A method for constructing spatio-temporal big data acquisition model based on crowdsourcing mode

Abstract

The present invention provides a method for constructing a spatio-temporal big data collection model based on the crowdsourcing mode. The release information module sends the initial information to the storage module, and the client obtains the initial information from the storage module by accessing the crowdsourcing platform; data classification The module sends the fragmented information to the screening module, and when the client gives feedback to the crowdsourcing platform, the screening module in the crowdsourcing platform compares the fragmented information stored in it with the user's feedback information Relevance identification, if the identification results are consistent, the screening module stores the feedback information into the storage module; the screening module generates characteristic fragment information, and the screening module sends the characteristic fragment information to the priority module of the server, so The priority module judges the priority of the characteristic segment information according to the pre-stored priority evaluation method; the collection module retrieves the feedback information from the storage module of the crowdsourcing platform according to the pre-stored calling rules.

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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