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Regional disease monitoring method driven by transfer learning and assisted by unmanned aerial vehicle

A technology of transfer learning and disease monitoring, applied to instruments, electrical components, data processing applications, etc., to shorten the data collection cycle, improve the quality of monitoring data, and ensure robustness

Inactive Publication Date: 2020-12-18
CHANGSHU INSTITUTE OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Existing monitoring systems and methods can only achieve one or two of the above goals

Method used

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  • Regional disease monitoring method driven by transfer learning and assisted by unmanned aerial vehicle
  • Regional disease monitoring method driven by transfer learning and assisted by unmanned aerial vehicle
  • Regional disease monitoring method driven by transfer learning and assisted by unmanned aerial vehicle

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Embodiment Construction

[0030]The present invention will be further described below in conjunction with the examples, but not as a limitation to the present invention.

[0031]Please combineFigure 1 to Figure 3 As shown, the migration learning-driven and UAV-assisted regional disease monitoring method involved in this embodiment starts from the bus network and finds the time for the UAV on the grid divided Ω between any two monitoring areas. Constrained shortest path; UAV rides on a bus to replenish its power and reaches the target monitoring area after several transfers; combined with migration learning algorithms, it calculates the monitoring points that meet the needs of disease monitoring in the unknown monitoring area, and uses robots to patrol. The inspection collects the data of these monitoring points and adjusts the robot's inspection scheduling path in real time; after the drone reaches the corresponding inspection area, it charges the robot while reading the monitoring data, and then returns to the...

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Abstract

The invention discloses a regional disease monitoring method for transfer learning driving and unmanned aerial vehicle assistance, and the method comprises the steps: starting from a bus network, searching a shortest path with time constraint for an unmanned aerial vehicle on grid division between any two monitoring regions; the unmanned aerial vehicle takes the bus to supplement electric quantityto the bus and arrives at the target monitoring area after a plurality of times of transfer; in combination with a transfer learning algorithm, monitoring points meeting disease monitoring requirements are calculated in an unknown monitoring area, data of the monitoring points are collected through robot inspection, and an inspection scheduling path of the robot is adjusted in real time; and theunmanned aerial vehicle reads the monitoring data while charging the robot after arriving at the corresponding detection area, then returns the monitoring data to the bus, and forwards the monitoringdata to the monitoring center through the bus wireless communication system. According to the invention, sustainability and robustness of the regional disease monitoring system are guaranteed.

Description

Technical field[0001]The invention relates to a regional disease monitoring method, in particular to a regional disease monitoring method driven by migration learning and UAV assistance.Background technique[0002]When the Internet of Things is applied to the field of smart medical care, the selection of disease monitoring points and the collection and forwarding of monitoring data require a large amount of monitoring equipment and computing and storage resources of the application platform, especially the selection of monitoring points in unknown monitoring areas and equipment charging issues. When the monitoring point is selected in some special environments such as buildings, underwater, forests, parks, etc., the existing wired communication (power transmission) method or wireless communication (charging) network cannot be used for data collection and charging. At the same time, the real-time and diversified needs of disease monitoring have made it a key issue to find an optimal co...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08G06Q50/26
CPCG06Q50/26H04L67/12
Inventor 靳勇钱振江
Owner CHANGSHU INSTITUTE OF TECHNOLOGY
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