Goods inbound and outbound management methods, systems, computer equipment, and storage media

Abstract

This application relates to a method, system, computer equipment, and storage medium for managing the inbound and outbound operations of goods. The method includes: collecting basic information about the goods and generating a unique identifier for each goods; based on the unique identifier and corresponding basic information, mapping the warehouse shelf status in real time using digital twin technology, dynamically allocating shelf locations, generating storage optimization plans, and guiding personnel and / or automated equipment to inbound the goods; receiving order data, generating picking routes based on the order data, guiding personnel and / or automated equipment to outbound the goods, and updating real-time inventory information; acquiring historical inventory data, predicting future inventory status, and dynamically generating replenishment plans based on future inventory status; performing inventory checks on warehouse goods according to a preset time period, obtaining corresponding inventory data, comparing the inventory data, identifying inventory discrepancies, and generating an inventory display table. This application improves warehouse management efficiency.

Description

Technical Field

[0001] This application relates to the technical field of warehouse management, and in particular to a method, system, computer equipment, and storage medium for managing the entry and exit of goods. Background Technology

[0002] Currently, warehouse management plays a crucial role in logistics, e-commerce, and the storage of high-value goods. It primarily encompasses processes such as receiving, storing, issuing, and managing inventory. Traditional goods management methods utilize barcodes or simple ERP systems to record and manage product information. However, these methods largely rely on fixed rules for shelf allocation and order fulfillment planning, lacking the ability to respond to real-time dynamic environments and struggling to cope with complex and ever-changing storage scenarios. This results in low efficiency in managing goods and shelves within the warehouse.

[0003] The existing technical solutions mentioned above have the following drawbacks: the existing methods for managing the entry and exit of goods are mostly based on fixed rules in terms of shelf allocation and order fulfillment planning, which are insufficient to respond to real-time dynamic environments, resulting in low management efficiency of goods, shelves and other items in the warehouse, and therefore there is room for improvement. Summary of the Invention

[0004] To improve warehouse management efficiency, this application provides a method, system, computer equipment, and storage medium for managing the inbound and outbound of goods.

[0005] The above-mentioned objective of this application is achieved through the following technical solution:

[0006] A method for managing the inbound and outbound movement of goods, the method comprising:

[0007] Collect basic information about the goods and generate a unique identifier for the goods based on the basic information;

[0008] Based on the unique identifier of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and shelf positions are dynamically allocated to generate storage optimization schemes. According to the shelf allocation schemes, personnel and / or automated equipment are guided to put the goods into the warehouse.

[0009] Receive order data, generate a distribution route based on the order data, guide personnel and / or automated equipment to dispatch the goods based on the distribution route, and update real-time inventory information;

[0010] Acquire historical inventory data, predict future inventory status based on the real-time inventory information and historical inventory data, and dynamically generate a replenishment plan based on the future inventory status;

[0011] The warehouse goods are inventoried according to a preset time period to obtain corresponding inventory data. The inventory data is then compared with the inventory data to identify inventory differences and generate an inventory display table.

[0012] By adopting the above technical solutions, and collecting basic information about goods, and generating unique identification codes for each item based on this information, the uniqueness and accuracy of each item in the system can be ensured, avoiding management chaos caused by duplicate or incorrect information, thereby improving the reliability and accuracy of goods data management. Based on the unique identification codes and corresponding basic information, and using digital twin technology, the real-time status of warehouse shelves can be mapped, and shelf locations can be dynamically allocated. This comprehensively reflects the real-time status of warehouse shelves and enables dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention. By receiving order data and generating picking routes based on it, the optimal picking routes can be automatically planned for different orders, reducing operation time and improving outbound efficiency, thus meeting the needs of rapid logistics response. By acquiring historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, inventory status can be predicted and optimized in advance, thereby avoiding overstocking or shortages and improving the economic efficiency of warehouse operations.

[0013] In one example, this application can be further configured as follows: the real-time mapping of warehouse shelf status using digital twin technology, and the dynamic allocation of shelf locations to generate storage optimization schemes, specifically includes:

[0014] Based on the basic information, the goods corresponding to the basic information are stored in a preset digital twin warehouse model, thereby mapping the warehouse shelf information and the basic information of the goods in real time.

[0015] Based on the real-time mapping results, the storage location of goods is dynamically adjusted through the shelf allocation algorithm to obtain the storage path guidance for goods entering the warehouse, and the storage optimization scheme is generated.

[0016] By adopting the above technical solution, goods are stored in a pre-set digital twin warehouse model based on basic information, which enables virtual mapping between goods information and warehouse shelf status. This ensures the visualization and real-time nature of goods storage information, thereby improving management efficiency and the scientific nature of decision-making. Based on the real-time mapping results, the goods storage location is dynamically adjusted through a shelf allocation algorithm. This allows for dynamic allocation of storage locations according to actual needs, avoiding waste or overloading of shelf resources and thus optimizing the overall utilization rate of the shelves.

[0017] In one example, this application can be further configured as follows: the dynamic adjustment of the goods storage location through the shelf allocation algorithm to obtain the storage path guidance for goods entering the warehouse, and the generation of the storage optimization scheme, specifically includes:

[0018] The basic information of the goods and the warehouse shelf information are evaluated for shelf availability using a preset shelf allocation algorithm, and the storage location of the goods is generated.

[0019] A path optimization algorithm is used to generate storage path guidance for goods entering the warehouse based on the storage location of the goods and the preset physical layout of the shelves.

[0020] By adopting the above technical solutions, the shelf availability can be assessed by using a preset shelf allocation algorithm to evaluate the basic information of goods and warehouse shelf information. Storage locations can be dynamically selected based on the attributes of goods and the status of shelves, thereby ensuring the suitability of goods storage and the effective utilization of shelves. By using a path optimization algorithm to generate storage path guidance for goods entering the warehouse, unnecessary path detours and time waste during the warehousing process can be reduced, thereby improving the efficiency of goods entering the warehouse.

[0021] In one example, this application can be further configured as follows: the shelf availability assessment of the basic information of the goods and the warehouse shelf information using a preset shelf allocation algorithm specifically includes:

[0022] The volume data of the goods is obtained from the basic information of the goods, and the remaining space information of the shelves is obtained from the warehouse shelf information. Based on the degree of matching between the volume data of the goods and the remaining space information of the shelves, the shelf where the goods are stored is determined.

[0023] Obtain the shelf partition category from the warehouse shelf information, and determine the shelf area for storing goods based on the shelf partition category;

[0024] The distribution of warehouse shelving information is assessed based on the shelving and shelving areas where goods are stored, in order to ensure the overall uniformity of goods distribution.

[0025] By adopting the above technical solutions, and by obtaining the volume data of goods from the basic information of the goods, and combining it with the remaining shelf space information, storage locations can be dynamically allocated based on the matching degree between storage needs and shelf capacity, thereby ensuring the rationality of goods storage and avoiding space waste. By obtaining the shelf zoning categories from the warehouse shelf information, and determining the storage area of ​​goods based on the zoning categories, suitable storage environments can be provided for different types of goods, thereby meeting the storage needs of special goods and improving the storage security of goods. By evaluating the distribution of shelf information, the distribution of goods can be dynamically adjusted to avoid shelf overloading or uneven distribution, thereby optimizing the overall space utilization of the warehouse and improving operational convenience.

[0026] In one example, this application can be further configured as follows: receiving order data and generating a delivery route based on the order data specifically includes:

[0027] Based on the received order data, query the unique identifier of the corresponding order item, and then query the corresponding shelf location based on the unique identifier.

[0028] Based on the delivery time requirements, delivery location requirements, and cargo quantity information of the received order data, the corresponding orders are prioritized to obtain the processing priority of different orders;

[0029] Based on the processing priority, the corresponding orders are processed sequentially, and the shortest path is calculated for the shelf location using a path optimization algorithm to obtain the picking path.

[0030] By adopting the above technical solutions, the location of goods in an order can be quickly determined by querying the unique identifier and corresponding shelf location of the goods based on the order data, thereby reducing outbound preparation time. By prioritizing orders based on delivery time requirements, delivery location requirements, and quantity information in the order data, the processing order of orders can be rationally arranged to meet the needs of urgent orders, thereby improving the flexibility and efficiency of order processing. By using path optimization algorithms to generate the shortest picking route, the path waste of operators can be reduced, picking efficiency can be improved, thereby reducing labor costs and speeding up order processing.

[0031] In one example, this application can be further configured as follows: the acquisition of historical inventory data and the prediction of future inventory status based on the real-time inventory information and historical inventory data specifically include:

[0032] The sales records, production cycles, and seasonal demand of the goods are extracted from the historical inventory data, and time series analysis is performed using a time series analysis model to identify the cyclical trends and sudden demand of the goods and obtain the corresponding sales characteristics.

[0033] The sales characteristics are modeled using machine learning algorithms, and the model parameters are optimized based on the real-time inventory information to generate inventory demand forecast results within a preset time period, thereby calculating the future inventory status.

[0034] By adopting the above technical solutions, sales records, production cycles, and seasonal demand of goods can be extracted from historical inventory data, and time series analysis models can be used to identify cyclical trends and sudden demands of goods, thereby providing accurate demand forecasting basis for inventory management. By using machine learning algorithms to model sales characteristics and optimizing model parameters based on real-time inventory information, the accuracy and adaptability of the inventory forecasting model can be dynamically adjusted, thereby generating more reasonable inventory replenishment plans, avoiding excess or shortage of inventory, and improving operational efficiency and economic benefits.

[0035] The second objective of this invention is achieved through the following technical solution:

[0036] A goods inbound and outbound management system, the goods inbound and outbound management system comprising:

[0037] The information collection module is used to collect basic information about the goods and generate a unique identifier for the goods based on the basic information.

[0038] The digital twin mapping module is used to map the warehouse shelf status in real time based on the unique identifier of the goods and the corresponding basic information, dynamically allocate shelf positions, generate storage optimization schemes, and guide personnel and / or automated equipment to put the goods into the warehouse according to the shelf allocation schemes.

[0039] The order processing module is used to receive order data, generate a picking route based on the order data, guide personnel and / or automated equipment to pick up the goods based on the picking route, and update real-time inventory information.

[0040] The inventory forecasting module is used to acquire historical inventory data, predict future inventory status based on the real-time inventory information and historical inventory data, and dynamically generate a replenishment plan based on the future inventory status.

[0041] The inventory management module is used to perform inventory counts on warehouse goods according to a preset time period, obtain corresponding inventory data, compare the inventory data with the inventory, identify inventory discrepancies, and generate an inventory display table.

[0042] By adopting the above technical solutions, and collecting basic information about goods, and generating unique identification codes for each item based on this information, the uniqueness and accuracy of each item in the system can be ensured, avoiding management chaos caused by duplicate or incorrect information, thereby improving the reliability and accuracy of goods data management. Based on the unique identification codes and corresponding basic information, and using digital twin technology, the real-time status of warehouse shelves can be mapped, and shelf locations can be dynamically allocated. This comprehensively reflects the real-time status of warehouse shelves and enables dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention. By receiving order data and generating picking routes based on it, the optimal picking routes can be automatically planned for different orders, reducing operation time and improving outbound efficiency, thus meeting the needs of rapid logistics response. By acquiring historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, inventory status can be predicted and optimized in advance, thereby avoiding overstocking or shortages and improving the economic efficiency of warehouse operations.

[0043] The above-mentioned objective three of this application is achieved through the following technical solution:

[0044] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described goods entry and exit management method.

[0045] The fourth objective of this application is achieved through the following technical solution:

[0046] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described goods entry and exit management method.

[0047] In summary, this application includes the following beneficial technical effects:

[0048] 1. By collecting basic information about goods and generating unique identification codes based on this information, the uniqueness and accuracy of each item in the system can be ensured, avoiding management chaos caused by duplicate or incorrect information, thereby improving the reliability and accuracy of goods data management. 2. By using digital twin technology to map warehouse shelf status in real time based on the unique identification codes and corresponding basic information, and dynamically allocating shelf positions, the real-time status of warehouse shelves can be comprehensively reflected, enabling dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention. 3. By receiving order data and generating picking routes based on it, the optimal picking routes can be automatically planned for different orders, reducing operation time and improving outbound efficiency, thus meeting the needs of rapid logistics response. 4. By acquiring historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, inventory status can be predicted and optimized in advance, thereby avoiding overstocking or shortages and improving the economic efficiency of warehouse operations.

[0049] 2. By storing goods into a pre-set digital twin warehouse model based on basic information, a virtual mapping between goods information and warehouse shelf status can be achieved, ensuring the visualization and real-time nature of goods storage information, thereby improving management efficiency and the scientific nature of decision-making; by dynamically adjusting the goods storage location based on the real-time mapping results and the shelf allocation algorithm, the storage location can be dynamically allocated according to actual needs, avoiding waste or overloading of shelf resources, thereby optimizing the overall utilization rate of the shelves;

[0050] 3. By obtaining product volume data from basic product information and combining it with remaining shelf space information, storage locations can be dynamically allocated based on the matching degree between storage needs and shelf capacity, thereby ensuring the rationality of product storage and avoiding space waste; by obtaining shelf zoning categories from warehouse shelf information and determining product storage areas based on zoning categories, suitable storage environments can be provided for different types of products, thereby meeting the storage needs of special products and improving product storage security; by evaluating the distribution of shelf information, product distribution can be dynamically adjusted to avoid shelf overloading or uneven distribution, thereby optimizing the overall warehouse space utilization and improving operational convenience. Attached Figure Description

[0051] Figure 1 This is a flowchart of a goods entry and exit management method according to one embodiment of this application;

[0052] Figure 2 This is a flowchart illustrating the implementation of step S20 in the goods inbound and outbound management method of one embodiment of this application;

[0053] Figure 3 This is a flowchart illustrating the implementation of step S22 in the goods inbound and outbound management method of one embodiment of this application;

[0054] Figure 4 This is a flowchart illustrating the implementation of step S221 in the goods inbound and outbound management method of one embodiment of this application;

[0055] Figure 5 This is a flowchart illustrating the implementation of step S30 in the goods inbound and outbound management method of one embodiment of this application;

[0056] Figure 6 This is a flowchart illustrating the implementation of step S40 in the goods inbound and outbound management method of one embodiment of this application;

[0057] Figure 7 This is a schematic diagram of a goods inbound and outbound management system according to one embodiment of this application;

[0058] Figure 8 This is a schematic diagram of a device according to one embodiment of this application. Detailed Implementation

[0059] The present application is further described in detail below with reference to the accompanying drawings.

[0060] In one embodiment, such as Figure 1 As shown, this application discloses a method for managing the inbound and outbound of goods, which specifically includes the following steps:

[0061] S10: Collect basic information about the goods and generate a unique identifier for the goods based on the basic information.

[0062] Specifically, basic information about the goods is obtained through barcode scanning devices or RFID readers, including characteristics such as the volume, weight, number of pieces, and category of the goods. At the same time, combined with the specific storage requirements of the goods, the information processing module organizes the collected information and inputs it into the warehouse management system. The information processing module uses a unique identifier generation algorithm to generate a unique identifier code for the goods. This unique identifier code contains the attribute information of the goods and the internal tracking identifier of the system. The generated unique identifier code is transmitted to the system database through the data interface and used in subsequent goods storage management and operation processes.

[0063] S20: Based on the unique identifier of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf positions are dynamically allocated to generate storage optimization plans. According to the shelf allocation plan, personnel and / or automated equipment are guided to put the goods into the warehouse.

[0064] Specifically, the warehouse management system constructs a virtual warehouse model using digital twin technology, receives unique identifiers and basic information of goods in real time, and obtains real-time status information of shelves from the shelf monitoring module through a data interface, including shelf space occupancy rate, remaining capacity, and goods distribution status. The system dynamically analyzes the volume, category, and special storage requirements of goods with shelf status information to calculate the optimal storage location of goods and generate the optimal path guidance from the entry point to the target shelf. The path planning module generates instructions and sends them to personnel operation terminals or automated equipment. After the entry task is executed, the storage status and inventory information of the goods are updated in real time.

[0065] S30: Receive order data, generate a picking route based on the order data, guide personnel and / or automated equipment to pick up goods based on the picking route, and update real-time inventory information.

[0066] Specifically, after receiving order data, the warehouse management system queries the storage location of goods based on the unique identifier of the goods contained in the order. At the same time, it analyzes the delivery time and delivery location information in the order, prioritizes the order tasks through the intelligent scheduling module, and generates a picking route in combination with the path optimization module. The system generates the optimal path guidance from the shelf to the outbound point and sends it to the operation terminal or automated equipment. Personnel or equipment complete the outbound operation according to the guidance. After the goods are outbound, the system updates the inventory information in real time and stores the operation record in the database.

[0067] S40: Obtain historical inventory data and predict future inventory status based on real-time inventory information and historical inventory data, and dynamically generate replenishment plans based on future inventory status.

[0068] Specifically, the system retrieves historical inventory data from the database, including sales records, production cycles, and seasonal demand. It extracts features from the historical data using a time series analysis model and identifies cyclical trends and sudden demand. Simultaneously, it dynamically adjusts model parameters based on real-time inventory information to generate inventory demand forecasts. The system calculates future inventory status based on the forecasts and generates replenishment plans. The replenishment plans include the types of goods to be replenished, the quantity to be replenished, and the replenishment priority. These plans are then distributed to the supply chain management module through the system interface.

[0069] S50: Perform inventory checks on warehouse goods according to a preset time period, obtain corresponding inventory data, compare the inventory data with the inventory, identify inventory discrepancies, and generate an inventory display table.

[0070] Specifically, the system generates an inventory task list based on a preset time period, such as setting each container to be inventoried at least 3 times per month, and randomly generates a daily inventory data table for the container. The system scans and collects inventory data one by one using drones carrying barcode scanning equipment or automated scanning devices. The inventory data includes the number of items, storage location, and status information. The system compares the inventory data with the inventory records in the database to identify inventory surpluses, shortages, and missed scans. The discrepancies are analyzed by the discrepancy analysis module to generate an inventory report. The system further generates an inventory display table through the data visualization module and synchronizes the inventory results to the ERP system to ensure the consistency of inventory data.

[0071] In one embodiment, such as Figure 2 As shown, in step S20, the warehouse shelf status is mapped in real time using digital twin technology, and shelf locations are dynamically allocated to generate a storage optimization plan. Specifically, this includes:

[0072] S21: Based on the basic information, store the goods corresponding to the basic information into the preset digital twin warehouse model, and then map the warehouse shelf information and the basic information of the goods in real time.

[0073] Specifically, the unique identifier and basic information of the goods are used as input data and loaded into the digital twin warehouse model. The digital twin warehouse model receives the status data of the shelves through the real-time monitoring module and associates and maps it with the basic information of the goods. It updates the storage location of the goods and the status of the shelves in real time, and displays the virtual storage status of the goods and the real-time utilization rate of the shelves through the system's visual interface, which is used for subsequent operational decisions and management optimization.

[0074] S22: Based on the real-time mapping results, the storage location of goods is dynamically adjusted through the shelf allocation algorithm to obtain the storage path guidance for goods entering the warehouse and generate a storage optimization plan.

[0075] Specifically, the system dynamically adjusts the storage location of goods based on the basic information of the goods and the status data of the shelves through the shelf allocation algorithm. It calculates the adaptability of the shelves by combining the volume, category and special storage requirements of the goods. It generates the target storage location of the goods based on the remaining capacity of the shelves and the partition attributes. At the same time, the path planning module generates the inbound path guidance of the goods based on the physical layout of the warehouse and the target storage location. The system sends the path information to the executor through the operation terminal. At the same time, it generates an optimization plan based on the balance of storage distribution to improve the overall space utilization of the warehouse.

[0076] In one embodiment, such as Figure 3 As shown, in step S22, the storage location of goods is dynamically adjusted through a shelf allocation algorithm to obtain the storage path guidance for goods entering the warehouse and generate a storage optimization plan, which specifically includes:

[0077] S221: The shelf availability is assessed based on the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm, and the storage location of the goods is generated.

[0078] Specifically, the warehouse management system dynamically analyzes the basic information of goods and the status information of warehouse shelves through the shelf allocation module. The basic information of goods includes the volume, weight, category and special storage requirements of the goods, while the warehouse shelf information includes the remaining space of the shelves, the partition category and the current load status. The system uses a shelf availability assessment algorithm to match the storage requirements of goods with the actual status of the shelves, selects target shelves suitable for storing goods, optimizes storage selection based on the distribution balance of the shelves, and finally generates the storage location of goods and records it in the system database for subsequent inbound path planning and dynamic adjustment.

[0079] S222: Employs a path optimization algorithm to generate storage path guidance for goods entering the warehouse based on the storage location of the goods and the preset physical layout of the shelves.

[0080] Specifically, the path optimization module receives the target storage location of the goods and the physical layout information of the warehouse. Combined with the current location of the goods' entry point, it uses path planning algorithms such as A* algorithm or Dijkstra's algorithm to calculate the shortest path from the entry point to the target shelf. During the path planning process, it considers possible obstacles and dynamic passage status in the warehouse to ensure that the generated path is the optimal path under the current environment. The system transmits the generated path information to personnel or automated equipment through the operation terminal to guide them to complete the goods entry task according to the path. At the same time, it updates the storage location of the goods and the shelf status in the system in real time.

[0081] In one embodiment, such as Figure 4As shown, in step S221, a shelf availability assessment is performed on the basic information of the goods and the warehouse shelf information using a preset shelf allocation algorithm. This specifically includes:

[0082] S2211: Obtain the volume data of the goods from the basic information of the goods, obtain the remaining space information of the warehouse shelves from the warehouse shelf information, and determine the shelf where the goods are stored based on the degree of matching between the volume data of the goods and the remaining space information of the shelves.

[0083] Specifically, the volume data and storage requirements of goods are extracted through the goods information interface, and the remaining space information of all shelves is obtained in real time through the shelf status interface. The shelf allocation module calculates the matching score based on the matching degree between the volume data of goods and the remaining space of the shelves. Shelves with high matching degree will be recommended as target shelves first. At the same time, the system filters according to the actual load-bearing capacity and ease of operation of the shelves to ensure that the allocated target shelves meet the storage requirements of goods and are easy to operate.

[0084] S2212: Obtain the shelf partition category from the warehouse shelf information, and determine the shelf area for storing goods based on the shelf partition category.

[0085] Specifically, the shelving allocation module extracts the partition category information of the warehouse shelving through the partition management module. The shelving partition categories are divided into high-frequency zones, moisture-proof zones, etc., according to the storage needs of the goods. The system matches the appropriate storage partition according to the category information of the goods. For example, the goods that are frequently retrieved are allocated to the high-frequency operation zone. This partition management ensures that the storage of goods meets their physical characteristics and retrieval needs, thereby improving the storage efficiency and safety of the goods.

[0086] S2213: Assess the distribution of warehouse shelving information based on the shelving and shelving areas where goods are stored, to ensure the overall uniformity of goods distribution.

[0087] Specifically, after the storage location of goods is determined, the overall distribution of warehouse shelves is evaluated through the distribution balancing module. The evaluation includes the distribution density of goods, the load uniformity of shelves, and the utilization rate of storage zones. By analyzing the load uniformity data of shelves, the system automatically adjusts the storage plan to avoid overloading or excessive vacancy of shelves in certain areas. At the same time, the distribution density of shelf areas is dynamically optimized to improve the overall space utilization of the warehouse and reduce the complexity of goods storage and retrieval.

[0088] In one embodiment, such as Figure 5 As shown, in step S30, which involves receiving order data and generating a delivery route based on the order data, the specific steps include:

[0089] S31: Query the unique identifier of the corresponding order item based on the received order data, and query the corresponding shelf location based on the unique identifier.

[0090] Specifically, the order management module retrieves the product list from the order, calls the database interface to query the unique identifier of each product, and matches the product's storage location based on the identifier. The shelf location includes the shelf number, level, and partition information of the shelf where the product is located. The query results are synchronized to the system's path planning module in real time to generate order picking route guidance.

[0091] S32: Based on the delivery time requirements, delivery location requirements, and cargo quantity information of the received order data, prioritize the corresponding orders to obtain the processing priority of different orders.

[0092] Specifically, the system calculates the time urgency of each order through the delivery time module, analyzes the delivery route and physical distance of the order in conjunction with the delivery location module, and finally analyzes the complexity and processing difficulty of the order through the cargo volume module. The system calculates the priority score of the order by weighting the three parameters. Orders with higher priority are processed first in the task queue and enter the route optimization stage.

[0093] S33: Based on the processing priority, the corresponding orders are processed sequentially, and the shortest path is calculated for the shelf location using a path optimization algorithm to obtain the picking path.

[0094] Specifically, the route optimization module processes the picking tasks of goods one by one through the priority task list of orders. It generates the shortest path from the shelf to the outbound point based on the storage location of the goods and the physical layout of the warehouse. The route optimization algorithm calculates the optimal path based on the A* algorithm or Dijkstra's algorithm, and adjusts the path results in combination with the dynamic environment of the warehouse. The generated picking path is sent to the personnel operation terminal or automated equipment in real time for the execution of the picking task.

[0095] In one embodiment, such as Figure 6 As shown, in step S40, historical inventory data is acquired, and future inventory status is predicted based on real-time inventory information and historical inventory data. This specifically includes:

[0096] S41: Extract sales records, production cycles, and seasonal demand for goods from historical inventory data, and conduct time series analysis using a time series analysis model to identify cyclical trends and sudden demand for goods, thereby obtaining corresponding sales characteristics.

[0097] Specifically, the system extracts sales records, production cycles, and seasonal demand characteristics of goods from historical inventory data. The feature extraction module processes the historical data to extract key data points, such as sales peaks during specific time periods, the production cycle of goods purchased based on sales data, and sales fluctuations caused by seasonal changes. Time series analysis models, such as ARIMA or Prophet, are used to perform time-series analysis on the extracted historical data. This analysis includes identifying cyclical trends, such as the length of peak and off-peak sales seasons, and capturing sudden demand events, such as the time distribution of promotional activities or abnormal peaks. Finally, a sales characteristic dataset for each product is generated, containing demand patterns and abnormal characteristics for subsequent modeling and inventory forecasting.

[0098] S42: Use machine learning algorithms to model sales characteristics, optimize model parameters based on real-time inventory information, generate inventory demand forecast results within a preset time period, and then calculate future inventory status.

[0099] Specifically, the extracted sales feature dataset is input into the modeling module. The modeling module uses machine learning algorithms to train the dataset to generate a predictive model that reflects the demand patterns of goods. This model includes a short-term prediction module and a long-term trend module, which are used to predict the sales volume in the short term, such as daily or weekly demand, and the long-term inventory demand trend, respectively. Real-time inventory information is input into the modeling module through the inventory status interface to dynamically optimize the model parameters, including adjusting the safety stock coefficient and replenishment cycle length in the prediction model. Through model training and optimization, the system generates inventory demand prediction results within a preset time period. The prediction results include the demand quantity, demand change rate, and corresponding time distribution of each product. Based on the prediction results and the current inventory level, the system calculates the future inventory status and finally generates a demand list and replenishment plan for the products to guide warehouse storage management and supply chain scheduling.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0101] In one embodiment, a goods inbound / outbound management system is provided, which corresponds one-to-one with the goods inbound / outbound management methods described in the above embodiments. For example... Figure 7 As shown, the goods in / out inventory management system includes an information collection module, a digital twin mapping module, an order processing module, an inventory forecasting module, and an inventory management module. Detailed descriptions of each functional module are as follows:

[0102] The information collection module is used to collect basic information about the goods and generate a unique identifier for the goods based on the basic information.

[0103] The digital twin mapping module is used to map the warehouse shelf status in real time based on the unique identifier of the goods and the corresponding basic information, dynamically allocate shelf positions, generate storage optimization plans, and guide personnel and / or automated equipment to put the goods into the warehouse according to the shelf allocation plan.

[0104] The order processing module is used to receive order data, generate picking routes based on the order data, guide personnel and / or automated equipment to pick up goods based on the picking routes, and update real-time inventory information.

[0105] The inventory forecasting module is used to acquire historical inventory data and predict future inventory status based on real-time inventory information and historical inventory data, and dynamically generate replenishment plans based on future inventory status.

[0106] The inventory management module is used to perform inventory counts on warehouse goods according to preset time periods, obtain corresponding inventory data, compare the inventory data with the inventory, identify inventory discrepancies, and generate an inventory display table.

[0107] Optionally, the digital twin mapping module specifically includes:

[0108] The information loading submodule is used to store the goods corresponding to the basic information into the preset digital twin warehouse model based on the basic information, thereby mapping the warehouse shelf information and the basic information of the goods in real time.

[0109] The shelf allocation submodule is used to dynamically adjust the storage location of goods based on real-time mapping results and through shelf allocation algorithms, obtain storage path guidance for goods entering the warehouse, and generate storage optimization solutions.

[0110] Optionally, the shelf allocation submodule specifically includes:

[0111] The availability assessment unit is used to assess the availability of goods and warehouse shelving information based on a preset shelving allocation algorithm, and to generate the storage location of the goods.

[0112] The path optimization unit is used to generate storage path guidance for goods entering the warehouse based on the storage location of the goods and the preset physical layout of the shelves, using a path optimization algorithm.

[0113] Optionally, the usability assessment unit may specifically include:

[0114] The data matching subunit is used to obtain the volume data of goods from the basic information of goods, obtain the remaining space information of the warehouse shelves from the warehouse shelf information, and determine the shelf where the goods are stored based on the degree of matching between the volume data of goods and the remaining space information of the shelves.

[0115] The partition selection sub-unit is used to obtain the shelf partition category from the warehouse shelf information and determine the shelf area for storing goods based on the shelf partition category;

[0116] The distribution assessment subunit is used to assess the distribution of warehouse shelving information based on the shelving and shelving areas where goods are stored, in order to ensure the overall uniformity of goods distribution.

[0117] Optionally, the order processing module specifically includes:

[0118] The order query submodule is used to query the unique identifier of the corresponding order item based on the received order data, and then query the corresponding shelf location based on the unique identifier.

[0119] The priority sorting submodule is used to sort the corresponding orders based on the delivery time requirements, delivery location requirements, and cargo quantity information of the received order data, so as to obtain the processing priority of different orders.

[0120] The path calculation submodule is used to process corresponding orders sequentially based on processing priority, and to calculate the shortest path to the shelf location using a path optimization algorithm to obtain the picking route.

[0121] Optionally, the inventory forecasting module specifically includes:

[0122] The data extraction submodule is used to extract sales records, production cycles and seasonal demand of goods from historical inventory data, and to perform time series analysis through a time series analysis model to identify the periodic trends and sudden demand of goods and obtain the corresponding sales characteristics.

[0123] The feature modeling submodule is used to model sales features using machine learning algorithms, optimize model parameters based on real-time inventory information, generate inventory demand forecast results within a preset time period, and then calculate future inventory status.

[0124] Specific limitations regarding the goods in / out inventory management system can be found in the limitations of the goods in / out inventory management methods described above, and will not be repeated here. Each module in the aforementioned goods in / out inventory management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for managing the inbound and outbound inventory of goods.

[0126] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0127] Collect basic information about the goods and generate a unique identification code for the goods based on the basic information;

[0128] Based on the unique identifier of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf positions are dynamically allocated to generate storage optimization plans. According to the shelf allocation plan, personnel and / or automated equipment are guided to put the goods into the warehouse.

[0129] Receive order data, generate a picking route based on the order data, guide personnel and / or automated equipment to pick up goods based on the picking route, and update real-time inventory information;

[0130] Acquire historical inventory data and predict future inventory status based on real-time inventory information and historical inventory data, and dynamically generate replenishment plans based on future inventory status;

[0131] The warehouse inventory is checked according to a preset time period to obtain corresponding inventory data. The inventory data is then compared with the inventory data to identify inventory discrepancies and generate an inventory display table.

[0132] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0133] Collect basic information about the goods and generate a unique identification code for the goods based on the basic information;

[0134] Based on the unique identifier of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf positions are dynamically allocated to generate storage optimization plans. According to the shelf allocation plan, personnel and / or automated equipment are guided to put the goods into the warehouse.

[0135] Receive order data, generate a picking route based on the order data, guide personnel and / or automated equipment to pick up goods based on the picking route, and update real-time inventory information;

[0136] Acquire historical inventory data and predict future inventory status based on real-time inventory information and historical inventory data, and dynamically generate replenishment plans based on future inventory status;

[0137] The warehouse inventory is checked according to a preset time period to obtain corresponding inventory data. The inventory data is then compared with the inventory data to identify inventory discrepancies and generate an inventory display table.

[0138] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0140] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for managing the inbound and outbound movement of goods, characterized in that, The goods inbound and outbound management method includes: Collect basic information about the goods and generate a unique identifier for the goods based on the basic information; Based on the unique identifier and corresponding basic information of the goods, the warehouse shelf status is mapped in real time using digital twin technology, and shelf locations are dynamically allocated to generate storage optimization plans. According to the shelf allocation plan, personnel and / or automated equipment are guided to store the goods. The warehouse management system constructs a virtual warehouse model using digital twin technology, receives the unique identifier and basic information of the goods in real time, and obtains real-time shelf status information from the shelf monitoring module through a data interface, including shelf space occupancy, remaining capacity, and goods distribution status. The system dynamically analyzes the volume, category, and special storage requirements of the goods in conjunction with the shelf status information to calculate the optimal storage location for the goods and generate a route from the entry point to the target shelf. The optimal path guidance system generates instructions through the path planning module and sends them to the operator's terminal or automated equipment. After the warehousing task is executed, the system updates the storage status and inventory information of the goods in real time. The system dynamically adjusts the storage location of goods based on the basic information of the goods and the status data of the shelves through the shelf allocation algorithm. It calculates the adaptability of the shelves by combining the volume, category and special storage requirements of the goods, and generates the target storage location of the goods based on the remaining capacity of the shelves and the partition attributes. At the same time, the path planning module generates the warehousing path guidance of the goods based on the physical layout of the warehouse and the target storage location. The system sends the path information to the executor through the operator's terminal, and generates an optimization plan based on the balance of storage distribution to improve the overall space utilization of the warehouse. The process of mapping warehouse shelf status in real time using digital twin technology and dynamically allocating shelf locations to generate storage optimization schemes specifically includes: storing goods corresponding to the basic information into a preset digital twin warehouse model based on the basic information, thereby performing real-time mapping between warehouse shelf information and the basic information of goods; dynamically adjusting the storage location of goods based on the real-time mapping result using a shelf allocation algorithm to obtain storage path guidance for goods entering the warehouse, and generating the storage optimization scheme, specifically including: evaluating shelf availability based on the basic information of goods and the warehouse shelf information using a preset shelf allocation algorithm, and generating the storage location of goods; and using a path optimization algorithm to adjust the storage location of goods based on the storage location and the preset... The physical layout of the shelves generates storage path guidance for goods entering the warehouse; the shelf availability assessment of the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm specifically includes: obtaining the volume data of the goods from the basic information of the goods, obtaining the remaining shelf space information from the warehouse shelf information, and determining the shelf to store the goods based on the degree of matching between the volume data of the goods and the remaining shelf space information; obtaining the shelf partition category from the warehouse shelf information, and determining the shelf area to store the goods based on the shelf partition category; and assessing the distribution of the warehouse shelf information based on the shelves where the goods are stored and the shelf area to ensure the overall uniformity of the goods distribution. Receive order data, generate a distribution route based on the order data, guide personnel and / or automated equipment to dispatch the goods based on the distribution route, and update real-time inventory information; Acquire historical inventory data, predict future inventory status based on the real-time inventory information and historical inventory data, and dynamically generate a replenishment plan based on the future inventory status; The system performs inventory checks on warehouse goods according to a preset time period, obtains corresponding inventory data, compares the inventory data with the actual inventory, identifies inventory discrepancies, and generates an inventory display table. The system generates an inventory task list based on the preset time period, requiring each container to be inventoried at least three times per month, and randomly generates daily inventory data tables for each container. Goods are scanned one by one using drones equipped with barcode scanning devices or automated scanning devices to collect inventory data, including the number of items, storage location, and status information. The system compares the inventory data with inventory records in the database to identify inventory surpluses, shortages, and missed scans. Discrepancies are analyzed using a discrepancy analysis module to generate an inventory report. The system further generates an inventory display table through a data visualization module and synchronizes the inventory results to the ERP system to ensure inventory data consistency.

2. The goods inbound and outbound management method according to claim 1, characterized in that, The process of receiving order data and generating a delivery route based on the order data specifically includes: Based on the received order data, query the unique identifier of the corresponding order item, and then query the corresponding shelf location based on the unique identifier. Based on the delivery time requirements, delivery location requirements, and cargo quantity information of the received order data, the corresponding orders are prioritized to obtain the processing priority of different orders; Based on the processing priority, the corresponding orders are processed sequentially, and the shortest path is calculated for the shelf location using a path optimization algorithm to obtain the picking path.

3. The method for managing the entry and exit of goods according to claim 1, characterized in that, The process of acquiring historical inventory data and predicting future inventory status based on the real-time inventory information and historical inventory data specifically includes: The sales records, production cycles, and seasonal demand of the goods are extracted from the historical inventory data, and time series analysis is performed using a time series analysis model to identify the cyclical trends and sudden demand of the goods and obtain the corresponding sales characteristics. The sales characteristics are modeled using machine learning algorithms, and the model parameters are optimized based on the real-time inventory information to generate inventory demand forecast results within a preset time period, thereby calculating the future inventory status.

4. A goods inbound and outbound management system, characterized in that, The goods in / out inventory management system includes: The information collection module is used to collect basic information about the goods and generate a unique identifier for the goods based on the basic information. The digital twin mapping module is used to map the warehouse shelf status in real time based on the unique identifier of the goods and the corresponding basic information, and dynamically allocate shelf positions to generate storage optimization plans. According to the shelf allocation plan, it guides personnel and / or automated equipment to store the goods. The warehouse management system constructs a virtual warehouse model using digital twin technology, receives the unique identifier and basic information of the goods in real time, and obtains real-time shelf status information from the shelf monitoring module through a data interface, including shelf space occupancy, remaining capacity, and goods distribution status. The system dynamically analyzes the volume, category, and special storage requirements of the goods in conjunction with the shelf status information to calculate the optimal storage location for the goods and generate a storage optimization plan from the entry point to the warehouse. The optimal path guidance for the target shelf is generated by the path planning module and sent to the operator terminal or automated equipment. After the warehousing task is executed, the storage status and inventory information of the goods are updated in real time. The system dynamically adjusts the storage location of goods based on the basic information of the goods and the shelf status data through the shelf allocation algorithm. It calculates the adaptability of the shelves by combining the volume, category and special storage requirements of the goods. The system generates the target storage location of the goods based on the remaining capacity and partition attributes of the shelves. At the same time, the path planning module generates the warehousing path guidance of the goods based on the physical layout of the warehouse and the target storage location. The system sends the path information to the executor through the operator terminal. At the same time, it generates an optimization plan based on the balance of storage distribution to improve the overall space utilization of the warehouse. The process of mapping warehouse shelf status in real time using digital twin technology and dynamically allocating shelf locations to generate storage optimization schemes specifically includes: storing goods corresponding to the basic information into a preset digital twin warehouse model based on the basic information, thereby performing real-time mapping between warehouse shelf information and the basic information of goods; dynamically adjusting the storage location of goods based on the real-time mapping result using a shelf allocation algorithm to obtain storage path guidance for goods entering the warehouse, and generating the storage optimization scheme, specifically including: evaluating shelf availability based on the basic information of goods and the warehouse shelf information using a preset shelf allocation algorithm, and generating the storage location of goods; and using a path optimization algorithm to adjust the storage location of goods based on the storage location and the preset... The physical layout of the shelving generates storage path guidance for goods entering the warehouse; the shelving availability assessment is performed on the basic information of the goods and the warehouse shelving information using a preset shelving allocation algorithm, specifically including: obtaining the volume data of the goods from the basic information of the goods, obtaining the remaining space information of the shelving from the warehouse shelving information, and determining the shelving for storing the goods based on the degree of matching between the volume data of the goods and the remaining space information of the shelving; obtaining the shelving partition category from the warehouse shelving information, and determining the shelving area for storing the goods based on the shelving partition category; and assessing the distribution of the warehouse shelving information based on the shelving for storing the goods and the shelving area to ensure the overall uniformity of the distribution of goods. The order processing module is used to receive order data, generate a picking route based on the order data, guide personnel and / or automated equipment to pick up the goods based on the picking route, and update real-time inventory information. The inventory forecasting module is used to acquire historical inventory data, predict future inventory status based on the real-time inventory information and historical inventory data, and dynamically generate a replenishment plan based on the future inventory status. The inventory management module is used to perform inventory counts on warehouse goods according to a preset time period, obtain corresponding inventory data, compare the inventory data with the inventory, identify inventory discrepancies, and generate an inventory display table. The system generates an inventory task list according to a preset time period, which requires each container to be inventoried at least 3 times per month, and randomly generates a daily inventory data table for the container. The system uses drones carrying barcode scanning devices or automated scanning devices to scan each item and collect inventory data. The inventory data includes the number of items, storage location, and status information. The system compares the inventory data with the inventory records in the database to identify inventory surpluses, shortages, and missed scans. The discrepancy data generates an inventory report through the discrepancy analysis module. The system further generates an inventory display table through the data visualization module and synchronizes the inventory results to the ERP system to ensure the consistency of inventory data.

5. The goods inbound and outbound management system according to claim 4, characterized in that, The digital twin mapping module specifically includes: The information loading submodule is used to store the goods corresponding to the basic information into a preset digital twin warehouse model based on the basic information, thereby mapping the warehouse shelf information and the basic information of the goods in real time. The shelf allocation submodule is used to dynamically adjust the storage location of goods based on real-time mapping results and through the shelf allocation algorithm, obtain the storage path guidance for goods entering the warehouse, and generate the storage optimization scheme.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the goods entry and exit management method as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the goods entry and exit management method as described in any one of claims 1 to 3.

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