System and method for implementing auto-correction to solve dynamic issues in a distributed network

A method comprising:

• detect an application issue associated with an application running at a network node at a particular timestamp, wherein the application issue comprises a user request with an issue statement and a user interaction associated with one or more operation parameters of the application;
• receive a set of data objects associated with the application issue occurring at the timestamp;
• classify, by a machine learning model, the set of the data objects of the application issue into one or more issue patterns, wherein the machine learning model is trained based on a plurality of sets of the data objects and the issue patterns associated with the corresponding previous application issues;
• process, through a neural network, the one or more issue patterns and application information associated with the application issue at the network node to determine a series of executable operations configured to solve the application issue, wherein the neural network is trained based on the plurality of the issue patterns and associations between the issue patterns and the plurality of series of the executable operations, wherein the series of executable operations comprises a network node address and an application identifier;
• determine whether the network node is communicating with the processor;
• in response to determining that the network node is communicating with the processor, determine whether the application is currently running at the network node;
• in response to determining that the application is currently running at the network node, deploy the series of the executable operations to the network node based at the network node address and the application identifier,
• wherein the series of the executable operations is configured to be automatically executed at the network node to correct the one or more parameters of the application to prevent a failure operation of the application.

A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:

• detect an application issue associated with an application running at a network node at a particular timestamp, wherein the application issue comprises a user request with an issue statement and a user interaction associated with one or more operation parameters of the application;
• receive a set of data objects associated with the application issue occurring at the timestamp;
• classify, by a machine learning model, the set of the data objects of the application issue into one or more issue patterns, wherein the machine learning model is trained based on the plurality of sets of the data objects and the issue patterns associated with the corresponding previous application issues;
• process, through a neural network, the one or more issue patterns and application information associated with the application issue at the network node to determine a series of executable operations configured to solve the application issue, wherein the neural network is trained based on the plurality of the issue patterns and associations between the issue patterns and the plurality of series of the executable operations, wherein the series of executable operations comprises a network node address and an application identifier;
• determine whether the network node is communicating with the processor;
• in response to determining that the network node is communicating with the processor, determine whether the application is currently running at the network node;
• and
• in response to determining that the application is currently running at the network node, deploy the series of the executable operations to the network node based at the network node address and the application identifier,
• wherein the series of the executable operations is configured to be automatically executed at the network node to correct the one or more parameters of the application to prevent a failure operation of the application.