Space crowdsourcing method based on block chain and deep reinforcement learning and terminal

Abstract

The invention provides a space crowdsourcing method and terminal based on a block chain and deep reinforcement learning, and the method comprises the steps: obtaining a registration request which is transmitted by a request end and comprises a requester identification and a requester level; acquiring block chain information, wherein the block chain information comprises a block chain grade; determining a corresponding blockchain level according to the requester level, and registering the requester identifier to a blockchain corresponding to the blockchain level, so that a request end corresponding to the requester identifier can query a block on the blockchain; obtaining task release information sent by a release end, wherein the task release information comprises position information; determining a corresponding blockchain level according to the position information, packaging the task release information into blocks, and releasing the blocks to a blockchain corresponding to the blockchain level corresponding to the task level. According to the method, the task release information, the block chain and the requestor are graded, and the task release information with the same grade is on the same block chain and can only be viewed by the requestor with the corresponding grade, so that high privacy of task release is achieved.

Description

technical field [0001] The present invention relates to the field of space crowdsourcing, in particular to a space crowdsourcing method and terminal based on blockchain and deep reinforcement learning. Background technique [0002] Spatial crowdsourcing means that the task is released to the spatial crowdsourcing server. The task includes a specific geographical location. The server assigns the task to the requester near the geographical location of the task. After accepting the assigned task, the requester goes to the designated location to perform the task. Collect relevant data during the task and upload it to the spatial crowdsourcing task server. With the rapid development of advanced low-cost sensors, communication technology, and smartphone technology, spatial crowdsourcing technology is widely used in the field of Internet of Things. Spatial crowdsourcing, as an efficient and convenient data collection technology, can provide data for various Internet of Things appl...

AI Technical Summary

Problems solved by technology

The main reasons for the privacy and security of space crowdsourcing data are as follows: First, the data collected by space missions has high privacy, but because there is no task protection mechanism for the existing task release, the task is unrestricted to all requesters. visible

There may be some malicious requesters going to the location of the space mission to steal the data they need to collect, resulting in the leakage of high privacy data

Second, the spatial crowdsourcing system does not have a perfect task allocation mechanism

Many existing space crowdsourcing systems adopt the server allocation mode or the requester's self-application method, and do not consider the quality of the requester in the process of sending tasks to the requester. If the requester is a malicious participant, it may be in the Steal the data collected by the task during the execution of the task

Third, the traditional spatial crowdsourcing system adopts a centralized structure, and the entire system relies too much on the crowdsourcing server. Once the server is attacked, all data will be lost

[0005] Existing research results can protect space crowdsourcing data privacy to a certain extent, but there are still the following deficiencies: (1) How to prevent malicious requesters from going to the mission site to steal the data privacy contained in highly sensitive space missions is not considered; ( 2) Does not consider how to assign tasks to trusted requesters for execution

(3) How to prevent data privacy leakage from the single point of failure of the spatial crowdsourcing server is not considered

(4) Does not consider how to strike a balance between enhancing data privacy security and ensuring system performance

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