Temperature monitoring and control method in cold chain commodity transportation process

Abstract

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

Description

Technical Field

[0001] This invention relates to the field of cold chain commodity transportation technology, and specifically to a method for temperature monitoring and control during cold chain commodity transportation. Background Technology

[0002] In the field of cold chain commodity transportation, the accuracy of temperature monitoring and control is directly related to the core quality indicators of cold chain commodities. Existing mainstream solutions generally adopt a dual-path technical architecture: the first is a PID feedback control mechanism based on a fixed temperature threshold, which collects discrete data by deploying multiple temperature sensors in the compartment. When the monitored value exceeds the preset threshold, the compressor is activated for cooling compensation, and GPS positioning is used to achieve remote transmission of temperature data; the second is a predictive control model based on a historical meteorological database, which analyzes the correlation between environmental temperature and humidity along historical transportation routes and performs fuzzy matching operations by setting up a segmented temperature control strategy library.

[0003] However, these technologies all suffer from a fundamental flaw: multi-dimensional data such as cargo thermal capacity characteristics, environmental disturbance factors, and equipment operating status are fragmented, resulting in an irreconcilable contradiction between temperature monitoring, real-time temperature control, and energy consumption optimization.

[0004] Therefore, how to balance temperature monitoring and control with energy consumption optimization is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In order to achieve a balance between temperature monitoring and control and energy consumption optimization, this application provides a method for temperature monitoring and control during cold chain commodity transportation.

[0006] This application provides a temperature monitoring and control method for cold chain commodity transportation, which adopts the following technical solution: A method for temperature monitoring and control during cold chain commodity transportation includes: Acquire real-time temperature data, and generate digital profiles of the thermal response of cold chain products based on the real-time temperature data and the cold chain product category database; Collect real-time location and external temperature data of cold chain goods, and predict the temperature threshold for the next period based on the heat capacity characteristic parameters reflected in the thermal response digital archive. Based on the temperature threshold of the next time period and the vibration data of the cold chain goods in the compartment, a three-dimensional temperature field distribution map is constructed. Based on the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution map, control commands are generated; Based on the control command and the preset energy consumption optimization objective function, the control weight coefficient is calculated, and then the temperature control operation is executed according to the control weight coefficient and the control command.

[0007] Furthermore, based on real-time temperature data and a database of cold chain product categories, the steps for generating digital thermal response profiles for cold chain products include: Based on real-time temperature data, acquire temperature changes of cold chain products and generate temperature fluctuation data; Based on temperature fluctuation data, calculate the temperature response rate and thermal inertia index of cold chain products; Based on the heat sensitivity level parameters in the cold chain commodity category database, and combined with the temperature response rate and thermal inertia index, a commodity-temperature interaction response matrix is ​​constructed. Extract the feature vectors from the product-temperature interaction response matrix to generate a heat capacity feature map; By mapping the heat capacity characteristic spectrum with historical temperature fluctuation data, a digital archive of thermal response is generated, which records the safety threshold gradient and critical phase transition point.

[0008] Furthermore, based on the heat capacity characteristics reflected in the thermal response digital archive, the steps for predicting the temperature threshold for the next time period include: Based on the safety threshold gradient and the critical phase transition point, a basic temperature change response curve is generated; Based on the transportation routes of cold chain goods, real-time road condition data and meteorological data are obtained, and the thermal disturbance impact factor is calculated. Based on the basic temperature change response curve and thermal disturbance influence factor, a dynamic heat conduction tensor is generated. By using the dynamic heat conduction tensor to predict the heat propagation state for the next time period, and obtaining the temperature deviation probability distribution, the temperature threshold for the next time period is obtained based on the temperature deviation probability distribution.

[0009] Furthermore, the steps of rolling prediction of the heat propagation state for the next time period using the dynamic heat conduction tensor to obtain the temperature deviation probability distribution, and then obtaining the temperature threshold for the next time period based on the temperature deviation probability distribution, include: Based on the eigenvalues ​​of the dynamic heat conduction tensor, the heat propagation state distribution field for the next time period is simulated; Based on the instantaneous heat flux of each node reflected by the heat propagation state distribution in the next time period, after extracting the temperature change feature vector, a temperature deviation evolution model is constructed to obtain the temperature deviation probability distribution. The confidence coefficient is obtained by adjusting the risk weights based on the probability distribution of temperature deviation. By combining the probability distribution of temperature deviation and the confidence coefficient, risk simulation is performed to obtain the temperature risk factor matrix. Then, the temperature threshold for the next time period is calculated based on the temperature risk factor matrix.

[0010] Furthermore, based on the temperature threshold for the next time period and the vibration data of the compartment containing the cold chain goods, the steps for constructing a three-dimensional temperature field distribution map include: Modal decomposition is performed on the vibration data to extract multi-scale vibration feature components; Based on the high-frequency vibration characteristic components in the multi-scale vibration characteristic components, a vibration entropy matrix is ​​generated; By coupling the vibration entropy matrix and the temperature threshold of the next time period, and constructing the heat source constraint matching condition, the local heat source distribution vector is obtained through the derivation of the heat source constraint matching condition. Obtain the compartment structure data of the cold chain goods, and construct a three-dimensional temperature field distribution map based on the compartment structure data and local heat source distribution vector.

[0011] Furthermore, the steps for generating control commands based on the coordinates of temperature anomaly regions in the three-dimensional temperature field distribution map include: Based on the coordinates of the temperature anomaly region, the corresponding thermodynamic gradient response values ​​are extracted from the three-dimensional temperature field distribution map, and then the refrigerant diffusion path matrix is ​​obtained through field strength coupling. Based on the refrigerant diffusion path matrix, the airflow distribution strategy is solved, and the variable frequency parameters of the multi-stage compressor are derived according to the airflow distribution strategy. Based on the variable frequency parameters of the multi-stage compressor and the structural constraints of the compartment where the cold chain goods are located, control commands are generated, including refrigeration control parameters and ventilation control parameters.

[0012] Furthermore, the steps for calculating the control weight coefficients based on the control commands and the preset energy consumption optimization objective function include: Based on the cooling control parameters and ventilation control parameters, the predicted energy consumption value is calculated; The initial weight vector is obtained by weighting the predicted energy consumption value and the preset real-time electricity price parameter in the preset energy consumption optimization objective function. Based on the transportation route of cold chain goods, the initial weight vector is adjusted to obtain the control weight coefficients, which include refrigeration power parameters and ventilation distribution parameters.

[0013] Furthermore, based on the control weighting coefficients and control commands, the steps for performing temperature control operations include: Based on the preset cooling power allocation ratio, the cooling power parameters are adjusted to generate a cooling control signal, and based on the preset damper opening priority, the ventilation distribution parameters are adjusted to generate a directional ventilation signal; Based on the preset system temperature mode switching threshold and real-time temperature data, output the system mode switching command; The system combines cooling control signals, directional ventilation signals, and system mode switching commands to generate temperature control operations.

[0014] Beneficial effects achieved: This application provides a method for temperature monitoring and control during cold chain commodity transportation, comprising: acquiring real-time temperature data; generating a thermal response digital profile of the cold chain commodity based on the real-time temperature data and a cold chain commodity category database; collecting real-time location and external air temperature data of the cold chain commodity; predicting the temperature threshold for the next time period based on the heat capacity characteristic parameters reflected in the thermal response digital profile; constructing a three-dimensional temperature field distribution map based on the temperature threshold for the next time period and the vibration data of the compartment where the cold chain commodity is located; generating control commands based on the coordinates of temperature anomaly areas in the three-dimensional temperature field distribution map; calculating control weight coefficients based on the control commands and a preset energy consumption optimization objective function; and executing temperature control operations based on the control weight coefficients and the control commands.

[0015] In this application, thermal response digital archives are used to quantify the thermal capacity parameters of cold chain goods, accurately distinguishing the differences in thermal response among different cold chain goods. This avoids the redundancy of monitoring highly sensitive goods using traditional uniform thresholds at the temperature monitoring level. Simultaneously, based on real-time location and external temperature data, combined with thermal capacity parameters, a temperature threshold for the next time period is generated, replacing passive response with predictive temperature monitoring, significantly reducing ineffective compressor start-stop due to conservative temperature monitoring. Next, the vibration data of the cargo compartment is reused to construct a three-dimensional temperature field distribution map, transforming traditional interference sources into a full-space, blind-spot-free temperature monitoring signal. This accurately locates the coordinates of temperature anomaly areas such as the corners of the cargo compartment, effectively improving temperature monitoring accuracy. Furthermore, control commands generated based on the coordinates of temperature anomaly areas are combined with an energy consumption optimization objective function to dynamically calculate control weight coefficients. Finally, the execution unit is driven to perform temperature control operations based on the control weight coefficients and control commands. By dynamically matching the local temperature control intensity with the thermal capacity characteristics of the goods, the standard deviation of temperature fluctuations is reduced while energy consumption is decreased. This solves the dilemma of the trade-off between temperature monitoring accuracy, control real-time performance, and energy efficiency with zero hardware modification cost. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for temperature monitoring and control during cold chain commodity transportation, as described in this application. Figure 2 This is a flowchart illustrating step S10 of the temperature monitoring and control method for cold chain goods transportation in this application. Figure 3 This is a flowchart illustrating step S20 of the method for temperature monitoring and control during cold chain transportation of goods in this application. Figure 4 This is a schematic diagram of a time-temperature coordinate system. Figure 5 This is a flowchart illustrating step S30 of the temperature monitoring and control method for cold chain goods transportation in this application. Figure 6This is a flowchart illustrating step S40 of the method for temperature monitoring and control during cold chain transportation of goods in this application. Figure 7 This is a flowchart illustrating step S50 of the temperature monitoring and control method for cold chain goods transportation in this application. Detailed Implementation

[0017] The following is in conjunction with the appendix Figure 1-7 This application will be described in further detail.

[0018] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] This application discloses a method for temperature monitoring and control during cold chain commodity transportation.

[0021] Please refer to Figure 1 In one embodiment of this application, a method for temperature monitoring and control during cold chain commodity transportation includes steps S10-S50: Step S10: Obtain real-time temperature data, and generate a digital profile of the thermal response of cold chain products based on the real-time temperature data and the cold chain product category database.

[0022] By acquiring real-time temperature data and combining it with a database of cold chain product categories to generate digital profiles of thermal response, the aim is to break through the extensive mode of temperature control in traditional cold chain transportation and achieve precise quantitative modeling of the thermal sensitivity characteristics of different product categories.

[0023] In this embodiment, by dynamically capturing real-time temperature data of goods in the actual transportation environment, a digital thermal response profile is established for different categories of cold chain goods, providing a physical basis for subsequent predictive temperature control. Its core effect is to upgrade cold chain goods from static temperature threshold management to a dynamic thermal characteristic adaptation mechanism, capturing the different temperature response patterns between highly sensitive cold chain goods such as vaccines and ordinary fruit and vegetable cold chain goods, eliminating the risk of temperature control redundancy or runaway caused by insufficient understanding of the thermal inertia of cold chain goods, and ensuring that the temperature control strategy is strictly matched with the inherent thermal requirements of cold chain goods, thus avoiding cargo damage and energy waste caused by over-cooling or local temperature rise from the source.

[0024] Step S20: Collect real-time location and external temperature data of cold chain goods, and predict the temperature threshold for the next period based on the heat capacity characteristic parameters reflected in the thermal response digital archive.

[0025] By collecting real-time location data of cold chain goods and external temperature data, and combining this with precisely quantified heat capacity parameters in the thermal response digital archive, the temperature threshold for the next time period can be predicted.

[0026] By integrating the inherent thermal capacity characteristics of cold chain goods with external temperature data and real-time location, the system accurately predicts the impact of the transportation route environment on the temperature of cold chain goods. This generates temperature thresholds that adapt to specific external conditions in the next period. The aim is to identify the thermal risks in areas with sudden environmental changes, such as tunnels and high-altitude road sections, in advance. This enables the refrigeration system to implement precise pre-adjustment of cooling energy based on the actual heat demand of cold chain goods and the trend of external temperature changes. This eliminates temperature control delays or resource mismatches caused by the mismatch between sudden environmental temperature changes and the thermal capacity characteristics of cold chain goods, and achieves dynamic optimization of temperature safety boundaries and on-demand allocation of temperature control resources.

[0027] Step S30: Based on the temperature threshold of the next time period and the vibration data of the compartment where the cold chain goods are located, construct a three-dimensional temperature field distribution map.

[0028] Based on the temperature threshold of the next time period and the vibration data of the cold chain goods in the container, a three-dimensional temperature field distribution map is constructed, aiming to break through the spatial limitations of traditional point-based temperature monitoring.

[0029] By cross-domain coupling of the temperature threshold for the next time period with real-time vibration data, the physical mechanism of vibration data on the temperature field of cold chain goods is deeply analyzed. This allows for the accurate reproduction of the temperature gradient distribution of cold chain goods in the spatial dimension of the compartment. The aim is to integrate discrete temperature monitoring data and vibration data into a continuous thermal field model throughout the entire space, accurately locate the coordinates of abnormal temperature areas caused by airflow obstruction or stacking of cold chain goods, provide spatial navigation basis for directional temperature control, and eliminate the monitoring gaps caused by blind spots in temperature sensor coverage. This enables accurate monitoring of the temperature status of cold chain goods inside the compartment, laying a spatial data foundation for refined temperature control.

[0030] Step S40: Generate control commands based on the coordinates of the temperature anomaly areas in the three-dimensional temperature field distribution map.

[0031] Control commands are generated based on the coordinates of temperature anomaly areas in the three-dimensional temperature field distribution map, aiming to achieve a fundamental leap in cold chain transportation temperature control mode from extensive full-area coverage to refined spatial directional intervention.

[0032] By analyzing the spatial geometric features and thermodynamic gradient data of temperature anomaly areas using their coordinates, abstract thermal field distribution information is transformed into control commands that drive the actuators of refrigeration equipment. This approach overcomes the inherent limitations of traditional temperature control strategies that respond to the average value of the entire compartment. It directly targets and quantitatively regulates localized areas of concentrated temperature change in cold chain goods, ensuring that high-precision temperature control behavior strictly matches the actual spatial distribution of thermal anomalies in cold chain goods. This eliminates redundant energy consumption and the risk of localized temperature control failure caused by global refrigeration, while shortening the decision-making link from anomaly identification to execution response. It provides spatial quantitative operational basis for optimized allocation of refrigeration power and directional guidance of ventilation flow, achieving precise deployment and efficient utilization of temperature control resources in the physical space dimension.

[0033] Step S50: Based on the control command and the preset energy consumption optimization objective function, calculate the control weight coefficient, and then execute the temperature control operation according to the control weight coefficient and the control command.

[0034] The control weight coefficient is calculated based on the control command and the preset energy consumption optimization objective function, and the temperature control operation is executed according to the control weight coefficient and the control command. The aim is to achieve a dynamic and flexible balance between temperature control accuracy and energy efficiency target in the cold chain transportation process.

[0035] By placing the actual intensity of control commands within the energy consumption game environment corresponding to the preset energy consumption optimization objective function and performing weighted modulation, the contradiction between energy consumption and temperature control accuracy under the fixed power output mode is overcome. Temperature control operations are executed based on the control weight coefficient and control commands, enabling the control command execution process to accurately respond to the local temperature change requirements of cold chain goods while also taking into account real-time electricity price policies and carbon emission constraints. This eliminates the problem of excessive energy consumption or insufficient temperature control response caused by global full-power operation, achieving a high-efficiency compatibility between temperature control intensity and low energy consumption. This provides a smart execution paradigm for cold chain transportation systems that balances economy, reliability, and regulatory compliance.

[0036] The specific implementation examples are as follows: Regarding step S10, refer to... Figure 2 As shown, this can be achieved through steps S11 to S15: Step S11: Obtain the temperature change of cold chain goods based on real-time temperature data and generate temperature fluctuation data.

[0037] By continuously collecting real-time temperature data of cold chain goods during transportation, and calculating the temperature change between adjacent time points based on temperature reading sequences at fixed time intervals, temperature fluctuation data reflecting the dynamic temperature change trajectory of cold chain goods is constructed. Specifically, this temperature fluctuation data is a continuous function record of the amplitude, frequency, and trend of temperature rise or fall of cold chain goods per unit time. Its core generation logic lies in transforming discrete real-time temperature data into smooth temperature change curves through time series interpolation algorithms, and then extracting the instantaneous temperature change rate at each time point through first derivative calculation. Finally, it is integrated into temperature fluctuation data that includes the direction, rate, and period of temperature change.

[0038] Step S12: Calculate the temperature response rate and thermal inertia index of cold chain products based on temperature fluctuation data.

[0039] The calculation of temperature response rate and thermal inertia index of cold chain products based on temperature fluctuation data is achieved by performing time-domain differentiation on the continuous temperature change curve in the temperature fluctuation data, extracting the instantaneous slope of the temperature change of cold chain products per unit time as the temperature response rate, and analyzing the time decay characteristics of cold chain products maintaining temperature stability under specific external thermal disturbances through integral calculation, quantifying the ratio of temperature change delay time to external temperature change amplitude as the thermal inertia index.

[0040] Step S13: Based on the heat sensitivity level parameters in the cold chain commodity category database, and combined with the temperature response rate and thermal inertia index, construct the commodity-temperature interaction response matrix.

[0041] This embodiment integrates the inherent heat sensitivity level parameters of cold chain products with the real-time calculated temperature response rate and thermal inertia index to establish a multi-dimensional feature mapping model. The model uses the heat sensitivity level as the row vector and the combined features of temperature response rate and thermal inertia index as the column vector. Matrix multiplication is then used to quantify the predicted temperature behavior of different categories of cold chain products under specific thermal disturbance environments.

[0042] The product-temperature interaction response matrix is ​​essentially a two-dimensional decision table. Its row coordinates correspond to the thermal sensitivity level classification, such as Grade A (high sensitivity), Grade B (medium sensitivity), and Grade C (low sensitivity). The column coordinates are formed by the intersection of the temperature response rate range and the thermal inertia index range. The matrix element values ​​represent the theoretical temperature change trajectory function index of a specific category of cold chain products under the corresponding heat capacity characteristic parameters.

[0043] Step S14: Extract the feature vectors from the product-temperature interaction response matrix to generate a heat capacity feature map.

[0044] The process of extracting eigenvectors from the product-temperature interaction response matrix to generate a heat capacity feature map involves performing singular value decomposition on the product-temperature interaction response matrix, extracting the orthogonal eigenvector group (i.e., eigenvector) corresponding to the largest singular value, and then mapping this orthogonal eigenvector group into a heat capacity feature map in the thermodynamic parameter space after tensor expansion in Hilbert space.

[0045] This heat capacity feature map is essentially a thermodynamic state space mapping table that integrates the three-dimensional characteristics of cold chain commodities, including heat sensitivity level, temperature response rate, and thermal inertia index. Its horizontal axis represents the segmented intervals of the heat sensitivity level parameter, the vertical axis is related to the value range of the composite function of temperature response rate and thermal inertia index, and the surface height corresponds to the heat capacity equivalent value of cold chain commodities under specific thermodynamic conditions.

[0046] Step S15: The thermal capacity characteristic spectrum and historical temperature fluctuation data are correlated and mapped to generate a thermal response digital archive, which records the safety threshold gradient and critical phase transition point.

[0047] By establishing the spatiotemporal correspondence between the thermodynamic state space coordinates in the heat capacity feature map and key temperature change events in historical temperature fluctuation data, such as rapid cooling and phased warming, a nonlinear regression algorithm is used to fit the heat capacity equivalent value in the heat capacity feature map to the constraint boundary function of the historical temperature change trajectory. This decouples the safety threshold gradient, which characterizes the temperature safety evolution law of cold chain commodities, i.e. the maximum allowable temperature change slope per unit time, and the critical phase transition point that identifies the risk of sudden changes in the state of cold chain commodities, such as the critical temperature for thawing frozen products.

[0048] Thermodynamic state space coordinates refer to a quantitative system that transforms the thermodynamic characteristics of cold chain products into digital spatial positioning parameters.

[0049] In this embodiment, the temperature response rate and thermal inertia index are calculated using temperature fluctuation data. A commodity-temperature interaction response matrix is ​​constructed by combining thermal sensitivity level parameters. Feature vectors are extracted to generate a thermal capacity feature map. Finally, a thermal response digital profile containing a safety threshold gradient and a critical phase transition point is generated. This thermal response digital profile accurately depicts the thermal behavior characteristics of cold chain commodities, transforming temperature monitoring from static threshold judgment to a dynamic thermal characteristic adaptation mechanism. This eliminates temperature monitoring errors caused by insufficient understanding of thermal inertia, enabling differentiated temperature monitoring strategies for highly sensitive commodities such as vaccines and ordinary fruits and vegetables. This effectively reduces the damage rate and ineffective refrigeration energy consumption in cold chain transportation. At the same time, the refrigeration rate is constrained by the safety threshold gradient to avoid sudden temperature changes damaging the cellular structure of cold chain commodities, and the incidence of temperature rise exceeding the standard for cold chain commodities is reduced based on the critical phase transition point.

[0050] Regarding step S20, refer to... Figure 3 As shown, this can be achieved through steps S21 to S24: Step S21: Generate the basic temperature change response curve based on the safety threshold gradient and the critical phase transition point.

[0051] By using the safety threshold gradient as a constraint boundary condition for the rate of temperature change, and the critical phase transition point as a segmented control node for the risk of sudden changes in the state of cold chain commodities, a basic temperature change response curve of the temperature evolution of cold chain commodities over time is constructed in the thermodynamic state space.

[0052] The fundamental temperature response curve is essentially an idealized temperature change path in the time-temperature coordinate system that strictly follows the safety threshold gradient slope limit and avoids the forbidden zone of the critical phase transition point. Specifically: First, referring to... Figure 4 As shown, the critical phase transition point (i.e. Figure 4 In this context, d0) is used as a segmented anchor point to divide the temperature variation range, such as the freezing zone (i.e., Figure 4 a1) - transition region (i.e. Figure 4 a2) - Refrigerated area (i.e. Figure 4 (a3 in the middle), then according to the safety threshold gradient in each interval. The maximum allowable temperature change slope is calculated, and finally, a continuously differentiable temperature change curve that satisfies all constraints is generated using a cubic spline interpolation algorithm, i.e., the basic temperature change response curve. .in, The initial temperature. It is a time variable.

[0053] Step S22: Based on the transportation route of cold chain goods, obtain real-time road condition data and meteorological data, and calculate the thermal disturbance impact factor.

[0054] By using the geographical coordinates of the transportation route, real-time traffic data is obtained through the API interface of the traffic information platform. At the same time, meteorological data is collected by connecting to the meteorological satellite data interface. The vibration energy spectral density in the real-time traffic data is converted into the excitation force input term in the structural dynamics equation through a physical conversion formula. Then, the structural dynamics equation is solved to obtain the stress distribution inside the compartment. Meanwhile, the atmospheric temperature gradient and solar radiation parameters in the meteorological data are input into the heat conduction boundary condition equation to construct the boundary heat flow constraint. A two-way energy transfer model is constructed by coupling the vibration heat source term with the heat conduction boundary condition.

[0055] Specifically, the physical conversion formula is as follows: in, For vibration excitation force density, For vibrational energy spectral density, The mass density of the compartment. Let be the vibration angular frequency. After calculating the vibration excitation force density, substitute the calculated vibration excitation force density into the structural dynamics equations: in, The mass density of the compartment. The instantaneous acceleration caused by vibration. For the vibration stress inside the compartment, This refers to the mechanical vibration load on the box body.

[0056] Vibration heat source conversion formula: in, As a vibration heat source, The thermoelastic loss coefficient of the body material. The vibration frequency is used to convert vibration stress into a vibration heat source.

[0057] The boundary condition equation for heat conduction is: in, For boundary heat flow constraints, This refers to the efficiency of heat exchange between airflow and the surface of the compartment. The ambient air temperature. This refers to the measured temperature of the outer shell of the compartment. The ability of the container surface to absorb solar radiation (generally set at 0.92 for black paint and 0.3 for white paint). The solar radiation power received per unit area (generally set to 1000 on a sunny day at noon). 300 on cloudy days The tunnel has a capacity of 0. ), The ability of the compartment to radiate heat outwards. Let be the blackbody radiation constant. It is the equivalent temperature of atmospheric radiation into outer space.

[0058] At this point, coupling the vibration heat source conversion formula and the heat conduction boundary condition equation yields a two-way energy transfer model. This two-way energy transfer model first converts vibration stress into a vibration heat source, injecting it into the source term of the heat conduction boundary condition equation. Simultaneously, it converts the vibration stress into a vibration heat source within the heat conduction boundary condition equation. Feedback to the material elastic modulus function To correct vibration stress ,because Therefore, the impact force of the box body due to uneven road surface Under the condition that remains unchanged, when the surface temperature of the compartment remains constant... Temperature-related material properties when changes occur It will decrease, and thus increase vibration stress. In this dynamic iterative equilibrium process, based on the three-dimensional temperature field distribution, the disturbance of vibration frequency to thermal equilibrium is quantified as a vibration disturbance term with a weight of 0.4; the influence of atmospheric temperature gradient on heat exchange is quantified as a temperature change disturbance term with a weight of 0.3; and the heat penetration effect of solar radiation on the body wall is quantified as a radiation disturbance term with a weight of 0.3. Finally, a thermal disturbance influence factor with a value between 0 and 1 is generated through time integration. As a reference property at the reference temperature, Temperature coefficient, a material property For reference temperature, This refers to the effective load-bearing area at the bolted connection.

[0059] This thermal disturbance influence factor characterizes the comprehensive disturbance intensity index of the external environment on the thermal stability of the compartment per unit time.

[0060] It should be noted that the time integration operation is as follows: in, Thermal disturbance influencing factors and For time window, For vibration stress term, The yield strength of the material can be set to 200 MPa. For atmospheric temperature variation, This is a solar radiation correction term.

[0061] Step S23: Based on the basic temperature change response curve and the thermal disturbance influence factor, generate the dynamic heat conduction tensor.

[0062] First, an initial thermal conductivity tensor is constructed using the safety threshold gradient from the basic temperature change response curve. This initial thermal conductivity tensor is determined by the material's inherent thermal conductivity and the direction of the temperature gradient. Next, the thermal perturbation influence factor is input into the material response model to dynamically correct the thermal conductivity. Finally, a dynamic thermal conductivity tensor is synthesized using Kirchhoff transform. The diagonal elements of this dynamic heat conduction tensor reflect the position-dependent thermal conductivity, while the off-diagonal elements characterize the thermal coupling strength in the cross directions.

[0063] The material response model is as follows: in, To correct the thermal conductivity, Based on the basic thermal conductivity, This is the temperature gradient gain coefficient. This is the disturbance attenuation coefficient. Represents a unit tensor. Let the initial heat conduction tensor be... To control the correction intensity, in the tunnel scenario, the control correction intensity is set to 0.4.

[0064] Step S24: After rolling the prediction of the heat propagation state in the next time period by using the dynamic heat conduction tensor to obtain the temperature deviation probability distribution, the temperature threshold for the next time period is obtained based on the temperature deviation probability distribution.

[0065] Furthermore, step S24 can be specifically implemented through steps S25~S28: Step S25: Based on the characteristic value of the dynamic heat conduction tensor, simulate the heat propagation state distribution field for the next time period.

[0066] By analyzing the dynamic heat conduction tensor Eigenvalue decomposition is performed to obtain the diagonal elements (i.e., eigenvalues) in the eigenvector matrix. Combined with the initial temperature field vector, a thermal state evolution equation is constructed to quantify the attenuation characteristics of thermal disturbances at different spatial frequencies. The global temperature distribution field for the next time period is obtained by solving the equation through finite element discretization.

[0067] It should be noted that the thermal state evolution equation is: in, for Temperature field distribution after time, The eigenvector matrix, For attenuation operators, It is the inverse of the eigenvector matrix. For the initial temperature field, the decay operator diagonal elements Quantify the attenuation characteristics of thermal disturbances at different spatial frequencies.

[0068] The specific method for obtaining the global temperature distribution field in the next time period by discretizing the equation using the finite element method is as follows: First, the cold chain compartment space is discretized into a finite number of element nodes, and the temperature of each node forms a node vector. At this time, the dynamic heat conduction tensor is decomposed into eigenvalues. , where the eigenvalue matrix diagonal elements The decay rate of different heat conduction modes is characterized, while the eigenvector matrix Q describes the spatial pattern of heat propagation, obtained through mode space transformation of the initial temperature field. and the evolution over time (Index Item) Precise quantization of each mode at the time step (the energy decay ratio within), ultimately through inverse transformation Reconstruct the temperature distribution in the physical space to obtain the global temperature distribution for the next time period.

[0069] Step S26: Based on the instantaneous heat flux of each node reflected by the heat propagation state distribution location in the next time period, extract the temperature change feature vector, construct the temperature deviation evolution model, and obtain the temperature deviation probability distribution.

[0070] Based on the instantaneous heat flux of each node in the heat propagation state distribution field for the next time period, the temperature change feature vector is extracted. First, the temperature change feature vector of the discrete node is converted into the instantaneous heat flux density vector using Fourier's law. Next, singular value decomposition is performed on the instantaneous heat flux density vector to obtain the temperature change eigenvector matrix, based on which a temperature deviation evolution model is constructed. After simulating the evolution trajectory under 1000 environmental disturbances using the Monte Carlo method, the distribution of temperature deviation values ​​at each node was statistically fitted to a temperature deviation probability distribution.

[0071] Step S27: Adjust the risk weights based on the probability distribution of temperature deviation to obtain the confidence coefficient.

[0072] First, the temperature deviation values ​​at each node output by the temperature deviation evolution model are fitted to a normal distribution. A risk weight function is then constructed based on this probability density function. (in, Risk weighting The sensitivity coefficient can be set to 5. Taking 8 as the nonlinear gain), this risk weighting function assigns a higher weight to high-temperature deviation, and then performs integration. (in, The confidence coefficient is... For a safe threshold gradient, Let be the probability density function of the probability distribution of temperature deviation. The confidence coefficients are generated for the integral infinitesimal element, and their physical essence represents the compressed mapping of the probability of temperature exceeding the limit.

[0073] Step S28: Combine the temperature deviation probability distribution and confidence coefficient to perform risk simulation, obtain the temperature risk factor matrix, and then calculate the temperature threshold for the next time period based on the temperature risk factor matrix.

[0074] Based on the probability distribution of temperature deviation, 1000 sets of random temperature deviation field samples are generated. Then, the confidence coefficient is injected as a failure probability compensation term into the risk simulation to calculate the temperature risk factor for each spatial location. Finally, all spatial nodes are integrated to form a temperature risk factor matrix. This temperature risk factor matrix quantifies the probability of local temperature rise runaway through the element values ​​of the temperature risk factors. Based on this temperature risk factor matrix, the temperature threshold for the next time period is calculated, and its equation is as follows: ,in, This is the temperature threshold for the next time period. This is a static threshold. This is the shrinkage coefficient.

[0075] In this embodiment, the physical boundary of temperature change of cold chain goods is constrained by the safety threshold gradient, and the state change protection threshold is set by combining the critical phase transition point. A basic temperature change response curve is generated as a theoretical benchmark. Real-time road condition data and meteorological data in the transportation route are integrated to calculate the thermal disturbance impact factor and quantify the environmental interference intensity. A dynamic heat conduction tensor is generated. The heat propagation state of the next period is predicted in a rolling manner through the dynamic heat conduction tensor, and the temperature deviation probability distribution is output. Finally, the temperature threshold of the next period is dynamically generated based on the temperature deviation probability distribution. This process effectively reduces the error of subsequent temperature control prediction.

[0076] Regarding step S30, refer to... Figure 5 As shown, this can be achieved through steps S31 to S34: Step S31: Modal decomposition is performed on the vibration data to extract multi-scale vibration feature components.

[0077] In this embodiment, the vibration data is modally decomposed using an empirical mode decomposition algorithm. Specifically, the vibration data is first identified by identifying local extrema and constructing upper and lower envelopes. After calculating the envelope mean curve m[n], candidate components h[n] = a[n] - m[n] are separated. The process is iteratively sieved until h[n] satisfies the intrinsic mode function condition, i.e., the difference between the number of extrema and zero-crossing points does not exceed 1 and the local mean is zero. The output h[n] that satisfies the intrinsic mode function condition is output as the first-order multi-scale vibration feature classification c1[n]. At this time, the vibration data a[n] is subtracted from the first-order multi-scale vibration feature classification c1[n] to obtain the residual term r1[n]. Local extrema are identified and upper and lower envelopes are constructed on the residual term r1[n] to calculate the second-order multi-scale vibration feature classification c2[n]. This process is repeated until the residual term is a monotonic trend term. The calculated multi-scale vibration feature classifications are arranged in descending order of frequency to form multi-scale vibration feature components.

[0078] Where h[n] is the candidate component, and a[n] is the original vibration data sequence formed after identifying local extreme points and constructing upper and lower envelopes from the vibration data.

[0079] Step S32: Generate a vibration entropy matrix based on the high-frequency vibration characteristic components in the multi-scale vibration characteristic components.

[0080] In this embodiment, the vibration entropy matrix is ​​generated by performing time-frequency energy analysis on the high-frequency vibration characteristic components in the multi-scale vibration characteristic components. Specifically, the time-frequency energy density matrix is ​​calculated by performing short-time Fourier transform on the high-frequency vibration characteristic components. ,in Let represent the energy intensity of time window i and frequency j. Then, based on the Shannon entropy definition, the energy probability distribution of each time window is calculated. ( ), generating vibration entropy value ( After performing the above calculations sequentially for each time window, the vibration entropy values ​​corresponding to each time window are integrated to generate a vibration entropy matrix. .

[0081] in, Let F be the time-frequency energy density matrix, where T is the total number of time windows and F is the total number of multi-scale vibration characteristic components.

[0082] Step S33: Couple the vibration entropy matrix and the temperature threshold of the next time period to construct the heat source constraint matching condition. Then, derive the local heat source distribution vector through the heat source constraint matching condition.

[0083] Vibration entropy matrix Temperature threshold for the next time period Input to tensor product operation In this process, the joint constraint tensor is obtained. The joint constraint tensor Through Kronek product Extended to heat source constraint matching conditions.

[0084] In this embodiment, the local heat source distribution vector is derived from the heat source constraint matching condition. Solve the following heat source constraint matching conditions: Under the condition of satisfying the transformation of the heat conduction equation Under the premise of minimizing the objective function (The heat source gradient is matched with the vibration entropy gradient), and then the local heat source distribution vector is obtained by iterative calculation using the Lagrange multiplier method. ( (This represents the heat source intensity of spatial node i).

[0085] in, The coupling strength between the local heat source distribution vector and the vibration entropy matrix is ​​given. For the fundamental thermoelastic tensor of the material, This represents the maximum permissible temperature rise.

[0086] Step S34: Obtain the compartment structure data of the cold chain goods, and construct a three-dimensional temperature field distribution map based on the compartment structure data and local heat source distribution vector.

[0087] By accessing the vehicle design database, geometric topological parameters of the compartment, such as length, width, height, partition position, and material thickness distribution, are extracted. In order to improve the accuracy of the structural data, this embodiment proposes to combine real-time scanned point cloud data to correct deformation errors caused by assembly tolerances, thereby obtaining the compartment structural data.

[0088] The computational domain boundary is defined based on the length, width, and height dimensions of the compartment. For example, a rectangular domain boundary of 12.5m × 2.4m × 2.2m generates spatial node coordinates. The partition positions divide the computational domain boundary into independent sub-regions. Heat flow continuity conditions are applied to each independent sub-region, and the material thickness distribution is incorporated into the equation discretization by modifying the integral weight of the dynamic heat conduction tensor of the modified element. Simultaneously, the local heat source distribution vector is injected as an equation term, and material property parameters, namely density, specific heat capacity, and thermal conductivity, are loaded.

[0089] With respect to the time derivative term Discretization using the post-Euler scheme is employed to generate transient iterative equations: At this point, the unsteady heat conduction equation... Discretization yields the element stiffness matrix K and heat capacity matrix C, specifically: The continuous temperature field T is approximated using shape functions, resulting in... , Let j be the shape function of node j. To find the nodal temperature, substitute the shape function into the unsteady-state heat conduction equation, and then perform a weighted integral over the residual in the computational domain, forcing it to zero, to obtain... By reducing its order using Green's theorem, we obtain... + Boundary terms, finally calculated as follows ,in, Let C be the heat capacity matrix. K is the element stiffness matrix.

[0090] Next, the element stiffness matrix K and the heat capacity matrix C are combined to form a global linear equation system: Apply convective heat transfer boundary at the nodes on the outer surface of the chamber. Furthermore, a forced thermal equilibrium is established at the interlayer interface, and the global linear equations are iteratively solved until the residual norm is less than 1. Finally, the global node temperature vector T is output, and a dynamic three-dimensional temperature field distribution map is generated through spatial interpolation mapping.

[0091] in, For density, ρ is the specific heat capacity, k is the thermal conductivity. Let be the temperature field at time t. For time step, Let be the shape function of node i, t be the time variable, and the boundary term be the heat dissipation quantization of the outer surface of the compartment. For the infinitesimal element when the volume integral is performed over the region Ω, Let be the rate of change of temperature over time at node j.

[0092] In this embodiment, multi-scale vibration feature components are extracted through modal decomposition of vibration data. A vibration entropy matrix is ​​generated using high-frequency vibration feature components to quantify the vibration data. The vibration entropy matrix is ​​coupled with the temperature threshold of the next time period to construct a heat source constraint matching condition. The local heat source distribution vector is derived through this heat source constraint matching condition to accurately locate vibration-induced heating risk points. Combined with the body structure data, a three-dimensional temperature field distribution map is constructed to realize spatial visualization of the internal thermal field of the body, reducing the temperature rise prediction error caused by local vibration.

[0093] Regarding step S40, refer to... Figure 6 As shown, this can be achieved through steps S41 to S43: Step S41: Based on the coordinates of the temperature anomaly area, extract the corresponding thermodynamic gradient response value from the three-dimensional temperature field distribution map, and then obtain the refrigerant diffusion path matrix through field strength coupling.

[0094] In this embodiment, based on the coordinates of the temperature anomaly region, the core nodes of the anomaly are located in the three-dimensional temperature field distribution map. A vertex sequence of the closed boundary contour is generated using an isosurface extraction algorithm. Simultaneously, the temperature gradient magnitude of each node within the closed boundary contour is calculated, and the average value is taken as the thermodynamic gradient response value. Then, a field strength coupling model is used to physically correlate the thermodynamic gradient response value with the refrigerant pressure field, i.e., a coupling equation is established. Solving this equation yields the pressure gradient distribution. Then through Darcy's Law The flow velocity field q is calculated, and finally, the flow velocity field is streamlined to generate the refrigerant diffusion path matrix. Where k1 is the refrigerant's permeability along the diffusion path. The frictional resistance of the refrigerant along the diffusion path. For time infinitesimal elements, This is the thermodynamic gradient transformation tensor.

[0095] Step S42: Based on the refrigerant diffusion path matrix, the airflow distribution strategy is solved, and the frequency conversion parameters of the multi-stage compressor are derived according to the airflow distribution strategy.

[0096] Analyze the refrigerant diffusion path matrix and construct the objective function. Solving for the total air volume constraint using linear programming and the diffusion requirements of each node Greater than 0.7 Airflow distribution strategy.

[0097] Based on the total air volume requirement in the airflow distribution strategy With maximum pressure loss compressor characteristic curve Derivation of inverter parameters for multi-stage compressors, including the fundamental frequency. Incremental frequency conversion The final output frequency conversion parameter combination ( , ), which refers to the frequency conversion parameters of a multi-stage compressor.

[0098] in, This represents the total number of damper zones that output refrigerant. Let be the flow resistance coefficient of the k-th duct. For the target air volume of zone k, The deviation penalty weighting coefficient, For the required air volume of zone k, This is the proportional gain coefficient of the compressor. The base air volume is the factory air volume under steady-state operating conditions.

[0099] Step S43: Based on the frequency conversion parameters of the multi-stage compressor and the structural constraints of the compartment where the cold chain goods are located, generate control commands, which include refrigeration control parameters and ventilation control parameters.

[0100] Input the variable frequency parameters of the multi-stage compressor into the compressor characteristic equation The output power was calculated. Then, combined with refrigerant flow requirements Generate refrigeration control parameters.

[0101] Simultaneously, the geometric boundaries of the ventilation ducts are defined based on the length, width, and height dimensions in the compartment structure data; independent wind control subdomains are divided according to the partition positions; and the drag coefficient is corrected based on the material thickness distribution. This is achieved by solving a constrained optimization problem. Generate ventilation control parameters Ultimately, the cooling control parameters and ventilation control parameters are integrated to form a control command set, which drives the actuators to respond in a coordinated manner.

[0102] in, The actual opening degree of the damper in zone k. Let K be the target opening degree of the damper in zone k.

[0103] In this embodiment, the thermodynamic gradient response value corresponding to the coordinates of the temperature anomaly area is extracted, and a refrigerant diffusion path matrix is ​​generated through field strength coupling to quantify the optimal transport path of the refrigerant in the compartment space. Using this refrigerant diffusion path matrix, the airflow distribution strategy is solved to achieve the targeted delivery of refrigeration resources. Based on the airflow distribution strategy, the frequency conversion parameters of the multi-stage compressor are derived to dynamically adapt to the local heat load demand. Finally, combined with the structural constraints of the compartment, control commands containing refrigeration control parameters and ventilation control parameters are generated to improve the temperature adjustment response speed at the temperature anomaly point, improve the temperature adjustment accuracy, and avoid the problem of excessive energy consumption caused by overall temperature adjustment.

[0104] Regarding step S50, refer to... Figure 7 As shown, this can be achieved through steps S51 to S56: Step S51: Calculate the predicted energy consumption value based on the cooling control parameters and ventilation control parameters.

[0105] Obtain the output power from the cooling control parameters Based on the ventilation control parameters and the wind resistance distribution parameters in the structural constraints of the enclosure, and based on the fan power consumption model... Calculate the total power consumption of the ventilation system. Then, the standby power consumption of the basic equipment is added to the total power consumption of the ventilation system. Energy consumption prediction values ​​were obtained. .

[0106] in, Let be the efficiency coefficient of the wind turbine in zone k. Let be the drag coefficient of the k-th zone.

[0107] Step S52: Perform weighted calculations on the predicted energy consumption value and the preset real-time electricity price parameter in the preset energy consumption optimization objective function to obtain the initial weight vector.

[0108] Energy consumption forecast Substituting into the first linear weighting function: Simultaneously, preset real-time electricity price parameters will be implemented. Substituting into the second linear weighting function: Obtain the initial weight vector .

[0109] in, Based on basic energy consumption value, This is the energy consumption weighting gain coefficient. The benchmark electricity price, This is the electricity price weighting gain coefficient.

[0110] Step S53: Adjust the weights of the initial weight vector according to the transportation route of the cold chain goods to obtain the control weight coefficients, wherein the control weight coefficients include refrigeration power parameters and ventilation distribution parameters.

[0111] First, the transportation route is analyzed to obtain its radius of curvature, elevation gradient, and tunnel density. Based on the radius of curvature... Altitude gradient Tunnel density Generate route feature vectors Then, the initial weight vector With route feature vector Input adjustment function The adjusted weights are obtained. .

[0112] Next, the adjusted weights are applied using a weight mapping model. Converted to control weighting coefficients, where the cooling power parameter Ventilation distribution parameters .

[0113] in, This is the route influence coefficient matrix, which is typically set to [0.15, -0.1, 0.2] for general applications. As an energy consumption weighting component, As a weighted component of electricity price, The reference damper opening parameter is used. The air guiding matrix for the compartment structure is set as follows: for example, the value is 0.8 for the corner area and 0.3 for the center area.

[0114] Step S54: Adjust the cooling power parameters according to the preset cooling power allocation ratio to generate a cooling control signal; and adjust the ventilation distribution parameters according to the preset damper opening priority to generate a directional ventilation signal.

[0115] It should be noted that the preset cooling power allocation ratio in this embodiment is a three-level cooling power allocation. Preset damper opening priority .

[0116] Based on the above-mentioned preset cooling power allocation ratio Adjust the cooling power parameters to generate tiered cooling control signals. For example, the frequency of the first-stage compressor. Secondary compressor frequency Three-stage compressor frequency .

[0117] Simultaneously, based on the aforementioned preset damper opening priority, the ventilation distribution parameters are adjusted to generate directional ventilation signals. For example, the directional ventilation signal for the third priority corner damper opening. .

[0118] Step S55: Output a system mode switching command based on the preset system temperature mode switching threshold and real-time temperature data.

[0119] It should be noted that the preset system temperature mode switching threshold in this embodiment... ,in This indicates the energy-saving mode, which can be set to 2.0℃; This indicates the balanced mode, which can be set to 1.2℃; This indicates a high-precision mode, which can be set to 0.5℃.

[0120] First, the real-time temperature data Input temperature rise deviation calculation formula In the middle, the current maximum temperature rise deviation is calculated. Next, determine the current maximum temperature rise deviation. The system mode switching command is determined by comparing the magnitudes of each mode with the preset system temperature mode switching threshold.

[0121] when ≤ The system mode switching command is to switch to energy-saving mode; when < ≤ The system mode switching command is to switch to balanced mode; when > The system mode switching command is to switch to high-precision mode.

[0122] Step S56: Combine the cooling control signal, directional ventilation signal, and system mode switching command to generate a temperature control operation.

[0123] Determining the control strength coefficient based on the system mode switching command The staged compressor frequency in the refrigeration control signal The refrigeration control signal is corrected by weighting it with the control strength coefficient. Simultaneously, directional ventilation signals will be sent. Weighted correction of the directional ventilation signal with control intensity coefficient .

[0124] Correct the refrigeration control signal and correct directional ventilation signals The system integrates and outputs temperature control operations, generating separate control strategies for different fans and compressors within the same compartment. This enables temperature control in different areas of the compartment, avoiding the imbalance between temperature control accuracy and energy consumption caused by using the same control strategy for fans and compressors in different areas of the same compartment.

[0125] In this embodiment, the predicted energy consumption value is calculated by using cooling control parameters and ventilation control parameters, and an initial weight vector is generated by weighting the parameters with preset real-time electricity price parameters. The initial weight vector is dynamically adjusted according to the transportation route to obtain the control weight coefficient. The control weight coefficient is adjusted according to the preset cooling power allocation ratio and the preset damper opening priority to generate cooling control signal and directional ventilation signal. At the same time, after outputting the system mode switching command according to the system temperature mode switching threshold and real-time temperature data, the temperature control operation is integrated to improve the response speed of the cooling system and the accuracy of ventilation flow distribution, as well as reduce control energy consumption.

[0126] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for temperature monitoring and control during cold chain commodity transportation, characterized in that, include: Acquire real-time temperature data, and generate a thermal response digital profile for the cold chain product based on the real-time temperature data and the cold chain product category database; Collect real-time location and external temperature data of the cold chain goods, and predict the temperature threshold for the next period based on the heat capacity characteristic parameters reflected in the thermal response digital file; Based on the temperature threshold of the next time period and the vibration data of the compartment where the cold chain goods are located, a three-dimensional temperature field distribution map is constructed. Based on the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution map, control commands are generated; Based on the control command and the preset energy consumption optimization objective function, the control weight coefficient is calculated, and then the temperature control operation is executed according to the control weight coefficient and the control command.

2. The method for temperature monitoring and control during cold chain commodity transportation according to claim 1, characterized in that, The step of generating the thermal response digital profile of the cold chain product based on the real-time temperature data and the cold chain product category database includes: Based on the real-time temperature data, the temperature change of the cold chain goods is obtained, and temperature fluctuation data is generated; Based on the temperature fluctuation data, the temperature response rate and thermal inertia index of the cold chain product are calculated; Based on the heat sensitivity level parameters in the cold chain commodity category database, and combined with the temperature response rate and the thermal inertia index, a commodity-temperature interaction response matrix is ​​constructed. Extract the feature vectors from the commodity-temperature interaction response matrix to generate a heat capacity feature map; The thermal capacity characteristic spectrum and historical temperature fluctuation data are correlated and mapped to generate the thermal response digital archive, wherein the thermal response digital archive records the safety threshold gradient and the critical phase transition point.

3. The method for temperature monitoring and control during cold chain commodity transportation according to claim 2, characterized in that, The step of predicting the temperature threshold for the next time period based on the heat capacity characteristic parameters reflected in the thermal response digital file includes: Based on the safety threshold gradient and the critical phase transition point, a basic temperature change response curve is generated; Based on the transportation route of the cold chain goods, real-time road condition data and meteorological data are obtained, and the thermal disturbance impact factor is calculated. Based on the basic temperature change response curve and the thermal disturbance influence factor, a dynamic heat conduction tensor is generated. By using the dynamic heat conduction tensor to predict the heat propagation state for the next time period, and obtaining the temperature deviation probability distribution, the temperature threshold for the next time period is obtained based on the temperature deviation probability distribution.

4. The temperature monitoring and control method for cold chain commodity transportation according to claim 3, characterized in that, The step of performing rolling prediction of the heat propagation state for the next time period using the dynamic heat conduction tensor to obtain the temperature deviation probability distribution, and then obtaining the temperature threshold for the next time period based on the temperature deviation probability distribution, includes: Based on the characteristic values ​​of the dynamic heat conduction tensor, the heat propagation state distribution field for the next time period is simulated; Based on the instantaneous heat flux of each node reflected by the heat propagation state distribution location in the next time period, after extracting the temperature change feature vector, a temperature deviation evolution model is constructed to obtain the temperature deviation probability distribution. The confidence coefficient is obtained by adjusting the risk weights based on the temperature deviation probability distribution. By combining the temperature deviation probability distribution and the confidence coefficient to perform risk simulation and obtain the temperature risk factor matrix, the temperature threshold for the next time period is calculated based on the temperature risk factor matrix.

5. The method for temperature monitoring and control during cold chain commodity transportation according to claim 1, characterized in that, The step of constructing a three-dimensional temperature field distribution map based on the temperature threshold of the next time period and the vibration data of the cold chain goods in the container includes: Modal decomposition is performed on the vibration data to extract multi-scale vibration feature components; Based on the high-frequency vibration characteristic components in the multi-scale vibration characteristic components, a vibration entropy matrix is ​​generated; After coupling the vibration entropy matrix and the temperature threshold of the next time period to construct the heat source constraint matching condition, the local heat source distribution vector is obtained by derivation through the heat source constraint matching condition. Obtain the compartment structure data of the compartment where the cold chain goods are located, and construct the three-dimensional temperature field distribution map based on the compartment structure data and the local heat source distribution vector.

6. The method for temperature monitoring and control during cold chain commodity transportation according to claim 1, characterized in that, The step of generating control commands based on the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution map includes: Based on the coordinates of the temperature anomaly region, the corresponding thermodynamic gradient response value is extracted from the three-dimensional temperature field distribution map, and then the refrigerant diffusion path matrix is ​​obtained through field strength coupling. Based on the refrigerant diffusion path matrix, after solving the airflow distribution strategy, the multi-stage compressor frequency conversion parameters are derived according to the airflow distribution strategy. Based on the variable frequency parameters of the multi-stage compressor and the structural constraints of the compartment where the cold chain goods are located, the control command is generated, wherein the control command includes refrigeration control parameters and ventilation control parameters.

7. The method for temperature monitoring and control during cold chain commodity transportation according to claim 6, characterized in that, The step of calculating the control weight coefficients based on the control command and the preset energy consumption optimization objective function includes: Based on the cooling control parameters and the ventilation control parameters, the predicted energy consumption value is calculated. The energy consumption prediction value and the preset real-time electricity price parameter in the preset energy consumption optimization objective function are weighted and calculated to obtain an initial weight vector; Based on the transportation route of the cold chain goods, the initial weight vector is adjusted to obtain the control weight coefficient, wherein the control weight coefficient includes refrigeration power parameters and ventilation distribution parameters.

8. The method for temperature monitoring and control during cold chain commodity transportation according to claim 7, characterized in that, The step of performing temperature control operation based on the control weight coefficient and the control command includes: Based on a preset cooling power allocation ratio, the cooling power parameters are adjusted to generate a cooling control signal; and based on a preset damper opening priority, the ventilation distribution parameters are adjusted to generate a directional ventilation signal. Based on the preset system temperature mode switching threshold and the real-time temperature data, a system mode switching command is output; The temperature control operation is generated by combining the cooling control signal, the directional ventilation signal, and the system mode switching command.

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