Cold chain transportation method and device, electronic equipment and storage medium

Abstract

The invention provides a cold chain transportation method and device, electronic equipment and a storage medium. The method comprises the steps that the cargo type of cargo transported by a target cold chain transportation vehicle is acquired; determining at least one vehicle operation parameter and a standard parameter range corresponding to each vehicle operation parameter according to the cargo type; in the running process of the target cold-chain transport vehicle, real-time measurement values of all the vehicle running parameters are monitored in real time, and environment data of the environment where the target cold-chain transport vehicle is located are obtained; according to the environment data, the real-time measurement value of each vehicle operation parameter and the standard parameter range, the driving parameter and the refrigeration parameter of the target cold-chain transport vehicle are adjusted; and acquiring environment monitoring data in the cargo hold of the target cold-chain transport vehicle, and generating a three-dimensional visual interface including temperature field distribution and cargo placement state according to the environment monitoring data.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a cold chain transportation method, device, electronic equipment and storage medium. Background Art

[0002] In recent years, with the rapid growth of demand for high-quality cold chain logistics such as fresh food and pharmaceutical products due to the upgrading of residents' consumption, the cold chain transportation industry is facing higher technical requirements and operational challenges. Especially in the transportation of temperature-sensitive goods such as vaccines and high-end fresh products, strict temperature control requirements and timeliness standards have made the high energy consumption and high cargo damage rate of traditional cold chain transportation models more prominent. How to reduce operating costs, improve transportation efficiency and ensure cargo quality through intelligent technology has become a key issue that needs to be urgently addressed in the field of cold chain logistics. At present, common cold chain transportation vehicles mainly rely on basic temperature control systems and manual driving modes. Their technical solutions are usually: monitor the cargo hold temperature through fixed-position temperature sensors, trigger an alarm when the threshold is exceeded, or simply adjust the refrigeration parameters.

[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] The present application provides a cold chain transportation method, device, electronic equipment and storage medium to solve the problem that the cold chain transportation solutions in the existing technology 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, the present application provides a cold chain transportation method, comprising: Obtain the cargo type of the cargo transported by the target cold chain transport vehicle; determining at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; During the operation of the target cold chain transport vehicle, real-time monitoring of the real-time measurement value of each vehicle operating parameter, and obtaining 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; Acquire environmental monitoring data in the cargo hold of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

[0006] In one possible implementation, 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 includes: Calculating the deviation between the real-time measurement value of each vehicle operating parameter and the corresponding standard parameter range, and normalizing each deviation to obtain a standardized deviation value; determining a parameter adjustment amount based on the standardized deviation value, and determining an environmental adjustment amount based on the environmental data; Performing a weighted sum operation on the parameter adjustment amount and the environment adjustment amount to obtain a target adjustment amount; The driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted according to the target adjustment amount.

[0007] In one possible implementation, obtaining environmental data of the environment in which the target cold chain transport vehicle is located includes: Environmental perception data is collected 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 collecting 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 the communication module, the first road condition data including: onboard sensor data from other vehicles; road infrastructure status data from a roadside unit; and regional traffic flow data from a traffic management center; The first road condition data is temporally and spatially aligned and feature-fused with the vehicle-mounted sensor data to generate the environmental data.

[0008] In one possible implementation, the method further includes: Acquiring multi-dimensional status data of the goods transported by the target cold chain transport vehicle through a multimodal non-contact detection device; Analyzing the multi-dimensional status data to generate a cargo status assessment result; In the event that the cargo status assessment result is abnormal, a corresponding emergency plan is triggered according to the type of the cargo status assessment result.

[0009] In one possible implementation, the method further includes: Obtaining optimization target parameters for the current transportation task and constructing a multi-objective evaluation function based on the optimization target parameters, wherein the optimization target parameters include energy consumption coefficient, time weight, and cargo status index; Calculating a priority score for each candidate path using the multi-objective evaluation function based on the second road condition data and the vehicle status data; The candidate path with the highest priority score is selected as the optimized driving route.

[0010] In one possible implementation, the method further includes: Obtaining thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, wherein the thermodynamic characteristic parameters include specific heat capacity, thermal conductivity, and temperature sensitivity coefficient; Establishing a cargo temperature change kinetic model based on the thermodynamic characteristic parameters; Acquire real-time traffic condition prediction data of the target cold chain transport vehicle, wherein the real-time traffic condition prediction data includes a road slope parameter, a congestion degree parameter, and an expected driving speed parameter; Calculating an impact coefficient of a road condition change on a cargo hold heat load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter; Based on the cargo temperature change dynamics model and the influence coefficient, predicting the thermal load change data of the cargo hold in the future period; An optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a hierarchical control strategy is executed on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0011] In one possible implementation, the method further includes: After the target cold chain transport vehicle completes the transport task, the transport data of the transport task is obtained, wherein the transport data includes energy consumption data, time data and path deviation data; optimizing an adjustment strategy of the driving parameter and the cooling parameter according to the energy consumption data, the time data, and the path deviation data; The operating effect of the optimized adjustment strategy is simulated through the digital twin system, and the adjustment strategy is deployed to the control system after verification.

[0012] In a second aspect, the present application provides a cold chain transport device, comprising: An acquisition module is used to obtain the cargo type of the cargo transported by the target cold chain transport vehicle; a determination module, configured to determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; A monitoring module, configured to monitor the real-time measurement value of each of the vehicle's operating parameters during the operation of the target cold chain transport vehicle, and to obtain environmental data of the environment in which the target cold chain transport vehicle is located; a parameter adjustment module, configured to adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the environmental data, the real-time measurement value of each vehicle operating parameter, and the standard parameter range; A generation module is used to obtain environmental monitoring data in the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

[0013] In one possible implementation, the parameter adjustment module is specifically configured to: Calculating the deviation between the real-time measurement value of each vehicle operating parameter and the corresponding standard parameter range, and normalizing each deviation to obtain a standardized deviation value; determining a parameter adjustment amount based on the standardized deviation value, and determining an environmental adjustment amount based on the environmental data; Performing a weighted sum operation on the parameter adjustment amount and the environment adjustment amount to obtain a target adjustment amount; The driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted according to the target adjustment amount.

[0014] In one possible implementation, the monitoring module is specifically configured to: Environmental perception data is collected 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 collecting 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 the communication module, the first road condition data including: onboard sensor data from other vehicles; road infrastructure status data from a roadside unit; and regional traffic flow data from a traffic management center; The first road condition data is temporally and spatially aligned and feature-fused with the vehicle-mounted sensor data to generate the environmental data.

[0015] In one possible embodiment, the device further includes an emergency module for: Acquiring multi-dimensional status data of the goods transported by the target cold chain transport vehicle through a multimodal non-contact detection device; Analyzing the multi-dimensional status data to generate a cargo status assessment result; In the event that the cargo status assessment result is abnormal, a corresponding emergency plan is triggered according to the type of the cargo status assessment result.

[0016] In one possible implementation, the device further includes a route adjustment module configured to: Obtaining optimization target parameters for the current transportation task and constructing a multi-objective evaluation function based on the optimization target parameters, wherein the optimization target parameters include energy consumption coefficient, time weight, and cargo status index; Calculating a priority score for each candidate path using the multi-objective evaluation function based on the second road condition data and the vehicle status data; The candidate path with the highest priority score is selected as the optimized driving route.

[0017] In one possible implementation, the device further includes an execution module configured to: Obtaining thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, wherein the thermodynamic characteristic parameters include specific heat capacity, thermal conductivity, and temperature sensitivity coefficient; Establishing a cargo temperature change kinetic model based on the thermodynamic characteristic parameters; Acquire real-time traffic condition prediction data of the target cold chain transport vehicle, wherein the real-time traffic condition prediction data includes a road slope parameter, a congestion degree parameter, and an expected driving speed parameter; Calculating an impact coefficient of a road condition change on a cargo hold heat load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter; Based on the cargo temperature change dynamics model and the influence coefficient, predicting the thermal load change data of the cargo hold in the future period; An optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a hierarchical control strategy is executed on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0018] In one possible implementation, the device further includes an optimization module configured to: After the target cold chain transport vehicle completes the transport task, the transport data of the transport task is obtained, wherein the transport data includes energy consumption data, time data and path deviation data; optimizing an adjustment strategy of the driving parameter and the cooling parameter according to the energy consumption data, the time data, and the path deviation data; The operating effect of the optimized adjustment strategy is simulated through the digital twin system, and the adjustment strategy is deployed to the control system after verification.

[0019] In a third aspect, the present application provides a device comprising: a processor and a memory, wherein the processor is used 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.

[0020] In a fourth aspect, the present application provides a storage medium storing one or more programs, which can be executed by one or more processors to implement the cold chain transportation method described in any one of the first aspects.

[0021] The above-mentioned technical solution provided by the embodiment of the present application has the following advantages over the existing technology: the method provided by the embodiment of the present application realizes precise temperature control management for different cargo characteristics by obtaining the specific type of transported cargo and determining the corresponding standard parameter range; dynamically adjusts driving parameters and refrigeration parameters in combination with real-time monitored vehicle operating parameters and environmental data, effectively solving the problems of delayed temperature control response and low energy efficiency in traditional cold chain transportation; this technical solution can not only significantly reduce the cargo damage rate and improve the 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 the transportation of vaccines, high-end fresh products and other cargo scenarios with strict temperature control requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0025] Figure 1 A flow chart of an embodiment of a cold chain transportation method provided in an embodiment of the present application; Figure 2 A flow chart of another cold chain transportation method provided in an embodiment of the present application; Figure 3 A flow chart of another embodiment of a cold chain transportation method provided in an embodiment of the present application; Figure 4 This is a block diagram of an embodiment of a cold chain transportation device provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The disclosure below provides many different embodiments or examples for implementing different structures of the present application. In order to simplify the disclosure of the present application, the components and settings of specific examples are described below. Of course, these are merely examples and are not intended to limit the present application. In addition, the present application may repeat reference numbers and / or letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0028] Figure 1 This is a flow chart of an embodiment of a cold chain transportation method provided in the embodiment of the present application. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the cargo type of the cargo transported by the target cold chain transport vehicle.

[0029] Target cold chain transport vehicle refers to the cold chain transport vehicle that performs the target transport mission.

[0030] In this application, a control system (e.g., a cloud-based system) first utilizes an intelligent scheduling model to determine the optimal set of vehicles for the mission, based on transport order data (including order priority, cargo characteristics, and destination information) and real-time fleet status data (including each vehicle's location coordinates, refrigeration system parameters, remaining battery life, and cargo hold load factor, which are automatically uploaded to the control system upon vehicle startup). This model aims to maximize efficiency, minimize transportation costs, vehicle utilization, and balance fleet load, while simultaneously determining the optimal set of vehicles for the mission. Subsequently, a path planning model generates a personalized route for each selected vehicle (i.e., the target cold chain transport vehicle), combining real-time road network status data with the optimization objectives of minimizing transport time, energy consumption, and route risk. The two models collaborate to optimize the route using hard constraints such as temperature control requirements and battery life limits, as well as soft constraints such as cost thresholds and safety margins. The resulting transport plan is verified by the digital twin system, and customized route instructions are then issued to each target cold chain transport vehicle for execution. During the target cold chain transport vehicle's mission, its driving and refrigeration parameters are adjusted in real time through steps 101-104.

[0031] Cargo type refers to the category divided according to the physical and chemical properties of the transported items (such as heat sensitivity, mechanical strength, oxidation sensitivity, etc.), such as vaccines, frozen foods, fresh agricultural products, etc. Different cargo types have different requirements for the transportation environment.

[0032] In one embodiment, RFID tags can be used to identify the type of goods being transported by a target cold chain transport vehicle. Specifically, an ultra-high frequency RFID reader / writer (operating at 902-928 MHz) deployed at the cargo door automatically scans a standard passive electronic tag affixed to the cargo packaging during loading. This tag contains encrypted cargo characteristic data, including cargo classification codes, basic temperature control parameters, and special handling requirements. The reader / writer uses an anti-collision algorithm to batch read tags and transmits the decrypted cargo type data to the vehicle control unit via the CAN bus. This data is then verified against cloud-based order data. If missing or conflicting tag information is detected, an audible and visual alarm is triggered, locking the refrigeration system until manual confirmation is complete.

[0033] In another embodiment, the type of goods transported by the target cold chain transport vehicle may be obtained through manual input.

[0034] Step 102: Determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type.

[0035] 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, battery remaining power, etc.

[0036] The standard parameter range refers to the safe operation threshold range preset according to the characteristics of different goods.

[0037] In this embodiment of the application, the system's built-in database stores standard parameters for various types of goods (e.g., vaccines: temperature 2-8°C, vibration frequency <5Hz). Based on the identified cargo type, the corresponding parameter standard is automatically retrieved to provide a benchmark for real-time monitoring.

[0038] Step 103: During the operation of the target cold chain transport vehicle, the real-time measurement value of each vehicle operating parameter is monitored in real time, and environmental data of the environment in which the target cold chain transport vehicle is located is obtained.

[0039] In one embodiment, the real-time measurement value of each vehicle operating parameter is monitored in real time, specifically including: monitoring the three-dimensional temperature field distribution through a temperature sensor array deployed in the cargo hold, capturing the vibration spectrum of each frequency band through a MEMS three-axis vibration sensor, and detecting humidity changes through a digital humidity sensor; at the same time, obtaining equipment parameters such as the refrigeration system operating current and compressor speed, as well as vehicle driving status data through the CAN bus; all sensor data are synchronized with timestamps and processed by Kalman filtering to generate standardized measurement values ​​with confidence scores, which are updated to the central controller at a preset frequency (such as 10Hz). When an abnormality is detected, a graded alarm mechanism is immediately triggered.

[0040] Environmental data refers to information about the vehicle's external environment, including road conditions, weather, traffic flow, and other external factors that affect transportation. For example, it includes road congestion information, weather data, obstacle detection data, and cold charging station distribution information.

[0041] In one embodiment, obtaining 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 obtaining three-dimensional point cloud data of the surrounding environment; a hyperspectral imaging sensor for collecting spectral feature data of environmental substances; 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 on-board sensor data; obtaining first road condition data in real time through a communication module, the first road condition data including: on-board sensor data from other vehicles; road infrastructure status data from a roadside unit; and regional traffic flow data from a traffic management center; performing spatiotemporal alignment and feature fusion on the first road condition data and the on-board sensor data to generate the environmental data.

[0042] This solution uses a multimodal sensor array to collect real-time information about the vehicle's surrounding environment. The array includes a lidar, a hyperspectral imager, and a millimeter-wave radar, which respectively acquire three-dimensional spatial data, material composition characteristics, and obstacle dynamic information. After data fusion processing, it forms accurate on-board environmental perception data. Simultaneously, the vehicle network communication module obtains real-time road condition information from surrounding vehicles, road infrastructure, and traffic management centers. This information is integrated with the on-board perception data through spatiotemporal registration and feature fusion algorithms to generate comprehensive and accurate environmental situation data. This technical solution significantly improves the integrity and reliability of environmental perception through the complementary fusion of multi-source information, providing an accurate environmental situation perception foundation for intelligent decision-making systems and effectively enhancing the safety and adaptability of vehicles in complex transportation environments.

[0043] Step 104: Adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the environmental data, the real-time measurement value of each vehicle operating parameter, and the standard parameter range.

[0044] Driving parameters refer to vehicle movement parameters such as speed and route that affect the transportation process.

[0045] Refrigeration parameters refer to the operating parameters of the temperature control system such as compressor speed and air volume.

[0046] In an embodiment of the present application, step 104 specifically includes: calculating the deviation between the real-time measurement 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 sum 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 based on the target adjustment amount.

[0047] This solution first calculates the deviation of each vehicle's real-time operating parameter measurements from its preset standard range in real time, normalizing it into standardized deviations. Simultaneously, environmental adjustments are generated based on environmental data analysis. During the integrated decision-making phase, the system dynamically assigns weights based on the current transport stage and cargo characteristics: For temperature-sensitive cargo, the weights of temperature-related parameters are prioritized (e.g., 0.6-0.8), while for time-sensitive cargo, the weights of driving parameters are increased (e.g., 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 based on cargo type and environmental conditions), optimal control instructions are generated, adjusting vehicle parameters such as speed, route selection, and cooling power in real time. This dynamic weighting mechanism ensures temperature control accuracy while achieving a balanced optimization of transport efficiency and energy consumption.

[0048] S105: Obtain environmental monitoring data in the cargo hold of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

[0049] Distributed sensors refer to multi-source sensing devices such as temperature and humidity sensors and gas detectors installed at various locations in the cargo hold to collect environmental data in real time.

[0050] The three-dimensional visualization interface is a virtual monitoring screen constructed through augmented reality technology, which can present the temperature distribution gradient and cargo placement status in the cargo hold in three dimensions.

[0051] In the embodiment of the present application, cargo hold environmental monitoring data is first acquired through a distributed sensor network, and then this data is used to construct a three-dimensional visualization interface that accurately reflects the actual transportation status, thereby making it easier for the driver to view the cargo hold conditions.

[0052] In another embodiment, after S105, the following steps may also be included: receiving user interaction instructions through a gesture recognition module, and dynamically adjusting the display viewing angle and display parameters of the visual interface according to the instructions; based on pre-stored identity authentication information, limiting the scope of operation permissions of different users on the interface.

[0053] The gesture recognition module is an interactive component developed based on computer vision technology that accurately identifies user gestures. Identity authentication information is user permission credentials stored by the system, including different levels of access permissions.

[0054] Users can dynamically adjust the interface display content and viewing angle through natural gestures; the system automatically matches the corresponding data viewing and operation permissions based on user 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 operations. At the same time, strict permission control ensures system data security and realizes comprehensive intelligent supervision of the transportation process.

[0055] In another embodiment, the method further includes the following steps: obtaining 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 goods status evaluation result; and in the event that the goods status evaluation result is abnormal, triggering a corresponding emergency plan according to the type of the goods status evaluation result.

[0056] Among them, the multimodal non-contact detection device may include: a millimeter-wave radar sensor for detecting surface deformation and displacement changes of goods to obtain deformation data; an infrared imaging sensor for obtaining surface temperature distribution data of goods; an ultrasonic sensor for detecting internal structural status data of goods, etc.

[0057] The emergency plan includes: when the type of cargo status assessment result is abnormal temperature distribution on the cargo surface, adjusting the airflow organization pattern in the cargo hold, starting the local temperature compensation mechanism, and charging cold air nearby; when the type of cargo status assessment result is deformation of the cargo or abnormal internal structure, reducing the driving parameters and activating the shock absorption mode, as well as planning the route to the nearest checkpoint.

[0058] It should be noted that steps 101-104 can be applied to the following scenarios: Manned cold chain transport: The driver receives real-time system-optimized route and temperature control parameter recommendations via the onboard human-machine interface, and makes adjustments based on manual judgment. In abnormal situations (such as temperature deviation exceeding a threshold), the system triggers audible and visual alarms and provides emergency operation instructions, assisting the driver in responding quickly.

[0059] Intelligent unmanned cold chain transportation: Fully automated execution of system instructions: The A or D algorithm output from the path planning model generates a route that is directly transmitted to the autonomous driving controller. Refrigeration parameters are closed-loop regulated by a PID controller. Real-time environmental data such as road slope and traffic light phases are acquired through V2X communication to dynamically optimize the matching strategy between vehicle speed and refrigeration power.

[0060] This solution breaks through the limitations of traditional contact detection and builds a comprehensive monitoring system covering the surface and interior of the goods through multimodal non-contact detection devices; the intelligent evaluation model based on multi-source data fusion can accurately identify different types of cargo status abnormalities such as abnormal temperature distribution and physical deformation; it automatically matches the optimal emergency strategy for different types of abnormalities (such as starting local precise temperature control when the temperature is abnormal, enabling shock absorption mode and planning emergency inspection routes when physical deformation occurs), which not only ensures the timeliness of abnormality handling, but also avoids the energy waste of traditional "one-size-fits-all" emergency response, and significantly improves the cargo safety level and energy efficiency of cold chain transportation.

[0061] In the application, during the operation of the vehicle, the microbial activity data in the cargo hold can also be monitored. When the microbial activity data exceeds a preset safety threshold, the refrigeration system operating parameters are adjusted to inhibit microbial reproduction.

[0062] 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, ATP bioluminescence value, etc.; preset safety thresholds are microbial activity limits pre-set according to the hygiene standards of different cargo types (such as fresh food and pharmaceutical products).

[0063] The solution monitors the microbial activity data in the cargo hold in real time. When it detects that the data exceeds the safety threshold of the corresponding cargo, it automatically adjusts the temperature, humidity and other operating parameters of the refrigeration system (such as 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.

[0064] In another embodiment, the method further includes the following steps: after the target cold chain transport vehicle completes the transport task, obtaining the transport data of the 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 according to the energy consumption data, the time data and the path deviation data; simulating the operating effect of the optimized adjustment strategy through a digital twin system, and deploying the adjustment strategy to the control system after verification.

[0065] Transportation data refers to the set of operating 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 (the difference between the actual driving time and the planned time of each section), and path deviation data (the offset between the planned path and the actual driving trajectory).

[0066] Adjustment strategy refers to a set of optimization rules that control vehicle driving parameters (such as speed and route) and cooling parameters (such as temperature and power), including decision algorithm parameters, control logic thresholds, etc.

[0067] The digital twin system is a vehicle operation simulation platform built through virtual modeling that can simulate vehicle behavior and cargo compartment status in real environments.

[0068] In an embodiment of the present application, after the transportation task is completed, the energy consumption, time and path deviation data of the entire process are automatically collected. Based on these data, a machine learning algorithm is used to optimize the adjustment strategy of driving parameters (such as speed control curve) and cooling parameters (such as temperature adjustment response speed). In the application, the weight parameters of the path planning model and the trigger threshold parameters of the emergency response mechanism can also be optimized; then, the execution effect of the optimized strategy is simulated in a virtual environment through the 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.

[0069] In another embodiment, the method may further include the following steps: obtaining a cargo operation request input by a user through voice; extracting operation elements from the cargo operation request through 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 feedback operation confirmation information.

[0070] A cargo operation request refers to the instruction information containing cargo handling requirements submitted by the user through voice; natural language processing technology refers to artificial intelligence technology that converts human language into computer-recognizable instructions; operation elements refer to the key control parameters parsed from the voice request; and control instructions refer to the execution commands generated by the system based on the parsing results.

[0071] This solution first captures the user's voice input request. Natural language processing technology then extracts key elements such as cargo identification (for identifying specific cargo), cargo hold environmental requirements (including storage conditions such as temperature and humidity), and time requirements (for operational timeliness). This then generates three types of control instructions: environmental parameter adjustment instructions (automatically adjusting cargo hold storage conditions based on requirements), transport plan update instructions (dynamically adjusting transport routes and timing), and operation confirmation feedback (returning execution results to the user). This enables a seamless transition from voice commands to transport control, ensuring both operational convenience and precise control of cold chain transportation.

[0072] In another embodiment, the method may further include the following steps: obtaining real-time road condition prediction data, the road condition prediction data including the distance to the obstacle ahead, the road slippery coefficient and sudden weather warning; calculating the safe braking distance based on the road condition prediction data, the determination of the safe braking distance is also related to the impact resistance level of the currently transported goods; when it is detected that the actual vehicle distance is less than the safe braking distance, triggering the emergency braking strategy.

[0073] Real-time traffic prediction data refers to road environment information obtained in real time through on-board sensors and V2X communication, including: the distance to the obstacle ahead (the real-time distance to the vehicle or obstacle ahead measured by millimeter-wave radar), the road slipperiness coefficient (the friction coefficient obtained based on the scanning analysis of the road surface texture by lidar), and sudden weather warnings (real-time weather alerts from meteorological departments and roadside units).

[0074] The 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 (such as fragile items, liquid medicines, and other cargo with different impact resistance levels).

[0075] The emergency braking strategy is a multi-stage deceleration plan that is automatically executed when the system determines that there is a risk of collision. The first stage: reduces the load on the refrigeration system and activates energy recovery; the second stage: starts the electronic braking system and adjusts the braking force distribution; the third stage: triggers the mechanical brake and links the cargo compartment locking device.

[0076] In this solution, a variety of real-time road condition data are first integrated with the physical characteristics of the transported goods to accurately calculate the personalized safe braking distance; when the actual vehicle distance is detected to be insufficient, a customized emergency braking response is immediately triggered to achieve smooth deceleration of the vehicle while ensuring the safety of the goods.

[0077] In another embodiment, the method may further include the following steps: continuously monitoring the deviation of the vehicle positioning data from the preset transportation path; when the deviation exceeds a threshold and lasts for a preset period of time, triggering an anti-hijacking tracking strategy.

[0078] Vehicle positioning data refers to the real-time latitude and longitude coordinates, driving speed and heading angle information obtained through the vehicle-mounted GPS module and Beidou dual-mode positioning system.

[0079] The preset transport route is a digital reference route generated by the path planning results, which includes the coordinate set of key points on the path and the allowable deviation range.

[0080] Deviation is the spatial offset between the real-time positioning coordinates and the preset path, which is calculated by the geo-fencing algorithm.

[0081] The anti-hijacking tracking strategy is a multi-level security response plan initiated after the system 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 environment 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.

[0082] This solution continuously compares the spatial relationship between the real-time positioning trajectory and the planned path. When it detects that the deviation exceeds the preset threshold (such as horizontal offset > 500 meters) and lasts for more than the safety time (such as 5 minutes), it automatically triggers comprehensive protection measures including covert positioning reporting, cargo compartment environment locking and remote alarm linkage, thereby ensuring vehicle driving safety.

[0083] In another embodiment, the method may further include the following steps: real-time collection 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 to output 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 instruction when it is detected that the remaining life of any component is lower than a preset threshold.

[0084] Operating status data refers to key parameters that reflect the working condition of the refrigeration equipment, including the compressor vibration spectrum (mechanical vibration characteristics collected by the acceleration sensor), the condenser temperature difference (the temperature difference between the inlet and outlet of the condenser), and the refrigerant pressure value (the pressure reading in the refrigeration cycle system).

[0085] The health prediction model is an equipment status assessment model built based on a machine learning algorithm. It can analyze the correlation between historical operating data and equipment loss. The remaining life prediction value is an estimate of the sustainable operating time of the equipment output by the model.

[0086] The maintenance priority queue is a repair order list generated based on the prediction results.

[0087] This solution first collects the operating parameters of each key component of the refrigeration system in real time. This data is then fed into a pre-trained health prediction model for analysis, resulting in a prediction of the remaining life of each component. The system then automatically generates a maintenance priority ranking based on the predicted values. If the predicted remaining life of any component falls below a safety threshold, a maintenance reminder is immediately triggered. By accurately predicting the extent of equipment wear, this solution can schedule maintenance in advance, avoiding transportation interruptions caused by sudden failures. It also optimizes maintenance resource allocation, significantly reducing equipment maintenance costs, extending the service life of key components, and ensuring the stable operation of the cold chain transportation system.

[0088] In another embodiment, the method may further include the following steps: obtaining real-time status data during the transportation process, wherein the status data includes cargo hold environmental parameters, vehicle operating parameters, and path execution data; constructing a digital twin model based on the real-time status data, wherein the model maps the three-dimensional spatial status and equipment operating status of the transportation system in real time.

[0089] Real-time status data refers to the multi-dimensional operational information continuously collected during transportation, including: cargo hold environmental parameters (such as sensor data such as temperature, humidity, and gas concentration), vehicle operating parameters (including speed, energy consumption, refrigeration system working status, etc.) and path execution data (the degree of match between the actual driving route and the planned route).

[0090] The digital twin model is a dynamic mirror of the transportation system constructed through virtual simulation technology, which can fully reproduce the real-time status of the physical entity in three-dimensional space.

[0091] This solution leverages IoT technology to continuously capture real-time status data from 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 simultaneously reflects the operating status of each vehicle system. This enables digital and visual monitoring of all elements of the transportation process. Through simulation analysis that integrates virtual and real-world scenarios, 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. This data also provides data support for subsequent improvements to transportation plans.

[0092] In another embodiment, the method may further include the following steps: generating a hash value for key operation data and storing it in a blockchain network, wherein the key operations include at least temperature and humidity adjustment records, path change instructions, and equipment maintenance operations.

[0093] 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).

[0094] A hash value is a fixed-length digital fingerprint generated by a cryptographic algorithm that can uniquely identify the original data.

[0095] The blockchain network is a decentralized storage system that uses distributed accounting technology.

[0096] 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 the blockchain network. Leveraging the blockchain's immutable nature, this technology ensures the authenticity and traceability of key operational records in cold chain transportation, effectively preventing the risk of data tampering and providing credible electronic evidence for transportation quality disputes. It also meets the compliance requirements of high-standard logistics such as pharmaceutical cold chains, significantly enhancing the credibility and transparency of the transportation process.

[0097] In another embodiment, the method may further include the following steps: when a preset condition is detected to be triggered, automatically executing associated business processes through a smart contract, wherein the business processes include insurance claim application and transportation fee settlement.

[0098] Preset conditions refer to the pre-set business rule trigger thresholds, including abnormal events such as transportation time limit breach, temperature and humidity exceeding the standard, etc.

[0099] Smart contracts are automated program codes stored on the blockchain that can autonomously execute predefined logic based on input conditions.

[0100] Associated business processes refer to subsequent processing processes related to transportation services.

[0101] This solution monitors system data in real time. When it detects situations that meet preset conditions (such as delayed cargo delivery or abnormal cargo hold temperature), it automatically triggers smart contracts to execute corresponding business processes: for transport anomalies, an insurance claim is immediately generated and submitted; for transport tasks that are completed normally, the transportation costs are automatically calculated and settled. This automates the post-transportation processing process. The tamper-proof and self-executing nature of smart contracts significantly improves business processing efficiency and transparency, reduces manual intervention and disputes, and ensures the fairness and timeliness of the claims and settlement processes, providing reliable digital protection for cold chain transportation services.

[0102] The technical solution provided in the embodiment of the present application realizes precise temperature control management for different cargo characteristics by obtaining the specific type of transported cargo and determining the corresponding standard parameter range; dynamically adjusts driving parameters and refrigeration parameters in combination with real-time monitored vehicle operating parameters and environmental data, effectively solving the problems of delayed temperature control response and low energy efficiency in traditional cold chain transportation; this technical solution can not only significantly reduce the cargo damage rate and improve the 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 particularly suitable for transportation scenarios of vaccines, high-end fresh products and other cargoes with strict temperature control requirements.

[0103] Figure 2 This is another example flow chart of cold chain transportation provided by the present application. Figure 2 As shown, the following steps are included: Step 201: Obtain optimization target parameters of the current transportation task, and construct a multi-objective evaluation function based on the optimization target parameters, wherein the optimization target parameters include energy consumption coefficient, time weight, and cargo status index; Step 202: Calculate the priority score of each candidate path using the multi-objective evaluation function based on the second road condition data and the vehicle status data; Step 203: Select the candidate path with the highest priority score as the optimized driving route.

[0104] For ease of understanding, steps 201 to 203 are described in a unified manner below: Optimization target parameters refer to a set of key indicators used to evaluate the quality of transportation routes. Specifically, they include: energy consumption coefficient (reflecting the impact of different routes on the energy consumption of the vehicle refrigeration system), time weight (indicating the importance of transportation timeliness requirements), and cargo status index (the need to maintain freshness determined by the type of cargo).

[0105] The multi-objective evaluation function is a mathematical evaluation model that integrates the above parameters according to preset rules; the priority score is the comprehensive evaluation value of the path calculated by this function.

[0106] Secondary traffic data refers to real-time external environmental information, including real-time traffic flow (obtained through V2X communication), road construction information (from the traffic management center), and weather information (temperature, precipitation, and other meteorological data). Secondary traffic data can be collected in real time or predicted based on big traffic and meteorological data. Using this predicted secondary traffic data to adjust driving routes can help mitigate extreme environmental risks in advance.

[0107] Vehicle status data: refers to monitoring parameters that reflect the real-time operating status of cold chain transport vehicles, which 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 of each compartment in the cargo hold), and positioning information (GPS coordinates, driving speed, heading angle).

[0108] In this solution, the optimization target parameters for the current transport mission are first obtained, including: 1) the energy consumption coefficient, which is calculated by weighting the real-time load rate of the vehicle's refrigeration system and route characteristics (including road slope, traffic congestion level, etc.); 2) the time weight, which is dynamically set according to the corresponding freshness requirements of the cargo type (such as flowers must be delivered within 4 hours, frozen foods must be maintained below -18°C, etc.); 3) the cargo status indicator, which is directly related to the real-time temperature and humidity data of each temperature zone in the cargo hold, vibration amplitude, gas concentration and other storage condition monitoring parameters.

[0109] A multi-objective evaluation function is then constructed based on these parameters. This function is combined with the real-time collected second road condition data and vehicle status data to comprehensively score each candidate path. Finally, the path with the highest score is selected as the optimized driving route.

[0110] This solution achieves intelligent optimization of cold chain logistics routes by accurately quantifying energy efficiency, time cost and cargo preservation requirements during transportation, thereby improving transportation efficiency while ensuring cargo quality.

[0111] Figure 3 This is another example flow chart of cold chain transportation provided by the present application. Figure 3 As shown, the following steps are included: Step 301: Obtain thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, where the thermodynamic characteristic parameters include specific heat capacity, thermal conductivity, and temperature sensitivity coefficient.

[0112] This step uses a cargo information database or sensor detection to obtain key thermophysical parameters of the transported cargo. These thermodynamic parameters include: Specific heat capacity (c_p): This represents the amount of heat required to raise the temperature of a unit mass of cargo by 1°C and directly influences the calculation of the refrigeration system's power requirements. For example, the typical specific heat capacity of vaccines is 3.5 kJ / (kg·K); Thermal conductivity (k): This reflects the cargo's internal heat transfer capacity and determines the uniformity of temperature distribution. The thermal conductivity of frozen meat is approximately 1.5 W / (m·K); and Temperature sensitivity coefficient (α): This indicates the cargo's sensitivity to temperature fluctuations. These parameters are obtained through RFID tags or cloud-based databases, providing foundational data for subsequent modeling.

[0113] Step 302: Establish a cargo temperature change kinetic model based on the thermodynamic characteristic parameters.

[0114] In this step, the mathematical model based on thermodynamic characteristics can be expressed as: dT / dt = (Q_in - Q_out) / (m·c_p); Where Q_in includes both conductive heat (related to the thermal conductivity k) and convective heat (related to the ambient temperature difference); Q_out is the heat removed by the refrigeration system (related to the evaporator efficiency); and m is the cargo mass. This model can simulate how cargo hold temperature changes under different environmental conditions. For example, it can predict the rate of cargo hold temperature rise after the vehicle door is closed, providing a basis for feedforward control of the refrigeration system.

[0115] Step 303: Acquire real-time traffic condition prediction data of the target cold chain transport vehicle, wherein the real-time traffic condition prediction data includes road slope parameters, congestion level parameters, and expected driving speed parameters.

[0116] In this step, accurate road condition predictions are generated by fusing the following multi-source data: road slope parameters derived from high-precision map data (e.g., a 3° uphill slope); congestion parameters derived from real-time traffic flow data acquired through V2X communication (e.g., a congestion index of 0.8); and estimated driving parameters, including predicted speed profiles and start-stop frequency. This data is updated at preset intervals (e.g., 30 seconds) and a Kalman filter algorithm is used to eliminate measurement noise, resulting in a road condition forecast for the next period (e.g., 15-30 minutes).

[0117] Step 304: Calculate the influence coefficient of the road condition change on the cargo hold thermal load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter.

[0118] In this step, a quantitative relationship model between road condition and heat load is established:

[0119] in, : slope influence term (θ is the slope angle); : speed influence term (f is the driving frequency); : Congestion impact item (N is the number of starts and stops).

[0120] For example, when a vehicle enters a 5° uphill congested road section, the coefficient will increase significantly (by about 40%), reminding the system to increase the cooling power in advance.

[0121] Step 305: Based on the cargo temperature change dynamics model and the influence coefficient, predict the thermal load change data of the cargo hold in the future period.

[0122] In this step, the kinetic model is combined with the influence coefficients:

[0123] Where, Q_pred(t) is the predicted heat load value at time t (unit: kW); Q_base is the base heat load (unit: kW), which is the steady-state heat load output by the cargo temperature change dynamics model (step 302), and is calculated as follows: , 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: ℃), calculation method: ,in, is the external ambient temperature, Set the temperature for the cargo hold (e.g. 5°C for vaccines).

[0124] Next, a formula is used to calculate the heat load change data ΔQ (including changes in cargo hold temperature distribution, cooling demand fluctuations, and hotspot risk areas) over the future timeframe (typically 5-10 minutes). The calculation 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 rated capacity of the system, i.e., the maximum cooling capacity of the refrigeration system under standard operating conditions. For example, if the temperature in a corner of the cargo hold is predicted to rise by 2°C in 15 minutes, air supply to that area should be increased in advance.

[0125] Step 306: Calculate the optimal refrigeration parameter adjustment amount according to the heat load change data, and execute a hierarchical control strategy on the refrigeration system of the target cold chain transport vehicle according to the optimal refrigeration parameter adjustment amount.

[0126] In this step, the established dynamic mapping model is used to convert the heat load change data into accurate cooling parameter adjustment values. The specific corresponding relationship is shown below: Heat load intensity → compressor speed: When the predicted heat load increases by 10-20%, the compressor speed is increased by 15-25% accordingly; when the heat load decreases, the speed is reduced proportionally but maintained at the minimum safe speed.

[0127] Heat load change rate → PID parameter adjustment: Rapid rise (>1℃ / min): Enhanced differential control (D value increased by 30%); Slow fluctuation (<0.3℃ / min): Enhanced integral control (I value increased by 50%).

[0128] Spatial distribution of heat load → Air supply strategy: Local hotspot areas: Directly increase air volume by 20-30%; Overall uniform temperature increase: Evenly increase air speed throughout the cabin.

[0129] Implement three levels of control based on the prediction results: Level 1 regulation (ΔQ≤15%): adjust the compressor speed (±15%); optimize the air supply angle (such as avoiding direct blowing during vaccine transportation).

[0130] Secondary regulation (15%<ΔQ≤30%): Start the standby refrigeration unit; dynamically plan alternative charging and cooling station options (display information of the last three stations).

[0131] Level 3 regulation (ΔQ>30%): Force navigation to the optimal cold charging station (comprehensive distance / service capacity / electricity price); start the transport box emergency cold storage module (maintain core area temperature control for 2-4 hours).

[0132] Emergency scenario: When the refrigeration system fails, an appointment will be automatically made with a maintenance station within 30km.

[0133] This solution achieves precise temperature control by establishing a coupled model of cargo thermodynamic properties and road conditions. First, it obtains cargo parameters such as specific heat capacity and constructs a temperature change kinetic model. Simultaneously, it calculates the heat load impact coefficient based on secondary road condition data (such as slope and congestion). These two factors are combined to predict future heat load trends. Finally, based on the predicted results, it dynamically adjusts refrigeration parameters (such as compressor speed and air volume) and achieves an optimal balance between energy consumption and temperature control accuracy through a hierarchical control strategy (such as three-level power regulation). By combining physical modeling with real-time prediction, this solution addresses the issues of lagging temperature control and excessive energy consumption in traditional cold chain transportation.

[0134] Figure 4 This is a block diagram of an embodiment of a cold chain transport device provided in an embodiment of the present application. Figure 4 As shown, the device includes: An acquisition module 41 is used to acquire the type of goods transported by the target cold chain transport vehicle; a determination module 42 for determining at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; A monitoring module 43 is configured to monitor the real-time measurement value of each vehicle operating parameter during the operation of the target cold chain transport vehicle, and to obtain environmental data of the environment in which the target cold chain transport vehicle is located; a parameter adjustment module 44 for adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the environmental data, the real-time measurement value of each vehicle operating parameter, and the standard parameter range; The generation module 45 is used to obtain environmental monitoring data in the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

[0135] In one possible implementation, the parameter adjustment module is specifically configured to: Calculating the deviation between the real-time measurement value of each vehicle operating parameter and the corresponding standard parameter range, and normalizing each deviation to obtain a standardized deviation value; determining a parameter adjustment amount based on the standardized deviation value, and determining an environmental adjustment amount based on the environmental data; Performing a weighted sum operation on the parameter adjustment amount and the environment adjustment amount to obtain a target adjustment amount; The driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted according to the target adjustment amount.

[0136] In one possible implementation, the monitoring module is specifically configured to: Environmental perception data is collected 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 collecting 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 the communication module, the first road condition data including: onboard sensor data from other vehicles; road infrastructure status data from a roadside unit; and regional traffic flow data from a traffic management center; The first road condition data is temporally and spatially aligned and feature-fused with the vehicle-mounted sensor data to generate the environmental data.

[0137] In one possible embodiment, the device further includes an emergency module for: Acquiring multi-dimensional status data of the goods transported by the target cold chain transport vehicle through a multimodal non-contact detection device; Analyzing the multi-dimensional status data to generate a cargo status assessment result; In the event that the cargo status assessment result is abnormal, a corresponding emergency plan is triggered according to the type of the cargo status assessment result.

[0138] In one possible implementation, the device further includes a route adjustment module configured to: Obtaining optimization target parameters for the current transportation task and constructing a multi-objective evaluation function based on the optimization target parameters, wherein the optimization target parameters include energy consumption coefficient, time weight, and cargo status index; Calculating a priority score for each candidate path using the multi-objective evaluation function based on the second road condition data and the vehicle status data; The candidate path with the highest priority score is selected as the optimized driving route.

[0139] In one possible implementation, the device further includes an execution module configured to: Obtaining thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, wherein the thermodynamic characteristic parameters include specific heat capacity, thermal conductivity, and temperature sensitivity coefficient; Establishing a cargo temperature change kinetic model based on the thermodynamic characteristic parameters; Acquire real-time traffic condition prediction data of the target cold chain transport vehicle, wherein the real-time traffic condition prediction data includes a road slope parameter, a congestion degree parameter, and an expected driving speed parameter; Calculating an impact coefficient of a road condition change on a cargo hold heat load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter; Based on the cargo temperature change dynamics model and the influence coefficient, predicting the thermal load change data of the cargo hold in the future period; An optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a hierarchical control strategy is executed on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

[0140] In one possible implementation, the device further includes an optimization module configured to: After the target cold chain transport vehicle completes the transport task, the transport data of the transport task is obtained, wherein the transport data includes energy consumption data, time data and path deviation data; optimizing an adjustment strategy of the driving parameter and the cooling parameter according to the energy consumption data, the time data, and the path deviation data; The operating effect of the optimized adjustment strategy is simulated through the digital twin system, and the adjustment strategy is deployed to the control system after verification.

[0141] like Figure 5 As shown, an embodiment of the present 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. Memory 113, for storing computer programs; In one embodiment of the present application, the processor 111 is configured to execute a program stored in the memory 113 to implement the cold chain transportation method provided by any of the aforementioned method embodiments, including: Obtain the cargo type of the cargo transported by the target cold chain transport vehicle; determining at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; During the operation of the target cold chain transport vehicle, real-time monitoring of the real-time measurement value of each vehicle operating parameter, and obtaining environmental data of the environment in which the target cold chain transport vehicle is located; Adjust 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.

[0142] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the cold chain transportation method provided in any of the aforementioned method embodiments are implemented.

[0143] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 may be selected based on actual needs to achieve the objectives of this embodiment.

[0144] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0145] It should be understood that the terms used herein are for the purpose of describing specific example embodiments only and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "comprise", "include", "contain" and "have" are inclusive and therefore specify the presence of stated features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the specific order described or illustrated, unless the order of execution is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0146] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present 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 the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A cold chain transportation method, characterized in that: The method comprises: Obtain the cargo type of the cargo transported by the target cold chain transport vehicle; determining at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; During the operation of the target cold chain transport vehicle, real-time monitoring of the real-time measurement value of each vehicle operating parameter, and obtaining 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; Acquire environmental monitoring data in the cargo hold of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

2. The method according to claim 1, characterized in that The adjusting the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the environmental data, the real-time measurement value of each vehicle operating parameter and the standard parameter range includes: Calculating the deviation between the real-time measurement value of each vehicle operating parameter and the corresponding standard parameter range, and normalizing each deviation to obtain a standardized deviation value; determining a parameter adjustment amount based on the standardized deviation value, and determining an environmental adjustment amount based on the environmental data; Performing a weighted sum operation on the parameter adjustment amount and the environment adjustment amount to obtain a target adjustment amount; The driving parameters and refrigeration parameters of the target cold chain transport vehicle are adjusted according to the target adjustment amount.

3. The method according to claim 1, characterized in that The obtaining of environmental data of the environment in which the target cold chain transport vehicle is located includes: Environmental perception data is collected 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 collecting 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 the communication module, the first road condition data including: onboard sensor data from other vehicles; road infrastructure status data from a roadside unit; and regional traffic flow data from a traffic management center; The first road condition data is temporally and spatially aligned and feature-fused with the vehicle-mounted sensor data to generate the environmental data.

4. The method according to claim 1, wherein The method further comprises: Acquiring multi-dimensional status data of the goods transported by the target cold chain transport vehicle through a multimodal non-contact detection device; Analyzing the multi-dimensional status data to generate a cargo status assessment result; In the event that the cargo status assessment result is abnormal, a corresponding emergency plan is triggered according to the type of the cargo status assessment result.

5. The method according to claim 1, wherein The method further comprises: Obtaining optimization target parameters for the current transportation task and constructing a multi-objective evaluation function based on the optimization target parameters, wherein the optimization target parameters include energy consumption coefficient, time weight, and cargo status index; Calculating a priority score for each candidate path using the multi-objective evaluation function based on the second road condition data and the vehicle status data; The candidate path with the highest priority score is selected as the optimized driving route.

6. The method according to claim 1, wherein The method further comprises: Obtaining thermodynamic characteristic parameters of the goods transported by the target cold chain transport vehicle, wherein the thermodynamic characteristic parameters include specific heat capacity, thermal conductivity, and temperature sensitivity coefficient; Establishing a cargo temperature change kinetic model based on the thermodynamic characteristic parameters; Acquire real-time traffic condition prediction data of the target cold chain transport vehicle, wherein the real-time traffic condition prediction data includes a road slope parameter, a congestion degree parameter, and an expected driving speed parameter; Calculating an impact coefficient of a road condition change on a cargo hold heat load based on the road slope parameter, the congestion level parameter, and the expected driving speed parameter; Based on the cargo temperature change dynamics model and the influence coefficient, predicting the thermal load change data of the cargo hold in the future period; An optimal refrigeration parameter adjustment amount is calculated based on the heat load change data, and a hierarchical control strategy is executed on the refrigeration system of the target cold chain transport vehicle based on the optimal refrigeration parameter adjustment amount.

7. The method according to claim 1, characterized in that The method further comprises: After the target cold chain transport vehicle completes the transport task, the transport data of the transport task is obtained, wherein the transport data includes energy consumption data, time data and path deviation data; optimizing an adjustment strategy of the driving parameter and the cooling parameter according to the energy consumption data, the time data, and the path deviation data; The operating effect of the optimized adjustment strategy is simulated through the digital twin system, and the adjustment strategy is deployed to the control system after verification.

8. A cold chain transport device, characterized in that: The device comprises: An acquisition module is used to obtain the cargo type of the cargo transported by the target cold chain transport vehicle; a determination module, configured to determine at least one vehicle operating parameter and a standard parameter range corresponding to each vehicle operating parameter according to the cargo type; A monitoring module, configured to monitor the real-time measurement value of each of the vehicle's operating parameters during the operation of the target cold chain transport vehicle, and to obtain environmental data of the environment in which the target cold chain transport vehicle is located; a parameter adjustment module, configured to adjust the driving parameters and refrigeration parameters of the target cold chain transport vehicle according to the environmental data, the real-time measurement value of each vehicle operating parameter, and the standard parameter range; A generation module is used to obtain environmental monitoring data in the cargo compartment of the target cold chain transport vehicle, and generate a three-dimensional visualization interface including temperature field distribution and cargo placement status based on the environmental monitoring data.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is used 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 to 7.

10. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the cold chain transportation method according to any one of claims 1 to 7.

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