11 results about "Pooled data" patented technology

According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive an updated data record from a data source and may determine a first conflated data record. The first conflated data record may be associated with the updated data record and include data records in a first grouping from among a plurality of data sources. The processor may identify the data records included in the first conflated data record and may generate a second conflated data record that updates conflations among the updated data record and the identified data records. The second conflated data record may include a second grouping of data records. The processor may replace the first conflated data record with the second conflated data record to incrementally update a set of conflated data records.

This invention discloses an adaptive multi-table data normalization method based on RPA and OCR technologies. Using RPA tools and OCR technology, this invention can synchronously adjust the corresponding data content in the "Floating Summary" and "Normal Transfer" sub-tables of the summary table after the client modifies the data. Then, it performs data aggregation and merging on the two sub-tables separately. Finally, after confirming that the data does not need modification, the data in the summary table is finally aggregated and merged into the group's audit standard notes to the financial statements, thereby generating an audit report. This invention achieves automated merging and aggregation processing of massive amounts of single-file data through RPA and OCR technologies. Under the premise of intelligently identifying basic data table information, quickly matching and capturing data, and accurately summarizing data, it achieves the normalization and reverse writing of audit standard notes to the financial statements, solving the problems of traditional manual data aggregation and merging methods, which may result in aggregation errors, large workloads, and high labor costs due to repeated data adjustments and modifications.

The embodiment of the invention provides a rendering method and system for large-scale data in a port scene. The method comprises the following steps: performing preliminary screening on obtained large-scale data about a target port to obtain preliminary screening data, and classifying the preliminary screening data into structured data and unstructured data; performing standardization processing on the structured data and the unstructured data to obtain standard structured data and standard unstructured data; in response to the standard structured data and the standard unstructured data, completing merging, and performing optimization processing on the merged data to generate optimized data; in response to completion of verification of the optimization data, performing three-dimensional modeling and rendering based on the optimization data passing verification to generate a three-dimensional model; and pushing the visual information representing the three-dimensional model to a display terminal. By utilizing the rendering method disclosed by the embodiment of the invention, the overall efficiency of large-scale data processing and the system stability can be remarkably improved.

The invention provides a non-contact offline data transmission method, system and equipment in a power system, and the method comprises the steps: obtaining a plurality of data files of different types at a coding end, carrying out the merging processing of the plurality of data files, and obtaining a merged data file; performing fragmentation processing on the merged data file to obtain a plurality of animation groups matched with the sampling frequency of the code scanning ends and the display refresh rate of the coding end, wherein the number of the animation groups corresponds to the number of the code scanning ends; performing data acquisition on the visual codes in each animation group by using a plurality of code scanning ends to obtain acquisition data, and sending target transmission data determined based on the acquisition data to a decoding end; and at the decoding end, decoding the target transmission data sent by the different code scanning ends to restore a plurality of data files. By adopting the non-contact off-line data transmission method, system and equipment in the power system, the reliability and fault tolerance of large file transmission under the condition of no network and no contact are improved.

The present application relates to the technical field of audit data integration, and specifically relates to a heterogeneous data integration method for off-site audit, a computer device and a medium. For different data groups in a single data type, the method identifies data groups with missing collection and combines the data groups with missing collection, and identifies data groups with repeated collection and combines the data groups with repeated collection, to obtain merged data corresponding to each data type. Based on the merged data, the method performs natural language processing and one-hot encoding on data of different data types, forms column vector combinations between different data categories of each data type, analyzes the matching degree between any two column vectors in the column vector combinations corresponding to different data types, obtains matching column vectors of the column vectors, and completes the integration between the same data categories in different data types. When performing off-site audit on the integrated data, data classification analysis can be more convenient, and the audit efficiency and accuracy can be improved.

The application discloses a data synchronization distribution method and device and a storage medium. The method comprises the following steps: using an open source framework to synchronize and take out to-be-processed data from a relational database, wherein the open source framework is a Canal open source framework developed by using a programming language java; performing grouping processing on the to-be-processed data to obtain a to-be-processed data group; using multi-threading to perform merging processing on the to-be-processed data in the to-be-processed data group, to obtain a merged data group; and performing synchronous distribution processing on the merged data group. The application solves the technical problem that the data distribution method in the prior art cannot guarantee data sequence in a high concurrency case.

The invention relates to a multidimensional data index optimization method for hydropower big data storage. The method comprises the following steps: data access and feature extraction: extracting physical dimension and service dimension attributes of time sequence data; dynamic weight calculation: calculating the comprehensive weight of the dimension combination in real time based on the query frequency, the data selectivity and the service priority; constructing and updating a hierarchical fusion index, constructing an adaptive grid partition R-tree main index and an inverted auxiliary index according to the weight, and establishing bidirectional pointer association; routing and execution are intelligently queried, an optimal index path is selected according to the weight, and data are quickly positioned and merged through a pointer; online monitoring and self-adaptive adjustment are carried out, data distribution and query performance are monitored in real time, and weight recalculation and index increment recombination are triggered; the method further comprises storage management of life cycle perception, and differential index strategies are implemented on hot, warm and cold data; the method can dynamically adapt to query load changes, improve the multi-dimensional query efficiency and reduce the storage cost.

The application provides an extended training data set, and a method for classification using a classification model trained by the extended training data set. The method for extending the training data set includes generating merged data based on original data and auxiliary data, the auxiliary data being associated with an application scenario of the classification model and coming from a source different from a source of the original data; and updating a category of the merged data using the classification model to generate a first extended data set for training the classification model. The scheme of the application can expand the number of labeled data samples and improve the labeling accuracy of the data, so that the classification performance of the trained classification model is optimized.

This application discloses a data query method, apparatus, device, and storage medium. The method includes: establishing a target data table in a target database, the target data table including all field attributes from at least two source data tables; merging data from at least two source data tables to obtain merged data; inserting the merged data into the target data table; obtaining a first operation on the data in the source data tables, wherein the first operation includes at least one of an add operation, a modify operation, and a delete operation; generating a target operation based on the first operation; and executing the target operation, which is an operation on the target data table used to synchronize the data in the target data table with the data in the source data tables; and performing a data query based on the target data table. According to the embodiments of this application, the speed of data query can be improved.

This invention relates to the field of enterprise management technology, specifically to a big data-based enterprise financial shared assessment method and system, comprising the following steps: acquiring and sorting procurement and acceptance data for cumulative comparison; determining settlement quantities based on amount and unit price to screen discrepancy nodes; locating acceptance intervals and associating them with cost centers and suppliers; combining multiple time periods to divide segments and merge data; and associating business segments with cost categories to calculate indicators. In this invention, by constructing a continuous sequence around acceptance time and quantity and performing cumulative verification, introducing settlement amount and unit price to derive quantity identifiers and identify discrepancy nodes, extracting acceptance segments by procurement batch and time interval and fusing associated identifiers, and dividing business segments according to multiple time nodes for integration and mapping, business events are aligned and linked within a unified time framework, strengthening data continuity and node traceability capabilities, improving anomaly identification capabilities and segment attribution consistency, and enhancing the stability and analytical consistency of assessment results.