Abstract: A material completeness detection method configured to detect whether materials of a target object in a physical production line are complete, includes: inputting an image of the target object in the physical production line into a material completeness detection algorithm to acquire a first detection result; inputting a virtual model of the target object in a virtual production line into the material completeness detection algorithm to acquire a second detection result, where the virtual production line is a DT of the physical production line; and acquiring a material completeness detection result of the target object based on the first detection result and the second detection result. The embodiments of the present disclosure can realize efficient and accurate material completeness detection.

Abstract: A method for digital twin virtual-reality synchronization mapping of a mechanical arm comprises acquiring a virtual mechanical arm model built based on an actual mechanical arm in a virtual environment, where the virtual mechanical arm model is configured to map the actual mechanical arm; acquiring real motion information collected when the actual mechanical arm moves; constructing a training set and a test set based on the real motion information; training a target multilayer perceptron model based on the training set, and testing the target multilayer perceptron model based on the test set, where the target multilayer perceptron model is configured to predict a motion of the virtual mechanical arm model in the virtual environment; and deploying the target multilayer perceptron model on the actual mechanical arm when the target multilayer perceptron model meets a preset condition. According to this method, operation accuracy and efficiency of the mechanical arm are improved.

Abstract: A method for intelligently controlling a mechanical arm includes building a twin model of a mechanical arm, and extracting a state parameter and an action parameter corresponding to task characteristics from the twin model; determining a reward function corresponding to the task characteristics; training a twin delayed deep deterministic policy gradient (TD3) reinforcement learning model; simulating in the twin model based on a physical state parameter of the mechanical arm by using the TD3 reinforcement learning model, to obtain a controllable parameter; and controlling the mechanical arm to execute a corresponding task by using the controllable parameter. The TD3 reinforcement learning model is built based on the state parameter and the action parameter corresponding to the task characteristics and the reward function corresponding to the task characteristics, which can adapt to a dynamically changing environment and requirements for multiple tasks.

Abstract: An intelligent identification and warning method for an uncertain object of a production line in a digital twin environment, includes: establishing a model library for uncertain physical objects from a non-production line system; adding attribute data to the uncertain physical objects from the non-production line system; importing an established model library and added attribute data for the uncertain physical objects from the non-production line system into a model library of an existing DT production line system; performing auto-detection on an uncertain physical object entering a production line system; performing auto-detection on an actual size of the uncertain physical object entering the production line system; warning a danger for an unsafe object by means of voice prompting, system alarming and information pushing; matching a corresponding three-dimensional (3D) model in the established model library for a safe object; and loading a matched 3D model to the DT production line system.

Abstract: A material completeness detection method configured to detect whether materials of a target object in a physical production line are complete, includes: inputting an image of the target object in the physical production line into a material completeness detection algorithm to acquire a first detection result; inputting a virtual model of the target object in a virtual production line into the material completeness detection algorithm to acquire a second detection result, where the virtual production line is a DT of the physical production line; and acquiring a material completeness detection result of the target object based on the first detection result and the second detection result. The embodiments of the present disclosure can realize efficient and accurate material completeness detection.

Abstract: An intelligent identification and warning method for an uncertain object of a production line in a digital twin environment, includes: establishing a model library for uncertain physical objects from a non-production line system; adding attribute data to the uncertain physical objects from the non-production line system; importing an established model library and added attribute data for the uncertain physical objects from the non-production line system into a model library of an existing DT production line system; performing auto-detection on an uncertain physical object entering a production line system; performing auto-detection on an actual size of the uncertain physical object entering the production line system; warning a danger for an unsafe object by means of voice prompting, system alarming and information pushing; matching a corresponding three-dimensional (3D) model in the established model library for a safe object; and loading a matched 3D model to the DT production line system.

Abstract: An intelligent material completeness detection and configuration method based on digital twin and augmented reality (AR) includes: constructing a digital twin model base and knowledge base of an incomplete material; importing the digital twin model base and knowledge base of the incomplete material respectively into a model database and a knowledge database in a digital twin system database; sending materials to be detected into a vision-based material completeness detection platform, sorting out an incomplete material, acquiring corresponding data, and importing the data into an incomplete material information database; performing, based on an AR device, perception and reconstruction of incomplete material configuration; matching a configuration plan of the incomplete material in a digital twin relational database, and performing a virtual-real fusion in the AR device; and allowing, a worker to rapidly and accurately find a location and problem type of the incomplete material and configuration the incomplete

Abstract: The present invention provides a road surface profile estimation device and so on with which any road surface profile, including that of a general road, can be estimated with a high degree of precision using a general-purpose vehicle.