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1.
公开(公告)号:US20210294317A1
公开(公告)日:2021-09-23
申请号:US17204381
申请日:2021-03-17
Applicant: Johnson Controls Technology Company
Inventor: Nidhi Sharma , Anantharaman Iyer
Abstract: A system for predicting the vulnerability of a building management system (BMS) includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to establish a first communication link to a first data source and receive a first data using a communication module communicatively coupled to the processor. The first data includes information related to at least one of a plurality of IoT-enabled devices. The system is further configured to generate a historical record composed of a plurality of received data feeds received from a plurality of data feeds at unanticipated time intervals and analyze at least one of the plurality of data feeds with at least one or more of: the first data, the historical record, and another of the plurality of data feeds to predict the vulnerability of the BMS.
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2.
公开(公告)号:US12248311B2
公开(公告)日:2025-03-11
申请号:US17204381
申请日:2021-03-17
Applicant: Johnson Controls Technology Company
Inventor: Nidhi Sharma , Anantharaman Iyer
Abstract: A system for predicting the vulnerability of a building management system (BMS) includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to establish a first communication link to a first data source and receive a first data using a communication module communicatively coupled to the processor. The first data includes information related to at least one of a plurality of IoT-enabled devices. The system is further configured to generate a historical record composed of a plurality of received data feeds received from a plurality of data feeds at unanticipated time intervals and analyze at least one of the plurality of data feeds with at least one or more of: the first data, the historical record, and another of the plurality of data feeds to predict the vulnerability of the BMS.
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