Multi-dimensional spatio-temporal data thinning and restoring algorithm

Abstract

The invention relates to data management, in particular to a multi-dimensional spatio-temporal data thinning and restoring algorithm, which comprises the following steps of: performing correlation classification on multi-dimensional spatio-temporal data, dividing the multi-dimensional spatio-temporal data into a plurality of data buckets, solving a key time value in each data bucket, obtaining a time value set of all the data buckets, extracting a corresponding measuring point value from original data based on the time value set, and restoring the measuring point value to obtain a multi-dimensional spatio-temporal data thinning and restoring algorithm. A compression result is obtained, an interval where a to-be-restored time point is located is obtained in the time value set, a measuring point value of the data bucket at the to-be-restored time point is calculated, and then a restoration result of each dimension is obtained; according to the technical scheme provided by the invention, the defects that data feature loss is difficult to reduce while resource consumption is reduced and the diluted data cannot be effectively restored in the prior art can be effectively overcome.

Description

technical field [0001] The invention relates to data management, in particular to a multi-dimensional space-time data thinning and restoration algorithm. Background technique [0002] With the explosive growth of IoT devices, when collecting, storing, processing, and applying IoT data, the consumption of resources such as network bandwidth, storage usage, and graphics rendering is increasing day by day. [0003] With the rapid development of satellite navigation, Internet technology and sharing economy, the demand for high-precision positioning is becoming more and more intense. In order to meet the demand for high-precision positioning, it is necessary to collect a large amount of monitoring data and convert the monitoring data into driving trajectories. Through the monitoring view, the supervision Personnel can know the operation status in time. [0004] When collecting the monitoring data, the monitoring data may be collected at a finer granularity, for example, the moni...

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Problems solved by technology

[0007] Aiming at the above-mentioned shortcomings existing in the prior art, the present invention provides a multi-dimensional spatio-temporal data thinning and restoration algorithm, which can effectively overcome the problems existing in the prior art that it is difficult to reduce resource consumption while reducing the loss of data features, and cannot Defects of effective restoration of the later data

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