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https://github.com/nilanoemi25/compsysproject
Project: HR Clocking System for CompSystems Module - 10 credit
https://github.com/nilanoemi25/compsysproject
html5 http iot mqtt python
Last synced: 16 days ago
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Project: HR Clocking System for CompSystems Module - 10 credit
- Host: GitHub
- URL: https://github.com/nilanoemi25/compsysproject
- Owner: nilanoemi25
- Created: 2024-11-18T13:23:28.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-15T20:43:51.000Z (2 months ago)
- Last Synced: 2024-12-15T21:28:57.919Z (2 months ago)
- Topics: html5, http, iot, mqtt, python
- Language: Python
- Homepage:
- Size: 369 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
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README
# Project Title
HR Clocking System
# Project Description
This project is 40% of CompSys module in semester two, year 1 Hdip in SETU.
The purpose of this project is to explore different ways that a HR Assistant may get the information about employee time keeping.
# Firstly:
On Raspberry PI the physical trigger is to press **button** to simulate 'clock in punch'.
1. When the button is pressed the temperature, humidity and time values are read, along with employeeID and employeeName.
2. A photo is taken of the employee as they clock in.
3. The name of the employee & 'late' or 'ontime' message appears on the Raspberry PI SenseHat
The temperature and humidity values are used to infer wether the weather is good or bad.
Bad weather may be a valid excuse for lateness of an employee as it may cause commuting issues, traffic build up, accidents on the road etc
Good weather there is no excuse of lateness of an employee
The current time is measured against logic to determine whether the employee is late or ontime **at the button press.**
***
Thingspeak public url: https://thingspeak.mathworks.com/channels/2753624
Blynk Generic url: https://blynk.io/
***
## MQTT:
All of this information is linked with an MQTT protocol. The publishing is done via the client_pub.py script and the subscription can be via the client_sub.py script AND/OR via MQTT app on mobile.
This allows the HR professional to get clock in punches and their information to their work mobile phones.
Employee ID and employeeName are taken from the employee_data(base).
### List of values:
+ "deviceId"
+ "temp"
+ "humidity"
+ "employee"
+ "employeeId"
+ "weather_condition"
+ "time_keeping"
+ "URLString"
## HTTP:
Data is sent to Thingspeak platform using HTTP protocol.
The Thingspeak platform visualises the temperature and humidity which are integers on it's dashboard.
An EmailHR React App is in place that will send an email to HR professional IF any employee is late.
Most importantly the data gathered from the Rasberry PI button press input, can be downloaded as an CSV file by HR professional where all fields (not just integers but strings also) can be used in office for deep diving.
### List of values:
+ 'field1': temperature,
+ 'field2': humidity,
+ 'field3': weather_condition,
+ 'field4': employeeId,
+ 'field5': employee,
+ 'field6': is_ontime,
+ 'field7': is_ontime_numeric,
+ 'field8': urlString
***
The photos taken is stored on Glitch platform, full gallery accessible here:
https://glitch.com/~gentle-calico-nautilus
Of all the data gathered **humidity**, **temperature** and the **photo** are sent to Blynk platform. This makes it more user friendly for the HR professional to access the data in contrast to the MyMQTT app.
A push notification is enabled for each employee clock in on the mobile phone.
# Secondly:
The Raspberry PI is linked to Packet Tracer and the RFID reader uses UDP to send the cardID to the PI.
The PI reads the temp, humidity and time stamp and sends this data along with RFID card ID to a JSON file.
A HTTP thingspeak script reads in this data from the json file and uses logic to determine 1) the name of the employee and 2) whether the employee was late or ontime.
Subsequently all this data is sent to ThingsSpeak platform, where it can be viewed and downloaded by the user.
The data is also sent to Blynk app with notifications enabled.
# Additionally:
The restAPIs trigger different websites with templates, styled with some CSS, logically diving the data that was gathered from the Raspberry PI.
***
/
/sensehat/environment
/sensehat/punctual
/sensehat/punctualpost
# How to Install / Run the project
You will need:
+ Raspberry PI
+ Sensehat
+ Camera for Raspberry PI
+ Computer
## HTTP
1. SSH from Desktop into Raspberry PI.
2. Download from github and run thingspeak_data_http.py from Terminal using the python command.
3. Click the button on Raspberry PI to start the process.
4. View data collected in Thingspeak Web dashboard and Blynk Web dashboard /app.
## MQTT
1. SSH from Desktop into Raspberry PI.
2. Download from github and run client_pub.py from Terminal using python command.
3. Click the button on Raspberry PI to start the process.
4. Check MyMQTT app or run client_sub.py script to receive the subscriber messages.
## PacketTracer
1. Download theproject_v2.pkt
2. Run the file & make sure the RFID reader programme is running
3. Bring the cards over the RFID reader and you will see the cardId printed in the terminal
4. Download and run sensor_listener.py script on RasbPI. Now you will see the cardId from the RFID reader and additional data from PI be logged to a JSON script called udp_data.json
5. Download and run udp_thingspeak_data_http.py from the HTTP Thingspeak folder and run it from the PI's terminal.
6. Observe the data sent to Thingspeak platform and Blynk app.
# Credits & Acknowledgements
SETU 2024 classmates & teachers
Badges:
https://shields.io/badges
Github ReadMe Style:
https://github.com/adam-p/markdown-here/wiki/markdown-cheatsheet
# Licence
N/A
# Contact Information
Noemi Lovei
[email protected]