Intelligent logistics management system based on AI

Abstract

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of smart logistics management technology, and specifically to an AI-based smart logistics management system. Background Art

[0002] With the rapid development of information technology, the warehousing and logistics industry has gradually realized informatization. However, the inertia of traditional warehousing construction and management has made enterprises rely on a large number of manual operations such as scheduling, inspection, recording, loading and unloading, and the corresponding information system construction also only adopts the traditional management mode. Information systems such as TMS transportation management system, CRM customer relationship management system, and WMS warehouse management system are closed and cannot provide data analysis and data operation for refined management. The mismatch between management needs and management methods has led to operational problems and risks in some businesses. This makes the scheduling efficiency of resources such as people and equipment low. Summary of the invention

[0003] In view of this, this application provides an AI-based smart logistics management system, which uses a digital twin supervision method and AI-assisted analysis method to pre-process and analyze warehouse data through an AI-based smart logistics management system, solving the problem that the traditional management model cannot provide data analysis and data operation for refined management. The mismatch between management needs and management methods has led to operational problems and risks in some businesses. This is a technical problem that results in low efficiency in scheduling resources such as people and equipment.

[0004] To achieve the above object, the present invention provides the following technical solutions: An AI-based intelligent logistics management system, including: a warehouse management unit, a data fusion collection unit and a digital twin supervision unit; The warehouse management unit is used to manage the warehouse's inbound and outbound information, cargo information, and inventory risk reminders, as well as to perform demand forecasting and inventory optimization, and upload cargo information, inbound and outbound information, and inventory information to the digital twin supervision unit; The data fusion collection unit is used to collect information about personnel, goods, equipment and environment in the warehouse through the deployed data collection network, and upload the collected information to the digital twin supervision unit; The digital twin supervision unit includes an intelligent AI recognition unit and a data analysis and processing unit. The intelligent AI recognition unit is used to identify and analyze the personnel, goods, equipment and environment of the warehouse and provide risk reminders through AI intelligent algorithms. The data analysis and processing unit is used to map the data of the physical warehouse to the digital twin warehouse, and perform visual interactive management of the digital twin warehouse and intelligent scheduling of personnel, goods and equipment.

[0005] Furthermore, the warehouse management unit includes a warehouse entry and exit management module, a goods management module and an inventory risk calculation module; The inbound and outbound management module includes an equipment management module and a business management module. The equipment management module is used to manage the status parameters and operation information of the inbound and outbound equipment in real time and in association with the operation tasks. The business management module is used to generate inbound and outbound tasks according to the goods inbound and outbound requirements input by the user, and match the corresponding transport equipment and transport path according to the inbound and outbound tasks. The goods management module includes a data list module, a data update module and a quality inspection management module. The data list module is used to record the inventory data of the warehouse goods according to the logistics parameter list, and establish a multi-level product classification model according to the commodity management list. According to the multi-level product classification model, the distribution, type, storage time and storage environment of the warehoused goods are managed. The data update module is used to update the recorded data in the list according to the data collected by the data fusion collection unit. The quality inspection management module is used to record and mark the surface damage and quality standards of the goods entering the warehouse, and screen the damaged goods; The inventory risk calculation module is used to calculate the inventory quantity in the warehouse, compare the calculation result with the preset risk threshold, and then output the risk level value.

[0006] Furthermore, the inventory risk calculation module includes an inventory calculation module and a risk level matching module; The inventory calculation module is used to add the current inventory value to the value of goods to be received to obtain the incoming inventory value, subtract the incoming inventory value from the outgoing goods value to obtain the new inventory value, compare the new inventory value with each risk threshold, and obtain the risk level value based on the comparison result; The risk level matching module is used to determine the risk level to which it belongs based on the obtained risk level value, and to highlight and voice remind the management personnel based on the risk level.

[0007] Furthermore, the warehouse management unit also includes a forecasting optimization module, which includes a demand forecasting module and an inventory optimization module; The demand forecasting module is used to classify logistics demand using clustering algorithms, model different categories of logistics demand time series and historical data using time series models, and train SVM models. The trained SVM model is used to perform nonlinear regression forecasting on logistics demand to obtain forecast data of logistics demand. The kernel function and parameters are determined when training the SVM model, and the firefly algorithm is used to optimize the parameters of the RBF kernel function in the SVM model. The inventory optimization module is used to determine the optimal order quantity based on ordering cost and holding cost, set the maximum and minimum inventory levels based on logistics demand forecast and safety stock, adjust the inventory level based on real-time demand data, and adjust the inventory plan based on updated demand forecast.

[0008] Furthermore, the data fusion acquisition unit includes a data acquisition device and a data fusion processing module. The data acquisition device includes a camera module, a sensor module, a UWB tag and a 5G communication module. The camera module is used to collect image data information of people and equipment. The UWB tag is used to collect movement trajectory and location information of personnel. The sensor module is used to collect temperature and humidity change information, gas change information and fire information of the warehouse area environment, as well as the speed, working time and pressure change information of each equipment. The camera module and the sensor module are both connected to the 5G communication module, and the 5G communication module is used to upload the processed data to the digital twin supervision unit through the 5G wireless network.

[0009] Furthermore, the intelligent AI recognition unit includes a personnel safety recognition module, a transportation equipment speed recognition module, an environmental safety recognition module, and a risk reminder module; The personnel safety identification module is used to determine whether the operator stays in the dangerous area based on the personnel positioning information collected by the UWB tag and the coordinate information of each area of ​​the warehouse. If the personnel positioning information is in the preset dangerous area for more than 1 minute, it is determined that there is a stay and dangerous operation; The transport equipment speed identification module is used to obtain the current operating speed of the corresponding equipment based on the collected equipment operation data, compare the current operating speed with the preset safety speed threshold, and determine whether the transport equipment is overspeeding or has a fault and stops operating based on the comparison result; The environmental safety identification module is used to detect environmental parameters according to the AI ​​environmental identification algorithm, extract flame and smoke video features according to the collected video information, and identify flame and smoke based on the video features; The risk reminder module is connected to the personnel safety identification module, the transportation equipment speed identification module and the environmental safety identification module. The risk reminder module issues risk alarms for abnormal identification results through sound and light reminders and highlight displays based on the obtained identification results. Furthermore, the data analysis and processing unit includes a modeling module, a data mapping module, and a visualization management module; The modeling module is used to obtain the coordinates and size information of the physical components of the warehouse's internal building space and equipment, and to establish static and dynamic models. Based on the established static, dynamic and digital models, a digital twin warehouse model is formed. The data mapping module is used to map the collected physical data information to the established digital model to drive the digital twin warehouse model. The digital twin warehouse model can monitor the temperature and humidity, pressure data, equipment operation status, personnel operation trajectory and abnormal conditions in the warehouse in real time, as well as the goods situation and production needs in the warehouse. It can also perform intelligent scheduling of personnel and equipment and distribution of goods according to production needs. The visual management module is used to visualize and manage warehouse monitoring data and alarm data through a user-friendly visual management interface.

[0010] Furthermore, the data analysis and processing unit also includes a data sharing module; the data sharing module is used to build a warehousing and logistics data resource pool, and share the data in the warehousing and logistics data resource pool with a third-party system.

[0011] It can be seen from the above technical solution that the advantages of the present invention are: 1. In this application, intelligent demand forecasting and inventory optimization are carried out through the warehouse management unit, and real-time management of the warehouse goods in and out operations is carried out, thereby improving the efficiency of goods storage and management. The data fusion collection unit realizes all-round monitoring of warehouse data, and realizes remote monitoring function through the 5G communication network. Finally, the digital twin supervision unit performs data analysis and intelligent AI recognition on the collected and uploaded warehouse and goods data, so as to achieve the purpose of real-time monitoring of the warehouse environment, equipment, personnel, goods and production needs through the digital twin warehouse model, as well as intelligent scheduling of personnel and equipment and allocation of goods according to production needs, making the management method more reasonable and less risky, and the resource scheduling efficiency higher.

[0012] 2. This application provides services for the connectivity and visibility of logistics enterprise networks through video technology, big data, artificial intelligence and other technologies, realizes the refined and intelligent management of warehousing logistics, makes warehouses fully monitorable, improves the efficiency of cargo storage and management, and realizes the migration of physical entities to virtual warehousing through digital twin technology, promotes the informatization, intelligence and wisdom of logistics warehousing and improves the efficiency of resource integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0014] Figure 1 This is a schematic diagram of the structure of this application.

[0015] Figure 2 This is a schematic diagram of the structure of the warehouse management unit of this application.

[0016] Figure 3 This is a schematic diagram of the structure of the intelligent AI recognition unit of this application.

[0017] Figure 4 This is a structural diagram of the data analysis and processing unit of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the implementation modes and the accompanying drawings. Here, the illustrative implementation modes and descriptions of the present application are used to explain the present application, but are not intended to limit the present application.

[0019] Smart warehousing uses AI prediction and recognition and digital twin information technology to improve and supervise the management of warehousing equipment and goods management, thereby improving the dispatching level of supply chain and business links. The development trend of smart warehousing is gradually moving from automation to highly flexible automation, digitalization, transparency, predictability, and intelligence.

[0020] refer to Figures 1 to 4 ,like Figure 1 As shown, this embodiment provides an AI-based intelligent logistics management system, which is a highly integrated and automated system for real-time monitoring, analysis and management of the operation of the warehousing logistics system, specifically including: a warehousing management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehousing management unit is used to manage the warehouse's in-and-out information, cargo information and inventory risk reminders, as well as to perform demand forecasting and inventory optimization, and upload cargo information, in-and-out and inventory information to the digital twin supervision unit. The digital twin supervision unit includes an intelligent AI recognition unit and a data analysis and processing unit. The intelligent AI recognition unit is used to identify and analyze the personnel, cargo, equipment and environment of the warehouse and to provide risk reminders through AI intelligent algorithms. The data analysis and processing unit is used to map the data of the physical warehouse to the digital twin warehouse, and to perform visual interactive management of the digital twin warehouse and intelligent scheduling of personnel, cargo and equipment.

[0021] Specifically, the warehouse management unit includes an in-and-out management module, a goods management module and an inventory risk calculation module; the in-and-out management module includes an equipment management module and a business management module, the equipment management module is used to associate and manage the state parameters and operation information of the in-and-out equipment with the operation tasks in real time, the business management module is used to generate in-and-out tasks according to the goods in-and-out requirements input by the user, and match the corresponding transportation equipment and transportation path according to the in-and-out tasks; the goods management module includes a data list module, a data update module and a quality inspection management module, the data list module is used to record the inventory data of the warehouse goods according to the logistics parameter list, and establish a multi-level product classification model according to the commodity management list, and manage the storage location distribution, type, storage time and storage environment of the warehouse goods according to the multi-level product classification model, the data update module is used to update the recorded data of the list according to the data collected by the data fusion collection unit, and the quality inspection management module is used to record and mark the surface damage and quality qualification standards of the goods entering the warehouse, and screen the damaged goods; the inventory risk calculation module is used to calculate the inventory quantity of the warehouse, and compare the calculation result with the preset risk threshold, and then output the risk level value.

[0022] More specifically, the inventory risk calculation module includes an inventory calculation module and a risk level matching module; the inventory calculation module is used to add the current inventory value to the value of goods to be received to obtain the incoming inventory value, subtract the incoming inventory value from the value of goods to be shipped out to obtain a new inventory value, compare the new inventory value with each risk threshold, and obtain a risk level value based on the comparison result; the risk level matching module is used to determine the risk level to which it belongs based on the obtained risk level value, and highlight and voice remind the management personnel based on the risk level.

[0023] The warehouse management unit also includes a forecasting and optimization module, which includes a demand forecasting module and an inventory optimization module; the demand forecasting module is used to classify logistics demand using a clustering algorithm, model different categories of logistics demand time series and historical data using time series models, and train the SVM model. The trained SVM model is used to perform nonlinear regression forecasting on logistics demand to obtain forecast data of logistics demand. The kernel function and parameters are determined when training the SVM model, and the firefly algorithm is used to optimize the parameters of the RBF kernel function in the SVM model; the inventory optimization module is used to determine the optimal order quantity based on the ordering cost and holding cost, and to set the maximum and minimum inventory levels based on the logistics demand forecast and safety stock, and adjust the inventory level based on the real-time demand data, and also to adjust the inventory plan based on the updated demand forecast. When training the SVM model, it is necessary to collect historical logistics demand data, including the characteristics of the predicted time point (such as activities, seasons, holidays, etc.), divide the data set into a training set and a test set (or validation set), and select the radial basis function RBF to handle nonlinear relationships. The model is trained using training data and target logistics demand values, and the test set is used to evaluate the performance of the model. Common evaluation indicators include mean square error (MSE), root mean square error (RMSE), etc. Finally, the feature data is input into the trained SVM model to obtain the predicted logistics demand value.

[0024] The data fusion acquisition unit is used to collect personnel, goods, equipment and environmental information in the warehouse through the deployed data acquisition network, and upload the collected information to the digital twin supervision unit.

[0025] The data fusion acquisition unit includes a data acquisition device and a data fusion processing module. The data acquisition device includes a camera module, a sensor module, a UWB tag and a 5G communication module. The camera module is used to collect image data information of people and equipment. The UWB tag is used to collect the movement trajectory and location information of personnel. The sensor module is used to collect temperature and humidity change information, gas change information and fire information of the warehouse area environment, as well as the speed, working time and pressure change information of each equipment. The camera module and the sensor module are both connected to the 5G communication module. The 5G communication module is used to upload the processed data to the digital twin supervision unit through the 5G wireless network. The data fusion processing module is used to clean, screen and standardize the collected data.

[0026] The data in the system are divided into business monitoring data and control monitoring data. Business monitoring data includes "decision analysis, information management, planning management, warehouse management, and cost management" data. By cleaning and sorting the data collected by the underlying equipment, data with a standard data structure is formed and uploaded to the digital twin supervision unit through the data interface. Control monitoring data includes "intelligent scheduling, fault warning, abnormal warning, status monitoring, preventive maintenance, temperature and humidity control" data, which is collected through intelligent sensing equipment, including but not limited to equipment operating parameters, temperature and humidity parameters, technical standard parameters, and control thresholds. Through the preprocessing of intelligent sensing equipment, the data is converted into a standardized data structure and uploaded to the digital twin supervision unit.

[0027] In this embodiment, the intelligent AI identification unit includes a personnel safety identification module, a transportation equipment speed identification module, an environmental safety identification module and a risk reminder module; the personnel safety identification module is used to determine whether the operator is staying in a dangerous area based on the personnel positioning information collected by the UWB tag and the coordinate information of each area of ​​the divided warehouse. When the personnel positioning information is in the preset dangerous area for more than 1 minute, it is determined that there is a stay and dangerous operation. The analysis of the personnel position and trajectory is used to calculate the distance between the person and each base station based on the signal of the indoor base station obtained by the UWB positioning tag, and to perform positioning calculations on the person based on the distance to each base station. The transportation equipment speed identification module is used to obtain the current operating speed of the corresponding equipment based on the collected equipment operation data, compare the current operating speed with the preset safety speed threshold, and determine whether the transportation equipment is overspeeding or malfunctioning according to the comparison results; the environmental safety identification module is used to detect environmental parameters according to the AI ​​environmental recognition algorithm, and extract flame and smoke video features according to the collected video information, and identify flames and smoke based on video features; the risk reminder module is connected to the personnel safety identification module, the transportation equipment speed identification module and the environmental safety identification module. The risk reminder module issues risk alarms for abnormal identification results through sound and light reminders and highlight displays based on the obtained identification results. The data analysis and processing unit includes a modeling module, a data mapping module and a visualization management module; the modeling module is used to obtain the coordinates and size information of the physical components of the internal building space of the warehouse and the equipment in the warehouse, and to establish static models and dynamic models, and form a digital twin warehouse model based on the established static models, dynamic models and digital models; the data mapping module is used to map the collected physical data information to the established digital model to drive the digital twin warehouse model, and through the digital twin warehouse model, the temperature and humidity, pressure data, equipment operation status, personnel operation trajectory and abnormal conditions in the warehouse are monitored in real time, and the goods situation and production needs in the warehouse are monitored, and personnel and equipment can be intelligently scheduled and goods are distributed according to production needs; the visualization management module is used to visualize and manage warehouse monitoring data and alarm data through a user-friendly visualization management interface.

[0028] The digital twin warehouse model is created by collecting data from the actual warehousing process, such as cargo information, warehouse equipment information, and environmental information, and converting it into a digital model. The digital twin warehouse model can truly reflect the actual warehousing situation and can perform various analyses and optimizations on it. The collected data is processed, and the digital twin models of the workshop environment, equipment, personnel, and key equipment nodes are created using graphic software, and the corresponding digital twin models are given the same data information as the physical entity, so that the shape, structure, parameters, and spatial position of the digital twin are consistent with the physical entity. At the same time, a mapping relationship between the digital twin and the physical entity is established to drive the digital twin to produce actions synchronized with the physical entity, truly restoring the actual operation scene of logistics warehousing.

[0029] The data analysis and processing unit also includes a data sharing module; the data sharing module is used to build a warehousing and logistics data resource pool, and share the data in the warehousing and logistics data resource pool with a third-party system.

[0030] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An AI-based intelligent logistics management system, characterized in that: include: Warehouse management unit, data fusion collection unit and digital twin supervision unit; The warehouse management unit is used to manage the warehouse's in-and-out information, cargo information, and inventory risk reminders, as well as to perform demand forecasting and inventory optimization, and upload cargo information, in-and-out information, and inventory information to the digital twin supervision unit; The data fusion acquisition unit is used to collect personnel, goods, equipment and environmental information of the warehouse through the deployed data acquisition network, and upload the collected information to the digital twin supervision unit; The digital twin supervision unit includes an intelligent AI recognition unit and a data analysis and processing unit. The intelligent AI recognition unit is used to identify and analyze the personnel, goods, equipment and environment of the warehouse and provide risk reminders through AI intelligent algorithms. The data analysis and processing unit is used to map the data of the physical warehouse to the digital twin warehouse, and perform visual interactive management of the digital twin warehouse and intelligent scheduling of personnel, goods and equipment.

2. The AI-based smart logistics management system according to claim 1 is characterized in that: The warehouse management unit includes a warehouse entry and exit management module, a cargo management module and an inventory risk calculation module; The in-and-out management module includes an equipment management module and a business management module. The equipment management module is used to manage the state parameters and operation information of the in-and-out equipment in real time in association with the operation tasks. The business management module is used to generate in-and-out tasks according to the goods in-and-out requirements input by the user, and match the corresponding transport equipment and transport path according to the in-and-out tasks. The goods management module includes a data list module, a data update module and a quality inspection management module. The data list module is used to record the inventory data of the warehouse goods according to the logistics parameter list, and establish a multi-level product classification model according to the commodity management list, and manage the storage location distribution, type, storage time and storage environment of the warehoused goods according to the multi-level product classification model. The data update module is used to update the recorded data of the list according to the data collected by the data fusion acquisition unit. The quality inspection management module is used to record and mark the surface damage and quality standards of the goods entering the warehouse, and screen the damaged goods; The inventory risk calculation module is used to calculate the inventory quantity of the warehouse, compare the calculation result with the preset risk threshold, and then output the risk level value.

3. The AI-based smart logistics management system according to claim 2 is characterized in that: The inventory risk calculation module includes an inventory calculation module and a risk level matching module; The inventory calculation module is used to add the current inventory value to the value of goods to be received to obtain the incoming inventory value, subtract the incoming inventory value from the outgoing goods value to obtain a new inventory value, compare the new inventory value with each risk threshold, and obtain a risk level value according to the comparison result; The risk level matching module is used to determine the risk level to which it belongs according to the obtained risk level value, and to highlight and voice remind the management personnel according to the risk level.

4. The AI-based smart logistics management system according to claim 2 is characterized in that: The warehouse management unit also includes a forecasting optimization module, which includes a demand forecasting module and an inventory optimization module; The demand forecasting module is used to classify logistics demand by using a clustering algorithm, model different categories of logistics demand time series and historical data by using a time series model, and train an SVM model. The trained SVM model is used to perform nonlinear regression forecasting on the logistics demand to obtain forecast data of the logistics demand. The kernel function and parameters are determined when training the SVM model, and the firefly algorithm is used to optimize the parameters of the RBF kernel function in the SVM model. The inventory optimization module is used to determine the optimal order quantity based on ordering cost and holding cost, set the maximum and minimum inventory levels based on logistics demand forecast and safety stock, adjust the inventory level based on real-time demand data, and adjust the inventory plan based on updated demand forecast.

5. The AI-based smart logistics management system according to claim 1 is characterized in that: The data fusion acquisition unit includes a data acquisition device and a data fusion processing module. The data acquisition device includes a camera module, a sensor module, a UWB tag and a 5G communication module. The camera module is used to collect image data information of people and equipment. The UWB tag is used to collect movement trajectory and location information of personnel. The sensor module is used to collect temperature and humidity change information, gas change information and fire information of the warehouse area environment, as well as the speed, working time and pressure change information of each equipment. The camera module and the sensor module are both connected to the 5G communication module, and the 5G communication module is used to upload processed data to the digital twin supervision unit through the 5G wireless network.

6. The AI-based smart logistics management system according to claim 5 is characterized in that: The intelligent AI recognition unit includes a personnel safety recognition module, a transportation equipment speed recognition module, an environmental safety recognition module and a risk reminder module; The personnel safety identification module is used to determine whether the operator stays in the dangerous area based on the personnel positioning information collected by the UWB tag and the coordinate information of each area of ​​the warehouse. If the personnel positioning information is in the preset dangerous area for more than 1 minute, it is determined that there is a stay and dangerous operation; The transport equipment speed identification module is used to obtain the current operating speed of the corresponding equipment according to the collected equipment operation data, compare the current operating speed with the preset safety speed threshold, and determine whether the transport equipment is overspeeding or faulty and stopped according to the comparison result; The environmental safety identification module is used to detect environmental parameters according to the AI ​​environmental identification algorithm, extract flame and smoke video features according to the collected video information, and identify flame and smoke based on the video features; The risk reminder module is connected to the personnel safety identification module, the transportation equipment speed identification module and the environmental safety identification module. The risk reminder module issues a risk alarm for abnormal identification results through sound and light reminders and highlight display according to the obtained identification results.

7. The AI-based smart logistics management system according to claim 5 is characterized in that: The data analysis and processing unit includes a modeling module, a data mapping module and a visual management module; The modeling module is used to obtain the coordinates and size information of the physical components of the internal building space of the warehouse and the equipment in the warehouse, and to establish a static model and a dynamic model, and to form a digital twin warehouse model based on the established static model, dynamic model and digital model; The data mapping module is used to map the collected physical data information to the established digital model to drive the digital twin warehouse model. The digital twin warehouse model can monitor the temperature and humidity, pressure data, equipment operation status, personnel operation trajectory and abnormal conditions in the warehouse in real time, and monitor the goods situation and production demand in the warehouse, and can perform intelligent scheduling of personnel and equipment and distribution of goods according to production needs; The visual management module is used to visually display and manage warehouse monitoring data and alarm data through a user-friendly visual management interface.

8. The AI-based smart logistics management system according to claim 7 is characterized in that: The data analysis and processing unit also includes a data sharing module; the data sharing module is used to build a warehousing and logistics data resource pool, and share the data in the warehousing and logistics data resource pool with a third-party system.

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