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Distributed new energy related multi-source heterogeneous data processing methods

A distributed new energy, multi-source heterogeneous data technology, applied in data processing applications, electrical digital data processing, special data processing applications, etc., can solve problems that do not involve fusion methods, reduce indirect investment costs, and improve business The effect of competency and professional level

Inactive Publication Date: 2019-03-15
STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JIAXING POWER SUPPLY CO +2
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AI Technical Summary

Problems solved by technology

[0008] However, its shortcoming is that it only divides the latitude and classifies the data, and does not involve the specific fusion method

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] The M1 mentioned above reads in comprehensive big data and analyzes data attributes;

[0045] M2, using machine learning to establish attribute index and classification of data;

[0046] M3, conduct independent data integrity assessment on the read-in data, and eliminate incomplete, redundant and ambiguous data;

[0047] M4, the first evaluation of the data, noise removal and reorganization and filtering, and finally reduction;

[0048] M5, Establish a structured low-rank representation model and structured sparse constraints and low-rank constraints based on the matrix restoration theory framework

[0049] M6, perform error separation on the data, and complement the error segmentation defect;

[0050] M8, security management of fusion data to prevent illegal data loss.

[0051]The fusion of multi-source heterogeneous data related to distributed new energy and the establishment of a distributed database are the basis for subsequent data relationship analysis. Theref...

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PUM

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Abstract

The distributed new energy related multi-source heterogeneous data processing method comprises the following steps: M1, synthesizing big data reading and analyzing data attributes; M2, establishing attribute index and classification of data using machine learning mode; M3, carry out independent data integrity assessment on that read-in data to eliminate incomplete, redundant and fuzzy data; M4, the data is evaluated for the first time, the noise is eliminated, the noise is reorganized and filtered, and the data is finally reduced; M5, based on the matrix recovery theory, the structured low rank representation model and the structured sparse constraint and low rank constraint M6 are established to separate the data errors and to compensate the error segmentation defects. M8, security management of fused data to prevent illegal data loss. This method can reduce the repeated investment of manpower and equipment in the distributed new energy business, and reduce the indirect investment cost.

Description

technical field [0001] The present invention relates to a data integration and fusion method related to distributed new energy, in particular to a multi-source heterogeneous data processing method related to distributed new energy. Background technique [0002] Under the background of large-scale access of distributed new energy, in order to realize the effective management of massive data of distributed new energy, research on multi-source heterogeneous data such as distributed energy access information, power grid production management information, geographic meteorological information, etc. Data processing technologies such as pre-evaluation, extraction, and quality control ensure the consistency and integrity of cross-departmental distributed new energy data; establish a distributed new energy data fusion computing model, propose a distributed database construction plan and a visual display method, and develop distributed A new energy information management database prov...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/27G06Q50/06
CPCG06Q50/06Y02D10/00Y04S50/16
Inventor 胡舟韩志军方景辉唐锦江邵炜平施海峰冯振宇雷象兵李峰沈浚胡明达王大健张一鸣
Owner STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JIAXING POWER SUPPLY CO
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