Cold chain fresh-keeping box temperature monitoring system and method

Abstract

The invention discloses a cold chain fresh-keeping box temperature supervision system and method, and relates to the technical field of cold chain supervision, the system comprises a data acquisition module, a data processing module, a supervision analysis module and a response supervision module, and the supervision analysis module comprises a temperature change influence unit, a damage risk unit and a fresh-keeping box regulation decision unit. The dynamic temperature change index is output through the temperature change influence unit, the problem of cold chain temperature shock damage is solved in combination with the real-time temperature, the species sensitivity coefficient, the respiration rate and the box leakage coefficient, fresh-keeping box type selection and maintenance are supported, the damage risk index is output through the damage risk unit, the temperature change index, the ethylene increment and the vibration damage coefficient are integrated, and fresh-keeping box type selection and maintenance are achieved. The method comprises the following steps: tracking a continuous effect, decomposing factor contributions, supporting responsibility division and process optimization, outputting a regulation and control decision index through a fresh-keeping box regulation and control decision unit, associating box body performance with operation interference factors, and directly mapping regulation and control actions, thereby reducing manual dependence and realizing monitoring, evaluation, regulation and control closed-loop management.

Description

Technical Field

[0001] This invention relates to the field of cold chain monitoring technology, specifically to a temperature monitoring system and method for cold chain preservation boxes. Background Technology

[0002] Cold chain preservation of agricultural products is a core link in ensuring food quality and safety and supply chain efficiency. Temperature monitoring is a key factor in determining the preservation effect. Tropical fruits, such as bananas and mangoes, temperate crops, such as apples and pears, and fresh products, such as seafood and dairy products, have significantly different requirements for storage temperature. Therefore, a temperature monitoring system for cold chain preservation boxes is needed to intelligently and specifically monitor the temperature of various types of agricultural products in the preservation boxes.

[0003] In current cold chain transportation, the temperature monitoring of refrigerated containers may mainly rely on the traditional threshold alarm mode, that is, to monitor whether the temperature inside the container exceeds the preset upper and lower limits through sensors, and to trigger an alarm when the temperature is abnormal. However, in reality, in order to reduce fuel consumption and for operational convenience, drivers of cold chain transportation may adopt a rough control method of drastically lowering the temperature and then letting it rise naturally. In this way, the temperature may not exceed the threshold in the short term, but the drastic temperature fluctuation may cause damage to the cell structure of agricultural products. The damage symptoms may not appear until several days after delivery. Traditional threshold monitoring cannot identify such hidden risks.

[0004] Furthermore, the preservation effect of agricultural products is affected by multiple factors such as temperature, respiration, ethylene release, and mechanical vibration. For example, the high respiration rate of leafy vegetables can exacerbate the stress effect of temperature fluctuations. Existing technologies may only monitor temperature and ignore the synergistic effect of physiological metabolism and environmental disturbances.

[0005] Furthermore, when different agricultural products are transported on the same cold chain platform, temperature parameters need to be manually adjusted, which can easily lead to control errors due to lack of experience. For example, when tropical fruits and root crops are mixed, the same temperature standard is used, and there is a lack of systematic assessment of the hardware status of the refrigeration box, such as its sealing performance, cold storage capacity, and operating procedures, such as the number of times the box is opened and the stacking density. This may make it difficult to ensure the safe transportation of agricultural products in the cold chain transport box, resulting in poor practicality. Summary of the Invention

[0006] The purpose of this invention is to provide a temperature monitoring system and method for cold chain preservation boxes, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for temperature monitoring of a cold chain preservation box, comprising the following steps:

[0009] Step S1: Obtain real-time temperature data, CO2 release rate of agricultural products, cold leakage rate, information on the types of agricultural products in the cold chain preservation box, information on the ethylene concentration in the box, vibration data of the box, material parameters of the box, information on phase change materials, and data on interference factors through the data acquisition module.

[0010] Step S2: Input the data collected by the data acquisition module into the data processing module. The data processing module performs outlier filtering and standardization on the input data, and synchronizes the processed data with the system clock so that the processed data can be input into the regulatory analysis module.

[0011] Step S3: Regulatory Analysis Module;

[0012] By analyzing real-time temperature data, the impact of temperature changes inside and outside the refrigerator on the agricultural products inside is analyzed. The CO2 release rate of the agricultural products is also considered to take into account the respiration rate. The leakage of the refrigerator is analyzed based on the cold leakage rate. The coefficients are adjusted according to the type of agricultural products inside the refrigerator to output a dynamic temperature change impact index.

[0013] By analyzing the changes in ethylene concentration inside the container and combining it with container vibration data, the damage to agricultural products caused by transportation bumps is considered. The dynamic temperature change impact index calculated at different times is summed to output a damage risk index.

[0014] By analyzing the material parameters of the cabinet, we can determine the impact of the thickness of the insulation layer and the thermal conductivity of the cabinet material on the insulation performance of the cabinet. Based on the information of phase change materials, we can consider the residual coefficient of cold energy. Furthermore, we can combine the dynamic temperature change influence index and the damage risk index to comprehensively evaluate the control and decision-making of the food storage box. We can also introduce interference factor data and output the control and decision-making index of the food storage box by using the door status data, stacking gap data, condensate generation, and pre-cooling sufficiency information.

[0015] Step S4: Input the dynamic temperature change impact index, damage risk index, and food storage box control decision index into the response and supervision module. Based on the input data, the response and supervision module performs real-time stress intervention, cumulative damage control, and system-level control decisions.

[0016] Optionally, the monitoring and analysis module includes: a temperature change impact unit, a damage risk unit, and a food storage box control decision unit.

[0017] Optionally, the processing flow of the temperature change influence unit is as follows:

[0018] Ⅰ: By inputting the real-time temperature value of the preservation box into the temperature change influence unit, and combining it with the optimal storage temperature value and upper and lower temperature limits corresponding to agricultural products, a calculation is performed to serve as the basis for dynamic temperature change influence index analysis;

[0019] II: By using the information on the types of agricultural products inside the box, the output value of the species sensitivity coefficient can be adjusted for different types of agricultural products, thereby achieving targeted preservation and supervision of the cold chain for agricultural products;

[0020] III: By obtaining the CO2 release rate of agricultural products in the preservation box, the respiration rate coefficient of crops can be analyzed;

[0021] IV: By combining the temperature values ​​inside the refrigerator with the external temperature values ​​over multiple time periods, the leakage coefficient of the refrigerator is analyzed, and the dynamic temperature change influence index is finally output.

[0022] Optionally, the processing flow of the damage risk unit is as follows:

[0023] Ⅰ: By inputting the dynamic temperature change impact index calculated over multiple time intervals into the damage risk unit and summing it in combination with the sampling interval, the cumulative damage caused by continuous deviation from the optimal temperature can be considered.

[0024] II: The change in ethylene concentration inside the refrigeration box is analyzed by calculating the difference between the current ethylene concentration and the initial ethylene concentration 30 minutes after loading, and combining this with the ethylene risk factor.

[0025] III: By sampling the acceleration information of the refrigerated container during cold chain transportation multiple times, and based on the total number of samplings of the acceleration information, the vibration damage coefficient is evaluated, and the damage risk index is finally output.

[0026] Optionally, the processing flow of the food storage box control decision unit is as follows:

[0027] Ⅰ: By combining the dynamic temperature change impact index and the damage risk index, the control and decision-making of the food storage box can be comprehensively evaluated;

[0028] II: The thermal insulation coefficient of the enclosure is evaluated by combining the insulation layer thickness and enclosure material parameters.

[0029] III: By monitoring the temperature of the refrigerant, the remaining cooling capacity can be determined, thereby obtaining the remaining cooling capacity coefficient;

[0030] IV: The number of times the door is opened, the duration of the door opening, the stacking density coefficient, the pre-cooling adequacy, and the amount of condensate generated in the interference factor data are used to evaluate the obtained interference factor value. The value of the interference factor is then normalized by combining the maximum value of the corresponding interference factor to finally output the control decision index of the food preservation box.

[0031] Optionally, the outlier filtering in step S2 specifically includes:

[0032] The continuous data obtained in step S1 is processed by moving average filtering, and instantaneous jump values ​​caused by transportation bumps are removed.

[0033] Optionally, the standardization process in step S2 specifically includes:

[0034] First, the units of the data acquired in step S1 are standardized and normalized. Then, all the data acquired by the sensors in step S1 are synchronized through the system clock to ensure the direct correlation calculation of multi-dimensional data under the same timestamp.

[0035] Optionally, the cold chain preservation box includes an outer shell, a control panel, a temperature sensor one, and a Hall effect magnetic sensor are provided on the side of the outer shell, an infrared CO2 sensor and a temperature sensor two are provided on the inner wall of the outer shell, an electrochemical ethylene sensor and a pressure sensor are installed at the bottom inside the outer shell, and a triaxial accelerometer is installed at the center of the bottom of the outer shell.

[0036] Secondly, the present invention also provides a cold chain preservation box temperature monitoring system, including a memory, a processor, and computer program code stored in the memory and running and executing on the processor. When the processor executes the computer program code, it performs the above-mentioned cold chain preservation box temperature monitoring method.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] I. This invention outputs a dynamic temperature change impact index through a temperature change impact unit. By using real-time temperature values, upper and lower temperature limits, and nonlinear amplification of temperature deviation based on the square term, it solves the problem of damage to agricultural cold chain transportation caused by sudden temperature drops and rises. This allows the system to detect risks before the temperature exceeds the threshold. Furthermore, it introduces a species sensitivity coefficient, setting specific weights for various agricultural products such as tropical fruits and temperate crops, enabling customized stress assessment under the same monitoring platform. This avoids a one-size-fits-all temperature standard. In addition, it combines the respiration rate coefficient and the container leakage coefficient to fully consider the impact of temperature changes from multiple perspectives, accurately calculating the dynamic temperature change impact index. This provides data support for the selection and maintenance of refrigeration boxes, preventing monitoring failures caused by box aging.

[0039] Second, this invention outputs a damage risk index through a damage risk unit. By combining the dynamic temperature change influence index and sampling interval, and based on an integral processing method, it tracks the continuous effect of temperature stress. Combined with ethylene increment and vibration damage coefficient, it quantifies the superimposed effect of accelerated metabolism and mechanical stress on damage. For example, cell rupture caused by transportation bumps and low temperature stress synergistically exacerbate decay. This fills the gap in existing technologies that only monitor the physical environment and ignore physiological responses. Moreover, the damage risk index value can be decomposed into the contribution ratio of various factors such as temperature fluctuation, ethylene release, and vibration, thereby providing data support for quality responsibility division and process optimization rather than relying on subjective judgment.

[0040] Third, this invention outputs a control decision index for the refrigerated container through a control decision unit. By correlating the container's thermal insulation coefficient and the remaining cold energy of the phase change material with the physical properties of the refrigerated container, and combining operational interference factors such as the number of times the container door is opened and the stacking density, the control measures are adapted to the actual hardware capabilities. The control decision index value of the refrigerated container directly maps to specific control actions. For example, when the stacking density is too high and causes local overheating, the system automatically pushes operational instructions such as adjusting the distance between goods and the container height, reducing reliance on human experience and realizing closed-loop management of monitoring, evaluation, and control. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0042] Figure 2 This is a schematic diagram of the data acquisition module in this invention;

[0043] Figure 3 This is a schematic diagram of the regulatory analysis module and the regulatory response module in this invention;

[0044] Figure 4 This is a schematic diagram of the operation flow of the monitoring and analysis module in this invention;

[0045] Figure 5 This is an axonometric view of the cold chain preservation box in this invention. Figure 1 ;

[0046] Figure 6 This is an axonometric view of the cold chain preservation box in this invention. Figure 2 .

[0047] In the diagram: 1. Outer casing; 2. Control panel; 3. Infrared CO2 sensor; 4. Temperature sensor one; 5. Temperature sensor two; 6. Hall effect sensor. Detailed Implementation

[0048] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figures 1 to 6 This embodiment provides a method for temperature monitoring of a cold chain preservation box, including the following steps:

[0050] Step S1: Obtain real-time temperature data, CO2 release rate of agricultural products, cold leakage rate, information on the types of agricultural products in the cold chain preservation box, information on the ethylene concentration in the box, vibration data of the box, material parameters of the box, information on phase change materials, and data on interference factors through the data acquisition module.

[0051] Step S2: Input the data collected by the data acquisition module into the data processing module. The data processing module performs outlier filtering and standardization on the input data, and synchronizes the processed data with the system clock so that the processed data can be input into the regulatory analysis module.

[0052] Outlier filtering involves processing the continuous data obtained in step S1 using a moving average filter and removing instantaneous jump values ​​caused by transportation bumps.

[0053] Standardization processing involves unifying and normalizing the units of data acquired in step S1, and then synchronizing all sensor-based data acquired in step S1 through the system clock to ensure direct correlation calculation of multi-dimensional data under the same timestamp.

[0054] Step S3: Regulatory Analysis Module;

[0055] By analyzing real-time temperature data, the impact of temperature changes inside and outside the refrigerator on the agricultural products inside is analyzed. The CO2 release rate of the agricultural products is also considered to take into account the respiration rate. The leakage of the refrigerator is analyzed based on the cold leakage rate. The coefficients are adjusted according to the type of agricultural products inside the refrigerator to output a dynamic temperature change impact index.

[0056] By analyzing the changes in ethylene concentration inside the container and combining it with container vibration data, the damage to agricultural products caused by transportation bumps is considered. The dynamic temperature change impact index calculated at different times is summed to output a damage risk index.

[0057] By analyzing the material parameters of the cabinet, we can determine the impact of the thickness of the insulation layer and the thermal conductivity of the cabinet material on the insulation performance of the cabinet. Based on the information of phase change materials, we can consider the residual coefficient of cold energy. Furthermore, we can combine the dynamic temperature change influence index and the damage risk index to comprehensively evaluate the control and decision-making of the food storage box. We can also introduce interference factor data and output the control and decision-making index of the food storage box by using the door status data, stacking gap data, condensate generation, and pre-cooling sufficiency information.

[0058] The monitoring and analysis module includes: a temperature change impact unit, a damage risk unit, and a food storage box control decision unit;

[0059] Step S4: Input the dynamic temperature change impact index, damage risk index, and food storage box control decision index into the response and supervision module. Based on the input data, the response and supervision module performs real-time stress intervention, cumulative damage control, and system-level control decisions.

[0060] The cold chain preservation box includes an outer shell 1. A control panel 2, a temperature sensor 4, and a Hall effect magnetic sensor 6 are installed on the side of the outer shell 1. An infrared CO2 sensor 3 and a temperature sensor 5 are installed on the inner wall of the outer shell 1. An electrochemical ethylene sensor and a pressure sensor are installed at the bottom inside the outer shell 1. A triaxial accelerometer is installed at the center of the bottom of the outer shell 1.

[0061] Based on the above, this embodiment provides a detailed description. The above-mentioned cold chain preservation box temperature monitoring method and system jointly construct a closed-loop monitoring system of real-time monitoring, damage assessment, and intelligent control. This method and system captures the immediate risks of temperature fluctuations through dynamic stress index to ensure no delay in monitoring, reveals long-term risks through cumulative calculation, clarifies the damage formation mechanism, and generates feasible control measures in combination with the box status of the cold chain preservation box, forming a complete chain of discovery, analysis, and solution.

[0062] Parameters such as species sensitivity coefficient (TSE), respiration rate coefficient (TSF), and current ethylene concentration (CDB) are used to adapt to the physiological characteristics of different crops, avoiding a one-size-fits-all temperature setting. Hardware performance is evaluated using parameters such as enclosure leakage coefficient (TSG), enclosure insulation coefficient (BRA), and residual cooling capacity coefficient (BRB) to ensure that regulatory measures match the actual capacity of the enclosure. The interference factor (BRC) value is obtained. kThis method and system overcomes the limitations of traditional methods that only monitor upper and lower temperature limits by quantifying the impact of human operation and promoting the standardization of management processes. It focuses on dynamic characteristics such as the rate of temperature change and the frequency of fluctuations, which is more in line with the biological laws of chilling injury in agricultural products. Furthermore, it incorporates physiological indicators such as respiration rate and ethylene into the monitoring, upgrading temperature control from maintaining a value to ensuring crop activity. It not only detects temperature anomalies but also automatically generates control plans based on the condition of the container and operational interference, reducing reliance on human experience. The dynamic temperature change impact index TS intercepts sudden temperature drops by the driver in real time, the damage risk index CD tracks the cumulative effect of low temperature, and the cold storage container control decision index BR automatically adjusts the cold energy distribution to maintain a stable temperature. It also distinguishes crop types through the species sensitivity coefficient TSE and the respiration rate coefficient TSF, achieving differentiated control on the same platform. By combining the container leakage coefficient and the number of times the container is opened, it ensures the temperature while taking into account the actual feasibility of logistics operations, such as allowing necessary opening inspections but maintaining temperature stability through compensation measures.

[0063] The three units work together through multi-dimensional parameter coupling and hierarchical logic design, enabling the cold chain preservation box temperature monitoring system to truly achieve the technical goals of accurate perception, dynamic evaluation and intelligent control, providing a systematic solution for the cold chain transportation of agricultural products from passive defense to proactive protection.

[0064] See Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 The processing flow for the temperature-affected unit is as follows:

[0065] S1: By inputting the real-time temperature value of the preservation box into the temperature change influence unit, and combining it with the optimal storage temperature value and upper and lower temperature limits corresponding to agricultural products, a calculation is performed to serve as the basis for dynamic temperature change influence index analysis.

[0066] S2: By using the information on the types of agricultural products inside the box, the output value of the species sensitivity coefficient can be adjusted for different types of agricultural products, thereby achieving targeted preservation and supervision of the cold chain for agricultural products;

[0067] S3: By obtaining the CO2 release rate of agricultural products in the preservation box, the respiration rate coefficient of crops can be analyzed;

[0068] S4: By combining the temperature values ​​inside the refrigerator with the external temperature values ​​over multiple time periods, the leakage coefficient of the refrigerator is analyzed, and the dynamic temperature change influence index is finally output.

[0069] The calculation process for the temperature change-affected element is as follows:

[0070]

[0071] in:

[0072] TS stands for Dynamic Temperature Change Impact Index, which is the risk of temperature deviation. This parameter can quickly identify the operational situation when the temperature drops or rises suddenly, and can distinguish the temperature sensitivity of different agricultural products. The smaller the Dynamic Temperature Change Impact Index TS is, the better, and the ideal value is close to 0.

[0073] TSA stands for real-time temperature value, which is the temperature data inside the refrigerator. It is collected by an NTC temperature sensor installed in the center of the top of the refrigerator. The unit is ℃, the range is -20-40℃, and the data is updated every 10 seconds.

[0074] TSB stands for Optimal Storage Temperature, which is the recommended storage temperature for agricultural products. This parameter is preset by the user and can be entered through the control panel 2 on the outer shell 1 of the cold chain safe, such as 3℃ for lychees and 4℃ for potatoes, etc. The unit is ℃, and the range is -18-15℃.

[0075] TSC stands for Upper Temperature Limit, which is the upper limit of the allowable temperature fluctuation based on the optimal storage temperature value TSB. The data can be collected based on the system's built-in database.

[0076] TSD stands for Lower Temperature Limit, which is the lower limit of the allowable temperature fluctuation based on the Optimal Storage Temperature (TSB) value. The data can be collected based on the system's built-in database. Taking bananas as an example, the Optimal Storage Temperature (TSB) value is 12℃, the Upper Temperature Limit (TSC) value is 14℃, and the Lower Temperature Limit (TSD) value is 7℃.

[0077] TSE stands for Species Sensitivity Coefficient, which measures the sensitivity of different agricultural products to temperature deviations. Data collection can be performed by the user through the control panel, selecting the corresponding agricultural product type. The system then automatically retrieves the parameters. The specific values ​​for several possible agricultural product types are shown below:

[0078] Tropical fruits, such as bananas and mangoes: TSE = 1.3;

[0079] Temperate fruits, such as apples and pears: TSE = 0.9;

[0080] Leafy greens, such as lettuce and spinach: TSE = 1.1;

[0081] Root vegetables, such as potatoes and carrots: TSE = 0.7;

[0082] Seafood, such as salmon and shrimp: TSE = 1.4;

[0083] Dairy products, such as yogurt and cheese: TSE = 1.2;

[0084] TSF stands for respiratory rate coefficient, which is the CO2 release rate of agricultural products and is used to reflect metabolic intensity. The data is collected by installing an infrared CO2 sensor 3 on the top of the inner wall of the cold chain preservation box 1. The unit is ppm / h, and the range is 8-40ppm / h.

[0085] TSG stands for the leakage coefficient of the container, which is the rate at which cold air leaks out of the cold chain container. It is also one of the core indicators of passive cold storage equipment. The data is collected by temperature sensor 25, which is set at the top of the inner wall of the outer shell 1, after the door of the cold chain container is closed, recording the temperature rise value within 1 hour. The specific calculation process is as follows:

[0086] TSG=(T1h-T0h) / (T0h-TT);

[0087] in:

[0088] T1h refers to the temperature value at time t1;

[0089] T0h refers to the temperature value at time t0;

[0090] TT refers to the ambient temperature, which is collected by the temperature sensor 4 on the side of the outer casing 1. The range of this parameter is TT≤5% / h for new cold chain preservation boxes and TT≤15% / h for old cold chain preservation boxes. If it exceeds this range, the sealing strip needs to be replaced.

[0091] T1 refers to the species sensitivity coefficient weighting factor;

[0092] T2 refers to the weighting factor of the respiratory rate coefficient;

[0093] T3 refers to the weighting factor for the leakage coefficient of the enclosure;

[0094] max(TSC-TSB,TSB-TSD) refers to the larger of the difference between the optimal storage temperature and the upper limit of the temperature range, and the difference between the lower limit of the optimal storage temperature and the lower limit of the temperature range. It serves as a benchmark for temperature deviation and is used to standardize temperature deviation, avoiding distortion of the dynamic temperature change index TS value caused by the asymmetry between the upper and lower limit ranges. When the lower limit range is wider, a larger value is taken in the denominator to prevent the low temperature deviation from being excessively amplified.

[0095] Based on the above, this embodiment solves the problem of hidden chilling injury caused by sudden drops and rises in transport temperature by cold chain transport drivers by capturing the dynamic process of temperature deviation from the optimal range in real time. Traditional monitoring only monitors whether the temperature exceeds the threshold, while this temperature change impact unit amplifies the degree of deviation by using the square term. At the same time, it introduces the species sensitivity coefficient TSE to distinguish the tolerance differences between tropical fruits and temperate crops, thereby ensuring that different agricultural products receive accurate stress assessment under the same monitoring platform. This unit upgrades the absolute temperature value monitoring to the monitoring of the dynamic temperature change process, filling the technical gap that traditional static thresholds cannot identify short-term drastic fluctuations. This allows the system to detect risks before chilling injury appears, such as 2 hours before bananas are frostbitten.

[0096] The dynamic temperature change impact index (TS) calculation result can be directly used as the system's first-level early warning trigger. When the value exceeds 1.5, the system immediately initiates real-time intervention, such as audible and visual alarms and locking the temperature setting panel to prevent driver misoperation. This temperature change impact unit is different from single temperature monitoring. The calculation of the dynamic temperature change impact index (TS) integrates the respiration rate and the box leakage coefficient, reflecting both the physiological state of agricultural products, such as the fact that crops with high respiration rates are more sensitive to temperature fluctuations, and the physical characteristics of the preservation box, such as the risk of temperature rebound caused by leakage in old boxes. This achieves collaborative monitoring of agricultural products and the preservation box from two dimensions. Moreover, this unit calculates every 5 minutes to ensure that temperature anomalies are detected in the early stages, avoiding irreversible damage caused by traditional delayed monitoring.

[0097] The respiration rate coefficient (TSF) in this temperature change influence unit is the first to introduce the physiological metabolic intensity of agricultural products into temperature monitoring, breaking through the limitations of traditional methods that only focus on the physical environment. This allows monitoring to shift from passive temperature control to proactive adaptation to crop needs. The leakage coefficient (TSG) of this temperature change influence unit quantifies sealing performance based on the rate of temperature recovery, providing data support for the selection and maintenance of refrigeration boxes and preventing monitoring failures due to box aging. Users can use the dynamic temperature change influence index (TS) to judge in real time whether the current temperature control strategy is suitable for the characteristics of the goods. For example, tropical fruits require a lower leakage coefficient and stricter fluctuation control, rather than relying on experience to set the temperature. The entire system can automatically push box health reports to remind users to replace the sealing strips or replace refrigeration boxes with high leakage rates, ensuring monitoring effectiveness from a hardware perspective.

[0098] See Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 The handling process for damage risk units is as follows:

[0099] S1: By inputting the dynamic temperature change impact index calculated over multiple time intervals into the damage risk unit and summing it in combination with the sampling interval, the cumulative damage caused by continuous deviation from the optimal temperature is taken into account.

[0100] S2: The change in ethylene concentration inside the refrigeration box is analyzed by calculating the difference between the current ethylene concentration value and the initial ethylene concentration value 30 minutes after loading, and combining it with the ethylene risk coefficient.

[0101] S3: By sampling the acceleration information of the refrigerated box during cold chain transportation multiple times, and based on the total number of samplings of the acceleration information, the vibration damage coefficient is evaluated, and the damage risk index is finally output.

[0102] The calculation process for the damage risk unit is as follows:

[0103]

[0104] in:

[0105] CD stands for Damage Risk Index, which is the cumulative damage over time. The calculation results are used to quantify the difference between short-term and long-term deviations. For example, a 2-hour deviation may be 3 times more risky than a 5-minute deviation. Furthermore, this parameter combines non-temperature factors such as ethylene increment and vibration to achieve multi-physics coupled damage assessment. The smaller this parameter is, the better, with the ideal value approaching 0.

[0106] n refers to the total number of intervals. For example, if the transportation time is 2 hours, then n = 24 5-minute intervals.

[0107] i refers to the time interval index, from 1 to n, with each interval being 5 minutes.

[0108] TSA i It refers to the dynamic temperature change impact index calculated in the i-th time interval, which integrates the temperature change stress effect on the time axis and quantifies the cumulative damage caused by continuous deviation from the optimal temperature.

[0109] CDA refers to the sampling interval, which means the time length for calculating the dynamic temperature change influence index (TSA) of a single dynamic temperature change. The acquisition method is that the system clock is fixed at 5 minutes, and the unit is h.

[0110] C1 refers to the ethylene risk coefficient, which is the fitting coefficient of experimental data. Physically, it means that for every 1 ppm increase in ethylene concentration, the risk of chilling injury increases by 3%. In this embodiment, C1 is set to 0.03.

[0111] CDB refers to the current ethylene concentration value;

[0112] CDC refers to the initial ethylene concentration 30 minutes after loading.

[0113] CDB-CDC refers to the change in ethylene concentration inside the box, in ppm. It means the change in ethylene concentration during transportation. An electrochemical ethylene sensor can be installed at the bottom inside the outer shell 1 to collect data. The range of the electrochemical ethylene sensor is 0-1 ppm. Tropical fruits exceeding 0.5 ppm will trigger a risk.

[0114] CDD stands for Vibration Damage Coefficient, which represents the degree of mechanical damage to cells caused by transportation bumps. The data is collected by installing a triaxial accelerometer at the center of the bottom of the outer casing 1. The specific calculation formula is as follows:

[0115]

[0116] in:

[0117] p refers to the total number of samples taken within 5 minutes;

[0118] 'e' refers to the index of a single vibration sample.

[0119] a e Let be the acceleration value of the e-th sample, where 0.3g is the safety threshold, p is the number of samples within 5 minutes, and g represents the acceleration due to gravity, 1g = 9.8 m / s². 2 The denominator, gravitational acceleration g, is used as the unit of acceleration to reflect the intensity of the bumps, such as 0.3g = 2.94 m / s². 2 , which is the tolerance threshold for agricultural products;

[0120] C2 refers to the weighting coefficient of vibration damage factors.

[0121] Based on the above, the intraday rolling comprehensive adjustment unit, through damage accumulation calculation over time, addresses the issue of latent damage caused by long-term fluctuations in surface temperature even when it meets the standard. For example, bananas experiencing repeated temperature fluctuations between 7-14℃, although not exceeding the threshold, still suffer from chilling injury due to cumulative effects. This damage risk unit extends instantaneous status monitoring to full-process damage assessment, using integral terms... By capturing the sustained effects of temperature stress and introducing the ethylene increment CDB-CDC and vibration damage coefficient CDD, we can quantify the cumulative effect of non-temperature factors, such as accelerated metabolism and transport turbulence, on damage, thus upgrading the regulatory assessment from a single physical indicator to a coupled assessment of physiological and physical factors.

[0122] The calculation results of the damage risk unit are directly used as the basis for the system's damage level classification. When the value exceeds 5, the system can automatically generate a damage risk report and mark the key period of cumulative damage. For example, the vibration damage coefficient CDD increases suddenly in the 3rd hour of transportation due to bumps. This damage risk unit is different from the traditional endpoint detection, such as detecting the rot rate and damage risk index CD after arrival. It can track the damage formation process in real time, clarify the risk contribution of loading, transportation and unloading at each stage, and provide data support for responsibility traceability. The ethylene increment considered in this damage risk unit reflects the accelerated ripening of fruit, and the vibration coefficient reflects mechanical damage. The two are calculated by superimposing temperature stress to avoid risk misjudgment caused by single factor assessment. For example, although the low temperature does not exceed the standard, the high ethylene environment will still aggravate the spoilage.

[0123] This damage risk unit incorporates plant hormone levels into temperature monitoring, revealing the chain relationship between temperature, hormones, and damage. It breaks through the limitations of traditional methods that only focus on physical parameters. The vibration damage coefficient (CDD) addresses the mechanical stress unique to transportation scenarios, quantifying the damage to cell structure caused by bumps through acceleration sensors. This explains the technical blind spot where rotting still occurs even when the temperature meets the standard. Users can identify the main causes of damage through the damage risk index (CD) and optimize transportation routes accordingly, such as reducing bumps or adjusting temperature strategies. The entire system can automatically generate damage warning thresholds, prompting users to take intervention measures when the damage risk index (CD) approaches the critical value, such as suspending transportation and adjusting loading methods, to avoid irreversible damage.

[0124] See Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 The processing flow of the food storage container's control decision unit is as follows:

[0125] S1: By combining the dynamic temperature change impact index and the damage risk index, the control and decision-making of the food storage box can be comprehensively evaluated.

[0126] S2: The thermal insulation coefficient of the enclosure is evaluated by combining the insulation layer thickness and enclosure material parameters.

[0127] S3: By monitoring the temperature of the refrigerant, the remaining cooling capacity is determined, thereby obtaining the remaining cooling capacity coefficient;

[0128] S4: The values ​​of interference factors are evaluated by taking into account the number of times the door is opened, the duration of the door opening, the stacking density coefficient, the pre-cooling adequacy, and the amount of condensate generated in the interference factor data. The values ​​are then normalized by combining the maximum values ​​of the corresponding interference factors to finally output the control decision index of the food preservation box.

[0129] The calculation process of the food storage container's control decision unit is as follows:

[0130]

[0131] in:

[0132] BR refers to the control decision index of the refrigerated container. The smaller this parameter is, the better. The safety threshold is 1.2. The ideal value range of this parameter is ≤1.2. If it exceeds the standard, the refrigerated container may malfunction and there is a risk of total loss of goods.

[0133] BRA refers to the thermal insulation coefficient of the food storage container, meaning its ability to prevent external heat from penetrating. It is measured at the factory using a heat flow meter. BRA = SA / SB, where SA is the insulation layer thickness and SB is the thermal conductivity. For example, for a PU insulation layer with SB = 0.022 W / (m·K) and SA = 50 mm, BRA = 2.27 m² / kJ. 2 • K / W, this parameter ranges from 1.8 to 3.0, the higher the value, the better the thermal insulation;

[0134] BRB stands for Remaining Cooling Capacity Coefficient. The remaining cooling capacity can be determined by monitoring the temperature of the refrigerant. The data is collected by embedding a temperature sensor in the phase change material (PCM) module built into the tank wall to monitor the temperature plateau period during the phase change process. For example, if the phase change temperature of the ice pack is 0°C and the plateau period lasts for 8 hours, the remaining cooling capacity is 50% when the temperature starts to rise. BRB = Z1 / Z2, where Z1 is the current remaining plateau period duration and Z2 is the total plateau period duration, with a range of 0.3-1.0.

[0135] It should be noted that phase change materials (PCMs) are materials that undergo a phase transition at specific temperatures, such as solid to liquid or liquid to solid, accompanied by the absorption or release of a large amount of latent heat. In cold chain preservation, the core function of the PCM module is to maintain a stable temperature inside the box through the phase change process, reducing the impact of temperature fluctuations on agricultural products. Depending on the phase change temperature and application scenario, commonly used PCM modules in cold chain preservation boxes include:

[0136] Water-based PCM: Phase change temperature 0℃, which is ice, suitable for low-temperature transportation close to 0℃, such as temperate fruits like apples and pears;

[0137] Paraffin-based PCM: It has a wide phase transition temperature range, such as 5-30℃, and can be customized by adjusting the carbon chain length. It is suitable for transporting subtropical fruits such as citrus and lychee at 10-15℃.

[0138] Fatty acid PCM: phase transition temperature 30-70℃, such as lauric acid phase transition temperature 44℃ and stearic acid phase transition temperature 69℃, but the cost is high and it is mostly used in high-end cold chain.

[0139] Inorganic salt PCMs: such as calcium chloride aqueous solution (phase transition temperature -5℃) and sodium nitrate aqueous solution (phase transition temperature -10℃ to 20℃), are concentration-dependent and suitable for scenarios with specific temperature requirements.

[0140] The characteristic of the phase transition temperature plateau period is that when a PCM undergoes a phase transition, such as from solid to liquid, the temperature will stabilize near the phase transition point. For example, during the ice phase transition, the temperature remains at 0°C until all phase transitions are completed. This stage is called the temperature plateau period, which is the key to maintaining the temperature stability of the PCM.

[0141] The necessity of high-precision sensors, the duration of the temperature plateau period (i.e., the phase transition duration) and temperature stability (i.e., the fluctuation range) directly affect the cold chain effect. A high-precision sensor with a temperature range of ±0.1℃ can accurately capture the start of the plateau period, the beginning and end of the temperature stabilization and the temperature change again, thereby evaluating the performance of the PCM, such as the latent heat value, phase transition rate and the temperature control effect inside the chamber.

[0142] BRC k Refers to the interference factor acquisition value, interference factor acquisition value BRC k Specifically, the following situations exist:

[0143] BRC1 refers to the number of times the cabinet door is opened, which is the cumulative number of times the cabinet door is opened during transportation. The data is collected by a Hall gate magnetic sensor 6 installed on the side of the outer shell 1. The unit is times, and the range is 0-20 times / day. The maximum interference factor of the number of cabinet door openings is BRD1 = 10 times. If it exceeds this value, the risk of cold air loss will increase sharply.

[0144] BRC2 refers to the stacking density coefficient, which is the effect of cargo gap ratio on temperature uniformity. It is collected by two infrared ranging sensors installed at the top of the inside of the cold chain container, which can monitor the distance between the top of the cargo and the top of the container diagonally. BRC2 = actual gap distance / standard gap distance. The standard gap distance is 20% of the container height, with a range of 0.5-1.5. The maximum interference factor of the stacking density coefficient is BRD2 = 1.5. Excessive density will cause local overheating.

[0145] BRC3 refers to the pre-cooling adequacy, calculated by the difference between the ambient temperature and the target temperature during loading. The temperature sensor 4 is located on the side of the outer casing 1. The initial temperature TP1 of the cargo is manually input during loading. BRC3 = (TT - TSB) / (TP1 - TSB), where TT is the ambient temperature. The range of pre-cooling adequacy BRC3 is 0.8-1.2. The maximum interference factor of pre-cooling adequacy is BRD3 = 1.5. When it is less than 0.8, it indicates insufficient pre-cooling.

[0146] BRC4 refers to the amount of condensate generated, which means the weight of condensate caused by excessive humidity inside the box. It is collected by a thin-film pressure sensor installed at the bottom of the cold chain preservation box, placed below the drip tray. The unit is g, and the range is 0-100g. The maximum interference factor of condensate generation BRD4 is 50g. If this value is exceeded, mold may grow.

[0147] BRC5 refers to the door opening duration, which means the cumulative duration of a single door opening. The data is collected by a door magnetic sensor recording the start and end times of each door opening, in seconds, ranging from 0 to 300 seconds per opening. The maximum interference factor for the door opening duration is BRD5 = 30 seconds, indicating significant cold energy loss if the opening time exceeds this limit.

[0148] BRD k Refers to the maximum value of the interference factor;

[0149] m refers to the total number of interference factors;

[0150] k refers to the index of the interference factor.

[0151] Based on the above, this refrigeration box control decision unit solves the compatibility problem of monitoring different products on the same platform by coupling calculations of multi-dimensional interference factors and box status. The contribution of this unit is to upgrade passive alarms to active control decisions. By integrating the dynamic temperature change impact index TS, damage risk index CD, box insulation coefficient BRA, cold energy surplus coefficient BRB, and 5 types of interference factors, specific control instructions are generated, thereby ensuring that the monitoring system can not only detect problems, but also solve them autonomously. The calculation results of the refrigeration box control decision unit are directly used as the basis for generating the system's control actions. When the refrigeration box control decision index BR is too high, the system automatically executes the preset control strategy, such as activating the backup cold storage and adjusting the ventilation mode.

[0152] Unlike traditional simple control methods that rely solely on temperature sensors, this insulated box's control decision unit integrates the physical characteristics of the box and operational disturbances to ensure that control measures are adapted to the actual state of the insulated box. For example, when the old box has poor insulation, it automatically relaxes the temperature fluctuation threshold to reduce cold energy consumption. Based on the species sensitivity coefficient (TSE) and damage mechanism of different agricultural products, the insulated box's control decision index (BR) can dynamically adjust the control priority. For example, tropical fruits are given priority to ensure temperature stability, while root vegetables are given priority to ensure the remaining cold energy, thereby achieving precise monitoring with a one-product-one-policy approach.

[0153] The insulation coefficient and remaining cold energy of the container are incorporated into the decision-making system to avoid idealized control that is detached from the container's capabilities. For example, for older containers with poor insulation, the system does not blindly pursue low temperatures but balances cold energy consumption with temperature stability. The acquisition values ​​of various interference factors can quantify operational processes, such as the impact of opening the container for inspection and loading goods on temperature. This ensures that control measures take into account both regulatory requirements and practical operational feasibility. For example, a reasonable number of times the container can be opened can be allowed, but the temperature can be maintained by compensating for cold energy. Moreover, no professional temperature control knowledge is required to use this method and system. The system can generate operation guidelines based on the container control decision index BR, such as "The current stacking density is too high. It is recommended to adjust the gap to 20% of the container height," to reduce the risk of human error. The system can also identify high-frequency risk operations through interference factor analysis, such as if a driver frequently opens the container, and optimize management processes accordingly, such as strengthening training or adopting intelligent lock control for the container door.

[0154] It is important to note that the refrigeration box control decision index BR is recalculated to influence the species sensitivity coefficient weighting factor T1, thereby adjusting the importance of the species sensitivity coefficient TSE in the temperature change impact unit. This adjusts the calculation result of the dynamic temperature change impact index TS, thus radiating to all units in the monitoring and analysis module, forming a cyclical iterative form that interconnects and intertwines multiple units. The iterative processing of the matrix is ​​as follows:

[0155] First, the iterative formula is as follows:

[0156]

[0157] Then, the iteration termination condition is set. This embodiment is based on two termination conditions to achieve iteration convergence. The iteration terminates when either of the following two conditions is met:

[0158] Termination condition 1: The maximum number of iterations is 50 to avoid getting stuck in an infinite loop;

[0159] Termination condition two: In three consecutive iterations |T1 u+1 -T1 u If the weight change is less than 0.05, it means the weight change is less than 5%.

[0160] in:

[0161] T1 u+1 Refers to the species sensitivity coefficient weighting factor in the (u+1)th iteration;

[0162] T1 u The species sensitivity coefficient weighting factor refers to the u-th iteration;

[0163] RR refers to the learning rate, which ranges from 0.1 to 0.3, balancing convergence speed and stability. The default value is 0.2.

[0164] BRu Refers to the control decision index of the insulated box in the u-th iteration;

[0165] BRS refers to the target threshold of the control decision index for refrigerated containers, which can be set according to the type of agricultural product, such as 1.2 for tropical fruits and 1.5 for root vegetables.

[0166] Based on the above, the species sensitivity coefficient (TSE) is the only parameter in the temperature change impact unit that reflects the characteristics of agricultural products. It directly determines the sensitivity of different categories to temperature deviations. The refrigeration container control decision index (BR) integrates temperature stress, cumulative damage, and interference factors, reflecting the overall risk status of the refrigeration container. The two are strongly correlated through the characteristics of agricultural products and regulatory risks. However, the static species sensitivity coefficient (TSE) may not be able to adapt to the dynamic changes in risk during transportation. For example, when the refrigeration container control decision index (BR) rises sharply, the warning priority of highly sensitive species needs to be increased. By adjusting the weight of the species sensitivity coefficient (TSE) through the feedback of the refrigeration container control decision index (BR), adaptive control can be achieved, where the higher the risk, the greater the weight of species sensitivity and the more accurate the warning.

[0167] This iterative approach breaks through the limitations of the traditional static threshold model. Through the closed-loop iteration from the control decision unit of the food storage box to the temperature change influence unit, the system can adjust the weight of sensitive factors according to real-time risks, thus resolving the contradiction between delayed early warning in high-risk situations and excessive intervention in low-risk situations. This forms an intelligent closed-loop form of monitoring, evaluation, control, and re-monitoring.

[0168] To address the differentiated needs of different agricultural products under varying risk scenarios, this approach enables refined control of multiple containers per vehicle to a certain extent, avoiding an unscientific, one-size-fits-all approach. The Dynamic Temperature Change Impact Index (TS) is based on a preset agricultural product type, with stable data sources, making it suitable as an iterative target. The Species Sensitivity Coefficient (TSE) directly influences the species specificity of the Dynamic Temperature Change Impact Index (TS), which forms the basis for calculating the Damage Risk Index (CD) and the Fresh-Keeping Container Control Decision Index (BR). It can be achieved through iteration of the Species Sensitivity Coefficient (TSE), resulting in system-level optimization where a change in one part affects the whole, with control efficiency far exceeding that of adjusting local parameters.

[0169] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the temperature of a cold chain preservation box, characterized in that, Includes the following steps: Step S1: Obtain real-time temperature data, CO2 release rate of agricultural products, cold leakage rate, information on the types of agricultural products in the cold chain preservation box, information on the ethylene concentration in the box, vibration data of the box, material parameters of the box, information on phase change materials, and data on interference factors through the data acquisition module. Step S2: Input the data collected by the data acquisition module into the data processing module. The data processing module performs outlier filtering and standardization on the input data, and synchronizes the processed data with the system clock so that the processed data can be input into the regulatory analysis module. Step S3: Regulatory Analysis Module; By analyzing real-time temperature data, the impact of temperature changes inside and outside the refrigerator on the agricultural products inside is analyzed. The CO2 release rate of the agricultural products is also considered to take into account the respiration rate. The leakage of the refrigerator is analyzed based on the cold leakage rate. The coefficients are adjusted according to the type of agricultural products inside the refrigerator to output a dynamic temperature change impact index. By analyzing the changes in ethylene concentration inside the container and combining it with container vibration data, the damage to agricultural products caused by transportation bumps is considered. The dynamic temperature change impact index calculated at different times is summed to output a damage risk index. By analyzing the material parameters of the cabinet, we can determine the impact of the thickness of the insulation layer and the thermal conductivity of the cabinet material on the insulation performance of the cabinet. Based on the information of phase change materials, we can consider the residual coefficient of cold energy. Furthermore, we can combine the dynamic temperature change influence index and the damage risk index to comprehensively evaluate the control and decision-making of the food storage box. We can also introduce interference factor data and output the control and decision-making index of the food storage box by using the door status data, stacking gap data, condensate generation, and pre-cooling sufficiency information. Step S4: Input the dynamic temperature change impact index, damage risk index, and food storage box control decision index into the response and supervision module. Based on the input data, the response and supervision module performs real-time stress intervention, cumulative damage control, and system-level control decisions.

2. The method for monitoring the temperature of a cold chain preservation box according to claim 1, characterized in that: The monitoring and analysis module includes: a temperature change impact unit, a damage risk unit, and a food storage box control decision unit.

3. The method for monitoring the temperature of a cold chain preservation box according to claim 2, characterized in that: The processing flow of the temperature change influence unit is as follows: Ⅰ: By inputting the real-time temperature value of the preservation box into the temperature change influence unit, and combining it with the optimal storage temperature value and upper and lower temperature limits corresponding to agricultural products, a calculation is performed to serve as the basis for dynamic temperature change influence index analysis; II: By using the information on the types of agricultural products inside the box, the output value of the species sensitivity coefficient can be adjusted for different types of agricultural products, thereby achieving targeted preservation and supervision of the cold chain for agricultural products; III: By obtaining the CO2 release rate of agricultural products in the preservation box, the respiration rate coefficient of crops can be analyzed; IV: By combining the temperature values ​​inside the refrigerator with the external temperature values ​​over multiple time periods, the leakage coefficient of the refrigerator is analyzed, and the dynamic temperature change influence index is finally output.

4. The method for monitoring the temperature of a cold chain preservation box according to claim 3, characterized in that: The processing flow for the damage risk unit is as follows: Ⅰ: By inputting the dynamic temperature change impact index calculated over multiple time intervals into the damage risk unit and summing it in combination with the sampling interval, the cumulative damage caused by continuous deviation from the optimal temperature can be considered. II: The change in ethylene concentration inside the refrigeration box is analyzed by calculating the difference between the current ethylene concentration and the initial ethylene concentration 30 minutes after loading, and combining this with the ethylene risk factor. III: By sampling the acceleration information of the refrigerated container during cold chain transportation multiple times, and based on the total number of samplings of the acceleration information, the vibration damage coefficient is evaluated, and the damage risk index is finally output.

5. The method for monitoring the temperature of a cold chain preservation box according to claim 4, characterized in that: The processing flow of the control decision unit of the food storage box is as follows: Ⅰ: By combining the dynamic temperature change impact index and the damage risk index, the control and decision-making of the food storage box can be comprehensively evaluated; II: The thermal insulation coefficient of the enclosure is evaluated by combining the insulation layer thickness and enclosure material parameters. III: By monitoring the temperature of the refrigerant, the remaining cooling capacity can be determined, thereby obtaining the remaining cooling capacity coefficient; IV: The number of times the door is opened, the duration of the door opening, the stacking density coefficient, the pre-cooling adequacy, and the amount of condensate generated in the interference factor data are used to evaluate the obtained interference factor value. The value of the interference factor is then normalized by combining the maximum value of the corresponding interference factor to finally output the control decision index of the food preservation box.

6. The method for monitoring the temperature of a cold chain preservation box according to claim 1, characterized in that: The outlier filtering in step S2 specifically involves: The continuous data obtained in step S1 is processed by moving average filtering, and instantaneous jump values ​​caused by transportation bumps are removed.

7. The method for monitoring the temperature of a cold chain preservation box according to claim 1, characterized in that: The standardization process in step S2 specifically includes: First, the units of the data acquired in step S1 are standardized and normalized. Then, all the data acquired by the sensors in step S1 are synchronized through the system clock to ensure the direct correlation calculation of multi-dimensional data under the same timestamp.

8. The method for temperature monitoring of a cold chain preservation box according to claim 1, characterized in that: The cold chain preservation box includes an outer shell (1), a control panel (2), a temperature sensor (4) and a Hall effect sensor (6) are provided on the side of the outer shell (1), an infrared CO2 sensor (3) and a temperature sensor (5) are provided on the inner wall of the outer shell (1), an electrochemical ethylene sensor and a pressure sensor are installed at the bottom inside the outer shell (1), and a triaxial accelerometer is installed at the center of the bottom of the outer shell (1).

9. A temperature monitoring system for a cold chain preservation box, characterized in that, It includes a memory, a processor, and computer program code stored in the memory and running and executing on the processor. When the processor executes the computer program code, it performs a method for monitoring the temperature of a cold chain preservation box as described in any one of claims 1-8.

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