IoT Monitoring in Catering Back-of-House: Intelligent Innovation in Temperature, Humidity, and Energy Consumption Management
As the core setting for food processing, the environmental control and energy management of catering back-of-house areas directly impact food safety, operational costs, and sustainable development. Traditional back-of-house operations rely on manual inspections and experience-based decision-making, leading to high risks of environmental mismanagement, significant energy waste, and inefficient operations. With the deep integration of IoT technology, an intelligent monitoring system centered on precise temperature and humidity regulation and dynamic energy optimization is reshaping the management paradigm of catering back-of-house areas. This article provides an in-depth analysis of the innovative practices of industrial panel PCs in back-of-house monitoring from four dimensions: technical architecture, application scenarios, implementation paths, and future trends.
The core of an IoT back-of-house monitoring system lies in constructing a closed-loop ecosystem of "perception-transmission-decision-execution," with its technical architecture divided into four layers:
Environmental Perception Layer: This layer achieves full-scene coverage through a high-precision sensor network. For example, temperature and humidity sensors can monitor real-time changes in critical areas such as cutting and preparation zones, cooking areas, and cold storage, with precision up to ±0.3°C and ±2%RH. PM2.5 sensors can be linked with fresh air systems to ensure air quality complies with the "Code for Food Safety Operations in Catering Services." Water immersion sensors can accurately locate water leaks, automatically close valves, and send alerts to management personnel. A chain restaurant successfully avoided three potential fire incidents by deploying gas sensors and an automatic exhaust linkage system, demonstrating the real-time reliability of environmental perception.
Data Transmission Layer: This layer employs multi-mode communication technologies such as LoRa, WiFi, and 4G to build low-latency, high-reliability data channels. For instance, intelligent gateways can be compatible with mainstream industrial protocols like Modbus and BACnet, supporting the simultaneous connection of hundreds of devices to ensure stable sensor data uploads to the cloud or local servers. A central kitchen of an international hotel group achieved (internet-connected) management of 200 devices by deploying over 100 gateways, providing data support for subsequent decision-making.
Intelligent Decision Layer: This layer enables localized decision-making based on edge computing and AI algorithms. For example, the industrial panel PC USR-EG628, equipped with a 1.0 TOPS NPU, can run lightweight AI models to analyze multidimensional data such as temperature, humidity, energy consumption, and equipment status in real time, generating compliance reports or optimization suggestions. Its built-in WukongEdge platform supports IEC 61131-3 standard PLC programming, allowing the definition of scenario-based strategies like "strong exhaust during peak hours" and "nighttime energy-saving mode," reducing reliance on the cloud.
Control Execution Layer: This layer achieves remote equipment control and automated linkage through intelligent controllers. For example, the system can automatically adjust actuators such as air conditioners, dehumidifiers, and ventilation equipment to ensure cold storage temperatures remain constant at 2-8°C and operating room humidity stays below 65%RH. Simultaneously, smart electricity/water/gas meters can sub-meter energy consumption data, identify high-energy-consuming equipment (e.g., old refrigerators, long-running stoves), and propose optimization plans. A hotel kitchen saved 120,000 yuan annually through intelligent temperature control and equipment start-stop optimization, validating the cost-saving and efficiency-enhancing value of the control execution layer.
The application scenarios of IoT back-of-house monitoring systems have expanded from single environmental monitoring to multi-dimensional solutions encompassing food safety, energy management, and equipment maintenance.
Food Safety Compliance: By integrating AI video surveillance with sensor data, systems achieve "transparent kitchens" and operational norm traceability. For example, high-definition cameras can capture whether back-of-house personnel are wearing masks and gloves in real time, automatically generating compliance reports and pushing them to regulatory platforms for violations. Temperature and humidity sensors can record temperature fluctuations during cold chain transportation, generating tamper-proof electronic temperature profiles to meet GSP certification requirements. An internet-famous restaurant increased its customer traffic by 15% and significantly enhanced its brand reputation by live-streaming back-of-house operations.
Fine-Grained Energy Management: Based on sub-metering and trend analysis, systems optimize energy usage strategies. For example, the system can identify non-essential equipment running at night (e.g., lighting, exhaust fans) and automatically switch to energy-saving mode. By comparing historical data, it can push optimization suggestions (e.g., adjusting stove firepower, replacing energy-saving light bulbs) when detecting a sharp increase in electricity consumption during a specific period. After standardized renovations across 300 stores nationwide, a chain catering brand reduced energy consumption costs by 28% and improved management efficiency by 50%.
Predictive Equipment Maintenance: By monitoring parameters such as vibration, current, and temperature, systems can provide early warnings of equipment failures. For example, the USR-EG628 can connect to vibration sensors to analyze the operating status of cold storage compressors in real time. When abnormal vibrations are detected, it automatically triggers maintenance work orders to avoid unplanned downtime. Simultaneously, the system can record equipment maintenance logs to provide a basis for lifespan assessment and spare parts management. An automotive parts factory reduced equipment failure rates by 25% and maintenance costs by 25% through such solutions.
The successful deployment of an IoT back-of-house monitoring system requires balancing technical feasibility with business adaptability, with its implementation path divided into three steps:
Requirement Decomposition and Scenario Definition: Clearly define key indicators such as monitoring objectives (e.g., food safety, energy conservation), equipment types (e.g., stoves, refrigerators), and communication protocols (e.g., Modbus RTU, DL/T 645). For example, an agricultural monitoring project initially planned to use a high-computing-power controller to run an AI pest and disease identification model. However, by compressing the model and accelerating it with the NPU of the USR-EG628, it reduced hardware costs by 60% while maintaining 95% accuracy, demonstrating precise alignment between requirements and technology.
Hardware Selection and Secondary Development: Choose industrial panel PCs with strong compatibility and high scalability as the core. For example, the USR-EG628 features a modular design, supporting various interfaces such as digital/analog input/output, RS485/CAN/LAN, enabling rapid connection to devices like temperature and humidity sensors and smart electricity meters. Its built-in OpenPLC platform supports five programming languages, including ladder diagrams and function blocks, allowing developers to define logical rules (e.g., starting a fan when the temperature exceeds a threshold) through a graphical interface, lowering development barriers.
System Integration and Testing Verification: Verify data collection accuracy, logical stability, and long-term operational reliability through simulators and protocol conversion tools. For example, a photovoltaic power station project configured the USR-EG628's protocol conversion engine to convert the inverter's DL/T 645 protocol into MQTT format for direct upload to the Alibaba Cloud IoT platform without additional gateway program development. Simultaneously, through 72 hours of continuous stress testing, it optimized data structures to reduce memory usage, ensuring system stability.
As technology advances, IoT back-of-house monitoring systems are exhibiting two major trends:
Low-Code Development: By employing graphical programming and drag-and-drop logic configuration, development barriers are lowered. For example, the next generation of the USR-EG628 already supports Blockly programming, enabling developers to complete basic logic development (e.g., scheduled equipment switching, data threshold alarms) without writing code, accelerating scenario implementation.
Intelligent Decision-Making: By integrating lightweight AI models, controllers gain autonomous decision-making capabilities. For example, in predictive equipment maintenance scenarios, the USR-EG628 can analyze vibration data using locally trained LSTM models to predict bearing failures in advance and trigger maintenance work orders. In energy optimization scenarios, the system can train reinforcement learning models based on historical data to dynamically adjust equipment operating parameters (e.g., stove firepower, air conditioning temperature) to balance energy consumption and efficiency. A steel enterprise has piloted such solutions, reducing unplanned downtime by 40%, validating the commercial value of intelligent decision-making.
Industrial panel PCs are evolving from simple data collection tools into the "intelligent hubs" of back-of-house management. Their value lies not only in leading technical parameters but also in their deep understanding and rapid response to scenario-specific needs. From precise temperature and humidity regulation to dynamic energy optimization, from food safety compliance to predictive equipment maintenance, IoT technology is driving catering back-of-house areas toward efficiency, safety, and sustainability. In the future, with the popularization of low-code development, edge AI, and other technologies, back-of-house monitoring systems will further reduce deployment costs, enhance decision-making intelligence, and inject new momentum into the digital transformation of the catering industry.
