There is proposed a model update device and method, and a process control system, which are capable of significantly reducing the labor, time, and monetary cost required for model update and are also easily applicable to a plant in which the existing model predictive control is introduced, without causing a loss of operating profit.

By converting a data format of operation data of a target process into a delay coordinate format and solving a regression problem including a regularization term for the operation data converted into the delay coordinate format, an update model reflecting secular change information of the target process is generated, and the model is replaced with the generated update model to be updated.

A method includes simulating one or more states of a movable object by implementing a movable object model. Each simulated state is associated with simulated state data of the movable object. The method further includes determining one or more sets of simulated sensor data corresponding to the one or more simulated states respectively by implementing a plurality of sensor models, determining environment data of a simulated environment surrounding the movable object by implementing an environment model, providing the one or more sets of simulated sensor data to a movable object controller configured for generating control signals to adjust states of the movable object, and providing the simulated state data and the environment data to a vision simulator configured for visualizing operations of the movable object in the one or more simulated states.

A control method includes sending, by a controller, a created context-aware model to a context-aware engine. The context-aware model is used to define a preset control performed when target data meets a trigger condition and to instruct the context-aware engine to send indication information to the controller when the context-aware engine determines that the target data meets the trigger condition. The preset control is used to implement a context-aware function. The indication information is used to indicate that the target data meets the trigger condition. The method also includes receiving, by the controller, the indication information. The method further includes performing, by the controller, the preset control based on the indication information.

A control method includes sending, by a controller, a created context-aware model to a context-aware engine. The context-aware model is used to define a preset control performed when target data meets a trigger condition and to instruct the context-aware engine to send indication information to the controller when the context-aware engine determines that the target data meets the trigger condition. The preset control is used to implement a context-aware function. The indication information is used to indicate that the target data meets the trigger condition. The method also includes receiving, by the controller, the indication information. The method further includes performing, by the controller, the preset control based on the indication information.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

Provided are techniques for proactively operating gas-oil separation plant (GOSP) type process facilities that include determining historical operational characteristics of a GOSP for a past time interval using historical operational data for the GOSP, determining expected operating characteristics of the GOSP for a subsequent time interval using the historical operational characteristics, determining an operating plan for the GOSP using the expected operating characteristics, and operating the GOSP in accordance with the operating plan.

Provided are techniques for proactively operating gas-oil separation plant (GOSP) type process facilities that include determining historical operational characteristics of a GOSP for a past time interval using historical operational data for the GOSP, determining expected operating characteristics of the GOSP for a subsequent time interval using the historical operational characteristics, determining an operating plan for the GOSP using the expected operating characteristics, and operating the GOSP in accordance with the operating plan.

A mechanical ventilator (10) is connected with a ventilated patient (12) to provide ventilation in accordance with ventilator settings of the mechanical ventilator. Physiological values (variables) are acquired for the ventilated patient using physiological sensors (32). A ventilated patient cardiopulmonary (CP) model (40) is fitted to the acquired physiological variables values to generate a fitted ventilated patient CP model by fine-tuning its parameters (50). Updated ventilator settings are determined by adjusting model ventilator settings of the fitted ventilated patient CP model to minimize a cost function (60). The updated ventilator settings may be displayed on a display component (22) as recommended ventilator settings for the ventilated patient, or the ventilator settings of the mechanical ventilator may be automatically changed to the updated ventilator settings so as to automatically control the mechanical ventilator.

A mechanical ventilator (10) is connected with a ventilated patient (12) to provide ventilation in accordance with ventilator settings of the mechanical ventilator. Physiological values (variables) are acquired for the ventilated patient using physiological sensors (32). A ventilated patient cardiopulmonary (CP) model (40) is fitted to the acquired physiological variables values to generate a fitted ventilated patient CP model by fine-tuning its parameters (50). Updated ventilator settings are determined by adjusting model ventilator settings of the fitted ventilated patient CP model to minimize a cost function (60). The updated ventilator settings may be displayed on a display component (22) as recommended ventilator settings for the ventilated patient, or the ventilator settings of the mechanical ventilator may be automatically changed to the updated ventilator settings so as to automatically control the mechanical ventilator.