SELF-FORMING COMMUNICATION AND CONTROL SYSTEM

1 . A computerized method for processing data of a self-forming communication and control system, the method comprising:

executing, by a processor, environment interpretation software from a first non-transitory memory causing the processor to detect and identify a plurality of manufacturing control devices of a manufacturing environment based on at least one of environment signaling of the manufacturing environment and premise messages exchanged with another processor to produce an identified manufacturing control device identifier for each manufacturing control device of the identified plurality of manufacturing control devices, each manufacturing control device of the plurality of manufacturing control devices comprising at least one of a physical object within the manufacturing environment when the manufacturing environment includes a physical environment and a virtual object within the manufacturing environment when the manufacturing environment includes a virtual environment, the environment signaling comprising at least one of an unencoded direct electromagnetic emission, an unencoded indirect electromagnetic emission, an encoded electromagnetic emission, an encoded electronic signal, an unencoded mechanical wave, and an encoded mechanical wave, the premise messages comprising object profile information for each identified manufacturing control device, the object profile information comprising one or more of object basics, object deployment information, and object availability information;

executing, by the processor, profile generation software from a second non-transitory memory to facilitate intercommunication between the environment interpretation software and the profile generation software causing the processor to exchange prescriptive information associated with each identified manufacturing control device identifier with an artificial intelligence (AI) memory, the prescriptive information comprising object learnings based on an interpretation of a plurality of historical object behavior observations associated with the plurality of manufacturing control devices associated with the manufacturing environment; and

executing, by the processor, object control software from a third non-transitory memory to facilitate intercommunication between the profile generation software and the object control software causing the processor to interpret further environment signaling for the plurality of manufacturing control devices within the manufacturing environment using the object profile information and at least some of the prescriptive information to produce object control information for storage within a digital twin memory, wherein the object control information is available to be subsequently recovered from the digital twin memory and utilized to virtually represent the plurality of manufacturing control devices within a virtual representation of the manufacturing environment.

2 . The method of claim 1 further comprising:

executing, by the processor, object learning software from a fourth non-transitory memory causing the processor to:

interpret other environment signaling for the corresponding plurality of manufacturing control devices associated with the manufacturing environment to produce other object control information,

store the other object control information in the AI memory as the plurality of historical object behavior observations associated with the corresponding plurality of manufacturing control devices,

recover a portion of the plurality of historical object behavior observations from the AI memory, and

infer the object learnings based on an interpretation of the portion of the plurality of historical object behavior observations as the prescriptive information, the object learnings predicting future object behavior of the plurality of manufacturing control devices.

3 . The method of claim 1 further comprising:

executing, by the processor, further profile generation software from the second non-transitory memory causing the processor to produce updated object profile information for at least some of the plurality of manufacturing control devices based on corresponding identified manufacturing control device identifiers and updated prescriptive information associated with a particular identified manufacturing control device of the plurality of manufacturing control devices within the AI memory, the updated object profile information comprising one or more of updated object basics, updated object deployment information, and updated object availability information, the updated prescriptive information comprising one or more of updated object learnings based on another interpretation of the plurality of historical object behavior observations associated with the corresponding plurality of manufacturing control devices associated with the manufacturing environment each of the manufacturing control devices associated with the manufacturing environment and an evaluation of the updated object learnings against a standard.

4 . The method of claim 1 further comprising:

executing, by the processor, dashboard software from a fifth non-transitory memory to facilitate intercommunication between the object control software and the dashboard software causing the processor to interpret a portion of the object control information for the plurality of manufacturing control devices recovered from the digital twin memory to produce dashboard information, the dashboard information comprising a representation of status of each identified manufacturing control device of the plurality of manufacturing control devices based on the further environment signaling and in accordance with the object profile information.

5 . The method of claim 4 further comprising:

executing, by the processor, further dashboard software from the fifth non-transitory memory causing the processor to:

obtain the portion of the object control information that corresponds to the further environment signaling for a particular identified manufacturing control device recovered from the digital twin memory, and

interpret the portion of the object control information in accordance with the object profile information to produce the dashboard information.

6 . The method of claim 4 further comprising:

executing, by the processor, prescriptive software from a sixth non-transitory memory to facilitate intercommunication between the dashboard software and the prescriptive software causing the processor to process a portion of the dashboard information to produce the prescriptive information within the AI memory, the prescriptive information comprising one or more of an interpretation of the portion of the dashboard information, an evaluation of the portion of the dashboard information against a standard, and adaptive processor-executable instructions for use with the object profile information and the further environment signaling to cause change with regards to the identified plurality of manufacturing control devices within the manufacturing environment.

7 . The method of claim 6 further comprising:

executing, by the processor, further prescriptive software from the sixth non-transitory memory causing the processor to:

determine control parameters of object control of the identified plurality of manufacturing control devices based on the object profile information,

determine signaling parameters of the further environment signaling based on the identified manufacturing control device, and

generate the processor-executable instructions based on the control parameters and the signaling parameters to facilitate subsequent collection of the further environment signaling associated with the identified manufacturing control device to provide the object control of the identified plurality of manufacturing control devices within the manufacturing environment.

8 . The method of claim 6 further comprising:

executing, by the processor, further prescriptive software from the sixth non-transitory memory causing the processor to:

obtain the portion of the dashboard information corresponding to a prescriptive timeframe from the digital twin memory,

process the portion of the dashboard information in accordance with the object profile information to produce preliminary prescriptive information,

determine a format for the prescriptive information based on the preliminary prescriptive information and an object knowledgebase of the AI memory,

interpret the portion of the dashboard information in accordance with the format for the prescriptive information to produce the prescriptive information, and

store the prescriptive information within the AI memory.

9 . The method of claim 1 , wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to detect the plurality of manufacturing control devices of the manufacturing environment based on the environment signaling of the manufacturing environment to produce the identified plurality of manufacturing control devices by:

obtaining the environment signaling of the manufacturing environment from an environment sensor module;

indicating the physical object as a particular identified manufacturing control device when identifying a physical object pattern from at least one of the unencoded direct electromagnetic emission, the unencoded indirect electromagnetic emission, and the unencoded mechanical wave of the environment signaling; and

indicating the virtual object as a particular detected object when identifying a virtual object pattern from at least one of the encoded electromagnetic emission, the encoded electronic signal, and the encoded mechanical wave of the environment signaling.

10 . The method of claim 1 , wherein the processor further executes the environment interpretation software from the first non-transitory memory causing the processor to:

access a portion of the digital twin memory that includes an object knowledgebase based on a particular identified manufacturing control device;

compare an attribute of detection of the particular identified manufacturing control device to the portion of the digital twin memory that includes the object knowledgebase to produce the identifier of the particular identified manufacturing control device; and

access the portion of the digital twin memory that includes the object knowledgebase based on the particular identified building control device to produce the object profile information.

11 . The method of claim 1 further comprising:

executing, by the processor, ledger software from a seventh non-transitory memory to facilitate intercommunication between the object control software and the ledger software causing the processor to memorialize the object control information in an object distributed ledger by:

synchronizing with the object distributed ledger to a finalized block height having at least a threshold number of confirmations,

computing, by a cryptographic engine associated with the processor, a transaction digest over a canonical representation of a portion of the object control information comprising a canonical object basics record, a validation timestamp, and an availability status, together with a nonce, a chain identifier, and a previous block hash,

encrypting, by the cryptographic engine, at least a portion of the portion of the object control information using a receiving public key associated with the object distributed ledger and generating a transaction signature by signing the transaction digest with a private key associated with the processor,

generating a next block of a blockchain of the object distributed ledger to include the portion of the object control information, the encrypted at least a portion, a Merkle root committing the portion of the object control information including a per-learning-object manifest, and the transaction signature, and

causing inclusion of the next block as a token in the object distributed ledger and recording a Merkle proof of inclusion for the portion of the object control information, wherein inclusion of the chain identifier, the previous block hash, and the nonce in the transaction digest prevents replay across ledgers or epochs, and the recorded Merkle proof together with the threshold number of confirmations provides tamper-evident provenance and finalized custody of the token within a bounded time window.