A global inventory visibility and intelligent allocation coordination system for complex supply chain network

Abstract

The application provides a global inventory visibility and intelligent allocation coordination system for a complex supply chain network, and belongs to the technical field of supply chain inventory management and intelligent coordination. The system solves the problems of information island, opaque inventory state, localized allocation decision, missing execution closed loop, and insufficient scene adaptability in the traditional supply chain mode. The system builds a full-link closed loop architecture from data aggregation, state visualization, supply and demand situation awareness, intelligent allocation decision, execution closed loop management and control to model self-iteration optimization, and sets corresponding function modules to realize full-process collaborative management and control. The application can realize end-to-end transparent visibility and global allocation coordination optimization of the full-link inventory of the complex supply chain, improve the operation efficiency of the supply chain, reduce the full-link operation cost, and adapt to the operation requirements of complex supply chain networks of different structures.

Description

Technical Field

[0001] This invention relates to the field of supply chain inventory management and intelligent collaboration technology, specifically to a global inventory visibility and intelligent allocation collaboration system for complex supply chain networks. Background Technology

[0002] As commodity distribution networks continue to expand, supply chain systems are gradually exhibiting a complex form involving multiple nodes, cross-regional operations, and multiple stakeholders. A complete supply chain network typically encompasses multiple participating entities, including multi-level suppliers, production and processing bases, regional central warehouses, city forward warehouses, multi-level distributors, and terminal retail stores. It also involves the entire process of storing, circulating, and fulfilling multiple categories of goods. Traditional inventory management and allocation coordination models are no longer adequate for the operational needs of complex supply chain networks and exhibit numerous shortcomings in practical applications.

[0003] In traditional inventory management models, each participating entity typically uses an independent business management system. Differences in data formats, statistical definitions, and semantic rules between these systems create fragmented information silos. Each entity can only view inventory data within its own jurisdiction, unable to grasp the inventory status, in-transit flow, and fulfillment capabilities of nodes across the entire supply chain. This results in a lack of transparency regarding the overall inventory status of the network, highlighting information asymmetry. This information barrier directly leads to resource misallocation across the entire supply chain, often resulting in situations where some nodes have long-term inventory backlogs while others face inventory shortages that prevent fulfillment. This wastes both warehousing and merchandise resources and negatively impacts the efficiency of fulfilling end orders.

[0004] In the inventory allocation process, traditional models often employ a single-node, localized decision-making approach. Each inventory node initiates allocation requests based on its own inventory status and order demands, lacking a holistic, supply chain-wide planning strategy. This localized decision-making model can only achieve supply and demand balance at a single node, failing to consider the optimal allocation of resources across the entire network. This easily leads to duplicate allocations and circuitous transportation, significantly increasing supply chain allocation, transportation, and warehousing management costs. Furthermore, traditional allocation decisions are often reactive, initiating operations only after stockouts or overstocking occur. This makes it impossible to anticipate supply and demand changes and potential risks in advance, resulting in weak capabilities to handle market fluctuations and sudden demand disruptions, and easily leading to untimely allocations and non-compliance.

[0005] Furthermore, in traditional allocation and coordination models, decision-making and execution are disconnected. After allocation plans are issued, it is impossible to track the execution progress and flow status of each entity in real time. Anomalies that occur during execution cannot be promptly reported to the decision-making level for adjustments, leading to a wider impact of these events and hindering the effective implementation of the allocation plan. Simultaneously, traditional decision-making rules are often fixed and cannot be autonomously optimized as the supply chain network changes or the market environment adjusts. Over time, the adaptability of the decision-making plans to actual business scenarios gradually decreases, making it impossible to continuously adapt to the dynamic operational needs of complex supply chain networks.

[0006] Furthermore, in multi-entity collaboration, the traditional model lacks unified interaction channels and standardized collaboration processes. Communication, instruction transmission, and document transfer between entities are mostly conducted in a decentralized manner, resulting in low collaboration efficiency and the inability to achieve full traceability of business operations, leading to insufficient management standardization. At the same time, the lack of a sound security control mechanism for data sharing among multiple entities makes it prone to data leakage and data tampering, affecting the operational security of the supply chain network.

[0007] To address this, a global inventory visibility and intelligent allocation coordination system for complex supply chain networks is proposed. Summary of the Invention

[0008] The present invention aims to solve the problems mentioned in the background art by providing a global inventory visibility and intelligent allocation and coordination system for complex supply chain networks.

[0009] The specific technical solution is as follows:

[0010] A global inventory visibility and intelligent allocation collaboration system for complex supply chain networks includes a global inventory data normalization and aggregation module, a real-time visualization engine for full-link inventory status, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation collaboration decision-making module, a multi-entity execution closed-loop control module, and a decision model self-iterative optimization module.

[0011] The input end of the global inventory data normalization and aggregation module connects to all heterogeneous business systems in the complex supply chain network, while the output end connects to the input end of the real-time visualization engine for the end-to-end inventory status and the input end of the global supply and demand situation perception and prediction module. The global inventory data normalization and aggregation module is used to collect basic inventory data, business flow data, and node attribute data from all nodes and categories of the supply chain. It performs format unification, semantic alignment, deduplication verification, and time-series synchronization processing on the collected multi-source heterogeneous data to generate a standardized global inventory basic dataset, providing a unified data foundation for subsequent visualization, situation perception, and decision-making.

[0012] The output of the end-to-end inventory status real-time visualization engine is connected to the input of the multi-entity closed-loop management module. The end-to-end inventory status real-time visualization engine is used to build a digital twin topology of the supply chain network based on a standardized global inventory base dataset. It renders the inventory quantity, age status, storage location, in-transit status, and available quota information of all nodes and all categories in real time, realizing end-to-end full-link visibility of global inventory. At the same time, it opens up the visualization view with corresponding permissions to each entity in the supply chain to support collaborative decision-making among the entities.

[0013] The output of the global supply and demand situation perception and prediction module is connected to the first input of the multi-constraint intelligent allocation collaborative decision-making module. The global supply and demand situation perception and prediction module is used to quantitatively predict and classify the future supply and demand gap, inventory backlog risk and node fulfillment capability of the entire supply chain network based on a standardized global inventory basic dataset, combined with historical supply and demand data, market fluctuation data and node operation data, and generate a global supply and demand situation map to provide supply and demand boundary conditions for allocation decisions.

[0014] The second input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the output of the full-link inventory status real-time visualization engine. The third input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the first output of the multi-entity execution closed-loop control module. The output of the multi-constraint intelligent allocation collaborative decision-making module is connected to the second input of the multi-entity execution closed-loop control module. The multi-constraint intelligent allocation collaborative decision-making module is used to generate a globally optimal multi-node multi-category collaborative allocation plan with the optimization objectives of the lowest total global supply chain cost, the best delivery time, and the highest inventory turnover rate. It uses the global supply and demand situation map as the input boundary and combines multi-dimensional constraints such as available inventory, node warehousing capacity, logistics capacity constraints, cross-regional circulation rules, and entity delivery priorities. It also supports dynamic adjustment of the allocation plan based on real-time execution feedback.

[0015] The third input of the multi-entity execution closed-loop control module is connected to the output of the full-link inventory status real-time visualization engine. The second output of the multi-entity execution closed-loop control module is connected to the feedback input of the global inventory data normalization and aggregation module. The third output of the multi-entity execution closed-loop control module is connected to the input of the decision model self-iterative optimization module. The multi-entity execution closed-loop control module is used to decompose the collaborative allocation plan into execution tasks corresponding to each supply chain entity, push task instructions to the corresponding entity, and collect the task execution progress, logistics flow data, node receipt data, and abnormal feedback information of each entity in real time to realize the closed-loop tracking of the entire allocation execution process. At the same time, the execution process data is synchronized to the corresponding upstream module to support data updates and dynamic adjustment of the plan.

[0016] The output of the decision model self-iterative optimization module is connected to the optimization input of the multi-constraint intelligent allocation collaborative decision module. The decision model self-iterative optimization module is used to collect historical data of the entire allocation process, perform multi-dimensional quantitative evaluation of the execution effect of the allocation plan, and optimize the parameters and iterate the structure of the decision model of the multi-constraint intelligent allocation collaborative decision module based on the evaluation results, so as to continuously improve the global optimality and scenario adaptability of the decision plan.

[0017] As a preferred embodiment of the present invention, the global inventory data normalization and aggregation module is equipped with a heterogeneous system adaptation unit, a data standardization processing unit, a data quality verification unit, and a time-series synchronization unit. The heterogeneous system adaptation unit has a built-in multi-protocol adaptation interface to connect with the ERP system, WMS system, TMS system, OMS system, and dealer management system of various entities in the supply chain network, realizing non-intrusive collection of multi-source heterogeneous data. The data standardization processing unit performs field mapping, semantic alignment, and format unification processing on the collected multi-source data based on a preset supply chain data ontology standard, generating inventory data that conforms to a unified standard. The data quality verification unit performs integrity verification, consistency verification, and logical rationality verification on the standardized inventory data, removes abnormal data, and fills in missing data. The time-series synchronization unit performs time-series alignment on the data collected from all nodes based on a unified time base, ensuring the consistency of the time dimension of the global inventory data.

[0018] As a preferred embodiment of the present invention, the full-chain inventory status real-time visualization engine is equipped with a supply chain digital twin construction unit, an inventory status real-time rendering unit, and a permission-level control unit. The supply chain digital twin construction unit constructs a digital twin model that maps one-to-one with the physical supply chain network based on the node attributes, topology, and flow rules of the supply chain network, achieving real-time synchronization of the physical and digital nodes. The inventory status real-time rendering unit updates and renders the static and dynamic flow information of the inventory of all nodes and all categories in real time in the digital twin model based on a standardized global inventory dataset, generating a global inventory panorama view, node detail view, category tracking view, and in-transit flow view. The permission-level control unit configures corresponding data access permissions and view operation permissions based on the role attributes and business scope of each entity in the supply chain, ensuring that each entity can only view the inventory data and visualization view within its permission scope.

[0019] As a preferred embodiment of the present invention, the global supply and demand situation perception and prediction module is equipped with a historical data feature extraction unit, a supply and demand prediction unit, and a risk situation classification unit. The historical data feature extraction unit is used to extract time-series features, periodic features, mutation features, and correlation features from the global inventory basic dataset, historical supply and demand data, and market fluctuation data to generate a feature dataset for supply and demand prediction. Based on the feature dataset, the supply and demand prediction unit uses a time-series prediction model to perform multi-period rolling predictions of the future demand, inbound quantity, and available inventory of each category at each node in the entire network, and calculates the supply and demand gap and inventory backlog of each category at each node. Based on the supply and demand prediction results, the risk situation classification unit combines the node's fulfillment capability, logistics timeliness guarantee, and market fluctuation range to quantitatively score and classify the stockout risk, backlog risk, and supply disruption risk of each node, and generates a global supply and demand situation map that includes risk level, scope of impact, and response priority.

[0020] As a preferred embodiment of the present invention, the multi-constraint intelligent allocation collaborative decision-making module is equipped with an optimization target configuration unit, a multi-constraint condition adaptation unit, a global optimal solution unit, and a scheme dynamic adjustment unit. The optimization target configuration unit is used to configure the core optimization targets and weight coefficients of each target for allocation decisions. The core optimization targets include minimizing the total cost of global supply chain allocation, optimizing the timeliness of terminal order fulfillment, maximizing the inventory turnover rate across the entire network, and maximizing the regional supply-demand balance. The multi-constraint condition adaptation unit is used to access and parse the full-dimensional constraints of the allocation decision, including constraints on available inventory, node warehousing throughput capacity, logistics capacity and route timeliness, and cross-regional circulation. The system includes constraints such as regulatory constraints, priority constraints for entity performance, and constraints related to special storage and transportation requirements for product categories. The global optimal solution unit, based on the set optimization objectives and constraints, constructs a mixed-integer linear programming model and, combined with a heuristic solution algorithm, generates a globally optimal multi-node collaborative allocation plan that satisfies all constraints. The allocation plan includes the allocation category, allocation quantity, outgoing node, incoming node, allocation path, execution time limit, and logistics carrier. The dynamic adjustment unit is used to access real-time feedback data during execution. When constraints change or abnormal events occur, the current allocation plan is quickly re-solved and dynamically adjusted to generate an updated allocation plan and push it to the execution stage.

[0021] As a preferred embodiment of the present invention, the multi-constraint intelligent allocation collaborative decision-making module is further provided with an emergency allocation priority control unit. The emergency allocation priority control unit is used to access terminal emergency orders and demand instructions for sudden supply and demand anomalies. Based on the urgency level, impact scope, and performance priority of the demand, it adjusts and optimizes the weight coefficient of the target and the priority of the constraint conditions to generate a priority allocation plan that meets the emergency performance requirements, while ensuring the orderly execution of the regular allocation plan.

[0022] As a preferred embodiment of the present invention, the multi-entity execution closed-loop management module is equipped with a task decomposition and dispatch unit, an execution progress real-time tracking unit, an abnormal event closed-loop handling unit, and an execution data synchronization unit. The task decomposition and dispatch unit is used to decompose the generated allocation plan into standardized execution tasks corresponding to each execution entity. These tasks include outbound tasks, transportation tasks, inbound tasks, and handover and acceptance tasks. Based on the entity's business scope and responsibilities, the corresponding task instructions are accurately pushed to the corresponding entity's business system and operating terminal. The execution progress real-time tracking unit is used to collect task execution status data, logistics node flow data, and inventory change data of each execution entity in real time. The execution progress is updated synchronously in the end-to-end visualization view, enabling full-process traceability of allocation execution. The abnormal event closed-loop handling unit is used to receive abnormal events reported by various entities during the execution process. Abnormal events include insufficient inventory, capacity interruption, node congestion, and compliance interception. Based on the level and scope of impact of abnormal events, the abnormal handling process is automatically triggered, and handling instructions are pushed to the corresponding responsible entities. The feedback is synchronously fed back to the decision-making module for plan adjustment, realizing the full closed-loop handling of abnormal events. The execution data synchronization unit is used to synchronize the full-process data of allocation execution, inventory change data, and abnormal handling data to the global inventory data normalization and aggregation module in real time, realizing the real-time update of global inventory data.

[0023] As a preferred embodiment of the present invention, the decision model self-iterative optimization module is provided with an execution effect evaluation unit, a model optimization unit, and an effect verification unit. The execution effect evaluation unit is used to construct a multi-dimensional decision effect evaluation system. Based on historical allocation execution data, it quantifies and comprehensively evaluates the execution effect of historical allocation decision schemes from multiple dimensions such as cost control, timeliness achievement, inventory optimization, fulfillment rate, and anomaly occurrence rate. Based on the evaluation results of the execution effect, the model optimization unit identifies the sources of deviation and optimization direction of the decision model, and optimizes and updates the parameter weights, constraint adaptation logic, and iterative rules of the solution algorithm of the decision model through reinforcement learning algorithms. The effect verification unit is used to perform offline simulation verification of the optimized decision model based on historical datasets, compare the decision effects before and after optimization, and when the verification results meet the preset optimization threshold, the optimized model is deployed to the multi-constraint intelligent allocation collaborative decision module to complete the iterative update of the model.

[0024] As a preferred embodiment of the present invention, the system is further provided with a cross-entity collaborative interaction module. The input and output ends of the cross-entity collaborative interaction module are respectively connected to the multi-entity execution closed-loop control module and the operation terminals of each entity in the supply chain. The cross-entity collaborative interaction module provides a unified collaborative interaction entry point for each entity in the supply chain, supporting online communication, instruction confirmation, document transfer, anomaly reporting, and progress feedback among entities for allocation tasks, realizing information synchronization and efficient collaboration among multiple entities, and at the same time, associating and storing all interactive data with business data to support traceability and auditing of the entire process.

[0025] As a preferred embodiment of the present invention, the system also includes a full-link data security management module. This module covers the entire data flow process, including a global inventory data normalization and aggregation module, a real-time inventory status visualization engine, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation and collaborative decision-making module, a multi-entity execution closed-loop management module, and a decision model self-iterative optimization module. The full-link data security management module is used to perform encrypted management and access control and permission auditing throughout the entire lifecycle of data collection, transmission, storage, calculation, application, and sharing. It provides hierarchical protection for inventory data and business data based on data classification standards, and performs full-process log retention and audit traceability for all data access, modification, and sharing operations, ensuring the security, confidentiality, and immutability of the entire supply chain network data.

[0026] The present invention has the following beneficial effects:

[0027] This system, through its closed-loop architecture, comprehensively addresses the various shortcomings of traditional complex supply chain networks in inventory management and allocation coordination. It achieves end-to-end full-link visibility of inventory across the entire supply chain and global collaborative optimization of allocation operations, significantly improving the operational efficiency and management level of complex supply chain networks.

[0028] This system breaks down information barriers between various participants in the supply chain by normalizing and aggregating multi-source heterogeneous data. It unifies the statistical definitions and format standards for inventory data across the entire network, enabling centralized management and collaborative use of inventory data across all nodes and product categories. This fundamentally solves the problems of information silos and asymmetry inherent in traditional models. The end-to-end visualization capabilities built on a unified data foundation achieve complete transparency of the supply chain's inventory status and flow process, allowing all participants to conduct business collaboration based on unified inventory information and avoiding decision-making biases caused by information asymmetry.

[0029] By establishing the ability to perceive and predict supply and demand trends, this system enables early prediction and risk identification of supply and demand changes across the entire supply chain network. It can proactively identify supply and demand gaps and potential operational risks across the entire network, transforming inventory allocation from a traditional reactive response to proactive planning. This significantly enhances the supply chain network's ability to cope with abnormal situations such as market fluctuations and sudden demand, and reduces stockouts and inventory backlogs.

[0030] This system, through a globally integrated, multi-constraint intelligent allocation decision-making design, breaks away from the limitations of traditional single-node local decision-making. Based on the supply and demand situation and actual operational constraints of the entire supply chain network, it can generate globally optimal allocation schemes, achieve optimized allocation of commodity resources and warehousing and logistics resources across the entire network, avoid problems such as duplicate allocation and detour transportation, reduce resource waste, lower allocation and transportation costs and warehousing management costs across the entire supply chain, and improve the fulfillment efficiency and stability of terminal orders.

[0031] This system, through a closed-loop design encompassing decision-making, execution, feedback, and optimization, achieves end-to-end control over allocation operations, from plan generation to implementation. It can track the progress of each stage of allocation execution in real time, quickly respond to and handle abnormal events during execution, prevent the escalation of the impact of anomalies, and ensure the effective implementation of allocation plans. Simultaneously, through a self-iterative optimization mechanism, the system's decision-making capabilities continuously adapt to the dynamic changes in the supply chain network and the needs of business development, constantly improving the adaptability and rationality of decision-making plans, and ensuring the system's long-term stable operation.

[0032] This system, through hierarchical access control and a unified collaborative interaction design, provides standardized collaborative interaction channels for all participating entities while ensuring network-wide data security. It clarifies the responsibilities of each entity, standardizes the entire allocation process, significantly improves collaboration efficiency among multiple entities, and reduces communication costs. Simultaneously, the system's correlation and retention of all business and interaction data throughout the entire process enables traceable auditing of the allocation operation, enhancing the management standardization of the supply chain network.

[0033] This system has excellent heterogeneous system adaptability, and can be compatible with various existing business systems of all entities in the supply chain. It can be deployed and implemented without large-scale modifications to the original system, which greatly reduces the difficulty and cost of system implementation. It can adapt to complex supply chain networks of different sizes and structures, and has broad scenario adaptability and promotion and application value. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the architecture of a global inventory visibility and intelligent allocation coordination system for complex supply chain networks provided in an embodiment of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0036] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0037] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0038] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] Example

[0040] This embodiment provides a global inventory visibility and intelligent allocation coordination system for complex supply chain networks, such as... Figure 1 As shown, it includes a global inventory data normalization and aggregation module, a real-time visualization engine for end-to-end inventory status, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation and collaborative decision-making module, a multi-entity execution closed-loop control module, and a decision model self-iterative optimization module, wherein:

[0041] The input end of the global inventory data normalization and aggregation module connects to all heterogeneous business systems in the complex supply chain network, while the output end connects to the input end of the real-time visualization engine for the end-to-end inventory status and the input end of the global supply and demand situation perception and prediction module. The global inventory data normalization and aggregation module is used to collect basic inventory data, business flow data, and node attribute data from all nodes and categories of the supply chain. It performs format unification, semantic alignment, deduplication verification, and time-series synchronization processing on the collected multi-source heterogeneous data to generate a standardized global inventory basic dataset, providing a unified data foundation for subsequent visualization, situation perception, and decision-making.

[0042] The output of the end-to-end inventory status real-time visualization engine is connected to the input of the multi-entity closed-loop management module. The end-to-end inventory status real-time visualization engine is used to build a digital twin topology of the supply chain network based on a standardized global inventory base dataset. It renders the inventory quantity, age status, storage location, in-transit status, and available quota information of all nodes and all categories in real time, realizing end-to-end full-link visibility of global inventory. At the same time, it opens up the visualization view with corresponding permissions to each entity in the supply chain to support collaborative decision-making among the entities.

[0043] The output of the global supply and demand situation perception and prediction module is connected to the first input of the multi-constraint intelligent allocation collaborative decision-making module. The global supply and demand situation perception and prediction module is used to quantitatively predict and classify the future supply and demand gap, inventory backlog risk and node fulfillment capability of the entire supply chain network based on a standardized global inventory basic dataset, combined with historical supply and demand data, market fluctuation data and node operation data, and generate a global supply and demand situation map to provide supply and demand boundary conditions for allocation decisions.

[0044] The second input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the output of the full-link inventory status real-time visualization engine. The third input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the first output of the multi-entity execution closed-loop control module. The output of the multi-constraint intelligent allocation collaborative decision-making module is connected to the second input of the multi-entity execution closed-loop control module. The multi-constraint intelligent allocation collaborative decision-making module is used to generate a globally optimal multi-node multi-category collaborative allocation plan with the optimization objectives of the lowest total global supply chain cost, the best delivery time, and the highest inventory turnover rate. It uses the global supply and demand situation map as the input boundary and combines multi-dimensional constraints such as available inventory, node warehousing capacity, logistics capacity constraints, cross-regional circulation rules, and entity delivery priorities. It also supports dynamic adjustment of the allocation plan based on real-time execution feedback.

[0045] The third input of the multi-entity execution closed-loop control module is connected to the output of the full-link inventory status real-time visualization engine. The second output of the multi-entity execution closed-loop control module is connected to the feedback input of the global inventory data normalization and aggregation module. The third output of the multi-entity execution closed-loop control module is connected to the input of the decision model self-iterative optimization module. The multi-entity execution closed-loop control module is used to decompose the collaborative allocation plan into execution tasks corresponding to each supply chain entity, push task instructions to the corresponding entity, and collect the task execution progress, logistics flow data, node receipt data, and abnormal feedback information of each entity in real time to realize the closed-loop tracking of the entire allocation execution process. At the same time, the execution process data is synchronized to the corresponding upstream module to support data updates and dynamic adjustment of the plan.

[0046] The output of the decision model self-iterative optimization module is connected to the optimization input of the multi-constraint intelligent allocation collaborative decision module. The decision model self-iterative optimization module is used to collect historical data of the entire allocation process, perform multi-dimensional quantitative evaluation of the execution effect of the allocation plan, and optimize the parameters and iterate the structure of the decision model of the multi-constraint intelligent allocation collaborative decision module based on the evaluation results, so as to continuously improve the global optimality and scenario adaptability of the decision plan.

[0047] This solution establishes a comprehensive system architecture encompassing data aggregation, status visualization, supply and demand situation awareness, intelligent allocation decision-making, closed-loop execution control, and model self-iterative optimization. This architecture streamlines information flow across complex supply chain networks, breaking down information barriers between different stakeholders and achieving end-to-end visibility of the entire network's inventory status. Through the linkage between supply and demand situation prediction and intelligent allocation decision-making, it adapts to the multi-dimensional operating conditions of complex supply chain networks, generating allocation plans tailored to global operational needs and avoiding resource misallocation and waste caused by localized decisions. The closed-loop connection between decision-making, execution, and optimization enables full-process control of allocation operations from plan generation to implementation, while continuously optimizing the system's decision-making capabilities as business operations progress, improving overall supply chain efficiency and reducing overall operating costs.

[0048] Specifically, in this embodiment, the global inventory data normalization and aggregation module is equipped with a heterogeneous system adaptation unit, a data standardization processing unit, a data quality verification unit, and a time-series synchronization unit. The heterogeneous system adaptation unit has a built-in multi-protocol adaptation interface to connect with the ERP system, WMS system, TMS system, OMS system, and dealer management system of various entities in the supply chain network, realizing non-intrusive collection of multi-source heterogeneous data. The data standardization processing unit performs field mapping, semantic alignment, and format unification processing on the collected multi-source data based on a preset supply chain data ontology standard, generating inventory data that conforms to a unified standard. The data quality verification unit performs integrity verification, consistency verification, and logical rationality verification on the standardized inventory data, removes abnormal data, and fills in missing data. The time-series synchronization unit performs time-series alignment on the data collected from all nodes based on a unified time base, ensuring the consistency of the time dimension of the global inventory data.

[0049] By setting up multi-protocol adaptation interfaces, it can be compatible with different types of heterogeneous business systems of various entities in the supply chain. Data collection from multiple sources can be completed without modifying existing business systems, reducing the difficulty and implementation cost of system integration. Through end-to-end processing of data standardization, quality verification, and time-series synchronization, the format and definition of data from different sources can be unified, abnormal data can be eliminated, and the consistency of data across the entire network in the time dimension can be ensured. This solves the problem of the inability to use multi-source heterogeneous data in a coordinated manner, providing a stable and accurate data foundation for subsequent visualization, supply and demand forecasting, and allocation decisions.

[0050] Specifically, in this embodiment, the end-to-end inventory status real-time visualization engine includes a supply chain digital twin construction unit, an inventory status real-time rendering unit, and a permission-based hierarchical management unit. The supply chain digital twin construction unit constructs a digital twin model that maps one-to-one with the physical supply chain network based on the node attributes, topology, and flow rules of the supply chain network, achieving real-time synchronization of the physical and digital nodes' statuses. The inventory status real-time rendering unit updates and renders the static and dynamic flow information of all nodes and all categories of inventory in real time within the digital twin model based on a standardized global inventory dataset, generating a global inventory panorama view, node detail view, category tracking view, and in-transit flow view. The permission-based hierarchical management unit configures corresponding data access permissions and view operation permissions based on the role attributes and business scope of each entity in the supply chain, ensuring that each entity can only view inventory data and visualization views within its authorized scope.

[0051] By constructing a digital twin model that maps one-to-one with the physical supply chain network, real-time synchronization of the physical inventory nodes and the digital view can be achieved. This intuitively presents the static information and dynamic flow status of inventory across the entire network and all product categories, solving the problems of information lag and opaque status in traditional inventory management. The generation of multiple view types can adapt to the viewing needs of different business scenarios, allowing each link in the supply chain to clearly grasp the corresponding inventory information. Through hierarchical access control, global information sharing can be achieved while standardizing the data access scope of each entity, preventing unauthorized data access, ensuring data security, and supporting efficient collaboration within authorized scopes.

[0052] Specifically, in this embodiment, the global supply and demand situation perception and prediction module is equipped with a historical data feature extraction unit, a supply and demand prediction unit, and a risk situation classification unit. The historical data feature extraction unit is used to extract time-series features, periodic features, mutation features, and correlation features from the global inventory basic dataset, historical supply and demand data, and market fluctuation data to generate a feature dataset for supply and demand prediction. Based on the feature dataset, the supply and demand prediction unit uses a time-series prediction model to perform multi-period rolling predictions of the future demand, inbound quantity, and available inventory of each category at each node in the entire network, and calculates the supply and demand gap and inventory backlog of each category at each node. Based on the supply and demand prediction results, the risk situation classification unit combines the node's fulfillment capability, logistics timeliness guarantee, and market fluctuation range to quantitatively score and classify the stockout risk, backlog risk, and supply disruption risk of each node, and generates a global supply and demand situation map that includes risk level, scope of impact, and response priority.

[0053] By extracting multi-dimensional features from historical and real-time data, we can accurately capture the inherent patterns of supply and demand changes in the supply chain, improving the alignment between supply and demand forecasts and actual operations. Through multi-period rolling supply and demand forecasts, we can identify supply and demand gaps and inventory backlogs at each node of the entire network in advance, predicting potential problems in supply chain operations. Through risk quantification scoring and grading, we can clearly present the supply and demand situation and risk distribution across the entire network, providing clear boundary conditions for allocation decisions, avoiding blind decisions, and improving the targeting and foresight of allocation plans.

[0054] Specifically, in this embodiment, the multi-constraint intelligent allocation collaborative decision-making module includes an optimization target configuration unit, a multi-constraint condition adaptation unit, a global optimal solution unit, and a scheme dynamic adjustment unit. The optimization target configuration unit is used to configure the core optimization targets and weight coefficients of each target for allocation decisions. The core optimization targets include the lowest total cost of global supply chain allocation, the best timeliness of terminal order fulfillment, the highest inventory turnover rate across the entire network, and the highest regional supply and demand balance. The multi-constraint condition adaptation unit is used to access and parse the full-dimensional constraints of allocation decisions, including constraints on available inventory, node warehousing throughput capacity, logistics capacity and route timeliness, and compliance of cross-regional circulation. Constraints include: performance constraints, priority constraints for main entities, and special storage and transportation requirements for product categories. The global optimal solution unit constructs a mixed-integer linear programming model based on the set optimization objectives and constraints. Combined with heuristic solution algorithms, it generates a globally optimal multi-node collaborative allocation plan that satisfies all constraints. The allocation plan includes the allocation category, allocation quantity, outgoing node, incoming node, allocation path, execution time limit, and logistics carrier. The dynamic adjustment unit is used to access real-time feedback data during the execution process. When constraints change or abnormal events occur, the current allocation plan is quickly re-solved and dynamically adjusted to generate an updated allocation plan and push it to the execution stage.

[0055] By setting configurable optimization objectives, the system can adapt to the management orientation of different stages of supply chain operation, ensuring that allocation decisions align with actual operational needs. Adapting to multi-dimensional constraints allows the generated allocation plans to fully consider the actual operational limitations of each link in the supply chain, guaranteeing the feasibility of decision-making. The global optimal solution setting transcends the limitations of local optimization at single nodes or in single regions, achieving overall optimal allocation plans for the entire supply chain network and reducing resource waste across the entire chain. A dynamic adjustment mechanism adapts to changes in conditions and unforeseen circumstances during execution, updating allocation plans in a timely manner to ensure the continuity and effectiveness of allocation execution.

[0056] Specifically, in this embodiment, the multi-constraint intelligent allocation collaborative decision-making module is also equipped with an emergency allocation priority control unit. The emergency allocation priority control unit is used to access terminal emergency orders and demand instructions for sudden supply and demand anomalies. Based on the urgency level, impact scope, and performance priority of the demand, it adjusts and optimizes the weight coefficient of the target and the priority of the constraint conditions to generate a priority allocation plan that meets the emergency performance requirements, while ensuring the orderly execution of the regular allocation plan.

[0057] By setting up priority control for emergency allocation, we can quickly respond to sudden needs and abnormal situations in the supply chain operation. We can adjust the decision-making adaptation logic according to needs of different urgency levels, ensure the rapid fulfillment of emergency needs, reduce interference with the execution of regular allocations, balance the needs of emergency fulfillment and regular operation, and improve the supply chain's ability to cope with emergencies and the overall stability of fulfillment.

[0058] Specifically, in this embodiment, the multi-entity execution closed-loop management module includes a task decomposition and dispatch unit, an execution progress real-time tracking unit, an abnormal event closed-loop handling unit, and an execution data synchronization unit. The task decomposition and dispatch unit decomposes the generated allocation plan into standardized execution tasks corresponding to each executing entity. These tasks include outbound tasks, transportation tasks, inbound tasks, and handover and acceptance tasks. Based on the entity's business scope and responsibilities, the corresponding task instructions are accurately pushed to the corresponding entity's business system and operating terminal. The execution progress real-time tracking unit collects task execution status data, logistics node flow data, and inventory change data from each executing entity in real time, synchronizing the data across the entire system. The execution progress is updated synchronously in the link visualization view, enabling full-process traceability of allocation execution; the abnormal event closed-loop handling unit is used to receive abnormal events reported by various entities during the execution process, including insufficient inventory, capacity interruption, node congestion, and compliance interception. Based on the level and scope of impact of the abnormal event, the abnormal handling process is automatically triggered, the handling instructions are pushed to the corresponding responsible entity, and the decision-making module is synchronously fed back to achieve full closed-loop handling of abnormal events; the execution data synchronization unit is used to synchronize the full-process data of allocation execution, inventory change data, and abnormal handling data to the global inventory data normalization and aggregation module in real time, realizing real-time updates of global inventory data.

[0059] By standardizing task breakdown and precise assignment, the overall allocation plan can be transformed into specific tasks that each entity can directly execute, clarifying the responsibilities of each entity and avoiding deviations and ambiguities in task transmission. Through full-process execution progress tracking, the status of each stage of allocation execution can be monitored in real time, achieving full traceability and controllability of the allocation process. A closed-loop mechanism for handling abnormal events allows for rapid response to various issues during execution, timely dissemination of handling instructions, and synchronized adjustments to decisions, preventing the escalation of the impact of abnormal events and ensuring the smooth completion of allocation tasks. Real-time synchronization of execution data ensures timely updates to global inventory data, guaranteeing the real-time performance and consistency of data across the entire system.

[0060] Specifically, in this embodiment, the decision model self-iterative optimization module includes an execution effect evaluation unit, a model optimization unit, and an effect verification unit. The execution effect evaluation unit is used to construct a multi-dimensional decision effect evaluation system. Based on historical allocation execution data, it quantifies and comprehensively evaluates the execution effect of historical allocation decision schemes from multiple dimensions, including cost control, timeliness achievement, inventory optimization, fulfillment rate, and anomaly occurrence rate. Based on the evaluation results of the execution effect, the model optimization unit identifies the sources of deviation and optimization direction of the decision model, and optimizes and updates the parameter weights, constraint adaptation logic, and iterative rules of the solution algorithm of the decision model through reinforcement learning algorithms. The effect verification unit is used to perform offline simulation verification of the optimized decision model based on historical datasets, compare the decision effects before and after optimization, and when the verification results meet the preset optimization threshold, the optimized model is deployed to the multi-constraint intelligent allocation collaborative decision module to complete the iterative update of the model.

[0061] A multi-dimensional performance evaluation system comprehensively quantifies the actual operational effectiveness of allocation decision-making schemes, accurately identifying deviations and optimization potential in the decision-making model. Model optimization based on the evaluation results allows the decision-making model to continuously adapt to the dynamic changes in supply chain operations, constantly improving the adaptability and rationality of decision-making schemes. Offline simulation verification verifies the optimization effect before formal model deployment, ensuring the optimized model stably achieves the expected operational results, continuously improving the overall system's decision-making capabilities and adapting to the long-term operational needs of the supply chain network.

[0062] Specifically, in this embodiment, the system also includes a cross-entity collaborative interaction module. The input and output ends of the cross-entity collaborative interaction module are respectively connected to the multi-entity execution closed-loop control module and the operation terminals of each entity in the supply chain. The cross-entity collaborative interaction module provides a unified collaborative interaction entry point for each entity in the supply chain, supporting online communication, instruction confirmation, document transfer, anomaly reporting, and progress feedback among entities for allocation tasks, realizing information synchronization and efficient collaboration among multiple entities. At the same time, all interactive data and business data are associated and stored to support traceability and auditing of the entire process.

[0063] Through a unified collaborative interaction portal, a standardized information exchange channel is provided for all participants in the supply chain, resolving issues such as fragmented information transmission, low communication efficiency, and non-standard document flow among multiple entities. It supports online communication, instruction confirmation, and anomaly feedback throughout the entire allocation execution process, enabling real-time information synchronization among entities and improving the efficiency of multi-entity collaboration. By linking and storing interactive data with business data, traceability and auditing of the entire allocation process are achieved, ensuring the traceability of business operations and standardizing the collaborative behavior of all entities.

[0064] Specifically, in this embodiment, the system also includes a full-link data security management module. This module covers the entire data flow process, including a global inventory data normalization and aggregation module, a real-time inventory status visualization engine, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation and collaborative decision-making module, a multi-entity execution closed-loop management module, and a decision model self-iterative optimization module. The full-link data security management module is used to perform encryption management and access control and permission auditing throughout the entire lifecycle of data collection, transmission, storage, calculation, application, and sharing. It provides hierarchical protection for inventory data and business data based on data classification standards, and performs full-process log retention and audit traceability for all data access, modification, and sharing operations, ensuring the security, confidentiality, and immutability of the entire supply chain network data.

[0065] By implementing comprehensive security controls across the entire data flow process, the system can provide standardized protection for the entire lifecycle of supply chain inventory and business data, reducing the risk of data leakage and tampering. Through tiered and categorized data protection, it can adapt to the security protection needs of different data types, ensuring data security without affecting the normal flow and use of data. Full-process operation log retention and audit traceability enable comprehensive control over all data operation activities, clarifying operational responsibilities and ensuring the security, confidentiality, and integrity of data across the entire supply chain network, providing secure support for information sharing and collaboration among multiple stakeholders.

[0066] Specifically, in this embodiment, the multi-constraint intelligent allocation collaborative decision-making module is equipped with an optimization target configuration unit, a multi-constraint condition adaptation unit, a global optimal solution unit, and a scheme dynamic adjustment unit;

[0067] The optimization target configuration unit is used to configure the core optimization objectives and weight coefficients of each objective in the allocation decision. The core optimization objectives include minimizing the total global allocation cost of the supply chain, optimizing the fulfillment timeliness of terminal orders, maximizing the inventory turnover rate of the entire network, and maximizing the regional supply and demand balance. The core optimization objective configured by the optimization target configuration unit is to minimize the overall global allocation cost, which is achieved through the following objective function:

[0068]

[0069] in:

[0070] The quantity of category k transferred from node i to node j during time period t;

[0071] The transportation cost for allocating a unit quantity of product category k from node i to node j;

[0072] The standard transit time is from node i to node j;

[0073] α is the time cost weighting coefficient, which converts transportation time into cost;

[0074] Let i be the available inventory of category k at time t;

[0075] ϵ is a pre-defined minimum positive number to prevent the denominator from being zero;

[0076] The out-of-stock priority of node j for category k at time t (provided by the global supply and demand situation perception and prediction module);

[0077] Let be the stock shortage quantity of node j for category k at time t (defined by the supply and demand balance constraint);

[0078] Let be the inventory of category k at node j at the end of time t;

[0079] Let j be the target inventory level for category k at time t;

[0080] β, γ, and δ are the weighting coefficients of the inventory turnover promotion item, the stockout penalty item, and the inventory balance item, respectively.

[0081] The multi-constraint adaptation unit is used to access and parse the full-dimensional constraints of the allocation decision. The constraints include inventory availability constraints, node warehousing throughput constraints, logistics capacity and route timeliness constraints, cross-regional circulation compliance constraints, entity performance priority constraints, and category-specific storage and transportation requirements constraints. The global optimal solution unit constructs a mixed-integer linear programming model based on the set optimization objectives and constraints. Combined with heuristic solution algorithms, it generates a globally optimal multi-node collaborative allocation scheme that satisfies all constraints. The allocation scheme includes the allocation category, allocation quantity, outgoing node, incoming node, allocation route, execution time limit, and logistics carrier.

[0082] The dynamic adjustment unit is used to access real-time feedback data during the execution process. When constraints change or abnormal events occur, the current allocation plan is quickly re-solved and dynamically adjusted to generate an updated allocation plan and push it to the execution stage.

[0083] Parameter description table:

[0084]

[0085] The weighting coefficients can be manually configured by the user according to the management priorities, or they can be automatically adjusted by the decision model's self-iterative optimization module based on historical execution results to achieve adaptive optimization.

[0086] Example: Assume a minimal supply chain: two nodes A (warehouse) and B (store), one product category, and one product cycle. Given:

[0087] =10 yuan / piece =2 days =100 pieces, =0 units (B has no inventory), forecast demand =80 items, priority =0.9 (High), Target Inventory =50, =20; Initial inventory =100, =0, weighting coefficients: α=5 yuan / day / piece, β=2, γ=50, δ=1;

[0088] If x=80 items are transferred from A to B, then:

[0089] Transportation cost: 10 × 80 = 800

[0090] Time cost: 5 × 2 × 80 = 800

[0091] Inventory turnover promotion items:

[0092] Out-of-stock penalty: γ·p·u, since B can meet the demand after the transfer, u=0, this item is 0.

[0093] Inventory equilibrium: End of period =100−80=20, =80, target deviation |20−50|+|80−20|=30+60=90, δ×90=90;

[0094] The total target value is 800 + 800 + 1.6 + 0 + 90 = 1691.6.

[0095] If no transfer is made, B will be out of stock by 80 units, with a stockout penalty of 50 × 0.9 × 80 = 3600, resulting in a balanced inventory. =50 + 20 = 70, the total objective value is 3600 + 70 = 3670, which is much higher than the allocation scheme, so the optimization will choose allocation. If there are other candidate paths or allocation amounts, the solver will compare all feasible schemes and select the global optimum.

[0096] Technical effects:

[0097] This objective function, through integrated modeling, unifies multiple previously scattered management objectives within a single optimization framework, avoiding the pitfalls of optimizing a single objective. Specific technical benefits include:

[0098] Global costs are explicitly controllable: transportation costs, time costs, stockout costs, and inventory deviation costs are all quantified, and the solution obtained is globally optimal in terms of economics.

[0099] Dynamic priority adaptation: Out-of-stock priority It reflects the importance and risk of nodes in real time, enabling the allocation of resources to automatically tilt towards key nodes.

[0100] Proactive inventory turnover adjustment: Inventory turnover promotion items automatically guide allocation behavior to flow out of high inventory nodes, accelerate overall turnover, and reduce capital occupation.

[0101] Automatic regional equilibrium is achieved: the inventory equilibrium item suppresses extreme inventory distribution and enhances the resilience of the supply chain.

[0102] Flexibility and scalability: The weight coefficients are adjustable, which can be adapted to supply chain management strategies in different industries and at different stages; the model is linear, with high solution efficiency, and is suitable for large-scale networks.

[0103] Working principle and process

[0104] During system runtime, the objective function is executed in the global optimal solution unit of the multi-constraint intelligent allocation collaborative decision-making module, and the specific process is as follows:

[0105] 1. Input Acquisition: Obtain available inventory from each node using the global inventory data normalization and aggregation module. Initial inventory Obtain node topology and transportation parameters from the real-time end-to-end inventory status visualization engine. , ; Obtain forecasted demand from the global supply and demand situation perception and forecasting module Out-of-stock priority Obtain the weighting coefficients α, β, γ, δ and the target inventory from the optimized target configuration unit. .

[0106] 2. Model Construction: Based on the above data, the global optimal solution unit constructs a mixed integer linear programming model that includes the objective function and all constraints, in conjunction with inventory balance constraints, available inventory constraints, and storage capacity constraints.

[0107] 3. Solution and Calculation: Use the built-in solver (such as branch and bound, heuristic algorithm) to solve the model and obtain the optimal allocation variables. and corresponding inventory variables Stockout variables .

[0108] 4. Solution Output: The solution results are parsed into a complete allocation plan (category, quantity, start and end points, route, time limit, etc.) and transmitted to the multi-entity execution closed-loop management module.

[0109] 5. Dynamic adjustment: If an anomaly occurs during execution, the dynamic adjustment unit re-acquires the latest data, quickly re-solves the model, and outputs an updated solution.

[0110] Working principle:

[0111] This system establishes a closed-loop operational architecture across the entire supply chain, connecting all aspects of data collection, status display, situation prediction, decision-making, execution control, and model optimization within a complex supply chain network. This enables full-chain visibility of global inventory and multi-entity collaboration for intelligent allocation.

[0112] During system operation, the system first connects to the heterogeneous business systems of all participants in the supply chain network through the global inventory data normalization and aggregation module. It non-intrusively collects basic inventory data, business flow data, and node attribute data from all nodes and categories. The collected multi-source heterogeneous data is then processed sequentially for format unification, semantic alignment, deduplication verification, and time-series synchronization to generate a standardized global inventory basic dataset with consistent caliber and reliable quality. This provides a unified data foundation for the entire system and solves the problem of multi-source data not being able to be used in conjunction.

[0113] The end-to-end inventory status real-time visualization engine is based on a standardized global inventory dataset. It constructs a digital twin topology that maps one-to-one with the physical supply chain network, synchronizing inventory quantity, age status, storage location, in-transit status, and available quota information of all nodes to the digital twin model in real time. This generates a multi-dimensional visualization view, achieving end-to-end visibility of the global inventory. Simultaneously, through hierarchical access control, it provides each participant in the supply chain with access to the visualization view corresponding to their permissions, enabling them to conduct business collaboration based on unified inventory information.

[0114] The global supply and demand situation perception and prediction module is based on a standardized global inventory dataset. It combines historical supply and demand data, market fluctuation data, and node operation data to extract multi-dimensional features of supply and demand changes. It performs multi-period rolling predictions on the future supply and demand situation, inventory backlog risk, and node fulfillment capabilities of the entire supply chain network. It calculates the supply and demand gap for each node and each product category, and quantifies and classifies various operational risks. It generates a global supply and demand situation map, providing clear supply and demand boundary conditions for allocation decisions and realizing the transformation from passive response to proactive prediction.

[0115] The multi-constraint intelligent allocation and collaborative decision-making module uses a global supply and demand situation map as input boundaries, combined with real-time inventory status data across the entire supply chain. Optimization directions include minimizing total supply chain cost, optimizing fulfillment timeliness, and maximizing inventory turnover. It also incorporates multi-dimensional operational constraints such as available inventory, node warehousing capacity, logistics capacity constraints, cross-regional circulation rules, and entity fulfillment priorities. A corresponding solution model is constructed to generate a globally optimal multi-node, multi-category collaborative allocation plan that satisfies all constraints. During plan execution, the module can access execution feedback data in real time. When constraints change or unexpected anomalies occur, the module quickly re-solves and dynamically adjusts the plan, ensuring its adaptability to actual operational conditions.

[0116] After receiving the generated allocation plan, the multi-entity closed-loop management module breaks it down into standardized execution tasks for each supply chain entity. These tasks are then precisely pushed to the corresponding entity's business systems and operational terminals. Simultaneously, the module collects real-time data on task execution progress, logistics flow, node receipt data, and anomaly feedback from each entity, updating the execution status in a full-link visualization view to achieve end-to-end tracking of allocation execution. For reported anomalies during execution, the module automatically triggers corresponding handling procedures, pushing handling instructions to the responsible entity. It also synchronizes the anomaly information to the decision-making module to adjust the plan, achieving closed-loop handling of anomalies. Furthermore, the module synchronizes all process data generated during execution to the data aggregation module in real-time, ensuring real-time updates of global inventory data and maintaining data consistency across the entire system.

[0117] The decision-making model's self-iterative optimization module continuously collects historical data from the entire allocation process. Through a multi-dimensional evaluation system, it quantitatively assesses the effectiveness of historical allocation plans, identifies sources of deviation and optimization directions for the decision-making model, and optimizes and updates the model's parameters, adaptation logic, and solution rules based on the evaluation results. The optimized model undergoes offline simulation verification using historical datasets. After successful verification, it is deployed to the decision-making module to complete iterative updates, enabling the system's decision-making capabilities to continuously adapt to the dynamic changes in the supply chain network and achieve continuous improvement in system operational capabilities.

[0118] How to use:

[0119] The first step is to complete system deployment and integration with heterogeneous systems. The system is deployed to the corresponding runtime environment and, through built-in multi-protocol adapter interfaces, connects to the existing business systems of various participants in the supply chain network, establishing data acquisition channels without requiring large-scale modifications to existing business systems. Simultaneously, the roles of each participant in the supply chain network are configured, clarifying their business scope and system operation permissions.

[0120] The second step is to complete the initialization of basic system information. This involves entering basic business information into the system, including the node topology of the supply chain network, basic information on all product categories, warehousing capacity information for each node, logistics capacity and route information, cross-regional distribution rules, and fulfillment priority rules. It also involves configuring system operation rules such as optimization directions for allocation decisions, constraint ranges, execution control rules, and effect evaluation dimensions. This initial configuration ensures the system can adapt to the operational characteristics of the corresponding supply chain network.

[0121] The third step is to aggregate and visualize global inventory data. After system startup, it automatically and continuously collects inventory and business flow data from all nodes of the supply chain, performs standardized data processing and quality verification, and generates a standardized global inventory dataset. Users can view corresponding visualizations within their authorized scope through the system terminal, including a global inventory overview view, a node inventory detail view, a category flow tracking view, and an in-transit goods status view, allowing them to monitor the inventory status and flow of goods across the entire supply chain in real time.

[0122] The fourth step is to view the overall supply and demand situation and risk prediction results of the supply chain. Based on real-time updated global inventory data, the system automatically extracts supply and demand characteristics, forecasts supply and demand over multiple periods, and classifies risk levels, generating a global supply and demand situation map. Users can view the supply and demand forecast results for each node and category across the entire network, as well as the distribution and impact of potential risks, through the system, gaining an early understanding of the future operation of the supply chain and providing a reference for subsequent allocation planning.

[0123] The fifth step is to generate and confirm the allocation and coordination plan. Based on the global supply and demand situation map, real-time inventory status data, and preset optimization directions and constraints, the system automatically generates a globally optimal multi-node collaborative allocation plan. Users can view the complete content of the allocation plan in the system, including information such as the allocation category, allocation quantity, transfer-out and transfer-in nodes, allocation path, execution time limit, and corresponding execution entity. Users can directly confirm the plan for execution, or adjust the weight of the optimization direction and the scope of the constraints according to actual business needs, triggering the system to regenerate the allocation plan, and then confirm and issue it after the adjustment. For sudden emergency needs, users can enter emergency request commands in the system, and the system will automatically adjust the decision logic, generate an emergency allocation plan with corresponding priority, and issue it for execution after confirmation.

[0124] Step 6: Implement full-process control and anomaly handling for allocation execution. After the plan is confirmed and issued, the system automatically breaks down the allocation plan into standardized tasks corresponding to each executing entity and accurately pushes them to the corresponding entity's operating terminal. After receiving the task, each executing entity carries out corresponding operations such as outbound, transportation, warehousing, and acceptance, and reports the execution progress in real time through the system. Users can view the execution progress of the entire process in real time through the system and track the flow status of goods synchronously in a visual view. When an anomaly occurs during execution, the executing entity reports the anomaly event through the system. The system automatically triggers the corresponding handling process, pushes handling instructions to the responsible entity, and synchronizes the anomaly information to the decision-making module, triggering dynamic adjustments to the plan. Users can track the progress of anomaly handling throughout the entire process to ensure the smooth completion of the allocation task.

[0125] The seventh step is to achieve continuous system optimization and iteration. During system operation, the system automatically collects historical data of the entire allocation process, regularly conducts multi-dimensional quantitative evaluations of the allocation plan's execution effectiveness, and optimizes and validates the decision-making model based on the evaluation results. Once validation is successful, the model is automatically iterated and updated. Users can view the effects and evaluation results of model optimization through the system, and can also adjust the evaluation dimensions and optimization directions according to business development changes, allowing the system to continuously adapt to the operational needs of the supply chain network.

[0126] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A global inventory visibility and intelligent allocation coordination system for complex supply chain networks, characterized in that, It includes a global inventory data normalization and aggregation module, a real-time visualization engine for the entire inventory status, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation and collaborative decision-making module, a multi-entity execution closed-loop control module, and a decision model self-iterative optimization module. The input end of the global inventory data normalization and aggregation module connects to all heterogeneous business systems in the complex supply chain network, while the output end connects to the input end of the real-time visualization engine for the end-to-end inventory status and the input end of the global supply and demand situation perception and prediction module. The global inventory data normalization and aggregation module is used to collect basic inventory data, business flow data, and node attribute data from all nodes and categories of the supply chain. It performs format unification, semantic alignment, deduplication verification, and time-series synchronization processing on the collected multi-source heterogeneous data to generate a standardized global inventory basic dataset, providing a unified data foundation for subsequent visualization, situation perception, and decision-making. The output of the end-to-end inventory status real-time visualization engine is connected to the input of the multi-entity closed-loop management module; The end-to-end inventory status real-time visualization engine is used to build a digital twin topology of the supply chain network based on a standardized global inventory dataset. It renders the inventory quantity, age status, storage location, in-transit status, and available quota information of all nodes and categories in real time, realizing end-to-end full-link visibility of global inventory. At the same time, it opens up corresponding permission-based visualization views to each entity in the supply chain to support collaborative decision-making among the entities. The output of the global supply and demand situation perception and prediction module is connected to the first input of the multi-constraint intelligent allocation collaborative decision-making module. The global supply and demand situation perception and prediction module is used to quantitatively predict and classify the future supply and demand gap, inventory backlog risk and node fulfillment capability of the entire supply chain network based on a standardized global inventory basic dataset, combined with historical supply and demand data, market fluctuation data and node operation data, and generate a global supply and demand situation map to provide supply and demand boundary conditions for allocation decisions. The second input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the output of the full-link inventory status real-time visualization engine. The third input of the multi-constraint intelligent allocation collaborative decision-making module is connected to the first output of the multi-entity execution closed-loop control module. The output of the multi-constraint intelligent allocation collaborative decision-making module is connected to the second input of the multi-entity execution closed-loop control module. The multi-constraint intelligent allocation collaborative decision-making module is used to generate a globally optimal multi-node multi-category collaborative allocation plan with the optimization objectives of the lowest total global supply chain cost, the best delivery time, and the highest inventory turnover rate. It uses the global supply and demand situation map as the input boundary and combines multi-dimensional constraints such as available inventory, node warehousing capacity, logistics capacity constraints, cross-regional circulation rules, and entity delivery priorities. It also supports dynamic adjustment of the allocation plan based on real-time execution feedback. The third input of the multi-entity execution closed-loop control module is connected to the output of the full-link inventory status real-time visualization engine. The second output of the multi-entity execution closed-loop control module is connected to the feedback input of the global inventory data normalization and aggregation module. The third output of the multi-entity execution closed-loop control module is connected to the input of the decision model self-iterative optimization module. The multi-entity execution closed-loop control module is used to decompose the collaborative allocation plan into execution tasks corresponding to each supply chain entity, push task instructions to the corresponding entity, and collect the task execution progress, logistics flow data, node receipt data, and abnormal feedback information of each entity in real time to realize the closed-loop tracking of the entire allocation execution process. At the same time, the execution process data is synchronized to the corresponding upstream module to support data updates and dynamic adjustment of the plan. The output of the decision model self-iterative optimization module is connected to the optimization input of the multi-constraint intelligent allocation collaborative decision module. The decision model self-iterative optimization module is used to collect historical data of the entire allocation process, perform multi-dimensional quantitative evaluation of the execution effect of the allocation plan, and optimize the parameters and iterate the structure of the decision model of the multi-constraint intelligent allocation collaborative decision module based on the evaluation results, so as to continuously improve the global optimality and scenario adaptability of the decision plan.

2. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The global inventory data normalization and aggregation module is equipped with a heterogeneous system adaptation unit, a data standardization processing unit, a data quality verification unit, and a time-series synchronization unit. The heterogeneous system adaptation unit has built-in multi-protocol adaptation interfaces to connect with the ERP system, WMS system, TMS system, OMS system, and dealer management system of various entities in the supply chain network, so as to realize non-intrusive collection of multi-source heterogeneous data. The data standardization processing unit, based on the preset supply chain data ontology standard, performs field mapping, semantic alignment, and format unification processing on the collected multi-source data to generate inventory data that conforms to the unified standard. The data quality verification unit performs integrity verification, consistency verification, and logical rationality verification on the standardized inventory data, removes abnormal data, and fills in missing data; the time synchronization unit performs time-series alignment on the data collected from all nodes based on a unified time benchmark to ensure the time dimension consistency of global inventory data.

3. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The end-to-end inventory status real-time visualization engine is equipped with a supply chain digital twin building unit, an inventory status real-time rendering unit, and a hierarchical access control unit. The supply chain digital twin building unit constructs a digital twin model that maps one-to-one with the physical supply chain network based on the node attributes, topological relationships, and flow rules of the supply chain network, thereby achieving real-time synchronization of the physical and digital nodes. The real-time inventory status rendering unit is based on a standardized global inventory dataset. In the digital twin model, it updates and renders the static and dynamic flow information of the inventory across all nodes and categories in real time, generating a global inventory panorama view, node detail view, category tracking view, and in-transit flow view. The permission-based hierarchical control unit configures corresponding data access permissions and view operation permissions based on the role attributes and business scope of each entity in the supply chain, ensuring that each entity can only view inventory data and visualization views within its permission scope.

4. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The global supply and demand situation perception and prediction module includes a historical data feature extraction unit, a supply and demand prediction unit, and a risk situation classification unit. The historical data feature extraction unit extracts time-series, periodic, mutation, and correlation features from the global inventory base dataset, historical supply and demand data, and market fluctuation data to generate a feature dataset for supply and demand prediction. Based on the feature dataset, the supply and demand prediction unit uses a time-series prediction model to perform multi-period rolling predictions of future demand, inbound volume, and available inventory for each category at each node in the entire network, calculating the supply and demand gap and inventory backlog for each category at each node. Based on the supply and demand prediction results, the risk situation classification unit combines the node's fulfillment capability, logistics timeliness guarantee, and market volatility to quantitatively score and classify the risks of stockouts, backlogs, and supply disruptions at each node, generating a global supply and demand situation map that includes risk level, scope of impact, and response priority.

5. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The multi-constraint intelligent allocation collaborative decision-making module is equipped with an optimization target configuration unit, a multi-constraint condition adaptation unit, a global optimal solution unit, and a scheme dynamic adjustment unit. The optimization target configuration unit is used to configure the core optimization targets of allocation decisions and the weight coefficients of each target. The core optimization targets include the lowest total cost of global allocation in the supply chain, the best timeliness of terminal order fulfillment, the highest inventory turnover rate in the entire network, and the highest regional supply and demand balance. The multi-constraint adaptation unit is used to access and parse the full-dimensional constraints of the allocation decision, including constraints on inventory availability, node warehousing throughput capacity, logistics capacity and route timeliness, cross-regional circulation compliance, entity performance priority, and category-specific storage and transportation requirements. The global optimal solution unit constructs a mixed-integer linear programming model based on the set optimization objectives and constraints, and combines a heuristic solution algorithm to generate a globally optimal multi-node collaborative allocation scheme that satisfies all constraints. The allocation scheme includes the allocation category, allocation quantity, sending node, receiving node, allocation path, execution time limit, and logistics carrier. The scheme dynamic adjustment unit is used to access real-time feedback data during the execution process. When constraints change or abnormal events occur, the current allocation scheme is quickly re-solved and dynamically adjusted to generate an updated allocation scheme and push it to the execution stage.

6. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 5, characterized in that, The multi-constraint intelligent allocation collaborative decision-making module is also equipped with an emergency allocation priority control unit. The emergency allocation priority control unit is used to access terminal emergency orders and demand instructions for sudden supply and demand anomalies. Based on the urgency level, impact scope, and performance priority of the demand, it adjusts and optimizes the weight coefficient of the target and the priority of the constraint conditions to generate a priority allocation plan that meets the emergency performance needs, while ensuring the orderly execution of the regular allocation plan.

7. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The multi-entity execution closed-loop management module is equipped with a task decomposition and dispatch unit, an execution progress real-time tracking unit, an abnormal event closed-loop handling unit, and an execution data synchronization unit. The task decomposition and dispatch unit is used to decompose the generated allocation plan into standardized execution tasks corresponding to each execution entity. The execution tasks include outbound tasks, transportation tasks, inbound tasks, and handover and acceptance tasks. Based on the business scope and responsibilities of the entity, the corresponding task instructions are accurately pushed to the business system and operation terminal of the corresponding entity. The real-time execution progress tracking unit collects task execution status data, logistics node flow data, and inventory change data from each executing entity in real time, and updates the execution progress synchronously in a full-link visualization view to achieve full-process traceability of allocation execution. The abnormal event closed-loop handling unit receives abnormal events reported by each entity during execution, including insufficient inventory, transportation interruption, node congestion, and compliance interception. Based on the level and scope of impact of the abnormal event, it automatically triggers the abnormal handling process, pushes handling instructions to the corresponding responsible entity, and synchronously feeds back to the decision-making module for plan adjustment, achieving full closed-loop handling of abnormal events. The execution data synchronization unit synchronizes the entire process data of allocation execution, inventory change data, and abnormal handling data to the global inventory data normalization and aggregation module in real time, achieving real-time updates of global inventory data.

8. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The decision model self-iterative optimization module is equipped with an execution effect evaluation unit, a model optimization unit, and an effect verification unit. The execution effect evaluation unit is used to construct a multi-dimensional decision effect evaluation system. Based on historical allocation execution data, it quantitatively scores and comprehensively evaluates the execution effect of historical allocation decision plans from multiple dimensions such as cost control, timeliness achievement, inventory optimization, fulfillment rate, and anomaly occurrence rate. Based on the evaluation results of the execution effect, the model optimization unit identifies the sources of deviation and optimization direction of the decision model, and optimizes and updates the parameter weights, constraint adaptation logic, and iteration rules of the solution algorithm of the decision model through reinforcement learning algorithm. The effect verification unit is used to perform offline simulation verification of the optimized decision model based on historical datasets, compare the decision effect before and after optimization, and when the verification result meets the preset optimization threshold, the optimized model is deployed to the multi-constraint intelligent allocation collaborative decision module to complete the iterative update of the model.

9. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to claim 1, characterized in that, The system also includes a cross-entity collaborative interaction module. The input and output ends of this module connect to the multi-entity execution closed-loop control module and the operation terminals of each entity in the supply chain, respectively. This module provides a unified collaborative interaction entry point for each entity in the supply chain, supporting online communication, instruction confirmation, document transfer, anomaly reporting, and progress feedback for allocation tasks. It enables information synchronization and efficient collaboration among multiple entities and associates all interactive data with business data for full-process traceability and auditing.

10. The global inventory visibility and intelligent allocation coordination system for complex supply chain networks according to any one of claims 1-9, characterized in that, The system also includes a full-link data security management module, which covers the entire data flow process, including a global inventory data normalization and aggregation module, a real-time inventory status visualization engine, a global supply and demand situation perception and prediction module, a multi-constraint intelligent allocation and collaborative decision-making module, a multi-entity execution closed-loop management module, and a decision model self-iterative optimization module. This module provides encrypted control and access auditing for the entire lifecycle of data collection, transmission, storage, computation, application, and sharing. It classifies and protects inventory and business data based on data classification standards, and maintains full-process logs and audit traceability for all data access, modification, and sharing operations, ensuring the security, confidentiality, and immutability of data across the entire supply chain network.

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