Offline store cargo allocation method and related equipment

Abstract

The invention relates to the technical field of cargo allocation, and particularly discloses an offline store cargo allocation method and related equipment, and the method comprises the steps: a system receives a cargo allocation order from a store, analyzes the information, number and priority of commodities in the order, and generates a cargo allocation task in combination with real-time inventory data; based on the warehouse layout, the commodity position and the real-time data, intelligent path planning is carried out by using an intelligent algorithm; goods sorting, packaging and carrying are completed through automatic equipment; inventory data is automatically updated after the commodities are delivered, and a replenishment instruction is triggered when the inventory is lower than a threshold value; the system dynamically allocates tasks to the distribution staff through the mobile terminal according to the position and the working state of the distribution staff, and monitors the progress and the abnormal condition in real time; after distribution, the system scans the code to complete handover, and the handheld intelligent distribution terminal equipment automatically uploads a distribution result to the warehouse management system; through a dynamic path planning algorithm, automatic sorting equipment and real-time inventory management, an efficient and accurate cargo allocation process is realized.

Description

Technical Field

[0001] This invention patent relates to the technical field of goods allocation, and specifically to an offline store goods allocation method and related equipment. Background Art

[0002] In today's retail industry, the operational efficiency of offline stores largely depends on the smoothness of the goods allocation process. Traditional offline store goods allocation usually relies on manual operations. In the manual goods allocation process, the goods allocators need to walk back and forth in the warehouse to find goods, and repeatedly perform physical labor such as carrying and sorting. Long-term high-intensity work is likely to make employees fatigued, reduce work efficiency and is prone to errors. The speed of manual operations is limited. When facing a large number of orders, from order reception, information entry to goods sorting and delivery arrangement, each link takes a lot of time. The path planning is unreasonable, resulting in time waste, low efficiency of the goods allocation process, manual inventory taking consuming manpower and time, and the inventory results cannot be updated in real time, which will lead to misjudgment of inventory quantity, resulting in out-of-stock or overstocking phenomena.

[0003] Content of the Invention Patent Aiming at the deficiencies of the prior art, this invention patent provides an offline store goods allocation method and related equipment, which have the advantages of automatic goods allocation process, path and task optimization, and real-time inventory management, etc., and solves the problems of low work efficiency and easy errors in manual goods allocation, and lagging inventory management.

[0004] An offline store goods allocation method of this invention patent includes: The system receives the goods allocation orders from the store, and parses the commodity information, quantity and priority in the orders, and generates goods allocation tasks in combination with real-time inventory data; the order reception system adopts distributed cache technology to handle order peaks, uses preset rules and algorithms to initially screen orders, automatically marks abnormal orders, and through data conversion algorithms, unifies the data formats of orders from different channels. The order parsing system relies on a large and accurate commodity information database, uses natural language processing technology and rule matching algorithms to accurately identify the commodity names, specifications, analyzes the composition and quantity of commodity combinations, and obtains information such as commodity inventory locations, purchase prices, and selling prices. Extracts the basic customer information from the orders and conducts format verification and integrity check, extracts customers' special preferences and requirements through text analysis algorithms and semantic recognition technologies, uses geographic information system (GIS) technology to analyze the delivery methods and expected delivery times, and conducts accurate positioning and path planning analysis on the delivery addresses to assist in confirming fuzzy addresses; Based on the warehouse layout, commodity locations and real-time data, uses intelligent algorithms for intelligent path planning. According to the order parsing results, in combination with preset inventory thresholds, delivery strategies, as well as historical sales data and market trend analysis, uses intelligent algorithms to generate personalized goods allocation plans, and dynamically adjusts the goods allocation plans considering factors such as store promotion activities and holidays; The commodity sorting, packing and handling are completed by automated equipment. After receiving the system instructions, the AGV picks up goods from the intelligent shelf and transports them to the packing area, and the sorting robot sorts the commodities according to the orders. After the commodities are out of the warehouse, the inventory data is automatically updated. When the inventory is lower than the threshold, a replenishment instruction is triggered. After each commodity is out of the warehouse, the RFID reader automatically updates the inventory database and records the operation log through blockchain technology. According to the position and working status of the order pickers, the system dynamically assigns tasks to the order pickers through the mobile terminal, and monitors the progress and abnormal situations in real time. The order pickers hold intelligent order picking terminal devices to receive the order picking plan, scan the barcodes of the goods in the warehouse, and upload the location and status information of the goods in real time. The distribution vehicle is equipped with a GPS positioning device and a temperature sensor (for commodities with temperature requirements), and uploads the location, speed, goods temperature and other information to the monitoring center in real time. The monitoring center monitors in real time through the monitoring software. If the vehicle deviates from the route or the goods temperature is abnormal, it will give an early warning in time and notify the relevant personnel to handle it. After the distribution, the handover is completed by scanning the code through the system. The intelligent order picking terminal device automatically uploads the order picking result to the warehouse management system, and the sales system and the customer relationship management system update the order status and customer information, and statistically analyze the data of the order picking process. Customer self-service: Customers scan the commodity QR code through the in-store self-service terminal. The system displays the inventory location in real time and generates a navigation path to guide the customers to pick up the goods.

[0005] A method for in-store order picking of the present invention for invention patent, wherein the intelligent path planning adopts the A* algorithm or the Dijkstra algorithm, and the algorithm introduces a congestion coefficient weight to dynamically adjust the path priority. The system plans the shortest path for the order pickers based on the digital map of the warehouse and dynamically adjusts it in combination with the real-time personnel location data.

[0006] A method for in-store order picking of the present invention for invention patent, wherein the handling of abnormal situations includes out-of-stock response and mis-picking traceability. Out-of-stock response: The system automatically recommends alternative commodities (such as "Brand A is out of stock, it is recommended to replace it with Brand B"); Mis-picking traceability: The mis-picking link is traced through the scanned code record, and the process is improved accordingly.

[0007] An in-store order picking device of the present invention for invention patent, including a warehouse management system, a data analysis and optimization module, an intelligent shelf system, automated sorting equipment and a handheld intelligent order picking terminal device, characterized in that: the warehouse management system realizes the integrated management of inventory visualization, order allocation and path planning.

[0008] An offline store goods allocation device for this invention patent, wherein the data analysis and optimization module includes a demand prediction engine and an efficiency analysis tool. Demand prediction engine: Based on historical sales data and external factors (such as weather, promotional activities), use the LSTM neural network to predict the demand for goods, set the safety stock threshold to avoid out-of-stock or overstock; Efficiency analysis tool: Statistically analyze the time consumed in each link of goods allocation (such as route planning, sorting, packing), identify bottlenecks and optimize algorithm parameters.

[0009] An offline store goods allocation device for this invention patent, wherein the intelligent shelf system includes integrated gravity sensors and electronic tags, which are used to monitor the weight and quantity of goods on the shelves in real time, and use Internet of Things technology to transmit to the warehouse management system, providing accurate real-time inventory information for generating the goods allocation plan, and automatically generating replenishment requests. The intelligent shelf system supports light guidance (such as LED indicators) to assist the goods allocator in quickly locating the target goods.

[0010] An offline store goods allocation device for this invention patent, wherein the automated sorting device includes an automated guided vehicle (AGV) and a sorting robot. Automated guided vehicle (AGV): Equipped with a laser navigation module, and automatically transports goods to the packing area according to system instructions; Sorting robot: Equipped with a robotic arm and a vision recognition system to achieve the grasping and sorting of multi-category goods.

[0011] An offline store goods allocation device for this invention patent, wherein the handheld intelligent goods allocation terminal device has data receiving, displaying, inputting and communication functions (such as smart watches, handheld PDAs). The goods allocator receives the goods allocation plan through the handheld intelligent goods allocation terminal device, and scans the barcodes or QR codes of the goods to upload the goods location and status information in real time.

[0012] Compared with the prior art, the beneficial effects of this invention patent are as follows: 1. The system of this invention patent receives the goods allocation orders from the store, parses the goods information, quantity and priority in the orders, combines the real-time inventory data to generate goods allocation tasks; Based on the warehouse layout, goods location and real-time data, use intelligent algorithms for intelligent route planning; Complete goods sorting, packing and transportation through automated equipment; Automatically update the inventory data after the goods are out of the warehouse, and trigger a replenishment instruction when the inventory is lower than the threshold; The system dynamically allocates tasks to the goods allocator through the mobile terminal according to the location and working status of the goods allocator, and monitors the progress and abnormal situations in real time; After distribution, complete the handover by scanning the code through the system, and the handheld intelligent goods allocation terminal device automatically uploads the goods allocation results to the warehouse management system; Customer self-service: Customers scan the QR codes of the goods through the self-service terminal in the store, the system displays the inventory location in real time, and generates a navigation path to guide the customers to pick up the goods. Through the dynamic route planning algorithm, automated sorting equipment and real-time inventory management module, an automated, efficient and accurate goods allocation process is realized.

[0013] 2. This invention patent integrates hardware such as intelligent shelves and AGVs, combines big data analysis and intelligent algorithms, avoids the problems of lagging inventory management, difficulty in updating inventory data in a timely manner, resulting in out-of-stock or overstock, and at the same time avoids problems such as easy misallocation and missing allocation in manual sorting, significantly reducing labor costs and error rates, and improving customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a schematic diagram of the method for distributing goods in offline stores of this invention patent; Figure 2 It is a flow chart of customer self-service of this invention patent; Figure 3 It is a schematic diagram of handling abnormal situations of this invention patent; Figure 4 It is a schematic diagram of the composition of the goods distribution equipment of this invention patent; Figure 5 It is a schematic diagram of the composition of the data analysis and optimization module of this invention patent; Figure 6 It is a schematic diagram of the composition of the intelligent shelf system of this invention patent; Figure 7 It is a schematic diagram of the composition of the automatic sorting equipment of this invention patent. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The following will disclose multiple embodiments of this invention patent in the form of diagrams. For the sake of clarity, many practical details will be described together in the following narrative. However, it should be understood that these practical details should not be used to limit this invention patent. That is to say, in some embodiments of this invention patent, these practical details are not necessary. In addition, for the purpose of simplifying the diagrams, some well-known and commonly used structures and components will be shown in a simple schematic manner in the diagrams.

[0016] Please refer to Figure 1-7, A method for allocating goods to offline stores in this invention patent for invention includes: The system receives the goods allocation order from the store, analyzes the product information, quantity and priority in the order, and generates the goods allocation task by combining the real-time inventory data; The order receiving system uses the distributed cache technology to handle the order peak, initially screens the orders by using the preset rules and algorithms, automatically marks the abnormal orders, and unifies the data format of orders from different channels through the data conversion algorithm. The order analysis system relies on a huge and accurate product information database, uses natural language processing technology and rule matching algorithms to accurately identify the product name and specifications, analyzes the composition and quantity of the product combination, and obtains information such as the inventory location, purchase price, and selling price of the product. It extracts the basic customer information from the order and conducts format verification and integrity check, extracts the special preferences and requirements of the customer through the text analysis algorithm and semantic recognition technology, uses the Geographic Information System (GIS) technology to analyze the delivery method and expected delivery time, accurately locates the delivery address and conducts path planning analysis, and assists in confirming the ambiguous address. During the peak of promotional activity orders, the data cache module of the order receiving system uses the distributed cache technology to easily handle a large number of concurrent orders. The initial screening module quickly marks abnormal orders according to the preset rules, such as orders with an empty product name or a negative quantity. The data integration and format unification module converts the order data from different channels into a unified format.

[0017] For example: Convert "3.5g lipstick" in the e-commerce platform order to the system standard format "Lipstick, Specification: 3.5 grams". The order analysis system compares the order product name with the product information database, such as identifying the name and specifications of "300 ml XX brand lotion", and obtains information such as the inventory location (the 2nd layer of the 3rd shelf in Area C of the warehouse), purchase price (50 yuan per bottle), and selling price (80 yuan per bottle). For the "beauty gift box" product combination order, analyze the composition (1 lipstick, 1 eyeshadow palette, 1 box of blush) and quantity of the combined products according to the preset rules.

[0018] For online platform orders, the customer information is verified in real time by the system after being filled in by the consumer. For offline store orders, the system conducts integrity check when the staff enters the information.

[0019] When the order note is "Needs delicate packaging, for gift giving", the system extracts the packaging requirements and marks them.

[0020] Use the GIS technology to analyze the delivery method and time, locate and plan the path for the delivery address "No. XX, XX Street, XX District, XX City". If the address is ambiguous as "Near XX Street", assist the staff to confirm the accurate address by comparing with the map database and intelligent prompts.

[0021] Based on the warehouse layout, product locations, and real-time data, intelligent algorithms are used for intelligent path planning. According to the order parsing results, combined with preset inventory thresholds, distribution strategies, as well as historical sales data and market trend analysis, intelligent algorithms are applied to generate personalized goods allocation plans, and the goods allocation plans are dynamically adjusted considering factors such as store promotion activities and holidays.

[0022] For example: According to the order parsing results, combined with the inventory threshold (such as the safety stock of lipsticks is 10 pieces and the replenishment point is 15 pieces), the distribution strategy (prioritize delivering orders with shorter distances), as well as historical sales data and market trends (such as the sales volume of a certain sunscreen is expected to increase by 20% in summer), intelligent algorithms are used to generate a goods allocation plan. For example, the lipstick inventory in a certain store is 12 pieces, and the order requires 5 pieces. Considering the sales trend and delivery time, it is planned to replenish 8 pieces and arrange for delivery before 10 am, and optimize the delivery route to avoid congested sections.

[0023] Automated equipment is used to complete product sorting, packing, and handling. After receiving the system instructions, the AGV picks up goods from the intelligent shelves and transports them to the packing area, and the sorting robot sorts products according to the orders. After the products are out of the warehouse, the inventory data is automatically updated. When the inventory is below the threshold, a replenishment instruction is triggered. After each product is out of the warehouse, the RFID reader automatically updates the inventory database and records the operation logs through blockchain technology.

[0024] For example: The system updates the lipstick inventory to 15 pieces (original 12 pieces - 5 pieces + 8 pieces).

[0025] The system dynamically assigns tasks to the order pickers through the mobile terminal according to their locations and working status, and real-time monitors the progress and abnormal situations. The order pickers hold intelligent order picking terminal devices to receive the goods allocation plan, go to the warehouse to scan the barcodes of the goods to sort products such as lipsticks and lotions, and upload the location and status information of the goods in real-time.

[0026] The delivery vehicles are installed with GPS positioning devices and temperature sensors (for products with temperature requirements), and upload information such as location, speed, and product temperature to the monitoring center in real-time. The monitoring center monitors in real-time through the monitoring software. If the vehicle deviates from the route or the product temperature is abnormal, it will give an early warning and notify the relevant personnel for handling.

[0027] After delivery, the handover is completed by scanning the code through the system. The intelligent order picking terminal device automatically uploads the goods allocation results to the warehouse management system, and the sales system and customer relationship management system update the order status and customer information, and conduct statistical analysis on the data of the goods allocation process.

[0028] For example: The time taken from order receipt to completion of this goods allocation is 4 hours, the sorting accuracy rate is 99.5%, and the delivery on-time rate is 98%. Adjust the inventory threshold according to the analysis results (the safety stock of lipsticks is adjusted to 12 pieces, and the replenishment point is 18 pieces), optimize the delivery route, improve the parameter settings of the sorting equipment, and enhance the efficiency and quality of goods allocation.

[0029] Customer self-service: Customers scan the product QR code through the in-store self-service terminal. The system displays the inventory location in real time and generates a navigation path to guide the customers to pick up the goods.

[0030] A method for offline store goods allocation of this invention patent, wherein intelligent path planning adopts the A* algorithm or the Dijkstra algorithm, and the algorithm introduces a congestion coefficient weight to dynamically adjust the path priority. The system plans the shortest path for the goods allocator based on the digital map of the warehouse and dynamically adjusts it in combination with the real-time personnel location data.

[0031] For example: When area A is congested, the task is automatically assigned to the idle goods allocator in area B, and the path is re-planned.

[0032] A method for offline store goods allocation of this invention patent, wherein the handling of abnormal situations includes out-of-stock response and misallocation traceability. Out-of-stock response: The system automatically recommends alternative products (such as "Brand A is out of stock, it is recommended to replace it with Brand B"); Misallocation traceability: Trace the misallocation link through the scan record and improve the process accordingly.

[0033] An offline store goods allocation device of this invention patent includes a warehousing management system, a data analysis and optimization module, an intelligent shelf system, an automated sorting device, and a handheld intelligent goods allocation terminal device. The warehousing management system realizes the integrated management of inventory visualization, order allocation, and path planning.

[0034] An offline store goods allocation device of this invention patent, wherein the data analysis and optimization module includes a demand prediction engine and an efficiency analysis tool. Demand prediction engine: Based on historical sales data and external factors (such as weather, promotional activities), use the LSTM neural network to predict the demand for products, set the safety stock threshold, and avoid out-of-stock or overstock; Efficiency analysis tool: Statistically analyze the time taken for each link of goods allocation (such as path planning, sorting, packing), identify bottlenecks and optimize algorithm parameters.

[0035] An offline store goods allocation device of this invention patent, wherein the intelligent shelf system includes an integrated gravity sensor and an electronic label, which are used to monitor the weight and quantity of goods on the shelf in real time, transmit them to the warehousing management system through the Internet of Things technology, provide accurate real-time inventory information for the generation of goods allocation plans, and automatically generate replenishment requests. The intelligent shelf system supports light guidance (such as LED indicator lights) to assist the goods allocator in quickly locating the target products. Through the intelligent shelf system, the problems of lagging inventory management and difficulty in updating inventory data in a timely manner, resulting in out-of-stock or overstock, are avoided.

[0036] An offline store goods allocation device for this invention patent, wherein the automatic sorting device includes an automated guided vehicle (AGV) and a sorting robot. The automated guided vehicle (AGV): is equipped with a laser navigation module and automatically transports goods to the packing area according to system instructions. The sorting robot: is equipped with a robotic arm and a vision recognition system to achieve the grasping and sorting of multi-category goods. By setting up the automatic sorting device, problems such as misallocation and missing allocation that are prone to occur in manual sorting are avoided.

[0037] An offline store goods allocation device for this invention patent, wherein the handheld intelligent goods allocation terminal device has data receiving, displaying, inputting, and communication functions (such as smart watches, handheld PDAs). The goods allocator receives the goods allocation plan through the handheld intelligent goods allocation terminal device and scans the barcodes or QR codes of the goods to upload the goods location and status information in real time.

[0038] When using this invention patent: the system receives the goods allocation order from the store, analyzes the product information, quantity, and priority in the order, and generates a goods allocation task in combination with the real-time inventory data; Based on the warehouse layout, product location, and real-time data, use intelligent algorithms for intelligent path planning; Complete the sorting, packing, and transportation of goods through automated equipment; Automatically update the inventory data after the goods leave the warehouse, and trigger a replenishment instruction when the inventory is below the threshold; The system dynamically allocates tasks to the goods allocator through the mobile terminal according to the location and working status of the goods allocator, and monitors the progress and abnormal situations in real time; After distribution, complete the handover by scanning the code through the system, and the handheld intelligent goods allocation terminal device automatically uploads the goods allocation result to the warehouse management system; Customer self-service: Customers scan the QR code of the product through the self-service terminal in the store, and the system displays the inventory location in real time and generates a navigation path to guide the customer to pick up the goods.

[0039] The above is only the implementation mode of this invention patent and is not used to limit this invention patent. For those skilled in the art, various changes and modifications can be made to this invention patent. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this invention patent shall be included within the scope of the claims of this invention patent.

Claims

1. An offline store distribution method, comprising: The system receives distribution orders from stores, analyzes the product information, quantity and priority in the orders, and generates distribution tasks based on real-time inventory data; Intelligent routing planning is performed using intelligent algorithms based on warehouse layout, product location and real-time data; Complete product sorting, packaging and transportation through automated equipment; Inventory data is automatically updated after the goods are shipped out, and replenishment instructions are triggered when the inventory falls below the threshold; The system dynamically assigns tasks to pickers through mobile terminals based on their locations and work status, and monitors progress and abnormal situations in real time; After delivery, the handover is completed by scanning the system code, and the handheld intelligent distribution terminal automatically uploads the distribution results to the warehouse management system; Customer self-service: Customers scan the product QR code through the store’s self-service terminal. The system displays the inventory location in real time and generates a navigation path to guide customers to pick up the goods.

2. An offline store distribution method according to claim 1, characterized in that: The intelligent path planning adopts A* algorithm or Dijkstra algorithm, and the algorithm introduces congestion coefficient weight to dynamically adjust the path priority.

3. The offline store distribution method according to claim 1, characterized in that: The abnormal situation handling includes out-of-stock response and mismatch tracing. Out-of-stock response: the system automatically recommends alternative products (such as "Brand A is out of stock, it is recommended to replace it with Brand B"); mismatch tracing: trace the mismatch link by scanning the code records and improve the process in a targeted manner.

4. An offline store distribution equipment, including a warehouse management system, a data analysis and optimization module, an intelligent shelf system, an automated sorting device and a handheld intelligent distribution terminal device, characterized in that: The warehouse management system realizes integrated management of inventory visualization, order allocation, and route planning.

5. The offline store distribution equipment according to claim 4, characterized in that: The data analysis and optimization module includes a demand forecasting engine and an efficiency analysis tool. The demand forecasting engine: based on historical sales data and external factors (such as weather, promotional activities), uses an LSTM neural network to predict commodity demand and set safety inventory thresholds to avoid out-of-stock or backlogs; the efficiency analysis tool: counts the time spent in each distribution link (such as path planning, sorting, and packaging), identifies bottlenecks, and optimizes algorithm parameters.

6. The offline store distribution equipment according to claim 4, characterized in that: The smart shelf system includes integrated gravity sensors and electronic tags for real-time monitoring of the weight and quantity of goods on the shelves. Using the Internet of Things technology, the information is transmitted to the warehouse management system to provide accurate real-time inventory information for distribution plan generation and automatically generate replenishment requests. The smart shelf system supports light guidance (such as LED indicator lights) to assist distribution staff in quickly locating target goods.

7. The offline store distribution equipment according to claim 4, characterized in that: The automated sorting equipment includes an automatic guided vehicle (AGV) and a sorting robot. The automatic guided vehicle (AGV) is equipped with a laser navigation module and automatically transports goods to the packaging area according to system instructions. The sorting robot is equipped with a robotic arm and a visual recognition system to realize the grabbing and sorting of multiple categories of goods.

8. The offline store distribution equipment according to claim 4, characterized in that: The handheld intelligent distribution terminal device has data receiving, displaying, inputting and communication functions (such as smart watches, handheld PDAs). Distribution personnel receive distribution plans through the handheld intelligent distribution terminal device, scan the barcode or QR code of the goods, and upload the location and status information of the goods in real time.

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