How Intelligent Loading/Unloading Point Detection Boosts Logistics Efficiency

What is Loading/Unloading Point Detection?

Loading/unloading point detection determines the precise start and end moments of continuous loading or unloading actions based on the beginning and completion of these operations. It serves as a key window for the G7 intelligent hanging platform, enhancing loading efficiency, reducing waiting time, and providing real‑time reminders for smoother scheduling.

Why It Matters for Logistics

For companies that prioritize platform utilization, accurate point detection enables precise vehicle dispatch, higher vehicle utilization, and improved loading efficiency. By analyzing real‑time data, managers can predict loading completion times and monitor progress, especially for scheduled departures.

Core Detection Algorithm

The algorithm relies on uniquely designed feature parameters such as loading‑rate acceleration, second‑order acceleration, acceleration energy, and zero‑crossing rate. These features allow the system to recognize loading/unloading start points by analyzing the speed of loading‑rate changes, even in complex environments with multiple actions.

Algorithm Workflow

Real‑time transport information query : Users input a time range to retrieve vehicle details, stop duration, loading rate, and volume, which are displayed on the G7 platform.

Feature engineering :

a) Define loading acceleration as a variable reflecting loading speed, calculated from loading time, rate, and its change; aggregate acceleration over a time window to assess trends.

b) Use the accumulated acceleration energy within the window to pinpoint start and end points.

c) Introduce the zero‑crossing rate of acceleration to reduce false detections caused by alternating positive and negative acceleration.

d) Compute the zero‑crossing rate as the number of axis crossings per 30‑second interval, normalized by sample count.

e) Set low and high energy thresholds to differentiate noise from genuine loading/unloading events.

f) Apply a zero‑crossing rate threshold (e.g., >3×Zs) based on a 3σ rule to confirm start/end phases.

g) Use cross‑threshold data to avoid misclassifying short‑distance events (e.g., distance <21).

Threshold determination and parameter optimization

Intelligent computation : Combine low‑threshold triggers with high‑threshold confirmations to accurately locate start and end points under complex conditions.

Performance Metrics

Precision=100%, Recall=91.7%, F1=95.67% – the algorithm exceeds a 95% overall evaluation score.

Result Integration

After computation, the G7 intelligent management platform displays the identified time points, further improving the platform’s recognition accuracy. The detection algorithm is a foundational technical support that will be continuously iterated for various business scenarios.