Can File-to-Zip Sensor Triggers Be Refined for More Accurate Event Responses?

[Rudolfff]

I’ve been working with multiple sensors that capture environmental data like temperature, pressure, and magnetic fields. These sensors send readings at short intervals, and I have scripts that trigger responses when values pass certain thresholds. The problem I face is when two or more sensors change nearly at the same time, causing conflicting or unpredictable responses. I tried offsetting the sampling times slightly, but sometimes the system still reacts in confusing ways. I’m wondering if there’s a way to prioritize or group sensor inputs so the system knows which signal to respond to first or most strongly.

I’m curious if others handle this by assigning priorities or confidence levels to sensor triggers. For example, if temperature and magnetic field readings spike at once, is there a standard approach to deciding which sensor’s event takes precedence? Maybe using a hierarchy or a scoring system?

On a related note, I recently came across a web tool feature that creates a zip file automatically when multiple related sensor log files are detected in a folder. This easy zip for folders method compresses all relevant files in that directory into one archive, which helps keep sensor data organized and easier to manage. The file-to-zip process here means converting multiple files in a folder into a single zipped file based on certain conditions like timing or file count. This idea got me thinking: could a similar “bundling” approach be applied to sensor signals themselves—combining multiple sensor events happening closely in time into one packaged event for the system to handle at once?

Another challenge I notice is false positives. Small fluctuations sometimes trigger alerts even if the changes are insignificant. Has anyone experimented with adding software hysteresis or minimum change thresholds over time, so sensors only trigger actions when changes are sustained or exceed a certain delta?

I also use alerts based on combinations of sensor readings, but when multiple alerts trigger simultaneously, the notifications overlap and cause confusion. Would adding delay buffers between different alert triggers help, or would that cause missed events in fast-changing situations?

I’m really interested in how others manage these rapid, overlapping sensor inputs and make their systems respond reliably. How do you avoid conflicting signals or “noise” when multiple sensors trigger almost at the same time?

[TomasVaverka]

Hi,

To manage simultaneous sensor triggers and reduce unpredictable behavior, assign priorities or confidence scores to each sensor so the system knows which to respond to first. Use event bundling - collect sensor inputs within short time windows and process them as a group - to avoid reacting to every spike independently. Apply hysteresis, deadbands and rate-of-change filters to reduce false positives. To prevent alert overload, add delay buffers or cool-down periods between triggers. For better control, consider implementing a state machine or a central event manager to coordinate responses. These strategies improve clarity, reduce noise  and help the system respond more intelligently to overlapping signals.

BRs, Tomas