A cloud global control cold chain logistics dynamic transfer scheduling method and system

Abstract

The application discloses a kind of based on cloud global overall planning's cold chain logistics dynamic transfer scheduling method and system, for the defect that existing cold chain logistics lacks dynamic quality prediction and urgent overall scheduling, the method is applied to cloud server, by issuing initial route and real-time monitoring multi-source state data;System executes double-track abnormality determination logic, identifies external delay abnormality or internal equipment abnormality;When determining that abnormality occurs, different quality decay models are called according to the type of abnormality and real-time temperature environment, and the expected remaining quality value of goods arriving at the destination is deduced;If the expected remaining quality value is lower than the preset acceptance threshold, an early warning is triggered;Then the system dynamically adjusts the evaluation weight according to the type of abnormality, and performs adaptive optimization based on multi-dimensional cost logic, sends a resource lock request to the optimal transfer location and issues a new route.

Description

Technical Field

[0001] This invention belongs to the field of supply chain technology, specifically a cloud-based global control method and system for dynamic transfer and scheduling of cold chain logistics. Background Technology

[0002] In existing cold chain logistics supply chain management, fixed route planning and post-transportation traceability mechanisms are typically employed. Most existing transportation monitoring systems can only provide basic GPS positioning and vehicle temperature alarms. However, cold chain goods such as frozen foods and biopharmaceuticals are extremely sensitive to temperature fluctuations in the transportation environment and transit time.

[0003] Current cold chain logistics systems suffer from the following technical deficiencies: When traffic congestion or extreme weather causes significant delays during transport, the system cannot dynamically predict the extent of damage to cargo quality caused by the extended time, often resulting in goods being found to be spoiled and damaged only after arriving at their destination. When vehicle refrigeration equipment malfunctions, traditional methods rely solely on drivers manually locating repair shops or temporary cold storage facilities, lacking automated and optimized emergency response plans based on the rate of cargo quality deterioration. Summary of the Invention

[0004] To address the shortcomings of existing technologies, a cloud-based, globally controlled dynamic transfer scheduling method for cold chain logistics is proposed.

[0005] The technical solution adopted by this invention to solve its technical problem is: A cloud-based, globally controlled dynamic transfer scheduling method for cold chain logistics, the method being applied to a cloud server, includes the following steps: S1: Obtain transportation task information for cold chain transport vehicles, generate initial driving routes for each vehicle, and send them to the corresponding vehicle terminals; S2: Receives real-time status data messages uploaded by each vehicle terminal through the wireless communication network, and simultaneously obtains dynamic road conditions and weather data of the road section ahead of each vehicle at its current location; S3: For any controlled vehicle, determine in real time and in parallel whether it has experienced external delays or internal equipment malfunctions. S4: When a vehicle is determined to have an anomaly, the cloud server simulates and extrapolates the estimated remaining quality value of the cargo when it arrives at its destination, based on the anomaly type, the current remaining driving time, and the real-time temperature in the status data message. S5: If the remaining quality value is expected to be lower than the preset acceptance quality threshold, an emergency transfer instruction will be generated for the vehicle; and based on the current location of the vehicle, the real-time available cold storage capacity and loading and unloading queue status of each candidate transfer point that is synchronously connected to the cloud database will be scanned. S6: Comprehensively evaluate the optimal transit point, send a cold storage resource lock request to the optimal transit point, and after successful lock, generate a new driving route containing the transit node and send it to the vehicle terminal of the corresponding vehicle.

[0006] Furthermore, the transportation task information in S1 includes preset acceptance quality thresholds and target delivery time windows for each departure point, destination, and cargo.

[0007] Furthermore, the status data message in S2 includes vehicle location information, real-time temperature and humidity data of each compartment in the vehicle, and the operating status of the refrigeration equipment.

[0008] Furthermore, S2 also includes breakpoint resume logic to handle communication dead zones: If the cloud server does not receive status data messages from the vehicle terminal within the set period, the vehicle will be marked as out of contact. Once the vehicle regains communication, the cloud server receives a set of historical status data with timestamps from the vehicle terminal during the period of loss of contact, and uses the set of historical status data to calibrate the current baseline of cargo quality degradation.

[0009] Furthermore, the specific determination in S3 is as follows: If dynamic road conditions or weather data cause the estimated arrival time to exceed the set threshold, it is judged as an external delay anomaly; If the refrigeration equipment displays a fault or the real-time temperature data of any zone deviates from the preset safe range and continues to exceed the judgment time window, it is judged as an internal equipment malfunction.

[0010] Furthermore, the prediction logic in step S4 is specifically as follows: If the external delay is determined to be abnormal and no internal equipment malfunction has occurred, the normal quality degradation model is invoked to calculate the natural quality loss at standard cold chain temperature caused by the extended time. If the internal equipment is determined to be faulty, the accelerated quality degradation model is invoked, using the real-time abnormal temperature change rate of the faulty zone as the core parameter to calculate the extreme survival time window of the goods; if the expected arrival time is longer than this time window, the expected remaining quality value is determined to be substandard.

[0011] Furthermore, the multi-dimensional cost logic for comprehensively evaluating each candidate transit point in S6 includes a dynamic weight adjustment step: For each candidate transit point, the costs of detours, warehousing and handling fees, and penalties for breach of contract are extracted as evaluation factors. If the current vehicle's trigger condition is an external delay anomaly, then the distance detour cost and the warehousing and unloading fee rate cost are given higher calculation weights. If the current vehicle's trigger condition is an internal equipment malfunction, then the distance detour cost and the fault repair service response capability of the candidate transfer point are assigned the highest calculation weight. And select the optimal transit point based on the weight.

[0012] Furthermore, it also includes step S7: recurrence logic after transit. After the vehicle arrives at the optimal transit point, the cloud server receives the warehouse entry confirmation instruction from the transit point and the vehicle maintenance status after unloading. After the abnormal situation is resolved or the cloud server dispatches a backup cold chain vehicle to the transit point, the cloud server regenerates the remaining driving route from the transit point to the final destination and issues a recurrence outbound instruction.

[0013] A cloud-based, globally controlled dynamic transfer and scheduling system for cold chain logistics includes: Global task scheduling module: used for initial task allocation and route distribution; Multi-concurrent data receiving module: used to receive and parse data packets uploaded by multiple cold chain vehicles in real time, and has the function of breaking point data synchronization processing; Anomaly detection and prediction module: used to perform dual-track detection of road delays and equipment failures, and to extrapolate the expected remaining quality value of the cargo; Cloud-based transit arbitration module: When an alert is triggered, it performs a global scan of candidate transit points, multi-dimensional cost assessment and matching; and when there are resource conflicts among multiple vehicles, it performs resource priority allocation based on the cargo's maximum survival time. Node collaborative communication module: used to send resource locking requests and warehouse entry reservations to the optimal transit point, and to send new navigation routes to the vehicle terminal.

[0014] The present invention provides a dynamic transfer and scheduling method and system for cold chain logistics based on cloud-based global coordination, which, compared with the prior art, has the following outstanding substantive features and significant progress: 1. This invention no longer relies solely on temperature exceedance alarms, but instead introduces a dynamic degradation model for cargo quality. By leveraging cloud computing power, real-time abnormal temperatures and remaining travel time are input into the model for extrapolation. Before cargo damage occurs, the model can predict its quality status upon delivery, thus buying time for emergency dispatch.

[0015] 2. This invention categorizes anomalies into external delay anomalies and internal equipment anomalies, and applies different attenuation models accordingly. When locking in a transit point, the system can dynamically adjust the weights of evaluation factors based on the anomaly type: for external delays, the focus is on evaluating the economics of distance and warehousing costs; for internal faults, the highest weight is assigned to fault repair response capability. This differentiated adaptive strategy ensures that each scheduling is the optimal solution for the current scenario.

[0016] 3. The scheduling core of this invention resides on a cloud server, which not only monitors the controlled vehicles but also simultaneously scans the real-time available cold storage capacity and operational status of candidate transfer points across the entire network. When a transfer command is triggered, a cold storage resource locking request is directly sent to the optimal transfer point via the cloud, achieving seamless collaboration of the Internet of Things and completely solving the delay problem caused by drivers blindly searching for cold storage in the traditional model. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the normal operation of the present invention; Figure 3 This is a flowchart illustrating the external delay process of this invention; Figure 4 This is a flowchart of the internal equipment malfunction process of this invention; Figure 5 This is a flowchart illustrating a complex scenario in this invention. Detailed Implementation

[0018] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0019] like Figure 1 As shown: The method of this invention runs on a cloud server and achieves global control through data interaction with the on-board terminals of multiple cold chain transport vehicles and the management systems of cold storage facilities in various locations. Its specific steps include: S1: Obtain transportation task information for cold chain transport vehicles, generate initial driving routes for each vehicle, and send them to the corresponding vehicle terminals; the cloud server acts as the scheduling brain, receiving global cold chain transportation orders. Before departure, the system includes transportation task information such as departure points, destinations, preset acceptance quality thresholds for the goods to be picked up (e.g., the core temperature of chilled meat upon delivery must not exceed -15°C, shelf-life loss must not exceed 20%), and the target delivery time window. Combining existing map data, a multi-objective optimization algorithm is used to generate the initial driving route with the shortest overall time, and this route is then sent to the vehicle terminals.

[0020] S2: Receives real-time status data messages uploaded by each vehicle terminal through the wireless communication network, and simultaneously obtains dynamic road conditions and weather data of the road section ahead of each vehicle's current location; while the vehicle is in motion, the vehicle IoT gateway collects GPS location, real-time temperature and humidity of each independent temperature zone, and operating condition codes of the refrigeration compressor at high frequency.

[0021] The S2 system also includes breakpoint resume logic to address communication blind spots: Considering vehicles may enter mountainous areas or long tunnels where communication is limited, the system incorporates breakpoint resume logic. If the cloud server does not receive a message within a given period, it determines the vehicle is out of contact. During this time, the vehicle terminal writes the data, along with a precise timestamp, to its local non-volatile memory. Upon restoration of communication, the vehicle terminal uses a high-priority channel to retransmit a set of historical data. The cloud then fills in the data gaps and refits the cargo temperature change curve for that period, eliminating the risk of misjudgment caused by monitoring blind spots.

[0022] S3: For any controlled vehicle, determine in real time and in parallel whether it has experienced external delays or internal equipment malfunctions. If dynamic traffic conditions or weather data cause the estimated arrival time to exceed a set threshold, it is judged as an external delay anomaly. Specifically, the system crawls the dynamic traffic congestion index and weather disaster warnings ahead of the vehicle in real time. If the recalculated estimated arrival time exceeds the delay tolerance value compared to the original plan, for example, by 2 hours, an external anomaly is triggered.

[0023] If the refrigeration equipment displays a fault status or the real-time temperature data of any zone deviates from the preset safe range and continues to exceed the judgment time window, it is judged as an internal equipment malfunction. Specifically, if the refrigeration equipment reports fault codes such as refrigerant leakage or compressor shutdown, or if the real-time temperature of a certain temperature zone rises abnormally and persists for a certain period of time, it is judged as a serious internal equipment malfunction.

[0024] In this invention, to achieve precise control over the cold chain transportation process, a dynamic transfer scheduling mathematical model based on multi-source state data is established. Specific parameter definitions are as follows: Current predicted arrival time at destination Preset arrival time tolerance threshold : Status parameters of refrigeration equipment, with values ​​of {0,1} (0 represents normal, 1 represents fault). Real-time temperature inside the carriage Preset safe temperature range : Initial quality of goods The minimum quality threshold for acceptance. Predicting the remaining quality upon arrival The cloud server uses the acquired real-time messages to perform parallel calculations of the external delay anomaly indication function. and internal device anomaly indication function : (1) The formula for judging external delay anomalies is: The formula for determining internal equipment malfunctions is: when or When any value is 1, the system determines that an anomaly has occurred and automatically triggers the subsequent quality deduction logic.

[0025] S4: When a vehicle is determined to have an anomaly, the cloud server simulates and extrapolates the estimated remaining quality value of the cargo when it arrives at its destination, based on the anomaly type, the current remaining driving time, and the real-time temperature in the status data message. The specific logic is as follows: If the delay is determined to be an external anomaly and no internal equipment malfunction occurs, the normal quality degradation model is invoked to calculate the natural quality loss at standard cold chain temperature due to the extended time. This is because the refrigeration system of the cargo compartment is still operating when only an external delay anomaly occurs. The system substitutes the extended estimated arrival time into the natural loss function at the baseline cold chain temperature to calculate the natural quality loss of the goods due to being stranded on the road.

[0026] If an internal equipment malfunction is detected, the accelerated quality degradation model is invoked. The real-time abnormal temperature change rate of the affected zone is used as the core parameter to calculate the cargo's critical survival time window. If the estimated arrival time exceeds this window, the estimated remaining quality value is deemed substandard. This is because when an internal equipment malfunction occurs, the temperature in the affected zone, lacking cooling capacity, rises rapidly. The system captures the temperature rise slope of that zone as a key variable, establishes an accelerated degradation curve, and calculates the critical survival time window before the cargo completely spoils, for example, only 90 minutes remaining. If the estimated arrival time is significantly longer than 90 minutes, the cargo will inevitably spoil.

[0027] For different anomaly types, the system dynamically calls segmented quality prediction functions. Predict the remaining quality upon arrival; (1) If it is determined that only external delays have occurred, i.e. , =0, the system invokes the normal exponential decay model: (2) If it is determined that an internal equipment malfunction has occurred, i.e. The system first determines the current rate of temperature change. Calculate the cargo's maximum survival time : The following accelerated decay model was used to calculate... : in, This represents the accelerated decay coefficient that changes with real-time temperature. If the expected arrival time exceeds the limit survival time, the system directly determines that the quality is substandard.

[0028] S5: If the remaining quality value is expected to be lower than the preset acceptance quality threshold, an emergency transfer instruction will be generated for the vehicle; and based on the current location of the vehicle, the real-time available cold storage capacity and loading and unloading queue status of each candidate transfer point that is synchronously connected to the cloud database will be scanned. S6: Comprehensively evaluate the optimal transit point, send a cold storage resource lock request to the optimal transit point, and after successful lock, generate a new driving route containing the transit node and send it to the vehicle terminal of the corresponding vehicle.

[0029] Specifically, a comprehensive evaluation of the multi-dimensional cost logic of each candidate transit point is conducted, including a dynamic weight adjustment step: For each candidate transit point, the costs of detours, warehousing and handling fees, and penalties for breach of contract are extracted as evaluation factors. If the current vehicle's trigger condition is an external delay anomaly, then the distance detour cost and the warehousing and unloading fee rate cost will be given higher calculation weights. This scheme prioritizes economic benefits because if it is simply a traffic jam, since there is no immediate risk of severe spoilage of the goods, the system will increase the weight of the warehousing fee rate cost and the distance detour cost. Therefore, it will find the cheapest nearby cold storage with the shortest detour to temporarily store the goods, in order to reduce additional logistics expenses.

[0030] If the current vehicle's trigger condition is an internal equipment malfunction, then the distance detour cost and the fault repair service response capability of the candidate transfer point are given the highest calculation weight. This solution focuses on reducing losses because if it is a cold-running failure, the system will greatly increase the weight of the fault repair service response capability and distance and time consumption of the transfer point, and even ignore the high temporary storage fees. This method is used to directly find the nearest transfer station that has cold-running repair capabilities or a backup vehicle.

[0031] The system selects the optimal transit point based on the weight of the nodes. After selecting the optimal node, the cloud sends a warehouse location lock request and a loading and unloading queue reservation directly to the warehouse management system of the transit station through the system, ensuring that the vehicle can unload as soon as it arrives without waiting. The new route is then sent to the vehicle navigation system.

[0032] When the deduction results At that time, the system selects from the candidate node set Searching for the comprehensive cost function Minimum optimal relay node : in, The weight allocation is dynamically adjusted based on the anomaly type: (1) In the event of an abnormal external delay, the system will implement an economic benefit-first mode, with the weighting biased towards distance cost. and warehousing rates : Set the weighting coefficient here. This is to minimize losses during routine operations.

[0033] (2) In the event of internal equipment failure, the system executes the emergency rescue priority mode, with the weighting biased towards the emergency repair response capability of the node. : Set the weighting coefficient here. This ensures that emergency repairs or temporary storage are completed before the goods spoil.

[0034] Step S7: Re-entry Logic After transit, once the vehicle arrives at the optimal transit point, the cloud server receives the warehousing confirmation instruction from the transit point and the vehicle's maintenance status after unloading. After the abnormal situation is resolved or the cloud server dispatches a backup cold chain vehicle to the transit point, the cloud server regenerates the remaining driving route from the transit point to the final destination and issues a re-entry instruction. This step is a system closure completion step and needs to continue operating after transit. After the goods are safely stored, the cloud system suspends the status of that batch of goods. The system continuously monitors external clearance signals, such as the highway reopening or internal clearance signals, such as the vehicle's refrigeration unit being repaired at the maintenance station. Once the conditions are met, the cloud replans the remaining route, issues a re-entry instruction, and ultimately transports the goods to their destination.

[0035] A cloud-based, globally controlled dynamic transfer and scheduling system for cold chain logistics, characterized in that it includes components deployed on a cloud server: Global task scheduling module: used for initial task allocation and route distribution; Multi-concurrent data receiving module: used to receive and parse data packets uploaded by multiple cold chain vehicles in real time, and has the function of breaking point data synchronization processing; Anomaly detection and prediction module: used to perform dual-track detection of road delays and equipment failures, and to extrapolate the expected remaining quality value of the cargo; Cloud-based transit arbitration module: When an alert is triggered, it performs a global scan of candidate transit points, multi-dimensional cost assessment and matching; and when there are resource conflicts among multiple vehicles, it performs resource priority allocation based on the cargo's maximum survival time. Node collaborative communication module: used to send resource locking requests and warehouse entry reservations to the optimal transit point, and to send new navigation routes to the vehicle terminal.

[0036] Example 1 (Routine Operation) A vehicle fully loaded with chilled beef is scheduled to travel from City A to City B. The system's preset acceptance quality requirements are that the meat temperature be maintained between -2°C and 2°C.

[0037] During the journey, real-time data from the ETC gantry and Gaode Maps both indicated that the road ahead was clear, and the estimated arrival time was a normal 4 hours. Meanwhile, the real-time temperature of Zone A, transmitted from the vehicle terminal, remained stable at 0°C.

[0038] The system executes steps S3 and S4, predicting that the remaining quality value of the goods upon arrival at the destination is as high as 95%, far exceeding the 80% acceptance threshold. The system determines everything is normal and does not intervene; the vehicle successfully arrives in City B along the initial route, completing the delivery.

[0039] Example 2 (Chain Collision and Traffic Jam) A scenario is set up where a vehicle encounters a multi-vehicle collision on a highway, resulting in a two-way closure of the highway. The cloud receives congestion data and recalculates the estimated arrival time, which jumps dramatically from the original 3 hours to 12 hours.

[0040] At this point, the onboard refrigeration unit was operating normally. The cloud-based model of normal quality degradation was invoked. The simulation results showed that although the refrigeration was normal, the additional 12 hours of enclosed and bumpy ride would cause some of the meat's freshness to deteriorate, resulting in a final quality level of 75%, below the 80% threshold.

[0041] The system executes the S6 scheduling strategy, entering an economy-first mode. Near the nearest highway exit, the system scans for two available transit depots, C and D. Depot C has higher storage fees, while depot D is slightly further away but cheaper. The system assigns a high weight to cost and ultimately selects depot D, which has the lowest overall cost, as the optimal transit point. The system then guides the vehicle off the highway to depot D for temporary storage, thus avoiding breach of contract penalties.

[0042] Example 3 (Sudden shutdown of the refrigeration unit) The vehicle was traveling on a clear national highway when it suddenly received a "compressor overload shutdown" fault code from the vehicle's IoT gateway. Data showed that the temperature in the freezer compartment had suddenly risen from -18°C to -5°C within 15 minutes.

[0043] The cloud system immediately detected an internal equipment malfunction and invoked an accelerated quality degradation model. Because the system detected an extremely steep temperature rise, it calculated that the critical survival window for high-value frozen seafood was only 45 minutes. At this point, the destination was still a 3-hour drive away.

[0044] The system executed the S6 scheduling strategy, entering an emergency rescue mode. A system scan of the surrounding area revealed that Warehouse E was 20 minutes away with extremely high storage fees, but had an on-site refrigeration unit repair workshop; Warehouse F was 40 minutes away with cheaper storage fees but lacked repair capabilities. The system forcibly assigned the highest weight to repair capabilities, ignoring costs, and instantly locked onto Warehouse E. Vehicle alarms sounded, and the driver followed the new navigation to transport the goods to Warehouse E within 20 minutes. The goods were quickly transferred to the freezer compartment of Warehouse E, and repair personnel simultaneously began emergency repairs on the vehicle, successfully averting significant economic losses.

[0045] Example 4 (Blind Spot Network Disconnection and Multi-Vehicle Contention Coordination) This embodiment illustrates an extreme environment scenario, where vehicle X, carrying frozen dumplings, and vehicle Y, carrying high-value imported vaccines, are simultaneously traveling in a remote mountainous area during thunderstorms. Vehicle X enters a 5-kilometer-long tunnel, where it loses signal. The cloud-based system triggers a resume transmission mechanism, and after vehicle X exits the tunnel, it retransmits the lost 5 minutes of data.

[0046] Cloud-based analysis of supplementary data and real-time data from vehicle Y revealed that vehicle X experienced a cold-start malfunction due to a lightning strike, and the road where vehicle Y was located was blocked by a mudslide. Both vehicles were predicted to face a quality exceeding the standard crisis, and system scanning revealed that there was only one small transfer station, Z, nearby, whose available warehouse capacity and unloading platform could only meet the immediate needs of one vehicle.

[0047] At this point, the anti-conflict scheduling logic in the claim is triggered. The cloud calculates the extreme survival time windows for the two vehicles: the frozen dumplings in vehicle X have a survival window of 3 hours due to the better insulation of the compartment; the vaccines in vehicle Y are extremely sensitive to temperature, and the survival window is only 1 hour.

[0048] The cloud immediately conducted a global arbitration, prioritizing the allocation of resources from transfer station Z to vehicle Y and sending a lock request to station Z. Simultaneously, vehicle X, with a longer survival time, was guided to a more distant, secondary backup transfer station, thus minimizing losses across the entire logistics system.

[0049] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A cloud-based, globally controlled dynamic transfer scheduling method for cold chain logistics, characterized in that: The method is applied to a cloud server and includes the following steps: S1: Obtain transportation task information for cold chain transport vehicles, generate initial driving routes for each vehicle, and send them to the corresponding vehicle terminals; S2: Receives real-time status data messages uploaded by each vehicle terminal through the wireless communication network, and simultaneously obtains dynamic road conditions and weather data of the road section ahead of each vehicle at its current location; S3: For any controlled vehicle, determine in real time and in parallel whether it has experienced external delays or internal equipment malfunctions. The specific judgment in S3 is as follows: If dynamic road conditions or weather data cause the estimated arrival time to exceed the set threshold, it is judged as an external delay anomaly; If the refrigeration equipment displays a fault or the real-time temperature data of any zone deviates from the preset safe range and continues to exceed the judgment time window, it is judged as an internal equipment malfunction. S4: When a vehicle is determined to have an anomaly, the cloud server simulates and extrapolates the estimated remaining quality value of the cargo when it arrives at its destination, based on the anomaly type, the current remaining driving time, and the real-time temperature in the status data message. The prediction logic in step S4 is as follows: If the external delay is determined to be abnormal and no internal equipment malfunction has occurred, the normal quality degradation model is invoked to calculate the natural quality loss at standard cold chain temperature caused by the extended time. If the internal equipment is determined to be faulty, the accelerated quality degradation model is invoked, and the real-time abnormal temperature change rate of the faulty zone is used as the core parameter to calculate the extreme survival time window of the goods. If the expected arrival time is longer than the time window, the expected remaining quality value is determined to be substandard. in: Predict the remaining quality upon arrival; : The initial quality of the goods; : The currently predicted time of arrival at the destination; : The acceleration decay coefficient as real-time temperature changes; The maximum survival time of goods; Real-time temperature inside the carriage; Maximum safe temperature; Current rate of temperature change; S5: If the remaining quality value is expected to be lower than the preset acceptance quality threshold, an emergency transfer instruction will be generated for the vehicle; and based on the current location of the vehicle, the real-time available cold storage capacity and loading and unloading queue status of each candidate transfer point that is synchronously connected to the cloud database will be scanned. S6: Evaluate the optimal transit point, send a cold storage resource lock request to the optimal transit point, and after successful lock, generate a new driving route containing the transit node and send it to the vehicle terminal of the corresponding vehicle. The evaluation in S6 includes the following steps: For each candidate transit point, the costs of detours, warehousing and handling fees, and penalties for breach of contract are extracted as evaluation factors. If the current vehicle's trigger condition is an external delay anomaly, then the distance detour cost and the warehousing and unloading fee rate cost are given higher calculation weights. If the current vehicle's trigger condition is an internal equipment malfunction, then the distance detour cost and the fault repair service response capability of the candidate transfer point are assigned the highest calculation weight. And select the optimal transit point based on the weight.

2. The cloud-based global control method for dynamic transfer scheduling of cold chain logistics according to claim 1, characterized in that: The transportation task information in S1 includes preset acceptance quality thresholds and target delivery time windows for each departure point, destination, and cargo.

3. The cloud-based global control method for dynamic transfer and scheduling of cold chain logistics according to claim 1, characterized in that: The status data message in S2 includes vehicle location information, real-time temperature and humidity data of each compartment in the carriage, and the operating status of the refrigeration equipment.

4. The cloud-based global control method for dynamic transfer and scheduling of cold chain logistics according to claim 1, characterized in that: S2 also includes breakpoint resume logic to handle communication blind spots: If the cloud server does not receive status data messages from the vehicle terminal within the set period, the vehicle will be marked as out of contact. Once the vehicle regains communication, the cloud server receives a set of historical status data with timestamps from the vehicle terminal during the period of loss of contact, and uses the set of historical status data to calibrate the current baseline of cargo quality degradation.

5. The cloud-based global control method for dynamic transfer scheduling of cold chain logistics according to claim 1, characterized in that: It also includes step S7: post-transfer recurrence logic: after the vehicle arrives at the optimal transfer point, the cloud server receives the warehouse entry confirmation instruction from the transfer point and the vehicle maintenance status after unloading. Once the abnormal situation is resolved or the cloud server dispatches a backup cold chain vehicle to the transit point, the cloud server will regenerate the remaining driving route from the transit point to the final destination and issue a re-delivery instruction.

6. A cloud-based, globally controlled dynamic transfer and scheduling system for cold chain logistics, used to execute the method described in claim 1, characterized in that: Including those deployed on cloud servers: Global task scheduling module: used for initial task allocation and route distribution; Multi-concurrent data receiving module: used to receive and parse data packets uploaded by multiple cold chain vehicles in real time, and has the function of breaking point data synchronization processing; Anomaly detection and prediction module: used to perform dual-track detection of road delays and equipment failures, and to extrapolate the expected remaining quality value of the cargo; Cloud-based transit arbitration module: When an alert is triggered, it performs a global scan of candidate transit points, multi-dimensional cost assessment and matching; and when there are resource conflicts among multiple vehicles, it performs resource priority allocation based on the cargo's maximum survival time. Node collaborative communication module: used to send resource locking requests and warehouse entry reservations to the optimal transit point, and to send new navigation routes to the vehicle terminal.

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