A phase change cold storage refrigeration module and its application in a cold chain transportation system

Abstract

This invention relates to the field of cold chain transportation technology, and discloses a phase change cold storage refrigeration module and its application in a cold chain transportation system. The refrigeration module is configured within the transport vehicle and includes a speed-regulating fan, return air and supply air temperature sensors, a communication gateway, and a microcontroller. The microcontroller acquires the real-time operating speed of the speed-regulating fan and collects return air and supply air temperature data to calculate the actual remaining cooling capacity. The communication gateway sends the actual remaining cooling capacity to a cloud server and receives the remaining effective cooling release time and estimated remaining travel time calculated by the cloud server in conjunction with the predicted external ambient temperature. The microcontroller performs a time-series comparison between the remaining effective cooling release time and the estimated remaining travel time, and controls the speed-regulating fan to execute either constant-temperature cooling release or passive cooling degradation mode based on the comparison result. This invention switches between degradation modes by comparing the remaining effective cooling release time and the estimated travel time, avoiding uncontrolled temperature in the vehicle compartment due to premature depletion of cooling capacity.

Description

Technical Field

[0001] This invention relates to the field of cold chain transportation technology, specifically to a phase change cold storage refrigeration module and its application in a cold chain transportation system. Background Technology

[0002] Phase change cold storage technology is widely used in the cold chain transportation of fresh produce and pharmaceuticals because it does not require a continuous reliance on the vehicle's power source and has stable temperature control. Current phase change cold storage equipment mainly uses the equipment base station to pre-charge the cold storage and uses internal fans to accelerate the convective heat exchange between the phase change material and the air in the vehicle during transportation to maintain the set target temperature.

[0003] However, in actual transportation scenarios, the external environmental heat load is constantly changing due to the influence of external climate, and external heat loss events such as unloading when the compartment is opened and the compartment door seals aging and leaking heat often occur during transportation. Existing cold storage equipment relies solely on temperature sensors inside the compartment to monitor the current status, making it difficult to accurately quantify and correct the actual remaining cold capacity inside the phase change material by combining the instantaneous cooling power of the equipment and environmental disturbance factors. Since it is impossible to pre-calculate the effective cooling time that the current cold capacity can sustain, existing equipment cannot perform supply and demand matching assessments between the remaining cold capacity status and the expected remaining travel time.

[0004] Due to the lack of operational status assessment, when encountering high external heat load or travel delays leading to a negative balance between cooling supply and demand, the existing system lacks an active energy consumption degradation control mechanism; the system can only passively trigger an alarm when the temperature inside the carriage has exceeded the limit, at which point the phase change material has often completely lost its heat exchange capacity; since the equipment cannot autonomously adjust the fan load or relax the temperature control parameters to extend the cooling period before the cooling capacity is exhausted, and cannot coordinate with the external dispatch system in advance to plan the supplementary cooling path for backup network points, it will eventually lead to uncontrolled temperature inside the carriage and damage to the cargo. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a phase change cold storage refrigeration module and its application in a cold chain transportation system. It solves the problems that existing phase change cold storage devices cannot accurately quantify the matching relationship between remaining cold capacity and travel distance, and lack an active degradation control mechanism when cold capacity is insufficient.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a phase change cold storage refrigeration module, configured inside a transport vehicle, including a speed-regulating fan, a return air temperature sensor, a supply air temperature sensor, a communication gateway, and a microcontroller. The speed-regulating fan, the return air temperature sensor, the supply air temperature sensor, and the communication gateway are each electrically connected to the microcontroller. The microcontroller is used to acquire the real-time operating speed of the speed-regulating fan and collect temperature data from the return air temperature sensor and the supply air temperature sensor to calculate the actual remaining cooling capacity. The communication gateway sends the actual remaining cooling capacity to a cloud server and receives the remaining effective cooling release time and the estimated remaining travel time calculated and returned by the cloud server based on the predicted temperature from the external environment. The microcontroller performs a time-series comparison between the remaining effective cooling release time and the estimated remaining travel time, and controls the speed-regulating fan to execute a constant-temperature cooling release or passive cooling degradation mode based on the comparison result.

[0007] The above technical solution compares the remaining effective cooling time with the expected remaining driving time in sequence, and realizes the switching control between constant temperature cooling and passive cooling degradation modes at the bottom layer of the refrigeration module, so as to avoid abnormal temperature caused by premature exhaustion of cooling capacity.

[0008] Furthermore, the specific process by which the microcontroller calculates the actual remaining cooling capacity is as follows: the microcontroller uses the air enthalpy difference formula to perform discrete integral calculation on the instantaneous cooling power of the phase change cold storage refrigeration module to obtain the basic remaining cooling capacity; the microcontroller calculates the time derivative feature of the return air temperature collected by the return air temperature sensor, and when the time derivative feature is detected to be greater than the set environmental disturbance judgment threshold and the duration of the violation exceeds the judgment filter time window parameter, it is determined that there is an external heat loss event; the microcontroller extracts the temperature jump peak feature within the event interval and calculates the cooling capacity penalty equivalent in combination with the preset overall effective heat capacity constant of the carriage, and calls the cooling capacity penalty equivalent to perform a forced step deduction on the basic remaining cooling capacity to obtain the actual remaining cooling capacity.

[0009] This feature identifies external heat loss events by calculating the time derivative of the return air temperature and performs a forced step deduction, which can correct the calculation error of the basic residual cooling capacity caused by the opening of the carriage door or sudden heat leakage.

[0010] Furthermore, the microcontroller internally stores an airflow mapping matrix for converting operating speed into air volumetric flow rate. When the microcontroller detects that the transport vehicle is in a steady-state stationary docking state, it cuts off the drive signal of the speed-regulating fan to obtain the natural heat leakage temperature rise slope under no forced convection heat transfer, and uses the natural heat leakage temperature rise slope to calculate the instantaneous heat leakage power. Subsequently, the microcontroller controls the speed-regulating fan to operate at full load to enter the forced cooling test stage, extracts the forced temperature drop slope and combines it with the instantaneous heat leakage power to back-calculate the actual cooling power, thereby deriving the actual air volumetric flow rate and using this flow rate to update the airflow mapping matrix.

[0011] This feature utilizes the natural heat leakage and forced cooling temperature drop slope under steady-state static docking conditions to back-calculate the actual cooling power and update the air volume mapping matrix, compensating for air volume calculation deviations caused by fan mechanical wear or changes in duct resistance.

[0012] Furthermore, the remaining effective cooling time received by the communication gateway is obtained by the cloud server through the following processing steps: acquiring forward path meteorological data to extract the predicted external environment temperature, and constructing a predicted instantaneous heat load function by combining the comprehensive heat transfer coefficient and total surface area of ​​the external enclosure structure of the transport vehicle; performing definite integral solution on the predicted instantaneous heat load function in the time domain to obtain the absolute time domain heat load, comparing the absolute time domain heat load with the actual remaining cooling capacity, and solving in reverse through integral equation iteration to obtain the unknown time variable representing the node of system cooling capacity depletion, and using the unknown time variable as the remaining effective cooling time.

[0013] This feature predicts the instantaneous heat load function by integration and compares it with the actual remaining cooling capacity, then iteratively solves for the remaining effective cooling release time, enabling the module to assess the cooling capacity based on the time dimension of external predicted meteorological data.

[0014] Furthermore, when the remaining effective cooling time is greater than or equal to the sum of the expected remaining driving time and the system redundancy time, the microcontroller controls the speed-regulating fan to perform the constant-temperature cooling; the microcontroller extracts the actual remaining cooling capacity and looks up the phase state attenuation feedforward gain coefficient in the phase change material attenuation characteristic curve; the microcontroller uses a proportional-integral-derivative control algorithm to process the dynamic deviation sequence between the return air temperature and the target set temperature to obtain the basic feedback control component, and performs a composite operation with the basic feedback control component and the phase state attenuation feedforward gain coefficient to construct a control law, and outputs a pulse width modulation signal after saturation limiting to control the speed-regulating fan.

[0015] This feature utilizes a composite operation of phase state attenuation feedforward gain coefficient and proportional-integral-derivative control to control the fan when there is sufficient residual cooling capacity, thus compensating for the attenuation of heat exchange capacity of phase change materials in the later stage of cooling release.

[0016] Furthermore, when the remaining effective cooling time is less than the sum of the expected remaining travel time and the system redundancy time, the microcontroller controls the speed-regulating fan to execute the passive cooling degradation mode. The microcontroller concatenates the standardized historical return air temperature sequence, the remaining effective cooling time, the expected remaining travel time, and the average predicted external environment temperature into a feature vector, inputs it into the built-in long short-term memory neural network model for forward inference, and outputs a dynamic bias of the target set temperature. The microcontroller limits the dynamic bias based on the maximum safe temperature rise tolerance allowed for the carried goods, increases the operating target temperature, and relaxes the temperature control dead zone to reduce instantaneous load output.

[0017] When the remaining effective cooling time is insufficient, this feature uses a long short-term memory neural network model to output a dynamic bias, combined with a temperature rise tolerance relaxation control dead zone, thereby reducing the instantaneous load output of the refrigeration module to extend the effective cooling time.

[0018] Furthermore, during the execution of the passive cooling degradation mode, the microcontroller calculates the expected time interval for cargo loss based on the dynamic approximation rate of the real-time return air temperature approaching the critical value of the limit cargo loss temperature. When the expected time interval for cargo loss is less than the set physical intervention time threshold, a circuit breaker status message is generated and uploaded via the communication gateway. The communication gateway is also used to receive a reconstructed navigation trajectory message. The reconstructed navigation trajectory message is received by the cloud server in response to the circuit breaker status message. The cloud server performs a topology search of the surrounding backup cooling replenishment points with the current coordinates as the center, and constructs a comprehensive scheduling cost evaluation model based on the road network travel distance, expected travel time, and cooling replenishment resource availability coefficient to traverse and filter the results.

[0019] This feature generates a circuit breaker status message when the expected time interval for cargo damage is too short under passive cold preservation degradation mode. It reconstructs the navigation trajectory using road network and cold replenishment resource information and provides cold replenishment route intervention under abnormal conditions.

[0020] Furthermore, when the reconstructed navigation trajectory message is sent, an abnormal performance electronic evidence generation step is carried out simultaneously. The abnormal performance electronic evidence is obtained by the cloud server by structurally encapsulating the associated timestamp that triggered the warning, the set of path coordinates before and after reconstruction, and the underlying temperature data, and then using a secure hash algorithm to generate an tamper-proof hash digital fingerprint, which is then solidified into a distributed blockchain storage node.

[0021] This feature uses hash digital fingerprints and blockchain storage nodes to structurally encapsulate and solidify the associated data that triggers the warning, ensuring the tamper-proof characteristics of the underlying temperature data related to trajectory reconstruction.

[0022] Furthermore, the communication gateway is also used to receive the updated heat transfer coefficient sent by the cloud server after the transport vehicle terminates its mission, so that the microcontroller can perform low-level parameter overwrite backup; the updated heat transfer coefficient is obtained by the cloud server extracting the net conduction cooling loss including the equivalent deduction of cooling penalty, and using the external environment evolution temperature sequence to re-integrate the actual absolute time-domain heat load. When the absolute value of the relative error between the two is within the effective update range, the error adaptive correction algorithm based on exponential decay weight is invoked to iteratively derive the coefficient in combination with the current deviation ratio.

[0023] This feature utilizes the relative error between the actual net heat loss due to conduction after the completion of the transportation task and the absolute time-domain heat load to iteratively derive and update the heat transfer coefficient, thereby achieving adaptive correction of parameters for changes in the thermal insulation performance of the carriage enclosure structure.

[0024] A second aspect of the present invention provides an application of the aforementioned phase change cold storage refrigeration module in a cold chain transportation system. The phase change cold storage refrigeration module is configured as a core temperature control node in the fresh or pharmaceutical transport vehicle compartment of the cold chain transportation system. The cold chain transportation system collects multi-dimensional physical state parameters through the underlying sensors of the phase change cold storage refrigeration module, and interacts with the geographic meteorological data of the cloud server to perform self-calibration of the air volume mapping matrix for the entire delivery cycle, energy supply and demand time series assessment, variable target parameter degradation control, and emergency route reconstruction intervention operations.

[0025] This feature applies the aforementioned phase change cold storage refrigeration module to the cold chain transportation system. Through the acquisition of physical state parameters and interaction with cloud data, it enables parameter self-calibration, degradation control, and routing intervention throughout the entire cold chain transportation cycle.

[0026] This invention provides a phase change cold storage refrigeration module and its application in a cold chain transportation system. It offers the following advantages: 1. The microcontroller of this invention compares the remaining effective cooling time with the expected remaining travel time in a time sequence, and controls the speed-regulating fan to execute constant temperature cooling or passive cooling degradation mode based on the comparison result; this mechanism changes the control logic of traditional cold storage equipment that only passively alarms after the cold energy is exhausted, and can actively adjust the fan operation strategy according to the matching state of cold energy supply and demand during transportation, so as to avoid the uncontrolled temperature inside the carriage caused by the premature exhaustion of cold energy.

[0027] 2. This invention uses the time derivative characteristics of return air temperature to identify external heat loss events, extracts the peak characteristics of temperature rise, and calculates the equivalent of cooling penalty by combining the effective heat capacity constant of the carriage, and performs a forced step deduction on the basic remaining cooling capacity. This calculation process quantitatively compensates for environmental disturbances such as carriage door opening or sudden heat leakage during actual transportation, eliminates the cumulative error generated by conventional integral calculation, and improves the accuracy of the calculation of actual remaining cooling capacity data.

[0028] 3. When the remaining effective cooling time is insufficient, this invention uses a long short-term memory neural network model to output a dynamic bias of the target set temperature to reduce the instantaneous load output. When the expected time interval for cargo damage is lower than the threshold, it links with the cloud to search for backup cooling network points and send a message to reconstruct the navigation trajectory. When the supply and demand of cooling capacity are in negative balance, this solution can first extend the cooling period by relaxing the temperature control parameters, and then actively guide the transport vehicle to change routes for supplemental cooling in the critical state, thereby reducing the probability of actual cargo damage. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall hardware topology and communication links of the cold chain transportation system of the present invention; Figure 2 This is a schematic diagram of the overall process of the cold chain distribution state composite constant temperature control method based on phase change cold storage of the present invention. Figure 3 This is a schematic diagram of the logic topology for determining external heat flow disturbance events and calculating the equivalent of cooling penalty in this invention. Figure 4 This is a schematic diagram of the closed-loop update architecture for heat leakage power calculation and air volume mapping matrix under docking conditions of the present invention. Figure 5 This is a schematic diagram of the nonlinear physical characteristic curve of the feedforward gain coefficient of the phase state attenuation in the phase change medium of the present invention. Figure 6 This is a schematic diagram of the neural network multidimensional feature fusion and underlying baseline reset signal topology under the passive cooling degradation mode of the present invention; Figure 7 This is a schematic diagram of the data flow topology for the self-learning closed-loop calibration of heat transfer parameters and green carbon emission reduction accounting of this invention. Figure 8 This is a schematic diagram showing the anti-disturbance timing response tracking comparison of the actual return air temperature in the carriage under the composite constant temperature control strategy of this invention. Figure 9 This is a schematic diagram comparing the actual residual cooling capacity consumption decay time sequence of the present invention and the traditional control group under the same operating conditions. Figure 10 This is a schematic diagram of the adaptive updating of long-term evolution parameters of the system and the comprehensive evaluation of carbon emission reduction benefits of the present invention. In this diagram, a is a schematic diagram of the evolution trend of the system's comprehensive heat transfer coefficient as it updates with continuous delivery tasks, and b is a bar chart comparing the absolute emission reduction of the equivalent carbon footprint of the present invention and traditional units.

[0030] Among them, 10, transport carrier; 20, phase change cold storage refrigeration module; 21, speed-regulating fan; 22, return air temperature sensor; 23, supply air temperature sensor; 24, microcontroller; 25, communication gateway; 26, cold storage medium; 27, heat exchange pipeline; 30, cloud server; 40, cold charging station; 41, cold charging pile. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] See attached document Figure 1 The present invention provides a phase change cold storage refrigeration module, wherein the phase change cold storage refrigeration module 20 is deployed inside the transport carrier 10.

[0033] The phase change cold storage refrigeration module 20 is equipped with a cold storage medium 26 and a heat exchange pipeline 27; a speed-regulating fan 21 is configured on the air circulation channel of the phase change cold storage refrigeration module 20; a return air temperature sensor 22 is configured on the return air side of the phase change cold storage refrigeration module 20, and a supply air temperature sensor 23 is configured on the supply air side of the phase change cold storage refrigeration module 20.

[0034] The electrical control terminal of the phase change cold storage refrigeration module 20 integrates a microcontroller 24 and a communication gateway 25; the return air temperature sensor 22, the supply air temperature sensor 23, the speed regulating fan 21, and the underlying communication bus of the transport carrier 10 are respectively electrically connected to the microcontroller 24; the communication gateway 25 establishes a data transmission connection with the microcontroller 24.

[0035] The communication gateway 25 establishes a wireless connection with the external cloud server 30; the phase change cold storage refrigeration module 20 establishes a physical circulation loop of refrigerant with the charging pile 41 of the charging station 40 through the heat exchange pipeline 27.

[0036] See attached document Figure 2 This invention provides a dynamic temperature control and self-calibration scheduling method based on a phase change cold storage refrigeration module, comprising the following steps: S100, the microcontroller 24 acquires the real-time operating speed of the speed-regulating fan 21 according to the set cycle, synchronously collects the temperature data of the return air temperature sensor 22 and the supply air temperature sensor 23, and performs discrete integral calculation on the instantaneous cold release power of the phase change cold storage refrigeration module 20 using the air enthalpy difference formula to obtain the basic remaining cold capacity. S200, microcontroller 24 calculates the time derivative characteristics of return air temperature, and when it is determined that there is an external heat loss event, it performs a cooling capacity penalty deduction on the basic remaining cooling capacity to obtain the actual remaining cooling capacity; S300, when the microcontroller 24 detects that the vehicle is in a stationary parking state, it sends a shutdown command to the speed-regulating fan 21, obtains the heat leakage temperature rise slope in the shutdown state, and after the speed-regulating fan 21 resumes operation, it combines the heat leakage temperature rise slope to calculate the actual cooling power of the system and updates the internal air volume mapping matrix. The updated air volume mapping matrix is ​​then applied to the calculation of the subsequent instantaneous cooling power and the actual remaining cooling capacity. S400, the communication gateway 25 sends the actual remaining cooling capacity to the cloud server 30. The cloud server 30 obtains the estimated remaining driving time and the meteorological data of the forward path, calculates the time-domain heat load to maintain the target temperature, and calculates the remaining effective cooling release time of the phase change cold storage refrigeration module 20 in combination with the actual remaining cooling capacity. S500, when the remaining effective cooling time is greater than or equal to the sum of the expected remaining driving time and the system redundancy time, the microcontroller 24 obtains the phase decay feedforward gain coefficient associated with the actual remaining cooling capacity, combines the phase decay feedforward gain coefficient with the proportional-integral-derivative control algorithm, and outputs a pulse width modulation signal to control the speed-regulating fan 21 to perform constant temperature cooling. S600, when the remaining effective cooling time is less than the sum of the expected remaining travel time and the system redundancy time, the microcontroller 24 triggers the passive cooling degradation mode. The cloud server 30 extracts the maximum safe temperature rise tolerance of the target cargo and sends it to the microcontroller 24 via the communication gateway 25. The microcontroller 24 uses the built-in neural network model to calculate the dynamic bias of the target set temperature and performs a limit based on the maximum safe temperature rise tolerance. Based on the limited dynamic bias, the target set temperature is increased and the temperature control dead zone is relaxed simultaneously. The speed-regulating fan 21 is controlled to perform derated cooling. After the S700 microcontroller 24 performs derating and cooling, it continuously monitors the dynamic approach rate of the return air temperature to the critical value of the limit cargo loss temperature. When the estimated cargo loss time interval calculated based on the approach rate is less than the set physical intervention time threshold, it triggers a circuit breaker warning. The cloud server 30 sends a routing instruction to the associated terminal to navigate to the nearest cooling station 40 based on the comprehensive scheduling cost evaluation model, or dispatches an emergency intervention work order to the manual dispatch center when there is no available cooling station. S800 After the transport vehicle 10 arrives at its final destination, the cloud server 30 extracts the actual cumulative cooling load and deducts the cooling load penalty equivalent, then compares it with the actual absolute time domain heat load, executes a parameter self-learning closed-loop calibration program to update the comprehensive heat transfer coefficient of the external enclosure structure of the transport vehicle 10, and generates a green logistics carbon reduction electronic certificate in combination with the equivalent power consumption.

[0037] In this embodiment, step S100 provided by the present invention may include the following steps in specific implementation: S110, the microcontroller 24 collects the real-time operating speed of the speed-regulating fan 21 at a preset time period, and simultaneously reads the return air temperature detected by the return air temperature sensor 22 and the supply air temperature detected by the supply air temperature sensor 23. The microcontroller 24 can obtain the real-time operating speed of the speed-regulating fan 21 by receiving the feedback pulse signal from the Hall sensor built into the speed-regulating fan 21, or by reading the pulse width modulation duty cycle value currently output by the microcontroller 24 as the speed characterization parameter. For the analog-to-digital conversion and high-frequency noise filtering of the temperature sensor data, the conventional moving average filtering algorithm can be used in this embodiment. The value of the preset time period is based on the thermal inertia response time calibration of the internal air circulation system of the phase change cold storage refrigeration module 20. In this embodiment, the value of the preset time period is in the range of 1 second to 10 seconds.

[0038] S120, the microcontroller 24 stores a pre-calibrated air volume mapping matrix through wind tunnel test. The microcontroller 24 calls the air volume mapping matrix to convert the acquired real-time operating speed data into the actual air volume flow rate at the current moment. This conversion mechanism is based on the empirical coupling mapping relationship between the duct resistance characteristics and the fan output characteristics in fluid mechanics. Under the condition that the inherent airflow resistance characteristics of the phase change cold storage refrigeration module 20 are constant, the specific numerical correspondence between the different speed nodes of the speed-regulating fan 21 and the system output air volume flow rate is constructed. The microcontroller 24 uses a lookup table combined with linear interpolation to analyze and obtain the air volume flow rate at the target speed. The discrete node density of this matrix is ​​configured according to the memory capacity of the microcontroller 24 and the desired airflow resolution.

[0039] S130, the microcontroller 24 calculates the instantaneous cooling power of the phase change cold storage module 20 based on the principle of air enthalpy difference thermodynamics. The specific heat exchange physical process is that when the circulating air flows through the heat exchange pipe 27, convective heat transfer occurs, generating a return air temperature difference. The microcontroller 24 substitutes the actual air volume flow rate and the return air temperature difference data into the air enthalpy difference formula for calculation. The resulting instantaneous cooling power calculation formula is as follows: ; In the formula, for The instantaneous theoretical cold release power of the phase change cold storage module; is the isobaric specific heat capacity constant of air; ρ is the density constant of air; for The volume and air volume of the circulating fan in the air circulation channel at all times; for The return air temperature of the phase change cold storage module at any given time; for The air supply temperature of the phase change cold storage module at any given time; This is the current running time variable.

[0040] S140, the microcontroller 24 records the initial full charge nominal value of the phase change cold storage refrigeration module 20 when it is disconnected from the cold storage facility. This nominal value is determined based on the product of the total physical mass of the filled cold storage medium 26 and the latent heat coefficient of the solid-liquid phase change. The microcontroller 24 performs an accumulation and integration operation on the calculated instantaneous cold release power in the time domain with a preset time period as the discrete time step to obtain the total cold release consumed in the historical operation phase of the equipment. This is to avoid repeated accumulation with the heat loss events calculated in subsequent steps. When the microcontroller 24 determines that there is an external heat loss event as defined in subsequent step S200 and its corresponding temperature recovery period, the temperature recovery period is defined as the time interval from the end of the over-limit state of the external heat loss event until the return air temperature drops again and the absolute difference between it and the reference return air temperature before the event or the current target set temperature is less than the set steady-state recovery tolerance threshold, the direct accumulation and integration calculation of the instantaneous cooling power is paused, and the integration time point is traced back to the initial over-limit moment of the event, and the abnormal integration increment generated during the over-limit confirmation period is eliminated. The microcontroller 24 uses the latest recorded steady-state instantaneous cooling power before the external heat loss event is triggered as a substitute value to continuously perform the substitution integral calculation. This ensures that the accumulated integral only represents the steady-state heat transfer consumption and does not miss the natural conduction cooling loss during this time period. After the temperature recovery period ends, the direct accumulation integral calculation of the instantaneous cooling power is resumed. The microcontroller 24 uses the initial full-charge nominal value of cooling capacity minus the total accumulated cooling capacity to obtain the basic remaining cooling capacity for the current cycle. The specific discrete integral calculation formula is as follows: ; In the formula, for The estimated baseline remaining cooling capacity at any given time; The full-load nominal cooling capacity of the phase change cold storage module at the initial moment of the delivery task; To step in historical discrete time The instantaneous theoretical cooling power is as follows; The step time for discretizing the sampling period; From the initial time to the current time Discrete summation variables over historical time; This is the current running time variable.

[0041] See attached document Figure 3 In specific implementations, step S200 provided by the present invention may include the following steps: S210, the microcontroller 24 extracts the return air temperature data queue of multiple consecutive sampling periods in the storage area and calculates the time derivative feature of the return air temperature at the current moment. The physical essence of this derivative feature represents the dynamic change rate of the internal ambient temperature of the transport vehicle 10. The microcontroller 24 uses a first-order backward difference algorithm to perform derivative operations on the return air temperature values ​​of adjacent time nodes to obtain the initial temperature derivative sequence. For the transient spike signal caused by high-frequency electrical noise coupling of the return air temperature sensor 22 line in the initial temperature derivative sequence, an infinite impulse response low-pass digital filter can be used in this embodiment to smooth it and obtain a stable and reliable temperature derivative feature. The specific filter transfer function construction and cutoff frequency parameter configuration are well known technologies in this field.

[0042] S220, the microcontroller 24 is configured with an environmental disturbance judgment threshold and a judgment filtering time window parameter in its internal non-volatile memory. The selection of the environmental disturbance judgment threshold is based on the natural heat leakage temperature rise limit slope of a conventional sealed insulation box under extreme external temperature difference. In this embodiment, the value range of the environmental disturbance judgment threshold is configured to be from 0.1℃ per second to 0.5℃ per second. The microcontroller 24 compares the value relationship between the temperature derivative characteristics after filtering and the value of the environmental disturbance judgment threshold in real time. When the microcontroller 24 detects that the value of the temperature derivative feature is continuously greater than the environmental disturbance judgment threshold and the duration of the temperature derivative feature exceeding the limit exceeds the judgment filter time window parameter, the value of the judgment filter time window parameter must be configured to be greater than the value of the preset time period in step S110. In this embodiment, the value range of the judgment filter time window parameter is 15 seconds to 30 seconds. The microcontroller 24 marks this continuous state interval as an independently occurring external heat loss event.

[0043] S230, when the condition for determining the occurrence of an external heat loss event is met and the temperature derivative characteristic is detected to change from positive to negative and then drop again, indicating that the car door is closed, the microcontroller 24 extracts the temperature jump peak characteristic within the event interval and calculates the scale of cold loss caused by a single event. The specific physical phenomenon is the instantaneous intrusion of heat caused by the convection of internal and external air due to the opening of the car compartment. The microcontroller 24 extracts the reference return air temperature in the steady state interval before the heat loss event is triggered and the highest return air temperature during the event evolution process. The microcontroller 24 substitutes the two temperature data points mentioned above, along with the overall effective heat capacity constant of the transport vehicle 10 compartment, into the penalty equivalent calculation formula to obtain the cold energy penalty equivalent corresponding to a single discrete event. The overall effective heat capacity constant of the transport vehicle 10 compartment is calculated by the cloud server 30 at the initial stage of the delivery task based on the net air volume heat capacity after deducting the effective volume occupied by the cargo from the physical space volume inside the compartment, and the equivalent coupled heat capacity of the cargo surface and the inner wall of the compartment. The calculation is then sent to the microcontroller 24 via the communication gateway 25.

[0044] The specific formula for calculating the penalty equivalent is as follows: ; In the formula, For the first Penalty for cold air loss caused by the second door opening incident; The overall volumetric heat constant of the 10 carriages of the transport carrier; For the first The peak temperature of the interior environment of the carriage caused by the opening of the second door; For the first The baseline steady-state value of the ambient temperature inside the carriage before the second door opening incident; This is a sequence index variable for the door opening event.

[0045] S240, the microcontroller 24 calls the sum of the cold energy penalty equivalents generated by all independent trigger events accumulated in the register to perform a forced step deduction correction on the basic remaining cold energy calculated in the previous step. This data operation process constructs a multi-dimensional composite evolution state mapping between the ideal attenuation amount of the physical model and the discrete disturbance loss amount of the environment. Since the enthalpy difference calculation inside the system cannot capture the mass loss of cold air that directly overflows at the moment the door is opened, the microcontroller 24 subtracts the sum of all cold energy penalty equivalents that occurred in the historical time domain from the basic remaining cold energy to avoid the cold energy calculation deviating from the actual physical state.

[0046] The specific formula for calculating the cooling capacity deduction is as follows: ; In the formula, for The actual remaining cooling capacity after adjusting for door opening losses; for The estimated baseline remaining cooling capacity at any given time; For the first Penalty for cold air loss caused by the second door opening incident; This is a sequence number index variable for the door opening event; From the initial time to the current time The total number of door opening events that occurred during the period; This is the current running time variable.

[0047] See attached document Figure 4 In specific implementations, step S300 provided by the present invention may include the following steps: S310, the microcontroller 24 periodically acquires the real-time driving speed status data of the vehicle through the vehicle's underlying communication bus, such as the controller local area network bus or the on-board diagnostic system interface. The microcontroller 24 continuously determines that the vehicle is in a stationary parking state with a speed of zero and no external heat loss event is detected, and the duration exceeds the set steady-state waiting threshold. Furthermore, when the difference between the critical value of the extreme cargo loss temperature and the current return air temperature is greater than the set calibration safety tolerance, the closed-loop online calibration program is triggered. In this embodiment, the steady-state waiting threshold ranges from 3 minutes to 10 minutes. After the calibration program is triggered, the microcontroller 24 sends a forced shutdown command to the speed-regulating fan 21 of the phase change cold storage refrigeration module 20 and simultaneously cuts off the pulse width modulation signal of the fan drive circuit to make its operating speed return to zero.

[0048] S320, after the speed-regulating fan 21 completely stops operating, the phase change cold storage refrigeration module 20 enters a natural environment heat leakage state without forced convection heat exchange. At this time, the physical loss of the total cooling capacity of the internal system of the transport carrier 10 is caused by the static heat conduction between the outer enclosure structure and the external environment. The microcontroller 24 calls the return air temperature sensor 22 to collect a continuous time temperature sequence according to the set heat leakage temperature measurement time window and executes a linear fitting algorithm to obtain the natural heat leakage temperature rise slope in the shutdown state. In this embodiment, the value range of the heat leakage temperature measurement time window is 2 minutes to 5 minutes. The microcontroller 24 multiplies the natural heat leakage temperature rise slope with the overall effective heat capacity constant of the aforementioned transport carrier 10 compartment to calculate the instantaneous heat leakage power transferred from the external environment to the internal physical space.

[0049] The specific formula for calculating instantaneous heat leakage power is as follows: ; In the formula, The heat leakage power of the transport vehicle 10 compartment when the speed regulating fan 21 is stopped; The overall volumetric heat constant of the 10 carriages of the transport carrier; The natural temperature rise rate of the return air temperature inside the carriage during the shutdown of the speed-regulating fan 21.

[0050] S330, after completing the extraction of heat leakage power parameters, the microcontroller 24 sends a full-load start command to the speed-regulating fan 21. The microcontroller 24 controls the speed-regulating fan 21 to run continuously at the maximum duty cycle to enter the active cooling forced cooling test stage. The microcontroller 24 synchronously collects the return air cooling data sequence and supply air temperature data sequence of the car environment during the preset test time window. In this embodiment, the test time window ranges from 3 minutes to 5 minutes. The microcontroller 24 extracts the absolute value of the forced temperature drop slope in the return air cooling data sequence within the window and, in conjunction with the pre-calculated instantaneous heat leakage power, calculates the current actual cooling power of the phase change cold storage refrigeration module 20 based on the law of conservation of energy. The data obtained in this physical calculation process covers the physical frosting phenomenon on the surface of the heat exchange pipeline and the passive attenuation characteristics of the system heat transfer parameters caused by changes in the internal air duct resistance.

[0051] The specific formula for calculating the actual cooling power is as follows: ; In the formula, This represents the current actual instantaneous cooling power released by the phase change cold storage refrigeration module. The heat leakage power of the transport vehicle 10 compartment when the speed regulating fan 21 is stopped; The overall heat capacity constant of the 10 carriages of the transport carrier; This refers to the forced cooling rate of the return air temperature inside the carriage during the operation of the speed-regulating fan 21.

[0052] S340, the microcontroller 24 substitutes the calculated actual cooling power and the inlet and outlet air temperature difference data after integral averaging within the test time window into the air enthalpy difference formula to derive the actual air volume flow rate that the duct system actually flows through at this time. After confirming that the vehicle remains stationary within the above test time window, the microcontroller 24 uses the actual air volume flow rate to overwrite the initial nominal flow rate value corresponding to the full-load operating speed node in the memory, for the underlying data of other speed nodes in the air volume mapping matrix. The microcontroller 24 extracts the proportional coefficient between the actual air volume flow rate and the corresponding full-load nominal flow rate value before the update. It uses this proportional coefficient to perform a proportional scaling and smooth update on the nominal flow rate value of the remaining speed node, regenerates the mapping table, and its specific matrix reconstruction numerical analysis process is a well-known technology in the field. The microcontroller 24 re-executes the underlying air volume mapping operation based on the updated air volume mapping matrix to replace the initial reference parameters, and applies the updated air volume mapping matrix to the subsequent calculation of instantaneous cooling power and actual remaining cooling capacity. It also releases the forced command to the speed-regulating fan 21 so that the system returns to the original temperature control mode. If the microcontroller 24 detects that the vehicle speed is greater than zero or triggers an external heat loss event during the entire test of shutdown and forced cooling, it will actively discard the current test data and stop the overwriting and updating of the underlying data, and simultaneously release the forced command to restore the system to the original temperature control mode.

[0053] In this embodiment, step S400 provided by the present invention may include the following steps in specific implementation: S410, the microcontroller 24 synchronously obtains the current global positioning system coordinates of the transport vehicle 10 through the vehicle's underlying communication bus, and the communication gateway 25 uploads the actual remaining cooling data obtained by the microcontroller 24 through continuous calculation, along with the global positioning system coordinates, to the cloud server 30 in real time via the wireless mobile communication network. The cloud server 30 analyzes the current GPS coordinates of the transport vehicle 10 and the destination coordinates configured in the mission plan based on the built-in geographic information system module. The cloud server 30 calls the third-party map navigation application interface to calculate the estimated remaining travel time from the current location to the destination. Based on the estimated remaining travel time and the planned travel trajectory, the cloud server 30 calls the meteorological service platform data interface to extract the spatiotemporal distribution of the external environment prediction temperature sequence along the forward path. For the calling and data parsing mechanism of the map navigation interface and the meteorological service platform data interface, this embodiment can adopt the conventional Hypertext Transfer Protocol request technology, and its network communication interaction process is a well-known technology in the field.

[0054] S420, after obtaining the spatiotemporal distribution of the predicted external environment temperature sequence, the cloud server 30 uses the numerical integration method to estimate the absolute time-domain heat load required to maintain the target set temperature. The cloud server 30 performs a difference calculation between the predicted external environment temperature and the target set temperature required by the goods and introduces a non-negative constraint operator in the calculation to eliminate the interference of natural cold backflow when the external temperature is lower than the target temperature. The cloud server 30 multiplies the non-negative truncated heat transfer temperature difference with the comprehensive heat transfer coefficient of the external enclosure structure of the transport vehicle 10 and the total surface area of ​​the external enclosure structure of the transport vehicle 10 participating in heat exchange, which is pre-stored in the database of the cloud server 30, to construct a predicted instantaneous heat load function. The cloud server 30 performs definite integral on the predicted instantaneous heat load function in the time domain with the current time as the lower limit of integration and the absolute time consisting of the current time plus the estimated remaining travel time as the upper limit of integration to obtain the absolute time domain heat load value.

[0055] The specific formula for solving the time-domain heat load numerical integral is as follows: ; In the formula, This refers to the absolute time-limited heat load required from the current moment until arrival at the destination. This is the initial time point triggered by the current prediction calculation; This represents the estimated remaining travel time from the current location to the destination. The overall heat transfer coefficient of the external enclosure structure of the transport vehicle 10; The total surface area of ​​the external enclosure structure of the transport vehicle 10 that participates in heat exchange; For the corresponding time on the forward path External environmental temperature prediction; Set the target temperature that needs to be maintained for the cargo currently being transported; For future time variables within the time integration interval; Let be an integral infinitesimal element in the time domain.

[0056] S430, the cloud server 30 compares the actual remaining cooling capacity uploaded by the communication gateway 25 with the absolute time-domain heat load obtained by the solution. When it is determined that the actual remaining cooling capacity is less than the absolute time-domain heat load, the cloud server 30 constructs a cooling capacity consumption integral equation that integrates the predicted instantaneous heat load function with zero as the lower limit of integration and unknown time variables as the upper limit of integration. The cloud server 30 establishes an equation with the actual remaining cooling capacity using the integral equation and uses Newton's iteration method to solve for the specific value of the unknown time variable. When the actual remaining cooling capacity is determined to be greater than or equal to the absolute time-domain heat load, or if the unknown time variable exceeds the time span of the acquired forward path meteorological data during the iterative solution process, the cloud server 30 determines that the cooling capacity is absolutely sufficient and directly sets the remaining effective cooling time to a preset ultra-large safety boundary value. Otherwise, the cloud server 30 outputs the solved unknown time variable as the current remaining effective cooling time of the system and configures it as the core timing evaluation indicator for triggering subsequent multi-level protection strategies.

[0057] The specific integral equation for calculating the remaining effective cooling time is as follows: ; In the formula, This is the scalar value of the actual remaining cooling capacity at the current moment; This is the remaining effective cooling time of the system obtained by inverse solving; The overall heat transfer coefficient of the external enclosure structure of the transport vehicle 10; The total surface area of ​​the external enclosure structure of the transport vehicle 10 that participates in heat exchange; For a relative moment in the future Predicted ambient temperature; Set the target temperature that needs to be maintained for the cargo currently being transported; For the integration variable representing a future relative time; It is an integral infinitesimal element in the relative time domain.

[0058] See attached document Figure 5 In specific implementations, step S500 provided by the present invention may include the following steps: S510, the cloud server 30 is configured with system redundancy time parameters in its internal database. In this embodiment, the value range of the system redundancy time parameters is configured to be 0.5 hours to 2 hours based on the statistical probability of urban delivery traffic congestion. The cloud server 30 performs an addition operation on the estimated remaining driving time and the system redundancy time to construct a conservative physical delivery time sequence boundary and sends the boundary data, the estimated remaining driving time and the remaining effective cooling time to the microcontroller 24 through the communication gateway 25. The microcontroller 24 compares the received remaining effective cooling time with the physical delivery time boundary in real time. When the microcontroller 24 determines that the remaining effective cooling time is greater than or equal to the physical delivery time boundary, the system confirms that the current cold storage is sufficient and triggers the state compensation composite constant temperature control program. When it determines that the remaining effective cooling time is less than the boundary, the microcontroller 24 generates an alarm message and executes the passive cooling degradation mode of derating output. Furthermore, once the passive cold preservation degradation mode is triggered, the system state will be forcibly locked until the transport vehicle 10 arrives at the cooling station to complete the recooling operation or the remaining effective cooling release time is greater than the sum of the physical delivery sequence boundary and the set safe recovery hysteresis time. Only then can the state compensation composite constant temperature control program be reset to avoid frequent oscillations and switching of control modes in the critical state of the system.

[0059] S520, the microcontroller 24 has a phase change material decay characteristic curve embedded in its internal read-only memory. This curve characterizes the physical phenomenon of nonlinear increase in heat transfer resistance caused by the thickening of the liquid boundary layer in the middle and late stages of the transition from solid to liquid phase of the cold storage medium 26. The microcontroller 24 extracts the actual remaining cold capacity after calibration at the current moment as the input index value and performs a lookup operation in the phase change material decay characteristic curve to obtain the corresponding phase decay feedforward gain coefficient. The phase decay feedforward gain coefficient is set to 1 in the initial stage when the cooling capacity is sufficient, and it exhibits a non-linear increasing property as the actual remaining cooling capacity decreases until it approaches the upper limit of the phase decay feedforward gain coefficient configured in the system. In this embodiment, the upper limit of the phase decay feedforward gain coefficient is in the range of 1.5 to 2.0. For the data acquisition of this non-linear characteristic curve, in this embodiment, a high and low temperature alternating test chamber combined with a heat flow meter can be used to perform offline material calibration, and the specific calibration data extraction process is a well-known technology in the field.

[0060] S530, the microcontroller 24 calls the built-in proportional-integral-derivative (PID) control algorithm module to receive the dynamic deviation sequence between the real-time return air temperature fed back by the current return air temperature sensor 22 and the set target temperature. Its core algorithm responds to the current error through a proportional element, eliminates the steady-state residual through an integral element, and extracts the error change trend through a derivative element. The microcontroller 24 uses the PID control algorithm module to process the deviation sequence to obtain the basic feedback control component, introduces the physical attenuation characteristic compensation parameter, and performs a direct multiplication and composite operation between the extracted phase attenuation feedforward gain coefficient and the basic feedback control component to construct a composite control law that couples state compensation and feedback, and outputs the original target operating speed command.

[0061] The specific formulas for constructing the composite control law and calculating the original target operating speed command are as follows: ; In the formula, For the current moment Output the target operating speed command; The phase decay feedforward gain coefficient is obtained based on the actual remaining cooling capacity; This represents the actual remaining cooling capacity. The proportional gain constant of the proportional-integral-derivative control algorithm; For the current moment The temperature deviation between the return air temperature and the target set temperature; The integral gain constant of the proportional-integral-derivative (PID) control algorithm; The differential gain constant of the proportional-integral-derivative (PID) control algorithm; This is the time variable for the current control operation; For time-domain integration and differentiation operations, the infinitesimal element is used.

[0062] S540, the microcontroller 24 converts the calculated original target operating speed command into the corresponding pulse width modulation duty cycle value according to the system's built-in motor drive mapping rules and performs a saturation limiting truncation operation for the command exceeding the limit state. The microcontroller 24 compares the converted duty cycle value with the full load limit duty cycle threshold and the zero lower limit allowed by the speed regulating fan 21 hardware respectively and forcibly truncates the abnormal command that exceeds the upper limit boundary to within the full load limit duty cycle threshold. Abnormal instructions below zero are truncated to the zero lower limit. The microcontroller 24 sends a pulse width modulation signal after saturation limiting to the speed-regulating fan 21 through its internal hardware timer pin. The power drive circuit inside the speed-regulating fan 21 responds to the pulse width modulation signal to dynamically adjust the effective drive voltage of the fan stator coil and forcibly increase the gas volume flow rate in the air circulation channel according to the composite pulse width modulation signal.

[0063] See attached document Figure 6 In specific implementations, step S600 provided by the present invention may include the following steps: S610, after triggering the passive cooling degradation mode, the microcontroller 24 extracts the historical return air temperature sequence of the last ten macroscopic feature sampling periods from the internal memory. In this embodiment, the value range of the macroscopic feature sampling period is configured to be 30 seconds to 1 minute according to the thermal inertia constant of the carriage. The microcontroller 24 simultaneously obtains the remaining effective cooling time, the estimated remaining driving time and the average predicted external environment temperature of the forward path issued by the cloud server 30. The average predicted external environment temperature is calculated by the cloud server 30 from the spatiotemporal distribution of the predicted external environment temperature sequence and then sent out. The physical state of the above data represents the thermal inertia trend inside the carriage and the supply and demand gap of the remaining cooling capacity of the system. The microcontroller 24 uses a standardization algorithm combined with the global statistical mean and standard deviation parameters pre-extracted during the offline training phase of the model to perform mean removal and variance scaling on the above data to obtain the standardized feature components that eliminate the difference in dimensions. For the matrix standardization numerical preprocessing calculation method of the input data, in this embodiment, the conventional statistical mean and standard deviation solution method can be used, and its specific mathematical operation process is a well-known technology in the field.

[0064] S620, the microcontroller 24 has a pre-trained long short-term memory neural network model deployed inside, and the model contains a time-series feature extraction layer and a fully connected mapping layer. The microcontroller 24 inputs the standardized historical return air temperature sequence, a time-series feature component, into the forget gate, input gate and output gate logic units of the time-series feature extraction layer to update the cell state and outputs the internal hidden layer state vector. The microcontroller 24 concatenates and aggregates the hidden layer state vector with the standardized remaining effective cooling time, the expected remaining driving time, and the average predicted external environment temperature as scalar feature components in the feature dimension to generate a high-dimensional fusion feature vector. This high-dimensional fusion feature vector deeply integrates the historical dynamic thermodynamic evolution law of the system and the expected state of the forward macroscopic environment. The microcontroller 24 inputs the high-dimensional fusion feature vector into the fully connected mapping layer and transmits the internal data flow to the output node through the neuron matrix multiplication and addition operation with nonlinear activation function.

[0065] S630, the output node of the long short-term memory neural network model gives a scalar value, and the specific business meaning of the output result corresponds to the target set temperature dynamic bias that can be relaxed under the current extreme cooling conditions. The microcontroller 24 calls the mean and standard deviation parameters of the target variable pre-extracted during the offline training phase of the model to perform inverse standardization transformation on the scalar value, and restores it to the actual temperature bias with the physical dimension of ℃. Then, the microcontroller 24 extracts the maximum safe temperature rise tolerance allowed by the physical and chemical properties of the cargo carried by the cloud server 30 based on the task order as a hard constraint threshold and performs a saturation limiting and truncation operation on the actual temperature bias with zero as the lower limit benchmark. The microcontroller 24 performs an addition operation on the initially fixed target set temperature and the actual temperature offset after the amplitude limiting process to obtain the actual operating target temperature in the degraded mode. It uses the preset proportional value or fixed increment of the actual temperature offset as the dead zone compensation threshold and simultaneously relaxes the temperature control dead zone of the proportional-integral-derivative control algorithm. The actual operating target temperature is configured as a new tracking reference for the closed-loop control of the underlying speed-regulating fan 21. The microcontroller 24 resets the input error sequence of the proportional-integral-derivative control algorithm module based on the updated actual operating target temperature and the relaxed temperature control dead zone, thereby forcing the system to reduce the instantaneous load output and slow down the consumption rate of the remaining cooling capacity.

[0066] The specific formula for calculating the actual target operating temperature is as follows: ; In the formula, The actual target operating temperature calculated under passive cooling degradation mode; Set the initial fixed target temperature required to maintain the current cargo; The dynamic bias output of the forward inference of the neural network model; The maximum safe temperature rise tolerance threshold is configured based on the physical properties of the cargo being carried.

[0067] S640, this long short-term memory neural network model extracts abnormal delivery records of severe traffic congestion and depletion of cold energy from the historical logistics database as training sample datasets during the offline construction phase. The label of this sample dataset is defined as the optimal target temperature bias that can ensure the remaining cold energy is maintained exactly until the delivery end under the physicochemical detection critical premise of ensuring that the carried goods do not undergo irreversible physical deterioration. The model training environment uses mean square error as the loss function to evaluate the difference between the network output value and the actual label value and relies on the adaptive moment estimation optimizer to execute the backpropagation gradient descent algorithm. The model training process iterates through multiple rounds to update the weight matrix and bias vector inside the network until the output of the loss function converges within the preset accuracy threshold range. Finally, the researchers convert the solidified network parameter matrix into the underlying executable file format and burn it into the non-volatile memory of the microcontroller 24 to configure it as a resident calling module.

[0068] In this embodiment, step S700 provided by the present invention may include the following steps in specific implementation: S710, when the microcontroller 24 detects that the real-time return air temperature is greater than the actual operating target temperature under the passive cold preservation and degradation mode, it continuously extracts the real-time return air temperature sequence and compares it with the extreme cargo loss temperature threshold value statically configured according to the cargo characteristics. The microcontroller 24 directly calls the time derivative feature of the current return air temperature as the dynamic approximation rate of the current temperature approaching the threshold value. The microcontroller 24 determines whether the current return air temperature is greater than or equal to the extreme cargo loss temperature threshold value. If the value is greater than or equal to the threshold, the estimated time interval for cargo loss is set to zero. If the value is less than the threshold, the microcontroller 24 determines whether the approximation rate is greater than the set minimum positive number approximation threshold. If the approximation rate is greater than the minimum positive number approximation threshold, the remaining temperature margin is calculated using the difference between the critical cargo loss temperature and the current return air temperature. The remaining temperature margin is then divided by the approximation rate to estimate the estimated time interval for cargo loss. If the approximation rate is less than or equal to the minimum positive number approximation threshold, the microcontroller 24 sets the estimated time interval for cargo loss to the system's set safety limit to avoid the dead zone of the underlying division. When the microcontroller 24 determines that the time interval between the expected cargo damage and the expected cargo damage is less than the physical intervention time threshold set by the system based on the vehicle's response capability, it triggers the highest priority software-level emergency exception interrupt. In this embodiment, the physical intervention time threshold ranges from 15 minutes to 30 minutes. The microcontroller 24 sets a global emergency dispatch flag in the interrupt service routine. The communication task program of the microcontroller 24 responds to the global emergency dispatch flag, packages the current system operation data including the expected time interval between the expected cargo damage and the expected cargo damage, generates a circuit breaker status message with timestamp information, and uploads it to the cloud server 30.

[0069] S720, after receiving the circuit breaker status message, the cloud server 30 extracts the current real-time location coordinates of the transport vehicle 10 and performs a topological search of the surrounding backup cooling network points through the internal geographic information system interface. The cloud server 30 extracts the set of geographic coordinates of all candidate cooling stations within the geographic range covered by the current coordinates as the center and with a preset search radius. In this embodiment, the value range of the search radius is 30 kilometers to 50 kilometers. The cloud server 30 calls the path planning engine to calculate the road network travel distance and estimated travel time from the current location to each candidate network point, and simultaneously obtains the number of currently idle cooling replenishment stations and the real-time cooling power reserve status parameters of the charging piles at each network point. The above parameters are then fused through a preset weighted normalization function to generate a cooling replenishment resource availability coefficient. For example, the coefficient is obtained by multiplying the number of currently idle cooling replenishment stations at the network point with the normalized value of the real-time cooling power reserve status parameters of the charging piles. In the data processing flow, cloud server 30 forcibly removes failed network points with a cooling resource availability coefficient of zero to eliminate abnormal boundaries where the denominator tends to zero in subsequent division operations. Simultaneously, it removes network points with estimated travel times greater than or equal to the aforementioned estimated cargo loss interval to avoid ineffective scheduling intervention. If the candidate network point set is empty after removing failed and timed-out network points, cloud server 30 further determines: If the set is empty because there is at least one timeout point in the original search range whose availability coefficient of the cooling resource is not zero but is removed because the estimated travel time is greater than or equal to the estimated time interval of cargo damage, it indicates that there are no physically reachable available cooling points within the estimated time interval of cargo damage. At this time, the cloud server 30 directly sends an emergency intervention work order to the manual dispatch center and stops the automatic optimization process to avoid meaningless topology expansion. If the set is empty because there are no alternative cooling sites in the original search range or all alternative cooling sites in the original search range are eliminated because the availability coefficient of cooling resources is equal to zero, then the cloud server 30 expands the search radius step by step according to the preset distance increment step and re-executes the topology search and evaluation until there are valid sites in the candidate site set or the maximum number of topology expansions is reached. If the candidate site set is still empty after the upper limit is reached, an emergency intervention work order will be directly dispatched to the manual dispatch center and the automatic optimization process will be stopped. If there are valid sites, the cloud server will combine the remaining valid site data to build a comprehensive dispatch cost assessment model and integrate the vehicle driving space consumption and operation time delay characteristics in the physical dimension.

[0070] S730, cloud server 30 uses the constructed comprehensive scheduling cost evaluation model to perform numerical calculations for each point in the candidate set and outputs the corresponding normalized comprehensive scheduling cost evaluation value. Cloud server 30 multiplies the road network travel distance by the distance weight coefficient configured by the system and multiplies the estimated travel time by the time weight coefficient to integrate the spatiotemporal costs. Cloud server 30 divides the sum of the costs in the above time and space dimensions by the availability coefficient of the cooling resources of the target point to obtain the final single-node evaluation value. Cloud server 30 traverses all candidate points to obtain the specific point corresponding to the minimum comprehensive scheduling cost evaluation value and anchors it as the preferred destination for emergency path reconstruction.

[0071] The specific formula for evaluating the overall scheduling cost is as follows: ; In the formula, In order to target the The normalized comprehensive scheduling cost evaluation value calculated from each candidate cooling replenishment point; Distance weighting coefficients configured globally for the system; For the vehicle's current location, arrive at the first The road network access distance of each candidate site; The time weighting coefficient is configured globally for the system; To call the path planning engine to obtain the arrival time of the first... Estimated travel time for each candidate destination; For the first The availability coefficient of cooling replenishment resources currently reported by each candidate site; This is the index variable for the candidate cooling point set.

[0072] S740, after locking the target destination, the cloud server 30 calls the map service interface to generate a reconstructed navigation trajectory message and sends the message to the vehicle navigation terminal of the transport vehicle 10 through the wireless communication network to guide the driver to perform a lane change and cooling operation. The cloud server 30 simultaneously encapsulates the associated timestamp of the circuit breaker warning and the set of path coordinates before and after reconstruction, along with the underlying temperature data, into a data structure. The cloud server 30 uses a secure hash algorithm to perform hash operations on the encapsulated data packet to generate a hash digital fingerprint with tamper-proof characteristics. The cloud server 30 writes the data packet and its corresponding hash digital fingerprint as electronic evidence of abnormal performance into the distributed underlying blockchain storage node for solidification. In this embodiment, the hash encryption of network data packets and the consensus mechanism for blockchain node on-chain can be implemented using conventional cryptographic components and smart contracts, and its specific block generation process is a well-known technology in the field.

[0073] See attached document Figure 7 In specific implementations, step S800 provided by the present invention may include the following steps: S810, after receiving the arrival and parking command broadcast by the vehicle's underlying communication bus, the microcontroller 24 triggers the delivery task termination program and stops the pulse width modulation signal output of the speed-regulating fan 21. The microcontroller 24 extracts the actual cumulative cooling consumption of the phase change cold storage refrigeration module, the total cumulative cooling penalty equivalent, and the external environmental temperature evolution sequence data collected by the underlying communication bus of the transport carrier 10 during the corresponding time period from the internal memory. The actual cumulative cooling load is composed of the sum of the total cooling release consumed during the entire delivery cycle and the total cooling load penalty equivalent. The microcontroller 24 packages the above system operation log data in a structured manner and uploads it to the cloud server 30 via the wireless link of the communication gateway 25. The cloud server 30 replaces the predicted external environment temperature with the actual cumulative cooling load and the external environment temperature evolution sequence data in the corresponding time period, and substitutes it into the actual absolute time domain heat load value calculated by re-integration in the aforementioned predicted instantaneous heat load function. Synchronous extraction and timestamp alignment are performed. The cloud server 30 queries the underlying blockchain storage node to see if there is any electronic evidence of abnormal performance of mid-way cooling associated with this task. If it exists or the passive cooling degradation mode was triggered during this delivery task, the parameter self-learning closed-loop calibration procedure is stopped. If it does not exist and the feedforward composite constant temperature control procedure is maintained throughout the process, the parameter self-learning closed-loop calibration procedure is started.

[0074] S820, the cloud server 30 subtracts the total accumulated cooling penalty from the actual accumulated cooling load to obtain the net conducted cooling load. The cloud server 30 determines whether the actual absolute time-domain heat load is greater than the set effective calculated load lower limit. When the actual absolute time-domain heat load is greater than the effective calculated load lower limit, the net conducted cooling load is compared with the actual absolute time-domain heat load and the absolute value of the relative error between the two is extracted. When the cloud server 30 determines that the absolute value of the relative error is greater than the internally configured tolerance lower limit and less than the abnormal isolation upper limit, it confirms that the comprehensive heat transfer coefficient of the external enclosure structure of the transport carrier 10 has substantially deviated. In this embodiment, the tolerance lower limit is 5% and the abnormal isolation upper limit is 20%. The cloud server 30 synchronously eliminates high-frequency noise disturbances from the sensor through the upper and lower limit bidirectional constraint mechanism and completely eliminates the interference of abnormal heat loss caused by the opening of the car door in the middle of the journey by combining the pre-deduction logic. If the absolute value of the relative error is less than or equal to the lower limit of tolerance or greater than or equal to the upper limit of abnormal isolation, or the actual absolute time domain heat load is less than or equal to the lower limit of the effective calculated load, resulting in the inability to perform the division evaluation, the cloud server 30 directly terminates the current heat transfer coefficient update operation. When it is confirmed that the update is in a valid update interval, the cloud server 30 calls the error adaptive correction algorithm based on exponential decay weight to couple and iteratively derive the historical comprehensive heat transfer coefficient with the current deviation ratio calculated from the net conduction cooling loss and the actual absolute time-domain heat load. The current deviation ratio is the difference between the net conduction cooling loss and the actual absolute time-domain heat load divided by the actual absolute time-domain heat load to obtain the updated heat transfer coefficient that reflects the latest physical insulation state. For the decay weight constant configured in the adaptive correction algorithm, the value range of the decay weight constant in this embodiment is 0.1 to 0.3, and the multiplicative iterative correction process based on the deviation ratio is a well-known technology in the art.

[0075] The specific formula for the self-learning update of the overall heat transfer coefficient is as follows: ; In the formula, The updated heat transfer coefficient is derived after self-learning closed-loop calibration. The historical comprehensive heat transfer coefficient was fixed within the system prior to the execution of this task; The decay weight constant configured in the exponentially weighted moving average algorithm; The net conducted cooling loss is extracted after deducting the total equivalent of the cooling penalty at the end of the mission. The actual absolute time-domain heat load value is obtained by recalculating using external environmental temperature evolution sequence data.

[0076] S830, cloud server 30 calculates the equivalent grid power consumption value of this cold chain delivery task based on the actual cumulative cooling consumption and the comprehensive energy efficiency ratio of the cold storage medium preparation process. The cloud server 30 calls the regional grid benchmark carbon emission factor interface published by the energy management department and multiplies the factor with the equivalent power consumption value to calculate the actual carbon footprint equivalent generated by using phase change cold storage technology. The cloud server 30 calculates the baseline carbon emission total based on the industry fuel consumption standard of traditional diesel refrigeration units under the same cooling capacity demand, and subtracts the baseline value from the actual carbon footprint equivalent to obtain the absolute carbon emission reduction value. The cloud server 30 binds the absolute carbon emission reduction value with the vehicle identification code of the transport carrier 10 and the current business order number to generate a standardized green logistics carbon emission reduction electronic certificate.

[0077] S840, the cloud server 30 will push the generated green logistics carbon emission reduction electronic certificate to the third-party carbon trading registration platform or the enterprise environmental and social governance disclosure database for archiving and solidification through the data interface. Simultaneously, the cloud server 30 will bind and overwrite the calculated updated heat transfer coefficient with the transport carrier 10 in the cloud database. The updated value is configured as a new initial static baseline for the cloud server 30 to perform the next logistics delivery task's cold load prediction calculation. At the same time, the cloud server 30 encapsulates it as a configuration overwrite message and sends it to the vehicle terminal system of the transport vehicle 10 using the over-the-air download technology protocol channel. After receiving the configuration overwrite message, the microcontroller 24 erases the original historical data in its internal non-volatile memory and writes the updated heat transfer coefficient as a local parameter backup. After confirming that the underlying parameter overwrite verification is successful, the microcontroller 24 cuts off the power supply circuit of the internal non-essential peripherals and enters a low-power sleep standby mode at the microampere level until it receives the wake-up signal for the next task.

[0078] Application Examples: To better understand the technical solution of the present invention, the following uses the summer cold chain logistics fresh food delivery application scenario as an application example and test example.

[0079] The current date is June 29, 2026. The average temperature of the external environment is 35℃. The transport carrier 10, equipped with the phase change cold storage refrigeration module 20, is carrying out a fresh goods delivery task from City A to City B. The target temperature is set at 4℃, and the critical value for the extreme cargo damage temperature is 7℃.

[0080] The transport vehicle 10 departs fully loaded with cooling capacity at 9:00 AM. The microcontroller 24 acquires the real-time operating speed of the speed-regulating fan 21 according to the set cycle and simultaneously collects the return air temperature and supply air temperature. It calculates the instantaneous cooling power of the phase change cold storage refrigeration module 20 using the air enthalpy difference formula and performs discrete integral calculation to obtain the basic remaining cooling capacity.

[0081] At 10:15 AM, transport vehicle 10 arrived at the unloading point to perform unloading operations. During the unloading process, the rear door of the carriage was open for 180 seconds. Microcontroller 24 detected that the time derivative characteristic of the return air temperature was greater than the set environmental disturbance judgment threshold of 0.5℃ per second and the duration of the violation exceeded 30 seconds. Microcontroller 24 marked this continuous state interval as an independent external heat loss event, extracted the temperature jump peak characteristics within the event interval, and combined them with the overall effective heat capacity constant of the carriage of transport vehicle 10. Substituting these characteristics into the penalty equivalent calculation formula, microcontroller 24 obtained the cold energy penalty equivalent corresponding to a single discrete event. Subsequently, a forced step deduction correction was performed on the basic remaining cold energy to obtain the actual remaining cold energy.

[0082] At 11:30 AM, the transport vehicle 10 was in a stationary stop state with zero speed for a period of 5 minutes, reaching the steady-state waiting threshold. The microcontroller 24 sent a shutdown command to the speed-regulating fan 21, and the system entered a natural heat leakage state. Within a 3-minute heat leakage temperature measurement window, the natural heat leakage temperature rise slope and instantaneous heat leakage power were obtained through a linear fitting algorithm. The microcontroller 24 then sent a full-load start command to the speed-regulating fan 21. Within a 4-minute test window, the forced temperature drop slope was extracted and the current real cooling power was calculated in reverse using the law of conservation of energy. The microcontroller 24 used the real air volume flow rate derived from the real cooling power to overwrite the initial nominal flow rate value in the memory, completed the proportional scaling and smooth update of the internal air volume mapping matrix, and applied it to subsequent calculations.

[0083] At 13:00, the transport vehicle 10 encountered traffic congestion. The cloud server 30 obtained the estimated remaining travel time increased by 120 minutes and extracted the meteorological data of the forward path for numerical integration prediction. The cloud server 30 calculated that the absolute time-domain heat load required to maintain the target set temperature was greater than the actual remaining cooling capacity, and the calculated remaining effective cooling release time was less than the sum of the estimated remaining travel time and the system redundancy time. The microcontroller 24 triggered the passive cooling degradation mode. The microcontroller 24 used a long short-term memory neural network model combined with the standardized historical return air temperature sequence and forward macroscopic environmental characteristics to output an actual temperature bias of 1.8℃ with the dimension of ℃. The system configured the actual operating target temperature containing this bias as a new tracking benchmark for the bottom closed-loop control and simultaneously relaxed the temperature control dead zone to control the speed-regulating fan 21 to perform derated cooling release.

[0084] At 14:30, during the passive cooling degradation mode, the transport vehicle 10 generated a real-time return air temperature of 6.5℃. Based on the dynamic approach rate of the current temperature approaching the critical value of 7℃ for cargo damage, the microcontroller 24 estimated that the time interval for cargo damage would be 25 minutes. This value is less than the 30-minute physical intervention time threshold set by the system, thus triggering the highest priority software-level emergency abnormal interruption. The cloud server 30 extracted the real-time location coordinates of the vehicle and retrieved a specific alternative cooling station 40 that meets the availability coefficient of cooling resources within 10 kilometers. Based on the comprehensive scheduling cost evaluation model, the cloud server 30 anchored the station as the preferred destination and generated a reconstructed navigation trajectory message to guide the lane change for cooling. The system simultaneously wrote the abnormal performance electronic evidence containing the underlying temperature data and the hash digital fingerprint generated by the secure hash algorithm into the distributed blockchain storage node for solidification.

[0085] After the transport vehicle 10 arrives at its destination at 16:00, the delivery task termination procedure is triggered. The cloud server 30 extracts the actual cumulative cooling consumption and the total amount of accumulated cooling penalty equivalents during the entire delivery cycle, and subtracts them to obtain the net conduction cooling consumption. The cloud server 30 compares this net conduction cooling consumption with the actual absolute time-domain heat load value calculated by re-integrating based on the external environment temperature evolution sequence and extracts the absolute value of the relative error between the two. When it is confirmed that it is in the valid update interval, the error adaptive correction algorithm based on exponential decay weight is called to iteratively derive the updated heat transfer coefficient by comparing the current deviation ratio calculated from the two parameters with the historical parameters. Finally, the cloud server 30 calculates the absolute carbon emission reduction value based on the equivalent power consumption value converted from the actual cumulative cooling consumption and the benchmark total carbon emission and generates a standardized green logistics carbon emission reduction electronic certificate.

[0086] Test example: Researchers used a phase change refrigeration device with a built-in conventional proportional-integral-derivative control algorithm and a static cooling capacity estimation logic based on theoretical nominal duration as a traditional control group and the system provided by this invention as a test group. Under a test environment with an average constant temperature of 35°C and simulated radiant heat, a comparative verification experiment was conducted on two groups of insulated wagons of the same specification with the same mass load and a target temperature of 4°C and a critical value of 7°C for extreme cargo loss. The experimental procedure included forcibly opening the rear door of the wagon for 180 seconds in the second and fourth hours and forcibly cutting off the vehicle's power output in the fifth hour to simulate 90 minutes of static congestion.

[0087] The data and conclusions extracted from the above comparative verification experiments are summarized into the following comparison table based on the core performance indicators.

[0088]

[0089] The cooling capacity calculation mechanism obtained by the test group of this invention combines the discrete penalty equivalent deduction operation executed for the heat loss event caused by the 180-second door opening operation, and superimposes the combined instruction sequence of extracting the natural heat leakage temperature rise slope and cooling slope in the shutdown state to back-calculate the cooling power and update the air volume mapping matrix, outputting a final system error data of 3.2%, which is more accurate than the 18.5% cooling capacity estimation error rate of the traditional control group.

[0090] When the test group of this invention encountered a passive cooling degradation mode node triggered by a static cooling shortage caused by power cut-off simulation for up to 90 minutes, it used a long short-term memory neural network model combined with the dynamic bias extracted from standardized data to raise the actual operating target temperature and reduce the load output. This extended the system cooling capacity depletion time from 45 minutes obtained by the traditional control group at full load to 110 minutes. The peak return air temperature recorded data remained at a value recording point of 6.2℃, which is lower than the peak data of the traditional control group of 8.5℃.

[0091] The parameter self-learning closed-loop calibration program executed at the end of the test task extracts the net conduction cooling value obtained by calculating the actual cumulative cooling and cooling penalty equivalent. The program obtains the residual ratio between the net conduction cooling and the actual absolute time-domain heat load obtained by re-integration using the evolution temperature. It uses an error adaptive correction algorithm based on exponential decay weighting combined with the deviation ratio to generate the updated heat transfer coefficient of the external enclosure structure of the transport vehicle 10 and executes the instruction to overwrite and replace the stored parameters.

[0092] See appendix Figure 8The two-dimensional coordinate system region contains waveforms of external environmental temperature evolution data, recording peak values ​​of 38°C and valley values ​​of 30°C. The return air temperature curve generated by the traditional control group produces a vertical discrete upward segment with extreme value abrupt changes at the corresponding forced opening of the rear door node, and crosses the reference limit of 7°C at the end of the static congestion test phase. The return air temperature curve generated by the test group of this invention produces a vertical temperature rise segment with a length smaller than that of the traditional control group at the same door opening node. The return air temperature curve generated by the test group of this invention contains a segment component with a geometric upward slope of 0.02°C per minute during the static congestion test phase and remains below the 7°C reference limit throughout the entire process.

[0093] See appendix Figure 9 The attenuation area boundary profile constructed by the traditional control group is a continuous linear decreasing segment with a constant absolute value of geometric slope. The attenuation area boundary profile constructed by the test group of this invention has a vertically downward discrete step segment with a corresponding cold energy penalty equivalent measurement value at the 180-second door opening test time node. The absolute value of the curve descent slope of the attenuation area boundary profile constructed by the test group of this invention after the passive degradation and cold preservation mode trigger time node is less than the absolute value of the geometric descent slope of the data statistical interval before the degradation trigger node.

[0094] See appendix Figure 10 The left-hand independent display area uses the sequential numbering of logistics and distribution task execution order as the horizontal coordinate set and the comprehensive heat transfer coefficient as the vertical scale to present a linear regression scatter cluster that conforms to the characteristics of a proportional geometric distribution. The right-hand independent display area is a parallel histogram comparison structure table containing two rectangular bars of entity data. The rectangular bars on the left of the parallel histogram record the baseline carbon footprint equivalent geometric height value obtained by traditional diesel refrigeration equipment accounting. The rectangular bars on the right of the parallel histogram record the actual carbon footprint equivalent geometric height value generated by accounting for electricity consumption value and carbon emission factor. The absolute height difference range at the top of the two rectangular bars is marked with the absolute carbon emission reduction value of green logistics generated by the method of this invention.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A phase change cold storage refrigeration module, characterized in that, The device is configured inside the transport vehicle and includes a speed-regulating fan, a return air temperature sensor, a supply air temperature sensor, a communication gateway, and a microcontroller. The speed-regulating fan, the return air temperature sensor, the supply air temperature sensor, and the communication gateway are each electrically connected to the microcontroller. The microcontroller is used to obtain the real-time operating speed of the speed-regulating fan and collect the temperature data from the return air temperature sensor and the supply air temperature sensor to calculate the actual remaining cooling capacity. The communication gateway sends the actual remaining cooling capacity to the cloud server and receives the remaining effective cooling release time and the estimated remaining driving time calculated by the cloud server in conjunction with the predicted temperature of the external environment. The microcontroller performs a time-series comparison between the remaining effective cooling release time and the estimated remaining driving time, and controls the speed-regulating fan to execute constant temperature cooling release or passive cooling degradation mode based on the comparison result.

2. The phase change cold storage refrigeration module according to claim 1, characterized in that, The specific process by which the microcontroller calculates the actual remaining cooling capacity is as follows: The microcontroller uses the air enthalpy difference formula to perform discrete integral calculation on the instantaneous cooling power of the phase change cold storage refrigeration module to obtain the basic remaining cooling capacity; The microcontroller calculates the time derivative feature of the return air temperature collected by the return air temperature sensor. When the time derivative feature is detected to be greater than the set environmental disturbance judgment threshold and the duration of the violation exceeds the judgment filter time window parameter, it is determined that there is an external heat loss event; The microcontroller extracts the temperature jump peak feature within the event interval and calculates the cooling capacity penalty equivalent in combination with the preset overall effective heat capacity constant of the carriage, and calls the cooling capacity penalty equivalent to perform a forced step deduction on the basic remaining cooling capacity to obtain the actual remaining cooling capacity.

3. The phase change cold storage refrigeration module according to claim 1, characterized in that, The microcontroller internally stores an airflow mapping matrix for converting operating speed into air volumetric flow rate. When the microcontroller detects that the transport vehicle is in a steady-state stationary docking state, it cuts off the drive signal of the speed-regulating fan to obtain the natural heat leakage temperature rise slope under no forced convection heat transfer, and uses the natural heat leakage temperature rise slope to calculate the instantaneous heat leakage power. Subsequently, the microcontroller controls the speed-regulating fan to operate at full load to enter the forced cooling test stage, extracts the forced temperature drop slope and combines it with the instantaneous heat leakage power to back-calculate the actual cooling power, and then derives the actual air volumetric flow rate and uses this flow rate to update the airflow mapping matrix.

4. The phase change cold storage refrigeration module according to claim 1, characterized in that, The remaining effective cooling time received by the communication gateway is obtained by the cloud server through the following processing steps: acquiring forward path meteorological data to extract the predicted external environment temperature, and constructing a predicted instantaneous heat load function by combining the comprehensive heat transfer coefficient and total surface area of ​​the external enclosure structure of the transport vehicle; performing definite integral solution on the predicted instantaneous heat load function in the time domain to obtain the absolute time domain heat load, comparing the absolute time domain heat load with the actual remaining cooling capacity, and solving in reverse through integral equation iteration to obtain the unknown time variable representing the node of system cooling capacity depletion, and using the unknown time variable as the remaining effective cooling time.

5. The phase change cold storage refrigeration module according to claim 1, characterized in that, When the remaining effective cooling time is greater than or equal to the sum of the expected remaining driving time and the system redundancy time, the microcontroller controls the speed-regulating fan to perform the constant-temperature cooling; the microcontroller extracts the actual remaining cooling capacity and looks up the phase state attenuation feedforward gain coefficient in the phase change material attenuation characteristic curve; the microcontroller uses a proportional-integral-derivative control algorithm to process the dynamic deviation sequence between the return air temperature and the target set temperature to obtain the basic feedback control component, and constructs a control law by combining the basic feedback control component with the phase state attenuation feedforward gain coefficient, and outputs a pulse width modulation signal after saturation limiting to control the speed-regulating fan.

6. The phase change cold storage refrigeration module according to claim 1, characterized in that, When the remaining effective cooling time is less than the sum of the expected remaining travel time and the system redundancy time, the microcontroller controls the speed-regulating fan to execute the passive cooling degradation mode. The microcontroller concatenates the standardized historical return air temperature sequence, the remaining effective cooling time, the expected remaining travel time, and the average predicted external environment temperature into a feature vector, inputs it into the built-in long short-term memory neural network model for forward inference, and outputs a dynamic bias of the target set temperature. The microcontroller limits the dynamic bias based on the maximum safe temperature rise tolerance allowed for the cargo being carried, increases the operating target temperature, and relaxes the temperature control dead zone to reduce instantaneous load output.

7. The phase change cold storage refrigeration module according to claim 6, characterized in that, During the execution of the passive cooling degradation mode, the microcontroller calculates the expected time interval for cargo loss based on the dynamic approximation rate of the real-time return air temperature approaching the critical value of the limit cargo loss temperature. When the expected time interval for cargo loss is less than the set physical intervention time threshold, a circuit breaker status message is generated and uploaded via the communication gateway. The communication gateway is also used to receive a reconstructed navigation trajectory message. The reconstructed navigation trajectory message is obtained by the cloud server in response to the circuit breaker status message, performing a topology search of the surrounding backup cooling network points with the current coordinates as the center, and traversing and filtering based on the road network travel distance, expected travel time, and cooling resource availability coefficient to construct a comprehensive scheduling cost evaluation model.

8. The phase change cold storage refrigeration module according to claim 7, characterized in that, When the reconstructed navigation trajectory message is sent, an abnormal performance electronic evidence generation step is carried out simultaneously. The abnormal performance electronic evidence is obtained by the cloud server by structurally encapsulating the associated timestamp that triggered the warning, the set of path coordinates before and after reconstruction, and the underlying temperature data, and then using a secure hash algorithm to generate an tamper-proof hash digital fingerprint and solidifying it in a distributed blockchain storage node.

9. The phase change cold storage refrigeration module according to claim 1, characterized in that, The communication gateway is also used to receive the updated heat transfer coefficient sent by the cloud server after the transport vehicle terminates its mission, so that the microcontroller can perform low-level parameter overwrite backup. The updated heat transfer coefficient is obtained by the cloud server extracting the net conduction cooling loss including the equivalent deduction of cooling penalty, and using the external environment evolution temperature sequence to re-integrate the actual absolute time-domain heat load. When the absolute value of the relative error between the two is within the effective update range, the error adaptive correction algorithm based on exponential decay weight is called to iteratively derive the coefficient in combination with the current deviation ratio.

10. The application of a phase change cold storage refrigeration module according to any one of claims 1 to 9 in a cold chain transportation system, characterized in that, The phase change cold storage refrigeration module is configured as a core temperature control node in the fresh or pharmaceutical transport vehicle compartment of the cold chain transportation system. The cold chain transportation system collects multi-dimensional physical state parameters through the underlying sensors of the phase change cold storage refrigeration module, and interacts with the geographic meteorological data of the cloud server to perform self-calibration of the air volume mapping matrix, energy supply and demand time series assessment, variable target parameter degradation control, and emergency route reconstruction intervention operations for the entire delivery cycle.

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