29 results about "Pooled data" patented technology

The invention discloses a distributed storage method and device based on a time sequence database, and relates to the technical field of data storage. Obtaining original time sequence data and carrying out time clustering segmentation to obtain a time sequence data fragment set; performing data merging on the time sequence data fragments in the time sequence data fragment set to obtain a merged data fragment set; determining a storage position of each merged data fragment in the merged data fragment set; constructing a time query index for the data in each merged data segment; and for the data in each merged data segment, obtaining a storage address of the data, enabling the storage address to correspond to the time query index of the data, and storing the data in the storage address. According to the method, data of a time sequence is clustered and then segmented, then the segmented data is merged, storage fragmentation is reduced, finally, a storage address and a time query index are associated by constructing a query tree, efficient storage is still met in the face of mass data, and the data management efficiency is improved.

The invention relates to the technical field of disability care modeling, and discloses a disability crowd care consultation digital twinborn modeling system based on a Longson scale. The system comprises seven functional modules, namely a data acquisition module, a data processing module, a condition screening module, a condition merging module, a time identification module, a digital twin modeling module and a decision-making module. The data acquisition module acquires the evaluation data of the Lungson scale of the disabled crowd, and the data processing module performs abnormal value processing and data filling on the data; the condition screening module is used for screening data meeting initial conditions and termination conditions of the care transient process from the processed data; the data merging module merges the two types of screened data, generates merged data and identifies the end time of the care transient process; the time identification module determines start time by using the merged data; the digital twinborn modeling module constructs a care process digital twinborn model, and the decision making module matches care decisions according to the start and end time of a care transient process, and assists in improving the care consultation accuracy and normalization of disabled people.

The invention belongs to the technical field of artificial intelligence, and relates to a data exception monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining compensation data in a preset time period; constructing a graph model based on the claim data; performing feature extraction on the graph model to obtain graph features, and merging the graph features with the compensation data to obtain merged data; splitting the merged data based on the time sequence to obtain target data; performing clustering analysis on the target data based on a target clustering algorithm to obtain a plurality of clustering groups; performing index analysis on all the clustering groups based on the target index, and judging whether an abnormal claim group exists in all the clustering groups or not; and if yes, executing alarm processing corresponding to the abnormal claim group. In addition, the invention also relates to a block chain technology, and the target clustering algorithm can be stored in a block chain. The method can be applied to case abnormity monitoring scenes in the fields of financial science and technology and medical health insurance, and the recognition accuracy of repeated claim is effectively improved.

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.

When a merging condition is present, first processing element circuitry is responsive to control signals received the decoding of N data update instructions that each specify updated data to be made available for stashing to buffer the updated data. When the merging condition is no longer present in response to a stashing specific merge end trigger, the first processing circuitry issues a merged data update stashing transaction to interconnect circuitry specifying, as merged updated data, the updated data, to trigger stashing control circuitry to cause the merged updated data to be made available for stashing in an associated storage structure of at least one further processing element.

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 merged task scheduling method and device, and relates to the technical field of cloud computing, in particular to the technical field of distributed table storage. A specific embodiment of the method comprises the steps of obtaining to-be-processed data; in response to triggering of the merging task, grouping the to-be-processed data according to the merging period, and generating a to-be-processed data group; based on the data volume of the to-be-processed data group and the merging period, determining the to-be-processed data volume of the current time period; selecting to-be-processed data matched with the to-be-processed data size from the to-be-processed data set according to the priority; and merging the selected to-be-processed data in the current time period to generate merged data.

The disclosure generally shows systems and methods for optimizing MVNO subscriber mappings to pooled data pricing plans offered by MNOs with different tiered data pools are variously linked to different MVNO subscribers. The system receives and processes raw CDR data provided by the MNO. The system may then poll a backend server to receive data concerning subscriber ID assignments and subscriber activations / deactivations for the MVNO. Then the system determines optimal plan changes for each subscriber based on expected usage and pushes those plan changes to a Wholesale API Proxy that communicates with the MNO to implement the mapping changes to the pooled data pricing plan.

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 invention provides a data merging method and a related device. The data merging method comprises the following steps: firstly, dividing received block data into at least one group of to-be-merged data; for each group of to-be-merged data, data conflict detection can be carried out according to the node path indexes recorded in the to-be-merged data, so that whether data node information with the same node path index exists in each group of to-be-merged data or not is detected. If yes, according to a source data timestamp corresponding to the to-be-merged data and a preset data priority, one piece of node information is determined from multiple pieces of data node information with the same node path index to serve as insertion node information. And carrying out data merging according to the determined inserted node information and the corresponding data type description. Therefore, the problem of data conflict in a data merging scene can be solved, so that the data can be accurately and completely restored according to the block data, and the data quality of the merged data can be improved.

The invention discloses a dimension reduction and equalization method for high-dimensional unbalanced data of a wind turbine, and the method comprises the steps: removing singular values in a to-be-processed high-dimensional unbalanced data set, and obtaining an updated data set; performing normalization processing on the updated data set to obtain a normalized data set; performing oversampling on the icing data set in the normalized data set to obtain an oversampled data set; carrying out undersampling on an unfrozen data set in the normalized data set to obtain an undersampled data set; and merging the oversampling data set and the undersampling data set to obtain a merged data set, and performing standardization and dimension reduction processing on the merged data set in sequence to obtain a dimension-reduced balanced data set. The dimension reduction equilibrium data set obtained by the invention can effectively identify the early characteristics of icing of the wind turbine blade of the wind turbine generator, equipment maintenance can be carried out in time, the occurrence risk of disastrous accidents such as blade breakage and gearbox failure is reduced, stable operation and economic benefits of a wind power plant are ensured, and meanwhile, the difficulty of data processing and analysis is reduced.

The invention provides a form data incremental interaction method and device under a front-end and rear-end separation architecture, and relates to the field of incremental data processing.The method comprises the steps that if a received data processing request needs to be subjected to rear-end incremental processing, local cache data are extracted according to a cache serial number in the data processing request; combining incremental data in the data processing request with the local cache data to obtain combined data; performing back-end increment processing on a service data processing result obtained by performing service processing based on the merged data, and returning a compressed back-end increment processing result to the client; wherein the business data processing operation comprises business data extraction, business data calculation and business data statistics. The invention can provide an efficient, reliable and safe incremental interaction data processing method so as to solve the problems of large bandwidth consumption, long processing time, insufficient security and the like in the existing full-quantity synchronization technology.

Various embodiments described herein support or provide operations for facilitating the reconciliation and storage of conflicting user data across multiple platforms. Specifically, an audit record that includes a plurality of data records of an attribute is identified. A consolidated data record of the attribute in a summary record is generated. A current value of the attribute is determined based on the consolidated data record of the attribute in the summary record. The current value of the attribute is stored in a final record for downstream processing.

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.

Provided are techniques for identifying and correcting model focus drift during model training. A model is trained using an original dataset with data, and the is classified into clusters. A representation is assigned to each of the clusters. A first visualization dashboard with visualizations is generated, where each visualization represents a first data distribution of an associated cluster using the representation assigned to that cluster. The model is fine-tuned using a fine-tune dataset. A second visualization dashboard is generated by updating each visualization, where each visualization represents a second data distribution of the associated cluster. It is determined that a cluster of the clusters has focus drift based on changes between the first data distribution and the second data distribution. The focus drift is corrected by: adding the data of the cluster to the fine-tune dataset to form a combined dataset and fine-tuning the model using the combined dataset.

The application discloses a network monitoring system based on a multi-pipeline switch, wherein a data plane of the switch is used for processing and forwarding various types of data messages on different ports of the switch, and reporting flows exceeding a pipeline threshold to a control plane of the switch; the control plane of the switch is used for controlling and managing the operation of all modules in the switch, and monitoring the reported flows. When a data packet enters a pipeline of the switch, a program on the pipeline judges whether the data packet is a potential target, and if yes, the pipeline reports the data packet to the control plane of the switch. A controller located at the control plane of the switch pulls and merges data of all pipelines of the data plane at the end of each epoch, and checks whether the flows reported by the data plane are real targets. After integrating information collected in the current epoch, the controller sends new control information to each pipeline of the data plane, so that the pipelines work more accurately and efficiently.

A system for assisting an additive manufacturing process of a workpiece comprises an interface configured to receive a first and second data stream generated while the workpiece is being manufactured. The first data stream comprises first data, provided by a control device, representative of a first form of the workpiece, and the second data stream comprises second data, provided by a sensor system, representative of a second form of the workpiece. Machine-executable components comprise a data fusion component configured to receive the first and second data stream and to generate a merged data stream by associating the first data stream with the second data stream so that there is a common time reference between the first and second data stream in the merged data stream, a geometrical classification component configured to determine the second form, and a quality inspection component configured to compare the second form with the first form.

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.

The disclosure generally shows systems and methods for optimizing MVNO subscriber mappings to pooled data pricing plans offered by MNOs with different tiered data pools are variously linked to different MVNO subscribers. The system receives and processes raw CDR data provided by the MNO. The system may then poll a backend server to receive data concerning subscriber ID assignments and subscriber activations / deactivations for the MVNO. Then the system determines optimal plan changes for each subscriber based on expected usage and pushes those plan changes to a Wholesale API Proxy that communicates with the MNO to implement the mapping changes to the pooled data pricing plan.

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.

A computer implemented method for recording data includes a first step of recording data of a predetermined first time-range in a block storage; and a second step of performing a batch operation of merging data in the block storage to record the generated block data in an object storage, if there is no data inflow for a predetermined second time-range or if the sum of the capacities of new blocks recorded in the block storage exceeds a predetermined capacity or the sum of the number of records exceeds a predetermined number of data. The block data recorded in the second step is data where the time range of a merged column is placed at the front and the data is merged based on a column name.

The invention relates to the field of intelligent traffic, in particular to a traffic flow prediction method fusing external time information and context space information. The method comprises the following steps: firstly, preprocessing a plurality of similar traffic flow data sets, and constructing a combined training set and a test set with uniform structures; inputting the merged data into a time sequence feature embedding module, and generating embedded representation of fusion time and traffic flow information; the embedded representation is input into a Transformer model containing two layers of encoders and decoders to be trained, and the model with the best performance on the merging test set is selected to store encoder parameters of the model; then, a space-time prediction model ST-SDNet is constructed; performing model optimization on each single data set through a back propagation and gradient descent algorithm; and finally, for each traffic flow data set, performing traffic flow prediction by adopting an optimal model obtained by corresponding training. According to the method, the multi-source time sequence features are effectively fused, and the accuracy and generalization ability of traffic flow prediction are improved.

There are provided a method and an apparatus for transmitting a plurality of data streams, a device, and a medium. The method of transmitting a plurality of data streams comprises: obtaining, at a first device, the plurality of data streams to be transmitted. The method further comprises merging the plurality of data streams based on a transmission resolution to generate a merged data stream, the transmission resolution being greater than a sum of resolutions of the plurality of data streams. The method further comprises transmitting the merged data stream to a second device. In this way, the user can transmit the plurality of data streams to another device through only a single link. This reduces the link maintenance cost and improves the reliability of data transmission.

An apparatus has decoder circuitry and processing circuitry within a first processing element. Whilst a merging condition is present, the processing circuitry is responsive to the decoder circuitry decoding N data update instructions each specifying updated data to be made available for stashing, where N is an integer greater than or equal to 1, to buffer the updated data specified by the N data update instructions. When the merging condition is determined to be no longer present, the processing circuitry issues a merged data update stashing transaction to interconnect circuitry specifying, as merged updated data, the updated data specified by the N data update instructions, to trigger the merged updated data to be made available for stashing in an associated storage structure of a further processing element. The processing circuitry is responsive to a stashing specific merge end trigger to determine that the merging condition is no longer present.