13 results about "Atomic data" patented technology

The invention belongs to the technical field of data management and intelligent analysis, and particularly relates to a data loading and conversion processing method under a lake-warehouse fusion architecture, which comprises the following steps of: after an externally uploaded file is received, performing compatibility verification and content verification on the externally uploaded file in sequence; identifying that the tables and the partitions which are influenced by data loading and need to be synchronously updated are added, executing atomic data loading operation, synchronously updating the tables and the partitions in the table and the partitions, and generating corresponding version numbers for each table and each partition at the same time; for a data block set under the lake-warehouse fusion architecture, performing hierarchical adjustment according to the access popularity of the data block set; income-cost evaluation is carried out on each data block, and the data blocks needing to be subjected to Z-sequence clustering rearrangement are recognized and added into a requeuing column; and performing Z-sequence clustering rearrangement on each data block in the requeuing column.

The invention discloses a supervision data processing method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the supervision data processing method comprises the steps: obtaining process basic data and instance operation data of a process business during the processing of the process business, and obtaining the process basic data and instance operation data of the process business based on a preset data specification; and cleaning abnormal data in the instance operation data, and processing the instance operation data and the process basic data based on service element configuration and preset processing rule configuration in a register book of a process service to obtain scene atomic data. The instance operation data is business data corresponding to the process basic data, and the process data and the business data can be associated through processing. And the service element configuration corresponds to the service scene, so that the scene atomic data can comprise the corresponding service scene through processing. Therefore, when supervision analysis is carried out based on the scene atomic data, the quality supervision analysis effect can be improved.

A system includes modules for DFT dataset generation, AGAT training, high-throughput prediction, CGAN generation, validation and augmentation, composition classification, and coordination. The DFT module generates and updates atomic data via spin-polarized DFT. The AGAT module trains attention-based models with translational, rotational, and permutational invariance. The prediction module estimates HER activity and ΔG(H) for new compositions. The CGAN module learns from predicted results and generates hypothetical compositions. The validation module runs DFT on selected candidates to update training data. The classification module uses KNN to group validated compositions and updates the formula list. The coordination module manages module interactions and iteration control.

The invention relates to an ICU (Intensive Care Unit) severe illness quality control system data dynamic acquisition and intelligent analysis method, which specifically comprises the following steps of quality control data acquisition, data preprocessing, quality control index intelligent calculation, intelligent analysis and quality control related chart output, and specifically comprises the following steps: receiving patient data information sent by a multi-source data system based on a data acquisition module; the method comprises the following steps: acquiring patient data information, sending the patient data information to a data preprocessing module for preprocessing, storing the patient data information in an original data database, calling the patient data information in the original database through a quality control index calculation module, calculating to obtain a quality control index result, and based on an intelligent analysis module, taking the quality control index result as a core analysis object. And atomic data in an original database is combined as an auxiliary and traceability object for analysis, data-driven intelligent support is provided for clinical decision making, and finally a chart and a formatted report are generated by a chart output module. Data sources can be integrated in a centralized mode, real-time updating of quality control indexes is achieved, and management pertinence and effectiveness are improved.

The invention discloses a method for observing spatial distribution characteristics of deuterium and impurities of a lower divertor by using hyperspectral imaging, which comprises the following steps of: when a magnetic confinement fusion device runs, realizing multiband 2D image observation of a lower divertor region through a hyperspectral camera, and realizing continuous recording of emission spectral lines of deuterium and impurity elements of the lower divertor; arranging the obtained multiband spectral intensity data in the 2D space, establishing a corresponding database according to corresponding shot numbers, and carrying out calibration analysis on spectral information intensity recorded by a camera through an integrating sphere standard light source to obtain absolute intensity data of emission spectral lines in the multiband; and determining corresponding element information by combining the special emission spectral line wavelengths of different elements recorded in the ADAS atom database. Multiband spectral information data recorded by hyperspectral imaging can provide a basis for research on spatial 2D profile distribution of boundary plasma parameters and tungsten impurity spectral lines, and provide data support for evaluation of first wall tungsten impurity source distribution and tungsten impurity sputtering conditions.

The invention relates to the technical field of large models, in particular to a thinking processing method and system based on a large model.The method comprises the steps that a large model atomic agent database is established, and basic parameters, operation scenes, thinking strategy design and performance feedback information of atomic agents are covered; the operation scenarios include user interaction and system resource scenarios, based on which streaming monitoring risk levels are evaluated. And constructing a streaming monitoring simulation environment through the basic parameters and thinking strategy design, and performing streaming simulation on the thinking process of the atomic agent. And loading a streaming monitoring risk level in a simulation environment, setting a time sampling window, carrying out streaming simulation on a plurality of thinking stages of the atomic agent based on the window, and obtaining a monitoring evaluation result through a stage simulation result. According to the method, through transparent decision paths, real-time risk quantitative evaluation, high-compliance scene adaptation and dynamic strategy optimization, the problems that a traditional large model black box cannot be explained and dynamic risk monitoring is missing are solved.

The application relates to a thermal conductivity prediction method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining an initial structure file of a target crystal; a plurality of atomic data are included in the initial structure file; a plurality of perturbation structure files are generated based on the initial structure file and a preset supercell size; atomic information corresponding to each perturbation structure file is obtained based on a plurality of the perturbation structure files and a preset potential function model; and a thermal conductivity prediction result of the target crystal is obtained based on a plurality of the atomic information. The method can improve the thermal conductivity prediction efficiency.

The embodiment of the invention provides a cache operation method, a cache, computing equipment and a system, and relates to the technical field of chips. The cache operation method is applied to a cache, the cache comprises a shared cache and an atomic cache, the shared cache comprises a plurality of shared cache blocks used for storing data corresponding to different types of cache operation requests, and the atomic cache comprises a plurality of atomic data cache blocks used for storing atomic data corresponding to atomic operation requests; the cache operation method comprises the steps that according to a cache operation request sent by a computing unit, a first cache block used for storing target data is determined in a plurality of shared cache blocks, and the target data is data corresponding to the cache operation request; determining whether a second cache block corresponding to the first cache block exists in a plurality of atomic data cache blocks of the atomic cache, wherein partial data of the first cache block is stored in the second cache block; and if the second cache block exists in the atomic cache, executing the cache operation request at least according to the second cache block.

The invention relates to the technical field of heavy metal pollution prediction, in particular to a heavy metal pollution prediction method based on a molecular dynamics model, which comprises the following steps: collecting atomic data of copper tailing geopolymers, heavy metal ion types, initial concentration and environmental condition data, and constructing an influence factor reference data set; based on a molecular dynamics algorithm and an influence factor reference data set, a molecular dynamics model of interaction of the copper tailing geopolymer and the heavy metal ions is constructed, and based on acid rain leaching erosion, dry-wet freeze-thaw cycle and carbonization, dissolution, competitive adsorption and migration behaviors of the heavy metal ions in the copper tailing geopolymer are tested. Parameters of the molecular dynamics model are optimized on the basis of the test result, the currently obtained environmental conditions are input into the optimized molecular dynamics model, the pollution condition prediction result of the heavy metal in the copper tailing geopolymer is obtained, and the problems that in the prior art, the prediction result error is large, and the precision is low are solved.

The application discloses a method and device for generating report data, electronic equipment and a computer readable medium, and relates to the technical field of big data intelligent analysis. A specific embodiment of the method comprises the following steps: obtaining an atomic data list from a database according to a query condition, and extracting a row element set and a column element set from the query condition; performing data completion on the atomic data list according to the row element set and the column element set; calculating a total result of the completed atomic data list, and merging the total result into the atomic data list; and converting the atomic data list into a report data list. The embodiment can solve the technical problems that low-quality codes are prone to appear and codes are difficult to reuse.

The invention discloses a machine learning force field molecular dynamics efficient parallel acceleration method, system and device based on a taftogen S60 chip architecture and a storage medium. The method comprises the following steps of: firstly, acquiring initial data of a molecular system, and converting the initial data into a structure array (SoA) format to be stored in a high-bandwidth memory (HBM) of a chip; then, the simulation space is divided into a plurality of local sub-domains based on a space decomposition strategy, and atomic data are efficiently transmitted to an on-chip temporary storage memory (SRAM) through a direct memory access (DMA) engine; then, constructing a neighbor list in an SRAM (Static Random Access Memory), generating a local environment descriptor, and executing forward reasoning of a machine learning force field model by calling a tensor calculation unit of a chip to obtain atomic energy or atomic force; and finally, finishing stress reduction summation on the chip, executing numerical integration by using a vector processing unit, and updating the speed and the position of the atom. According to the method, through SoA data layout and a DMA strategy, the memory bandwidth utilization rate is improved, non-bonding force calculation is accelerated, and the expandability of parallel simulation is improved; and through a mixed precision strategy, the calculation efficiency is improved while the precision is ensured, and the method is suitable for long-time-scale high-precision simulation.

The application discloses a kind of robustness evaluation method, device and equipment of neural network, and storage medium, belong to computer technical field.The method comprises: data in benchmark dataset is annotated according to target multi-level structure, and the annotation information of data is obtained;Target multi-level structure includes m levels, and the lowest level in m levels includes at least one leaf characteristic;Data is classified based on the annotation information of data, and at least one atomic dataset is obtained, and the data in atomic dataset has the same annotation information;At least one atomic dataset is used to test the neural network to be evaluated, and the evaluation index value of at least one leaf characteristic is obtained;Based on the evaluation index value of at least one leaf characteristic, the evaluation index value of the neural network to be evaluated is determined, and the evaluation index value of the neural network to be evaluated is used to indicate the robustness of the neural network to be evaluated.The present application helps to improve the robustness evaluation efficiency of neural network.

The invention provides a fund data detection method and device, equipment, a medium and a program product, and relates to the technical field of data processing. The method comprises the steps of obtaining target detection information in response to a received data detection request sent by client equipment; the data detection request comprises target detection information; the target detection information is detection demand data of the target fund data; the target fund data is to-be-detected fund data; obtaining a target atomic data model based on the target detection information; adopting the target atomic data model and constructing a target fund data detection model based on the target detection information; constructing a target task table based on the target detection information, and associating the target task table with the target fund data detection model; and determining target fund abnormal data based on the target task table by adopting the target fund data detection model.