Multi-target multi-vehicle type low-carbon emission integrated optimization architecture for industrial cold-chain logistics system

Abstract

The invention discloses a multi-target multi-vehicle type low-carbon emission integrated optimization architecture for an industrial cold-chain logistics system, and belongs to the technical field of intelligent logistics management and low-carbon operation. According to the method, a hierarchical cross-scale feature fusion demand prediction model facing spatio-temporal dynamic perception is adopted, and non-stationary demand evolution is deconstructed by adopting multi-granularity tensor coupling; the method comprises the following steps: injecting demand map prior for a low-carbon emission reduction-oriented enhanced multi-target multi-vehicle optimization model, taking green energy efficiency potential energy as a boundary, constructing a'transportation-temperature control-storage-aging-carbon emission 'five-dimensional quantitative normal form, solving multi-agent collaborative game equilibrium, and issuing a flow topology constraint; based on a graph neural representation-based multi-constraint collaborative reinforcement learning warehouse site selection model, hub layout self-adaptive optimization and reverse flux calibration are realized by using cascade graph attention and spectral domain convolution and strategy gradient and branch and bound decision. According to the invention, module splitting and single-target limitation are eliminated, and economic and environmental benefits are considered.

Description

Technical Field

[0001] This invention relates to the fields of intelligent logistics management and low-carbon logistics technology, and in particular to a multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems. Background Technology

[0002] Currently, industrial cold chain logistics is a core link in maintaining the quality and safety of perishable goods (such as food, pharmaceuticals, and biological agents), and its market size is growing rapidly. Efficient cold chain logistics operations rely heavily on accurate demand forecasting and dynamic scheduling optimization to achieve synergy among multiple objectives such as temperature control stability, delivery timeliness, and cost controllability. However, in practice, complex transportation network topologies, variable environmental conditions (such as temperature control energy consumption fluctuations and extreme weather), and spatiotemporal heterogeneity of demand are intertwined, causing enterprises to face a complex game between delivery efficiency, operating costs, and service quality. For example, in rugged terrain delivery, vehicle energy consumption costs rise non-linearly, while fresh food delivery requires simultaneous optimization of cold chain equipment start-up and shutdown strategies in high-temperature environments. Such scenarios all require dynamic adjustment of operational and carbon emission accounting logic. Traditional phased, single-objective optimization strategies are difficult to cope with the systemic complexity of such tightly coupled multi-objectives, and an integrated solution that can perceive the environment in real time and integrate forecasting and decision-making is urgently needed.

[0003] To ensure the delivery efficiency of industrial cold chain logistics systems and reduce carbon emissions, existing academic methods can be mainly divided into two categories: one focuses on demand forecasting-based optimization, using machine learning or statistical models to predict order volume to guide resource allocation; the other focuses on route planning and real-time scheduling, using heuristic algorithms to dynamically adjust vehicle routes. However, these methods have significant limitations: insufficient ability to model complex spatiotemporal relationships, resulting in prediction models that are sensitive to abnormal fluctuations and have poor robustness; most methods either ignore carbon emission constraints or have huge computational overhead, making it difficult to achieve an effective balance between green and low-carbon development and real-time response; existing methods often optimize prediction, scheduling, and location selection in isolation, ignoring the inherent synergistic effects between them, resulting in limited overall system performance.

[0004] To overcome the above-mentioned shortcomings, this invention proposes a multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems. This architecture integrates spatiotemporal characteristics to jointly optimize demand forecasting, route scheduling, and warehouse location selection, aiming to achieve efficient and low-carbon overall planning of cold chain logistics. This invention aims to solve the problems of conflicting optimization objectives, poor environmental adaptability, and low overall energy efficiency caused by the modular fragmentation of traditional methods. Summary of the Invention

[0005] This invention aims to provide a multi-objective, multi-vehicle, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, achieving three main functions: improving the accuracy of customer demand forecasting by accurately characterizing the spatiotemporal evolution of demand through a multi-granularity tensor feature coupling mechanism, and deriving a non-stationary stochastic demand evolution spectrum; realizing multi-vehicle collaborative scheduling by constructing a five-dimensional unified quantitative evaluation paradigm of "transportation-temperature control-warehousing-timeliness-carbon emission" using green energy efficiency potential energy as the implicit boundary of the system, balancing transportation costs, delivery efficiency, and carbon emissions; establishing a dynamic collaborative mechanism for warehouse site selection and route planning by dynamically representing the network state using cascaded graph attention and spectral domain convolution operators, combined with hyperplane decision-making techniques of policy gradient and branch-bound, to achieve endogenous adaptive optimization and reverse throughput calibration of hub node layout, enhancing the system's adaptability to complex environments, and ultimately reducing system operating costs and improving the economic and environmental benefits of cold chain logistics through integrated collaboration.

[0006] The present invention solves its technical problem by adopting the following technical solution: a multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, including a hierarchical cross-scale feature fusion demand prediction model for spatiotemporal dynamic perception, an enhanced multi-objective, multi-vehicle type optimization model for low-carbon emission reduction, and a multi-constraint collaborative reinforcement learning warehouse location optimization model for graph neural representation perception. The models work together and achieve low-power backhaul of resource instructions through a backscatter modulation mechanism.

[0007] Furthermore, the hierarchical cross-scale feature fusion demand prediction model for spatiotemporal dynamic perception, by feeding in historical data streams and employing a multi-granularity tensor feature coupling mechanism, deeply deconstructs and deduces the non-stationary random demand evolution map, providing spatiotemporal state priors for subsequent model optimization. This model integrates convolutional neural networks (CNN), Transformer self-attention mechanism, and U-Net encoder-decoder architecture, enabling accurate extraction of multi-scale demand features and effective modeling of demand spatiotemporal evolution.

[0008] Furthermore, the encoder extracts multi-scale local temporal features through multi-layer CNN convolution and downsampling operations, while using skip connections to preserve high-resolution details. Then, in the global dependency modeling stage, the encoder's output features are divided into several patches, which are then linearly mapped and positionally encoded before being input into the Transformer self-attention module to capture long-range spatiotemporal correlations in parallel. Finally, the decoder restores the spatial resolution through multiple upsampling and convolution operations, and then concatenates and fuses the upsampled features with the corresponding scale CNN-encoded features to generate a high-precision non-stationary random demand evolution map.

[0009] Furthermore, the enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction incorporates the spatiotemporal state prior of the demand evolution map, uses green energy efficiency potential energy as the implicit boundary of the system, and constructs a five-dimensional unified quantitative evaluation paradigm covering the entire set K of heterogeneous transport units and "transportation-temperature control-warehousing-timeliness-carbon emission". Under the strict conditions of physical spatiotemporal hard constraints, road network geometric topology adaptability, and full life cycle operation and maintenance boundary, it solves the multi-agent cooperative game equilibrium under complex networks and issues traffic topology constraints. This model revolves around multi-vehicle parameter modeling, integrated cost construction, carbon emission-driven planning model, and dynamic scheduling mechanism to achieve a synergistic balance between cost, efficiency, and low-carbon goals in cold chain distribution.

[0010] Furthermore, the enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction first performs multi-vehicle parameter modeling, defining a heterogeneous vehicle unit set K encompassing multiple power topologies and load energy levels. This set is discretized into eleven specific physical entity subclasses: internal combustion engine-driven diesel light / medium / heavy-duty truck units, electrochemical energy storage-driven electric light / medium-duty truck units, hybrid range-extended light / medium-duty truck units, hydrogen fuel cell-driven light / heavy-duty truck units, and diesel / electric refrigerated special units specifically for temperature control scenarios (i.e., 11 vehicle types: diesel light truck, diesel medium truck, diesel heavy truck, electric light truck, electric medium truck, range-extended light truck, range-extended medium truck, hydrogen fuel cell light truck, hydrogen fuel cell heavy truck, diesel refrigerated truck, and electric refrigerated truck). All heterogeneous subclasses are projected onto a normalized physical feature parameter matrix for calibration, clarifying the core parameters of each vehicle type. Maximum load capacity of diesel light truck =1.5–4.5 tons, range =600–800km, empty-load carbon emission coefficient =0.25–0.3 kg CO2e / km, cargo sensitivity coefficient =0.6–0.8, unit distance transportation cost =3–4 yuan / km, average driving speed =60–80km / h, no cooling power required wheelbase =3.2–3.8m, overall vehicle length =5.5–6.8m, turning radius =6.5–8.0m, maximum climbing ability =20%–30% of the unit mileage maintenance cost =0.2–0.3 yuan / km, warranty mileage =4 years or 250,000–300,000 kilometers; Maximum load capacity of diesel medium trucks =5–10 tons, range =700–900km, empty-load carbon emission coefficient =0.35–0.45 kgCO2e / km, cargo sensitivity coefficient =0.7–0.9, unit distance transportation cost =4–5.5 yuan / km, average driving speed =70–90km / h, no cooling power required wheelbase =3.8–4.5m, overall vehicle length =7.0–8.5m, turning radius =8.0–9.5m, maximum climbing ability =25%–35%, unit mileage maintenance cost =0.3–0.4 yuan / km, warranty mileage =5 years or 350,000–450,000 kilometers; Maximum load capacity of diesel heavy trucks =10–25 tons, range =800–1200km, empty-load carbon emission coefficient =0.45–0.6 kgCO2e / km, cargo sensitivity coefficient =0.8–1.0, unit distance transportation cost =5.5–7 yuan / km, average driving speed =70–85km / h, no cooling power required wheelbase =4.8–5.8m, overall vehicle length =9.5–12.0m, turning radius =10.5–12.5m, maximum climbing ability =32%–42%, Maintenance cost per unit mileage =0.45–0.55 yuan / km, warranty mileage =6 years or 450,000–550,000 kilometers; Maximum load capacity of electric light truck =1.0–3.0 tons, range =200–300km, empty-load carbon emission coefficient =0 kgCO2e / km, Cargo Sensitivity Factor =0.5–0.7, unit distance transportation cost =1.0–2.0 yuan / km, average driving speed =50–70km / h, no cooling power required wheelbase =3.0–3.6m, overall vehicle length =5.2–6.5m, turning radius =6.2–7.8m, maximum climbing ability =18%–28%, Maintenance cost per unit mileage =0.15–0.25 yuan / km, warranty mileage =5 years or 300,000–400,000 kilometers; Maximum load capacity of electric medium-duty trucks =5–8 tons, range =250–350km, empty-load carbon emission coefficient =0 kgCO2e / km, Cargo Sensitivity Factor =0.6–0.8, unit distance transportation cost =1.5–2.5 yuan / km, average driving speed =55–75km / h, no cooling power required wheelbase =3.7–4.4m, overall vehicle length =6.8–8.2m, turning radius =7.8–9.3m, maximum climbing ability =22%–32%, Maintenance cost per unit mileage =0.2–0.3 yuan / km, warranty mileage =6 years or 350,000–450,000 kilometers; Maximum load capacity of range-extended light trucks =1.5–4.0 tons, range =500–600km, empty-load carbon emission coefficient =0.08–0.12 kgCO2e / km, cargo sensitivity coefficient =0.6–0.8, unit distance transportation cost =1.5–2.5 yuan / km, average driving speed =60–80km / h, no cooling power required wheelbase =3.1–3.7m, overall vehicle length =5.4–6.7m, turning radius =6.4–7.9m, maximum climbing ability =19%–29%, Maintenance cost per unit mileage =0.18–0.28 yuan / km, warranty mileage =5 years or 300,000–350,000 kilometers; Maximum load capacity of range-extended medium-duty trucks =5–9 tons, range =550–700km, empty-load carbon emission coefficient =0.1–0.15 kgCO2e / km, cargo sensitivity coefficient =0.7–0.9, unit distance transportation cost =2.0–3.0 yuan / km, average driving speed =65–85km / h, no cooling power required wheelbase =3.8–4.5m, overall vehicle length =6.9–8.4m, turning radius =8.0–9.5m, maximum climbing ability =24%–34%, Maintenance cost per unit mileage =0.25–0.35 yuan / km, warranty mileage =5 years or 350,000–400,000 kilometers; Maximum load capacity of hydrogen fuel cell light truck =1.5–4.0 tons, range =500–700km, empty-load carbon emission coefficient =0 kgCO2e / km, Cargo Sensitivity Factor =0.5–0.7, unit distance transportation cost =2.5–4.0 yuan / km, average driving speed =60–80km / h, no cooling power required wheelbase =3.1–3.6m, overall vehicle length =5.3–6.6m, turning radius =6.3–7.8m, maximum climbing ability =19%–29%, Maintenance cost per unit mileage =0.22–0.32 yuan / km, warranty mileage =6 years or 350,000–450,000 kilometers; Maximum load capacity of hydrogen fuel cell heavy trucks =10–20 tons, range =600–800km, empty-load carbon emission coefficient =0 kgCO2e / km, Cargo Sensitivity Factor =0.4–0.6, unit distance transportation cost =3.5–5.0 yuan / km, average driving speed =70–90km / h, no cooling power required wheelbase =4.5–5.5m, overall vehicle length =9.0–11.0m, turning radius =10.0–12.0m, maximum climbing ability =30%–40%, unit mileage maintenance cost =0.4–0.5 yuan / km, warranty mileage =6 years or 400,000–500,000 kilometers; Maximum load capacity of diesel refrigerated truck =1.5–4.0 tons, range =800–1000km, empty-load carbon emission coefficient =0.25–0.3 kg CO2e / km, cargo sensitivity coefficient =0.7–0.9, unit distance transportation cost =3.5–4.5 yuan / km, average driving speed =60–80km / h, cooling power =3.0–5.0kW, wheelbase =3.3–3.9m, overall vehicle length =5.8–7.2m, turning radius =6.8–8.3m, maximum climbing ability =20%–30% of the unit mileage maintenance cost =0.25–0.35 yuan / km, warranty mileage =4 years or 250,000–300,000 kilometers; Maximum load capacity of electric refrigerated truck =1.0–2.5 tons, range =200–250km, empty-load carbon emission coefficient =0 kgCO2e / km, Cargo Sensitivity Factor =0.5–0.7, unit distance transportation cost =2.0–3.0 yuan / km, average driving speed =50–70 km / h, cooling power =3.0–5.0kW, wheelbase =3.0–3.5m, overall vehicle length =5.3–6.6m, turning radius =6.3–7.7m, maximum climbing ability =18%–28%, Maintenance cost per unit mileage =0.2–0.3 yuan / km, warranty mileage =5 years or 300,000–400,000 kilometers.

[0011] Next, we model the total cost and total carbon emission cost, integrating transportation costs, refrigeration costs, warehousing costs, and time default / reward costs. We also incorporate the costs corresponding to the three types of carbon emissions: transportation, refrigeration, and pre-cooling in warehousing, forming a unified cost function. This function strictly adheres to the calculation axiom of "discrete metric orthogonal analysis and global loss linear reconstruction." The specific formulas and parameter explanations are as follows: Total carbon emission cost: , in, For total carbon emission costs, For transportation carbon emission costs, For the carbon emission cost of the refrigeration system, Pre-cooling for storage reduces carbon emissions costs; Transportation carbon emission costs: , in, As a carbon emission cost weight, For vehicle type, For nodes, For the set of all nodes, For model No-load carbon emission factor For nodes and Inter-space distance, For model Is the driving arc Binary decision variables, For model Cargo sensitivity coefficient To avoid auxiliary variables in the nonlinear product of "cargo volume × carbon emission coefficient"; Carbon emission costs of refrigeration systems: , in, Carbon emission factor per unit of electricity / fuel For vehicle model Refrigeration equipment power, For model Transportation time; Carbon emission costs of pre-cooling in warehousing: , in, Carbon emission factor per unit of electricity Number the warehouse. For warehouse collection, For warehouse Power consumption; Total cost (excluding carbon emissions): , in, This is the total cost (excluding carbon emissions). For warehouse costs, For transportation costs, To reduce cold chain transportation costs, The total time penalty cost for breach of contract, Total time reward cost; Transportation costs: , in, For model Unit distance transportation cost; Cold chain transportation costs: , in, Unit refrigeration cost For model Time-dependent refrigeration efficiency factor (with transport time) (Related) Single customer time default penalty cost: , in, Number the customer. Penalty cost per unit of time For the vehicle to reach the customer Time, For customers The end of the time window; Total time penalty cost for breach of contract: , The total cost of time-based penalties for all customers.

[0012] Subsequently, a carbon emission-driven linear integer programming model is constructed. By building a multi-dimensional constrained hyperplane system orthogonally mapped to the customer service level threshold, the vehicle's full lifecycle operating state vector, and hard time window boundaries, the high-dimensional solution space is strictly geometrically cut and the feasible region is defined. This forces convergence to a delivery solution set with physical execution effectiveness and global Pareto optimality. The specific objective function and constraint formulas are as follows: Objective function: , in, This is the total cost (excluding carbon emissions). For total carbon emission costs, This is the carbon emission cost weighting coefficient; Constraint 1 (Customer Service Uniqueness): , in, For model Is the driving arc Binary decision variables, For the set of all nodes, For a set of client nodes; Constraint 2 (Vehicle start and end point closed loop - departure): , in, Represents the starting point of the warehouse. A collection of car models; Constraint 3 (Vehicle start and end point closed loop - return): , in, The total number of customer nodes. Represents the end point of the warehouse; Constraint 4 (Flow Conservation): , in, For any customer node, ensure that the vehicle leaves after entering the customer node; Constraint 5 (Capacity Limitation): , in, For customers The demand quantity, For model Maximum load capacity (core performance and low carbon parameters); Constraint 6 (Battery Range Constraint): , in, For nodes and Inter-space distance, For model Driving range (core performance and low-carbon parameters); Constraint 7 (Time Window Constraint): , in, For customers Time window start point, For customers End of time window For model Arrival at the customer Time; Constraint 8 (Time Accumulation): , in, For model Driving arc Time, For model Average driving speed (core performance and low carbon parameters). Use a sufficiently large constant for linearization; Constraint 9 (Initial Departure Time): , in, For model The initial time from the warehouse; Constraint 10 (Road Slope Adaptation): , in, For road section Actual slope For model Maximum climbing ability; Constraint 11 (Road segment turning radius adaptation): , in, For road section Minimum allowable turning radius For model Turning radius; Constraint 12 (Warranty Mileage Constraint): , in, For model Warranty mileage; Constraint 13 (Maintenance Cost Budget): , in, For model Maintenance cost per unit mileage For model Maximum permissible maintenance cost; Constraint 14 (Total Carbon Emission Allowance): , in, For model No-load carbon emission factor For model Cargo sensitivity coefficient To linearize auxiliary variables, For the region's total carbon emission allowance; Constraint 15 (Speed ​​Limit on Road Section): , in, , Each is a road segment Minimum and maximum speed limits; Constraint 16 (Cooling power adaptation): , in, For model Cooling power, For goods Minimum cooling power requirement Represents a node Transporting goods ; Constraint 17 (Load-Slope Coordination): , in, For model In the arc The actual load capacity; Constraint 18 (Ratio of Unloaded Distance): , in, This represents the upper limit for the percentage of unloaded distance. Constraint 19 (Battery Range Safety Margin): , in, The distance the vehicle travels from the last customer back to the warehouse. For battery life safety factor.

[0013] Furthermore, the nonlinear terms in the model are linearized by introducing auxiliary variables: Dynamically update loaded goods (linearization): in, For model At the node Cargo capacity, A sufficiently large constant is used for linearization; Auxiliary variables Linearization: in, For model In the arc The actual cargo capacity on board A sufficiently large constant is used for linearization; Linearization of product terms: Defining auxiliary variables Avoid nonlinear product terms.

[0014] Finally, a dynamic scheduling mechanism is established based on demand forecasting results and real-time traffic conditions to dynamically adjust vehicle routes and cargo allocation, taking into account both delivery efficiency and low-carbon goals. The mechanism solves the multi-agent collaborative game equilibrium under complex networks and issues traffic topology constraints.

[0015] Furthermore, the multi-constraint collaborative reinforcement learning warehouse location optimization model for graph neural representation perception performs high-order graph topology mining based on traffic topology constraints. It uses data space association mapping technology—namely, cascaded graph attention and spectral domain convolution operators—to dynamically represent the network state. Combined with hyperplane decision-making technology based on policy gradient and branch-bound, it achieves endogenous adaptive optimization and reverse throughput calibration of hub node layout. This model achieves dynamic collaborative adaptation of warehouse node layout and delivery path through graph structure encoding, deep reinforcement learning decision-making, mixed integer programming optimization, and collaborative optimization mechanisms.

[0016] Furthermore, the model first performs graph structure encoding, using a graph neural network (GNN) to construct a logistics network topology graph, encoding the spatial relationships between warehousing nodes and demand areas, as well as the heterogeneity of demand. The graph attention encoder extracts high-order features of nodes through a three-level cascaded attention aggregation structure (the first two levels use a self-attention topology aggregation mechanism, and the third level uses a cascaded spectral domain convolution operator), and encodes the static Euclidean space attributes of the nodes—including Cartesian coordinates. Scalar demand scale Time window closed interval (start time) Deadline Static information such as traffic speed is mapped to embedded vectors; simultaneously, real-time traffic speed is introduced to dynamically adjust the edge weights, with the adjustment formula being: ,in, The generalized impedance weights are corrected for flow velocity. For nodes and The Riemann distance between them For nodes and Instantaneous transport rate is used to enhance the model's robustness to unsteady road network environments.

[0017] Subsequently, deep reinforcement learning decision-making was conducted, employing the Deep Deterministic Policy Gradient (DDPG) algorithm combined with an actor-critic architecture to explore Pareto optimal solutions in a continuous action space, generating joint location-scheduling decisions. The state space design strictly adhered to the physical constraints and optimization objectives of the mathematical model: the vehicle's state subspace consisted of the remaining payload potential energy... Cumulative trajectory length and current system clock (Current system clock directly coupled to time violation penalty operator) With time-incentive operator The node state subspace is then integrated with the static attribute vector. With the time-varying width of the dynamic remaining time window This provides a multi-dimensional spatiotemporal awareness foundation for policy generation networks.

[0018] The action space generates a constraint system that inherits from the mathematical model to ensure the feasibility of the solution. When the carrier unit resides at node i, a ternary joint constraint check is performed on the candidate node j, including capacity saturation constraints. Temporal hard window constraints and energy range constraints A hard threshold masking mechanism is introduced to eliminate all singular actions that violate the above constraints. The node selector performs random probability sampling decisions in the compliant candidate set based on the weighted attention coefficients output by the feature extractor, thereby realizing the modeling of stochastic processes that satisfy the constraint boundary conditions.

[0019] Reward Function and Mathematical Model Objective Strict alignment is applied, with the immediate reward for each decision step defined as a negative incremental cost, as shown in the formula: ,in, The carbon footprint cost covers the entire lifecycle of transportation flow, thermodynamic refrigeration, and storage; when the trajectory of the transport unit fails to converge to the node within the closed time window. (Right now When the large norm negative penalty signal is triggered, the effectiveness of gradient guidance under the time window boundary constraint is enhanced.

[0020] Furthermore, mixed-integer programming optimization is performed, and a multi-warehouse location-allocation model is constructed based on the service cost output by the path scheduling module. With the objective of "minimizing warehouse fixed costs + delivery costs", a binary location variable is introduced. (representing warehouse) Whether it is activated), the decision rule is: ,in, Indicates customer Is it from the warehouse? Serve, For warehouse Fixed operating costs, For warehouse To the customer Transportation costs, For warehouse To the customer While reducing cold chain costs, it also meets warehouse capacity constraints. ( For warehouse Maximum capacity and customer attribution constraints The Pareto optimal solution set is obtained by using the branch and bound algorithm, and the warehouse combination with the minimum total supply chain cost is selected.

[0021] Furthermore, relying on the collaborative optimization mechanism, the site selection results are fed back to the enhanced multi-objective multi-vehicle optimization model oriented towards low-carbon emission reduction, thereby achieving dynamic adaptation of warehousing resource allocation and delivery routes.

[0022] Furthermore, the various models work together to advance the optimization process. The hierarchical cross-scale feature fusion demand prediction model for spatiotemporal dynamic perception takes historical data containing spatiotemporal features and environmental interference factors as input and uses a multi-granularity tensor feature coupling mechanism to output a non-stationary stochastic demand evolution map. The enhanced multi-objective multi-vehicle optimization model for low-carbon emission reduction generates a delivery plan that takes into account cost, efficiency, and carbon emissions based on the demand evolution map, vehicle parameters, and constraints, and outputs warehouse-demand node service cost data. The multi-constraint collaborative reinforcement learning warehouse location optimization model for graph neural representation perception optimizes the allocation of warehousing resources based on the service cost of the delivery plan and network topology characteristics. Through a backscatter modulation mechanism, the location results and scheduling instructions are transmitted back to the logistics execution system with low power consumption.

[0023] The beneficial effects of this invention are that, through the aforementioned multi-objective, multi-vehicle, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, the MAE of the hierarchical cross-scale feature fusion demand prediction model based on spatiotemporal dynamic perception is reduced by approximately 61.67%, and the MSE is reduced by approximately 85.42%, providing accurate data support for subsequent optimization. Simultaneously, delivery costs are reduced by up to 46.84%, carbon emissions are reduced by an average of 46.80%, and vehicle utilization is increased by 10.78%, achieving a win-win situation for both economic and environmental benefits. It can also effectively balance multiple objectives such as transportation costs, delivery timeliness, carbon emissions, and service quality, overcoming the limitations of single-objective optimization in traditional methods. Furthermore, it supports multi-vehicle and multi-scenario delivery needs, and can adapt to the characteristics of different regional logistics networks through dynamic parameter adjustment, exhibiting strong adaptability and scalability. Additionally, it achieves low-power instruction return through a backscatter modulation mechanism, and improves execution efficiency through a processor-free scheduling strategy, outperforming traditional solution methods. Attached Figure Description

[0024] Figure 1 This is a detailed flowchart of the multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems in this invention. Detailed Implementation

[0025] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention proposes a multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, the process of which is described below. Figure 1 Among them are a hierarchical cross-scale feature fusion demand prediction model for spatiotemporal dynamic perception, a multi-objective multi-vehicle optimization model for low-carbon emission reduction, and a multi-constraint collaborative reinforcement learning warehouse location optimization model for graph neural representation perception. Each model achieves real-time data communication through a distributed data interface and completes low-power backhaul of resource instructions by relying on a backscatter modulation mechanism.

[0027] In the multi-objective, multi-vehicle, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, the hierarchical cross-scale feature fusion demand prediction model oriented towards spatiotemporal dynamic perception is the "data cornerstone" of the entire chain optimization. Its core revolves around five key aspects: data preprocessing, encoder design for multi-scale feature extraction, long-range spatiotemporal correlation modeling, decoder design for feature fusion and prediction output, and model training and optimization. Through the collaborative operation of each link, the complex characteristics of "local sudden fluctuations and long-range spatiotemporal correlations" of cold chain demand are accurately captured by the multi-granularity tensor feature coupling mechanism, and the evolution map of non-stationary stochastic demand is deduced, providing highly reliable demand data support for subsequent multi-objective path scheduling and warehouse location models.

[0028] Data preprocessing is fundamental to ensuring the quality of model input. It requires collecting multi-dimensional historical data from the past 12 months, including cold chain delivery order volume, spatiotemporal coordinates of each demand node, daily ambient temperature and weather type, and promotional activity schedules for regional business districts. Outliers are cleaned using the 3σ criterion. Numerical features such as order volume and temperature are normalized to eliminate dimensional differences. Finally, the processed data is divided into training, validation, and test sets in a 7:2:1 ratio to ensure high-quality, highly representative data support for model training.

[0029] The encoder design for multi-scale feature extraction focuses on capturing local demand patterns in different dimensions. It adopts a 3-layer CNN convolutional structure to build the encoder. The first layer uses a 3×3 convolutional kernel to extract short-term demand fluctuation features at the hourly level. The second layer uses a 5×5 convolutional kernel to capture regional demand features at the daily level. The third layer uses a 3×3 convolutional kernel to strengthen the cyclical demand features at the weekly level.

[0030] Each convolutional layer is followed by a ReLU activation function to activate non-linear features. The convolution operation is defined as follows: ,in, For time step Input value, For the convolution kernel at position The weight, For bias terms, To activate the output, and simultaneously via Max pooling downsampling compresses the feature dimension. The pooling operation is defined as follows: ,in, For The pooling window is centered on the center. The output is downsampled, and the output features of each layer are temporarily stored through skip connections to avoid losing high-resolution detail information during the downsampling process.

[0031] The long-range spatiotemporal correlation modeling process mainly relies on the Transformer self-attention mechanism. First, the multi-scale features output by the encoder are divided into segments with a size of... The feature patch, after being linearly mapped to a 256-dimensional feature vector, is then augmented with a sine-cosine positional encoding to mark the spatiotemporal location information of the feature. The encoding formula is as follows: ,in, For patch embedding matrix, For patch tokens, For location encoding, the input is a Transformer self-attention module containing four attention heads. This module is processed through multiple Transformer encoder layers, each containing multi-head self-attention (MSA) and multi-layer perceptron (MLP) blocks. It can compute the correlation weights between different feature patches in parallel, accurately capturing long-range spatiotemporal correlations across regions and periods. For example, it can identify the linkage pattern of "after the demand for fresh food in supermarkets in the main urban area increases, the replenishment demand of suburban distribution centers increases synchronously within 3 hours". This makes up for the limitation of traditional CNNs that can only capture local features and provides support for multi-granularity tensor feature coupling mechanisms.

[0032] The core design of the decoder for feature fusion and prediction output is to balance the macroscopic regularities and microscopic details of the requirements. It employs three deconvolution upsampling operations to progressively restore the spatial resolution of the features. After each upsampling, the current global features are concatenated and fused with the local features temporarily stored in the corresponding layer of the encoder—for example, fusing the weekly periodic features obtained from upsampling with the hourly fluctuation features retained in the first layer of the encoder. This ensures that the output contains both macroscopic periodic regularities and microscopic sudden fluctuations. The decoding operation is defined as follows: ,in, The final representation encoded for a Transformer. For skip connection features of the CNN encoder path, This indicates feature splicing.

[0033] The fused features are processed by the final convolutional operation and mapped to predicted probabilities using the Softmax function, as shown in the formula: ,in, For category The logarithmic odds, For category The probability is calculated, and the final output is a non-stationary random demand evolution graph. For example, it can accurately output "In the three days before the Mid-Autumn Festival, the demand for fresh food in a community store in a certain area increased compared to usual from 8 to 10 am". Specific data for "".

[0034] The model training and optimization phases focus on improving prediction accuracy and generalization ability, using MAE (Mean Absolute Error) and MSE (Mean Squared Error) as evaluation metrics. The MAE formula is: The MSE formula is: ,in, To predict the number of samples, For the first The true value of each sample For the first The predicted values ​​for each sample are used, with the weighted sum of MAE and MSE as the loss function to quantify the deviation between the predicted and actual values; this is combined with the Adam optimizer (learning rate set to...). Iterative training is performed to efficiently update model parameters; simultaneously, an early stopping strategy is implemented, terminating training when the validation set loss shows no decrease for five consecutive rounds to prevent overfitting. Testing showed that in the fresh food cold chain demand prediction scenario in Yilong County, Sichuan Province, the MAE of this model is reduced by approximately [percentage missing] compared to the traditional LSTM model. MSE decreased by approximately This can effectively reduce delivery delays and damage caused by deviations in demand forecasting.

[0035] The non-stationary stochastic demand evolution map output by the hierarchical cross-scale feature fusion demand prediction model oriented towards spatiotemporal dynamic perception provides core support for the refined decision-making of the enhanced multi-objective multi-vehicle optimization model for low-carbon emission reduction. As the "execution center" of cold chain logistics operation, this model mainly revolves around four core aspects: multi-vehicle parameter modeling, integrated cost and carbon emission quantification, carbon emission-driven planning model construction, and dynamic scheduling mechanism implementation. Through the coordinated cooperation of each link, and with green energy efficiency potential energy as the implicit boundary of the system, a five-dimensional unified quantitative evaluation paradigm of "transportation-temperature control-warehousing-timeliness-carbon emission" is constructed. Under the strict conditions of physical spatiotemporal hard constraints, road network geometric topology adaptability, and full life cycle operation and maintenance boundary, the multi-agent collaborative game equilibrium under complex network is solved, and flow topology constraints are issued to achieve the goal of "delivering economically, quickly, and environmentally friendly" in cold chain distribution.

[0036] Multi-vehicle parameter modeling is the foundation for scheduling schemes to adapt to different demand scenarios. First, a complete set K of heterogeneous transport units covering multiple power topologies and load energy levels is defined (i.e., 11 types of vehicles, including diesel light trucks, diesel medium trucks, diesel heavy trucks, electric light trucks, electric medium trucks, range-extended light trucks, range-extended medium trucks, hydrogen fuel cell light trucks, hydrogen fuel cell heavy trucks, diesel refrigerated trucks, and electric refrigerated trucks). All heterogeneous subclasses are then projected onto a normalized physical feature parameter matrix for calibration, clarifying the core parameters of each type of vehicle, providing accurate data basis for vehicle selection and resource allocation during subsequent scheduling.

[0037] An integrated cost and carbon emission quantification framework is a key prerequisite for achieving a balance among multiple objectives. Strictly adhering to the calculation axioms of "discrete metric orthogonal analysis and global loss linear reconstruction," it unifies various costs and carbon emission consequences in cold chain operations into a quantification system. Transportation costs are calculated based on vehicle type and unit distance cost, along with delivery mileage. For example, the unit distance cost for a traditional diesel light truck is 3-4 yuan / km, so a round trip of 100km would correspond to a transportation cost of 300-400 yuan. Refrigeration costs are calculated for refrigerated vehicles, combining refrigeration power, delivery time, and unit refrigeration cost. If a fresh produce delivery task takes 4 hours, the corresponding refrigeration cost is 3.0-5.0kW × 4h × unit kWh cost. Warehouse costs are allocated to each delivery task based on the stock quantity and warehousing unit price. Time-related default / reward costs are calculated based on the customer's time window in the demand forecast. If the time window ends, the default cost is calculated based on the penalty cost per unit of time. If the delivery is made early or on time, the cost is deducted according to the reward coefficient. At the same time, carbon emission costs are calculated separately, covering the transportation, refrigeration, and warehousing pre-cooling processes, to ensure that low-carbon goals are quantifiable and can be included in the optimization scope.

[0038] Carbon emission-driven linear integer programming models are the core means of generating optimal scheduling schemes. With the goal of "minimizing the weighted sum of total cost and carbon emission cost", a multi-dimensional constrained hyperplane system orthogonally mapped to customer service level thresholds, vehicle life cycle operating state vectors, and hard time window boundaries is constructed to strictly cut the high-dimensional solution space geometrically and define the feasible region. This forces convergence to a delivery solution set with physical execution effectiveness and global Pareto optimality. Multiple constraints are set to ensure the feasibility of the scheme. For nonlinear terms in the model, auxiliary variables are introduced to transform the product terms into linear expressions, ensuring that the model can be efficiently calculated by solvers such as Gurobi.

[0039] The dynamic scheduling mechanism is an important supplement to make the scheduling plan fit the actual operation scenario. It makes flexible adjustments based on demand forecast results and real-time traffic data. If the demand forecast shows that the demand for fresh food in a certain area increases by 30% on weekends compared to weekdays, the number of new energy pure electric refrigerated trucks will be temporarily increased to match the surge in temperature control demand. If the real-time traffic data shows that the traffic speed on a certain main road drops by 40% during the morning rush hour, the delivery route for that section will be adjusted to a non-main road, and some heavy-load orders will be split into multiple light trucks for delivery to avoid time window defaults due to congestion. Through such dynamic adjustments, the forward-looking nature of demand forecasts can be fully utilized, and unexpected situations in actual operation can be dealt with. Ultimately, a delivery plan that takes into account cost, efficiency and carbon emissions can be generated. The mechanism solves the equilibrium of multi-agent collaborative game and issues traffic topology constraints, while providing accurate service cost data for the subsequent warehouse location module.

[0040] The enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction outputs a delivery solution that balances cost, efficiency, and carbon emissions, along with service cost data from each candidate warehouse to the demand node. This provides a crucial basis for accurate decision-making in a multi-constraint collaborative reinforcement learning warehouse location optimization model oriented towards graph neural representation perception. As a "spatial hub" for cold chain logistics resource layout, the core of this model revolves around four aspects: logistics network topology graph construction and feature encoding, deep reinforcement learning-based joint decision-making for location selection and scheduling, mixed integer programming for multi-warehouse location selection and allocation, and dynamic collaborative feedback between location selection and scheduling. Based on flow topology constraints, it performs high-order graph topology structure mining, dynamically represents the network state using cascaded graph attention and spectral domain convolution operators, and combines policy gradient and branch-bound hyperplane decision-making techniques to achieve endogeneous adaptive optimization and reverse throughput calibration of hub node layout. Through scientific layout of warehouse nodes, it further reduces the overall operating cost and improves the response efficiency of cold chain services, forming a collaborative optimization closed loop of "spatial layout-dynamic execution" with the scheduling module.

[0041] The construction and feature encoding of the logistics network topology are the foundation of site selection modeling. It requires building a logistics network graph covering all business scenarios based on a Graph Neural Network (GNN). The nodes in the graph contain two core types of objects: first, candidate warehouse locations, which need to be entered, including their maximum capacity, fixed operating costs (such as rent and labor costs), and surrounding road conditions; second, demand nodes, which need to import data such as time-period demand scale, temperature control requirements, and time window restrictions provided by the multi-objective path scheduling module. The edge features between nodes cover information such as the actual distance between two points, regular travel time, peak-hour congestion coefficient (such as the percentage decrease in traffic speed on main roads during morning rush hour), and temperature-controlled road section restrictions (such as the time requirements for refrigerated trucks on certain road sections). Simultaneously, the unit mileage cost and carbon emission data of each vehicle type output by the scheduling module are associated with the corresponding edge features, adjusted using an edge weight adjustment formula. Dynamically adapting to real-time traffic conditions ensures that the topology map fully reflects the operational constraints and cost patterns between "warehousing and demand," providing a foundation for mining high-order graph topology structures.

[0042] Deep reinforcement learning-based joint decision-making for site selection and scheduling focuses on solving the adaptability problem between site selection and scheduling. It constructs a decision model by fusing graph neural networks (GNNs) and deep reinforcement learning, and achieves efficient collaborative optimization based on an actor-critic architecture. The actor network outputs the specific geographical coordinates of candidate warehouse locations as the site selection action, closely referencing the vehicle type adaptation rules of the scheduling module. For example, for short-distance urban delivery areas primarily using new energy pure electric refrigerated trucks, it prioritizes candidate locations close to demand node clusters and with well-developed surrounding charging facilities, aligning with the range and refrigeration energy consumption requirements of pure electric refrigerated trucks. The critic network, based on service cost data provided by the scheduling module, comprehensively evaluates the total delivery cost, total carbon emissions, and service compliance rate corresponding to the site selection scheme, generating accurate value assessment results to provide a basis for optimizing site selection decisions. This decision model encodes the topological structure and spatial correlation between warehouse nodes and demand areas through the GNN module, combined with the strategy optimization capabilities of reinforcement learning, to achieve dynamic adaptation between site selection schemes and scheduling needs, ensuring efficient collaboration between warehouse layout and transportation scheduling.

[0043] The state-space design strictly follows the constraints of the mathematical model, and the vehicle's state subspace consists of the residual load potential energy. Cumulative trajectory length and current system clock ; Node state subspace fusion static attribute vector With the time-varying width of the dynamic remaining time window The motion space generation must satisfy capacity constraints. Time window constraints Battery life constraints Illegal actions are filtered out using a hard mask mechanism; the reward function uses a formula. This ensures alignment with the optimization goals.

[0044] The policy optimization uses the Monte Carlo policy gradient method, and the loss function is: ,in, This is the cumulative reward for the sampling and decoding path. The baseline reward for greedy decoding is used to update parameters via the Adam optimizer. Maximize expected cumulative reward .

[0045] Hybrid integer programming for multi-warehouse location-allocation is a key means to achieve optimal resource allocation. It aims to minimize the sum of warehouse fixed operating costs and regional distribution costs (the specific formula is consistent with the invention content). It also incorporates warehouse capacity constraints, customer order attribution constraints, and distribution radius constraints. During the planning process, it calls the unit distribution cost and carbon emission data between each warehouse and demand node output by the scheduling module, and uses a branch and bound algorithm to quickly narrow down the solution space.

[0046] The dynamic collaborative feedback mechanism of site selection and scheduling is an important supplement to ensure the optimization effect of the entire chain. The final determined warehouse site selection plan is fed back to the enhanced multi-objective multi-vehicle optimization model for low carbon emission reduction. The scheduling module will adjust the vehicle configuration and route planning based on the new warehouse layout. For example, after a new warehouse covers the demand node cluster originally served by the suburban warehouse, the scheduling module will reduce the long-distance delivery tasks of hydrogen fuel cell heavy trucks and increase the proportion of short-distance delivery of new energy pure electric light trucks, while optimizing the route to shorten the average delivery mileage. If a warehouse is positioned as a "dedicated cold storage warehouse" in the site selection plan, the scheduling module will prioritize the allocation of refrigerated vehicles to the warehouse for delivery tasks to ensure that temperature control requirements are met. In addition, the site selection results will also be synchronized to the logistics execution system through a backscatter modulation mechanism to guide the planning of the types and quantities of goods prepared at the warehouse terminal, forming a complete optimization chain of "site selection-scheduling-execution".

[0047] In summary, the multi-objective, multi-vehicle, low-carbon emission integrated optimization architecture proposed in this invention for industrial cold chain logistics systems accurately outputs non-stationary stochastic demand evolution maps through a hierarchical cross-scale feature fusion demand prediction model oriented towards spatiotemporal dynamic perception; generates delivery solutions that balance cost, efficiency, and low carbon emissions through a reinforced multi-objective, multi-vehicle optimization model oriented towards low-carbon emission reduction; and optimizes warehouse layout through a multi-constraint collaborative reinforcement learning warehouse location optimization model oriented towards graph neural network representation perception. Furthermore, it relies on a backscatter modulation mechanism to achieve data interoperability among models and low-power instruction backhaul. Ultimately, this results in a significant improvement in the accuracy of cold chain logistics demand prediction, a substantial reduction in end-to-end operating costs, effective reduction in carbon emissions, and improved vehicle utilization. At the same time, it overcomes the limitations of traditional single-objective optimization methods, supports multi-vehicle and multi-scenario delivery needs, and possesses strong adaptability and scalability, achieving a win-win situation for both economic and environmental benefits.

Claims

1. A multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems, characterized in that, include: By feeding in historical data streams and employing a multi-granularity tensor feature coupling mechanism through a hierarchical cross-scale feature fusion demand prediction model oriented towards spatiotemporal dynamic perception, a non-stationary stochastic demand evolution map is derived. This map is then used to inject spatiotemporal state priors, activating a strengthened multi-objective, multi-vehicle optimization model oriented towards low-carbon emission reduction. Using green energy efficiency potential energy as the implicit boundary of the system, a five-dimensional unified quantitative evaluation paradigm is constructed, encompassing the entire set K of heterogeneous transport units and "transportation-temperature control-warehousing-timeliness-carbon emission." Under strict constraints of physical spatiotemporal hard constraints, road network geometric topology adaptability, and full lifecycle operation and maintenance boundaries, the equilibrium of multi-agent collaborative games in complex networks is solved, and flow topology constraints are imposed. Based on this, a multi-constraint collaborative reinforcement learning warehouse location optimization model oriented towards graph neural representation perception is driven, performing high-order graph topology mining. Data space association mapping technology—namely, cascaded graph attention and spectral domain convolution operators—is used to dynamically represent the network state. Combined with hyperplane decision-making technology based on policy gradient and branch-bound, endogenous adaptive optimization and reverse throughput calibration of hub node layout are achieved.

2. The multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems according to claim 1, characterized in that, The enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction defines a heterogeneous vehicle unit set K encompassing diverse power topologies and load energy levels. This set is discretized into eleven specific physical entity subclasses: internal combustion engine-driven diesel light / medium / heavy-duty truck units, electrochemical energy storage-driven electric light / medium-duty truck units, hybrid range-extended light / medium-duty truck units, hydrogen fuel cell-driven light / heavy-duty truck units, and diesel / electric refrigerated special units specifically designed for temperature control scenarios. All of these heterogeneous subclasses are projected onto a normalized physical characteristic parameter matrix for calibration. The specific dimensions of this parameter matrix are defined as follows: the maximum load capacity related to core performance and low carbon emissions. Battery life Carbon emission coefficient when unloaded Cargo Sensitivity Coefficient Unit distance transportation cost Average driving speed Cooling power Wheelbase related to road condition adaptation Overall vehicle length Turning radius Maximum climbing ability Maintenance costs per unit mileage related to total lifecycle cost Warranty mileage .

3. The multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems according to claim 1, characterized in that, The enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction executes a homogeneous scalar mapping of economic potential and carbon footprint externalities for heterogeneous subsystems such as transport kinematics, thermodynamic refrigeration dissipation, and nodal storage and residence. It embeds a bidirectional time-domain value anchoring logic, constructing a closed-loop expression of the value stream throughout the entire lifecycle through an asymmetric time-default gradient penalty function and a time-accurate response incentive function. The analysis of the aforementioned cost elements strictly follows the computational axiom of "discrete metric orthogonal analysis and global loss linear reconstruction," and its functional composition and parameter definitions are as follows: S301, Calculate the total carbon emission cost: , in For transportation carbon emission costs, For the carbon emission cost of the refrigeration system, Pre-cooling for storage reduces carbon emissions costs; S302， ， in As a carbon emission cost weight, For vehicle type, For nodes, For the set of all nodes, For model No-load carbon emission factor For nodes and Inter-space distance, For vehicle model Is the driving arc Binary decision variables, For model Cargo sensitivity coefficient To avoid auxiliary variables in the nonlinear product of "cargo volume × carbon emission coefficient"; S303， ， in Carbon emission factor per unit of electricity / fuel For model Refrigeration equipment power, For model Transportation time; S304， ， in Carbon emission factor per unit of electricity Number the warehouse. For warehouse collection, For warehouse Power consumption; S305， ， in This is the total cost (excluding carbon emissions). For warehouse costs, For transportation costs, To reduce cold chain transportation costs, The total time penalty cost for breach of contract, Total time reward cost; S306， ， in For model Unit distance transportation cost; S307， ， in Unit refrigeration cost For model Time-dependent refrigeration efficiency factor (with transport time) (Related) S308， ， in Number the customer. Penalty cost per unit of time For the vehicle to reach the customer Time, For customers The end of the time window; S309， ， The total cost of time-based penalties for all customers.

4. The multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems according to claim 1, characterized in that, The enhanced multi-objective, multi-vehicle optimization model for low-carbon emission reduction constructs a multi-dimensional constrained hyperplane system orthogonally mapped to customer service level thresholds, vehicle lifecycle operating state vectors, and hard time window boundaries. This system rigorously geometrically divides and defines the feasible region of the high-dimensional solution space, thereby forcing convergence to a delivery solution set with physical execution effectiveness and global Pareto optimality. The specific objective functional and boundary constraint equations are as follows: S401, Objective function: , in, This is the total cost (excluding carbon emissions). For total carbon emission costs, This is the carbon emission cost weighting coefficient; S402, Constraint 1 (Customer Service Uniqueness): , in, For model Is the driving arc Binary decision variables, For the set of all nodes, For a set of client nodes; S403, Constraint 2 (Vehicle start / end point closed loop - departure): , in, Represents the starting point of the warehouse. A collection of car models; S404, Constraint 3 (Vehicle start and end point closed loop - return): , in, The total number of customer nodes. Represents the end point of the warehouse; S405, Constraint 4 (Flow Conservation): , in, For any customer node, ensure that the vehicle leaves after entering the customer node; S406, Constraint 5 (Capacity Limitation): , in, For customers The demand quantity, For model Maximum load capacity (core performance and low carbon parameters); S407, Constraint 6 (Battery Range Constraint): , in, For nodes and Inter-space distance, For model Driving range (core performance and low carbon parameters); S408, Constraint 7 (Time Window Constraint): , in, For customers Time window start point For customers End of time window For model Arrival at the customer Time; S409, Constraint 8 (Time Accumulation): , in, For model Driving arc Time, For model Average driving speed (core performance and low carbon parameters). Use a sufficiently large constant for linearization; S410, Constraint 9 (Initial Departure Time): ; in, For model The initial time from the warehouse; S411, Constraint 10 (Road Slope Adaptation): , in, For road section Actual slope For model Maximum climbing ability; S412, Constraint 11 (Road segment turning radius adaptation): , in, For road section Minimum allowable turning radius For model Turning radius; S413, Constraint 12 (Warranty Mileage Constraint): , in, For model Warranty mileage; S414, Constraint 13 (Maintenance Cost Budget): , in, For model Maintenance cost per unit mileage For model Maximum permissible maintenance cost; S415, Constraint 14 (Total Carbon Emission Allowance): , in, For model No-load carbon emission factor For model Cargo sensitivity coefficient To linearize auxiliary variables, For the region's total carbon emission allowance; S416, Constraint 15 (Segment Speed ​​Limit): , in, , Each is a road segment Minimum and maximum speed limits; S417, Constraint 16 (Cooling Power Adaptation): , in, For model Cooling power, For goods Minimum cooling power requirement Represents a node Transporting goods ; S418, Constraint 17 (Load-Slope Coordination): , in, For model In the arc The actual load capacity; S419, Constraint 18 (No-load distance ratio): , in, This represents the upper limit for the percentage of unloaded distance. S420, Constraint 19 (Battery Range Safety Margin): , in, The distance the vehicle travels from the last customer back to the warehouse. For battery life safety factor.

5. The multi-objective, multi-vehicle type, low-carbon emission integrated optimization architecture for industrial cold chain logistics systems according to claim 1, characterized in that, The graph neural representation-based perceptual multi-constraint collaborative reinforcement learning repository location optimization module constructs a hierarchical nonlinear representation computation architecture coupled with a non-Euclidean space topology extraction mechanism. The computational kernel of this architecture consists of a set of topological feature mapping units responsible for capturing the anisotropic dependencies between nodes, and a set of probability pointer generation units responsible for outputting discretized decision trajectories. Its underlying operating mechanism is detailed below: S501, Construction of a High-Order Topological Feature Extractor: Deploying a three-level cascaded attention aggregation structure to extract the latent space high-order features of logistics network nodes (the first two levels employ a self-attention topological aggregation mechanism, and the third level cascades spectral domain convolution operators), aiming to extract the static Euclidean space attributes of nodes—encompassing Cartesian coordinates. Scalar demand scale Time window closed interval (start time) Deadline Nonlinear projection mappings are used to construct high-dimensional node embedding tensors; real-time traffic flow velocity field information is synchronously coupled to dynamically reweight network edge weights, and its anisotropic adjustment kernel function is defined as... (in Characterizing the generalized impedance weights after velocity correction. For nodes and The Riemann distance between them, For nodes and (Instantaneous transport rate), thereby enhancing the system's robustness and adaptability to unsteady road network environments; S502, System State Phase Space Reconstruction: The physical boundary conditions and functional optimization objective of the mathematical programming model are rigorously anchored to construct the state vector, where the vehicle state subspace is composed of the residual load potential energy. Cumulative trajectory length and current system clock Composition (current system clock directly coupled time violation penalty operator) With time-incentive operator The node state subspace is then integrated with the static attribute vector. With the time-varying width of the dynamic remaining time window This provides a multi-dimensional spatiotemporal awareness foundation for policy generation networks; S503, Feasible Control Action Space Generation: A hyperplane constraint system based on a mathematical model delineates the set of reachable actions for the vehicle to verify the physical feasibility of the strategy solution. This applies when the transport unit resides at a node. At that time, for candidate nodes Implement ternary joint constraint verification – including capacity saturation constraints Temporal hard window constraints and energy range constraints A hard threshold masking mechanism is introduced to eliminate all singular actions that violate the above constraints. The node selector performs random probability sampling decisions in the compliant candidate set based on the weighted attention coefficients output by the feature extractor, thereby realizing the modeling of stochastic processes that satisfy the constraint boundary conditions. S504, Construction of a Multi-Objective Feedback Mechanism: Establishing a Functional Framework Related to the Overall System Objective A strictly isomorphic feedback loop defines the instantaneous feedback signal of the discrete decision step as the negative gradient increment of the cost functional, specifically expressed as: in, The carbon footprint cost covers the entire lifecycle of transportation flow, thermodynamic refrigeration, and storage; when the trajectory of the transport unit fails to converge to the node within the closed time window. (Right now When the large norm negative penalty signal is triggered, the effectiveness of gradient guidance under the time window boundary constraint is enhanced.

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