A supply chain management method and system based on trackable container adaptive optimization

Abstract

This invention discloses a supply chain management method and system based on adaptive optimization using traceable containers. The method collects multi-dimensional supply chain data in real time using standardized traceable containers, including inbound volume, outbound volume, inventory, transportation location, environmental parameters, and carbon emission estimates; calculates data volatility and reliability to determine the importance of data collection time; integrates target smoothing parameters and uses reinforcement learning to correct predicted demand; generates multimodal transport schemes using K-D tree matching and the dragonfly algorithm; optimizes production and transportation scheduling based on simulated annealing algorithm, embedding carbon footprint constraints to minimize total green costs; and forms an adaptive closed loop through container feedback to improve system robustness. This method achieves real-time tracking, adaptive optimization, and sustainable development of the supply chain, and is suitable for complex and ever-changing supply chain environments.

Description

Technical Field

[0001] This invention belongs to the field of logistics and transportation technology, specifically a supply chain management method and system based on adaptive optimization of traceable containers. Background Technology

[0002] With the acceleration of globalization and digital transformation, supply chain management has become a core component of modern enterprise operations. Existing supply chain management systems typically rely on traditional ERP, WMS, and TMS systems, which can handle basic inventory tracking, order fulfillment, and logistics scheduling. For example, some systems use RFID tags or GPS devices to track the location of goods and utilize historical data for demand forecasting. However, these technologies are often limited to static data analysis and cannot cope with real-time fluctuations, such as sudden changes in demand, environmental disruptions, or supply chain disruptions. Furthermore, existing optimization algorithms often focus on cost minimization, neglecting carbon emissions and sustainability factors, leading to inefficiencies in green supply chain transformation. Simultaneously, data privacy protection and reverse logistics support in multi-supplier collaboration are inadequate, easily creating information silos and response delays. Summary of the Invention

[0003] To address the shortcomings of existing technologies, a supply chain collaborative management system and method based on traceable containers and adaptive optimization is proposed.

[0004] The technical solution adopted by this invention to solve its technical problem is:

[0005] A supply chain management method based on adaptive optimization of traceable containers includes the following steps:

[0006] S1: At each collection point, collect supply chain data for various dimensions of the product using standardized, traceable containers;

[0007] S2: Calculate the degree of fluctuation by comparing the supply chain data of each dimension at the time of collection with the preset length of historical supply chain data;

[0008] S3: Calculate the reliability of data changes using the Euclidean distance between the destinations of outbound transportation data;

[0009] S4: Calculate the importance of the data collection time. The importance is positively correlated with the degree of fluctuation in the supply chain data at that time and the reliability of the changes in the supply chain data.

[0010] S5: Integrate the target smoothing parameters, use a reinforcement learning model to correct the weights in the EWMA algorithm, and obtain the supply chain demand forecast for the next time step.

[0011] S6: Use the KD tree nearest neighbor search algorithm to match the material transportation feature text data with the supply chain material transportation scheme data set, and use the improved dragonfly algorithm to search for multimodal transport schemes and execution order to generate supply chain material transportation mode and transport execution order identification data. At the same time, optimize the algorithm initialization to incorporate quantum heuristic mechanism to improve global search efficiency.

[0012] S7: Calculate the edge weights of various optional vehicles in the commodity supply chain graph from the sales node;

[0013] S8: Based on simulated annealing algorithm, optimize production and transportation scheduling, embed carbon footprint into the optimization, and extend it to multi-objective optimization, including minimizing total green cost and maximizing supply chain resilience;

[0014] S9: By tracking and updating dynamic location identifiers in real time through containers, a feedback loop is formed, and machine learning is used to adjust prediction and scheduling parameters to improve system adaptability.

[0015] Furthermore, the confidence level of the calculated data changes in S3 satisfies the formula:

[0016]

[0017] Among them The reliability of supply chain data changes at time t. The number of historical outbound data. Let H be the Euclidean distance between the k-th and h-th outbound data transport destinations. This represents the average of the location indicators;

[0018] Furthermore, the formula for calculating the target smoothing parameter in S5 is as follows:

[0019]

[0020] Let be the target smoothing parameter for the i-th sales node. The standard deviation of historical supply data. For the DTW distance, the reinforcement learning model uses prediction accuracy and carbon emission reduction as reward functions and dynamically adjusts the weights;

[0021] The specific implementation of the reinforcement learning model includes: inputting historical and real-time data, outputting weight correction values, and the reward function is: R = prediction accuracy + β × carbon emission reduction rate, where β is the environmental weight.

[0022] Furthermore, the formula is satisfied in S7:

[0023]

[0024] in For carbon footprint items ( ,in , (where h is the emission factor of vehicle type), and the effective route for each type of optional vehicle is planned according to the edge weight, and the route selectivity is calculated according to the demand of nodes on the route to realize commodity scheduling.

[0025] Furthermore, the objective function in S8 is:

[0026]

[0027] It is a sustainable punishment. It can be dynamically adjusted; It is a path selectivity penalty. Weights are assigned to ensure efficient paths;

[0028] Formula for calculating total carbon footprint:

[0029]

[0030] Where CF represents the total carbon footprint; h and i represent the paths from vehicle type to node i; The edge weight; Path distance; This refers to the load weight; Emission factor; To adjust the factor, ;

[0031] The steps for optimizing production and transportation scheduling further include: integrating time window constraints and vehicle capacity limits, using deep reinforcement learning to assist in the generation of initial solutions for simulated annealing, and improving optimization efficiency and sustainability.

[0032] Furthermore, the standardized traceable container in S1 further includes: associated storage of container identifiers and location identifiers, supporting complete traceability of multi-supplier inventory items, providing dynamic location updates for forward and reverse movement from supply chain input to order fulfillment, and achieving low-latency data synchronization through 5G edge computing.

[0033] Furthermore, the search process of the Dragonfly algorithm in S6 includes initializing the maximum number of iterations T, dynamically adjusting the selection of transportation execution companies through behavioral parameters, realizing numerical analysis of the optimal transportation execution company search parameters, and adding a particle swarm optimization hybrid mechanism to accelerate convergence.

[0034] Furthermore, Q1: Standardized trackable container modules are used to store inventory items and collect data in real time, including built-in sensors to collect location, inventory, outbound data and carbon emission estimates;

[0035] Q2: Data acquisition and fluctuation calculation module, used to calculate the degree of fluctuation and the reliability of data changes;

[0036] Q3: Prediction module, used to calculate importance and fuse smoothing parameters, and use reinforcement learning to correct EWMA prediction requirements;

[0037] Q4: Integrated scheduling module, used for KD tree matching and dragonfly algorithm search for multimodal transport schemes, calculating edge weights and path selectivity;

[0038] Q5: Optimization module, used to simulate the annealing algorithm to minimize cost and carbon footprint, considering outsourcing and time windows;

[0039] Q6: Tracking feedback module, used to update the location identifier and form an adaptive closed loop through reinforcement learning, adjusting parameters in each iteration cycle;

[0040] Q7: Central processing unit and non-transitory computer-readable storage medium, performing the calculations and data processing of the above modules, storing container identifiers, location identifiers and vendor identifiers.

[0041] Furthermore, the standardized traceable container module described in Q1 is compatible with the index storage location of node facilities and transport vehicles, providing complete traceability from input to fulfillment, including carbon emission tracking and reverse logistics support, and reducing container failures through AI-driven predictive maintenance;

[0042] The prediction module described in Q3 further includes a DTW distance calculation submodule, which is used to enhance the accuracy of smoothing parameters and support a temporal attention mechanism to capture long-term dependencies;

[0043] The Dragonfly algorithm of the integrated scheduling module described in Q4 includes behavior parameter optimization to ensure the global optimum of the multimodal transport scheme, and incorporates quantum-inspired algorithms to handle high-dimensional optimization problems;

[0044] The carbon footprint calculation of the optimization module described in Q5 further includes: real-time estimation of vehicle emission coefficients, dynamic adjustment of λ weights to prioritize green paths; the tracking feedback module uses cloud platform API interfaces to achieve hourly uploading of container data and model iteration, and integrates digital twin technology to simulate supply chain scenario optimization.

[0045] This invention utilizes standardized, traceable containers to achieve real-time data acquisition and fluctuation calculation, improving data accuracy and predictive adaptability. Reinforcement learning is used to modify the EWMA algorithm, introducing a multi-objective reward function to enhance prediction accuracy and sustainability. KD-tree matching combined with an improved dragonfly algorithm efficiently generates multimodal transport solutions, enhancing global optimization and burst response. Simulated annealing multi-objective optimization incorporates carbon footprint constraints to minimize total green costs and achieve cost-environment balance. Container feedback closed-loop integration with anomaly detection improves system robustness and pre-emptive adjustments. Overall, this invention significantly improves supply chain efficiency, sustainability, and adaptability, and is suitable for complex multi-supplier scenarios. Detailed Implementation

[0046] The embodiments of the present invention will be described in further detail below with reference to examples. These examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0047] Example 1:

[0048] A supply chain management method based on adaptive optimization of traceable containers includes the following steps:

[0049] S1: At each collection point, standardized traceable containers are used to collect supply chain data for various dimensions of the product, including inbound quantity, outbound quantity, inventory quantity, transportation location latitude and longitude, environmental parameters, and carbon emission estimation data. The standardized traceable containers are equipped with built-in IoT sensors, RFID tags, and edge computing modules, and are compatible with the index storage locations of node facilities and transportation vehicles. This ensures that the data is transmitted to the central management system in real time and supports blockchain verification to enhance the immutability of the data.

[0050] S2: Calculate the degree of fluctuation by comparing the supply chain data of each dimension at the time of collection with the preset length of historical supply chain data. Specifically, this includes: taking the average absolute value of the difference between the supply chain data of each dimension at the time of collection and the historical supply chain data as the fluctuation index of that dimension, and accumulating and normalizing the fluctuation indices of all dimensions at the time of collection to obtain the degree of fluctuation of the supply chain data at the time of collection.

[0051] S3: Calculate the reliability of data changes using the Euclidean distance between the destinations of outbound transportation data;

[0052] Based on the simulated annealing algorithm, production and transportation scheduling are optimized, taking into account transportation outsourcing, time window constraints, total cost and carbon footprint, to minimize the total green cost.

[0053] S4: Calculate the importance of the data collection time. The importance is positively correlated with the degree of fluctuation of the supply chain data and the reliability of the supply chain data changes at that time. Specifically, the product of the degree of fluctuation of the supply chain data and the reliability of the supply chain data changes at each collection time is recorded as the degree of data change at that time. The ratio of the degree of data change at the collection time to that of the previous collection time is normalized and multiplied by the adaptive factor output by the reinforcement learning to obtain the importance of the collection time, ensuring that the prediction adapts to real-time fluctuations.

[0054] S5: Integrate the target smoothing parameters, use a reinforcement learning model to correct the weights in the EWMA algorithm, and obtain the supply chain demand forecast for the next time step.

[0055] S6: Use the KD tree nearest neighbor search algorithm to match the material transportation feature text data with the supply chain material transportation scheme data set, and use the improved dragonfly algorithm to search for multimodal transport schemes and execution order to generate supply chain material transportation mode and transport execution order identification data. At the same time, optimize the algorithm initialization to incorporate quantum heuristic mechanism to improve global search efficiency.

[0056] S7: Calculate the edge weights of various optional vehicles in the commodity supply chain graph from the sales node;

[0057] S8: Optimize production and transportation scheduling based on simulated annealing algorithm, embed carbon footprint into the optimization, and extend it to multi-objective optimization, specifically including minimizing total green cost and maximizing supply chain resilience;

[0058] S9: By tracking and updating dynamic location identifiers in real time through containers, a feedback loop is formed. Machine learning is used to adjust prediction and scheduling parameters to improve system adaptability. Specifically, this includes: container feedback of delay data, hourly iteration of reinforcement learning models, updating smoothing parameters and weight correction values, and integrating anomaly detection algorithms to predict potential supply chain disruptions, thereby achieving pre-emptive adjustments.

[0059] The reliability of the calculated data changes in S3 satisfies the following formula:

[0060]

[0061] Among them The reliability of supply chain data changes at time t. The number of historical outbound data. Let H be the Euclidean distance between the k-th and h-th outbound data transport destinations. This represents the average of the location indicators;

[0062] The formula for calculating the target smoothing parameter in S5 is as follows:

[0063]

[0064] Let be the target smoothing parameter for the i-th sales node. The standard deviation of historical supply data. For the DTW distance, the reinforcement learning model uses prediction accuracy and carbon emission reduction as reward functions and dynamically adjusts the weights;

[0065] The specific implementation of the reinforcement learning model includes: inputting historical and real-time data, outputting weight correction values, and the reward function is: R = prediction accuracy + β × carbon emission reduction rate, where β is the environmental weight.

[0066] The formula is satisfied in S7:

[0067]

[0068] in For carbon footprint items ( ,in , (where h is the emission factor of vehicle type), and the effective route for each type of optional vehicle is planned according to the edge weight, and the route selectivity is calculated according to the demand of nodes on the route to realize commodity scheduling.

[0069] The objective function in S8 is:

[0070]

[0071] It is a sustainable punishment. It can be dynamically adjusted; It is a path selectivity penalty. Weights are assigned to ensure efficient paths;

[0072] Formula for calculating total carbon footprint:

[0073]

[0074] Where CF represents the total carbon footprint; h and i represent the paths from vehicle type to node i; The edge weight; Path distance; This refers to the load weight; Emission factor; To adjust the factor, ;

[0075] The steps for optimizing production and transportation scheduling further include: integrating time window constraints and vehicle capacity limits, using deep reinforcement learning to assist in the generation of initial solutions for simulated annealing, and improving optimization efficiency and sustainability.

[0076] The standardized traceable container in S1 further includes: associated storage of container identifiers and location identifiers, supporting complete traceability of multi-supplier inventory items, providing dynamic location updates for forward and reverse movement from supply chain input to order fulfillment, and achieving low-latency data synchronization through 5G edge computing.

[0077] The search process of the Dragonfly algorithm in S6 includes initializing the maximum number of iterations T, dynamically adjusting the selection of transportation execution companies through behavioral parameters, realizing numerical analysis of the optimal transportation execution company search parameters, and adding a particle swarm optimization hybrid mechanism to accelerate convergence.

[0078] A supply chain management system based on adaptive optimization of traceable containers, comprising:

[0079] Q1: Standardized trackable container modules for storing inventory items and collecting data in real time, including location, inventory, outbound data and carbon emission estimates collected by built-in sensors; the standardized trackable container modules described in Q1 are compatible with the indexed storage locations of node facilities and transport vehicles, providing complete traceability from input to fulfillment, including carbon emission tracking and reverse logistics support, and reducing container failures through AI-driven predictive maintenance;

[0080] Q2: Data acquisition and fluctuation calculation module, used to calculate the degree of fluctuation and the reliability of data changes;

[0081] Q3: Prediction module, used to calculate importance and fuse smoothing parameters, uses reinforcement learning to correct EWMA prediction requirements. The prediction module in Q3 further includes a DTW distance calculation submodule, used to enhance the accuracy of smoothing parameters and support temporal attention mechanism to capture long-term dependencies.

[0082] Q4: An integrated scheduling module is used for KD tree matching and Dragonfly algorithm search for multimodal transport schemes, calculating edge weights and path selectivity; the Dragonfly algorithm of the integrated scheduling module in Q4 includes behavioral parameter optimization to ensure the global optimum of the multimodal transport scheme, and incorporates quantum-inspired algorithms to handle high-dimensional optimization problems;

[0083] Q5: Optimization module, used to simulate the annealing algorithm to minimize cost and carbon footprint, considering outsourcing and time windows; the carbon footprint calculation of the optimization module in Q5 further includes: real-time estimation of vehicle emission coefficients, dynamic adjustment of λ weights to prioritize green paths; the tracking feedback module uses cloud platform API interfaces to realize hourly uploading of container data and model iteration, and integrates digital twin technology to simulate supply chain scenario optimization.

[0084] Q6: Tracking feedback module, used to update the location identifier and form an adaptive closed loop through reinforcement learning, adjusting parameters in each iteration cycle;

[0085] Q7: Central processing unit and non-transitory computer-readable storage medium, performing the calculations and data processing of the above modules, storing container identifiers, location identifiers and vendor identifiers.

[0086] Example 2

[0087] Food supply chain applications

[0088] Assume a food company's supply chain, including supplier warehouses, transport vehicles, and retail nodes. The system deployment is as follows:

[0089] Data collection: 100 standardized containers are deployed in the warehouse and vehicles to collect data every 5 minutes. For example, data collected includes: 500kg inbound, 300kg outbound, 200kg in stock, location (latitude and longitude: 39.9, 116.3), temperature 25°C, humidity 60%, and an estimated carbon emission of 10kg CO2.

[0090] Volatility Calculation: Compare with historical data from the past 24 hours to calculate the volatility index for each dimension. For example, inventory volatility = |200 - average historical inventory| / N = 0.15, and the cumulative normalization yields a volatility level of 0.25.

[0091] Credibility calculation: based on the mean Euclidean distance of 5 historical outbound destinations. =50km, calculate =0.8 (high confidence).

[0092] Importance calculation: degree of change = 0.25 * 0.8 = 0.2, ratio to the previous time point = 1.1, normalized importance = 0.6.

[0093] Demand Forecast: =0.3 (based on) =0.5, =0.67), reinforcement learning corrects EWMA, predicts demand of 400kg in the next hour, reward R=0.95 (accuracy 0.9 + 0.05*carbon emission reduction).

[0094] Transportation plan: KD tree matching for the "cold chain transportation" feature, Dragonfly algorithm (T=50) generates the plan: road + rail, sequence: warehouse → transit station → retail, choose company A (low cost).

[0095] Path planning: edge weights =100 + 0.5 * (0.250km300kg) = 115, the planned route covers 3 nodes, and the selectivity is 0.85.

[0096] Optimization: Simulated annealing optimization, total cost 1500 yuan, carbon footprint 500 kg, adjust λ=0.6 to prioritize the green path.

[0097] Feedback: Container feedback is delayed by 10 minutes, and the iterative model is updated to α=0.35.

[0098] Results: The system improved prediction accuracy by 15% and reduced carbon emissions by 10%, making it suitable for peak demand fluctuations.

[0099] Example 3

[0100] Reverse logistics applications for electronic products

[0101] For return scenarios:

[0102] Data Acquisition: The container collects reverse data and updates the location to the central system.

[0103] Fluctuation and Importance: Similar to Example 1, fluctuation level 0.3, confidence level 0.7, importance level 0.65.

[0104] Forecast: 200 return requests are expected, which will be incorporated into carbon emission reduction incentives.

[0105] Solution and Planning: Dragonfly algorithm generates reverse multimodal transport with a path selectivity of 0.9.

[0106] Optimization: Minimize outsourcing costs + sustainability penalty, total carbon footprint 300kg.

[0107] Feedback: Updated hourly, anomalies detected (such as delays), and path adjustments made in advance of emptying.

[0108] Results: Reverse engineering efficiency improved by 20%, supporting multi-supplier collaboration.

[0109] 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 supply chain management method based on adaptive optimization of traceable containers, characterized in that, Includes the following steps: S1: At each data collection moment, supply chain data for various dimensions of the product is collected through standardized traceable containers; S2: By collecting multi-dimensional supply chain data at various times and calculating the difference between it and the preset historical supply chain data length, the degree of fluctuation can be determined. S3: Calculate the reliability of changes in outbound data by measuring the Euclidean distance between transportation destinations; S4: Calculate the importance of the data collection time. The importance is positively correlated with the degree of fluctuation in the supply chain data at that time and the reliability of the changes in the supply chain data. S5: Integrate the target smoothing parameters and correct the weights in the EWMA algorithm through a reinforcement learning model to obtain the supply chain demand forecast for the next time step. S6: Use the KD tree nearest neighbor search algorithm to match the material transportation feature text data with the supply chain material transportation scheme dataset. Use the improved dragonfly algorithm to search for multimodal transport schemes and execution order, generate supply chain material transportation mode and transportation execution order identification data, and optimize the algorithm initialization process. S7: Calculate the edge weights of various optional vehicles in the commodity supply chain graph from the sales node; S8: Based on simulated annealing algorithm, optimize production and transportation scheduling, embed carbon footprint into the optimization, and extend it to multi-objective optimization, including minimizing total green cost and maximizing supply chain resilience; S9: By tracking and updating dynamic location identifiers in real time through containers, a feedback loop is formed, and machine learning is used to adjust prediction and scheduling parameters to improve system adaptability.

2. The supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The reliability of the calculated data changes in S3 satisfies the following formula: Among them The reliability of supply chain data changes at time t. The number of historical outbound data. Let H be the Euclidean distance between the k-th and h-th outbound data transport destinations. This represents the average of the location indicators.

3. The supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The formula for calculating the target smoothing parameter in S5 is as follows: Let be the target smoothing parameter for the i-th sales node. The standard deviation of historical supply data. For the DTW distance, the reinforcement learning model uses prediction accuracy and carbon emission reduction as reward functions and dynamically adjusts the weights; The specific implementation of the reinforcement learning model includes: inputting historical and real-time data, outputting weight correction values, and the reward function is: R = prediction accuracy + β × carbon emission reduction rate, where β is the environmental weight.

4. The supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The formula is satisfied in S7: in For carbon footprint items ( ,in , (where h is the emission factor of vehicle type), and the effective route for each type of optional vehicle is planned according to the edge weight, and the route selectivity is calculated according to the demand of nodes on the route to realize commodity scheduling.

5. A supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The objective function in S8 is: It is a sustainable punishment. It can be dynamically adjusted; It is a path selectivity penalty. Weights are assigned to ensure efficient paths; Formula for calculating total carbon footprint: Where CF represents the total carbon footprint; h and i represent the paths from vehicle type to node i; The edge weight; Path distance; This refers to the load weight; Emission factor; To adjust the factor, ; The steps for optimizing production and transportation scheduling further include: integrating time window constraints and vehicle capacity limits, using deep reinforcement learning to assist in the generation of initial solutions for simulated annealing, and improving optimization efficiency and sustainability.

6. The supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The standardized traceable container in S1 further includes: associated storage of container identifiers and location identifiers, supporting complete traceability of multi-supplier inventory items, providing dynamic location updates for forward and reverse movement from supply chain input to order fulfillment, and achieving low-latency data synchronization through 5G edge computing.

7. A supply chain management method based on adaptive optimization of traceable containers according to claim 1, characterized in that: The search process of the Dragonfly algorithm in S6 includes initializing the maximum number of iterations T, dynamically adjusting the selection of transportation execution companies through behavioral parameters, realizing numerical analysis of the optimal transportation execution company search parameters, and adding a particle swarm optimization hybrid mechanism to accelerate convergence.

8. A supply chain management system based on adaptive optimization of traceable containers, characterized in that: include: Q1: Standardized trackable container modules are used to store inventory items and collect data in real time, including built-in sensors to collect location, inventory, outbound data and carbon emission estimates; Q2: Data acquisition and fluctuation calculation module, used to calculate the degree of fluctuation and the reliability of data changes, the formula of which is as described in claim 1; Q3: Prediction module, used to calculate importance and fuse smoothing parameters, and use reinforcement learning to correct EWMA prediction requirements; Q4: Integrated scheduling module, used for KD tree matching and dragonfly algorithm search for multimodal transport schemes, calculating edge weights and path selectivity; Q5: Optimization module, used to simulate the annealing algorithm to minimize cost and carbon footprint, considering outsourcing and time windows; Q6: Tracking feedback module, used to update the location identifier and form an adaptive closed loop through reinforcement learning, adjusting parameters in each iteration cycle; Q7: Central processing unit and non-transitory computer-readable storage medium, performing the calculations and data processing of the above modules, storing container identifiers, location identifiers and vendor identifiers.

9. A supply chain management system based on adaptive optimization of traceable containers according to claim 8, characterized in that: The standardized traceable container module described in Q1 is compatible with the index storage location of node facilities and transport vehicles, providing complete traceability from input to fulfillment, including carbon emission tracking and reverse logistics support, and reducing container failures through AI-driven predictive maintenance; The prediction module described in Q3 further includes a DTW distance calculation submodule, which is used to enhance the accuracy of smoothing parameters and support a temporal attention mechanism to capture long-term dependencies; The Dragonfly algorithm of the integrated scheduling module described in Q4 includes behavior parameter optimization to ensure the global optimum of the multimodal transport scheme, and incorporates quantum-inspired algorithms to handle high-dimensional optimization problems; The carbon footprint calculation of the optimization module described in Q5 further includes: real-time estimation of vehicle emission coefficients and dynamic adjustment of λ weights to prioritize green paths; The tracking and feedback module uses cloud platform API interfaces to enable hourly uploads of container data and model iterations, and integrates digital twin technology to simulate supply chain scenario optimization.

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