An intelligent monitoring and energy-saving optimization system for cold chain transportation based on Internet of Things
Patent Information
- Application Number
- CN202611079701.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-21
- Publication Date
- 2026-10-09
AI Technical Summary
现有双模通信系统多采用固定周期的全量数据上传模式，5G 通信模块长期保持在线状态，终端设备功耗较高；同时传感器原始数据全部直接上传至云端，造成流量与云端算力的浪费，且传感器受环境干扰产生的瞬时异常数据易触发误报警，系统可靠性不足
[0003]本发明的目的在于针对现有技术的不足，提供一种基于物联网的冷链运输智能监控与节能优化系统，通过三维温度场分区控制、路况-热惯性耦合节能调度、动态功耗适配通信架构与分级容错预警机制，在保障货物温控精度的前提下大幅降低运输能耗，同时提升系统运行可靠性与预警精准度。为实现上述目的，本发明采用如下技术方案：一种基于物联网的冷链运输智能监控与节能优化系统，包括全方位环境感知子系统、物联网通信子系统、智能控制与节能优化子系统、远程监控与预警子系统。所述全方位环境感知子系统用于采集冷链车厢内部与货物本体的多维度状态数据，包括温度传感器阵列、湿度传感器、震动传感器、开门检测传感器与货物内置微型传感器。温度传感器阵列由多个高精度铂电阻温度传感器组成，均匀分布于车厢内部的不同高度、前后端与侧壁位置，采样精度不低于 ±0.05°C，用于采集车厢内多点位温度数据，支撑三维温度场重构；湿度传感器采用电容式湿度传感器，安装于车厢中部侧壁，采样精度 ±2% RH，用于实时监测车厢内环境湿度；震动传感器采用压电式震动传感器，布置于车厢底板与货物固定架位置，用于检测运输过程中的震动强度，判断货物碰撞与道路颠簸风险；开门检测传感器采用磁性接近传感器，安装于车厢门闭合处，用于实时检测车厢门的开启与关闭状态，记录开门时长；货物内置微型传感器为微型低功耗温度传感器，放置于货物包装内部，用于直接采集货物本体温度。所述物联网通信子系统用于实现感知数据的本地处理与广域传输，包括边缘计算单元、LoRa 内网通信模块与 5G 广域通信模块。边缘计算单元采用 ARM 架构微处理器，安装于车厢内部前端，通过 LoRa 内网通信模块与所有感知传感器连接，用于对采集到的原始数据进行均值滤波、异常值剔除等预处理，同时完成本地温度场重构、异常事件预判断，仅将聚合后的特征数据向外传输；LoRa 内网通信模块构建车厢内部星形低功耗通信网络，传感器节点采用定时唤醒机制传输数据，实现短距离、低功耗的内网数据交互；5G 广域通信模块采用动态触发传输机制，正常工况下每 5 分钟上传一次聚合后的状态数据，模块保持低功耗待机状态，当边缘计算单元检测到温度超限、开门超时、强震动等异常事件时，立即切换为高频传输模式，按 10 秒 / 次的频率上传实时数据与告警信息。所述智能控制与节能优化子系统用于实现制冷设备的智能调节与节能调度，包括智能温控模块、节能调度模块与制冷执行单元。智能温控模块接收温度数据，基于 PID 控制算法结合模糊逻辑控制规则，动态调节制冷执行单元的运行功率，其核心功能包括三维温度场分区控制与开门扰动自适应补偿；三维温度场分区控制基于温度传感器阵列的多点数据，通过空间插值算法重构车厢内部三维温度场，识别高温热点区域与低温区域，对应调节各分区制冷出风口的风量开度，对热点区域定向补冷，实现车厢内温度均匀控制；开门扰动自适应补偿内置开门扰动补偿模型，当开门检测传感器检测到开门事件时，实时采集开门时长、外界温湿度参数，计算冷量泄漏总量，关门后根据车厢热惯性参数生成阶梯式制冷功率恢复曲线，采用先快速拉温、后稳态维持的功率调整策略，替代传统满功率冲击模式。所述节能调度模块对接外部导航与路况系统，结合车厢热惯性参数动态优化制冷运行模式，其核心功能包括：预存目标车厢的热惯性参数，包括箱体导热系数、货物蓄冷系数、总热容量，建立车厢温度变化预测模型；获取前方5-30 公里路段的实时路况数据，包括平均车速、道路坡度、拥堵预计时长、外界环境温度；基于热惯性模型预测未来时段温度变化趋势，输出对应制冷运行模式，包括预冷模式、稳态节能模式、拥堵降载模式、自然冷却模式。制冷执行单元包括压缩机制冷机组与多组分区制冷出风口，接收智能温控模块的控制指令，调节制冷功率与各分区出风量。所述远程监控与预警子系统用于实现运输过程的远程管控与风险预警，包括远程监控平台与分级预警模块。远程监控平台基于云计算架构搭建，接收 5G 广域通信模块上传的数据，以可视化界面展示车辆位置、车厢温湿度、震动、门状态、制冷功率等实时信息，同时存储历史数据，生成温度变化曲线、能耗统计报表等分析文件；分级预警模块内置不同品类货物的温度-耐受时间曲线，建立超温当量累计模型，设置三级预警机制，一级容错级为累计超温当量达到阈值的 30% 时，系统自动提升制冷功率进行本地容错调节，不触发人工告警；二级预警级为累计超温当量达到阈值的 70% 时，向管理人员推送 APP 预警通知，提示风险并建议干预；三级紧急级为累计超温当量达到阈值的 100% 时，触发短信加电话联动紧急告警，同步推送风险评估与处置建议。与现有技术相比，本发明具有以下有益效果：第一，温控精度与均匀性显著提升，通过分布式温度阵列重构三维温度场，结合分区制冷控制，可将车厢内温度均匀性控制在 ±0.2°C 以内，开门扰动自适应补偿策略可使开门后温度恢复时间缩短 30%，有效降低温度波动对货物品质的影响；第二，节能降耗效果突出，通过开门扰动补偿、路况-热惯性耦合调度等多维度节能策略，相比传统固定模式制冷的冷链系统，全程运输能耗可降低 15%-25%，大幅减少车辆燃油与电力消耗，降低运营成本；第三，系统功耗更低、可靠性更强，边缘计算本地预处理加 5G 动态触发传输机制，可使终端设备整体通信功耗降低40% 以上，同时减少云端数据处理压力与流量成本，且不影响异常事件的实时响应速度；第四，预警精准度大幅提升，基于货物耐受度曲线的分级容错预警机制，可将误报警率降低70%，同时实现货物品质风险的量化评估，减少无效人工干预，提升管理效率。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of cold chain transportation and Internet of Things (IoT) monitoring technology, specifically to an IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation, which can be applied to the whole-process environmental monitoring, intelligent temperature control and energy consumption optimization management of road refrigerated transport vehicles. Background Technology
[0002] Cold chain transportation is a core link in ensuring the quality of perishable goods such as fresh produce and pharmaceuticals. Temperature stability during transportation directly determines the quality and spoilage rate of the goods. With the development of IoT technology, existing cold chain transportation systems have gradually achieved temperature acquisition and remote monitoring functions, but many technical shortcomings still exist in practical applications. First, the accuracy of temperature monitoring and control is insufficient. Most systems only have a few temperature measurement points inside the carriage, assuming a uniform temperature distribution within the carriage, which cannot accurately reflect the differences in the three-dimensional temperature field distribution inside the carriage. This easily leads to localized hot spots that cause spoilage of goods. At the same time, refrigeration systems mostly adopt a unified overall control mode, unable to adjust for regional temperature differences. Furthermore, cold air is rapidly lost when the carriage doors are opened, and usually resumes full-power refrigeration immediately after the doors are closed, resulting in large temperature fluctuations, high energy consumption, and a lack of targeted disturbance compensation mechanisms. Second, the energy-saving scheduling methods are crude. Existing energy-saving solutions mostly reduce cooling power only when vehicles are parked for extended periods, failing to consider dynamic conditions such as road gradients, vehicle speed changes, congestion duration, and ambient temperature during transportation. They also fail to fully utilize the thermal inertia of the vehicle body and cargo for pre-cooling or load reduction, resulting in limited energy savings and high overall operating energy costs. Third, communication and data processing efficiency is low. Existing dual-mode communication systems mostly use a fixed-cycle full data upload mode, with 5G communication modules remaining online for extended periods, leading to high power consumption of terminal devices. Simultaneously, all raw sensor data is directly uploaded to the cloud, wasting bandwidth and cloud computing power. Furthermore, instantaneous abnormal data from sensors due to environmental interference easily triggers false alarms, resulting in insufficient system reliability. Fourth, the early warning mechanism is not sufficiently rational. Existing early warning systems generally use fixed temperature thresholds to trigger alarms, failing to consider the temperature tolerance characteristics of different types of goods and the cumulative damage effect of overheating. Even slight instantaneous overheating triggers manual alarms, leading to a high false alarm rate. Moreover, they cannot accurately quantify the actual impact of overheating on cargo quality, making it difficult for managers to assess the risk level. Summary of the Invention
[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing an IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation. Through three-dimensional temperature field zoning control, road condition-thermal inertia coupling energy-saving scheduling, dynamic power consumption adaptation communication architecture, and a hierarchical fault-tolerant early warning mechanism, this system significantly reduces transportation energy consumption while ensuring accurate temperature control of goods, and simultaneously improves system reliability and early warning accuracy. To achieve the above objectives, this invention adopts the following technical solution: an IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation, comprising an all-round environmental perception subsystem, an IoT communication subsystem, an intelligent control and energy-saving optimization subsystem, and a remote monitoring and early warning subsystem. The all-round environmental perception subsystem is used to collect multi-dimensional state data of the interior of the cold chain vehicle and the goods themselves, including a temperature sensor array, humidity sensor, vibration sensor, door opening detection sensor, and micro-sensors built into the goods. The temperature sensor array consists of multiple high-precision platinum resistance temperature sensors, evenly distributed at different heights, front and rear ends, and side walls within the carriage, with a sampling accuracy of no less than ±0.05°C. This array collects temperature data from multiple points within the carriage, supporting three-dimensional temperature field reconstruction. The humidity sensor, a capacitive humidity sensor, is installed on the middle side wall of the carriage, with a sampling accuracy of ±2% RH, used for real-time monitoring of the humidity inside the carriage. The vibration sensor, a piezoelectric vibration sensor, is positioned on the carriage floor and cargo mounting frames to detect vibration intensity during transportation and assess the risk of cargo collisions and road bumps. The door opening detection sensor, a magnetic proximity sensor, is installed at the carriage door's closing point to detect the door's opening and closing status in real-time and record the opening duration. The cargo-embedded miniature sensor is a low-power temperature sensor placed inside the cargo packaging to directly collect the cargo's temperature. The IoT communication subsystem is used for local processing and wide-area transmission of the sensed data, including an edge computing unit, a LoRa intranet communication module, and a 5G wide-area communication module. The edge computing unit uses an ARM architecture microprocessor and is installed at the front end inside the carriage. It connects to all sensing sensors via a LoRa intranet communication module to perform preprocessing on the collected raw data, such as mean filtering and outlier removal. It also reconstructs the local temperature field and pre-judges abnormal events, transmitting only the aggregated feature data outwards. The LoRa intranet communication module constructs a star-shaped low-power communication network inside the carriage. Sensor nodes use a timed wake-up mechanism to transmit data, enabling short-range, low-power intranet data interaction. The 5G wide-area communication module uses a dynamic trigger transmission mechanism, uploading aggregated status data every 5 minutes under normal operating conditions. The module remains in low-power standby mode. When the edge computing unit detects abnormal events such as temperature exceeding limits, door opening timeout, or strong vibration, it immediately switches to a high-frequency transmission mode, uploading real-time data and alarm information at a frequency of 10 seconds per transmission.The intelligent control and energy-saving optimization subsystem is used to realize intelligent adjustment and energy-saving scheduling of refrigeration equipment, including an intelligent temperature control module, an energy-saving scheduling module, and a refrigeration execution unit. The intelligent temperature control module receives temperature data and dynamically adjusts the operating power of the refrigeration execution unit based on PID control algorithm combined with fuzzy logic control rules. Its core functions include three-dimensional temperature field zoning control and door opening disturbance adaptive compensation. The three-dimensional temperature field zoning control is based on multi-point data from a temperature sensor array. It reconstructs the three-dimensional temperature field inside the carriage through a spatial interpolation algorithm, identifies high-temperature hot spots and low-temperature areas, and adjusts the airflow opening of the refrigeration outlets of each zone accordingly. It provides targeted cooling to hot spots to achieve uniform temperature control inside the carriage. The door opening disturbance adaptive compensation has a built-in door opening disturbance compensation model. When the door opening detection sensor detects a door opening event, it collects the door opening duration and external temperature and humidity parameters in real time, calculates the total amount of cold energy leakage, and generates a stepped refrigeration power recovery curve based on the thermal inertia parameters of the carriage after the door is closed. It adopts a power adjustment strategy of first rapidly raising the temperature and then maintaining a steady state, replacing the traditional full-power impact mode. The energy-saving scheduling module interfaces with external navigation and road condition systems, dynamically optimizing the refrigeration operation mode based on the thermal inertia parameters of the vehicle compartment. Its core functions include: pre-storing the thermal inertia parameters of the target vehicle compartment, including the thermal conductivity of the compartment, the cold storage coefficient of the cargo, and the total heat capacity; establishing a temperature change prediction model for the compartment; acquiring real-time road condition data for the road section 5-30 kilometers ahead, including average vehicle speed, road gradient, estimated congestion duration, and ambient temperature; predicting future temperature change trends based on the thermal inertia model; and outputting corresponding refrigeration operation modes, including pre-cooling mode, steady-state energy-saving mode, congestion load reduction mode, and natural cooling mode. The refrigeration execution unit includes a compressor refrigeration unit and multi-zone refrigeration air outlets, receiving control commands from the intelligent temperature control module to adjust the refrigeration power and the air volume of each zone. The remote monitoring and early warning subsystem is used to realize remote control and risk warning of the transportation process, including a remote monitoring platform and a hierarchical early warning module. The remote monitoring platform is built on a cloud computing architecture. It receives data uploaded by a 5G wide-area communication module and displays real-time information such as vehicle location, compartment temperature and humidity, vibration, door status, and cooling power through a visual interface. It also stores historical data and generates analytical files such as temperature change curves and energy consumption statistics reports. The tiered early warning module incorporates temperature-tolerance time curves for different types of goods, establishes a cumulative over-temperature equivalent model, and sets up a three-level early warning mechanism. The first level, the fault tolerance level, automatically increases the cooling power for local fault tolerance adjustment when the cumulative over-temperature equivalent reaches 30% of the threshold, without triggering manual alarms. The second level, the early warning level, pushes an APP warning notification to management personnel when the cumulative over-temperature equivalent reaches 70% of the threshold, indicating the risk and suggesting intervention. The third level, the emergency level, triggers an emergency alarm via SMS and telephone when the cumulative over-temperature equivalent reaches 100% of the threshold, simultaneously pushing risk assessment and handling suggestions.Compared with existing technologies, this invention has the following beneficial effects: First, temperature control accuracy and uniformity are significantly improved. By reconstructing a three-dimensional temperature field through a distributed temperature array and combining it with zoned refrigeration control, the temperature uniformity inside the carriage can be controlled within ±0.2°C. The adaptive compensation strategy for door opening disturbances can shorten the temperature recovery time after door opening by 30%, effectively reducing the impact of temperature fluctuations on cargo quality. Second, energy saving and consumption reduction are outstanding. Through multi-dimensional energy-saving strategies such as door opening disturbance compensation and road condition-thermal inertia coupling scheduling, compared with traditional fixed-mode refrigeration cold chain systems, the energy consumption of the entire transportation process can be reduced by 15%-25%, significantly reducing vehicle fuel and electricity consumption and lowering operating costs. Third, the system has lower power consumption and stronger reliability. Edge computing local preprocessing combined with a 5G dynamic triggering transmission mechanism can reduce the overall communication power consumption of terminal devices by 40%. In addition, it reduces cloud data processing pressure and traffic costs without affecting the real-time response speed of abnormal events; fourth, the accuracy of early warning is greatly improved. The graded fault-tolerant early warning mechanism based on the cargo tolerance curve can reduce the false alarm rate by 70%, while realizing the quantitative assessment of cargo quality risks, reducing ineffective manual intervention and improving management efficiency. Attached Figure Description Figure 1 is a block diagram of the overall system architecture of the present invention; Figure 2 is a schematic diagram of the working process of the intelligent temperature control module of the present invention; Figure 3 is a schematic diagram of the working condition decision logic of the energy-saving scheduling module of the present invention; Figure 4 is a schematic diagram of the overall structure of the cold chain transport vehicle of the present invention.
Claims
1. A smart monitoring and energy-saving optimization system for cold chain transportation based on the Internet of Things, characterized in that, The system comprises a comprehensive environmental perception subsystem, an IoT communication subsystem, an intelligent control and energy-saving optimization subsystem, and a remote monitoring and early warning subsystem. The comprehensive environmental perception subsystem includes a temperature sensor array, humidity sensor, vibration sensor, door opening detection sensor, and cargo-embedded micro-sensors to collect multi-dimensional status data of the vehicle interior and the cargo itself. The IoT communication subsystem includes an edge computing unit, a LoRa intranet communication module, and a 5G wide-area communication module for local preprocessing and wide-area transmission of the perceived data. The intelligent control and energy-saving optimization subsystem includes an intelligent temperature control module, an energy-saving scheduling module, and a refrigeration execution unit for zoned intelligent adjustment and condition-adaptive energy-saving scheduling of the refrigeration equipment. The remote monitoring and early warning subsystem includes a remote monitoring platform and a tiered early warning module for remote control and tiered risk warning during transportation.
2. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 1, characterized in that, The temperature sensor array consists of multiple high-precision platinum resistance temperature sensors, distributed at different heights, front and rear ends, and side walls of the carriage. The intelligent temperature control module has a built-in three-dimensional temperature field reconstruction unit, which reconstructs the three-dimensional temperature field of the carriage based on multi-point temperature data through a spatial interpolation algorithm, identifies high-temperature hot spots, and adjusts the airflow opening of the zoned cooling air outlets of the cooling execution unit accordingly to achieve zoned directional cooling.
3. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 2, characterized in that, The intelligent temperature control module has a built-in door opening disturbance compensation model. When the door opening detection sensor detects a door opening event, it collects the door opening duration and external temperature and humidity parameters in real time to calculate the total amount of cold energy leakage. After the door is closed, it generates a stepped cooling power recovery curve based on the thermal inertia parameters of the carriage and adopts a power adjustment strategy of first rapidly raising the temperature and then maintaining a steady state.
4. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 1, characterized in that, The energy-saving scheduling module pre-stores the thermal inertia parameters of the carriage, including the thermal conductivity of the carriage, the cold storage coefficient of the cargo, and the total heat capacity, and establishes a temperature change prediction model for the carriage. The energy-saving scheduling module connects to the external road condition system to obtain the average vehicle speed, road gradient, congestion duration, and ambient temperature data of the road ahead. Based on the thermal inertia model, it predicts the temperature change trend and outputs refrigeration operation strategies such as pre-cooling mode, steady-state energy-saving mode, congestion load reduction mode, or natural cooling mode.
5. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 1, characterized in that, The edge computing unit connects to all sensing sensors through the LoRa intranet communication module, performs mean filtering and outlier removal preprocessing on the raw data, and completes local temperature field reconstruction and abnormal event pre-judgment; the 5G wide area communication module adopts a dynamic trigger transmission mechanism, which uploads aggregated data according to a preset low frequency cycle under normal working conditions, and immediately switches to high frequency transmission mode when an abnormal event occurs.
6. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 1, characterized in that, The graded early warning module incorporates temperature-tolerance time curves for different types of goods and establishes a cumulative over-temperature equivalent model. The graded early warning module sets up a three-level early warning mechanism: Level 1 fault tolerance level, Level 2 early warning level, and Level 3 emergency level. Based on the cumulative over-temperature equivalent, the module triggers alarms and handling strategies of the corresponding level.
7. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 6, characterized in that, The first-level fault tolerance level is when the cumulative over-temperature equivalent reaches 30% of the threshold, the system automatically increases the cooling power for local adjustment without triggering manual alarms; the second-level early warning level is when the cumulative over-temperature equivalent reaches 70% of the threshold, an early warning notification is pushed to the APP. The third emergency level is triggered when the cumulative over-temperature equivalent reaches 100% of the threshold, thus triggering an emergency alarm via SMS and telephone.
8. The IoT-based intelligent monitoring and energy-saving optimization system for cold chain transportation according to claim 1, characterized in that, The refrigeration execution unit includes a compressor refrigeration unit and multiple independently controlled zoned refrigeration air outlets. It receives control commands from the intelligent temperature control module and independently adjusts the refrigeration air volume of each zone.