Intelligent decision support system and method for big data analysis

Abstract

This invention relates to the field of big data analytics and discloses an intelligent decision support system and method for big data analytics, comprising: a data acquisition module for collecting raw data from multiple data sources; a data processing module for cleaning, organizing, and preprocessing the collected data; a data integration module for uniformly integrating data from different platforms and systems; an intelligent decision model generation module for automatically generating a decision model based on the results of big data analytics; a decision execution module for executing decision tasks based on the intelligent decision model; and a real-time feedback module for optimizing the decision model based on real-time data stream feedback on the decision effect. This invention provides intelligent support for massive data analysis and dynamic decision-making, enabling cross-platform data integration, real-time data processing, and intelligent decision model generation and optimization. It effectively improves the accuracy, efficiency, and intelligence level of decision-making and has broad application prospects.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to an intelligent decision support system and method for big data analytics. Background Technology

[0002] With the advent of the big data era, the continuous advancement of information technology, the sustained growth of various data sources, and the explosive growth of data volume, how to effectively extract valuable information from massive amounts of data and make scientific decisions has become a crucial issue facing all industries. Traditional decision support systems mainly rely on manual analysis and static data models, which are insufficient to cope with the challenges of ever-increasing data scale, increasingly complex data types, and rapid changes.

[0003] While current decision support systems can perform some data analysis and assist in decision-making, they typically have the following drawbacks:

[0004] Insufficient data processing capabilities: Traditional decision support systems perform poorly when processing big data, making it difficult to efficiently handle massive amounts of data and complex data analysis tasks.

[0005] Single decision-making model: Existing decision support systems often rely on static rules or models, lack the ability to process dynamic data in real time, and cannot flexibly respond to changing market environments or business needs.

[0006] Low level of intelligence: Most existing systems only process data through simple analysis methods, failing to delve into the deep-seated patterns in the data and thus unable to provide intelligent support for complex decision-making.

[0007] Cross-platform integration is difficult: Big data comes from diverse sources, and existing decision support systems often struggle to effectively integrate information from different platforms and data sources, leading to data silos and affecting the comprehensiveness and accuracy of decision-making.

[0008] To address the aforementioned issues, this invention proposes an intelligent decision support system and method for big data analytics, which provides a more efficient, accurate, and intelligent decision support solution through intelligent data processing, dynamic decision models, and cross-platform data integration. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent decision support system and method for big data analysis, which can provide intelligent support for massive data analysis and dynamic decision-making, and realize functions such as cross-platform data integration, real-time data processing, intelligent decision model generation and optimization.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent decision support system for big data analysis, comprising:

[0011] The data acquisition module is used to collect raw data from multiple data sources;

[0012] The data processing module is used to clean, organize, and preprocess the collected data;

[0013] The data integration module is used to uniformly integrate data from different platforms and systems;

[0014] The intelligent decision model generation module is used to automatically generate decision models based on big data analysis results;

[0015] The decision execution module is used to execute decision-making tasks based on the intelligent decision-making model.

[0016] The real-time feedback module is used to optimize the decision-making model based on the feedback of real-time data streams.

[0017] As a further limitation of the above solution, the data acquisition module includes multiple data interfaces, which can connect to different types of data sources and automatically acquire data.

[0018] As a further limitation of the above scheme, the data processing module adopts a distributed architecture, which can efficiently process large-scale data and perform parallel computing.

[0019] As a further limitation of the above scheme, the intelligent decision model generation module includes a machine learning algorithm that can train and optimize the decision model based on historical and real-time data.

[0020] As a further limitation of the above scheme, the intelligent decision model generation module also includes a deep learning algorithm, which can automatically extract features from the data and generate a more accurate decision model.

[0021] As a further limitation of the above scheme, the real-time feedback module automatically adjusts the decision-making model and optimizes the decision-making process based on changes in the data stream.

[0022] As a further limitation of the above solution, the decision execution module can select and execute appropriate decision strategies according to different business scenarios.

[0023] As a further limitation of the above solution, the data integration module adopts data lake technology, which can centrally manage and analyze data from different platforms.

[0024] As a preferred technical solution of the present invention, the system has good scalability and can flexibly configure and adjust system modules according to different business needs.

[0025] A method for intelligent decision support for big data analytics, comprising the aforementioned intelligent decision support system for big data analytics, specifically includes the following steps:

[0026] S1: Collect raw data from multiple data sources;

[0027] S2: Clean, organize, and preprocess the collected data;

[0028] S3: Unifies and integrates data from different platforms and systems;

[0029] S4: Automatically generates intelligent decision-making models based on big data analysis results;

[0030] S5: Execute decision-making tasks based on the intelligent decision-making model;

[0031] S6: Optimize the decision-making model based on the feedback of decision-making effects from real-time data streams.

[0032] This invention provides an intelligent decision support system and method for big data analysis. It has the following beneficial effects:

[0033] 1. This intelligent decision support system and method for big data analysis, through a distributed architecture, enables the system to efficiently process massive amounts of data from different platforms and data sources, ensuring that the data processing process is fast and stable.

[0034] 2. This intelligent decision support system and method for big data analysis introduces machine learning and deep learning technologies, enabling the system to automatically generate and optimize decision models, greatly improving the intelligence level of decision-making, reducing human intervention, and ensuring the scientific nature and accuracy of decisions.

[0035] 3. This is an intelligent decision support system and method for big data analysis. The system can efficiently integrate data from multiple data platforms, eliminate the problem of data silos, and ensure the comprehensiveness of decision-making and the accuracy of information.

[0036] 4. This is an intelligent decision support system and method for big data analysis. The system has real-time data processing and feedback capabilities and can automatically adjust the decision model according to data changes and decision effects, thereby improving the flexibility and adaptability of decision-making.

[0037] 5. This is an intelligent decision support system and method for big data analysis. The system adopts a modular design, has strong scalability and adaptability, and can be flexibly adjusted and configured according to different business scenarios and needs to achieve personalized decision support. Attached Figure Description

[0038] Figure 1 This is a system framework diagram of the present invention. Detailed Implementation

[0039] Example 1

[0040] Please see Figure 1 The aforementioned intelligent decision support system for big data analysis includes:

[0041] The data acquisition module is used to collect raw data from multiple data sources;

[0042] The data processing module is used to clean, organize, and preprocess the collected data;

[0043] The data integration module is used to uniformly integrate data from different platforms and systems;

[0044] The intelligent decision model generation module is used to automatically generate decision models based on big data analysis results;

[0045] The decision execution module is used to execute decision-making tasks based on the intelligent decision-making model.

[0046] The real-time feedback module is used to optimize the decision-making model based on the feedback of real-time data streams.

[0047] The data acquisition module includes multiple data interfaces, enabling it to connect to different types of data sources and automatically acquire data.

[0048] The data processing module adopts a distributed architecture, which can efficiently process large-scale data and perform parallel computing.

[0049] The intelligent decision-making model generation module includes machine learning algorithms that can train and optimize decision-making models based on historical and real-time data.

[0050] The intelligent decision model generation module also includes deep learning algorithms, which can automatically extract features from data and generate more accurate decision models.

[0051] The real-time feedback module automatically adjusts the decision-making model and optimizes the decision-making process based on changes in the data stream.

[0052] The decision execution module can select and execute appropriate decision strategies based on different business scenarios.

[0053] The data integration module uses data lake technology, which enables centralized management and analysis of data from different platforms.

[0054] The system of this invention has good scalability and can flexibly configure and adjust system modules according to different business needs.

[0055] A method for intelligent decision support for big data analytics, comprising the aforementioned intelligent decision support system for big data analytics, specifically includes the following steps:

[0056] S1: Collect raw data from multiple data sources;

[0057] S2: Clean, organize, and preprocess the collected data;

[0058] S3: Unifies and integrates data from different platforms and systems;

[0059] S4: Automatically generates intelligent decision-making models based on big data analysis results;

[0060] S5: Execute decision-making tasks based on the intelligent decision-making model;

[0061] S6: Optimize the decision-making model based on the feedback of decision-making effects from real-time data streams.

[0062] Specifically, the goal will be achieved through the following technical means:

[0063] 1. Big Data Processing and Integration: This invention adopts a distributed data processing architecture, which can efficiently process large-scale data from multiple data sources and achieve efficient data integration and cleaning.

[0064] 2. Intelligent Decision Model Generation and Optimization: This invention introduces intelligent algorithms such as machine learning and deep learning to automatically generate and optimize decision models based on big data analysis results, supporting real-time dynamic adjustments to adapt to different business scenarios and decision-making needs.

[0065] 3. Cross-platform data integration: This invention supports the integration of cross-platform and multi-data source data, enabling unified analysis of data from different data platforms and systems, eliminating data silos, and improving the accuracy and comprehensiveness of decision-making.

[0066] 4. Real-time analysis and feedback: The system has real-time data processing and feedback capabilities, enabling rapid analysis based on real-time data streams and real-time updates to the decision-making model, providing effective support for rapid decision-making.

[0067] 5. Scalability and Adaptability: This invention adopts a modular design, and the system has good scalability and adaptability, which can flexibly adjust and configure the decision model and data processing flow according to different business needs.

[0068] Working principle of the invention:

[0069] This invention, based on a distributed architecture and combining big data analytics, machine learning, and deep learning technologies, constructs an intelligent decision support system. Its specific working principles include the following aspects:

[0070] Data Acquisition and Processing: The system automatically collects raw data from multiple data sources (such as databases, APIs, sensors, etc.) through the data acquisition module and performs preprocessing. This process includes deduplication, data cleaning, and format conversion to ensure the quality and accuracy of the input data.

[0071] Data integration and unified management: Collected data often comes from multiple platforms and different systems, resulting in issues such as inconsistent formats and scattered storage locations. The data integration module uses data lake technology to unify and integrate data from different data sources, providing a consistent data foundation for subsequent analysis.

[0072] Intelligent Decision Model Generation and Optimization: After data processing, the system uses machine learning and deep learning algorithms to analyze patterns in the data and automatically generate decision models. The generation of these models not only relies on historical data but can also be dynamically adjusted and optimized based on real-time data streams to adapt to constantly changing business needs and environmental conditions.

[0073] Decision Execution and Feedback: During the decision execution phase, the system selects the optimal decision strategy based on the generated decision model and executes the relevant tasks. Simultaneously, the real-time feedback module provides feedback and adjustments to the decision model based on the post-execution results, ensuring that the decision model remains consistent with actual business needs.

[0074] Example 2

[0075] Building upon Example 1, this example presents a big data intelligent decision support system based on e-commerce data. This example is based on an e-commerce platform scenario, which generates a large amount of user behavior data, sales data, inventory data, etc., daily. The intelligent decision support system of this invention is applied to this e-commerce platform, aiming to help the platform optimize business decisions such as inventory management, pricing strategies, and personalized user recommendations through real-time data analysis and intelligent decision support.

[0076] Implementation steps

[0077] 1. Data Collection

[0078] E-commerce platforms collect raw data from multiple data sources through data acquisition modules, including but not limited to:

[0079] User behavior data (browsing history, purchase history, review data)

[0080] Sales data (product sales volume, promotional activity data)

[0081] Inventory data (inventory levels, product purchase data)

[0082] User information data (basic user information, purchase preferences, consumption history)

[0083] For example, the following raw data was collected from data within one month:

[0084] User behavior data: browsing and purchase records of 1 million users, with an average of 20 browsing records per user, and each record includes product ID, browsing time, browsing category, etc.

[0085] Sales data: 300,000 transaction records, each transaction includes product ID, transaction price, buyer information, etc.

[0086] Inventory data: Includes inventory levels, purchase times, and prices for 1,000 products.

[0087] 2. Data Processing and Preprocessing

[0088] The data processing module cleans and preprocesses the collected data, removing invalid data (such as invalid browsing records and duplicate transaction records), standardizing the formats of different data sources, and ensuring data consistency.

[0089] Missing values ​​are filled in, and missing purchase records are estimated and filled in using the user's historical data.

[0090] For example, to address missing values ​​in inventory data, the system predicts and fills in the missing inventory data by analyzing trends in historical purchasing and sales data.

[0091] 3. Data Integration

[0092] The data integration module manages data from different platforms in a unified manner, forming a unified big data repository.

[0093] For example, by correlating user behavior data with sales data on e-commerce platforms, we can identify user purchasing patterns and link this information with inventory data to form a mapping relationship between user purchasing behavior and inventory changes.

[0094] 4. Intelligent Decision Model Generation and Optimization

[0095] The system uses an intelligent decision model generation module and machine learning algorithms, such as decision trees, random forests, and support vector machines (SVM), to analyze cleaned data and generate intelligent decision models based on user behavior and inventory status.

[0096] Based on historical data, the system can identify seasonal sales patterns for specific products. For example, sales of winter coats peak between November and January each year, and the system will automatically adjust its inventory strategy accordingly.

[0097] Based on user behavior data and historical purchase records, the system can recommend personalized products to different types of user groups, improving the accuracy of recommendations.

[0098] For example, a decision tree model generated by the system from training data shows that for users who like sports, the system should recommend sports shoes, sports equipment, and other products; for users who frequently buy home furnishings, it should recommend home furnishings, decorations, and other products.

[0099] 5. Decision Implementation and Feedback

[0100] The decision-making and execution module uses an intelligent decision-making model to adjust product recommendations, inventory management, and pricing strategies in real time. For example, the system will automatically adjust product prices or inventory levels based on predicted sales volume. If the predicted sales volume for a certain product is too low, the system can automatically lower its price or reduce its inventory.

[0101] The system can also adjust pricing strategies during promotional events (such as Singles' Day) to maximize profits and maintain inventory balance.

[0102] 6. Real-time feedback and optimization

[0103] The real-time feedback module automatically adjusts the decision-making model by monitoring real-time data streams. For example, when the actual sales of a product exceed expectations, the system will promptly adjust the model and include it in the list of key products for promotion; if there is excessive inventory buildup, the system will suggest promotions or price reductions.

[0104] Real-time feedback example: The projected sales volume for a certain type of athletic shoe was 500 units, but the actual sales volume was 700 units. The system will automatically update the model and increase the recommendation frequency for this product.

[0105] Through the above steps, this embodiment can achieve the following effects in e-commerce platforms:

[0106] Inventory optimization: Intelligent decision-making models can predict product sales trends and dynamically adjust inventory levels to reduce inventory backlogs or stockouts. For example, the system can increase inventory of popular products in advance during peak seasons and reduce inventory of low-demand products during off-seasons.

[0107] Precision marketing and personalized recommendations: By analyzing user behavior through machine learning, the system can provide personalized product recommendations for each user, improving purchase conversion rates. For example, if the system discovers that user A likes to buy sporting goods, it will recommend more related products, such as athletic shoes and sports equipment.

[0108] Dynamic pricing: Based on market demand and competition, the system can adjust product pricing strategies in real time to optimize profits and market share. For example, when a product's sales are lower than expected, the system will automatically suggest a price reduction.

[0109] Example 3

[0110] Based on Example 1, this example is an intelligent decision support system based on medical and health data. This example applies the intelligent decision support system to the medical and health management of hospitals, aiming to help hospitals make decisions such as patient health risk assessment, treatment plan recommendation, and resource optimization.

[0111] Implementation steps

[0112] 1. Data Collection

[0113] The system collects various medical and health data within the hospital through a data acquisition module. Data sources include, but are not limited to:

[0114] Patient basic information (such as age, gender, and past medical history)

[0115] Clinical examination data (such as physiological data like blood glucose, blood pressure, and electrocardiogram)

[0116] Doctor's medical records (such as disease diagnosis, treatment plan, medication prescriptions, etc.)

[0117] Patient hospitalization records (duration of hospitalization, surgical records, etc.)

[0118] For example, the following data was collected from medical data within one month:

[0119] Patient basic information: Basic information of 5,000 patients, including age, gender, disease type, etc.

[0120] Clinical examination data: 500,000 clinical data records, including patients' physical examination results (blood sugar, blood pressure, etc.).

[0121] Medical records: 10,000 doctor's medical records, including diagnoses and treatment plans.

[0122] 2. Data Processing and Preprocessing

[0123] The system preprocesses the collected medical data, removing outliers (such as incorrect blood pressure records), standardizing the examination data formats of different hospitals, and filling in missing data (such as supplementing missing medical history information through relevant rules).

[0124] For example, for a patient's blood glucose data, the system uses historical data models to predict and fill in the missing records.

[0125] 3. Data Integration

[0126] The data integration module manages data from different sources in a unified manner, forming a comprehensive patient health data repository.

[0127] For example, linking a patient's clinical examination data, hospitalization records, and doctor's treatment records can create a complete patient health record to support subsequent decision-making.

[0128] 4. Intelligent Decision Model Generation and Optimization

[0129] The system uses machine learning and deep learning algorithms, combined with patient health data, to generate intelligent decision-making models, primarily for:

[0130] Health risk assessment: Analyzing a patient's health status and assessing their risk of developing certain diseases.

[0131] Recommended treatment plan: Based on the patient's medical history and clinical examination results, a personalized treatment plan will be recommended.

[0132] For example, the system analyzes patients' health data using a decision tree algorithm, discovers the correlation between smoking history and hypertension, and feeds this information back to doctors to provide patients with personalized health management plans.

[0133] 5. Decision Implementation and Feedback

[0134] Based on the results of the intelligent decision-making model, the decision execution module can provide doctors with health risk assessments and treatment recommendations. For example, the system recommends appropriate antihypertensive medications for a 50-year-old patient with a history of hypertension and reminds the patient to have regular blood glucose checks.

[0135] The real-time feedback module provides feedback based on the patient's treatment progress. If the patient's blood pressure is not effectively controlled, the system will automatically adjust the treatment plan and recommend more potent medications or other treatment methods.

[0136] 6. Real-time feedback and optimization

[0137] The system updates its decision-making model in real time based on changes in the patient's health data. For example, when a patient's blood sugar level rises, the system will automatically remind the patient to take more effective control measures, such as adjusting their diet or increasing exercise.

[0138] The system can also recommend personalized health management plans based on different patient groups, thereby improving patient treatment outcomes and satisfaction.

[0139] Through the above steps, this embodiment can achieve the following effects:

[0140] Health risk assessment: Through in-depth analysis of patient data, the system can accurately assess the patient's health risk, helping doctors to identify high-risk patients in advance and carry out early intervention.

[0141] Personalized treatment plan recommendation: The system automatically recommends personalized treatment plans based on the patient's specific condition, improving treatment effectiveness and reducing the waste of medical resources.

[0142] Dynamic health management: Through a real-time feedback mechanism, the system can dynamically adjust the health management plan based on changes in the patient's treatment effects and health data, thereby optimizing the patient's treatment outcomes.

[0143] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. An intelligent decision support system for big data analysis, characterized in that, include: The data acquisition module is used to collect raw data from multiple data sources; The data processing module is used to clean, organize, and preprocess the collected data; The data integration module is used to uniformly integrate data from different platforms and systems; The intelligent decision model generation module is used to automatically generate decision models based on big data analysis results; The decision execution module is used to execute decision-making tasks based on the intelligent decision-making model. The real-time feedback module is used to optimize the decision-making model based on the feedback of real-time data streams.

2. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The data acquisition module includes multiple data interfaces, enabling it to connect to different types of data sources and automatically acquire data.

3. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The data processing module adopts a distributed architecture, which can efficiently process large-scale data and perform parallel computing.

4. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The intelligent decision-making model generation module includes machine learning algorithms that can train and optimize decision-making models based on historical and real-time data.

5. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The intelligent decision model generation module also includes deep learning algorithms, which can automatically extract features from data and generate more accurate decision models.

6. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The real-time feedback module automatically adjusts the decision-making model and optimizes the decision-making process based on changes in the data stream.

7. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The decision execution module can select and execute appropriate decision strategies based on different business scenarios.

8. The intelligent decision support system for big data analysis according to claim 1, characterized in that: The data integration module employs data lake technology, enabling centralized management and analysis of data from different platforms.

9. The intelligent decision support system for big data analysis according to claim 1, characterized in that, The system has good scalability and can flexibly configure and adjust system modules according to different business needs.

10. An intelligent decision support method for big data analysis, characterized in that, The intelligent decision support system for big data analysis as described in any one of claims 1-9 specifically includes the following steps: S1: Collect raw data from multiple data sources; S2: Clean, organize, and preprocess the collected data; S3: Unifies and integrates data from different platforms and systems; S4: Automatically generates intelligent decision-making models based on big data analysis results; S5: Execute decision-making tasks based on the intelligent decision-making model; S6: Optimize the decision-making model based on the feedback of decision-making effects from real-time data streams.

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