137 results about "Cloud base" patented technology

Aspects presented herein relate to perception data collection and curation. In one aspect, a network entity receives, from a first set of user equipments (UEs), a first set of perception data collected by the first set of UEs. The network entity configures, for a second set of UEs based on the first set of perception data, at least one of: a set of planned routes and time plans for collecting a second set of perception data or a set of embeddings centroids. The network entity receives, from the second set of UEs, the second set of perception data based on at least one of the configured set of planned routes and time plans or the configured set of embeddings centroids.

The invention discloses an automatic monitoring system and method for hydrological data and a storage medium. The automatic monitoring system comprises a multi-modal data acquisition module, an edge intelligent processing module and a cloud platform. The multi-modal data acquisition module acquires water level, water quality and meteorological data in real time through a radar water level gauge, a multi-parameter water quality meter and a rain gauge, and transmits the data to the edge intelligent processing module; the module extracts and aligns spatio-temporal features through a multi-scale spatio-temporal convolution unit, an uncertainty quantization unit is fused with Bayesian to calculate confidence, and complete data or abstract features are selectively transmitted to a cloud according to an abnormal classification judgment result; the cloud platform fuses multi-source features through a space-time attention mechanism, dynamically adjusts an early warning threshold based on a near-end strategy optimization algorithm, aggregates edge node model parameters through a federated learning framework in combination with a homomorphic encryption technology, updates a global model, and then synchronizes the global model to edge nodes to form closed-loop optimization.

The invention provides a sea fog detection method and device based on a satellite-borne laser radar, and belongs to the field of atmosphere remote sensing monitoring. The method comprises the following steps: setting a multi-parameter joint threshold value for cloud-aerosol distinguishing; based on a set multi-parameter combined threshold value, primary distinguishing of cloud-aerosol is completed, and the number of cloud layers within the specified height from the sea surface is screened out from the cloud-aerosol range; in combination with the cloud base height and the scattering characteristic parameters of the cloud layer, the area meeting the scattering characteristic criterion is calibrated as a sea fog layer; calculating continuity characteristics of sea fog levels in vertical and horizontal distribution, and identifying signal interruption points and abnormal distribution areas; and carrying out interpolation completion on scattering characteristic data of a signal interruption point region, eliminating an abnormal distribution region, re-correcting hierarchical characteristics of vertical and horizontal distribution, and outputting an optimized sea fog hierarchical distribution result. According to the sea fog detection method and device based on the satellite-borne laser radar provided by the invention, detection of vertical distribution of sea fog can be accurately and effectively realized.

Embodiments are directed to a cloud based rotation of a secret stored in a secrets storage and stored in a target system. Embodiments receive an identifier of a function for rotating the secret or an identifier of the target system when the target system includes a management Application Programming Interface (“API”) for rotating the secret. Embodiments determine that the secret needs to be rotated based on a rotating schedule. When the identifier of the function is received, embodiments rotate the secret using the function and when the identifier of the target system is received, embodiments rotate the secret using the management API. Rotating the secret includes updating the secret at the secret storage and at the target system.

Confidential workload provisioning is disclosed. To overcome various hardware constraints, a workload (e.g., image or container) to be executed confidentially in an enclave is verified and then re-signed by the cloud service. Re-signing the workload allows workloads to be provisioned confidentially in enclaves or other secure environments while eliminating hardware constraints that may impact workload provisioning.

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power station ultra-short-term power prediction method based on double foundation cloud atlas, which comprises the following steps: collecting real-time foundation cloud atlas of two imagers of a photovoltaic power station, and carrying out image preprocessing; dividing the preprocessed foundation cloud picture into different cloud cluster areas by using an image segmentation algorithm; according to a binocular vision principle, calculating cloud bottom height and horizontal distance of each cloud cluster relative to an observation point; and simulating the ground cloud shadow coverage condition of the photovoltaic power station, obtaining an attenuation coefficient, correcting the irradiation intensity by using the attenuation coefficient, and inputting into the power prediction model to obtain an ultra-short-term power prediction value. Through the prediction method, the shielding condition of the cloud layer projection on each area of the photovoltaic power station can be accurately judged, and the ultra-short-term power prediction precision and accuracy of the large-scale photovoltaic power station under the cloudy weather condition are improved.

The invention discloses a microwave laser composite radar cloud identification method and a computer program product, and the method comprises the steps: removing ground clutters based on transfer learning and an R-FCN network, and eliminating the noise caused by the ground clutters of a microwave radar; a deep learning model is constructed by using transfer learning and R-FCN algorithms, microwave radar and laser radar echo images are input, an attention mechanism is optimized and used in the model to realize adaptive feature extraction, a multi-scale feature pyramid is introduced on the basis of an RPN network, and the recognition and detection capability of cloud regions with different scales is enhanced. Single-result joint inversion of microwave radar and laser radar cloud identification is realized by utilizing cloud boundary self-inspection, cloud feature marking, initial matching and joint inversion of cloud boundaries, and a composite identification cloud area is obtained. The method has a cloud identification function under complex atmospheric conditions such as multilayer clouds and equipment noise interference, is accurate in inversion result, is not sensitive to ground clutter and daytime background noise, and fully integrates the identification advantage of a laser radar on a thin-roll cloud area and a cloud bottom and the cloud top identification advantage of a microwave radar.

The invention discloses an industrial software cloud base intelligent operation and maintenance and fault self-healing method based on digital twinning, and relates to the technical field of digital twinning. According to the method, the hierarchical digital twinborn model is constructed and dynamically updated, and the operation state of the cloud base is accurately mapped and visually displayed in combination with independent visual modules of a physical layer, a logic layer and an application layer; the method comprises the following steps: calibrating a reference value in real time through a dynamic reference library, quickly identifying abnormity of each layer, generating graded early warning, improving abnormity response timeliness, constructing a dynamically updated fault knowledge graph based on historical fault data, integrating a multi-dimensional correlation analysis and confidence coefficient verification mechanism, positioning a unique fault reason, greatly shortening a fault diagnosis period, and improving fault diagnosis efficiency. And a strategy library is constructed based on a fault knowledge graph and historical self-healing cases, simulation verification is carried out on candidate strategies through a digital twin model, the effectiveness of the self-healing strategies is ensured, and then the self-healing strategies are issued and executed, so that intelligent and automatic fault repair is realized, and stable operation of the cloud base is ensured.

According to one embodiment of the present disclosure, A visual localization method comprises generating at least two or more anchor points from three-dimensional point clouds; generating three-dimensional ray clouds by connecting three-dimensional points included in the three-dimensional point clouds with one of the generated anchor points; extracting feature points of an input image; and clustering a plurality of lines included in the three-dimensional ray clouds based on the at least two or more anchor points, sampling two ray cloud clusters out of the clustered ray cloud clusters, and estimating a pose of a camera that captured the input image based on the sampled ray cloud clusters and the feature points.

The invention relates to the field of atmospheric science, and discloses a method for calculating the uniformity degree of a coil-clamping mixing process in cloud based on a remote sensing means, and the method comprises the steps: building a function relation between a parameter b and a uniform mixing degree psi in a radar reflectivity factor and water content function relation (Z-LWC) based on a high-resolution numerical mode; according to the radar reflectivity factor output by the cloud radar and the inverted water content, a Z-LWC relation based on remote sensing observation is established, and the value of a parameter b is obtained; according to the value of the parameter b obtained through remote sensing observation, the function relation between the parameter b and the psi obtained through the numerical mode is combined, and the uniform mixing degree of the coil clamping mixing process is calculated. According to the invention, the observation and research of the clip-coil mixing process can be expanded from expensive and discontinuous aircraft observation data to economic, continuous and widely used remote sensing observation data, the limitation of the conventional research method is broken through, new means for the observation and research of the clip-coil mixing process are added, and the research efficiency of the clip-coil mixing process is improved.

The invention provides an aviation support system efficiency evaluation method and system based on a combined weighting and cloud gravity center theory. The method comprises the following steps: determining an aviation guarantee efficiency evaluation object, and establishing an aviation guarantee efficiency evaluation index system; the method comprises the following steps: based on an aviation guarantee efficiency evaluation index system, acquiring data of quantitative indexes through simulation deduction, rating the qualitative indexes by experts, converting the qualitative indexes into quantitative data through an evaluation grade scale table, standardizing the index data, and calculating weights of the indexes by adopting a combined weighting method combining an analytic hierarchy process and an entropy weight method; the method comprises the following steps: determining digital characteristics of an index cloud based on aviation guarantee efficiency evaluation index data, calculating a comprehensive cloud gravity center, determining a cloud gravity center position in an ideal state based on standardized index data, and calculating a weighted deviation degree; and calculating an efficiency evaluation value of the aviation guarantee system based on the comprehensive weight and the weighted deviation degree of the aviation guarantee efficiency evaluation index, and forming a final evaluation conclusion.

The invention relates to the technical field of three-dimensional wind field inversion, in particular to a three-dimensional wind field inversion method in a thunderstorm cloud strong echo region based on a single Doppler weather radar, which comprises the following steps of: 1, carrying out lattice processing on a radar reflectivity factor to obtain a radar reflectivity factor; the reflectivity factor is converted from a polar coordinate system to a Cartesian rectangular coordinate system through interpolation; 2, performing same-layer continuous moment strong echo graph similarity recognition through an image similarity recognition technology; 3, calculating the horizontal movement speed of the thunderstorm cloud at different heights; and 4, comparing the observation result of the radial speed of the radar with the horizontal movement speed of the thunderstorm, and calculating the rising or sinking airflow speed. According to the method, the grid data of the single Doppler weather radar and the observation data of the radial speed of the radar can be utilized to accurately describe the three-dimensional wind field of the strong echo region in the thunderstorm cloud.

According to aspects of the disclosure, an uninterruptible power supply (UPS) system is presented, the UPS System comprising at least one first input configured to be coupled to a primary power source and to a secondary power source, a second input configured to be coupled to a backup power source, an output configured to be coupled to at least one load, and at least one controller configured to receive a signal indicating that the secondary power source is prepared to provide secondary power to the UPS system, determine, responsive to receiving the signal, whether an energy level of the backup power source is above a threshold energy level, and control, responsive to determining that the energy level of the backup power source is above the threshold energy level, the UPS system to provide output power derived from the backup power source to the output.

A detection method for determining the communication availability of a satellite-to-ground laser link based on sky infrared remote sensing images comprises the following steps: 1) obtaining meteorological information and sky infrared remote sensing images above a satellite-to-ground laser communication ground station; 2) calculating the corresponding elevation angle and position relationship between the satellite and the ground station at each moment, and calculating the cloud base height and cloud optical depth of the spatial region through which the satellite-to-ground link passes based on the meteorological information and the sky infrared remote sensing images; 3) using a Monte Carlo laser vector transmission model to model the cloud laser channel based on the corresponding elevation angle and position relationship, cloud base height, and cloud optical depth results between the satellite and the ground station at each moment, and normalizing the residual to serve as a reference value for the link communication availability; 4) judging the communication availability of the laser link between the satellite and the ground station based on the atmospheric turbulence obtained in step 1; and 5) predicting future sky infrared remote sensing image change information of a specified satellite-to-ground laser ground station using machine learning technology.

The invention provides a train material volume detection method, device and equipment based on millimeter wave radar, and relates to the technical field of railway transportation intelligent detection and three-dimensional point cloud data processing. The method comprises the following steps: acquiring point cloud data obtained by scanning a train through a millimeter wave radar, and preprocessing the point cloud data to obtain a train main body point cloud; wherein the point cloud data comprises position coordinates of points; on the basis of density change characteristics of the train main body point cloud in the train advancing direction, carriage connection gaps are recognized; based on the compartment connection gap and the preset compartment length, compartment section point clouds are divided from the train main body point clouds; and based on the coordinates of the points in the compartment section point clouds in the train height direction, material surface point clouds are extracted, and material volume calculation is carried out based on the material surface point clouds. The system can stably run in a loading building environment with large dust, strong shielding and complex illumination, and provides a reliable technical means for real-time loading monitoring of bulk materials of a train.

The invention provides a vehicle driving control method and device and a computer readable storage medium, in the vehicle driving process, the current terrain is sensed to obtain vehicle end sensing information, the cloud reference information of the current terrain is obtained from the cloud based on the current position of the vehicle, and the vehicle end sensing information and the cloud reference information are arbitrated to obtain the vehicle driving control information. And the vehicle end sensing accuracy is improved. After the arbitrated target information is determined, the vehicle driving parameters adapting to the current terrain are determined based on the accurate target information, a driver is helped to better adapt to the current road condition, and the driving comfort and safety are effectively improved.

A system for metric collection and performance analysis of an asset in a network having one or more processors, a memory, and one or more programs stored in a memory, the one or more programs comprising instructions configured to identify a plurality of products associated with the asset, wherein each product is configured to generate one or more metrics associated with performance of said product, retrieve one or more metrics associated with one or more products, determine a key performance indicator for one or more of the metrics, assign a threshold value to one or more key performance indicator to determine performance of the one or more products, transmit a prompt to a user, when the key performance indicator associated with one or more products is below the threshold value and generate a dashboard, said dashboard presenting a report corresponding to one or more key performance indicator for associated metrics and the threshold value.

Some embodiments provide a novel method for configuring components of a software-defined network (SDN) to automatically deploy and monitor a logical edge router for a user. The method configures a policy parser to parse an intent-based Application Programming Interface (API) request to identify a set of attributes for the logical edge router. The method also configures a set of multi-cloud edge orchestrators (1) to use the set of attributes to derive an edge deployment plan specifying a set of two or more edge instances to implement the logical edge router, and (2) to deploy the set of edge instances in a set of two or more clouds based on the edge deployment plan.

The invention discloses a cloud supercooled water content prediction method based on deep learning and spatio-temporal data driving, and the method comprises the steps: obtaining satellite multi-channel radiation transmission data, carrying out the channel screening and feature extraction, obtaining radar reflectivity data, and carrying out the data interpolation and feature extraction; the method comprises the following steps: acquiring conventional meteorological data of a ground automatic meteorological station, performing feature extraction, fusing the features by using a multi-head attention mechanism to obtain fused features, establishing a deep learning neural network model, and predicting the content of supercooled water in cloud in a future time period. The method can more comprehensively and accurately predict the content and the distribution condition of the supercooled water in the cloud, and has important scientific and practical significance for relieving water resource shortage, improving the ecological environment and guaranteeing low-altitude economic safety.

The invention discloses a thunderstorm cloud electric field prediction method, device and equipment based on a deep learning algorithm, and relates to the technical field of thunderstorm cloud electric field prediction. The method comprises the following steps: firstly, acquiring a target convection parameter group of a region of interest, and acquiring historical observation data of each convection parameter in the target convection parameter group and historical observation data of an electric field in a thunderstorm cloud; constructing a convection parameter prediction model and a thunderstorm cloud internal electric field inversion model based on historical observation data; integrating the two models to obtain a thunderstorm cloud internal electric field prediction model corresponding to each convection parameter set; and performing thunderstorm cloud internal electric field prediction on the region of interest according to the thunderstorm cloud internal electric field prediction model corresponding to each convection parameter set. According to the application, convection parameter prediction and in-cloud electric field inversion are fused, and prediction of the thunderstorm in-cloud electric field can be realized by using observable convection parameters.

A system and method for determining performance of connections between an endpoint device and a Cloud region is disclosed. A Cloud based host server communicates with the endpoint device operated by a user through a network. A data collection module is coupled to the Cloud server and the endpoint device. The data collection module periodically collects connection data associated with the endpoint device and collects context data of the endpoint device. An analysis engine compares a connection data metric derived from the connection data to a baseline value of the connection data metric associated with the context data of the endpoint device and determines substandard performance. The analysis engine provides an alert when a substandard performance is determined.

The invention relates to the field of weather prediction, and particularly discloses an intelligent hail monitoring and early warning method and system based on morphological learning, equipment and a medium, and the method mainly comprises the steps: obtaining the multi-source data of hail cloud, and carrying out the recognition and extraction of the key morphological features of the hail cloud based on the multi-source data; a space-time evolution model is constructed, the space-time evolution model comprises a ConvLSTM space-time prediction model and optical flow method motion estimation, and predicted hail cloud key morphological characteristics are output based on the space-time evolution model; and the multi-feature fusion module is configured to perform multi-feature fusion on the key morphological features of the hail cloud, the multi-feature fusion comprises weighted fusion and an attention mechanism, a loss function is set for optimization, and a hail prediction result is obtained. According to the method, the hail cloud form change under different hail disasters is learned through the form learning network according to the form change in the time sequence change process of the hail cloud, the hail is dynamically predicted in combination with the form change, and accurate and dynamic prediction of the hail is achieved.

The present disclosure provides a method for facilitating managing clouds for a user. Further, the method includes receiving requests from user devices associated with users. Further, the method includes receiving information from the devices based on the requests. Further, the information is associated with clouds of the users. Further, the clouds are provided by cloud platforms. Further, the method includes analyzing the information. Further, the method includes determining cloud management actions for managing the clouds based on the analyzing of the information. Further, the method includes generating management information based on the determining of the cloud management actions. Further, the management information facilitates a performing of the cloud management actions. Further, the method includes transmitting the management information.

1. Name of the product in this design: Intelligent All-Sky Imager. 2. Purpose of this design: This design is used for all-weather intelligent observation of cloud cover and cloud base, and directly outputs cloud cover information and cloud height information. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This application provides a method, apparatus, and device for determining the yaw rate of a vehicle, comprising: determining multiple reference point clouds of a reference frame; for any point cloud, i.e., a first point cloud, establishing an equation relationship between the energy of the first point cloud and each reference point cloud based on a first distance between the first point cloud and each reference point cloud, and the weight corresponding to each reference point cloud, and determining multiple weights corresponding to the multiple reference point clouds; determining the energy of the second point cloud after rotation based on any point cloud, i.e., a second point cloud, among multiple target point clouds in the current frame, a preset rotation angle, and the weights of the multiple reference point clouds; determining relevant parameters corresponding to the preset rotation angle based on the energies of the multiple second point clouds and the energies of the multiple target point clouds; determining the yaw angle among the multiple preset rotation angles based on the multiple relevant parameters corresponding to the multiple preset rotation angles; and determining the yaw rate of the vehicle based on the yaw angle and the time interval between the current frame and the reference frame.

The application discloses a kind of foil cloud cluster time-domain scattering characteristic analysis method based on modulated Gaussian pulse radar, specifically includes: the size of foil cloud cluster, the number of foil, length, radius, position coordinate, attitude angle are determined by Matlab;Foil cloud cluster modeling is completed using the method of Matlab-CST joint simulation;The scattering characteristics of foil cloud cluster are analyzed using finite integration algorithm, and the time-domain echo of foil cloud cluster is obtained;Change receiving angle, obtain the time-domain echo of foil cloud cluster from the same angle incidence at different angles receiving electromagnetic wave.The application completes foil cloud cluster modeling by the method of Matlab-CST joint simulation, and the time-domain echo of foil cloud cluster is obtained by time-domain finite integration algorithm, fully considers the influence of randomness and overlapping of foil distribution on time-domain echo.

The invention provides an integrated TBOX remote diagnosis data optimization transmission method and system, and relates to the technical field of Internet of Vehicles. Comprising the steps that a cloud side sends a diagnosis instruction to a TBOX in an encrypted communication mode, and the TBOX conducts physical layer transparent transmission on the diagnosis instruction and transmits the diagnosis instruction to a vehicle-mounted MCU through a UART interface in a transparent mode; the vehicle-mounted MCU identifies a diagnosis instruction from the cloud based on an integrated UDS / J1939 protocol analysis engine, triggers and executes a diagnosis task, obtains a diagnosis result and carries out structured packaging; the vehicle-mounted MCU transmits the diagnosis result subjected to structured packaging back to the TBOX through the UART interface, and the TBOX finally transmits the diagnosis result subjected to structured packaging back to the cloud end in an encryption communication mode. According to the invention, the end-to-end transmission delay of the diagnosis instruction and the vehicle-mounted network protocol is reduced, the diagnosis response efficiency is improved, and high-reliability and low-delay remote diagnosis technical support is provided for the health management of the whole life cycle of the vehicle.

The invention relates to the technical field of meteorological prediction, in particular to a three-dimensional atmosphere and three-dimensional cloud fusion analysis system based on big data. According to the system, the cloud bottom height of a target cloud cluster is determined based on a laser ceilometer, radiation corresponding to the cloud top height of the target cloud cluster is collected based on a meteorological satellite, the cloud top temperature of the target cloud cluster is calculated based on a radiation transmission mode, and the cloud top height of the target cloud cluster is determined based on the cloud top temperature. And detecting precipitation particles of the marked segmented cloud layer, deducing the water content density of the marked segmented cloud layer according to the precipitation particles, obtaining an aerosol particle extinction coefficient and a backscattering coefficient of the marked segmented cloud layer, and obtaining a meteorological characterization value of the target cloud cluster based on the water content density, the aerosol particle extinction coefficient and the backscattering coefficient. The reasonability of the target cloud cluster is analyzed based on the meteorological characterization value of the target cloud cluster, the cloud layer meteorological prediction precision can be improved, and the meteorological prediction timeliness is improved.

The present invention relates to a method for 3-D reconstruction of a leaked gas cloud based on a dual FTIR telemetry scanning imaging system, comprising the following steps: Step A: Select a monitoring area where gas leakage occurs, deploy two FTIR telemetry scanning imaging systems at different azimuths in the monitoring area, and obtain the GPS positions of each system; Step B: During the normal monitoring process, each system measures the 2-D projection column concentration image of the gas cloud and the pointing information of each pixel, such as azimuth and elevation angle; Step C: Use a geometric method to locate the cross-monitoring area and stratify it by elevation angle to obtain the monitoring area information divided by a three-dimensional grid; Step D: Calculate the path length coefficient sparse matrix of each layer, and use the simultaneous algebraic reconstruction technique (SART) to reconstruct the gas cloud distribution of each layer; Step E: Combine the gas cloud distributions of each layer into a 3-D gas cloud, calculate the center position of the gas cloud by the concentration weighting method, and display it through a false color combined with a 3-D map.

To highly accurately measure cloud heights of clouds in the whole sky.SOLUTION: A cloud measurement system comprises: a stereo camera 30 that can change zoom magnification in an imaging direction and optically; a picture reception unit 204 for acquiring the imaged picture; a cloud edge detection unit 207 that recognizes a cloud displayed on the picture, detects a boundary of the cloud, and acquires an edge region including the boundary from a plurality of sub regions in the picture; a cloud imaging order setting unit 209 that on the basis of the edge region, sets target sub regions to be imaged while changing the zoom magnification of the camera and their imaging order; a camera control unit 210 that controls the stereo camera 30 so as to perform enlargement imaging on the target sub regions according to the set imaging order to acquire a stereo picture for each target sub region, a stereo vision distance measurement unit 202 for acquiring a distance to a cloud displayed on the stereo picture using a parallax, and a cloud bottom height estimation unit 203 for acquiring a cloud bottom height of the cloud on the basis of the acquired distance.SELECTED DRAWING: Figure 1