Multi-source data fusion method and device based on spatio-temporal information weighting

Abstract

The invention discloses a multi-source data fusion method based on spatio-temporal information weighting. The method comprises the following steps: S1, establishing a reference database; S2, collecting a plurality of incremental data sets through a plurality of data collection channels; S3, fusing DS1... DS5 into the reference database in sequence; S4, reviewing the data fusion process and optimizing the data fusion algorithm. According to the method, a data island is broken, the construction cost is reduced, the geographic position is a carrier for association and intersection of various data, the information barrier is broken through in order based on data connection and correction of the geographic position, safe sharing of the data is achieved with higher efficiency and lower cost, and different data consumers can accurately obtain the data according to needs through the same way, the storage and maintenance cost and the transmission cost are reduced, and meanwhile the accuracy and cleanliness of the data are guaranteed.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a method and device for multi-source data fusion based on spatio-temporal information weighting. Background technique [0002] In the digital age, it is an effective way for enterprises to achieve precise operations and enhance their competitiveness by conveniently obtaining and analyzing a large amount of high-quality data, and in-depth mining and innovating data value through machine learning and other artificial intelligence technologies. As one of the new production factors, Data can bring more than expected benefits to enterprises. In the face of the two-way rapid growth of data demand in width and breadth, the traditional data production process has the following problems: [0003] Problem 1: Data redundancy, repeated production, different construction and use units among enterprises, departments, business layers, etc., there is a large amount of duplicate data,...

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Problems solved by technology

[0003] Problem 1: Data redundancy, repeated production, different construction and use units among enterprises, departments, business layers, etc., there is a large amount of duplicate data, poor readability, information blockage forms data islands, data is neither effectively used nor Safety sharing is not realized, resulting in an N-fold increase in production costs, storage costs, maintenance costs, and production cycles;

[0004] Problem 2: The data is incomplete and unreliable. Data production and quality control lack effective automation technology. They rely too much on the production personnel's cognition and control of quality and the standardization of the production process, resulting in poor data integrity and lack of reliability. Reliability;

[0005] Problem 3: The measurement is inconsistent. Taking house prices as an example, the measurement units are not uniform. Some use "yuan per square meter" as the unit, and some use "10,000 yuan per square meter" as the unit. When using it, it is necessary to identify the validity of the data sex, increase the cost of use;

[0006] Problem 4: Inconsistent versions, scattered data, even the same data, will generate different copies during the transfer process, lack of data baseline, unable to achieve continuous and stable updates and version backtracking and control, resulting in users being unable to obtain on-demand Accurate and complete data, increasing the cost of data usage

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