Cold chain temperature control remote monitoring and early warning system and method driven by Internet of Things

Abstract

The invention discloses a cold chain temperature control remote monitoring and early warning system and method driven by the Internet of Things, and the system comprises a monitoring terminal module which is disposed in a cold chain environment and is used for collecting temperature and environment state signals and generating original sensing data signals; the wireless communication module is connected with the monitoring terminal module, receives an original sensing data signal and packages the original sensing data signal into an Internet of Things transmission frame signal; the cloud platform processing module receives the Internet of Things transmission frame signal, analyzes and stores the data, and generates a temperature abnormity early warning signal through an integrated artificial intelligence analysis algorithm; the early warning service module receives the temperature abnormity early warning signal and generates a multi-stage early warning notification signal according to a predefined warning strategy; the terminal execution module is connected with the cloud platform processing module and the early warning service module. According to the cold chain temperature control remote monitoring and early warning system and method driven by the internet of things, the problems of temperature control information lag, inaccurate early warning and difficulty in tracing in the cold chain transportation process can be solved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology and cold chain logistics monitoring, specifically to an IoT-driven remote monitoring and early warning system and method for cold chain temperature control. Background Technology

[0002] Cold chain logistics is a crucial link in ensuring the quality of special commodities such as food and medicine, and the reliability of its temperature control monitoring directly affects product quality and safety. Traditional cold chain monitoring mainly relies on manual inspections combined with recorders, which suffers from drawbacks such as data lag, susceptibility to tampering, and inability to provide real-time alerts. With the development of Internet of Things (IoT) technology, existing technologies are beginning to adopt a combination of wireless temperature sensors and cloud platforms, triggering alarms by setting fixed thresholds.

[0003] However, this approach still has significant shortcomings: First, simple threshold alarms cannot identify slow temperature change trends, often only issuing alerts after an incident has occurred, lacking predictability; second, unstable communication networks can easily lead to data loss or delays, resulting in false alarms and missed alarms; third, temperature data exists in isolation from geographical location, environmental parameters, and other information, making it difficult to conduct root cause analysis of incidents; finally, most existing systems remain at the monitoring level, lacking intelligent linkage capabilities with execution equipment. While some improvements have emerged in recent years, such as using redundant transmission with multiple communication protocols or adding auxiliary sensors, there is still room for improvement in intelligent data analysis, early warning accuracy, and closed-loop system control. Especially in fields like pharmaceutical cold chain and high-end food transportation, the high sensitivity to temperature fluctuations and legal requirements for quality traceability make existing technologies insufficient to meet practical needs. Therefore, there is an urgent need for a cold chain temperature control monitoring solution that can achieve intelligent prediction, accurate early warning, and complete traceability capabilities. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide an IoT-driven remote monitoring and early warning system and method for cold chain temperature control, to solve the problems of delayed temperature control information, inaccurate early warning, and difficulty in traceability during cold chain transportation. This invention deploys multi-sensor monitoring terminals to collect temperature and environmental data in real time, which is then transmitted to a cloud platform via a multi-mode communication network. The cloud platform uses artificial intelligence algorithms to establish a dynamic prediction model to achieve early identification of abnormal trends. The system activates a graded early warning mechanism based on the anomaly level, and simultaneously binds geographical location information with temperature control data to generate a traceable evidence chain. Finally, the terminal execution module realizes visual display and equipment linkage control, forming a closed-loop management from data acquisition to execution.

[0005] This invention provides an IoT-driven remote monitoring and early warning system for cold chain temperature control, comprising: The monitoring terminal module is deployed in the cold chain environment to collect temperature and environmental status signals and generate raw sensor data signals. The wireless communication module connects to the monitoring terminal module, receives raw sensor data signals, and encapsulates them into IoT transmission frame signals. The cloud platform processing module receives IoT transmission frame signals, parses and stores the data, and generates temperature anomaly warning signals through integrated artificial intelligence analysis algorithms. The early warning service module receives temperature anomaly early warning signals and generates multi-level early warning notification signals according to predefined alarm strategies. The terminal execution module is connected to the cloud platform processing module and the early warning service module respectively. It receives temperature abnormality early warning signals and multi-level early warning notification signals, and generates equipment control signals and visual interactive signals to feed back to the user terminal.

[0006] In one embodiment of the present invention, the monitoring terminal module further includes an environmental state sensing unit, which integrates a vibration sensor for detecting vibration intensity and a light sensor for detecting changes in light intensity. The monitoring terminal module generates a comprehensive environmental state signal by fusing temperature, vibration and light signals, and transmits this comprehensive environmental state signal as part of the original sensing data signal to the wireless communication module, thereby providing multi-dimensional data for assessing the stability of goods and the integrity of packaging during transportation.

[0007] In one embodiment of the present invention, the wireless communication module has a built-in multi-network access unit that supports at least two different low-power wide-area network communication protocols. The wireless communication module can monitor the link quality of the current primary network in real time and automatically and seamlessly switch to the backup network to send IoT transmission frame signals when the quality is lower than the available threshold, thereby ensuring the continuity and reliability of the data upload link and effectively avoiding data interruption caused by network blind spots or signal fluctuations.

[0008] In one embodiment of the present invention, the artificial intelligence analysis algorithm integrated in the cloud platform processing module is a time series-based deep learning model. This model establishes a dynamic prediction baseline by learning the normal fluctuation patterns in historical temperature data. By continuously comparing the real-time incoming parsed data with the dynamic prediction baseline, the model can identify abnormal trends that exceed the normal fluctuation range and generate an early temperature anomaly warning signal before the temperature actually exceeds the static threshold.

[0009] In one embodiment of the present invention, the predefined alarm strategy in the early warning service module includes multiple early warning levels divided according to the degree and duration of temperature anomaly deviation; the early warning service module matches the corresponding early warning level and generates a multi-level early warning notification signal of the corresponding level according to the anomaly severity parameter contained in the received temperature anomaly early warning signal, and different levels of notification signals will trigger different notification methods and escalation reporting processes.

[0010] In one embodiment of the present invention, the device control signal generated by the terminal execution module can be directly sent to the auxiliary control unit connected to the monitoring terminal module; the device control signal can drive the control unit to perform linkage operation on the field execution device, the linkage operation including but not limited to starting the backup cooling equipment, activating the audible and visual alarm or sending a remote locking command, thereby realizing automated closed-loop control from cloud early warning to field execution.

[0011] In one embodiment of the present invention, the cloud platform processing module is also connected to a traceable database for storing all historical sensor data and early warning event records; the module can respond to query requests from the terminal execution module, generate temperature traceability report signals based on time dimension or transportation batch, and feed the signals back to the visualization interface of the terminal execution module for users to query and analyze the temperature control situation throughout the process.

[0012] In one embodiment of the present invention, the monitoring terminal module also has a local cache and an intermittent working unit; this unit can be automatically activated when the signal strength of the wireless communication module is insufficient, temporarily storing the collected raw sensor data signals in the local non-volatile memory, and automatically resending the cached time-series data to the wireless communication module after the communication is detected to be restored, so as to ensure the continuity and integrity of data recording.

[0013] In one embodiment of the present invention, when the cloud platform processing module generates a temperature anomaly warning signal, it simultaneously calls a geographic information service; the service binds real-time geographic location information with the temperature anomaly event and performs coordinate labeling in the geographic information system, thereby generating a temperature anomaly warning signal containing a location label of the location where the anomaly occurred, providing spatial information support for subsequent logistics route optimization and responsibility definition.

[0014] This invention also includes an IoT-driven remote monitoring and early warning method for cold chain temperature control, comprising: S1: Acquire temperature and environmental status signals and generate raw sensor data signals; S2: Receive raw sensor data signals and encapsulate them into IoT transmission frame signals; S3: Receives IoT transmission frame signals, parses and stores the data, and generates temperature anomaly warning signals through integrated artificial intelligence analysis algorithms; S4: Receive temperature anomaly warning signals and generate multi-level warning notification signals according to predefined alarm strategies; S5: Receives abnormal temperature warning signals and multi-level warning notification signals, and generates equipment control signals and visual interactive signals to feed back to the user terminal.

[0015] This invention provides an IoT-driven remote monitoring and early warning system and method for cold chain temperature control. It deploys multi-sensor monitoring terminals to collect temperature and environmental data in real time, transmitting the data to a cloud platform via a multi-mode communication network. The cloud platform uses artificial intelligence algorithms to establish a dynamic prediction model, enabling early identification of abnormal trends. The system activates a tiered early warning mechanism based on the level of abnormality, simultaneously binding geographical location information with temperature control data to generate a traceable evidence chain. Finally, the terminal execution module enables visual display and coordinated equipment control, forming a closed-loop management system from data acquisition to execution. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 System architecture diagram of a cold chain temperature control remote monitoring and early warning system driven by the Internet of Things; Figure 2 A schematic diagram illustrating the operational flow of an enterprise data security management system; Figure 3 A flowchart illustrating a method for remote monitoring and early warning of cold chain temperature control driven by the Internet of Things. Detailed Implementation

[0018] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0021] Please see Figure 1-3 The IoT-driven remote monitoring and early warning system for cold chain temperature control of the present invention includes: a monitoring terminal module, which is deployed in the cold chain environment to collect temperature and environmental status signals and generate raw sensor data signals; a wireless communication module, which is connected to the monitoring terminal module, receives the raw sensor data signals and encapsulates them into IoT transmission frame signals; a cloud platform processing module, which receives the IoT transmission frame signals, parses and stores the data, and generates temperature anomaly early warning signals through integrated artificial intelligence analysis algorithms; an early warning service module, which receives the temperature anomaly early warning signals and generates multi-level early warning notification signals according to a predefined alarm strategy; and a terminal execution module, which is connected to both the cloud platform processing module and the early warning service module, receives the temperature anomaly early warning signals and multi-level early warning notification signals, and generates equipment control signals and visual interactive signals to feed back to the user terminal.

[0022] like Figure 1As shown, its core innovation lies in its core architecture, which consists of five mutually cooperating modules, forming a complete closed loop from data acquisition, transmission, intelligent analysis, decision-making and early warning to final execution. The monitoring terminal module, acting as the system's nerve endings, is directly deployed in various cold chain environments requiring monitoring, such as refrigerated truck compartments, frozen warehouses, medical insulated boxes, or fresh produce packaging boxes. Its core responsibility is to sense changes in the physical world's state. This module typically uses a high-precision digital temperature sensor as its main sensing unit, capable of continuously collecting environmental temperature parameters at specific intervals and converting these analog signals into digital signals, thus generating the most basic sensor data signals. However, the module's function is not limited to this. To more comprehensively reflect the cold chain status, it often integrates other environmental sensors, such as vibration sensors to monitor whether goods have suffered severe bumps or impacts, and light sensors to indirectly determine whether refrigerated box doors have been abnormally opened. These diverse sensor data are initially summarized and packaged within the monitoring terminal module, forming a structured data packet awaiting upload, thus constituting the system's front-end and most fundamental data source. The wireless communication module, acting as an information bridge for the system, establishes a close connection with the monitoring terminal module. It is primarily responsible for receiving raw sensor data signals from the monitoring terminal module and reliably transmitting the data to the remote cloud platform. Internally, this module includes a signal modulation / demodulation unit, a protocol stack processing unit, and a radio frequency antenna unit. Its workflow involves first verifying and packaging the received raw data into a data frame format conforming to a specific IoT communication protocol (such as MQTT, CoAP, etc.). This packaging process includes adding information such as the target address and data checksum, thereby generating a standard IoT transmission frame signal. Subsequently, the module transmits the aforementioned data frame signal to a network base station or gateway through its built-in wireless transmission unit (which may support multiple network standards), ultimately routing it to the designated cloud platform processing module via the public internet. This process ensures that data from monitoring points scattered across various locations can be collected in real time and centrally.The cloud platform processing module, the brain and data center of the system, is deployed on a remote server cluster and possesses powerful computing and storage capabilities. This module continuously monitors the network port, receiving IoT transmission frame signals from numerous wireless communication modules. Its primary task is to parse these data frames, stripping the protocol headers, extracting the valid sensor data payload, and storing it permanently in a time-series database for historical querying and analysis. Beyond this, the module's core value lies in its integrated artificial intelligence analysis algorithm. This algorithm is not a simple threshold comparison but employs complex machine learning models, such as a time-series analysis model based on Long Short-Term Memory (LSTM) networks. By learning and analyzing massive amounts of historical temperature data, this model can establish a dynamic baseline for normal temperature fluctuations, adapting to different seasons, different goods, and different transportation stages. It continuously compares the real-time incoming parsed data with this dynamic baseline, not only identifying instantaneous exceedances but also keenly capturing slow but continuous temperature drift trends. This allows it to calculate the probability of anomalies and generate predictive temperature anomaly warning signals before the temperature actually exceeds the static threshold, achieving a fundamental shift from "post-event alarm" to "pre-event warning." The early warning service module is the system's decision-making and command center. It receives temperature anomaly early warning signals from the cloud platform processing module. This module has a pre-set, comprehensive, and configurable alarm strategy system. These strategies typically classify early warnings into multiple levels (e.g., alert, warning, severe warning) based on factors such as the severity of the temperature anomaly, the duration of the anomaly, and the sensitivity level of the transported goods. The early warning service module's job is to analyze the abnormal parameters in the input signal and match them with the built-in strategies to determine the appropriate early warning level for the event. Once the determination is complete, it generates a corresponding multi-level early warning notification signal. This signal not only contains basic information about the abnormal event but, more importantly, specifies the notification method and emergency procedures to be triggered, providing clear instructions for subsequent early warning information distribution.The terminal execution module is the end effector for system-user interaction and physical world control. It is connected to the cloud platform processing module and the early warning service module. This module receives temperature anomaly early warning signals and multi-level early warning notification signals from the former two and processes and executes them. Its functions are mainly reflected in two aspects: First, it generates equipment control signals, which can be sent to the monitoring terminal or independent field controller through the downlink to drive actuators such as backup refrigeration units, audible and visual alarms, and vent switches to perform linkage operations, attempting to automatically correct anomalies or provide on-site warnings. Second, it generates visual interactive signals, which are sent to the user's operating terminal (such as computer, tablet, or mobile phone) through WebSocket or other real-time push technologies. The early warning information is presented to the user intuitively in various forms on the graphical user interface, such as map annotations, trend curves, pop-up notifications, and sound prompts, and provides interactive buttons such as confirmation, processing, and ignoring, thereby completing the entire early warning handling closed loop and realizing transparent and intelligent management of the entire cold chain environment.

[0023] Furthermore, the monitoring terminal module has been further enhanced and specified. Its key feature is that the monitoring terminal module also includes a dedicated environmental state sensing unit, which is a highly integrated sensor group containing at least a vibration sensor for detecting vibration intensity and a light sensor for detecting changes in light intensity. The vibration sensor typically employs a triaxial accelerometer based on a microelectromechanical system (MEMS), capable of capturing mechanical impact events such as vibrations, bumps, and even drops experienced by goods during transportation with extremely high sensitivity, and converting these physical quantities into electrical signals. The light sensor is a photoelectric detection element capable of sensing changes in ambient light intensity; a key application scenario is inferring the open / closed state of refrigerated transport container doors—when the door is opened, external light floods in, causing a drastic change in the sensor reading. The core microcontroller unit of the monitoring terminal module is capable of fusing these multi-source sensor signals. It no longer simply reports temperature, vibration, and light signals independently, but uses specific logical algorithms to correlate and comprehensively analyze them. For example, it can determine whether a severe vibration is accompanied by an abnormal temperature rise, or whether a sudden change in light intensity leads to a continuous temperature loss trend. Through this fusion process, it ultimately generates a comprehensive environmental status signal that more fully and profoundly describes the current environmental conditions of the goods. Compared to a single temperature signal, this comprehensive signal has significantly enhanced information dimensions and value. Subsequently, this comprehensive environmental status signal is transmitted as a core component of the original sensor data signal to the wireless communication module. The fundamental purpose of this design is to provide a solid and multi-dimensional data basis for the cloud system to assess the stability and packaging integrity of goods during transportation. The cloud algorithm can analyze this data to determine whether the goods have undergone improper loading and unloading, whether the transportation route is in poor condition, and whether the container door seal has been damaged, thus expanding the system's monitoring scope from simple "temperature control" to comprehensive "quality control," greatly improving the system's practical value and reliability.

[0024] Specifically, the reliability and robustness of its wireless communication module have been significantly enhanced. Its key feature is that the wireless communication module integrates a multi-network access unit, which is a hardware and software solution supporting at least two different low-power wide-area network communication protocols. Common combinations include, but are not limited to, narrowband IoT communication protocols for cellular networks and long-range radio communication protocols for non-cellular networks. This design enables terminal devices to connect to different network infrastructures. The wireless communication module does not simply connect to multiple networks simultaneously; it includes an intelligent network link management unit. This unit continuously and in real-time monitors the link quality of the currently used primary network, evaluating parameters including, but not limited to, signal strength, network signal-to-noise ratio, packet loss rate, and network latency. The management unit compares these parameters with a preset quality threshold used to determine network availability. Once any key quality parameter of the primary network is detected to be below the aforementioned availability threshold, indicating potential network instability or impending interruption, the module's internal switching control unit immediately initiates an automatic, seamless switching process. This process includes a series of operations such as rapidly scanning the availability of the backup network, performing network authentication, and re-establishing the data connection. All these operations are efficiently completed in the background, ultimately smoothly switching the data stream from the degraded primary network to the higher-performance backup network. After a successful switch, the transmission of IoT transmission frames will continue to be handled by the new network connection. The ultimate goal of this entire mechanism is to maximize the continuity and reliability of the data upload link. It effectively solves the communication interruption problem caused by vehicles passing through tunnels, underground parking garages, remote mountainous areas, and other areas with blind or weak network signal coverage during long-distance cold chain transportation. It significantly reduces packet loss or transmission delays caused by network fluctuations, ensuring that the cloud platform can receive continuous, complete, and timely monitoring data, laying a solid foundation for subsequent accurate analysis and early warning.

[0025] like Figure 2As shown, the predefined alarm strategy in the early warning service module is a multi-dimensional, intelligent rule engine. Its core lies in comprehensively evaluating two key parameters: the degree of temperature anomaly deviation and its duration, thereby classifying multiple early warning levels with different response priorities. The degree of anomaly deviation refers to the extent to which real-time temperature data deviates from a preset safety range; this is a spatial dimension indicator measuring the severity of the anomaly. The duration measures the length of time the anomaly persists; this is a temporal dimension indicator of persistence. Upon receiving a temperature anomaly early warning signal from the cloud platform processing module, the early warning service module immediately analyzes the anomaly severity parameters contained in the signal. These parameters typically include the specific value of the deviation and the duration of its occurrence. Subsequently, the strategy matching engine within the module compares these two parameters with a preset strategy matrix. This matrix defines the early warning levels corresponding to different combinations of values, such as slight deviation... A poor, short-term deviation might only trigger a low-level alert, while a severe, prolonged deviation will inevitably trigger the highest-level alarm. Once a match is found, the early warning service module generates a multi-level early warning notification signal of the corresponding level. This signal is a set of instructions that not only includes a description of the abnormal event but, more importantly, clearly defines the notification method to be triggered and the escalation reporting process to be executed. Different levels of notification signals will activate drastically different response mechanisms. A low-level alert might only trigger a push notification within the application, while a high-level alert will sequentially initiate mass SMS messaging, automatic telephone calls, and even an emergency process to be reported to the highest management level. This hierarchical and escalable early warning mechanism ensures that alarm information is delivered to relevant personnel with an urgency commensurate with the severity of the event. This avoids overreacting to minor issues and ensures that critical crises are given immediate attention and handling, thereby greatly improving the intelligence and practicality of the entire early warning system.

[0026] Furthermore, the device control signals generated by the terminal execution module are not merely for user interface alerts, but can be directly transmitted to the auxiliary control unit connected to the monitoring terminal module via a pre-set secure downlink communication link. This auxiliary control unit is an independent field controller with input / output interfaces, acting as a highly reliable local execution agent for cloud commands. The device control signal is a digital command containing specific operational instructions, enabling the control unit to perform precise coordinated operations on a series of field actuators. These operations are designed to automatically mitigate abnormal conditions or prevent their further deterioration, and their specific forms include, but are not limited to, activating pre-installed backup... The refrigeration compressor unit enhances cooling capacity and activates an audible and visual alarm consisting of a high-brightness flashlight and a high-volume buzzer to alert on-site personnel, or sends a remote locking command to the access control system of the transport vehicle to prevent the goods from being mistakenly removed in abnormal circumstances (this operation needs to be closely integrated with business logic). By realizing direct drive from cloud analysis to on-site execution, this invention completes the automated closed-loop control from "perception-early warning" to "perception-early warning-execution", which greatly reduces the human delay between the discovery of anomalies and the implementation of physical intervention measures, improves the system's response speed and automation level, and provides a crucial technical means for the safety of valuable or sensitive goods in emergency situations.

[0027] like Figure 3 The diagram illustrates the IoT-driven remote monitoring and early warning method for cold chain temperature control according to the present invention. S1: Collect temperature and environmental status signals and generate raw sensor data signals; S2: Receive the raw sensor data signals and encapsulate them into IoT transmission frame signals; S3: Receive the IoT transmission frame signals, parse and store the data, and generate a temperature anomaly early warning signal through an integrated artificial intelligence analysis algorithm; S4: Receive the temperature anomaly early warning signal and generate multi-level early warning notification signals according to a predefined alarm strategy; S5: Receive the temperature anomaly early warning signal and multi-level early warning notification signals, and generate equipment control signals and visual interactive signals to feed back to the user terminal.

[0028] Specifically, the cloud platform processing module enhances powerful data traceability and post-event analysis capabilities, which are crucial for quality dispute identification and process optimization. Its key feature is that the cloud platform processing module also connects to a specially designed traceable database for storing all historical sensor data and all warning event records. This database typically employs a dedicated database system suitable for efficient reading, writing, and compression of time-series data. It indexes and stores raw sensor data from all monitoring endpoints and all warning event records generated by the system, indexed by timestamps and unique transport batch identifiers, forming a complete and tamper-proof data archive. This module possesses powerful data retrieval and report generation functions, capable of responding to user query requests from the terminal execution module. These requests typically contain a specific time range or a unique transport batch number. Upon receiving a request, the cloud platform processing module's report engine quickly extracts data from the traceable database. All relevant data undergoes cleaning, aggregation, and visualization formatting to generate a detailed temperature traceability report based on time or transportation batch. This report is not merely a list of data points; it typically includes temperature curves, anomaly event annotations, overlaid geographic location trajectories, and relevant environmental data (such as vibration records). This report is then fed back to the terminal execution module's visualization interface and presented to the user in a clear and user-friendly format. This design eliminates the need for users to deal with raw and difficult-to-understand massive amounts of data. Instead, users can directly query and analyze the temperature control status and compliance of any batch of goods throughout the entire transportation process, providing valuable objective data support for consignee acceptance, quality traceability, carrier performance evaluation, and transportation process optimization.

[0029] Furthermore, the reliability of the monitoring terminal module in harsh communication environments has been significantly enhanced, ensuring uninterrupted data flow. The monitoring terminal module is characterized by an additional local cache and intermittent working unit. This unit consists of a non-volatile memory (whose data is not lost after power failure) and a low-power microprocessor unit responsible for managing its operation. This unit continuously monitors the signal strength of the connected wireless communication module. Once it detects that the signal strength of the wireless communication module is consistently below the threshold level required for reliable communication, indicating that the device may have entered a network dead zone, the unit automatically activates its cache working mode. In this mode, the main sensor of the monitoring terminal module continues to collect data at a predetermined period, but the generated raw sensor data signals no longer attempt to be immediately transmitted via the wireless link (because transmission at this time would inevitably fail and consume power), but are instead written to the local non-volatile memory in real time and chronologically. Temporary secure storage is performed during the process; simultaneously, to maximize energy conservation, other high-power-consuming circuits in the monitoring terminal module may enter a dormant state, maintaining only the most basic sensing and caching functions; when the device moves and detects that the signal strength of the wireless communication module has recovered to a usable level, the cache management unit will automatically wake up the system and immediately initiate the data retransmission process, sequentially reading and packaging the time-series data stored in the local memory, and retransmitting it to the cloud through the restored wireless communication link; this mechanism ensures the continuity and integrity of data recording, effectively addressing the network coverage instability issues that are unavoidable in cold chain transportation, such as crossing tunnels and remote areas, so that the data curve finally obtained by the cloud is continuous and uninterrupted, providing a solid data foundation for subsequent accurate analysis and responsibility determination.

[0030] This invention relates to an IoT-driven remote monitoring and early warning system and method for cold chain temperature control. It involves deploying multi-sensor monitoring terminals to collect temperature and environmental data in real time, which is then transmitted to a cloud platform via a multi-mode communication network. The cloud platform employs artificial intelligence algorithms to establish a dynamic prediction model, enabling early identification of abnormal trends. The system activates a tiered early warning mechanism based on the level of abnormality, simultaneously binding geographical location information with temperature control data to generate a traceable chain of evidence. Finally, the terminal execution module enables visual display and coordinated control of equipment, forming a closed-loop management system from data acquisition to execution.

[0031] Therefore, the IoT-driven remote monitoring and early warning system and method for cold chain temperature control of the present invention solves the problems of delayed temperature control information, inaccurate early warning, and difficulty in tracing the source during cold chain transportation.

[0032] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An IoT-driven remote monitoring and early warning system for cold chain temperature control, characterized in that, include: A monitoring terminal module, which is deployed in a cold chain environment, is used to collect temperature and environmental status signals and generate raw sensor data signals; A wireless communication module is connected to the monitoring terminal module, which receives raw sensor data signals and encapsulates them into Internet of Things transmission frame signals. The cloud platform processing module receives IoT transmission frame signals, parses and stores the data, and generates temperature anomaly warning signals through integrated artificial intelligence analysis algorithms. The early warning service module receives temperature anomaly early warning signals and generates multi-level early warning notification signals according to a predefined alarm strategy. The terminal execution module is connected to the cloud platform processing module and the early warning service module respectively. It receives temperature abnormality early warning signals and multi-level early warning notification signals, and generates device control signals and visual interaction signals to feed back to the user terminal.

2. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The monitoring terminal module also includes an environmental state sensing unit, which integrates a vibration sensor for detecting vibration intensity and a light sensor for detecting changes in light intensity. The monitoring terminal module generates a comprehensive environmental state signal by fusing temperature, vibration, and light signals, and transmits this comprehensive environmental state signal as part of the original sensor data signal to the wireless communication module, thereby providing multi-dimensional data for assessing the stability of goods and the integrity of packaging during transportation.

3. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The wireless communication module has a built-in multi-network access unit that supports at least two different low-power wide-area network communication protocols. The wireless communication module can monitor the link quality of the current primary network in real time and automatically and seamlessly switch to the backup network to send IoT transmission frame signals when the quality is lower than the available threshold. This ensures the continuity and reliability of the data upload link and effectively avoids data interruption caused by network blind spots or signal fluctuations.

4. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The artificial intelligence analysis algorithm integrated in the cloud platform processing module is a time-series-based deep learning model. This model establishes a dynamic prediction baseline by learning the normal fluctuation patterns in historical temperature data. By continuously comparing the real-time incoming parsed data with the dynamic prediction baseline, the model can identify abnormal trends that exceed the normal fluctuation range and generate an early temperature anomaly warning signal before the temperature actually exceeds the static threshold.

5. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The predefined alarm strategy in the early warning service module includes multiple early warning levels based on the degree and duration of temperature anomaly deviation. The early warning service module matches the corresponding early warning level and generates a multi-level early warning notification signal based on the severity parameter of the received temperature anomaly early warning signal. Different levels of notification signals will trigger different notification methods and escalation reporting processes with varying degrees of urgency.

6. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The device control signal generated by the terminal execution module can be directly sent to the auxiliary control unit connected to the monitoring terminal module. The device control signal can drive the control unit to perform linkage operations on the field actuators. The linkage operations include, but are not limited to, starting the backup cooling equipment, activating the audible and visual alarm, or sending a remote locking command, thereby realizing automated closed-loop control from cloud early warning to field execution.

7. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The cloud platform processing module is also connected to a traceable database for storing all historical sensor data and early warning event records. This module can respond to query requests from the terminal execution module, generate temperature traceability report signals based on time dimension or transportation batch, and feed the signals back to the visualization interface of the terminal execution module for users to query and analyze the temperature control situation throughout the process.

8. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, The monitoring terminal module also has a local cache and intermittent working unit; this unit can be automatically activated when the signal strength of the wireless communication module is insufficient, temporarily storing the collected raw sensor data signals in the local non-volatile memory, and automatically resending the cached time-series data to the wireless communication module after the communication is detected to be restored, so as to ensure the continuity and integrity of data recording.

9. The IoT-driven remote monitoring and early warning system for cold chain temperature control according to claim 1, characterized in that, When generating a temperature anomaly warning signal, the cloud platform processing module simultaneously calls a geographic information service. This service binds real-time geographic location information with the temperature anomaly event and marks the coordinates in the geographic information system, thereby generating a temperature anomaly warning signal containing a location tag of the location where the anomaly occurred, providing spatial information support for subsequent logistics route optimization and responsibility determination.

10. The monitoring and early warning method of the IoT-driven remote monitoring and early warning system for cold chain temperature control according to claims 1-9, comprising: S1: Acquire temperature and environmental status signals and generate raw sensor data signals; S2: Receive raw sensor data signals and encapsulate them into IoT transmission frame signals; S3: Receives IoT transmission frame signals, parses and stores the data, and generates temperature anomaly warning signals through integrated artificial intelligence analysis algorithms; S4: Receive temperature anomaly warning signals and generate multi-level warning notification signals according to predefined alarm strategies; S5: Receives abnormal temperature warning signals and multi-level warning notification signals, and generates equipment control signals and visual interactive signals to feed back to the user terminal.

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