Intelligent prediction and optimization decision-making system driven by artificial intelligence

Abstract

The invention discloses an intelligent prediction and optimization decision system driven by artificial intelligence. According to the invention, a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module and a creative generation sub-module are arranged in a creative thinking module; the knowledge fusion sub-module ensures that the decision is based on a comprehensive and multivariate knowledge basis, and the limitation of a single information source is avoided; the association discovery sub-module deeply mines potential relationships among the data and provides unexpected insight for decision making; the mode recognition sub-module further extracts valuable information and trend from complex data, and the prediction reliability is enhanced. The creative generation sub-module injects creative thinking into the core decision-making process, not only provides a traditional solution, but also can generate a novel and unique strategy. Through the synergistic effect of the modules, the system can make a more accurate and prospective decision in a complex and changeable environment, and the competitiveness and coping ability of an enterprise are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent optimization decision-making, and specifically relates to an intelligent prediction and optimization decision-making system driven by artificial intelligence. Background Art

[0002] With the rapid development of information technology, data has become an important resource in all walks of life. The wide application of technologies such as the Internet, Internet of Things, big data, and cloud computing has led to an exponential growth in the data scale and an increasing diversification of data types, including structured data, semi-structured data, and unstructured data. However, in the face of massive and complex data, how to effectively make predictions and decisions has become a major challenge. An intelligent prediction and optimization decision-making system is an advanced system that integrates technologies such as artificial intelligence, big data analysis, and operations research. 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 based on this. This system is widely used in fields such as finance, transportation, energy, and manufacturing. 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, rely on fixed algorithms and patterns, and are 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 capabilities and cannot self-adjust and optimize according to new data or environmental changes, thus affecting the accuracy and timeliness of decisions. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent prediction and optimization decision-making system driven by artificial intelligence in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: An intelligent prediction and optimization decision-making system driven by artificial intelligence, the system includes: 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 internal of the creative thinking module is provided with a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module, and a creative 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-making 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-making module to improve the decision-making plan;

[0011] The input end of the learning and adaptation module is connected to the intelligent prediction module and the optimization decision-making 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-making module includes a decision model library, a decision engine, a solution 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 a suitable model for decision calculation according to the prediction result and the decision-making goal; the solution evaluator evaluates the generated decision-making plan, considering various factors such as cost, benefit, and risk; the decision output unit outputs the optimal decision-making plan in an intuitive form for the user to refer to; the optimization decision-making module realizes the scientific and intelligent decision-making by integrating various decision-making technologies and methods.

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

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

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

[0019]

[0020] Among them, X and Y represent item sets, T_XY represents the transaction set that contains both X and Y, T represents the set of all transactions, and T_X represents the transaction set that contains X; Support represents the support degree, indicating the frequency of the simultaneous occurrence of item sets X and Y; Confidence represents the confidence degree, indicating the probability that Y is also included in the transactions that contain X.

[0021] In a preferred embodiment, the association discovery sub-module is responsible for mining potential association relationships in the fused knowledge base, providing new perspectives and ideas for creative thinking.

[0022] In a preferred embodiment, the recurrence pattern recognition calculation formula of the pattern recognition sub-module is:

[0023]

[0024] where \(C_i\) represents the \(i\)-th cluster center, \(S_i\) represents the set of samples belonging to the \(i\)-th cluster, \(u_i\) represents the mean of the \(i\)-th cluster, and \(P(x - \mu)\) i )P 2 represents the square of the Euclidean distance between the data point \(x\) and the \(i\)-th cluster center \(\mu_i\).

[0025] In a preferred embodiment, the creative generation sub-module includes a creative stimulation unit, a thinking simulator, a creative evaluator, and a creative optimizer.

[0026] In a preferred embodiment, the learning and adaptation module includes a data monitor, a model updater, a performance evaluator, and a feedback learner;

[0027] The user interaction module includes a user interface, an instruction parser, a result displayer, and a feedback collector.

[0028] 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.

[0029] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows:

[0030] 1. In the present invention, the knowledge fusion sub-module ensures that decisions are based on a comprehensive and diverse knowledge base, avoiding the limitations of a single information source; the association discovery sub-module deeply explores the potential connections between data, providing unexpected insights for decision-making; the pattern recognition sub-module further extracts valuable information and trends from complex data, enhancing the reliability of predictions. 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 enhancing the competitiveness and response ability of enterprises.

[0031] 2. In the present invention, the learning and adaptive module enables the system to self-optimize during the process of continuously receiving new data and analysis results, continuously improving the quality of prediction and decision-making. The user interaction module, through an intuitive and friendly interface design, enables users to easily understand complex data and decision-making processes, while providing a customized interaction experience to meet the needs of different users. The system security and monitoring module ensures the stable operation of the entire system and data security, providing users with a reliable guarantee for use. In summary, this artificial intelligence-driven intelligent prediction and optimized decision-making system not only improves the innovation and accuracy of decision-making, but also enhances the system's adaptability and user satisfaction, providing a powerful decision-making support tool for various organizations and individuals. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the overall system block diagram of the present invention;

[0033] Figure 2 is the system block diagram of the creative thinking module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] Embodiment:

[0036] Referring to Figure 1-2 , an artificial intelligence-driven intelligent prediction and optimized decision-making system, the system includes: a data acquisition and processing module, an intelligent prediction module, an optimized decision-making module, a creative thinking module, a learning and adaptive module, a user interaction module, and a system security and monitoring module;

[0037] Inside the creative thinking module, there are a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module, and a creative generation sub-module;

[0038] The output end of the data acquisition and processing module is connected to the input end of the intelligent prediction module;

[0039] The output end of the intelligent prediction module is connected to the input end of the optimized decision-making module;

[0040] The output end of the optimized decision-making module is connected to the input end of the creative thinking module;

[0041] The output end of the creative thinking module is connected back to the optimized decision-making module to improve the decision-making plan;

[0042] The input end of the learning and adaptive module is connected to the intelligent prediction module and the optimization decision-making module, and the output end of the learning and adaptive module is connected to the data acquisition and processing module and the intelligent prediction module;

[0043] The input end of the user interaction module is connected to the user, and the output end is connected to the learning and adaptive module;

[0044] 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.

[0045] The data acquisition and processing module is the front-end module of the system, responsible for collecting raw data from diverse data sources; it consists of a data collector, a data cleaner, a data converter, and a feature extractor; the data collector obtains structured, semi-structured, and unstructured data through API interfaces, database connections, file reading, etc.; the data cleaner performs operations such as deduplication, denoising, and filling missing values 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; the entire module realizes the efficient acquisition and processing of data through an automated process, providing a reliable data foundation for subsequent modules.

[0046] The intelligent prediction module is responsible for in-depth analysis of the processed data to generate prediction results; it consists of a data preprocessing unit, a model training unit, a prediction engine, and a result output unit; the data preprocessing unit performs operations such as standardization and normalization on the input data to prepare for model training; the model training unit uses algorithms such as machine learning and deep learning to train prediction models, such as regression analysis, classification algorithms, time series prediction, etc.; the prediction engine analyzes new data based on the trained model to generate prediction results; the result output unit outputs the prediction results in a structured form for use by the optimization decision-making module; the intelligent prediction module improves the accuracy and reliability of predictions by continuously iterating and optimizing the model.

[0047] The optimization decision-making module generates an optimal decision-making plan based on the output of the intelligent prediction module; it consists of a decision model library, a decision engine, a plan 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 a suitable model for decision calculation based on the prediction results and decision-making goals; the plan evaluator evaluates the generated decision-making plan, considering multiple factors such as cost, benefit, and risk; the decision output unit outputs the optimal decision-making plan in an intuitive form for user reference; the optimization decision-making module realizes the scientific and intelligent decision-making by integrating various decision-making technologies and methods.

[0048] The knowledge fusion sub-module is responsible for integrating knowledge from different sources and in different formats to form a unified knowledge representation, providing a rich knowledge base for creative thinking. The calculation formula of the knowledge fusion algorithm is as follows:

[0049] F(K1,K2) = Kf;

[0050] Among them, K1 and K2 represent two knowledge bases to be fused, and Kf represents the fused knowledge base. The calculation formula is as follows:

[0051]

[0052] Among them, X and Y represent item sets, T_XY represents the transaction set that contains both X and Y, T represents the set of all transactions, and T_X represents the transaction set that contains X; Support represents the support degree, indicating the frequency of the simultaneous occurrence of item sets X and Y; Confidence represents the confidence degree, indicating the probability that Y is also included in the transactions that contain X.

[0053] The association discovery sub-module is responsible for mining potential association relationships in the fused knowledge base, providing new perspectives and ideas for creative thinking.

[0054] The pattern recognition sub-module uses the K-means clustering algorithm to discover recurring patterns or structures in the data, providing the ability to identify and utilize these patterns for creative thinking. The calculation formula is as follows:

[0055]

[0056] Among them, C_i represents the i-th clustering center, S_i represents the set of samples belonging to the i-th cluster, u_i represents the mean of the i-th cluster, and Px-μ i )P 2 represents the square of the Euclidean distance between the data point x and the i-th clustering center μi.

[0057] The creative generation sub-module integrates a variety of creative thinking techniques and algorithms to achieve efficient creative generation; its internal structure includes a creative stimulation unit, a thinking simulator, a creative evaluator, and a creative optimizer; the creative stimulation unit stimulates initial ideas through means such as randomness introduction and scenario simulation, providing a starting point for the thinking process; the thinking simulator uses artificial intelligence technology to simulate the creative thinking process of humans, such as analogical thinking and associative thinking, to deeply expand and transform the initial ideas; the creative evaluator conducts a preliminary screening and evaluation of the generated ideas, judging the feasibility and value of the ideas based on preset criteria and indicators; the creative optimizer further refines and optimizes the selected ideas to improve the possibility and effect of their implementation; through the collaborative work of these units, the entire creative generation sub-module continuously iterates, and finally outputs innovative and practical creative solutions, providing unique decision-making support for the intelligent prediction and optimization decision-making system.

[0058] The learning and adaptation module is responsible for continuous learning and optimizing the 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 the prediction model and decision model according to new data and feedback information; the performance evaluator regularly evaluates the system performance to identify potential problems; the feedback learner collects user feedback and actual effect data for improving the model and algorithm; through a closed-loop learning mechanism, the learning and adaptation module ensures that the system always adapts to environmental changes and user needs.

[0059] 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 displayer, and a feedback collector; the user interface provides an intuitive and friendly operation interface, supporting multiple interaction methods; the instruction parser parses the instructions input by the user and converts them into a format understandable by the system; the result displayer displays the decision results to the user in the form of charts, reports, etc.; the feedback collector collects the feedback opinions of the user on the decision results; through a user-friendly design, the user interaction module enhances the user's usage experience and satisfaction.

[0060] 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 security measures such as access control, data encryption, and firewalls; the system monitoring unit monitors the system operation status in real time, such as memory usage and CPU load; the anomaly detector identifies potential security threats and system anomalies; the emergency responder activates the emergency plan when problems are found to minimize losses; through a multi-level security mechanism and monitoring system, the system security and monitoring module provides a solid security guarantee for the entire system.

[0061] It can be known from the above that:

[0062] In the present invention, the knowledge fusion sub-module ensures that decisions are based on a comprehensive and diverse knowledge base, avoiding the limitations of a single information source; the association discovery sub-module delves deep into the potential connections between data, providing unexpected insights for decision-making; the pattern recognition sub-module further extracts valuable information and trends from complex data, enhancing the reliability of predictions. The creative generation sub-module injects creative thinking into the core decision-making process, providing not only 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 enhancing the competitiveness and response capabilities of enterprises.

[0063] In the present invention, the learning and adaptation module enables the system to self-optimize during the continuous reception of new data and analysis results, continuously improving the quality of predictions and decisions. The user interaction module, through an intuitive and user-friendly interface design, enables users to easily understand complex data and decision-making processes, while providing a customized interaction experience to meet the needs of different users. The system security and monitoring module ensures the stable operation of the entire system and data security, providing users with a reliable guarantee for use. In summary, this artificial intelligence-driven intelligent prediction and optimization decision-making system not only enhances the innovation and accuracy of decisions but also strengthens the self-adaptability and user satisfaction of the system, providing a powerful decision-making support tool for various organizations and individuals.

[0064] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An artificial intelligence-driven intelligent prediction and optimization decision-making system, characterized in that: The system includes: a data acquisition and processing module, an intelligent prediction module, an optimization decision-making module, a creative thinking module, a learning and adaptation module, a user interaction module, and a system security and monitoring module; Inside the creative thinking module, there are a knowledge fusion sub-module, an association discovery sub-module, a pattern recognition sub-module, and a creative generation sub-module; The output end of the data acquisition and 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-making module; The output end of the optimization decision-making 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-making module to improve the decision-making scheme; The input end of the learning and adaptation module is connected to the intelligent prediction module and the optimization decision-making 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; 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; 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.

2. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to claim 1, characterized in that: The data acquisition and processing module includes a data collector, a data cleaner, a data converter, and a feature extractor; the data collector obtains structured, semi-structured, and unstructured data through API interfaces, database connections, and file reading; the data cleaner performs operations such as deduplication, denoising, and filling missing values 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 prediction and optimization decision-making system according to claim 1, wherein: The intelligent prediction module includes a data preprocessing unit, a model training unit, a prediction engine, and a result output unit.

4. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to claim 1, characterized in that: The optimization decision-making module includes a decision model library, a decision engine, a solution 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 a suitable model for decision-making calculations based on the prediction results and decision-making goals; the solution evaluator evaluates the generated decision-making solutions, considering multiple factors such as cost, benefit, and risk; the decision output unit outputs the optimal decision-making solution in an intuitive form for users to refer to; the optimization decision-making module realizes the scientific and intelligent decision-making by integrating various decision-making technologies and methods.

5. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to claim 1, wherein: The calculation formula of the knowledge fusion algorithm of the knowledge fusion sub-module is: F(K1,K2)=Kf; where K1 and K2 represent two knowledge bases to be fused, and Kf represents the fused knowledge base. The calculation formula is: where X and Y represent item sets, T_XY represents the transaction set that contains both X and Y, T represents the set of all transactions, and T_X represents the transaction set that contains X; Support represents the support degree, indicating the frequency of the simultaneous occurrence of item sets X and Y; Confidence represents the confidence degree, indicating the probability that Y is also included in the transactions that contain X.

6. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to claim 1, characterized in that: The associated discovery sub-module 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 prediction and optimization decision-making system according to claim 1, characterized in that: The calculation formula for repeated pattern recognition of the pattern recognition sub-module is: Among them, \(C_i\) represents the \(i\)-th clustering center, \(S_i\) represents the set of samples belonging to the \(i\)-th cluster, \(u_i\) represents the mean of the \(i\)-th cluster, \(P_{x - \mu}\) i )P 2 represents the square of the Euclidean distance between the data point \(x\) and the \(i\)-th clustering center \(\mu_i\).

8. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to claim 1, characterized in that: The creative generation sub-module includes a creative inspiration unit, a thinking simulator, a creative evaluator, and a creative optimizer.

9. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to 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 displayer, and a feedback collector.

10. The artificial intelligence-driven intelligent prediction and optimization decision-making system according to 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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