초록 ▼

According to some embodiments, a model building platform may receive a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time. The model building platform may automatically create a generative model based on relationships detec

According to some embodiments, a model building platform may receive a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time. The model building platform may automatically create a generative model based on relationships detected within the set of historic industrial plant parameters. A model execution platform may then receive incomplete industrial plant information associated with a particular industrial plant, and automatically generate supplemented industrial plant data based on the received incomplete industrial plant information and the generative model. An indication of the supplemented industrial plant data may then be output.

대표청구항 ▼

1. A method, comprising: receiving, at a model building platform, a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time;automatically creating, by the model building platform, a generative model based on relationships detect

1. A method, comprising: receiving, at a model building platform, a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time;automatically creating, by the model building platform, a generative model based on relationships detected within the set of historic industrial plant parameters;receiving, at a model execution platform, incomplete industrial plant information associated with a particular industrial plant;automatically generating supplemented industrial plant data based on the received incomplete industrial plant information and the generative model; andoutputting an indication of the supplemented industrial plant data. 2. The method of claim 1, wherein the industrial plants comprise power plants that produce electricity. 3. The method of claim 1, wherein the set of historic industrial plant parameters are associated with at least one of: (i) economic information, (ii) regulatory information, (iii) configuration information, and (iv) operational information. 4. The method of claim 1, further comprising: prior to the automatic creation of the generative model, pre-processing the historic industrial plat parameters to create normalized data. 5. The method of claim 4, wherein the automatic creation of the generative model is associated with a machine deep learning process and a validation test set. 6. The method of claim 1, further comprising: prior to the automatic generation of the supplemented industrial plant data, pre-processing the received incomplete industrial plant information to created normalized data. 7. The method of claim 1, wherein the generative model comprises a stochastic generative model. 8. The method of claim 7, wherein the model execution platform uses a Gibbs sampling Markov Chain Monte Carlo algorithm to obtain an observation approximated from a specified multivariate probability distribution. 9. The method of claim 1, wherein the incomplete industrial plant information and the supplemented industrial plant data comprise complete industrial plant operational information. 10. The method of claim 9, wherein the supplemented industrial plant data includes likelihood information. 11. A non-transitory, computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform a medium, the medium comprising: receiving, at a model building platform, a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time;automatically creating, by the model building platform, a generative model based on relationships detected within the set of historic industrial plant parameters;receiving, at a model execution platform, incomplete industrial plant information associated with a particular industrial plant;automatically generating supplemented industrial plant data based on the received incomplete industrial plant information and the generative model; andoutputting an indication of the supplemented industrial plant data. 12. The medium of claim 11, wherein the industrial plants comprise power plants that produce electricity, and the set of historic industrial plant parameters are associated with at least one of: (i) economic information, (ii) regulatory information, (iii) configuration information, and (iv) operational information. 13. The medium of claim 1, wherein execution of the instructions further results in: prior to the automatic creation of the generative model, pre-processing the historic industrial plat parameters to create normalized data, and the automatic creation of the generative model is associated with a machine deep learning process and a validation test set; andprior to the automatic generation of the supplemented industrial plant data, pre-processing the received incomplete industrial plant information to created normalized data. 14. The medium of claim 11, wherein the generative model comprises a stochastic generative model and the model execution platform uses a Gibbs sampling Markov Chain Monte Carlo algorithm to obtain an observation approximated from a specified multivariate probability distribution. 15. The medium of claim 11, wherein the incomplete industrial plant information and the supplemented industrial plant data comprise complete industrial plant operational information, and the supplemented industrial plant data includes likelihood information. 16. A system, comprising: a database storing a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time;a model building platform coupled to the database to: receive the set of historic industrial plant parameters, and automatically create a generative model based on relationships detected within the set of historic industrial plant parameters; anda model execution platform to: receive incomplete industrial plant information associated with a particular industrial plant, automatically generate supplemented industrial plant data based on the received incomplete industrial plant information and the generative model, and output an indication of the supplemented industrial plant data. 17. The system of claim 16, wherein the industrial plants comprise power plants that produce electricity, and the set of historic industrial plant parameters are associated with at least one of: (i) economic information, (ii) regulatory information, (iii) configuration information, and (iv) operational information. 18. The system of claim 16, wherein: the model building platform is further to, prior to the automatic creation of the generative model, pre-process the historic industrial plat parameters to create normalized data, and the automatic creation of the generative model is associated with a machine deep learning process and a validation test set; andthe model execution platform is further to, prior to the automatic generation of the supplemented industrial plant data, pre-process the received incomplete industrial plant information to created normalized data. 19. The system of claim 16, wherein the generative model comprises a stochastic generative model and the model execution platform uses a Gibbs sampling Markov Chain Monte Carlo algorithm to obtain an observation approximated from a specified multivariate probability distribution. 20. The system of claim 16, wherein the incomplete industrial plant information and the supplemented industrial plant data comprise complete industrial plant operational information, and the supplemented industrial plant data includes likelihood information.