24 results about "Cloud simulation" patented technology

The invention discloses a map generation method, electronic equipment and a robot, and relates to the technical field of artificial intelligence. The method comprises the steps of generating a structured prior map representing an environmental space occupation state according to environmental geometric shape design data of a target physical task space; determining a simulation path of the intelligent mobile device moving in the passage area of the structured prior map, and performing simulation scanning operation on the virtual physical model of the target physical task space along the simulation path to obtain laser point cloud simulation data and motion state simulation data; and generating a navigation map based on a pose map according to the laser point cloud simulation data and the motion state simulation data. According to the method and the device, the problems of high dependence on manpower, time consumption and uncertain generation results during map generation in the prior art can be solved, the map with high accuracy can be efficiently and automatically generated at low cost, and the task execution efficiency and the task execution precision of the intelligent mobile equipment are improved.

This application provides a testing method and apparatus for an autonomous driving cloud simulation platform. The method includes: acquiring multi-source data from the autonomous driving cloud simulation platform; classifying and structurally extracting the multi-source data to obtain corresponding knowledge fragments and storing them in a knowledge source database; vectorizing the knowledge fragments according to their types using different language models to obtain document vector sets, code vector sets, and scene parameter vector sets; receiving test requirements described in natural language and quantizing them; performing vector retrieval in the document vector set and scene parameter vector set using a retrieval enhancement generation algorithm; inputting the retrieval results into an artificial intelligence model to understand the test scenario requirements; obtaining test case logic output by the model; retrieving test code templates from the code vector set based on the test case logic; and injecting input parameters into the test code templates to obtain test code for testing. This application can improve the efficiency and accuracy of testing autonomous driving cloud simulation platforms.

This invention discloses a remote sensing cloud simulation method and apparatus based on a classification-guided diffusion model, belonging to the field of remote sensing image cloud and fog simulation. The method includes: acquiring and preprocessing multi-temporal remote sensing images to construct a dataset containing registered pairs of cloudless and cloudy images; pre-training a feature extraction network based on cloud mask data and performing unsupervised clustering using the cloud features extracted by the network to achieve automated cloud classification; training a pre-constructed cloud simulation diffusion model using the cloudless image and cloud classification results as input, and the corresponding cloudy image as the ground truth supervision signal, to learn the mapping relationship from cloudless to cloudy states; and inputting the target cloudless image and a specified cloud category into the trained cloud simulation diffusion model to generate a simulated cloud-containing remote sensing image. This invention effectively improves the realism of generated clouds and fog, the naturalness of integration with ground features, and the controllability of generating specific cloud categories, providing sufficient and realistic training samples for cloud-related data-driven models.

The invention provides a data processing method and device, equipment, a storage medium and a program product, and relates to application of a simulation data processing technology in the field of vehicles. The method comprises the following steps: acquiring driving data acquired by a vehicle; processing the driving data in a cloud simulation environment matched with the vehicle to obtain sensing result data; the cloud simulation environment matched with the vehicle is configured to be a simulation environment simulating an actual scene of a vehicle end; and outputting the sensing result data according to a structured format to be used as training data of an auxiliary driving model. According to the method, the cloud simulation environment equivalent to the actual scene of the vehicle end can be constructed, and the driving data acquired by the vehicle can be stably and reliably processed by utilizing the sufficient computing power of the cloud end, so that the complete, consistent and traceable high-quality sensing data can be obtained. The sensing result can be output according to a preset format to be used for assisting training of the driving model, more accurate and richer learning signals are provided for the model, and the processing capacity of the model on a real driving scene is effectively improved.

1. The name of the design product: cloud simulation detection system information management display screen panel. 2. The use of the design product: the interface can be used on electronic devices such as physical carrier mobile phones, notebook computers, desktop computers, tablet computers, etc., for cloud simulation scene arrangement and monitoring related information display. 3. The design points of the design product: the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: clicking the "deploy" button in the upper right corner of the main view interface can enter interface change figure 1, which is used as a simulation scene management interface; clicking the task icon in the middle of interface change figure 1 enters interface change figure 2, which is used as a simulation scene monitoring interface.

The present application relates to cloud computing and clean room equipment operation and maintenance management technical field, specifically to clean room equipment operation and maintenance system based on cloud computing;Contain data access, cloud simulation processing and terminal execution module;The system constructs management benchmark model through collecting business plan and operation and maintenance rule data;Its core is to combine resource account to generate theoretical damaged data through abnormal disturbance simulation, and calculate the dynamic time warping similarity and time series cross-correlation value of actual and theoretical residual vector to generate state decision result;When outputting state decision, the system will issue corresponding operation and maintenance instruction work order, and dynamically correct the disturbance operator weight;The present application realizes the change from single judgment depending on fixed threshold to accurate distinction between normal load deviation and abnormal equipment degradation, which can reduce the false positive rate of unnecessary room inspection and the false negative rate of equipment out of control.

This invention discloses a nuclear power industry mechanism model platform architecture for integration with an industrial internet platform, comprising a runtime environment layer, a service layer, an interface layer, a view layer, and an application layer. The runtime environment layer abstracts the hardware, provides the necessary library files for the upper-layer environment, and handles inter-cluster scheduling and communication. The service layer includes AI models, real-time computing models, cloud simulation models, industrial cloud software models, auxiliary analysis tools, and integrated development and execution tools. The interface layer standardizes and uniformly manages the interfaces of the service layer. The view layer includes mechanism model library interfaces, an operation management module, a data management module, and an engineering management module. The application layer includes artificial intelligence application scenarios, real-time computing scenarios, cloud simulation scenarios, and cloud-based industrial software scenarios. This invention provides an integrated development and execution environment for nuclear power industry mechanism models.

The invention discloses a report management system, method, equipment and medium for virtual device cloud simulation, and the system comprises a storage server which is configured to store report content; a database configured to store report records; the report container is configured to query and modify the report record in the database, access the report content of the storage server according to the report record, and display and modify the report content; the virtual tester container is configured to manage starting, running and ending of the virtual device container and read reports in the virtual device container; the server is further configured to store the report content to the storage server and add or modify accessible report records in the database; and the virtual device container is configured to read the numerical value in the virtual tester container, simulate actual device operation and record an operation report. The technical framework of the container cloud platform can be met, the virtual device test report can be stored, checked and shared, and the simulation test efficiency and the user experience can be effectively improved.

This application discloses a map generation method, electronic device, and robot, relating to the field of artificial intelligence technology. The method includes: generating a structured prior map representing the occupancy state of the environmental space based on environmental geometry design data of the target physical task space; determining a simulated path for the intelligent mobile device to move within the traversable area of ​​the structured prior map, and performing a simulated scanning operation on a virtual physical model of the target physical task space along the simulated path to obtain laser point cloud simulation data and motion state simulation data; and generating a pose-based navigation map based on the laser point cloud simulation data and motion state simulation data. This application can solve the problems of related technologies in map generation, which are highly dependent on manual labor, time-consuming, and produce uncertain results. It can efficiently, cost-effectively, and automatically generate highly accurate maps, improving the task execution efficiency and accuracy of intelligent mobile devices.

The application relates to a kind of super-high-speed collision space debris cloud simulation calculation method and device in aerospace technology field, the method comprises the following steps: establishing the three-dimensional geometric model of projectile and target plate, the model is meshed, each grid corresponds one SPH particle, reads initial parameter and assigns to the SPH particle, searches the particle adjacent to the SPH particle and the particle adjacent to the SDPH particle, calculates the update parameter of the SPH particle and the update parameter of the SDPH particle based on the search result, judges whether SPH particle is converted into SDPH particle or not;If not, the update parameter of SPH particle is used to update SPH particle parameter;If yes, the update parameter of the SDPH particle obtained by using the update parameter calculation method of SDPH particle is used to update the SDPH particle parameter, after the end of the above loop calculation, the debris cloud shape distribution and scale distribution can be visualized and displayed.The application reduces the amount of calculation, and has better accuracy.

Systems and methods are provided for implementing virtual cloud simulation within a public cloud network. In examples, a domain name system ("DNS") server in a virtual cloud network receives, from a service under test that is running within the virtual cloud network, a request to access a resource, the request including non-routable cloud configurations for the resource. A central configuration repository in the virtual cloud network maps the non-routable cloud configurations for the resource that are identified in the request from the service under test to public cloud configurations of the resource. A reverse proxy server in the virtual cloud network directs the request to a public cloud instance of the resource that is running in another portion of the public cloud network, directs return data traffic from the public cloud instance of the resource back to the service under test in the virtual cloud network.

This invention discloses a radar echo simulation method based on layered chaff cloud simulation, constructing the chaff cloud as a layered mathematical model segmented according to vertical height. The relationship between the shape parameters of the chaff layers and the chaff scattering coefficient is studied, solving the problems of excessive chaff unit numbers and slow computation speed caused by traditional ellipsoidal modeling based on small cube equivalents. First, the chaff cloud is divided into chaff units at different heights according to its density; second, the relationship between the shape of the chaff unit and the radar scattering coefficient is considered; finally, the echo signal generated by the motion law corresponding to the chaff unit is modeled, and the signals of the overall chaff units are superimposed to obtain the final echo. This invention only requires estimating the shape and size of the chaff cloud, thus achieving fast and accurate simulation of radar echoes from chaff clouds.

The present application relates to the field of AI. The present application specifically relates to a point cloud simulation model training method and a related device. The method comprises: acquiring a plurality of pieces of first point cloud data at a plurality of different positions in a target scene; on the basis of the plurality of pieces of first point cloud data, constructing an initial point cloud simulation model, and determining a plurality of pieces of pose information of a target object, parameters of the initial point cloud simulation model comprising the reflection intensity and loss rate of a Gaussian sphere corresponding to a point in a point cloud, and the target object is an object in the plurality of pieces of first point cloud data; on the basis of camera intrinsic parameters, respectively converting the plurality of pieces of first point cloud data into a plurality of first depth maps; and training the initial point cloud simulation model on the basis of the plurality of pieces of pose information and the plurality of first depth maps, so as to obtain a target point cloud simulation model. Real-time rendering of point clouds can be achieved, and rendering precision of point clouds can be improved.

The application discloses a kind of city emergency management simulation method, device, electronic equipment and storage medium.The present application includes: obtaining the emergency knowledge and emergency data corresponding to emergency task;According to the emergency knowledge and the emergency data determine emergency role;The nested execution pair of the emergency role is constructed;According to the nested execution pair, the system requirement of the emergency role is extracted, and the emergency system analysis template is generated according to the system requirement;Task decomposition is carried out on the emergency task using the emergency system analysis template, and the task decomposition result is obtained;Interactive logic modeling is carried out using the task decomposition result, and the logic flow is formed;The logic flow is used to generate Agent execution graph;Executable simulation model of the Agent execution graph is generated, and the executable simulation model is combined with cloud simulation model.

The invention discloses a rapid expansion cloud chamber environment simulation method and system based on artificial intelligence, and the method comprises the steps: obtaining the experimental data and experimental conditions of a to-be-simulated expansion cloud chamber, carrying out the structural classification based on the experimental data according to the experimental conditions and expansion stages, and obtaining the feature vector of each stage, meta learning is carried out through a neural network according to the feature vectors, a cloud simulation model and simulation errors are obtained, a cloud chamber working condition interval is identified based on the simulation errors and experimental data, an output result of the cloud simulation model in a high-deviation interval is obtained according to the working condition interval, and an interval result is corrected through a CFD equation based on the output result. And obtaining a cloud room environment simulation result. According to the method, the cloud expansion stage is split, the fog drop growth process is fitted, CFD local correction is carried out on the simulated high-deviation interval, the prediction error control precision is improved, the prediction and collaborative analysis efficiency of the fog forming process in the complex environment is improved, and meanwhile good interpretability is achieved.

The invention relates to the field of AI. The invention particularly relates to a point cloud simulation model training method and related equipment. The method comprises the following steps: acquiring a plurality of pieces of first point cloud data of a plurality of different positions in a target scene; constructing an initial point cloud simulation model based on the plurality of pieces of first point cloud data and determining a plurality of pieces of pose information of the target object, wherein parameters of the initial point cloud simulation model comprise reflection intensity and loss rate of a Gaussian ball corresponding to a point in the point cloud; the target object is an object in the multiple pieces of first point cloud data; converting the plurality of first point cloud data into a plurality of first depth maps based on camera internal reference; and training the initial point cloud simulation model based on the plurality of pose information and the plurality of first depth maps to obtain a target point cloud simulation model. The real-time rendering of the point cloud can be realized, and the rendering precision of the point cloud can be improved.

This invention provides a structured hierarchical management method, device, and server for cloud simulation data, relating to the technical field of industrial simulation applications. The method includes: performing hierarchical storage processing on cloud simulation result data based on access popularity and usage demand information, storing the cloud simulation result data according to corresponding data storage structures to obtain multi-level result data; preloading simulation animations in the multi-level result data using a preset animation preloading switch, and independently indexing and rendering components in static cloud maps within the multi-level result data to obtain target multi-level result data; and performing automated screenshot processing and screenshot validity verification on the target multi-level result data to obtain a target simulation report. This invention can significantly reduce storage costs and improve the accuracy of displayed results by optimizing the storage and display process of cloud simulation data.

The application relates to a cloud simulation computing resource prediction method, device and equipment based on ranking learning, which comprises the following steps: acquiring a feature data set of a simulation application on a cloud computing node; the data in the feature data set comprises pre-operation parameters and runtime parameters of the simulation application; relevant feature data which significantly affects the running time of the simulation application is extracted from the feature data set based on a SHAP feature extraction method; the relevant feature data is input into a constructed ranking learning model to predict the priority ranking of different cloud computing resource usage; and the cloud computing resource usage with the highest ranking output by the ranking learning model is taken as an optimal cloud simulation computing resource prediction result. The relevant feature data which significantly affects the running time of the simulation application is extracted as model input by using the SHAP explainability-based feature extraction method, and the ranking learning model is used to predict the cloud computing resource usage with the highest ranking, so that the resource prediction performance is greatly improved.

Systems and methods are provided for implementing virtual cloud simulation within a public cloud network. In examples, a domain name system (“DNS”) server in a virtual cloud network receives, from a service under test that is running within the virtual cloud network, a request to access a resource, the request including non-routable cloud configurations for the resource. A central configuration repository in the virtual cloud network maps the non-routable cloud configurations for the resource that are identified in the request from the service under test to public cloud configurations of the resource. A reverse proxy server in the virtual cloud network directs the request to a public cloud instance of the resource that is running in another portion of the public cloud network, directs return data traffic from the public cloud instance of the resource back to the service under test in the virtual cloud network.

The application relates to the field of agricultural robots and discloses an autonomous operation intelligent fruit and vegetable picking robot carrying a deep learning model, which integrates a mobile vehicle frame, a multi-degree-of-freedom mechanical arm, a visual perception module and an edge computing control module. In the motion planning layer, the obstacle avoidance speed is mapped into the self-motion joint speed component by using the Jacobian matrix null space projection technology, so that the linkage posture is adjusted to avoid obstacles under the premise of keeping the end operation trajectory locked. In the end-cloud cooperation layer, high-value difficult example samples are screened based on information entropy and value deviation, and the model capacity is continuously enhanced by combining cloud simulation calibration and knowledge distillation technology. In the system maintenance layer, the double-buffer memory architecture and atomic-level pointer switching technology are adopted to complete the seamless hot update of the model in the zero-speed state of the mechanical arm. The application effectively solves the motion planning conflict and model iteration lag problem in the unstructured environment, and significantly improves the operation robustness and long-term unattended operation reliability of the robot.

The utility model discloses a temperature monitoring device for a tall tower, which relates to the structure monitoring technology, and comprises a tall tower and an external measuring assembly, and the external measuring assembly comprises an external contact type temperature sensor, an internal contact type temperature sensor, a wind speed sensor and a non-contact type temperature sensor. The external measurement assembly comprises an external contact type temperature sensor, an internal contact type temperature sensor, a wind speed sensor and a non-contact type temperature sensor, the temperature distribution condition of the tower can be obtained in real time through the external measurement assembly, temperature stress calculation is conducted through the cloud system, the health condition of the tower can be monitored in real time, and the system is simple in structure and convenient to use. According to the utility model, the strain gauges are arranged, and strain gauge data can be compared with a structure field in a cloud simulation system for correcting stress field data of the cloud simulation system, so that the data accuracy is higher, and the simulation is more real.

The invention belongs to the technical field of nuclear power industry internet platform optimization, aims to solve the problem that a nuclear power industry mechanism model lacks an integrated development environment, and discloses a nuclear power industry mechanism model integration platform which comprises an operation environment layer, a service layer, an interface layer, a view layer and an application layer. The operation environment layer is used for providing library files for an upper-layer environment and carrying out scheduling and communication among clusters, the service layer is used for carrying out unified management, scheduling and operation on various mechanism models and tools, and the interface layer is used for carrying out standard packaging and unified management on interfaces of the service layer. The view layer is used for presenting a complex model capability and a data processing process of a bottom layer, and the application layer is used for providing an artificial intelligence application scene, a real-time computing scene, a clouded simulation scene and an industrial clouded software scene which are quickly constructed based on lower-layer services. According to the application, an integrated development operation environment is provided, and an online arrangement and debugging operation environment and a service packaging and publishing function are provided for four components of a service layer.

The application belongs to the field of artificial intelligence, computer vision and computer graphics, and discloses a method and system for VR real-time rendering of low-code-rate cloud rendering streaming, which comprises the following steps: obtaining a rendering frame image; constructing a lightweight super-resolution model; inputting the rendering frame image into the lightweight super-resolution model to obtain a reconstructed 4K resolution image; and performing real-time rendering and display on a VR device based on the reconstructed 4K resolution image. The application not only avoids the high bandwidth pressure caused by direct transmission of 4K video, but also overcomes the hardware limitation that low-config devices cannot perform complex rendering tasks. Through the combination of scene customization model and video coding, high-quality and low-delay visual experience is realized under limited resources, and the application is suitable for various application scenarios with high requirements for image quality and real-time performance, such as large-scale cloud simulation, VR tourism, intelligent power grid monitoring and the like.

The application discloses a cloud simulation method and system for simulating train operation, and the method comprises the following steps: determining devices needing direct manipulation, and deploying the devices in a front end, wherein the devices needing direct manipulation comprise a plurality of first CTC units; determining a plurality of second CTC units and a simulation system to be deployed, and deploying the plurality of second CTC units and the simulation system in a back end cloud platform to simulate train operation in the back end cloud platform. The application can realize flexible configuration of system environments required by various application software without changing the use habits of users to the devices, and has the characteristics of easy deployment and investment saving.