A cold chain box constant temperature intelligent control method

Abstract

This application relates to a method for intelligent temperature control of cold chain containers, belonging to the fields of cold chain logistics and constant temperature storage and transportation technology. The method includes: acquiring multi-dimensional temperature and humidity data within the container; constructing a nonlinear thermodynamic model including non-uniform heat capacity region division and various physical parameter inputs; calculating a thermal imbalance factor L characterizing the temperature field uniformity based on the model; comparing L with an adaptive threshold Lth dynamically calculated based on the external ambient temperature; when L is not greater than Lth, executing a first refrigeration control strategy based on PID regulation of compressor speed and superheat feedback regulation of expansion valve opening; when L is greater than Lth, executing a second refrigeration control strategy that increases compressor speed to above rated value and forcibly reduces expansion valve opening to form an asymmetric operating condition. This application can achieve active equalization control of the temperature field inside the cold chain container, quickly eliminate local hot spots, and improve temperature uniformity, system energy efficiency, and operational reliability.

Description

Technical Field

[0001] This application relates to the fields of cold chain logistics and constant temperature storage and transportation technology, specifically to a method for intelligent constant temperature control of cold chain containers. Background Technology

[0002] In cold chain logistics and sectors requiring constant temperature storage and transportation such as pharmaceuticals and food, ensuring the uniformity and stability of the temperature field within the transport container is a key technical indicator for guaranteeing cargo quality and meeting regulatory requirements. Traditional cold chain container temperature control methods generally rely on temperature sensor readings at a single point or a few fixed locations, combined with preset upper and lower temperature thresholds, to start or stop the compressor or adjust the fan to control the average temperature inside the container. The core of this control logic lies in maintaining a macroscopic, average temperature target. This is typically achieved by monitoring temperature changes at specific points, triggering the operation or shutdown of refrigeration equipment when the value exceeds the set range, thereby attempting to maintain the overall temperature inside the container within the preset range.

[0003] However, in existing technologies, this control method based on average temperature or single-point temperature is difficult to effectively address the local temperature imbalance caused by complex changes in physical conditions inside the container, resulting in poor control of the temperature field uniformity within the container. When local hot spots appear inside the container due to cargo obstructing airflow, uneven airflow organization, or changes in evaporator efficiency, existing control systems often cannot detect them in time or take targeted intervention measures, easily leading to situations where the temperature in some areas exceeds the standard while other areas become too cold, affecting the preservation quality of the goods. Summary of the Invention

[0004] This application provides a method for intelligent temperature control of cold chain containers to solve the technical problem that existing technologies struggle to achieve uniform temperature control within the container under complex operating conditions.

[0005] To achieve the above objectives, this application provides the following technical solution: a method for intelligent temperature control of a cold chain container, comprising the following steps: S1: Acquire temperature and humidity data for multiple spatial dimensions within the cold chain container; S2: Construct a nonlinear thermodynamic model. The nonlinear thermodynamic model divides the interior of the box into at least three non-uniform heat capacity regions and uses the anisotropic thermal conductivity of the box wall material, the local convection resistance coefficient caused by cargo stacking, and the dynamic heat transfer efficiency decay factor caused by frost on the evaporator fin surface as input parameters of the nonlinear thermodynamic model. S3: Based on the aforementioned nonlinear thermodynamic model, calculate the thermal imbalance factor L used to characterize the uniformity of the temperature field inside the chamber. The formula for calculating the thermal imbalance factor L is: L = A · (Tmax - Tmin) + B · tcomp; Where Tmax is the highest temperature inside the chamber, Tmin is the lowest temperature inside the chamber, tcomp is the cumulative compressor running time since the last defrosting operation, and A and B are dimensionless weighting coefficients calibrated through experiments. S4: Compare the thermal imbalance factor L with the dynamic adaptive threshold Lth, which is calculated based on the external ambient temperature Tenv using the following formula: Lth = Lbase + C · ln(Tenv / Tref); Where Lbase is the basic imbalance tolerance, C is the correction factor, and Tref is the standard reference temperature; S5: When L ≤ Lth, execute the first refrigeration control strategy. The first refrigeration control strategy is to control the speed Nc of the variable frequency compressor to be continuously adjusted according to the heat load Qload in the box using the PID algorithm, and at the same time control the opening degree Ee of the electronic expansion valve to be adjusted according to the superheat ΔTsh at the evaporator outlet. S6: When L>Lth, execute the second refrigeration control strategy. The second refrigeration control strategy is to increase the speed Nc of the variable frequency compressor to 110%-120% of the rated speed and maintain it for a first preset time, while forcibly reducing the opening Ee of the electronic expansion valve to 30%-50% of the normal operating opening, forming an asymmetrical operating condition of low-pressure side flashing and high-pressure side throttling, so as to generate a low-temperature jet below the normal dew point, which acts on the high-temperature region identified by the thermal imbalance factor L.

[0006] Furthermore, in step S2, the division of the three non-uniform heat capacity regions is based on the flow field distribution characteristics of the airflow inside the box, including the forced convection region near the evaporator, the natural convection stagnation region far from the evaporator, and the local vortex region formed by the stacking of goods. The equivalent heat capacity value Ci of each region is determined by the convective heat transfer coefficient hi and the region volume Vi, where Ci = K · hi^M · Vi^N, and K, M, and N are region characteristic coefficients.

[0007] Furthermore, in step S5, the heat load Qload inside the box is calculated using a simplified heat load estimation model. The calculation formula is: Qload = Ueff · (Tenv - Tin), where Tenv is the external ambient temperature, Tin is the average temperature inside the box, and Ueff is the equivalent comprehensive heat transfer coefficient that integrates the effects of heat leakage from the box, heat from the breathing of the goods, and heat intrusion from opening the door. Ueff is pre-calibrated through on-site calibration experiments.

[0008] Furthermore, in step S6, after the first preset duration ends, a recovery period of the second preset duration begins, and the compressor speed Nc is driven to linearly decrease to 80% of the normal speed, while the opening degree Ee of the electronic expansion valve is increased.

[0009] Furthermore, when the thermal imbalance factor L is detected to be continuously greater than an upper limit threshold Lmax for more than a third preset time, any currently executed cooling strategy is forcibly interrupted, and electric heating defrosting is initiated, while the speed of the internal circulating fan is increased to the maximum value.

[0010] Further, in step S5, the feedforward quantity is determined based on the heat load prediction value Qloadpred output by the simplified heat load estimation model, and the feedback quantity is calculated by performing PID calculation based on the deviation between the actual superheat ΔTsh and the target superheat ΔTshset. The opening degree Ee of the electronic expansion valve is the algebraic sum of the feedforward quantity and the feedback quantity.

[0011] This application provides a method for intelligent temperature control of cold chain boxes. This method acquires temperature and humidity data in multiple spatial dimensions inside the cold chain box and constructs a nonlinear thermodynamic model that divides the inside of the box into at least three non-uniform heat capacity regions, enabling the system to accurately perceive the complex thermal environment inside the box.

[0012] Based on this model, by introducing the anisotropic thermal conductivity of the enclosure wall material, the local convection resistance coefficient caused by cargo stacking, and the dynamic heat transfer efficiency attenuation factor caused by frost formation on the evaporator fin surface as input parameters, a thermal imbalance factor L is calculated, which integrates the difference between the highest and lowest temperatures inside the enclosure and the cumulative compressor operating time. This quantitatively characterizes the uniformity of the temperature field inside the enclosure and the cumulative effect of the system's operating state. By comparing the thermal imbalance factor L with an adaptive threshold Lth dynamically calculated based on the external ambient temperature, the system can adaptively determine the current thermal equilibrium state.

[0013] When the thermal imbalance factor does not exceed the threshold, the variable frequency compressor speed is continuously adjusted by the PID algorithm based on the heat load inside the box, and the opening of the electronic expansion valve is adjusted by the feedback of the superheat at the evaporator outlet, so as to achieve precise matching of cooling capacity and stable and energy-saving operation of the system. When the thermal imbalance factor exceeds the threshold, the compressor speed is increased to 110%-120% of the rated speed and the opening of the electronic expansion valve is forcibly reduced to form an asymmetric operating condition, so as to generate a low temperature jet below the normal dew point to act on the high temperature area, thereby quickly and specifically eliminating local hot spots.

[0014] Therefore, this solution effectively solves the problem of poor temperature field uniformity control inside the box caused by the simplification of sensing information and the lack of spatial specificity of control commands in traditional control technology. It improves the temperature control accuracy, system reliability and energy efficiency of cold chain boxes under complex working conditions, and ensures the stable quality of stored goods.

[0015] In summary, this application, through nonlinear thermodynamic modeling, quantitative evaluation of thermal imbalance factors, dynamic adaptive threshold determination, and the synergistic effect of dual-mode refrigeration strategies, not only achieves proactive identification and precise intervention of microscopic non-uniformity of temperature field within the chamber, but also possesses adaptive capabilities to changes in the external environment and a fault recovery mechanism under extreme operating conditions, significantly enhancing the overall performance and application value of the cold chain temperature control system. Attached Figure Description

[0016] Figure 1 A flowchart of a method for intelligent temperature control of a cold chain container is provided in this application; Figure 2 This is a flowchart for calculating the opening degree of the electronic expansion valve in step S5. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] In cold chain logistics and sectors requiring constant temperature storage and transportation such as pharmaceuticals and food, ensuring the uniformity and stability of the temperature field within the transport container is a key technical indicator for guaranteeing cargo quality and meeting regulatory requirements. Traditional cold chain container temperature control methods generally rely on temperature sensor readings at a single point or a few fixed locations, combined with preset thresholds to start or stop the compressor or adjust the fan to control the average temperature inside the container. However, this traditional control method has fundamental shortcomings: it cannot effectively detect and respond to local temperature imbalances caused by complex changes in physical conditions inside the container. Data from a limited number of temperature measurement points cannot accurately reflect the details of the dynamic and non-uniform temperature distribution within the three-dimensional space of the container caused by changes in cargo stacking, airflow organization, and the state of refrigeration system components. At the same time, traditional control strategies usually respond to the overall heat load or average temperature, lacking the ability to target and intensify intervention in high-temperature hotspots. As a result, when local thermal imbalances occur inside the container, the control system either reacts with lag or adopts a one-size-fits-all approach of intensified global refrigeration, which is not only energy-intensive but may also cause low-temperature damage in other areas due to over-cooling, and cannot accurately eliminate local hotspots.

[0019] Based on the above issues, please refer to Figure 1, which is a flowchart of a method for intelligent temperature control of a cold chain container provided in an embodiment of this application. The method includes the following steps: S1: Acquire temperature and humidity data for multiple spatial dimensions within the cold chain container; Acquiring temperature and humidity data across multiple spatial dimensions within the cold chain container involves using a sensor network deployed at various locations within the container to collect raw data reflecting the overall and localized thermal and humidity environment. This step is executed by the central control unit or embedded processor of the cold chain container, which is configured to periodically or in real-time receive signals from temperature and humidity sensors distributed across multiple dimensions, including the top, bottom, corners, and gaps between goods. Specifically, temperature data characterizes the instantaneous thermal state at each monitoring point, while humidity data helps determine the dew point and potential frost risk.

[0020] For example, in a standard pharmaceutical cold chain box, high-precision digital temperature sensors can be deployed near the evaporator outlet, the farthest dead corner of the box, the center layer of the stacked goods, and near the door, forming a three-dimensional sensing grid covering the entire box. This multi-dimensional data acquisition method avoids control deviations that may result from relying on data from a single measuring point, ensuring the completeness and representativeness of the information used to subsequently construct thermodynamic models and calculate control parameters. This provides a fundamental input for accurately sensing the spatial heterogeneity of the temperature field within the box.

[0021] This step aims to comprehensively capture the real-time status of the thermal environment inside the chamber. Through high-density spatial sampling, the previously invisible internal temperature distribution is transformed into a quantifiable digital signal, thereby solving the control lag problem caused by sensing blind spots in traditional technologies.

[0022] S2: Construct a nonlinear thermodynamic model. The nonlinear thermodynamic model divides the interior of the box into at least three non-uniform heat capacity regions and uses the anisotropic thermal conductivity of the box wall material, the local convection resistance coefficient caused by cargo stacking, and the dynamic heat transfer efficiency decay factor caused by frost on the evaporator fin surface as input parameters of the nonlinear thermodynamic model. Among them, constructing a nonlinear thermodynamic model can refer to establishing a mathematical model that can simulate the heat transfer process involving complex boundary conditions and dynamic changes in the actual operation of a cold chain container.

[0023] The nonlinear thermodynamic model divides the interior of the enclosure into at least three non-uniform heat capacity regions: a forced convection region near the evaporator, a natural convection stagnation region away from the evaporator, and a local vortex region formed by the stacked goods. The equivalent heat capacity of each region is determined based on the convective heat transfer coefficient and the region's volume, quantifying the differences in the ability of different regions to store and transfer heat. For example, the forced convection region, due to its high airflow velocity, exhibits an equivalent heat capacity that is easily cooled, while the natural convection stagnation region, with its slow airflow, shows a heat capacity characteristic of high temperature inertia.

[0024] The anisotropic thermal conductivity of the enclosure wall material refers to the difference in heat transfer capacity of the enclosure insulation material in different directions (such as the thickness direction and the planar direction). This parameter is used to accurately reflect the non-uniform influence of the wall structure on heat leakage of the enclosure. The local convection drag coefficient generated by cargo stacking is a dimensionless parameter obtained by analyzing the shape and density of cargo placement. It is used to quantify the obstruction of cargo to airflow inside the enclosure, and this drag directly affects the local heat transfer efficiency.

[0025] The dynamic heat transfer efficiency attenuation factor caused by frost formation on the evaporator fins is a correction coefficient estimated based on compressor operating time or frost thickness. It is used to correct the evaporator's heat transfer capacity deteriorating due to frost thickening in real time. These parameters together serve as inputs to the nonlinear thermodynamic model, enabling the model to more realistically simulate the thermal behavior of the enclosure under complex operating conditions such as cargo obstruction, uneven airflow, and component performance degradation.

[0026] This step significantly improves the accuracy of the thermal model's prediction of the temperature field inside the box by discretizing the box into multiple non-uniform regions that conform to physical reality and introducing key dynamic boundary parameters, laying a solid physical foundation for subsequent accurate calculation of thermal state indicators.

[0027] S3: Based on the nonlinear thermodynamic model, calculate the thermal imbalance factor L used to characterize the uniformity of the temperature field inside the chamber. The formula for calculating the thermal imbalance factor L is: L=A⋅(Tmax⋅Tmin)+B⋅tcomp; where Tmax is the highest temperature inside the chamber, Tmin is the lowest temperature inside the chamber, tcomp is the cumulative compressor running time since the last defrosting operation, and A and B are dimensionless weighting coefficients calibrated through experiments. The thermal imbalance factor L, used to characterize the uniformity of the temperature field within the chamber, can be calculated by combining the temperature field distribution data output from the aforementioned nonlinear thermodynamic model with system operating time parameters to generate an index that quantifies the degree of thermal imbalance within the chamber. Tmax represents the highest temperature value among all monitoring points within the chamber at the current moment, and Tmin represents the lowest temperature value among all monitoring points within the chamber at the current moment; the difference between the two directly reflects the spatial non-uniformity of the temperature distribution at the current moment. tcomp represents the cumulative operating time of the compressor since the last successful defrosting operation; this parameter indirectly characterizes the degree of frost formation on the evaporator fins and the resulting decline in heat exchange efficiency.

[0028] A and B are dimensionless weighting coefficients calibrated experimentally, used to balance the weights of the temperature range term and the time accumulation term on the overall thermal imbalance. For example, in a calibration experiment, if it is found that the temperature range has a more direct impact on the quality of goods, A can be set to a larger value (e.g., 0.8), while B can be set to a smaller value (e.g., 0.2). Conversely, if the latent imbalance caused by frost during long-term operation is more critical, the weight of B can be adjusted. Through this weighted calculation, the thermal imbalance factor L not only reflects the instantaneous uneven temperature distribution but also relates to the potential risk that long-term operation and evaporator efficiency degradation may exacerbate the uneven temperature distribution.

[0029] This step aims to integrate complex spatial temperature distribution information and time-dimensional system state information into a single criterion, enabling the control system to scientifically characterize the comprehensive imbalance state of the thermal environment inside the chamber, and providing a clear quantitative basis for the adaptive switching of control strategies.

[0030] S4: Compare the thermal imbalance factor L with the dynamic adaptive threshold Lth. The dynamic adaptive threshold Lth is calculated based on the external ambient temperature Tenv using the following formula: Lth=Lbase+C⋅ln(Tenv / Tref); where Lbase is the basic imbalance tolerance, C is the correction coefficient, and Tref is the standard reference temperature. The comparison between the thermal imbalance factor L and the dynamic adaptive threshold Lth indicates whether the current level of thermal imbalance within the enclosure exceeds the allowable range. The dynamic adaptive threshold Lth is not a fixed value but a variable calculated in real-time based on the external ambient temperature Tenv. Tenv is the ambient temperature data obtained from a temperature sensor installed outside the enclosure. Lbase is the basic imbalance tolerance, representing the maximum allowable level of thermal imbalance at the standard reference temperature Tref. C is a correction factor used to adjust the sensitivity of the threshold to changes in ambient temperature.

[0031] The formula Lth=Lbase+C⋅ln(Tenv / Tref) implements a logarithmic adjustment of the threshold value with respect to ambient temperature. For example, when the ambient temperature Tenv is significantly higher than the standard reference temperature Tref (such as in high-temperature transportation scenarios in summer), ln(Tenv / Tref) is positive, leading to an increase in Lth. This means that the system appropriately relaxes its tolerance for temperature field uniformity to avoid frequent triggering of aggressive control modes due to oversensitivity under high-temperature and high-load conditions, resulting in a surge in energy consumption. Conversely, in low-temperature environments, the threshold value decreases accordingly, and the system has stricter requirements for temperature uniformity.

[0032] This step introduces ambient temperature as a correction variable, enabling the control system's judgment criteria to have adaptive capabilities. It can maintain the rationality and economy of the control logic under different external disturbances, avoiding malfunctions or insufficient response caused by applying a one-size-fits-all threshold.

[0033] S5: When L ≤ Lth, the first refrigeration control strategy is executed. The first refrigeration control strategy is to control the speed Nc of the variable frequency compressor to be continuously adjusted according to the heat load Qload in the box using the PID algorithm, and at the same time control the opening degree Ee of the electronic expansion valve to be adjusted according to the superheat ΔTsh at the evaporator outlet. The first refrigeration control strategy can refer to maintaining system stability by using a conventional, continuous, and smooth adjustment method when the internal thermal environment is in a relatively balanced or slightly unbalanced state. Controlling the variable frequency compressor speed Nc based on the internal heat load Qload using a PID algorithm involves first calculating the current real-time heat load using a simplified heat load estimation model (e.g., Qload = Ueff⋅(Tenv - Tin)), and then dynamically adjusting the compressor speed using a proportional-integral-derivative (PID) controller based on the deviation between the actual and target heat load values. For example, when a slight increase in heat load is detected, the PID algorithm outputs an incremental signal, causing the compressor speed to gradually increase to match the increased cooling demand, thereby achieving energy-saving operation.

[0034] The opening degree Ee of the electronic expansion valve is controlled by feedback adjustment based on the evaporator outlet superheat ΔTsh. This can be achieved by collecting the difference between the refrigerant temperature at the evaporator outlet and the saturation temperature corresponding to the evaporation pressure (i.e., superheat), comparing it with the target superheat, and adjusting the expansion valve opening through a feedback loop. Specifically, if the actual superheat is higher than the target value, it indicates insufficient refrigerant supply, so the opening degree is increased; if it is lower than the target value, the opening degree is decreased to ensure that the evaporator is in a high-efficiency heat exchange state and to prevent liquid slugging in the compressor.

[0035] This step aims to achieve a close match between cooling capacity output and actual demand through a composite control method that combines feedforward prediction based on heat load with feedback correction based on superheat. This will maintain stable internal temperature while minimizing system energy consumption and ensuring smooth operation.

[0036] S6: When L>Lth, execute the second refrigeration control strategy. The second refrigeration control strategy is to increase the speed Nc of the variable frequency compressor to 110%-120% of the rated speed and maintain it for the first preset time. At the same time, the opening of the electronic expansion valve Ee is forcibly reduced to 30%-50% of the normal operating opening, forming an asymmetrical operating condition of low-pressure side flashing and high-pressure side throttling, so as to generate a low-temperature jet below the normal dew point, which acts on the high-temperature area marked by the thermal imbalance factor L.

[0037] The second refrigeration control strategy can refer to a strong, asymmetric intervention to quickly correct the temperature distribution when a significant imbalance in the internal temperature field is detected (i.e., L>Lth). Increasing the inverter compressor's speed Nc to 110%-120% of its rated speed and maintaining it for a first preset duration can refer to driving the compressor to operate at overclocked speeds, for example, increasing a compressor with a rated speed of 3000 rpm to 3300-3600 rpm and continuing to run it for a preset time (e.g., 5-10 minutes) to instantly and significantly increase the system's refrigerant mass flow rate and discharge pressure.

[0038] At the same time, the opening degree Ee of the electronic expansion valve is forcibly reduced to 30%-50% of the normal operating opening degree. This can refer to artificially and significantly restricting the refrigerant flow while the compressor is running at high speed, for example, closing the valve with a normal opening degree of 200 pulses to 60-100 pulses.

[0039] This creates an asymmetric operating condition of low-pressure side flashing and high-pressure side throttling: the high-pressure side experiences a sharp increase in pressure due to compressor overspeed, while the low-pressure side experiences a sudden drop in pressure due to valve throttling. The extreme pressure difference causes the refrigerant to undergo violent flashing after the expansion valve, generating a low-temperature gas-liquid two-phase flow (i.e., a low-temperature jet) with a temperature significantly lower than the system's normal operating dew point.

[0040] The action applied to the high-temperature area identified by the thermal imbalance factor L can refer to the control system using the Tmax location information calculated above, adjusting the air guide vanes or activating a local high-power fan to direct the generated low-temperature jet to the area with the highest temperature inside the chamber. For example, if the thermal imbalance factor calculation shows that the right rear corner of the chamber is a high-temperature hotspot, the airflow guiding device will concentrate the low-temperature jet towards that area, using the extremely strong cooling impact to rapidly reduce the local temperature.

[0041] This step aims to generate super-strong cooling output by creating asymmetric refrigeration conditions, breaking local heat accumulation, and quickly eliminating dead zones or hot spots that are difficult to handle by traditional methods. In this way, the uniformity of the temperature field inside the container can be quickly restored in an emergency, ensuring the safety of the goods.

[0042] This application achieves precise perception of the complex thermal environment inside the cold chain container by acquiring multi-dimensional temperature and humidity data and constructing a nonlinear thermodynamic model that includes the division of non-uniform heat capacity regions and input of various dynamic physical parameters. Based on this, by introducing a thermal imbalance factor L that integrates temperature range and compressor operating time, and combining it with an adaptive threshold Lth that dynamically adjusts with ambient temperature, the system can intelligently determine the degree of thermal equilibrium within the container.

[0043] When the system is in normal operating condition, it executes the first refrigeration control strategy based on heat load feedforward and superheat feedback, which realizes fine and continuous adjustment of refrigeration capacity, effectively reduces energy consumption and maintains operational stability. When a significant thermal imbalance is detected, the system immediately switches to the second refrigeration control strategy, which artificially creates an asymmetric operating condition by forcibly increasing the compressor speed to above the rated value and significantly reducing the opening of the electronic expansion valve to generate a low-temperature jet below the normal dew point, which is then precisely applied to the high-temperature area.

[0044] This dual-modal control mechanism not only solves the problem that traditional technologies cannot detect and respond to local temperature imbalances, but also adapts to changes in the external environment through dynamic thresholds. It achieves rapid and targeted elimination of local hot spots through low-temperature jet technology under asymmetric operating conditions, thereby optimizing the overall energy efficiency and reliability of the system while ensuring the stability of cargo storage quality.

[0045] This application achieves a shift from empirical selection to data-driven, precise calibration of the weighting coefficients a1, b1, and c1 through the aforementioned calibration method. By sampling and solving for the optimal solution that minimizes variance under multiple typical steady-state operating conditions, the constructed nonlinear coupled model can be deeply adapted to the physical characteristics of the current cold water tank system.

[0046] The equivalent heat capacity Ci of each region is determined by the convective heat transfer coefficient hi and the region volume Vi, where Ci = K · hi^M · Vi^N, and K, M, and N are region characteristic coefficients. The equivalent heat capacity Ci refers to a comprehensive parameter characterizing the heat storage capacity and resistance to temperature changes of the i-th region. Its value directly determines the rate of temperature rise or fall of the region under the same heat load. The convective heat transfer coefficient hi is a physical quantity reflecting the heat transfer intensity between the fluid and the wall or cargo surface in the i-th region. It is obtained by calculating the average wind speed, fluid properties, and characteristic length in the region, combined with the Nusselt number correlation, or by inverting the measured data from an on-site anemometer. Its function is to quantify the airflow state into a heat transfer capacity index.

[0047] The region volume Vi can refer to the geometric volume occupied by the i-th non-uniform heat capacity region in three-dimensional space. It is derived from the geometric modeling of the box structure dimensions and the cargo stacking position. Its function is to characterize the total amount of material participating in heat exchange in this region.

[0048] The regional characteristic coefficients K, M, and N are dimensionless or dimensional correction parameters calibrated experimentally. K is a proportionality coefficient used to unify dimensions and calibrate the baseline heat capacity level; M is the exponential weight of the convective heat transfer coefficient, used to describe the degree of nonlinear influence of flow velocity changes on the equivalent heat capacity, and M is usually taken between 0.5 and 0.8 to reflect the efficiency of forced convection; N is the exponential weight of volume, used to describe the contribution of spatial scale to thermal inertia, and N is usually taken between 0.8 and 1.0.

[0049] For example, in forced convection regions, due to the large value of hi, the hi^M term in the formula will significantly increase the dynamic response component in the calculated value of Ci, making the model predict rapid temperature changes in this region; while in natural convection stagnation regions, the hi value is extremely small, causing Ci to be mainly dominated by Vi^N, exhibiting large thermal inertia, i.e., slow temperature changes.

[0050] The calculation formula Ci = K · hi^M · Vi^N introduces exponential terms M and N to construct a nonlinear coupling relationship between heat transfer intensity and spatial volume, so that the equivalent heat capacity not only depends on the size of the space, but also more profoundly reflects the modulation effect of airflow field characteristics on the heat transfer process.

[0051] Therefore, the equivalent heat capacity value determined in this step can provide high-precision partitioning parameters for the nonlinear thermodynamic model, ensuring that the model can accurately distinguish the degree of cooling difficulty in different regions when calculating the evolution of the temperature field, thus laying a solid physical foundation for the accurate calculation of the subsequent thermal imbalance factor.

[0052] This application divides the box into a forced convection zone, a natural convection stagnation zone, and a local vortex zone based on the airflow field distribution characteristics, and calculates the equivalent heat capacity of each zone using a nonlinear formula that includes the convective heat transfer coefficient and the regional volume, thereby improving the physical realism of the spatial discretization of the thermodynamic model.

[0053] The high heat transfer coefficient of the forced convection zone and the low flow velocity of the natural convection stagnation zone are differentiated in the formula through exponential weights, enabling the model to accurately capture the differences in thermal inertia between different regions. This zoning and parameterization method based on flow field characteristics works closely with the nonlinear thermodynamic model constructed in the above embodiments, ensuring that the model input parameters are no longer merely static geometric or material data, but dynamically reflect the airflow organization state during actual operation within the chamber.

[0054] Furthermore, the thermal imbalance factor L calculated based on this high-precision model can more accurately identify local hot spots caused by cargo obstruction or airflow short-circuiting, rather than being masked by the overall average temperature. This not only solves the technical problem that traditional models cannot detect differences in local thermal response, but also enhances the adaptability of the control strategy to complex operating conditions, ensuring that the low-temperature jet in the second refrigeration control strategy can accurately act on the real high-temperature area identified by this refined model, thereby significantly improving the temperature uniformity control effect inside the cold chain box.

[0055] In step S5, the heat load Qload inside the box is calculated using a simplified heat load estimation model, and its calculation formula is: Qload = Ueff · (Tenv - Tin); The simplified heat load estimation model is based on the fundamental principle of Newton's law of cooling and aims to quantify the total heat entering the enclosure in real time through a mechanism driven by the internal and external temperature difference. In the formula, Tenv can refer to the ambient temperature outside the cold chain enclosure, typically collected by temperature sensors placed on or near the outer wall of the enclosure, reflecting the intensity of the thermal shock from external heat sources; Tin can refer to the average temperature inside the enclosure, a value representing the overall thermal state of the enclosure obtained by weighted averaging or arithmetic averaging the temperature data from multiple spatial dimensions acquired in step S1.

[0056] Ueff is an equivalent comprehensive heat transfer coefficient that integrates the effects of heat leakage from the container, heat generated by the respiration of goods, and heat intrusion from door opening. This coefficient is not a single physical heat transfer parameter, but a comprehensive correction factor pre-calibrated through on-site calibration experiments. Specifically, Ueff's role is to treat complex heat load sources that are difficult to monitor individually in real time (such as heat conduction from insulation layers, heat generation from the biological metabolism of fresh goods, and hot air intrusion caused by frequent door openings) as a black box, and uniformly map them into a coefficient proportional to the temperature difference.

[0057] For example, in a calibration experiment for a specific model of pharmaceutical cold chain box, technicians simulated different combinations of external ambient temperatures (e.g., 25℃, 30℃, 35℃), different cargo loading rates (e.g., 50%, 80%), and different door opening frequencies (e.g., 2 times per hour, 5 times per hour). They recorded the temperature change curve inside the box and the actual work done by the compressor, and used the least squares method to backfit the Ueff value (e.g., calibrated to 1.85W / ℃) that best reflects the thermal behavior characteristics of the box in actual use scenarios.

[0058] Once the Ueff value is calibrated and written into the control system, the system no longer needs to install sensors separately to monitor whether the door is open or the specific breathing rate of the goods during operation. It only needs to read the real-time Tenv and Tin to quickly calculate the current Qload using the above formula.

[0059] By setting this integrated coefficient and simplifying the calculation method, the computational complexity of the control system can be greatly reduced. At the same time, the influence of various uncertain disturbance factors is implicitly compensated, ensuring that the output heat load estimate has sufficient accuracy and robustness. This provides a reliable feedforward input signal for the subsequent PID continuous adjustment of the variable frequency compressor speed, avoiding mismatch of cooling capacity caused by lag or deviation in heat load estimation.

[0060] Where Tenv is the external ambient temperature, Tin is the average temperature inside the box, and Ueff is the equivalent comprehensive heat transfer coefficient that integrates the effects of heat leakage from the box, heat from the breathing of the goods, and heat intrusion from opening the door. Ueff is pre-calibrated through on-site calibration experiments. The specific sources and determination methods of each parameter further clarify the applicable boundaries and accuracy assurance mechanisms of the model. Tenv, as an external perturbation variable, directly determines the driving force of heat leakage in the enclosure; Tin, as an internal state variable, reflects the current level of heat accumulation within the enclosure, and the difference between the two constitutes the potential difference for heat transfer. The determination of Ueff relies on on-site calibration experiments, which are typically conducted before the enclosure is put into use or during periodic maintenance. By constructing a standard test profile containing typical transportation scenarios, multiple sets of input and output data are collected to solve for the optimal coefficients in reverse.

[0061] This allows Ueff to not only include the inherent thermal insulation performance parameters of the enclosure, but also dynamically incorporate the impact of specific user group operating habits (such as door opening frequency) and typical cargo characteristics (such as respiratory heat intensity). Ueff is used in conjunction with the temperature difference term (Tenv - Tin) to transform nonlinear, multi-source coupled heat load problems into linear algebraic relationships that are easy to calculate in real time.

[0062] Specifically, when the external environment changes drastically or the enclosure undergoes frequent door opening operations, traditional fixed-coefficient models often produce large errors. However, the Ueff model in this embodiment, calibrated under comprehensive operating conditions, has stronger environmental adaptability. This step, by introducing a pre-calibrated equivalent comprehensive heat transfer coefficient, achieves a unified characterization of complex heat load sources, thereby obtaining a real-time and accurate estimate of the heat load Qload inside the enclosure.

[0063] This result provides a key control basis for implementing the first refrigeration control strategy in step S5, enabling the variable frequency compressor to adjust its speed in advance according to actual heat demand, effectively avoiding temperature overshoot, and significantly improving the system's energy efficiency ratio and temperature control stability under normal operating conditions.

[0064] This application achieves low-cost, high-precision real-time sensing of the heat load inside the container by introducing a simplified heat load estimation model and an equivalent comprehensive heat transfer coefficient calibrated on-site. Based on this, by leveraging Ueff's integrated compensation capabilities for container heat leakage, cargo breathing heat, and door opening intrusion heat, the system can cope with diverse real-world transportation scenarios without the need to deploy complex dedicated sensors.

[0065] Furthermore, by directly inputting the calculated Qload as a feedforward into the compressor's PID control loop, the cooling capacity output can closely follow the changing trend of the heat load. This not only eliminates the lag inherent in traditional feedback control but also effectively suppresses temperature fluctuations within the compressor caused by heat load variations. This technique, combining model simplification and parameter calibration, significantly reduces the computational power required for the algorithm while ensuring control accuracy, thus guaranteeing the high efficiency and reliability of the first refrigeration control strategy during long-term operation.

[0066] The present application provides a second refrigeration control strategy execution process and recovery period compressor speed and electronic expansion valve opening change curve. The method further includes entering a second preset period after the first preset time ends, driving the compressor speed Nc to linearly decrease to 80% of the normal speed, and increasing the opening of the electronic expansion valve Ee.

[0067] After the first preset duration ends, a recovery period of the second preset duration begins. The first preset duration can refer to the duration of high-intensity asymmetric cooling in the second cooling control strategy. The end of this duration indicates that the high-temperature region inside the chamber has been effectively suppressed by the low-temperature jet, and the temperature field imbalance has been initially corrected. The second preset recovery period is a transitional phase between the high-intensity intervention mode and the normal stable operation mode. Its function is to provide a buffer window for the system to eliminate residual energy disturbances caused by the drastic parameter adjustments in the previous stage.

[0068] The recovery period can be set according to the chamber's thermal capacity characteristics and the current temperature field convergence rate. For example, when the maximum temperature difference inside the chamber is detected to have decreased to a preset safe range but a local gradient still exists, the recovery period can be set to 5 to 10 minutes. By setting this recovery period, system oscillations caused by the control strategy jumping directly from strong intervention to steady-state maintenance can be avoided, ensuring smooth switching of operating conditions.

[0069] The compressor speed Nc decreases linearly to 80% of its normal speed. Here, normal speed can refer to the baseline operating speed determined by the variable frequency compressor under the first refrigeration control strategy, based on the current heat load PID control. The linear decrease in compressor speed Nc can mean that the control unit uniformly reduces the compressor's operating frequency over time at a predetermined slope, rather than using a step-like shutdown or abrupt speed reduction.

[0070] Setting the target speed to 80% of the normal speed means that during the recovery period, the compressor maintains a cooling output capacity slightly higher than the fully steady-state requirement. This setting aims to utilize the remaining excess cooling capacity to further consolidate the previous cooling results and prevent a temperature rebound in the suppressed high-temperature areas due to excessively rapid withdrawal of cooling capacity. For example, if the normal speed under current operating conditions is 3000 rpm, then during the recovery period, the compressor speed will be controlled to decrease linearly from the over-rated speed (e.g., 3600 rpm) at a rate of 200 rpm per minute until it stabilizes at 2400 rpm (i.e., 80% of 3000 rpm).

[0071] Simultaneously, the opening degree Ee of the electronic expansion valve is increased. This increase in the opening degree Ee is a coordinated operation performed in sync with the compressor's speed reduction. During the execution of the second refrigeration control strategy, the expansion valve opening is forcibly reduced to achieve a high-pressure ratio throttling condition. During the recovery period, as the compressor speed decreases and the system's high-pressure side pressure drops, the expansion valve opening needs to be gradually increased to match the new refrigerant flow demand, preventing the evaporator from experiencing a sharp increase in superheat due to insufficient refrigerant supply or from experiencing return gas superheat.

[0072] The process of increasing the refrigerant opening can be linear or adaptively adjusted based on the rate of change of superheat at the evaporator outlet. The aim is to gradually return the refrigerant flow from the flash evaporation cooling mode to the high-efficiency heat exchange mode. For example, when the compressor speed decreases linearly, the opening of the electronic expansion valve starts at 40% of the normal operating opening and increases synchronously in fixed steps or proportions until it reaches the optimal opening value required to adapt to 80% of the speed. This synchronous linkage between speed and opening maintains the dynamic balance of mass flow rate in the refrigeration cycle, avoiding system fluctuations caused by supply-demand mismatch.

[0073] This application achieves a seamless transition from asymmetric high-power refrigeration conditions to normal stable operation by setting a second preset recovery period and combining a linear decrease in compressor speed to 80% of normal speed with an increased electronic expansion valve opening. The linear decay of compressor speed eliminates the mechanical shock and electrical stress caused by abrupt changes, extending the service life of core components. By locking the recovery period speed at 80% of normal, a moderate excess refrigeration capacity is retained, effectively suppressing the risk of temperature rebound and ensuring the complete elimination of residual thermal gradients within the unit.

[0074] At the same time, the synchronous increase in the opening of the electronic expansion valve ensures real-time matching between refrigerant flow and compressor displacement, maintaining the stability of the two-phase flow state inside the evaporator. This multi-parameter coordinated recovery mechanism not only improves the continuity and robustness of the control process, but also ensures that the cold chain enclosure can quickly and stably return to a high-precision constant temperature control state after undergoing extreme thermal imbalance correction.

[0075] When the thermal imbalance factor L is detected to be continuously greater than an upper limit threshold Lmax for more than a third preset time period, this step is the condition for triggering the system's abnormal protection mechanism. The thermal imbalance factor L, as a comprehensive indicator characterizing the uniformity of the temperature field inside the chamber and the system's operating status, indicates that a severe temperature distribution imbalance has occurred inside the chamber when it is continuously greater than the upper limit threshold Lmax, and this state cannot be corrected by the conventional first refrigeration control strategy or the powerful second refrigeration control strategy.

[0076] The upper limit threshold Lmax is a fixed safety limit higher than the dynamic adaptive threshold Lth, used to define whether the system has entered a fault or extreme condition. The third preset duration is a time delay window set to prevent misjudgment due to instantaneous fluctuations in the sensor or brief environmental disturbances. Only when the state of L>Lmax is continuously maintained for more than this window is it considered a valid trigger signal.

[0077] For example, the upper limit threshold Lmax is set to 15.0 (dimensionless), and the third preset duration is 30 minutes. If the monitoring data shows that the thermal imbalance factor L remains above 16.0 within 30 minutes, the trigger condition is met. If L reaches 16.0 but falls back to 14.0 after 20 minutes, this step is not triggered. This dual judgment logic of numerical limit exceedance + time duration ensures that the system only activates the protection program in extreme cases where the evaporator is suspected of severe frosting and blockage, or a sharp drop in heat exchange efficiency, thus avoiding the impact of frequent start-stop cycles on the equipment.

[0078] Forcefully interrupt any currently executing cooling strategy, activate electric defrosting, and simultaneously increase the internal circulation fan speed to its maximum value.

[0079] This step involves specific corrective and recovery actions performed by the system after confirming extreme operating conditions. Forcibly interrupting any currently executing cooling strategy can mean immediately stopping the compressor's cooling operation and cutting off refrigerant circulation, regardless of whether the system was previously under a first cooling control strategy based on PID regulation or a second cooling control strategy under asymmetrical operating conditions. This prevents continued forced cooling in the event of duct blockage, which could lead to energy waste and equipment damage.

[0080] Activating electric heating defrosting refers to activating the electric heating element pre-placed on the surface of the evaporator fins, using the Joule heating effect to directly and quickly melt the frost layer, thereby eliminating physical barriers that hinder airflow and heat exchange.

[0081] Increasing the internal fan speed to its maximum value can refer to driving the fan at its rated maximum speed during the defrosting process and initially after defrosting. On one hand, the high-speed airflow can accelerate the removal of moisture generated during defrosting, preventing secondary icing; on the other hand, during cooling pauses, the maximum fan speed helps to agitate stagnant air inside the cabinet, promoting a more even distribution of residual cold energy and preventing localized excessively high or low temperatures.

[0082] The forced interruption of cooling strategy cuts off ineffective energy input, while the activation of electric heating defrosting provides an active and efficient heat source to quickly remove obstacles. Combined with the maximum increase in the speed of the circulating fan, it not only accelerates the defrosting process and moisture removal, but also enhances the mixing effect of airflow inside the chamber, effectively preventing drastic temperature fluctuations during defrosting.

[0083] Please refer to Figure 2 As shown, the feedforward is determined based on the heat load prediction value Qloadpred output by the simplified heat load estimation model. The feedforward can refer to the electronic expansion valve opening adjustment component pre-calculated to offset known or predictable heat load disturbances. This feedforward is determined based on the heat load prediction value Qloadpred output by the aforementioned simplified heat load estimation model, and its function is to predict the trend of refrigerant flow rate changes required by the system in the early stages of changes in the external thermal environment.

[0084] Specifically, the control system inputs the Qloadpred calculated in real time to the feedforward control module. This module stores a mapping table or function curve between heat load and expansion valve opening, thereby directly looking up the table or calculating the corresponding feedforward opening value.

[0085] For example, when a sudden increase in the ambient temperature Tenv is detected, causing Qloadpred to increase instantaneously by 20%, the feedforward control module immediately outputs a positively increasing opening increment signal. This causes the electronic expansion valve to open wider before the superheat actually deviates, matching the increased refrigerant demand. This model-predictive feedforward mechanism significantly overcomes the response lag problem caused by waiting for errors in traditional pure feedback control, laying the foundation for subsequent rapid stabilization.

[0086] The feedback quantity is calculated using PID control based on the deviation between the actual superheat ΔTsh and the target superheat ΔTshset. This feedback quantity can be a closed-loop correction component used to eliminate system steady-state errors and unmodeled disturbances. The feedback quantity is obtained by collecting the actual superheat ΔTsh at the evaporator outlet, comparing it with the preset target superheat ΔTshset to obtain the deviation value, and then inputting this deviation value into the proportional-integral-derivative (PID) controller for calculation.

[0087] The actual superheat ΔTsh is calculated from real-time data from temperature and pressure sensors installed on the evaporator outlet pipe, while the target superheat ΔTshset is a constant or dynamic setpoint determined based on the compressor's operating conditions and safety margin. The PID calculation process includes: the proportional term reacts immediately to the current deviation, the integral term accumulates historical deviations to eliminate steady-state error, and the derivative term predicts the deviation trend to suppress overshoot.

[0088] For example, if the actual superheat ΔTsh is 1.5K lower than the target value ΔTshset due to fluctuations in cargo respiration heat, the PID controller will quickly calculate a negative opening correction value based on the set parameters, instructing the electronic expansion valve to close less until the deviation returns to zero. This step compensates for potential inaccuracies in the feedforward model and handles random disturbances that the feedforward model cannot cover, such as fluctuations in fan speed and small changes in refrigerant charge, ensuring long-term stability of the superheat.

[0089] The opening degree Ee of the electronic expansion valve is the algebraic sum of the feedforward and feedback quantities. The final opening degree Ee of the electronic expansion valve is the linear superposition of the output signals from the feedforward and feedback control paths. In practice, the controller algebraically adds the calculated feedforward opening component to the calculated feedback opening component, and the resulting sum signal serves as the final pulse sequence driving the stepper motor of the electronic expansion valve. This synthesis method achieves an organic combination of coarse and fine adjustment: the feedforward quantity handles large, predictable load changes, providing rapid main action; the feedback quantity handles fine adjustment, eliminating residual errors.

[0090] For example, in a single thermal shock event caused by a door opening, the feedforward may contribute 80% of the total opening change, causing the valve to open rapidly to prevent the evaporator from drying out. The remaining 20% ​​is dynamically supplemented or reduced by the feedback based on the actual superheat recovery, preventing excessive opening and liquid slugging. Thus, the opening Ee of the electronic expansion valve can respond quickly to drastic fluctuations in heat load while maintaining extremely high control precision, ensuring that the evaporator always operates within its optimal heat exchange efficiency range, while simultaneously guaranteeing the safe operation of the compressor.

Claims

1. A method for intelligent temperature control of a cold chain container, characterized in that, Includes the following steps: S1: Acquire temperature and humidity data for multiple spatial dimensions within the cold chain container; S2: Construct a nonlinear thermodynamic model. The nonlinear thermodynamic model divides the interior of the box into at least three non-uniform heat capacity regions and uses the anisotropic thermal conductivity of the box wall material, the local convection resistance coefficient caused by cargo stacking, and the dynamic heat transfer efficiency decay factor caused by frost on the evaporator fin surface as input parameters of the nonlinear thermodynamic model. S3: Based on the aforementioned nonlinear thermodynamic model, calculate the thermal imbalance factor L used to characterize the uniformity of the temperature field inside the chamber. The formula for calculating the thermal imbalance factor L is: L = A · (Tmax - Tmin) + B · tcomp; Where Tmax is the highest temperature inside the chamber, Tmin is the lowest temperature inside the chamber, tcomp is the cumulative compressor running time since the last defrosting operation, and A and B are dimensionless weighting coefficients calibrated through experiments. S4: Compare the thermal imbalance factor L with the dynamic adaptive threshold Lth, which is calculated based on the external ambient temperature Tenv using the following formula: Lth = Lbase + C · ln(Tenv / Tref); Where Lbase is the basic imbalance tolerance, C is the correction factor, and Tref is the standard reference temperature; S5: When L ≤ Lth, execute the first refrigeration control strategy. The first refrigeration control strategy is to control the speed Nc of the variable frequency compressor to be continuously adjusted according to the heat load Qload in the box using the PID algorithm, and at the same time control the opening degree Ee of the electronic expansion valve to be adjusted according to the superheat ΔTsh at the evaporator outlet. S6: When L > Lth, execute the second refrigeration control strategy. The second refrigeration control strategy is to increase the speed Nc of the variable frequency compressor to 110%-120% of the rated speed and maintain it for a first preset time, while forcibly reducing the opening Ee of the electronic expansion valve to 30%-50% of the normal operating opening, forming an asymmetrical operating condition of low-pressure side flashing and high-pressure side throttling, so as to generate a low-temperature jet below the normal dew point, which acts on the high-temperature region identified by the thermal imbalance factor L.

2. The method according to claim 1, characterized in that, In step S2, the division of the three non-uniform heat capacity regions is based on the flow field distribution characteristics of the airflow inside the box, including the forced convection region near the evaporator, the natural convection stagnation region far from the evaporator, and the local vortex region formed by the stacking of goods. The equivalent heat capacity value Ci of each region is determined by the convective heat transfer coefficient hi and the region volume Vi, where Ci = K · hi^M · Vi^N, and K, M, and N are region characteristic coefficients.

3. The method according to claim 1, characterized in that, In step S5, the heat load Qload inside the box is calculated using a simplified heat load estimation model. The calculation formula is: Qload = Ueff · (Tenv - Tin), where Tenv is the external ambient temperature, Tin is the average temperature inside the box, and Ueff is the equivalent comprehensive heat transfer coefficient that integrates the effects of heat leakage from the box, breathing heat from the cargo, and heat intrusion from opening the door. Ueff is pre-calibrated through on-site calibration experiments.

4. The method according to claim 1, characterized in that, In step S6, after the first preset duration ends, a recovery period of the second preset duration begins, and the compressor speed Nc is driven to decrease linearly to 80% of the normal speed, while the opening degree Ee of the electronic expansion valve is increased.

5. The method according to claim 1, characterized in that, When the thermal imbalance factor L is detected to be continuously greater than an upper limit threshold Lmax for more than a third preset time, any currently executed cooling strategy is forcibly interrupted, and electric heating defrosting is started, while the speed of the internal circulating fan is increased to the maximum value.

6. The method according to claim 1, characterized in that, In step S5, the feedforward quantity is determined based on the heat load prediction value Qloadpred output by the simplified heat load estimation model, and the feedback quantity is calculated by performing PID calculation based on the deviation between the actual superheat ΔTsh and the target superheat ΔTshset. The opening degree Ee of the electronic expansion valve is the algebraic sum of the feedforward quantity and the feedback quantity.

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