MATLAB_Anomaly_Filter.m.txt

% MATLAB Code for Anomaly Detection (ThingSpeak Analysis App)
%
% This script runs on ThingSpeak's cloud platform. 
% It reads the raw data from Field 1, applies a Moving Average Filter 
% for noise reduction (DSP), and writes the anomaly status to a new field (e.g., Field 2).

% 1. Read the last 60 data points (approx 10 minutes) from Field 1
% Note: Replace YOUR_CHANNEL_ID and YOUR_READ_API_KEY with your credentials
readChannelID = YOUR_CHANNEL_ID;
fieldID = 1;
readAPIKey = 'YOUR_READ_API_KEY'; 

tempData = thingSpeakRead(readChannelID, 'Field', fieldID, 'NumPoints', 60, 'ReadKey', readAPIKey);

% 2. Digital Signal Processing (DSP): Apply a Moving Average Filter
% We use a window size of 5 data points (50 seconds) to smooth the noise.
windowSize = 5;
b = (1/windowSize)*ones(1,windowSize);
a = 1;
tempFiltered = filter(b, a, tempData);

% Get the latest filtered value
latestFilteredTemp = tempFiltered(end);
latestRawTemp = tempData(end);

% 3. Anomaly Detection and Threshold Logic
% Critical Threshold set at 60.0 C
CRITICAL_THRESHOLD = 60.0; 

% Default status: 0 (Normal)
anomalyStatus = 0; 
statusMessage = 'Normal';

% Check if the latest RAW data is above the threshold (confirming the anomaly)
if latestRawTemp > CRITICAL_THRESHOLD
    anomalyStatus = 1; % Status 1 means Anomaly Detected
    statusMessage = '!!! ANOMALY DETECTED (70C Spike) !!!';
end

% 4. Write the results back to ThingSpeak 
% We write the Filtered Temperature to Field 2 and the Anomaly Status to Field 3.
outputField2 = latestFilteredTemp; % Filtered Value
outputField3 = anomalyStatus;      % Anomaly Flag (1 or 0)

writeChannelID = YOUR_CHANNEL_ID;
writeAPIKey = 'YOUR_WRITE_API_KEY'; % Use the WRITE_API_KEY for the output channel

thingSpeakWrite(writeChannelID, [outputField2, outputField3], 'WriteKey', writeAPIKey);

% 5. Output for Debugging
disp(['Latest Filtered Temp (Field 2): ', num2str(latestFilteredTemp)]);
disp(['Anomaly Status (Field 3): ', num2str(anomalyStatus)]);
disp(statusMessage);