56 results about "Cloud base" patented technology

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.

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 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.

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.

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.

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

The application discloses a cloud bottom height inversion method based on Mie scattering laser radar data, and comprises the following steps: S1, obtaining an atmospheric extinction coefficient based on Mie scattering laser radar inversion; S2, using the atmospheric extinction coefficient based on Mie scattering laser radar inversion, drawing an atmospheric extinction coefficient profile, and processing an original atmospheric extinction coefficient profile; S3, using a wavelet method to analyze the processed atmospheric extinction coefficient profile, and screening a significant minimum point; S4, using the significant minimum point, screening an effective feature point; and S5, using the effective feature point, outputting a CBH. The application provides a new method for further inverting the cloud bottom height by analyzing the vertical profile features of the atmospheric extinction coefficient based on the Mie scattering laser radar data. The application aims to enrich the application neighborhood of the Mie scattering laser radar and provide a new method for inverting the cloud bottom height.

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 a chaff cloud cluster electromagnetic scattering calculation method based on a binary-voxel method, and belongs to the technical field of computational electromagnetism and electronic countermeasures. According to the method, an incidence voxel coordinate system and a scattering voxel coordinate system are respectively established through two times of dynamic coordinate rotation transformation, voxel division, chaff concentration statistics and attenuation factor calculation are independently executed on a bidirectional path, and accurate modeling of incidence and scattering path attenuation is realized in combination with a linear moment method basic scattering field solution. According to the method, the scattered field calculation precision of the non-uniform chaff cloud cluster in the complex electromagnetic environment is remarkably improved, meanwhile, the calculation complexity is reduced through voxel-level discretization, and the method can be applied to electronic countermeasure efficiency evaluation.

The application provides a method and system for real-time monitoring of the operation state of a photovoltaic power station, and relates to the technical field of photovoltaic power station monitoring. The method integrates a multi-modal sensor array in a photovoltaic module junction box, and collects sensor data of the photovoltaic power station in real time. The sensor data is pre-processed locally by an edge computing node, and different priority data is transmitted to the cloud based on a dynamic hybrid communication protocol. The sensor data is fused in the cloud to construct a four-dimensional digital twin model, and spatiotemporal continuous weather prediction, component aging and dust evolution data are fused. The fault mode of the photovoltaic power station is identified, and the string topology of the photovoltaic power station is dynamically optimized in combination with the real-time operation state, equipment health data and fault mode identification result of the photovoltaic power station. By integrating distributed sensors, edge computing modules and intelligent alarm mechanisms, the application can effectively solve the problems of delay, insufficient intelligent analysis and response lag in the prior art.

This invention discloses a method for identifying cavitation bubbles in high-velocity clouds based on adaptive multi-threshold segmentation, belonging to the field of cavitation bubble image recognition and fluid measurement technology. The method first uses a high-speed camera to acquire images of the evolution of dense bubble groups in a high-velocity cloud cavitation flow field. Based on the image's grayscale distribution characteristics, the OTSU adaptive global thresholding method is used to determine the background noise threshold and the scattered light noise threshold, respectively. On this basis, an adaptive multi-threshold segmentation algorithm is constructed, combining grayscale threshold sequences and bubble diameter threshold sequences for multi-round identification. Connected component analysis and area compensation are used to separate adherent bubbles and accurately extract small-scale bubbles. This invention can adapt to environments with high flow rates, non-uniform illumination, and uneven bubble distribution, achieving high-precision, non-invasive identification and statistical analysis of tiny bubbles inside cloud cavitation.

The cloud recognition method and computer program product of the microwave laser composite radar are based on transfer learning and R-FCN network, remove the ground clutter, and remove the noise caused by the ground clutter of the microwave radar; a deep learning model is constructed by using transfer learning and R-FCN algorithm, microwave radar and laser radar echo images are input, the attention mechanism is used in the model to realize adaptive feature extraction, a multi-scale feature pyramid is introduced on the basis of the RPN network, and the recognition and detection capability for different scale cloud areas is enhanced. The single result of the microwave radar and the laser radar cloud recognition is realized by using cloud boundary self-checking, cloud feature marking, initial matching and joint inversion of the cloud boundary, and the composite recognition cloud area is obtained. The cloud recognition function is possessed under complex atmospheric conditions such as multi-layer clouds and under the interference of equipment noise, the inversion result is accurate, the ground clutter and the daytime background noise are not sensitive, and the recognition advantages of the laser radar for the thin roll cloud area and the cloud bottom and the recognition advantage of the microwave radar for the cloud top are fully integrated.

The invention discloses an air conditioner noise reduction method, an air conditioner and a computer readable storage medium, and relates to the technical field of air conditioners, and the air conditioner is in communication connection with a cloud. The air conditioner is controlled to operate on the basis of the limited compressor frequency and the limited fan air speed corresponding to the set noise level and the preset air outlet angle till the environment temperature reaches the set temperature, and then current operation data are sent to the cloud end on the basis of the first preset frequency so that the cloud end can determine corresponding control data, the current operation data at least comprise the current compressor frequency, the fan rotating speed, the set temperature, the air outlet angle and the set noise level, and the control data at least comprise the compressor frequency control, the fan rotating speed control and the air outlet angle control; and receiving control data sent by the cloud, and controlling the air conditioner to operate based on the control data. The technical problems that the noise reduction scheme of an existing air conditioner is high in cost and poor in temperature control effect are solved.

A system onboard an aircraft is configured to determine when an aircraft enters or detects a cloud top or cloud base, and record a corresponding altitude. The correlated altitude, location, and time are shared to establish a set of data points corresponding to cloud ceilings and tops. Onboard cameras may be used to identify when the aircraft enters and leaves a cloud. The system correlates the recorded data points to satellite data such as satellite-based cloud images and / or clout top elevations based on location. The correlated images and data points may establish a more complete map of clouds in a wide area. Data points from multiple aircraft may be correlated and combined over time. Data points from multiple aircraft may be shared between aircraft directly or via a network including one or more ground stations.

A networked wireless model rocket launching and tracking system. Rocket launches, users, and other activity can be remotely monitored and tracked, and stored in a remote database. Safety checking and advanced functionality to be performed wirelessly using remote servers to retrieve location information.

The invention provides a data processing method and device based on a cloud base station, equipment and a storage medium. The method is applied to an acceleration server, a physical layer accelerator is deployed in the acceleration server, the acceleration server is in communication connection with a plurality of base station servers, cloud base stations are deployed on the base station servers, and the base station servers are used for executing services of the cloud base stations. The method comprises the following steps: receiving to-be-processed data sent by a base station server; wherein the to-be-processed data represents data generated by executing the service of the cloud base station, the to-be-processed data comprises a server identifier of a base station server, and the server identifier is used for representing the base station server; processing the to-be-processed data based on a physical layer accelerator in the acceleration server to obtain processed data; and feeding back the processed data to the base station server according to the server identifier in the to-be-processed data. According to the method, the hardware use cost is saved, the data processing efficiency is improved, and the flexibility of cloud base station service deployment is improved.

The invention relates to the field of atmospheric remote sensing, and provides a solar irradiance prediction method, system and device, and electronic equipment, and the method comprises the steps: obtaining cloud bottom image data of a target cloud body collected by a target unmanned plane; performing attitude correction on the cloud bottom image data, and performing semantic segmentation to obtain a binary cloud body mask sequence; performing feature extraction on the binary cloud body mask sequence; and inputting the extracted feature vector into a pre-trained regression model to obtain a solar irradiance prediction result below the flight path of the target unmanned aerial vehicle. The method, the device and the system are used for solving the technical blank that high-temporal-spatial-resolution and foresight local solar irradiance prediction on a front route cannot be provided for a moving unmanned aerial vehicle in the prior art, and according to the scheme, through a visual sensor carried by the unmanned aerial vehicle, microscopic dynamic characteristics of a cloud base are quantitatively analyzed in real time; and a quantitative relation between the characteristics and a ground irradiance slope event is established, so that active early warning of the flight path risk is realized.

The application discloses a volcanic ash cloud multi-task monitoring method based on a Transformer and field adaptation, and relates to the technical field of volcanic ash cloud detection and parameter inversion. The application adopts an uncertain-based weighting mode combined with a multi-task loss function, and uses an Adam optimizer to minimize the loss function, so that the model can better capture the relationship between bands from a large amount of simulation data, and is transferred to a real scene, thereby realizing the simultaneous and accurate prediction of information such as the position of a volcanic ash cloud, the cloud top height of the volcanic ash cloud and the volcanic ash concentration.

This application discloses a target-aware observation point cloud alignment method, device, and storage medium, relating to the field of target tracking technology. It aims to at least improve the accuracy of acquiring observation point clouds of moving targets, thereby improving the accuracy of target processing using these observation point clouds. The method includes: performing at least one round of trajectory processing operations on the moving target. The trajectory processing operations include: obtaining prior trajectory information of the moving target; performing point cloud alignment operations on each sensor based on the prior trajectory information and the observation point clouds corresponding to each sensor to obtain aligned point clouds corresponding to each sensor; the point cloud alignment operations include: performing trajectory filtering based on the prior trajectory information and the observation point clouds corresponding to the sensors to obtain posterior trajectory information associated with the sensors; estimating target trajectory information at least through the posterior trajectory information; and correcting the observation point clouds based on the trajectory offset information of the posterior trajectory information and the target trajectory information to obtain the corresponding aligned point clouds.

The invention provides a remote fault identification method and system for a new energy automobile, and belongs to the technical field of new energy automobiles, and the method specifically comprises the steps: determining a storage management strategy of the driving data of the new energy automobile in a cloud end based on the output data of a power battery of the new energy automobile in the driving process, based on the storage management strategy, storage processing of the driving data of the new energy vehicle is carried out, and when it is determined that updating processing of the driving data of a cloud platform needs to be carried out according to the storage result of the driving data of the cloud platform, it is determined that the driving data of the new energy vehicle is updated within different idle durations according to the idle monitoring data of the vehicle and the updating result of the driving data; according to the remote fault diagnosis method of the driving data based on the cloud platform, the reliability of remote fault identification processing is improved.

The invention discloses a cloud recognition method based on multiple observation devices, which is characterized in that a weather phenomenon video intelligent observation instrument, an aerosol laser radar, a millimeter wave cloud radar and other observation devices are used for comprehensive judgment to finally obtain a recognition result, and comprises the following steps: acquiring a sky image from the weather phenomenon video intelligent observation instrument, and judging whether cloud exists or not; the cloud base height measured by the laser radar is combined to judge whether the cloud is high cloud or low cloud; non-cloud echoes and noise echoes are filtered through a quality control algorithm, and a reliable cloud radar product is obtained; the boundary of the cloud is identified through the cloud radar product, so that the cloud information such as the cloud bottom height, the cloud top height, the cloud layer thickness and the cloud layer number is obtained. The method is small in calculation amount, high in recognition rate and wide in application range, and overcomes the defects that in the prior art, manual work is relied on, and the processing speed is low.

The application provides a kind of intelligent automobile end cloud model version collaborative management system and method, it is related to intelligent automobile technical field.Intelligent automobile end cloud model version collaborative management system includes cloud and side.The cloud is used to respond to the cloud base model upgrade is monitored, determine and the side inference model of the cloud base model exists version dependency relationship;Compatibility evaluation result obtained by executing multidimensional compatibility evaluation to the side inference model is acquired, multidimensional compatibility evaluation includes interface compatibility evaluation, precision compatibility evaluation and performance compatibility evaluation;And, according to compatibility evaluation result and the cloud base model after upgrading, the update package corresponding to the side inference model is generated, and the update package is pushed to the side.The side is used to update the side inference model according to the update package.Through the cooperation of cloud and side, the automatic collaborative management of end cloud model version is realized, and the reliability and stability of inference service are improved.

To measure a cloud height with high accuracy for a long time.SOLUTION: A cloud measurement device includes: a stereo camera 30 for photographing a stereo image by a plurality of cameras that can change a pan angle and a tilt angle; a camera control unit 210 for controlling the pan angle and the tilt angle of each camera to control photographing; an image reception unit 204 for receiving the stereo image from the stereo camera; a matching coordinate acquisition unit for acquiring feature points of a subject captured in the stereo image, and acquiring a combination of reference coordinates at which the feature points should be located on the image and coordinates of the acquired feature points as matching coordinates; a direction difference detection unit 209 for acquiring a displacement amount of the feature points on the basis of the matching coordinates, and acquiring a difference amount of pan directions and tilt directions of the plurality of cameras to generate correction information; a correction processing unit 201 for correcting the stereo image on the basis of the correction information; and a cloud base height estimation unit 203 for estimating a cloud base height on the basis of the corrected stereo image.SELECTED DRAWING: Figure 1

This application provides a cloud measurement system and method based on a wave glider. By integrating a laser cloud-measuring radar with a marine meteorological observation instrument onto a wave glider platform with ultra-long endurance, autonomous navigation, and virtual mooring capabilities, a sea-based mobile cloud observation system is constructed. This system overcomes the inherent shortcomings of existing satellite remote sensing observations, such as discontinuity and lack of vertical structure information, as well as the high cost, poor maneuverability, and limited coverage of ship and buoy observations. It achieves long-term, continuous, and mobile observation of cloud systems over vast ocean areas and can acquire high-precision cloud base height, vertical structure, and conventional meteorological elements from the sea surface, significantly improving the spatiotemporal continuity and data quality of marine meteorological observation.

The application discloses a railway line safety risk assessment method and system based on a two-dimensional cloud model, and the method comprises the following steps: constructing an index system for railway line safety risk assessment; determining the subjective weight of each index in the index system by using interval analytic hierarchy process, determining the objective weight of each index in the index system by using entropy weight method, and determining the combined weight by using improved game theory; for the railway line to be subjected to safety risk assessment, obtaining sequence data of each index including a plurality of sample observation values, and calculating two-dimensional cloud characteristic values of each index from two dimensions of risk occurrence probability and risk influence degree based on the sequence data of each index and the weight, and the two-dimensional cloud characteristic values are recorded as a cloud to be assessed; calculating the closeness between the cloud to be assessed and each level of risk standard cloud based on fuzzy distance, and determining the risk level with the largest closeness as the final assessment level of the railway line to be assessed. The application can simultaneously consider the risk occurrence probability and the consequence severity, and has high assessment accuracy and practicability.

Methods and systems for estimating and using excess path length (EPL) corrections in GNSS receivers are described. A method can estimate the EPLs using a selection of line of sight and non line of sight pseudorange measurements, and these EPLs can be used to correct non selected non-line of sight pseudoranges. In one embodiment, a cloud based system can receive data from a crowd source set of EPL corrections (e.g., from GNSS receivers in an urban canyon environment) and then can develop a crowd sourced set of EPL corrections and then provide to GNSS receivers (some of which may part of the crowd of GNSS receivers) the crowd sourced set of EPL corrections. The EPL corrections can be used to improve position solutions in, for example, an urban canyon.

An example method of performing a scaling operation for a distributed load balancer in a multi-cloud system includes: initiating, by a controller of the distributed load balancer, a scaling operation targeting a plurality of clouds in the multi-cloud system; determining, by the controller, implementations of the scaling operation for the plurality of clouds based on networking infrastructures of the plurality of clouds; and executing, by the controller, an implementation of the scaling operation for a first cloud of the plurality of clouds, the implementation including operations of: configuring a network interface on a data plane device of the distributed load balancer and configuring an upstream network of the first cloud.