Digitized AI intelligent analysis system and method based on big data

Abstract

The invention discloses a digital AI intelligent analysis system and method based on big data, and relates to the technical field of financial data protection systems. Comprising a data arrangement module which is responsible for collecting original data and carrying out cleaning and normalized data arrangement operation; the feature engineering module is used for automatically extracting key features of data through a data mining technology; and the model training module is used for performing model training by applying a machine learning algorithm. According to the method, key features are automatically extracted by adopting an advanced data mining technology based on a feature engineering module, and a feature set is optimized through methods such as recursive feature elimination; model training time is shortened, the performance of the model is improved, the model training module supports multiple machine learning algorithms and allows a user-defined model, and the performance of the model is further improved through the hyper-parameter tuning function; in addition, the model storage and management unit simplifies the deployment and iteration process of the model.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data protection systems, and in particular to a digital AI intelligent analysis system and method based on big data. Background Art

[0002] With the surge in data volume and the improvement of computing power, big data analysis and artificial intelligence have become key tools to promote scientific research and business decision-making. Traditional data analysis methods cannot effectively process massive amounts of complex data and it is difficult to extract deep information from the data.

[0003] After searching, the Chinese patent application number 202211360554.3 discloses a digital AI intelligent analysis system based on big data and its method, including an intelligent analysis system body, which includes a security management module, an AI analysis module, a self-service query module, a data processing module, a large database module, a central control module, a result storage module, and a visualization display module. The intelligent analysis system in the above patent is still insufficient in terms of model evaluation and optimization capabilities; and the practicality and adaptability of the business need to be improved. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a digital AI intelligent analysis system and method based on big data.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A digital AI intelligent analysis system based on big data, including:

[0007] Data sorting module: responsible for collecting raw data and performing data sorting operations such as cleaning and normalization;

[0008] Feature Engineering Module: Automatically extract key features of data through data mining technology;

[0009] Model training module: Apply machine learning algorithms to train the model. The specific optimization objective function formula is:

[0010]

[0011] sty (i) (w T φ(x (i) )+b)≥1-G i ,G i ≥0

[0012] Among them, w is the weight vector, b is the bias term, P is the penalty parameter, G iis the slack variable;

[0013] Reinforcement learning module: uses reinforcement learning algorithm to learn the optimal strategy through interaction with the environment;

[0014] Quantum Computing Module: Using the principles of quantum computing to tackle specific types of computational problems;

[0015] Result evaluation module: The performance of the model is evaluated through cross-validation and ROC curve methods.

[0016] As a preferred embodiment of the present invention: the data sorting module includes:

[0017] Data collection unit: automatically collects data from multiple data sources; supports the capture and processing of real-time data streams;

[0018] Data cleaning unit: automatically detects and processes missing values, outliers, and duplicate data; provides data quality reports and visualizes data problems;

[0019] Data conversion unit: realizes data standardization, normalization, and encoding conversion; supports custom conversion logic and adapts to different algorithm requirements;

[0020] Data integration unit: merges data from different sources to solve data redundancy and inconsistency problems; performs data fusion to form a unified analysis data set.

[0021] As a preferred embodiment of the present invention, the feature engineering module includes:

[0022] Feature extraction unit: extracts features using statistical analysis and spectrum analysis techniques; supports extracting features from unstructured data such as text and images;

[0023] Feature selection unit: implements feature importance assessment, including information gain and correlation coefficient; provides automated feature selection algorithms, including recursive feature elimination and model-based feature selection;

[0024] Feature construction unit: creates new features by mathematically transforming and combining existing features; supports feature construction driven by domain knowledge;

[0025] Feature dimensionality reduction unit: Apply PCA, LDA, and t-SNE methods to reduce feature dimensions; optimize feature sets to improve model training efficiency and performance;

[0026] The feature engineering module uses recursive feature elimination to eliminate features backwards, obtaining fewer and fewer features, and thus selecting features. The specific method is as follows:

[0027] Training model: Train the SVM model on the current feature set;

[0028] Sorting features: Sort each feature according to its contribution to the SVM hyperplane decision function;

[0029] Eliminate features: remove the lowest ranked features;

[0030] Iterative process: Repeat the above steps using the remaining features;

[0031] Among them, the decision function of SVM is:

[0032]

[0033] Where x is the input vector, N is the number of samples, and a i is the Lagrange multiplier, y i is the class label, K(x,x i ) is the kernel function and c is the bias term.

[0034] As a preferred embodiment of the present invention: the feature engineering module uses the t-SNE method to reduce the feature dimension, and the loss function of t-SNE is:

[0035]

[0036] in:

[0037] P is the probability distribution of similarity in high-dimensional space;

[0038] Q is the similarity probability distribution in low-dimensional space;

[0039] KL is the Kullback-Leibler divergence;

[0040] p ij is the probability that object i chooses object j as a neighbor in high-dimensional space;

[0041] q ij is the probability that object i chooses object j as a neighbor in the low-dimensional space.

[0042] As a preferred embodiment of the present invention: the model training module includes:

[0043] Model selection unit: provides a variety of machine learning algorithms and supports the import and use of custom models;

[0044] Hyperparameter tuning unit: Use grid search, random search, and Bayesian optimization techniques to tune hyperparameters;

[0045] Model training unit: efficiently processes large-scale data sets and supports distributed computing; provides training progress monitoring and performance evaluation indicators;

[0046] Model storage and management unit: supports persistent storage of models to facilitate subsequent use and deployment; provides model version control and tracks model iteration history;

[0047] The enhanced learning module includes:

[0048] Environment modeling unit: builds and simulates the environment for reinforcement learning, defines the state space and action space; supports environment visualization and helps understand complex environment structures;

[0049] Strategy training unit: implements various strategy algorithms and supports iterative training and adjustment of strategies;

[0050] Reward design unit: design reasonable reward functions to guide agents to learn target strategies; support customized reward mechanisms to meet the needs of different business scenarios;

[0051] Strategy Evaluation and Optimization Unit: Evaluate the performance of training strategies, identify possible overfitting or underfitting problems, and provide strategy optimization suggestions.

[0052] As a preferred embodiment of the present invention, the quantum computing module includes:

[0053] Quantum Algorithm Design and Implementation Unit: Provides basic quantum algorithms and supports the implementation and testing of user-defined quantum algorithms;

[0054] Quantum circuit simulation unit: simulates the operation of quantum circuits on classical computers to verify the correctness of quantum algorithms; provides a visual editing and operation interface for quantum logic gates;

[0055] Quantum noise simulation and processing unit: simulates the noise in quantum systems and its impact on algorithms; provides error correction solutions to enhance the robustness of algorithms;

[0056] Quantum Resource Optimization Unit: Optimizes the resource consumption of quantum algorithms and explores more efficient quantum algorithms that achieve the same computing goals;

[0057] The quantum computing module uses quantum Fourier transform to first find the random r value of a given integer N, and then calculate the period of the periodicity r; the period detection process is accelerated, and the formula is:

[0058]

[0059] Among them, F n is the quantum Fourier transform operator, |x> is the input state, and |y> is the output state.

[0060] As a preferred embodiment of the present invention: the result evaluation module includes:

[0061] Performance measurement calculation unit: calculates various performance indicators and supports the calculation of multiple categories of evaluation indicators, which is suitable for different model evaluation requirements;

[0062] Error Analysis Unit: Analyzes the errors in the model prediction results, identifies the sources and patterns of errors; provides visual reports of errors to help improve model performance;

[0063] Cross-validation unit: implements K-fold cross-validation technology to evaluate the generalization ability of the model; supports custom cross-validation strategies and parameters;

[0064] Model comparison and selection unit: compares the performance of different models on the same data set and conducts a comprehensive performance comparison; provides decision support tools to assist users in selecting the optimal model for deployment.

[0065] As a preferred embodiment of the present invention: it also includes:

[0066] Self-Organizing Map Module: The Self-Organizing Map Module performs cluster analysis on data without a target output; it maps multidimensional data into a low-dimensional space for visualization and analysis of the intrinsic structure of the data;

[0067] Wherein, the self-organizing map module comprises:

[0068] Data Mapping and Visualization Unit: Map high-dimensional data to a low-dimensional space, usually a two-dimensional grid; provide intuitive visualization to facilitate understanding of the internal structure of the data;

[0069] Topology-preserving training unit: ensures that the topological relationship of the data is maintained during the mapping process, and similar inputs are close to each other after mapping;

[0070] Adaptive learning rate unit: sets and adjusts the learning rate, affecting the training speed and mapping quality; supports adaptive adjustment of the learning rate, and dynamically adjusts it according to the training situation;

[0071] Cluster analysis and interpretation unit: Perform cluster analysis on the generated SOM to identify the main patterns and trends in the data; provide cluster interpretation and feature identification to help users understand the characteristics of each cluster.

[0072] As a preferred embodiment of the present invention: in the self-organizing map module, K-means is used to group the activated BMUs, and the specific steps include:

[0073] ① Calculate BMUs:

[0074] During the SOM training process, for each input sample, find the neuron BMU that best matches it. The specific formula is as follows:

[0075]

[0076] Where x is the input sample, w i is the weight vector of neuron i;

[0077] ②Use K-means for clustering:

[0078] The BMU of all input samples is used as input to the K-means algorithm; K-means attempts to minimize the following objective function, which is the sum of the distances of all points to their assigned centers:

[0079]

[0080] Where N is the number of clusters, C i is the set of data points in cluster i, c i is the center of cluster i;

[0081] ③ Update cluster center:

[0082] K-means optimizes the above objective function by iteratively updating the cluster center:

[0083]

[0084] Among them, |C i | is the number of data points in cluster i;

[0085] ④Identify patterns and trends:

[0086] Through the final cluster configuration, the distribution patterns in the dataset are identified.

[0087] As a preferred embodiment of the present invention, the analysis method of the intelligent analysis system comprises the following steps:

[0088] S1: Data preprocessing: Automatically collect raw data from multiple data sources and preprocess them through data cleaning and normalization operations;

[0089] S2: Feature Engineering: Automatically extract key features of data, evaluate feature importance, and perform feature selection through recursive feature elimination; construct new features through mathematical transformation and combination of existing features, and apply PCA, LDA, t-SNE methods for feature dimensionality reduction;

[0090] S3: Model training and optimization: Select appropriate machine learning algorithms and perform hyperparameter tuning; train models on large-scale data sets and support distributed computing to improve training efficiency; store and manage trained models to ensure version control and convenience of subsequent deployment;

[0091] S4: Reinforcement Learning: Build and simulate reinforcement learning environments, design reasonable reward functions and evaluate the performance of strategies, and adjust the exploration rate and reward discount factor as needed;

[0092] S5: Quantum computing applications: Implement basic quantum algorithms and simulate quantum circuits to verify the correctness of the algorithms; optimize the resource consumption of quantum algorithms and handle noise in quantum systems;

[0093] S6: Results Evaluation and Validation: Calculate performance metrics and evaluate the generalization ability of the model through cross-validation; perform error analysis and compare the performance of different models to select the best model for deployment.

[0094] The beneficial effects of the present invention are:

[0095] 1. The present invention uses a data sorting module and an advanced data mining technology based on a feature engineering module to automatically extract key features, and optimizes the feature set through methods such as recursive feature elimination; it reduces the time of model training and improves the performance of the model.

[0096] 2. The model training module of the present invention supports multiple machine learning algorithms and allows customized models, and the hyperparameter tuning function further improves the performance of the model. In addition, the model storage and management unit simplifies the deployment and iteration process of the model. The combination of the reinforcement learning module and the quantum computing module enables the system to handle complex environmental simulations and computationally intensive tasks, thereby expanding the application field of the AI ​​system.

[0097] 3. The result evaluation module of the present invention not only provides the calculation of multiple performance indicators, but also evaluates the generalization ability of the model through cross-validation and other techniques to ensure that the optimal model is selected for deployment.

[0098] 4. The present invention can perform unsupervised cluster analysis on data based on the self-organizing map module, reveal the inherent structure and pattern of the data, and can better cope with exploratory data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 The present invention is a flowchart of an analysis method of a digital AI intelligent analysis system based on big data. DETAILED DESCRIPTION

[0100] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0101] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0102] Embodiment 1:

[0103] A digital AI intelligent analysis system based on big data, including:

[0104] Data sorting module: responsible for collecting raw data and performing pre-processing operations such as cleaning and normalization;

[0105] Feature Engineering Module: Automatically extract key features of data through data mining technology;

[0106] Model training module: Apply machine learning algorithms to train the model. The specific optimization objective function formula is:

[0107]

[0108] sty (i) (w T φ(x (i) )+b)≥1-G i ,G i ≥0

[0109] Among them, w is the weight vector, b is the bias term, P is the penalty parameter, G i is the slack variable;

[0110] Reinforcement learning module: uses reinforcement learning algorithm to learn the optimal strategy through interaction with the environment;

[0111] Quantum Computing Module: Using the principles of quantum computing to tackle specific types of computational problems;

[0112] Result evaluation module: Evaluate the performance of the model through cross-validation, ROC curve and other methods.

[0113] Wherein, the data sorting module includes:

[0114] Data collection unit: automatically collects data from multiple data sources, such as API crawling, database access, etc.; supports the capture and processing of real-time data streams;

[0115] Data cleaning unit: automatically detects and processes missing values, outliers, and duplicate data; provides data quality reports and visualizes data problems;

[0116] Data conversion unit: realizes data standardization, normalization, encoding conversion, etc.; supports custom conversion logic and adapts to different algorithm requirements;

[0117] Data integration unit: merges data from different sources to solve data redundancy and inconsistency problems; performs data fusion to form a unified analysis data set.

[0118] Wherein, the feature engineering module includes:

[0119] Feature extraction unit: extracts features using statistical analysis, spectrum analysis and other techniques; supports extracting features from unstructured data such as text and images;

[0120] Feature selection unit: implements feature importance assessment, such as based on information gain, correlation coefficient, etc.; provides automated feature selection algorithms, such as recursive feature elimination, model-based feature selection, etc.;

[0121] Feature construction unit: creates new features by mathematically transforming and combining existing features; supports feature construction driven by domain knowledge;

[0122] Feature dimensionality reduction unit: Apply PCA, LDA, t-SNE and other methods to reduce feature dimensions; optimize feature sets to improve model training efficiency and performance.

[0123] The feature engineering module uses recursive feature elimination to eliminate features backwards, obtaining fewer and fewer features, and thus selecting features. The specific method is as follows:

[0124] Train model: Train the SVM model on the current feature set.

[0125] Sort features: Sort each feature based on its contribution to the SVM hyperplane decision function.

[0126] Eliminate features: Remove the lowest ranked features.

[0127] Iterative process: Repeat the above steps using the remaining features;

[0128] Among them, the decision function of SVM is:

[0129]

[0130] Where x is the input vector, N is the number of samples, and a i is the Lagrange multiplier, y i is the class label, K(x,x i ) is the kernel function and c is the bias term.

[0131] Among them, the feature engineering module uses the t-SNE method to reduce the feature dimension, and the loss function (optimization target) of t-SNE is:

[0132]

[0133] in:

[0134] P is the probability distribution of similarity in high-dimensional space;

[0135] Q is the similarity probability distribution in low-dimensional space;

[0136] KL is the Kullback-Leibler divergence;

[0137] p ij is the probability that object i chooses object j as a neighbor in high-dimensional space;

[0138] q ij is the probability that object i chooses object j as a neighbor in the low-dimensional space.

[0139] Wherein, the model training module includes:

[0140] Model selection unit: provides a variety of machine learning algorithms, including classification, regression, clustering, etc.; supports the import and use of custom models;

[0141] Hyperparameter tuning unit: Use grid search, random search, Bayesian optimization and other techniques to tune hyperparameters; support cross-validation and other methods to evaluate the effectiveness of hyperparameters;

[0142] Model training unit: efficiently processes large-scale data sets and supports distributed computing; provides training progress monitoring and performance evaluation indicators;

[0143] Model storage and management unit: supports persistent storage of models to facilitate subsequent use and deployment; provides model version control and tracks model iteration history.

[0144] Wherein, the enhanced learning module includes:

[0145] Environment modeling unit: builds and simulates the environment for reinforcement learning, defines the state space and action space; supports environment visualization and helps understand complex environment structures;

[0146] Strategy training unit: implements various strategy algorithms, such as Q-learning, SARSA, Deep Q Networks (DQN), etc.; supports iterative training and adjustment of strategies;

[0147] Reward design unit: design reasonable reward functions to guide agents to learn target strategies; support customized reward mechanisms to meet the needs of different business scenarios;

[0148] Strategy Evaluation and Optimization Unit: Evaluate the performance of the training strategy and identify possible overfitting or underfitting problems; provide strategy optimization suggestions, such as adjusting the exploration rate, reward discount factor, etc.

[0149] Wherein, the quantum computing module includes:

[0150] Quantum algorithm design and implementation unit: provides basic quantum algorithms, including Shor's algorithm, Grover's algorithm, etc.; supports the implementation and testing of user-defined quantum algorithms;

[0151] Quantum circuit simulation unit: simulates the operation of quantum circuits on classical computers to verify the correctness of quantum algorithms; provides a visual editing and operation interface for quantum logic gates;

[0152] Quantum noise simulation and processing unit: simulates the noise in quantum systems and its impact on algorithms; provides error correction solutions to enhance the robustness of algorithms;

[0153] Quantum Resource Optimization Unit: Optimize the resource consumption of quantum algorithms, such as the number of quantum bits and the number of gate operations; explore more efficient quantum algorithms that achieve the same computing goals.

[0154] The quantum computing module uses quantum Fourier transform to first find the random r value of a given integer N, and then calculate the period of the periodicity r; the period detection process is accelerated, and the formula is:

[0155]

[0156] Among them, F n is the quantum Fourier transform operator, |x> is the input state, and |y> is the output state.

[0157] Wherein, the result evaluation module includes:

[0158] Performance measurement calculation unit: calculates various performance indicators, such as accuracy, recall rate, F1 score, etc.; supports the calculation of multiple categories of evaluation indicators, suitable for different model evaluation needs;

[0159] Error Analysis Unit: Analyzes the errors in the model prediction results, identifies the sources and patterns of errors; provides visual reports of errors to help improve model performance;

[0160] Cross-validation unit: Implement K-fold cross-validation and other techniques to evaluate the generalization ability of the model; support custom cross-validation strategies and parameters;

[0161] Model comparison and selection unit: compares the performance of different models on the same data set and conducts a comprehensive performance comparison; provides decision support tools to assist users in selecting the optimal model for deployment.

[0162] The analysis method of the intelligent analysis system comprises the following steps:

[0163] S1: Data preprocessing: Automatically collect raw data from multiple data sources and preprocess them through data cleaning and normalization operations;

[0164] S2: Feature Engineering: Automatically extract key features of data, evaluate feature importance, and perform feature selection through recursive feature elimination; construct new features through mathematical transformation and combination of existing features, and apply PCA, LDA, t-SNE methods for feature dimensionality reduction;

[0165] S3: Model training and optimization: Select appropriate machine learning algorithms and perform hyperparameter tuning; train models on large-scale data sets and support distributed computing to improve training efficiency; store and manage trained models to ensure version control and convenience of subsequent deployment;

[0166] S4: Reinforcement Learning: Build and simulate reinforcement learning environments, design reasonable reward functions and evaluate the performance of strategies, and adjust the exploration rate and reward discount factor as needed;

[0167] S5: Quantum computing applications: Implement basic quantum algorithms and simulate quantum circuits to verify the correctness of the algorithms; optimize the resource consumption of quantum algorithms and handle noise in quantum systems;

[0168] S6: Results Evaluation and Validation: Calculate performance metrics and evaluate the generalization ability of the model through cross-validation; perform error analysis and compare the performance of different models to select the best model for deployment.

[0169] Embodiment 2:

[0170] A digital AI intelligent analysis system based on big data, this embodiment makes the following improvements on the basis of embodiment 1: it also includes:

[0171] Self-Organizing Map Module: The Self-Organizing Map Module performs cluster analysis on data without a target output; it maps multidimensional data into a low-dimensional space for visualization and analysis of the intrinsic structure of the data;

[0172] Wherein, the self-organizing map module comprises:

[0173] Data Mapping and Visualization Unit: Map high-dimensional data to a low-dimensional space, usually a two-dimensional grid; provide intuitive visualization to facilitate understanding of the internal structure of the data;

[0174] Topology-preserving training unit: ensures that the topological relationship of the data is maintained during the mapping process, that is, similar inputs are close to each other after mapping; different distance measurement methods are used, such as Euclidean distance, Manhattan distance, etc.

[0175] Adaptive learning rate unit: sets and adjusts the learning rate, affecting the training speed and mapping quality; supports adaptive adjustment of the learning rate, and dynamically adjusts it according to the training situation;

[0176] Cluster analysis and interpretation unit: Perform cluster analysis on the generated SOM to identify the main patterns and trends in the data; provide cluster interpretation and feature identification to help users understand the characteristics of each cluster.

[0177] Wherein, in the self-organizing map module, K-means is used to group the activated BMUs (Best Matching Units), and the specific steps include:

[0178] ① Calculate BMUs:

[0179] During the SOM training process, for each input sample, the neuron (BMU) that "best matches" it is found. The specific formula is as follows:

[0180]

[0181] Where x is the input sample, w i is the weight vector of neuron i;

[0182] ②Use K-means for clustering:

[0183] The BMU of all input samples is used as input to the K-means algorithm; K-means attempts to minimize the following objective function, which is the sum of the distances of all points to their assigned centers:

[0184]

[0185] Where N is the number of clusters, C i is the set of data points in cluster i, c i is the center of cluster i;

[0186] ③ Update cluster center:

[0187] K-means optimizes the above objective function by iteratively updating the cluster center:

[0188]

[0189] Among them, |C i | is the number of data points in cluster i;

[0190] ④Identify patterns and trends:

[0191] Through the final cluster configuration, the distribution patterns in the data set are identified. For example, some clusters may contain specific types of data points, indicating that these data points are similar in the original space.

[0192] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A digital AI intelligent analysis system based on big data, characterized by: include: Data sorting module: responsible for collecting raw data and performing data sorting operations such as cleaning and normalization; Feature Engineering Module: Automatically extract key features of data through data mining technology; Model training module: Apply machine learning algorithms to train the model. The specific optimization objective function formula is: s.t.y (i) (w T φ(x (i) )+b)≥1-G i ,G i ≥0 Among them, w is the weight vector, b is the bias term, P is the penalty parameter, G i is a slack variable; Reinforcement learning module: uses reinforcement learning algorithms to learn optimal strategies through interaction with the environment; Quantum Computing Module: Utilizes the principles of quantum computing to address specific types of computational problems; Result evaluation module: Evaluate the performance of the model through cross-validation and ROC curve method.

2. A digital AI intelligent analysis system based on big data according to claim 1, characterized in that: The data collating module includes: Data collection unit: automatically collects data from multiple data sources; supports the capture and processing of real-time data streams; Data cleaning unit: automatically detects and processes missing values, outliers, and duplicate data; provides data quality reports and visualizes data problems; Data conversion unit: realizes data standardization, normalization, and encoding conversion; supports custom conversion logic and adapts to different algorithm requirements; Data integration unit: merges data from different sources to solve data redundancy and inconsistency problems; performs data fusion to form a unified analysis data set.

3. The digital AI intelligent analysis system based on big data according to claim 1 is characterized in that: The feature engineering module includes: Feature extraction unit: uses statistical analysis and spectrum analysis techniques to extract features; supports extracting features from unstructured data such as text and images; Feature selection unit: implements feature importance assessment, including information gain and correlation coefficient; provides automated feature selection algorithms, including recursive feature elimination and model-based feature selection; Feature construction unit: creates new features by mathematically transforming and combining existing features; supports domain knowledge-driven feature construction; Feature Dimensionality Reduction Unit: Apply PCA, LDA, and t-SNE methods to reduce feature dimensions; optimize feature sets to improve model training efficiency and performance; The feature engineering module uses recursive feature elimination to eliminate features backwards, obtaining fewer and fewer features, and thus selecting features. The specific method is as follows: Training model: train the SVM model on the current feature set; Sorting features: rank each feature based on its contribution to the SVM hyperplane decision function; Eliminate features: remove the lowest-ranked features; Iterative process: Repeat the above steps using the remaining features; Among them, the decision function of SVM is: Where x is the input vector, N is the number of samples, and a i is the Lagrange multiplier, y i is the class label, K(x,x i ) is the kernel function and c is the bias term.

4. The digital AI intelligent analysis system based on big data according to claim 3 is characterized in that: The feature engineering module uses the t-SNE method to reduce feature dimensions. The loss function of t-SNE is: in: P is the probability distribution of similarity in high-dimensional space; Q is the similarity probability distribution in low-dimensional space; KL is the Kullback-Leibler divergence; p ij is the probability that object i chooses object j as a neighbor in high-dimensional space; q ij is the probability that object i chooses object j as a neighbor in the low-dimensional space.

5. The digital AI intelligent analysis system based on big data according to claim 3 is characterized in that: The model training module includes: Model selection unit: provides a variety of machine learning algorithms and supports the import and use of custom models; Hyperparameter tuning unit: Use grid search, random search, and Bayesian optimization techniques to tune hyperparameters; Model training unit: Efficiently processes large-scale data sets and supports distributed computing; provides training progress monitoring and performance evaluation indicators; Model storage and management unit: supports persistent storage of models to facilitate subsequent use and deployment; provides model version control and tracks model iteration history; The enhanced learning module includes: Environment Modeling Unit: Builds and simulates the environment for reinforcement learning, defines the state space and action space; supports environment visualization to help understand complex environment structures; Strategy training unit: implements various strategy algorithms and supports iterative training and adjustment of strategies; Reward Design Unit: Designs reasonable reward functions to guide agents to learn target strategies; supports custom reward mechanisms to adapt to different business scenarios; Strategy Evaluation and Optimization Unit: Evaluates the performance of training strategies, identifies possible overfitting or underfitting issues, and provides strategy optimization recommendations.

6. The digital AI intelligent analysis system based on big data according to claim 5, characterized in that: The quantum computing module comprises: Quantum Algorithm Design and Implementation Unit: Provides basic quantum algorithms and supports the implementation and testing of user-defined quantum algorithms; Quantum Circuit Simulation Unit: simulates the operation of quantum circuits on classical computers to verify the correctness of quantum algorithms; provides a visual editing and operation interface for quantum logic gates; Quantum noise simulation and processing unit: simulates noise in quantum systems and its impact on algorithms; provides error correction solutions to enhance algorithm robustness; Quantum Resource Optimization Unit: Optimizes the resource consumption of quantum algorithms and explores more efficient quantum algorithms that achieve the same computing goals; The quantum computing module uses quantum Fourier transform to first find a random r value for a given integer N, and then calculate the period of the periodicity r; the period detection process is accelerated, and the formula is: Among them, F n is the quantum Fourier transform operator, |x> is the input state, and |y> is the output state.

7. The digital AI intelligent analysis system based on big data according to claim 6, characterized in that: The result evaluation module includes: Performance measurement calculation unit: calculates various performance indicators and supports the calculation of multiple categories of evaluation indicators, suitable for different model evaluation requirements; Error Analysis Unit: Analyzes errors in model prediction results, identifies the sources and patterns of errors, and provides visual reports of errors to help improve model performance. Cross-validation unit: Implements K-fold cross-validation technology to evaluate the generalization ability of the model; supports custom cross-validation strategies and parameters; Model comparison and selection unit: Compare the performance of different models on the same dataset and conduct a comprehensive performance comparison; provide decision support tools to assist users in selecting the optimal model for deployment.

8. The digital AI intelligent analysis system based on big data according to claim 6, characterized in that: Also includes: Self-Organizing Map Module: The self-organizing map module performs cluster analysis on data without target output; Mapping multidimensional data into a low-dimensional space to visualize and analyze the intrinsic structure of the data; Wherein, the self-organizing map module includes: Data Mapping and Visualization Unit: Maps high-dimensional data into a low-dimensional space, usually a two-dimensional grid; provides intuitive visualization to facilitate understanding of the inherent structure of the data; Topology-preserving training unit: ensures that the topological relationship of the data is maintained during the mapping process, and similar inputs are close to each other after mapping; Adaptive learning rate unit: sets and adjusts the learning rate, affecting training speed and mapping quality; supports adaptive adjustment of the learning rate and dynamic adjustment based on training conditions; Cluster analysis and interpretation unit: performs cluster analysis on the generated SOM to identify the main patterns and trends in the data; provides cluster interpretation and feature identification to help users understand the characteristics of each cluster.

9. The digital AI intelligent analysis system based on big data according to claim 8, characterized in that: In the self-organizing map module, K-means is used to group the activated BMUs. The specific steps include: ① Calculate BMUs: During the SOM training process, for each input sample, the best matching neuron BMU is found. The specific formula is as follows: Among them, x is the input sample, w i is the weight vector of neuron i; ② Use K-means for clustering: The BMU of all input samples is used as input to the K-means algorithm; K-means attempts to minimize the following objective function, which is the sum of the distances of all points to their assigned centers: Where N is the number of clusters, C i is the set of data points in cluster i, c i is the center of cluster i; ③Update cluster center: K-means optimizes the above objective function by iteratively updating the cluster centers: Among them, |C i | is the number of data points in cluster i; ④Identify patterns and trends: Through the final cluster configuration, the distribution patterns in the dataset are identified.

10. The digital AI intelligent analysis system based on big data according to claim 8, characterized in that: The analysis method of the intelligent analysis system comprises the following steps: S1: Data preprocessing: Automatically collect raw data from multiple data sources and preprocess them through data cleaning and normalization operations; S2: Feature Engineering: Automatically extract key features of the data, evaluate the importance of features, and perform feature selection through recursive feature elimination; construct new features by mathematically transforming and combining existing features, and apply PCA, LDA, and t-SNE methods for feature dimensionality reduction; S3: Model Training and Optimization: Select appropriate machine learning algorithms and perform hyperparameter tuning; train models on large-scale datasets and support distributed computing to improve training efficiency; store and manage trained models to ensure version control and ease of subsequent deployment; S4: Reinforcement Learning: Build and simulate reinforcement learning environments, design reasonable reward functions and evaluate the performance of strategies, and adjust the exploration rate and reward discount factor as needed; S5: Quantum computing applications: Implement basic quantum algorithms and simulate quantum circuits to verify the correctness of the algorithms; optimize the resource consumption of quantum algorithms and handle noise in quantum systems; S6: Results Evaluation and Validation: Calculate performance metrics and evaluate the generalization ability of the model through cross-validation; perform error analysis and compare the performance of different models to select the optimal model for deployment.

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