1187 results about "Ancillary data" patented technology

Computer-implemented systems and methods for synchronizing data for dataset execution. The system includes a source database that stores a canonical dataset, a secondary database that stores a processed dataset, and a synchronization server that comprises a processor and a memory. The processor is configured to monitor for a publication of one or more source tables and when the publication is detected, identify the processed tables, corresponding to the source tables, to be updated in the processed dataset. The processor determines a tolerance level corresponding to each processed table and updates the processed tables in the processed dataset. The processor determines whether the processed tables in the processed dataset were successfully updated within the tolerance levels and transmits a notification based on determining whether the processed tables in the processed dataset were successfully updated within the tolerance levels.

Computer-implemented systems and methods for synchronizing data for dataset execution. The system includes a source database that stores a canonical dataset, a secondary database that stores a processed dataset, and a synchronization server that comprises a processor and a memory. The processor is configured to monitor for a publication of one or more source tables and when the publication is detected, identify the processed tables, corresponding to the source tables, to be updated in the processed dataset. The processor determines a tolerance level corresponding to each processed table and updates the processed tables in the processed dataset. The processor 10 determines whether the processed tables in the processed dataset were successfully updated within the tolerance levels and transmits a notification based on determining whether the processed tables in the processed dataset were successfully updated within the tolerance levels.

The invention provides a cross-domain data integration and fusion method based on a large model, a terminal and a storage medium, and the method comprises the steps: carrying out the format conversion and cleaning of cross-domain original data through a unified data access adaptation layer, and outputting a standardized data set; carrying out semantic understanding integration by utilizing a large model, analyzing field names, generating field semantic tags in combination with a business term knowledge base, judging business relevance by calculating similarity, and generating an inter-domain field mapping relationship; and performing technical standard conversion and data fusion processing based on the mapping relationship, and performing intelligent quality treatment by adopting a mode of combining a quality treatment standard library and a large model. According to the method, a large model is introduced to assist data understanding and treatment, so that the technical problems of insufficient semantic understanding, non-uniform standards and the like in existing cross-domain data integration are effectively solved, and the accuracy and automation level of data integration are remarkably improved.

The invention belongs to the technical field of big data analysis, and discloses a supplier price management system and method based on big data analysis, and the method comprises the steps: collecting core data and auxiliary data, and constructing a multi-dimensional data cube; dynamically calculating a grid availability index, forming a dynamic space-time grid reference library, and synchronously generating a supply chain topological graph; receiving and quantifying a business target instruction, and selecting a path through a rule engine; generating purchase strategy suggestions with confidence rating and a dynamic priority supplier list; the authority is dynamically adjusted through an ABAC model, an intelligent contract engine is triggered, and an order or bargaining invitation is generated; parallel approval is carried out through a workflow engine, and a purchase order set and a permission change log are generated; receiving a real-time data stream of the Internet of Things and a historical case library, and triggering a disposal logic; and matching similar historical cases, pushing a historical optimal strategy, synchronously updating a weight rule, a data cube structure and a supplier credit sub-threshold, and forming full-link closed-loop iteration.

Techniques are provided for enabling on-demand positioning reference signals (PRS) with a user equipment (UE). An example method for determining a location of a mobile device includes sending a request for downlink positioning reference signals to a network server, where the request includes positioning reference signal configuration information, receiving assistance data based on the positioning reference signal configuration information, measuring one or more downlink positioning reference signals based at least in part on the positioning reference signal configuration information, and determining a location of the mobile device based at least on part on the measurements obtained from the one or more downlink positioning reference signals and the assistance data.

Disclosed are apparatuses, systems, and techniques for implementing automatic runtime selection and tuning of MLM processing pipelines using stream augmentation. In one embodiment, the techniques include augmenting data stream(s) with auxiliary data to obtain an augmented data stream. The techniques further include performing an inference processing of the augmented data stream using a machine learning model (MLM) to obtain a characterization of a presence of the auxiliary data in the augmented data stream and adjusting one or more runtime settings of the MLM using the obtained characterization.

A method and system of the device may include obtaining hardware constraints of an FHE accelerator configured to execute the FHE program. In addition, the device may include selecting an optimal bootstrapping configuration that corresponds to the hardware constraints. The device may include identifying repetitive data patterns in the auxiliary data to be used in the bootstrapping process. Moreover, the device may include reducing the auxiliary data by applying at least one auxiliary data optimization technique based on the repetitive data patterns. Also, the device may include modifying the FHE program to include an instruction to load at least a portion of the reduced auxiliary data into an internal memory of the FHE accelerator, where the at least a portion of the reduced auxiliary data is loaded to the internal memory once prior to the execution of the plurality of bootstrapping processes.

An intelligent safety supervision system applied to a ship is provided. An image acquisition module is configured to acquires high-definition images in real time. An automatic recognition module is configured to obtains ship dynamic and static data. A ship server to-performs feature recognition on the ship dynamic and static data to obtain a data processing result, to-transmits the ship dynamic and static data and the data processing result, and receives alarm indication information. An alarm module outputs an alarm. A ship client displays the data processing result, and determines whether to transmit the alarm indication information according to the data processing result. A communication module receives and transmits the ship dynamic and static data and the data processing result. A shore-side supervision system includes a ship safety supervision big data analysis platform for performing secondary feature recognition on the ship dynamic and static data, so as to obtain a secondary data processing result.

Techniques are provided for utilizing a user equipment (UE) which is registered as a reference location device (RLD) and a location-target device. An example method of obtaining and reporting reference signal measurements includes providing an indication of a capability to operate as a reference location device and a location-target device, receiving assistance data associated with at least two positioning sessions occurring simultaneously, wherein a first positioning session is associated with the capability to operate as the reference location device and a second positioning session associated with the capability to operate as the location-target device, obtaining one or more reference signal measurements based at least in part on the assistance data, and reporting the one or more of the reference signal measurements to a positioning entity.

The invention relates to the technical field of environment management, in particular to a land coverage change monitoring method based on fusion of a remote sensing image and a geographic information system, which comprises the following steps: S1, data acquisition: acquiring multi-temporal remote sensing and auxiliary data; s2, data preprocessing: preprocessing the collected remote sensing and auxiliary data; s3, land feature classification and land cover change identification: classifying land cover types, and performing land cover change identification in combination with the spatial texture features of the remote sensing image data; and S4, result output and analysis: performing fusion and analysis on the terrain classification and land cover change identification result and the existing land cover data in the geographic information system, and evaluating the change trend of land utilization. According to the method, different types of land coverage can be identified carefully, and particularly, when complex remote sensing data is processed, the classification precision and the accuracy of change identification are remarkably improved.

The invention discloses an Internet of Things dynamic weighing and data tracing method and system, and relates to the technical field of industrial Internet data processing, and the method comprises the steps: obtaining dynamic weighing data of a weighing event and at least one group of auxiliary data synchronized with the dynamic weighing data; based on the auxiliary data, generating a credibility weight value for the dynamic weighing data through a preset credibility model; performing weighted statistical analysis on the one or more dynamic weighing data based on the weight value to obtain a weighted statistical analysis result; and fusing the weighted statistical analysis result and the change trend of the credibility weight value, and carrying out two-dimensional fusion diagnosis to obtain a diagnosis result capable of accurately decoupling a problem root. And generating a high-credibility tracing record or issuing a control instruction according to the diagnosis result. According to the invention, through real-time quantification and management of data credibility, intelligent distinguishing of production process abnormity and measurement system abnormity is realized, and the reliability of a dynamic weighing system and the value of tracing data are improved.

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention discloses a content creation and multi-mode display platform based on AIGC. The system comprises six modules of user interaction, content creation intelligent collaboration, AIGC auxiliary creation, data management, multi-modal deep fusion display and user feedback. The user interaction module supports various inputs and provides a creation template; the content creation intelligent collaboration module analyzes the intention and intelligently distributes tasks; the AIGC auxiliary creation module provides multi-mode creation assistance; the data management module stores various data; the multi-modal deep fusion display module is used for mining modal connection to realize deep fusion display; and the user feedback module collects and feeds back optimization contents and algorithms. Through cooperative work of the modules, the problems that an existing platform is difficult in creation cooperation, single in content generation, insufficient in multi-mode display and the like are effectively solved, creation efficiency and content quality are improved, immersive experience is brought to users, and innovative development of the field of content creation and display is promoted.

In some implementations, a server may send a request to a first wireless device for capability information, wherein the first wireless device is capable of transmitting both cellular and ultra-wideband (UWB) wireless signals. The server may receive a response from the first wireless device, the response comprising the capability information regarding a capability of the first wireless device for performing UWB positioning. The server may determine first UWB assistance data for the first wireless device based at least in part on (i) the capability information and (ii) information regarding one or more positioning signals transmitted in a cellular wireless network, wherein the first UWB assistance data comprises one or more parameters for a first set of one or more UWB positioning sessions between the first wireless device and a second wireless device. The server may send the first UWB assistance data from the server to the first wireless device.

The invention discloses a meta-learning and multi-modal large model-based few-sample anomaly detection method and system, and the method comprises the steps: dividing an auxiliary data set into a task set composed of N tasks according to the type of a detected target object, and dividing each task into a training set and a test set, the method comprises the following steps: constructing a semantic-anomaly network based on a multi-modal large model, the semantic-anomaly network comprising a visual encoder, a language encoder, an image-image anomaly discriminator, an image-text anomaly detector and a fusion module; and training a semantic-exception network by using the training set and the test set of each task in the task set, and using the semantic-exception network to predict an exception score of the query sample to determine whether the query sample is exceptional. The invention aims to solve the problems that the generalization ability of cross-category anomaly detection is poor and semantic information cannot be effectively converted into anomaly information in a traditional anomaly detection method, and the accuracy and robustness of image anomaly detection and the cross-category anomaly detection ability are improved.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention relates to the technical field of point cloud completion, and provides a point cloud completion method based on cross-modal and deep repair and related equipment. The method comprises the following steps: acquiring target point cloud data and auxiliary data corresponding to the target point cloud data; encoding the target point cloud data and the auxiliary data by using an encoder to obtain point cloud encoding features; decoding the point cloud coding features by using a decoder to obtain point cloud coordinate features under a plurality of density scales, and performing deep repair on each point cloud coordinate feature according to the auxiliary data to obtain a complemented point cloud under each density scale; constructing a comprehensive loss function according to all the complemented point clouds, and optimizing an encoder and a decoder by using the comprehensive loss function to obtain an optimized encoder and an optimized decoder; and complementing the to-be-complemented point cloud data by using the optimized encoder and the optimized decoder to obtain a final complemented point cloud. According to the method, the accuracy of point cloud repair can be improved.

Implementations of this disclosure provide for automated monitoring of configuration parameters of a primary data intake and query system instance operating within a distributed deployment environment. Further implementations provide for automatically generating instructions in response to a detected change in a configuration parameter of the primary data intake and query system instance and transmitting those instructions to one or more secondary data intake and query system instances. The instructions, upon execution by one or more processors, cause the configuration parameters of the one or more secondary data intake and query system instances to be updated in accordance with the detected change in the configuration parameter of the primary data intake and query system instance.

Disclosed are techniques for wireless positioning. In an aspect, a network entity may determine that a positioning reference signal (PRS) processing load for a UE exceeds a PRS processing capacity of the UE. The network entity may send, to the UE, assistance data that reduces the PRS processing load for the UE. In another aspect, a user equipment (UE) may determine, based on information received from a network entity, that one or more PRS measurements is associated with a low-latency location request. The UE may modify one or more parameters associated with the one or more PRS measurements to reduce measurement latency. The UE may perform the one or more PRS measurements associated with the low-latency location request according to the modified one or more parameters.

The invention provides a wide-area multi-level soil attribute fine mapping method based on spatial downscaling. According to the method, long-time-sequence optical data information and ground elevation information are collected through a satellite sensor, long-time-sequence temperature and rainfall data are collected through a meteorological station, and spatial position auxiliary data extracted from the long-time-sequence temperature and rainfall data are combined with existing national-level soil grid database data to construct a high-quality data set; the method is used for training a random forest model constructed based on an integrated learning technology so as to realize high-precision and high-reliability refined soil attribute mapping under a wide-area scale. The method is reasonable in conception, high-precision and high-reliability refined soil attribute mapping under a wide-area scale is realized, a specific region multi-level soil attribute database with higher resolution can be constructed, and the method is suitable for popularization and application.

The invention provides a bidding total factor compliance examination method based on multi-modal model space-time sequence analysis, which relates to the technical field of data processing and comprises the steps of acquiring space-time sequence data and auxiliary data of bidding items, constructing a space-time double-flow feature network, constructing a knowledge graph based on output, and calculating an abnormal score by adopting a space-time mode encoder. And optimizing an anomaly detection result through a graph attention network, and dynamically generating a bidding compliance verification path by using deep reinforcement learning. According to the method, comprehensive analysis of bidding and tendering data can be realized, the abnormal behavior detection accuracy is improved, the manual review cost is reduced, and the verification path efficiency is optimized.

Techniques for environment-based positioning and communication are disclosed. In some aspects, techniques include performing one or more positioning operations with respect to a user device in a wireless communication network, which may include obtaining first environmental information using one or more sensors of a wireless-enabled device; sending the first environmental information to a network entity of the wireless communication network; receiving first enhanced assistance data from the network entity, the first enhanced assistance data generated based at least on the first environmental information sent to the network entity; and performing the one or more positioning operations using the first enhanced assistance data received from the network entity, wherein the wireless-enabled device comprises the user device or a base station. In some aspects, techniques include improving communication in the wireless communication network, which may include receiving enhanced beamforming information from a network entity; and performing wireless communication using enhanced beamforming information.

The invention relates to the field of forest resource monitoring, and discloses a large-scale forest canopy height inversion method based on ICESat-2 and multi-source remote sensing data, and the method comprises the following steps: S1, collecting multi-source data; s2, carrying out data preprocessing and resampling; s3, multi-temporal data of Sentinel-2 are analyzed, a key phenological period is determined, normalized spectral features are developed, and vegetation is classified; s4, constructing a response canopy height of an explanatory variable and ICESat-2 ATL08, and training a forest canopy height regression model based on forest features based on a random forest regression algorithm; and S5, performing spatial inversion on the continuous interpretation variables by using the trained model, and generating a large-scale forest canopy height map. According to the method, discrete height data of ICESat-2, Sentinel-1 / 2 and auxiliary data are combined, and the forest canopy height is accurately inverted by adopting a random forest regression algorithm.

The invention provides an RFID label positioning error correction method based on deep learning, and the method comprises the following steps: 1, deploying a multi-mode sensing network in a target region, synchronously collecting radio wave signals and environment auxiliary data of an RFID label, and constructing a space-time multi-dimensional feature data set; step 2, constructing a joint architecture hybrid model composed of a space-time Transform network and a generative adversarial network; according to the method, a space-time multi-dimensional feature data set is constructed by fusing RFID signals and environment auxiliary data through a multi-mode sensing network, adaptive parameters are dynamically generated in combination with a model-independent meta-learning algorithm so as to quickly respond to environment changes, multi-path signal correlation is captured by using a space-time Transform and generative adversarial network combined architecture, feature expression is optimized, and the robustness of the system is improved. The problems that a single signal feature is missing, model parameters cannot dynamically adapt to the environment and the anti-interference robustness of a traditional network is insufficient are effectively solved, and the effectiveness, the real-time performance and the precision of RFID label positioning in the complex environment are remarkably improved.

The invention relates to the technical field of quality evaluation, and discloses a lightweight beef quality evaluation method based on unsupervised feature learning, and the method comprises the steps: obtaining original hyperspectral data of beef as real data, and generating high-quality sample data based on a generative adversarial network model; preprocessing the high-quality sample data, mapping, reconstructing and fusing the preprocessed sample data, extracting spectral and spatial features of different levels, and fusing or splicing the spectral and spatial features with feature values of other auxiliary data; the fusion sample features are mapped and fused, sample data of specific tiny disturbance similar to real data are generated, the sample data and the real data are alternately input into a Transform model, and parameters of the Transform model are updated; the beef quality is evaluated on the basis of the Transform model with the updated parameters; according to the method, the accuracy, objectivity, rapidness, non-destructive property and comprehensiveness of beef quality evaluation are improved.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The invention is suitable for the technical field of forest fire disaster early warning, and provides a forest fire disaster risk early warning method considering multi-source satellite information fusion, and the method comprises the steps: obtaining multi-source satellite information data which comprise stationary meteorological satellite data, polar orbit meteorological satellite data, high-resolution satellite data and auxiliary data; performing data preprocessing on the obtained multi-source satellite information data, and then performing multi-scale space-time fusion to obtain a fused target feature vector; performing fire point identification on the target feature vector by using a eucalyptus forest fire point enhancement detection algorithm, and judging whether a risk fire point exists or not; if yes, performing multi-dimensional coupling analysis on the risk fire point; according to the multi-dimensional coupling analysis result, a grading early warning graph is generated, decision data including fire point coordinates, spreading speed and emergency paths are output, an accurate and timely data basis is provided for disaster prevention and reduction, and power grid and economic losses caused by mountain fire disasters are effectively reduced.