20 results about "Numeric data" patented technology

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The invention relates to the technical field of data analysis and recognition, in particular to an intelligent recognition method for subject data logic conflicts based on a thinking chain technology, which comprises the following steps: converting original data of a subject into a logic proposition sequence with a time sequence mark, the original data comprising numeric data and semantic data, the logic proposition sequence comprises a numeric proposition unit and a semantic proposition unit; inputting the logic proposition sequence into a thinking chain reasoning model, and generating a dynamic logic association network comprising a main reasoning path and an auxiliary reasoning path; and based on the dynamic logical association network, executing bidirectional reasoning verification and rule template matching, and outputting a verification result set comprising conflict type marks and confidence rating. According to the method, the reasoning network adaptability is improved, the implicit logic contradiction recognition capability is remarkably improved, and the method is particularly suitable for conflict tracking in chronic disease evolution paths and multi-stage test processes.

The invention discloses a financial behavior anomaly detection system based on big data, and relates to the field of financial behavior anomaly detection. Multi-source data such as customer transaction, account operation and basic information are collected and formats are unified; processing numeric data by using an isolated forest, extracting text features by using TF-IDF, and constructing a composite feature vector; constructing a customer behavior baseline model based on an LSTM network, and dynamically updating and triggering early warning; carrying out anomaly detection by combining ensemble learning and a graph neural network; and triggering a multi-level response mechanism according to the abnormal confidence coefficient, and feeding back an optimization model. According to the invention, multi-source data and an advanced algorithm are fused, a dynamic behavior baseline is constructed, millisecond-level anomaly detection is realized, complex abnormal behaviors are accurately identified, and the rate of missing report and false report is reduced; federal learning is adopted to guarantee data security, systematic risks are predicted and prevented through risk propagation, the response efficiency is improved through automatic grading disposal, and fund security is comprehensively guaranteed.

The invention provides a method and system for generating a transformer area line loss abnormity cause discrimination model of a power system, and relates to the technical field of transformer area line loss of the power system, and the method comprises the steps: obtaining transformer areas in a designated area, line loss influence parameters of each transformer area, and a line loss rate and a line loss abnormity label corresponding to the line loss influence parameters, the line loss influence parameters comprise text type data and first numerical type data; quantifying the text type data to obtain second numeric type data; constructing an optimization function according to the first numeric data, the second numeric data and respective corresponding line loss rates; solving the optimal solution of the optimization function to obtain the influence degree values of the first numeric data and the second numeric data of each transformer area on the line loss of the transformer area; and forming a training data set by adopting the influence degree value, the line loss rate and the line loss abnormal label, training a preset model to obtain an abnormal cause judgment model, and matching the line loss abnormal label for the to-be-detected data set by adopting the abnormal cause judgment model, so that the matching accuracy is improved.

Methods and apparatus for sparse column-aware encodings for numeric data types, including integer data and floating-point data (float, double, etc.). The encoding schemes are tailored to take advantage of column addressable memories such as stochastic associative memories (SAM) to enable Stochastic Associative Search (SAS), which is a highly efficient and fast way of searching through a very large database of records (order of Billions) and finding similar records to a given query record (search key). Techniques are also disclosed for performing range searches for both integer and floating-point data types. The integer or float data is converted to Hexadecimal form and encoded using an m-of-n constant weight encoding. Only the columns with set bits in search keys need to be read, which significantly reduces the number of reads required for searches.

The invention discloses a converter valve fault diagnosis method and system based on state analysis. Acquiring operation data of a converter valve thyristor assembly, a valve cooling assembly and a valve lightning arrester, and processing the non-numeric state quantity into numeric data according to the influence degree of the non-numeric state quantity on the operation of the converter valve; constructing a multi-criterion feature selection method for the preprocessed operation data to screen feature subsets in the operation data; based on the data samples of the screened feature subsets, constructing a converter valve fault diagnosis model by adopting a dual-channel hybrid neural network in combination with multi-task branch output; operating data of the converter valve are collected in real time, and after preprocessing and feature screening, the fault level distribution probability of each assembly is output through the converter valve fault diagnosis model. According to the converter valve fault diagnosis method provided by the invention, intelligent and refined diagnosis and division of the overall operation state of the converter valve can be realized.

Systems and techniques for data-driven precision memory access of cache block data are described. Computing system components are informed as to instances where access operations involve deducing a necessary precision of the data format and expressing the requested data in a lower-precision data format with minimal to no accuracy loss. In one example, executable code for a computational task includes hints that identify when memory requests involve accessing data in a numeric data format based on a deduced precision of the stored data during memory access. The described techniques thus overcome conventional drawbacks facing systems that transmit and compute data in a higher-precision data format than required by the stored values.

Systems and techniques for data-driven precision memory access of cache block data are described. Computing system components are informed as to instances where access operations involve deducing a necessary precision of the data format and expressing the requested data in a lower-precision data format with minimal to no accuracy loss. In one example, executable code for a computational task includes hints that identify when memory requests involve accessing data in a numeric data format based on a deduced precision of the stored data during memory access. The described techniques thus overcome conventional drawbacks facing systems that transmit and compute data in a higher-precision data format than required by the stored values.

The invention relates to an eight-bit numerical value type data encryption algorithm, which comprises the following steps of: arranging actual eight-bit numerical values in left and right eight bits according to a way of comparing the actual eight-bit numerical values with 5, forming bit numbers according to a way that the actual eight-bit numerical values are arranged from one bit to ten millions of bits or from ten millions of bits to one bit, and filling the corresponding bit numbers with the actual numerical values which are subtracted from five (numerical values smaller than five) or subtracted from five (numerical values larger than five), if data exist on the bit number, the bit number is set to be 1, finally, two 8-bit binary numbers are formed on the left side and the right side, the two binary numbers are secret keys of the left data and the right data, the two binary numbers and the processed data are reprocessed in an XOR mode and the like, and then new data are formed. And after the data is formed, executing a specific function according to a function code according to a frame structure of a frame header, a secret key, a data length, data and a frame tail so as to complete a specific task. In the application, the system is convenient to compile and debug, has the characteristics of clear principle, simple structure and easiness in implementation, and has extremely high application value.

The application relates to the technical field of index creation, and provides an index creation method based on thumbnail comparison of numeric data in Opengauss, which comprises the following steps: judging whether the column of the data to be created is of the numeric type, setting an optimization mark for thumbnail comparison in the tuple in a target table according to the judgment result; scanning the tuples in the target table one by one, saving the value of the index column of the tuple according to the optimization mark of the tuple; sorting the tuples in step S2 through a thumbnail comparison algorithm; and inserting the tuples sorted in step S3 into a btree index. According to the index creation method based on thumbnail comparison of numeric data in Opengauss of the exemplary embodiment of the application, the single int data direct comparison is optimized from the character array comparison mode in the prior art, the comparison algorithm performance is improved, the number of CPU operation times is reduced, the time consumption of the numeric data type index creation is greatly reduced, the original algorithm overhead in the prior art is reduced, and the index creation efficiency is improved.

Methods and apparatus for sparse column-aware encodings for numeric data types, including integer data and floating-point data (float, double, etc.). The encoding schemes are tailored to take advantage of column addressable memories such as stochastic associative memories (SAM) to enable Stochastic Associative Search (SAS), which is a highly efficient and fast way of searching through a very large database of records (order of Billions) and finding similar records to a given query record (search key). Techniques are also disclosed for performing range searches for both integer and floating-point data types. The integer or float data is converted to Hexadecimal form and encoded using an m-of-n constant weight encoding. Only the columns with set bits in search keys need to be read, which significantly reduces the number of reads required for searches.

The invention is systems and methods directed to a method of training a machine learning model that is particularly adept at handling numeric data and is able to learn from prior models trained using the same method.

The invention discloses a medical data desensitization traceability auditing method based on differential privacy, and relates to the technical field of medical data security, and the method specifically comprises the following steps: collecting two types of medical data and standardizing the format, extracting the clinical decision weight of numeric data and the privacy risk tag of text data, and implementing differential desensitization based on the weight and the tag, the method comprises the following steps: adding weight-associated Laplacian noise to numeric data, hierarchically replacing text data or masking key information, embedding a unique traceability identifier and verifying privacy compliance and availability, finally establishing a closed-loop auditing mechanism, supporting identifier extraction, compliance verification and leakage tracking, continuously optimizing related parameters and strategies based on feedback, and finally realizing the real-time auditing of the privacy. And the data security and traceability are effectively ensured.

An information processing system comprising: an input portion configured to receive, as input to a main storage device that stores data including an attribute having a unit, input information comprising numeric data for the attribute; an arithmetic portion, configured to evaluate, based on the input information and unit information, whether the numeric data is appropriate when regarded as being expressed in each of the plurality of unit candidates, wherein the unit information includes information on a plurality of unit candidates that may be assigned to the attribute, information on a specified unit of the attribute that is selected from the plurality of unit candidates, and information on conversion from the plurality of unit candidates to the specified unit; and the display portion configured to display, on a display device, a unit candidate, which differs from the specified unit, from among the plurality of unit candidates that are evaluated as appropriate.

A system and method including receiving numeric data of a first dataset including a plurality of columns having numeric values with one of the plurality of columns specified as a target column; generating a trained generative model based on numeric values in non-target columns of the plurality of columns; generating a trained predictive model based on numeric values in non-target columns of the plurality of columns being input variables and the target column being a target variable; generating, by the trained generative model, a new set of numeric data for the non-target columns; generating predicted target values for the non-target columns by the trained predictive model using the new set of numeric data as an input to the predictive model; and generating anonymized numeric data for the first dataset by combining the new set of numeric data and the target column populated with the generated predicted target values.

The invention discloses a pipeline anomaly detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a plurality of pipeline segments corresponding to a target detection pipeline, and collecting real-time pipeline data of each pipeline segment; dividing the real-time pipeline data according to the division value to obtain at least one piece of numeric data and at least one piece of non-numeric data, and determining a first pipeline time sequence corresponding to each piece of numeric data and a second pipeline time sequence corresponding to each piece of numeric data; determining a first abnormal coefficient corresponding to the target detection pipeline according to the plurality of first pipeline time sequences, and determining a second abnormal coefficient corresponding to the target detection pipeline according to the plurality of second pipeline time sequences; and determining a pipeline abnormal factor according to the first abnormal coefficient and the second abnormal coefficient, and determining an abnormal detection result corresponding to the target detection pipeline according to the pipeline abnormal factor. According to the invention, the effect of determining the pipeline anomaly result through deep analysis of the pipeline data can be realized, and the accuracy of pipeline anomaly detection is improved.

A processor comprises a memory to store numeric data in bit plane format and circuitry coupled to the memory to perform matrix operations on the stored data using adders. The bit plane format allows dynamic variations in bit sizes of values. A system includes a memory to store numeric data in bit plane format, a processor coupled to the memory, and a matrix multiplication module to perform matrix multiplication using adders. A method involves storing numeric data in bit plane format, retrieving the stored data, and performing matrix operations using adders. The processor, system, and method enable faster processing and reduced memory consumption compared to consecutive aligned bit sequences.

This invention discloses an AI-based method and system for collecting price information in the textile industry. The method includes the following steps: S1, Image Repair and Cleaning: Processing the tabular price quote images from textile factories, retaining only the table area; S2, Table Cropping: Cropping the repaired image according to the table format, dividing it into multiple smallest sub-table images without table sub-tables; S3, Sub-table OCR Recognition: Extracting the row, column, and header names of the sub-tables, as well as corresponding text and / or numeric data, and returning sub-table data containing complete structured information; S4, Header Row to Column Conversion: Extracting the header row information and converting it into column information to generate a uniformly formatted intermediate table; S5, Multi-Table Concatenation and Integration: Concatenating and integrating multiple intermediate tables into a single large table with a unified format. Compared with existing technologies, this invention eliminates the need for manual secondary processing, significantly reducing the cost of price information collection and significantly improving the efficiency, accuracy, and timeliness of data collection.

A system is provided.SOLUTION: A system comprising: means for receiving a natural language input; means for parsing the received natural language input and generating a corresponding structured query; means for executing the generated structured query to obtain numerical data; and means for formatting and outputting the obtained numerical data to a user.SELECTED DRAWING: Figure 1

The invention discloses a logistics industry green innovation performance evaluation method based on multi-modal data fusion, and the method comprises the steps: integrating enterprise operation data, social media public opinion and government policy texts based on a TOE framework, and constructing a multi-modal database; normalizing the numeric data, the text data and the image data respectively; constructing an index system: constructing a VIF-FA double screening method to screen indexes, and establishing a TOE three-dimensional evaluation system; aHP subjective weight and CRITIC method objective weight are fused based on the game theory, performance is calculated by constructing a DEA-TOPSIS two-stage model, and the first stage DEA calculates an enterprise comprehensive efficiency value; in the second stage, the DEA efficiency value serves as a newly-added index to extend a TOPSIS decision matrix, and a performance value is output in combination with the optimal combination weight. According to the method, the problem of singleness of traditional evaluation data is improved, subjective deviation and index redundancy are avoided, the index weighting scientificity is improved, and the quantitative logistics industry green innovation performance accuracy is improved.