4 results about "Model builder" patented technology

The invention relates to a multi-agent system and method for Simulink modeling in the aviation field. The existing automatic modeling method for Simulink modeling in the aviation field highly depends on hard coding, converts formalized requirements into model design, and has the defects of high development cost, dependence on expert knowledge, incapability of automatic verification, no support of a third-party library and the like. According to the multi-agent system provided by the invention, a plurality of expert agents are integrated and designed and workflows are strictly arranged, so that the design, implementation and verification processes of Simulink modeling in the aviation field are fully automated. With reference to abstract drawings, the whole system comprises a model designer, a test review device, a model builder, an execution and debugger, a report compiler and an Agent RAG subsystem, and different sub-modules depend on large model capability and a context management strategy to collaboratively complete tasks in a specific process. Compared with a traditional automatic modeling technology, the system and the method have the advantage that the modeling flexibility and efficiency are greatly improved.

Methods, systems, apparatuses, devices, and computer program products are described. A data service may receive a first user input indicating a first set of entities for training an artificial intelligence (AI) model for propensity score-prediction. The data service may receive a second user input indicating a set of outcome conditions which define what a user would like to predict about a customer (e.g., propensity to purchase). The data service may generate the AI model accordingly, and based on executing the AI model, generate a set of prediction metrics (propensity scores) for a second set of entities. The data service may store an indication of the AI model for review by a user. When the user approves the AI model and publishes the AI model to the data service, the generated propensity scores may be used to generate a segment of entities of the second set of entities.

The hereby disclosed invention pertains to a method and system for creating and executing customized software-based models for simulating production processes. It involves defining simulation targets, selecting suitable equations from a digital catalog, and using a Model Builder Module to assemble these equations into a model. The digital catalog consists of equations stored in the Functional Mock-up Unit (FMU) format, which can be easily updated and reused. The method allows for rapid model creation and modification by simply adjusting a configuration file, eliminating the need for repeated qualification. This approach enhances the accuracy and efficiency of simulations, enabling real-time adjustments to production parameters based on model predictions. Additionally, the invention includes a workflow for adding new equations to the digital catalog, ensuring it remains current and comprehensive. The system supports various modeling types, including mechanistic, empirical, and machine learning models, and is compatible with GxP environments. This flexible and adaptable solution is particularly useful for optimizing bioprocesses in bioreactors, providing precise control over production conditions and outcomes.

System and method are disclosed for multi-phase process of automated data capture for photogrammetry and 3D model building of an unknown object (311) in an unknown environment. Planner module (152) generates a flight plan (413) for a camera drone (110) to fly autonomously on a flight path along a virtual polygon grid (302) defined above the target object (311) during a survey phase. Model builder computer (153) receives a point cloud dataset (321) captured by LiDAR sensor on camera drone (301) during survey flight and constructs low resolution 3D mesh (331) of the target object (311). Planner module (152) generates a flight path (413) for camera drone inspection phase with virtual waypoints surrounding the target object (311) at a marginal distance from the surface defined by the low resolution 3D mesh (331). Model builder (153, 163) builds a high resolution 3D model (422) of the target object (311) using photogrammetry processing of high resolution images captured by camera drone (411, 412) during inspection phase.