27 results about "Multi level data" patented technology

The invention relates to the technical field of data cache management, and discloses a segmented multi-level data cache management method and device, computer equipment and a storage medium. The method comprises the following steps: defining a hierarchical data management module, wherein the data management module comprises a first-level data interaction sub-module and a second-level cache hit management sub-module; receiving an upper-layer data access request, and performing data splitting according to the logic address relationship; querying the module state of the position according to the actual situation, and marking the cache hit management module state of the corresponding position; if the second-level cache hit management sub-modules to which the first-level data interaction sub-modules belong are all hit, realizing data communication with lower-layer storage equipment according to large granularity; and if part of data enters the cache, according to the continuity of the data hit state, respectively constructing commands with corresponding lengths to lower-layer storage equipment. By implementing the method provided by the invention, access requirements of different types of data can be effectively realized, the cache hit rate is improved, and better performance improvement is obtained.

A computer-implemented method of using a multi-level (ML) data chain protocol to embed a data chain on a core blockchain, comprising: obtaining one or more ML transactions, wherein each ML transaction comprises one or more carrier pairs, each carrier pair comprising an input and an output, wherein each output comprises data associated with the data chain, and wherein each input comprises a signature that signs the carrier pair; generating a first ML block of the ML data chain, wherein the first ML block is a core blockchain transaction and comprises the respective carrier pairs of the obtained one or more ML transactions, wherein for each carrier pair, a respective position index of the respective input corresponds to a respective position index of the respective output, and b) a first chain output, wherein the first chain output is for being spent by a respective chain input of a subsequent ML block.

Hierarchical data objects are generated via a computer-based system for applying a series of rules to establish episode-specific data objects reflecting a plurality of discrete claim records before further dissecting the generated episode-specific data objects prior to finalization of those episode-specific data objects to identify claim records within the episode-specific data objects that are eligible for generation of one or more sub-episodes within the episode-specific data objects. The identified sub-episodes are reflected within the episode-specific data object to designate complete episodes of care that additionally reflect interactions with the corresponding parent episode.

In an example, a multi-level data structure is defined including fine grid (FG) and coarse levels. The FG level is configured to store FG data of FG points. The coarse level is configured to store, for a respective chunk of FG points, compressed FG data and / or a pointer to corresponding FG data of the respective chunk. First chunks are identified by a graphics processing unit (GPU) and include each chunk of the FG points including one or more of: (i) that includes a FG point in a level set layer L0 (LSL0), and (ii) that neighbors a chunk that includes the FG point in the LSL0. Memory of the GPU is allocated for the first chunks that have respective compressed FG data to be decompressed. Level set values of the FG points in the LSL0 stored in the FG level in the allocated memory are updated by the GPU.

The invention provides a data storage and reading method, device and equipment, and the data storage method comprises the steps: extracting the features of to-be-stored data, and applying for an index storage position in a traversed current data volume according to a level sequence from a higher level to a lower level, when it is determined that first to-be-stored data which does not meet the data similar compression condition exists, the first to-be-stored data is written into an applied target storage position, and the target storage position is written into an index storage position; when it is determined that second to-be-stored data meeting the data similar compression condition exists, the second to-be-stored data is compressed, the to-be-stored data is updated, the index storage position applied by the to-be-stored data in the next-level data volume is written into the current data volume, and if the level of the current data volume is the lowest level, the next-level data volume is created, and a data storage instruction is triggered. Through the method, the to-be-stored data is stored after being subjected to multi-stage compression based on the multi-stage data volume, so that the compression rate of the data is improved.

An improved data system supports creation of multiple hierarchical database catalogs under a particular account. Users or organizations have the flexibility to organize their data, native or external, into an n-level hierarchy of their choice. There is a root database catalog of the account, and other catalogs (1 or n-levels) are created under it. The data system has extract, transform, and load (ETL) features that allows data to be moved between different data sources and targets.

To provide a device for computers to query and display collections of big data in a more efficient manner.SOLUTION: In a system 100, a server computer 103 associates a multi-level data structure hierarchy of index 103 with a particular date to reduce linearity of search. A client computer 112, 116, or 118 receiving results of a query stores the query results in a linked list with a record format consistent with the resulting records. The client computer also periodically updates a graphical user interface with the query results until all the results are received.SELECTED DRAWING: Figure 1

This invention provides an arithmetic circuit that calculates the product of weight coefficients, which are analog or multi-level data, and data. [Solution] In the arithmetic circuit MAC2, transistors F2 and F2m are operated in the subthreshold region, and a wiring VE is provided that flows a current corresponding to the product of the weight coefficient and the data between the source and drain of the transistors.

An improved data system supports creation of multiple hierarchical database catalogs under a particular account. Users or organizations have the flexibility to organize their data, native or external, into an n-level hierarchy of their choice. There is a root database catalog of the account, and other catalogs (1 or n-levels) are created under it. The data system has extract, transform, and load (ETL) features that allows data to be moved between different data sources and targets.

The invention discloses a virtual character driving method based on real-time facial feature point detection and Kalman filtering, and the performance of virtual character driving is improved through the four aspects of multi-stage data optimization, high-robustness feature point selection, a lightweight communication protocol and time sequence association optimization. A two-stage filtering framework is combined with Kalman filtering and PD control, real-time denoising and smoothing are carried out on coordinates of feature points, jitter is effectively restrained, stability is improved, and therefore the problem of tracking instability of a traditional method in a fast movement or shielding scene is solved. Then, by selecting high-stability feature points and combining improved dynamic mixed function design, the adaptability of the system to complex scenes is enhanced, meanwhile, feature point detection errors are reduced, and cross-domain generalization performance is improved; then, a lightweight binary communication protocol and an efficient real-time driving architecture are designed, data transmission efficiency and model calculation performance are optimized, millisecond-level interaction requirements are met, the CPU occupancy rate is reduced, and high-frame-rate operation of a mobile terminal is supported.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a multi-stage data processing MEC system based on an unmanned aerial vehicle and an implementation method. According to different performances of the unmanned aerial vehicles, the unmanned aerial vehicles are divided into three levels, including MEC unmanned aerial vehicles, a master unmanned aerial vehicle and slave unmanned aerial vehicles, and the master unmanned aerial vehicle can sense existence of surrounding MEC unmanned aerial vehicles and establishes wireless connection with the MEC unmanned aerial vehicles. Dividing the task according to the details of the calculation task and the current environmental factors, and transmitting the divided task to a platform of a proper level for processing. Meanwhile, communication between the master unmanned aerial vehicle and the slave unmanned aerial vehicles adopts an orthogonal frequency division multiplexing mode, simultaneous communication between the master unmanned aerial vehicle and the multiple slave unmanned aerial vehicles is supported, and the task uploading efficiency is improved. The computing resources are allocated and the flight path of the unmanned aerial vehicle is optimized through a convex optimization technology, so that the task processing efficiency is improved, and the problem that task scheduling and resource allocation are difficult due to mobility and heterogeneity of the unmanned aerial vehicle is solved.

The present application relates to an interactive concurrency method, and parallel processing method and apparatus for multi-level data transmission and data computation of big data or large models. In order to achieve the timeliness of big data or large model multi-access data transmission and multi-task computing, three application modes of a multi-level site / node remote, short-range and local remote / local short-range data communication system and a supercomputer / processor / computing core cluster having distributed computing resources are established. The interactive concurrency method establishes a data block forwarding and computing mechanism, uses relatively short time to complete big data or large model multi-level site / node concurrent transmission, and aggregates computing power of sites / nodes to complete big data or large model computing tasks, and thus has extremely high data transmission and computing benefits. The present application efficiently satisfies the demands on different application types of super-computing power for big data or large model multi-access data communication and multi-task computing.

The invention relates to the technical field of data processing, and discloses a multi-domain multi-level data feedback method and device. The method comprises the steps of confirming data request information needing to be fed back; according to the data request information, establishing a binding relationship between the multi-level through data block and the reverse feedback link block; and obtaining request data needing to be fed back according to the binding relationship. According to the method, the data request information is confirmed, and the binding relationship between the multi-stage through data block and the reverse feedback link block is established, so that the accurate matching of the data composition form and the stack path is realized, the data feedback range is defined, the controllability of the data flow direction is ensured, and the data security and reliability are improved.

This application discloses a method and electronic device for parsing executable and linkable files, relating to the field of computer technology. It identifies the data structure of executable and linkable files, queries a multi-level cache to see if multi-level data is stored based on the data structure, and skips the parsing of the current-level data in the current-level cache, directly retrieving the executable and linkable file result from the cache if the current-level data is stored there. If the current-level data is not stored in the current-level cache, the current-level data is loaded from the hard disk into the current-level cache. By storing the parsed multi-level data of the executable and linkable files in a multi-level cache, the same executable and linkable files can be parsed without repeated parsing, reducing memory usage and improving the parsing efficiency of executable and linkable files. Furthermore, it avoids the need for frequent I / O operations to load data from the hard disk, thus preventing impact on operating system performance.

The present invention relates to a parallel processing method and apparatus for multi-level data transmission and data calculation of big data, including an interactive concurrency method for multi-level data transmission and data calculation. The method and apparatus include four parts: system structure, parallel mechanism, system function, and system module. To solve the timeliness problem of multi-address data transmission and multi-task calculation of big data, three application modes of data communication systems with multi-level sites for remote or short-range or local remote and local short-range and supercomputer clusters with independent distributed computing resources are established. The interactive concurrency method establishes a data block forwarding calculation mechanism, which can complete the concurrent transmission of big data at multi-level sites in relatively little time, and pools the computing power of each site to complete the big data calculation task, with very high data transmission and data calculation efficiency. It provides a parallel mechanism and application mode for multi-address transmission and multi-task calculation of big data, and efficiently solves the different types of requirements for multi-address data communication of big data and the super computing power for multi-task calculation of big data.

This invention discloses a multi-level data directory processing method and apparatus for a trusted data space. The method includes: a client receiving a data query request initiated by a user and sending it to a general data directory node; the general data directory node obtaining an industry code; obtaining an industry data directory service address based on the industry code, and sending the query request to the industry data directory node based on that address; the industry data directory node obtaining metadata and a master data code, obtaining an industry master data service address based on the master data code, and sending the query request to the industry master data service node based on the industry master data service node address; the industry master data service node retrieving the master data and sending it to the industry data directory node; the industry data directory node returning the master data and metadata; and the general data directory node assembling the general data directory, metadata, and master data and returning it to the client. This invention can reduce the load on the general data directory and lower system maintenance pressure.

The present invention proposes a service process processing system and method using multi-level data caching, relating to the fields of cloud computing and distributed storage technology. The system comprises: a local cache unit, a Redis centralized cache unit, a database and a service process processing module; wherein the local cache unit, the Redis centralized cache unit and the database adopt a multi-level data caching mode to store data, and the access priority from high to low is: local cache unit, Redis centralized cache unit and database; the service process processing module is used to preferentially access the local cache unit when the service process is started; if no data is hit in the local cache unit, the Redis centralized cache unit is accessed; if the data is hit, the data is read and transaction processing is continued; if no data is hit in the Redis centralized cache unit, the database is accessed; if the data is hit, the data is read and transaction processing is continued.

A computer-implemented method of using a multi-level (ML) data chain protocol to embed a data chain on a core blockchain, comprising: obtaining one or more ML transactions, wherein each ML transaction comprises one or more carrier pairs, each carrier pair comprising an input and an output, wherein each output comprises data associated with the data chain, and wherein each input comprises a signature that signs the carrier pair; generating a first ML block of the ML data chain, wherein the first ML block is a core blockchain transaction and comprises the respective carrier pairs of the obtained one or more ML transactions, wherein for each carrier pair, a respective position index of the respective input corresponds to a respective position index of the respective output, and a first chain output that is for being spent by a respective chain input of a subsequent ML block.

The present invention relates to the field of data processing technology, specifically a multi-level data optimization method and system based on big data, the method comprising: obtaining pre-selected data and browsing data of a user; screening out a plurality of first video data from the browsing data; fusing and screening the first video data to obtain video data to be played; when the type of the browsed video is a negative type, determining whether the duration of browsing the negative type video is greater than a second preset duration, and determining whether the number of negative type videos browsed is greater than a preset number; if all are negative, playing the video data to be played; if one of them is yes, using a video screening model to screen out video data that meets preset rules from a video information library and playing the video data. The present invention can solve the problem that existing video intelligent recommendation algorithms do not take into account user interests, but continue to recommend negative videos to users, causing users to continue to fall into negative emotions and affecting their viewing mood.

A method for visual representation includes receiving a point cloud of an environment. The method also includes processing the point cloud at multiple levels of detail to produce multi-level data, the multi-level data including simplified geometric descriptions of the point cloud and / or a subset of points of the point cloud. The method further includes forming intermediate representations based on the multi-level data. The method still further includes determining features for the intermediate representations based on the simplified geometric descriptions and / or the subset of points. The method also includes generating a visual representation of the environment based on the determined features.

The invention discloses an executable and linkable file analysis method and electronic equipment, and relates to the technical field of computers, a data structure of an executable and linkable file is identified, whether multi-level data is stored in a multi-level cache or not is queried based on the data structure, and when current-level data is stored in a current-level cache, the executable and linkable file is analyzed. If the current-level data is not stored in the cache, skipping the analysis of the current-level data, directly obtaining an executable and linkable file result from the cache, and if the current-level data is not stored in the current-level cache, loading the current-level data from the hard disk to the current-level cache. According to the method, the same executable and linkable files are analyzed, repeated analysis is not needed, memory occupation is reduced, then the analysis efficiency of the executable and linkable files is improved, data of the executable and linkable files do not need to be loaded from a hard disk through frequent I / O operation, and the performance of an operating system is prevented from being affected.

A method and apparatus for computer operation improvement by flattening multi-level data structures to optimize pointer chase is provided. Within computer program code, one or more chains of multiple pointers which ultimately reference stored information to be processed are identified. These chains are analyzed to determine the feasibility of replacing the chains with a flatter data structure. In the flatter data structure, the stored information is accessible more directly. For example the data may be stored in a single array by which it can be accessed directly. When feasible, a chain is replaced with such a data structure and the computer program code is adjusted to implement the improved data structure in place of the associated chain. Candidate chains can be identified or prioritized for example on the basis of the expected impact to computer performance, e.g. in terms of operating speed or resource usage.

The invention relates to a data processing method and device, equipment and a storage medium. The data processing method comprises the steps that a first instruction is received, the first instruction is a processing instruction for at least one data block in target data, and the target data is configuration data related to a mobile network operator; writing at least one label in the first instruction and a byte length corresponding to the at least one label into a pre-constructed target stack structure, wherein the at least one label corresponds to a label involved in a multi-level data structure of the target data; determining a to-be-processed target label in the at least one label based on the target stack structure; and processing a target data block corresponding to the target label in the first instruction, wherein at least one data block comprises the target data block. According to the method provided by the invention, complete data does not need to be stored in the data analysis process, and the received data blocks are processed, so that the memory occupancy rate is reduced, continuous processing of the complete data is ensured through the stack structure, and the processing speed is further improved.

The invention provides a multi-stage data backflow system and method supporting multiple modes. The multi-stage data backflow system comprises an intelligent task scheduling engine, a full-amount synchronization module and an increment synchronization module. The intelligent task scheduling engine is used for determining a data backflow mode based on the data features of the first-level data source and a preset rule; wherein the data backflow mode comprises a full-amount mode and an incremental mode; if the data backflow mode is a full-amount mode, the full-amount synchronization module is used for segmenting full-amount data and synchronizing the segmented data to a second-level node and a third-level node; and if the data backflow mode is an incremental mode, the incremental synchronization module is used for acquiring incremental data based on the database log and the upstream business system change notification, and synchronizing the incremental data to the second-level node and the third-level node. The method can intelligently adapt to different data scenes, and the data synchronization efficiency is improved.

The invention discloses an education management platform fusing multi-level data, and particularly relates to the field of education informatization, and the platform comprises a terminal collection module which is used for collecting an evaluation record, an IEP record, a teaching record, a teaching and research record, a training record and a supervision record, and outputting a process record with an object identifier, an item identifier, a record content and a time identifier; and the edge decoupling module is used for receiving a process record at an edge computing node, constructing a process tensor according to an object identifier, an item identifier and a time identifier, performing tensor decomposition on the process tensor to obtain decomposition factors representing object continuation, item continuation and time sequence continuation respectively, and outputting a corresponding process factor sequence. According to the method, field change content is extracted from front and back process records, a process factor sequence is generated, an action chain is constructed, actual effect verification is carried out in combination with inheritance probability, deviation and a convergence relation, and the process record which truly causes subsequent changes and the form record which only completes trace leaving can be relatively distinguished.

The present application belongs to the technical field of label management processing, and particularly relates to a multi-level data interaction processing method, system and platform based on labels. The present application acquires label data containing business information in real time by a method, and generates a label data set corresponding to the label data; the label data set is subjected to category splitting processing, and the label data content is subjected to rendering processing in combination with a scene corresponding to the label; a multi-level data interaction operation interface corresponding to the label is generated in real time according to the data after splitting processing and rendering processing, and a system, a platform and a storage medium corresponding to the method, make up for the fact that the existing functions on the market cannot be applied to the scene of the present system, interface the data of other systems, visualize the data, make the operation convenient, improve the user experience, and provide an efficient label management scheme, so that the functions of related modules are more unified, and the operation learning cost of the system is reduced.

The invention provides a multi-level data backflow account checking method and system, electronic equipment and a medium, and the method comprises the steps: obtaining a backflow task information summary table when a main platform synchronizes backflow data to each secondary platform; after each secondary platform receives the backflow data, generating a backflow reconciliation table of each secondary platform based on a preset reconciliation strategy, and reporting the backflow reconciliation table to the main platform; and the main platform integrates the backflow reconciliation tables and the backflow task information summary tables of the secondary platforms to obtain reconciliation data, and performs multi-dimensional analysis on the reconciliation data to obtain a reconciliation analysis result. According to the invention, the efficiency of backflow reconciliation can be improved, and the timeliness and integrity of data backflow are improved.