156 results about "Data grid" patented technology

The invention relates to the technical field of data visualization, in particular to an earthquake emergency information rapid visualization method and system. The method comprises the following steps: acquiring earthquake monitoring real-time data; based on earthquake monitoring real-time data grid division, earthquake region population density distribution and key facility positions are generated, and an earthquake region sensitivity matrix is generated; dynamically simulating a seismic wave propagation process based on the seismic monitoring real-time data and the seismic region sensitivity matrix, and generating fluctuation propagation time sequence data; and identifying epicenter topography and landform vector data of the earthquake monitoring real-time data, and respectively evaluating topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. According to the invention, rapid and accurate assessment and visualization of earthquake damage distribution are realized through a data visualization technology, so that the earthquake emergency response efficiency and the disaster rescue decision scientificity are improved.

The invention provides a running state monitoring method and monitoring platform for an intelligent coffee machine, and relates to the technical field of running state monitoring, and the method comprises the steps: connecting a built-in sensor of the coffee machine, and obtaining a self-sensing state parameter; acquiring external monitoring data through external monitoring equipment of the coffee machine; performing internal data grid mapping according to the self-sensing state parameters, and constructing an internal sensing data network; performing alignment segmentation on external monitoring data, and establishing internal and external response data pairs; operating state recognition is conducted, an operating state recognition result is obtained, and coffee machine regulation and control feedback is conducted. The technical problems that in the prior art, due to the fact that operation state monitoring mostly adopts single-point data collection or passive alarm, the timeliness of state monitoring is weak, and the user experience and the service life of the coffee machine are affected are solved, intelligent recognition and regulation and control feedback of the operation state of the coffee machine are achieved by combining external operation monitoring with self-inspection parameters, and the user experience is improved. And the operation efficiency of the coffee machine is improved.

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 relates to the technical field of electric digital data processing, and discloses a 3D intelligent stocktaking method and device, and the device comprises a 4D millimeter wave radar disposed at the top of a stock bin, S1, obtaining the data of a corresponding coverage area detected by the 4D millimeter wave radar; s2, preprocessing the acquired data; S3, processing the preprocessed data by a point cloud data grid filtering algorithm to obtain point cloud data on grids on a global grid map; s4, checking the integrity of the data, and if blank grids exist in the global grid map, filling the grids of the blank data in the global grid map by using a point cloud smooth interpolation algorithm to form a final smooth point cloud; and S5, reconstructing the surface of the material, and constructing a three-dimensional diagram of the surface of the material according to the final smooth point cloud determined in the step S4. Through the 4D millimeter wave radar and various algorithms, the real-time and all-around monitoring of the material bin is realized, and accurate three-dimensional material level imaging is provided.

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.

The invention relates to the technical field of blood cell analysis, and particularly discloses a blood cell analysis system and method based on computer vision, which uses an image recognition and generation technology based on computer vision to carry out grid division and semantic feature extraction on blood image data. Performing global text generation and semantic coding on each blood image data grid block feature after semantic extraction so as to perform adjustment response aggregation on attention of different blood local areas in each blood image data grid block feature according to blood image text description semantic features; therefore, intelligent optimization of the blood image data is realized. In this way, the complex deep-layer mutual influence association relationship between cells can be captured, so that a more accurate diagnosis basis is provided. Meanwhile, the processing strategy can be dynamically adjusted according to the specific description content of the blood image so as to better adapt to diversified samples, and the method does not only depend on fixed preprocessing parameters.

The invention discloses an alkaline electrolytic cell multi-physics field distribution prediction system and method based on deep learning, and in the prediction system, a multi-physics field coupling simulation module is used for obtaining unstructured multi-physics field coupling simulation data of an alkaline electrolytic cell; the grid data conversion module is used for acquiring structured grid data, geometric features and a symbol distance matrix, and constructing a data form required by a subsequent deep learning model; the deep learning training module is used for training the structured grid data through a deep convolutional neural network to generate a multi-physics field prediction model; and the prediction and visualization module is used for loading the trained model for real-time prediction and realizing visual presentation of a result. The method can be used for real-time prediction and design optimization of the running state of the alkaline electrolytic cell.

The invention relates to the technical field of geographic information systems and space computing, in particular to an amusement equipment safety risk early warning system based on big data. Comprising the following parts: a geospatial data gridding storage module, which is used for collecting multi-dimensional geographic information and space-time state information of an amusement park; the multi-dimensional geographic information refers to space coordinates and proximity relations of amusement equipment; the space-time state information refers to temperature change data in each starting and stopping process of the amusement equipment; and the geographic space correlation degree calculation module is used for calculating an accumulated risk and an instantaneous risk of the amusement equipment based on the multi-dimensional geographic information and the space-time state information. According to the method, geographic space grid storage, dynamic topological association and risk visualization technologies are fused, and intelligent safety monitoring of amusement equipment is achieved; spatial and temporal characteristics are analyzed based on a cumulative / instantaneous risk formula, the early warning capability is improved in combination with closed-loop management of a people flow thermodynamic layer, missed reports are reduced, the operation and maintenance efficiency and the equipment life are optimized, and intelligent decision making is assisted.

The invention relates to a sea surface wind wave field parameter integrated inversion method combining HFR and SAR, and the method mainly comprises the steps: extracting the positive and negative first-order echo spectrum peak values of HFR, carrying out the inversion of a sea surface wind direction, and carrying out the interpolation of an HFR wind direction result to an SAR data grid through bilinear interpolation; based on the registered HFR wind direction result, utilizing a CMOD5. N model and an SAR backscattering coefficient to invert the wind speed; constructing a wind wave spectrum based on the inverted wind speed and wind direction results; calculating a surge spectrum peak wave number, a surge spectrum peak amplitude and a surge propagation direction by using the SAR image information, and constructing a surge spectrum based on the surge spectrum peak wave number, the surge spectrum peak amplitude and the surge propagation direction; and superposing the obtained wind wave spectrum and the surge spectrum to obtain a sea wave spectrum result, and further inverting the significant wave height. According to the method, the defect of sea state parameter observation through a single means is overcome, integrated high-precision inversion of the wind wave parameters is achieved, and the method has important significance on marine environment monitoring, disaster forecasting, shipping safety and the like.

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.

Systems and methods for real-time data integration, analysis, and notification within a mobile application system are provided. Embodiments include initializing a data layer and a pre-processing phase to store structured and unstructured data with a distributed database policy. A real-time data grid (RTDM) continuously extracts data from various platforms using signal processing methods and machine learning techniques for noise removal and data feature extraction. An advanced analysis and machine learning (AAML) engine processes the data, while a decision construct derives appropriate actions. A push notification service is activated based on an event-driven architecture (EDA) or a publish-subscribe (publish-subscribe) system to deliver customized notifications. The technical precision ensures that relevant information reaches the end user in time. The system optimizes operation through a self-adaptive feedback mechanism and protects data through encryption.

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.

The invention belongs to the technical field of artificial intelligence, and relates to an AI psychological tutoring system with emotion intelligence and privacy calculation double engines, comprising: a data acquisition and fusion module for generating a fusion input stream with time-space alignment; the emotional state analysis module is used for generating a dynamic emotional state matrix; the privacy strategy generation module is used for triggering a privacy calculation engine to construct a differential privacy domain according to the dynamic emotional state matrix and generating hierarchical data grids; the privacy recoding module is used for executing privacy recoding on the fused input stream according to the hierarchical data grids to generate an anti-reverse engineering ciphertext packet; the self-adaptive strategy generation module is used for generating a self-adaptive tutoring strategy tree; and the closed-loop intervention execution module is used for driving the terminal equipment to execute feedback of the self-adaptive tutoring strategy tree and generating a closed-loop psychological intervention flow. According to the invention, the problem that a real-time capture and response mechanism for user feedback is lacked due to the fact that most interaction modes are one-way output is solved.

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.

The present invention relates to a data grid system for realizing data collaboration based on a private message structure, which includes a control plane, a data plane, and data packets; the control plane, the data plane, and the data packets are connected in sequence; the system performs data transfer collaboration in a decentralized manner of the data grid. The present invention changes the concept that a large amount of data must be centralized in traditional centralized data collaboration for use, focuses on the domain attributes of data, and allows each domain to participate in data collaboration and transfer to achieve the goal of data joint governance and decentralization.

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 gravity anomaly upward continuation calculation method based on a central grid data block separation method, belongs to the technical field of geophysical exploration data processing, and solves the problems that when an existing method is used for processing an ultra-low altitude upward continuation problem, singularity occurs when an integral kernel function coincides with a calculation point and a data point; the method comprises the following steps: stripping a central data grid where a calculation point is located from an integral domain based on a block separation method, and carrying out practical modification processing on a global integral model based on the block separation method to obtain a modified model without integral singularity; according to the method, a practical modification scheme for converting a gravity anomaly upward continuation global integral model into a local numerical integral model is provided, numerical calculation verification is carried out on a kernel function singularity solution and the practical modification scheme of a calculation model by utilizing an ultrahigh-order bit model EGM2008, and model internal coincidence precision superior to 1mGal can be obtained; therefore, the high-precision earth external gravitational field assignment requirement is met.

The invention relates to a data braiding system based on a data grid architecture. The data braiding system comprises a global processing device and a production node processing device used for realizing field autonomy, the global processing device comprises a zero-trust access control module for interacting with external information and a global agent; wherein the received external information is dynamically analyzed by the global agent, and a task of the domain agent allocated to the production node processing device is generated; the production node processing device comprises a dynamic strategy coordination layer which is used for interacting with the global processing device and realizing cross-node access consistency; a plurality of domain agents; and the dynamic strategy coordination layer selects at least one domain agent for processing based on tasks distributed by the global agent. The method has the beneficial effects that the balance of'separate treatment 'and'unification' is realized, the data barrier is broken, the problem of data islands is solved, the data processing time is greatly shortened, and the cross-domain organization coordination cost is saved.

The present invention discloses a method for dynamic optimization of weather routes based on the A-star algorithm, comprising: obtaining a recommended weather route for the current voyage, and translating the recommended weather route to both sides by a first distance threshold to obtain a recommended route belt; obtaining a weather data grid node; generating a navigation grid node with obstacles and wind, wave, and current with obstacle attributes in the recommended route belt; obtaining a weather route from the departure port position to the destination port position based on the A-star algorithm; determining the optimal weather route for the current voyage; calculating the ship position when the weather update time threshold is passed starting from the departure port; obtaining all ship positions in the optimal weather route, thereby obtaining the entire optimal weather route. The present invention utilizes forecasted weather data to continuously update the navigation environment, continuously optimize the remaining segments, and ultimately obtain the optimal route, thereby maximizing the use of weather conditions to minimize the total cost of ship operation and promote the healthy development of smart shipping.

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.