A distributed large-scale data acquisition system and method based on mobile devices

Abstract

The invention discloses a distributed massive data acquisition system and method based on mobile devices. The system comprises a mobile terminal subsystem and a service management subsystem, wherein the mobile terminal subsystem comprises a data acquisition unit, a data processor and a state controller; the data acquisition unit can automatically apply for an acquisition task and executes the task; the data processor extracts, analyzes, compresses and transmits the acquired data; the state controller records operation and execution conditions of the mobile devices and controls schedule; the service management subsystem comprises a device manager, a task manager, a data analyzer and a central controller; the device manager manages all the mobile devices in the network and operation and network states of the mobile devices; the task manager manages all acquisition tasks in a concentrated manner and dynamically manages acquisition frequency and task states; the data analyzer cleans and processes data returned by the mobile devices; the central control unit controls and manages other manager modules. With adoption of the system and the method, the massive data acquisition network can be flexibly deployed, idle mobile devices and network bandwidth are effectively utilized, and the data acquisition cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to an information collection system and a method thereof under large-scale data. Background technique [0002] With the advent of the era of big data, information, especially social information, has grown exponentially, and the demand for data from various applications has also increased. At present, the collection of massive information mostly relies on traditional centralized collection or PC distributed collection. Its shortcomings are extremely obvious. On the one hand, the client is complicated, and there is a certain learning cost for operation and use; on the other hand, the bandwidth overhead of data collection is very large, and the cost of equipment is too high; in addition, the data is not dynamic during the collection process. The simplified transmission processing also leads to the waste of related resources and the low efficiency. [0003] A...

AI Technical Summary

Problems solved by technology

[0002] With the advent of the era of big data, information, especially social information, has grown exponentially, and the demand for data from various applications has also increased. At present, the collection of massive information mostly relies on traditional centralized collection or PC distributed collection. Its shortcomings are extremely obvious. On the one hand, the client is complicated, and there is a certain learning cost for operation and use; on the other hand, the bandwidth overhead of data collection is very large, and the cost of equipment is too high; in addition, the data is not dynamic during the collection process. Optimized transmission processing also leads to waste of related resources and low performance

[0003] At present, the development of network facilities is also extremely rapid. Unlike the current situation of high bandwidth costs in IDC computer rooms, the idle network environment, mainly in homes and social places, is not fully utilized.

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