Steam‑oven cooking performance is highly dependent on precise temperature and humidity coordination. Conventional single‑stage control logic easily causes violent fluctuations of cavity temperature and steam content during long‑time steaming. Uneven heat and moisture distribution leads to inconsistent ripening degree of food in different areas of the cavity. Optimizing the multi‑stage temperature and humidity regulation software logic of the steam‑oven control board can realize segmented dynamic adjustment of heating and steam supply, stabilize the cavity environment and greatly improve the consistency of constant‑temperature steaming.

Analyze the defects of traditional control logic. Most control programs adopt simple threshold triggering. Once the measured value deviates from the set point, the heater or water pump is turned on and off sharply, resulting in obvious temperature overshoot and humidity oscillation. In the initial heating stage, a large amount of cold air exists in the cavity, and steam is quickly condensed, which fails to establish a stable hot‑humid environment. In the constant‑temperature holding period, frequent start‑stop of steam supply will cause local super‑heating or insufficient moisture. The fixed control parameters cannot adapt to different cooking loads, food volumes and initial cavity temperatures, so the steaming effect varies greatly batch by batch.

Build a segmented multi‑stage temperature and humidity linkage control framework. Divide the whole steaming process into preheating, constant‑temperature steaming and gentle holding stages with independent target parameters. In the preheating stage, the program prioritizes rapid heating and moderate steam injection to eliminate cold zones and reduce condensation on the cavity wall. In the core constant‑temperature stage, temperature and humidity are controlled synchronously instead of separately. When the temperature rises too fast, the system appropriately increases steam output to absorb excess heat and suppress overshoot. If humidity drops, micro‑steam replenishment is adopted rather than large‑volume water injection to avoid sharp temperature drop.

Optimize sensor data filtering and sampling logic. Steam generates instantaneous signal jitter of temperature and humidity probes. The software adds sliding average filtering and mutation value rejection algorithms to filter out interference signals, prevent false triggering of actuators caused by noise. Set adaptive sampling intervals: collect data densely in the temperature rising phase and extend the sampling cycle properly in the stable holding phase, reducing frequent actuator switching and prolonging the service life of pumps and heating tubes.

Adopt adaptive PID parameter tuning for different working conditions. Fixed PID parameters cannot meet light‑load and heavy‑load cooking scenarios simultaneously. The control board automatically identifies the cavity temperature change rate at startup, judges the load size, and switches to matched groups of PID parameters. For low‑temperature slow steaming, reduce the response intensity to avoid oscillation; for high‑temperature rapid steaming, strengthen the regulation speed while limiting overshoot amplitude, realizing stable constant‑temperature control under diverse loads.

Add cavity temperature equalization and steam distribution compensation logic. Combine the feedback of multiple temperature sensors installed at different heights of the cavity. When obvious temperature difference between upper and lower positions is detected, adjust the fan speed and steam injection timing to promote hot‑steam circulation and eliminate temperature stratification. Avoid continuous steam injection toward a fixed direction, and adopt intermittent and scattered steam supply strategy to improve humidity uniformity in the whole chamber.

Carry out repeated cooking verification and parameter iteration. Complete cyclic tests with different food loads and preset programs, record temperature‑humidity curves and the consistency of finished food. Track the steady‑state error and fluctuation range of each stage, continuously correct the switching threshold and compensation coefficient of the software logic. Reserve independent parameter groups for steaming, fermenting and moisturizing modes to realize targeted environmental control for different cooking requirements.

Through multi‑stage segmented process planning, temperature‑humidity linkage algorithm optimization, adaptive PID regulation and multi‑point temperature equalization compensation, the steam‑oven control board achieves stable and repeatable hot‑humid working conditions, effectively solves the problem of uneven steaming quality, and enhances the cooking reliability of intelligent steam ovens.