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Python Module to Calibrate Humidity and Dust Sensors After Construction Work

#webdev #programming #ai #javascript

After a renovation or building project, the environment is often left in a highly polluted state. From fine drywall dust to elevated humidity levels caused by wet materials, the conditions can delay cleaning, finishing, or painting. That’s where sensor calibration plays a crucial role.

Whether you're working in residential, commercial, or Post Construction Cleaning Chicago scenarios, ensuring that your sensors are accurately calibrated can prevent health risks and ensure high-quality cleaning standards.

Why Sensor Calibration Matters

Sensors straight out of the box are often inaccurate due to:

  • Environmental drift
  • Sensor variance during manufacturing
  • Power supply inconsistencies

By calibrating humidity and dust sensors using Python, you can ensure stable readings before using the data in a live cleaning job.

Required Tools

  • Raspberry Pi or any microcontroller with GPIO
  • DHT22 or DHT11 humidity sensor
  • GP2Y1010AU0F dust sensor or similar
  • Python 3.x installed on the device
  • Libraries: Adafruit_DHT, RPi.GPIO, statistics, json

Step-by-Step Code: Calibrating Humidity Sensor

We’ll begin with a function that collects 30 readings from the humidity sensor and calculates their average and standard deviation.

import Adafruit_DHT
import statistics
import time

SENSOR_TYPE = Adafruit_DHT.DHT22
SENSOR_PIN = 4  # GPIO4

def read_humidity():
    humidity, _ = Adafruit_DHT.read_retry(SENSOR_TYPE, SENSOR_PIN)
    return humidity

def calibrate_humidity(samples=30, delay=1):
    readings = []
    print(" Gathering humidity samples...")
    for i in range(samples):
        value = read_humidity()
        if value:
            readings.append(value)
        time.sleep(delay)
    avg = round(statistics.mean(readings), 2)
    stdev = round(statistics.stdev(readings), 2)
    return {"average": avg, "stdev": stdev}
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Step-by-Step Code: Simulating Dust Sensor Calibration

For dust sensors, we simulate analog input values. In production, this would be replaced by actual sensor input using ADC (analog-to-digital converters).

import random

def read_dust():
    # Replace this with actual analog input from your dust sensor
    return round(random.uniform(0.1, 0.5), 3)

def calibrate_dust(samples=30, delay=1):
    readings = []
    print("Gathering dust level samples...")
    for i in range(samples):
        value = read_dust()
        readings.append(value)
        time.sleep(delay)
    avg = round(statistics.mean(readings), 3)
    stdev = round(statistics.stdev(readings), 3)
    return {"average": avg, "stdev": stdev}
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In real cleaning environments, especially during Post Construction Cleaning Chicago il, this helps technicians determine when air scrubbers should be activated.

Saving Calibration Results

Let’s store the calibration into a .json file for later use in real-time monitoring or automation.

import json

def save_to_file(data, filename="sensor_calibration.json"):
    with open(filename, "w") as f:
        json.dump(data, f, indent=4)
    print(f" Calibration data saved to {filename}")
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Full Calibration Routine

Now, let’s bring everything together:

if __name__ == "__main__":
    print(" Starting full sensor calibration...\n")

    humidity_data = calibrate_humidity()
    dust_data = calibrate_dust()

    all_data = {
        "humidity": humidity_data,
        "dust": dust_data
    }

    save_to_file(all_data)

    print("\n Calibration complete. You may now proceed with data-driven cleaning operations.")
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Real-Life Use Case

Imagine you're tasked with cleaning a newly renovated medical clinic. The air feels damp and there's visible residue on surfaces. With your calibrated sensors, you can:

  • Determine when conditions are optimal to start disinfection
  • Avoid painting in high humidity
  • Monitor air quality to reduce worker exposure
  • Report compliance data to clients

In a market as competitive as Post Construction Cleaning in Chicago, this kind of data-driven approach sets your service apart.

Final Thoughts

Sensor calibration might sound like a technical step, but it plays a pivotal role in the health, safety, and efficiency of post-construction cleaning. Python makes it incredibly accessible—even to non-engineers—and enables you to create smarter cleaning workflows with minimal setup.

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