Space-time big data acquisition model construction method based on crowdsourcing mode

Abstract

The invention provides a space-time big data acquisition model construction method based on a crowdsourcing mode, and the method comprises the steps: enabling an information publishing module to transmit initial information to a storage module, and enabling a user side to obtain the initial information from the storage module through accessing a crowdsourcing platform; allowing a data classification module to send the fragmented information to the screening module; when the user side feeds back to the crowdsourcing platform, allowing the screening module in the crowdsourcing platform to perform correlation identification on fragmented information stored in the screening module and feedback information of a user, and if an identification result is consistent, storing the feedback information into the storage module by the screening module, wherein the screening module generates characteristic fragment information, the screening module sends the characteristic fragment information to a priority module of the server, and the priority module performs priority judgment on the characteristic fragment information according to a pre-stored priority evaluation method; and calling feedback information from the storage module of the crowdsourcing platform by the acquisition module according to a pre-stored calling rule.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for constructing a spatio-temporal big data collection model based on a crowdsourcing model. Background technique [0002] With the development of smart mobile devices and wireless networks, urban life in today's society is moving towards a smart life. From smart homes in daily life to smart planning of the entire city, using mobile devices to collect information as a data source for future planning has become a very popular direction. However, after the data collation is completed, the customers who need to use the data often have to pay a very high cost, resulting in very slow and high costs for the entire data transmission chain. [0003] The shortcomings of the existing technology are mainly reflected in the sustainability of the entire data collection process and maintenance costs; in the existing technology, the subject of data acquisition first needs to b...

AI Technical Summary

Problems solved by technology

However, after the data collation is completed, the customers who need to use the data often have to pay a very high cost, resulting in very slow and high-cost delivery of the entire data delivery chain.

[0003] The shortcomings of the existing technology are mainly reflected in the sustainability of the entire data collection process and maintenance costs; in the existing technology, the subject of data acquisition first needs to bear the cost of crowdsourcing, and then can obtain the corresponding amount data, and it still takes a lot of manpower and material resources in the process of sorting and merging the data, and the entire return can only be recovered after the entire data is finally obtained

However, the entire data collection process is lengthy, and the material chain may be broken, making it difficult to guarantee the quality and efficiency of data collection.

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