96 results about "Data grid" patented technology

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention relates to the field of ocean current remote sensing, and relates to a vector flow inversion method combining a single-station ground wave radar and a satellite-borne SAR, and the method mainly comprises the steps: calculating the frequency deviation of a first-order Bragg peak of the ground wave radar, carrying out the inversion of the sea surface radial flow of the ground wave radar, and carrying out the inversion of the sea surface radial flow of the SAR through employing a Doppler centroid anomaly method; interpolating a ground wave radar radial flow result to an SAR data grid through bilinear interpolation; inverting a single-station vector flow based on a ground wave radar radial flow result, projecting single-station vector flow data with high confidence coefficient to an SAR range direction, calculating a difference mean value of a projection flow velocity and an SAR radial flow velocity, and further calibrating the SAR radial flow; and combining a ground wave radar radial flow result with the calibrated SAR radial flow to invert a vector flow. According to the method, the defect of ocean current observation through a single means is overcome, high-precision inversion of the combined vector flow is achieved, and the method has important significance on ocean environment monitoring, disaster forecasting, shipping safety and the like.

The present invention discloses an unified multiple access (MA) based system for uplink transmission of data by multiple users. The system comprises an user interface integrated transmitter modules for transmitting the data from multiple user transmitters over waveforms through a cooperative unified data grid structure including grid points, said unified data grid structure including each user transmitter allocation of a few non-overlapping grid points for loading respective data bearing symbols, with zeros loaded onto unallocated grid points. The user transmitter is configured to transmit a sparsely loaded grid resembling to the unified two-dimensional data grid structure involving data-bearing Quadrature Amplitude Modulation (QAM), where a part of available grid points of the grid as allocated is loaded with QAM symbols and remaining grid points are loaded with zero symbols. A waveform modulator corresponding to user transmitter is also provided in the present system for waveform modulation of said sparsely loaded grid as per the waveforms enabling transmission for multi-antenna reception of the waveforms to a base station or an access point.

A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making.

The invention discloses a seasonal frozen region salinization roadbed deformation monitoring and early warning method based on a neural network. Comprising the steps of static attribute data acquisition, dynamic monitoring data acquisition, action process data acquisition, data gridding fusion, construction and training of a first neural network model, construction and training of a second neural network model, regular monitoring and intelligent diagnosis and intelligent prediction and beforehand early warning. Data fusion is performed on spatial continuous data, investigation data, traffic load, freeze-thaw cycle and other action process data of a vertical deformation field and a salt concentration field of a roadbed, and then a serial neural network model is utilized to predict the disease type and severity of the roadbed under current and future working conditions. Full-chain intelligent analysis from multi-source data fusion, deformation prediction to disease identification is realized, the problems of monitoring fragmentation and early warning lag of a traditional method are solved, and the perception ability and the beforehand early warning level of the health state of the salinized roadbed in the seasonal frozen region are remarkably improved.

A wireless communication system for enhanced reliability of wireless communication and reducing peak power in wireless data transmission across different waveforms comprising transmitter modules coupled to waveform modulators including waveform two-dimensional resource or data grids representative of a signal processing domain for transmitting data bit bearing symbols across varied waveforms under controlled sparsely / partially loading of fewer modulated data symbols of the available grid points of a larger two-dimensional resource or data grid for transmission as spaced fewer data symbols with loading zero symbols at remaining empty grid points. The modulated data symbols under controlled sparsely / partially loading including said fewer modulated data symbols being allocated in the resource or data grid and distanced there between based on a factor for partial loading and full number of symbols accommodable in the resource or data grid to reduce cross interference amongst symbols and minimize error rate.

In various example embodiments, systems and methods for data grid platforms are proposed. User-associated attribute data is accessed from multiple attribute sources. Analysis of the attribute data is performed. Based on this analysis, actions corresponding to the user are executed.

In accordance with an embodiment, described herein are systems and methods for use of an in-memory data grid as a vector database, with linearly-scalable data ingestion, for use in generative artificial intelligence (AI), data visualization, or other applications that include the use of a large language model (LLM) or a retrieval-augmented generation (RAG) process. In accordance with an embodiment, where AI-related tasks or processes, such as content ingestion and vectorization, or vector similarity searches, can be performed in parallel, the in-memory data grid provides efficient scaling and execution of such processes. When tasked with large amounts of content to be vectorized—for example in a cloud environment or as part of an on-premise solution—the system can scale its processing of the content, in parallel where indicated, to perform an optimal utilization of available computing hardware resources, and expeditiously perform required tasks or processes.

The invention provides a method for obtaining a three-dimensional body through two-dimensional seismic reflection data gridding, and belongs to the field of seismic data processing. Through a technical means of constructing a virtual grid through rotation, a two-dimensional measuring line is converted into a three-dimensional data body which is uniformly distributed in space, and subsequent data processing is not limited to one direction any more. Specifically, latitude and longitude coordinates of each discrete reflection data are obtained and converted into UTM projection rectangular coordinate values, and coordinate values of a longitude minimum point are determined; calculating the optimal azimuth angle of the grid according to the plane distribution characteristics of the discrete points, and determining the grid spacing along the azimuth angle direction and the vertical azimuth angle direction; the grid is rotated to the three-dimensional grid direction, the coordinates of the point with the minimum longitude value after rotation are extended outwards by a half grid interval, and the coordinates of the point serve as base points of the grid; all the coordinate points are rotated in the same way, data falling into the same grid are superposed, then the coordinate number of each point in the grid is calculated, and a three-dimensional data volume is obtained accordingly.

This invention relates to the field of atmospheric environmental risk early warning technology, and in particular to an atmospheric environmental simulation diffusion method and system based on GIS and virtual reality. In simulating atmospheric environmental diffusion, this invention first acquires multi-source data for the simulation, and then processes the multi-source data. During the data processing step, when simulating atmospheric diffusion of pollution sources, a connection is established between the resolution of the geographic grid and the resolution of the pollution source data grid. This reduces information loss due to data mismatch during data fusion and improves the reliability of the atmospheric environmental diffusion simulation results.

The invention relates to a method for processing data grids representative of a scene captured by a set of sensors, a grid being divided into cells (C1, C2, C3, Ci), each associated with a probability distribution (DE) of states for a region of the scene covered by the cell at a current time step, the probability distributions being determined on the basis of data delivered by at least one of the sensors and / or data predicted by a prediction model on the basis of data delivered by the sensor at a previous time step. The method comprises a step of determining, for at least one cell of the grid, a data item of interest (DI), representative of a degree of interest of the cell in light of predetermined criteria relating to a context in which the scene is captured, a characteristic of the captured scene, and / or an application associated with the capture, this determination step delivering an enhanced data grid (GDA).

The invention is suitable for the technical field of environment management, and provides a rural environment data block chain evidence storage method based on multi-source verification, and the method comprises the following steps: determining an unmanned plane inspection plan and a grid member inspection plan based on historical evidence storage data; monitoring station data are received and analyzed in real time, and when the monitoring station is abnormal, an abnormality detection order sending announcement is sent, so that grid members or villagers can carry out detection; receiving unmanned aerial vehicle inspection data, grid member inspection data and villager uploading data, wherein each source data is marked with an environment project, acquisition time and place; performing cross validation on the monitoring station data of each time window based on the unmanned aerial vehicle inspection data, the grid member inspection data and the villager uploading data, and determining the data credibility; and determining block evidence storage data according to the data credibility and the corresponding multi-source environment data, and performing uplink evidence storage on the block evidence storage data. According to the invention, multi-source cross validation is carried out, and the accuracy and reliability of environmental data are improved.

The application discloses a kind of for the slope strength variation monitoring method of waste dump side slope, it is related to slope strength variation monitoring field.The application is based on strength reduction method, combines three-dimensional data grid and monitoring data fusion, dynamically evaluates slope strength variation, and real-time identification side slope instability risk is carried out through intelligent early warning mechanism.The application is based on reinforcement strength reduction method, combines geological data, considers the nonlinear deformation characteristics of soil body, uses nonlinear constitutive model to dynamically describe the stress-strain relationship of soil body, and adjusts strength reduction coefficient in real time in monitoring process, improves the accuracy of side slope stability evaluation.

In accordance with an embodiment, described herein are systems and methods for use of an in-memory data grid as a vector database, with linearly-scalable data ingestion, for use in generative artificial intelligence (AI), data visualization, or other applications that include the use of a large language model (LLM) or a retrieval-augmented generation (RAG) process. In accordance with an embodiment, the in-memory data grid provides functionality to represent content as document chunks containing text, embedding, and metadata, which allows the system to support a variety of RAG framework integrations in a consistent manner. To further support the use of RAG processes, the system can support document ingestion via various types of document sources, such as the use of HTTP URLs that allow retrieval of documents using HTTP GET calls; or, for example in cloud environments, the use of object storage and / or other cloud provider storage services as appropriate.

The invention discloses a power distribution system configuration device and method. The method comprises the steps that a data acquisition module acquires global basic data of a to-be-configured power distribution system; the grid division module determines the current time period state of the to-be-configured power distribution system, and performs grid division on the power distribution network in combination with the global basic data; the system configuration module obtains global basic data and a grid division result, and determines initial configuration of the power distribution system to be configured in the next time period; the user access module obtains the current time period state of the to-be-configured power distribution system, provides current user access configuration, and determines user access optimal configuration of the next time period in combination with the initial configuration of the next time period of the to-be-configured power distribution system; and the system configuration module also obtains the given user access optimization configuration, optimizes the determined initial configuration of the power distribution system to be configured in the next time period, and completes the configuration of the power distribution system. According to the method, the load demand difference of different areas can be accurately identified, so that the load prediction error rate is reduced, and the configuration precision is improved.

The invention discloses an estimation method of a two-dimensional plane numerical value of a semiconductor device and a related device. The estimation method comprises the following steps: constructing a cutting straight line segment penetrating through a two-dimensional grid; calculating position information of intersection points of the cut straight line segments and the two-dimensional grid; estimating physical information of the intersection points; based on the position information and the physical information of the intersection point, a function model for cutting the straight line segment is constructed, and the function model is used for describing the relation between the physical information and the position information of any point on the cut straight line segment; and estimating physical information of any point on the cut straight line segment based on the function model. According to the method, the numerical value of the specific point on the data grid of the semiconductor device can be estimated more quickly under the same grid density, and a user can be helped to quickly obtain the numerical value of the specific data point in performance analysis and defect detection of the semiconductor device.

The invention relates to the field of multi-robot cooperative scheduling, and discloses a multi-robot cooperative scheduling unmanned underground mechanical garage system and method, and the system comprises a hardware subsystem which comprises a transfer robot, a parking space frame, a vertical elevator, and an entrance and exit junction area; the control system is deployed in the central control server, and the control system comprises a multi-layer dynamic data grid generation module which is used for constructing a discretized data grid corresponding to a garage physical space; and the central data calculation module is used for continuously updating the data of each data layer in the multi-layer dynamic data grid according to the real-time state of the garage. In the regional congestion degree and traffic control layer, the congestion index is calculated according to the density, the speed variance and the dispersion in the movement direction of the transfer robot, and the task gravitation modulation coefficient is generated, so that the tendency of the robot to enter the crowded region is reduced, the adaptive dispersion of the traffic flow is promoted, and the continuity of garage operation is maintained.

The invention discloses a slope body strength change monitoring method for a waste slag field side slope, and relates to the field of slope body strength change monitoring. The method is based on a strength reduction method, combines a three-dimensional data grid and monitoring data fusion, dynamically evaluates the slope strength change, and identifies the slope instability risk in real time through an intelligent early warning mechanism. According to the method, the stress-strain relation of the soil body is dynamically described by adopting the nonlinear constitutive model on the basis of the strengthening strength reduction method in combination with geological data and considering the nonlinear deformation characteristic of the soil body, the strength reduction coefficient is adjusted in real time in the monitoring process, and the accuracy of slope stability evaluation is improved.

The invention relates to the technical field of big data processing, and discloses a data grid point rarefaction method and device, computer equipment and a storage medium, and the method comprises the steps: determining the longitude and latitude range and the grid point density of a target research region, and generating a plurality of grid point regions based on the longitude and latitude range and the grid point density; determining a plurality of lattice point positions in the specified lattice point area, and selecting lattice points meeting a preset condition from the plurality of lattice point positions as representative sites; according to the method, a representative site group significantly related to a representative site is screened out based on the correlation between the representative site and other sites except the representative site in a specified grid point region, and a representative result of the specified grid point region is obtained based on data of all representative sites in the representative site group. And calculation is carried out through the screened representative station group, so that the overall characteristics of regional meteorological elements can be accurately reflected while the data processing amount is reduced and the calculation efficiency is improved.

Computerized systems and methods are described for managing vendor-independent configuration-on-order (CTO) and offer-on-order (QTO) processes. A real-time data grid (RTDM) is provided for aggregating, normalizing, and normalizing real-time data from different sources. A single pane view user interface (SPoG UI) facilitates dynamic interaction and visibility to vendor performance. An advanced analysis and machine learning (AAML) module analyzes product compatibility, optimizes a pricing strategy and predicts a market trend. A vendor independent CTO / QTO integration module (VACIM) includes a process standardization engine and a vendor data conversion gateway to ensure consistency of different vendors. According to the method, automatic data processing and data conversion gateway integration are carried out to ensure data consistency, and decision making is carried out by adopting a rule engine driven by machine learning, so that a retailer process is simplified in an extensible and high-adaptability framework, the expandability is enhanced, and a pricing strategy is optimized.

The application discloses a heat supply data grid-connected diagnosis analysis alarm system and method, relates to the technical field of data analysis, and comprises the following steps: acquiring heat supply data, transmitting the heat supply data to a cloud database of a monitoring center, setting a data capsule and a mirror capsule in the cloud database, acquiring state information of the data capsule, destroying the data capsule and enabling the mirror capsule based on the state information, setting a diagnosis library corresponding to the monitoring center, wherein the diagnosis library comprises heat supply data types and corresponding preset diagnosis conditions, performing real-time diagnosis on the heat supply data based on the diagnosis library, and performing alarm on heat supply data that does not meet the preset diagnosis conditions based on the diagnosis library. The application can destroy the database in time when unauthorized network accesses the information of the database, and the converted and mirrored capsule is used, so that the storage of the heat supply data is not affected, and the data protection effect is good.

Method for processing at least one data grid representing a scene captured by a set of sensors, device and corresponding program. The invention relates to a method for processing data grids representing a scene captured by sensors, a grid being divided into cells (C1, C2, C3, Ci) each associated with a probability distribution of states (PD) of an area of ​​the scene covered by the cell at a current time step, determined as a function of data reported by at least one of the sensors and / or data predicted by a prediction model as a function of data reported at a previous time step by said sensor.The process includes determining, for at least one cell of the grid, a data point of interest (DP) representative of the degree of interest of said cell with regard to predetermined criteria related to a scene capture context, a characteristic of the captured scene, and / or an application associated with said capture, said determination delivering an augmented data grid (ADG). Abbreviated figure: Figure 1.

The invention discloses a method and a system for dynamically generating a rainstorm disaster alarm based on a grid actual condition. The method comprises the following steps: collecting site observation rainfall data, grid actual rainfall data, radar data, geographical environment data and rainstorm disaster alarm events in a preset time period; performing deviation analysis and correction on the grid real-time rainfall data by using the site observation rainfall data, and calculating a grid rainfall storm disaster alarm threshold according to the corrected grid real-time rainfall data and the site observation rainfall storm disaster alarm threshold; constructing a training data set, and performing training optimization on the rainstorm disaster alarm model by using the training data set; and carrying out red and orange rainstorm disaster alarm on villages, towns and cities based on the trained and optimized rainstorm disaster alarm model. According to the method, the defects of observation station network layout can be made up, effective monitoring of the rainstorm disaster in a station-free area is realized, and the accuracy, the spatial resolution and the disaster prevention applicability of rainstorm disaster risk early warning are remarkably improved.

The application discloses a method for applying seven-grade anomaly division method to medium-large scale geochemical exploration data, and comprises the following steps: step S1: the point position of the discrete data of the medium-large scale regional investigation is generally not located at the center position of the regular grid, and the average value gridding interpolation can be adopted to make the data points be distributed on the grid center; step S2: the WIG, Al2O3 / TiO2 and K2O / SiO2 three parameters of the 1:200,000 geochemical exploration data of the research area are calculated to obtain the three parameters of the 1:200,000 data; step S3: the nearest point gridding interpolation is performed on the three parameters of the 1:200,000 data according to the above-mentioned regular grid of the medium-large scale, so that the data gridding densities of the two are same; and step S4: the two kinds of grid data are matched, and then the seven-grade anomaly division method is used to determine and grade the evaluation of the medium-large scale geochemical exploration data; the problem that the medium-large scale geochemical exploration data lacking of background values can also use the seven-grade anomaly division method to determine and grade the evaluation is solved.

Computerized systems and methods are described for managing multi-tiered subscription-based models of technology products, including hardware, software, and cloud services. The system performs automated optimizing of subscription models and pricing using a Real-Time Data Mesh (RTDM) and Advanced Analytics and Machine Learning (AAML) Module. The system employs a Single Pane of Glass User Interface (SPoG UI) to enhance user interaction and subscription management. Automated processes facilitate dynamic adjustment of subscription terms, pricing, and configurations based on real-time data and analytics. API integrations for perform data exchange for comprehensive management of multi-tiered subscriptions within a unified ecosystem.

This invention relates to the field of data processing technology, specifically a comprehensive evaluation system for large-scale mass sports events based on image recognition technology. The system includes a time-frame synchronization module, a target tracking and detection module, a participation analysis module, a heatmap generation module, a trend prediction module, and a report generation module. First, it synchronizes multiple event video streams. Then, it tracks and detects the movement trajectories of participants, statistically analyzing the number of participants and their trajectory distribution to obtain real-time participation. The participation data is then processed into a grid to generate a dynamic heatmap, and the spatiotemporal evolution trend of participation concentration is predicted based on the event schedule. Finally, the real-time participation, dynamic heatmap, and prediction results are integrated to generate a comprehensive event evaluation report. This invention can improve the efficiency of generating reference information for the comprehensive evaluation of large-scale mass sports events based on image recognition technology.

The embodiment of the invention discloses a mixed collection seismic data separation method and device. According to the invention, a three-dimensional FKK domain variable grid sparse inversion technology is provided, the data grid partitioning scale is dynamically adjusted in the iteration process, the physical law of signal extraction is matched, and the essence of signal extraction from a strong axis to a weak axis and from a low frequency to a high frequency during inversion is better met, so that a better separation effect is obtained; a self-adaptive empty channel compensation technology is provided, empty channels are filled through regularized interpolation in a sparse transform domain, energy abnormal attenuation is avoided, the processed data whitening phenomenon caused by variable-view missing channels is effectively eliminated, and meanwhile the effect is improved; meanwhile, a three-dimensional FKK transform domain is selected, signal coherence recognition, transform reversibility and calculation efficiency are considered, finally, the separation precision and efficiency are improved, the field production operation efficiency is remarkably improved, the operation period is shortened, and the exploration cost is reduced.

The invention discloses a microstructure identification method and system based on trend line fitting and surplus calculation. The microstructure identification method comprises the steps of obtaining a coordinate range of a selected research area, burial depth data of any interface and dip angle distribution characteristics of a top interface; after the burial depth data is scattered, a new regular grid coordinate system is generated through equal-interval division according to the coordinate range of the research area, burial depth data closest to each grid node of the new regular grid coordinate system is given to the grid node, and a target layer series depth data grid is obtained; determining the length of a sliding window, and carrying out trend line fitting and surplus calculation on the depth data grid of the target strata series according to the length of the sliding window; screening and processing the residual amount of each network node to obtain a final residual amount; and performing micro-structure quantitative identification on the final residual amount and the dip angle distribution characteristics by using the threshold value of the residual amount to obtain a quantitative identification result. The method does not necessarily depend on three-dimensional seismic data, and the quantization degree is high.

Methods and systems for generating synthetic point cloud data are described. A projected 2D data grid is generated by projecting a 3D point cloud into a 2D grid with rotational homogeneity. A generative model is learned using the projected 2D data grid, where the generative model is implemented using flexible convolution and transposed flexible convolution operations, for example, in a generative adversarial network. The learned generative model is used to generate synthetic point clouds.

The invention relates to a drift trajectory reconstruction method and device of a target object, electronic equipment and a storage medium, and the method comprises the steps: converting a coordinate of a preset starting point of the target object into a geographic coordinate in a target coordinate system; determining the row number of the preset starting point in the sea ice drift data grid, determining the speed of the target object based on the row number, and determining the inference point location of the target object based on the speed of the target object and the preset starting point coordinate; updating a next inferred point location of the target object based on a preset time interval, and recording an accumulated inferred point location of the target object until a preset termination condition is reached; and obtaining a reconstruction trajectory coordinate of the target object based on the accumulated inference point positions updated each time, and drawing a trajectory result map based on the reconstruction trajectory coordinate. Therefore, the technical problems that the search range is large, the cost is high, consumed time is unstable, long-term research is not facilitated and the like due to the fact that the target object is replied in the mode of reporting outward expansion of the positioning point for the last time in the prior art are solved.