15 results about "Simulated reality" patented technology

The present application relates to the technical field of graphic simulation system, specifically to a hydraulic fracturing process simulation and optimization system based on digital twin technology; containing real-time input, probabilistic simulation, adaptive assimilation and risk analysis and control unit; the system generates prior fracture probability cloud by combining agent model with real-time data; the core is to generate posterior fracture probability cloud by assimilating monitoring data, and to calculate simulation-realistic credibility index and probabilistic risk boundary accordingly; the system generates adaptive control signals according to the index and feeds back to the assimilation unit; the present application overcomes the false accuracy of traditional deterministic simulation, and realizes the quantification and intuitive display of fracture uncertainty.

The utility model discloses a limited space operation and rescue drilling platform, which belongs to the technical field of drilling platforms, and comprises a platform main body and a simulation pipeline assembly arranged at the bottom of the platform main body, the top of the platform main body is provided with a main well mouth and side well mouths positioned on two sides of the main well mouth; the simulation pipeline assembly comprises a main well pipe, a side well pipe, a transverse well pipe and a maintenance bin, the main well pipe, the side well pipe and the transverse well pipe are the same in structure and each comprise two same semicircular pipes, and the semicircular pipe located on the front face is a transparent pipe. Through the arrangement of the platform main body and the simulation pipeline assembly, real ground and underground pipelines are simulated, multi-person drilling operation can be carried out, and the use is flexible; through the arrangement of the main well mouth, the main well pipe and the maintenance bin, the situation of entering the maintenance bin for drilling can be simulated, and through the arrangement of the side well pipe, when drilling rescue work is simulated, ventilation from the side well pipe and rescue from the main well pipe can be simulated.

Computer-readable medium containing instructions that configure a computer to: present an application programming interface (API), wherein the API includes a user-specified sound level parameter; receive, via the API, one or more values ​​for the user-specified sound level parameter, which are assigned to a digital audio asset;and incorporating code into a simulated reality application that is currently being assembled, wherein the code determines a loudness correction gain for the digital audio asset by determining a difference between i) the one or more values ​​for the user-specified sound level parameter and ii) a parameter of the playback hardware device that represents a known or predefined sound level that can be produced by a playback hardware device on which the simulated reality application is running, wherein the loudness correction gain compensates for the difference, and the loudness correction gain is then to be applied to the digital audio asset during the runtime of the simulated reality application.

The invention relates to a social network subject simulation system and method based on a large language model, and belongs to the technical field of cognitive social simulation. In order to fill up evolvable psychological and action mechanisms existing in the cognitive social simulation field, the social network subject simulation system and method are provided, virtual subjects with dynamic memory, reflection and social behavior capabilities are constructed, and interaction and information spreading of multiple subjects in a virtual social environment are realized. Therefore, the public opinion forming and spreading process in a real social network is simulated.

The application provides a defect detection and positioning method based on regional anomaly generation and multi-level reverse distillation, and relates to the technical field of industrial defect detection. The new synthetic anomaly method provided by the application simulates the randomness of the abnormal area in the real scene by generating pseudo-anomaly images of different sizes and shapes, provides rich training data for the model, proposes a single-scale repair strategy including convolutional transformer processing and C-CBAM attention mechanism, enables the student network to efficiently capture global and local features, and realizes basic feature repair, further proposes a multi-scale repair strategy, removes redundant features by using a similarity redundancy removal module to enhance cross-layer feature integration, retains key information, and removes redundant features, simultaneously uses a multi-scale patch block fusion module to strengthen local fine-grained anomaly feature reconstruction, and finally realizes image anomaly detection and positioning through loss calculation and anomaly scoring. The method has good defect positioning effect and can meet the needs of industrial production.

A method is provided for evaluation of data, where it was surprisingly found out that raw data can be simulated using hardware devices. The method actively operates and control hardware components to establish a network that provides hardware components that simulate the relationships between real world concepts. The method is able to automatically perform technical processes such that firstly no human ado is required and secondly the resulting data is not prone to errors. The method suggests iterations on evolving data sets and hence a bias is excluded or at least minimized in each iteration. Also provided is a respectively arranged system along with a computer program product and a computer-readable medium.

Methods and systems are provided for digitally replicating a real-world infrastructure to simulate the behavior of a real-world object, the methods and systems supporting the ability to extend a wireless network by providing services implemented in the network, and supporting optimization of network usage and digital representation accuracy. In one embodiment, the method includes instantiating one or more digital representation (D-Rep) instances corresponding to a digital replica of the real world object included in the real world infrastructure to form a digital infrastructure (D-Inf), and generating one or more digital representation (D-Rep) instances corresponding to a digital replica of the real world object included in the real world infrastructure. The method further includes providing a communication plane to provide a service to the D-Inf. The method further includes connecting to the real world object using the communication plane to collect real world data defining a state of the real world object, and associating the real world data with the D-Rep instance; the behavior of the real-world object is simulated by the D-Rep instance using a simulator that can be accessed by the D-Inf instance using the communication plane.

A first world is one of a real world and a virtual world simulating the real world, and a second world is another of them. A first image is captured by a first camera in the first world, and a second image is captured by a second camera in the second world. A visual positioning system executes common processing that generates a scene graph representing a positional relationship between objects included in the image and extracts a feature amount of the scene graph. The visual positioning system performs matching between a first feature amount extracted by the common processing on the first image and a second feature amount extracted by the common processing on the second image, and then associates the first camera position in the first world and the second camera position in the second world with each other based on a result of the matching.

The invention provides a defect detection and positioning method based on regional anomaly generation and multi-level reverse distillation, and relates to the technical field of industrial defect detection. According to the new anomaly synthesis method provided by the invention, the randomness of an abnormal region in a real scene is simulated by generating pseudo-anomaly images with different sizes and shapes, and rich training data is provided for a model; a single-scale repair strategy is proposed, the single-scale repair strategy comprises convolution transformer processing and a C-CBAM attention mechanism, so that the student network efficiently captures global and local features, and basic feature repair is realized; a multi-scale repair strategy is also provided, cross-layer feature integration is enhanced by means of a similarity redundancy removal module, key information is retained, redundant features are removed, and local fine-grained abnormal feature reconstruction is enhanced by means of a multi-scale patch block fusion module; and finally, through loss calculation and anomaly scoring, realizing anomaly detection and positioning of the image. The method has a good defect positioning effect and can meet the requirements of industrial production.

The invention discloses an artificial intelligence generated image detection model optimization method, system and device and a medium, which are corresponding schemes, and in the scheme, a dynamic interaction optimization mechanism is constructed between a finite available generation model and a to-be-optimized detection model; the performance of the detection model is continuously optimized by simulating a generative model-detection model alternate evolution path in the real world, so that the stable detection performance of the detection model in a real scene is ensured; the method can be applied to various current mainstream detection model architectures, and is wide in application range; meanwhile, images generated by various latest generation models can be detected, generalization is high, practicability is high, and the average identification precision in an experiment can reach 85% or above.

The invention provides a project recommendation model training method and device, a project recommendation method and device and electronic equipment, and belongs to the technical field of artificial intelligence, and the method comprises the steps: constructing a directional missing sample set, a random missing sample set and a non-missing sample set based on a training sample set comprising a plurality of training samples; the training sample comprises a user multi-modal operation feature vector sample and a corresponding item recommendation result label; and performing bidirectional optimization training on the project recommendation model and the corresponding deviation model by utilizing the directional missing sample set, the random missing sample set and the non-missing sample set to obtain the project recommendation model. According to the invention, the directional missing sample set, the random missing sample set and the non-missing sample set are constructed based on the training sample set to simulate the data deviation in reality, and the three sample sets are utilized to carry out bidirectional optimization training on the project recommendation model and the corresponding deviation model at the same time. Through fusion of multi-modal data and a deviation elimination mechanism, the deviation of a project recommendation model is significantly reduced.

The invention discloses a testing method and testing equipment for a positioning device and a computer readable storage medium. The method comprises the steps of obtaining to-be-simulated environment parameters, and generating a test scene based on the to-be-simulated environment parameters; the test scene comprises at least one of urban canyons, tunnels, indoor and outdoor transition, high-speed movement, ground-satellite network switching and complex electromagnetic interference; the to-be-simulated environment parameters comprise test scene environment parameters, motion parameters, trigger parameters and evaluation parameters; generating a dynamic track of a target object corresponding to the test scene, wherein the target object is correspondingly coupled with a positioning device; controlling the target object to run along the dynamic track, and collecting position information of the target object through a positioning device; and evaluating the performance of the positioning device by using the position information collected by the positioning device and the error information of the actual position information of the target object in the test scene. The positioning module is automatically tested by simulating a complex environment in reality, and the testing efficiency is improved.

A computer implemented method of creating a simulated realistic virtual model of a geographical area for training an autonomous driving system, comprising obtaining geographic map data of a geographical area, obtaining visual imagery data of the geographical area, classifying static objects identified in the visual imagery data to corresponding labels to designate labeled objects, superimposing the labeled objects over the geographic map data, generating a virtual 3D realistic model emulating the geographical area by synthesizing a corresponding visual texture for each of the labeled objects and injecting synthetic 3D imaging feed of the realistic model to imaging sensor(s) input(s) of the autonomous driving system controlling movement of an emulated vehicle in the realistic model where the synthetic 3D imaging feed is generated to depict the realistic model from a point of view of emulated imaging sensor(s) mounted on the emulated vehicle.

The application belongs to the technical field of sensor data group behavior recognition, and discloses a group behavior recognition method and device based on self-supervised global-local contrast learning, and constructs two contrast learning tasks of global-global and global-local. In the global-global task, a learnable data enhancement mechanism is designed to generate high-quality enhanced samples, and a high-order hypergraph reasoning module is introduced to capture complex collaboration patterns beyond pairwise interaction in the group. In the global-local task, a cross-group individual mixing strategy is used to simulate a realistic interference scene, and a separable key-value memory network is used to extract local features that can represent the core identity of the group. Through joint optimization of the two tasks, the model can learn a comprehensive group representation that contains both the overall dynamics and focuses on the core members.