Goods warehouse-in and warehouse-out management method and system, computer equipment and storage medium

Abstract

The invention relates to a goods warehouse-in and warehouse-out management method and system, computer equipment and a storage medium, and the method comprises the steps: collecting the basic information of goods, and generating a unique identification code of the goods; based on the unique identification code and the corresponding basic information, the warehouse shelf state is mapped in real time through the digital twinning technology, the shelf position is dynamically allocated, a storage optimization scheme is generated, and personnel and / or automatic equipment are / is guided to warehouse goods; order data are received, a cargo allocation path is generated according to the order data, personnel and / or automatic equipment are guided to carry out warehouse-out on cargos, and real-time inventory information is updated; acquiring historical inventory data, predicting a future inventory state, and dynamically generating a replenishment plan based on the future inventory state; according to a preset time period, inventory operation is performed on warehouse goods to obtain corresponding inventory data, inventory comparison is performed on the inventory data, inventory differences are identified, and an inventory display table is generated. The method has the effect of improving the warehouse management efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of warehouse management, and in particular to a method, system, computer equipment and storage medium for goods in and out of warehouse management. Background Art

[0002] At present, warehouse management plays a vital role in logistics, e-commerce and high-value goods storage, which mainly includes the processes of goods warehousing, storage, outbound and inventory management. Traditional goods management methods use barcodes or simple ERP systems to record and manage goods information, but most of them are based on fixed rules in terms of shelf allocation and distribution path planning. They are not able to respond to real-time dynamic environments and are difficult to cope with complex and changing storage scenarios, resulting in low management efficiency of goods and shelves in warehouses.

[0003] The above-mentioned existing technical solutions have the following defects: the existing methods for managing the entry and exit of goods in terms of shelf allocation and distribution path planning are mostly based on fixed rules, and their ability to respond to real-time dynamic environments is insufficient, resulting in low management efficiency of goods, shelves, etc. in the warehouse, so there is room for improvement. Summary of the invention

[0004] In order to improve warehouse management efficiency, the present application provides a method, system, computer equipment and storage medium for goods in and out of warehouse management.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A method for managing the entry and exit of goods, the method comprising: Collect basic information of the goods and generate a unique identification code for the goods based on the basic information; Based on the unique identification code of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf location is dynamically allocated to generate a storage optimization plan. According to the shelf allocation plan, personnel and / or automated equipment are guided to warehouse the goods; Receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship the goods out of the warehouse based on the distribution path, and update real-time inventory information; Acquire historical inventory data, and 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; Perform inventory operations on warehouse goods according to a preset time period to obtain corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

[0006] By adopting the above technical solution, by collecting the basic information of the goods and generating a unique identification code for the goods based on the basic information, the uniqueness and accuracy of each item in the system can be ensured, and management confusion caused by information duplication or errors can be avoided, thereby improving the reliability and accuracy of goods data management; by mapping the warehouse shelf status in real time based on the unique identification code and corresponding basic information of the goods through digital twin technology, and dynamically allocating shelf positions, it can fully reflect the real-time status of the warehouse shelves and realize dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention; by receiving order data and generating distribution paths based on the order data, it can automatically plan the optimal distribution paths for different orders, reduce operation time, and improve outbound efficiency, thereby meeting the logistics needs of rapid response; by obtaining historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, it can predict and optimize the inventory status in advance, thereby avoiding inventory surplus or shortage problems and improving the economic benefits of warehouse operations.

[0007] In one example, the present application may be further configured as follows: the real-time mapping of warehouse shelf status through digital twin technology, and the dynamic allocation of shelf locations to generate a storage optimization plan specifically include: According to the basic information, the goods corresponding to the basic information are stored in a preset digital twin warehouse model, and then the warehouse shelf information and the basic information of the goods are mapped in real time; Based on the real-time mapping results, the storage location of the goods is dynamically adjusted through the shelf allocation algorithm, the storage path guidance for the goods entering the warehouse is obtained, and the storage optimization plan is generated.

[0008] By adopting the above technical solution, by storing the goods in the preset digital twin warehouse model according to the basic information, it is possible to realize the virtual mapping of the goods information and the warehouse shelf status, ensuring the visualization and real-time nature of the goods storage information, thereby improving management efficiency and the scientific nature of decision-making; by dynamically adjusting the storage location of goods based on the real-time mapping results through the shelf allocation algorithm, the storage location can be dynamically allocated according to actual needs, avoiding waste or overload of shelf resources, thereby optimizing the overall utilization of the shelf.

[0009] In one example, the present application may be further configured as follows: dynamically adjusting the storage location of goods through the shelf allocation algorithm, obtaining the storage path guidance for goods entering the warehouse, and generating the storage optimization plan specifically include: Perform shelf availability evaluation on the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm, and generate the storage location of the goods; A path optimization algorithm is used to generate the storage path guidance for goods entering the warehouse according to the storage location of the goods and the preset physical layout of the shelves.

[0010] By adopting the above technical solution, the shelf availability evaluation is performed on the basic information of the goods and the warehouse shelf information through the preset shelf allocation algorithm, and the storage location can be dynamically screened according to the goods attributes and shelf status, so as to ensure the adaptability of the goods storage and the effective use of the shelves; by adopting the path optimization algorithm to generate storage path guidance for the goods entering the warehouse, it is possible to reduce unnecessary path detours and time waste in the warehousing process, thereby improving the efficiency of goods entering the warehouse.

[0011] In one example, the present application may be further configured as follows: the shelf availability evaluation is performed on the basic information of the goods and the warehouse shelf information by using a preset shelf allocation algorithm, specifically including: Acquire the volume data of the goods from the basic information of the goods, acquire the shelf remaining space information from the warehouse shelf information, and determine the shelf where the goods are stored based on the matching degree between the volume data of the goods and the shelf remaining space information; Acquire a shelf partition category from the warehouse shelf information, and determine a shelf area for storing goods based on the shelf partition category; The distribution of the warehouse shelf information is evaluated based on the shelves and shelf areas where goods are stored to ensure the overall uniformity of the distribution of goods.

[0012] By adopting the above technical solution, by obtaining the volume data of the goods from the basic information of the goods and combining it with the remaining space information of the shelf, the storage location can be dynamically allocated based on the matching degree between the storage demand and the shelf capacity, thereby ensuring the rationality of the storage of goods and avoiding space waste; by obtaining the shelf partition category from the warehouse shelf information and determining the storage area of ​​the goods based on the partition category, it is possible to provide an adaptive storage environment for different categories of goods, thereby meeting the storage needs of special goods and improving the storage safety of the goods; by evaluating the distribution of shelf information, the distribution of goods can be dynamically adjusted to avoid shelf overload or uneven distribution, thereby optimizing the overall space utilization of the warehouse and improving operational convenience.

[0013] In one example, the present application may be further configured as follows: the receiving of order data and generating a distribution path according to the order data specifically includes: Query the unique identification code of the corresponding order product according to the received order data, and query the corresponding shelf location according to the unique identification code; Based on the delivery time requirement, delivery location requirement and cargo quantity information of the received order data, the corresponding orders are prioritized to obtain the processing priorities of different orders; The corresponding orders are processed in sequence based on the processing priority, and the shortest path of the shelf location is calculated using a path optimization algorithm to obtain the distribution path.

[0014] By adopting the above technical solution, by querying the unique identification code of the goods and the corresponding shelf position according to the order data, the location of the ordered goods can be quickly located, thereby reducing the preparation time for delivery; by prioritizing the orders based on the delivery time requirements, delivery location requirements and cargo quantity information of the order data, the processing sequence of the orders can be reasonably arranged to meet the needs of urgent orders, thereby improving the flexibility and efficiency of order processing; by adopting the path optimization algorithm to generate the shortest distribution path, the path waste of operators can be reduced, the distribution efficiency can be improved, thereby reducing labor costs and speeding up order processing.

[0015] In one example, the present application may be further configured as follows: the acquiring of historical inventory data and predicting future inventory status according to the real-time inventory information and the historical inventory data specifically includes: Extracting the sales records, production cycles and seasonal demands of goods from the historical inventory data, and performing time series analysis through a time series analysis model to identify the cyclical trends and sudden demands of goods, and obtain corresponding sales characteristics; The sales characteristics are modeled using a machine learning algorithm, and model parameters are optimized according to the real-time inventory information to generate inventory demand forecast results within a preset time period, thereby calculating the future inventory status.

[0016] By adopting the above technical solution, by extracting the sales records, production cycles and seasonal demands of goods from historical inventory data and performing time series analysis through the time series analysis model, it is possible to identify the cyclical trends and sudden demands of goods, thereby providing accurate demand forecasting basis for inventory management; by adopting machine learning algorithms to model sales characteristics and optimizing model parameters based on real-time inventory information, it is possible to dynamically adjust the accuracy and adaptability of the inventory forecasting model, thereby generating a more reasonable inventory replenishment plan, avoiding inventory surplus or shortage, and improving operational efficiency and economic benefits.

[0017] The second object of the invention is achieved by the following technical solutions: A goods in and out of warehouse management system, the goods in and out of warehouse management system comprising: An information collection module is used to collect basic information of goods and generate a unique identification code for the goods based on the basic information; A digital twin mapping module is used to map the warehouse shelf status in real time based on the unique identification code of the goods and the corresponding basic information through digital twin technology, and dynamically allocate shelf locations to generate a storage optimization plan, and guide personnel and / or automated equipment to warehouse the goods according to the shelf allocation plan; An order processing module, used to receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship the goods out of the warehouse based on the distribution path, and update real-time inventory information; An inventory forecasting module is used to obtain historical inventory data, and 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 operations on warehouse goods according to a preset time period, obtain corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

[0018] By adopting the above technical solution, by collecting the basic information of the goods and generating a unique identification code for the goods based on the basic information, the uniqueness and accuracy of each item in the system can be ensured, and management confusion caused by information duplication or errors can be avoided, thereby improving the reliability and accuracy of goods data management; by mapping the warehouse shelf status in real time based on the unique identification code and corresponding basic information of the goods through digital twin technology, and dynamically allocating shelf positions, it can fully reflect the real-time status of the warehouse shelves and realize dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention; by receiving order data and generating distribution paths based on the order data, it can automatically plan the optimal distribution paths for different orders, reduce operation time, and improve outbound efficiency, thereby meeting the logistics needs of rapid response; by obtaining historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, it can predict and optimize the inventory status in advance, thereby avoiding inventory surplus or shortage problems and improving the economic benefits of warehouse operations.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned goods in-and-out warehouse management method are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned goods in-and-out warehouse management method.

[0021] In summary, this application includes the following beneficial technical effects: 1. By collecting the basic information of the goods and generating a unique identification code for the goods based on the basic information, the uniqueness and accuracy of each item in the system can be ensured, and management confusion caused by duplication or errors of information can be avoided, thereby improving the reliability and accuracy of goods data management; by mapping the warehouse shelf status in real time based on the unique identification code and corresponding basic information of the goods through digital twin technology, and dynamically allocating shelf positions, it can fully reflect the real-time status of the warehouse shelves and realize dynamic management of goods, thereby improving the utilization efficiency of warehouse resources and reducing manual intervention; by receiving order data and generating distribution paths based on order data, it can automatically plan the optimal distribution path for different orders, reduce operation time, and improve outbound efficiency, thereby meeting the logistics needs of rapid response; by obtaining historical inventory data and predicting future inventory status based on real-time inventory information and historical inventory data, it can predict and optimize the inventory status in advance, thereby avoiding inventory surplus or shortage problems and improving the economic benefits of warehouse operations; 2. By storing goods in the preset digital twin warehouse model based on basic information, virtual mapping of 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 scientific decision-making; by dynamically adjusting the storage location of goods based on real-time mapping results through shelf allocation algorithms, storage locations can be dynamically allocated according to actual needs, avoiding waste or overload of shelf resources, thereby optimizing the overall utilization of shelves; 3. By obtaining the volume data of goods from the basic information of the goods and combining it with the remaining space information of the shelf, the storage location can be dynamically allocated based on the matching degree between storage demand and shelf capacity, thereby ensuring the rationality of goods storage and avoiding space waste; by obtaining the shelf partition category from the warehouse shelf information and determining the storage area of ​​the goods based on the partition category, it is possible to provide an adaptive storage environment for different categories of goods, thereby meeting the storage needs of special goods and improving the storage safety of goods; by evaluating the distribution of shelf information, the distribution of goods can be dynamically adjusted to avoid shelf overload or uneven distribution, thereby optimizing the overall space utilization of the warehouse and improving operational convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for managing the entry and exit of goods in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S20 in the goods in and out of the warehouse management method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S22 in the goods in and out management method in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S221 in the goods in and out of the warehouse management method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in the goods in and out of warehouse management method in one embodiment of the present application; Figure 6 This is a flowchart for implementing step S40 in the goods in and out management method in one embodiment of the present application; Figure 7 This is a principle block diagram of a goods in-and-out warehouse management system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a method for managing goods in and out of warehouse, which specifically includes the following steps: S10: Collect basic information of the goods and generate a unique identification code for the goods based on the basic information.

[0025] Specifically, the basic information of the goods, including the volume, weight, number of pieces and category of the goods, is obtained through a barcode scanning device or an RFID reader. At the same time, combined with the special storage requirements of the goods, the collected information is sorted out through the information processing module and input into the warehouse management system. The information processing module uses a unique identification generation algorithm to generate a unique identification code for the goods. The unique identification code contains the attribute information of the goods and the system's internal tracking identification. The generated unique identification code is transmitted to the system database through the data interface and used in subsequent goods storage management and operation processes.

[0026] S20: Based on the unique identification code and corresponding basic information of the goods, the digital twin technology is used to map the warehouse shelf status in real time, dynamically allocate shelf locations, generate storage optimization plans, and guide personnel and / or automated equipment to put goods into storage according to the shelf allocation plan.

[0027] Specifically, the warehouse management system builds a virtual warehouse model through digital twin technology, receives the unique identification code and basic information of the goods in real time, and obtains the real-time status information of the shelf from the shelf monitoring module through the data interface, including the shelf's space occupancy rate, remaining capacity and goods distribution status. The system combines the volume, category and special storage requirements of the goods with the shelf status information for dynamic analysis, calculates the optimal storage location of the goods, and generates the optimal path guidance from the entry point to the target shelf. The path planning module generates instructions and sends them to the personnel operation terminal or automated equipment. After executing the warehousing task, the storage status and inventory information of the goods are updated in real time.

[0028] S30: Receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship goods out of the warehouse based on the distribution path, and update real-time inventory information.

[0029] Specifically, after receiving the order data, the warehouse management system queries the storage location of the goods based on the unique identification code of the goods contained in the order, analyzes the delivery time and delivery location information in the order, prioritizes the order tasks through the intelligent scheduling module, and generates the distribution path 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 operating terminal or automated equipment. The personnel or equipment completes the outbound operation of the goods according to the guidance. After outbound delivery, the system updates the inventory information in real time and stores the operation records in the database.

[0030] S40: Acquire historical inventory data, and predict future inventory status based on real-time inventory information and historical inventory data, and dynamically generate a replenishment plan based on the future inventory status.

[0031] Specifically, the system obtains historical inventory data from the database. The historical data includes sales records, production cycles and seasonal demand of goods. The system uses a time series analysis model to extract features from the historical data and identify cyclical trends and sudden demands. At the same time, the system dynamically adjusts model parameters in combination with real-time inventory information to generate inventory demand forecast results. The system calculates the future inventory status based on the forecast results and generates a replenishment plan. The replenishment plan includes the replenishment type, replenishment quantity and replenishment priority of the goods, and is allocated to the supply chain management module through the system interface.

[0032] S50: performing an inventory operation on the warehouse goods according to a preset time period, obtaining corresponding inventory data, and performing an inventory comparison on the inventory data to identify inventory differences and generate an inventory display table.

[0033] Specifically, the system generates an inventory task list according to a preset time period, such as setting each container to be counted at least three times a month, and randomly generates a daily inventory container data table. The goods are scanned one by one and the inventory data is collected by means of drones carrying barcode scanning equipment or automated scanning devices. The inventory data includes the number of items, storage location and status information of the goods. The system compares the inventory data with the inventory records in the database to identify surplus, shortage and missed scanning. The difference data generates an inventory report through the difference 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 the inventory data.

[0034] In one embodiment, if Figure 2 As shown, in step S20, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf location is dynamically allocated to generate a storage optimization plan, which specifically includes: S21: Based on the basic information, the goods corresponding to the basic information are stored in the preset digital twin warehouse model, and then the warehouse shelf information and the basic information of the goods are mapped in real time.

[0035] Specifically, the unique identification code and basic information of the goods are loaded into the digital twin warehouse model as input data. The digital twin warehouse model receives the status data of the shelf through the real-time monitoring module and associates and maps it with the basic information of the goods. It updates the storage location and shelf status of the goods in real time, and displays the virtual storage status of the goods and the real-time utilization of the shelves through the system visualization interface for subsequent operational decisions and management optimization.

[0036] S22: Based on the real-time mapping results, the storage location of the goods is dynamically adjusted through the shelf allocation algorithm, the storage path guidance for the goods entering the warehouse is obtained, and a storage optimization plan is generated.

[0037] Specifically, the system dynamically adjusts the storage location of goods according to the basic information of the goods and the shelf status data through the shelf allocation algorithm, calculates the adaptability of the shelf based on the volume, category and special storage requirements of the goods, and generates the target storage location of the goods according to the remaining capacity and partition attributes of the shelf. At the same time, the path planning module generates the warehousing path guidance for the goods according to the physical layout of the warehouse and the target storage location. The system sends the path information to the executor through the operation terminal, and generates an optimization plan based on the balance of the storage distribution to improve the overall space utilization of the warehouse.

[0038] In one embodiment, if Figure 3 As shown, in step S22, the storage location of the goods is dynamically adjusted through the shelf allocation algorithm, the storage path guidance for the goods entering the warehouse is obtained, and a storage optimization plan is generated, which specifically includes: S221: Evaluate shelf availability of the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm, and generate a storage location for the goods.

[0039] Specifically, the warehouse management system dynamically analyzes the basic information of the goods and the status information of the warehouse shelves through the shelf allocation module. The basic information of the goods includes the volume, weight, category and special storage requirements of the goods, and the warehouse shelf information includes the remaining space of the shelf, partition category and current load status. The system uses the shelf availability evaluation algorithm to match and calculate the storage requirements of the goods with the actual status of the shelf, and screens out target shelves suitable for storing goods. At the same time, it optimizes the storage selection according to the distribution balance of the shelves, and finally generates the storage location of the goods and records it in the system database for subsequent warehousing path planning and dynamic adjustment.

[0040] S222: Using a path optimization algorithm, a storage path guide for goods entering the warehouse is generated according to the goods storage location and the preset shelf physical layout.

[0041] Specifically, the path optimization module receives the target storage location of the goods and the physical layout information of the warehouse, and combines the current location of the goods entry point with the path planning algorithm such as the A* algorithm or the Dijkstra algorithm to calculate the shortest path from the entry point to the target shelf. During the path planning process, possible obstacles and dynamic channel status in the warehouse are taken into account 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 operating terminal, guiding them to complete the goods entry task according to the path, and at the same time updates the storage location and shelf status of the goods in the system in real time.

[0042] In one embodiment, if Figure 4 As shown, in step S221, the shelf availability is evaluated on the basic information of the goods and the warehouse shelf information by using a preset shelf allocation algorithm, specifically including: S2211: Obtain the volume data of the goods from the basic information of the goods, obtain the shelf remaining space information from the warehouse shelf information, and determine the shelf for storing the goods based on the degree of matching between the volume data of the goods and the shelf remaining space information.

[0043] Specifically, the volume data and storage requirements of the 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 adaptation score according to the matching degree between the volume data of the goods and the remaining space of the shelf. The shelves with high matching degree will be recommended as target shelves. At the same time, the system filters according to the actual carrying capacity and ease of operation of the shelves to ensure that the allocated target shelves meet the storage requirements of the goods and are easy to operate.

[0044] S2212: Obtain shelf partition categories from warehouse shelf information, and determine shelf areas for storing goods based on the shelf partition categories.

[0045] Specifically, the shelf allocation module extracts the partition category information of the warehouse shelves through the partition management module, where the shelf partition categories are divided into high-frequency areas, moisture-proof areas, etc. according to the storage requirements of the goods. The system matches the appropriate storage partitions according to the category information of the goods. For example, goods with high frequency of pickup are allocated to high-frequency operation areas. Through this partition management, it ensures that the storage of goods meets their physical characteristics and pickup requirements, thereby improving the storage efficiency and safety of goods.

[0046] S2213: Evaluate the distribution of warehouse shelf information based on the shelves and shelf areas where goods are stored to ensure the overall uniformity of goods distribution.

[0047] Specifically, after the storage location of the goods is determined, the overall distribution of the warehouse shelves is evaluated through the distribution balance module. The evaluation content includes the distribution density of the goods, the load uniformity of the shelves and the utilization rate of the storage partitions. By analyzing the load uniformity data of the 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 the shelf area is dynamically optimized to improve the overall space utilization of the warehouse and reduce the complexity of goods storage and retrieval.

[0048] In one embodiment, if Figure 5 As shown, in step S30, order data is received and a distribution path is generated according to the order data, which specifically includes: S31: query the unique identification code of the corresponding order product according to the received order data, and query the corresponding shelf location according to the unique identification code.

[0049] Specifically, the order management module retrieves the list of goods in the order, calls the database interface to query the unique identification code of each item and matches the storage location of the item according to the identification code. The shelf location includes the number, level and partition information of the shelf where the item is located. The query results are synchronized to the system's path planning module in real time to generate the order's distribution path guidance.

[0050] S32: Based on the delivery time requirement, delivery location requirement and cargo quantity information of the received order data, the corresponding orders are prioritized to obtain processing priorities of different orders.

[0051] Specifically, the delivery time module is used to calculate the time urgency of each order, and the delivery location module is used to analyze the order's delivery path and physical distance. Finally, the cargo volume module is used to analyze the complexity and processing difficulty of the order. The system weights the three parameters to generate an order priority score. Orders with high priority are given priority in the task queue and enter the path optimization stage.

[0052] S33: Based on the processing priority, the corresponding orders are processed in sequence, and the shortest path of the shelf location is calculated using the path optimization algorithm to obtain the distribution path.

[0053] Specifically, the path optimization module processes the picking tasks of goods one by one through the priority task list of the order, and generates the shortest path from the shelf to the delivery point according to the storage location of the goods and the physical layout of the warehouse. The path optimization algorithm calculates the optimal path based on the A* algorithm or the Dijkstra algorithm, and adjusts the path results in combination with the dynamic environment of the warehouse. The generated distribution path is sent in real time to the personnel operation terminal or automated equipment for executing the distribution task.

[0054] In one embodiment, if Figure 6As shown, in step S40, historical inventory data is obtained, and future inventory status is predicted based on real-time inventory information and historical inventory data, specifically including: S41: Extract the sales records, production cycles and seasonal demands of goods from historical inventory data, and perform time series analysis through the time series analysis model to identify the cyclical trends and sudden demands of goods and obtain the corresponding sales characteristics.

[0055] Specifically, the sales records, production cycles and seasonal demand characteristics of goods are extracted from historical inventory data. The system processes the historical data through the feature extraction module to extract key data points, such as sales peaks in specific time periods, the production cycles of goods when purchasing according to sales data, and sales fluctuations caused by seasonal changes. The extracted historical data are subjected to time series analysis through time series analysis models such as the ARIMA model or the Prophet model. The analysis process includes the identification of cyclical trends such as the cycle lengths of peak and off-season sales, and the capture of sudden demands such as the time distribution of promotional activities or abnormal peaks. Finally, a sales feature data set of goods is generated, which contains the demand patterns and abnormal characteristics of each product for subsequent modeling and inventory forecasting.

[0056] 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.

[0057] Specifically, the extracted sales feature data set is input into the modeling module. The modeling module uses a machine learning algorithm to train the data set to generate a prediction model that can reflect the law of product demand. The 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 in the future, such as daily demand or weekly demand, and the long-term inventory demand change trend. Real-time inventory information is input into the modeling module through the inventory status interface, which is used to dynamically optimize the model parameters, including adjusting the safety stock coefficient and the replenishment cycle length in the prediction model. The inventory demand forecast results within a preset time period are generated through model training and optimization. The forecast results include the demand for each product, the demand change rate and the corresponding time distribution. The system calculates the future inventory status based on the forecast results combined with the current inventory level, and finally generates a demand list and replenishment plan for the goods to guide the warehouse's storage management and supply chain scheduling.

[0058] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0059] In one embodiment, a goods in and out of warehouse management system is provided, and the goods in and out of warehouse management system corresponds one-to-one to the goods in and out of warehouse management method in the above embodiment. Figure 7 As shown in the figure, the goods in and out warehouse management system includes information collection module, digital twin mapping module, order processing module, inventory forecasting module, and inventory management module. The detailed description of each functional module is as follows: The information collection module is used to collect basic information of the goods and generate a unique identification code 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 identification code and corresponding basic information of the goods through digital twin technology, dynamically allocate shelf locations, generate storage optimization plans, and guide personnel and / or automated equipment to warehouse the goods according to the shelf allocation plan; The order processing module is used to receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship goods out of the warehouse based on the distribution path, and update real-time inventory information; Inventory forecasting module, which is used to 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; The inventory management module is used to perform inventory operations on warehouse goods according to a preset time period, obtain corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

[0060] Optionally, the digital twin mapping module specifically includes: The information loading submodule is used to store the goods corresponding to the basic information into the preset digital twin warehouse model according to the basic information, and then perform real-time mapping of the warehouse shelf information and the basic information of the goods; The shelf allocation submodule is used to dynamically adjust the storage location of goods based on the real-time mapping results through the shelf allocation algorithm, obtain the storage path guidance for the goods entering the warehouse, and generate a storage optimization plan.

[0061] Optionally, the shelf allocation submodule specifically includes: An availability evaluation unit, used to evaluate the shelf availability of the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm, and generate a storage location for the goods; 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 by using a path optimization algorithm.

[0062] Optionally, the usability evaluation unit specifically includes: The data matching subunit is used to obtain the volume data of the goods from the basic information of the goods, obtain the shelf remaining space information from the warehouse shelf information, and determine the shelf where the goods are stored based on the matching degree between the volume data of the goods and the shelf remaining space information; A partition selection subunit 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; The distribution assessment subunit is used to evaluate the distribution of warehouse shelf information based on the shelves and shelf areas where the goods are stored to ensure the overall uniformity of the goods distribution.

[0063] Optionally, the order processing module specifically includes: The order query submodule is used to query the unique identification code of the corresponding order goods according to the received order data, and query the corresponding shelf location according to the unique identification code; The priority sorting submodule is used to sort the corresponding orders according to the delivery time requirements, delivery location requirements and cargo quantity information of the received order data, and obtain the processing priority of different orders; The path calculation submodule is used to process the corresponding orders in sequence based on the processing priority, and use the path optimization algorithm to calculate the shortest path of the shelf location to obtain the distribution path.

[0064] Optionally, the inventory forecasting module specifically includes: The data extraction submodule is used to extract the sales records, production cycles and seasonal demand of goods from historical inventory data, and perform time series analysis through the time series analysis model to identify the cyclical trend and sudden demand of goods and obtain the corresponding sales characteristics; 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.

[0065] For the specific definition of the goods in and out of the warehouse management system, please refer to the definition of the goods in and out of the warehouse management method above, which will not be repeated here. Each module in the above-mentioned goods in and out of the warehouse management system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for managing the entry and exit of goods is implemented.

[0067] 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 implements the following steps when executing the computer program: Collect basic information of the goods and generate a unique identification code for the goods based on the basic information; Based on the unique identification code and corresponding basic information of the goods, the digital twin technology is used to map the warehouse shelf status in real time, dynamically allocate shelf locations, generate storage optimization plans, and guide personnel and / or automated equipment to warehouse the goods according to the shelf allocation plan; Receive order data, generate a distribution path based on the order data, and guide personnel and / or automated equipment to ship goods out of the warehouse based on the distribution path, and update real-time inventory information; 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; Carry out inventory operations on warehouse goods according to the preset time period, obtain the corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

[0068] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Collect basic information of the goods and generate a unique identification code for the goods based on the basic information; Based on the unique identification code and corresponding basic information of the goods, the digital twin technology is used to map the warehouse shelf status in real time, dynamically allocate shelf locations, generate storage optimization plans, and guide personnel and / or automated equipment to warehouse the goods according to the shelf allocation plan; Receive order data, generate a distribution path based on the order data, and guide personnel and / or automated equipment to ship goods out of the warehouse based on the distribution path, and update real-time inventory information; 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; Carry out inventory operations on warehouse goods according to the preset time period, obtain the corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

[0069] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0070] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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.

[0071] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for managing goods in and out of warehouse, characterized in that: The goods in and out management method comprises: Collect basic information of the goods and generate a unique identification code for the goods based on the basic information; Based on the unique identification code of the goods and the corresponding basic information, the warehouse shelf status is mapped in real time through digital twin technology, and the shelf location is dynamically allocated to generate a storage optimization plan. According to the shelf allocation plan, personnel and / or automated equipment are guided to warehouse the goods; Receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship the goods out of the warehouse based on the distribution path, and update real-time inventory information; Acquire historical inventory data, and 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; Perform inventory operations on warehouse goods according to a preset time period to obtain corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

2. The method for managing goods in and out of warehouse according to claim 1, characterized in that: The digital twin technology is used to map the warehouse shelf status in real time, dynamically allocate shelf locations, and generate storage optimization solutions, including: According to the basic information, the goods corresponding to the basic information are stored in a preset digital twin warehouse model, and then the warehouse shelf information and the basic information of the goods are mapped in real time; Based on the real-time mapping results, the storage location of the goods is dynamically adjusted through the shelf allocation algorithm, the storage path guidance for the goods entering the warehouse is obtained, and the storage optimization plan is generated.

3. The method for managing goods in and out of warehouse according to claim 2, characterized in that: The method of dynamically adjusting the storage location of goods through the shelf allocation algorithm, obtaining the storage path guidance for goods entering the warehouse, and generating the storage optimization plan specifically includes: Perform shelf availability evaluation on the basic information of the goods and the warehouse shelf information through a preset shelf allocation algorithm, and generate the storage location of the goods; A path optimization algorithm is used to generate the storage path guidance for goods entering the warehouse according to the storage location of the goods and the preset physical layout of the shelves.

4. The method for managing goods in and out of warehouse according to claim 3, characterized in that: The step of evaluating the shelf availability of the basic information of the goods and the warehouse shelf information by using a preset shelf allocation algorithm specifically includes: Acquire the volume data of the goods from the basic information of the goods, acquire the shelf remaining space information from the warehouse shelf information, and determine the shelf where the goods are stored based on the matching degree between the volume data of the goods and the shelf remaining space information; Acquire a shelf partition category from the warehouse shelf information, and determine a shelf area for storing goods based on the shelf partition category; The distribution of the warehouse shelf information is evaluated based on the shelves and shelf areas where goods are stored to ensure the overall uniformity of the distribution of goods.

5. The method for managing goods in and out of warehouse according to claim 1, characterized in that: The receiving order data and generating a distribution path according to the order data specifically includes: Query the unique identification code of the corresponding order product according to the received order data, and query the corresponding shelf location according to the unique identification code; Based on the delivery time requirement, delivery location requirement and cargo quantity information of the received order data, the corresponding orders are prioritized to obtain the processing priorities of different orders; The corresponding orders are processed in sequence based on the processing priority, and the shortest path of the shelf location is calculated using a path optimization algorithm to obtain the distribution path.

6. The method for managing goods in and out of warehouse according to claim 1, characterized in that: The acquiring of historical inventory data and predicting future inventory status according to the real-time inventory information and the historical inventory data specifically includes: Extracting the sales records, production cycles and seasonal demands of goods from the historical inventory data, and performing time series analysis through a time series analysis model to identify the cyclical trends and sudden demands of goods, and obtain corresponding sales characteristics; The sales characteristics are modeled using a machine learning algorithm, and model parameters are optimized according to the real-time inventory information to generate inventory demand forecast results within a preset time period, thereby calculating the future inventory status.

7. A goods in and out warehouse management system, characterized in that: The goods in and out storage management system includes: An information collection module is used to collect basic information of goods and generate a unique identification code for the goods based on the basic information; A digital twin mapping module is used to map the warehouse shelf status in real time based on the unique identification code of the goods and the corresponding basic information through digital twin technology, and dynamically allocate shelf locations to generate a storage optimization plan, and guide personnel and / or automated equipment to warehouse the goods according to the shelf allocation plan; An order processing module, used to receive order data, generate a distribution path according to the order data, and guide personnel and / or automated equipment to ship the goods out of the warehouse based on the distribution path, and update real-time inventory information; An inventory forecasting module is used to obtain historical inventory data, and forecast 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 operations on warehouse goods according to a preset time period, obtain corresponding inventory data, perform inventory comparison on the inventory data, identify inventory differences and generate an inventory display table.

8. The goods in and out of warehouse management system according to claim 7, characterized in that: The digital twin mapping module specifically includes: An information loading submodule, for storing the goods corresponding to the basic information into a preset digital twin warehouse model according to the basic information, and then performing real-time mapping between the warehouse shelf information and the basic information of the goods; The shelf allocation submodule is used to dynamically adjust the storage location of goods through the shelf allocation algorithm based on the real-time mapping results, obtain the storage path guidance for the goods entering the warehouse, and generate the storage optimization plan.

9. 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, the steps of the method for managing the entry and exit of goods as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for managing the entry and exit of goods as claimed in any one of claims 1 to 6 are implemented.

Images