Intelligent logistics management system and optimization method

Abstract

This invention discloses an intelligent logistics management system and optimization method, relating to the field of logistics supply chain management technology. Specifically, it includes an order management module, a warehouse management module, a delivery management module, a route optimization module, and an AI prediction module. This invention constructs a logistics information platform based on the fusion of multi-source heterogeneous data to achieve real-time monitoring and dynamic scheduling of order, warehouse, and delivery processes. It employs big data analysis and route optimization algorithms to globally optimize transportation routes, load matching, and resource allocation, and combines AI prediction models to improve the accuracy of logistics demand prediction. This enables intelligent management and efficient operation of the entire process of order management, warehouse management, and delivery management, promoting the intelligent and automated development of the entire logistics system, significantly improving logistics operational efficiency, reducing transportation costs, maximizing customer satisfaction with logistics services, and supporting enterprises in enhancing their market competitiveness.

Description

Technical Field

[0001] This invention relates to the field of logistics supply chain management technology, specifically to an intelligent logistics management system and optimization method. Background Technology

[0002] Logistics management refers to the process of planning, implementing, and controlling the efficient and orderly flow of goods, information, and related resources from the supply location to the receiving location. It covers all aspects of the supply chain, including but not limited to procurement, production, warehousing, transportation, and distribution. The purpose of logistics management is to meet customer needs at the lowest cost, improve response speed, enhance customer satisfaction, and achieve optimal allocation and utilization of resources throughout the process. A Chinese patent discloses a logistics supply chain management system, application number CN202110797606.2. This patent integrates customers, platform providers, and suppliers into one system to achieve unified management of logistics information, effectively process order information, and improve the efficiency and intelligence of logistics management. In the current intelligent logistics management process, the order module, warehousing module, and delivery module are usually deployed independently, using different databases with inconsistent data formats, making effective synchronization impossible. This results in the inability to achieve real-time monitoring and dynamic scheduling of order, warehousing, and delivery processes. Furthermore, the data between the various modules cannot meet the real-time requirements, making it impossible to optimize transportation routes, load matching, and resource allocation globally. Consequently, logistics operation efficiency is low, transportation costs are increased, and customer experience is poor. Summary of the Invention

[0003] This invention provides an intelligent logistics management system and optimization method, which can effectively solve the problems mentioned in the background art. In the current intelligent logistics management process, the order module, warehousing module, and delivery module are usually deployed independently, using different databases with inconsistent data formats, making effective synchronization impossible. This results in the inability to achieve real-time monitoring and dynamic scheduling of order, warehousing, and delivery links, and the inability of data between modules to meet real-time requirements. Consequently, it is impossible to perform global optimization of transportation routes, load matching, and resource allocation, leading to low logistics operation efficiency, increased transportation costs, and a poor customer experience.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent logistics management system, which achieves efficient operation of order management, warehouse management, and delivery management by deeply integrating multi-source heterogeneous data and combining big data analysis, path optimization algorithms, and AI prediction models; Specifically, it includes an order management module, a warehouse management module, a delivery management module, a route optimization module, and an AI prediction module. These modules are interconnected through multi-party data integration, interaction, and intelligent prediction technology.

[0005] According to the above technical solution, the order management module is responsible for the management of the entire life cycle of the order and is the starting point and core of the logistics information flow. Specifically, it includes an order receiving unit, an order review unit, a sorting and allocation unit, and a tracking and monitoring unit. The order receiving unit supports multi-channel order access and ensures order compliance through data exchange and verification; The order review unit verifies the compliance and validity of orders based on business rules. Abnormal orders trigger alarms or rollbacks, and the review log is recorded for traceability to avoid subsequent operational errors. The sorting and distribution unit sorts orders according to rules, generates task assignments to designated warehouse locations or carriers, and balances transportation costs with customer demand. The tracking and monitoring unit enables real-time updates of order status, allows customers to view logistics routes, collects customer feedback, and drives continuous system optimization.

[0006] According to the above technical solution, the warehouse management module is responsible for managing the warehousing, storage, picking, sorting and outbound of materials, and specifically includes a material receiving and storage unit, an automated handling and positioning unit, and a real-time monitoring and intelligent decision-making unit. The material receiving and storage unit identifies material information through RFID or barcode, automatically synchronizes it to the database, and uses warehouse robots and stacker cranes to complete automated warehousing. Automated handling and positioning units refer to handling robots that use 3D vision or LiDAR to accurately locate goods, combine algorithms to optimize storage layout, and dynamically adjust the storage location and sorting route of goods. The real-time monitoring and intelligent decision-making unit monitors the warehouse environment and material status by linking temperature and humidity sensors and vehicle tracking equipment, and optimizes the inventory structure based on big data and AI to predict inventory demand.

[0007] According to the above technical solution, the delivery planning module is responsible for delivering orders from the warehouse to the customer's last mile, and specifically includes an order allocation and task generation unit, a route planning and optimization unit, a vehicle and driver scheduling unit, and a real-time monitoring and adjustment unit; The order allocation and task generation unit categorizes tasks according to rules and generates customized delivery tasks based on specific needs. The route planning and optimization unit combines real-time traffic data and weather conditions with algorithms to calculate the shortest path, balancing delivery time, delivery distance, and fuel consumption. The vehicle and driver dispatching unit matches tasks and resources based on personnel status and vehicle performance using advanced dispatching algorithms to ensure balance and seamless connection between multiple tasks. The real-time monitoring and adjustment unit tracks delivery progress via GPS, uses predictive algorithms to warn of delay risks, and makes dynamic adjustments.

[0008] According to the above technical solution, the route optimization module is based on global data and uses algorithms and models to achieve global optimization of transportation routes, load matching and resource allocation. Specifically, it includes a global route planning unit, a vehicle and route matching unit, and a real-time dynamic adjustment unit. The global route planning unit identifies high-cost and congested routes and selects the optimal transportation route by relying on GIS maps and model-driven algorithms. The vehicle and route matching unit combines vehicle capabilities and uses genetic algorithms and linear programming to optimize the matching of carriers and tasks, thereby achieving dynamic scheduling. The real-time dynamic adjustment unit monitors external variables such as weather and traffic in real time, uses machine learning to predict the risks of weather and accidents, and adjusts routes and deploys emergency transportation capacity in advance.

[0009] According to the above technical solution, the AI ​​prediction module uses machine learning to mine historical and real-time data to predict demand, inventory and risk, specifically including a demand prediction unit, a transportation model optimization unit, and an inventory decision and risk management unit. The demand forecasting unit uses models to analyze historical orders, seasonality, holidays, and market dynamics, and uses these models to predict future demand, guiding production and inventory adjustments. The transportation model optimization unit is based on historical transportation data and real-time road conditions and weather to build transportation models and dynamically optimize routes and transportation plans; The inventory decision and risk management unit is responsible for predicting inventory turnover and safety stock to avoid overstocking and stockouts, monitoring supply chain nodes, and providing real-time alerts for anomalies.

[0010] The optimization method of intelligent logistics management, through technological collaboration and a closed-loop process, achieves efficiency improvement and cost reduction, specifically including the following steps: Step 1: Multi-source data acquisition and fusion; Step two, demand and inventory forecasting; Step 3: Order processing and task allocation; Step 4: Intelligent warehouse scheduling; Step 5: Route optimization and delivery execution.

[0011] According to the above technical solution, step one involves collecting order data, warehousing data, transportation data, and market data. Order data includes customers, products, and time; warehousing data includes inventory and environment; transportation data includes vehicle status, road conditions, and weather; and market data includes demand fluctuations and competitor dynamics. After collecting data from multiple sources, ETL tools are used to clean and standardize the data, remove redundant and erroneous data, and integrate and process the data through a data lake or central database to form a usable logistics dataset. In step two, based on the AI ​​prediction module, combined with historical orders, seasonality, and market dynamics, short-term and long-term demand are predicted using machine learning models. Based on the forecast results, production plans, inventory targets and transportation plans are formulated, safety stock levels are adjusted to avoid stockpiling and shortages, and inventory optimization strategies are generated to clarify the resource requirements of each link.

[0012] According to the above technical solution, step three specifically involves receiving orders from multiple channels through the order management module, completing the review and sorting, and verifying the validity of the orders; Based on warehouse inventory status and delivery capacity, order tasks are allocated to the warehouse picking and delivery process, and time windows and resource requirements are clearly defined; In step four, the warehouse management module uses automated equipment to pick and sort materials according to the order tasks. Meanwhile, inventory dynamics are monitored in real time, and when demand exceeds expectations, inventory is automatically adjusted and emergency replenishment is triggered.

[0013] According to the above technical solution, in step five, the route optimization module combines real-time traffic and weather data, uses algorithms to plan the globally optimal route, balances distance, time and cost, and matches vehicles. The system integrates a delivery management module to dispatch vehicles and drivers, execute delivery tasks, monitor progress in real time via GPS, and dynamically adjust routes or increase capacity in the event of sudden delays due to congestion, sudden weather changes, or order modifications.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a logistics information platform based on the fusion of multi-source heterogeneous data to achieve real-time monitoring and dynamic scheduling of order, warehousing, and delivery processes. It employs big data analysis and route optimization algorithms to globally optimize transportation routes, load matching, and resource allocation, and combines AI prediction models to improve the accuracy of logistics demand prediction. This enables intelligent management and efficient operation of the entire process of order management, warehousing management, and delivery management, promotes the intelligent and automated development of the entire logistics system, significantly improves logistics operation efficiency, and reduces transportation costs. Furthermore, the system possesses high flexibility, controllability, and global optimization capabilities, enabling intelligent operation of the entire process from demand forecasting to delivery execution. This continuously improves operational efficiency and effectively reduces logistics costs, thereby maximizing customer satisfaction with logistics services and supporting enterprises in enhancing their market competitiveness. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0016] In the attached diagram: Figure 1 This is a structural block diagram of the management system of the present invention; Figure 2 This is a flowchart of the steps of the optimization method of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] Example: Figure 1 As shown, this invention provides a technical solution: an intelligent logistics management system. By deeply integrating multi-source heterogeneous data and combining big data analysis, path optimization algorithms, and AI prediction models, it achieves efficient operation of order management, warehouse management, and distribution management, promotes the intelligent and automated development of the entire logistics system, and comprehensively improves logistics operation efficiency and effectiveness. Specifically, it includes an order management module, a warehouse management module, a delivery management module, a route optimization module, and an AI prediction module. These modules are interconnected through multi-party data integration, interaction, and intelligent prediction technology, thereby achieving dynamic monitoring and global optimization of the entire logistics process.

[0019] Based on the above technical solution, the order management module is responsible for the management of the entire life cycle of the order. It is the starting point and core of the logistics information flow, ensuring the smooth flow and timeliness of information during the order processing. Specifically, it includes the order receiving unit, the order review unit, the sorting and allocation unit, and the tracking and monitoring unit. The order receiving unit supports order access from multiple channels, including e-commerce platforms, B2B platforms, and manual entry. It ensures order compliance through data exchange and verification, specifically verifying minimum order requirements, special delivery conditions, and filtering invalid orders. The order review unit verifies the compliance and validity of orders based on business rules. These business rules include inventory checks, pricing terms, and total quantity limits. Alarms are triggered for abnormal orders, including those with insufficient inventory. Review logs are recorded for traceability and to prevent subsequent operational errors. The sorting and distribution unit sorts orders according to rules such as customer category, order amount, and product type, generates task assignments to designated warehouse locations, balances transportation costs with customer demand, and optimizes resource scheduling. The tracking and monitoring unit enables real-time updates of order status, including received, processing, shipped, and delivery status. It allows customers to view the logistics trajectory, collects customer feedback synchronously, and drives continuous system optimization. Specific customer feedback includes delivery speed and service quality.

[0020] Based on the above technical solutions, the warehouse management module is responsible for managing the inbound, storage, picking, sorting and outbound of materials. It reduces warehousing costs and improves efficiency through automation and intelligent technologies. Specifically, it includes a material receiving and storage unit, an automated handling and positioning unit, and a real-time monitoring and intelligent decision-making unit. The material receiving and storage unit identifies material information through barcodes, including production number and production date, which is automatically synchronized to the database. It also utilizes warehouse robots and stacker cranes to complete automated warehousing, thereby improving the speed and accuracy of material receiving and storage. Automated handling and positioning units refer to handling robots that use LiDAR to accurately locate goods, combine algorithms to optimize storage layout, dynamically adjust the storage location and sorting route of goods, and improve the utilization rate of warehouse space. The real-time monitoring and intelligent decision-making unit monitors the warehouse environment and material status by linking temperature and humidity sensors and vehicle tracking equipment. Based on big data and AI, it predicts inventory demand, optimizes inventory structure, responds quickly to market changes, improves delivery efficiency, and specifically reduces the backlog of slow-moving goods.

[0021] Based on the above technical solution, the delivery planning module is responsible for delivering orders from the warehouse to the customer's last mile, so as to achieve efficient delivery of products from the warehouse to the customer. Specifically, it includes an order allocation and task generation unit, a route planning and optimization unit, a vehicle and driver scheduling unit, and a real-time monitoring and adjustment unit. The order allocation and task generation unit classifies tasks according to rules and combines them with special needs to generate customized delivery tasks and optimize resource allocation. Specific rules include order priority and delivery time window, and special needs include specified packaging and specified personnel. The route planning and optimization unit combines real-time traffic data and weather conditions, including traffic congestion and accidents, and uses Dijkstra's algorithm to calculate the shortest path, balancing delivery time, delivery distance, and fuel consumption in order to pursue global optimization. The vehicle and driver scheduling unit is based on personnel status and vehicle performance, specifically including load capacity, capacity, speed and range. It uses advanced scheduling algorithms to match tasks and resources, ensuring balance and connection between multiple tasks and improving capacity utilization efficiency. The system also ensures efficient execution during the delivery process through dynamic resource allocation. Based on real-time factors such as traffic, weather, and order changes, the system can dynamically adjust task allocation and resource scheduling to cope with emergencies and ensure the timeliness and accuracy of logistics delivery. The real-time monitoring and adjustment unit tracks delivery progress via GPS, including estimated arrival time and actual route. It uses predictive algorithms to warn of delay risks and makes dynamic adjustments to improve on-time delivery rates. Delay risks include congestion, and dynamic adjustments include replanning routes and dispatching backup vehicles.

[0022] Based on the above technical solution, the route optimization module is based on global data and uses algorithms and models to achieve global optimization of transportation routes, load matching and resource allocation, thereby reducing transportation costs. Specifically, it includes a global route planning unit, a vehicle and route matching unit, and a real-time dynamic adjustment unit. The global route planning unit identifies high-cost and congested routes and selects the optimal transportation route by relying on GIS maps and model-driven algorithms. The specific model-driven algorithm is the minimum spanning tree (MST). The route optimization module combines real-time traffic data, weather information, and transportation conditions, using advanced algorithms to calculate the optimal route to ensure reduced delivery costs and improved transportation efficiency. Specifically, the optimization objective is to minimize the total transportation distance and cost. in, Indicates the distance of a path segment. This indicates the travel time for that route. It refers to the transportation costs associated with the route.

[0023] The vehicle and route matching unit combines vehicle capacity and uses genetic algorithms and linear programming to optimize the matching of carriers and tasks, thereby achieving dynamic scheduling. Vehicle capacity includes vehicle capacity and vehicle speed. The real-time dynamic adjustment unit monitors external variables such as weather and traffic in real time, predicts weather and accident risks through machine learning, adjusts routes and deploys emergency transportation capacity in advance, and ensures logistics stability. Specifically, route adjustment means avoiding construction sections.

[0024] Based on the above technical solution, the AI ​​prediction module uses machine learning to mine historical and real-time data to predict demand, inventory and risks, supporting system decision-making. Specifically, it includes a demand prediction unit, a transportation model optimization unit, and an inventory decision and risk management unit. The demand forecasting unit uses ARIMA and random forest models to analyze historical orders, seasonality, holidays, and market dynamics, and uses the models to predict future demand to guide production and inventory adjustments. These forecasts enable the system to guide production and inventory adjustments, thereby improving supply chain efficiency. The forecasting model is as follows: in, It is the demand forecast for the next k days, where X represents external factors (such as market dynamics).

[0025] The transportation model optimization unit is based on historical transportation data and real-time road conditions and weather to build transportation models, predict congestion, dynamically optimize routes and transportation plans, and reduce time and costs, including switching transportation modes. The inventory decision and risk management unit is responsible for predicting inventory turnover and safety stock to avoid overstocking and stockouts, monitoring supply chain nodes, providing real-time early warnings of anomalies, and ensuring supply chain resilience. Supply chain nodes include raw materials, production, and distribution, and anomalies include supplier delays.

[0026] The warehouse management module uses an intelligent decision-making system for dynamic inventory management. Based on order demand and forecast data, it adjusts inventory levels in real time to avoid insufficient or excessive inventory. The inventory optimization formula is as follows: in, This represents the inventory level at time t. It's the replenishment quantity. It refers to demand.

[0027] like Figure 2 As shown, the optimization method of intelligent logistics management achieves efficiency improvement and cost reduction through technological collaboration and a closed-loop process. Specifically, it includes the following steps: Step 1: Multi-source data acquisition and fusion; Step two, demand and inventory forecasting; Step 3: Order processing and task allocation; Step 4: Intelligent warehouse scheduling; Step 5: Route optimization and delivery execution.

[0028] Based on the above technical solution, step one involves collecting order data, warehousing data, transportation data, and market data. Order data includes customers, products, and time; warehousing data includes inventory and environment; transportation data includes vehicle status, road conditions, and weather; and market data includes demand fluctuations and competitor dynamics. After collecting data from multiple sources, ETL tools are used to clean and standardize the data, remove redundant and erroneous data, and integrate and process the data through a central database to form a usable logistics dataset. Step 2: Based on the AI ​​prediction module, combined with historical orders, seasonality, and market dynamics, short-term and long-term demand is predicted using a machine learning model. The machine learning model adopts random forest, with short-term demand divided into daily and weekly segments, and long-term demand divided into monthly and quarterly segments. Based on the forecast results, production plans, inventory targets, and transportation plans are formulated, safety stock levels are adjusted to avoid stockpiling and shortages, and inventory optimization strategies are generated to clarify the resource requirements of each link. Specific strategies include promotion of slow-moving products and replenishment of fast-moving products, and resource requirements include warehouse capacity and number of vehicles.

[0029] Based on the above technical solution, step three specifically involves receiving orders from multiple channels through the order management module, completing the review and sorting, and verifying the validity of the orders. Specifically, the review includes verifying inventory and rules, and sorting is carried out according to priority and region. Based on warehouse inventory status and delivery capacity, order tasks are allocated to the warehouse picking and delivery process, and time windows and resource requirements are clearly defined; Step four: The warehouse management module uses automated equipment, including robots and stacker cranes, to pick and sort materials according to the order tasks. In the layout optimization, high-frequency products are specifically placed near the exit. Meanwhile, inventory dynamics are monitored in real time, and when demand exceeds expectations, inventory is automatically adjusted and emergency replenishment is triggered.

[0030] Based on the above technical solution, in step five, the route optimization module combines real-time traffic and weather data to use an algorithm to plan the globally optimal route. The algorithm selects Dijkstra's algorithm, balances distance, time and cost, and matches vehicles, specifically according to vehicle capacity and speed. The system integrates a delivery management module to dispatch vehicles and drivers, execute delivery tasks, monitor progress in real time via GPS, and dynamically adjust routes to ensure on-time delivery in the event of sudden delays caused by congestion, sudden weather changes, or order modifications.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent logistics management system, characterized by: By deeply integrating multi-source heterogeneous data and combining big data analysis, path optimization algorithms, and AI prediction models, we can achieve efficient operation of order management, warehouse management, and delivery management. Specifically, it includes an order management module, a warehouse management module, a delivery management module, a route optimization module, and an AI prediction module. These modules are interconnected through multi-party data integration, interaction, and intelligent prediction technology.

2. The intelligent logistics management system according to claim 1, characterized in that: The order management module is responsible for the management of the entire order lifecycle and is the starting point and core of the logistics information flow. Specifically, it includes an order receiving unit, an order review unit, a sorting and allocation unit, and a tracking and monitoring unit. The order receiving unit supports multi-channel order access and ensures order compliance through data exchange and verification; The order review unit verifies the compliance and validity of orders based on business rules. Abnormal orders trigger alarms or rollbacks, and the review log is recorded for traceability to avoid subsequent operational errors. The sorting and distribution unit sorts orders according to rules, generates task assignments to designated warehouse locations or carriers, and balances transportation costs with customer demand. The tracking and monitoring unit enables real-time updates of order status, allows customers to view logistics routes, collects customer feedback, and drives continuous system optimization.

3. The intelligent logistics management system according to claim 1, characterized in that: The warehouse management module is responsible for managing the inbound, storage, picking, sorting and outbound processes of materials, and specifically includes a material receiving and storage unit, an automated handling and positioning unit, and a real-time monitoring and intelligent decision-making unit. The material receiving and storage unit identifies material information through RFID or barcode, automatically synchronizes it to the database, and uses warehouse robots and stacker cranes to complete automated warehousing. Automated handling and positioning units refer to handling robots that use 3D vision or LiDAR to accurately locate goods, combine algorithms to optimize storage layout, and dynamically adjust the storage location and sorting route of goods. The real-time monitoring and intelligent decision-making unit monitors the warehouse environment and material status by linking temperature and humidity sensors and vehicle tracking equipment, and optimizes the inventory structure based on big data and AI to predict inventory demand.

4. The intelligent logistics management system according to claim 1, characterized in that: The delivery planning module is responsible for delivering orders from the warehouse to the customer's last mile, and specifically includes an order allocation and task generation unit, a route planning and optimization unit, a vehicle and driver scheduling unit, and a real-time monitoring and adjustment unit. The order allocation and task generation unit categorizes tasks according to rules and generates customized delivery tasks based on specific needs. The route planning and optimization unit combines real-time traffic data and weather conditions with algorithms to calculate the shortest path, balancing delivery time, delivery distance, and fuel consumption. The vehicle and driver dispatching unit matches tasks and resources based on personnel status and vehicle performance using advanced dispatching algorithms to ensure balance and seamless connection between multiple tasks. The real-time monitoring and adjustment unit tracks delivery progress via GPS, uses predictive algorithms to warn of delay risks, and makes dynamic adjustments.

5. The intelligent logistics management system according to claim 1, characterized in that: The route optimization module is based on global data and uses algorithms and models to achieve global optimization of transportation routes, load matching and resource allocation. Specifically, it includes a global route planning unit, a vehicle and route matching unit, and a real-time dynamic adjustment unit. The global route planning unit identifies high-cost and congested routes and selects the optimal transportation route by relying on GIS maps and model-driven algorithms. The vehicle and route matching unit combines vehicle capabilities and uses genetic algorithms and linear programming to optimize the matching of carriers and tasks, thereby achieving dynamic scheduling. The real-time dynamic adjustment unit monitors external variables such as weather and traffic in real time, uses machine learning to predict the risks of weather and accidents, and adjusts routes and deploys emergency transportation capacity in advance.

6. The intelligent logistics management system according to claim 1, characterized in that: The AI ​​prediction module uses machine learning to mine historical and real-time data to predict demand, inventory, and risk. Specifically, it includes a demand prediction unit, a transportation model optimization unit, and an inventory decision-making and risk management unit.

7. The optimization method for intelligent logistics management according to any one of claims 1-6, characterized in that: Through technological collaboration and a closed-loop process, efficiency improvements and cost reductions are achieved, specifically including the following steps: Step 1: Multi-source data acquisition and fusion; Step two, demand and inventory forecasting; Step 3: Order processing and task allocation; Step 4: Intelligent warehouse scheduling; Step 5: Route optimization and delivery execution.

8. The optimization method for intelligent logistics management according to claim 7, characterized in that: Step one involves collecting order data, warehousing data, transportation data, and market data. Order data includes customers, products, and time; warehousing data includes inventory and environment; transportation data includes vehicle status, road conditions, and weather; and market data includes demand fluctuations and competitor dynamics. After collecting data from multiple sources, ETL tools are used to clean and standardize the data, remove redundant and erroneous data, and integrate and process the data through a data lake or central database to form a usable logistics dataset. In step two, based on the AI ​​prediction module, combined with historical orders, seasonality, and market dynamics, short-term and long-term demand are predicted using machine learning models. Based on the forecast results, production plans, inventory targets and transportation plans are formulated, safety stock levels are adjusted to avoid stockpiling and shortages, and inventory optimization strategies are generated to clarify the resource requirements of each link.

9. The optimization method for intelligent logistics management according to claim 7, characterized in that: Step three specifically involves receiving orders from multiple channels through the order management module, completing the review and sorting, and verifying the validity of the orders; Based on warehouse inventory status and delivery capacity, order tasks are allocated to the warehouse picking and delivery process, and time windows and resource requirements are clearly defined; In step four, the warehouse management module uses automated equipment to pick and sort materials according to the order tasks. Meanwhile, inventory dynamics are monitored in real time, and when demand exceeds expectations, inventory is automatically adjusted and emergency replenishment is triggered.

10. The optimization method for intelligent logistics management according to claim 7, characterized in that: In step five, the route optimization module combines real-time traffic and weather data to use algorithms to plan the globally optimal route, balance distance, time, and cost, and match vehicles. The system integrates a delivery management module to dispatch vehicles and drivers, execute delivery tasks, monitor progress in real time via GPS, and dynamically adjust routes or increase capacity in the event of sudden delays due to congestion, sudden weather changes, or order modifications.

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