Systems and methods for configuring network architecture using advanced computational models for data analysis and automated processing

1 . A system for configuring network architecture using advanced computational models for data analysis and automated processing, the system comprising:

a processing device;

a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:

receive a service request, wherein the service request comprises configuring a node to complete the service request;

determine a protocol based on the service request, wherein the protocol comprises evaluating the service request using application servers;

determine the node to be used in a network configuration, wherein the node is determined in response to decision compute requirements;

arrange, using an artificial intelligence (AI) model, the node into the network configuration, wherein the AI model optimizes the network configuration based on the decision compute requirements and one or more network parameters;

monitor the network configuration using a configuration monitor, wherein the configuration monitor analyzes the one or more network parameters; and

determine, using a quantum simulation, a probabilistic distribution of the network configuration to analyze performance of the network configuration, wherein determining, using the quantum simulation, the probabilistic distribution of the network configuration to analyze performance of the network configuration further comprises recommending a different network configuration than the network configuration recommended by the AI model, and wherein the different network configuration is pre-staged to minimize downtime during implementation.

2 . The system of claim 1 , wherein the application servers comprise application rules, and wherein the application rules comprise:

mapping the service request to a network type; and

adjusting, in real time, the network type in response to the service request.

3 . The system of claim 1 , wherein the decision compute requirements further comprise:

service request parameters, wherein the service request parameters comprise resource requirements associated with the service request;

a node configuration, wherein the node configuration comprises the processing capabilities of the node, and health status of the node; and

a node priority, wherein the node priority comprises assigning a role to the node, wherein the role comprises a leader node and a worker node.

4 . The system of claim 3 , wherein the decision compute requirements further comprise a prior node configuration, wherein the prior node configuration comprises the node's state from prior iterations.

5 . The system of claim 1 , wherein the network configuration comprises a circular network, a wheel network, or a star network.

6 . The system of claim 1 , wherein the AI model comprises a generative AI model, wherein the generative AI model considers previous network configurations to determine the network configuration.

7 . The system of claim 1 , wherein the one or more network parameters comprise:

a network health, wherein the network health comprises the health of the network configuration; and

a network feed, wherein the network feed comprises the amount of data transmitted via the service request.

8 . The system of claim 1 , wherein executing the instructions further causes the processing device to implement, in response to detecting an anomaly, a secure zone, wherein the secure zone corresponds with the network configuration where the anomaly was detected.

9 . A computer program product for configuring network architecture using advanced computational models for data analysis and automated processing, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

receive a service request, wherein the service request comprises configuring a node to complete the service request;

determine a protocol based on the service request, wherein the protocol comprises evaluating the service request using application servers;

determine the node to be used in a network configuration, wherein the node is determined in response to decision compute requirements;

arrange, using an artificial intelligence (AI) model, the node into the network configuration, wherein the AI model optimizes the network configuration based on the decision compute requirements and one or more network parameters;

monitor the network configuration using a configuration monitor, wherein the configuration monitor analyzes the one or more network parameters; and

determine, using a quantum simulation, a probabilistic distribution of the network configuration to analyze performance of the network configuration, wherein determining, using the quantum simulation, the probabilistic distribution of the network configuration to analyze performance of the network configuration further comprises recommending a different network configuration than the network configuration recommended by the AI model and wherein the different network configuration is pre-staged to minimize downtime during implementation.

10 . The computer program product of claim 9 , wherein the application servers comprise application rules, and wherein the application rules comprise:

mapping the service request to a network type; and

adjusting, in real time, the network type in response to the service request.

11 . The computer program product of claim 9 , wherein the decision compute requirements further comprise:

service request parameters, wherein the service request parameters comprise resource requirements associated with the service request;

a node configuration, wherein the node configuration comprises the processing capabilities of the node, and health status of the node; and

a node priority, wherein the node priority comprises assigning a role to the node, wherein the role comprises a leader node and a worker node.

12 . The computer program product of claim 11 , wherein the compute requirements further comprise a prior node configuration, wherein the prior node configuration comprises the node's state from prior iterations.

13 . The computer program product of claim 9 , wherein the network configuration comprises a circular network, a wheel network, or a star network.

14 . The computer program product of claim 9 , wherein the AI model comprises a generative AI model, wherein the generative AI model considers previous network configurations to determine the network configuration.

15 . The computer program product of claim 9 , wherein the one or more network parameters comprise:

a network health, wherein the network health comprises the health of the network configuration; and

a network feed, wherein the network feed comprises the amount of data transmitted via the service request.

16 . The computer program product of claim 9 , wherein the code further causes the apparatus to implement, in response to detecting an anomaly, a secure zone, wherein the secure zone corresponds with the network configuration where the anomaly was detected.

17 . A method for configuring network architecture using advanced computational models for data analysis and automated processing, the method comprising:

receiving a service request, wherein the service request comprises configuring a node to complete the service request;

determining a protocol based on the service request, wherein the protocol comprises evaluating the service request using application servers;

determining the node to be used in a network configuration, wherein the node is determined in response to decision compute requirements;

arranging, using an artificial intelligence (AI) model, the node into the network configuration, wherein the AI model optimizes the network configuration based on the decision compute requirements and one or more network parameters;

monitoring the network configuration using a configuration monitor, wherein the configuration monitor analyzes the one or more network parameters; and

determining, using a quantum simulation, a probabilistic distribution of the network configuration to analyze performance of the network configuration, wherein determining, using the quantum simulation, the probabilistic distribution of the network configuration to analyze performance of the network configuration further comprises recommending a different network configuration than the network configuration recommended by the AI model, and wherein the different network configuration is pre-staged to minimize downtime during implementation.

18 . The method of claim 17 , wherein the application servers comprise application rules, and wherein the application rules comprise:

mapping the service request to a network type; and

adjusting, in real time, the network type in response to the service request.