Methods and systems for prioritization of group computing tasks

1 . A system for prioritization of group computing tasks, the system comprising:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

detect a plurality of active nodes communicatively connected in a group computing environment, wherein the group computing environment comprises a virtual environment using at least a secondary computing device, as a function of hardware allocation;

receive a plurality of computing tasks associated with the plurality of active nodes for execution in the group computing environment;

train a demand machine-learning model using demand training data, wherein training the demand machine-learning model comprises:

obtaining the demand training data by querying a communicatively connected database, wherein the communicatively connected database comprises past inputs and outputs of the demand machine-learning model;

training the demand machine-learning model as a function of the demand training data;

receiving demand feedback as a function of user input associated with an accuracy of an output of the trained demand machine-learning model;

retraining the demand machine-learning model as a function of the user input;

determine a computing demand associated with each of the plurality of computing tasks for the group computing environment using the retrained demand machine-learning model; and

generate priority data for the plurality of computing tasks as a function of the computing demand associated with each of the plurality of computing tasks, wherein generating the priority data comprises utilizing a secure proof protocol, wherein the secure proof protocol comprises a zero-knowledge proof.

2 . The system of claim 1 , wherein the at least a processor is further configured to determine a criticality factor for each of the plurality of computing tasks.

3 . The system of claim 2 , wherein determining the criticality factor for each of the plurality of computing tasks comprises:

receiving criticality training data comprising a plurality of exemplary computing task inputs correlated to a plurality of criticality factor outputs;

training a criticality machine learning model as a function of the criticality training data; and

determining the criticality factor for each of the plurality of computing tasks as a function of the trained criticality machine learning model.

4 . The system of claim 3 , wherein training the criticality machine learning model as a function of the criticality training data comprises:

receiving priority feedback following each complete iteration of processing of the system; and

iteratively training the criticality machine learning model as a function of the priority feedback.

5 . The system of claim 4 , wherein generating the priority data for the plurality of computing tasks further comprises generating the priority data as a function of the criticality factor for each of the plurality of computing tasks.

6 . The system of claim 1 , wherein the at least a processor is further configured to determine a security factor for each of the plurality of computing tasks.

7 . The system of claim 6 , wherein determining the security factor for each of the plurality of computing tasks comprises:

receiving security factor training data comprising a plurality of exemplary computing task inputs correlated to a plurality of security factor outputs;

training a security factor machine learning model as a function of the security factor training data; and

determining the security factor for each of the plurality of computing tasks as a function of the trained security factor machine learning model.

8 . The system of claim 7 , wherein training the security factor machine learning model as a function of the security factor training data comprises:

receiving priority feedback following each complete iteration of execution of the plurality of computing tasks; and

iteratively training the security factor security factor machine learning model as a function of the priority feedback.

9 . The system of claim 8 , wherein generating the priority data for the plurality of computing tasks further comprises generating the priority data as a function of the security factor for each of the plurality of computing tasks.

10 . The system of claim 1 , wherein the at least a processor is further configured to communicate the priority data for the plurality of computing tasks to the plurality of active nodes.

11 . A method for prioritization of group computing tasks comprising:

detecting, by at least a processor, a plurality of active nodes communicatively connected in a group computing environment, wherein the group computing environment comprises a virtual environment using at least a secondary computing device, as a function of hardware allocation;

receiving, by the at least a processor, a plurality of computing tasks associated with the plurality of active nodes for execution in the group computing environment;

training, by the at least a processor, a demand machine-learning model using demand training data, wherein training the demand machine-learning model comprises:

obtaining the demand training data by querying a communicatively connected database, wherein the communicatively connected database comprises past inputs and outputs of the demand machine-learning model;

training the demand machine-learning model as a function of the demand training data;

receiving demand feedback as a function of user input associated with an accuracy of an output of the trained demand machine-learning model;

retraining the demand machine-learning model as a function of the user input;

determining, by the at least a processor, a computing demand associated with each of the plurality of computing tasks for the group computing environment using the retrained demand machine-learning model; and

generating, by the at least a processor, priority data for the plurality of computing tasks as a function of the computing demand associated with each of the plurality of computing tasks, wherein generating the priority data comprises utilizing a secure proof protocol, wherein the secure proof protocol comprises a zero-knowledge proof.

12 . The method of claim 11 , further comprising determining, by the at least a processor, a criticality factor for each of the plurality of computing tasks.

13 . The method of claim 12 , wherein determining the criticality factor for each of the plurality of computing tasks comprises:

receiving criticality training data comprising a plurality of exemplary computing task inputs correlated to a plurality of criticality factor outputs;

training a criticality machine learning model as a function of the criticality training data; and

determining the criticality factor for each of the plurality of computing tasks as a function of the trained criticality machine learning model.

14 . The method of claim 13 , wherein training the criticality machine learning model as a function of the criticality training data comprises:

receiving priority feedback following each complete iteration of processing of the method; and

iteratively training the criticality machine learning model as a function of the priority feedback.

15 . The method of claim 14 , wherein generating the priority data for the plurality of computing tasks further comprises generating the priority data as a function of the criticality factor for each of the plurality of computing tasks.

16 . The method of claim 11 , wherein the method further comprises determining, by the at least a processor, a security factor for each of the plurality of computing tasks.

17 . The method of claim 16 , wherein determining the security factor for each of the plurality of computing tasks comprises:

receiving security factor training data comprising a plurality of exemplary computing task inputs correlated to a plurality of security factor outputs;

training a security factor machine learning model as a function of the security factor training data; and

determining the security factor for each of the plurality of computing tasks as a function of the trained security factor machine learning model.

18 . The method of claim 17 , wherein training the security factor machine learning model as a function of the security factor training data comprises:

receiving priority feedback following each complete iteration of execution of the plurality of computing tasks; and

iteratively training the security factor machine learning model as a function of the priority feedback.

19 . The method of claim 18 , wherein generating the priority data for the plurality of computing tasks further comprises generating the priority data as a function of the security factor for each of the plurality of computing tasks.

20 . The method of claim 11 , further comprising communicating, by the at least a processor, the priority data for the plurality of computing tasks to the plurality of active nodes.