Trend analysis and reporting in SCADA systems - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start logging sensor data every minute.

SCADA systems

scheduler.schedule_task(sensor.read_data, interval=[1])

Drag options to blanks, or click blank then click option'

A60

B10

C120

D30

Attempts:

3 left

💡 Hint

Common Mistakes

Using 30 or 10 seconds instead of 60 seconds.

The interval is set in seconds. To log data every minute, use 60 seconds.

2fill in blank

medium

Complete the code to filter data points where temperature is above the threshold.

SCADA systems

filtered_data = [d for d in data if d.temperature [1] threshold]

Drag options to blanks, or click blank then click option'

A<

B>

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '<=' which select lower temperatures.

We want data points where temperature is greater than the threshold.

3fill in blank

hard

Fix the error in the code to calculate the average value of sensor readings.

SCADA systems

average = sum(readings) [1] len(readings)

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' or '+' which do not compute average.

To find the average, divide the sum by the number of readings.

4fill in blank

hard

Fill both blanks to create a dictionary of sensor IDs and their latest values.

SCADA systems

latest_values = {sensor.[1]: sensor.[2] for sensor in sensors}

Drag options to blanks, or click blank then click option'

Aid

Bvalue

Ctimestamp

Dstatus

Attempts:

3 left

💡 Hint

Common Mistakes

Using timestamp or status instead of id or value.

The dictionary keys are sensor IDs and values are the latest sensor readings.

5fill in blank

hard

Fill all three blanks to filter and report sensors with values above threshold.

SCADA systems

report = {sensor.[1]: sensor.[2] for sensor in sensors if sensor.[3] > threshold}

Drag options to blanks, or click blank then click option'

Aid

Bvalue

Dstatus

Attempts:

3 left

💡 Hint

Common Mistakes

Using status or timestamp instead of value for filtering or reporting.

The report maps sensor IDs to their values, filtering those with values above the threshold.

Practice

1. What is the main purpose of trend analysis in SCADA systems?

easy

A. To configure hardware devices

B. To track changes in data over time

C. To write code for automation

D. To backup system files

Solution

1. Step 1: Understand trend analysis concept

   Trend analysis means observing how data changes over a period.

2. Step 2: Match purpose with options

   Only tracking data changes over time fits the definition of trend analysis.

3. Final Answer:

   To track changes in data over time -> Option B

4. Quick Check:

   Trend analysis = track data changes [OK]

Hint: Trend analysis = watching data over time [OK]

Common Mistakes:

• Confusing trend analysis with system backup
• Thinking it configures devices
• Mixing it with coding tasks

2. Which of the following is the correct command to generate a trend report in a SCADA system CLI?

easy

A. generate_trend_report --start 2024-01-01 --end 2024-01-31

B. trend report create start=2024-01-01 end=2024-01-31

C. create report trend from 2024-01-01 to 2024-01-31

D. report --trend --from 2024-01-01 --to 2024-01-31

Solution

1. Step 1: Identify correct command syntax

   SCADA CLI commands usually follow verb_action --option value format.

2. Step 2: Compare options

   generate_trend_report --start 2024-01-01 --end 2024-01-31 matches the expected syntax with clear flags and dates.

3. Final Answer:

   generate_trend_report --start 2024-01-01 --end 2024-01-31 -> Option A

4. Quick Check:

   Correct CLI syntax = generate_trend_report --start 2024-01-01 --end 2024-01-31 [OK]

Hint: Look for commands with clear flags and date ranges [OK]

Common Mistakes:

• Using incorrect command order
• Missing dashes before options
• Using natural language instead of CLI syntax

3. Given this snippet of a SCADA trend report script:

data = [10, 15, 20, 25, 30]
trend = []
for i in range(1, len(data)):
    trend.append(data[i] - data[i-1])
print(trend)

What is the output?

medium

A. [10, 5, 5, 5]

B. [10, 15, 20, 25, 30]

C. [5, 10, 15, 20]

D. [5, 5, 5, 5]

Solution

1. Step 1: Calculate differences between consecutive data points

   Subtract each previous value from current: 15-10=5, 20-15=5, 25-20=5, 30-25=5.

2. Step 2: Collect results in trend list and print

   The trend list is [5, 5, 5, 5], which is printed.

3. Final Answer:

   [5, 5, 5, 5] -> Option D

4. Quick Check:

   Differences between data points = [5,5,5,5] [OK]

Hint: Subtract previous from current values to find trend [OK]

Common Mistakes:

• Printing original data instead of differences
• Off-by-one errors in loop range
• Appending wrong values to trend list

4. You run this SCADA report command but get an error:

generate_trend_report --start 2024-02-30 --end 2024-03-01

What is the likely cause?

medium

A. End date is before start date

B. Missing required --format option

C. Invalid date: February 30 does not exist

D. Command syntax is incorrect

Solution

1. Step 1: Check date validity

   February has at most 29 days; 30 is invalid.

2. Step 2: Confirm error cause

   Invalid date causes command to fail before other checks.

3. Final Answer:

   Invalid date: February 30 does not exist -> Option C

4. Quick Check:

   Invalid date causes error [OK]

Hint: Verify dates exist on calendar before running commands [OK]

Common Mistakes:

• Assuming syntax error without checking dates
• Ignoring invalid date and blaming options
• Confusing start and end date order

5. You want to create a report showing average temperature trends per day from hourly SCADA data. Which approach is best?

hard

A. Aggregate hourly data by day, then calculate daily averages and plot trend

B. Plot hourly data directly without aggregation

C. Calculate weekly averages ignoring daily details

D. Use raw data without any calculations for reporting

Solution

1. Step 1: Understand goal of daily average trends

   We need daily summaries from hourly data to see daily trends clearly.

2. Step 2: Choose method to aggregate and analyze data

   Aggregating hourly data by day and calculating averages fits the goal best.

3. Final Answer:

   Aggregate hourly data by day, then calculate daily averages and plot trend -> Option A

4. Quick Check:

   Daily average trend needs daily aggregation [OK]

Hint: Group data by day before averaging for daily trends [OK]

Common Mistakes:

• Using raw hourly data without aggregation
• Skipping daily grouping and using weekly averages
• Ignoring calculations and plotting raw data