Storehouse management method and system based on big data

Abstract

The invention discloses a warehouse management method and system based on big data, and relates to the technical field of warehouse management, and the method comprises the following steps: 1, data collection and integration; 2, data storage and management; 3, data analysis and intelligent decision making; 4, real-time monitoring and process automation are carried out; and 5, performing performance evaluation and continuous improvement. According to the invention, intelligent decision making and efficiency improvement are realized, through real-time data analysis and prediction analysis, the system can provide real-time inventory state and future demand prediction, and an automatic process is realized, so that decision making quality and warehouse operation efficiency are improved, and human errors are reduced. The intelligent decision support system can provide scientific inventory management suggestions for managers according to historical data and market trends, ensure that the inventory level is matched with market demands, and avoid inventory overstock or shortage.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to a warehouse management method and system based on big data. Background Art

[0002] The warehouse of modern enterprises has become the logistics center of the enterprise. Its role is not only to keep the goods, but more to be the material circulation center. The focus of warehouse management is no longer just on the safety of material storage, but more on how to use modern technologies, such as information technology and automation technology to improve the speed and efficiency of warehouse operations. Modern warehousing plays a vital role in the logistics supply chain. If the correct purchase, inventory control and delivery cannot be guaranteed, it will lead to an increase in management costs and it will be difficult to guarantee the service quality, thus affecting the competitiveness of the enterprise. Therefore, a commodity warehouse management system is needed to solve the problems of concentrated accumulation of goods, high pressure of logistics transportation, failure to ship out goods in time, and long transportation time during the peak period of logistics.

[0003] The existing technology has the following problems: In existing systems, data updates often require manual operations, which may lead to inaccurate inventory information and decision makers cannot obtain real-time inventory data. Manual recording and retrieval of data takes a lot of time and is prone to errors, which reduces overall work efficiency. Existing systems are often difficult to adapt to the expansion of business scale. When the warehouse scale or inventory volume increases, the system may not be able to respond effectively. Existing paper records or simple spreadsheets are prone to damage or leakage, and it is difficult to implement data encryption and access control. Many existing systems only provide basic inventory management functions and lack advanced functions such as automated inventory replenishment and predictive analysis. Existing systems are often not highly integrated with other business systems, resulting in data silos and duplication of work. Existing systems may lack powerful data analysis tools and cannot generate complex reports or conduct in-depth data analysis. The interface design may be outdated, the operation is complicated, and it is not easy for users to use. Some existing systems may require specific hardware equipment, such as scanners, which increases the cost of maintenance and upgrades. With the increasing popularity of mobile devices, existing systems may not be able to provide mobile support, limiting the operational flexibility of staff inside and outside the warehouse. Summary of the invention

[0004] The present invention provides a warehouse management method and system based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a warehouse management method based on big data, the warehouse management method based on big data comprising the following steps: Step 1: Data collection and integration; Step 2: Data storage and management; Step 3: Data analysis and intelligent decision-making; Step 4: Real-time monitoring and process automation; Step 5: Performance evaluation and continuous improvement.

[0006] A further improvement of the technical solution of the present invention is that the step 1 further includes the following steps: A1: Demand analysis: First, we need to clarify the specific business needs of warehouse management, analyze the types of data to be collected and the sources of data, and ensure the pertinence and effectiveness of data collection; A2: Data source identification: Next, identify and determine the source of data collection, which may include sensors, barcodes, RFID, manual input, etc., to ensure the comprehensiveness and accuracy of the data; A3: Equipment deployment: Install and configure data collection equipment, such as sensors, barcode scanners, etc., to ensure the accuracy and efficiency of data collection and lay a solid foundation for subsequent data processing; A4: Data collection: Collect data in real time or regularly through automated equipment or manual input to ensure the timeliness and integrity of the data; A5: Data cleaning: pre-process the collected data, including deduplication, error correction, and missing data completion, to improve data quality and provide a reliable basis for subsequent analysis; A6: Data integration: Format data from different sources uniformly and convert them into a standard format for subsequent processing to ensure data consistency and comparability; A7: Data synchronization: Ensure that the collected data is synchronized with the data in the warehouse management system to maintain data consistency and avoid data redundancy and inconsistency issues.

[0007] A further improvement of the technical solution of the present invention is that the step 2 further includes the following steps: B1: Database design: Design the database model, including table structure, fields, indexes and relationships, to ensure the efficiency and scalability of the database; B2: Database deployment: Select a suitable database system, install and configure it, and ensure the stability and security of the database; B3: Data import: import the integrated data into the database, which may involve batch import or real-time import to ensure the timeliness and accuracy of the data; B4: Data index: Create indexes for commonly used query fields to improve query efficiency and ensure fast response of data retrieval; B5: Data security: Implement security measures such as data encryption, access control, data backup, etc. to ensure data security and integrity; B6: Data maintenance: Perform data maintenance regularly, such as data compression, cleaning, and optimization, to ensure efficient operation of the database.

[0008] A further improvement of the technical solution of the present invention is that the step three further includes the following steps: C1: Data analysis model construction: Build data analysis models according to business needs, such as inventory forecasting, demand analysis, etc., to support intelligent decision-making; C2: Data mining: Use data mining technology to extract valuable information and patterns from large amounts of data to provide data support for decision-making; C3: Decision support: Use analysis results to provide decision support to managers, such as inventory adjustment, purchasing decisions, etc., to improve the scientificity and accuracy of decision-making; C4: Intelligent algorithm application: Apply machine learning algorithms for intelligent prediction and optimization to improve the intelligence level of warehouse management; C5: Result visualization: Display the analysis results in the form of charts, dashboards, etc. to facilitate understanding and use, and improve the intuitiveness and convenience of decision-making.

[0009] A further improvement of the technical solution of the present invention is that the step 4 further includes the following steps: D1: Monitoring platform construction: Establish a real-time monitoring platform to display key indicators such as warehouse status and inventory status, and realize real-time monitoring of the warehouse; D2: Event triggering: Set thresholds and rules to trigger events when specific conditions are met, such as insufficient inventory, expired items, etc., to ensure timely response; D3: Process Automation: Automated warehouse management processes, such as automatic replenishment and automatic inventory, improve the efficiency and accuracy of warehouse management; D4: Exception handling: Establish an exception handling mechanism to promptly respond to and handle abnormal situations in warehouse management to ensure the stable operation of warehouse management; D5: Logging: Record all operations and events for easy tracking and auditing, ensuring transparency and traceability of warehouse management.

[0010] A further improvement of the technical solution of the present invention is that the step five further includes the following steps: E1: Performance indicator setting: Determine key indicators for evaluating warehouse management performance, such as inventory accuracy, response time, etc., to provide a basis for performance evaluation; E2: Performance evaluation: Regularly evaluate the performance of warehouse management and compare it with preset indicators to ensure continuous improvement of warehouse management; E3: Feedback collection: Collect feedback from users and managers to understand system problems and improvement needs, and provide direction for continuous improvement; E4: Process optimization: Based on the evaluation results and feedback, optimize and improve the warehouse management process to improve the efficiency and accuracy of warehouse management; E5: System upgrade: With the development of business and technological progress, the system is regularly upgraded and expanded to ensure the advancement and adaptability of the warehouse management system.

[0011] In the second aspect, the present invention also provides a warehouse management system based on big data, including a data acquisition and automation layer, a data transmission and integration layer, a data storage and management layer, a data processing and analysis layer, an application and service layer, a user interface and security monitoring layer, the data acquisition and automation layer includes sensors and equipment, an automated control system, and data acquisition software; the data transmission and integration layer includes a communication protocol, a data transmission mechanism, and a data integration platform; the data storage and management layer includes a database system, a data warehouse, and data backup and recovery; the data processing and analysis layer includes data processing tools, data mining and analysis algorithms, and a big data platform; the application and service layer includes business logic processing, middleware, and an API interface; the user interface and security monitoring layer includes a user interface, authority management, logging and auditing, and a security mechanism.

[0012] A further improvement of the technical solution of the present invention is that: the data acquisition and automation layer is mainly responsible for collecting data from entities such as items and shelves in the warehouse, and transmitting the data to the data transmission and integration layer. The function of the data transmission and integration layer is to transmit the collected data from the data acquisition and automation layer to the data storage and management layer, and is responsible for integrating data from different sources to ensure the uniformity and integrity of the data. The data storage and management layer is responsible for storing data transmitted from the data transmission and integration layer, and providing data support for the data processing and analysis layer. The data processing and analysis layer processes and analyzes the data in the data storage and management layer, extracts useful information, and provides support for decision-making. The application and service layer uses the information provided by the data processing and analysis layer to perform warehouse management tasks and support the automation of business processes. The user interface and security monitoring layer is the interface for users to interact with the warehouse management system, and is also responsible for monitoring the security of the system to ensure the confidentiality, integrity and availability of the data.

[0013] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides a warehouse management method and system based on big data, which realizes intelligent decision-making and efficiency improvement. Through real-time data analysis and predictive analysis, the system can provide instant inventory status and future demand forecasts, realize automated processes, thereby improving decision-making quality and warehouse operation efficiency, and reducing human errors. This intelligent decision support system can provide managers with scientific inventory management suggestions based on historical data and market trends, ensure that inventory levels match market demand, and avoid inventory backlogs or shortages.

[0014] 2. The present invention provides a warehouse management method and system based on big data to achieve cost optimization and integrated management. The system reduces inventory, transportation and labor costs by optimizing inventory management and operation processes, and is closely integrated with other systems of the enterprise to achieve seamless data flow and improve overall operational efficiency. This integrated management method can not only reduce the phenomenon of data islands, but also achieve optimal resource allocation through cross-departmental collaboration, thereby reducing overall operating costs.

[0015] 3. The present invention provides a warehouse management method and system based on big data to achieve user experience and security expansion. The warehouse management system based on big data provides an intuitive and easy-to-use user interface and mobile support to enhance user experience. At the same time, it has powerful data security measures and cloud-based scalability to ensure the security of sensitive data and adapt to business growth. This system not only focuses on the convenience of user operation, but also ensures the security of data during transmission and storage through multi-level security protection measures to prevent data leakage and illegal access.

[0016] 4. The present invention provides a warehouse management method and system based on big data to achieve data analysis and continuous improvement. The system has powerful reporting and analysis capabilities, can generate complex reports and provide multi-dimensional data analysis, help management to deeply understand the business situation, and achieve self-optimization and performance improvement by continuously collecting user feedback and business data. This continuous improvement mechanism can ensure that the warehouse management system always keeps pace with the times, constantly adapts to market changes and business needs, thereby improving overall operational efficiency and management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the warehouse management method based on big data of the present invention; Figure 2 This is a system framework diagram of a warehouse management system based on big data of the present invention; Figure 3 It is a system flow chart of the warehouse management system based on big data of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with embodiments: Example 1 First, as Figure 1 As shown, the present invention provides a warehouse management method based on big data, and the warehouse management method based on big data includes the following steps: Step 1: Data collection and integration; Step 2: Data storage and management; Step 3: Data analysis and intelligent decision-making; Step 4: Real-time monitoring and process automation; Step 5: Performance evaluation and continuous improvement.

[0019] Step one also includes the following steps: A1: Demand analysis: First, we need to clarify the specific business needs of warehouse management, analyze the types of data to be collected and the sources of data, and ensure the pertinence and effectiveness of data collection; A2: Data source identification: Next, identify and determine the source of data collection, which may include sensors, barcodes, RFID, manual input, etc., to ensure the comprehensiveness and accuracy of the data; A3: Equipment deployment: Install and configure data collection equipment, such as sensors, barcode scanners, etc., to ensure the accuracy and efficiency of data collection and lay a solid foundation for subsequent data processing; A4: Data collection: Collect data in real time or regularly through automated equipment or manual input to ensure the timeliness and integrity of the data; A5: Data cleaning: pre-process the collected data, including deduplication, error correction, and missing data completion, to improve data quality and provide a reliable basis for subsequent analysis; A6: Data integration: Format data from different sources uniformly and convert them into a standard format for subsequent processing to ensure data consistency and comparability; A7: Data synchronization: Ensure that the collected data is synchronized with the data in the warehouse management system to maintain data consistency and avoid data redundancy and inconsistency issues.

[0020] Automated data collection can update inventory information in real time and reduce human input errors, while data integration ensures the uniformity and consistency of information between different systems, improves data accuracy, provides a reliable data basis for subsequent analysis, speeds up decision-making, and improves the transparency of warehouse operations.

[0021] Step 2 also includes the following steps: B1: Database design: Design the database model, including table structure, fields, indexes and relationships, to ensure the efficiency and scalability of the database; B2: Database deployment: Select a suitable database system, install and configure it, and ensure the stability and security of the database; B3: Data import: import the integrated data into the database, which may involve batch import or real-time import to ensure the timeliness and accuracy of the data; B4: Data index: Create indexes for commonly used query fields to improve query efficiency and ensure fast response of data retrieval; B5: Data security: Implement security measures such as data encryption, access control, data backup, etc. to ensure data security and integrity; B6: Data maintenance: Perform data maintenance regularly, such as data compression, cleaning, and optimization, to ensure efficient operation of the database.

[0022] Ensure efficient storage and fast access to large amounts of data, while data cleaning ensures the quality and availability of stored data, reduces data redundancy and errors, improves data processing efficiency, and provides high-quality data support for data analysis and decision-making.

[0023] Step three also includes the following steps: C1: Data analysis model construction: Build data analysis models according to business needs, such as inventory forecasting, demand analysis, etc., to support intelligent decision-making; C2: Data mining: Use data mining technology to extract valuable information and patterns from large amounts of data to provide data support for decision-making; C3: Decision support: Use analysis results to provide decision support to managers, such as inventory adjustment, purchasing decisions, etc., to improve the scientificity and accuracy of decision-making; C4: Intelligent algorithm application: Apply machine learning algorithms for intelligent prediction and optimization to improve the intelligence level of warehouse management; C5: Result visualization: Display the analysis results in the form of charts, dashboards, etc. to facilitate understanding and use, and improve the intuitiveness and convenience of decision-making.

[0024] Through analytical tools and forecasting models, we help companies understand inventory trends and customer demand, so as to make more accurate inventory and layout decisions, optimize inventory levels, reduce inventory backlogs and out-of-stock situations, improve inventory turnover, and reduce operating costs.

[0025] Step 4 also includes the following steps: D1: Monitoring platform construction: Establish a real-time monitoring platform to display key indicators such as warehouse status and inventory status, and realize real-time monitoring of the warehouse; D2: Event triggering: Set thresholds and rules to trigger events when specific conditions are met, such as insufficient inventory, expired items, etc., to ensure timely response; D3: Process Automation: Automated warehouse management processes, such as automatic replenishment and automatic inventory, improve the efficiency and accuracy of warehouse management; D4: Exception handling: Establish an exception handling mechanism to promptly respond to and handle abnormal situations in warehouse management to ensure the stable operation of warehouse management; D5: Logging: Record all operations and events for easy tracking and auditing, ensuring transparency and traceability of warehouse management.

[0026] Monitor inventory changes in real time, reduce manual intervention through automated processes, improve warehouse operation efficiency, reduce operation time, reduce human errors, improve the accuracy and efficiency of warehouse operations, and improve overall operation speed.

[0027] Step five also includes the following steps: E1: Performance indicator setting: Determine the key indicators for evaluating warehouse management performance, such as inventory accuracy, response time, etc., to provide a basis for performance evaluation; E2: Performance evaluation: Regularly evaluate the performance of warehouse management and compare it with preset indicators to ensure continuous improvement of warehouse management; E3: Feedback collection: Collect feedback from users and managers to understand system problems and improvement needs, and provide direction for continuous improvement; E4: Process optimization: Based on the evaluation results and feedback, optimize and improve the warehouse management process to improve the efficiency and accuracy of warehouse management; E5: System upgrade: With the development of business and technological progress, the system is regularly upgraded and expanded to ensure the advancement and adaptability of the warehouse management system.

[0028] By setting and monitoring key performance indicators, evaluating the performance of the warehouse management system, and making improvements based on feedback, we can continuously improve warehouse management efficiency and service levels, enhance the company's market competitiveness, and at the same time ensure the stable operation of the system and the professionalism of employees through user training and technical support.

[0029] In this embodiment, the warehouse management method based on big data reduces human intervention and improves warehouse operation efficiency through automated processes and intelligent decision support, reduces inventory costs and space waste by optimizing inventory management and storage layout, improves inventory accuracy and reduces errors and losses through real-time monitoring and data analysis, and adjusts inventory strategies in a timely manner by predicting future demand, thereby enhancing the ability to respond to market changes.

[0030] Example 2 Second, as Figure 2As shown, the present invention also provides a warehouse management system based on big data, including a data collection and automation layer, a data transmission and integration layer, a data storage and management layer, a data processing and analysis layer, an application and service layer, a user interface and a security monitoring layer. These layers together constitute a structural framework of a warehouse management system based on big data. Each layer has different functions. Through the close cooperation of these layers, the entire system realizes the automation and intelligence of warehouse management, thereby improving the efficiency and accuracy of warehouse operation. The data collection and automation layer includes sensors and equipment, automated control systems, and data collection software. Sensors and equipment include barcode scanners, RFID tag readers, and automated shelf systems, which are used to automatically collect information about items in the warehouse. Automated control systems include automated warehouse management systems and automated guided vehicles, which are used to control the handling and storage of items in the warehouse. Data collection software is used to collect data from sensors and equipment and perform preliminary processing. The data transmission and integration layer includes communication protocols, data transmission mechanisms, and data integration platforms. Communication protocols are used to define the rules for data transmission. Data transmission mechanisms include message queues, Web services, and database replication, which are used to transmit data between different systems. The data integration platform includes ETL tools, which are used to integrate data from different sources and formats into a unified data warehouse. The data storage and management layer includes database system, data warehouse, data backup and recovery. The database system uses relational database or non-relational database to store and manage data. The data warehouse is used to store and analyze large amounts of data for a long time. Data backup and recovery can ensure data security and cope with data loss or damage. The data processing and analysis layer includes data processing tools, data mining and analysis algorithms, and big data platforms. Data processing tools include data cleaning, data conversion, data aggregation and other tools. Data mining and analysis algorithms use machine learning algorithms to discover patterns and trends from data. Big data platforms use Hadoop, Spark, etc. to process and analyze large-scale data sets. The application and service layer includes business logic processing, middleware, and API interfaces. Business logic processing uses application logic such as inventory management, order processing, and inbound and outbound management. Middleware includes enterprise service buses and application servers, which are used to connect different business applications and services. The API interface provides an interface for integration with other systems or services. The user interface and security monitoring layer include user interface, permission management, log and audit, and security mechanism. The user interface is used to provide a graphical user interface or web interface for warehouse managers to operate and monitor. Permission management ensures that only authorized users can access the system. Log and audit record system operations and events for monitoring and auditing. Security mechanisms include firewalls, intrusion detection systems, data encryption, etc., to protect the security of the system.

[0031] Example 3 like Figure 3 As shown, on the basis of Example 2, the present invention provides a technical solution: preferably, the data acquisition and automation layer is mainly responsible for collecting data from entities such as items and shelves in the warehouse, and transmitting the data to the data transmission and integration layer. The function of the data transmission and integration layer is to transmit the collected data from the data acquisition and automation layer to the data storage and management layer, and is responsible for integrating data from different sources to ensure the uniformity and integrity of the data. The data storage and management layer is responsible for storing the data transmitted from the data transmission and integration layer, and providing data support for the data processing and analysis layer. The data processing and analysis layer processes and analyzes the data in the data storage and management layer, extracts useful information, and provides support for decision-making. The application and service layer uses the information provided by the data processing and analysis layer to perform warehouse management tasks and support the automation of business processes. The user interface and security monitoring layer is the interface for users to interact with the warehouse management system, and is also responsible for monitoring the security of the system to ensure the confidentiality, integrity and availability of the data.

[0032] In this embodiment, data starts from the data acquisition and automation layer, passes through the data transmission and integration layer, is stored in the data storage and management layer, is processed by the data processing and analysis layer, and finally is provided to users through the application and service layer; Users issue commands through the user interface and security monitoring layer, which are passed through the application and service layer to the data processing and analysis layer, thereby affecting the data in the data storage and management layer; The output of the system will be fed back to the user to assist in the decision-making process, while system performance and security feedback will be used to optimize system configuration and strategy.

[0033] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A warehouse management method based on big data, characterized by: The warehouse management method based on big data includes the following steps: Step 1: Data collection and integration; Step 2: Data storage and management; Step 3: Data analysis and intelligent decision-making; Step 4: Real-time monitoring and process automation; Step 5: Performance evaluation and continuous improvement.

2. A warehouse management method based on big data according to claim 1, characterized in that: The step 1 also includes the following steps: A1: Demand analysis: First, we need to clarify the specific business needs of warehouse management, analyze the types of data to be collected and the sources of data, and ensure the pertinence and effectiveness of data collection; A2: Data source identification: Next, identify and determine the source of data collection, which may include sensors, barcodes, RFID, manual input, etc., to ensure the comprehensiveness and accuracy of the data; A3: Equipment deployment: Install and configure data collection equipment, such as sensors and barcode scanners, to ensure the accuracy and efficiency of data collection and lay a solid foundation for subsequent data processing; A4: Data collection: Collect data in real time or regularly through automated equipment or manual input to ensure the timeliness and integrity of the data; A5: Data cleaning: pre-process the collected data, including deduplication, error correction, and missing data completion, to improve data quality and provide a reliable basis for subsequent analysis; A6: Data integration: Format data from different sources uniformly and convert them into a standard format for subsequent processing to ensure data consistency and comparability; A7: Data synchronization: Ensure that the collected data is synchronized with the data in the warehouse management system to maintain data consistency and avoid data redundancy and inconsistency issues.

3. A warehouse management method based on big data according to claim 1, characterized in that: The step 2 also includes the following steps: B1: Database design: Design the database model, including table structure, fields, indexes and relationships, to ensure the efficiency and scalability of the database; B2: Database deployment: Select a suitable database system, install and configure it, and ensure the stability and security of the database; B3: Data import: import the integrated data into the database, which may involve batch import or real-time import to ensure the timeliness and accuracy of the data; B4: Data index: Create indexes for commonly used query fields to improve query efficiency and ensure fast response of data retrieval; B5: Data security: Implement security measures such as data encryption, access control, and data backup to ensure data security and integrity; B6: Data maintenance: Perform data maintenance regularly, such as data compression, cleaning, and optimization, to ensure efficient operation of the database.

4. The warehouse management method based on big data according to claim 1, characterized in that: The step three also includes the following steps: C1: Data analysis model construction: Build data analysis models according to business needs, such as inventory forecasting and demand analysis, to support intelligent decision-making; C2: Data mining: Use data mining technology to extract valuable information and patterns from large amounts of data to provide data support for decision-making; C3: Decision support: Use analysis results to provide decision support to managers, such as inventory adjustment and purchasing decisions, to improve the scientificity and accuracy of decision-making; C4: Intelligent algorithm application: Apply machine learning algorithms for intelligent prediction and optimization to improve the intelligence level of warehouse management; C5: Result visualization: Display the analysis results in the form of charts and dashboards to facilitate understanding and use, and improve the intuitiveness and convenience of decision-making.

5. The warehouse management method based on big data according to claim 1 is characterized in that: The step 4 also includes the following steps: D1: Monitoring platform construction: Establish a real-time monitoring platform to display the key indicators of warehouse status and inventory status, and realize real-time monitoring of the warehouse; D2: Event triggering: Set thresholds and rules to trigger events when specific conditions are met, such as insufficient inventory or expired items, to ensure timely response; D3: Process Automation: Automated warehouse management processes, such as automatic replenishment and automatic inventory, improve the efficiency and accuracy of warehouse management; D4: Exception handling: Establish an exception handling mechanism to promptly respond to and handle abnormal situations in warehouse management to ensure the stable operation of warehouse management; D5: Logging: Record all operations and events for easy tracking and auditing, ensuring transparency and traceability of warehouse management.

6. The warehouse management method based on big data according to claim 1 is characterized by: The step five also includes the following steps: E1: Performance indicator setting: Determine key indicators for evaluating warehouse management performance, such as inventory accuracy and response time, to provide a basis for performance evaluation; E2: Performance evaluation: Regularly evaluate the performance of warehouse management and compare it with preset indicators to ensure continuous improvement of warehouse management; E3: Feedback collection: Collect feedback from users and managers to understand system problems and improvement needs, and provide direction for continuous improvement; E4: Process optimization: Based on the evaluation results and feedback, optimize and improve the warehouse management process to improve the efficiency and accuracy of warehouse management; E5: System upgrade: With the development of business and technological progress, the system is regularly upgraded and expanded to ensure the advancement and adaptability of the warehouse management system.

7. A warehouse management system based on big data, characterized by: It includes data acquisition and automation layer, data transmission and integration layer, data storage and management layer, data processing and analysis layer, application and service layer, user interface and security monitoring layer. The data acquisition and automation layer includes sensors and equipment, automatic control system, and data acquisition software. The data transmission and integration layer includes communication protocol, data transmission mechanism, and data integration platform. The data storage and management layer includes database system, data warehouse, data backup and recovery. The data processing and analysis layer includes data processing tools, data mining and analysis algorithms, and big data platform. The application and service layer includes business logic processing, middleware, and API interface. The user interface and security monitoring layer includes user interface, authority management, log and audit, and security mechanism.

8. A warehouse management system based on big data according to claim 7, characterized in that: The data acquisition and automation layer is mainly responsible for collecting data from items and shelf entities in the warehouse, and transmitting the data to the data storage and integration layer. The function of the data storage and integration layer is to transmit the collected data from the data acquisition and automation layer to the data storage and management layer, and is responsible for integrating data from different sources to ensure the uniformity and integrity of the data. The data storage and management layer is responsible for storing data transmitted from the data transmission and integration layer, and providing data support for the data processing and analysis layer. The data processing and analysis layer processes and analyzes the data in the data storage and management layer, extracts useful information, and provides support for decision-making. The application and service layer uses the information provided by the data processing and analysis layer to perform warehouse management tasks and support the automation of business processes. The user interface and security monitoring layer is the interface for users to interact with the warehouse management system, and is also responsible for monitoring the security of the system to ensure the confidentiality, integrity and availability of data.

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