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Title:Brezkontaktni sistem za spremljanje delovnih ur s pomočjo platforme Raspberry Pi
Authors:ID Gačnik, Jan Alojz (Author)
ID Črepinšek, Matej (Mentor) More about this mentor... New window
Files:.pdf VS_Gacnik_Jan_Alojz_2022.pdf (2,00 MB)
MD5: 994AA01F16671026DCEAA34BC0EA4E63
PID: 20.500.12556/dkum/ab2d16fd-bae6-4a91-808d-0235055bbd94
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Namen diplomskega dela je izdelava sistema, s katerim bi lahko brezkontaktno evidentirali delovne ure. To smo storili s pomočjo računalnika Raspberry Pi, na katerem smo z orodjem Node-RED preverjali prisotnost posameznika preko njegovih delovnih postaj in pametnih naprav. Za shranjevanje podatkov uporabimo dokumentno podatkovno bazo MongoDB, za prikaz informacij pa razvijemo Android aplikacijo. Vse to povezujemo s spletno aplikacijo Spring Boot, katera zbrane podatke tudi obdeluje, nato pa razvito rešitev primerjamo z že obstoječimi načini evidentiranja delovnih ur.
Keywords:Raspberry Pi, Spring Boot, Android, Node-RED
Year of publishing:2022
PID:20.500.12556/DKUM-82155 New window
Publication date in DKUM:04.08.2022
Categories:KTFMB - FERI
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License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:22.07.2022

Secondary language

Title:Contactless system for monitoring work-hours using the Raspberry Pi platform
Abstract:The purpose of this diploma thesis is to create a system with whom we could monitor work hours without direct contact. For this matter we use a Raspberry Pi board with the Node-RED tool installed, on which we can examine the presence of an individual via their workstation and smart devices. For data storage we use the document-oriented database MongoDB and develop an Android app for displaying the collected data. We connect everything with a Spring Boot web application, which also process the collected data. After that we compare our solution to already existing ones.
Keywords:Raspberry Pi, Spring Boot, Android, Node-RED


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