AI extensions and intelligent model validation for an industrial digital twin

    公开(公告)号:US11900277B2

    公开(公告)日:2024-02-13

    申请号:US17744980

    申请日:2022-05-16

    IPC分类号: G06N5/04 G06N20/10 G06N5/048

    CPC分类号: G06N5/048 G06N20/10

    摘要: Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

    METHOD FOR EVALUATING COLD TOLERANCE OF HEVEA BRASILIENSIS

    公开(公告)号:US20230400443A1

    公开(公告)日:2023-12-14

    申请号:US18332018

    申请日:2023-06-09

    IPC分类号: G01N33/00 G06N5/048

    CPC分类号: G01N33/0098 G06N5/048

    摘要: A method for evaluating cold tolerance of Hevea brasiliensis includes: (1) taking different one-year-old germplasm plants of the Hevea brasiliensis with second whorls of leaves entering a stable period as materials, firstly culturing the materials at a normal temperature, then treating the materials at a low temperature, and finally respectively measuring relative electrical conductivities of the materials cultured at the normal temperature and physiological indexes of the cold tolerance of the materials treated at the low temperature, where a variety 93114 is used as a cold tolerance control and a Reyan 73397 is used as a sensitive control; and (2) according to changes of the physiological indexes of the cold tolerance in the germplasm plants of the Hevea brasiliensis, comprehensively evaluating the cold tolerance of the materials by using a fuzzy membership function method. When comprehensive indexes of the cold tolerance are larger, the cold tolerance of the materials is better.

    Abnormal air pollution emission prediction

    公开(公告)号:US11836644B2

    公开(公告)日:2023-12-05

    申请号:US16532543

    申请日:2019-08-06

    摘要: A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

    METHOD AND SYSTEM FOR SMART DETECTION OF BUSINESS HOT SPOTS

    公开(公告)号:US20230325693A1

    公开(公告)日:2023-10-12

    申请号:US18194679

    申请日:2023-04-03

    申请人: INTUIT INC.

    IPC分类号: G06N5/048 G06Q10/04

    CPC分类号: G06N5/048 G06Q10/04

    摘要: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.