Cold-chain logistics collaborative management system based on cloud computing

Abstract

The invention discloses a cold-chain logistics collaborative management system based on cloud computing, and the system comprises a collaborative modeling module which builds a multi-body cold-chain collaborative management model at a cloud end; the acquisition and preprocessing module is used for acquiring logistics related data and executing preprocessing; the resource allocation module is used for executing consistency verification to generate a resource allocation scheme and generating a soft lock; the scheduling optimization module is used for calling an improved jellyfish search algorithm to generate a cold chain transportation plan; the plan issuing and state updating module issues the transportation plan difference to the execution terminal; the risk optimization module is used for calculating temperature control safety margin and regenerating a transportation plan; the traceability recording module is used for writing the transportation plan version and the resource lock state into a tamper-resistant evidence chain and recording a receipt to form a traceability chain; and the priority adjustment module is used for adjusting the scheduling priority according to the multi-agent collaborative performance indicators. According to the invention, the cooperation efficiency and plan stability of cold-chain transportation scheduling are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cloud computing collaborative management and intelligent transportation scheduling, and particularly relates to a cold chain logistics collaborative management system based on cloud computing. BACKGROUND

[0002] With the continuous growth of cold chain logistics demand and the strict requirements of fresh food, e-commerce and pharmaceutical industry on temperature control and on-time delivery throughout the transportation, multi-agent collaborative management and intelligent scheduling optimization technology based on cloud computing have attracted widespread attention. Existing cold chain logistics collaboration schemes mostly rely on manual scheduling or simple transportation management systems for order allocation and path planning, but in actual application, the following problems are generally present:

[0003] There is a serious data island among multi-agents, and the data standards of consignors, carriers, warehousing parties and regulators are not unified, and the update frequencies are inconsistent, resulting in delayed resource state information and frequent conflicts, making it difficult to achieve global optimization scheduling; the data collected during cold chain transportation, such as vehicle position, compartment temperature and humidity, and platform occupation, have many abnormal values and different timestamps, and existing standardized processing methods cannot achieve high-precision synchronization and missing compensation, affecting the accuracy of transportation state perception; transportation plan optimization under multiple constraints still mainly relies on static algorithms or simplified heuristic algorithms, which are difficult to cope with sudden insert orders, road congestion and temperature control out-of-bounds risks, resulting in a lack of dynamic updating capability of transportation plans; existing cold chain traceability is mostly based on single-point database records, lacks support for tamper-proof evidence chain, and is difficult to meet the needs of full-chain compliance audit and ex post facto accountability; there is a lack of multi-agent collaborative performance evaluation mechanism, and the scheduling priority cannot be dynamically adjusted according to the on-time rate, temperature control compliance rate and receipt timeliness, resulting in a lack of continuous optimization of resource utilization efficiency and transportation service level.

[0004] Therefore, how to provide a cold chain logistics collaborative management system based on cloud computing is a problem that those skilled in the art need to solve. SUMMARY

[0005] One object of the present application is to provide a cold chain logistics collaborative management system based on cloud computing, which fully integrates multi-agent collaborative modeling, data acquisition standardization, consistency verification and resource allocation, improved jellyfish search algorithm optimization scheduling, dynamic risk iterative update, blockchain traceability and collaborative performance feedback mechanism, realizes real-time perception, intelligent scheduling and traceable management throughout the cold chain transportation, and has the advantages of high resource utilization rate, dynamic feasible transportation plan, high temperature control compliance rate and on-time rate.

[0006] According to the cold chain logistics collaborative management system based on cloud computing of the present application, the cold chain logistics collaborative management system based on cloud computing comprises: a collaborative modeling module, configured to establish a multi-agent cold chain collaborative management model on the cloud, and configure each agent role, data domain and shared data contract; a collection and preprocessing module configured to collect vehicle records, platform records, order records, and supervision records, and perform standardized processing on the cloud to form a cloud collaborative dataset; a resource allocation module configured to perform consistency checking and priority calculation on the cloud collaborative dataset, generate a resource allocation scheme, and generate a soft lock; a scheduling optimization module configured to upgrade the soft lock to a hard lock after confirmation by each subject, and call an improved jellyfish search algorithm to generate a cold chain transportation plan under temperature control, time window, and capacity constraints; a plan issuing and state updating module configured to issue a transportation plan difference to an execution terminal, collect a transportation process receipt, and update the cloud collaborative dataset; a risk optimization module configured to calculate a temperature control safety margin, and trigger an improved jellyfish search algorithm to iteratively update and regenerate a transportation plan when a transportation abnormal event is detected; a traceability recording module configured to write a transportation plan version and a resource lock state into an unalterable evidence chain, and record an execution receipt to form a traceability chain; a priority adjustment module configured to update multi-subject collaborative performance indicators according to on-time rate, temperature control compliance rate, and receipt timeliness, and adjust scheduling priority.

[0007] Optionally, the modules are implemented through the following methods: S1, establishing a multi-subject cold chain collaborative management model on the cloud, and configuring roles, data domains, and shared data contracts of each subject; S2, collecting vehicle records, platform records, order records, and supervision records, and performing standardized processing on the cloud to form a cloud collaborative dataset; S3, performing consistency checking and priority calculation on the cloud collaborative dataset, outputting a resource allocation scheme, and generating a soft lock; S4, upgrading the soft lock to a hard lock after confirmation by each subject, and calling an improved jellyfish search algorithm to generate a cold chain transportation plan under temperature control, time window, and capacity constraints; S5, issuing a transportation plan difference to an execution terminal, collecting a transportation process receipt, and updating the cloud collaborative dataset; S6, calculating a temperature control safety margin based on the updated cloud collaborative dataset, and triggering an improved jellyfish search algorithm to iteratively update and regenerate a transportation plan when a transportation abnormal event is detected; S7, writing a transportation plan version and a resource lock state into an unalterable evidence chain, and recording an execution receipt to form a traceability chain; S8, updating multi-subject collaborative performance indicators according to on-time rate, temperature control compliance rate, and receipt timeliness, and adjusting scheduling priority.

[0008] Optionally, the S1 specifically includes: S11, create a multi-agent collaborative space in the cloud, and assign unique agent instance identifiers to the shipper, carrier, warehousing party, and regulatory party respectively; S12, each agent establishes a user table and a role table, and configures agent roles, operation permissions, and data access ranges; S13, define order data fields, transport capacity data fields, platform operation data fields, environmental monitoring data fields, and compliance audit data fields, and set the data structure, field type, timestamp precision, and update frequency of each data field; S14, configure a shared data contract, including a shared field list, a minimum data granularity, a synchronization period, a visible range, and a use validity period; S15, establish an access control policy, set role-based access rules, cross-agent data authorization processes, and data masking rules; S16, generate agent digital certificates and keys, and enable authentication, authorization, and request signature verification for interface calls; S17, allocate independent namespaces to each agent instance in the cloud, create database instances and message queue channels, set computing resource and storage resource quotas, and enable isolation strategies; S18, configure audit logs and configuration change log recording strategies, generate log entries for access behavior, data writing, and policy adjustment, and access evidence chain writing interfaces.

[0009] Optionally, the S2 specifically includes: S21, collect real-time position, driving state, vehicle capacity parameters, vehicle temperature, vehicle humidity, and door opening and closing events of the refrigerated vehicle through the vehicle terminal, and generate a vehicle record; S22, collect cold storage loading and unloading plans, platform occupancy, platform temperature control levels, temperature control partition states, and operation completion times through a warehouse data interface, and generate a platform record; S23, collect cold chain transportation order numbers, categories, quantities, volumes, temperature control levels, arrival time requirements, order priorities, and special temperature control requirements through an order data interface, and generate an order record; S24, collect road traffic conditions, weather information, cold chain sampling records, and temporary regulatory instructions through a regulatory data interface, and generate a regulatory record; S25, aggregate the vehicle record, platform record, order record, and regulatory record to form a collection data set; S26, synchronize the collection data set by timestamp, filter abnormal data that exceeds the preset temperature interval, humidity interval, or position jump threshold, and interpolate missing data to form a cold chain transportation standardized data record; S27, write the cold chain transportation standardization data record into the cloud collaborative dataset, and establish an index with order number, vehicle identification, platform number, and time window as key values.

[0010] Optionally, the S3 specifically comprises: S31, read the vehicle record, platform record, order record, and supervision record in the cloud collaborative dataset; S32, compare the arrival time requirement in the order record with the driving state time in the vehicle record, and the operation completion time in the platform record, identify time mismatch records, and generate a time conflict set; S33, compare the quantity and volume in the order record with the capacity parameters in the vehicle record, identify overload records, and generate a capacity conflict set; S34, compare the temperature control level in the order record with the vehicle compartment temperature interval in the vehicle record and the temperature control partition state in the platform record, identify mismatch records, and generate a temperature control conflict set; S35, merge the time conflict set, the capacity conflict set, and the temperature control conflict set to form a consistency conflict set; S36, calculate the priority weight of each conflict record in the consistency conflict set, including: assigning a basic weight according to the order priority in the order record, weighting and correcting the overdue risk and default risk according to the arrival time requirement in the order record, adjusting the weight value according to the temporary supervision instruction and cold chain sampling record in the supervision record, and generating a conflict resolution priority list; S37, allocate vehicles, platforms, and time resources according to the conflict resolution priority list, and output a resource allocation scheme; S38, write the resource allocation scheme into the cloud collaborative dataset and generate a soft lock record.

[0011] Optionally, the improved jellyfish search algorithm specifically comprises: Encode the transportation task, vehicle, and platform in the resource allocation scheme into jellyfish individuals, which include order assignment sequence, vehicle path sequence, platform operation time period, and loading and unloading sequence; generate a jellyfish individual group by combining heuristic seeding and random initialization, and write the task fragments corresponding to the soft lock and the hard lock into the individual as fixed fragments; Standardize the total transportation time of each jellyfish individual, the temperature control safety margin deviation, the platform occupation conflict frequency, the vehicle capacity utilization rate, the planned change amount, and the supervision risk; weight and sum according to the benchmark weight, and take the weighted result as the fitness value of the jellyfish individual; Performing ocean current drift update, referring to the order assignment sequence and the platform operation period of the individual whose current fitness meets the preset preferred condition, moving the global position of the population, and keeping the position projection of the segment involving the hard lock unchanged; performing active foraging update, preferentially adjusting the order whose temperature control level meets the preset threshold or whose temperature control safety margin is less than the preset safety threshold, generating candidate individuals by using local operations of path insertion, adjacent exchange and platform period translation, and correcting the time window, capacity, platform occupation and partition temperature control constraints of the candidate individuals in turn, and eliminating the candidate individuals that do not meet the constraints; Adopting an adaptive weight mechanism to dynamically adjust the population search parameters, adjusting the corresponding index weight when detecting that the platform congestion rate exceeds the preset threshold, the temperature control violation quantity exceeds the preset threshold or the fitness improvement amplitude is lower than the preset improvement value; reserving the elite individuals whose fitness values meet the preset preferred condition, and performing disturbance suppression on the individual whose plan change amount exceeds the threshold, eliminating or reverting the individual to the last feasible state; Terminating the search when the number of iterations reaches the preset upper limit or the fitness improvement in continuous multiple iterations is lower than the threshold, and outputting the cold chain transportation plan corresponding to the jellyfish individual whose fitness meets the preset preferred condition.

[0012] Optionally, the S5 specifically includes: S51, comparing the cold chain transportation plan with the previous version of the cold chain transportation plan, extracting order assignment changes, vehicle path changes and platform operation period changes, and forming a differential data package; S52, delivering the differential data package to the vehicle terminal, the warehouse terminal and the monitoring terminal through the cloud interface; S53, receiving the driving state receipt, the temperature and humidity receipt and the loading and unloading completion receipt uploaded by the vehicle terminal; S54, receiving the platform occupation receipt and the operation completion receipt uploaded by the warehouse terminal; S55, writing the driving state receipt, the temperature and humidity receipt, the platform occupation receipt and the operation completion receipt into the cloud collaborative data set in chronological order, and updating the order execution state, the vehicle running state and the platform occupation state.

[0013] Optionally, the S6 specifically includes: S61, reading the vehicle running state, the car temperature, the car humidity and the order arrival time requirement in the updated cloud collaborative data set, calculating the margin from the difference between the car temperature and the upper limit of the order target temperature control, and the difference between the car temperature and the lower limit of the order target temperature control, and calculating the difference margin of the loading, driving and unloading time periods respectively, and selecting the difference margin for judgment as the temperature control safety margin according to the preset safety judgment rule; S62, compare the temperature control safety margin with a preset temperature control safety threshold, identify transportation tasks with a temperature control safety margin less than the preset threshold, and generate a temperature control risk list; S63, receive real-time road congestion information uploaded by a road traffic monitoring interface, identify path segments with an average travel speed lower than a preset speed threshold, and generate a congestion path list; S64, receive supervision interface uploaded supervision order information, extract the arrival time requirement, temperature control level and special constraints of the new order, and generate an order list; S65, determine whether the temperature control risk list, congestion path list and order list are empty, and only when any list is not empty, trigger the improved jellyfish search algorithm to perform iterative update; S66, write the new transportation plan generated by the iterative update into the cloud collaborative dataset, and assign a new plan version identifier.

[0014] Optionally, the S7 specifically includes: S71, pack the latest generated transportation plan version, the corresponding soft lock record and the hard lock record into block data; S72, calculate the hash value of the block data using a preset hash algorithm and write it into the blockchain ledger to generate an unalterable transportation plan version record; S73, index the order number, vehicle identifier, platform number and plan version number to form a searchable resource lock state mapping; S74, receive travel state receipts, temperature and humidity receipts, loading and unloading completion receipts and platform occupation receipts uploaded by the execution terminal, and associate them to the corresponding order and plan version in chronological order; S75, write the associated receipts into the blockchain ledger to generate a receipt block and link it to the plan version block to form a traceable chain; S76, provide a query interface to support retrieving transportation plans, resource lock states and execution receipts according to order numbers, vehicle identifiers or plan version numbers.

[0015] Optionally, the S8 specifically includes: S81, compare the actual arrival time of each transportation task with the arrival time requirement in the order record, count the number of tasks that meet the time requirement and the total number of tasks, calculate the on-time completion ratio, and generate the on-time rate index of each subject; S82, compare the temperature and humidity of the carriage recorded in the transportation process with the target temperature control interval in the order record, count the duration within the target temperature control interval and the total transportation time, calculate the temperature control satisfaction ratio, and generate the temperature control compliance rate index of each subject; S83, compare the time of each execution terminal uploading the receipt with the transport plan node time, count the number of uploaded receipts within the preset time window and the total number of receipts, calculate the receipt timeliness ratio, and generate a receipt timeliness index; S84, the punctuality rate index, the temperature control compliance rate index and the receipt timeliness index are aggregated according to the subject to form a collaborative performance evaluation result; S85, compare the collaborative performance evaluation result with the preset performance threshold, when any index is lower than the preset performance threshold, reduce the scheduling priority of the subject, and when all indexes are greater than or equal to the preset performance threshold, increase the scheduling priority of the subject; S86, write the adjusted scheduling priority into the cloud collaborative data set, and call the updated scheduling priority in the next round of resource allocation and transport plan generation step.

[0016] The beneficial effects of the present application are: The present application establishes a multi-agent cold chain collaborative management model in the cloud, configures each subject role, data domain and shared data contract, adopts a unified data structure and timestamp synchronization mechanism to standardize the writing of vehicle records, platform records, order records and supervision records into the cloud collaborative data set, realizes the real-time sharing of cross-agent resource state information, introduces an improved jellyfish search algorithm in the scheduling link, encodes the transportation task, vehicle and platform as individuals, generates a group through heuristic seeding and random initialization, combines the total transportation time, temperature control safety margin deviation, platform conflict frequency, vehicle capacity utilization and supervision risk to calculate a comprehensive score, dynamically adjusts the weight and executes the drift of the ocean current and active foraging update, generates a feasible transportation plan under the constraints of temperature control, time window and capacity, issues the plan in the execution link, real-time collects the driving state, car temperature and humidity and platform occupation receipt, calculates the temperature control safety margin based on the updated data, triggers algorithm iteration update when temperature control out-of-bounds, road congestion or supervision insertion is detected, ensures the dynamic feasibility of the plan, records the transportation plan version, soft lock and hard lock into the blockchain ledger and associates the execution receipt to form an unalterable traceability chain, realizes the whole process query, finally updates the multi-agent collaborative performance index according to the punctuality rate, temperature control compliance rate and receipt timeliness, adjusts the scheduling priority, realizes the continuous optimization of resource utilization and service level, and finally realizes the efficient operation and whole process traceability of the cold chain logistics collaborative management system based on cloud computing. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0018] Fig. 1A structural schematic diagram of a cold chain logistics collaborative management system based on cloud computing is provided for the present application. Fig. 2 A flow chart of a cold chain logistics collaborative management system based on cloud computing is provided for the present application. Fig. 3 A flow chart of a cold chain logistics collaborative management system based on cloud computing is provided for the present application. DETAILED DESCRIPTION

[0019] The present application will now be further described in greater detail in conjunction with the accompanying drawings, in which the drawings are simplified schematic diagrams and only schematically illustrate the basic structure of the present application, and thus only show the components relevant to the present application.

[0020] REFERENCE Figs. 1-3 A cold chain logistics collaborative management system based on cloud computing comprises: A collaborative modeling module is configured to establish a multi-agent cold chain collaborative management model on the cloud, configure each agent role, data domain and shared data contract; A collection and preprocessing module is configured to collect vehicle records, platform records, order records and supervision records, and perform standardized processing on the cloud to form a cloud collaborative dataset; A resource allocation module is configured to perform consistency verification and priority calculation on the cloud collaborative dataset, generate a resource allocation scheme and generate a soft lock; A scheduling optimization module is configured to upgrade the soft lock to a hard lock after confirmation by each agent, and call an improved jellyfish search algorithm to generate a cold chain transportation plan under temperature control, time window and capacity constraints; A plan issuing and state updating module is configured to issue the transportation plan difference to the execution terminal, collect the transportation process receipt and update the cloud collaborative dataset; A risk optimization module is configured to calculate the temperature control safety margin, and trigger the improved jellyfish search algorithm to iteratively update and regenerate the transportation plan when detecting a transportation abnormal event; A traceability record module is configured to write the transportation plan version and resource lock state into an unalterable evidence chain, and record the execution receipt to form a traceability chain; A priority adjustment module is configured to update the multi-agent collaborative performance indicators according to the on-time rate, temperature control compliance rate and receipt timeliness, and adjust the scheduling priority.

[0021] The application realizes the whole-process collaborative management from multi-agent modeling, data acquisition, resource allocation, transportation plan generation, execution feedback to traceability and performance optimization by constructing a system including a collaborative modeling module, an acquisition and preprocessing module, a resource allocation module, a scheduling optimization module, a plan issuing and state updating module, a risk optimization module, a traceability recording module and a priority adjustment module. The system structure is clear, and each module cooperates to run, so that the whole process of cold chain transportation can be monitored, dynamically optimized and traceable, the feasibility and execution efficiency of the transportation plan are improved, the temperature control standard and on-time delivery are ensured, and reliable technical support is provided for cold chain logistics collaboration.

[0022] In the embodiment, the modules are realized by the following method: S1, a multi-agent cold chain collaborative management model is established in the cloud, and the roles, data domains and shared data contracts of each agent are configured; S2, vehicle records, platform records, order records and supervision records are acquired, and standardized processing is performed in the cloud to form a cloud collaborative data set; S3, consistency verification and priority calculation are performed on the cloud collaborative data set, a resource allocation scheme is output, and a soft lock is generated; S4, after confirmation of each agent, the soft lock is upgraded to a hard lock, and an improved jellyfish search algorithm is called to generate a cold chain transportation plan under temperature control, time window and capacity constraints; S5, the transportation plan difference is issued to the execution terminal, the transportation process receipt is acquired, and the cloud collaborative data set is updated; S6, based on the updated cloud collaborative data set, the temperature control safety margin is calculated, and when a transportation abnormal event is detected, the improved jellyfish search algorithm is triggered to update and regenerate the transportation plan; S7, the transportation plan version and resource lock state are written into an unalterable evidence chain, and the execution receipt is recorded to form a traceability chain; S8, the multi-agent collaborative performance indicators are updated according to the on-time rate, the temperature control compliance rate and the timeliness of the receipt, and the scheduling priority is adjusted.

[0023] The application establishes a multi-agent collaborative management model in the cloud and configures roles, data domains and shared contracts; acquires and standardizes vehicle, platform, order and supervision data to form a cloud collaborative data set; performs consistency verification and priority calculation on the data set to generate a resource allocation scheme and a soft lock; after confirmation, an improved jellyfish search algorithm is called to generate a transportation plan; the plan difference is issued and the receipt is acquired to update the data; the temperature control safety margin is calculated, and the plan is iteratively optimized when risks or congestion, inserted orders are detected; write into a blockchain to form a traceability chain; dynamically adjust the scheduling priority according to the performance indicators, realize intelligent collaboration and optimization of the whole process of cold chain transportation.

[0024] In the embodiment, the S1 specifically includes: S11, a multi-agent collaborative space is created in the cloud, and unique agent instance identifiers are assigned to the consignor, the carrier, the warehousing party and the regulatory party respectively; S12, each agent establishes a user table and a role table, and configures agent roles, operation permissions and data access ranges; S13, order data domains, transport capacity data domains, platform operation data domains, environmental monitoring data domains and compliance audit data domains are defined, and the data structure, field type, timestamp precision and update frequency of each data domain are set; S14, a shared data contract is configured, including a shared field list, a minimum data granularity, a synchronization period, a visible range and a use validity period; S15, an access control strategy is established, and role-based access rules, cross-agent data authorization processes and data screening rules are set; S16, a subject digital certificate and key are generated, and authentication, authorization and request signature verification are enabled for interface calls; S17, independent namespaces are allocated to each agent instance in the cloud, database instances and message queue channels are created, computing resources and storage resource quotas are set and isolation strategies are enabled; S18, an audit log and a configuration change log recording strategy are configured, log entries are generated for access behavior, data writing and policy adjustment, and an evidence chain writing interface is accessed.

[0025] The application creates a multi-agent collaborative space in the cloud, assigns agent instance identifiers to the consignor, the carrier, the warehousing party and the regulatory party, establishes a user table and a role table, configures role permissions and data access ranges, defines order, transport capacity, platform operation, environmental monitoring and compliance audit data domains, configures data structure, field type, timestamp precision and update frequency, sets shared data contracts and access control strategies, generates agent digital certificates and keys, allocates namespaces, database instances and message channels and sets resource isolation strategies, records access and configuration change logs and accesses evidence chains, and realizes initialization of a multi-agent collaborative management model and construction of a safe operation environment.

[0026] In the embodiment, the S2 specifically includes: S21, the real-time position, driving state, vehicle capacity parameters, vehicle compartment temperature, vehicle compartment humidity and door opening and closing events of the refrigerated vehicle are collected through the vehicle-mounted terminal to generate a vehicle record; S22, the refrigeration warehouse loading and unloading plan, platform occupation, platform temperature control level, temperature control partition state and operation completion time are collected through the warehousing data interface to generate a platform record; S23, the cold chain transportation order number, category, quantity, volume, temperature control level, arrival time requirement, order priority and special temperature control requirement are collected through the order data interface to generate an order record; S24, collect road traffic conditions, weather information, cold chain sampling records and temporary supervision instructions through a supervision data interface to generate supervision records; S25, aggregate the vehicle records, platform records, order records and supervision records to form a collection of collected data; S26, synchronize the collection of collected data according to timestamps, filter abnormal data exceeding preset temperature intervals, humidity intervals or position jump thresholds, and interpolate missing data to form cold chain transportation standardized data records; S27, write the cold chain transportation standardized data records into a cloud collaborative data set, and establish an index with order numbers, vehicle identifiers, platform numbers and time windows as key values.

[0027] The application collects vehicle positions, driving states, capacity parameters, carriage temperature and humidity, platform occupancy, loading and unloading plans, order information and supervision data, synchronizes timestamps and performs abnormal value filtering and interpolation, generates standardized data records and writes them into a cloud collaborative data set, establishes an index with order numbers, vehicle identifiers, platform numbers and time windows as key values, provides complete, synchronized and retrievable collaborative data basis for subsequent consistency verification, priority calculation and transportation plan optimization, and ensures that multi-agent scheduling decisions are based on consistent and reliable data environment.

[0028] In the embodiment, S3 specifically includes: S31, read vehicle records, platform records, order records and supervision records in the cloud collaborative data set; S32, compare the arrival time requirement in the order record with the driving state time in the vehicle record and the operation completion time in the platform record, identify time mismatch records and generate a time conflict set; S33, compare the quantity and volume in the order record with the capacity parameters in the vehicle record, identify overload records and generate a capacity conflict set; S34, compare the temperature control level in the order record with the carriage temperature interval in the vehicle record and the temperature control partition state in the platform record, identify mismatch records and generate a temperature control conflict set; S35, merge the time conflict set, the capacity conflict set and the temperature control conflict set to form a consistency conflict set; S36, calculate the priority weight of each conflict record in the consistency conflict set, including: assigning a basic weight according to the order priority in the order record, weighting and correcting the overdue risk and default risk according to the arrival time requirement in the order record, adjusting the weight value according to the temporary supervision instructions and cold chain sampling records in the supervision record, and generating a conflict resolution priority list; S37. Allocate vehicle, platform, and time resources according to the conflict resolution priority list, and output the resource allocation plan; S38. Write the resource allocation scheme into the cloud collaborative dataset and generate a soft lock record.

[0029] This invention identifies and merges time, capacity, and temperature control conflicts into a consistency conflict set by comparing order arrival time, quantity, volume, and temperature control level with vehicle and platform records. Conflict records are assigned weights based on order priority, and adjustments are made based on arrival time differences and regulatory instructions. A conflict resolution priority list is generated, and vehicle, platform, and time resources are allocated. The resource allocation scheme is output and written into a cloud-based collaborative dataset to generate soft lock records, providing a constraint basis for transportation plan optimization.

[0030] In this embodiment, the improved jellyfish search algorithm specifically includes: The transportation tasks, vehicles, and platforms in the resource allocation scheme are encoded as jellyfish individuals. Each jellyfish individual includes an order assignment sequence, a vehicle route sequence, a platform operation period, and a loading / unloading sequence. A jellyfish individual population is generated by combining heuristic seeding and random initialization. The task fragments corresponding to soft locks and hard locks are written into the individuals as fixed fragments. The total transportation time, temperature control safety margin deviation, number of platform occupancy conflicts, vehicle capacity utilization rate, plan change volume, and regulatory risks of each jellyfish individual are standardized; a weighted sum is performed based on the benchmark weights, and the weighted result is used as the fitness value of the jellyfish individual. Perform ocean current drift update, using the order assignment sequence and platform operation time of individuals whose current fitness meets the preset preference conditions as a reference to globally move the group's position, and keep the position projection unchanged for segments involving hard locks; perform active foraging update, prioritizing the adjustment of orders with temperature control levels that meet the preset threshold or orders with temperature control safety margins less than the preset safety threshold, and generating candidate individuals using local operations such as path insertion, proximity swapping and platform time shifting, and sequentially correcting the time window, capacity, platform occupancy and partition temperature control constraints for the candidate individuals, and removing candidate individuals that do not meet the constraints; The preset optimization conditions refer to the overall fitness value of an individual jellyfish reaching a set threshold, and the order assignment, path, and platform time slot corresponding to that individual all meeting the comprehensive conditions of time window, capacity, temperature control, and locking constraints. Jellyfish individuals that meet these conditions are considered preferred individuals and are used to guide group updates or as the final output plan.

[0031] An adaptive weighting mechanism is used to dynamically adjust the group search parameters. When the platform congestion rate exceeds the preset threshold, the number of temperature control violations exceeds the preset threshold, or the fitness improvement is lower than the preset improvement value, the corresponding indicator weights are adjusted. Elite individuals whose fitness values ​​meet the preset optimization conditions are retained. For individuals whose plan change exceeds the threshold, disturbance suppression is performed, and the individual is removed or rolled back to the most recent feasible state. The search terminates when the number of iterations reaches the preset upper limit or the fitness improvement falls below the threshold after multiple consecutive iterations, and outputs the cold chain transportation plan corresponding to the jellyfish individual whose fitness meets the preset optimization conditions.

[0032] In this embodiment, S5 specifically includes: S51. Compare the cold chain transportation plan with the previous version of the cold chain transportation plan, extract changes in order assignment, vehicle route, and platform operation time, and form a differential data package. S52. The differential data packet is sent to the vehicle terminal, warehouse terminal and monitoring terminal through the cloud interface; S53. Receive the driving status receipt, temperature and humidity receipt, and loading / unloading completion receipt uploaded by the vehicle terminal. S54. Receive the platform occupancy receipt and operation completion receipt uploaded by the warehouse terminal; S55. Write the driving status receipt, temperature and humidity receipt, platform occupancy receipt and work completion receipt into the cloud collaborative dataset in chronological order, and update the order execution status, vehicle operation status and platform occupancy status.

[0033] This invention extracts differential data packets by comparing the changes in order assignment, vehicle routes, and platform operation periods from the old and new transportation plans, and sends them to vehicle, warehouse, and monitoring terminals. It also collects driving status, temperature and humidity, loading and unloading completion, and platform occupancy receipts, and writes them into a cloud collaborative dataset in chronological order to update the order execution status, vehicle operation status, and platform occupancy status, providing a real-time execution data foundation for subsequent risk detection and iterative optimization.

[0034] In this embodiment, S6 specifically includes: S61. Read the vehicle operating status, compartment temperature, compartment humidity and order delivery time requirements from the updated cloud collaborative dataset. Calculate the margin based on the difference between the compartment temperature and the upper limit of the order target temperature control and the lower limit of the order target temperature control. Calculate the margin difference for the loading, driving and unloading time periods respectively. Select the margin difference used for judgment as the temperature control safety margin according to the preset safety judgment rules. S62. Compare the temperature control safety margin with the preset temperature control safety threshold, identify transportation tasks where the temperature control safety margin is less than the preset threshold, and generate a temperature control risk list. S63. Receive real-time road congestion information uploaded by the road traffic monitoring interface, identify path segments with average driving speeds below a preset speed threshold, and generate a list of congested paths. S64. Receive the regulatory order insertion information uploaded by the regulatory interface, extract the delivery time requirements, temperature control level and special constraints of the new orders, and generate an order insertion list; S65. Determine whether the temperature control risk list, congestion path list and order insertion list are empty, and trigger the improved jellyfish search algorithm to perform iterative updates only when any of the lists is not empty; S66. Write the new transportation plan generated by the iterative update into the cloud collaborative dataset and assign a new plan version identifier.

[0035] This invention reads vehicle operating status, compartment temperature and humidity, and order delivery time requirements from a cloud-based collaborative dataset, calculates temperature control safety margins, and compares them with preset thresholds to identify risky tasks; it receives road congestion information to generate a list of congested routes, receives regulatory order insertion information to generate a list of inserted orders, and triggers an improved jellyfish search algorithm to iteratively update the transportation plan only when any list is not empty, and writes the new plan into the cloud-based collaborative dataset to generate a new version identifier.

[0036] In this embodiment, S7 specifically includes: S71. Package the latest generated transport plan version with the corresponding soft lock record and hard lock record to generate block data; S72. Calculate the hash value of the block data using a preset hash algorithm and write it into the blockchain ledger to generate an immutable transportation plan version record; S73. Index the order number, vehicle identifier, platform number and plan version number to form a searchable resource lock status mapping; S74. Receive driving status receipts, temperature and humidity receipts, loading and unloading completion receipts, and platform occupancy receipts uploaded by the execution terminal, and associate them with the corresponding order and plan version in chronological order. S75. Write the associated receipt into the blockchain ledger, generate a receipt block and link it with the planned version block to form a traceability chain; S76. Provides a query interface to support retrieving transportation plans, resource lock status, and execution receipts by order number, vehicle identifier, or plan version number.

[0037] This invention packages transportation plan versions with soft and hard lock records to generate block data, calculates hash values, and writes them into a blockchain ledger to form an immutable record; it establishes an index of orders, vehicles, platforms, and plan version numbers, receives driving status, temperature and humidity, loading and unloading completion, and platform occupancy receipts and associates them with the corresponding orders and versions, writes them into the blockchain to generate receipt blocks, forms a traceability chain, and supports the retrieval of the entire process information by order, vehicle, or plan version.

[0038] In this embodiment, S8 specifically includes: S81. Compare the actual arrival time of each transportation task with the arrival time requirements in the order record, count the number of tasks that meet the time requirements and the total number of tasks, calculate the on-time completion rate, and generate on-time rate indicators for each entity. S82. Compare the temperature and humidity of the carriage recorded during the transportation process with the target temperature control range in the order record, calculate the duration within the target temperature control range and the total transportation time, calculate the temperature control compliance rate, and generate temperature control compliance rate indicators for each entity. S83. Compare the time of the uploaded receipts from each execution terminal with the time of the transportation plan node, count the number of receipts uploaded within the preset time window and the total number of receipts, calculate the timely receipt ratio, and generate a timely receipt indicator. S84. Aggregate the on-time rate indicator, temperature control compliance rate indicator, and timely receipt indicator by subject to form a collaborative performance evaluation result; S85. Compare the collaborative performance evaluation results with a preset performance threshold. When any indicator is lower than the preset performance threshold, reduce the scheduling priority of the subject. When all indicators are greater than or equal to the preset performance threshold, increase the scheduling priority of the subject. S86. Write the adjusted scheduling priority into the cloud collaborative dataset, and call the updated scheduling priority in the next round of resource allocation and transportation plan generation steps.

[0039] This invention calculates on-time rate, temperature control compliance rate, and timely receipt rate by comparing actual arrival time, transportation temperature and humidity, and receipt upload time. The results are then aggregated by subject to form a collaborative performance evaluation. The evaluation results are compared with preset thresholds. If the results are below the threshold, the scheduling priority is reduced. If the results meet the threshold, the priority is increased. The updated priority is written to the cloud collaborative dataset for use in the next round of resource allocation and plan generation.

[0040] Example 1: To verify the feasibility of this invention in a real-world cold chain logistics scenario, it was applied to a fresh food cold chain distribution center in a city. This distribution center handles over 1200 fresh food orders daily, covering various temperature-controlled categories such as fruits and vegetables, meat, seafood, and dairy products. It operates a total of 80 delivery vehicles, including refrigerated trucks, frozen trucks, and multi-temperature zone vehicles. Due to frequent traffic congestion during peak hours, limited warehouse platform resources, and strict order delivery time requirements in this area, traditional dispatching methods often result in vehicles waiting idly at platforms, temperature control issues exceeding vehicle limits, and delayed order delivery, impacting customer experience and increasing operating costs.

[0041] In practical applications, a multi-entity cold chain collaborative management model is first established in the cloud. This involves configuring roles, data domains, and sharing agreements for shippers, carriers, warehouse operators, and regulators, clarifying the fields, update frequencies, and visibility scope for data sharing among all parties. Real-time data collection is achieved through onboard terminals, including the location, driving status, compartment temperature, humidity, and capacity usage of each cold chain truck. Simultaneously, warehouse platform occupancy, loading / unloading plans, and temperature control zone status are also collected. This multi-source heterogeneous data is standardized and written into a cloud-based collaborative dataset. Consistency checks are performed to compare order quantity with vehicle capacity, arrival time with driving status, and platform temperature control level with order temperature control requirements. A conflict set is generated, conflict priorities are calculated, and a resource allocation plan is output.

[0042] In the scheduling optimization phase, an improved jellyfish search algorithm is used to generate transportation plans while considering temperature control, time windows, and capacity constraints. For example, during the morning peak hours, by adjusting vehicle routes and loading / unloading sequences, three vehicles that were originally waiting at the platform are rescheduled to warehouses with available platforms, reducing waiting time by an average of 17 minutes per vehicle. High-risk orders with a detected temperature control safety margin below 2°C are prioritized for scheduling, reducing cargo damage caused by temperature control exceeding limits. The generated transportation plan is distributed to the vehicle terminals and warehouse terminals, and the driving status feedback, temperature and humidity feedback, and loading / unloading completion feedback during transportation are written to the cloud in real time to ensure that status information is updated in real time.

[0043] During operation, the system continuously calculates the temperature control safety margin. When it detects that a vehicle's estimated arrival delay exceeds 30 minutes due to congestion and the temperature control safety margin is insufficient, the system automatically triggers an iterative update of the improved jellyfish search algorithm to generate a new transportation plan. This plan adjusts the order to a backup refrigerated truck or reorders the loading and unloading process, ensuring delivery is completed within the temperature control range. All transportation plans and receipts are recorded and traceable via blockchain, ensuring data immutability and full-process traceability.

[0044] To verify the effectiveness of this invention, it was compared with the traditional manual scheduling + fixed time slot vehicle arrangement method. 30 days of operational data were collected, and the results of the comparative experiment are shown in Table 1. Table 1. Comparison of Indicators between the Invention and Traditional Methods in Cold Chain Logistics Scenarios

[0045] As can be seen from the comparative data in Table 1 above, the present invention significantly improves on-time delivery rate, temperature control compliance rate, resource utilization rate, and order insertion response capability. Among them, the average waiting time at the platform is reduced by more than 60%, and the temperature control violation rate is reduced to less than 1%, significantly reducing the loss of fresh produce. At the same time, due to more reasonable resource allocation, the average on-time delivery rate is increased by about 8%, the success rate of responding to temporary order insertions is increased by about 46%, and the order error rate is reduced by about 66%, effectively improving the stability of the cold chain logistics system and customer satisfaction, and verifying the feasibility and superiority of the technical solution of the present invention in actual large-scale cold chain scenarios.

[0046] Therefore, the cloud-based cold chain logistics collaborative management system proposed in this invention not only significantly improves the on-time delivery rate and temperature control compliance rate, but also effectively reduces platform waiting time and temperature control violation rate. It has obvious advantages in complex logistics scenarios involving multiple stakeholders, large order fluctuations, and frequent road congestion, verifying its engineering feasibility and promotional value in actual cold chain distribution scenarios.

[0047] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A cloud-based cold chain logistics collaborative management system, characterized in that, include: The collaborative modeling module is used to establish a multi-entity cold chain collaborative management model in the cloud, and configure the roles, data domains and shared data contracts of each entity. The data acquisition and preprocessing module is used to collect vehicle records, platform records, order records, and regulatory records, and to perform standardized processing in the cloud to form a cloud-based collaborative dataset. The resource allocation module is used to perform consistency checks and priority calculations on the cloud-based collaborative dataset, generate a resource allocation scheme, and generate a soft lock. The scheduling optimization module is used to upgrade the soft lock to a hard lock after confirmation by each entity, and call the improved jellyfish search algorithm to generate a cold chain transportation plan under temperature control, time window and capacity constraints; The plan distribution and status update module is used to distribute the transportation plan difference to the execution terminal, collect transportation process receipts, and update the cloud collaborative dataset; The risk optimization module is used to calculate the temperature control safety margin and trigger the improved jellyfish search algorithm to iteratively update and regenerate the transportation plan when an abnormal transportation event is detected. The traceability recording module is used to write the transportation plan version and resource lock status into an immutable evidence chain and record execution receipts to form a traceability chain; The priority adjustment module is used to update multi-entity collaborative performance indicators based on on-time rate, temperature control compliance rate, and timely feedback, and to adjust scheduling priorities.

2. The cloud-based cold chain logistics collaborative management system according to claim 1, characterized in that, The modules are connected in the following way: S1. Establish a multi-entity cold chain collaborative management model in the cloud and configure the roles, data domains, and shared data contracts of each entity; S2. Collect vehicle records, platform records, order records, and monitoring records, and perform standardized processing in the cloud to form a cloud-based collaborative dataset; S3. Perform consistency verification and priority calculation on the cloud collaborative dataset, output resource allocation scheme and generate soft lock; S4. After confirmation by all parties, upgrade the soft lock to a hard lock and call the improved jellyfish search algorithm to generate a cold chain transportation plan under temperature control, time window and capacity constraints. S5. Send the transportation plan difference to the execution terminal, collect transportation process receipts and update the cloud collaborative dataset; S6. Calculate the temperature control safety margin based on the updated cloud collaborative dataset. When an abnormal transportation event is detected, trigger the improved jellyfish search algorithm to iteratively update and regenerate the transportation plan. S7. Write the transportation plan version and resource lock status into an immutable evidence chain and record the execution receipt to form a traceability chain; S8. Update multi-entity collaborative performance indicators and adjust scheduling priorities based on on-time performance, temperature control compliance rate and timely feedback.

3. The cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S1 specifically includes: S11. Create a multi-entity collaborative space in the cloud and assign a unique entity instance identifier to the shipper, carrier, warehousing party, and regulator respectively; S12. For each entity, establish a user table and a role table, and configure the entity's roles, operation permissions, and data access scope. S13. Define the order data field, capacity data field, platform operation data field, environmental monitoring data field, and compliance audit data field, and set the data structure, field type, timestamp precision, and update frequency for each data field; S14. Configure the shared data contract, including the list of shared fields, the minimum granularity of data, the synchronization cycle, the visibility scope, and the validity period of use; S15. Establish access control policies, and set role-based access rules, cross-subject data authorization processes, and data masking rules; S16. Generate the main digital certificate and key to enable authentication, authorization and request signature verification for interface calls; S17. Allocate independent namespaces for each entity instance in the cloud, create database instances and message queue channels, set computing and storage resource quotas and enable isolation policies. S18. Configure audit logs and configuration change log recording policies, generate log entries for access behavior, data writing and policy adjustments, and connect to the evidence chain writing interface.

4. The cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S2 specifically includes: S21. Collect real-time location, driving status, vehicle capacity parameters, compartment temperature, compartment humidity and door opening / closing events of refrigerated vehicles through the vehicle terminal, and generate vehicle records. S22. Collect cold storage loading and unloading plans, platform occupancy, platform temperature control level, temperature control zone status and operation completion time through the warehouse data interface, and generate platform records. S23. Collect cold chain transportation order number, category, quantity, volume, temperature control level, delivery time requirement, order priority and special temperature control requirements through the order data interface, and generate order records; S24. Collect road traffic conditions, weather information, cold chain inspection records, and temporary regulatory instructions through the regulatory data interface to generate regulatory records; S25. The vehicle records, platform records, order records, and supervision records are aggregated to form a collection of data; S26. Synchronize the collected data set according to timestamps, filter out abnormal data that exceeds the preset temperature range, humidity range or location jump threshold, and interpolate and fill in missing data to form a standardized data record for cold chain transportation. S27. Write the standardized cold chain transportation data records into the cloud collaborative dataset and establish an index with order number, vehicle identifier, platform number and time window as key values.

5. A cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S3 specifically includes: S31. Read the vehicle records, platform records, order records, and monitoring records from the cloud-based collaborative dataset; S32. Compare the delivery time requirement in the order record with the driving status time in the vehicle record and the operation completion time in the platform record, identify time mismatch records and generate a time conflict set; S33. Compare the quantity and volume in the order records with the capacity parameters in the vehicle records to identify overload records and generate a capacity conflict set; S34. Compare the temperature control level in the order record with the temperature range of the vehicle compartment and the temperature control zoning status of the platform record, identify mismatched records and generate a set of temperature control conflicts. S35. Merge the time conflict set, capacity conflict set, and temperature control conflict set to form a consistency conflict set; S36. Calculate the priority weight for each conflict record in the consistency conflict set, including: assigning a basic weight according to the order priority in the order record, weighting and correcting the overdue risk and default risk according to the delivery time requirement of the order record, adjusting the weight value according to the temporary regulatory instructions and cold chain sampling records in the regulatory record, and generating a conflict resolution priority list; S37. Allocate vehicle, platform, and time resources according to the conflict resolution priority list, and output the resource allocation plan; S38. Write the resource allocation scheme into the cloud collaborative dataset and generate a soft lock record.

6. The cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, The improved jellyfish search algorithm specifically includes: The transportation tasks, vehicles, and platforms in the resource allocation scheme are encoded as jellyfish individuals. Each jellyfish individual includes an order assignment sequence, a vehicle route sequence, a platform operation period, and a loading / unloading sequence. A jellyfish individual population is generated by combining heuristic seeding and random initialization. The task fragments corresponding to soft locks and hard locks are written into the individuals as fixed fragments. The total transportation time, temperature control safety margin deviation, number of platform occupancy conflicts, vehicle capacity utilization rate, plan change volume, and regulatory risks of each jellyfish individual are standardized; a weighted sum is performed based on the benchmark weights, and the weighted result is used as the fitness value of the jellyfish individual. Perform ocean current drift update, using the order assignment sequence and platform operation time of individuals whose current fitness meets the preset preference conditions as a reference to globally move the group's position, and keep the position projection unchanged for segments involving hard locks; perform active foraging update, prioritizing the adjustment of orders with temperature control levels that meet the preset threshold or orders with temperature control safety margins less than the preset safety threshold, and generating candidate individuals using local operations such as path insertion, proximity swapping and platform time shifting, and sequentially correcting the time window, capacity, platform occupancy and partition temperature control constraints for the candidate individuals, and removing candidate individuals that do not meet the constraints; An adaptive weighting mechanism is used to dynamically adjust the group search parameters. When the platform congestion rate exceeds the preset threshold, the number of temperature control violations exceeds the preset threshold, or the fitness improvement is lower than the preset improvement value, the corresponding indicator weights are adjusted. Elite individuals whose fitness values ​​meet the preset optimization conditions are retained. For individuals whose plan change exceeds the threshold, disturbance suppression is performed, and the individual is removed or rolled back to the most recent feasible state. The search terminates when the number of iterations reaches the preset upper limit or the fitness improvement falls below the threshold after multiple consecutive iterations, and outputs the cold chain transportation plan corresponding to the jellyfish individual whose fitness meets the preset optimization conditions.

7. The cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S5 specifically includes: S51. Compare the cold chain transportation plan with the previous version of the cold chain transportation plan, extract changes in order assignment, vehicle route, and platform operation time, and form a differential data package. S52. The differential data packet is sent to the vehicle terminal, warehouse terminal and monitoring terminal through the cloud interface; S53. Receive the driving status receipt, temperature and humidity receipt, and loading / unloading completion receipt uploaded by the vehicle terminal. S54. Receive the platform occupancy receipt and operation completion receipt uploaded by the warehouse terminal; S55. Write the driving status receipt, temperature and humidity receipt, platform occupancy receipt and work completion receipt into the cloud collaborative dataset in chronological order, and update the order execution status, vehicle operation status and platform occupancy status.

8. A cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S6 specifically includes: S61. Read the vehicle operating status, compartment temperature, compartment humidity and order delivery time requirements from the updated cloud collaborative dataset. Calculate the margin based on the difference between the compartment temperature and the upper limit of the order target temperature control and the lower limit of the order target temperature control. Calculate the margin difference for the loading, driving and unloading time periods respectively. Select the margin difference used for judgment as the temperature control safety margin according to the preset safety judgment rules. S62. Compare the temperature control safety margin with the preset temperature control safety threshold, identify transportation tasks where the temperature control safety margin is less than the preset threshold, and generate a temperature control risk list. S63. Receive real-time road congestion information uploaded by the road traffic monitoring interface, identify path segments with average driving speeds below a preset speed threshold, and generate a list of congested paths. S64. Receive the regulatory order insertion information uploaded by the regulatory interface, extract the delivery time requirements, temperature control level and special constraints of the new orders, and generate an order insertion list; S65. Determine whether the temperature control risk list, congestion path list and order insertion list are empty, and trigger the improved jellyfish search algorithm to perform iterative updates only when any of the lists is not empty; S66. Write the new transportation plan generated by the iterative update into the cloud collaborative dataset and assign a new plan version identifier.

9. A cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, Specifically, S7 includes: S71. Package the latest generated transport plan version with the corresponding soft lock record and hard lock record to generate block data; S72. Calculate the hash value of the block data using a preset hash algorithm and write it into the blockchain ledger to generate an immutable transportation plan version record; S73. Index the order number, vehicle identifier, platform number and plan version number to form a searchable resource lock status mapping; S74. Receive driving status receipts, temperature and humidity receipts, loading and unloading completion receipts, and platform occupancy receipts uploaded by the execution terminal, and associate them with the corresponding order and plan version in chronological order. S75. Write the associated receipt into the blockchain ledger, generate a receipt block and link it with the planned version block to form a traceability chain; S76. Provides a query interface to support retrieving transportation plans, resource lock status, and execution receipts by order number, vehicle identifier, or plan version number.

10. A cloud-based cold chain logistics collaborative management system according to claim 2, characterized in that, S8 specifically includes: S81. Compare the actual arrival time of each transportation task with the arrival time requirements in the order record, count the number of tasks that meet the time requirements and the total number of tasks, calculate the on-time completion rate, and generate on-time rate indicators for each entity. S82. Compare the temperature and humidity of the carriage recorded during the transportation process with the target temperature control range in the order record, calculate the duration within the target temperature control range and the total transportation time, calculate the temperature control compliance rate, and generate temperature control compliance rate indicators for each entity. S83. Compare the time of the uploaded receipts from each execution terminal with the time of the transportation plan node, count the number of receipts uploaded within the preset time window and the total number of receipts, calculate the timely receipt ratio, and generate a timely receipt indicator. S84. Aggregate the on-time rate indicator, temperature control compliance rate indicator, and timely receipt indicator by subject to form a collaborative performance evaluation result; S85. Compare the collaborative performance evaluation results with a preset performance threshold. When any indicator is lower than the preset performance threshold, reduce the scheduling priority of the subject. When all indicators are greater than or equal to the preset performance threshold, increase the scheduling priority of the subject. S86. Write the adjusted scheduling priority into the cloud collaborative dataset, and call the updated scheduling priority in the next round of resource allocation and transportation plan generation steps.

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