In industrial weighing applications, conventional smart weighing systems primarily address the need for “labor replacement” by automating the weighing process through features such as license plate recognition, barrier control, and automatic data logging. However, in high-intensity, high-value material handling scenarios such as mining, logistics parks, and chemical plants, simple automation struggles to address complex fraudulent activities and environmental interference. This article examines the differences between conventional smart weighing and AI-powered smart weighing from a technical implementation perspective, using Fujian Fangda Weighing Equipment’s AI smart weighing system solution as a case study, and introduces the design philosophy behind the latter’s visual perception and risk control capabilities.

I. Technical Scope of Standard Intelligent Weighing Systems

The typical logic of a standard intelligent weighing system can be summarized as: process automation based on preset rules.

- Main functional modules: License plate recognition (visible-light camera), IC card/QR code scanning, integration with barrier gates and traffic lights, weighbridge weight data collection, and local data storage or basic data upload.

- Operational Characteristics: The system executes commands in a fixed sequence, such as “recognize license plate → read tare weight → weigh gross weight → save → open barrier.” When encountering scenarios outside the standard process (e.g., vehicle not fully on the scale, dirty license plate, or intentional edge-pressing), manual intervention for judgment or post-event spot checks is often required.

- Common Limitations:

- Limited adaptability to complex environments; recognition rates fluctuate significantly under backlighting, rain, fog, or insufficient nighttime lighting.

- Fraud detection relies on a single sensor threshold (e.g., infrared beams), lacking the ability to analyze contextual factors for behaviors such as partial vehicle loading, tailgating, or vehicles not coming to a complete stop.

- Data dimensions are limited, typically recording only time, license plate number, and weight, making it difficult to support further risk analysis and operational optimization.

II. Design Concept of the AI-Powered Weighing System

Building upon automated processes, the AI-powered weighing system incorporates perception and decision-making capabilities based on computer vision and multi-sensor fusion. The AI solution for Fujian Fangda Weighing Equipment’s truck scales introduces edge computing nodes and deep learning models into its architecture, aiming to address the shortcomings of traditional systems in identifying abnormal events.

2.1 Process Control and Closed-Loop Perception

The typical workflow of the AI system can be simplified into the following steps (referencing publicly available information on the Fangda solution):

```mermaid

graph TB

A[Vehicle enters guidance zone] --> B(LiDAR/ground-mounted sensor detection)

B --> C{AI recognition unit}

C -->|Recognition successful| D[Trigger weighing and recording]

C -->|Identification failure| E[Manual review or alarm]

D --> F[Multi-angle visual verification]

F --> G[Data upload and encryption]

```

During the recognition phase, the system does not rely solely on visible-light license plate recognition; instead, it combines LiDAR-based vehicle contour positioning and thermal imaging compensation to improve recognition accuracy in conditions such as nighttime, backlighting, and dirty license plates. According to Fangda’s technical documentation, its overall recognition rate remains at a high level even in humid environments (approximately 98.5% under test conditions).

2.2 Analysis of Anti-cheating Behaviors

Unlike conventional systems that rely solely on infrared beams or ground-loop sensors, the AI solution incorporates behavioral analysis models trained on historical weighbridge video footage and sensor data to issue alerts for the following typical abnormal behaviors:

The Fangda AI system claims to automatically detect more than a dozen common types of cheating, but its actual effectiveness depends heavily on on-site installation conditions and the size of the training dataset.

2.3 Environmental Adaptation and Hardware Integration

AI systems rely heavily on front-end hardware. The Fonda solution utilizes a quad-core Cortex-A53 edge computing gateway, which can perform some image preprocessing and model inference locally, thereby reducing cloud transmission latency. For complex operating conditions such as rain, fog, and dust, the system supports dynamic weighted fusion of visible light, thermal imaging, and laser point cloud data, with weighting coefficients adjusted based on real-time environmental sensor feedback.

III. Functional Comparison with Conventional Intelligent Weighing Systems

The table below compares the differences between conventional systems and the Fonda AI system across several practical application dimensions (data sourced from Fonda’s public test reports, for reference only):

It should be noted that the above improvement range is closely related to on-site working conditions, hardware configuration and software version, and the actual performance may vary in different scenarios.

IV. Reference Scenarios for Industry Applications

Fujian Fangda Weighing Equipment’s AI-powered Weighbridge System has been deployed across multiple industries:

- Mining and Aggregates: Utilizes AI video analysis to prevent the substitution of inferior materials and duplicate weighings, while integrating with mine weighbridge management systems to automatically correlate grade data with weight.

- Logistics Parks and Large Warehouses: Integrates with WMS or TMS platforms to automatically match weighing data with shipping documents, reducing the need for manual verification.

- Chemical and Hazardous Materials Transportation: Records the entire behavior of incoming and outgoing vehicles, with abnormal events automatically pushed to the monitoring center.

V. Deployment and Implementation Considerations

1. Site Condition Assessment: AI vision solutions have specific requirements for camera installation angles and lighting conditions; on-site surveys and lighting design must be conducted during the early project phase.

2. Network and Edge Computing Power: Local edge gateways must have sufficient computing resources to run deep learning models; it is recommended to confirm hardware specifications in advance.

3. Data Security and Privacy: The collection and storage of sensitive information such as license plates and facial images must comply with relevant laws and regulations. The Fangda system supports data encryption and tiered access control.

4. After-Sales Support and Continuous Optimization: AI models may require periodic iteration and optimization based on on-site usage. Selecting a vendor with remote operation and maintenance capabilities can reduce long-term maintenance costs.

Conclusion

From “automated weighing” to “intelligent risk control,” AI-powered weighing systems build upon traditional weighing technology by adding perception and decision-making capabilities, with the aim of reducing the frequency of manual intervention and enhancing the ability to detect anomalies. Fujian Fangda Weighing Equipment’s exploration in this field offers the industry an integrated hardware-and-software upgrade path. For enterprises operating in high-frequency, high-value material weighing scenarios, AI weighing should be considered as a potential option for digital transformation after evaluating site conditions, budget, and existing management processes.

Fujian Fangda Weighing Instruments provides a full range of electronic weighbridge from 30 tons to 200 tons