Artificial intelligence driven intelligent forecasting and optimization decision system

Abstract

The application discloses an artificial intelligence driven intelligent prediction and optimization decision system.In the application, the inside of the creative thinking module is internally provided with a knowledge fusion submodule, an association discovery submodule, a pattern recognition submodule and a creative generation submodule; the knowledge fusion submodule ensures that the decision is based on a comprehensive and diversified knowledge base, avoiding the limitations of a single information source; the association discovery submodule further excavates potential connections among data, providing unexpected insights for decision-making; the pattern recognition submodule further refines valuable information and trends from complex data, enhancing the reliability of prediction; and the creative generation submodule injects creative thinking into the core decision-making process, not only providing traditional solutions, but also generating novel and unique strategies.The synergistic effect of these modules enables the system to make more accurate and forward-looking decisions in complex and changing environments, effectively improving the competitiveness and response capability of enterprises.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent optimization decision, specifically an artificial intelligence driven intelligent prediction and optimization decision system. BACKGROUND

[0002] With the rapid development of information technology, data has become an important resource in various industries. The widespread application of Internet, Internet of Things, big data, cloud computing and other technologies has made the data scale grow exponentially, and the data types have become increasingly diversified, including structured data, semi-structured data and unstructured data. However, in the face of massive and complex data, how to effectively predict and make decisions has become a major challenge. Intelligent prediction and optimization decision system is an advanced system that integrates artificial intelligence, big data analysis, operations research and other technologies. It can accurately predict future trends and potential problems through deep learning of a large amount of historical and real-time data, and make optimization decisions on this basis. The system is widely used in finance, transportation, energy, manufacturing and other fields, and through intelligent algorithm models, it helps enterprises and organizations achieve efficient allocation of resources, effective control of risks and intelligent upgrading of decisions, thereby significantly improving operational efficiency, reducing costs and enhancing market competitiveness. It is an important tool for promoting the informatization and intelligent development of modern society.

[0003] However, traditional decision-making systems often lack innovation, relying on fixed algorithms and patterns, making it difficult to cope with complex and changing environments, resulting in single and lagging decision-making results. Secondly, these systems usually do not have self-adaptive ability, and cannot adjust and optimize themselves according to new data or environmental changes, thereby affecting the accuracy and timeliness of the decisions. SUMMARY

[0004] The purpose of the present application is to solve the above-mentioned problems, and to provide an artificial intelligence driven intelligent prediction and optimization decision system.

[0005] The technical solution adopted by the present application is as follows: an artificial intelligence driven intelligent prediction and optimization decision system, the system comprising: a data acquisition and processing module, an intelligent prediction module, an optimization decision module, a creative thinking module, a learning and self-adaptive module, a user interaction module and a system security and monitoring module.

[0006] The creative thinking module is internally provided with a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module and an idea generation sub-module.

[0007] The output end of the data acquisition and processing module is connected to the input end of the intelligent prediction module;

[0008] The output end of the intelligent prediction module is connected to the input end of the optimization decision module;

[0009] The output end of the optimization decision module is connected to the input end of the creative thinking module;

[0010] The output end of the creative thinking module is connected back to the optimization decision module to perfect the decision scheme;

[0011] The input end of the learning and adaptation module is connected to the intelligent prediction module and the optimization decision module, and the output end of the learning and adaptation module is connected to the data acquisition and processing module and the intelligent prediction module;

[0012] The input end of the user interaction module is connected to the user, and the output end is connected to the learning and adaptation module;

[0013] The monitoring end of the system security and monitoring module is connected to the key nodes of all other modules, and the control end implements security measures and system optimization.

[0014] In a preferred embodiment, the intelligent prediction module includes a data preprocessing unit, a model training unit, a prediction engine, and a result output unit.

[0015] In a preferred embodiment, the optimization decision module includes a decision model library, a decision engine, a scheme evaluator, and a decision output unit; the decision model library stores various decision models such as linear programming, integer programming, multi-objective optimization, etc.; the decision engine selects the appropriate model for decision calculation according to the prediction results and decision objectives; the scheme evaluator evaluates the generated decision scheme, considering factors such as cost, benefit, risk, etc.; the decision output unit outputs the optimal decision scheme in an intuitive form for user reference; the optimization decision module integrates various decision techniques and methods to realize the scientific and intelligent decision-making.

[0016] In a preferred embodiment, the knowledge fusion algorithm of the knowledge fusion sub-module has the following calculation formula:

[0017] F(K1,K2)=Kf;

[0018] Where K1 and K2 represent two knowledge bases to be fused, Kf represents the fused knowledge base, and the calculation formula is:

[0019] ;

[0020] ;

[0021] Where X and Y represent item sets, T_XY represents the transaction set containing both X and Y, T represents the set of all transactions, T_X represents the transaction set containing X; Support represents the support, which represents the frequency of the simultaneous occurrence of item sets X and Y; Confidence represents the confidence, which represents the probability of containing Y in the transaction containing X.

[0022] In a preferred embodiment, the association discovery submodule is responsible for mining potential association relationships in the fused knowledge base, providing new perspectives and ideas for creative thinking.

[0023] In a preferred embodiment, the repetitive pattern recognition calculation formula of the pattern recognition submodule is:

[0024] ;

[0025] wherein C_i represents the i-th cluster center, S_i represents the sample set belonging to the i-th cluster, u_i represents the mean value of the i-th cluster, represents the square of the Euclidean distance between the data point x and the i-th cluster center μi.

[0026] In a preferred embodiment, the creative generation submodule includes a creative inspiration unit, a thinking simulator, a creative evaluator, and a creative optimizer.

[0027] In a preferred embodiment, the learning and adaptation module includes a data monitor, a model updater, a performance evaluator, and a feedback learner.

[0028] The user interaction module includes a user interface, an instruction parser, a result presenter, and a feedback collector.

[0029] In a preferred embodiment, the system security and monitoring module includes a security protection unit, a system monitoring unit, an anomaly detector, and an emergency responder.

[0030] In summary, due to the adoption of the above technical solutions, the present application has the following advantages:

[0031] 1. In the present application, the knowledge fusion submodule ensures that the decision is based on a comprehensive and diverse knowledge base, avoiding the limitations of a single information source. The association discovery submodule deeply mines the potential relationships between data, providing unexpected insights for decision-making. The pattern recognition submodule further refines valuable information and trends from complex data, enhancing the reliability of predictions. The creative generation submodule injects creative thinking into the core decision-making process, not only providing traditional solutions but also generating novel and unique strategies. The synergistic effect of these modules enables the system to make more accurate and forward-looking decisions in complex and changing environments, effectively improving the competitiveness and response capabilities of enterprises.

[0032] 2、In the present application, the learning and adaptive module enables the system to optimize itself in the process of continuously receiving new data and analyzing results, continuously improving the quality of prediction and decision-making. The user interaction module, through intuitive and friendly interface design, enables users to easily understand complex data and decision-making process, while providing customized interactive experience to meet the needs of different users. The system security and monitoring module ensures the stable operation and data security of the entire system, providing reliable use guarantee for users. In summary, the artificial intelligence driven intelligent prediction and optimization decision-making system not only improves the innovation and accuracy of decision-making, but also enhances the adaptability and user satisfaction of the system, providing a powerful decision-making support tool for various organizations and individuals. BRIEF DESCRIPTION OF DRAWINGS

[0033] Fig. 1 is the overall system block diagram of the present application;

[0034] Fig. 2 is the system block diagram of the creative thinking module in the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0036] Example:

[0037] Referring to Figs. 1-2 , the artificial intelligence driven intelligent prediction and optimization decision-making system, the system comprises: a data acquisition and processing module, an intelligent prediction module, an optimization decision-making module, a creative thinking module, a learning and adaptive module, a user interaction module and a system security and monitoring module;

[0038] The creative thinking module is internally provided with a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module and an idea generation sub-module;

[0039] The output end of the data acquisition and processing module is connected to the input end of the intelligent prediction module;

[0040] The output end of the intelligent prediction module is connected to the input end of the optimization decision-making module;

[0041] The output end of the optimization decision-making module is connected to the input end of the creative thinking module;

[0042] The output end of the creative thinking module is connected back to the optimization decision-making module to perfect the decision-making scheme;

[0043] The input end of the learning and adaptation module is connected to the intelligent prediction module and the optimization decision module, and the output end of the learning and adaptation module is connected to the data acquisition and processing module and the intelligent prediction module;

[0044] The input end of the user interaction module is connected to the user, and the output end is connected to the learning and adaptation module;

[0045] The monitoring end of the system security and monitoring module is connected to the key nodes of all other modules, and the control end implements security measures and system optimization.

[0046] The data acquisition and processing module is the front-end module of the system, responsible for collecting raw data from diverse data sources; its composition includes data collectors, data cleaners, data converters, and feature extractors; data collectors obtain structured, semi-structured, and unstructured data through API interfaces, database connections, file reading, etc.; data cleaners handle collected data for de-duplication, noise reduction, and missing value filling to ensure data quality; data converters convert cleaned data into a unified format for subsequent analysis; feature extractors extract key features from data to provide effective input for the intelligent prediction module; the entire module achieves efficient data collection and processing through automated processes, providing a reliable data foundation for subsequent modules.

[0047] The intelligent prediction module is responsible for in-depth analysis of processed data to generate prediction results; its composition includes data preprocessing units, model training units, prediction engines, and result output units; data preprocessing units standardize and normalize input data to prepare for model training; model training units train prediction models using machine learning, deep learning, and other algorithms, such as regression analysis, classification algorithms, and time series prediction; prediction engines analyze new data based on trained models to generate prediction results; result output units output prediction results in a structured format for the optimization decision module; the intelligent prediction module improves prediction accuracy and reliability by continuously iterating and optimizing models.

[0048] The optimization decision module generates optimal decision schemes based on the output of the intelligent prediction module; its composition includes a decision model library, a decision engine, a scheme evaluator, and a decision output unit; the decision model library stores various decision models such as linear programming, integer programming, and multi-objective optimization; the decision engine selects appropriate models for decision calculation based on prediction results and decision objectives; the scheme evaluator evaluates generated decision schemes considering cost, benefit, risk, and other factors; the decision output unit outputs optimal decision schemes in an intuitive format for user reference; the optimization decision module integrates various decision techniques and methods to achieve scientific and intelligent decision-making.

[0049] The knowledge fusion sub-module is responsible for integrating knowledge from different sources and different formats to form a unified knowledge representation and provide a rich knowledge base for creative thinking. The calculation formula of the knowledge fusion algorithm is:

[0050] F(K1,K2)=Kf;

[0051] where K1 and K2 represent two knowledge bases to be fused, and Kf represents the fused knowledge base. The calculation formula is:

[0052] ;

[0053] ;

[0054] where X and Y represent item sets, T_XY represents the set of transactions that contain both X and Y, T represents the set of all transactions, and T_X represents the set of transactions that contain X. Support represents the support, which is the frequency of the simultaneous occurrence of item sets X and Y. Confidence represents the confidence, which is the probability of containing Y in the transactions that contain X.

[0055] The association discovery sub-module is responsible for mining potential association relationships in the fused knowledge base to provide new perspectives and ideas for creative thinking.

[0056] The pattern recognition sub-module uses the K-means clustering algorithm to discover repeatedly occurring patterns or structures in the data, providing the ability to recognize and utilize these patterns for creative thinking. The calculation formula is:

[0057] ;

[0058] where C_i represents the i-th cluster center, S_i represents the sample set belonging to the i-th cluster, u_i represents the mean of the i-th cluster, and represents the square of the Euclidean distance between the data point x and the i-th cluster center μi.

[0059] The creative generation submodule integrates various creative thinking techniques and algorithms to achieve efficient creative generation. Its internal structure includes a creative inspiration unit, a thinking simulator, a creative evaluator, and a creative optimizer. The creative inspiration unit stimulates initial creativity through means such as randomness introduction and scenario simulation, providing a starting point for the thinking process. The thinking simulator simulates human creative thinking processes such as analogical thinking and associative thinking using artificial intelligence techniques, deeply expanding and transforming the initial creativity. The creative evaluator performs preliminary screening and evaluation of the generated creativity, judging its feasibility and value based on pre-set standards and indicators. The creative optimizer further refines and optimizes the screened creativity, improving its implementation possibility and effectiveness. Through the coordinated work of these units, the entire creative generation submodule continuously iterates and eventually outputs innovative and practical creative solutions, providing unique decision support for the intelligent prediction and optimization decision system.

[0060] The learning and adaptation module is responsible for continuous learning and optimizing system performance. Its components include a data monitor, a model updater, a performance evaluator, and a feedback learner. The data monitor monitors data changes and model performance in real time. The model updater updates and optimizes prediction models and decision models based on new data and feedback information. The performance evaluator periodically evaluates system performance and identifies potential problems. The feedback learner collects user feedback and actual effect data for improving models and algorithms. The learning and adaptation module ensures that the system always adapts to environmental changes and user needs through a closed-loop learning mechanism.

[0061] The user interaction module is the interface between the system and the user, responsible for receiving user instructions and displaying decision results. Its components include a user interface, an instruction parser, a result presenter, and a feedback collector. The user interface provides an intuitive and friendly operation interface, supporting multiple interaction methods. The instruction parser parses user input instructions and converts them into a format that the system can understand. The result presenter presents decision results to users in the form of charts, reports, etc. The feedback collector collects user feedback on decision results. The user interaction module improves user experience and satisfaction through humanized design.

[0062] The system security and monitoring module is responsible for ensuring the stable operation and data security of the system. Its components include a security protection unit, a system monitoring unit, an anomaly detector, and an emergency responder. The security protection unit implements access control, data encryption, firewall, and other security measures. The system monitoring unit monitors system running status in real time, such as memory usage, CPU load, etc. The anomaly detector identifies potential security threats and system anomalies. The emergency responder starts emergency plans when problems are found to minimize losses. The system security and monitoring module provides solid security protection for the entire system through multiple security mechanisms and monitoring systems.

[0063] From the above, it can be seen that:

[0064] In the present application, the knowledge fusion sub-module ensures that the decision is based on a comprehensive and diverse knowledge base, avoiding the limitations of a single information source; the association discovery sub-module deeply excavates the potential relationship between data, providing unexpected insights for decision-making; the pattern recognition sub-module further refines valuable information and trends from complex data, enhancing the reliability of prediction. And the creative generation sub-module injects creative thinking into the core decision-making process, not only providing traditional solutions, but also generating novel and unique strategies. The synergistic effect of these modules enables the system to make more accurate and forward-looking decisions in complex and changing environments, effectively improving the competitiveness and response ability of enterprises.

[0065] In the present application, the learning and adaptive module enables the system to optimize itself in the process of continuously receiving new data and analyzing results, continuously improving the quality of prediction and decision-making. The user interaction module provides an intuitive and friendly interface design, allowing users to easily understand complex data and decision-making processes, while providing customized interactive experiences to meet the needs of different users. The system security and monitoring module ensures the stable operation and data security of the entire system, providing reliable use protection for users. In summary, the artificial intelligence-driven intelligent prediction and optimization decision-making system not only improves the innovation and accuracy of decision-making, but also enhances the adaptability and user satisfaction of the system, providing a powerful decision-making support tool for various organizations and individuals.

[0066] It should be noted that in this paper, relationship terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0067] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An artificial intelligence driven intelligent forecasting and optimization decision system, characterized in that: The system comprises a data collection processing module, an intelligent prediction module, an optimization decision module, a creative thinking module, a learning and self-adaptive module, a user interaction module and a system security and monitoring module. The creative thinking module is internally provided with a knowledge fusion submodule, an association discovery submodule, a pattern recognition submodule and an idea generation submodule. The output end of the data collection processing module is connected to the input end of the intelligent prediction module. The output end of the intelligent prediction module is connected to the input end of the optimization decision module. The output end of the optimization decision module is connected to the input end of the creative thinking module. The output end of the creative thinking module is connected back to the optimization decision module to perfect the decision scheme. The input end of the learning and self-adaptive module is connected to the intelligent prediction module and the optimization decision module, and the output end of the learning and self-adaptive module is connected to the data collection processing module and the intelligent prediction module. The input end of the user interaction module is connected to the user, and the output end is connected to the learning and self-adaptive module. The monitoring end of the system security and monitoring module is connected to the key nodes of all other modules, and the control end implements safety measures and system optimization.

2. The artificial intelligence driven, intelligent forecasting and optimized decision system as claimed in claim 1 wherein: The data collection processing module comprises a data collector, a data cleaner, a data converter and a feature extractor; the data collector acquires structured, semi-structured and unstructured data through an API interface, a database connection and a file reading mode; the data cleaner performs de-duplication, de-noising and missing value filling processing on the collected data to ensure data quality; The data converter converts the cleaned data into a unified format for subsequent analysis; the feature extractor extracts key features from the data to provide effective input for the intelligent prediction module.

3. The artificial intelligence driven, intelligent forecasting and optimized decision system of claim 1, wherein: The intelligent prediction module comprises a data preprocessing unit, a model training unit, a prediction engine and a result output unit.

4. The artificial intelligence driven, intelligent forecasting and optimized decision system of claim 1, wherein: The optimization decision module comprises a decision model library, a decision engine, a scheme evaluator and a decision output unit; the decision model library stores various decision models, including linear programming, integer programming and multi-objective optimization; the decision engine selects an appropriate model for decision calculation according to the prediction results and decision objectives; the scheme evaluator evaluates the generated decision scheme, considering factors such as cost, benefit and risk; the decision output unit outputs the optimal decision scheme in an intuitive form for user reference; the optimization decision module integrates various decision techniques and methods to realize the scientific and intelligent decision-making.

5. The artificial intelligence driven, intelligent forecasting and optimized decision system as claimed in claim 1, wherein: The calculation formula of the knowledge fusion algorithm of the knowledge fusion submodule is: F(K1,K2)=Kf; Where K1 and K2 represent two knowledge bases to be fused, and Kf represents the fused knowledge base, and the calculation formula is: ； ； Where X and Y represent item sets, T_XY represents a transaction set containing both X and Y, T represents a set of all transactions, and T_X represents a transaction set containing X; Support represents support, which represents the frequency of the simultaneous occurrence of item sets X and Y; Confidence represents confidence, which represents the probability of containing Y in the transaction containing X.

6. The artificial intelligence driven, intelligent forecasting and optimized decision system as claimed in claim 1 wherein: The association discovery submodule is responsible for mining potential association relationships in the fused knowledge base, providing new perspectives and ideas for creative thinking.

7. The artificial intelligence driven, intelligent forecasting and optimized decision system of claim 1, wherein: The repetitive pattern recognition calculation formula of the pattern recognition submodule is: ; where C_i denotes the i-th cluster center, S_i denotes the set of samples belonging to the i-th cluster, u_i denotes the mean of the i-th cluster, denotes the square of the Euclidean distance between the data point x and the i-th cluster center μi.

8. The artificial intelligence driven, intelligent forecasting and optimized decision system of claim 1, wherein: The creative generation submodule includes a creative inspiration unit, a thinking simulator, a creative evaluator, and a creative optimizer.

9. The artificial intelligence driven, intelligent forecasting and optimized decision system as claimed in claim 1, wherein: The learning and adaptation module includes a data monitor, a model updater, a performance evaluator, and a feedback learner. The user interaction module includes a user interface, an instruction parser, a result presenter, and a feedback collector.

10. The artificial intelligence driven, intelligent forecasting and optimized decision system of claim 1, wherein: The system security and monitoring module includes a security protection unit, a system monitoring unit, an anomaly detector, and an emergency responder.

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