343results about "Data sorting" patented technology

An information processing method, an electronic device, and a storage medium. The method includes: obtaining a query statement of a user, determining at least one model identifier of at least one candidate service model based on the query statement; generating at least one first prompt word based on the query statement and the at least one model identifier, inputting the at least one first prompt word into a pre-trained target large model, and outputting, by the target large model, at least one screening parameter of the at least one candidate service model based on the at least one first prompt word; determining a target service model from the at least one candidate service model based on the at least one screening parameter; and inputting the query statement into the target service model, and obtaining feedback information corresponding to the query statement.

Embodiments of the present disclosure relate to the technical field of cloud computing, and provide a server failure prediction method and apparatus, an electronic device, and a storage medium. The method comprises: first, acquiring time series data of the operating state of a server; cutting the time series data of the operating state according to a period, and stacking a plurality of pieces of sub-time series data obtained by cutting; extracting time series multi-dimensional features from the stacked data; and performing failure prediction on the server by using the time series multi-dimensional features. In the embodiments, the time series multi-dimensional features are extracted on the basis of the time series data of the operating state for failure prediction, so that a server failure can be predicted in advance. Additionally, the time series data of the operating state are cut according to the period and stacked, and then the time series multi-dimensional features are extracted, so that related information on the fluctuation trend of data over time and different performances of the data before and after the period can be extracted, thereby improving the accuracy and recall rate of failure prediction.

The invention discloses a mathematical algorithm calculation system and optimization method based on big data, and the method comprises the steps: calculating the local entropy density of a data set, constructing an entropy gradient field, and extracting a representative data subset with global sorting information through employing a multi-level recursive strategy; calculating initial partition mapping based on the subset, and introducing an entropy difference driven partition adjustment mechanism to dynamically optimize a data partition boundary; constructing a relative sequence relation of data points in each partition, calculating an optimal exchange sequence by using a minimum cost and maximum flow method, and reducing global adjustment times through a recursive hierarchical structure to achieve local sorting lower than linear time; after sorting, local optimal exchange or global correction is carried out through a dynamic entropy difference adjustment strategy; in a real-time data stream scene, a dynamic sorting topological graph is constructed, only a local area is updated, and global resorting is avoided. Compared with a traditional sorting method, the method has remarkable advantages in the aspects of real-time performance, high efficiency and calculation cost.

The invention discloses a computer system based on a unified information calculation model of electric power Internet of Things terminal equipment, and relates to the field of computer systems, in which a data acquisition module acquires information of a target power grid; the power supply path analysis module determines on-off state change information and a load point path and analyzes to obtain an initial power supply path; a path search module extracts a load point to be transferred and searches for alternative power supply paths to obtain a potential transfer path list; a marking module calls the unified calculation model for analysis, line capacity limitation information and switch operation sequence constraints are obtained, a potential transfer path list is marked through the line capacity limitation information, and a feasible transfer path set is obtained; and the load transfer execution module controls the target power grid to perform load transfer based on the deduced switching operation strategy on the basis of the constraints. According to the method, a data basis is provided for the computer system to execute accurate decision-making of load transfer, and meanwhile, the execution quality and accuracy of the computer system are improved.

The invention provides a data sorting method for a tensor processor, a tensor processor system and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring input data and the storage capacity of a single tensor processor TPU register, and distributing core id for marking an initial position for each element; when the data size of the input data is smaller than or equal to the storage capacity of the single TPU register, single register level sorting operation is executed, and a double-tone sequence of the single register is obtained; otherwise, performing double-tone sorting recursion on the input data to divide the input data into a plurality of subsequences, executing single-register-level sorting operation on each subsequence, and merging the sorted subsequences to obtain a double-tone sequence of multiple registers; wherein the core id of the element is transmitted when the single register level sorting operation is executed for each sub-sequence and the sub-sequences are combined; and adjusting the sequence of the equal elements according to the core id after the double-tone sequence is obtained. The stability of the data sorting result can be improved.

The invention provides a data rearrangement method and device, electronic equipment and a storage medium. The data rearrangement method comprises the steps of obtaining input data comprising sub-data of multiple dimensions; determining a sub-instruction meeting a preset rule from a pre-stored rearrangement instruction; determining first position information of the sub-data in the input data by utilizing the sub-instruction; splitting the rearrangement instruction based on the sub-instruction to obtain a first instruction and a second instruction; executing a first rearrangement operation on the sub-data according to the first position information and the first instruction to obtain cached data; the first instruction indicates a mapping relation between the first position information and second position information of the sub-data in the cache data; executing a second rearrangement operation on the sub-data in the cache data according to the second position information and the second instruction to obtain output data; the second instruction indicates a mapping relation between the second position information and third position information of the sub-data in the output data. The invention relates to the technical field of data rearrangement, and the data rearrangement efficiency can be improved.

Provided are systems, methods, and apparatuses of data processing for machine learning. In one or more examples, the systems, devices, and methods include determining priority values for elements of a weight matrix based on a gradient of a loss function of an AI model and the weight matrix; determining an index value based on a number of elements in the weight importance matrix and a sparsity ratio; determining a threshold based on sorting the elements of the weight importance matrix in sequential order and determining a value of an element of the sorted weight importance matrix based on using the index value as an index of the sorted weight importance matrix; determining a pruned weights matrix based on the threshold; and processing a query using an updated AI model, the updated AI model being based on the pruned weights matrix being implemented in the AI model.

Apparatuses, systems, and techniques to perform non-maximum suppression (NMS) with a bit-reduced radix sort to remove redundant bounding boxes are described. In at least one embodiment, one or more circuits perform i) a bit-reduced radix sort operation to sort a list of confidence scores associated with a set of bounding boxes corresponding to one or more objects within one or more digital images and ii) a non-maximum suppression (NMS) operation on the sorted list to remove one or more redundant bounding boxes from the set.

A data input sub-system of a parallelized database system includes processing core resources operable to obtain divisions of data slabs of a dataset and compress the divisions of data slabs using a null elimination compression scheme to produce divisions of compressed data slabs, A first data slab of a first division of data slabs of the divisions of data slabs is compressed using the null elimination compression scheme to produce a first compressed data slab. The first compressed data slab includes first compressed data and first compression information. The processing core resources are further operable to store a respective division of compressed data slabs of the divisions of compressed data slabs.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

Techniques for handling block format floating point and / or integer numbers are described. In some examples, circuitry for handling block format floating point and / or integer numbers includes a plurality of multiplexers to select between the output of the mantissa multiplier circuits and outputs of the shift circuits to allow for support for block and non-block numbers.

Provided are a data processing method, an electronic device, a computer-readable storage medium and a computer program product, which relate to the field of databases. The data processing method comprises: determining, from a database, M data groups to be integrated, wherein M is a positive integer greater than 1, each data group to be integrated comprises a plurality of data records having sorting key values and has a corresponding sorting key value interval, a minimum endpoint value and a maximum endpoint value of the sorting key value interval are respectively a minimum sorting key value and a maximum sorting key value in each data record in the corresponding data group, and for any data group to be integrated, there is at least one other data group to be integrated, the sorting key value interval of which overlaps with the sorting key value interval thereof; and reintegrating the M data groups to be integrated into at least one target data group, and adding the at least one reintegrated target data group to the database.

Methods and systems of verifying a hardware design for a sorter are disclosed. The methods include generating a modified version of the hardware design of the sorter accepting extended inputs and performing formal verification comprising: implementing a constraint that the least significant bits of each input in a set of extended inputs represent a unique value; and formally verifying aspects of the modified version of the hardware design of the sorter for the set of extended inputs.

The invention relates to a method for rapidly counting and analyzing massive time sequence violation paths. The method comprises the following steps of: performing read operation on a time sequence report file generated by a static time sequence analysis tool by adopting a storage I / O mapping technology in a shared memory, and separating an original file into a plurality of independent fragments according to a multi-mode character string matching algorithm, starting a plurality of sub-threads and distributing information of corresponding fragments of the original file to the sub-threads; adopting a multi-mode character string matching algorithm to obtain specific attributes of a time sequence violation path according to the keyword information; the sub-threads are used for carrying out attribute extraction and distribution statistics according to specific attributes in sequence, and after the main thread receives that the sub-threads complete attribute extraction and distribution statistics, various kinds of distribution statistics information of the sub-threads is merged into a total statistical table of the main thread according to a rapid sorting algorithm to generate various kinds of distribution statistics sorting information. By adopting the method, rapid statistics and analysis of massive time sequence violation paths can be realized.

Provided are systems, methods, and apparatuses of data processing for machine learning. In one or more examples, the systems, devices, and methods include determining priority values for elements of a weight matrix based on a gradient of a loss function of an AI model and the weight matrix; determining an index value based on a number of elements in the weight importance matrix and a sparsity ratio; determining a threshold based on sorting the elements of the weight importance matrix in sequential order and determining a value of an element of the sorted weight importance matrix based on using the index value as an index of the sorted weight importance matrix; determining a pruned weights matrix based on the threshold; and processing a query using an updated AI model, the updated AI model being based on the pruned weights matrix being implemented in the AI model.

A method for enhancing hybrid-classical algorithms for combinatorial optimization includes executing, on a quantum processor, a variational algorithm, the execution producing a subset of a set of solutions (solution space) of the variational algorithm within a predefined period, the variational algorithm computing a quantum state of a quantum system corresponding to a combinatorial optimization problem, each solution in the subset having a corresponding value; sorting, according to a sorting criterion, the subset of solutions; isolating, based on the sorting, a portion of the subset of solutions wherein a value corresponding to each solution in the portion is within a boundary defined by a threshold value; computing an average value of the solutions from the portion of the subset of solutions; and altering the variational algorithm to produce a second subset of solutions such that the second subset of solutions comprises solutions having values within the boundary.

A list sort instruction is provided to perform a sort and / or merge operation. The instruction is an architected machine instruction of an instruction set architecture and is executed by a general-purpose processor of a computing environment. The execution includes sorting a plurality of input lists to obtain one or more sorted output lists as output.

A method is provided that includes performing, by a processor in response to a vector matrix multiply instruction, multiplying an m×n matrix (A matrix) and a n×p matrix (B matrix) to generate elements of an m×p matrix (R matrix), and storing the elements of the R matrix in a storage location specified by the vector matrix multiply instruction.

This specification discloses a data processing and visualization method, apparatus, medium, and device, which relate to the computer technology field. The method includes selecting a data platform to acquire source data of a target company, organizing the source data and loading it into a data system as a source database table, performing primary data operations to process the source data into target data, performing secondary data operations to process the target data into value-added data, performing tertiary data operations to process related data into graphical data, and performing data visualization to render the graphical data into a visualization diagram. The method of the present invention at least partially solves the problems of inability to timely and completely acquire source data and inability to effectively utilize source data without processing it, thereby improving data processing efficiency, increasing the accuracy of data analysis of the target company through data modeling, and automatically combining background data with front-end displays such as panel interfaces through data visualization, thereby improving the effectiveness of data presentation and the availability of the data system.

The application discloses a circuit and method for long sequence data sorting. The circuit is used for sorting N data in K base, and the maximum element in the data contains m bits in K base. The circuit comprises a radix counting unit, a first address generating unit, a data distribution unit and two sorting buffers. The two sorting buffers are used as a source buffer and a target buffer respectively, and can read data in a given address or write a data into a specified address. The radix counting unit, the first address generating unit and the data distribution unit are connected in sequence. The two sorting buffers are connected with the data distribution unit and the radix counting unit respectively. The data sorting circuit of the application has simple structure, can be flexibly adjusted according to specific requirements, and the data sorting method has linear order time complexity and short sorting time.

A processor and a vector sort instruction for the processor to execute are provided, in which the vector sort instructions includes instructions for comparing a first element of a set of vector elements of a vector to a remainder of the set of vector elements; determining, based on the comparing, a control vector that specifies a respective sorted position for each element of the set of vector elements; and reordering the set of vector elements based on the control vector.

A method and apparatus for finding a signal in a frequency range includes converting an analog input signal to a digital domain using an analog-to-digital converter to create a digital input signal. A first fast Fourier transform (FFT) converts a digital input signal into a frequency input signal that is binned based on frequency and thresholds to create a binary filter. A digital input signal (from an analog-to-digital converter) is classified into a container (e.g., using the Chinese remainder theorem), and then converted to a frequency intermediate signal by a second FFT. The frequency intermediate signal is classified into bins and multiplied using a binary filter to create an output frequency signal.

A parallelized data input sub-system of a database system includes a first set of loader nodes of pluralities of computing nodes of a plurality of computing device clusters. The first set of loader nodes includes a plurality of memory devices and a plurality of processing modules. The first set of loader node is operable to ingest at least a portion of a dataset (“data”), a set of the memory devices stores the data, and a set of the processing modules determines whether the data is regarding a query. When it is regarding the query, the set of processing modules provides the data to a query and response sub-system. When it is not regarding the query, the set of processing modules determines long term storage parameters, processes the data in accordance with the parameters to produce formatted data, and provides the formatted data to a store and compute sub-system.

A data paging automation method is used for processing a plurality of pieces of data, and comprises the following steps: determining a sorting basis for sorting the plurality of pieces of data according to an ordered non-repetitive key of the plurality of pieces of data; sorting and paging the plurality of pieces of data according to the sorting basis; and updating a paging information table of the plurality of pieces of data and collecting corresponding data according to the ordered non-repetitive key and at least one filtering condition.

A data processing platform, method, and program product perform compression and decompression on a set of data items. Suffix data and prefixes are selected for each corresponding data item in the set based on the data content of the corresponding data item. The set of data items are classified based on the prefixes. The prefixes are encoded by querying multiple encoding tables to create codewords containing compressed information representing the values ​​of all prefixes in the set of data items. The codewords and suffix data for each of the data items are stored in a memory. The codewords are decompressed to recover the prefixes. The recovered prefixes are paired with their corresponding suffix data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for finding splits in gradient boosting systems. One example method includes identifying, based on multi-party computation (MPC), a first split among possible first splits in a first layer of a decision tree. Identifying the first split comprises determining a first derivative and a second derivative for each sample in a dataset of n samples and m features; generating a sorting array indicating a sorting order of at least one feature; identifying the first split based on first derivatives, second derivatives and the sorting array; and generating an index vector that indicates whether each sample belongs to a first or a second sub-dataset. The method further includes identifying a second split among possible second splits in a second layer of the decision tree based on the first derivatives, the second derivatives, the sorting array, and the index vector.

An example includes receiving a read request specifying a target range and a target checkpoint, and executing a first query to retrieve a first entry of a metadata table. The first entry is a most recent entry of those entries that are older than the target checkpoint and record write ranges overlapping the target range. The example also includes, if the write range in the first entry does not cover all of the target range, determining a remaining portion of the target range that excludes the write range in the first entry. The example also includes executing a second query to retrieve a second entry of the metadata table, where the second entry is a most recent record in a second subset of entries that are older that the target checkpoint and record write ranges overlapping the remaining portion of the target location range.

Example embodiments of systems and methods for data transmission between a contactless card, a client device, and one or more servers are provided. The memory of the contactless card may include one or more applets and a counter. The client device may be in data communication with the contactless card and one or more servers, and the one or more servers may include an expected counter value. The client device maybe configured to read the counter from the contactless card and transmit it to the one or more servers. The one or more servers may compare the counter to the expected counter value for synchronization. The contactless card and the one or more servers may resynchronize the counter, via one or more processes, based on one or more reads of the one or more applets. The one or more servers may authenticate the contactless card based on the resynchronization.