Warehouse logistics management method based on logistics big data

Abstract

The invention relates to the technical field of big data application, and discloses a warehouse logistics management method based on logistics big data, which comprises the following specific steps: step 1, firstly collecting data from various logistics links, collecting warehouse data, transportation data and customer data, then cleaning and integrating data from different sources, and collecting data from different logistics links; a unified big data warehouse is established, the operation conditions of storage facilities, equipment and inventory are monitored in real time through the logistics big data warehouse, and abnormal conditions are found and processed in time. Through an automatic and intelligent management mode, storage is divided into different resource areas, waste of human resources is reduced according to different resource areas, and through analysis and optimization of sorting paths, cargo storage positions and arrangement of workstations, working efficiency is improved, repeated work and unnecessary movement are reduced, storage operation efficiency is greatly improved, and the labor intensity of workers is reduced. The labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of big data application technology, and in particular to a warehousing logistics management method based on logistics big data. Background Art

[0002] Big data technology is not only the result of the high development of social economy, but also the inevitable development of information technology. It has opened up a major era of transformation, changing the way of life and understanding the world, and is a great change in life, work and thinking. Warehousing logistics management based on logistics big data is to use big data technology and analysis methods to optimize and manage the logistics operations within the warehouse. It covers the data collection, analysis and application of the entire process from the goods entering the warehouse to the final delivery.

[0003] The current warehousing and logistics management methods require a large amount of human resources to perform various tasks, which makes the labor cost relatively high. In addition, since it is difficult to accurately predict changes in market demand and supply, common problems in warehousing and logistics are over-inventory or out-of-stock, and the efficiency of warehousing inventory management is low. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a warehousing logistics management method based on logistics big data, which solves the problems of high labor costs and low management efficiency caused by excessive inventory or out-of-stock.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a warehousing logistics management method based on logistics big data, comprising the following specific steps:

[0006] Step 1: First, collect data from various logistics links, including warehousing data, transportation data, and customer data. Then, clean and integrate data from different sources to establish a unified big data warehouse. Through the logistics big data warehouse, monitor the operation of storage facilities, equipment, and inventory in real time, and discover and handle abnormal situations in a timely manner.

[0007] Step 2: Based on the big data warehouse established in step 1, use data mining technology to analyze the big data warehouse, extract valuable information, and use logistics big data to analyze storage demand and trends, inventory turnover, seasonal demand information, demand forecasting, optimal layout, transportation optimization, and anomaly detection to further identify key trends and patterns;

[0008] Step 3: Based on the data analysis results, build an intelligent warehouse management system, analyze the layout of warehouse equipment and warehouse areas through logistics big data, propose the optimal warehouse facility layout and inventory storage method, improve inventory turnover rate and warehouse capacity utilization rate, conduct real-time monitoring according to the warehouse management system, timely monitor data changes and abnormal situations in the warehouse logistics link, adjust operations and decisions in real time according to the monitoring results, ensure the smooth operation of the warehouse process, and enable the warehouse management system to automate inventory management, intelligent logistics scheduling and personalized customer service;

[0009] Step 4: The warehouse management system divides the warehouse into different resource areas. According to the different resource areas, AGV or shuttle vehicles transport goods. The real-time monitoring system and warehouse management system collect the real-time location and cargo location data of AGV or shuttle vehicles. Combined with historical data, the cargo demand and traffic flow information in different time periods are analyzed to plan routes, ensure the selection of the shortest and best paths, and avoid congestion and idling.

[0010] Step 5: Then, based on the logistics big data, analyze customer orders and delivery needs, optimize cargo delivery routes and vehicle scheduling, reduce delivery costs, and improve delivery efficiency. At the same time, based on the constantly updated big data warehouse, feed back the data analysis results to the warehouse logistics management system, continuously optimize the system and processes, and establish an abnormal warning system based on the big data warehouse to monitor real-time data and generate warning information, and provide real-time feedback on abnormal situations.

[0011] Preferably, the data collection in step 1 includes the following specific steps:

[0012] Warehouse data collection: used to collect inventory data, inbound and outbound records, shelf information and equipment status;

[0013] Transportation data collection: used to collect delivery routes, vehicle information and transportation time;

[0014] Customer data collection: used to collect order information, customer needs and customer evaluations.

[0015] Preferably, the establishment of the big data warehouse includes the following specific steps:

[0016] (1) Determine the data sources that need to be collected, including internal system data and external data sources, clean the data, ensure data quality and integrity, integrate data from various data sources, and establish a unified data model;

[0017] (2) Establish a data model dimension model and fact model suitable for data analysis, and design a star model or snowflake model for the data warehouse model;

[0018] (3) Establish an operation and maintenance and real-time monitoring mechanism for the big data warehouse, and regularly perform performance optimization, backup and recovery, and security updates.

[0019] Preferably, the demand and trend are used to understand the demand trend of different products and services, the inventory turnover rate is used to predict the inventory turnover rate, the seasonal demand information is used for seasonal demand analysis, scheduling production and procurement plans, the demand forecast is used to predict inventory demand in the future, the optimal layout is used to optimize the warehouse layout, the transport optimization is used to optimize the transport routes, and the anomaly detection is used to timely identify inventory anomalies and equipment failures.

[0020] Preferably, the warehouse management system adopts the ABC classification method, classifies commodities into three categories, A, B, and C, according to commodity value and turnover rate, and adopts different management strategies for commodities of different categories.

[0021] Preferably, the warehouse management system allocates fixed regional cargo locations according to the size, weight, and nature of the goods, plans the shortest path for the AGV or shuttle vehicle, monitors the warehouse based on real-time monitoring, and plans the optimal path for the AGV or shuttle vehicle.

[0022] Preferably, the real-time monitoring system uses barcode or RFID technology. A barcode or RFID tag will be generated when goods are put into storage. The barcode or RFID tag will be scanned when the goods are put into storage, stored, transferred and shipped out of the storage, and the scanning records will be updated and uploaded in real time, and can be identified and tracked based on the information.

[0023] Preferably, the abnormal warning system includes the following specific parts:

[0024] Data collection and integration: The system first needs to collect and integrate various logistics-related data, including cargo flow information, warehouse internal operation data, supply chain information, and transportation data. Data sources include barcode or RFID technology;

[0025] Data analysis and model building: Through data analysis and modeling, the system can identify the baseline pattern of normal logistics operations and detect abnormal situations that do not conform to it;

[0026] Abnormal detection and early warning mechanism: Based on the established models and algorithms, the system can monitor abnormal situations in logistics operations in real time, such as delayed goods, abnormal inventory changes, and abnormal transportation routes;

[0027] Visualization and decision support: The system usually provides an intuitive visualization interface to display the real-time status of logistics operations and historical data analysis results;

[0028] Continuous optimization and feedback mechanism: The anomaly warning system is not just a one-time implementation, but also has the ability to be continuously optimized. Through feedback analysis of historical data, the system can continuously adjust and improve the anomaly detection model.

[0029] The present invention provides a warehousing logistics management method based on logistics big data. It has the following beneficial effects:

[0030] 1. The present invention adopts an automated and intelligent management method, and divides the warehouse into different resource areas. According to the different resource areas, it reduces the waste of human resources, improves work efficiency by analyzing and optimizing the picking path, cargo storage location and workstation arrangement, reduces duplication of work and unnecessary movement, greatly improves warehouse operation efficiency and reduces labor costs.

[0031] 2. The present invention utilizes data mining technology to analyze big data warehouses, extract valuable information, and use logistics big data to analyze storage demand and trends, inventory turnover rate and seasonal demand information for accurate forecasting and planning. According to product characteristics, sales trends and market demand, appropriate inventory holdings are formulated, and the ABC classification method is used to help distinguish which products require closer management, thereby avoiding excessive inventory or out-of-stock phenomena and improving warehouse inventory management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a warehousing logistics management method based on logistics big data of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0034] Example:

[0035] Please see attached Figure 1 The embodiment of the present invention provides a warehousing logistics management method based on logistics big data, comprising the following specific steps:

[0036] Step 1: First, collect data from various logistics links, including warehousing data, transportation data, and customer data. Then, clean and integrate data from different sources to establish a unified big data warehouse. Through the logistics big data warehouse, monitor the operation of storage facilities, equipment, and inventory in real time, and discover and handle abnormal situations in a timely manner.

[0037] Step 2: Based on the big data warehouse established in step 1, use data mining technology to analyze the big data warehouse, extract valuable information, and use logistics big data to analyze storage demand and trends, inventory turnover, seasonal demand information, demand forecasting, optimal layout, transportation optimization, and anomaly detection to further identify key trends and patterns;

[0038] Step 3: Based on the data analysis results, build an intelligent warehouse management system, analyze the layout of warehouse equipment and warehouse areas through logistics big data, propose the optimal warehouse facility layout and inventory storage method, improve inventory turnover rate and warehouse capacity utilization rate, conduct real-time monitoring according to the warehouse management system, timely monitor data changes and abnormal situations in the warehouse logistics link, adjust operations and decisions in real time according to the monitoring results, ensure the smooth operation of the warehouse process, and enable the warehouse management system to automate inventory management, intelligent logistics scheduling and personalized customer service;

[0039] Step 4: The warehouse management system divides the warehouse into different resource areas. According to the different resource areas, AGV or shuttle vehicles transport goods. The real-time monitoring system and warehouse management system collect the real-time location and cargo location data of AGV or shuttle vehicles. Combined with historical data, the cargo demand and traffic flow information in different time periods are analyzed to plan routes, ensure the selection of the shortest and best paths, and avoid congestion and idling.

[0040] Step 5: Then, based on the logistics big data, analyze customer orders and delivery needs, optimize cargo delivery routes and vehicle scheduling, reduce delivery costs, and improve delivery efficiency. At the same time, based on the constantly updated big data warehouse, feed back the data analysis results to the warehouse logistics management system, continuously optimize the system and processes, and establish an abnormal warning system based on the big data warehouse to monitor real-time data and generate warning information, and provide real-time feedback on abnormal situations.

[0041] Data collection in step 1 includes the following specific steps:

[0042] Warehouse data collection: used to collect inventory data, inbound and outbound records, shelf information and equipment status;

[0043] Transportation data collection: used to collect delivery routes, vehicle information and transportation time;

[0044] Customer data collection: used to collect order information, customer needs and customer evaluations.

[0045] Preferably, the establishment of a big data warehouse includes the following specific steps:

[0046] (1) Determine the data sources that need to be collected, including internal system data and external data sources, clean the data, ensure data quality and integrity, integrate data from various data sources, and establish a unified data model;

[0047] (2) Establish a data model dimension model and fact model suitable for data analysis, and design a star model or snowflake model for the data warehouse model;

[0048] (3) Establish an operation and maintenance and real-time monitoring mechanism for the big data warehouse, and regularly perform performance optimization, backup and recovery, and security updates.

[0049] Demand and trends are used to understand demand trends for different products and services, inventory turnover is used to predict inventory turnover, seasonal demand information is used to analyze seasonal demand, arrange production and procurement plans, demand forecasting is used to predict inventory demand in the future, optimal layout is used to optimize warehouse layout, transportation optimization is used to optimize transportation routes, and anomaly detection is used to timely identify inventory anomalies and equipment failures.

[0050] The warehouse management system adopts the ABC classification method, which divides the goods into three categories: A, B, and C according to their value and turnover rate, and adopts different management strategies for different categories of goods.

[0051] The warehouse management system allocates fixed area cargo locations according to the size, weight, and nature of the goods, plans the shortest path for the AGV or shuttle vehicle, and monitors the warehouse based on real-time monitoring to plan the optimal path for the AGV or shuttle vehicle.

[0052] The real-time monitoring system uses barcode or RFID technology. A barcode or RFID tag will be generated when goods enter the warehouse. The barcode or RFID tag will be scanned when the items are entered, stored, transferred, and shipped out of the warehouse, and the scanning records will be updated and uploaded in real time, so that they can be identified and tracked based on the information.

[0053] The abnormal warning system includes the following specific parts:

[0054] Data collection and integration: The system first needs to collect and integrate various logistics-related data, including cargo flow information, warehouse internal operation data, supply chain information, and transportation data. Data sources include barcode or RFID technology;

[0055] Data analysis and model building: Through data analysis and modeling, the system can identify the baseline pattern of normal logistics operations and detect abnormal situations that do not conform to it;

[0056] Abnormal detection and early warning mechanism: Based on the established models and algorithms, the system can monitor abnormal situations in logistics operations in real time, such as delayed goods, abnormal inventory changes, and abnormal transportation routes;

[0057] Visualization and decision support: The system usually provides an intuitive visualization interface to display the real-time status of logistics operations and historical data analysis results;

[0058] Continuous optimization and feedback mechanism: The anomaly warning system is not just a one-time implementation, but also has the ability to be continuously optimized. Through feedback analysis of historical data, the system can continuously adjust and improve the anomaly detection model.

[0059] 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. A warehousing logistics management method based on logistics big data, characterized by: The specific steps include: Step 1: First, collect data from various logistics links, including warehousing data, transportation data, and customer data. Then, clean and integrate data from different sources to establish a unified big data warehouse. Through the logistics big data warehouse, monitor the operation of storage facilities, equipment, and inventory in real time, and discover and handle abnormal situations in a timely manner. Step 2: Based on the big data warehouse established in step 1, use data mining technology to analyze the big data warehouse, extract valuable information, and use logistics big data to analyze storage demand and trends, inventory turnover, seasonal demand information, demand forecasting, optimal layout, transportation optimization, and anomaly detection to further identify key trends and patterns; Step 3: Based on the data analysis results, build an intelligent warehouse management system, analyze the layout of warehouse equipment and warehouse areas through logistics big data, propose the optimal warehouse facility layout and inventory storage method, improve inventory turnover rate and warehouse capacity utilization rate, conduct real-time monitoring according to the warehouse management system, timely monitor data changes and abnormal situations in the warehouse logistics link, adjust operations and decisions in real time according to the monitoring results, ensure the smooth operation of the warehouse process, and enable the warehouse management system to automate inventory management, intelligent logistics scheduling and personalized customer service; Step 4: The warehouse management system divides the warehouse into different resource areas. According to the different resource areas, AGV or shuttle vehicles transport goods. The real-time monitoring system and warehouse management system collect the real-time location and cargo location data of AGV or shuttle vehicles. Combined with historical data, the cargo demand and traffic flow information in different time periods are analyzed to plan routes, ensure the selection of the shortest and best paths, and avoid congestion and idling. Step 5: Then, based on the logistics big data, analyze customer orders and delivery needs, optimize cargo delivery routes and vehicle scheduling, reduce delivery costs, and improve delivery efficiency. At the same time, based on the constantly updated big data warehouse, feed back the data analysis results to the warehouse logistics management system, continuously optimize the system and processes, and establish an abnormal warning system based on the big data warehouse to monitor real-time data and generate warning information, and provide real-time feedback on abnormal situations.

2. A warehousing logistics management method based on logistics big data according to claim 1, characterized in that: The data collection in step 1 includes the following specific steps: Warehouse data collection: used to collect inventory data, inbound and outbound records, shelf information and equipment status; Transportation data collection: used to collect delivery routes, vehicle information and transportation time; Customer data collection: used to collect order information, customer needs and customer evaluations.

3. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The establishment of the big data warehouse includes the following specific steps: (1) Determine the data sources that need to be collected, including internal system data and external data sources, clean the data, ensure data quality and integrity, integrate data from various data sources, and establish a unified data model; (2) Establish a data model dimension model and fact model suitable for data analysis, and design a star model or snowflake model for the data warehouse model; (3) Establish an operation and maintenance and real-time monitoring mechanism for the big data warehouse, and regularly perform performance optimization, backup and recovery, and security updates.

4. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The demand and trend are used to understand the demand trend of different products and services, the inventory turnover rate is used to predict the inventory turnover rate, the seasonal demand information is used for seasonal demand analysis, scheduling production and procurement plans, the demand forecast is used to predict inventory demand in the future, the optimal layout is used to optimize the warehouse layout, the transportation optimization is used to optimize the transportation route, and the anomaly detection is used to timely identify inventory anomalies and equipment failures.

5. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The warehouse management system adopts the ABC classification method, which divides the commodities into three categories: A, B, and C according to the commodity value and turnover rate, and adopts different management strategies for different categories of commodities.

6. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The warehouse management system allocates fixed regional cargo locations according to the size, weight, and nature of the goods, plans the shortest path for the AGV or shuttle vehicle, monitors the warehouse based on real-time monitoring, and plans the optimal path for the AGV or shuttle vehicle.

7. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The real-time monitoring system uses barcode or RFID technology. A barcode or RFID tag will be generated when goods enter the warehouse. The barcode or RFID tag will be scanned when the goods enter the warehouse, are stored, transferred, and leave the warehouse, and the scanning records will be updated and uploaded in real time, and can be identified and tracked based on the information.

8. The warehousing logistics management method based on logistics big data according to claim 1 is characterized by: The abnormal warning system includes the following specific parts: Data collection and integration: The system first needs to collect and integrate various logistics-related data, including cargo flow information, warehouse internal operation data, supply chain information, and transportation data. Data sources include barcode or RFID technology; Data analysis and model building: Through data analysis and modeling, the system can identify the baseline pattern of normal logistics operations and detect abnormal situations that do not conform to it; Abnormal detection and early warning mechanism: Based on the established models and algorithms, the system can monitor abnormal situations in logistics operations in real time, such as delayed goods, abnormal inventory changes, and abnormal transportation routes; Visualization and decision support: The system usually provides an intuitive visualization interface to display the real-time status of logistics operations and historical data analysis results; Continuous optimization and feedback mechanism: The anomaly warning system is not just a one-time implementation, but also has the ability to be continuously optimized. Through feedback analysis of historical data, the system can continuously adjust and improve the anomaly detection model.

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