Complex system dynamic modeling and flow control method based on C-topological structure

Abstract

The invention discloses a complex system dynamic modeling and flow control method and system based on a C-topological structure, and relates to the technical field of complex system modeling and intelligent information processing. The method comprises the following steps: acquiring complex system unit state data, and constructing a C-topology adjacent structure; executing dimensionality change according to a system state to form a dynamic dimensionality space; calculating and updating a state vector and a global vector field in the dynamic dimension space; executing mobility evolution on the C-topological structure by taking a vector field as a drive to realize dynamic conduction and convergence of information, energy, resources, probability or causal relationship; and finally outputting a system state, prediction information or a control instruction. According to the method, the C-topological structure, dimension dynamic transformation, vector field evolution and mobility driving are organically fused to form a unified underlying processing framework suitable for various complex systems, and the method has the advantages of high structural adaptability, high modeling precision, wide universality, high expansibility and the like; the method can be widely applied to the fields of intelligent decision, dynamic prediction, intelligent control, network optimization, artificial intelligence and the like.

Description

Technical Field

[0001] This invention relates to the field of dynamic modeling and flow control technology for complex systems. Background Technology

[0002] In various complex systems such as artificial intelligence, industrial control, network communication, financial systems, and smart cities, the system structure is characterized by dynamism, openness, and high dimensionality. Traditional modeling methods often use fixed dimensions and fixed topological structures for description, making it difficult to adaptively match changes in system structure and state evolution. At the same time, they lack a unified expression mechanism for internal trends, transmission relationships, and global flows, resulting in weak model generalization ability, low prediction accuracy, and poor control robustness.

[0003] Existing technologies fail to organically integrate topology, dynamic dimensional transformations, vector field driving, and system fluidity, thus failing to form a unified underlying processing framework applicable to various complex systems. Therefore, there is an urgent need for a general modeling and control scheme that can dynamically adapt to structural changes, support adaptive dimensional adjustment, and quantitatively describe system trends and flow patterns.

[0004] Purpose of the invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for dynamic modeling and flow control of complex systems based on C-topology, so as to achieve unified modeling and efficient processing of the structure, dimensions, trends and flow of complex systems, thereby improving the accuracy and versatility of complex system analysis, prediction and control.

[0006] Technical solution

[0007] This invention discloses a method for dynamic processing of complex systems based on C-topology, comprising the following steps:

[0008] Acquire state data of multiple units in a complex system, construct a C-topology adjacency structure, and define the neighborhood relationships, connection strength, and boundary constraints between units;

[0009] Perform dimensional change processing based on the current operating state of the system, including dimensional lifting, dimensional compression, or manifold projection, to form a dynamic dimensional space that matches the system state;

[0010] The state vectors corresponding to each unit are calculated in the dynamic dimension space, and the global vector field is updated in real time. The direction and magnitude of the vectors represent the motion trend, intensity of action and correlation of the system units.

[0011] Driven by a global vector field, fluid evolution is performed on a C-topological adjacency structure to realize the dynamic transmission, diffusion, and convergence of information, energy, resources, probability, or causal relationships within the system.

[0012] Based on the results of liquidity evolution, output system state identification results, prediction information, or control instructions.

[0013] Furthermore, the C-topology adjacency structure supports dynamic addition and deletion of nodes, dynamic adjustment of connection relationships, and adaptive reconstruction of the structure.

[0014] Furthermore, the dimensionality change process maintains the topological invariants of the C-topology, ensuring modeling stability.

[0015] Furthermore, the state vector includes at least one of a trend vector, an intensity vector, a weight vector, and a correlation vector.

[0016] Furthermore, the liquidity evolution adopts an iterative update mechanism until the system state reaches a stable condition or a preset target threshold.

[0017] Furthermore, the method is applicable to artificial intelligence models, dynamic prediction systems, intelligent control systems, network optimization systems, and data processing systems.

[0018] This invention also provides a dynamic processing system for complex systems based on C-topology, comprising:

[0019] The topology building module is used to acquire complex system unit data and construct C-topology adjacency structures;

[0020] The dimension transformation module is used to perform dimensional transformation processing and generate a dynamic dimension space;

[0021] The vector field calculation module is used to calculate and update the state vector and the global vector field;

[0022] The liquidity evolution module is used to drive the iterative evolution of system liquidity on the C-topology.

[0023] The output module is used to output system status, prediction results, or control commands.

[0024] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the methods described above.

[0025] Beneficial effects

[0026] The present invention has the following significant advantages over the prior art:

[0027] 1. This invention uses C-topology as the underlying connection skeleton, which can dynamically describe the structural evolution and adjacency relationship of complex systems. It has stronger adaptability to the structure of open, dynamic, and non-stationary complex systems, and the modeling is closer to the actual system operation law.

[0028] 2. This invention introduces a dimensionality change mechanism, supporting adaptive dimensionality adjustment and manifold mapping, which can uniformly express system features at different granularities, avoid information loss or redundancy, and significantly improve the completeness and accuracy of modeling.

[0029] 3. This invention dynamically characterizes the trend, intensity, and correlation direction of a system through vector fields, transforming abstract system behavior into a calculable and controllable quantitative representation, thus providing a unified data foundation for the analysis, prediction, and control of complex systems.

[0030] 4. This invention uses vector fields to drive fluid evolution, which can uniformly describe the transmission and distribution laws of objects such as information, energy, resources, probability, and causality within the system, realize global dynamic modeling, and has extremely strong versatility and scalability.

[0031] 5. This invention integrates C-topology, dimensionality changes, vector field updates, and fluidity evolution into a general underlying framework that can be widely applied in multiple fields such as intelligent decision-making, dynamic prediction, intelligent control, artificial intelligence, data security, and network optimization. It has extremely high application value and protection scope, and provides a brand-new technical path for the intelligent processing of complex systems. Detailed Implementation

[0032] The method for dynamic modeling and flow control of complex systems based on C-topology described in this invention has the following specific implementation steps:

[0033] 1. Data Acquisition and Topology Construction: Collect the state, attribute, and association data of each unit in the target complex system, construct a C-topology adjacency structure, and determine the nodes, neighborhood relationships, connection strength, and boundary constraints.

[0034] 2. Dynamic Dimensional Transformation: Based on the system's data density, feature complexity, and operational stage, adaptive dimensional enhancement or compression is performed to form a dynamic dimensional space while maintaining topological invariants.

[0035] 3. Vector field generation: Calculate the state vector for each unit in the dynamic dimensional space, and combine them to form a global vector field, which represents the overall trend of the system and the relationship between local actions.

[0036] 4. Iterative Evolution of Liquidity: Driven by vector fields, multiple rounds of iterative updates are performed on the C-topology to simulate the internal transmission, diffusion, convergence and stabilization processes of the system.

[0037] 5. Results Output and Application: Output state recognition, trend prediction or control commands based on the evolution results, and apply them to prediction, decision-making, optimization or control scenarios.

[0038] The embodiments described above are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the protection scope of the present invention.

Claims

1. A method for dynamic processing of complex systems based on C-topology, characterized in that, Includes the following steps: Acquire state data of multiple units in a complex system, construct a C-topology adjacency structure, and define the neighborhood relationships, connection strength, and boundary constraints between units; Perform dimensional change processing based on the current operating state of the system, including dimensional lifting, dimensional compression, or manifold projection, to form a dynamic dimensional space that matches the system state; The state vectors corresponding to each unit are calculated in the dynamic dimension space, and the global vector field is updated in real time. The direction and magnitude of the vectors represent the motion trend, intensity of action and correlation of the system units. Driven by a global vector field, fluid evolution is performed on a C-topological adjacency structure to realize the dynamic transmission, diffusion, and convergence of information, energy, resources, probability, or causal relationships within the system. Based on the results of liquidity evolution, output system state identification results, prediction information, or control instructions.

2. The method according to claim 1, characterized in that, The C-topology adjacency structure supports dynamic addition and deletion of nodes, dynamic adjustment of connection relationships, and adaptive reconstruction of the structure.

3. The method according to claim 1, characterized in that, The dimensionality change process maintains the topological invariants of the C-topology, ensuring modeling stability.

4. The method according to claim 1, characterized in that, The state vector includes at least one of the following: trend vector, intensity vector, weight vector, and correlation vector.

5. The method according to claim 1, characterized in that, The liquidity evolution adopts an iterative update mechanism until the system state reaches a stable condition or a preset target threshold.

6. The method according to claim 1, characterized in that, The method is applicable to artificial intelligence models, dynamic prediction systems, intelligent control systems, network optimization systems, and data processing systems.

7. A dynamic processing system for complex systems based on C-topology, characterized in that, include: The topology building module is used to acquire complex system unit data and construct C-topology adjacency structures; The dimension transformation module is used to perform dimensional transformation processing and generate a dynamic dimension space; The vector field calculation module is used to calculate and update the state vector and the global vector field; The liquidity evolution module is used to drive the iterative evolution of system liquidity on the C-topology. The output module is used to output system status, prediction results, or control commands.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-6.