Logistics center intelligent optimization scheduling system based on data analysis

Abstract

The invention discloses a logistics center intelligent optimization scheduling system based on data analysis, and relates to the technical field of intelligent optimization scheduling, during operation of the system, key data in logistics center operation is collected and processed, after preprocessing, the inventory state of a logistics center is managed and optimized, and based on real-time inventory data and order demand information, the logistics center intelligent optimization scheduling system based on data analysis is obtained. Based on real-time traffic data, order demands and vehicle positions, calculation is carried out to obtain a comprehensive optimization index SOF, according to the order demands, the cargo capacity, the vehicle capacity and the real-time positions, vehicles are intelligently distributed, the loading sequence and the loading rate are optimized, and through the Internet of Things technology and a data analysis tool, the real-time traffic data and the real-time traffic data are analyzed. And the vehicle position, the transportation state, the environment change and the order processing progress are monitored in real time, the scheduling optimization level of the logistics system is evaluated in real time by comparing the comprehensive optimization index SOF with a preset threshold value, and an abnormal early warning and feedback mechanism is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent optimization scheduling, and in particular to an intelligent optimization scheduling system for a logistics center based on data analysis. Background Art

[0002] Logistics centers are the core hubs in modern supply chains and distribution networks, undertaking a large number of order processing, inventory management and distribution tasks. However, with the complexity of logistics needs and the sharp increase in order volume, the operational efficiency of traditional logistics centers faces severe challenges. Route planning and transportation optimization have become key links in improving the efficiency of logistics centers. Using data analysis technology, order, inventory, traffic and environmental data can be obtained and processed in real time, and more efficient and accurate logistics operations can be achieved through intelligent scheduling and optimization.

[0003] Traditional methods often rely on fixed rules and experience, and lack effective use of real-time data, resulting in inflexible transportation route planning, low vehicle loading rates, and insufficient ability to respond to emergencies. In addition, in the face of adverse conditions such as high traffic density or bad weather, the scheduling optimization capabilities of existing systems are limited, which can easily lead to delivery delays and waste of resources. Therefore, an intelligent optimization scheduling system based on data analysis is needed to make up for these shortcomings and improve the overall efficiency and resilience of the logistics system through real-time monitoring and dynamic adjustment.

[0004] The shortcomings of traditional logistics systems mainly stem from the lack of agile response capabilities to dynamically changing environments, such as traffic conditions, order fluctuations, and weather changes. When the logistics system cannot adjust routes and scheduling strategies in real time, it will lead to delivery delays, low vehicle utilization, poor inventory management, and other problems. As a result, not only will the operational efficiency and customer satisfaction of the logistics center be affected, but it may also cause resource waste and increase operating costs. In more serious cases, when the system is in an unoptimized state for a long time, the company may face the risk of reduced market competitiveness and reduced service levels. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent optimization and scheduling system for a logistics center based on data analysis, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent optimization and scheduling system for a logistics center based on data analysis, including a data acquisition module, a data preprocessing module, an inventory management module, a path planning module, a vehicle scheduling 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, and transmits them 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, dynamically adjust the inventory layout based on real-time inventory data and order demand information, and optimize the storage and pickup paths of goods; The path planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand and vehicle location, adopt a dynamic path planning algorithm, update and adjust the delivery route in real time, avoid traffic congestion and adverse weather conditions, and calculate and obtain: comprehensive optimization index SOF; The vehicle dispatching and loading module is used to intelligently allocate vehicles according to order requirements, cargo volume, vehicle capacity and its real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically dispatching vehicles; The real-time monitoring module is used to monitor the vehicle location, transportation status, environmental changes and order processing progress in real time through Internet of Things technology and data analysis tools, and to evaluate the scheduling optimization level of the logistics system in real time by comparing the comprehensive optimization index SOF with the preset threshold, and to provide an abnormal warning and feedback mechanism, and automatically trigger scheduling adjustments when delays, route changes or other abnormal situations occur.

[0007] Preferably, the data acquisition module includes internal and external data acquisition units; The internal and external data collection unit is 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 the total number of orders N, the total number of vehicles M, the total number of deliveries L, the order demand time ODT, the transportation time TT, the vehicle capacity utilization VCU, the delivery load DL, the traffic density TD and the weather delay WD to form internal and external data sets.

[0008] Preferably, the data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean up the collected raw data, eliminate 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.

[0009] Preferably, the inventory management module includes an inventory layout optimization unit and a pickup path optimization unit; The inventory layout optimization unit is used to track the inventory quantity and storage location of each commodity by receiving inventory data updates in real time, dynamically adjust the storage layout of commodities based on inventory status and order requirements, and calculate the optimal commodity storage location through intelligent algorithms to maximize picking efficiency and storage space utilization; The picking path optimization unit is used to calculate the optimal picking path according to the real-time order demand and inventory location. The unit obtains the path efficiency coefficient PEC through the optimization algorithm, generates the shortest path plan, reduces the walking distance and time of the picking personnel, improves the picking efficiency, and ensures that the order can be processed quickly and accurately; The path efficiency coefficient PEC is calculated by the following formula: ; 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.

[0010] Preferably, the path planning module includes a real-time traffic analysis unit, a path calculation unit and a dynamic path adjustment unit; The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, obtain road congestion, traffic accidents and weather impact data through real-time interaction with the traffic information system, and provide analysis results of traffic conditions for route planning; The path calculation unit is used to calculate the optimal delivery path according to the order demand, vehicle location and traffic data, and obtains the following after calculation using the path planning algorithm: the comprehensive optimization index SOF, the vehicle utilization coefficient VUC and the transport load coefficient TLC; The dynamic path adjustment unit is used to monitor and adjust the delivery path in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the path planning to avoid traffic jams and bad weather.

[0011] Preferably, the vehicle utilization coefficient VUC is calculated and obtained by the following formula: ; Where VCU represents the vehicle capacity utilization rate, and M represents the total number of vehicles.

[0012] Preferably, the transport load factor TLC is calculated by the following formula: ; Where DL represents the cargo volume of each delivery, and L represents the total number of deliveries.

[0013] Preferably, the comprehensive optimization index SOF is calculated by the following formula: ; In the formula, PEC represents the path efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transportation load coefficient. They represent the proportional coefficients of the path efficiency coefficient PEC, the vehicle utilization coefficient VUC and the transport load coefficient TLC, respectively, and are used to balance the weights of different optimization objectives.

[0014] Preferably, the vehicle dispatching and loading module includes a vehicle allocation unit, a loading optimization unit and a dynamic dispatching unit; The vehicle allocation unit is used to intelligently allocate the most suitable vehicle for delivery tasks according to current order requirements, vehicle status and cargo capacity, and achieve optimal configuration of vehicle resources through scheduling algorithms to ensure that each delivery task is performed by a suitable vehicle; The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle, and ensure that the loading capacity of each vehicle is maximized by calculating the optimal loading plan of the vehicle, thereby reducing the number of transportation times and transportation costs; The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time, dynamically update the vehicle scheduling arrangement according to the delivery task progress, vehicle location and real-time traffic conditions, ensure that the vehicle can respond to delivery needs in a timely manner, and improve the flexibility and response speed of the system.

[0015] Preferably, the real-time monitoring module includes a vehicle position monitoring unit, a transportation status monitoring unit and an abnormality early warning unit; The vehicle location monitoring unit is used to track the geographic location of the delivery vehicle in real time, and monitor the vehicle's driving route and current location through real-time connection with the vehicle's GPS system to ensure visualization and transparency of the logistics operation process; The transport status monitoring unit is used to monitor the execution of the transport task in real time, including the cargo status, transport progress and environmental changes, and can quickly identify abnormal situations during the transport process, including delays or cargo damage; The abnormal warning unit is used to issue a warning in time and trigger corresponding adjustment measures 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 a corresponding level strategy is formulated; When the comprehensive optimization index SOF ≤ the first scheduling threshold H, obtain the first level evaluation, normal level, maintain the existing scheduling strategy, and conduct data review and trend analysis regularly; When the first dispatch threshold H < comprehensive optimization index SOF ≤ the second preset dispatch threshold V, obtain the second level evaluation, re-plan the route for high traffic density sections or delayed orders, reduce transportation time, focus on checking route planning efficiency and vehicle loading, identify specific bottlenecks, enhance real-time monitoring, and dynamically adjust routes in high traffic density and bad weather areas; When the comprehensive optimization index SOF> the second preset scheduling threshold V, the third level evaluation is obtained, and a comprehensive scheduling review is immediately initiated to quickly adjust all affected routes to avoid traffic congestion and adverse weather, prioritize the on-time delivery of emergency orders, dynamically allocate logistics resources, including personnel, vehicles and equipment, concentrate on handling the most important logistics tasks, report abnormal situations in real time, and issue early warnings to the operation team and relevant personnel to ensure a quick response from all parties.

[0016] The present invention provides a logistics center intelligent optimization scheduling system based on data analysis, which has the following beneficial effects: (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 pickup paths 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 and optimizes the loading sequence and loading rate. Through the Internet of Things technology and data analysis tools, it monitors the 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 an abnormal warning and feedback mechanism.

[0017] (2) By introducing six modules, namely data collection, data preprocessing, inventory management, route planning, vehicle scheduling and loading, and real-time monitoring, the intelligent optimization scheduling system of the logistics center has effectively improved the overall operational efficiency. The data collection module ensures the comprehensiveness and real-time nature of multi-source data, while the data preprocessing module ensures the accuracy and consistency of the data. The inventory management module significantly improves the efficiency of warehousing and picking by dynamically adjusting the inventory layout and optimizing the pickup route, thereby reducing warehousing costs and manual operation time.

[0018] (3) The route planning module and vehicle scheduling and loading module optimize the transportation route and resource allocation through real-time traffic analysis, dynamic route adjustment and intelligent scheduling. The loading rate and utilization rate of vehicles are greatly improved, and the delays and empty loads in the transportation process are significantly reduced. The real-time monitoring module further ensures the visualization and transparency of the logistics process and provides the ability to quickly respond to abnormal situations, thereby reducing transportation risks and customer complaints.

[0019] (4) Compared with traditional logistics scheduling technology, this system realizes 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a block diagram of an intelligent optimization and scheduling system for a logistics center based on data analysis in the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1 The present invention provides a logistics center intelligent optimization scheduling system based on data analysis, please refer to Figure 1 , including data acquisition module, data preprocessing module, inventory management module, path planning module, vehicle scheduling and loading module and 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, and transmits them 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, dynamically adjust the inventory layout based on real-time inventory data and order demand information, and optimize the storage and pickup paths of goods; The path planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand and vehicle location, adopt a dynamic path planning algorithm, update and adjust the delivery route in real time, avoid traffic congestion and adverse weather conditions, and calculate and obtain: comprehensive optimization index SOF; The vehicle dispatching and loading module is used to intelligently allocate vehicles according to order requirements, cargo volume, vehicle capacity and its real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically dispatching vehicles; The real-time monitoring module is used to monitor the vehicle location, transportation status, environmental changes and order processing progress in real time through Internet of Things technology and data analysis tools, and to evaluate the scheduling optimization level of the logistics system in real time by comparing the comprehensive optimization index SOF with the preset threshold, and to provide an abnormal warning and feedback mechanism, and automatically trigger scheduling adjustments when delays, route changes or other abnormal situations occur.

[0023] In this embodiment, key data in the operation of the logistics center is collected and processed, and transmitted to the data preprocessing module, the logistics data obtained by the data acquisition module is preprocessed, the inventory status of the logistics center is managed and optimized, and the inventory layout is dynamically adjusted based on real-time inventory data and order demand information, and the storage and pickup paths of the goods are optimized. Based on real-time traffic data, order demand and vehicle location, the following is calculated: the comprehensive optimization index SOF is obtained, and vehicles are intelligently allocated according to order demand, cargo volume, vehicle capacity and its real-time location, and the loading sequence and loading rate are optimized. Through dynamic vehicle scheduling, the vehicle utilization rate is maximized and the empty and overloaded phenomena are reduced. Through the Internet of Things technology and data analysis tools, the vehicle location, transportation status, environmental changes and order processing progress are monitored in real time. By comparing the comprehensive optimization index SOF with the preset threshold, the scheduling optimization level of the logistics system is evaluated in real time, and an abnormal warning and feedback mechanism is provided. When delays, route changes or other abnormal situations occur, scheduling adjustments are automatically triggered.

[0024] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes internal and external data acquisition units; The internal and external data collection unit is 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 the total number of orders N, the total number of vehicles M, the total number of deliveries L, the order demand time ODT, the transportation time TT, the vehicle capacity utilization VCU, the delivery load DL, the traffic density TD and the weather delay WD to form internal and external data sets.

[0025] The data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean up the collected raw data, eliminate 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.

[0026] In this embodiment, through the cooperation of the data acquisition module and the data preprocessing module, the system can build a high-quality internal and external data set to provide a reliable data foundation for subsequent analysis and optimization. The internal and external data acquisition units in the data acquisition module realize the real-time collection of multi-dimensional data such as order information, inventory status, vehicle location, real-time traffic and weather through sensors, Internet of Things devices and database interfaces, thereby ensuring the comprehensiveness and real-time nature of the data. The data preprocessing module further cleans, formats and completes the collected raw data, eliminates invalid and duplicate data, ensures the consistency and accuracy of the data, and effectively solves the problem of inconsistent formats caused by the diversity of data sources. Through this high-quality data foundation, the system realizes intelligent support for logistics scheduling, provides an accurate basis for subsequent path planning, inventory optimization and scheduling decisions, thereby significantly improving the scheduling accuracy and response speed of the logistics system.

[0027] Example 3 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the inventory management module includes an inventory layout optimization unit and a pickup path optimization unit; The inventory layout optimization unit is used to track the inventory quantity and storage location of each commodity by receiving inventory data updates in real time, dynamically adjust the storage layout of commodities based on inventory status and order requirements, and calculate the optimal commodity storage location through intelligent algorithms to maximize picking efficiency and storage space utilization; The picking path optimization unit is used to calculate the optimal picking path according to the real-time order demand and inventory location. The unit obtains the path efficiency coefficient PEC through the optimization algorithm, generates the shortest path plan, reduces the walking distance and time of the picking personnel, improves the picking efficiency, and ensures that the order can be processed quickly and accurately; The path efficiency coefficient PEC is calculated by the following formula: ; 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.

[0028] The path planning module includes a real-time traffic analysis unit, a path calculation unit and a dynamic path adjustment unit; The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, obtain road congestion, traffic accidents and weather impact data through real-time interaction with the traffic information system, and provide analysis results of traffic conditions for route planning; The path calculation unit is used to calculate the optimal delivery path according to the order demand, vehicle location and traffic data, and obtains the following after calculation using the path planning algorithm: the comprehensive optimization index SOF, the vehicle utilization coefficient VUC and the transport load coefficient TLC; The dynamic path adjustment unit is used to monitor and adjust the delivery path in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the path planning to avoid traffic jams and bad weather.

[0029] The vehicle utilization coefficient VUC is calculated by the following formula: ; Where VCU represents the vehicle capacity utilization rate, and M represents the total number of vehicles.

[0030] The transport load factor TLC is calculated by the following formula: ; Where DL represents the cargo volume of each delivery, and L represents the total number of deliveries.

[0031] The comprehensive optimization index SOF is calculated by the following formula: ; In the formula, PEC represents the path efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transportation load coefficient. They represent the proportional coefficients of the path efficiency coefficient PEC, the vehicle utilization coefficient VUC and the transport load coefficient TLC, respectively, and are used to balance the weights of different optimization objectives.

[0032] In this embodiment, the inventory management module and path planning module of the system realize efficient inventory layout and dynamic path planning through intelligent algorithms, which significantly improves the operational efficiency of the logistics center. The inventory layout optimization unit can dynamically adjust the commodity storage layout by updating inventory data in real time, so that high-frequency demand commodities are placed in a location that is easy to pick up, thereby improving the utilization rate of storage space and maximizing picking efficiency. The pickup path optimization unit optimizes the picking path through the path efficiency coefficient (PEC), reduces the walking distance and time of the picking personnel, and ensures that the order can be processed quickly and accurately. The path planning module further uses the real-time traffic analysis unit, the path calculation unit and the dynamic path adjustment unit to realize dynamic distribution path optimization based on external traffic conditions and order requirements. The path calculation unit calculates the comprehensive optimization index (SOF), the vehicle utilization coefficient (VUC) and the transport load coefficient (TLC) to achieve the optimal distribution plan, maximize the vehicle capacity utilization rate, and reduce transportation costs. The dynamic path adjustment unit updates the path planning in real time to avoid traffic jams and bad weather and ensure the on-time delivery rate. Overall, through efficient scheduling of routes and resources, the system significantly reduces operating costs, improves picking and delivery efficiency, and effectively improves the flexibility and accuracy of overall logistics scheduling.

[0033] Example 4 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: 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 according to current order requirements, vehicle status and cargo capacity, and achieve optimal configuration of vehicle resources through scheduling algorithms to ensure that each delivery task is performed by a suitable vehicle; The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle, and ensure that the loading capacity of each vehicle is maximized by calculating the optimal loading plan of the vehicle, thereby reducing the number of transportation times and transportation costs; The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time, dynamically update the vehicle scheduling arrangement according to the delivery task progress, vehicle location and real-time traffic conditions, ensure that the vehicle can respond to delivery needs in a timely manner, and improve the flexibility and response speed of the system.

[0034] In this embodiment, the vehicle scheduling and loading module of the system significantly improves the efficiency and resource utilization of logistics distribution through intelligent vehicle allocation, loading optimization and dynamic scheduling. The vehicle allocation unit accurately matches the most suitable vehicle according to the current order demand, vehicle status and cargo capacity, thereby achieving the optimal configuration of vehicle resources, ensuring that each distribution task can be efficiently executed and reducing the empty load rate. The loading optimization unit maximizes the vehicle's loading rate and space utilization through intelligent loading solutions, effectively reduces the number of transportation times, and significantly reduces logistics operating costs. The dynamic scheduling unit monitors the vehicle status and distribution progress in real time, adjusts the scheduling arrangements according to road and environmental changes, and ensures that the vehicle can flexibly respond to emergencies and new tasks. Overall, this module greatly improves the flexibility and response speed of the logistics system, enabling the system to always maintain an efficient operating state in a dynamic environment, and effectively reduces transportation costs and shortens delivery time, providing strong support for the intelligent scheduling of the logistics center.

[0035] Example 5 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the real-time monitoring module includes a vehicle position monitoring unit, a transportation status monitoring unit and an abnormality warning unit; The vehicle location monitoring unit is used to track the geographic location of the delivery vehicle in real time, and monitor the vehicle's driving route and current location through real-time connection with the vehicle's GPS system to ensure visualization and transparency of the logistics operation process; The transport status monitoring unit is used to monitor the execution of the transport task in real time, including the cargo status, transport progress and environmental changes, and can quickly identify abnormal situations during the transport process, including delays or cargo damage; The abnormal warning unit is used to issue a warning in time and trigger corresponding adjustment measures 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 a corresponding level strategy is formulated; When the comprehensive optimization index SOF ≤ the first scheduling threshold H, obtain the first level evaluation, normal level, maintain the existing scheduling strategy, and conduct data review and trend analysis regularly; When the first dispatch threshold H < comprehensive optimization index SOF ≤ the second preset dispatch threshold V, obtain the second level evaluation, re-plan the route for high traffic density sections or delayed orders, reduce transportation time, focus on checking route planning efficiency and vehicle loading, identify specific bottlenecks, enhance real-time monitoring, and dynamically adjust routes in high traffic density and bad weather areas; When the comprehensive optimization index SOF> the second preset scheduling threshold V, the third level evaluation is obtained, and a comprehensive scheduling review is immediately initiated to quickly adjust all affected routes to avoid traffic congestion and adverse weather, prioritize the on-time delivery of emergency orders, dynamically allocate logistics resources, including personnel, vehicles and equipment, concentrate on handling the most important logistics tasks, report abnormal situations in real time, and issue early warnings to the operation team and relevant personnel to ensure a quick response from all parties.

[0036] In this embodiment, the real-time monitoring module of the system realizes the visualization and dynamic management of the logistics operation process through comprehensive monitoring of the vehicle location, transportation status and abnormal warning, and effectively improves the safety, transparency and emergency response capability of the logistics system. The vehicle location monitoring unit ensures accurate tracking of the vehicle's driving route and current location through real-time connection with the vehicle's GPS system, making the entire logistics process clear at a glance, and facilitating the operation team to fully control the route and progress. The transportation status monitoring unit further strengthens the detailed monitoring of transportation tasks. By real-time tracking of cargo status, transportation progress and environmental changes, it can quickly discover and identify abnormal situations such as delays or cargo damage, ensuring the stability of logistics tasks. The abnormal warning unit compares the comprehensive optimization index (SOF) with the set scheduling threshold in real time, dynamically evaluates the scheduling optimization level of the logistics system under different thresholds, and divides it into three levels: normal, optimization required, and emergency adjustment, helping the system to quickly identify potential problems and trigger response measures in time. When the system is abnormal, it immediately issues an early warning to the operation team and automatically adjusts the scheduling strategy, such as replanning the route, adding resources, or giving priority to emergency orders. Through multi-level monitoring and early warning mechanisms, this module has greatly improved 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.

[0037] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent optimization and scheduling system for logistics centers based on data analysis, characterized by: It includes data acquisition module, data preprocessing module, inventory management module, route planning module, vehicle dispatching and loading module and 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, and transmits them 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, dynamically adjust the inventory layout based on real-time inventory data and order demand information, and optimize the storage and pickup paths of goods; The path planning module is used to calculate the optimal delivery route based on real-time traffic data, order demand and vehicle location, adopt a dynamic path planning algorithm, update and adjust the delivery route in real time, avoid traffic congestion and adverse weather conditions, and calculate and obtain: comprehensive optimization index SOF; The vehicle dispatching and loading module is used to intelligently allocate vehicles according to order requirements, cargo volume, vehicle capacity and its real-time location, optimize loading sequence and loading rate, and ensure maximum vehicle utilization and reduce empty and overloaded phenomena by dynamically dispatching vehicles; The real-time monitoring module is used to monitor the vehicle location, transportation status, environmental changes and order processing progress in real time through Internet of Things technology and data analysis tools, and to evaluate the scheduling optimization level of the logistics system in real time by comparing the comprehensive optimization index SOF with the preset threshold, and to provide an abnormal warning and feedback mechanism, and automatically trigger scheduling adjustments when delays, route changes or other abnormal situations occur.

2. According to claim 1, a logistics center intelligent optimization scheduling system based on data analysis is characterized in that: The data acquisition module includes internal and external data acquisition units; The internal and external data collection unit is 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 the total number of orders N, the total number of vehicles M, the total number of deliveries L, the order demand time ODT, the transportation time TT, the vehicle capacity utilization VCU, the delivery load DL, the traffic density TD and the weather delay WD to form internal and external data sets.

3. According to claim 2, a logistics center intelligent optimization and scheduling system based on data analysis is characterized in that: The data preprocessing module includes a data preprocessing unit; The data preprocessing unit is used to clean up the collected raw data, eliminate 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 and scheduling system for logistics centers based on data analysis according to claim 3 is characterized by: The inventory management module includes an inventory layout optimization unit and a pickup path optimization unit; The inventory layout optimization unit is used to track the inventory quantity and storage location of each commodity by receiving inventory data updates in real time, dynamically adjust the storage layout of commodities based on inventory status and order requirements, and calculate the optimal commodity storage location through intelligent algorithms to maximize picking efficiency and storage space utilization; The picking path optimization unit is used to calculate the optimal picking path according to the real-time order demand and inventory location. The unit obtains the path efficiency coefficient PEC through the optimization algorithm, generates the shortest path plan, reduces the walking distance and time of the picking personnel, improves the picking efficiency, and ensures that the order can be processed quickly and accurately; The path efficiency coefficient PEC is calculated by the following formula: ； 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.

5. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 4 is characterized by: The path planning module includes a real-time traffic analysis unit, a path calculation unit and a dynamic path adjustment unit; The real-time traffic analysis unit is used to receive and process external traffic data, analyze the current traffic conditions, obtain road congestion, traffic accidents and weather impact data through real-time interaction with the traffic information system, and provide analysis results of traffic conditions for route planning; The path calculation unit is used to calculate the optimal delivery path according to the order demand, vehicle location and traffic data, and obtains the following after calculation using the path planning algorithm: the comprehensive optimization index SOF, the vehicle utilization coefficient VUC and the transport load coefficient TLC; The dynamic path adjustment unit is used to monitor and adjust the delivery path in real time. During the delivery process, it receives information on traffic and environmental changes in real time and dynamically updates the path planning to avoid traffic jams and bad weather.

6. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 5 is characterized by: The vehicle utilization coefficient VUC is calculated by the following formula: ； Where VCU represents the vehicle capacity utilization rate, and M represents the total number of vehicles.

7. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 6 is characterized by: The transport load factor TLC is calculated by the following formula: ； Where DL represents the cargo volume of each delivery, and L represents the total number of deliveries.

8. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 7 is characterized by: The comprehensive optimization index SOF is calculated by the following formula: ； In the formula, PEC represents the path efficiency coefficient, VUC represents the vehicle utilization coefficient, and TLC represents the transportation load coefficient. They represent the proportional coefficients of the path efficiency coefficient PEC, the vehicle utilization coefficient VUC and the transport load coefficient TLC, respectively, and are used to balance the weights of different optimization objectives.

9. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 1 is characterized by: The vehicle dispatching and loading module includes a vehicle allocation unit, a loading optimization unit and a dynamic dispatching unit; The vehicle allocation unit is used to intelligently allocate the most suitable vehicle for delivery tasks according to current order requirements, vehicle status and cargo capacity, and achieve optimal configuration of vehicle resources through scheduling algorithms to ensure that each delivery task is performed by a suitable vehicle; The loading optimization unit is used to optimize the loading sequence and loading rate of each vehicle, and ensure that the loading capacity of each vehicle is maximized by calculating the optimal loading plan of the vehicle, thereby reducing the number of transportation times and transportation costs; The dynamic scheduling unit is used to adjust the vehicle scheduling plan in real time, dynamically update the vehicle scheduling arrangement according to the delivery task progress, vehicle location and real-time traffic conditions, ensure that the vehicle can respond to delivery needs in a timely manner, and improve the flexibility and response speed of the system.

10. The intelligent optimization and scheduling system for logistics centers based on data analysis according to claim 1 is characterized in that: The real-time monitoring module includes a vehicle position monitoring unit, a transportation status monitoring unit and an abnormality early warning unit; The vehicle location monitoring unit is used to track the geographic location of the delivery vehicle in real time, and monitor the vehicle's driving route and current location through real-time connection with the vehicle's GPS system to ensure visualization and transparency of the logistics operation process; The transport status monitoring unit is used to monitor the execution of the transport task in real time, including the cargo status, transport progress and environmental changes, and can quickly identify abnormal situations during the transport process, including delays or cargo damage; The abnormal warning unit is used to issue a warning in time and trigger corresponding adjustment measures 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 a corresponding level strategy is formulated; When the comprehensive optimization index SOF ≤ the first scheduling threshold H, obtain the first level evaluation, normal level, maintain the existing scheduling strategy, and conduct data review and trend analysis regularly; When the first dispatch threshold H < comprehensive optimization index SOF ≤ the second preset dispatch threshold V, obtain the second level evaluation, re-plan the route for high traffic density sections or delayed orders, reduce transportation time, focus on checking route planning efficiency and vehicle loading, identify specific bottlenecks, enhance real-time monitoring, and dynamically adjust routes in high traffic density and bad weather areas; When the comprehensive optimization index SOF> the second preset scheduling threshold V, the third level evaluation is obtained, and a comprehensive scheduling review is immediately initiated to quickly adjust all affected routes to avoid traffic congestion and adverse weather, prioritize the on-time delivery of emergency orders, dynamically allocate logistics resources, including personnel, vehicles and equipment, concentrate on handling the most important logistics tasks, report abnormal situations in real time, and issue early warnings to the operation team and relevant personnel to ensure a quick response from all parties.

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