In the field of quality management, traditional Statistical Process Control (SPC) often faces the dual challenges of "data silos" and "analysis lag." With the deepening of industrial digitalization, we believe that SPC should not be merely a drawing tool, but rather a real-time pulse connecting the production site and management decisions.

1. Real-time: Bridging the time lag between data and analysis

The system adopts a B/S architecture, achieving seamless integration from data generation to chart feedback. Through integrated HTTP, MQTT, TCP, OPC, and other interface protocols, data from sensors or detection equipment on the production line can be synchronized to the system in real time. This mechanism ensures that control charts are updated automatically without manual refresh when the page is open, truly transforming quality control from "post-event statistics" to "process prevention."

2. Multi-dimensional dashboards: Building a comprehensive monitoring view

To meet the quality management needs at different levels, we have designed three types of core monitoring dashboards, supporting the creation of an unlimited number of display pages:

• Dynamic Dashboard: Designed specifically for production workstations, it is projected onto the workshop's large screen via an independent URL. Data changes in real time as it flows in from the inspection points, allowing frontline personnel to immediately grasp the stability of the process.

• Integrated Dashboard: Supports cross-process configuration, allowing control charts, rainbow charts, or histograms of different testing items to be freely combined in the same view, enabling centralized monitoring of complex processes.

• Statistical Dashboard: Provides data summaries for management, intuitively displaying daily alarm rates, number of updated detection items, and anomaly lists, identifying systemic quality risks from a global perspective.

3. Closed-loop alarm: From anomaly detection to improvement record

A robust backend monitoring service is the core of the system's proactive defense. Its key feature lies in the independent configuration of anomaly detection rules and alerting rules:

• Omnichannel reach: The backend monitors SPC anomaly detection and CPK/PPK process capability alerts in real time. Once a rule is triggered, the system can immediately push alarm information via email, WeChat Work, DingTalk, Lark, and MQTT interface.

• Closed-loop management: Personnel receiving alarms can directly register improvement measures in the system. This closed-loop model, from anomaly detection and message push to the recording of processing results, ensures that nursing measures are traceable and that the improvement process is clear and transparent.

4. In-depth technological development: AI empowerment and reliability assurance

Beyond basic statistics, we've introduced more cutting-edge tools to deeply mine the value of data. The MSA (Measurement System Analysis) module covers gauge linearity and bias studies, Gage R&R studies, and more, ensuring the stability and accuracy of the gauges themselves before statistical analysis. Simultaneously, the system innovatively integrates large models such as ChatGPT and DeepSeek, enabling one-click intelligent interpretation of statistical results and assisting quality managers in quickly analyzing the causes of anomalies.

We are committed to helping enterprises build efficient digital quality systems with a lightweight architecture and extremely low technical barriers. By enabling data to "speak" and anomalies to be "detected in real time," we help China's manufacturing industry steadily move towards high-quality development.