Description:SDROT-AI-OV: OPTIMISING COMPLEX SYSTEMS WITH STOCHASTIC REAL-TIME AI-POWERED ALGORITHM TECHNIQUE.

FIELD OF THE INVENTION

[01] Various embodiments of the present invention relate to optimisation systems for complex systems. More particularly, the invention relates to a method and system for providing advanced Stochastic Dynamic Real-Time Optimisation Techniques with Artificial Intelligence and Optimal Value Algorithm (SDROT-AI-OV) for optimising complex systems, such as those found in transportation, logistics, supply chain management, energy management and healthcare.

BACKGROUND OF THE INVENTION
[01] Complex systems are ubiquitous in various domains, including transportation, logistics, supply chain management, energy management and healthcare. These systems are often characterised by uncertainty, randomness and non-linearity, making it challenging to optimise their performance.
[02] Traditional optimisation techniques often rely on simplifying assumptions, such as linearity or stationarity, which may not accurately capture the underlying dynamics of the complex system. Furthermore, these techniques may not be able to handle high-dimensional data or complex relationships between variables.
[03] Stochastic optimisation techniques have been proposed to address the challenges of optimising complex systems. However, these techniques often require a large amount of computational resources and time to converge to effective solutions.
[04] Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used to optimise complex systems. However, these techniques often require large amounts of data and may not be able to handle uncertain or dynamic environments.
[05] There is a need for a method and system that can optimise complex systems in real-time, using a combination of stochastic optimisation, AI and ML techniques.
[06] The existing art of optimisation techniques for complex systems has several limitations, including:
• Inability to handle high-dimensional data and complex relationships between variables
• requirement for large amounts of computational resources and time to converge to effective solutions
• Inability to handle uncertain or dynamic environments
• Limited scalability and flexibility
[07] Therefore, there exists a need for a method and system that can address the aforementioned problems in an efficient and focused manner. Accordingly, the disclosed invention enables the method and system to provide a stochastic dynamic real-time optimisation technique with artificial intelligence and optimal value algorithm (SDROT-AI-OV) for optimising complex systems.

SUMMARY OF THE INVENTION

[01] The present invention provides a novel stochastic dynamic real-time optimisation technique with artificial intelligence and optimal value algorithm (SDROT-AI-OV) for optimising complex systems. The SDROT-AI-OV algorithm combines stochastic processes, machine learning and optimisation techniques to provide a more accurate and reliable approach to optimisation.
[02] The SDROT-AI-OV algorithm consists of a data integration module to combine data from various sources, a stochastic process model to capture the uncertainty and randomness of the complex system, a machine learning algorithm to learn patterns and relationships in the data and an optimisation technique to find the optimal solution.
[03] The SDROT-AI-OV algorithm leverages artificial intelligence techniques to understand the behaviour of complex systems, adapt to changing conditions, and continuously enhance the optimisation process. By incorporating real-time data and feedback, the algorithm ensures continuous learning and improvement, providing a more accurate and reliable approach to optimisation.
[04] One or more advantages of traditional optimisation techniques are overcome and additional advantages are provided through the invention. Additional features are realised through the technique of the invention. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the invention.
BRIEF DESCRIPTION OF THE FIGURES
[01] The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, serve to further illustrate various embodiments of the stochastic dynamic real-time optimisation technique with artificial intelligence and optimal value algorithm (SDROT-AI-OV) and to explain various principles and advantages of the invention.
[02] FIG. 1 is a block diagram illustrating the overall architecture of the SDROT-AI-OV system, including the data integration module, stochastic process model, machine learning algorithm and optimisation technique.
[03] FIG. 2 is a detailed diagram illustrating the stochastic process model and machine learning algorithm used in the SDROT-AI-OV system.
[04] FIG. 3 is a flowchart illustrating the steps of the SDROT-AI-OV algorithm, including data integration, stochastic process modelling, machine learning, optimisation and feedback analysis.
[05] FIG. 4 is a graph illustrating the performance of the SDROT-AI-OV algorithm in optimising a complex system.
[06] Skilled artisans will appreciate that the elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[01] The present invention provides a novel stochastic dynamic real-time optimisation technique with artificial intelligence and optimal value algorithm (SDROT-AI-OV) for optimising complex systems.
[02] FIG. 2 illustrates a system 200 for implementing various embodiments of the SDROT-AI-OV algorithm. The system 200 comprises:
• A data integration module 202 for combining data from various sources and extracting relevant features.
• A stochastic process model 204 for capturing the uncertainty and randomness of the complex system.
• A machine learning algorithm 206 for learning patterns and relationships in the data.
• An optimisation technique 208 for finding the optimal solution based on the learned patterns and relationships.
• A feedback analysis module 210 for evaluating the effectiveness of the optimisation and updating the stochastic process model and machine learning algorithm accordingly.
[03] The data integration module 202 integrates data from various sources, extracts relevant features, and prepares the data for analysis.

[04] The stochastic process model 204 captures the uncertainty and randomness of the complex system using a stochastic differential equation (SDE):

dX(t) = µ(X(t), t)dt + s(X(t), t)dW(t)
where µ(X(t), t) is the drift term, s(X(t), t) is the diffusion term, and W(t) is a standard Brownian motion process.

[05] The machine learning algorithm 206 learns patterns and relationships in the data using a supervised learning approach, where the goal is to minimise the loss function L(f(x), y) between the predicted value and the true value:

L(f(x), y) = (1/n) * ?[i=1 to n] (f(x[i]) - y[i])^2
Where n is the number of training samples, x[i] is the input data for the i-th sample, and y[i] is the corresponding true value.

[06] FIG. 3 illustrates the flow and steps of implementing the SDROT-AI-OV algorithm.
[07] In Step 302, the method integrates data from various sources and extracts relevant features.
[08] In Step 304, the method employs a stochastic process model to capture the uncertainty and randomness of the complex system.
[09] In Step 306, the method uses a machine learning algorithm to learn patterns and relationships in the data.
[10] In Step 308, the method applies an optimisation technique to find the optimal solution based on the learned patterns and relationships.
[11] In Step 310, the method evaluates the effectiveness of the optimisation using a feedback analysis module.
[12] The SDROT-AI-OV algorithm provides a more accurate and reliable approach to optimisation by combining stochastic processes, machine learning and optimisation techniques.
[13] The SDROT-AI-OV algorithm has various applications, including optimising complex systems in transportation, logistics, supply chain management, energy management and healthcare.
[14] The SDROT-AI-OV algorithm provides several advantages, including improved accuracy, increased efficiency and enhanced flexibility.
[15] The algorithm is particularly useful for optimising complex systems that involve uncertainty, randomness and non-linearity.
[16] The SDROT-AI-OV algorithm can be implemented using various machine learning algorithms, including reinforcement learning, deep learning, and supervised learning.
[17] The algorithm can be deployed in various environments, including cloud computing, edge computing and on-premises computing.
[18] The SDROT-AI-OV algorithm has the potential to revolutionise the field of optimisation by providing a more accurate and reliable approach to optimising complex systems.
[19] The algorithm can be used to optimise various complex systems, including traffic flow, supply chain management, energy consumption and healthcare systems.
[20] The SDROT-AI-OV algorithm has the potential to provide significant benefits, including improved efficiency, reduced costs and enhanced decision-making.
[21] The algorithm can be used in various industries, including transportation, logistics, energy, healthcare and finance.
[22] The SDROT-AI-OV algorithm is a novel and innovative approach to optimisation that has the potential to provide significant benefits in various fields.
[23] The present invention provides a method and system for the SDROT-AI-OV algorithm for optimising complex systems.
[24] The SDROT-AI-OV algorithm combines stochastic processes, machine learning, and optimisation techniques to provide a more accurate and reliable approach to optimisation.
[25] While the present invention has been described in detail concerning specific embodiments, it will be appreciated that various modifications and changes can be made without departing from the scope of the invention.
[26] It is intended that the specification and figures be considered as exemplary rather than restrictive and all modifications that fall within the scope of the scope of the invention are intended to be included.
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, Claims:I CLAIM:
1. A stochastic dynamic real-time optimisation system with artificial intelligence and optimal value algorithm (SDROT-AI-OV) for optimising complex systems, comprising:
• A data integration module to combine data from various sources and extract relevant features;
• A stochastic process model to capture the uncertainty and randomness of the complex system;
• A machine learning algorithm to learn patterns and relationships in the data;
• An optimisation technique to find the optimal solution based on the learned patterns and relationships; and
• A feedback analysis module to evaluate the effectiveness of the optimisation and update the stochastic process model and machine learning algorithm.
2. The SDROT-AI-OV system of claim 1, wherein the data integration module further comprises a feature extraction component to extract high-dimensional features from the data.
3. The SDROT-AI-OV system of claim 1, wherein the stochastic process model further comprises a probabilistic modelling component to model the uncertainty and randomness of the complex system.
4. The SDROT-AI-OV system of claim 1, wherein the machine learning algorithm further comprises a deep learning component to learn complex patterns and representations from the data.
5. The SDROT-AI-OV system of claim 1, wherein the optimisation technique further comprises a real-time optimisation component to find the optimal solution in real-time.
6. A method for stochastic dynamic real-time optimisation with artificial intelligence and optimal value algorithm (SDROT-AI-OV) for optimising complex systems, comprising:
• Integrating data from various sources and extracting relevant features;
• Employing a stochastic process model to capture the uncertainty and randomness of the complex system;
• Using a machine learning algorithm to learn patterns and relationships in the data;
• Applying an optimisation technique to find the optimal solution based on the learned patterns and relationships;
• Evaluating the effectiveness of the optimisation using a feedback analysis module; and
• Updating the stochastic process model and machine learning algorithm based on the feedback analysis
7. The method of claim 6, further comprises creating a digital twin of the complex system to simulate and predict its behaviour.
8. The method of claim 6, further comprises incorporating expert knowledge and domain-specific constraints into the optimisation process.
9. The method of claim 6, further comprises edge computing or fog computing to enable real-time optimisation and decision-making.
10. The method of claim 6, further comprised applying the SDROT-AI-OV algorithm to various domains, including transportation, logistics, supply chain management, energy management and healthcare.