Digital Warehousing and Logistics Management System Based on AI Intelligent Scheduling Technology

Abstract

This invention relates to the field of logistics management technology, and more particularly to a digital warehousing and logistics management system based on AI intelligent scheduling technology. The system first acquires real-time inventory and scheduling request data to construct a warehousing logistics status map and decision support text. Second, it establishes a digital operational status model based on key logistics node components and constructs a strategy model for controlling resource allocation. Subsequently, it selects specific operational points and performs coupled simulations of the strategy model and status model to generate decision result data. Finally, combining the simulation results, status map, and support text, it outputs optimized decisions or abnormal risk indicators. This solution, through multi-model coupling and end-to-end data fusion, achieves dynamic and precise control of warehousing resources and early warning of operational risks, significantly improving the intelligence level, resource utilization, and overall operational efficiency of the logistics system.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to a digital warehousing and logistics management system based on AI intelligent scheduling technology. Background Technology

[0002] With the rapid development of e-commerce, intelligent manufacturing, and new retail, modern warehousing and logistics systems face increasingly complex operating environments and higher service requirements. Traditional warehousing and logistics management models mainly rely on human experience for inventory management and scheduling decisions, resulting in slow response times, low resource utilization, and poor collaboration efficiency. They struggle to cope with challenges such as large order fluctuations, high delivery timeliness requirements, and complex multi-warehouse collaboration. Although some enterprises have introduced automated equipment and information systems, significant shortcomings remain in data fusion and analysis, global resource optimization, and dynamic intelligent decision-making.

[0003] While existing scheduling systems based on rule engines or simple algorithms have been applied to warehousing and logistics scenarios, these systems typically lack the ability to dynamically model the overall operational status, making it difficult to achieve collaborative optimization across multiple stages such as inventory, sorting, and transportation. Furthermore, the semantic relationships between massive amounts of real-time monitoring data and customer scheduling requests are not fully explored, resulting in limited decision support capabilities and an inability to effectively identify potential resource bottlenecks or predict operational risks. In addition, most systems lack closed-loop feedback mechanisms, leading to a disconnect between simulation decisions and actual execution, and insufficient adaptability and robustness of optimization strategies.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a digital warehousing and logistics management system based on AI intelligent scheduling technology. This system aims to address the technical problems of low resource utilization, slow response speed, and insufficient identification of potential risks in existing warehousing and logistics management technologies. These problems stem from the lack of global dynamic modeling and multi-stage collaborative optimization capabilities, which makes it difficult to deeply integrate real-time data and scheduling requests for accurate perception, intelligent decision-making, and closed-loop feedback.

[0006] To achieve the above objectives, the present invention provides a digital warehousing and logistics management system based on AI intelligent scheduling technology, the system comprising: The data mapping module is used to acquire real-time inventory monitoring data and logistics distribution scheduling request data of digital warehousing, and to construct a digital warehousing logistics status map and scheduling decision support text based on the real-time inventory monitoring data and logistics distribution scheduling request data; The model building module is used to build a digital operation status model of the digital warehouse based on the target logistics node components of the warehouse logistics, wherein the target logistics node components are components related to the resource flow of the warehouse logistics process. The strategy control module is used to construct a warehousing and logistics resource scheduling and control strategy model, which is used to regulate the output allocation of logistics resources in the digital operation status model. The coupled simulation module is used to perform coupled decision simulation on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model based on the selected warehousing and logistics operation conditions, and obtain the corresponding simulation decision result data. The decision optimization module is used to determine optimization decisions or abnormal risk indications in digital warehousing and logistics management based on the simulation decision result data, combined with the digital warehousing and logistics status map and the scheduling decision auxiliary text.

[0007] Optionally, when the target logistics node components include inventory storage units, sorting systems, and distribution networks, the digital operational status model of the digital warehousing based on the target logistics node components includes: Based on AI-powered intelligent scheduling, target logistics node components are used to construct dynamic inventory update models, sorting operation load models, and delivery route planning models. The inventory dynamic update model, the sorting operation load model, and the delivery route planning model are integrated to form the digital operation status model; wherein, the output data of the inventory dynamic update model is used to update the input parameters of the sorting operation load model, and the input parameters of the delivery route planning model are adjusted based on the output data of the sorting operation load model.

[0008] Optionally, the step of performing coupled decision simulation on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model based on the selected warehousing and logistics operation condition points to obtain corresponding simulation decision result data includes: Based on the selected warehousing and logistics operation points, a resource allocation control signal is generated through the warehousing and logistics resource scheduling and control strategy model to dynamically adjust the logistics resource parameters in the digital operation status model. Under the action of resource allocation control signals, a warehousing and logistics operation scenario is simulated, and data on logistics resource utilization and customer delivery timeliness are recorded as simulation decision results. The logistics resource utilization and delivery timeliness data are fed back to the warehousing and logistics resource scheduling and control strategy model to iteratively optimize the resource allocation control signals.

[0009] Optionally, the system further includes: Extract a set of candidate scheduling scheme entries related to scheduling decisions from a knowledge base in the warehousing and logistics field; Based on logistics delivery scheduling request data and contextual logistics information, semantic disambiguation processing is performed on the candidate scheduling scheme entry set to obtain an optimized scheduling option list; wherein, the optimized scheduling option list is used to initialize the decision parameters of the warehousing logistics resource scheduling control strategy model.

[0010] Optionally, determining optimization decisions or abnormal risk indicators in digital warehousing and logistics management based on the simulation decision result data, combined with the digital warehousing and logistics status map and the scheduling decision auxiliary text, includes: If the simulation decision result data meets the preset resource threshold conditions, the digital warehousing and logistics status map and the scheduling decision auxiliary text are used to determine the existence of warehousing and logistics resource bottlenecks or idle waste risks. Generate optimized resource allocation suggestions or risk warning signals; wherein, the optimized resource allocation suggestions are verified with reference to the contextual logistics information in the scheduling decision support text.

[0011] Optionally, in cases where there are bottlenecks or risks of idle and wasted warehousing and logistics resources, the control strategy parameters in the warehousing and logistics resource scheduling and control strategy model can be optimized. Based on the optimized control strategy parameters, the warehousing and logistics resource scheduling control strategy model and the digital operation status model are re-coupled for decision simulation until the target simulation decision result data is obtained. The target simulation decision result data represents the elimination of service resource bottlenecks or waste risks.

[0012] Optionally, the construction of the dynamic inventory update model includes: Based on the product categories and storage location distribution of digital warehousing, the inventory storage status is discretized into an equivalent state transition sub-model. The state transition sub-model is connected by state transition rules, and the inventory inbound / outbound sequence logic is set to construct an inventory dynamic update model; wherein, the output data of the inventory inbound / outbound sequence logic is used to drive the input update of the sorting operation load model.

[0013] Optionally, the process of constructing the delivery route planning model includes: Using delivery timeliness indicators and transportation costs as scheduling boundaries, delivery scheduling is divided into transportation capacity allocation units and route optimization units; The capacity allocation unit and the route optimization unit are defined as independent scheduling modules; The independent scheduling modules are integrated through a feedback compensation mechanism to construct a delivery route planning model; wherein, the scheduling output data of the independent scheduling modules is calibrated based on real-time road conditions and inventory monitoring values ​​in the digital warehousing and logistics status map.

[0014] Optionally, the step of acquiring real-time inventory monitoring data and logistics delivery scheduling request data of digital warehousing, and constructing a digital warehousing logistics status map and scheduling decision support text based on the real-time inventory monitoring data and logistics delivery scheduling request data, includes: Text normalization and semantic structure parsing are performed on logistics delivery scheduling request data to obtain a list of scheduling request entities and unstructured context fragments. Quantify the correlation between real-time inventory monitoring and transportation capacity status data, and generate indicators of scheduling influencing factors; Based on the list of scheduling request entities, unstructured context fragments, and scheduling influencing factor indicators, the scheduling decision support text is constructed. The digital warehousing and logistics status map is constructed based on real-time inventory monitoring data and transportation capacity operation data; wherein, the scheduling influencing factor indicators are input into the warehousing and logistics resource scheduling control strategy model to adjust the decision weights.

[0015] Optionally, the system further includes: After the determined optimization decisions are put into actual warehousing and logistics scheduling operations, real-time data on logistics resource utilization, delivery timeliness, and order completion rate are collected during the actual operation process. The actual collected operation data is matched with the simulation decision results of the corresponding optimization decision to calculate the actual simulation deviation value. When the actual simulation deviation value exceeds the preset deviation threshold, the scheduling context is updated based on the currently updated digital warehousing and logistics status map, triggering AI adaptive rescheduling, readjusting the decision weights of the warehousing and logistics resource scheduling control strategy model, and generating a new optimization decision.

[0016] In this invention, a digital warehousing and logistics management system based on AI intelligent scheduling technology, a digital warehousing and logistics status map and scheduling decision-making auxiliary text are constructed to achieve accurate perception and semantic expression of the entire warehousing and logistics chain status. A digital operation status model is established by combining target logistics node components and coupled with a resource scheduling control strategy model for simulation, which improves the scientific nature and dynamic adaptability of resource allocation decisions. Through condition-driven simulation analysis and multi-source information fusion decision-making, operational risks can be predicted and optimization space can be identified before actual scheduling, thereby realizing intelligent control of the warehousing and logistics system, efficient resource utilization, and early warning of abnormal risks, which significantly improves the automation and intelligence level and overall operational efficiency of warehousing and logistics management. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the first embodiment of the digital warehousing and logistics management system based on AI intelligent scheduling technology of the present invention; Figure 2 This is a flowchart illustrating the specific steps involved in constructing a digital warehousing and logistics status map and scheduling decision support text within the AI-based intelligent scheduling technology-based digital warehousing and logistics management system of this invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] In one embodiment, such as Figure 1 As shown, a digital warehousing and logistics management system based on AI intelligent scheduling technology is provided. The system includes: The data mapping module 10 is used to acquire real-time inventory monitoring data and logistics distribution scheduling request data of digital warehousing, and to construct a digital warehousing logistics status map and scheduling decision support text based on the real-time inventory monitoring data and logistics distribution scheduling request data; The data mapping module can be a system component used to integrate multi-source heterogeneous data and generate structured semantic expressions, enabling accurate perception and semantic expression of the entire warehousing and logistics chain status. In this embodiment, the data mapping module can receive real-time inventory monitoring data and logistics delivery scheduling request data, and output a status map and auxiliary text through knowledge graph construction technology and natural language generation technology, respectively. Furthermore, the data mapping module can be a front-end perception unit that works in collaboration with other objects, and its output provides a semantic basis for subsequent modeling and decision-making. For example, the digital warehousing and logistics status map generated by the data mapping module can include, but is not limited to, one or more of facility-level maps, operation-level maps, and resource-level maps; the scheduling decision-making auxiliary text can include, but is not limited to, task summary text, constraint description text, and risk warning text. Real-time inventory monitoring data can be dynamic data reflecting the current quantity, location, status, and other attributes of goods in the warehouse, which can be used as the basic input for status perception to support real-time modeling and scheduling judgment at the inventory level. In an exemplary embodiment, real-time inventory monitoring data can be collected in real time through RFID, barcode scanning, IoT sensors, or WMS system interfaces. Logistics delivery scheduling request data can be a set of customer scheduling instructions containing information such as order requirements, delivery time, destination, and cargo type, and can be used as an external trigger signal to drive resource scheduling decisions. In one specific embodiment, logistics delivery scheduling request data can be pushed to the scheduling platform by an OMS, TMS, or customer interface system.

[0021] Acquiring real-time inventory monitoring data and logistics delivery scheduling request data from digital warehousing can be achieved by pulling or subscribing to relevant data streams from the warehouse management system and order scheduling system in real time. Furthermore, this operation can be implemented through API polling or by subscribing to real-time data streams via message queues, thus providing raw input for state perception and decision-making modeling. Constructing a digital warehousing logistics state graph and scheduling decision support text based on real-time inventory monitoring data and logistics delivery scheduling request data can be achieved by semantic parsing and knowledge fusion of structured and unstructured data, generating graphs and text respectively. Further, this operation can be achieved by using graph neural networks for relational reasoning to construct the graph and using large language models to generate support text; or by using rule templates combined with entity linking technology to generate graphs and text, thus achieving the technical effect of structured perception and semantic association expression of the entire chain state. The digital warehousing logistics state graph can be a semantic knowledge representation of the entities and their relationships across the entire warehousing logistics chain in graph structure form, which can be used to achieve structured perception of operational status and cross-stage association mining. In this embodiment, the digital warehousing and logistics status map can be generated based on real-time inventory monitoring data and logistics delivery scheduling request data, using entity recognition, relation extraction, and map construction algorithms. The scheduling decision support text can be semantic text information that describes the scheduling context in natural language, which can be used to enhance the interpretability of decisions and assist human-machine collaboration in understanding scheduling intentions and constraints. In a specific embodiment, the scheduling decision support text can be automatically generated based on structured data through template filling or a neural text generation model.

[0022] Model building module 20 is used to build a digital operation status model of digital warehousing based on target logistics node components of warehousing and logistics. The target logistics node components are components related to the resource flow of warehousing and logistics processes. The model building module can be a system component that constructs a digital operational state model based on target logistics node components, and can be used to characterize the dynamic behavior of resource flow in the warehousing and logistics process. In this embodiment, the model building module can use target logistics node components as modeling units to construct a computable and evolvable model using state-space modeling or process simulation modeling methods. Target logistics node components can be physical or logical functional units directly related to resource flow in the warehousing and logistics process, and can be used as basic modeling units for the digital operational state model. For example, target logistics node components can include, but are not limited to, one or more of storage units, sorting equipment, transport vehicles, loading and unloading nodes, etc. Constructing a digital operational state model of digitized warehousing based on target logistics node components can be achieved by establishing their state variables and dynamic evolution equations, using the target logistics node components as units. Furthermore, this operation can be implemented using discrete event system modeling methods or agent-based simulation modeling methods, thereby achieving the technical effect of forming a computable and evolvable operational state representation.

[0023] A digital operational status model (DRM) can be a digital representation of the operational status of a target logistics node component under specific working conditions, achieved through mathematical or simulation modeling. It can be used to support resource behavior prediction and state evolution deduction. In an exemplary embodiment, the DRM can be constructed based on the physical characteristics, historical operational data, and process logic of the target logistics node component. Furthermore, the DRM can interact with the warehousing and logistics resource scheduling and control strategy model through a coupled simulation module; its output state influences resource allocation results while simultaneously accepting regulatory inputs from the strategy model.

[0024] The strategy control module 30 is used to construct a warehousing and logistics resource scheduling and control strategy model, which is used to regulate the output allocation of logistics resources in the digital operation status model. The strategy control module can be a system component for constructing a resource scheduling control strategy model, providing dynamic control capabilities for the allocation of logistics resources. In this embodiment, the strategy control module can generate scheduling strategies based on optimization algorithms, rule engines, or reinforcement learning methods, and encapsulate them into a callable control model. Constructing a warehousing and logistics resource scheduling control strategy model can involve designing or training a strategy model capable of outputting resource allocation schemes. Further, this operation can be implemented by constructing an optimization model based on linear programming or integer programming, and training a strategy network based on deep reinforcement learning, thereby achieving the technical effect of enabling strategy-driven dynamic control capabilities for resource allocation. The warehousing and logistics resource scheduling control strategy model can be a strategic model for determining how logistics resources are allocated and scheduled, guiding the optimal configuration of resources in terms of time, space, and task dimensions. In a specific embodiment, the warehousing and logistics resource scheduling control strategy model can be trained and generated using operations research, machine learning, or hybrid intelligent methods. Further, the warehousing and logistics resource scheduling control strategy model can run collaboratively with a digital operation status model in a coupled simulation module, adjusting the strategy output based on status feedback. Regulating the output allocation of logistics resources in a digital operation state model can be achieved by applying the output of the scheduling strategy model to resource variables within the digital operation state model. Furthermore, this operation can be implemented by adjusting resource allocation ratios through parameter injection and by driving state transitions through action sequences, thereby achieving the technical effect of policy intervention and guidance over state evolution.

[0025] The coupled simulation module 40 is used to perform coupled decision simulation on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model based on the selected warehousing and logistics operation conditions, and obtain the corresponding simulation decision result data. The coupled simulation module can be a system component that drives the collaborative simulation of the digital operation state model and the resource scheduling control strategy model. It can be used to pre-simulate the execution effect of scheduling strategies in a virtual environment, exposing potential bottlenecks and conflicts. In this embodiment, the coupled simulation module can simultaneously run the two models and record state changes and resource responses during the interaction process based on selected warehousing and logistics operation conditions. Warehouse and logistics operation conditions can be typical combinations of state parameters representing specific operational scenarios or load conditions, which can be used as input conditions for coupled simulation, driving the model to run under representative scenarios. For example, warehousing and logistics operation conditions may include, but are not limited to, peak order conditions, equipment failure conditions, and multi-warehouse collaborative conditions.

[0026] Based on the selected warehousing and logistics operation points, a coupled decision simulation is performed on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model. This can involve running the two models simultaneously under specified operating conditions and recording the interaction process. Furthermore, this operation can employ a joint simulation framework to synchronously advance the time steps of the two models and use a feedback closed-loop mechanism to achieve iterative interaction between strategy and state, thereby achieving the technical effect of early exposure of resource bottlenecks and scheduling conflicts. Obtaining the corresponding simulation decision result data can involve collecting key performance indicators and state trajectories during the coupled simulation process. Further, this operation can record resource utilization and task delay indicators, collect node congestion status and task completion path implementation, thereby achieving the technical effect of providing quantitative basis for subsequent decision optimization. The simulation decision result data can be simulated data on resource allocation effects and system response output by the coupled simulation module, which can be used to provide a preliminary basis for decision optimization. In an exemplary embodiment, the simulation decision result data can record indicators such as state changes, resource utilization, and task completion rate of each node during the coupled simulation process.

[0027] The decision optimization module 50 is used to determine the optimization decisions or abnormal risk indications in digital warehousing and logistics management based on the simulation decision result data, combined with the digital warehousing and logistics status map and scheduling decision auxiliary text.

[0028] The decision optimization module can be a system component that integrates multi-dimensional information to generate final scheduling decisions or risk warnings, enabling intelligent decision generation and early identification of abnormal risks. In this embodiment, the decision optimization module can integrate simulation decision result data, digital warehousing and logistics status maps, and scheduling decision support text, outputting optimization suggestions or risk indications through a multi-source information fusion algorithm. Based on simulation decision result data, combined with digital warehousing and logistics status maps and scheduling decision support text, the optimization decisions or abnormal risk indications in digital warehousing and logistics management are determined, which can be achieved by integrating numerical simulation results and semantic context information for comprehensive judgment. Furthermore, this operation can use a multi-modal fusion model to jointly process maps, text, and numerical data, and use a rule engine combined with semantic matching and threshold judgment to generate results, thereby achieving the technical effect of intelligent decision generation or risk warning under multi-dimensional information fusion. The optimization decision can be an improved scheduling instruction or resource allocation scheme generated after multi-source information fusion, which can be used to improve resource utilization efficiency and scheduling scientificity. In a specific embodiment, the optimization decision can be derived by the decision optimization module based on simulation results and semantic information through comprehensive reasoning. Anomaly risk indicators can serve as early warning markers of potential operational risks or resource bottlenecks, enabling proactive warnings of abnormal risks. For example, anomaly risk indicators can be obtained by comparing simulation results with preset thresholds or historical patterns to identify deviation signals.

[0029] Taking multi-warehouse collaborative scheduling during e-commerce promotions as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can be as follows: Before the Double Eleven promotion, the system uses a data mapping module to obtain real-time inventory level data and massive order delivery requests from warehouses in various regions, constructs a warehousing and logistics status map covering the whole country, and generates scheduling decision support text that includes the priority of urgent orders and regional delivery pressure; the model building module uses sorting robots, conveyor belts, outbound platforms, etc. as target logistics node components to establish digital operation status models for each warehouse; the strategy control module loads a resource scheduling control strategy model trained based on historical promotion data; the coupled simulation module selects high-concurrency order working points, drives the strategy model and status model to run collaboratively in a virtual environment, and discovers that a certain East China warehouse has a sorting capacity bottleneck; the decision optimization module combines simulation results, regional correlations in the status map, and time constraints in the support text to generate an optimized decision to divert some orders to the neighboring North China warehouse, and issues an abnormal risk indication of congestion in the East China warehouse sorting area.

[0030] In one embodiment, where the target logistics node components include inventory storage units, sorting systems, and distribution networks, a digital operational status model of digital warehousing is constructed based on the target logistics node components, including: Based on AI-powered intelligent scheduling, target logistics node components are used to construct dynamic inventory update models, sorting operation load models, and delivery route planning models. The inventory dynamic update model, sorting operation load model, and delivery route planning model are integrated to form a digital operation status model. The output data of the inventory dynamic update model is used to update the input parameters of the sorting operation load model, and the input parameters of the delivery route planning model are adjusted based on the output data of the sorting operation load model.

[0031] The inventory storage unit can be a physical or logical storage space unit used to store goods, and can be used as a modeling object for a dynamic inventory update model to reflect changes in the storage status of goods. In this embodiment, the inventory storage unit can be dynamically updated using a time-series prediction model or state transition equation, combined with real-time inventory monitoring data. Furthermore, the inventory storage unit can be one or more of the following: shelf storage area, pallet stacking area, automated storage and retrieval system (AS / RS) area, etc., categorized by storage form. The sorting operation system can be a set of operation units that perform order picking and consolidation tasks, and can be used as a modeling object for a sorting operation load model to characterize the intensity and efficiency of human or equipment operations. For example, the sorting operation system can generate an operation load assessment result based on order structure, SKU characteristics, and inventory location information through operation simulation or load estimation algorithms. In an exemplary embodiment, the sorting operation system may include a manual sorting area, a semi-automatic sorting line, a cluster of fully automated sorting robots, etc.

[0032] The distribution and transportation network can be a logistics transportation topology connecting warehouses and customer nodes, and can be used as a modeling object for distribution route planning models to support transportation resource scheduling and route optimization. In a specific embodiment, the distribution and transportation network can use graph search algorithms, operations research optimization, or reinforcement learning methods to generate feasible route solutions under dynamic input conditions. Furthermore, the distribution and transportation network can include one or more of the following: urban distribution branch network, trunk transfer network, and last-mile delivery network. The inventory dynamic update model can be a sub-model built based on AI intelligent scheduling methods to reflect changes in inventory status in real time, and can be used to capture the impact of operations such as warehousing, outbound, and transfer on inventory levels and distribution. In this embodiment, the inventory dynamic update model can use time-series prediction models or state transition equations, combined with real-time inventory monitoring data, for dynamic updates. For example, the output data of the inventory dynamic update model can be used as input parameters for a sorting operation load model to drive the dynamic calculation of sorting task volume.

[0033] The sorting workload model can be a sub-model used to quantify the operational pressure and resource requirements of the sorting system under current order and inventory conditions. It can be used to assess the occupancy of manpower, equipment, or time resources in the sorting process. In an exemplary embodiment, the sorting workload model can be generated based on order structure, SKU characteristics, and inventory location information through operational simulation or load estimation algorithms. Furthermore, the sorting workload model can receive the output of the inventory dynamic update model as input and output its processing results to the delivery route planning model to adjust transportation task parameters. The delivery route planning model can be a sub-model used to generate the optimal delivery route under given tasks and constraints. It can be used to optimize vehicle scheduling, route selection, and timeliness control. In a specific embodiment, the delivery route planning model can employ graph search algorithms, operations research optimization, or reinforcement learning methods to generate feasible route solutions under dynamic input conditions. For example, the delivery route planning model can dynamically adjust its input parameters based on the output of the sorting workload model, such as task quantity, cargo volume, and timeliness requirements. AI intelligent scheduling can be a technical means of using artificial intelligence algorithms to achieve resource allocation and task scheduling decisions, and can be used to provide intelligent modeling and reasoning capabilities for each sub-model. In this embodiment, AI intelligent scheduling may include one or more of the following: reinforcement learning-based scheduling, operations research-based scheduling, and graph neural network-based scheduling.

[0034] Output data can be the result information generated after the model runs, which can be used as input for downstream models to achieve data transfer between models. Furthermore, output data can be generated by the model's internal computational logic and output through a standardized interface. Input parameters can be initial or external condition variables required for model operation, which can be used to determine the specific form of model behavior and output results. For example, input parameters can come from raw monitoring data, other model outputs, or preset configurations. When the target logistics node components include inventory storage units, sorting operation systems, and distribution and transportation networks, constructing a digital operational status model of digital warehousing based on the target logistics node components can involve specifying the target logistics node components into three core units and modeling based on these. Furthermore, this operation can be achieved through methods such as modeling and integrating functional modules separately, or modeling and decomposing end-to-end processes uniformly, thereby clarifying the modeling boundaries and granularity and improving the model's coverage of actual business processes.

[0035] The construction of dynamic inventory update models, sorting load models, and delivery route planning models for target logistics node components based on AI intelligent scheduling can be achieved by applying AI methods to build dedicated sub-models for each type of target logistics node component. In a specific embodiment, this operation can be implemented by using LSTM networks to build dynamic inventory update models, using queuing theory combined with machine learning to build sorting load models, or using graph attention networks to build delivery route planning models, or using mixed integer programming combined with heuristic algorithms to build delivery route planning models, thereby enabling refined and intelligent modeling of the state of each link. Integrating the dynamic inventory update model, sorting load model, and delivery route planning model to form a digital operation state model can be achieved by coupling the three sub-models through data interfaces and logical dependencies. For example, this operation can be implemented by encapsulating each sub-model using a microservice architecture and integrating them through a message bus, or by embedding the three into the same simulation engine using a unified state space, thereby forming a unified state representation covering the entire warehousing and logistics chain.

[0036] Updating the input parameters of the sorting workload model using the output data of the inventory dynamic update model can be achieved by injecting information such as SKU location, availability, and batch status from the inventory model into the sorting model. In an exemplary embodiment, this operation can be implemented by triggering recalculation of the sorting model through real-time push of inventory change events via API or by synchronizing key inventory indicators through shared memory, thereby making the sorting workload calculation closer to the real-time inventory status. Adjusting the input parameters of the delivery route planning model based on the output data of the sorting workload model can be achieved by inputting results such as sorting completion time, package volume and weight, and collection location as delivery task attributes into the route planning model. Furthermore, this operation can be achieved by triggering route replanning after packaging the sorting results into a delivery task order or by dynamically inserting new task nodes into the route model and re-optimizing it, thereby making the delivery route planning responsive to the actual output status of the front-end operations.

[0037] Taking the next-day delivery scenario of fresh food e-commerce as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can identify the target logistics node components as the cold storage unit of the front warehouse, the automatic sorting operation system, and the urban distribution and transportation network; build an inventory dynamic update model based on AI intelligent scheduling to track the inventory loss and replenishment status of high-turnover fresh products in real time; the available inventory and location information output by the model are used to update the sorting operation load model to accurately estimate the sorting human and machine resources required for the day's orders; the package collection completion time and volume data output by the sorting model are then used to adjust the input parameters of the delivery route planning model, so that the route algorithm can dynamically generate the optimal delivery sequence according to the actual outbound rhythm; the three sub-models are tightly coupled through data flow and finally integrated into a digital operation status model that can truly reflect the status of the entire link from front warehouse inventory to last-mile delivery, providing a high-fidelity virtual environment for subsequent coupled simulation with the scheduling strategy model.

[0038] In one embodiment, based on selected warehousing and logistics operation points, a coupled decision simulation is performed on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model to obtain corresponding simulation decision result data, including: Based on the selected warehousing and logistics operation points, resource allocation control signals are generated through the warehousing and logistics resource scheduling and control strategy model to dynamically adjust the logistics resource parameters in the digital operation status model.

[0039] The resource allocation control signal can be a set of dynamic instructions output by the warehousing and logistics resource scheduling control strategy model to guide the allocation of logistics resources. It can drive the adjustment of logistics resource parameters in the digital operation state model, enabling the strategy to intervene in the virtual operation state. In an exemplary embodiment, the resource allocation control signal can be generated by the strategy model through optimization calculation or learning inference based on the current working point input. Furthermore, logistics resource parameters can be adjustable variables describing the configuration state of logistics resources in the digital operation state model, and can be used to characterize the allocation level and operating mode of resources in the simulation environment. For example, logistics resource parameters can include, but are not limited to, one or more of the following: manpower scheduling parameters, equipment start / stop parameters, and transportation path weight parameters. Generating the resource allocation control signal through the warehousing and logistics resource scheduling control strategy model based on the selected warehousing and logistics operation working point can be achieved by using the working point as input and calling the strategy model to output the corresponding resource scheduling instructions. Furthermore, this operation can be implemented by using an online optimization solver to generate control signals in real time and calling a pre-trained strategy neural network for forward inference, thereby enabling the targeted generation of strategies under specific operational scenarios. Dynamically adjusting logistics resource parameters in a digital operation status model can be achieved by mapping resource allocation control signals to update state variables within the model. In one specific embodiment, this operation can be implemented by directly replacing the original configuration through parameter overriding or by gradually correcting resource parameters through incremental adjustment, ensuring that the virtual operation status remains consistent with the scheduling strategy and supporting high-fidelity simulation.

[0040] Under the influence of resource allocation control signals, a warehousing and logistics operation scenario is simulated, and data on logistics resource utilization and customer delivery timeliness are recorded as simulation decision-making results.

[0041] The warehousing and logistics operation scenario can be a virtual reproduction of the real warehousing and distribution operation process simulated in a coupled simulation, and can be used to provide a testing environment for the execution effect of resource scheduling strategies. In this embodiment, the warehousing and logistics operation scenario can be dynamically generated by a digital operation status model based on adjusted logistics resource parameters. Logistics resource utilization rate data can be a quantitative indicator reflecting the actual utilization efficiency of various logistics resources during the simulation process, and can be used as one of the core bases for evaluating the effectiveness of scheduling strategies. For example, logistics resource utilization rate data can include, but is not limited to, one or more of equipment occupancy rate, personnel man-hour utilization rate, and storage space turnover rate. Customer delivery timeliness index data can be a set of performance indicators that measure the time taken from order receipt to delivery to the customer, and can be used to evaluate the degree to which the scheduling strategy meets service commitments. In a specific embodiment, customer delivery timeliness index data can include, but is not limited to, one or more of order fulfillment on-time rate, average delivery time, and emergency order response delay.

[0042] Under the influence of resource allocation control signals, simulating warehousing and logistics operation scenarios can drive the digital operation status model to run the complete operation process based on updated logistics resource parameters. Furthermore, this operation can be achieved by using a discrete event simulation engine to advance the operation process and simulating the interaction behavior of each node based on agent modeling methods, thereby generating realistic operational response results for strategy effectiveness verification. Recording logistics resource utilization data and customer delivery timeliness index data as simulation decision-making result data can be achieved by collecting key performance indicators and storing them in a structured manner during simulation operation. In an exemplary embodiment, this operation can be achieved by embedding an indicator listener in the simulation engine for automatic recording and extracting utilization and timeliness data through post-event log analysis, thereby forming a quantifiable basis for strategy evaluation.

[0043] Among them, logistics resource utilization rate data and delivery timeliness index data are fed back to the warehousing and logistics resource scheduling and control strategy model to iteratively optimize resource allocation control signals.

[0044] Feeding logistics resource utilization data and delivery timeliness index data back to the warehousing logistics resource scheduling and control strategy model to iteratively optimize resource allocation control signals can be achieved by using evaluation indicators as feedback signals to input into the strategy model, triggering updates to its parameters or logic. Furthermore, this operation can be implemented by updating the policy network with reward signals used in reinforcement learning, or as a constraint or objective function adjustment basis for multi-objective optimization problems, thereby constructing a closed-loop adaptive mechanism and improving the dynamic adaptability and robustness of the strategy.

[0045] Taking the emergency dispatch of a sudden surge in orders in a regional warehouse as an example, the digital warehousing and logistics management system based on AI intelligent dispatching technology in this embodiment can select a warehousing and logistics operation point that represents the surge in orders caused by regional epidemic lockdowns. Based on this, the strategy model generates resource allocation control signals to increase nighttime sorting shifts and temporarily activate backup transport fleets. These signals dynamically adjust the manpower scheduling parameters and vehicle dispatching parameters in the digital operation status model. Subsequently, the system simulates the entire order processing process for the next 24 hours, recording data such as the sorting equipment utilization rate rising to 92% and the average delivery time for emergency medicine orders shortening to 3.5 hours. These indicators are fed back to the strategy model, triggering it to re-balance cost and timeliness goals and generate a second round of more refined resource allocation plans, such as prioritizing the medical supplies channel and restricting the outbound shipment of non-urgent goods, thereby ensuring critical services while avoiding system overload.

[0046] In one embodiment, the system further includes: Extract a set of candidate scheduling scheme entries related to scheduling decisions from a knowledge base in the warehousing and logistics field; Based on logistics delivery scheduling request data and contextual logistics information, semantic disambiguation processing is performed on the candidate scheduling scheme item set to obtain an optimized scheduling option list; the optimized scheduling option list is used to initialize the decision parameters of the warehousing and logistics resource scheduling control strategy model.

[0047] The warehousing and logistics knowledge base can be a knowledge system that stores structured scheduling experience, rules, and historically effective strategies within the warehousing and logistics domain. It can serve as a priori knowledge source, providing a semantic and reusable foundation for generating scheduling strategies. In this embodiment, the warehousing and logistics knowledge base can be constructed through expert system accumulation, historical scheduling log mining, or industry standard rule bases, organizing knowledge entries in the form of graphs, rule sets, or vectors. The candidate scheduling scheme entry set can be a collection of scheduling strategy fragments potentially related to the current scheduling decision task, selected from the warehousing and logistics knowledge base. This can be used to narrow down the strategy search space and provide high-quality initial strategy candidates. For example, the candidate scheduling scheme entry set can retrieve matching entries from the knowledge base based on meta-tags such as scheduling task type, resource constraints, or geographical region. Furthermore, the candidate scheduling scheme entry set can include, but is not limited to, one or more of expert rule entries, historical success case entries, and industry standard process entries.

[0048] Contextual logistics information can be real-time or near-real-time environmental state information that influences scheduling decisions, in addition to scheduling requests. It can be used to provide the necessary context for semantic disambiguation, improving the accuracy of scheduling options. In one embodiment, contextual logistics information may include, but is not limited to, inventory level status, transportation route congestion, and order service level requirements. Semantic disambiguation processing can be a context-aware screening and clarification process for items in the candidate scheduling scheme set that have ambiguity, vagueness, or conflict. It can be used to eliminate candidate schemes that are not applicable to the current context, retaining semantically consistent and feasible scheduling options. In a specific embodiment, semantic disambiguation processing can use a semantic matching model to calculate the similarity or compatibility score between candidate items and the current request and context, filtering out low-confidence items. The optimized scheduling option list can be a high-quality subset of scheduling strategies that highly match the current scheduling context after semantic disambiguation. It can be used to initialize the decision parameters of the warehousing and logistics resource scheduling control strategy model, improving the quality of the strategy starting point. Furthermore, the optimized scheduling option list is output by semantic disambiguation processing, retaining highly relevant, conflict-free, and constraint-satisfied candidate items. In this embodiment, the optimized scheduling option list, as input, directly affects the initial parameter configuration of the warehousing and logistics resource scheduling control strategy model; its content depends on the joint processing results of the candidate scheduling scheme entry set and contextual logistics information.

[0049] Decision parameters can be adjustable variables or configuration items used in the warehousing and logistics resource scheduling and control strategy model to guide resource allocation behavior. They can determine the behavioral tendencies and optimization directions of the strategy model during simulation and execution phases. For example, decision parameters can exist in the form of weight vectors, action space boundaries, or initial parameters of the strategy network, and can be generated by mapping from the list of optimized scheduling options. Extracting a set of candidate scheduling scheme entries related to scheduling decisions from the warehousing and logistics domain knowledge base can be achieved by retrieving relevant strategy entries from the knowledge base based on the characteristics of the current scheduling task (such as order type, region, and timeliness requirements). Furthermore, this operation is implemented through rule retrieval based on keywords and meta-tags, and semantic similarity retrieval based on embedding vectors, thereby formalizing expert experience and historically effective strategies into the current decision-making process. Based on logistics delivery scheduling request data and contextual logistics information, semantic disambiguation processing is performed on the set of candidate scheduling scheme entries. This can involve fusing scheduling request semantics and contextual states, evaluating the applicability of each candidate entry, and eliminating conflicting or mismatched items. Furthermore, this operation can accurately select the scheduling option that best matches the current situation by using a contrastive learning model to calculate item-context compatibility and using a logical rule engine to verify constraint satisfaction, thereby avoiding policy misuse.

[0050] Obtaining the optimized scheduling option list can be achieved by outputting a subset of high-confidence scheduling strategies retained after semantic disambiguation. Further, this operation can be implemented by sorting the top N items by relevance score and selecting representative items from each strategy type cluster, thus forming a high-quality, context-aware initial strategy space. Initializing the decision parameters of the warehouse logistics resource scheduling control strategy model using the optimized scheduling option list can be achieved by mapping the optimized scheduling option list to the initial parameter configuration of the strategy model. Further, this operation can be achieved by encoding the option list as pre-trained weights of the strategy network and transforming the options into initial feasible solutions or constraint boundaries of the optimization problem, thereby enabling the model to possess a high-quality initial strategy before simulation and reducing ineffective exploration.

[0051] For example, in the scenario of emergency replenishment scheduling for fresh food cold chain, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment could be as follows: A sudden high temperature in a certain area leads to a surge in fresh food orders, and the system receives an emergency delivery request. The strategy control module first extracts a set of candidate scheduling scheme entries containing tags such as "high temperature emergency," "cold chain priority," and "cross-warehouse transfer" from the warehousing and logistics knowledge base; then, combining the current logistics delivery scheduling request data (such as 30-minute delivery requirement) with contextual logistics information (such as insufficient local warehouse inventory, redundant cold chain capacity in neighboring warehouses, and main road congestion), semantic disambiguation is performed on the candidate entries to eliminate schemes that rely on direct delivery from the local warehouse or ordinary transportation; finally, an optimized scheduling option list is obtained, including strategies such as enabling pre-cooling of inventory in neighboring warehouses and delivery via alternative routes; this list is used to initialize the decision parameters of the warehousing and logistics resource scheduling control strategy model, so that subsequent coupled simulations can run directly in a high-quality strategy space, quickly generate feasible scheduling schemes, and provide early warnings of timeliness risks of the original routes.

[0052] In one embodiment, based on simulation decision-making result data, combined with a digital warehousing and logistics status map and scheduling decision support text, optimization decisions or abnormal risk indicators in digital warehousing and logistics management are determined, including: If the simulation decision results data meet the preset resource threshold conditions, the existence of warehousing and logistics resource bottlenecks or idle waste risks can be identified by combining the digital warehousing and logistics status map and scheduling decision auxiliary text.

[0053] The preset resource threshold conditions can be predefined numerical values ​​or logical boundary conditions used to determine whether the resource usage status is abnormal. These can serve as a benchmark for identifying resource bottlenecks or idle waste risks. In this embodiment, the preset resource threshold conditions may include, but are not limited to, one or more of equipment utilization thresholds, inventory turnover thresholds, and task backlog thresholds. A warehousing and logistics resource bottleneck can be a state where insufficient resource supply leads to obstructed workflows or decreased efficiency. It can be used to characterize constraints where the system cannot meet scheduling requirements under specific operating conditions. For example, warehousing and logistics resource bottlenecks may include inbound processing bottlenecks, sorting capacity bottlenecks, and outbound delivery bottlenecks. Idle waste risk can be a potential problem where resources are not effectively utilized due to over-allocation or scheduling imbalances. It can be used to indicate the possibility of cost redundancy or inefficient asset operation. In an exemplary embodiment, idle waste risk may include equipment idling risk, manpower waiting risk, and inventory stagnation risk.

[0054] If the simulation decision-making results meet the preset resource threshold conditions, the existence of warehousing and logistics resource bottlenecks or idle waste risks can be identified by combining the digital warehousing and logistics status map and scheduling decision-making auxiliary text. This can be achieved by comparing the resource indicators output by the simulation with the preset thresholds. If the limits are exceeded, the structural relationships in the map and the semantic context in the text are further integrated for comprehensive judgment. Furthermore, this operation can be achieved by using a rule engine to match thresholds and trigger joint map-text inference, using a multimodal classification model to directly input simulation data, and using map embedding vectors and text representations for end-to-end risk classification. This can achieve accurate identification of abnormal resource states and avoid misjudgment based on a single data indicator.

[0055] Generate optimized resource allocation suggestions or risk warning signals.

[0056] The optimized resource allocation suggestions can be improved scheduling or allocation schemes proposed to address identified resource problems, guiding the system to dynamically adjust resources to improve overall efficiency. In one embodiment, the optimized resource allocation suggestions can be generated based on simulation decision result data and semantic context, and their business rationality can be verified through contextual logistics information. Risk warning signals can be warning indicators issued for potential abnormal resource states, triggering early intervention mechanisms to avoid operational interruptions or cost losses. Furthermore, risk warning signals can be generated when simulation results trigger preset resource threshold conditions and are cross-validated using graphs and text. Generating optimized resource allocation suggestions or risk warning signals can be achieved by automatically generating corresponding countermeasures or warning information based on the identified risk type. For example, this operation can be implemented by retrieving and instantiating matching optimization templates from a strategy knowledge base and dynamically synthesizing suggestion content through a generative model, thereby forming executable intelligent output to support early intervention.

[0057] Among them, the optimization of resource allocation suggestions should be verified by referring to the contextual logistics information in the scheduling decision support text.

[0058] The contextual logistics information can be business context information related to the current scheduling task contained in the scheduling decision support text, which can be used to provide a basis for business logic verification of optimization suggestions. In this embodiment, the contextual logistics information may include order urgency information, customer priority information, historical fulfillment record information, etc. Verifying the optimized resource allocation suggestions by referring to the contextual logistics information in the scheduling decision support text can involve checking the consistency between the generated suggestions and the business constraints, priorities, or historical patterns described in the support text. Furthermore, this operation can verify the consistency between the suggestions and the context through semantic similarity calculation and construct lightweight logic verification rules to determine whether the suggestions violate known constraints, thereby ensuring that the optimization suggestions conform to actual business logic and improving decision feasibility.

[0059] Taking the overload warning and optimization of regional warehouse sorting capacity as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can be coupled with the simulation module outputting that the average load rate of a sorting line in a South China warehouse reaches 92% during a simulated peak sales period, exceeding the preset resource threshold condition of 85%. The system then combines the inventory relationship between the warehouse and the surrounding forward warehouses in the digital warehousing and logistics status map, as well as the contextual logistics information mentioned in the scheduling decision auxiliary text, which states that 30% of the orders on that day are fresh produce and need to be delivered within 2 hours, to determine that there is a bottleneck in sorting capacity. The decision optimization module generates an optimized resource allocation suggestion to divert some non-fresh produce orders to the nearby Dongguan warehouse, and confirms in the verification stage that the suggestion does not violate the contextual constraints of local fulfillment of fresh produce orders. At the same time, a risk warning signal that the sorting area of ​​the South China warehouse is about to be congested is issued, triggering the automatic scheduling system to add temporary manpower.

[0060] In one embodiment, when there is a bottleneck or risk of idle waste in warehousing and logistics resources, the control strategy parameters in the warehousing and logistics resource scheduling and control strategy model are optimized. The control strategy parameters can be adjustable configuration variables or weight coefficients in the warehousing and logistics resource scheduling control strategy model, and can be used to determine the specific execution methods of scheduling behaviors such as resource allocation, task sequencing, or path selection. In an exemplary embodiment, the control strategy parameters may include, but are not limited to, one or more of task allocation weights, resource release thresholds, and path priority coefficients. When there is a risk of warehousing and logistics resource bottlenecks or idle waste, optimizing the control strategy parameters in the warehousing and logistics resource scheduling control strategy model can be achieved by adjusting the corresponding parameters in the strategy model according to the identified risk type to change the resource allocation logic. Furthermore, this operation can be achieved by automatically adjusting parameters based on rule mapping (such as increasing the task diversion weight corresponding to bottleneck equipment), or by using algorithms such as gradient descent or Bayesian optimization to search for better parameter combinations, thereby enabling the scheduling strategy to have dynamic adaptability and specifically alleviate specific resource problems.

[0061] Based on the optimized control strategy parameters, the warehousing and logistics resource scheduling control strategy model and the digital operation status model are re-coupled and simulated until the target simulation decision result data is obtained. The target simulation decision result data represents the elimination of service resource bottlenecks or waste risks.

[0062] The optimized control strategy parameters can be updated versions of the control strategy parameters after risk feedback adjustments, and can be used to drive a new round of coupled simulation to verify the risk mitigation effect. In one embodiment, the optimized control strategy parameters can be generated based on identified warehousing and logistics resource bottlenecks or idle waste risks through parameter tuning algorithms or rule mapping. Coupled decision simulation can be the joint operation process of the warehousing and logistics resource scheduling control strategy model and the digital operation status model under unified operating conditions, and can be used to verify the actual effect of the control strategy in the simulated environment. For example, the coupled decision simulation can receive the optimized control strategy parameters as input and output simulation decision result data to determine whether the target state has been reached. The target simulation decision result data can be the simulation output result representing that the resource bottleneck or waste risk has been eliminated, and can be used as an iteration termination condition to confirm that the scheduling strategy has met the optimization objective. Furthermore, the target simulation decision result data can be generated when the relevant resource indicators in the simulation decision result data return to the range of preset resource threshold conditions.

[0063] Eliminating service resource bottlenecks or waste risks can be demonstrated by simulation results showing that the system no longer experiences resource overload or inefficient idleness, marking the successful completion of a closed-loop optimization process. In an exemplary embodiment, eliminating service resource bottlenecks or waste risks can include one or more of the following: restoring sorting capacity to equilibrium, achieving inventory turnover efficiency targets, and ensuring reasonable utilization of transport vehicles. Based on the optimized control strategy parameters, the warehousing and logistics resource scheduling control strategy model and the digital operation state model are re-coupled for decision simulation. This can involve injecting the updated parameters into the strategy model and running the coupled simulation again under the same or similar operating conditions. Furthermore, this operation can be achieved by reproducing the simulation at the original operating conditions to compare the effects and testing the robustness of the strategy under disturbed operating conditions, thereby verifying the actual impact of parameter adjustments on the overall system state. Repeating the coupled decision simulation until the target simulation decision result data is obtained can be achieved by iteratively executing parameter optimization and coupled simulation until the simulation output meets the risk elimination conditions. Furthermore, this operation can be implemented by setting a maximum number of iterations to prevent infinite loops and introducing an early stopping mechanism to terminate when multiple consecutive rounds of improvement are less than a threshold, thereby achieving strategy self-evolution and virtual optimization oriented towards business objectives.

[0064] Taking the online optimization of AGV scheduling strategy in a smart warehouse as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can be as follows: The first run of the coupled simulation reveals that the AGV congestion rate in a certain picking area reaches 85%, exceeding the preset threshold, indicating a bottleneck in transportation resources. Based on this, the system increases the area avoidance weight in the AGV path planning module from 0.6 to 0.9 and reduces the task allocation priority in that area. The coupled simulation is then re-driven using the optimized control strategy parameters and run under the same peak operating conditions. The results show that the average AGV waiting time decreases by 32%, and the congestion rate drops to 68%. Since the target is still not met, the system further adjusts the task splitting granularity parameters and simulates again. In the third round of simulation, all indicators return to the normal range, generating target simulation decision result data, confirming that the service resource bottleneck has been eliminated, and finally, this set of parameters is solidified into a new scheduling strategy version.

[0065] In one embodiment, a dynamic inventory update model is constructed, including: Based on the product categories and storage location distribution of digital warehousing, the inventory storage status is discretized into an equivalent state transition sub-model.

[0066] In this context, the product category can be a set of goods with the same or similar attributes, management strategies, or business processing logic. It can serve as the basis for discretizing inventory status and influence the granularity of the state transition sub-model. In an exemplary embodiment, the product category can include, but is not limited to, one or more of high-turnover fast-moving consumer goods, temperature-controlled fresh produce, and large household appliances. Storage location distribution can be the physical or logical layout structure of the goods' storage locations within the warehouse space. It can be used to determine the spatial dimension division of inventory status and to construct location-related state transition sub-models. For example, storage location distribution can adopt high-bay shelving areas, floor stacking areas, and temporary storage handover areas. Inventory storage status can be a comprehensive description of the quantity, batches, and availability of a specific product category in a specific storage location. It can be used as the original state variable for modeling and discretized into multiple equivalent state transition sub-models. Furthermore, inventory storage status can be generated by aggregating real-time inventory monitoring data. The equivalent state transition sub-model can be a state machine unit that abstracts the inventory status changes under a certain product category-storage location combination. It can be used to transform high-dimensional continuous inventory changes into a traceable and computable discrete state transition process. In one specific embodiment, the equivalent state transition sub-model may include, but is not limited to, vacant state, low inventory state, and full-load state. Based on the product categories and storage location distribution in digital warehousing, the inventory storage state is discretized into an equivalent state transition sub-model. This can be achieved by using product category-storage location combinations as the partitioning dimension to structurally discretize continuous inventory states. Furthermore, this operation is implemented by using a clustering algorithm to discretize and partition historical inventory states, pre-setting state intervals according to business rules, and mapping them to real-time values. This enables the computationalization of inventory states and provides the foundation for state machine modeling.

[0067] By connecting the state transition sub-models through state transition rules and setting the inventory inbound and outbound sequence logic, a model for dynamically updating inventory can be constructed.

[0068] The state transition rules can be logical constraints defining how equivalent state transition sub-models switch states with inbound / outbound events. These rules connect the sub-models, forming a holistic state evolution mechanism with causal and temporal relationships. In this embodiment, state transition rules can be generated inductively based on business rules, historical operation logs, or reinforcement learning strategies. For example, state transition rules may include, but are not limited to, inbound trigger rules, outbound trigger rules, and transfer trigger rules. The inventory inbound / outbound sequence logic can be a process-oriented expression reflecting the timing, frequency, and dependencies of inbound / outbound operations in actual business. It can be used to imbue state transitions with a real business rhythm, driving the temporal behavior of the inventory dynamic update model. Furthermore, the inventory inbound / outbound sequence logic can be extracted and structured from WMS operation logs or order fulfillment processes. In a specific embodiment, the output data of the inventory inbound / outbound sequence logic can be directly used to drive the input updates of the sorting operation load model, such as task volume, SKU distribution, and time windows.

[0069] Connecting state transition sub-models through state transition rules allows for defining the state transition conditions and objectives of different sub-models under inbound / outbound events. Furthermore, this operation can be implemented by formally describing the transition rules using finite state automata and representing the sub-models and their transition edges using a graph structure, thereby constructing a holistic inventory evolution mechanism with logical coherence and temporal consistency. Setting the inventory inbound / outbound sequence logic to construct a dynamically updated inventory model can be achieved by abstracting the operational sequences in actual business operations into temporal logic driving state transitions. Further, this operation can be implemented by extracting the inbound / outbound event sequence from the order fulfillment chain and modeling it as a Markov process, and pre-setting future inbound / outbound sequence templates based on a scheduling plan, thereby making the model behavior closely match the real operational rhythm and enhancing simulation fidelity.

[0070] The output data of the inventory inbound / outbound sequence logic is used to drive the input update of the sorting operation load model.

[0071] Input updates can be the result of dynamically replacing or correcting model input parameters, ensuring that downstream models always calculate based on the latest upstream state. In this embodiment, input updates can receive upstream model output through a data pipeline and trigger parameter reloading. Using the output data of the inventory inbound / outbound sequence logic to drive input updates for the sorting workload model can involve passing task characteristics (such as outbound SKU list, time window, and quantity) generated by the inbound / outbound sequence logic to the sorting model. Furthermore, this operation can be achieved by packaging the outbound sequence into a sorting task queue and injecting it into the sorting model in real time, and by broadcasting inbound / outbound completion signals via an event bus to trigger sorting load recalculation, thereby enabling data flow and dynamic linkage from the inventory level to the operational level.

[0072] For example, in the scenario of dynamic modeling of multi-category e-commerce warehouse inventory, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can identify that a certain area of ​​the warehouse contains three categories of goods: cosmetics, 3C products, and home furnishings, and the storage location distribution covers high-bay shelves, flow racks, and floor stacking areas. Based on this, the inventory storage state is discretized into multiple equivalent state transition sub-models, such as cosmetics-flow rack-low inventory state, 3C products-high-bay shelf-full-load state, etc. The state transition rules define that when the inventory falls below a threshold due to the outbound shipment of a certain SKU, the system transitions from the full-load state to the low-inventory state. At the same time, the inventory inbound and outbound sequence logic is set to simulate the peak of batch outbound shipments per hour during promotional periods and the nighttime replenishment rhythm. The outbound SKU list, time distribution, and storage location path information output by this logic are pushed to the sorting operation load model in real time, so that it dynamically adjusts the robot scheduling density and manpower scheduling. The inventory dynamic update model constructed in this way not only accurately reflects the inventory evolution path, but also becomes a key pre-configuration link to drive the pre-configuration of sorting resources.

[0073] In one embodiment, the process of constructing the delivery route planning model includes: Using delivery timeliness indicators and transportation costs as scheduling boundaries, delivery scheduling is divided into transportation capacity allocation units and route optimization units; The delivery timeliness index can be a performance constraint parameter that measures the time required to complete a delivery task. It can be used as one of the scheduling boundary conditions to constrain the feasibility range of route planning and capacity allocation. In an exemplary embodiment, the delivery timeliness index can be set in conjunction with the service type, and may include, but is not limited to, one or more of same-day delivery, next-day delivery, and scheduled delivery. Capacity cost can be the economic or resource cost of manpower, vehicles, energy, and other resources consumed in executing a delivery task. It can be used as another scheduling boundary condition to balance service efficiency and operational economy. For example, capacity cost can be divided according to cost composition, and may include fixed vehicle depreciation costs, variable fuel costs, and piece-rate labor costs. The scheduling boundary can be a set of constraints that limit the feasible domain of scheduling decisions, and can be used to ensure that the generated scheduling scheme is effective within business rules and resource constraints. Furthermore, the scheduling boundary can be jointly defined by the delivery timeliness index and capacity cost, forming a constraint space for multi-objective optimization. The capacity allocation unit can be a scheduling function module responsible for matching available transportation resources to delivery tasks, and can be used to achieve the rational allocation of capacity resources such as vehicles, drivers, or delivery personnel. In one specific embodiment, the capacity allocation unit can perform matching calculations based on task volume, capacity, and delivery time window, and may include, but is not limited to, vehicle scheduling subunits, rider allocation subunits, and outsourced capacity access subunits. The route optimization unit can be a scheduling function module that generates the optimal driving route under given task set and capacity conditions, and can be used to minimize driving distance, time, or energy consumption, thereby improving the efficiency of a single delivery. In this embodiment, the route optimization unit can use graph algorithms or intelligent optimization methods to solve constrained path problems, and may include, but is not limited to, shortest time path subunits, lowest energy consumption path subunits, and least transfer path subunits.

[0074] Using delivery timeliness indicators and transportation costs as scheduling boundaries, delivery scheduling is divided into transportation capacity allocation units and route optimization units, which can decouple the overall delivery scheduling problem based on dual constraints. Furthermore, this operation can be achieved by dividing according to the decision-making stage (allocating transportation capacity first and then optimizing routes) or by separating according to optimization objectives (handling resource matching and spatiotemporal path problems separately), thereby realizing the professional division of scheduling tasks and improving the optimization depth of each link.

[0075] The capacity allocation unit and the route optimization unit are defined as independent scheduling modules; The independent scheduling module can be a scheduling function encapsulation with independent input / output interfaces and internal logic, which can be used to support modular decoupling and specialized optimization. For example, the independent scheduling module can encapsulate the capacity allocation unit and the route optimization unit into independently runnable and callable software modules. Defining the capacity allocation unit and the route optimization unit as independent scheduling modules can be a software encapsulation of two functional units, giving them independent interfaces and operating logic. Furthermore, this operation can be implemented by deploying them as two independent services using a microservice architecture or by integrating them into a unified scheduling framework using a plug-in design, thereby supporting independent module iteration and flexible combination.

[0076] An independent scheduling module is integrated through a feedback compensation mechanism to construct a delivery route planning model; the scheduling output data of the independent scheduling module is calibrated based on real-time road conditions and inventory monitoring values ​​in the digital warehousing and logistics status map.

[0077] The feedback compensation mechanism can be a closed-loop control structure used to coordinate the outputs of multiple independent scheduling modules and correct deviations. It can be used to achieve dynamic coordination and error correction between modules, avoiding local optima. In one specific embodiment, the feedback compensation mechanism can generate compensation signals by comparing the consistency between module outputs and the global target, and then adjust the parameters of preceding modules in reverse. This can include, but is not limited to, error integral compensation, predictive feedforward compensation, and state observer compensation. The scheduling output data can be the preliminary scheduling results generated by independent scheduling modules, which can be used as the raw input for feedback compensation and calibration. Furthermore, the scheduling output data can be calculated by each module according to its internal logic, including a capacity assignment list or path sequence. Real-time traffic conditions can be the current road network traffic status information, which can be used as a calibration basis to improve the responsiveness of path planning to traffic disturbances. In this embodiment, real-time traffic conditions can be obtained from map APIs, vehicle GPS, or traffic management department interfaces. Inventory monitoring values ​​can be real-time data reflecting the current quantity and status of goods available for shipment, which can be used to determine whether a delivery task is ready for execution, avoiding invalid order dispatches. For example, inventory monitoring values ​​can be collected in real time through a WMS system or IoT sensors.

[0078] By integrating independent scheduling modules through a feedback compensation mechanism to construct a delivery route planning model, a closed-loop feedback path can be established between the two modules to dynamically adjust output consistency. Furthermore, this operation can be achieved by adjusting the capacity allocation scheme if the route optimization result exceeds the capacity carrying capacity, or by triggering a reallocation if the route becomes infeasible after capacity allocation. This avoids strategy fragmentation caused by decoupling and enhances overall synergy. Calibrating the scheduling output data of the independent scheduling module based on real-time road conditions and inventory monitoring values ​​in the digital warehousing and logistics status graph can inject external real-time semantic information into the post-processing stage of the scheduling output to correct the initial plan. Further, this operation can be achieved by updating the route weights using road condition entities in the graph and then re-optimizing, or filtering out non-fulfillable orders based on inventory monitoring values ​​before executing capacity allocation. This ensures that the scheduling results closely match the real operating environment and improves execution feasibility.

[0079] Taking peak-hour delivery for urban instant retail as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can use the 30-minute delivery time indicator and the daily average cost limit per vehicle as the scheduling boundary. The delivery scheduling is broken down into a capacity allocation unit (determining which rider will accept each order) and a route optimization unit (generating the optimal pickup and delivery order after the rider accepts the order). The two are encapsulated as independent scheduling modules and connected through a feedback compensation mechanism: when the route optimization finds that a rider's task has timed out, it will send a feedback request to reassign some orders. At the same time, the initial outputs of the two modules will be dynamically calibrated based on real-time road conditions (such as a sudden traffic jam in a business district) and inventory monitoring values ​​(such as a temporary shortage of a store's best-selling products) in the digital warehousing and logistics status map, automatically skipping stores with shortages or detouring through congested sections, and finally generating a highly feasible delivery route planning model output.

[0080] In one embodiment, real-time inventory monitoring data and logistics delivery scheduling request data of digital warehousing are acquired, and a digital warehousing logistics status map and scheduling decision support text are constructed based on the real-time inventory monitoring data and logistics delivery scheduling request data, including: Text normalization and semantic structure parsing are performed on the logistics delivery scheduling request data to obtain a list of scheduling request entities and unstructured context fragments.

[0081] Text normalization can be a preprocessing operation that standardizes the original logistics delivery scheduling request text, eliminating differences in expression and improving the consistency and accuracy of subsequent semantic parsing. In this embodiment, text normalization can clean and standardize the original text through regular expression matching, synonym replacement, abbreviation expansion, and format unification rules. Semantic structure parsing is a technical process of extracting structured semantic components from the normalized scheduling request text, which can be used to transform unstructured instructions into machine-understandable semantic units. For example, semantic structure parsing can use dependency parsing, named entity recognition, or intent-slot filling models to parse task elements in the text. The scheduling request entity list can be a set of key task elements extracted from the scheduling request, such as cargo type, destination, and timeliness requirements, which can be used as structured input for constructing scheduling decision support text. Further, the scheduling request entity list can include, but is not limited to, one or more of cargo attribute entities, spatiotemporal constraint entities, and service level entities. Unstructured context fragments can be descriptive text fragments in the scheduling request that are not fully structured but have semantic value, which can be used to retain contextual information in the original instructions and enhance the interpretability of the decision. In one exemplary embodiment, unstructured context fragments may include, but are not limited to, notes fragments, exception condition fragments, customer preference fragments, etc.

[0082] Text normalization and semantic structure parsing of logistics delivery scheduling request data can be achieved by first standardizing and cleaning the original scheduling text, and then extracting structured semantic components. Further, this operation can be implemented by using rule-based regularization templates for normalization combined with entity recognition using a CRF model, or by directly performing normalization and structured parsing using an end-to-end large language model. This transforms unstructured scheduling instructions into computable and fusionable semantic units. Obtaining the scheduling request entity list and unstructured context fragments can be achieved by classifying the output of semantic structure parsing into structured entities and context-preserving unstructured fragments. Further, this operation can be implemented by using a slot-filling model to output structured entities, with the remaining text serving as context fragments, or by using an attention masking mechanism to distinguish core elements from auxiliary descriptions. This separates quantifiable elements from semantic context, supporting multimodal fusion.

[0083] Quantify the correlation between real-time inventory monitoring and transportation capacity status data to generate indicators of scheduling influencing factors.

[0084] The capacity status data can reflect the real-time capacity and load status of currently available transportation resources (such as vehicles, personnel, and equipment), and can be used as a key constraint for assessing scheduling feasibility. In a specific embodiment, capacity status data can be collected in real time through a TMS system, vehicle-mounted terminals, or dispatcher reporting interfaces. The correlation metric can be a calculation method used to quantify the strength of the statistical or logical correlation between real-time inventory monitoring data and capacity status data. It can be used to reveal the coupling relationship between resource supply and demand, supporting the generation of influencing factors. For example, the correlation metric can be implemented using Pearson correlation coefficient, mutual information, Granger causality test, or graph neural network embedding similarity calculation. The scheduling influencing factor index can be a quantitative indicator representing scheduling difficulty or priority, generated based on the correlation metric between inventory and capacity data. It can be used to dynamically adjust the decision weights of resource scheduling strategies, improving strategy adaptability. Furthermore, the scheduling influencing factor index can be output as a numerical feature that can be input into the strategy model after normalizing, weighted fusion, or threshold mapping the correlation metric results. In an exemplary embodiment, scheduling influencing factor indicators can be used as input parameters to directly affect the decision weights in the warehousing and logistics resource scheduling and control strategy model; at the same time, they can participate in the construction of scheduling decision auxiliary texts and provide quantitative basis.

[0085] Quantifying the correlation between real-time inventory monitoring and capacity status data can be achieved by calculating the statistical or semantic association strength between the two types of heterogeneous data across time or task dimensions. Furthermore, this operation can be implemented by calculating the Pearson correlation coefficient between inventory fluctuations and capacity utilization based on a sliding window, or by using a graph neural network to embed inventory points and capacity nodes into the same vector space and then calculating cosine similarity. This reveals the coupling relationship between resource supply and demand, providing a basis for assessing scheduling difficulty. Generating indicators of scheduling influencing factors can be achieved by transforming the correlation measurement results into standardized indicators that can be used as input to the strategy model. Further, this operation can be achieved by binning the correlation scores and using them as classification feature input, or by mapping continuous correlation values ​​to 0-1 interval weight factors using a Sigmoid function. This allows for dynamic feedback of resource constraints on scheduling strategies.

[0086] Based on the list of scheduling request entities, unstructured context fragments, and indicators of scheduling influencing factors, a scheduling decision support text is constructed.

[0087] Constructing decision support text for scheduling can involve fusing structured entities, contextual fragments, and quantitative indicators to generate natural language decision support text. Furthermore, this operation can be achieved by combining the three types of inputs using template splicing, or by using a conditional text generation model to generate a coherent description based on these three factors, thereby enhancing the interpretability and contextual integrity of scheduling decisions.

[0088] A digital warehousing and logistics status map is constructed based on real-time inventory monitoring data and transportation capacity operation data.

[0089] The transportation capacity operation data can be dynamic behavioral data recording the trajectory, speed, and load changes of transportation resources during task execution, which can be used to support the state modeling of transportation capacity nodes in the digital warehousing and logistics state graph. In a specific embodiment, transportation capacity operation data can be continuously collected through GPS positioning, vehicle-mounted IoT sensors, or a scheduling log system. Constructing a digital warehousing and logistics state graph based on real-time inventory monitoring data and transportation capacity operation data can be achieved by modeling the entities and their dynamic relationships in the inventory and transportation capacity data as a graph structure. Furthermore, this operation can be implemented by constructing a dynamic knowledge graph with warehouses, storage locations, and vehicles as nodes and inventory flow and task assignment as edges, or by using a spatiotemporal graph neural network to directly construct an implicit graph from the original data stream, thereby realizing the topological mapping and dynamic representation of the physical operating status in the digital space.

[0090] Among them, the scheduling influencing factor indicators are input into the warehousing and logistics resource scheduling control strategy model to adjust the decision weights.

[0091] Decision weights can be parameters used in the warehousing and logistics resource scheduling and control strategy model to adjust the priorities of different optimization objectives or constraints. They can be used to determine the degree of emphasis on factors such as timeliness, cost, and load balancing when allocating resources. In an exemplary embodiment, decision weights can be dynamically adjusted by scheduling influencing factor indicators, which can be represented as coefficients in the loss function, weights in the reinforcement learning reward function, or priority factors in the rule engine. Inputting scheduling influencing factor indicators into the warehousing and logistics resource scheduling and control strategy model to adjust decision weights can be achieved by injecting the generated indicators as external features into the strategy model and dynamically modifying its internal weight configuration. Furthermore, this operation can be achieved by using the indicators as part of the state input in the reinforcement learning strategy network, or by using them as dynamic coefficients of each sub-item in the objective function in the optimization model, thereby enabling the strategy model to adaptively optimize resource allocation priorities according to real-time operating conditions.

[0092] Taking the emergency replenishment scheduling of fresh food cold chain as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can be as follows: A sudden high temperature in a certain area leads to a surge in fresh food orders, and the system receives a large number of scheduling requests containing expressions such as "urgent," "delivery within 2 hours," and "refrigerated truck." First, the requests are normalized, and expressions such as "urgent," "emergency," and "fast" are standardized as priority tags. Then, the system extracts a list of scheduling request entities such as cargo temperature control requirements, time windows, and delivery addresses through semantic structure parsing, while retaining unstructured context fragments such as "the customer is a hospital canteen." At the same time, the system quantifies the current cold chain... The negative correlation between warehouse water levels and the number of available refrigerated trucks generates a high-value scheduling influencing factor index. This index is input into the resource scheduling control strategy model, automatically increasing the timeliness weight and decreasing the cost weight. It is also used in conjunction with entity lists and context fragments to generate scheduling decision support text such as "High-priority fresh food order, needs to be delivered to the hospital within 2 hours, current refrigerated transport capacity is tight." At the same time, the status map constructed based on cold storage temperature and humidity monitoring data and refrigerated truck GPS trajectories shows that the eastern distribution area node is overloaded. Based on this, the system finally generates an optimized decision to prioritize the use of nearby forward warehouse inventory and temporarily activate backup refrigerated trucks.

[0093] In one embodiment, the system further includes: After the determined optimization decisions are put into actual warehousing and logistics scheduling operations, real-time data on logistics resource utilization, delivery timeliness, and order completion rate are collected during the actual operation process. The actual collected operation data is matched with the simulation decision results of the corresponding optimization decision to calculate the actual simulation deviation value. When the actual simulation deviation exceeds the preset deviation threshold, the scheduling context is updated based on the updated digital warehousing and logistics status map, triggering AI adaptive rescheduling, readjusting the decision weights of the warehousing and logistics resource scheduling and control strategy model, and generating new optimized decisions.

[0094] Logistics resource utilization data can be indicators reflecting the effective use of warehousing and logistics equipment, manpower, or space in actual operations. It can be used to evaluate the effectiveness of scheduling strategies and serves as a key input for deviation calculation. In one exemplary embodiment, logistics resource utilization data can be collected in real-time via IoT sensors, work logs, or WMS / TMS systems to determine resource occupancy and idle status. Further, logistics resource utilization data can include, but is not limited to, one or more of equipment utilization, manpower saturation, and warehouse space occupancy. Delivery timeliness data can record the actual time consumed from order departure to delivery to the customer, and can be used to measure the time-based fulfillment capability of the scheduling plan. For example, delivery timeliness data can be automatically calculated and generated by the logistics tracking system based on node check-in times. Order completion rate data can be the proportion of successfully fulfilled orders per unit time to the total number of scheduled orders, and can be used to characterize the overall execution completeness of the scheduling task. In one specific embodiment, order completion rate data can be obtained by comparing planned orders in the OMS with completed orders in the TMS.

[0095] The actual collected operational data can be a set of multi-dimensional performance indicators collected after the optimization decision is implemented in actual operation. This data can serve as a benchmark for simulation results and support deviation analysis. In this embodiment, the actual collected operational data may include, but is not limited to, logistics resource utilization data, delivery timeliness data, and order completion rate data. Real-time collection of logistics resource utilization data, delivery timeliness data, and order completion rate data during the actual operation process can be achieved by continuously monitoring the operational system interface during the optimization decision execution to obtain key performance indicators. Furthermore, this operation can be implemented by subscribing to operational event streams through message middleware and periodically polling the WMS / TMS performance monitoring API, thereby constructing a data foundation for simulation-execution closed-loop feedback. Deviation matching calculation can be a process of quantitatively analyzing the differences between actual operational data and corresponding simulation decision result data. This can be used to generate measurable actual simulation deviation values ​​to determine the effectiveness of the strategy. In an exemplary embodiment, deviation matching calculation can employ methods such as normalized error calculation, dynamic time warping, or semantic similarity matching.

[0096] The deviation matching calculation between the actual collected operation data and the corresponding simulation decision results can be used to align and measure the difference between the simulation predictions and actual observations of the same batch of scheduling tasks. Further, this operation can be implemented by using weighted mean square error to calculate multidimensional deviations and using sequence similarity algorithms to match task completion trajectories, thereby quantifying strategy execution deviations and identifying model inaccuracies. The actual simulation deviation value can be a quantitative difference index between the simulation prediction results and the actual operation results, which can be used as a basis for determining whether to trigger rescheduling. In a specific embodiment, the actual simulation deviation value can be obtained by weighting the deviation matching calculation output with multidimensional indicators. Obtaining the actual simulation deviation value can be achieved by summarizing the deviation matching calculation results and outputting a single or composite deviation index. Further, this operation can be implemented by outputting a scalar comprehensive deviation value or outputting the deviation vectors of each dimension, thereby providing clear input for threshold judgment. The preset deviation threshold can be a critical value pre-set by the system to allow the maximum acceptable deviation between simulation and reality, which can be used to determine whether the current scheduling strategy is still applicable. For example, the preset deviation threshold can be configured based on historical operation data statistical analysis or expert experience.

[0097] The updated digital warehousing and logistics status graph can be a semantic graph of warehousing and logistics reconstructed by integrating the latest actual operational status after the optimization decision execution. It can be used to provide the latest global status awareness foundation for rescheduling. In this embodiment, the updated digital warehousing and logistics status graph can be incrementally updated by injecting real-time operational feedback data into the original status graph. The scheduling context can be semantic background information such as environmental constraints, task priorities, and resource status on which the current scheduling decision depends, which can be used to guide the direction and boundary conditions of AI adaptive rescheduling. In a specific embodiment, the scheduling context can extract key semantic fragments from the updated digital warehousing and logistics status graph and organize them in a structured manner. Furthermore, the scheduling context can include, but is not limited to, task constraint context, resource availability context, and risk warning context.

[0098] When the actual simulation deviation exceeds a preset deviation threshold, the scheduling context is updated based on the currently updated digital warehousing and logistics state graph. This can be achieved by extracting current constraints, resource states, and task relationships from the latest state graph when the deviation exceeds the limit, thus reconstructing the scheduling context. Furthermore, this operation can be implemented by extracting context from graph subgraphs and generating context vectors by encoding state semantics using graph neural networks, ensuring that rescheduling is based on the latest real state. AI adaptive rescheduling can be an intelligent scheduling strategy regeneration mechanism that is automatically triggered based on deviation feedback and updated state, enabling rapid response to sudden disturbances or model mismatches. In an exemplary embodiment, AI adaptive rescheduling can invoke the strategy control module and decision optimization module to rerun the scheduling process in a new context. Triggering AI adaptive rescheduling can activate the internal rescheduling process, initiating strategy adjustment and decision regeneration. Further, this operation can asynchronously trigger background rescheduling tasks, synchronously interrupt the current scheduling flow, and immediately replan the implementation, thereby enabling autonomous response to abnormal operating conditions.

[0099] The decision weights of the warehousing and logistics resource scheduling and control strategy model can be adjustable parameters in the control strategy model that affect the objective function or constraint priority. These weights can be used to adjust resource allocation preferences, such as time-priority, cost-priority, or equilibrium modes. For example, the decision weights of the warehousing and logistics resource scheduling and control strategy model can be dynamically adjusted through online learning, meta-optimization, or rule mapping. Furthermore, the decision weights of the warehousing and logistics resource scheduling and control strategy model can include, but are not limited to, time-priority weights, cost weights, and robustness weights. Readjusting the decision weights of the warehousing and logistics resource scheduling and control strategy model can be achieved by dynamically modifying the objective function coefficients or constraint relaxation parameters in the strategy model based on the deviation type and scheduling context. Further, this operation can be implemented based on online fine-tuning of weights using reinforcement learning and mapping deviation patterns to weight configurations using a rule engine, thereby enabling the strategy model to adapt to changes in the current operating environment. The new optimization decision can be an updated scheduling instruction generated after AI adaptive rescheduling, which can be used to replace the original decision to better fit the current actual working conditions. In a specific embodiment, the new optimization decision can be re-output by the decision optimization module under the updated context and adjusted strategy model. Generating new optimized decisions can be achieved by rerunning the decision optimization process within the adjusted strategy model and updated context. Furthermore, this operation can be implemented by fully re-optimizing the generated decisions or by making local corrections based on the original decisions, thereby outputting a scheduling scheme that better reflects the current actual state.

[0100] Taking a sudden equipment failure in a regional warehouse leading to a decrease in sorting efficiency as an example, the digital warehousing and logistics management system based on AI intelligent scheduling technology in this embodiment can be as follows: During the morning peak optimization decision-making process, a warehouse in South China experienced a sudden failure of the sorting machine, resulting in a sharp drop in actual resource utilization and order backlog. The system collected data on delivery delays and order completion rate declines in real time. Deviation matching calculations revealed that the actual simulation deviation value exceeded the preset threshold. The system then extracted a new scheduling context based on the updated state graph containing fault information, in which the sorting area's capacity was marked as limited. The AI ​​adaptive rescheduling mechanism was triggered, and the strategy model automatically increased the weight of human resource allocation and reduced the dependence on automated equipment, generating a new optimization decision to transfer some orders to the manual review area for processing, effectively alleviating the fulfillment pressure.

[0101] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A digital warehousing and logistics management system based on AI intelligent scheduling technology, characterized in that, The system includes: The data mapping module is used to acquire real-time inventory monitoring data and logistics distribution scheduling request data of digital warehousing, and to construct a digital warehousing logistics status map and scheduling decision support text based on the real-time inventory monitoring data and logistics distribution scheduling request data; The model building module is used to build a digital operation status model of the digital warehouse based on the target logistics node components of the warehouse logistics, wherein the target logistics node components are components related to the resource flow of the warehouse logistics process. The strategy control module is used to construct a warehousing and logistics resource scheduling and control strategy model, which is used to regulate the output allocation of logistics resources in the digital operation status model. The coupled simulation module is used to perform coupled decision simulation on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model based on the selected warehousing and logistics operation conditions, and obtain the corresponding simulation decision result data. The decision optimization module is used to determine optimization decisions or abnormal risk indications in digital warehousing and logistics management based on the simulation decision result data, combined with the digital warehousing and logistics status map and the scheduling decision auxiliary text.

2. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, When the target logistics node components include inventory storage units, sorting systems, and distribution networks, the digital operational status model of the digital warehousing based on the target logistics node components includes: Based on AI-powered intelligent scheduling, target logistics node components are used to construct dynamic inventory update models, sorting operation load models, and delivery route planning models. The inventory dynamic update model, the sorting operation load model, and the delivery route planning model are integrated to form the digital operation status model; wherein, the output data of the inventory dynamic update model is used to update the input parameters of the sorting operation load model, and the input parameters of the delivery route planning model are adjusted based on the output data of the sorting operation load model.

3. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, The process involves performing coupled decision simulations on the warehousing and logistics resource scheduling and control strategy model and the digital operation status model based on selected warehousing and logistics operation conditions, to obtain corresponding simulation decision result data, including: Based on the selected warehousing and logistics operation points, a resource allocation control signal is generated through the warehousing and logistics resource scheduling and control strategy model to dynamically adjust the logistics resource parameters in the digital operation status model. Under the action of resource allocation control signals, a warehousing and logistics operation scenario is simulated, and data on logistics resource utilization and customer delivery timeliness are recorded as simulation decision results. The logistics resource utilization and delivery timeliness data are fed back to the warehousing and logistics resource scheduling and control strategy model to iteratively optimize the resource allocation control signals.

4. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, The system also includes: Extract a set of candidate scheduling scheme entries related to scheduling decisions from a knowledge base in the warehousing and logistics field; Based on logistics delivery scheduling request data and contextual logistics information, semantic disambiguation processing is performed on the candidate scheduling scheme entry set to obtain an optimized scheduling option list; wherein, the optimized scheduling option list is used to initialize the decision parameters of the warehousing logistics resource scheduling control strategy model.

5. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, Based on the simulation decision results data, combined with the digital warehousing and logistics status map and the scheduling decision support text, the optimization decisions or abnormal risk indicators in digital warehousing and logistics management are determined, including: If the simulation decision result data meets the preset resource threshold conditions, the digital warehousing and logistics status map and the scheduling decision auxiliary text are used to determine the existence of warehousing and logistics resource bottlenecks or idle waste risks. Generate optimized resource allocation suggestions or risk warning signals; wherein, the optimized resource allocation suggestions are verified with reference to the contextual logistics information in the scheduling decision support text.

6. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 5, characterized in that, In situations where there are bottlenecks or risks of idle and wasted warehousing and logistics resources, optimize the control strategy parameters in the warehousing and logistics resource scheduling and control strategy model. Based on the optimized control strategy parameters, the warehousing and logistics resource scheduling control strategy model and the digital operation status model are re-coupled for decision simulation until the target simulation decision result data is obtained. The target simulation decision result data represents the elimination of service resource bottlenecks or waste risks.

7. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 2, characterized in that, The construction of the dynamic inventory update model includes: Based on the product categories and storage location distribution of digital warehousing, the inventory storage status is discretized into an equivalent state transition sub-model. The state transition sub-model is connected by state transition rules, and the inventory inbound / outbound sequence logic is set to construct an inventory dynamic update model; wherein, the output data of the inventory inbound / outbound sequence logic is used to drive the input update of the sorting operation load model.

8. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 2, characterized in that, The process of constructing the delivery route planning model includes: Using delivery timeliness indicators and transportation costs as scheduling boundaries, delivery scheduling is divided into transportation capacity allocation units and route optimization units; The capacity allocation unit and the route optimization unit are defined as independent scheduling modules; The independent scheduling modules are integrated through a feedback compensation mechanism to construct a delivery route planning model; wherein, the scheduling output data of the independent scheduling modules is calibrated based on real-time road conditions and inventory monitoring values ​​in the digital warehousing and logistics status map.

9. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, The process of acquiring real-time inventory monitoring data and logistics delivery scheduling request data for digital warehousing, and constructing a digital warehousing logistics status map and scheduling decision support text based on the real-time inventory monitoring data and logistics delivery scheduling request data, includes: Text normalization and semantic structure parsing are performed on logistics delivery scheduling request data to obtain a list of scheduling request entities and unstructured context fragments. Quantify the correlation between real-time inventory monitoring and transportation capacity status data, and generate indicators of scheduling influencing factors; Based on the list of scheduling request entities, unstructured context fragments, and scheduling influencing factor indicators, the scheduling decision support text is constructed. The digital warehousing and logistics status map is constructed based on real-time inventory monitoring data and transportation capacity operation data; wherein, the scheduling influencing factor indicators are input into the warehousing and logistics resource scheduling control strategy model to adjust the decision weights.

10. The digital warehousing and logistics management system based on AI intelligent scheduling technology as described in claim 1, characterized in that, The system also includes: After the determined optimization decisions are put into actual warehousing and logistics scheduling operations, real-time data on logistics resource utilization, delivery timeliness, and order completion rate are collected during the actual operation process. The actual collected operation data is matched with the simulation decision results of the corresponding optimization decision to calculate the actual simulation deviation value. When the actual simulation deviation value exceeds the preset deviation threshold, the scheduling context is updated based on the currently updated digital warehousing and logistics status map, triggering AI adaptive rescheduling, readjusting the decision weights of the warehousing and logistics resource scheduling control strategy model, and generating a new optimization decision.

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