustment based on in-depth analysis to better adapt to the rapidly changing market environment. Summary of the invention

[0005] Based on the above objectives, the present invention provides a data intelligent analysis and optimization system for the financial industry.

[0006] A data intelligent analysis and optimization system for the financial industry, including a multi-level data receiving module, a data cleaning and enhancement module, a blockchain data verification module, a deep semantic analysis module, a multi-dimensional risk assessment module and a smart contract execution module; wherein:

[0007] Multi-level data receiving module: used to receive raw data from multiple financial data sources, including market data, transaction data, customer behavior data and social media data;

[0008] Data cleaning and enhancement module: Based on natural language processing and image recognition technology, it cleans, denoises, completes and standardizes the received raw data to improve the data quality;

[0009] Blockchain data verification module: Use blockchain technology to verify and store data processed by the data cleaning enhancement module to ensure data transparency, integrity and non-tamperability;

[0010] Deep semantic analysis module: Based on deep learning and natural language processing algorithms, it analyzes and extracts hidden information and complex relationships of data in the blockchain data verification module to identify potential market trends and investment opportunities, and transmits the analysis results to the multi-dimensional risk assessment module;

[0011] Multi-dimensional risk assessment module: Combines the analytical data output by the deep semantic analysis module with external risk factors to conduct multi-dimensional risk assessment and prediction to provide comprehensive risk management recommendations;

[0012] Smart contract execution module: Based on the evaluation results of the multi-dimensional risk assessment module, it automatically triggers and executes the preset smart contracts to conduct real-time investment strategy adjustments and risk control.

[0013] Furthermore, the multi-level data receiving module includes a market data receiving unit, a transaction data receiving unit, a customer behavior data receiving unit and a social media data receiving unit; wherein:

[0014] Market data receiving unit: used to receive real-time market data from financial markets, including stock prices, bond interest rates, foreign exchange rates and commodity futures prices, to ensure the comprehensiveness of market data;

[0015] Transaction data receiving unit: used to receive transaction record data from the trading platform, including buy and sell orders, transaction records, transaction time and transaction amount, to ensure the accuracy and completeness of transaction data;

[0016] Customer behavior data receiving unit: used to receive behavior data from financial institution customers, including customer account activity records, transaction preferences, and risk tolerance assessments, to ensure the diversity and accuracy of customer behavior data;

[0017] Social media data receiving unit: used to receive relevant data from social media platforms, including news updates, market sentiment, user comments and hot topics, to ensure the timeliness and relevance of social media data.

[0018] Furthermore, the data cleaning enhancement module includes a data cleaning unit, a noise removal unit, a data completion unit and a data standardization unit; wherein:

[0019] Data cleaning unit: Based on natural language processing technology, it is used to perform syntax analysis, entity recognition and sentiment analysis on the received text data, remove redundant information and erroneous records in the data, and ensure the accuracy and consistency of the data;

[0020] Noise Removal Unit: Uses image recognition technology to denoise received image and video data, identifies and removes noise and interference information in images through convolutional neural networks, and improves data clarity and usability;

[0021] Data Completion Unit: Using machine learning algorithms, predict and complete missing data, analyze data patterns through training models, complete missing values, and ensure data integrity;

[0022] Data Standardization Unit: Based on statistical analysis methods, uniformly standardize data from different sources, convert data into a unified format and dimension, and facilitate subsequent analysis and processing.

[0023] Furthermore, the blockchain data verification module includes a data hash generation unit, a block generation unit, a consensus mechanism unit, and a data storage unit; among them:

[0024] Data Hash Generation Unit: Generate a unique hash value for the data processed by the data cleaning and enhancement module. Specifically, use the SHA-256 algorithm to generate the hash value H(D) = SHA-256(D), where D is the data to be verified, to ensure the uniqueness and integrity of the data;

[0025] Block Generation Unit: Package the generated hash value and data into a block, and add a timestamp and the hash value of the previous block to each block to form a blockchain structure. The block content includes the current data hash value H(D i ), the hash value of the previous block H(D i-1 ) and the timestamp T i ;

[0026] Consensus Mechanism Unit: Adopt the proof-of-work consensus algorithm to verify and confirm the process of block generation. The proof-of-work consensus algorithm makes the hash value meet the predetermined condition by solving a mathematical problem: H(D) < T, where T is the target difficulty value;

[0027] Data Storage Unit: Store the blocks verified by the consensus mechanism in a distributed ledger to ensure the immutability and traceability of the data. All stored blocks are linked into a chain in chronological order to form a complete data record.

[0028] Furthermore, the deep semantic parsing module includes a data preprocessing unit, a feature extraction unit, a pattern recognition unit, and a trend analysis unit; among them:

[0029] Data Preprocessing Unit: Perform preliminary processing on the data transmitted by the blockchain data verification module, including data format conversion, noise filtering, and text tokenization, to ensure that the data is suitable for input into deep learning and natural language processing algorithms;

[0030] Feature Extraction Unit: Use a deep learning algorithm based on a recurrent neural network to extract features from the preprocessed data, and extract predetermined features from the data, including relevant words, sentiment tendencies in the text, and time series features in the data;

[0031] Pattern recognition unit: Based on the extracted features, the Transformer natural language processing algorithm is used to identify the relationships and hidden patterns in the data, analyze the correlation and potential logical relationships between the data, and provide an understanding of market dynamics;

[0032] Trend analysis unit: Combining the identified hidden patterns with external market data, using time series analysis and forecasting models, it analyzes market trends and investment opportunities, identifies potential market changes and investment hotspots, generates trend analysis reports and transmits them to the multi-dimensional risk assessment module.

[0033] Furthermore, the feature extraction unit includes:

[0034] Data input: The preprocessed data output by the data preprocessing unit is input into the recursive neural network model. The input data includes text data after word segmentation and format conversion, as well as time series data;

[0035] Embedding layer processing: convert the input text data into word vector representation, and use the embedding matrix to map each word to a high-dimensional vector space. The formula is: t,k =E·x t,k , where v t,k is the word vector representation of time step t and the kth word, E is the embedding matrix, x t,k is the kth word input;

[0036] Recurrent neural network layer processing: The input sequence is processed through a recurrent neural network layer, using hidden states to capture the time dependency and sequence information in the data. The formula is:

[0037] h t =σ(W h ·h t-1 +U h ·v t,k +b h ), where h t is the hidden state at the current time step t, h t-1 is the hidden state of the previous time step, W h and U h is the weight matrix, b h is the bias vector, σ is the activation function;

[0038] Feature extraction: extracting predetermined features from the hidden state output by the recurrent neural network layer, including related word extraction, sentiment analysis, and time series feature extraction;

[0039] The related word extraction is to determine the related words in the text by identifying the corresponding high-frequency words and important words in the hidden state;

[0040] The sentiment analysis is to determine the sentiment tendency of the text by scoring the sentiment of the hidden state, and specifically use the sentiment analysis function: S t,k =softmax(W s ·h t,k +b s ), where S t,k is the sentiment score of time step t and word k, W s is the sentiment analysis weight matrix, h t,k is the hidden state of time step t and the kth word, b s bias for sentiment analysis;

[0041] The time series feature extraction is to extract time-dependent features from the hidden state sequence, and use the time change pattern of the hidden state to analyze the trend and periodicity of the time series data;

[0042] Feature output: The extracted related words, sentiment tendencies, and time series features are combined and output as input to the pattern recognition unit.

[0043] Furthermore, the pattern recognition unit includes:

[0044] Feature input: The relevant words, sentiment tendencies, and time series features output by the feature extraction unit are input into the Transformer model;

[0045] Position encoding: Add position encoding to the input features to ensure that Transformer can capture sequence information. The specific formula is:

[0046]

[0047] Among them, P is the position code, pos is the position index, i is the dimension index, and d is the dimension of the feature vector;

[0048] Multi-head self-attention mechanism: The multi-head self-attention mechanism is used to perform weighted calculations on the input features to capture the dependencies between features. The formula is: Among them, Q is the query matrix, K is the key matrix, V is the value matrix, and d k is the dimension of the key vector;

[0049] Feedforward neural network: The output of the multi-head self-attention mechanism is transformed nonlinearly through a feedforward neural network. The formula is: FFN(x) = max(0, xW1+b1)W2+b2, where FFN is a feedforward neural network, x is the input, W1 and W2 are weight matrices, and b1 and b2 are bias vectors.

[0050] Layer normalization: Normalize the output of the feedforward neural network to improve the stability of the model. The formula is: Among them, μ is the mean of the input, σ is the standard deviation of the input, and ∈ is a small constant;

[0051] Relationship and pattern recognition: By stacking multiple Transformer layers, the input features are deeply processed and analyzed to identify complex relationships and hidden patterns in the data; and the identified relationships and patterns are transmitted to the trend analysis unit.

[0052] Further, the trend analysis unit includes:

[0053] Data collection: Collect hidden pattern data output by the pattern recognition unit and external market data, including historical market prices, trading volumes, and economic indicators;

[0054] Data integration: Integrate hidden pattern data with external market data to form a complete time series data set;

[0055] Time series preprocessing: preprocessing of time series data, including differential processing, detrending and standardization, the formula is: Δy t =y t -y t-1 , where Δy t is the difference data at time step t, y t is the original data at time step t;

[0056] Feature selection: feature variables that have an impact on market trends and investment opportunities are selected through feature selection algorithms. Specifically, principal component analysis is used to extract features. The formula is: Z = XW, where Z is the feature matrix after dimensionality reduction, X is the original feature matrix, and W is the feature vector matrix;

[0057] Model training: Use the time series prediction model ARIMA to train the preprocessed time series data. The formula is: Among them, y t is the predicted value at time step t, c is a constant term, is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term;

[0058] Perform trend analysis: Use the trained ARIMA model to predict future market trends, which includes the following steps:

[0059] Step 1: Input the latest time series data into the trained ARIMA model;

[0060] Step 2: Use the prediction formula of the ARIMA model to calculate the predicted values ​​for several future time steps. The prediction formula is: in, is the predicted value of the future time step t+h, and h is the number of predicted steps;

[0061] Step 3: According to the residual analysis results of the model, adjust the predicted value to reduce the prediction error;

[0062] Step 4: Based on the time series trend of the forecast value, determine the direction and magnitude of market trend changes and identify potential investment opportunities and risks;

[0063] Step 5: Generate a trend analysis report based on the forecast results and trend judgment, including forecast values, trend charts and investment recommendations.

[0064] Furthermore, the multi-dimensional risk assessment module includes a risk factor collection unit, a data fusion unit, a risk assessment model unit, a risk prediction unit and a risk management suggestion unit; wherein:

[0065] Risk factor collection unit: used to collect and integrate the analysis data output by the deep semantic analysis module and external risk factors, including market fluctuations, economic indicators, policy changes and industry dynamics;

[0066] Data fusion unit: Fusion of collected risk factors and analytical data to form a comprehensive risk assessment data set to ensure data consistency and integrity;

[0067] Risk assessment model unit: Based on the multi-dimensional risk assessment model, the fused data is quantitatively analyzed and risk assessed, which specifically includes the following steps:

[0068] First, based on historical data and current market conditions, calculate the weight of each risk factor using the weighted average formula: Among them, w i is the weight of the i-th risk factor, σ i is the standard deviation of the ith risk factor, and n is the total number of risk factors;

[0069] Then, the risk assessment model is used to calculate the risk exposure value of each risk factor. The formula is: E i =w i ·F i , where E i is the exposure value of the ith risk factor, F i is the current value of the ith risk factor;

[0070] Finally, the overall risk value is calculated by summing up the exposure values ​​of all risk factors. The formula is: Among them, R is the overall risk value;

[0071] Risk prediction unit: Use time series prediction models to predict future risks;

[0072] Risk Management Recommendation Unit: Generates comprehensive risk management recommendations based on risk assessment and forecasting results, including risk avoidance strategies, hedging solutions and emergency plans.

[0073] Furthermore, the smart contract execution module includes an evaluation result receiving unit, a strategy matching unit, a smart contract triggering unit, an execution monitoring unit, and a feedback adjustment unit; wherein:

[0074] Assessment result receiving unit: used to receive the assessment results from the multi-dimensional risk assessment module, including the overall risk value, risk exposure value and risk management recommendations;

[0075] Strategy matching unit: According to the received evaluation results, it matches the preset smart contract strategy library and selects the investment strategy and risk control plan with the highest matching degree to ensure the pertinence and effectiveness of the strategy;

[0076] Smart contract triggering unit: automatically triggers the corresponding smart contract according to the strategy selected by the strategy matching unit. The contract content includes investment adjustment instructions and risk hedging measures. The specific process is to first verify whether the triggering conditions of the smart contract are met, including market conditions, risk levels and investment goals; then when the conditions are met, the smart contract will be automatically signed and enter the execution state;

[0077] Execution monitoring unit: used to monitor the execution process of smart contracts in real time, ensure that each step is carried out according to the preset conditions and plans, and record the execution results and feedback information;

[0078] Feedback adjustment unit: According to the monitoring results and feedback information of the execution monitoring unit, the strategy is adjusted in real time to ensure dynamic optimization of investment strategy and risk control.

[0079] Beneficial effects of the present invention:

[0080] The present invention, by introducing a multi-level data receiving module, an intelligent data cleaning and enhancement module, and a deep semantic analysis module, the system can efficiently integrate and analyze information from multiple data sources, including real-time market data, transaction records, and social media dynamics. This integrated processing method not only optimizes the quality and availability of data, but also greatly reduces the possibility of human errors and data processing time through automated data processing processes, allowing the decision support system to respond to market changes more quickly.

[0081] The present invention greatly enhances the risk management capabilities and investment strategy flexibility of financial institutions by introducing a multi-dimensional risk assessment module and an intelligent contract execution module. The multi-dimensional risk assessment module allows in-depth analysis of multi-source data and comprehensive assessment of market risks, while the intelligent contract execution module can automatically adjust investment strategies according to the assessment results to achieve real-time risk control. This not only improves the accuracy of risk management, but also provides financial institutions with an efficient and automated risk response mechanism, enhancing their ability to respond to market emergencies, thereby protecting the interests of investors and optimizing resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0083] Figure 1 A schematic diagram of a data intelligent analysis and optimization system according to an embodiment of the present invention;

[0084] Figure 2 Schematic diagram of a deep semantic parsing module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0085] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0086] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The words "include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0087] like Figure 1-Figure 2 As shown, a data intelligent analysis and optimization system for the financial industry includes a multi-level data receiving module, a data cleaning and enhancement module, a blockchain data verification module, a deep semantic analysis module, a multi-dimensional risk assessment module and a smart contract execution module; wherein:

[0088] Multi-level data receiving module: used to receive raw data from multiple financial data sources, including market data, transaction data, customer behavior data and social media data, to ensure the diversity and comprehensiveness of data sources;

[0089] Data cleaning and enhancement module: Based on natural language processing and image recognition technology, it cleans, denoises, completes and standardizes the received raw data to improve the data quality, and transmits the processed data to the blockchain data verification module;

[0090] Blockchain data verification module: Use blockchain technology to verify and store the data processed by the data cleaning enhancement module to ensure the transparency, integrity and immutability of the data, and transmit the verified data to the deep semantic analysis module;

[0091] Deep semantic analysis module: Based on deep learning and natural language processing algorithms, it analyzes and extracts hidden information and complex relationships of data in the blockchain data verification module to identify potential market trends and investment opportunities, and transmits the analysis results to the multi-dimensional risk assessment module;

[0092] Multi-dimensional risk assessment module: Combines the analysis data output by the deep semantic analysis module with external risk factors to conduct multi-dimensional risk assessment and prediction to provide comprehensive risk management suggestions, and transmits the risk assessment results to the smart contract execution module;

[0093] Smart contract execution module: Based on the evaluation results of the multi-dimensional risk assessment module, it automatically triggers and executes the preset smart contracts to conduct real-time investment strategy adjustments and risk control.

[0094] The multi-level data receiving module includes a market data receiving unit, a transaction data receiving unit, a customer behavior data receiving unit and a social media data receiving unit; wherein:

[0095] Market data receiving unit: used to receive real-time market data from financial markets, including stock prices, bond interest rates, foreign exchange rates and commodity futures prices, to ensure the comprehensiveness of market data;

[0096] Transaction data receiving unit: used to receive transaction record data from the trading platform, including buy and sell orders, transaction records, transaction time and transaction amount, to ensure the accuracy and completeness of transaction data;

[0097] Customer behavior data receiving unit: used to receive behavior data from financial institution customers, including customer account activity records, transaction preferences, and risk tolerance assessments, to ensure the diversity and accuracy of customer behavior data;

[0098] Social media data receiving unit: used to receive relevant data from social media platforms, including news trends, market sentiment, user comments and hot topics, to ensure the timeliness and relevance of social media data; by refining the multi-level data receiving module into a market data receiving unit, a transaction data receiving unit, a customer behavior data receiving unit and a social media data receiving unit, the present invention ensures the comprehensiveness, accuracy and real-time nature of financial data reception. This refined receiving and classification processing mechanism provides a high-quality data foundation for subsequent data processing and analysis, thereby improving the data processing efficiency and accuracy of the entire system.

[0099] The data cleaning enhancement module includes a data cleaning unit, a noise removal unit, a data completion unit and a data standardization unit; wherein:

[0100] Data cleaning unit: Based on natural language processing technology, it is used to perform syntax analysis, entity recognition and sentiment analysis on the received text data, remove redundant information and erroneous records in the data, and ensure the accuracy and consistency of the data;

[0101] Noise Removal Unit: Uses image recognition technology to denoise received image and video data, identifies and removes noise and interference information in images through convolutional neural networks (CNN), and improves data clarity and usability;

[0102] Data completion unit: Use machine learning algorithms to predict and complete missing data, analyze data patterns through training models, complete missing values, and ensure data integrity;

[0103] Data standardization unit: Based on statistical analysis methods, data from different sources are uniformly standardized and processed, and the data are converted into a unified format and dimension to facilitate subsequent analysis and processing; by refining the data cleaning enhancement module into data cleaning units, noise removal units, data completion units and data standardization units, the present invention ensures comprehensive processing of original data and improves data accuracy, consistency, completeness and availability. This refined processing mechanism provides a high-quality data foundation for subsequent data verification and analysis, thereby significantly improving the data processing efficiency and analysis capabilities of the entire system.

[0104] The blockchain data verification module includes a data hash generation unit, a block generation unit, a consensus mechanism unit, and a data storage unit; among which:

[0105] Data hash generation unit: Generates a unique hash value for the data processed by the data cleaning enhancement module, specifically using the SHA-256 algorithm to generate a hash value H(D)=SHA-256(D), where D is the data to be verified, to ensure the uniqueness and integrity of the data;

[0106] Block Generation Unit: Encapsulates the generated hash value and data into a block, and adds a timestamp and the hash value of the previous block to each block to form a blockchain structure. The block content includes the current data hash value H(D i )、the hash value of the previous block H(D i-1 ) and the timestamp T i ;

[0107] Consensus Mechanism Unit: Adopts the Proof of Work (PoW) consensus algorithm to verify and confirm the block generation process. The Proof of Work consensus algorithm makes the hash value meet the predetermined condition by solving a mathematical problem: H(D) < T, where T is the target difficulty value;

[0108] Data Storage Unit: Stores the blocks verified by the consensus mechanism in a distributed ledger to ensure the immutability and traceability of the data. All stored blocks are linked into a chain in chronological order to form a complete data record; By refining the blockchain data verification module into a data hash generation unit, a block generation unit, a consensus mechanism unit, and a data storage unit, the present invention ensures the comprehensive verification and secure storage of the processed data. Using blockchain technology to generate hash values, verify data, and store them in a distributed ledger ensures the transparency, integrity, and immutability of the data. This verification mechanism provides a reliable data basis for subsequent in-depth semantic parsing and risk assessment, significantly improving the security and reliability of the system.

[0109] The in-depth semantic parsing module includes a data preprocessing unit, a feature extraction unit, a pattern recognition unit, and a trend analysis unit; Among them:

[0110] Data Preprocessing Unit: Performs preliminary processing on the data transmitted by the blockchain data verification module, including data format conversion, noise filtering, and text tokenization, to ensure that the data is suitable for input into deep learning and natural language processing algorithms;

[0111] Feature Extraction Unit: Utilizes a deep learning algorithm based on a Recurrent Neural Network (RNN) to extract features from the preprocessed data, extracting predetermined features in the data, including relevant words, sentiment tendencies in the text, and time series features in the data;

[0112] Pattern Recognition Unit: Based on the extracted features, through the natural language processing algorithm of Transformer, identifies the relationships and hidden patterns in the data, analyzes the correlations and potential logical relationships between the data, to provide an understanding of market dynamics;

[0113] Trend analysis unit: combining the identified hidden patterns and external market data, using time series analysis and forecasting models, analyzing market trends and investment opportunities, identifying potential market changes and investment hotspots, and generating trend analysis reports and transmitting them to the multi-dimensional risk assessment module; by refining the deep semantic parsing module into a data preprocessing unit, a feature extraction unit, a pattern recognition unit, and a trend analysis unit, the present invention ensures a comprehensive analysis and in-depth understanding of the data, uses a recursive neural network for feature extraction, and uses the Transformer natural language processing algorithm to identify and analyze complex relationships and hidden patterns in the data, combines external market data for trend analysis, and effectively identifies potential market changes and investment opportunities. This refined parsing mechanism provides accurate data analysis results for subsequent risk assessment and decision support, significantly improving the system's analytical capabilities and decision-making accuracy.

[0114] The feature extraction unit includes:

[0115] Data input: The preprocessed data output by the data preprocessing unit is input into the recursive neural network model. The input data includes text data after word segmentation and format conversion, as well as time series data;

[0116] Embedding layer processing: convert the input text data into word vector representation, and use the embedding matrix to map each word to a high-dimensional vector space. The formula is: t,k =E·x t,k , where v t,k is the word vector representation of time step t and the kth word, E is the embedding matrix, x t,k is the kth word input;

[0117] Recurrent neural network layer processing: The input sequence is processed through a recurrent neural network layer, using hidden states to capture the time dependency and sequence information in the data. The formula is:

[0118] h t =σ(W h ·h t-1 +U h ·v t,k +b h ), where h t is the hidden state of the current time step h, h t-1 is the hidden state of the previous time step, W h and U h is the weight matrix, b h is the bias vector, σ is the activation function;

[0119] Feature extraction: extracting predetermined features from the hidden state output by the recurrent neural network layer, including related word extraction, sentiment analysis, and time series feature extraction;

[0120] Related word extraction is to determine the related words in the text by identifying the corresponding high-frequency words and important words in the hidden state;

[0121] Sentiment analysis is to determine the sentiment of the text by scoring the sentiment of the hidden state. The specific sentiment analysis function is: t,k =softmax(W s ·h t,k +b s ), where S t,k is the sentiment score of time step t and word k, W s is the sentiment analysis weight matrix, h t,k is the hidden state of time step t and the kth word, b s bias for sentiment analysis;

[0122] Time series feature extraction is to extract time-dependent features from hidden state sequences and use the time-varying patterns of hidden states to analyze the trends and periodicity of time series data.

[0123] Feature output: The extracted related words, sentiment tendencies, and time series features are combined and output as the input of the pattern recognition unit; by breaking down the feature extraction unit into multiple steps and using a deep learning algorithm based on a recursive neural network, comprehensive feature extraction of the preprocessed data is ensured. Each step sequentially performs data input, embedding layer processing, RNN layer processing, feature extraction, and feature output to ensure the accuracy and reliability of the extracted related words, sentiment tendencies, and time series features. This refined feature extraction mechanism provides high-quality feature input for subsequent pattern recognition and trend analysis, significantly improving the system's analytical capabilities and decision-making accuracy.

[0124] The pattern recognition unit includes:

[0125] Feature input: The relevant words, sentiment tendencies, and time series features output by the feature extraction unit are input into the Transformer model;

[0126] Position encoding: Add position encoding to the input features to ensure that Transformer can capture sequence information. The specific formula is:

[0127]

[0128] Among them, P is the position code, pos is the position index, i is the dimension index, and d is the dimension of the feature vector;

[0129] Multi-head self-attention mechanism: The multi-head self-attention mechanism is used to perform weighted calculations on the input features to capture the dependencies between features. The formula is: Among them, Q is the query matrix, K is the key matrix, V is the value matrix, and d k is the dimension of the key vector;

[0130] Feedforward neural network: The output of the multi-head self-attention mechanism is transformed nonlinearly through a feedforward neural network. The formula is: FFN(x) = max(0, xW1+b1)W2+b2, where FFN is a feedforward neural network, x is the input, W1 and W2 are weight matrices, and b1 and b2 are bias vectors.

[0131] Layer normalization: Normalize the output of the feedforward neural network to improve the stability of the model. The formula is: Among them, μ is the mean of the input, σ is the standard deviation of the input, and ∈ is a small constant;

[0132] Relationship and pattern recognition: By stacking multiple Transformer layers, the input features are deeply processed and analyzed to identify complex relationships and hidden patterns in the data; the identified relationships and patterns are transmitted to the trend analysis unit for further analysis of market trends and investment opportunities; the above steps work together to ensure that the pattern recognition unit can use the Transformer's natural language processing algorithm to comprehensively analyze the extracted features, identify complex relationships and hidden patterns in the data, and provide a high-quality data foundation for subsequent trend analysis and risk assessment.

[0133] The trend analysis unit includes:

[0134] Data collection: Collect hidden pattern data output by the pattern recognition unit and external market data, including historical market prices, trading volumes, and economic indicators;

[0135] Data integration: Integrate hidden pattern data with external market data to form a complete time series data set;

[0136] Time series preprocessing: preprocessing of time series data, including differential processing, detrending and standardization, the formula is: Δy t =y t -y t-1 , where Δy t is the difference data at time step t, y t is the original data at time step t;

[0137] Feature selection: feature variables that have an impact on market trends and investment opportunities are selected through feature selection algorithms. Specifically, principal component analysis (PCA) is used to extract features. The formula is: Z = XW, where Z is the feature matrix after dimensionality reduction, X is the original feature matrix, and W is the feature vector matrix;

[0138] Model training: Use the time series prediction model ARIMA to train the preprocessed time series data. The formula is: Among them, y t is the predicted value at time step t, c is a constant term, is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term;

[0139] Perform trend analysis: Use the trained ARIMA model to predict future market trends, which includes the following steps:

[0140] Step 1: Input the latest time series data into the trained ARIMA model;

[0141] Step 2: Use the prediction formula of the ARIMA model to calculate the predicted values ​​for several future time steps. The prediction formula is: in, is the predicted value of the future time step t+h, h is the number of predicted steps, and the other parameter definitions are the same as the model training steps;

[0142] Step 3: According to the residual analysis results of the model, adjust the predicted value to reduce the prediction error;

[0143] Step 4: Based on the time series trend of the forecast value, determine the direction and magnitude of market trend changes and identify potential investment opportunities and risks;

[0144] Step 5: Generate a trend analysis report based on the forecast results and trend judgment, including forecast values, trend charts and investment recommendations;

[0145] By breaking down the trend analysis unit into multiple steps, combining hidden patterns and external market data, and utilizing time series analysis and forecasting models, the present invention ensures accurate analysis of market trends and investment opportunities. Each step sequentially performs data collection, data integration, time series preprocessing, feature selection, model training, and trend analysis to ensure the accuracy and reliability of identified market changes and investment hotspots. This refined analysis mechanism provides a high-quality data foundation for subsequent investment strategy adjustments and risk control, significantly improving the system's forecasting capabilities and decision-making accuracy.

[0146] The multi-dimensional risk assessment module includes a risk factor collection unit, a data fusion unit, a risk assessment model unit, a risk prediction unit, and a risk management suggestion unit; among which:

[0147] Risk factor collection unit: used to collect and integrate the analysis data output by the deep semantic analysis module and external risk factors, including market fluctuations, economic indicators, policy changes and industry dynamics;

[0148] Data fusion unit: Fusion of collected risk factors and analytical data to form a comprehensive risk assessment data set to ensure data consistency and integrity;

[0149] Risk assessment model unit: Based on the multi-dimensional risk assessment model (such as VaR, CVaR), the fused data is quantitatively analyzed and risk assessed, which specifically includes the following steps:

[0150] First, based on historical data and current market conditions, calculate the weight of each risk factor using the weighted average formula: Among them, w i is the weight of the i-th risk factor, σ i is the standard deviation of the ith risk factor, and n is the total number of risk factors;

[0151] Then, the risk assessment model is used to calculate the risk exposure value of each risk factor. The formula is: E i =w i ·F i , where E i is the exposure value of the ith risk factor, F i is the current value of the ith risk factor;

[0152] Finally, the overall risk value is calculated by summing up the exposure values ​​of all risk factors. The formula is: Among them, R is the overall risk value;

[0153] Risk prediction unit: Use time series prediction models (such as ARIMA) to predict future risks. For specific steps, refer to the model training and prediction calculation of the trend analysis unit;

[0154] Risk management recommendation unit: Based on the risk assessment and prediction results, it generates comprehensive risk management recommendations, including risk avoidance strategies, hedging plans and emergency plans; by breaking down the multi-dimensional risk assessment module into multiple units, combined with the analysis data and external risk factors output by the deep semantic analysis module, the comprehensiveness and accuracy of risk assessment and prediction are ensured. Each unit collects risk factors, integrates data, calculates risk assessment models, predicts risks and provides risk management recommendations in turn to ensure the accuracy and reliability of the risk management recommendations provided. This refined assessment mechanism provides financial institutions with high-quality risk management tools and significantly improves the system's risk identification and response capabilities.

[0155] The smart contract execution module includes an evaluation result receiving unit, a strategy matching unit, a smart contract triggering unit, an execution monitoring unit, and a feedback adjustment unit; wherein:

[0156] Assessment result receiving unit: used to receive the assessment results from the multi-dimensional risk assessment module, including the overall risk value, risk exposure value and risk management recommendations;

[0157] Strategy matching unit: According to the received evaluation results, it matches the preset smart contract strategy library and selects the investment strategy and risk control plan with the highest matching degree to ensure the pertinence and effectiveness of the strategy;

[0158] Smart contract triggering unit: automatically triggers the corresponding smart contract according to the strategy selected by the strategy matching unit. The contract content includes investment adjustment instructions and risk hedging measures. The specific process is to first verify whether the triggering conditions of the smart contract are met, including market conditions, risk levels and investment goals; then when the conditions are met, the smart contract will be automatically signed and enter the execution state;

[0159] Execution monitoring unit: used to monitor the execution process of smart contracts in real time, ensure that each step is carried out according to the preset conditions and plans, and record the execution results and feedback information;

[0160] Feedback adjustment unit: According to the monitoring results and feedback information of the execution monitoring unit, the strategy is adjusted in real time to ensure the dynamic optimization of investment strategy and risk control; by refining the smart contract execution module into multiple units and combining the evaluation results of the multi-dimensional risk assessment module, the present invention ensures the real-time and automation of investment strategy adjustment and risk control. Each unit receives the evaluation results, matches the strategy, triggers the smart contract, monitors the execution and makes feedback adjustments in turn to ensure the execution accuracy and effectiveness of the smart contract. This refined execution mechanism provides financial institutions with efficient investment and risk management tools, significantly improving the automation level and decision-making ability of the system.

[0161] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data intelligent analysis and optimization system for the financial industry, characterized in that: It includes a multi-level data receiving module, a data cleaning and enhancement module, a blockchain data verification module, a deep semantic analysis module, a multi-dimensional risk assessment module, and a smart contract execution module; among which: Multi-level data receiving module: used to receive raw data from multiple financial data sources, including market data, transaction data, customer behavior data and social media data; Data cleaning and enhancement module: Based on natural language processing and image recognition technology, it cleans, denoises, completes and standardizes the received raw data to improve the data quality; Blockchain data verification module: Use blockchain technology to verify and store data processed by the data cleaning enhancement module to ensure data transparency, integrity and non-tamperability; Deep semantic analysis module: Based on deep learning and natural language processing algorithms, it analyzes and extracts hidden information and complex relationships of data in the blockchain data verification module to identify potential market trends and investment opportunities, and transmits the analysis results to the multi-dimensional risk assessment module; Multi-dimensional risk assessment module: Combines the analytical data output by the deep semantic analysis module with external risk factors to conduct multi-dimensional risk assessment and prediction to provide comprehensive risk management recommendations; Smart contract execution module: Based on the evaluation results of the multi-dimensional risk assessment module, it automatically triggers and executes the preset smart contracts to conduct real-time investment strategy adjustments and risk control.

2. According to claim 1, a data intelligent analysis and optimization system for the financial industry is characterized in that: The multi-level data receiving module includes a market data receiving unit, a transaction data receiving unit, a customer behavior data receiving unit and a social media data receiving unit; wherein: Market data receiving unit: used to receive real-time market data from financial markets, including stock prices, bond interest rates, foreign exchange rates and commodity futures prices, to ensure the comprehensiveness of market data; Transaction data receiving unit: used to receive transaction record data from the trading platform, including buy and sell orders, transaction records, transaction time and transaction amount, to ensure the accuracy and completeness of transaction data; Customer behavior data receiving unit: used to receive behavior data from financial institution customers, including customer account activity records, transaction preferences, and risk tolerance assessments, to ensure the diversity and accuracy of customer behavior data; Social media data receiving unit: used to receive relevant data from social media platforms, including news updates, market sentiment, user comments and hot topics, to ensure the timeliness and relevance of social media data.

3. According to claim 1, a data intelligent analysis and optimization system for the financial industry is characterized in that: The data cleaning enhancement module includes a data cleaning unit, a noise removal unit, a data completion unit and a data standardization unit; wherein: Data cleaning unit: Based on natural language processing technology, it is used to perform syntax analysis, entity recognition and sentiment analysis on the received text data, remove redundant information and erroneous records in the data, and ensure the accuracy and consistency of the data; Noise Removal Unit: Uses image recognition technology to denoise received image and video data, identifies and removes noise and interference information in images through convolutional neural networks, and improves data clarity and usability; Data Completion Unit: Using machine learning algorithms, it predicts and completes missing data. By training a model to analyze data patterns, it completes missing values to ensure data integrity. Data Standardization Unit: Based on statistical analysis methods, it uniformly standardizes data from different sources, converting the data into a unified format and dimension for subsequent analysis and processing.

4. According to claim 1, a data intelligent analysis and optimization system for the financial industry is characterized in that: The blockchain data verification module includes a data hash generation unit, a block generation unit, a consensus mechanism unit, and a data storage unit; among them: Data Hash Generation Unit: Generates a unique hash value for the data processed by the data cleaning and enhancement module. Specifically, it uses the SHA-256 algorithm to generate the hash value H(D)=SHA-256(D), where D is the data to be verified, ensuring the uniqueness and integrity of the data. Block generation unit: encapsulates the generated hash value and data into blocks, and adds a timestamp and the hash value of the previous block to each block to form a blockchain structure. The block content includes the current data hash value H(D i ), the hash value H(D i-1 ) and timestamp T i ; Consensus Mechanism Unit: Adopts the proof-of-work consensus algorithm to verify and confirm the process of block generation. The proof-of-work consensus algorithm makes the hash value meet the predetermined condition by solving a mathematical problem: H(D)<T, where T is the target difficulty value. Data Storage Unit: Stores the blocks verified by the consensus mechanism in the distributed ledger to ensure the immutability and traceability of the data. All stored blocks are linked into a chain in chronological order to form a complete data record.

5. The data intelligent analysis and optimization system for the financial industry according to claim 1 is characterized in that: The deep semantic parsing module includes a data preprocessing unit, a feature extraction unit, a pattern recognition unit, and a trend analysis unit; among them: Data Preprocessing Unit: Performs preliminary processing on the data transmitted by the blockchain data verification module, including data format conversion, noise filtering, and text tokenization, to ensure that the data is suitable for input to deep learning and natural language processing algorithms. Feature Extraction Unit: Uses a deep learning algorithm based on a recurrent neural network to extract features from the preprocessed data, extracting predetermined features in the data, including relevant words, sentiment tendencies, and time series features in the text. Pattern Recognition Unit: Based on the extracted features, through the natural language processing algorithm of Transformer, it recognizes the relationships and hidden patterns in the data, analyzes the correlations and potential logical relationships between the data, to provide an understanding of market dynamics. Trend Analysis Unit: Combining the identified hidden patterns and external market data, using time series analysis and prediction models, it analyzes market trends and investment opportunities, identifies potential market changes and investment hotspots, and generates a trend analysis report and transmits it to the multi-dimensional risk assessment module.

6. The data intelligent analysis and optimization system for the financial industry according to claim 5, characterized in that: The feature extraction unit includes: Data Input: Inputs the preprocessed data output by the data preprocessing unit into the recurrent neural network model. The input data includes text data that has been tokenized and format-converted, as well as time series data. Embedding layer processing: convert the input text data into word vector representation, and use the embedding matrix to map each word to a high-dimensional vector space. The formula is: t,k =E·x t,k , where v t,k is the word vector representation of time step t and the kth word, E is the embedding matrix, x t,k is the kth word input; Recurrent Neural Network Layer Processing: Processes the input sequence through the recurrent neural network layer, using the hidden state to capture the time dependence and sequence information in the data. The formula is: h t =σ(W h ·h t-1 +U h ·v t,k +b h ), where h t is the hidden state at the current time step t, h t-1 is the hidden state of the previous time step, W h and U h is the weight matrix, b h is the bias vector, σ is the activation function; Feature Extraction: Extracts predetermined features from the hidden state output by the recurrent neural network layer, including relevant word extraction, sentiment tendency analysis, and time series feature extraction. The relevant word extraction is to determine the relevant words in the text by identifying the corresponding high-frequency words and important vocabulary in the hidden state. The sentiment analysis is to determine the sentiment tendency of the text by scoring the sentiment of the hidden state, and specifically use the sentiment analysis function: S t,k =softmax(W s ·h t,k +b s ), where S t,k is the sentiment score of time step t and word k, W s is the sentiment analysis weight matrix, h t,k is the hidden state of time step t and the kth word, b s bias for sentiment analysis; The time series feature extraction is to extract time-dependent features from the hidden state sequence, and use the time change pattern of the hidden state to analyze the trend and periodicity of the time series data; Feature output: The extracted related words, sentiment tendencies, and time series features are combined and output as input to the pattern recognition unit.

7. The data intelligent analysis and optimization system for the financial industry according to claim 6, characterized in that: The pattern recognition unit include: Feature input: The relevant words, sentiment tendencies, and time series features output by the feature extraction unit are input into the Transformer model; Position encoding: Add position encoding to the input features to ensure that Transformer can capture sequence information. The specific formula is: Among them, P is the position code, pos is the position index, i is the dimension index, and d is the dimension of the feature vector; Multi-head self-attention mechanism: The multi-head self-attention mechanism is used to perform weighted calculations on the input features to capture the dependencies between features. The formula is: Among them, Q is the query matrix, K is the key matrix, V is the value matrix, and d k is the dimension of the key vector; Feedforward neural network: The output of the multi-head self-attention mechanism is transformed nonlinearly through a feedforward neural network. The formula is: FFN(x) = max(0, xW1+b1)W2+b2, where FFN is a feedforward neural network, x is the input, W1 and W2 are weight matrices, and b1 and b2 are bias vectors. Layer normalization: Normalize the output of the feedforward neural network to improve the stability of the model. The formula is: Among them, μ is the mean of the input, σ is the standard deviation of the input, and ∈ is a small constant; Relationship and pattern recognition: By stacking multiple Transformer layers, the input features are deeply processed and analyzed to identify complex relationships and hidden patterns in the data; and the identified relationships and patterns are transmitted to the trend analysis unit.

8. The data intelligent analysis and optimization system for the financial industry according to claim 7 is characterized in that: The trend analysis unit comprises: Data collection: Collect hidden pattern data output by the pattern recognition unit and external market data, including historical market prices, trading volumes, and economic indicators; Data integration: Integrate hidden pattern data with external market data to form a complete time series data set; Time series preprocessing: preprocessing time series data, including difference processing, detrending and standardization processing, the formula is: Δy t =y t -y t-1 , where Δy t is the difference data at time step t, y t is the original data at time step t; Feature selection: feature variables that have an impact on market trends and investment opportunities are selected through feature selection algorithms. Specifically, principal component analysis is used to extract features. The formula is: Z = XW, where Z is the feature matrix after dimensionality reduction, X is the original feature matrix, and W is the feature vector matrix; Model training: Use the time series prediction model ARIMA to train the preprocessed time series data. The formula is: Among them, y t is the predicted value at time step t, c is a constant term, is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t is the error term; Perform trend analysis: Use the trained ARIMA model to predict future market trends, which includes the following steps: Step 1: Input the latest time series data into the trained ARIMA model; Step 2: Use the prediction formula of the ARIMA model to calculate the predicted values ​​for several future time steps. The prediction formula is: in, is the predicted value of the future time step t+h, and h is the number of predicted steps; Step 3: According to the residual analysis results of the model, adjust the predicted value to reduce the prediction error; Step 4: Based on the time series trend of the forecast value, determine the direction and magnitude of market trend changes and identify potential investment opportunities and risks; Step 5: Generate a trend analysis report based on the forecast results and trend judgment, including forecast values, trend charts and investment recommendations.

9. The data intelligent analysis and optimization system for the financial industry according to claim 1 is characterized in that: The multi-dimensional risk assessment module includes a risk factor collection unit, a data fusion unit, a risk assessment model unit, a risk prediction unit and a risk management suggestion unit; wherein: Risk factor collection unit: used to collect and integrate the analysis data output by the deep semantic analysis module and external risk factors, including market fluctuations, economic indicators, policy changes and industry dynamics; Data fusion unit: Fusion of collected risk factors and analytical data to form a comprehensive risk assessment data set to ensure data consistency and integrity; Risk assessment model unit: Based on the multi-dimensional risk assessment model, the fused data is quantitatively analyzed and risk assessed, which specifically includes the following steps: First, based on historical data and current market conditions, calculate the weight of each risk factor using the weighted average formula: Among them, w i is the weight of the i-th risk factor, σ i is the standard deviation of the ith risk factor, and n is the total number of risk factors; Then, the risk assessment model is used to calculate the risk exposure value of each risk factor. The formula is: E i =w i ·F i , where E i is the exposure value of the ith risk factor, F i is the current value of the ith risk factor; Finally, the overall risk value is calculated by summing up the exposure values ​​of all risk factors. The formula is: Among them, R is the overall risk value; Risk prediction unit: Use time series prediction models to predict future risks; Risk Management Recommendation Unit: Generates comprehensive risk management recommendations based on risk assessment and forecasting results, including risk avoidance strategies, hedging solutions and emergency plans.

10. The data intelligent analysis and optimization system for the financial industry according to claim 1, characterized in that: The smart contract execution module includes an evaluation result receiving unit, a strategy matching unit, a smart contract triggering unit, an execution monitoring unit, and a feedback adjustment unit; wherein: Assessment result receiving unit: used to receive the assessment results from the multi-dimensional risk assessment module, including the overall risk value, risk exposure value and risk management recommendations; Strategy matching unit: According to the received evaluation results, it matches the preset smart contract strategy library and selects the investment strategy and risk control plan with the highest matching degree to ensure the pertinence and effectiveness of the strategy; Smart contract triggering unit: automatically triggers the corresponding smart contract according to the strategy selected by the strategy matching unit. The contract content includes investment adjustment instructions and risk hedging measures. The specific process is to first verify whether the triggering conditions of the smart contract are met, including market conditions, risk levels and investment goals; then when the conditions are met, the smart contract will be automatically signed and enter the execution state; Execution monitoring unit: used to monitor the execution process of smart contracts in real time, ensure that each step is carried out according to the preset conditions and plans, and record the execution results and feedback information; Feedback adjustment unit: According to the monitoring results and feedback information of the execution monitoring unit, the strategy is adjusted in real time to ensure dynamic optimization of investment strategy and risk control.

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