A data analysis-based intelligent optimization scheduling system for logistics centers

Abstract

This invention discloses a data analysis-based intelligent optimization scheduling system for logistics centers, belonging to the field of intelligent optimization scheduling technology. During operation, the system collects and processes key data from the logistics center's operations. After preprocessing, it manages and optimizes the logistics center's inventory status. Based on real-time inventory data and order demand information, it dynamically adjusts inventory layout, optimizes the storage and retrieval routes of goods, and calculates a comprehensive optimization index (SOF) based on real-time traffic data, order demand, and vehicle location. It intelligently allocates vehicles according to order demand, cargo capacity, vehicle capacity, and their real-time location, optimizing loading sequence and loading rate. Through IoT technology and data analysis tools, it monitors vehicle location, transportation status, environmental changes, and order processing progress in real time. By comparing the SOF with a preset threshold, it evaluates the scheduling optimization level of the logistics system in real time and provides anomaly warning and feedback mechanisms.

Description

Technical Field

[0001] This invention relates to the field of intelligent optimization scheduling technology, specifically to an intelligent optimization scheduling system for logistics centers based on data analysis. Background Technology

[0002] Logistics centers are core hubs in modern supply chains and distribution networks, handling a large volume of order processing, inventory management, and delivery tasks. However, with the increasing complexity of logistics demands and the surge in order volume, the operational efficiency of traditional logistics centers faces severe challenges. Route planning and transportation optimization have become key aspects of improving logistics center efficiency. Utilizing data analytics, order, inventory, traffic, and environmental data can be acquired and processed in real time, enabling more efficient and precise logistics operations through intelligent scheduling and optimization.

[0003] Traditional methods often rely on fixed rules and experience, lacking effective utilization of real-time data. This results in inflexible transportation route planning, low vehicle load rates, and insufficient ability to respond to emergencies. Furthermore, existing systems have limited scheduling optimization capabilities in the face of high traffic density or adverse weather conditions, easily leading to delivery delays and resource waste. Therefore, a data-driven intelligent optimization scheduling system is needed to overcome these shortcomings, improving the overall efficiency and responsiveness of the logistics system through real-time monitoring and dynamic adjustments.

[0004] The shortcomings of traditional logistics systems primarily stem from a lack of agile responsiveness to dynamically changing environments, such as traffic conditions, order fluctuations, and weather changes. When a logistics system cannot adjust its routes and scheduling strategies in real time, it leads to problems such as delivery delays, low vehicle utilization, and poor inventory management. This not only impacts the operational efficiency of logistics centers and customer satisfaction but can also result in resource waste and increased operating costs. More seriously, when the system remains in a suboptimal state for extended periods, businesses may face the risk of declining market competitiveness and reduced service levels. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data analysis-based intelligent optimization scheduling system for logistics centers, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a data analysis-based intelligent optimization scheduling system for logistics centers, comprising a data acquisition module, a data preprocessing module, an inventory management module, a route planning module, a vehicle scheduling and loading module, and a real-time monitoring module;

[0007] The data acquisition module is used to collect and process key data in the operation of the logistics center. Through sensors, IoT devices and database interfaces, it collects internal and external data including order information, inventory status, vehicle location, real-time traffic conditions and weather conditions. The data is then transmitted to the data preprocessing module through the comprehensive collection of real-time and historical data.

[0008] The data preprocessing module is used to clean, format and denoise the logistics data acquired by the data acquisition module, filter outliers, correct data errors, fill in data gaps, and convert the processed data into a standardized format.

[0009] The inventory management module is used to manage and optimize the inventory status of the logistics center. Based on real-time inventory data and order demand information, it dynamically adjusts the inventory layout and optimizes the storage and retrieval paths of goods.

[0010] The route planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand, and vehicle location. It adopts a dynamic route planning algorithm to update and adjust the delivery route in real time, avoid adverse conditions such as traffic congestion and severe weather, and calculate and obtain the comprehensive optimization index SOF.

[0011] The vehicle scheduling and loading module is used to intelligently allocate vehicles based on order requirements, cargo volume, vehicle capacity and real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically scheduling vehicles.

[0012] The real-time monitoring module is used to monitor vehicle location, transportation status, environmental changes and order processing progress in real time through IoT technology and data analysis tools. By comparing the comprehensive optimization index SOF with a preset threshold, it evaluates the scheduling optimization level of the logistics system in real time, provides anomaly warning and feedback mechanism, and automatically triggers scheduling adjustments when delays, route changes or other abnormal situations occur.

[0013] Preferably, the data acquisition module includes internal and external data acquisition units;

[0014] The internal and external data acquisition units are used to collect order information, inventory status, vehicle location, real-time traffic conditions and weather conditions through sensors, IoT devices and database interfaces, and obtain total number of orders N, total number of vehicles M, total number of deliveries L, order demand time ODT, transportation time TT, vehicle capacity utilization rate VCU, delivery load DL, traffic density TD and weather delay WD, forming internal and external datasets.

[0015] Preferably, the data preprocessing module includes a data preprocessing unit;

[0016] The data preprocessing unit is used to clean the collected raw data, remove invalid, duplicate or abnormal data, convert multi-source data into a unified standard format so that it can be effectively used by subsequent modules and algorithms, correct erroneous values ​​in the data and fill in missing data.

[0017] Preferably, the inventory management module includes an inventory layout optimization unit and a picking route optimization unit;

[0018] The inventory layout optimization unit is used to receive real-time inventory data updates, track the inventory quantity and storage location of each product, dynamically adjust the storage layout of products based on inventory status and order demand, and calculate the optimal product storage location through intelligent algorithms to maximize picking efficiency and warehouse space utilization.

[0019] The picking route optimization unit is used to calculate the optimal picking route based on real-time order demand and inventory location. This unit obtains the path efficiency coefficient (PEC) through optimization algorithms, generates the shortest path solution, reduces the walking distance and time of picking personnel, improves picking efficiency, and ensures that orders can be processed quickly and accurately.

[0020] The path efficiency coefficient PEC is calculated using the following formula:

[0021] ;

[0022] In the formula, TT represents the transportation time, WD represents the weather delay time, ODT represents the order demand time, and N represents the total number of orders.

[0023] Preferably, the route planning module includes a real-time traffic analysis unit, a route calculation unit, and a dynamic route adjustment unit;

[0024] The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, and obtain data on road congestion, traffic accidents, and weather impacts through real-time interaction with the traffic information system, providing traffic condition analysis results for route planning.

[0025] The route calculation unit is used to calculate the optimal delivery route based on order requirements, vehicle location and traffic data. After calculation using a route planning algorithm, the following are obtained: Overall Optimization Index (SOF), Vehicle Utilization Coefficient (VUC), and Transport Load Coefficient (TLC).

[0026] The dynamic route adjustment unit is used to monitor and adjust the delivery route in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the route plan to avoid traffic congestion and severe weather.

[0027] Preferably, the vehicle utilization factor (VUC) is calculated using the following formula;

[0028] ;

[0029] In the formula, VCU represents vehicle capacity utilization rate, and M represents the total number of vehicles.

[0030] Preferably, the transport load factor TLC is calculated using the following formula;

[0031] ;

[0032] In the formula, DL represents the cargo volume of each delivery, and L represents the total number of deliveries.

[0033] Preferably, the comprehensive optimization index SOF is calculated using the following formula;

[0034] ;

[0035] In the formula, PEC represents the route efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transport load coefficient. These represent the proportional coefficients of the path efficiency coefficient (PEC), vehicle utilization coefficient (VUC), and transport load coefficient (TLC), respectively, used to balance the weights of different optimization objectives.

[0036] Preferably, the vehicle scheduling and loading module includes a vehicle allocation unit, a loading optimization unit, and a dynamic scheduling unit;

[0037] The vehicle allocation unit is used to intelligently allocate the most suitable vehicle for delivery tasks based on current order demand, vehicle status and cargo capacity. Through scheduling algorithms, it achieves optimal allocation of vehicle resources and ensures that a suitable vehicle is available for each delivery task.

[0038] The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle. By calculating the optimal loading scheme for the vehicle, it ensures that the loading capacity of each vehicle is maximized, thereby reducing the number of transport trips and transportation costs.

[0039] The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time. Based on the delivery task progress, vehicle location and real-time traffic conditions, it dynamically updates the vehicle scheduling arrangement to ensure that vehicles can respond to delivery needs in a timely manner and improve the system's flexibility and response speed.

[0040] Preferably, the real-time monitoring module includes a vehicle location monitoring unit, a transportation status monitoring unit, and an anomaly early warning unit;

[0041] The vehicle location monitoring unit is used to track the geographical location of delivery vehicles in real time. By connecting with the vehicle's GPS system in real time, it monitors the vehicle's driving route and current location, ensuring the visualization and transparency of the logistics operation process.

[0042] The transportation status monitoring unit is used to monitor the execution of transportation tasks in real time, including cargo status, transportation progress and environmental changes, and can quickly identify abnormal situations in the transportation process, including delays or cargo damage.

[0043] The abnormality early warning unit is used to issue early warnings and trigger corresponding adjustment measures in a timely manner when delays, route changes or other abnormal situations occur during transportation. By comparing the comprehensive optimization index SOF with the first scheduling threshold H and the second preset scheduling threshold V, the scheduling optimization level of the logistics system is evaluated in real time, and corresponding level strategies are formulated.

[0044] When the comprehensive optimization index SOF ≤ the first scheduling threshold H, the first level evaluation is obtained, the normal level is maintained, the existing scheduling strategy is maintained, and data review and trend analysis are carried out regularly.

[0045] When the first scheduling threshold H < comprehensive optimization index SOF ≤ second preset scheduling threshold V, the second level evaluation is obtained. The routes for high traffic density road sections or delayed orders are replanned to reduce transportation time. The focus is on checking the route planning efficiency and vehicle loading, identifying specific bottlenecks, enhancing real-time monitoring, and dynamically adjusting routes in high traffic density and severe weather areas.

[0046] When the overall optimization index SOF exceeds the second preset scheduling threshold V, a third-level evaluation is obtained. A comprehensive scheduling review is immediately initiated, all affected routes are quickly adjusted to avoid traffic congestion and adverse weather, priority is given to ensuring the timely delivery of urgent orders, logistics resources, including personnel, vehicles and equipment, are dynamically allocated, the most important logistics tasks are handled centrally, abnormal situations are reported in real time, and early warnings are issued to the operations team and relevant personnel to ensure a rapid response from all parties.

[0047] This invention provides a data analysis-based intelligent optimization scheduling system for logistics centers, which has the following beneficial effects:

[0048] (1) When the system is running, it collects and processes key data in the operation of the logistics center. After preprocessing, it manages and optimizes the inventory status of the logistics center. Based on real-time inventory data and order demand information, it dynamically adjusts the inventory layout and optimizes the storage and retrieval routes of goods. Based on real-time traffic data, order demand and vehicle location, it calculates and obtains the comprehensive optimization index SOF. According to order demand, cargo volume, vehicle capacity and its real-time location, it intelligently allocates vehicles, optimizes loading sequence and loading rate. Through IoT technology and data analysis tools, it monitors vehicle location, transportation status, environmental changes and order processing progress in real time. By comparing the comprehensive optimization index SOF with the preset threshold, it evaluates the scheduling optimization level of the logistics system in real time and provides anomaly warning and feedback mechanism.

[0049] (2) By introducing six modules—data acquisition, data preprocessing, inventory management, route planning, vehicle dispatching and loading, and real-time monitoring—the intelligent optimization scheduling system for the logistics center effectively improves overall operational efficiency. The data acquisition module ensures the comprehensiveness and real-time nature of multi-source data, while the data preprocessing module guarantees the accuracy and consistency of the data. The inventory management module significantly improves warehousing and picking efficiency and reduces warehousing costs and manual operation time by dynamically adjusting inventory layout and optimizing picking routes.

[0050] (3) The route planning module and vehicle scheduling and loading module optimize transportation routes and resource allocation through real-time traffic analysis, dynamic route adjustment, and intelligent scheduling. The vehicle loading rate and utilization rate are greatly improved, and delays and empty loads during transportation are significantly reduced. The real-time monitoring module further ensures the visualization and transparency of the logistics process and provides the ability to respond quickly to abnormal situations, thereby reducing transportation risks and customer complaint rates.

[0051] (4) Compared with traditional logistics scheduling technology, this system achieves real-time scheduling and dynamic adjustment through data-driven intelligent optimization, which greatly improves the flexibility and response speed of the logistics system. Overall, the system not only improves operational efficiency and service level, but also reduces operating costs and enhances the competitiveness and adaptability of enterprises in a rapidly changing market environment. Attached Figure Description

[0052] Figure 1 This is a block diagram of a data analysis-based intelligent optimization scheduling system for logistics centers according to the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] This invention provides a data analysis-based intelligent optimization scheduling system for logistics centers. Please refer to [link / reference]. Figure 1 It includes a data acquisition module, a data preprocessing module, an inventory management module, a route planning module, a vehicle dispatching and loading module, and a real-time monitoring module;

[0056] The data acquisition module is used to collect and process key data in the operation of the logistics center. Through sensors, IoT devices and database interfaces, it collects internal and external data including order information, inventory status, vehicle location, real-time traffic conditions and weather conditions. The data is then transmitted to the data preprocessing module through the comprehensive collection of real-time and historical data.

[0057] The data preprocessing module is used to clean, format and denoise the logistics data acquired by the data acquisition module, filter outliers, correct data errors, fill in data gaps, and convert the processed data into a standardized format.

[0058] The inventory management module is used to manage and optimize the inventory status of the logistics center. Based on real-time inventory data and order demand information, it dynamically adjusts the inventory layout and optimizes the storage and retrieval paths of goods.

[0059] The route planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand, and vehicle location. It adopts a dynamic route planning algorithm to update and adjust the delivery route in real time, avoid adverse conditions such as traffic congestion and severe weather, and calculate and obtain the comprehensive optimization index SOF.

[0060] The vehicle scheduling and loading module is used to intelligently allocate vehicles based on order requirements, cargo volume, vehicle capacity and real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically scheduling vehicles.

[0061] The real-time monitoring module is used to monitor vehicle location, transportation status, environmental changes and order processing progress in real time through IoT technology and data analysis tools. By comparing the comprehensive optimization index SOF with a preset threshold, it evaluates the scheduling optimization level of the logistics system in real time, provides anomaly warning and feedback mechanism, and automatically triggers scheduling adjustments when delays, route changes or other abnormal situations occur.

[0062] In this embodiment, key data from the operation of the logistics center is collected and processed, and transmitted to the data preprocessing module. The logistics data acquired by the data acquisition module is preprocessed to manage and optimize the inventory status of the logistics center. Based on real-time inventory data and order demand information, the inventory layout is dynamically adjusted to optimize the storage and retrieval routes of goods. Based on real-time traffic data, order demand, and vehicle location, the Comprehensive Optimization Index (SOF) is calculated and obtained. Vehicles are intelligently allocated according to order demand, cargo volume, vehicle capacity, and their real-time location to optimize loading sequence and loading rate. By dynamically scheduling vehicles, vehicle utilization is maximized, and empty and overloaded phenomena are reduced. Through IoT technology and data analysis tools, vehicle location, transportation status, environmental changes, and order processing progress are monitored in real time. By comparing the SOF with a preset threshold, the scheduling optimization level of the logistics system is evaluated in real time, and an anomaly warning and feedback mechanism is provided. When delays, route changes, or other abnormal situations occur, scheduling adjustments are automatically triggered.

[0063] Example 2

[0064] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the data acquisition module includes internal and external data acquisition units;

[0065] The internal and external data acquisition units are used to collect order information, inventory status, vehicle location, real-time traffic conditions and weather conditions through sensors, IoT devices and database interfaces, and obtain total number of orders N, total number of vehicles M, total number of deliveries L, order demand time ODT, transportation time TT, vehicle capacity utilization rate VCU, delivery load DL, traffic density TD and weather delay WD, forming internal and external datasets.

[0066] The data preprocessing module includes a data preprocessing unit;

[0067] The data preprocessing unit is used to clean the collected raw data, remove invalid, duplicate or abnormal data, convert multi-source data into a unified standard format so that it can be effectively used by subsequent modules and algorithms, correct erroneous values ​​in the data and fill in missing data.

[0068] In this embodiment, through the cooperation of the data acquisition module and the data preprocessing module, the system can construct a high-quality internal and external dataset, providing a reliable data foundation for subsequent analysis and optimization. The internal and external data acquisition units in the data acquisition module, through sensors, IoT devices, and database interfaces, achieve real-time acquisition of multi-dimensional data such as order information, inventory status, vehicle location, real-time traffic, and weather, thus ensuring the comprehensiveness and real-time nature of the data. The data preprocessing module further cleans, formats, and completes the collected raw data, eliminating invalid and duplicate data to ensure data consistency and accuracy, and effectively solving the problem of inconsistent formats caused by diverse data sources. Through this high-quality data foundation, the system achieves intelligent support for logistics scheduling, providing precise basis for subsequent route planning, inventory optimization, and scheduling decisions, thereby significantly improving the scheduling accuracy and response speed of the logistics system.

[0069] Example 3

[0070] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the inventory management module includes an inventory layout optimization unit and a pickup route optimization unit;

[0071] The inventory layout optimization unit is used to receive real-time inventory data updates, track the inventory quantity and storage location of each product, dynamically adjust the storage layout of products based on inventory status and order demand, and calculate the optimal product storage location through intelligent algorithms to maximize picking efficiency and warehouse space utilization.

[0072] The picking route optimization unit is used to calculate the optimal picking route based on real-time order demand and inventory location. This unit obtains the path efficiency coefficient (PEC) through optimization algorithms, generates the shortest path solution, reduces the walking distance and time of picking personnel, improves picking efficiency, and ensures that orders can be processed quickly and accurately.

[0073] The path efficiency coefficient PEC is calculated using the following formula:

[0074] ;

[0075] In the formula, TT represents the transportation time, WD represents the weather delay time, ODT represents the order demand time, and N represents the total number of orders.

[0076] The route planning module includes a real-time traffic analysis unit, a route calculation unit, and a dynamic route adjustment unit.

[0077] The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, and obtain data on road congestion, traffic accidents, and weather impacts through real-time interaction with the traffic information system, providing traffic condition analysis results for route planning.

[0078] The route calculation unit is used to calculate the optimal delivery route based on order requirements, vehicle location and traffic data. After calculation using a route planning algorithm, the following are obtained: Overall Optimization Index (SOF), Vehicle Utilization Coefficient (VUC), and Transport Load Coefficient (TLC).

[0079] The dynamic route adjustment unit is used to monitor and adjust the delivery route in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the route plan to avoid traffic congestion and severe weather.

[0080] The vehicle utilization factor (VUC) is calculated using the following formula;

[0081] ;

[0082] In the formula, VCU represents vehicle capacity utilization rate, and M represents the total number of vehicles.

[0083] The transport load factor (TLC) is calculated using the following formula;

[0084] ;

[0085] In the formula, DL represents the cargo volume of each delivery, and L represents the total number of deliveries.

[0086] The comprehensive optimization index SOF is calculated using the following formula;

[0087] ;

[0088] In the formula, PEC represents the route efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transport load coefficient. These represent the proportional coefficients of the path efficiency coefficient (PEC), vehicle utilization coefficient (VUC), and transport load coefficient (TLC), respectively, used to balance the weights of different optimization objectives.

[0089] In this embodiment, the system's inventory management module and route planning module achieve efficient inventory layout and dynamic route planning through intelligent algorithms, significantly improving the operational efficiency of the logistics center. The inventory layout optimization unit dynamically adjusts the product storage layout by updating inventory data in real time, placing high-frequency demand items in easily accessible locations, improving warehouse space utilization and maximizing picking efficiency. The picking route optimization unit optimizes picking routes using the route efficiency coefficient (PEC), reducing the walking distance and time for picking personnel and ensuring orders are processed quickly and accurately. The route planning module further utilizes a real-time traffic analysis unit, a route calculation unit, and a dynamic route adjustment unit to achieve dynamic delivery route optimization based on external traffic conditions and order demand. The route calculation unit calculates the overall optimization index (SOF), vehicle utilization coefficient (VUC), and transport load coefficient (TLC) to achieve the optimal delivery plan, maximizing vehicle capacity utilization and reducing transportation costs. The dynamic route adjustment unit updates route planning in real time, avoiding traffic congestion and severe weather to ensure on-time delivery. Overall, through efficient scheduling of routes and resources, the system significantly reduces operating costs, improves picking and delivery efficiency, and effectively enhances the flexibility and accuracy of overall logistics scheduling.

[0090] Example 4

[0091] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the vehicle scheduling and loading module includes a vehicle allocation unit, a loading optimization unit, and a dynamic scheduling unit;

[0092] The vehicle allocation unit is used to intelligently allocate the most suitable vehicle for delivery tasks based on current order demand, vehicle status and cargo capacity. Through scheduling algorithms, it achieves optimal allocation of vehicle resources and ensures that a suitable vehicle is available for each delivery task.

[0093] The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle. By calculating the optimal loading scheme for the vehicle, it ensures that the loading capacity of each vehicle is maximized, thereby reducing the number of transport trips and transportation costs.

[0094] The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time. Based on the delivery task progress, vehicle location and real-time traffic conditions, it dynamically updates the vehicle scheduling arrangement to ensure that vehicles can respond to delivery needs in a timely manner and improve the system's flexibility and response speed.

[0095] In this embodiment, the system's vehicle dispatching and loading module significantly improves the efficiency and resource utilization of logistics and distribution through intelligent vehicle allocation, loading optimization, and dynamic scheduling. The vehicle allocation unit accurately matches the most suitable vehicle based on current order demand, vehicle status, and cargo capacity, thereby achieving optimal allocation of vehicle resources and ensuring efficient execution of each delivery task, reducing empty load rates. The loading optimization unit maximizes vehicle loading rates and space utilization through intelligent loading schemes, effectively reducing the number of transport trips and significantly lowering logistics operating costs. The dynamic scheduling unit monitors vehicle status and delivery progress in real time, adjusting scheduling arrangements according to road and environmental changes, ensuring vehicles can flexibly respond to emergencies and new tasks. Overall, this module greatly improves the flexibility and responsiveness of the logistics system, enabling the system to maintain efficient operation in dynamic environments, effectively reducing transportation costs and shortening delivery times, providing strong support for intelligent scheduling in logistics centers.

[0096] Example 5

[0097] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the real-time monitoring module includes a vehicle location monitoring unit, a transportation status monitoring unit, and an anomaly early warning unit;

[0098] The vehicle location monitoring unit is used to track the geographical location of delivery vehicles in real time. By connecting with the vehicle's GPS system in real time, it monitors the vehicle's driving route and current location, ensuring the visualization and transparency of the logistics operation process.

[0099] The transportation status monitoring unit is used to monitor the execution of transportation tasks in real time, including cargo status, transportation progress and environmental changes, and can quickly identify abnormal situations in the transportation process, including delays or cargo damage.

[0100] The abnormality early warning unit is used to issue early warnings and trigger corresponding adjustment measures in a timely manner when delays, route changes or other abnormal situations occur during transportation. By comparing the comprehensive optimization index SOF with the first scheduling threshold H and the second preset scheduling threshold V, the scheduling optimization level of the logistics system is evaluated in real time, and corresponding level strategies are formulated.

[0101] When the comprehensive optimization index SOF ≤ the first scheduling threshold H, the first level evaluation is obtained, the normal level is maintained, the existing scheduling strategy is maintained, and data review and trend analysis are carried out regularly.

[0102] When the first scheduling threshold H < comprehensive optimization index SOF ≤ second preset scheduling threshold V, the second level evaluation is obtained. The routes for high traffic density road sections or delayed orders are replanned to reduce transportation time. The focus is on checking the route planning efficiency and vehicle loading, identifying specific bottlenecks, enhancing real-time monitoring, and dynamically adjusting routes in high traffic density and severe weather areas.

[0103] When the overall optimization index SOF exceeds the second preset scheduling threshold V, a third-level evaluation is obtained. A comprehensive scheduling review is immediately initiated, all affected routes are quickly adjusted to avoid traffic congestion and adverse weather, priority is given to ensuring the timely delivery of urgent orders, logistics resources, including personnel, vehicles and equipment, are dynamically allocated, the most important logistics tasks are handled centrally, abnormal situations are reported in real time, and early warnings are issued to the operations team and relevant personnel to ensure a rapid response from all parties.

[0104] In this embodiment, the system's real-time monitoring module achieves visualization and dynamic management of the logistics operation process through comprehensive monitoring of vehicle location, transportation status, and anomaly warnings, effectively improving the security, transparency, and emergency response capabilities of the logistics system. The vehicle location monitoring unit, through real-time connection with the vehicle's GPS system, ensures accurate tracking of the vehicle's route and current location, making the entire logistics process clear at a glance and facilitating comprehensive control of routes and progress by the operations team. The transportation status monitoring unit further enhances detailed monitoring of transportation tasks. By tracking cargo status, transportation progress, and environmental changes in real time, it can quickly detect and identify anomalies such as delays or cargo damage, ensuring the stability of logistics tasks. The anomaly warning unit compares the Comprehensive Optimization Index (SOF) with set scheduling thresholds in real time, dynamically evaluating the scheduling optimization level of the logistics system under different thresholds, categorizing it into three levels: normal, requiring optimization, and requiring urgent adjustment. This helps the system quickly identify potential problems and trigger timely countermeasures. When an anomaly occurs in the system, an alert is immediately issued to the operations team, and scheduling strategies are automatically adjusted, such as replanning routes, allocating additional resources, or prioritizing urgent orders. Through multi-level monitoring and early warning mechanisms, this module significantly improves the agility and risk response capabilities of the logistics system, enabling the logistics center to maintain efficient and stable operations in a dynamic environment, effectively reducing risks such as delays and losses, and improving customer satisfaction and operational reliability.

[0105] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data-driven intelligent optimization scheduling system for logistics centers, characterized in that: It includes a data acquisition module, a data preprocessing module, an inventory management module, a route planning module, a vehicle dispatching and loading module, and a real-time monitoring module; The data acquisition module is used to collect and process key data in the operation of the logistics center. Through sensors, IoT devices and database interfaces, it collects internal and external data including order information, inventory status, vehicle location, real-time traffic conditions and weather conditions. The data is then transmitted to the data preprocessing module through the comprehensive collection of real-time and historical data. The data preprocessing module is used to clean, format and denoise the logistics data acquired by the data acquisition module, filter outliers, correct data errors, fill in data gaps, and convert the processed data into a standardized format. The inventory management module is used to manage and optimize the inventory status of the logistics center. Based on real-time inventory data and order demand information, it dynamically adjusts the inventory layout and optimizes the storage and retrieval paths of goods. The route planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand, and vehicle location. It adopts a dynamic route planning algorithm to update and adjust the delivery route in real time, avoid adverse conditions such as traffic congestion and severe weather, and calculate and obtain the comprehensive optimization index SOF. The vehicle scheduling and loading module is used to intelligently allocate vehicles based on order requirements, cargo volume, vehicle capacity and real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically scheduling vehicles. The real-time monitoring module is used to monitor vehicle location, transportation status, environmental changes and order processing progress in real time through IoT technology and data analysis tools. By comparing the comprehensive optimization index SOF with a preset threshold, it evaluates the scheduling optimization level of the logistics system in real time, provides anomaly warning and feedback mechanism, and automatically triggers scheduling adjustments when delays, route changes or other abnormal situations occur. The inventory management module includes an inventory layout optimization unit and a pickup route optimization unit; The inventory layout optimization unit is used to receive real-time inventory data updates, track the inventory quantity and storage location of each product, dynamically adjust the storage layout of products based on inventory status and order demand, and calculate the optimal product storage location through intelligent algorithms to maximize picking efficiency and warehouse space utilization. The picking route optimization unit is used to calculate the optimal picking route based on real-time order demand and inventory location. This unit obtains the path efficiency coefficient (PEC) through optimization algorithms, generates the shortest path solution, reduces the walking distance and time of picking personnel, improves picking efficiency, and ensures that orders can be processed quickly and accurately. The path efficiency coefficient PEC is calculated using the following formula: ； In the formula, TT represents the shipping time, WD represents the weather delay time, ODT represents the order demand time, and N represents the total number of orders. i WD represents the shipping time for the i-th order. i ODT represents the delay time of the i-th order due to weather. i This represents the required time for the i-th order; The route planning module includes a real-time traffic analysis unit, a route calculation unit, and a dynamic route adjustment unit. The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, and obtain data on road congestion, traffic accidents, and weather impacts through real-time interaction with the traffic information system, providing traffic condition analysis results for route planning. The route calculation unit is used to calculate the optimal delivery route based on order requirements, vehicle location and traffic data. After calculation using a route planning algorithm, the following are obtained: Overall Optimization Index (SOF), Vehicle Utilization Coefficient (VUC), and Transport Load Coefficient (TLC). The dynamic route adjustment unit is used to monitor and adjust the delivery route in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the route plan to avoid traffic congestion and severe weather.

2. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 1, characterized in that: The data acquisition module includes internal and external data acquisition units; The internal and external data acquisition units are used to collect order information, inventory status, vehicle location, real-time traffic conditions and weather conditions through sensors, IoT devices and database interfaces, and obtain total number of orders N, total number of vehicles M, total number of deliveries L, order demand time ODT, transportation time TT, vehicle capacity utilization rate VCU, delivery load DL, traffic density TD and weather delay WD, forming internal and external datasets.

3. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 1, characterized in that: The data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean the collected raw data, remove invalid, duplicate or abnormal data, convert multi-source data into a unified standard format so that it can be effectively used by subsequent modules and algorithms, correct erroneous values ​​in the data and fill in missing data.

4. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 2, characterized in that: The vehicle utilization factor (VUC) is calculated using the following formula; ； In the formula, VCU represents vehicle capacity utilization rate, M represents the total number of vehicles, and VCU j This represents the vehicle capacity utilization rate of the j-th vehicle.

5. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 2, characterized in that: The transport load factor (TLC) is calculated using the following formula; ； In the formula, DL represents the cargo volume of each delivery, L represents the total number of deliveries, and DL k This represents the cargo volume of the k-th delivery.

6. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 2, characterized in that: The comprehensive optimization index SOF is calculated using the following formula; ； In the formula, PEC represents the route efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transport load coefficient. These represent the proportional coefficients of the path efficiency coefficient (PEC), vehicle utilization coefficient (VUC), and transport load coefficient (TLC), respectively, used to balance the weights of different optimization objectives.

7. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 1, characterized in that: The vehicle scheduling and loading module includes a vehicle allocation unit, a loading optimization unit, and a dynamic scheduling unit; The vehicle allocation unit is used to intelligently allocate the most suitable vehicle for delivery tasks based on current order demand, vehicle status and cargo capacity. Through scheduling algorithms, it achieves optimal allocation of vehicle resources and ensures that a suitable vehicle is available for each delivery task. The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle. By calculating the optimal loading scheme for the vehicle, it ensures that the loading capacity of each vehicle is maximized, thereby reducing the number of transport trips and transportation costs. The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time. Based on the delivery task progress, vehicle location and real-time traffic conditions, it dynamically updates the vehicle scheduling arrangement to ensure that vehicles can respond to delivery needs in a timely manner and improve the system's flexibility and response speed.

8. The intelligent optimization scheduling system for logistics centers based on data analysis according to claim 1, characterized in that: The real-time monitoring module includes a vehicle location monitoring unit, a transportation status monitoring unit, and an anomaly warning unit. The vehicle location monitoring unit is used to track the geographical location of delivery vehicles in real time. By connecting with the vehicle's GPS system in real time, it monitors the vehicle's driving route and current location, ensuring the visualization and transparency of the logistics operation process. The transportation status monitoring unit is used to monitor the execution of transportation tasks in real time, including cargo status, transportation progress and environmental changes, and can quickly identify abnormal situations in the transportation process, including delays or cargo damage. The abnormality early warning unit is used to issue early warnings and trigger corresponding adjustment measures in a timely manner when delays, route changes or other abnormal situations occur during transportation. By comparing the comprehensive optimization index SOF with the first scheduling threshold H and the second preset scheduling threshold V, the scheduling optimization level of the logistics system is evaluated in real time, and corresponding level strategies are formulated. When the comprehensive optimization index SOF ≤ the first scheduling threshold H, the first level evaluation is obtained, the normal level is maintained, the existing scheduling strategy is maintained, and data review and trend analysis are carried out regularly. When the first scheduling threshold H < comprehensive optimization index SOF ≤ second preset scheduling threshold V, the second level evaluation is obtained. The routes for high traffic density road sections or delayed orders are replanned to reduce transportation time. The focus is on checking the route planning efficiency and vehicle loading, identifying specific bottlenecks, enhancing real-time monitoring, and dynamically adjusting routes in high traffic density and severe weather areas. When the overall optimization index SOF exceeds the second preset scheduling threshold V, a third-level evaluation is obtained. A comprehensive scheduling review is immediately initiated, all affected routes are quickly adjusted to avoid traffic congestion and adverse weather, priority is given to ensuring the timely delivery of urgent orders, logistics resources, including personnel, vehicles and equipment, are dynamically allocated, the most important logistics tasks are handled centrally, abnormal situations are reported in real time, and early warnings are issued to the operations team and relevant personnel to ensure a rapid response from all parties.

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