Cold chain cabinet air flow control method and system based on independent air duct distribution

Abstract

The present application relates to the field of cold chain cabinet control, in particular to a cold chain cabinet air flow control method and system based on independent air duct distribution, by real-time statistics of each temperature zone static pressure and extraction of fluctuation characteristics, differential precision control is carried out according to fluctuation type, air flow coupling interference is eliminated, at the same time, angle time sequence and environmental parameters are obtained before door opening, cold air overflow and heat shock amount are calculated, results are estimated in advance and PID is driven to adjust quickly, temperature overshoot is eliminated, further, coupling difference between actual correction amount and deviation is calculated by statistics of fine adjustment and quick adjustment results, parameter drift is judged when exceeding threshold value, and static pressure reference and feedforward gain are corrected reversely, so that the system has long-term self-adaptive ability and improves storage stability.

Description

Technical Field

[0001] This invention relates to the field of cold chain cabinet control, specifically to a method and system for controlling the airflow of cold chain cabinets based on independent air duct distribution. Background Technology

[0002] With the rapid development of the pharmaceutical cold chain, fresh food e-commerce and biological product transportation industries, multi-temperature zone cold chain cabinets, as core storage equipment, are directly related to the quality and safety of stored items in terms of airflow organization control precision and thermal stability. Currently, mainstream multi-temperature zone cold chain cabinets usually use a single evaporator combined with damper opening and closing to achieve cold capacity distribution, or configure an independent refrigeration system for each temperature zone. Their airflow control strategy is generally based on passive adjustment based on return air vent temperature feedback.

[0003] However, traditional technologies still have shortcomings. First, traditional solutions rely on mechanical dampers or a single fan for global air supply, and there is a lack of physical air duct isolation between temperature zones. This leads to cold air cross-flow due to pressure imbalance between adjacent temperature zones. When a temperature zone experiences a sudden change in heat load due to opening the door to retrieve goods or storing goods, it triggers a chain reaction of temperature disturbances in multiple temperature zones, making it difficult to meet the differentiated storage environment requirements of different types of cold chain products.

[0004] Secondly, in terms of control strategies, traditional methods typically only activate cooling regulation after the temperature deviates from the set threshold by a certain range. This lack of prediction of environmental changes and early response makes it difficult to maintain stable temperature control accuracy.

[0005] To address the aforementioned technical problems, this invention proposes a solution. Summary of the Invention

[0006] This invention addresses the aforementioned technical deficiencies by real-time statistical analysis of static pressure in each temperature zone and extraction of fluctuation characteristics. It employs differentiated and precise control based on fluctuation type to eliminate airflow coupling interference. Simultaneously, it acquires angle timing and environmental parameters before door opening, calculates cold air overflow and thermal shock, predicts results in advance, and drives PID rapid adjustment to eliminate temperature overshoot. Furthermore, by statistically analyzing the results of fine-tuning and rapid adjustment, it calculates the coupling difference between the actual correction and the deviation. When the threshold is exceeded, parameter drift is determined, and the static pressure reference and feedforward gain are corrected in reverse, enabling the system to possess long-term adaptive capability and improving storage stability. Therefore, it proposes a cold chain cabinet airflow control method and system based on independent air duct allocation to solve the aforementioned technical defects.

[0007] The objective of this invention can be achieved through the following technical solution: a cold chain cabinet airflow control method based on independent air duct distribution, comprising the following steps:

[0008] Step 1: Divide the cold chain cabinet into temperature zones, namely the first low temperature zone, the second low temperature zone, and the third low temperature zone, with different temperature zones corresponding to different storage conditions;

[0009] Step 2: Statistically analyze the static pressure in different temperature zones, plot the real-time static pressure fluctuation curve, compare the static pressure fluctuation curve with the static pressure setting result, extract the fluctuation characteristics, and send them to the independent air duct management module.

[0010] Step 3: Update the control parameters of the independent air ducts based on the identified fluctuation characteristics;

[0011] Step 4: Obtain the temperature zone opening requirements of the cold chain cabinet through the cloud, calculate the static pressure opening disturbance and temperature opening disturbance of the temperature zone before the temperature zone opening requirements, and generate environmental change prediction results in advance based on the static pressure opening disturbance and temperature opening disturbance.

[0012] Step 5: The independent air duct management module fine-tunes the air supply between multiple temperature zones based on the received fluctuation characteristics, and quickly adjusts a single temperature zone based on the environmental change prediction results of the single temperature zone.

[0013] Step Six: The post-feedback adjustment module statistically analyzes the fine-tuning and rapid adjustment results from Step Four, and compares the adjustment results with the fluctuation characteristics and environmental change prediction results from Steps Two and Four, adjusting the generation parameters of the fluctuation characteristics and environmental change prediction results.

[0014] In a preferred embodiment of the present invention, the fluctuation features extracted in step two include: extracting the maximum overshoot, rise slope, and fluctuation frequency of the static pressure fluctuation curve relative to the static pressure setting result.

[0015] When the maximum overshoot exceeds the first preset threshold, or the fluctuation frequency exceeds the preset frequency band range, a duct coupling disturbance marker is generated and sent to the independent duct management module as the core identifier of the fluctuation characteristics.

[0016] In a preferred embodiment of the present invention, the method for updating the control parameters of the independent air duct in step three is as follows:

[0017] Based on the identified fluctuation characteristics, at least one target parameter should be adjusted. Specific target parameter types include: the reference speed value of the circulating fan in the independent air duct of each temperature zone, the PID proportional gain coefficient of the frequency converter, and the opening response rate of the evaporator bypass valve.

[0018] When the fluctuation characteristic indicates high-frequency, low-amplitude fluctuations, the PID proportional gain coefficient should be adjusted first. High-frequency, low-amplitude fluctuations are defined as fluctuation frequencies exceeding a set threshold and maximum overshoot below a set threshold.

[0019] When the fluctuation characteristic is low-frequency high-amplitude fluctuation, the fan speed reference value is adjusted first. The low-frequency high-amplitude fluctuation is defined as a fluctuation frequency lower than a set threshold and a maximum overshoot greater than a set threshold.

[0020] In a preferred embodiment of the present invention, the static pressure door opening disturbance and the temperature door opening disturbance are obtained as follows:

[0021] Acquire the door opening angle timing signal and the ambient temperature and humidity outside the door for this temperature zone;

[0022] The cold air overflow rate is calculated based on the door opening angle timing signal, and the heat load impact is generated by combining the temperature and humidity of the outside environment.

[0023] The heat load impact is mapped to predicted values ​​of static pressure disturbance and temperature disturbance, wherein the predicted value of static pressure disturbance is positively correlated with the duration of door opening, and the predicted value of temperature disturbance is positively correlated with the temperature difference between the door and the outside environment.

[0024] In a preferred embodiment of the present invention, the method for obtaining the environmental change prediction results is as follows:

[0025] The predicted values ​​of static pressure disturbance and temperature disturbance are input into the pre-trained thermal field feedforward model, and the predicted values ​​of temperature field distribution and static pressure distribution in the temperature zone after the door is opened are output as the prediction results of environmental change.

[0026] Among them, the thermal field feedforward model is a lightweight neural network model obtained by pre-simulation training based on the physical structure of the cold chain cabinet.

[0027] In a preferred embodiment of the present invention, the method by which the independent air duct management module rapidly adjusts a single temperature zone is as follows:

[0028] The temperature field distribution forecast in the environmental change prediction results is used as the feedforward compensation amount and superimposed on the real-time feedback value of the return air temperature sensor in this temperature zone to form a composite deviation signal.

[0029] Based on the composite deviation signal, the compressor cooling capacity and fan air volume of the independent evaporator in this temperature zone are dynamically adjusted through a closed-loop PID algorithm until the measured temperature at the return air inlet enters the allowable deviation zone of the target temperature zone.

[0030] In a preferred embodiment of the present invention, the method for coupling and comparing the adjustment results with the fluctuation characteristics and the environmental change prediction results in step six is ​​as follows:

[0031] Calculate the actual static pressure correction and actual temperature correction for both the fine-tuning and rapid adjustment results;

[0032] Calculate the first coupling difference between the actual static pressure correction and the static pressure deviation in the fluctuation characteristics, and the second coupling difference between the actual temperature correction and the temperature prediction deviation in the environmental change prediction results.

[0033] When the first coupling difference or the second coupling difference exceeds the preset residual threshold, it is determined that there is parameter drift in the static pressure setting result in step two or the environmental change prediction model in step four.

[0034] In a preferred embodiment of the present invention, adjusting the generation parameters in step six specifically includes:

[0035] The objective is to minimize the first and second coupling differences, and then reverse-correct the baseline value of the static pressure setting result and the output gain coefficient of the thermal field feedforward model.

[0036] The present invention also proposes a cold chain cabinet airflow control system based on independent air duct allocation, including a temperature zone management module, a static pressure balance module, an independent air duct management module, a dynamic feedforward calculation module, and a feedback adjustment module.

[0037] The temperature zone management module divides the temperature zones;

[0038] The static pressure balance module is used to generate real-time static pressure fluctuation curves and extract fluctuation characteristics.

[0039] The dynamic feedforward calculation module calculates the static pressure opening disturbance and temperature opening disturbance of the temperature zone and generates environmental change prediction results.

[0040] The independent air duct management module controls the air duct parameters based on fluctuation characteristics and environmental change prediction results.

[0041] The post-feedback adjustment module is used to adjust the generation parameters of fluctuation characteristics and environmental change prediction results.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. In this invention, by performing real-time statistics on the static pressure of each temperature zone and plotting the fluctuation curve, quantitative features such as the maximum overshoot, the rise slope, and the fluctuation frequency are extracted. Based on different fluctuation types such as high frequency and low amplitude or low frequency and high amplitude, the fan speed reference value, PID proportional gain coefficient, or bypass valve opening response rate are adaptively adjusted to achieve differentiated and precise control of each independent air duct. This eliminates airflow coupling interference between temperature zones from both physical isolation and dynamic adjustment levels, enabling multiple temperature zones to operate stably for a long time under their respective set storage conditions.

[0044] 2. Before the door opening action occurs, the present invention also acquires the door opening angle timing signal and the temperature and humidity of the outside environment, calculates the cold air overflow rate and heat load impact, and uses a pre-trained thermal field feedforward model to output the temperature field and static pressure distribution prediction results in advance. Then, the prediction results are used as feedforward compensation and superimposed on the return air outlet temperature feedback value to form a composite deviation signal to drive the closed-loop PID algorithm for rapid adjustment, eliminating the temperature overshoot and heat island blind zone problems caused by door opening disturbance, greatly shortening the recovery time after the temperature zone is disturbed, and achieving a balance between high precision and low power consumption.

[0045] 3. This invention also statistically analyzes the fine-tuning and rapid adjustment results, calculates the first coupling difference between the actual static pressure correction and the static pressure deviation in the fluctuation characteristics, and the second coupling difference between the actual temperature correction and the temperature prediction deviation in the environmental change prediction results. When the residual exceeds the threshold, parameter drift is determined, and then the static pressure setting benchmark value and the output gain coefficient of the thermal field feedforward model are corrected in reverse with the goal of minimizing the double residual. This enables the control system to have adaptive capability under long-term operation, effectively distinguishing whether the root cause of temperature deviation is external disturbance or internal parameter degradation, and improving the stability of the cold chain storage system. Attached Figure Description

[0046] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 This is a system block diagram of the present invention;

[0048] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1: Please refer to Figure 1 - Figure 2 As shown, the airflow control method for cold chain cabinets based on independent air duct allocation includes the following steps:

[0051] The cold chain cabinet is divided into temperature zones: a first low temperature zone, a second low temperature zone, and a third low temperature zone. Different temperature zones correspond to different storage conditions, and each temperature zone is equipped with an independent evaporator, a circulating fan, and an independent return air duct.

[0052] The static pressure in different temperature zones is statistically analyzed, and real-time static pressure fluctuation curves are plotted. The static pressure fluctuation curves are compared with the static pressure setting results, the fluctuation characteristics are extracted, and the data is sent to the independent air duct management module.

[0053] MEMS static pressure sensors are installed in the return air chambers of each temperature zone to collect the voltage signals output by the static pressure sensors in real time. After A / D conversion, the signals are converted into the actual static pressure value Pi(t), where i represents the temperature zone number (i=1,2,3) and t represents the sampling time. A static pressure fluctuation curve is plotted using a sliding time window method with time t as the horizontal axis and static pressure value Pi(t) as the vertical axis. The width W of the sliding window is set to 60 seconds, and the sliding step size is set to 1 second.

[0054] Specifically, for the i-th temperature zone, the static pressure fluctuation curve segment at time t is derived from the dataset. The curve represents the continuous change trajectory of static pressure over time in this temperature region over the most recent W seconds.

[0055] The extraction of volatility characteristics includes three quantitative indicators: maximum overshoot, rise slope, and volatility frequency. The specific calculation methods are as follows:

[0056] First, calculate the maximum positive deviation of the curve from the static pressure setpoint within the sliding window, i.e., the maximum overshoot.

[0057] Next, calculate the maximum rate of change of the static pressure value within the window as it rises from below the set value to the peak value, i.e., the rate of increase.

[0058] The number of times the static pressure value crosses the static pressure setpoint within the window is counted again, and the fluctuation frequency is calculated accordingly.

[0059] When the maximum overshoot exceeds the preset threshold or the fluctuation frequency exceeds the preset frequency band, the control system determines that there is abnormal airflow disturbance in the temperature zone, and then generates a duct coupling disturbance flag, and sends the flag along with the three values ​​of maximum overshoot, rise slope and fluctuation frequency to the independent duct management module.

[0060] After receiving fluctuation characteristic data from the static pressure monitoring link, the independent air duct management module performs differentiated control parameter update operations according to the specific type of fluctuation characteristics.

[0061] When the fluctuation characteristics are high frequency but low maximum overshoot, the control system judges the fluctuation as high frequency low amplitude fluctuation. This mode is usually caused by pulsating airflow generated by the rotation of the fan itself or small vortices in the duct. It belongs to the disturbance mode with low energy but high frequency.

[0062] To address such fluctuations, the control system prioritizes adjusting the PID proportional gain coefficient of the frequency converter. By slightly increasing or decreasing the proportional gain, high-frequency oscillations are suppressed. In specific operation, the adjustment range of the proportional gain coefficient is related to the degree to which the current fluctuation frequency deviates from the preset frequency band center value. The greater the deviation of the fluctuation frequency, the greater the adjustment range.

[0063] When the fluctuation characteristics are characterized by a low fluctuation frequency but a high maximum overshoot, the control system judges this type of fluctuation as a low-frequency high-amplitude fluctuation. This pattern is usually caused by a significant change in the heat load of the temperature zone, such as a large number of new goods entering the warehouse or the door being opened for a long time, which leads to a fundamental change in the cooling demand.

[0064] In response to such fluctuations, the control system prioritizes adjusting the reference speed of the circulating fan in the independent air duct of that temperature zone. By increasing or decreasing the fan speed, the cooling capacity delivery rate is changed at the source. The compensation amount for the fan speed is calculated and determined based on the relative ratio of the maximum overshoot to the static pressure setpoint.

[0065] After completing the above priority adjustment, if the fluctuation characteristics still do not converge to the preset allowable range within three consecutive fluctuation curve update cycles, the control system further adjusts the opening response rate of the evaporator bypass valve to accelerate the dynamic response of the cooling supply.

[0066] Each temperature zone door of the cold chain cabinet is equipped with a Hall angle sensor. When the door is opened, the sensor outputs the opening angle signal in real time. At the same time, the temperature and humidity sensor installed on the outside of the cabinet continuously collects the ambient temperature and humidity data outside the cabinet door.

[0067] In addition, the control system obtains the expected opening plan information of each temperature zone through the cloud scheduling platform. For example, in the pharmaceutical cold chain scenario, the smart medicine cabinet's medicine retrieval appointment system will push the medicine retrieval opening plan for the next five minutes to the cold chain cabinet control system in advance.

[0068] When the control system detects that a door opening action is about to occur in a certain temperature zone, the dynamic feedforward calculation module immediately starts the quantitative calculation process of the door opening disturbance;

[0069] First, the cold air overflow rate is estimated based on the door opening angle timing signal. The larger the door opening angle, the larger the effective leakage area of ​​the door gap and door frame gap, and the more cold air is leaked out per unit time. At the same time, the difference between the ambient temperature outside the cabinet and the set temperature inside the cabinet, and the difference between the humidity outside the cabinet and the set humidity inside the cabinet are included in the calculation to generate a comprehensive heat load impact.

[0070] The control system maps the heat load impact to predicted values ​​of static pressure disturbance and temperature disturbance. The predicted value of static pressure disturbance increases with the extension of the door opening duration because the longer the cold air overflows, the more significant the drop in static pressure inside the cabinet.

[0071] The predicted temperature disturbance increases as the difference between the ambient temperature outside the cabinet and the set temperature inside the cabinet increases. When the ambient temperature outside the cabinet is much higher than the set temperature inside the cabinet, the heat introduced by opening the door will cause the temperature inside the cabinet to rise rapidly.

[0072] After obtaining the predicted values ​​of static pressure disturbance and temperature disturbance, the control system inputs them into the pre-trained thermal field feedforward model. This model is a lightweight neural network model pre-trained through computational fluid dynamics simulation based on the actual physical structure of the cold chain cabinet. After receiving the input parameters, the trained model can output the predicted values ​​of temperature field distribution and static pressure distribution in the future time range after the door opening action, covering the changing trends of multiple key monitoring points in the temperature zone.

[0073] After receiving the fluctuation characteristics and environmental change prediction results, the independent air duct management module performs a two-layer adjustment operation, namely fine adjustment of air supply between multiple temperature zones and rapid adjustment of a single temperature zone.

[0074] At the multi-temperature zone fine-tuning level, when a certain temperature zone is marked as having duct coupling disturbance, the independent duct management module temporarily offsets the static pressure setpoint of that temperature zone by appropriately reducing the setpoint to offset part of the disturbance effect. At the same time, it performs micro-level linkage compensation on the fan speed of other adjacent temperature zones. The compensation amount is determined based on the difference in static pressure setpoint of adjacent temperature zones. The amplitude of this fine-tuning operation is controlled within a small range. Its purpose is not to replace the main control adjustment, but to reduce the transmission level of coupling disturbance between multiple temperature zones without triggering a violent response in the main control loop of adjacent temperature zones.

[0075] At the level of rapid adjustment of a single temperature zone, the independent air duct management module uses the temperature field distribution prediction in the environmental change prediction results as the feedforward compensation amount. In specific operation, the control system uses the temperature prediction average of all monitoring points in the temperature zone as the feedforward compensation signal of the temperature zone, and superimposes it on the real-time feedback value of the return air temperature sensor to form a composite deviation signal. Since the feedforward compensation signal contains the temperature change trend information that will occur soon, the composite deviation signal can reflect the real impact of door opening disturbance on the temperature control system in advance.

[0076] Subsequently, based on the composite deviation signal, the control system dynamically adjusts the compressor cooling capacity and fan air volume of the independent evaporator in this temperature zone through a closed-loop PID algorithm. When the composite deviation signal indicates that a temperature rise trend is about to occur, the PID controller outputs an enhanced cooling command in advance before the temperature actually rises, driving the compressor and fan to respond in advance.

[0077] Conversely, when the prediction results show that the disturbance is weak, the PID controller reduces the cooling output accordingly to avoid energy waste caused by excessive cooling.

[0078] The entire adjustment process continues until the measured temperature at the return air vent enters the allowable deviation range set for the target temperature zone.

[0079] After each rapid adjustment process is completed, the feedback adjustment module statistically analyzes and evaluates the actual effect of the adjustment. It records the actual static pressure correction and temperature correction applied during the adjustment process. The actual static pressure correction is calculated based on the change in the fan speed reference value before and after the adjustment, reflecting the actual impact of the fan speed adjustment on the static pressure. The actual temperature correction is the difference between the return air temperature at the start and end of the adjustment.

[0080] The feedback adjustment module obtains the first coupling difference and the second coupling difference, wherein the first coupling difference is the absolute value of the deviation between the actual static pressure correction amount and the static pressure correction requirement represented by the fluctuation characteristics, and the second coupling difference is the absolute value of the deviation between the actual temperature correction amount and the temperature prediction deviation represented by the environmental change prediction result.

[0081] When the first coupling difference or the second coupling difference exceeds their respective preset difference thresholds, the control system determines that there is parameter drift in the static pressure setting result in step two or the environmental change prediction model in step four. Parameter drift may be caused by various factors such as zero-point offset of the sensor after long-term use, changes in airflow resistance caused by dust accumulation in the duct, or natural degradation of compressor performance over time.

[0082] After determining that parameter drift exists, the control system starts the parameter self-correction program: when the first coupling difference exceeds the threshold multiple times, it indicates that the static pressure setting reference value is not matched with the current actual state of the equipment. The control system makes small step corrections to the static pressure setting value of the temperature zone, gradually approaching the reasonable setting value under the current operating conditions, and synchronously updates the corrected value to the independent air duct management module as the target reference for subsequent control.

[0083] When the second coupling difference exceeds the threshold multiple times, it indicates that there is a systematic deviation in the predicted output amplitude of the thermal field feedforward model. The control system adjusts the output gain coefficient of the model in the opposite direction. If the actual temperature correction is consistently less than the predicted deviation, the gain coefficient is appropriately increased to enhance the feedforward compensation.

[0084] If the actual temperature correction is consistently greater than the predicted deviation, the gain coefficient should be appropriately reduced to avoid overcompensation.

[0085] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

[0086] Thresholds, preset values, or preset ranges are set for result comparison and analysis to determine whether they are good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of the sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influence conditions. Similarly, the weighting ratio coefficients and influence factors are set based on the magnitude of each parameter's influence on the results. These values ​​are assigned to reflect the overall impact on the results. They are also determined by a combination of large-scale model analysis of the sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influence conditions.

Claims

1. A method for controlling airflow in a cold chain cabinet based on independent air duct distribution, characterized in that, Includes the following steps: Step 1: Divide the cold chain cabinet into temperature zones, namely the first low temperature zone, the second low temperature zone, and the third low temperature zone, with different temperature zones corresponding to different storage conditions; Step 2: Statistically analyze the static pressure in different temperature zones, plot the real-time static pressure fluctuation curve, compare the static pressure fluctuation curve with the static pressure setting result, extract the fluctuation characteristics, and send them to the independent air duct management module. Step 3: Update the control parameters of the independent air ducts based on the identified fluctuation characteristics; Step 4: Obtain the temperature zone opening requirements of the cold chain cabinet through the cloud, calculate the static pressure opening disturbance and temperature opening disturbance of the temperature zone before the temperature zone opening demand occurs, and generate environmental change prediction results in advance based on the static pressure opening disturbance and temperature opening disturbance. Step 5: The independent air duct management module fine-tunes the air supply between multiple temperature zones based on the received fluctuation characteristics, and quickly adjusts a single temperature zone based on the environmental change prediction results of the single temperature zone. Step Six: The post-feedback adjustment module statistically analyzes the fine-tuning and rapid adjustment results from Step Four, and compares the adjustment results with the fluctuation characteristics and environmental change prediction results from Steps Two and Four, adjusting the generation parameters of the fluctuation characteristics and environmental change prediction results.

2. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The fluctuation characteristics extracted in step two include: extracting the maximum overshoot, rise slope, and fluctuation frequency of the static pressure fluctuation curve relative to the static pressure setting result. When the maximum overshoot exceeds the first preset threshold, or the fluctuation frequency exceeds the preset frequency band range, a duct coupling disturbance marker is generated and sent to the independent duct management module as the core identifier of the fluctuation characteristics.

3. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The method for updating the control parameters of the independent air duct in step three is as follows: Based on the identified fluctuation characteristics, at least one target parameter should be adjusted. Specific target parameter types include: the reference speed value of the circulating fan in the independent air duct of each temperature zone, the PID proportional gain coefficient of the frequency converter, and the opening response rate of the evaporator bypass valve. When the fluctuation characteristic indicates high-frequency, low-amplitude fluctuation, the PID proportional gain coefficient should be adjusted first. High-frequency, low-amplitude fluctuation refers to a fluctuation frequency greater than a set threshold and a maximum overshoot less than a set threshold. When the fluctuation characteristic is indicated as low-frequency high-amplitude fluctuation, the fan speed reference value is adjusted first. The low-frequency high-amplitude fluctuation refers to the fluctuation frequency being lower than the set threshold and the maximum overshoot being greater than the set threshold.

4. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The static pressure door opening disturbance and temperature door opening disturbance are obtained as follows: Acquire the door opening angle timing signal and the ambient temperature and humidity outside the door for this temperature zone; The cold air overflow rate is calculated based on the door opening angle timing signal, and the heat load impact is generated by combining the temperature and humidity of the outside environment. The heat load impact is mapped to predicted values ​​of static pressure disturbance and temperature disturbance, wherein the predicted value of static pressure disturbance is positively correlated with the duration of door opening, and the predicted value of temperature disturbance is positively correlated with the temperature difference between the door and the outside environment.

5. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The method for obtaining the environmental change prediction results is as follows: The predicted values ​​of static pressure disturbance and temperature disturbance are input into the pre-trained thermal field feedforward model, and the predicted values ​​of temperature field distribution and static pressure distribution in the temperature zone after the door is opened are output as the prediction results of environmental change. Among them, the thermal field feedforward model is a lightweight neural network model obtained by pre-simulation training based on the physical structure of the cold chain cabinet.

6. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The method by which the independent air duct management module quickly adjusts a single temperature zone is as follows: The temperature field distribution forecast in the environmental change prediction results is used as the feedforward compensation amount and superimposed on the real-time feedback value of the return air temperature sensor in this temperature zone to form a composite deviation signal. Based on the composite deviation signal, the compressor cooling capacity and fan air volume of the independent evaporator in this temperature zone are dynamically adjusted through a closed-loop PID algorithm until the measured temperature at the return air outlet enters the allowable deviation zone of the target temperature zone.

7. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The method for coupling and comparing the adjustment results with the fluctuation characteristics and the environmental change prediction results in step six is ​​as follows: Calculate the actual static pressure correction and actual temperature correction for both the fine-tuning and rapid adjustment results; Calculate the first coupling difference between the actual static pressure correction and the static pressure deviation in the fluctuation characteristics, and the second coupling difference between the actual temperature correction and the temperature prediction deviation in the environmental change prediction results. When the first coupling difference or the second coupling difference exceeds the preset residual threshold, it is determined that there is parameter drift in the static pressure setting result in step two or the environmental change prediction model in step four.

8. The airflow control method for cold chain cabinets based on independent air duct distribution according to claim 1, characterized in that, The adjustment of the generation parameters in step six specifically includes: The objective is to minimize the first and second coupling differences, and then reverse-correct the baseline value of the static pressure setting result and the output gain coefficient of the thermal field feedforward model.

9. A cold chain cabinet airflow control system based on independent air duct distribution, employing the cold chain cabinet airflow control method based on independent air duct distribution as described in any one of claims 1-8, characterized in that, It includes a temperature zone management module, a static pressure balance module, an independent air duct management module, a dynamic feedforward calculation module, and a feedback adjustment module; The temperature zone management module divides the temperature zones; The static pressure balance module is used to generate real-time static pressure fluctuation curves and extract fluctuation characteristics. The dynamic feedforward calculation module calculates the static pressure opening disturbance and temperature opening disturbance of the temperature zone and generates environmental change prediction results. The independent air duct management module controls the air duct parameters based on fluctuation characteristics and environmental change prediction results. The post-feedback adjustment module is used to adjust the generation parameters of fluctuation characteristics and environmental change prediction results.

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