A cold chain transportation method, apparatus, electronic device and storage medium

Abstract

This application provides a cold chain transportation method, apparatus, electronic device, and storage medium. The method includes: acquiring the cargo type of a target cold chain transport vehicle; determining at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the cargo type; monitoring the real-time measurement value of each vehicle operating parameter during the operation of the target cold chain transport vehicle; acquiring environmental data of the environment in which the target cold chain transport vehicle is located; adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, the real-time measurement value of each vehicle operating parameter, and the standard parameter range; acquiring environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle; and generating a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a cold chain transportation method, apparatus, electronic device and storage medium. Background Technology

[0002] In recent years, with the rapid growth in demand for high-quality cold chain logistics for fresh food and pharmaceutical products driven by rising consumer spending, the cold chain transportation industry faces higher technical requirements and operational challenges. Particularly in the transportation of temperature-sensitive goods such as vaccines and high-end fresh produce, stringent temperature control requirements and timeliness standards have exacerbated the high energy consumption and high damage rates associated with traditional cold chain transportation models. How to reduce operating costs, improve transportation efficiency, and ensure cargo quality through intelligent technologies has become a critical issue that urgently needs to be addressed in the cold chain logistics field. Currently, common cold chain transport vehicles mainly rely on basic temperature control systems and manual driving. Their technical solutions typically involve monitoring the cargo compartment temperature using temperature sensors at fixed locations, triggering alarms or simply adjusting refrigeration parameters when the temperature exceeds a threshold.

[0003] However, this approach lacks real-time collaborative analysis of vehicle operating parameters (such as vibration frequency) and external environment (such as road conditions and weather), resulting in a high risk of cargo damage. Summary of the Invention

[0004] This application provides a cold chain transportation method, apparatus, electronic device, and storage medium to solve the problem that existing cold chain transportation solutions lack real-time collaborative analysis of vehicle operating parameters (such as vibration frequency) and external environment (such as road conditions and weather), resulting in a high risk of cargo damage.

[0005] In a first aspect, this application provides a cold chain transportation method, including:

[0006] Obtain the type of goods transported by the target cold chain transport vehicle;

[0007] Determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods.

[0008] During the operation of the target cold chain transport vehicle, real-time measurement values ​​of each vehicle operation parameter are monitored, and environmental data of the environment in which the target cold chain transport vehicle is located are obtained.

[0009] Based on the environmental data, the real-time measured values ​​of each of the vehicle's operating parameters, and the standard parameter range, the driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted.

[0010] The environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle is acquired, and a three-dimensional visualization interface containing temperature field distribution and cargo placement status is generated based on the environmental monitoring data.

[0011] In one possible implementation, adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, real-time measurements of each vehicle operating parameter, and standard parameter ranges includes:

[0012] Calculate the deviation between the real-time measured value of each vehicle operating parameter and the corresponding standard parameter range, and normalize each deviation to obtain a standardized deviation value.

[0013] The parameter adjustment amount is determined based on the standardized deviation value, and the environmental adjustment amount is determined based on the environmental data;

[0014] The target adjustment amount is obtained by performing a weighted summation operation on the parameter adjustment amount and the environmental adjustment amount;

[0015] Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the target adjustment amount.

[0016] In one possible implementation, acquiring environmental data of the environment in which the target cold chain transport vehicle is located includes:

[0017] Environmental perception data is acquired through a multimodal sensor array, which includes: a lidar sensor for acquiring three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for acquiring spectral characteristic data of environmental substances; and a millimeter-wave radar sensor for detecting obstacle data.

[0018] The three-dimensional point cloud data, the spectral feature data, and the obstacle data are fused together to generate vehicle sensor data.

[0019] The first traffic condition data is acquired in real time through the communication module. The first traffic condition data includes: vehicle sensor data from other vehicles; road infrastructure status data from roadside units; and regional traffic flow data from the traffic management center.

[0020] The environmental data is generated by spatiotemporal alignment and feature fusion of the first road condition data and the vehicle sensor data.

[0021] In one possible implementation, the method further includes:

[0022] Multidimensional status data of the goods transported by the target cold chain transport vehicle are obtained through a multimodal non-contact detection device.

[0023] The multidimensional status data is analyzed to generate cargo status assessment results;

[0024] If the cargo status assessment result is abnormal, the corresponding emergency plan will be triggered according to the type of cargo status assessment result.

[0025] In one possible implementation, the method further includes:

[0026] Obtain the optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters. The optimization target parameters include energy consumption coefficient, time weight and cargo status index.

[0027] Based on the second road condition data and vehicle status data, the priority score of each candidate path is calculated using the multi-objective evaluation function;

[0028] The candidate path with the highest priority score is selected as the optimized driving route.

[0029] In one possible implementation, the method further includes:

[0030] The thermodynamic properties of the goods transported by the target cold chain transport vehicle are obtained, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient.

[0031] A dynamic model of cargo temperature change is established based on the aforementioned thermodynamic characteristic parameters;

[0032] The real-time road condition prediction data of the target cold chain transport vehicle is obtained, including road slope parameters, congestion level parameters, and expected driving speed parameters.

[0033] Based on the road slope parameters, the congestion level parameters, and the expected driving speed parameters, calculate the impact coefficient of road condition changes on cargo hold heat load;

[0034] Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period;

[0035] The optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a graded control strategy is implemented on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0036] In one possible implementation, the method further includes:

[0037] After the target cold chain transport vehicle completes the transport task, the transport data for the operation of the transport task is obtained. The transport data includes energy consumption data, time data and route deviation data.

[0038] Based on the energy consumption data, the time data, and the path deviation data, optimize the adjustment strategy for the driving parameters and the cooling parameters;

[0039] The operational effect of the optimized adjustment strategy is simulated using a digital twin system, and the adjustment strategy is deployed to the control system after successful verification.

[0040] Secondly, this application provides a cold chain transportation device, comprising:

[0041] The acquisition module is used to acquire the type of goods transported by the target cold chain transport vehicle;

[0042] The determination module is used to determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods.

[0043] The monitoring module is used to monitor the real-time measurement values ​​of each vehicle operating parameter during the operation of the target cold chain transport vehicle, and to acquire environmental data of the environment in which the target cold chain transport vehicle is located.

[0044] The parameter adjustment module is used to adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, the real-time measurement values ​​of each vehicle operating parameter, and the standard parameter range.

[0045] The generation module is used to acquire environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface containing temperature field distribution and cargo placement status based on the environmental monitoring data.

[0046] In one possible implementation, the parameter adjustment module is specifically used for:

[0047] Calculate the deviation between the real-time measured value of each vehicle operating parameter and the corresponding standard parameter range, and normalize each deviation to obtain a standardized deviation value.

[0048] The parameter adjustment amount is determined based on the standardized deviation value, and the environmental adjustment amount is determined based on the environmental data;

[0049] The target adjustment amount is obtained by performing a weighted summation operation on the parameter adjustment amount and the environmental adjustment amount;

[0050] Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the target adjustment amount.

[0051] In one possible implementation, the monitoring module is specifically used for:

[0052] Environmental perception data is acquired through a multimodal sensor array, which includes: a lidar sensor for acquiring three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for acquiring spectral characteristic data of environmental substances; and a millimeter-wave radar sensor for detecting obstacle data.

[0053] The three-dimensional point cloud data, the spectral feature data, and the obstacle data are fused together to generate vehicle sensor data.

[0054] The first traffic condition data is acquired in real time through the communication module. The first traffic condition data includes: vehicle sensor data from other vehicles; road infrastructure status data from roadside units; and regional traffic flow data from the traffic management center.

[0055] The environmental data is generated by spatiotemporal alignment and feature fusion of the first road condition data and the vehicle sensor data.

[0056] In one possible implementation, the device further includes an emergency module for:

[0057] Multidimensional status data of the goods transported by the target cold chain transport vehicle are obtained through a multimodal non-contact detection device.

[0058] The multidimensional status data is analyzed to generate cargo status assessment results;

[0059] If the cargo status assessment result is abnormal, the corresponding emergency plan will be triggered according to the type of cargo status assessment result.

[0060] In one possible implementation, the device further includes a route adjustment module for:

[0061] Obtain the optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters. The optimization target parameters include energy consumption coefficient, time weight and cargo status index.

[0062] Based on the second road condition data and vehicle status data, the priority score of each candidate path is calculated using the multi-objective evaluation function;

[0063] The candidate path with the highest priority score is selected as the optimized driving route.

[0064] In one possible implementation, the apparatus further includes an execution module for:

[0065] The thermodynamic properties of the goods transported by the target cold chain transport vehicle are obtained, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient.

[0066] A dynamic model of cargo temperature change is established based on the aforementioned thermodynamic characteristic parameters;

[0067] The real-time road condition prediction data of the target cold chain transport vehicle is obtained, including road slope parameters, congestion level parameters, and expected driving speed parameters.

[0068] Based on the road slope parameters, the congestion level parameters, and the expected driving speed parameters, calculate the impact coefficient of road condition changes on cargo hold heat load;

[0069] Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period;

[0070] The optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a graded control strategy is implemented on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0071] In one possible implementation, the device further includes an optimization module for:

[0072] After the target cold chain transport vehicle completes the transport task, the transport data for the operation of the transport task is obtained. The transport data includes energy consumption data, time data and route deviation data.

[0073] Based on the energy consumption data, the time data, and the path deviation data, optimize the adjustment strategy for the driving parameters and the cooling parameters;

[0074] The operational effect of the optimized adjustment strategy is simulated using a digital twin system, and the adjustment strategy is deployed to the control system after successful verification.

[0075] Thirdly, this application provides an apparatus comprising: a processor and a memory, the processor being configured to execute a cold chain transportation program stored in the memory to implement the cold chain transportation method described in any one of the first aspects.

[0076] Fourthly, this application provides a storage medium storing one or more programs that can be executed by one or more processors to implement the cold chain transportation method described in any one aspect.

[0077] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application achieves precise temperature control management for different cargo characteristics by obtaining the specific type of transported goods and determining the corresponding standard parameter range; by dynamically adjusting driving parameters and refrigeration parameters in combination with real-time monitored vehicle operating parameters and environmental data, it effectively solves the problems of delayed temperature control response and low energy efficiency in traditional cold chain transportation; This technical solution can not only significantly reduce cargo damage rate and improve abnormal response speed, but also reduce energy consumption through intelligent regulation, thereby comprehensively improving the safety, economy and reliability of cold chain transportation, and is especially suitable for cargo transportation scenarios with strict temperature control requirements such as vaccines and high-end fresh food. Attached Figure Description

[0078] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0079] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0081] Figure 1 A flowchart illustrating an embodiment of a cold chain transportation method provided in this application;

[0082] Figure 2 A flowchart illustrating an embodiment of another cold chain transportation method provided in this application;

[0083] Figure 3 A flowchart illustrating another embodiment of a cold chain transportation method provided in this application;

[0084] Figure 4 A block diagram illustrating an embodiment of a cold chain transportation device provided in this application;

[0085] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0087] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0088] Figure 1 This is a flowchart illustrating an embodiment of a cold chain transportation method provided in this application. Figure 1 As shown, the method includes the following steps:

[0089] Step 101: Obtain the type of goods transported by the target cold chain transport vehicle.

[0090] Target cold chain transport vehicles refer to cold chain transport vehicles that perform target transport tasks.

[0091] In the application, the control system (such as a cloud system) first utilizes an intelligent scheduling model based on transportation order data (including order priority, cargo characteristic parameters, and destination information) and real-time fleet status data (including vehicle location coordinates, refrigeration system parameters, remaining power, and cargo hold loading rate, etc., which are automatically uploaded to the control system after vehicle startup). The goal is to maximize efficiency, minimize transportation costs, optimize vehicle scale, and balance fleet load to determine the optimal set of vehicles for task execution. Subsequently, a route planning model, for each selected vehicle (i.e., the target cold chain transport vehicle), combines real-time road network status data to generate personalized driving routes with the optimization objective of minimizing transportation time, energy consumption, and route risks. The two models collaboratively optimize based on hard constraints such as temperature control requirements and battery range limitations, as well as soft constraints such as cost thresholds and safety margins. The final generated transportation plan, after verification by a digital twin system, sends customized route instructions to each target cold chain transport vehicle for execution. During the task execution by the target cold chain transport vehicle, its driving and refrigeration parameters are adjusted in real-time through steps 101-104.

[0092] Cargo type refers to the category classified according to the physical and chemical properties of the transported goods (such as heat sensitivity, mechanical strength, oxidation sensitivity, etc.), such as vaccines, frozen food, fresh agricultural products, etc. Different cargo types have different requirements for the transportation environment.

[0093] In one embodiment, the type of goods transported by a target cold chain transport vehicle can be identified using RFID tags. Specifically: an UHF RFID reader (operating frequency 902-928MHz) deployed at the cargo door automatically scans the standard-compliant passive electronic tags affixed to the cargo packaging during loading. These tags pre-store encrypted cargo characteristic data, including cargo classification codes, basic temperature control parameters, and special handling requirements. The reader uses an anti-collision algorithm to read tags in batches and transmits the decrypted cargo type data to the vehicle control unit via the CAN bus. Simultaneously, it verifies the data against cloud-based order data. When missing or conflicting tag information is detected, an audible and visual alarm is triggered, and the refrigeration system is locked until manual confirmation is completed.

[0094] In another embodiment, the type of goods transported by the target cold chain transport vehicle can be obtained through manual input.

[0095] Step 102: Determine at least one vehicle operating parameter and the standard parameter range corresponding to each vehicle operating parameter based on the type of goods.

[0096] Vehicle operating parameters refer to vehicle status indicators that affect the quality of cargo transportation, such as cargo hold temperature, cargo hold humidity, cargo hold vibration frequency, refrigeration system operating power, and remaining battery power.

[0097] The standard parameter range refers to the preset safe operating threshold range based on the characteristics of different goods.

[0098] In this embodiment, the system has a built-in database that stores standard parameters for various types of goods (e.g., vaccines: temperature 2-8℃, vibration frequency <5Hz). The system automatically retrieves the corresponding standard parameters based on the identified type of goods, providing a benchmark for real-time monitoring.

[0099] Step 103: During the operation of the target cold chain transport vehicle, monitor the real-time measurement values ​​of each vehicle operation parameter and obtain environmental data of the environment in which the target cold chain transport vehicle is located.

[0100] In one embodiment, real-time monitoring of the real-time measured values ​​of each of the vehicle's operating parameters specifically includes: monitoring the three-dimensional temperature field distribution through a temperature sensor array deployed in the cargo compartment, capturing vibration spectra in various frequency bands through a MEMS triaxial vibration sensor, and detecting humidity changes through a digital humidity sensor; simultaneously acquiring equipment parameters such as the operating current of the refrigeration system and the compressor speed, as well as vehicle driving status data, through the CAN bus; all sensor data are time-stamped and processed by Kalman filtering to generate standardized measurement values ​​with confidence scores, which are then updated to the central controller at a preset frequency (e.g., 10Hz), and a graded alarm mechanism is immediately triggered when an anomaly is detected.

[0101] Environmental data refers to information about the vehicle's external environment, including external factors that affect transportation such as road conditions, weather, and traffic flow. Examples include road congestion information, weather data, obstacle detection data, and information on the distribution of charging and cooling stations.

[0102] In one embodiment, acquiring environmental data of the environment in which the target cold chain transport vehicle is located includes: collecting environmental perception data through a multimodal sensor array, the multimodal sensor array including: a lidar sensor for acquiring three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for acquiring spectral feature data of environmental substances; and a millimeter-wave radar sensor for detecting obstacle data; performing data fusion processing on the three-dimensional point cloud data, the spectral feature data, and the obstacle data to generate vehicle-mounted sensor data; acquiring first road condition data in real time through a communication module, the first road condition data including: vehicle-mounted sensor data from other vehicles; road infrastructure status data from roadside units; and regional traffic flow data from a traffic management center; and performing spatiotemporal alignment and feature fusion on the first road condition data and the vehicle-mounted sensor data to generate the environmental data.

[0103] This solution utilizes a multi-modal sensor array to collect real-time information about the vehicle's surrounding environment. This array includes LiDAR, a hyperspectral imager, and millimeter-wave radar, acquiring three-dimensional spatial data, material composition characteristics, and obstacle dynamics, respectively. After data fusion processing, precise onboard environmental perception data is generated. Simultaneously, a vehicle-to-everything (V2X) communication module acquires real-time information on surrounding vehicles, road infrastructure, and traffic conditions provided by the traffic management center. This information is then integrated with the onboard perception data through spatiotemporal registration and feature fusion algorithms to generate comprehensive and accurate environmental situational awareness data. This technical solution significantly improves the completeness and reliability of environmental perception through the complementary fusion of multi-source information, providing a precise environmental situational awareness foundation for intelligent decision-making systems and effectively enhancing the safety and adaptability of vehicles in complex transportation environments.

[0104] Step 104: Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, the real-time measurement values ​​of each vehicle operating parameter, and the standard parameter range.

[0105] Driving parameters refer to vehicle movement parameters such as speed and route that affect the transportation process.

[0106] Refrigeration parameters refer to the operating parameters of the temperature control system, such as compressor speed and air volume.

[0107] In this embodiment of the application, step 104 specifically includes: calculating the deviation between the real-time measured value of each vehicle operating parameter and the corresponding standard parameter range, and normalizing each deviation to obtain a standardized deviation value; determining the parameter adjustment amount based on the standardized deviation value, and determining the environmental adjustment amount based on the environmental data; performing a weighted summation operation on the parameter adjustment amount and the environmental adjustment amount to obtain a target adjustment amount; and adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the target adjustment amount.

[0108] In this scheme, the deviation between the real-time measured values ​​of each vehicle's operating parameters and their preset standard range is first calculated and normalized to a standardized deviation value. Simultaneously, environmental adjustment parameters are generated based on environmental data analysis. During the fusion decision-making phase, the system dynamically configures weighting coefficients according to the current transportation stage and cargo characteristics: for temperature-sensitive goods, the weight of temperature-related parameters is prioritized (e.g., set to 0.6-0.8); for goods with high time-sensitivity requirements, the weight of driving parameters is increased (e.g., set to 0.5-0.7). Using the weighted summation formula: Target adjustment = α × Parameter adjustment + β × Environmental adjustment (where α + β = 1, and α and β are dynamically adjusted according to cargo type and environmental conditions), optimal control commands are generated to adjust vehicle speed, route selection, and cooling power in real time. This dynamic weighting configuration mechanism ensures temperature control accuracy while achieving a balance between transportation efficiency and energy consumption.

[0109] S105. Obtain environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface containing temperature field distribution and cargo placement status based on the environmental monitoring data.

[0110] Distributed sensors refer to multi-source sensing devices such as temperature and humidity sensors and gas detectors installed in various locations in the cargo hold to collect environmental data in real time.

[0111] The 3D visualization interface is a virtual monitoring screen built using augmented reality technology, which can present the temperature distribution gradient and cargo placement status in the cargo hold in three dimensions.

[0112] In this embodiment, cargo hold environmental monitoring data is first acquired through a distributed sensor network. Then, this data is used to construct a three-dimensional visualization interface that accurately reflects the actual transportation status, thus facilitating the driver's view of the cargo hold.

[0113] In another embodiment, after S105, the following steps may be included: receiving user interaction instructions through a gesture recognition module, dynamically adjusting the display perspective and display parameters of the visualization interface according to the instructions; and restricting the scope of operation permissions of different users on the interface based on pre-stored identity authentication information.

[0114] The gesture recognition module is an interactive component developed based on computer vision technology, capable of accurately recognizing user gestures. Identity authentication information consists of user permission credentials stored by the system, containing different levels of access permission settings.

[0115] Users can dynamically adjust the interface content and viewing angle through natural gestures; the system automatically matches the corresponding data viewing and operation permission level according to the user's identity to ensure information security. This technology significantly improves the efficiency and accuracy of cold chain transportation management through intuitive visual monitoring and convenient interactive operation, while strict access control ensures system data security, achieving comprehensive intelligent supervision of the transportation process.

[0116] In another embodiment, the method further includes the following steps: acquiring multidimensional status data of the goods transported by the target cold chain transport vehicle through a multimodal non-contact detection device; analyzing the multidimensional status data to generate a cargo status assessment result; and triggering a corresponding emergency plan based on the type of the cargo status assessment result if the cargo status assessment result is abnormal.

[0117] The multimodal non-contact detection device may include: a millimeter-wave radar sensor for detecting deformation and displacement changes on the surface of the cargo to obtain deformation data; an infrared imaging sensor for acquiring temperature distribution data on the surface of the cargo; an ultrasonic sensor for detecting internal structural state data of the cargo, etc.

[0118] The emergency response plan includes: adjusting the airflow pattern in the cargo hold, activating the local temperature compensation mechanism, and providing nearby cooling when the cargo condition assessment result indicates abnormal cargo surface temperature distribution; and reducing driving parameters and activating shock absorption mode when the cargo condition assessment result indicates cargo deformation or abnormal internal structure, as well as planning a route to the nearest checkpoint.

[0119] It should be noted that steps 101-104 can be applied to the following scenarios:

[0120] Manned cold chain transportation: Drivers receive real-time suggestions on optimized routes and temperature control parameters from the system via an onboard human-machine interface, and then make adjustments based on their own judgment. In abnormal situations (such as temperature control deviation exceeding a threshold), the system triggers audible and visual alarms and pushes emergency operation guidelines to assist drivers in responding quickly.

[0121] Intelligent unmanned cold chain transportation: The fully automated system executes commands: the route generated by the A or D algorithm output from the path planning model is directly sent to the autonomous driving controller, and refrigeration parameters are adjusted in a closed loop via a PID controller. Real-time environmental data such as road gradient and traffic light phases are acquired through V2X communication to dynamically optimize the matching strategy between vehicle speed and refrigeration power.

[0122] This solution breaks through the limitations of traditional contact-based inspection by constructing a comprehensive monitoring system covering both the surface and interior of goods through multimodal non-contact inspection devices. Based on a multi-source data fusion-based intelligent evaluation model, it can accurately identify different types of cargo anomalies, such as abnormal temperature distribution and physical deformation. It automatically matches the optimal emergency strategy for different anomaly types (such as activating local precise temperature control when there is a temperature anomaly, and activating shock absorption mode and planning an emergency delivery route when there is physical deformation). This ensures the timeliness of anomaly handling and avoids the energy waste of traditional "one-size-fits-all" emergency response, significantly improving the safety and energy efficiency of cold chain transportation.

[0123] In application, during vehicle operation, the activity data of microorganisms in the cargo compartment can also be monitored. When the activity data of microorganisms exceeds a preset safety threshold, the operating parameters of the refrigeration system are adjusted to inhibit the reproduction of microorganisms.

[0124] Microbial activity data refers to indicator data reflecting the degree of microbial reproduction collected by biosensors in the cargo hold, including but not limited to total colony count and ATP biofluorescence value; preset safety thresholds are microbial activity limits set in advance according to the hygiene standards of different types of goods (such as fresh food and pharmaceutical products).

[0125] This solution monitors the microbial activity data in the cargo hold in real time. When the detected data exceeds the safety threshold of the corresponding cargo, it automatically adjusts the operating parameters of the refrigeration system, such as temperature and humidity (e.g., lowering the temperature to the microbial inhibition range and adjusting the humidity to the antibacterial range), thereby effectively inhibiting the reproduction of microorganisms in the cargo hold.

[0126] In another embodiment, the method further includes the following steps: after the target cold chain transport vehicle completes the current transport task, acquiring the transport data for performing the current transport task, the transport data including energy consumption data, time data, and path deviation data; optimizing the adjustment strategy of the driving parameters and the refrigeration parameters based on the energy consumption data, the time data, and the path deviation data; simulating the operation effect of the optimized adjustment strategy through a digital twin system, and deploying the adjustment strategy to the control system after verification.

[0127] Transportation data refers to the set of operational indicators automatically recorded during the execution of cold chain transportation tasks, including: energy consumption data (energy consumption of the refrigeration system and drive system), time data (difference between actual travel time and planned time for each road segment), and route deviation data (the deviation between the planned route and the actual travel trajectory).

[0128] The adjustment strategy refers to the set of optimization rules for controlling vehicle driving parameters (such as speed and route) and cooling parameters (such as temperature and power), including decision algorithm parameters, control logic thresholds, etc.

[0129] A digital twin system is a vehicle operation simulation platform built through virtual modeling, which can simulate vehicle behavior and cargo compartment status in a real environment.

[0130] In this embodiment, after the transportation task is completed, energy consumption, time, and path deviation data are automatically collected throughout the process. Based on this data, machine learning algorithms are used to optimize the adjustment strategies of driving parameters (such as speed control curves) and cooling parameters (such as temperature regulation response speed). In application, the weight parameters of the path planning model and the trigger threshold parameters of the emergency response mechanism can also be optimized. Subsequently, the execution effect of the optimized strategy is simulated in a virtual environment through a digital twin system to verify its reliability in different scenarios (such as extreme weather and congested road conditions). Finally, the verified strategy is deployed to the vehicle control system through OTA update to achieve continuous self-improvement of transportation efficiency and improve the accuracy and safety of parameter adjustment.

[0131] In another embodiment, the method may further include the following steps: obtaining a cargo operation request input by the user via voice; extracting operation elements from the cargo operation request using natural language processing technology, the operation elements including cargo identification, environmental requirements, and time requirements; generating control instructions based on the operation elements, the control instructions being used to: adjust the storage environment parameters of the cargo hold, update the transportation plan, and provide feedback operation confirmation information.

[0132] Cargo handling request refers to the instruction information containing cargo handling requirements submitted by the user via voice; Natural Language Processing technology refers to artificial intelligence technology that converts human language into computer-recognizable instructions; Operational elements refer to the key control parameters parsed from the voice request; Control instructions refer to the execution commands generated by the system based on the parsing results.

[0133] This solution first acquires the user's voice input request, then uses natural language processing technology to extract key elements such as cargo identification (for identifying specific goods), cargo hold environmental requirements (including storage conditions such as temperature and humidity), and time requirements (operational timeliness). This generates three types of control commands: environmental parameter adjustment commands (automatically adjusting cargo hold storage conditions according to requirements), transportation plan update commands (dynamically adjusting transportation routes and timing), and operation confirmation feedback information (returning the execution result to the user). This achieves a seamless conversion from voice commands to transportation control, ensuring both operational convenience and precise control of cold chain transportation.

[0134] In another embodiment, the method may further include the following steps: acquiring real-time traffic prediction data, the traffic prediction data including the distance to obstacles ahead, the road slipperiness coefficient, and sudden weather warnings; calculating a safe braking distance based on the traffic prediction data, the determination of the safe braking distance also being related to the impact resistance level of the currently transported goods; and triggering an emergency braking strategy when the actual vehicle distance is detected to be less than the safe braking distance.

[0135] Real-time traffic prediction data refers to road environment information acquired in real time through vehicle-mounted sensors and V2X communication. Specifically, it includes: distance to obstacles ahead (real-time distance to vehicles or obstacles ahead measured by millimeter-wave radar), road slip coefficient (friction coefficient derived from the scanning and analysis of road surface texture by lidar), and sudden weather warnings (real-time weather alerts from meteorological departments and roadside units).

[0136] Safe braking distance is the minimum safe stopping distance dynamically calculated based on the current vehicle speed, road conditions, and the characteristics of the cargo being carried (such as fragile items, liquid medicines, etc., with different impact resistance levels).

[0137] The emergency braking strategy is a multi-stage deceleration scheme that is automatically executed when the system determines that there is a collision risk. The first stage is to reduce the load on the refrigeration system and activate energy recovery; the second stage is to activate the electronic braking system and adjust the braking force distribution; and the third stage is to trigger the mechanical braking and link the cargo compartment locking device.

[0138] This solution first integrates various real-time road condition data and combines them with the physical characteristics of the transported goods to accurately calculate personalized safe braking distances. When the actual distance to the vehicle is detected to be insufficient, a customized emergency braking response is immediately triggered to achieve smooth vehicle deceleration while ensuring the safety of the goods.

[0139] In another embodiment, the method may further include the following steps: continuously monitoring the deviation between vehicle positioning data and a preset transportation route; when the deviation exceeds a threshold and continues for a preset duration, triggering an anti-hijacking tracking strategy.

[0140] Vehicle positioning data refers to real-time latitude and longitude coordinates, driving speed, and heading angle information obtained through the vehicle's GPS module and BeiDou dual-mode positioning system.

[0141] The preset transportation route is a digital reference route generated from the route planning results, which includes the coordinate set of key points of the route and the allowable deviation range.

[0142] Deviation is the spatial offset between the real-time positioning coordinates and the preset path, calculated using a geofencing algorithm.

[0143] The anti-hijacking tracking strategy is a multi-level security response scheme activated by the system after it determines that the vehicle has deviated abnormally. It includes: uploading real-time positioning data to the monitoring platform through a covert communication channel; activating the emergency lock mode of the cargo compartment environmental control system; and sending an alarm signal containing vehicle characteristics and cargo information to law enforcement agencies. The emergency lock mode includes maintaining the cargo compartment temperature within a safe range and recording operation logs.

[0144] This solution continuously compares the spatial relationship between the real-time positioning trajectory and the planned path. When a deviation is detected that exceeds a preset threshold (e.g., horizontal offset > 500 meters) and continues for more than a safe duration (e.g., for more than 5 minutes), it automatically triggers comprehensive protective measures, including concealed positioning reporting, cargo compartment environment locking, and remote alarm linkage, thereby ensuring vehicle driving safety.

[0145] In another embodiment, the method may further include the following steps: real-time acquisition of operating status data of key components of the refrigeration system, including compressor vibration spectrum, condenser temperature difference and refrigerant pressure value; inputting the operating status data into a pre-trained health prediction model and outputting the remaining life prediction value of each component; generating a maintenance priority queue based on the remaining life prediction value; and automatically triggering a maintenance reminder command when the remaining life of any component is detected to be lower than a preset threshold.

[0146] Operating status data refers to key parameters that reflect the working status of refrigeration equipment, including compressor vibration spectrum (mechanical vibration characteristics collected by an accelerometer), condenser temperature difference (temperature difference between condenser inlet and outlet), and refrigerant pressure (pressure reading in the refrigeration cycle system).

[0147] The health prediction model is an equipment condition assessment model built on machine learning algorithms. It can analyze the correlation between historical operating data and equipment wear and tear. The remaining life prediction value is the estimated sustainable operating time of the equipment output by the model.

[0148] The maintenance priority queue is a maintenance sorting list generated based on the prediction results.

[0149] This solution first collects the operating parameters of key components of the refrigeration system in real time. This data is then input into a pre-trained health prediction model for analysis, yielding predicted remaining lifespans for each component. The system automatically generates maintenance priority rankings based on these predictions. When the predicted remaining lifespan of any component is detected to be below a safety threshold, a maintenance alert is immediately triggered. By accurately predicting equipment wear and tear, this solution allows for advance maintenance planning, preventing transportation disruptions caused by sudden failures. It also optimizes maintenance resource allocation, significantly reduces equipment maintenance costs, extends the lifespan of critical components, and ensures the stable operation of the cold chain transportation system.

[0150] In another embodiment, the method may further include the following steps: acquiring real-time status data during transportation, the status data including cargo hold environmental parameters, vehicle operating parameters, and route execution data; and constructing a digital twin model based on the real-time status data, the model mapping the three-dimensional spatial status of the transportation system and the equipment operating status in real time.

[0151] Real-time status data refers to multi-dimensional operational information continuously collected during transportation, specifically including: cargo hold environmental parameters (such as sensor data on temperature, humidity, and gas concentration), vehicle operating parameters (including speed, energy consumption, and refrigeration system operating status), and route execution data (the degree of matching between the actual driving route and the planned route).

[0152] A digital twin model is a dynamic mirror image of a transportation system constructed through virtual simulation technology, which can completely reproduce the real-time state of the physical entity in three-dimensional space.

[0153] This solution utilizes IoT technology to continuously acquire real-time status data throughout the entire transportation process. Based on this data, a high-fidelity digital twin model is constructed. This model not only accurately maps the three-dimensional environment within the cargo hold but also synchronously reflects the operational status of various vehicle systems. This achieves full-element digital and visual monitoring of the transportation process. Through virtual-real fusion simulation analysis, potential risks can be identified in advance, transportation strategies can be optimized, and the reliability and management efficiency of cold chain logistics can be significantly improved. Simultaneously, it provides data support for subsequent improvements to transportation solutions.

[0154] In another embodiment, the method may further include the following steps: generating hash values ​​for key operation data and storing them in a blockchain network, wherein the key operations include at least temperature and humidity adjustment records, path change instructions, and equipment maintenance operations.

[0155] Key operational data refers to core operational records that affect transportation quality and safety, including temperature and humidity adjustment records (cargo hold environmental parameter change logs), route change instructions (driving route adjustment decisions), and equipment maintenance operations (refrigeration system maintenance records).

[0156] A hash value is a fixed-length digital fingerprint generated by a cryptographic algorithm that can uniquely identify the original data.

[0157] A blockchain network is a decentralized storage system that uses distributed ledger technology.

[0158] This solution first identifies key operational nodes in the transportation process, converts this operational data into irreversible digital fingerprints using a hash algorithm, and then distributes the hash values ​​across multiple nodes in a blockchain network. Leveraging the immutability of blockchain, this technology ensures the authenticity and traceability of critical operational records in cold chain transportation, effectively preventing data tampering risks, providing credible electronic evidence for transportation quality disputes, and meeting the compliance requirements of high-standard logistics such as pharmaceutical cold chain logistics, significantly improving the credibility and transparency of the transportation process.

[0159] In another embodiment, the method may further include the following steps: when a preset condition is detected to be triggered, an associated business process is automatically executed through a smart contract, the business process including an insurance claim application and a transportation fee settlement.

[0160] Preset conditions refer to the threshold values ​​that are set in advance for business rules to trigger, including abnormal events such as failure to meet delivery deadlines and exceeding temperature and humidity limits.

[0161] Smart contracts are automated program code stored on the blockchain that can autonomously execute predefined logic based on input conditions.

[0162] Related business processes refer to the follow-up processing processes related to transportation services.

[0163] This solution monitors system data in real time. When preset conditions are detected (such as delayed delivery or abnormal cargo temperature), it automatically triggers smart contracts to execute corresponding business processes: for transportation anomalies, insurance claims are generated and submitted immediately; for completed transportation tasks, transportation costs are automatically calculated and settled. This automates the post-transportation processing flow. Through the immutability and automatic execution of smart contracts, it significantly improves business processing efficiency and transparency, reduces human intervention and disputes, and ensures fairness and timeliness in claims and settlement processes, providing reliable digital protection for cold chain transportation services.

[0164] The technical solution provided in this application achieves precise temperature control management for different cargo characteristics by obtaining the specific type of transported goods and determining the corresponding standard parameter range. By dynamically adjusting driving and refrigeration parameters in conjunction with real-time monitored vehicle operating parameters and environmental data, it effectively solves the problems of delayed temperature control response and low energy efficiency in traditional cold chain transportation. This technical solution can not only significantly reduce cargo damage rate and improve anomaly response speed, but also reduce energy consumption through intelligent control, thereby comprehensively improving the safety, economy, and reliability of cold chain transportation. It is especially suitable for cargo transportation scenarios with strict temperature control requirements, such as vaccines and high-end fresh produce.

[0165] Figure 2 A flowchart illustrating another embodiment of cold chain transportation provided in this application. Figure 2 As shown, it includes the following steps:

[0166] Step 201: Obtain the optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters. The optimization target parameters include energy consumption coefficient, time weight and cargo status index.

[0167] Step 202: Based on the second road condition data and vehicle status data, calculate the priority score of each candidate path using the multi-objective evaluation function;

[0168] Step 203: Select the candidate path with the highest priority score as the optimized driving route.

[0169] For ease of understanding, steps 201-203 will be explained uniformly below:

[0170] The optimization target parameters refer to the set of key indicators used to evaluate the merits of transportation routes, specifically including: energy consumption coefficient (reflecting the impact of different routes on the energy consumption of vehicle refrigeration systems), time weight (characterizing the importance of transportation timeliness requirements), and cargo status indicators (the freshness maintenance requirements determined according to the type of cargo).

[0171] The multi-objective evaluation function is a mathematical evaluation model formed by integrating the above parameters according to preset rules; the priority score is the comprehensive evaluation value of the path calculated by this function.

[0172] Secondary road condition data refers to real-time acquired external environmental information, including: real-time traffic flow (obtained via V2X communication), road construction information (from the traffic management center), and weather information (temperature, precipitation, and other meteorological data). This secondary road condition data can be collected in real-time or predicted based on traffic big data and meteorological data. Adjusting driving routes based on predicted secondary road condition data can help avoid extreme environmental risks in advance.

[0173] Vehicle status data refers to monitoring parameters that reflect the real-time operating status of cold chain transport vehicles. Specifically, it may include: power system parameters (remaining battery power, motor output power, etc.), refrigeration system status (compressor load rate, refrigerant pressure value, current refrigeration power), cargo environment parameters (real-time temperature and humidity sensor readings for each compartment of the cargo compartment), and positioning information (GPS coordinates, driving speed, heading angle).

[0174] The scheme first obtains the optimization target parameters of the current transportation task, including: 1) energy consumption coefficient, which is obtained by weighting the real-time load rate of the vehicle refrigeration system with route characteristics (including road slope, traffic congestion, etc.); 2) time weight, which is dynamically set according to the preservation time requirements corresponding to the type of goods (such as flowers needing to be delivered within 4 hours, frozen food needing to be maintained below -18℃, etc.); 3) cargo status indicators, which are directly related to the real-time temperature and humidity data of each temperature zone in the cargo hold, vibration amplitude, and gas concentration and other storage condition monitoring parameters.

[0175] Subsequently, a multi-objective evaluation function was constructed based on these parameters. This function, combined with real-time collected second road condition data and vehicle status data, was used to comprehensively score each candidate path. Finally, the path with the highest score was selected as the optimized driving route.

[0176] This solution achieves intelligent optimization of cold chain logistics routes by accurately quantifying energy efficiency, time costs, and cargo preservation requirements during transportation, thereby improving transportation efficiency while ensuring cargo quality.

[0177] Figure 3 A flowchart illustrating another embodiment of cold chain transportation provided in this application. Figure 3 As shown, it includes the following steps:

[0178] Step 301: Obtain the thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient.

[0179] This step involves acquiring key thermophysical parameters of the transported goods through a cargo information database or sensor detection. These thermodynamic parameters specifically include: specific heat capacity (c_p): representing the amount of heat required to raise the temperature of a unit mass of cargo by 1°C, directly affecting the power demand calculation of the refrigeration system. For example, the typical specific heat capacity of vaccines is 3.5 kJ / (kg·K); thermal conductivity (k): reflecting the heat transfer capacity within the cargo and determining the uniformity of temperature distribution. The thermal conductivity of frozen meat is approximately 1.5 W / (m·K); and temperature sensitivity coefficient (α): characterizing the sensitivity of cargo quality to temperature fluctuations. These parameters are obtained through RFID tags or cloud databases, providing foundational data for subsequent modeling.

[0180] Step 302: Establish a dynamic model of cargo temperature change based on the thermodynamic characteristic parameters.

[0181] In this step, the mathematical model constructed based on thermodynamic properties can be expressed as:

[0182] dT / dt = (Q_in - Q_out) / (m·c_p);

[0183] Wherein, Q_in includes conductive heat (related to thermal conductivity k) and convective heat (related to ambient temperature difference); Q_out is the heat transferred by the refrigeration system (related to evaporator efficiency); and m is the mass of the cargo. This model can simulate the temperature variation of the cargo compartment under different environmental conditions. For example, it can predict the rate of temperature rise in the cargo compartment after the doors are closed, providing a basis for feedforward control of the refrigeration system.

[0184] Step 303: Obtain real-time road condition prediction data for the target cold chain transport vehicle. The real-time road condition prediction data includes road slope parameters, congestion level parameters, and expected driving speed parameters.

[0185] In this step, accurate traffic condition predictions are obtained through the fusion of the following multi-source data: road slope parameters: from high-precision map data (e.g., 3° ​​uphill sections); congestion level parameters: real-time traffic flow data acquired based on V2X communication (e.g., congestion index of 0.8); and predicted driving parameters: including predicted speed curves and start / stop frequencies. These data are updated every preset interval (e.g., 30 seconds) and measurement noise is eliminated using a Kalman filter algorithm to form a traffic condition prediction for a future period (e.g., 15-30 minutes).

[0186] Step 304: Calculate the impact coefficient of road condition changes on cargo hold heat load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter.

[0187] In this step, a quantitative relationship model between road conditions and heat load is established:

[0188]

[0189] in, : Slope influence term (θ is the slope angle); Speed-related factors (f is the driving frequency); : Congestion impact factor (N is the number of starts and stops).

[0190] For example, when a vehicle enters a congested section of road with a 5° uphill slope, this coefficient will increase significantly (by about 40%), prompting the system to increase its cooling capacity in advance.

[0191] Step 305: Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period.

[0192] In this step, the dynamic model is combined with the influence coefficient:

[0193]

[0194] Where, Q_pred(t): the predicted heat load value at time *t* (unit: kW); Q_base: the base heat load (unit: kW), which is the steady-state heat load output by the cargo temperature change dynamics model (step 302), and the calculation formula is:

[0195] Where k is the thermal conductivity, A is the heat transfer area, d is the cargo thickness, and h is the convective heat transfer coefficient; Road condition heat load influence coefficient; Ambient temperature difference (unit: °C), calculation method: ,in, External ambient temperature, Set a temperature for the cargo hold (e.g., 5°C for vaccines).

[0196] Then, the heat load change data ΔQ within the future time domain (usually 5-10 minutes) is calculated using a formula (including: changes in cargo hold temperature distribution; trends in cooling demand fluctuations; and risk areas for hotspot formation). The formula is: ΔQ = [max(Q_pred(t)) - Q_current] / Q_rated ×100%, where Q_current is the current actual heat load, and Q_rated is the system's rated capacity, i.e., the maximum cooling capacity of the cooling system under standard operating conditions. For example, if it is predicted that the temperature in a corner of the cargo hold will rise by 2°C in 15 minutes, the air supply to that area needs to be increased in advance.

[0197] Step 306: Calculate the optimal refrigeration parameter adjustment amount based on the heat load change data, and implement a graded control strategy for the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0198] In this step, a dynamic mapping model is established to transform heat load variation data into precise cooling parameter adjustment quantities. A specific example of the correspondence is shown below:

[0199] Heat load intensity → compressor speed: When the predicted heat load increases by 10-20%, the compressor speed should be increased by 15-25% accordingly; when the heat load decreases, the speed should be reduced proportionally but the minimum safe speed should be maintained.

[0200] Heat load change rate → PID parameter adjustment: rapid rise (>1℃ / min): enhance derivative control (D value increased by 30%); slow fluctuation (<0.3℃ / min): enhance integral control (I value increased by 50%).

[0201] Heat load spatial distribution → air supply strategy: Local hot spots: increase air volume by 20-30% in a targeted manner; Overall uniform temperature rise: evenly increase the air velocity throughout the cabin.

[0202] Implement three levels of control based on the forecast results:

[0203] Level 1 adjustment (ΔQ≤15%): Adjust compressor speed (±15%); optimize air delivery angle (e.g., avoid direct airflow during vaccine transportation).

[0204] Secondary regulation (15% < ΔQ ≤ 30%): Start the standby refrigeration unit; dynamically plan alternative charging station schemes (display information of the three most recent stations).

[0205] Level 3 regulation (ΔQ>30%): Force navigation to the optimal cooling station (considering distance / service capacity / electricity price); activate the emergency cold storage module of the transport container (maintain core area temperature control for 2-4 hours).

[0206] Emergency scenario: In case of refrigeration system failure, automatically schedule a repair station within 30km.

[0207] This solution achieves precise temperature control by establishing a coupled model of cargo thermodynamic properties and road conditions: First, it acquires characteristic parameters such as the specific heat capacity of the cargo and constructs a dynamic model of temperature change; simultaneously, it calculates the heat load influence coefficient based on secondary road condition data (such as slope, congestion, etc.); combining the two, it predicts the heat load change trend in future periods; finally, it dynamically adjusts refrigeration parameters (such as compressor speed and air volume) based on the prediction results, and achieves an optimal balance between energy consumption and temperature control accuracy through a graded control strategy (such as three-level power regulation). This solution, through the combination of physical modeling and real-time prediction, solves the problems of lagging temperature control and excessive energy consumption in traditional cold chain transportation.

[0208] Figure 4 This is a block diagram illustrating an embodiment of a cold chain transportation device provided in this application. Figure 4 As shown, the device includes:

[0209] The acquisition module 41 is used to acquire the type of goods transported by the target cold chain transport vehicle;

[0210] The determining module 42 is used to determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods.

[0211] The monitoring module 43 is used to monitor the real-time measurement values ​​of each vehicle operating parameter during the operation of the target cold chain transport vehicle, and to acquire environmental data of the environment in which the target cold chain transport vehicle is located.

[0212] The parameter adjustment module 44 is used to adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, the real-time measurement values ​​of each vehicle operating parameter and the standard parameter range.

[0213] The generation module 45 is used to acquire environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface containing temperature field distribution and cargo placement status based on the environmental monitoring data.

[0214] In one possible implementation, the parameter adjustment module is specifically used for:

[0215] Calculate the deviation between the real-time measured value of each vehicle operating parameter and the corresponding standard parameter range, and normalize each deviation to obtain a standardized deviation value.

[0216] The parameter adjustment amount is determined based on the standardized deviation value, and the environmental adjustment amount is determined based on the environmental data;

[0217] The target adjustment amount is obtained by performing a weighted summation operation on the parameter adjustment amount and the environmental adjustment amount;

[0218] Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the target adjustment amount.

[0219] In one possible implementation, the monitoring module is specifically used for:

[0220] Environmental perception data is acquired through a multimodal sensor array, which includes: a lidar sensor for acquiring three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for acquiring spectral characteristic data of environmental substances; and a millimeter-wave radar sensor for detecting obstacle data.

[0221] The three-dimensional point cloud data, the spectral feature data, and the obstacle data are fused together to generate vehicle sensor data.

[0222] The first traffic condition data is acquired in real time through the communication module. The first traffic condition data includes: vehicle sensor data from other vehicles; road infrastructure status data from roadside units; and regional traffic flow data from the traffic management center.

[0223] The environmental data is generated by spatiotemporal alignment and feature fusion of the first road condition data and the vehicle sensor data.

[0224] In one possible implementation, the device further includes an emergency module for:

[0225] Multidimensional status data of the goods transported by the target cold chain transport vehicle are obtained through a multimodal non-contact detection device.

[0226] The multidimensional status data is analyzed to generate cargo status assessment results;

[0227] If the cargo status assessment result is abnormal, the corresponding emergency plan will be triggered according to the type of cargo status assessment result.

[0228] In one possible implementation, the device further includes a route adjustment module for:

[0229] Obtain the optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters. The optimization target parameters include energy consumption coefficient, time weight and cargo status index.

[0230] Based on the second road condition data and vehicle status data, the priority score of each candidate path is calculated using the multi-objective evaluation function;

[0231] The candidate path with the highest priority score is selected as the optimized driving route.

[0232] In one possible implementation, the apparatus further includes an execution module for:

[0233] The thermodynamic properties of the goods transported by the target cold chain transport vehicle are obtained, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient.

[0234] A dynamic model of cargo temperature change is established based on the aforementioned thermodynamic characteristic parameters;

[0235] The real-time road condition prediction data of the target cold chain transport vehicle is obtained, including road slope parameters, congestion level parameters, and expected driving speed parameters.

[0236] Based on the road slope parameters, the congestion level parameters, and the expected driving speed parameters, calculate the impact coefficient of road condition changes on cargo hold heat load;

[0237] Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period;

[0238] The optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a graded control strategy is implemented on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0239] In one possible implementation, the device further includes an optimization module for:

[0240] After the target cold chain transport vehicle completes the transport task, the transport data for the operation of the transport task is obtained. The transport data includes energy consumption data, time data and route deviation data.

[0241] Based on the energy consumption data, the time data, and the path deviation data, optimize the adjustment strategy for the driving parameters and the cooling parameters;

[0242] The operational effect of the optimized adjustment strategy is simulated using a digital twin system, and the adjustment strategy is deployed to the control system after successful verification.

[0243] like Figure 5 As shown in the figure, this application provides a device including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0244] Memory 113 is used to store computer programs;

[0245] In one embodiment of this application, the processor 111, when executing a program stored in the memory 113, implements the cold chain transportation method provided in any of the foregoing method embodiments, including:

[0246] Obtain the type of goods transported by the target cold chain transport vehicle;

[0247] Determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods.

[0248] During the operation of the target cold chain transport vehicle, real-time measurement values ​​of each vehicle operation parameter are monitored, and environmental data of the environment in which the target cold chain transport vehicle is located are obtained.

[0249] Based on the environmental data, the real-time measured values ​​of each vehicle operating parameter, and the standard parameter range, the driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted.

[0250] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the cold chain transportation method provided in any of the foregoing method embodiments.

[0251] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0252] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0253] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0254] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A cold chain transportation method, characterized in that, The method includes: Obtain the type of goods transported by the target cold chain transport vehicle; Determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods. During the operation of the target cold chain transport vehicle, real-time measurements of each vehicle operating parameter are monitored, and environmental data of the environment in which the target cold chain transport vehicle is located are obtained. Based on the environmental data, the real-time measured values ​​of each of the vehicle's operating parameters, and the standard parameter range, the driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted. Obtain environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface containing temperature field distribution and cargo placement status based on the environmental monitoring data; The method further includes: The thermodynamic properties of the goods transported by the target cold chain transport vehicle are obtained, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient. A dynamic model of cargo temperature change is established based on the aforementioned thermodynamic characteristic parameters; The real-time road condition prediction data of the target cold chain transport vehicle is obtained, including road slope parameters, congestion level parameters, and expected driving speed parameters. Based on the road slope parameters, the congestion level parameters, and the expected driving speed parameters, calculate the impact coefficient of road condition changes on cargo hold heat load; Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period; The optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a graded control strategy is implemented on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

2. The method according to claim 1, characterized in that, The step of adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, real-time measured values ​​of each vehicle operating parameter, and standard parameter ranges includes: Calculate the deviation between the real-time measured value of each vehicle operating parameter and the corresponding standard parameter range, and normalize each deviation to obtain a standardized deviation value. The parameter adjustment amount is determined based on the standardized deviation value, and the environmental adjustment amount is determined based on the environmental data; The target adjustment amount is obtained by performing a weighted summation operation on the parameter adjustment amount and the environmental adjustment amount; Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the target adjustment amount.

3. The method according to claim 1, characterized in that, The acquisition of environmental data of the environment in which the target cold chain transport vehicle is located includes: Environmental perception data is acquired through a multimodal sensor array, which includes: a lidar sensor for acquiring three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for acquiring spectral characteristic data of environmental substances; and a millimeter-wave radar sensor for detecting obstacle data. The three-dimensional point cloud data, the spectral feature data, and the obstacle data are fused together to generate vehicle sensor data. The first traffic condition data is acquired in real time through the communication module. The first traffic condition data includes: vehicle sensor data from other vehicles; road infrastructure status data from roadside units; and regional traffic flow data from the traffic management center. The environmental data is generated by spatiotemporal alignment and feature fusion of the first road condition data and the vehicle sensor data.

4. The method according to claim 1, characterized in that, The method further includes: Multidimensional status data of the goods transported by the target cold chain transport vehicle are obtained through a multimodal non-contact detection device. The multidimensional status data is analyzed to generate cargo status assessment results; If the cargo status assessment result is abnormal, the corresponding emergency plan will be triggered according to the type of cargo status assessment result.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters. The optimization target parameters include energy consumption coefficient, time weight and cargo status index. Based on the second road condition data and vehicle status data, the priority score of each candidate path is calculated using the multi-objective evaluation function; The candidate path with the highest priority score is selected as the optimized driving route.

6. The method according to claim 1, characterized in that, The method further includes: After the target cold chain transport vehicle completes the transport task, the transport data for the operation of the transport task is obtained. The transport data includes energy consumption data, time data and route deviation data. Based on the energy consumption data, the time data, and the path deviation data, optimize the adjustment strategy for the driving parameters and the cooling parameters; The operational effect of the optimized adjustment strategy is simulated using a digital twin system, and the adjustment strategy is deployed to the control system after successful verification.

7. A cold chain transportation device, characterized in that, The device includes: The acquisition module is used to acquire the type of goods transported by the target cold chain transport vehicle; The determination module is used to determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter based on the type of goods. The monitoring module is used to monitor the real-time measurement values ​​of each vehicle operating parameter during the operation of the target cold chain transport vehicle, and to acquire environmental data of the environment in which the target cold chain transport vehicle is located. The parameter adjustment module is used to adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle based on the environmental data, the real-time measurement values ​​of each vehicle operating parameter, and the standard parameter range. The generation module is used to acquire environmental monitoring data inside the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface containing temperature field distribution and cargo placement status based on the environmental monitoring data. The device further includes an execution module for: The thermodynamic properties of the goods transported by the target cold chain transport vehicle are obtained, including specific heat capacity, thermal conductivity and temperature sensitivity coefficient. A dynamic model of cargo temperature change is established based on the aforementioned thermodynamic characteristic parameters; The real-time road condition prediction data of the target cold chain transport vehicle is obtained, including road slope parameters, congestion level parameters, and expected driving speed parameters. Based on the road slope parameters, the congestion level parameters, and the expected driving speed parameters, calculate the impact coefficient of road condition changes on cargo hold heat load; Based on the cargo temperature change dynamics model and the influence coefficient, predict the heat load change data of the cargo hold in the future period; The optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a graded control strategy is implemented on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

8. An electronic device, characterized in that, include: A processor and a memory, the processor being configured to execute a cold chain transportation program stored in the memory to implement the cold chain transportation method according to any one of claims 1-6.

9. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the cold chain transportation method according to any one of claims 1-6.

Images