A Cloud–Edge Collaborative Multi-Parameter Fusion Approach for Adaptive Early Warning and Condition Assessment of Production Control Systems

To address the limitations of traditional fixed-threshold alarms for distributed control systems (DCS), programmable logic controllers (PLC), safety instrumented systems (SIS), and remote terminal unit (RTU) production control systems in oil-gas stations, which suffer from delayed response, binary-only outputs, and an inability to characterize the continuous degradation process of equipment, this paper proposes a cloud–edge collaborative, human-reviewed adaptive diagnosis and early-warning algorithm for production control systems under a cloud–edge collaborative architecture integrating deterministic rules and multi-parameter fusion. Here, adaptive denotes cloud-assisted, human-reviewed parameter updating rather than autonomous online learning. A lightweight rule engine and risk fusion model are deployed at the edge side. Monitoring points are classified into nine risk groups according to functional semantics, and heterogeneous measurements are normalized into dimensionless risk scores within the range of [0, 1] through linear and binary mappings. A hierarchical judgment logic is designed to distinguish critical hard faults (e.g., communication and power supply failures) from ordinary parameter threshold-exceedance warnings, enabling precise point-level traceability. A three-stage fusion model is established. By introducing the pressure term, exponential smoothing filter and hysteresis latch, it generates a continuous condition-assessment score ranging from 0 to 100. The cloud side is responsible for threshold parameter management, global condition analysis across multiple stations, and iterative update of the fault knowledge base, forming a collaborative closed-loop of “real-time diagnosis at the edge and continuous optimization in the cloud”. Tests are conducted using seven-day time-series data from five PLC devices. A total of 3358 threshold-exceedance events are identified, and the proportion of continuous-variable anomalies ranges from 7.08% to 7.34%. Most exceedance events are one-minute transient pulses, and the temporal overlap coefficient between state-variable anomalies and continuous-variable anomalies reaches 0.61. Hard faults can forcibly constrain the condition-assessment interval, while fine-grained load differentiation can be realized under normal operating conditions. Ablation experiments verify the benefits of the three optimization mechanisms for interval constraint, stable output and hysteretic fault recovery.