Abstract: Systems, computer program products, and methods are described herein for real-time overload detection using image processing analysis. The present disclosure is configured to receive a first set of images associated with a device, wherein the one or more images are associated with a resiliency status of the device; deploy, using a machine learning (ML) subsystem, a trained ML model on the first set of images of the device; determine, using the trained ML model, a change in the resiliency status of the device based on the first set of images; and generate a notification indicating the change in the resiliency status of the device; and transmit control signals configured to cause a first user input device to display the notification.

Abstract: Systems, computer program products, and methods are described herein for performance monitoring using aggregated telemetry. The present disclosure is configured to receive, from the first performance monitoring engine, a first metadata associated with the first resiliency status; receive, from the second performance monitoring engine, a second metadata associated with the second resiliency status; determine, using a machine learning (ML) subsystem, an overall resiliency status of the device based on at least the first metadata, the second metadata, the first resiliency status, and the second resiliency status; determine one or more actions to be executed on the device, wherein the one or more actions are associated with the overall resiliency status; generate a notification indicating the overall resiliency status of the device and the one or more actions associated with the overall resiliency status; and transmit control signals configured to cause a user input device to display the notification.

Abstract: Aspects of the disclosure relate to digital twin simulation. A computing platform may receive historical information. The computing platform may train, using the historical information, a digital twin model, configured to identify agricultural information based on input of a query requesting the agricultural information. The computing platform may receive, from a user device, a query requesting the agricultural information. The computing platform may input, into the digital twin model, the query, to output the agricultural information based on the historical information and the relationships between the feature models. The computing platform may direct a vendor computing system to execute actions based on the agricultural information, which may cause the vendor computing system to execute the one or more actions.

Abstract: Aspects of the disclosure relate to outage prevention. A computing platform may train, using historical parameter information and historical outage information, an outage prediction model. The computing platform may receive, from at least one system, current parameter information, and may normalize the current parameter information. The computing platform may convert, using a CNN of the outage prediction model, the normalized current parameter information to a frequency domain. The computing platform may input, into at least one RNN of the outage prediction model, the frequency domain information, to produce a likelihood of outage score. The computing platform may compare the likelihood of outage score to a predetermined outage threshold. Based on identifying that the likelihood of outage score meets or exceeds the predetermined outage threshold, the computing platform may direct the at least one system to execute a performance modification to prevent a predicted outage.

Abstract: Aspects of the disclosure relate to digital twin simulation. A computing platform may train, using historical vehicle information, a digital twin vehicle evaluation engine, configured to model a vehicle based on characteristics of the vehicle using a computer simulation. The computing platform may receive, from a client device, an event processing request identifying a first vehicle. The computing platform may generate, using the digital twin vehicle evaluation engine, a computer simulation of the first vehicle. The computing platform may execute, over a simulated period of time, the computer simulation of the first vehicle to output event processing information for the first vehicle. The computing platform may send, to the client device, the event processing information and one or more commands directing the client device to display the event processing information, which may cause the client device to display the event processing information.

Abstract: Aspects of the disclosure relate to digital twin simulation. A computing platform may train, using historical property information, a digital twin property evaluation engine, configured to model a physical property based on characteristics of the physical property using a computer simulation. The computing platform may receive, from a client device, an event processing request identifying a first physical property. The computing platform may generate, using the digital twin property evaluation engine, a computer simulation of the first physical property. The computing platform may execute, over a simulated period of time, the computer simulation of the first physical property to output event processing information for the first physical property. The computing platform may send, to the client device, the event processing information and one or more commands directing the client device to display the event processing information, which may cause the client device to display the event processing information.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may configure a client application to use a custom driver when communicating with an enterprise database. The computing platform may receive a database query formatted in a first database format corresponding to a first database. The computing platform may translate, using a query translation library, the database query from the first database format into a second database format corresponding to a second database, which may cause the custom driver to execute a transliteration process using pre-verified query keys stored in the query translation library to convert the database query from the first database format into the second database format. The computing platform may execute the translated database query on the second database to obtain a query result, and may send the query result to the client application.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform receive a source query formatted in a first format for execution on a source database. The computing platform may execute the source query on the source database to produce a first data result. The computing platform may input the first data result into a reversal logic engine to produce a target query formatted in a second format corresponding to a target database. The computing platform may execute the target query on the target database to produce a second data result. Based on identifying that the second data result matches the first data result, the computing platform may validate the target query. Based on identifying that the second data result does not match the first data result, the computing platform may adjust the reversal logic engine based on the discrepancy.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a request to perform a data migration from a first database configured in a first format to a second database configured in a second format. The computing platform may receive, from the client application and at an abstraction layer, a query. Based on identifying that the query is formatted for execution at the second database, the computing platform may route the query to the second database for execution. Based on identifying that the query is not formatted for execution at the second database, the computing platform may: 1) translate the query from the first format to the second format by using pre-verified query keys to convert the query from the first format into the second format, and 2) route the translated query to the second database for execution.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. The computing platform may receive a query, formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by:1) extracting non-essential parameters from the query to create a query key; 2) storing the non-essential parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key; 4) based on identifying that the query library includes portions of the query key rather than the query key, recursively identify the translated query by nesting the portions of the query key; and 5) input the non-essential parameters into the translated query to create an output query. The computing platform may execute the output query on the second database.

Abstract: Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may train a machine learning model using source syntax trees and target dialect syntax trees, which may configure the model to output source dialect keys and their corresponding target dialect queries. The computing platform may execute the corresponding target dialect queries to identify whether they are valid. For a valid target dialect query, the computing platform may store the valid target dialect query and first source dialect keys corresponding to the valid target dialect query in a lookup table. For an invalid target dialect query resulting in error, the computing platform may: 1) identify a cause of the error; 2) generate a transliteration rule to correct the error; and 3) store, in the lookup table, the invalid target dialect query, second source dialect keys corresponding to the invalid target dialect query, and the transliteration rule.

Abstract: Aspects of the disclosure relate to data transmission with encryption of protected data. A computing platform may separate, via a computing device and in electronic data associated with a user, protected data from non-protected attribute data. Subsequently, the computing platform may generate, for the protected data, an encryption key. Then, the computing platform may modify the electronic data by replacing the protected data with the encryption key. Then, the computing platform may store, in a database, an association between the user, the protected data, and the encryption key. Subsequently, the computing platform may provide, via a network and to an external vendor, the modified electronic data comprising the encryption key and the non-protected attribute data.

Abstract: Aspects of the disclosure relate to data transmission with encryption of protected data. A computing platform may separate, via a computing device and in electronic data associated with a user, protected data from non-protected attribute data. Subsequently, the computing platform may generate, for the protected data, an encryption key. Then, the computing platform may modify the electronic data by replacing the protected data with the encryption key. Then, the computing platform may store, in a database, an association between the user, the protected data, and the encryption key. Subsequently, the computing platform may provide, via a network and to an external vendor, the modified electronic data comprising the encryption key and the non-protected attribute data.

Abstract: Aspects of the disclosure relate to encryption of protected data for data transmission over a web interface. A computing platform may submit, via a first computing device, a query for data associated with a user. The computing platform may receive a search result comprising an attribute of the user. Then, the computing platform may generate, based on the attribute of the user, a data file comprising an attribute identifier associated with the attribute of the user. Then, the computing platform may upload, via a web interface and to a second computing device, the data file comprising the attribute identifier. Subsequently, the computing platform may receive, via the web interface and from the second computing device, an encryption key corresponding to the attribute identifier. Then, the computing platform may store, via the first computing device and in a database, an association between the attribute, the attribute identifier, and the encryption key.