Visual intelligent electronic product warehouse management method and system
By generating cargo related heat maps and digital twin maps, combining temperature, humidity and weight data, the problem of manual dependence and data in electronic product warehouse management is solved, visualization and real-time optimization of warehouse management is realized, and manpower consumption is reduced.
Patent Information
- Application Number
- CN202510471678.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-07-25
AI Technical Summary
In the prior art, electronic product warehouse management relies on manual inventory, resulting in data inconsistency, data lag and product errors and missed products. The labor consumption during the management process is large, and the current situation of warehouse management cannot be intuitively reflected.
By obtaining historical order data, a cargo correlation heat map is generated, a warehouse three-dimensional map and a digital twin map are built, and a storage arrangement plan is generated based on temperature and humidity, weight and image data, and picking tasks and employee efficiency are monitored in real time. The inventory and safety status are marked with a visual instrument panel to trigger an early warning signal.
It improves the visualization and intuitiveness of electronic product warehouse management, reduces human resource consumption, reduces data lag and missed outages, and realizes real-time optimization and security monitoring of warehouse management.
Smart Images
Figure CN120374014A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of warehouse management, and in particular to a visual intelligent electronic product warehouse management method and system. Background Art
[0002] Electronic products are related products based on electric energy, mainly including watches, mobile phones, computers, game consoles, etc. Electronic products need to be stored during the production and sales process, and the storage conditions of electronic products are different from those of general products.
[0003] In the prior art, when storing electronic products, there are a wide variety of electronic products in the warehouse. Whether it is the inbound and outbound of electronic products, it depends on manual operation, and manual methods are used to conduct inventory and cleaning of the electronic products in the warehouse. However, in the actual application process, manual inventory is prone to data discrepancies. At the same time, there are many batches and miscellaneous models of electronic products. Especially for similar models of electronic products, frequent inbound and outbound will lead to data lag and product misplacement or omission. Finally, the data of electronic products in the electronic product warehouse is recorded in sheets of tables, and the data in the tables needs to be interpreted during the management of electronic products, which increases the labor consumption and cannot intuitively reflect the management status of the warehouse. Summary of the Invention
[0004] The purpose of the present invention is to provide a visual intelligent electronic product warehouse management method and system to solve the problems raised in the above background art.
[0005] In a first aspect, this application provides a visual intelligent electronic product warehouse management method, and the method includes: Obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation heat map according to the historical inbound and outbound data; Construct a three-dimensional map of the warehouse, superimpose the goods correlation heat map on the three-dimensional map of the warehouse to generate a warehouse heat map, generate a real-time storage location layout of electronic products according to the warehouse heat map, and generate a storage location arrangement plan according to the real-time storage location layout; According to the real-time storage location layout, collect temperature and humidity data, storage location weight data, and image data in the warehouse to generate a warehouse data packet, and construct a digital twin map in combination with the warehouse data packet, the three-dimensional map of the warehouse, and the warehouse heat map; Obtain current order data, generate a picking task according to the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat map; Monitor the product quantity and storage environment of electronic products in the warehouse according to the digital twin map, and mark the inventory status and storage safety status according to the visualization dashboard, triggering inventory warning signals and safety emergency instructions.
[0006] Preferably, the steps of obtaining historical order data, parsing the historical order data to obtain historical inbound and outbound data, and generating a goods correlation heat map according to the historical inbound and outbound data are specifically as follows: Obtain historical order data, perform data splitting and aggregation on the historical order data to obtain historical inbound and outbound data of historical orders; Perform data splitting on the historical inbound and outbound data to obtain the historical product quantity, historical product type, and product inbound and outbound data of electronic products; Obtain the historical storage data of electronic products, and generate a historical heat map of electronic products in the warehouse according to the historical storage data, the historical product quantity, and the historical product type; Continuously update the historical heat map according to the product inbound and outbound data to generate the latest goods correlation heat map.
[0007] Preferably, the steps of superimposing the goods correlation heat map on the three-dimensional map of the warehouse to generate a warehouse heat map, generating a real-time storage location layout of electronic products according to the warehouse heat map, and generating a storage location arrangement plan according to the real-time storage location layout are specifically as follows: Superimpose the goods correlation heat map on the three-dimensional map of the warehouse, and perform heat display on the three-dimensional map of the warehouse to generate a warehouse heat map; According to the warehouse heat map, divide the warehouse into regions to obtain multiple heat zones, and extract the electronic product data in the heat zones; Extract the product type and current location data in the electronic product data, and obtain the real-time storage location layout of electronic products according to the product type and the current location data; Score the real-time storage location layout to obtain the layout score of each electronic product, and generate a storage location arrangement plan according to the layout score.
[0008] Preferably, the steps of scoring the real-time storage location layout to obtain the layout score of each electronic product, and generating a storage location arrangement plan according to the layout score are specifically as follows: Score the real-time storage location layout to obtain the layout score of each electronic product; According to the historical inbound and outbound data, obtain the inbound and outbound frequency of each electronic product, and combine the layout score and the inbound and outbound frequency to obtain the first comprehensive storage score of the electronic product; Obtain the environmental distribution data in the warehouse, and generate the second comprehensive storage score of electronic products by combining the environmental distribution data and the product type; According to the first comprehensive storage score and the second comprehensive storage score, three most suitable storage areas are generated for each electronic product; According to the three most suitable storage areas corresponding to each product, perform area attribution division to generate a storage location arrangement plan for electronic products.
[0009] Preferably, according to the real-time storage location layout, the steps of collecting temperature and humidity data, storage location weight data, and image data in the warehouse to generate a warehouse data packet, and constructing a digital twin map by combining the warehouse data packet, the three-dimensional map of the warehouse, and the heat map of the warehouse are as follows: According to the real-time storage location layout, collect the temperature and humidity data, storage location weight data, and image data of each storage location in the warehouse and generate a warehouse data packet; Construct an initial digital twin map according to the three-dimensional map of the warehouse, and overlay the heat map of the warehouse on the initial digital twin map to generate a heat twin map; Generate a temperature and humidity display layer according to the temperature and humidity data and the real-time storage location layout; Generate a weight display layer according to the storage location weight data and the real-time storage location layout; Generate a regional detail picture display layer according to the image data and the real-time storage location layout; Overlay the temperature and humidity display layer, the weight display layer, and the regional detail picture display layer on the heat twin map to construct a digital twin map.
[0010] Among them, display switches are added to the heat map of the warehouse, the temperature and humidity display layer, the weight display layer, and the regional detail picture display layer.
[0011] Preferably, the steps of generating a picking task according to the current order data and arranging employees to perform picking according to the picking task are as follows: Obtain the target product type, target product quantity, and target product storage location according to the current order data; Obtain the personnel location of the employee, and generate a picking task by combining the current personnel location, the target product type, the target product quantity, and the target product storage location; According to the picking task, obtain a predetermined target picking route and a predetermined target picking time; Generate an AR navigation plan according to the predetermined target picking route and the predetermined target picking time, and send the AR navigation plan to the employee; Obtain the current moving speed and current picking efficiency of the employee, and generate a fatigue degree value of the employee according to the current moving speed and the current picking efficiency; Judge whether the fatigue degree value is greater than a preset fatigue threshold. If it is judged that the fatigue degree value is greater than the fatigue threshold, send a rest reminder to the employee.
[0012] Preferably, the steps of collecting the picking efficiency of the employee, generating an efficiency heat zone map according to the picking efficiency, and performing real-time dynamic optimization on the picking task according to the efficiency heat zone map are specifically as follows: Collect the moving trajectory data and order completion duration of the employee during the picking process, compare the moving trajectory data and the order completion duration with the predetermined target picking route and the predetermined target picking time, and obtain a route efficiency value and a duration efficiency value; Generate a route efficiency heat zone map and a duration efficiency heat zone map according to the route efficiency value and the duration efficiency value respectively; Fuse the route efficiency heat zone map and the duration efficiency heat zone map to generate an efficiency heat zone map, extract the efficiency area of the duration efficiency heat zone map, and obtain a to-be-optimized efficiency area with insufficient efficiency; Extract the to-be-optimized route and to-be-optimized duration in the to-be-optimized efficiency area to perform real-time dynamic optimization on the picking task.
[0013] Preferably, the steps of monitoring the product quantity and storage environment of electronic products in the warehouse, marking the inventory status and storage safety status according to the visual dashboard, and triggering an inventory warning signal and a safety emergency instruction are specifically as follows: Monitor the product quantity and storage environment of electronic products in the warehouse, obtain the inventory status according to the product quantity, and generate the storage safety status according to the storage environment; Generate an inventory label according to the inventory status, and generate an environmental safety label according to the storage safety status. Among them, the inventory label displays the inventory quantity and the purchase demand value, and the environmental safety label displays the environmental data and the environmental safety value; Mark the inventory label and the environmental safety label in the visual dashboard, and monitor the purchase demand value and the environmental safety value; Judge whether the purchase demand value is greater than a preset demand threshold; If it is judged that the purchase demand value is greater than the demand threshold, generate a purchase reminder, and mark the purchase reminder in yellow and display it in the visual dashboard; Judge whether the environmental safety value is less than a preset safety threshold; If it is determined that the environmental safety value is less than the safety threshold, an environmental adjustment reminder is generated and the environmental adjustment reminder is highlighted in red and displayed on the visual dashboard.
[0014] In a second aspect, the present application provides a visual intelligent electronic product warehouse management system, and the system includes: Thermodynamic map generation module: used to obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation thermodynamic map according to the historical inbound and outbound data; Storage location arrangement module: used to construct a three-dimensional map of the warehouse, superimpose the goods correlation thermodynamic map on the three-dimensional map of the warehouse to generate a warehouse thermodynamic map, generate a real-time storage location layout of electronic products according to the warehouse thermodynamic map, and generate a storage location arrangement plan according to the real-time storage location layout; Digital twin module: used to collect temperature and humidity data, storage location weight data, and image data in the warehouse according to the real-time storage location layout to generate a warehouse data packet, and construct a digital twin map in combination with the warehouse data packet, the three-dimensional map of the warehouse, and the warehouse thermodynamic map; Intelligent optimization module: used to obtain current order data, generate a picking task according to the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat zone map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat zone map; Monitoring and warning module: used to monitor the product quantity and storage environment of electronic products in the warehouse according to the digital twin map, and mark the inventory status and storage safety status according to the visual dashboard, and trigger an inventory warning signal and a safety emergency instruction.
[0015] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining historical order data, a goods correlation heat map of electronic products in the warehouse is obtained based on the historical order data. Then, a three-dimensional map of the warehouse is constructed, and the goods correlation heat map and the three-dimensional map of the warehouse are overlapped to generate a warehouse heat map. Based on the warehouse heat map, a real-time storage location layout of electronic products is generated, and a storage location arrangement plan is generated according to the real-time storage location layout. Temperature and humidity data, storage location weight data, and image data in the warehouse are collected, and a digital twin map is constructed based on the above three types of data combined with the warehouse heat map. A picking task is generated according to the current order data, and then picking is carried out according to the picking task arrangement. At the same time, the picking efficiency of employees is monitored, an efficiency heat zone map is generated, and the picking tasks of employees are dynamically optimized in real time according to the efficiency heat zone map. Finally, the inventory quantity and storage environment of electronic products in the warehouse are monitored according to the digital twin map. When there is a shortage of inventory or a safety problem, feedback is sent to the monitoring personnel in a timely manner. This improves the visualization and intuitiveness of the management of electronic product warehouses and reduces the consumption of human resources. Brief Description of the Drawings
[0016] Figure 1 is a flowchart of the steps of a visual intelligent electronic product warehouse management method provided by an embodiment of the present application; Figure 2 is a block diagram of the modules of a visual intelligent electronic product warehouse management system provided by an embodiment of the present application.
[0017] Description of the reference numerals: 1. Heat map generation module; 2. Storage location arrangement module; 3. Digital twin module; 4. Intelligent optimization module; 5. Monitoring and warning module. Detailed Embodiments
[0018] The following is combined with Figure 1 - Figure 2 This application is further described in detail below, but the embodiments of the present invention are not limited thereto.
[0019] An embodiment of the present application discloses a visual intelligent electronic product warehouse management method and system.
[0020] In this embodiment, a visual intelligent electronic product warehouse management method includes: S100: Obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation heat map according to the historical inbound and outbound data; S200: Construct a three-dimensional map of the warehouse, overlay the goods correlation heat map in the three-dimensional map of the warehouse to generate a warehouse heat map, generate a real-time storage location layout of electronic products according to the warehouse heat map, and generate a storage location arrangement plan according to the real-time storage location layout; S300: According to the real-time storage location layout, collect the temperature and humidity data, storage location weight data, and image data in the warehouse to generate a warehouse data packet, and construct a digital twin map by combining the warehouse data packet, the three-dimensional map of the warehouse, and the heat map of the warehouse; S400: Obtain the current order data, generate a picking task according to the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat zone map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat zone map; S500: Based on the digital twin map, monitor the quantity of electronic products and the storage environment in the warehouse, and mark the inventory status and storage safety status according to the visual dashboard, triggering an inventory warning signal and a safety emergency instruction.
[0021] It should be noted that the above modules are only the basic steps of this embodiment. In the specific implementation process, on the premise of not affecting the overall implementation effect, some steps can be appropriately added, reduced, or modified.
[0022] The steps of obtaining historical order data, parsing the historical order data to obtain historical inbound and outbound data, and generating a goods correlation heat map according to the historical inbound and outbound data are specifically as follows: Obtain historical order data, perform data splitting and aggregation on the historical order data to obtain the historical inbound and outbound data of the historical order; Perform data splitting on the historical inbound and outbound data to obtain the historical product quantity, historical product type, and product inbound and outbound data of the electronic products; Obtain the historical storage data of the electronic products, and generate a historical heat map of the electronic products in the warehouse according to the historical storage data, historical product quantity, and historical product type; Continuously update the historical heat map according to the product inbound and outbound data to generate the latest goods correlation heat map.
[0023] In operation, taking an electronic product warehouse as an example, the warehouse stores a total of 3,000 items such as mobile phones, tablets, and smart watches, and the historical order data includes 5,000 inbound and outbound records in the past two years. First, split the historical order data: Classify the data of order numbers 2023-001 to 2023-500 according to the "outbound / inbound" label. It is found that the average monthly outbound of mobile phone products is 120 times and the inbound is 80 times, and the outbound of tablet products is 90 times and the inbound is 60 times. Through set operations, it is found that among the 50 records of order numbers 2023-356 to 2023-400, the proportion of mobile phones and chargers being out of the warehouse together reaches 85%, generating a correlation data matrix. Subsequently, extract the historical storage data and find that the peak historical storage volume of mobile phone products at storage location A1 is 200 pieces / month. Combining with the product type data, generate an initial heat map: In the 3D map, storage location A1 is displayed as dark red (high-frequency area), and storage location B3 (where slow-selling tablet models are stored) is displayed as light blue. Finally, update according to the real-time inbound and outbound data. When the outbound volume of mobile phones suddenly increases to 150 times in January 2024, the system automatically adjusts the heat value of storage location A1 from 90 to 120, generating the latest goods correlation heat map, in which the A1-C5 channel forms a continuous high-heat zone.
[0024] The steps of superimposing the goods correlation heat map on the 3D map of the warehouse to generate the warehouse heat map, generating the real-time storage location layout of electronic products based on the warehouse heat map, and generating the storage location arrangement plan based on the real-time storage location layout are as follows: Superimpose the goods correlation heat map on the 3D map of the warehouse, and the 3D map of the warehouse performs heat display to generate the warehouse heat map; According to the warehouse heat map, divide the warehouse into regions to obtain multiple heat zones, and extract the electronic product data in the heat zones; Extract the product type and current location data in the electronic product data, and obtain the real-time storage location layout of the electronic products according to the product type and the current location data; Score the real-time storage location layout to obtain the layout score of each electronic product, and generate the storage location arrangement plan according to the layout score.
[0025] In operation, taking an electronic product warehouse as an example, when the goods correlation heat map is overlaid on the three-dimensional map of the warehouse, the system identifies that the heat values of three storage locations, A1, B2, and C3, exceed the threshold of 100 and automatically marks them as the red heat core areas. The 2000-square-meter warehouse is divided into 15 heat zones through the Voronoi algorithm. Among them, the A1 zone (50 square meters) contains 98% of the storage records of mobile phone products. When extracting electronic product data, it is found that there is an order correlation (the co-shipment rate is 72%) between the "X-type smart watch" stored in the C3 storage location and the "Y-type mobile phone" in the A1 storage location. According to the real-time storage location layout scoring rules: the A1 storage location gets a score of 95 because it is close to the shipping outlet (20 meters) and the temperature and humidity meet the standards (25°C / 45%RH), while the D5 storage location only gets 60 points because it is remote (80 meters). Based on this, the system generates a storage location arrangement plan, requiring that high-frequency products with a monthly sales volume of more than 100 pieces be centrally deployed in the A1-B2 area.
[0026] The steps of scoring the real-time storage location layout, obtaining the layout score of each electronic product, and generating a storage location arrangement plan based on the layout score are specifically as follows: Score the real-time storage location layout to obtain the layout score of each electronic product; Based on the historical inbound and outbound data, obtain the inbound and outbound frequency of each electronic product, and combine the layout score and the inbound and outbound frequency to obtain the first comprehensive storage score of the electronic product; Obtain the environmental distribution data in the warehouse, and combine the environmental distribution data and the product type to generate the second comprehensive storage score of the electronic product; Based on the first comprehensive storage score and the second comprehensive storage score, three most suitable storage areas are generated for each electronic product; According to the regional attribution division of the three most suitable storage areas corresponding to each product, generate a storage location arrangement plan for the electronic products.
[0027] In operation, taking an electronic product warehouse as an example, the system conducts a two-dimensional scoring on the "Z-type tablet computer": According to historical data, its inbound and outbound frequency is calculated to be 5 times per day (belonging to high-frequency products). Combining the layout score of 80 points for the A3 storage location, the first comprehensive storage score of 86 points is obtained according to the formula (frequency weight × 0.6 + layout score × 0.4). At the same time, it is detected that the environmental data of the B2 storage location (the temperature of 28°C exceeds the storage standard for electronic products), which reduces the second comprehensive storage score of this storage location to 65 points. Through the multi-objective optimization algorithm, the system generates three recommended storage locations for the "Z-type tablet computer": the first choice is A3 (comprehensive score 86), the alternative is C1 (82), and the second choice is A2 (78). The final plan stores similar products in different grades according to the sales frequency. High-frequency products form a "golden triangle" layout in area A, while low-frequency products are allocated to the edge storage locations in area D.
[0028] According to the real-time storage location layout, the steps of collecting temperature and humidity data, storage location weight data, and image data in the warehouse to generate a warehouse data packet, and constructing a digital twin map by combining the warehouse data packet, the three-dimensional warehouse map, and the warehouse heat map are as follows: According to the real-time storage location layout, collect the temperature and humidity data, storage location weight data, and image data of each storage location in the warehouse and generate a warehouse data packet; Construct an initial digital twin map based on the three-dimensional warehouse map, and overlay the warehouse heat map on the initial digital twin map to generate a heat twin map; Generate a temperature and humidity display layer according to the temperature and humidity data and the real-time storage location layout; Generate a weight display layer according to the storage location weight data and the real-time storage location layout; Generate a regional detail view display layer according to the image data and the real-time storage location layout; Overlay the temperature and humidity display layer, the weight display layer, and the regional detail view display layer on the heat twin map to construct a digital twin map.
[0029] Among them, display switches are added to the warehouse heat map, the temperature and humidity display layer, the weight display layer, and the regional detail view display layer.
[0030] In operation, taking an electronic product warehouse as an example, 50 temperature and humidity sensors (accuracy ±0.5°C), 200 intelligent shelves (weighing accuracy 0.1 kg), and 40 4K cameras are installed in the warehouse. When collecting data for storage location A1, the measured temperature is 25.3°C, the humidity is 48%RH, the current load of the shelf is 152.3 kg (standard capacity 200 kg), and the camera captures an alarm of empty box accumulation on the third layer of the shelf. When constructing the digital twin map, the initial map loads the steel frame structure model of the warehouse, and after overlaying the heat layer, area A1 is displayed as an orange warning (inventory saturation 85%). The alarm threshold of the temperature and humidity display layer is specially set to >28°C / <30%RH. When a temperature of 29.5°C is detected at a certain storage location in area D, the digital twin automatically triggers a red flashing warning. Staff can independently control each display layer through the HMI interface. For example, after closing the weight display layer, the map focuses on presenting the heat distribution and the correlation between temperature and humidity.
[0031] The steps of generating a picking task according to the current order data and arranging employees to perform picking according to the picking task are as follows: Obtain the types of target products, the quantity of target products, and the storage locations of target products according to the current order data; Obtain the personnel location of the employee, and generate a picking task by combining the current personnel location, the types of target products, the quantity of target products, and the storage locations of target products; According to the picking task, obtain the predetermined target picking route and the predetermined target picking time; Generate an AR navigation plan according to the predetermined target picking route and the predetermined target picking time, and send the AR navigation plan to the employee; Obtain the current moving speed and the current picking efficiency of the employee, and generate a fatigue level value of the employee according to the current moving speed and the current picking efficiency; Judge whether the fatigue level value is greater than the preset fatigue threshold. If it is judged that the fatigue level value is greater than the fatigue threshold, send a rest reminder to the employee.
[0032] In operation, taking an electronic product warehouse as an example, when processing order 20240215-001 (20 mobile phones and 50 pairs of headphones need to be picked), the system locates that the nearest employee E002 is located in the B area passage (coordinates X35, Y72), and generates a picking path: B12→A1→C5. The preset optimal route length is 82 meters, and the estimated time is 8 minutes. After the AR navigation is started, the enhanced path arrow is displayed on the employee's smart glasses, and a prompt box of "Prioritize picking the left shelf" automatically pops up when scanning the code at the A1 storage location. Through the UWB positioning system, it is monitored that the actual moving speed of the employee is 1.2 m / s (lower than the preset 1.5 m / s). Combining with the picking accuracy rate dropping from 98% to 85%, the system calculates that his fatigue value reaches 78 (threshold 70), and immediately pushes a voice prompt: "Fatigue state detected. It is recommended to charge in the rest area for 15 minutes."
[0033] Collect the picking efficiency of the employee, generate an efficiency heat zone map according to the picking efficiency, and perform real-time dynamic optimization of the picking task according to the efficiency heat zone map. The specific steps are as follows: Collect the moving trajectory data and the order completion duration of the employee during the picking process, compare the moving trajectory data and the order completion duration with the predetermined target picking route and the predetermined target picking time, and obtain the route efficiency value and the duration efficiency value; Generate a route efficiency heat zone map and a duration efficiency heat zone map according to the route efficiency value and the duration efficiency value respectively; Fuse the route efficiency heat zone map and the duration efficiency heat zone map to generate an efficiency heat zone map, extract the efficiency area of the duration efficiency heat zone map, and obtain the to-be-optimized efficiency area with insufficient efficiency; Extract the to-be-optimized route and the to-be-optimized duration in the to-be-optimized efficiency area to perform real-time dynamic optimization of the picking task.
[0034] In operation, taking an electronic product warehouse as an example, for the picking task of employee E005, the system records that his actual movement trajectory detours through the C3-C5 area (an increase of 35 meters) more than the predetermined route, and the order completion time is 12 minutes (50% overtime). Through comparative analysis, the route efficiency value is 65 points (benchmark value 100), and the duration efficiency value is 42 points. The integrated efficiency heat map shows that the efficiency value of the C3 channel is only 30 points (red alert area), and the efficiency value at the B2 corner is 55 points (yellow reminder area). The system identifies that the main reason for the low efficiency in the C3 area is insufficient shelf spacing (1.2 meters makes it difficult for forklifts to pass), and the automatic optimization plan includes: ① Adjust the products in this area to low-frequency small items; ② Set "17:00-19:00 every day" as the exclusive replenishment time period for robots, which increases the efficiency value of this area to 72 points.
[0035] Steps for monitoring the quantity of electronic products and the storage environment in the warehouse, and marking the inventory status and storage safety status according to the visual dashboard, triggering inventory warning signals and safety emergency instructions, specifically: Monitor the quantity of electronic products and the storage environment in the warehouse, obtain the inventory status based on the product quantity, and generate the storage safety status based on the storage environment; Generate an inventory label based on the inventory status and an environmental safety label based on the storage safety status. Among them, the inventory label shows the inventory quantity and the purchase demand value, and the environmental safety label shows the environmental data and the environmental safety value; Mark the inventory label and the environmental safety label on the visual dashboard, and monitor the purchase demand value and the environmental safety value; Judge whether the purchase demand value is greater than the preset demand threshold; If it is judged that the purchase demand value is greater than the demand threshold, generate a purchase reminder and mark the purchase reminder in yellow on the visual dashboard for display; Judge whether the environmental safety value is less than the preset safety threshold; If it is judged that the environmental safety value is less than the safety threshold, generate an environmental adjustment reminder and mark the environmental adjustment reminder in red on the visual dashboard for display.
[0036] During operation, taking an electronic product warehouse as an example, the monitoring module detects that the inventory of "flagship mobile phones" at storage location A1 has dropped to the safety threshold (currently 82 units, threshold 100 units), and the purchase demand value soars to 185%. The visual dashboard synchronously shows that the label of this storage location flashes yellow, marked with "It is recommended to replenish 118 units immediately". At the same time, due to an air conditioner failure at storage location D7, the temperature rises to 32°C (safety threshold 28°C), and the environmental safety value drops to 45 points. The system triggers a red alarm and pushes an emergency instruction: ① Start the standby refrigeration unit; ② Temporarily transfer the affected goods to buffer storage location B5. Through the overall view interface of the dashboard, the management personnel can see that 85% of the entire warehouse area shows a green safety sign, and 12 warning points are concentrated and displayed on the side panel.
[0037] An embodiment of the present invention provides a visual intelligent electronic product warehouse management system, which uses a visual intelligent electronic product warehouse management method as described in any one of the above, and the system includes the following.
[0038] Thermodynamic map generation module 1: used to obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation thermodynamic map according to the historical inbound and outbound data; Storage location arrangement module 2: used to construct a three-dimensional map of the warehouse, superimpose the goods correlation thermodynamic map on the three-dimensional map of the warehouse to generate a warehouse thermodynamic map, generate a real-time storage location layout of electronic products according to the warehouse thermodynamic map, and generate a storage location arrangement plan according to the real-time storage location layout; Digital twin module 3: used to collect temperature and humidity data, storage location weight data, and image data in the warehouse according to the real-time storage location layout to generate a warehouse data packet, and construct a digital twin map in combination with the warehouse data packet, the three-dimensional map of the warehouse, and the warehouse thermodynamic map; Intelligent optimization module 4: used to obtain current order data, generate a picking task according to the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat zone map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat zone map; Monitoring and warning module 5: used to monitor the product quantity and storage environment of electronic products in the warehouse based on the digital twin map, and mark the inventory status and storage safety status according to the visual dashboard, triggering an inventory warning signal and a safety emergency instruction.
[0039] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.
Claims
1. A method for visual intelligent electronic product warehouse management, characterized in that, Including the following steps: Obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation heat map based on the historical inbound and outbound data; Construct a three-dimensional map of the warehouse, overlay the goods correlation heat map on the three-dimensional map of the warehouse to generate a warehouse heat map, generate a real-time storage location layout of electronic products based on the warehouse heat map, and generate a storage location arrangement plan based on the real-time storage location layout; According to the real-time storage location layout, collect temperature and humidity data, storage location weight data, and image data in the warehouse to generate a warehouse data packet, and construct a digital twin map by combining the warehouse data packet, the three-dimensional map of the warehouse, and the warehouse heat map; Obtain current order data, generate a picking task based on the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat zone map based on the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat zone map; According to the digital twin map, monitor the quantity of electronic products and the storage environment in the warehouse, and mark the inventory status and storage safety status according to the visualization dashboard, and trigger an inventory warning signal and a safety emergency instruction.
2. The visualization intelligent electronic product warehouse management method according to claim 1, characterized in that The step of obtaining historical order data, parsing the historical order data to obtain historical inbound and outbound data, and generating a goods correlation heat map based on the historical inbound and outbound data is specifically as follows: Obtain historical order data, perform data splitting and aggregation on the historical order data to obtain historical inbound and outbound data of historical orders; Perform data splitting on the historical inbound and outbound data to obtain the historical quantity of electronic products, historical product types, and product inbound and outbound data; Obtain the historical storage data of electronic products, and generate a historical heat map of electronic products in the warehouse based on the historical storage data, the historical quantity of electronic products, and the historical product types; Continuously update the historical heat map according to the product inbound and outbound data to generate the latest goods correlation heat map.
3. A visualized intelligent electronic product warehouse management method according to claim 2, characterized in that, The step of overlaying the goods correlation heat map on the three-dimensional map of the warehouse to generate a warehouse heat map, generating a real-time storage location layout of electronic products based on the warehouse heat map, and generating a storage location arrangement plan based on the real-time storage location layout is specifically as follows: Overlay the goods correlation heat map on the three-dimensional map of the warehouse, and perform heat display on the three-dimensional map of the warehouse to generate a warehouse heat map; According to the warehouse heat map, divide the warehouse into regions to obtain multiple heat zones, and extract the electronic product data in the heat zones; Extract the product types and current location data in the electronic product data, and obtain the real-time storage location layout of electronic products according to the product types and the current location data; Score the real-time storage location layout to obtain the layout score of each electronic product, and generate a storage location arrangement plan according to the layout score.
4. A method for visual intelligent electronic product warehouse management according to claim 3, characterized in that The step of scoring the real-time storage location layout to obtain the layout score of each electronic product, and generating a storage location arrangement plan according to the layout score is specifically as follows: Score the real-time storage location layout to obtain the layout score of each electronic product; Based on the historical inbound and outbound data, obtain the inbound and outbound frequency of each electronic product, and combine the layout score and the inbound and outbound frequency to obtain the first comprehensive storage score of the electronic product; Obtain the environmental distribution data in the warehouse, and combine the environmental distribution data and the product type to generate the second comprehensive storage score of the electronic product; Based on the first comprehensive storage score and the second comprehensive storage score, generate three most suitable storage areas for each electronic product; According to the three most suitable storage areas corresponding to each product, conduct regional attribution division to generate a storage location arrangement plan for the electronic product.
5. A method for visual intelligent electronic product warehouse management according to claim 3, characterized in that, The steps of generating a warehouse data packet according to the real-time storage location layout by collecting temperature and humidity data, storage location weight data, and image data in the warehouse, and constructing a digital twin map by combining the warehouse data packet, the three-dimensional warehouse map, and the warehouse heat map are specifically as follows: According to the real-time storage location layout, collect the temperature and humidity data, storage location weight data, and image data of each storage location in the warehouse and generate a warehouse data packet; Construct an initial digital twin map according to the three-dimensional warehouse map, and superimpose the warehouse heat map on the initial digital twin map to generate a heat twin map; Generate a temperature and humidity display layer according to the temperature and humidity data and the real-time storage location layout; Generate a weight display layer according to the storage location weight data and the real-time storage location layout; Generate a regional detail picture display layer according to the image data and the real-time storage location layout; Superimpose the temperature and humidity display layer, the weight display layer, and the regional detail picture display layer on the heat twin map to construct a digital twin map; Among them, add display switches to the warehouse heat map, the temperature and humidity display layer, the weight display layer, and the regional detail picture display layer.
6. The visualization intelligent electronic product warehouse management method according to claim 1, characterized in that The steps of generating a picking task according to the current order data and arranging employees to pick goods according to the picking task are specifically as follows: Obtain the target product type, target product quantity, and target product storage location according to the current order data; Obtain the personnel location of the employee, and combine the current personnel location, the target product type, the target product quantity, and the target product storage location to generate a picking task; According to the picking task, obtain a predetermined target picking route and a predetermined target picking time; Generate an AR navigation plan according to the predetermined target picking route and the predetermined target picking time, and send the AR navigation plan to the employee; Obtain the current moving speed and current picking efficiency of the employee, and generate a fatigue degree value of the employee according to the current moving speed and the current picking efficiency; Judge whether the fatigue degree value is greater than a preset fatigue threshold. If it is judged that the fatigue degree value is greater than the fatigue threshold, send a rest reminder to the employee.
7. A method for visual intelligent electronic product warehouse management according to claim 6, characterized in that Collect the picking efficiency of the employee, generate an efficiency heat zone map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat zone map. The specific steps are as follows: Collect the movement trajectory data and order completion duration of employees during the picking process, compare the movement trajectory data and the order completion duration with the predetermined target picking route and the predetermined target picking time, and obtain a route efficiency value and a duration efficiency value; Generate a route efficiency heat map and a duration efficiency heat map according to the route efficiency value and the duration efficiency value respectively; Fuse the route efficiency heat map and the duration efficiency heat map to generate an efficiency heat map, extract the efficiency area from the duration efficiency heat map, and obtain the to-be-optimized efficiency area with insufficient efficiency; Extract the to-be-optimized route and the to-be-optimized duration in the to-be-optimized efficiency area to perform real-time dynamic optimization on the picking task.
8. A method for visual intelligent electronic product warehouse management according to claim 1, characterized in that, The steps of monitoring the product quantity and storage environment of electronic products in the warehouse, and marking the inventory status and storage safety status according to the visual dashboard, and triggering the inventory warning signal and the safety emergency instruction are specifically as follows: Monitor the product quantity and storage environment of electronic products in the warehouse, obtain the inventory status according to the product quantity, and generate the storage safety status according to the storage environment; Generate an inventory label according to the inventory status, and generate an environmental safety label according to the storage safety status, wherein the inventory label displays the inventory quantity and the purchase demand value, and the environmental safety label displays the environmental data and the environmental safety value; Mark the inventory label and the environmental safety label in the visual dashboard, and monitor the purchase demand value and the environmental safety value; Judge whether the purchase demand value is greater than a preset demand threshold; If it is judged that the purchase demand value is greater than the demand threshold, generate a purchase reminder, and mark the purchase reminder in yellow in the visual dashboard for display; Judge whether the environmental safety value is less than a preset safety threshold; If it is judged that the environmental safety value is less than the safety threshold, generate an environmental adjustment reminder, and mark the environmental adjustment reminder in red in the visual dashboard for display.
9. A visual intelligent electronic product warehouse management system, which uses a visual intelligent electronic product warehouse management method as described in any one of claims 1-8, characterized in that, The system includes: Heat map generation module: used to obtain historical order data, parse the historical order data to obtain historical inbound and outbound data, and generate a goods correlation heat map according to the historical inbound and outbound data; Storage location arrangement module: used to construct a three-dimensional map of the warehouse, overlay the goods correlation heat map in the three-dimensional map of the warehouse to generate a warehouse heat map, generate a real-time storage location layout of electronic products according to the warehouse heat map, and generate a storage location arrangement plan according to the real-time storage location layout; Digital twin module: used to collect the temperature and humidity data, storage location weight data and image data in the warehouse according to the real-time storage location layout to generate a warehouse data packet, and construct a digital twin map in combination with the warehouse data packet, the three-dimensional map of the warehouse and the warehouse heat map; Intelligent optimization module: used to obtain the current order data, generate a picking task according to the current order data, arrange employees to pick goods according to the picking task, collect the picking efficiency of the employees, generate an efficiency heat map according to the picking efficiency, and perform real-time dynamic optimization on the picking task according to the efficiency heat map; Monitoring and warning module: used to monitor the quantity of electronic products and the storage environment in the warehouse according to the digital twin map, and mark the inventory status and storage safety status based on the visual dashboard, triggering inventory warning signals and safety emergency instructions.