3results about How to "Reduce storage redundancy" patented technology

The application belongs to the technical field of databases, and specifically discloses a multi-mode time series data processing method and system. The method comprises the following steps: establishing a row storage table and a column storage table according to a table creation request, and establishing an associated mapping relationship between the row storage table and the column storage table; receiving time series data to be inserted and extracting label data and index data, determining a target data node based on label data hash calculation, writing the label data into the row storage table, and writing the index data into a target time series data block of the column storage table; performing partition pruning and scanning on the stored time series data based on a screening condition, and generating a query result by parallel execution of split query tasks and merging of results based on cross-mode query requirements; determining the affected time series data block based on an operation request, and updating the label data and the index data or deleting invalid data according to the type of the operation request. The application can realize low-redundancy storage, high-concurrency read-write and fast query analysis of time series data.

The application provides a time-dependent label constraint efficient query method based on partition tree decomposition, relates to the technical field of data processing, and is used for path planning application in an intelligent transportation system and comprises the following steps: acquiring road network information and generating a road data graph; performing reduction on the road data graph based on a label partition tree decomposition algorithm and generating a tree structure; constructing a parameter separation index mechanism and converting the tree structure into a parameter separation index; acquiring a departure point and a destination point and taking the departure point and the destination point as a source vertex and a target vertex respectively; and calculating a shortest path from the given source vertex to the target vertex based on the parameter separation index. Through the design of the label partition tree decomposition and the parameter separation index, the application greatly reduces the index storage redundancy of a time-dependent label constraint graph, can efficiently and compatibly support ordered label constraints and time-dependent weights to adapt to an intelligent transportation scenario, and can linearly expand with the scale of a data set and maintain stable performance in a network with more than ten million vertices.

The application provides an efficient matrix storage method and system based on wind power prediction data, and relates to the technical field of wind power data storage. The method comprises the following steps: constructing multi-dimensional time series data according to wind power prediction data of each wind turbine in a wind farm; constructing a table structure based on an array field for the multi-dimensional time series data, each row corresponding to prediction data of a prediction model of the wind turbine at a prediction release time, and each feature storing the numerical value of the corresponding feature at multiple prediction target times in the form of an array field; performing hierarchical storage management on the wind power prediction data according to the table structure, storing wind power prediction data within a first time range as hot data in a cache table in the form of an original array; and storing the remaining wind power prediction data as cold data, obtaining multiple time blocks, performing singular value decomposition on the original matrix corresponding to each time block, and compressing and storing the cold data into a cold data table. The application helps to improve data calling efficiency and reduce overall storage space occupation.