Cold-chain logistics remote monitoring system and method based on Internet of Things

Abstract

The invention discloses a cold-chain logistics remote monitoring system and method based on the Internet of Things. The system comprises a sensing layer, an edge computing gateway and a cloud platform. The sensing layer is provided with a multi-dimensional sensor group which at least comprises a high-precision temperature and humidity sensor, a vibration sensor, an inclination sensor, a door magnetic sensor, an electronic lock and a position tracking module and is used for collecting environment, equipment and position information in real time. And the edge computing gateway performs format conversion, exception filtering and validity verification on the original data, and triggers a local response operation according to a preset rule. The preset rules comprise data verification, sensor diagnosis, threshold linkage, data supplementary transmission and a real-time response function. The cloud platform integrates early warning grading, closed-loop management, data storage and prediction analysis functions, and adjusts parameters or early warning risks in advance based on a modeling result. The system realizes intelligent monitoring and quick response of the whole process of the cold chain, and is suitable for the fields of food, biological agents, medicines and the like with strict temperature control requirements.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of Internet of Things technology and cold chain logistics management, and particularly relates to a cold chain logistics full-link remote monitoring system and method based on Internet of Things. BACKGROUND

[0002] With the rapid development of the cold chain logistics industry, the monitoring requirements for temperature and humidity, equipment status and other parameters during transportation and storage of food, medicine and other goods are becoming increasingly stringent, but the existing technology still has many shortcomings:

[0003] Data validity and real-time performance are poor. The data collected by sensors lack effective verification mechanisms and are easily disturbed to produce abnormal values, resulting in distorted monitoring results. At the same time, data transmission relies on cloud centralized processing, and the edge side response is lagging. When the temperature is out of limits or the equipment is abnormal, it often takes several minutes to several hours to trigger an early warning, delaying the disposal opportunity.

[0004] There is a lack of equipment failure warning. The status monitoring of key equipment such as refrigeration units and refrigerated truck compartments relies on manual inspection and cannot predict fault risks based on parameters such as vibration, inclination and running time. Sudden failures often lead to temperature out of control and cause goods loss. According to statistics, about 30% of cold chain goods loss is caused by equipment failure not discovered in time.

[0005] The abnormal response has low automation. When temperature fluctuations, equipment abnormalities and other situations occur, manual judgment and adjustment operations are required, which is low in response efficiency and easy to cause improper disposal due to human error. For example, delayed power adjustment of refrigeration equipment can cause temperature to deviate from the threshold, increasing energy consumption and increasing the risk of goods deterioration.

[0006] The present application proposes a remote monitoring system and method integrating perception, edge computing and cloud intelligence to improve the integrity, real-time performance and intelligent level of cold chain logistics monitoring.

[0007] Cold chain logistics is widely used in food, medicine and biological products, etc. in fields with strict temperature control requirements. In order to protect the quality of goods during transportation, it is often necessary to continuously monitor information such as temperature and humidity, equipment operating status and transportation location. However, in actual use, the existing cold chain monitoring system still faces some key technical problems:

[0008] First, the accuracy of collected data is insufficient. Some systems lack effective verification mechanisms for raw data, and sensors may produce abnormal values after being disturbed. These data, if not filtered in time, will cause monitoring information to be distorted. At the same time, the system relies on cloud analysis and the edge side has little local judgment ability, causing response delay.

[0009] Secondly, the abnormality of the equipment operation state is difficult to be perceived in advance. The existing scheme mainly relies on manual inspection to find the abnormality of the refrigeration equipment, cannot analyze and predict the health condition of the equipment by using dynamic information such as vibration, inclination, door magnetic switch and the like, and is prone to cause temperature out of control due to sudden failure.

[0010] In addition, the system response process mainly depends on manual operation. When the temperature fluctuation or equipment abnormality occurs, manual judgment and adjustment of related parameters are often required, the reaction time is slow and errors are prone to occur, which is not conducive to maintaining the stability of the transportation environment and increases the energy consumption risk.

[0011] In summary, the current cold chain monitoring system still has obvious improvement space in data reliability, equipment abnormality early warning capability and response automation. SUMMARY

[0012] In view of the problems in the above background art, the present application proposes a cold chain logistics remote monitoring system and method based on Internet of Things, and the specific scheme is as follows:

[0013] A cold chain logistics remote monitoring system based on Internet of Things, characterized in that it comprises:

[0014] a perception layer containing a multi-dimensional sensor group deployed in the whole link of cold chain logistics, at least comprising a high-precision temperature and humidity sensor, a vibration sensor, an inclination sensor, a door magnetic sensor, an electronic lock and a position tracking module;

[0015] an edge computing gateway connected with the perception layer, containing a preprocessing unit and a threshold judgment unit, the preprocessing unit performs abnormal filtering, format standardization conversion and offline caching on the collected original data; the threshold judgment unit triggers a local response operation based on a preset rule and uploads valid data to a cloud platform;

[0016] a cloud platform connected with the edge computing gateway through a communication network, the communication network is 5G, cellular network or Ethernet; the cloud platform is composed of a multi-level early warning module, a closed-loop automatic management module, a data storage module and an intelligent analysis and prediction module, and is used for realizing graded early warning, remote disposal and operation state prediction analysis.

[0017] Preferably, the vibration sensor monitors the transportation vibration amplitude, the inclination sensor collects the inclination angle, and the door magnetic sensor records the cabin door opening and closing state; the high-precision temperature and humidity sensor monitors the precision which meets the cold chain temperature control requirement, and the position tracking module adopts 5G and Beidou dual-mode positioning and the trajectory precision meets the transportation path tracing standard.

[0018] Preferably, the cloud platform further comprises an intelligent analysis and prediction module, and the intelligent analysis and prediction module is used for constructing a temperature fluctuation prediction model, and the expression of the temperature fluctuation prediction model is:

[0019]

[0020] Where T(t+Δt) is the predicted temperature at time t+Δt, T(t) is the current temperature at time t, n is the number of influencing factors, and w i f represents the weight of the i-th influencing factor. i (X i (t) represents the function value of the i-th influencing factor at time t, X i (t) represents the parameter value of the i-th influencing factor at time t, and ∈ represents the error term; this model is linked to the real-time threshold response function of claim 4: when the predicted temperature is close to T min or T max In such cases, the device parameters are adjusted in advance to trigger response level 1, preventing temperature fluctuations from reaching the warning range, reducing energy waste caused by frequent start-ups and shutdowns of the refrigeration equipment, and lowering operating costs.

[0021] Preferably, in the cold chain logistics remote monitoring system according to claim 1, the cloud platform further includes an intelligent analysis and prediction module, used to predict the operating status of key equipment based on the following equipment failure probability model:

[0022] P=σ(α1·T norm +α2·H norm +α3·A+α4·θ+α5·F+α6·L+b

[0023] Where: P is the predicted probability of equipment failure, with a value range of [0,1];

[0024] σ(x) is the Sigmoid function, defined as:

[0025]

[0026] T_norm and H_norm are the standardized values ​​of the temperature and humidity sensor output data, respectively, and are calculated using the following formulas:

[0027]

[0028] A is the acceleration amplitude monitored by the vibration sensor, calculated using the following formula:

[0029] A = max(a1, a2, ..., a...) n )

[0030] θ is the tilt angle monitored by the tilt sensor, and the calculation formula is:

[0031] θ = atan2(y,x)

[0032] F is the switching frequency recorded by the door magnetic sensor, and the calculation formula is:

[0033]

[0034] L is the offset distance of the transportation trajectory measured by the location tracking module, and the calculation formula is:

[0035]

[0036] α1 to α6 are the model weight coefficients obtained during training, and b is the bias term; the P value output by the model is used to help determine whether the equipment is in a potential fault risk range, and when the set threshold is reached, the multi-level early warning module and the closed-loop automated management module will be linked to perform corresponding control operations.

[0037] Preferably, the preset rules include data validity verification rules, sensor anomaly diagnosis rules, multi-level threshold linkage rules, network disconnection reconnection data retransmission rules, and real-time threshold trigger response functions; wherein, the data validity verification rules are used to remove abnormal data that exceeds the physical range or has logical contradictions, and the removal principle is as follows:

[0038] If X <X min Or X>X max Then remove X

[0039] Among them, X is the raw data value collected by the X sensor; X_min is the lower limit of the sensor's physical range; X_max is the upper limit of the sensor's physical range; in addition, if the data does not conform to the equipment specifications, or logically conflicts with other parameters (for example, humidity is higher than 100% or temperature is lower than -50°C), the same rejection operation should be performed.

[0040] Sensor anomaly diagnosis rules determine fault states by monitoring sensor communication frequency and data stability, including continuous communication interruptions and numerical deviations exceeding set thresholds. The operating principle is as follows:

[0041] If |f(t)-f(t-1)|>T threshold If the passage fails n times, the sensor will be marked as faulty.

[0042] Where is the sensor data at time t; f(t-1) is the data at the previous time point; is the maximum allowable data fluctuation range; and n is the threshold number of consecutive communication failures.

[0043] The multi-level threshold linkage rule defines the collaborative response logic when different parameter thresholds are triggered. The network reconnection data retransmission rule specifies the upload order and integrity verification mechanism for locally cached data after network recovery. The collaborative response logic is defined as follows:

[0044] When multiple monitoring parameters (such as temperature, humidity, vibration, etc.) simultaneously trigger different thresholds, corresponding actions are taken according to the preset linkage logic:

[0045] Let T be the current temperature value, V be the vibration value, T_max be the maximum temperature threshold, and V_max be the maximum vibration threshold. The linkage rule is obtained using the following formula:

[0046] If T>T max and V>V max This triggers a local response.

[0047] The data retransmission rule for network disconnection and reconnection is as follows: In the event of a network interruption, the system caches data locally. After the network is restored, the missing data is retransmitted in timestamp order, and integrity checks are performed to ensure no data loss. Let D(t) be the raw data collected by the sensor at time t, C(t) be the data stored in the cache, and t1 and t2 be the time points of network disconnection and restoration. The data retransmission rule can be expressed as:

[0048] If t∈[t1,t2], the data is stored in the cache and reordered during reconnection.

[0049] When retransmitting:

[0050] For any t∈[t1,t2], send the data to the cloud platform in the form D(t).

[0051] Preferably, the real-time threshold trigger response function in the edge computing gateway is:

[0052]

[0053] Where R represents the response level, 0 indicates no response, 1 indicates issuing a warning and adjusting relevant equipment parameters, and 2 indicates issuing an emergency alarm and freezing some operating permissions. T represents the current monitored temperature. max and T min T represents the upper and lower limits of the normal temperature range. warn_high and T warn_low This model sets the upper and lower limits of the warning temperature range; it is used to automatically trigger graded responses based on the current temperature to quickly handle temperature anomalies; by linking with the temperature fluctuation prediction model, this function can transform passive response into active regulation, reducing ineffective equipment operation while ensuring temperature control accuracy, thereby achieving energy saving and cost reduction.

[0054] A method for remote monitoring of cold chain logistics based on the Internet of Things, characterized by comprising the following steps:

[0055] S1. Data is collected through a multi-dimensional sensor group deployed throughout the entire cold chain logistics process. This sensor group includes at least a high-precision temperature and humidity sensor, a vibration sensor, a tilt sensor, a door magnetic sensor, an electronic lock, and a location tracking module. The high-precision temperature and humidity sensor's monitoring accuracy is adapted to the temperature control requirements of the cold chain, and the location tracking module uses 5G and Beidou dual-mode positioning with trajectory accuracy that meets the requirements for tracing the transportation path. The multi-dimensional sensor group is distributed at a preset density at various monitoring points in the cold storage environment to simultaneously collect temperature and humidity, vibration amplitude, tilt angle, switch status, lock status, and real-time location data.

[0056] S2. The edge computing gateway receives raw data and performs preprocessing, including abnormal data identification and filtering, data format standardization conversion, local caching when the network is disconnected, and data compression; it analyzes data in real time based on a preset threshold trigger response mechanism, and initiates the corresponding level of response operation when the monitored parameters reach the preset threshold;

[0057] S3 and the edge computing gateway upload the processed data to the cloud platform. The cloud platform's data storage module stores the data in categories. The real-time visualization module converts the data into visual charts. The multi-level early warning module calls the graded threshold parameters and sends notifications with anomaly details through multiple channels when the monitored parameters exceed the threshold.

[0058] S4. After an abnormal event is triggered, the cloud platform's closed-loop automated management module and multi-level early warning module work together to automatically execute preset response measures according to the level and type of the abnormality. These measures include at least automatically generating and dispatching maintenance work orders, retrieving and scheduling nearby backup resources based on real-time location, and remotely accessing the equipment control system for parameter adjustment and maintenance.

[0059] Compared with the prior art, the advantages of the present invention are as follows:

[0060] (1) It achieves high-precision perception and monitoring across the entire link, solving the problems of monitoring blind spots and data disconnection in existing technologies, and realizing synchronous acquisition of environmental parameters, equipment status and location information across the entire domain.

[0061] (2) Improve the real-time performance of data processing and response, realize local real-time threshold response, avoid the delay caused by traditional reliance on centralized cloud processing, and shorten the time for handling anomalies.

[0062] (3) To achieve intelligent prediction and active control, the intelligent analysis and prediction module constructs a temperature fluctuation prediction model and combines it with the real-time threshold response function of the edge computing gateway to achieve active temperature control, reducing energy waste caused by frequent equipment adjustment; at the same time, it constructs a equipment failure probability model, quantifies the failure probability based on sensor data such as vibration and tilt, solves the problem of sudden equipment failure and insufficient early warning in the existing technology, and reduces unplanned downtime losses. Attached Figure Description

[0063] Figure 1This invention presents a remote monitoring system and method for the entire cold chain logistics chain based on the Internet of Things. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0065] Example 1: Monitoring of same-city delivery of fresh produce through e-commerce (leafy vegetables)

[0066] S1, Data Acquisition at the Perception Layer

[0067] A multi-dimensional sensor array is deployed in the electric refrigerated delivery box: three high-precision temperature and humidity sensors, two door magnetic sensors (main and auxiliary doors), one tilt sensor (monitoring the tilt angle of the box), and one position tracking module (5G positioning) are installed on the upper, middle, and lower parts of the box. Synchronously collected data includes: real-time temperature range of 0–4℃, humidity (85–90%), door open / close status (30 times per day), tilt angle (≤10°), and real-time position.

[0068] S2, Edge Computing Gateway Processing

[0069] Preprocessing: Remove abnormal values ​​from the temperature and humidity sensors that exceed the physical range of -2℃ to 10℃; according to the sensor anomaly diagnosis rules, when a sensor experiences two consecutive communication interruptions, it is marked as a fault and the corresponding backup sensor data is activated; cache data locally when the network is down, and retransmit it according to the timestamp after network recovery.

[0070] Real-time threshold response: Based on the response function, the normal temperature range is set to [0,4]℃ and the warning range is set to [-1,5]℃. When the monitored temperature T = 4.3℃, response level 1 is triggered (indicating that parameter adjustment is required), and the cold air circulation frequency is automatically adjusted from 3 times / minute to 4 times / minute.

[0071] S3, Cloud Platform Processing and Response

[0072] Intelligent analysis and prediction: The temperature fluctuation prediction model takes into account factors such as outdoor temperature (28℃) and number of door openings and closings. It predicts that the temperature T(t+30min) will be 4.7℃ after 30 minutes, which exceeds the set maximum value Tmax = 4℃. This triggers response level 1 in advance, pre-adjusting the cooling power to 60%. The equipment failure probability model is based on the tilt angle (8°) and operating time (2 hours), calculating a failure probability P = 0.06 (low risk).

[0073] Closed-loop management: When the door magnetic sensor detects that the cabinet door has been open for more than 2 minutes, the multi-level early warning module pushes an alert to the deliveryman's APP. The closed-loop automated management module simultaneously reduces the cooling power to 30% to reduce the loss of cold air. The operation data is stored in real time to the data storage module and a full-chain traceability code is generated.

[0074] Example 2: Monitoring of Agricultural Product Storage (Citrus Fruits)

[0075] S1, Data Acquisition at the Perception Layer

[0076] Deployment in a 500㎡ cold storage warehouse: 25 temperature and humidity sensors (one per 20㎡), 4 vibration sensors (forklift aisles), 1 door magnetic sensor (warehouse door), and a location tracking module (for locating shelving areas). Data collected includes: internal temperature (5-8℃), humidity (75-80%), vibration sensor monitoring of acceleration during handling (with a threshold of ≤0.2g), and warehouse door opening / closing frequency (twice per hour).

[0077] S2, Edge Computing Gateway Processing

[0078] Preprocessing: Outliers with humidity <50% or >100% are removed by data validity verification rules; based on multi-level threshold linkage rules, when the warehouse door is continuously opened for more than 5 minutes, the sampling frequency of the temperature and humidity sensor is increased to 1 time / 10 seconds.

[0079] Real-time threshold response: Set the normal temperature range [5,8]℃ and the warning range [4,9]℃. When the temperature T = 8.5℃ in a certain area, trigger response level 1 and automatically turn on the backup air cooler in that area.

[0080] S3, Cloud Platform Processing and Response

[0081] Intelligent analysis and prediction: The temperature fluctuation prediction model takes into account factors such as ambient temperature (15℃) and the frequency of warehouse door opening, and predicts that the temperature T(t+8h) after 8 hours will be 5.2℃. Since this is close to Tmin = 5℃, the power of the air coolers in adjacent areas will be reduced in advance. The equipment failure probability model calculates the failure probability P = 0.12 (low to medium risk) based on the air cooler's operating time (120 hours) and vibration data (0.15g), and pushes a maintenance warning.

[0082] Closed-loop management: When the humidity in a certain area suddenly drops to 60%, the multi-level early warning module alarms to the central control room, the closed-loop automated management module starts the humidification device, and at the same time dispatches a work order to the inspector. Within 10 minutes, the pipeline blockage is investigated and repaired, the humidity is restored to 78%, and the data is synchronized to the traceability system.

[0083] Example 3: Monitoring of Deep-Sea Cold Storage of Aquatic Products (Frozen Shrimp)

[0084] S1, Data Acquisition at the Perception Layer

[0085] The following sensors are deployed in the refrigerated compartments of the deep-sea fishing vessel: 8 low-temperature sensors (-30~0℃), 6 vibration sensors (around the hull), 1 tilt sensor (for hull attitude), and an electronic lock with anti-pry alarm function. Data collected includes: internal temperature (≤-18℃), vibration amplitude (≤0.8g), hull tilt angle (≤30°), and hatch status.

[0086] S2, Edge Computing Gateway Processing

[0087] Preprocessing: Locally cached data during network outages (supports 48-hour storage), and re-transmitted according to "timestamp + integrity verification" after network recovery; based on sensor anomaly diagnosis rules, when the temperature drift rate of the low-temperature sensor exceeds 3℃ / minute per unit time, the system performs data smoothing or removes the data segment.

[0088] Real-time threshold response: Set the normal temperature range [-22, -18]℃ and the warning range [-25, -17]℃. When the temperature T = -17.5℃, trigger response level 1 and reduce the evaporation temperature by 1℃.

[0089] S3, Cloud Platform Processing and Response

[0090] Intelligent analysis and prediction: The temperature fluctuation prediction model, taking into account factors such as ambient temperature (25℃) and vibration frequency (5 times / minute), predicts that the temperature T(t+24h) after 24 hours will be -17.8℃. Because this is close to the warning limit of -17℃, the compressor frequency is increased to 55Hz in advance. The equipment failure probability model, based on the tilt angle (25°) and system pressure (0.8MPa), calculates a failure probability P = 0.35 (medium risk) and pushes inspection warning information to the shore-based center or designated maintenance personnel.

[0091] Closed-loop management: When the temperature rises to -16℃, the multi-level early warning module notifies the shore-based center via satellite. The closed-loop automated management module starts the backup unit and marks the current batch of goods as "priority unloading" for subsequent shore-based cold chain scheduling and processing. After docking, it automatically connects with the cold chain truck scheduling and generates a commodity inspection traceability report for the entire process.

[0092] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote monitoring system for cold chain logistics based on the Internet of Things, characterized in that, include: The perception layer includes a multi-dimensional sensor group deployed throughout the entire cold chain logistics process, including at least a high-precision temperature and humidity sensor, vibration sensor, tilt sensor, door magnetic sensor, electronic lock, and location tracking module. An edge computing gateway, connected to the perception layer, includes a preprocessing unit and a threshold judgment unit. The preprocessing unit performs anomaly filtering, format standardization conversion, and local caching when the network is disconnected on the collected raw data. The threshold judgment unit triggers a local response operation based on preset rules and uploads valid data to the cloud platform. The cloud platform is connected to the edge computing gateway via a communication network, which can be 5G, cellular network, or Ethernet. The cloud platform consists of a multi-level early warning module, a closed-loop automated management module, a data storage module, and an intelligent analysis and prediction module, and is used to realize graded early warning, remote handling, and predictive analysis of operational status.

2. The IoT-based remote monitoring system for cold chain logistics according to claim 1, characterized in that, The vibration sensor monitors the vibration amplitude during transportation, the tilt sensor collects the tilt angle, and the door magnetic sensor records the opening and closing status of the cabin door; the high-precision temperature and humidity sensor monitors the accuracy to meet the cold chain temperature control requirements, and the position tracking module adopts 5G and Beidou dual-mode positioning with trajectory accuracy that meets the transportation path traceability standards.

3. The IoT-based remote monitoring system for cold chain logistics according to claim 1, characterized in that, The intelligent analysis and prediction module is used to construct a temperature fluctuation prediction model, and the expression of the temperature fluctuation prediction model is: Where T(t+Δt) is the predicted temperature at time t+Δt, T(t) is the current temperature at time t, n is the number of influencing factors, and w i f represents the weight of the i-th influencing factor. i (X i (t) represents the function value of the i-th influencing factor at time t, X i (t) represents the parameter value of the i-th influencing factor at time t, and ∈ represents the error term.

4. The IoT-based remote monitoring system for cold chain logistics according to claim 1, characterized in that, The cloud platform further includes an intelligent analysis and prediction module, used to predict the operating status of key equipment based on the following equipment failure probability model: P=σ(α1·T norm +α2·H norm +α3·A+α4·θ+α5·F+α6·L+b; Where: P is the predicted probability of equipment failure, with a value range of [0,1]; σ(x) is the Sigmoid function, defined as: T_norm and H_norm are the standardized values ​​of the temperature and humidity sensor output data, respectively, and are calculated using the following formulas: A is the acceleration amplitude monitored by the vibration sensor, calculated using the following formula: A=max(a1,a2,...,a n ); θ is the tilt angle monitored by the tilt sensor, and the calculation formula is: θ = atan2(y, x); F is the switching frequency recorded by the door magnetic sensor, and the calculation formula is: L is the offset distance of the transportation trajectory measured by the location tracking module, and the calculation formula is: α1 to α6 are the model weight coefficients obtained during training, and b is the bias term; the P value output by the model is used to help determine whether the equipment is in a potential fault risk range, and when the set threshold is reached, the multi-level early warning module and the closed-loop automated management module will be linked to perform corresponding control operations.

5. The IoT-based remote monitoring system for cold chain logistics according to claim 1, characterized in that, The preset rules include data validity verification rules, sensor anomaly diagnosis rules, multi-level threshold linkage rules, network disconnection reconnection data retransmission rules, and real-time threshold trigger response functions. Among these, the data validity verification rules are used to remove abnormal data that exceeds the physical range or contains logical contradictions. The removal principle is as follows: if X... <X min Or X>X max Then remove X; Among them, X is the raw data value collected by the X sensor; X_min is the lower limit of the sensor's physical range; X_max is the upper limit of the sensor's physical range; in addition, if the data does not conform to the equipment specifications, or logically conflicts with other parameters (for example, humidity is higher than 100% or temperature is lower than -50°C), the same rejection operation should be performed. Sensor anomaly diagnosis rules determine fault states by monitoring sensor communication frequency and data stability, including continuous communication interruptions and numerical deviations exceeding set thresholds. The operating principle is: if |f(t)-f(t-1)|>T threshold Or, if communication fails n times, the sensor is then marked as faulty; Where f(t) is the sensor data at time t; f(t-1) is the data at the previous time point; T_threshold is the maximum allowable data fluctuation range; and n is the threshold for the number of consecutive communication failures. The multi-level threshold linkage rule defines the collaborative response logic when different parameter thresholds are triggered. The network reconnection data retransmission rule specifies the upload order and integrity verification mechanism for locally cached data after network recovery. The collaborative response logic is defined as follows: when multiple monitoring parameters (such as temperature, humidity, vibration, etc.) simultaneously trigger different thresholds, corresponding operations are taken according to the preset linkage logic: Let T be the current temperature value, V be the vibration value, T_max be the maximum temperature threshold, and V_max be the maximum vibration threshold. The linkage rule is obtained through the following formula: if T>T max and V>V max If the network is interrupted, the system will cache the data locally. After the network is restored, the missing data will be retransmitted in the order of timestamps and integrity will be checked to ensure that the data is not lost. Let D(t) be the original data collected by the sensor at time t, C(t) be the data stored in the cache, and t1 and t2 be the time points of network disconnection and restoration. The data retransmission rule can be expressed as: if t∈[t1, t2], then the data is stored in the cache and reordered when reconnected. During retransmission: For any t∈[t1, t2], the data is sent to the cloud platform in the form of D(t).

6. The IoT-based remote monitoring system for cold chain logistics according to claim 5, characterized in that, The real-time threshold trigger response function in the edge computing gateway is: Where R represents the response level, 0 indicates no response, 1 indicates issuing a warning and adjusting relevant equipment parameters, and 2 indicates issuing an emergency alarm and freezing some operating permissions. T represents the current monitored temperature. min and T max T represents the upper and lower limits of the normal temperature range. warn_low and T warn_high This model sets the upper and lower limits of the warning temperature range; it is used to automatically trigger graded responses based on the current temperature to quickly handle temperature anomalies; by linking with the temperature fluctuation prediction model, this function can transform passive response into active regulation, reducing ineffective equipment operation while ensuring temperature control accuracy, thereby achieving energy saving and cost reduction.

7. The IoT-based remote monitoring system for cold chain logistics according to claim 1, characterized in that, The grading thresholds for cold chain parameters in the multi-level early warning module are obtained using the following formula: T k =T±z·σ t +β·E k ; Among them, T k The threshold value is the k-th level (warning value, alarm value, or legal accountability value); T is the mean of historical monitoring data; σ t denoted as temperature standard deviation; z is the confidence interval factor, selected according to the set level, such as warning level z = 1.64, alarm level z = 1.96; E k External influencing factors in a specific scenario include ambient temperature, transportation time, and door magnetic switch frequency; β is the sensitivity coefficient of this influencing factor, obtained based on historical samples training; the external influencing factor E k Includes one or more of the following, and its value is obtained by the following calculation formula: E k =γ1·(T a -T e )+γ2·F+γ3·H+γ4·L; Where T a T represents the current ambient temperature. e Historical average environmental temperature, all from temperature and humidity sensors; F is the door magnetic switch frequency per unit time, in times / hour; H is the continuous operating time of the chiller, in hours; L is the transport trajectory offset distance detected by the location tracking module, in meters; γ1~γ4 are factor weight parameters, obtained from training with historical risk samples; all variables have been standardized using the following general formula: Where X is the current monitored value, and X_min and X_max are the historical minimum and maximum values ​​of this type of data, or the upper and lower limits of the sensor range, respectively; The formula is used to adaptively set the threshold values ​​of cold chain parameters based on historical data statistical patterns and current environmental dynamic factors.

8. A remote monitoring system for cold chain logistics based on the Internet of Things according to claim 1, characterized in that, In the closed-loop automated management module: When the probability of equipment failure P≥P1 and the duration of failure ≥Δt1, a maintenance work order is automatically generated. The priority scheduling response function is expressed as follows: Score i =w1·d i +w2·τ i ; Where, d i τ is the distance between maintenance personnel and the equipment. i For the predicted response time, w1 and w2 are weighting coefficients.

9. A remote monitoring system for cold chain logistics based on the Internet of Things, characterized in that, Remote handling and predictive analysis of operational status include: Cooling power adjustment (unit: %): Q = Q0 + ΔQ; Compressor frequency adjustment (in Hz): f = f0 + Δf; Evaporator temperature setting (in °C): T evap =T0-δT.

10. A method for remote monitoring of cold chain logistics based on the Internet of Things, characterized in that, Includes the following steps: S1. Data is collected through a multi-dimensional sensor group deployed throughout the entire cold chain logistics process. This sensor group includes at least a high-precision temperature and humidity sensor, a vibration sensor, a tilt sensor, a door magnetic sensor, an electronic lock, and a location tracking module. The high-precision temperature and humidity sensor's monitoring accuracy is adapted to the temperature control requirements of the cold chain, and the location tracking module uses 5G and Beidou dual-mode positioning with trajectory accuracy that meets the requirements for tracing the transportation path. The multi-dimensional sensor group is distributed at a preset density at various monitoring points in the cold storage environment to simultaneously collect temperature and humidity, vibration amplitude, tilt angle, switch status, lock status, and real-time location data. S2. The edge computing gateway receives raw data and performs preprocessing, including abnormal data identification and filtering, data format standardization conversion, offline local caching, and data compression. Based on a preset threshold trigger response mechanism, data is analyzed in real time, and corresponding level response operations are initiated when the monitored parameters reach the preset threshold. S3 and the edge computing gateway upload the processed data to the cloud platform. The cloud platform's data storage module stores the data in categories. The real-time visualization module converts the data into visual charts. The multi-level early warning module calls the graded threshold parameters and sends notifications with anomaly details through multiple channels when the monitored parameters exceed the threshold. S4. After an abnormal event is triggered, the cloud platform's closed-loop automated management module and multi-level early warning module work together to automatically execute preset response measures according to the level and type of the abnormality. These measures include at least automatically generating and dispatching maintenance work orders, retrieving and scheduling nearby backup resources based on real-time location, and remotely accessing the equipment control system for parameter adjustment and maintenance.

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