Intelligent stereoscopic warehouse and operation method thereof

Abstract

The invention relates to the technical field of information, in particular to an intelligent stereoscopic warehouse and an operation method thereof. The method comprises the following steps: obtaining automatic stereoscopic warehouse design data, and carrying out stereoscopic warehouse layout feature extraction according to the automatic stereoscopic warehouse design data so as to obtain stereoscopic warehouse layout data; carrying out logistics process design according to the stereoscopic warehouse layout data so as to obtain logistics process data; performing logistics operation simulation according to the logistics process data so as to obtain logistics operation simulation data; carrying out logistics emergency analysis according to the logistics operation simulation data so as to obtain logistics emergency data; performing stereoscopic warehouse structure feature extraction according to the automatic stereoscopic warehouse design data so as to obtain stereoscopic warehouse structure data; and carrying out storage density analysis according to the stereoscopic warehouse structure data so as to obtain stereoscopic warehouse storage density data. The operation efficiency and flexibility of the stereoscopic warehouse are improved based on the information technology.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an operation method of an intelligent stereoscopic warehouse. Background Art

[0002] With the rapid development of the global economy and the booming e-commerce, the traditional warehousing model can no longer meet the modern logistics industry's demand for efficiency, flexibility and intelligence. Traditional flat warehouses are gradually being replaced by more advanced stereoscopic warehouses due to their shortcomings such as low space utilization, low manual operation efficiency and complex management. As a new type of warehousing solution, stereoscopic warehouses achieve the goals of high-density storage and fast access to warehouses, and have become an important part of modern logistics systems. Traditional stereoscopic warehouses usually adopt a fixed layout of shelves and storage units. Once the design is completed, it is very difficult to change the layout or storage strategy. This fixedness limits the warehouse's ability to adjust according to changes in demand. As market demand changes, product types and specifications become increasingly diversified, and traditional warehouses find it difficult to quickly adapt to new storage requirements, resulting in low storage efficiency. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an intelligent high-bay warehouse and an operation method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, an operation method of an intelligent three-dimensional warehouse comprises the following steps:

[0005] Step S1: obtaining automated stereoscopic warehouse design data, and extracting stereoscopic warehouse layout features based on the automated stereoscopic warehouse design data, thereby obtaining stereoscopic warehouse layout data; performing logistics process design based on the stereoscopic warehouse layout data, thereby obtaining logistics process data;

[0006] Step S2: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data; performing logistics emergency situation analysis according to the logistics operation simulation data, thereby obtaining logistics emergency situation data;

[0007] Step S3: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse; performing storage density analysis according to the structural data of the stereoscopic warehouse, thereby obtaining the storage density data of the stereoscopic warehouse; performing cargo turnover rate analysis according to the logistics process data, thereby obtaining the cargo turnover rate data;

[0008] Step S4: Acquire the warehouse automation equipment data, and perform equipment utilization analysis based on the warehouse automation equipment data, thereby obtaining equipment utilization data; perform warehouse flexibility calculation based on the warehouse storage density data and cargo turnover rate data, thereby obtaining warehouse flexibility data;

[0009] Step S5: Perform logistics process scheduling on the logistics emergency data according to the equipment utilization rate data, so as to obtain logistics process scheduling data; perform warehouse operation process efficiency analysis according to the warehouse flexibility data and the logistics process scheduling data, so as to obtain warehouse operation process efficiency data;

[0010] Step S6: Optimize the warehouse process according to the warehouse operation process efficiency data, so as to obtain warehouse process optimization data, and upload it to the warehouse operation management platform to execute the warehouse process operation optimization task.

[0011] By acquiring the design data of the automated stereoscopic warehouse and extracting the layout features, the present invention can ensure that the design of the warehouse maximizes the use of available space and improves storage density. The logistics process design is based on the actual layout data, ensuring the smoothness of goods entering and leaving the warehouse, reducing unnecessary handling and waiting time, and thus improving the overall operation efficiency. This step provides an accurate data basis for subsequent logistics operation simulations, which helps to better predict and plan the operation of the warehouse. Logistics operation simulation can effectively predict the flow of goods, help identify potential bottlenecks and inefficient links, and make adjustments in advance. Emergency analysis can ensure that the warehouse can respond quickly to unexpected events (such as equipment failures, order surges, etc.), formulate emergency plans, and reduce the impact on overall logistics efficiency. Through simulation and analysis, the warehouse's risk resistance can be improved to ensure efficient operation in a dynamic market environment. The structural feature extraction of stereoscopic warehouses can help confirm the rationality of warehouse structures, including design elements such as shelf height and spacing, and further improve storage efficiency. Storage density analysis provides a quantitative assessment of warehouse storage space utilization, helps identify whether there is space waste, and provides data basis for subsequent optimization. Cargo turnover analysis can reveal the flow efficiency of goods, ensure that high-demand products can be quickly turned over, reduce inventory retention, and improve capital utilization efficiency. Equipment utilization analysis can reveal the actual use of automated equipment, help managers identify problems of idle or overused equipment, and thus optimize equipment configuration. By combining storage density and cargo turnover data for flexibility calculation, the resilience of stereoscopic warehouses in the face of market changes can be evaluated, guiding future design and operation strategies. Improving warehouse flexibility can ensure that warehouses can quickly adapt and maintain efficient operations when demand fluctuates. Process scheduling for logistics emergencies based on equipment utilization data can effectively optimize resource allocation and ensure that the logistics system can still operate efficiently during peak periods or emergencies. The efficiency analysis of the stereoscopic warehouse operation process can help identify the efficiency of each link, guide managers to optimize key links, and improve overall operational efficiency. The implementation of this step can ensure the continuous and efficient operation of warehouses in a dynamic environment and improve customer satisfaction. The optimization of the three-dimensional warehouse process can not only improve operational efficiency, but also reduce operating costs, reduce resource waste, and improve overall economic benefits. The optimized data can be uploaded to the three-dimensional warehouse operation management platform to achieve real-time monitoring and automated adjustments to ensure that the warehouse is continuously in the best operating state. Through continuous optimization and data management, a virtuous feedback loop can be formed, enabling warehouse operations to gradually move towards intelligence and automation, thereby improving competitiveness.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring the design data of the automated stereoscopic warehouse, and extracting the layout features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the layout data of the stereoscopic warehouse;

[0014] Step S12: dividing the three-dimensional warehouse into functional areas according to the three-dimensional warehouse layout data, thereby obtaining the three-dimensional warehouse functional area data;

[0015] Step S13: designing the warehouse cargo flow direction according to the functional area data of the three-dimensional warehouse, thereby obtaining the warehouse cargo flow direction data;

[0016] Step S14: Acquire the location data of the goods in the three-dimensional warehouse and the data of goods entering and leaving the three-dimensional warehouse;

[0017] Step S15: drawing a cargo location heat map according to the cargo location data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, thereby obtaining a cargo location heat map;

[0018] Step S16: Design the logistics process according to the cargo location heat map and the warehouse cargo flow data, so as to obtain the logistics process data.

[0019] The present invention can fully understand the key elements of the warehouse space structure, shelf arrangement, channel design, etc. by acquiring the stereoscopic warehouse design data and extracting its layout features. The accurate layout feature extraction provides a scientific basis for the subsequent functional area division and logistics process optimization, which is helpful for the efficient use of space and the smoothness of goods flow. According to the warehouse layout data, the different functional areas are reasonably divided (such as storage area, picking area, shipping area, etc.), which can improve the efficiency of the internal operation of the warehouse. For example, the setting of fast channels can reduce the time of goods storage and retrieval, optimize the matching of space and function, and thus improve the operation efficiency of the warehouse. At the same time, reasonable area division can better cope with different types of goods storage needs. Goods flow design is a key link in warehouse efficiency optimization. By analyzing the functional area data and conducting reasonable goods flow design, the transportation distance of goods can be shortened, and the intersection or blockage phenomenon in the flow of goods can be reduced. The optimized logistics path can not only speed up the speed of storage, but also reduce equipment loss and manual operation costs, and improve the overall operation efficiency of the warehouse. Obtaining the real-time location and in-and-out data of goods can help grasp the dynamic changes inside the warehouse and form a clear goods management system. These data can provide basic support for the rapid storage and retrieval of goods and the optimization of goods distribution, and also help to achieve precise control and operation of automated equipment inside the warehouse. The cargo space heat map can intuitively display the frequency and heat of use of different cargo spaces. By drawing a heat map, warehouse managers can identify high-frequency cargo spaces and inefficiently used areas, and then adjust the storage location of goods to avoid excessive use or idleness of certain cargo spaces. Optimizing the distribution of cargo spaces can shorten the storage and retrieval time of high-frequency goods and improve warehouse space utilization and work efficiency. Combining the cargo space heat map with cargo flow data for logistics process design can optimize the storage and retrieval path of goods in the warehouse and ensure efficient movement of goods. By rationally planning the logistics process, time waste in the process of cargo handling can be reduced, equipment operation efficiency can be improved, and the complexity and risk of logistics operations can be reduced. The optimized logistics process can also better cope with order fluctuations and changes in market demand, and improve the flexibility and response speed of the warehouse.

[0020] Optionally, step S2 specifically includes:

[0021] Step S21: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data;

[0022] Step S22: performing logistics equipment failure analysis according to the logistics operation simulation data, thereby obtaining logistics equipment failure data;

[0023] Step S23: performing logistics order load analysis according to the logistics operation simulation data, thereby obtaining logistics order load data;

[0024] Step S24: Integrate logistics emergency data according to logistics equipment failure data and logistics order carrying data, so as to obtain logistics emergency data.

[0025] The present invention can identify potential bottlenecks or inefficient links by simulating the logistics operation process in the warehouse, thereby optimizing the logistics operation. The simulation results are helpful to adjust the storage, sorting, and transportation paths of goods inside the warehouse, and improve the overall efficiency of the warehouse internal operation. Through simulation before the actual operation, it is possible to foresee the problems of equipment conflicts and path congestion, and solve them in advance in the actual operation, thereby avoiding delays and efficiency losses in the actual operation. The logistics operation simulation can test the warehouse's coping ability under different scenarios (such as order fluctuations, large order influx, etc.), and help warehouse managers better cope with logistics pressure and emergencies. By analyzing the logistics operation simulation data, the risk of equipment failure can be discovered in advance, especially when operating at high load, which enables the warehouse to better perform preventive maintenance of equipment and reduce equipment downtime. Based on the equipment failure analysis, the warehouse manager can arrange a more reasonable equipment operation plan to reduce the problem of excessive use or idle equipment, thereby improving the overall utilization of the equipment. Identifying equipment failures in advance and taking preventive measures can greatly reduce the operational interruption caused by equipment failures in logistics operations, thereby improving the stability of the logistics system. By simulating the workload under different order levels, the warehouse's carrying capacity under different order fluctuation scenarios can be evaluated, helping warehouse managers to better cope with the processing needs of a large number of orders during peak periods. Logistics order load analysis can help warehouse managers reasonably arrange manpower, equipment and transportation resources to ensure that the warehouse can operate normally under high order pressure, improve operating efficiency and reduce delays. Through load analysis, the warehouse's operational bottlenecks during peak or off-peak orders can be identified, allowing managers to adjust storage strategies in a timely manner and improve the warehouse's flexibility in responding to order fluctuations. By integrating equipment failure data with order load data, the operational risks faced by the warehouse in emergencies such as high-load operation and equipment failure can be identified, which helps the warehouse respond quickly to emergencies and reduce the negative impact on overall operations. By analyzing emergency data, the warehouse can formulate emergency plans in advance and quickly enable them in actual operations, reducing stagnation or delays caused by equipment failure or order surges, and ensuring the continuous and stable operation of the logistics system in emergencies. Integrating logistics equipment failure and order load data can help warehouses identify high-risk areas or links, thereby better allocating resources and optimizing processes, and improving the overall risk management level of warehouses.

[0026] Optionally, step S22 is specifically:

[0027] Step S221: extracting logistics operation simulation fault features according to the logistics operation simulation data, thereby obtaining logistics operation simulation fault data;

[0028] Step S222: classifying the logistics operation simulation fault data into the types of logistics operation equipment faults, thereby obtaining the automation control system fault data and the equipment network communication fault data;

[0029] Step S223: performing automatic control system fault frequency statistics on the automatic control system fault data, thereby obtaining high-frequency automatic control system fault data and low-frequency automatic control system fault data;

[0030] Step S224: performing statistics on the device network communication failure time for the device network communication failure data, thereby obtaining the device network communication failure data during the peak period and the device network communication failure data during the non-peak period;

[0031] Step S225: performing an intersection operation of high-risk logistics equipment failures according to the high-frequency automation control system failure data and the peak period equipment network communication failure data, thereby obtaining high-risk logistics equipment failure data;

[0032] Step S226: performing an intersection operation of low-risk logistics equipment failures according to the low-frequency automation control system failure data and the off-peak equipment network communication failure data, thereby obtaining low-risk logistics equipment failure data;

[0033] Step S227: Integrate the logistics equipment failure data according to the high-risk logistics equipment failure data and the low-risk logistics equipment failure data, so as to obtain the logistics equipment failure data.

[0034] The present invention can find the potential failure risk of the equipment during the operation process by extracting the fault features in the simulation data, which helps the warehouse manager to perform preventive maintenance on the equipment before the actual operation, thereby reducing downtime. By analyzing the fault features in the simulation, the operation logic of the equipment can be better optimized, the stability of the logistics equipment and the automation system can be enhanced, and the continuity and efficiency of the warehouse operation can be ensured. Classifying the faults can help accurately identify whether it is an automation control system fault or a network communication fault, reduce the troubleshooting time and quickly lock the source of the problem. According to the type of fault, the warehouse can formulate different maintenance strategies, for example, for the failure of the automation control system, software debugging or hardware replacement; for network communication failures, troubleshooting communication modules or signal interference and other problems. Through the statistics of fault frequency, the frequently occurring high-frequency fault points can be identified, so as to optimize the system design or improve the quality of the faulty parts in a targeted manner. Through the analysis of high-frequency faults, warehouse managers can prevent these problems in advance, improve the reliability of the overall system, and reduce operational interruptions caused by frequent faults. Through statistical analysis of peak-period fault data, the warehouse can identify the links in the network communication system that are prone to problems during busy periods, and then optimize the bandwidth, configuration or hardware equipment of the communication network to ensure stable operation during peak periods. Analyzing fault data during non-peak periods helps the warehouse make adjustments when network resources are not tight to improve the efficiency and stability of overall network communication. Through the intersection analysis of high-frequency automation faults and peak-period network faults, it is possible to more accurately lock in high-risk equipment that is prone to failure during critical operation times and links, and then focus on monitoring and maintaining these equipment. Timely identification and handling of the failure risk of high-risk equipment will help reduce the overall operational risk of the system and ensure the safety and continuity of warehouse operations. By analyzing low-frequency and non-peak-period fault data, it can help warehouses optimize the maintenance cycle of low-risk equipment, reduce unnecessary maintenance and downtime, and reduce maintenance costs. According to the characteristics of low-risk faults, warehouses can reasonably allocate technicians and maintenance equipment to avoid investing too many resources in non-critical periods and improve the economy of overall operations. By integrating high-risk and low-risk equipment failure data, the warehouse can establish a systematic fault prevention and maintenance system, and perform maintenance and inspection at different frequencies on equipment with different risk levels, thereby improving the stability of equipment operation. The warehouse management system can quickly make a fault response plan based on the integrated fault data, reduce the impact time of the fault, and ensure the efficient operation of the logistics system.

[0035] Optionally, step S23 is specifically:

[0036] Step S231: extracting logistics order features according to the logistics operation simulation data, thereby obtaining logistics order data;

[0037] Step S232: performing logistics order fluctuation statistics according to the logistics order data, thereby obtaining large fluctuation order data;

[0038] Step S233: extracting the picking area features of the stereoscopic warehouse and the transport area features of the stereoscopic warehouse according to the functional area data of the stereoscopic warehouse, thereby obtaining the picking area data of the stereoscopic warehouse and the transport area data of the stereoscopic warehouse;

[0039] Step S234: extracting the maximum picking data of the automated equipment in the picking area according to the picking area data of the stereoscopic warehouse, thereby obtaining the maximum picking data of the automated equipment in the picking area;

[0040] Step S235: extracting the maximum transportation data of the transportation area automation equipment according to the transportation area data of the stereoscopic warehouse, thereby obtaining the maximum transportation data of the transportation area automation equipment;

[0041] Step S236: performing order picking overload calculation on the large fluctuation order data according to the maximum picking data of the automated equipment in the picking area, thereby obtaining order picking overload data;

[0042] Step S237: performing order transportation overload calculation on the large fluctuation order data according to the maximum transportation data of the transportation area automation equipment, thereby obtaining order transportation overload data;

[0043] Step S238: Integrate the logistics order carrying data according to the order picking overload data and the order transportation overload data, so as to obtain the logistics order carrying data.

[0044] The present invention helps to identify the quantity, type, weight, volume and other characteristics of an order by extracting logistics order data, thereby laying a foundation for subsequent order fluctuation analysis. By analyzing the characteristics of logistics orders, the order processing flow of the warehouse can be better optimized, and corresponding allocation and processing can be performed according to different order requirements. By statistically analyzing order fluctuations, the trend and law of large order fluctuations can be identified, helping warehouse managers to make resource scheduling and equipment allocation in advance during peak periods. For large fluctuation orders, planning and preparation can be carried out in advance to avoid equipment overload or manpower shortage in the warehouse when orders are processed in a centralized manner, thereby improving the overall response capability of the warehouse. By extracting functional area data and clarifying the characteristics of each functional area in the stereoscopic warehouse, the operation process in the area can be optimized according to different order requirements, thereby improving the overall operation efficiency of the warehouse. According to regional characteristics, picking and transportation tasks can be more reasonably allocated to avoid overload work in local areas. By calculating the maximum picking data of the automated equipment in the picking area, the utilization rate of the equipment can be effectively planned to prevent overloading of the picking equipment under large order fluctuations, while improving the maximum working efficiency of the equipment, clarifying the maximum picking capacity of the picking equipment, and preventing the risk of equipment overload or shutdown when orders surge, thereby ensuring the stability of warehouse operation. By calculating the maximum transport capacity of the transport equipment, we can arrange logistics tasks reasonably to ensure that the equipment can efficiently complete the transportation tasks under large order fluctuations without overloading or reduced efficiency. By reasonably allocating the workload of the transport equipment, we can ensure that the overall transportation system of the warehouse can still operate smoothly during peak hours and avoid logistics delays caused by equipment bottlenecks. When facing order fluctuations, we can calculate the picking overload situation and promptly identify the overload risk of the picking equipment, so as to take scheduling or diversion measures to reduce equipment pressure and prevent order processing delays caused by overloading of the picking equipment. Through scientific overload calculation, we can ensure that the warehouse can respond to large order fluctuations in a timely manner and realize efficient processing of orders. Through overload calculation, we can judge in advance whether the transport equipment is overloaded when processing large fluctuation orders, and ensure that the equipment will not reduce efficiency or malfunction due to excessive load. In the case of transport overload, we can promptly dispatch other transport resources or divert tasks to ensure the efficient operation of the transport chain and avoid logistics congestion. By integrating the overload data of picking and transportation equipment, we can comprehensively evaluate the overall carrying capacity of the high-bay warehouse under large order fluctuations, and help warehouse managers formulate reasonable response strategies. Based on the carrying data, we can dynamically adjust warehouse equipment and resources according to different situations of order fluctuations, thereby improving the flexibility and response speed of the warehouse during peak periods.

[0045] Optionally, step S3 specifically includes:

[0046] Step S31: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse;

[0047] Step S32: Calculating the shelf area and the shelf height of the stereoscopic warehouse according to the stereoscopic warehouse structure data, thereby obtaining the shelf area data and the shelf height data of the stereoscopic warehouse;

[0048] Step S33: Calculate the total weight of the order goods according to the logistics order data, so as to obtain the total weight data of the order goods;

[0049] Step S34: Calculate the storage density of the three-dimensional warehouse according to the total weight data of the ordered goods, the area data of the three-dimensional warehouse shelves, and the height data of the three-dimensional warehouse shelves, so as to obtain the storage density data of the three-dimensional warehouse;

[0050] Step S35: Calculate the quarterly average inventory of the three-dimensional warehouse according to the in-and-out data of the three-dimensional warehouse goods, so as to obtain the quarterly average inventory data of the three-dimensional warehouse;

[0051] Step S36: Calculate the cargo turnover rate based on the quarterly average inventory data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, so as to obtain cargo turnover rate data.

[0052] By extracting the structural feature data of the stereoscopic warehouse, the present invention can accurately identify the specific structures such as shelves, channels, and functional areas in the warehouse, ensure the rationality of the layout, and improve the space utilization rate of the warehouse. The structural data of the stereoscopic warehouse is helpful to optimize the warehouse structure in the design stage, so that it can be adjusted accordingly according to the changes in demand in the future, and the scalability of the warehouse can be improved. By accurately calculating the area and height of the shelf, the vertical storage space of the stereoscopic warehouse can be maximized, the storage capacity per unit area can be improved, and the high-density storage target can be achieved. The data of the shelf height and area provide a basis for the allocation of cargo positions and warehouse management, which helps to reasonably arrange the location of goods, improve access efficiency, and reduce storage costs. By calculating the total weight of the ordered goods, the storage area can be reasonably arranged according to the weight of each batch of orders to prevent the shelf from being overloaded due to excessive weight. The cargo weight data can help warehouse managers dynamically adjust the inventory management strategy according to the actual cargo demand, and ensure the reasonable circulation and space allocation of the goods in the warehouse. By calculating the storage density of the stereoscopic warehouse, the storage capacity per unit space of the warehouse can be quickly understood, the arrangement of the shelves can be optimized, and high-density storage can be achieved. The storage density data can be used as an important indicator for evaluating the design and operation of the warehouse, helping managers to improve the overall operation efficiency of the warehouse by adjusting the shelf layout or storage strategy. The calculation of quarterly average inventory can help warehouse managers develop more reasonable inventory management strategies to avoid excessive inventory occupying warehouse space or too low inventory causing shortages. Quarterly average inventory can help companies better control inventory levels, reduce capital occupation or losses caused by excess inventory, and improve inventory turnover. The calculation of goods turnover can directly reflect the circulation of goods in the warehouse, help speed up the circulation of goods, reduce retention time, and improve the overall efficiency of warehouse operations. By improving the goods turnover rate, companies can convert inventory into cash flow more quickly, reduce the capital occupied by backlogs, and enhance the company's capital utilization efficiency and market competitiveness.

[0053] Optionally, step S5 specifically includes:

[0054] Step S51: extracting the real-time utilization rate feature of the stacker according to the equipment utilization rate data, thereby obtaining the real-time utilization rate data of the stacker;

[0055] Step S52: extracting the stacker sudden failure data according to the logistics emergency situation data, thereby obtaining the stacker sudden failure data;

[0056] Step S53: performing utilization statistics according to the real-time utilization data of the stacker, thereby obtaining low utilization data of the stacker;

[0057] Step S54: performing stacker equipment allocation on the stacker sudden failure data according to the stacker low utilization rate data, thereby obtaining stacker equipment allocation data;

[0058] Step S55: performing a three-dimensional warehouse operation process efficiency analysis based on the three-dimensional warehouse flexibility data and the logistics process scheduling data, thereby obtaining the three-dimensional warehouse operation process efficiency data.

[0059] The present invention can dynamically track the use of the stacker by extracting the real-time utilization data of the stacker, effectively avoiding long-term idleness or overload of the equipment. The real-time utilization data provides a basis for equipment allocation for the warehouse manager, optimizes the utilization rate of the equipment, and thus improves the overall operation efficiency of the warehouse. By extracting the sudden failure data of the stacker, the abnormal situation of the equipment can be discovered in time, and emergency measures can be taken quickly to avoid the expansion of the failure and affect the continuity of the logistics operation. The rapid response to the sudden failure helps to reduce the logistics stagnation caused by the equipment failure and ensure the efficient operation and stability of the system. By counting the low utilization data of the stacker, the waste or redundancy of equipment resources can be identified, the allocation strategy of the equipment can be adjusted, and it is ensured that all equipment can play a full role, the inefficient utilization of the stacker can be reduced, and it is helpful to optimize the equipment scheduling, reduce the maintenance and operation costs of the equipment, and thus improve the overall economic benefits of the warehouse. Combined with the low utilization and failure data, the equipment can be deployed intelligently to avoid the situation where some equipment is overloaded and some equipment is idle, and the overall use efficiency of the equipment can be improved. The equipment allocation data helps to quickly dispatch the stacker, respond in time in the case of sudden logistics, and reduce the impact of equipment failure on logistics operations. By analyzing the efficiency of the warehouse operation process, we can identify bottlenecks in the process and optimize it to improve the overall operation efficiency of the warehouse. According to the flexibility and scheduling data of the warehouse, we can dynamically adjust the logistics process to ensure that the warehouse can adapt to changing order demands and emergencies, and improve its resilience and operational flexibility.

[0060] Optionally, step S54 is specifically:

[0061] Step S541: classifying the stacker sudden fault data by fault type, thereby obtaining the stacker electrical fault data and the stacker electrical software data;

[0062] Step S542: performing fault time statistics according to the stacker electrical fault data and the stacker electrical software data, thereby obtaining the long-term stacker electrical fault data and the long-term stacker electrical software data;

[0063] Step S543: Draw an operation flow chart according to the logistics process data, thereby obtaining an operation flow chart;

[0064] Step S544: performing a work flow sequence analysis according to the work flow chart, thereby obtaining work flow sequence data;

[0065] Step S545: performing operation delay calculation on the operation process sequence data according to the long-term stacker electrical fault data and the long-term stacker electrical software data, thereby obtaining the stacker electrical fault operation delay data and the stacker electrical software operation delay data;

[0066] Step S546: Obtain order priority data;

[0067] Step S547: Optimizing order scheduling according to the order priority data, the stacker electrical fault operation delay data, and the stacker electrical software operation delay data, thereby obtaining order scheduling optimization data;

[0068] Step S548: Perform stacker equipment allocation on the order scheduling optimization data according to the stacker low utilization data, thereby obtaining stacker equipment allocation data.

[0069] The present invention can clearly distinguish the fault types, such as electrical problems or software problems, by classifying the stacker faults, which is convenient for subsequent targeted repairs and maintenance, and reduces the troubleshooting time. Accurate fault classification helps to quickly locate the problem, optimize the maintenance process, shorten the downtime of the stacker, and improve the availability of the equipment. Through statistical analysis of the fault time, it can be identified which electrical or software problems cause the equipment to shut down for a long time, which helps to prioritize the resolution of these high-risk faults. By prioritizing the processing of long-term faults, the impact of equipment downtime on logistics operations can be effectively reduced, and the stability and continuity of logistics operations can be maintained. By drawing an operation flow chart, the various operation processes in the stereoscopic warehouse can be intuitively displayed, which helps managers understand and optimize the workflow more clearly. The operation flow chart can reveal the bottlenecks in the logistics process, which helps to analyze how to optimize the process and improve overall efficiency. By analyzing the process sequence, it can ensure that each link is executed in the optimal order, reduce the waiting time for the operation and the waste of resources, and improve the fluency of the operation. Reasonable operation sequence arrangement helps to speed up the work process, avoid process conflicts, and improve the operation efficiency of the warehouse. By calculating the operation delay, the impact of the failure of the stacker on the entire logistics operation can be estimated in advance, providing data support for order scheduling. By early detection and warning of delay risks, a time window can be provided for equipment maintenance to avoid sudden interruptions in the production process. By obtaining the priority data of the order, urgent or high-value orders can be given priority to ensure that important orders are completed on time and improve customer satisfaction. Priority data helps to reasonably allocate warehouse resources, ensure that key orders are given priority when resources are limited, and improve overall operational efficiency. According to the order priority and equipment failure situation, scheduling optimization ensures that important orders are completed with the least impact of the stacker failure and minimizes order delays. Optimized scheduling can improve the warehouse's ability to respond to failures and ensure that order processing can be maintained even in emergencies. Adjust the allocation of stackers based on low utilization data to ensure that the equipment is fully utilized, avoid idle or overloaded resources, and improve the efficiency of equipment use. The reasonable allocation of stackers can improve the overall efficiency of logistics operations and ensure that the utilization rate of equipment remains at the optimal level during order peaks and sudden equipment failures.

[0070] Optionally, step S55 is specifically:

[0071] Step S551: classifying the storage methods according to the high-bay warehouse flexibility data, thereby obtaining pallet storage method data and bulk storage method data;

[0072] Step S552: Perform process scheduling efficiency statistics based on the logistics process scheduling data, thereby obtaining low-efficiency process scheduling data;

[0073] Step S553: ​​Calculating the order processing time according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage order processing time data and the bulk storage order processing time data;

[0074] Step S554: Calculating the space utilization rate according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage space utilization rate data and the bulk storage space utilization rate data;

[0075] Step S555: Draw a pallet storage value flow diagram according to the pallet storage order processing time data and the pallet storage space utilization rate data, thereby obtaining a pallet storage value flow diagram;

[0076] Step S556: Draw a bulk storage value flow diagram according to the bulk storage order processing time data and the bulk storage space utilization rate data, thereby obtaining a bulk storage value flow diagram;

[0077] Step S557: Perform a three-dimensional warehouse process efficiency analysis based on the pallet storage value flow diagram and the bulk storage value flow diagram to obtain the three-dimensional warehouse operation process efficiency data.

[0078] The present invention can help warehouses select the most suitable storage method according to the characteristics of goods by classifying pallet storage and bulk storage, improve the storage flexibility of warehouses, and reasonably classified storage methods can reduce the loss of items during storage and retrieval, and improve the overall storage efficiency. By statistically analyzing the inefficient process scheduling data, warehouse managers can find out the unreasonable scheduling links in logistics operations, optimize the process, and after clarifying the inefficient process, targeted improvements can be made to improve the scheduling efficiency of the overall logistics process and reduce invalid waiting and operation delays. By analyzing the order processing time of pallet storage and bulk storage, warehouses can grasp the operation efficiency under different storage modes, provide data support for optimizing the order processing process, compare the order processing time of different storage methods, help adjust the storage strategy, give priority to using a more efficient storage method to process orders, and shorten the order completion time. By calculating the space utilization of the two storage methods, the warehouse can select the most suitable storage method to maximize the storage capacity of the warehouse, avoid resource waste, clarify the space utilization of different storage methods, and help the warehouse to reasonably layout the storage area and improve the utilization efficiency of the storage space. By drawing a pallet storage value flow diagram, you can intuitively show the balance between pallet storage processing time and space utilization, helping managers evaluate the value of pallet storage methods. The pallet storage value flow diagram provides data support for decision-making and helps managers make the best decision in future storage strategy selection. By visualizing the relationship between bulk storage processing time and space utilization, managers can intuitively determine whether bulk storage is an efficient storage method and make corresponding adjustments. The bulk storage value flow diagram helps evaluate its role in overall warehousing and helps warehouses choose the optimal storage method in different scenarios. By comparing the value flow diagrams of pallet storage and bulk storage, you can find the best storage method in the overall warehouse operation process and improve the operating efficiency of the entire warehouse.

[0079] Optionally, the present specification further provides a three-dimensional warehouse, which includes a three-dimensional warehouse main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the three-dimensional warehouse main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the three-dimensional warehouse main body and control the three-dimensional warehouse main body, and the electrical control unit includes:

[0080] Warehouse logistics process design module: used to obtain automated warehouse design data, and extract warehouse layout features based on the automated warehouse design data, thereby obtaining warehouse layout data; perform logistics process design based on warehouse layout data, thereby obtaining logistics process data;

[0081] Warehouse logistics operation simulation module: used to simulate logistics operations according to logistics process data, so as to obtain logistics operation simulation data; analyze logistics emergencies according to logistics operation simulation data, so as to obtain logistics emergencies data;

[0082] Warehouse density analysis module: used to extract the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, so as to obtain the structural data of the stereoscopic warehouse; to perform storage density analysis according to the structural data of the stereoscopic warehouse, so as to obtain the storage density data of the stereoscopic warehouse; to perform cargo turnover rate analysis according to the logistics process data, so as to obtain the cargo turnover rate data;

[0083] Warehouse flexibility evaluation module: used to obtain warehouse automation equipment data, and perform equipment utilization analysis based on warehouse automation equipment data to obtain equipment utilization data; perform warehouse flexibility calculation based on warehouse storage density data and cargo turnover rate data to obtain warehouse flexibility data;

[0084] Warehouse efficiency analysis module: used to schedule logistics processes based on equipment utilization data and logistics emergency data, thereby obtaining logistics process scheduling data; to analyze the efficiency of the warehouse operation process based on the warehouse flexibility data and logistics process scheduling data, thereby obtaining the warehouse operation process efficiency data;

[0085] Warehouse optimization design module: used to optimize the warehouse process according to the warehouse operation process efficiency data, so as to obtain the warehouse process optimization data, and upload it to the warehouse operation management platform to perform the warehouse process operation optimization task.

[0086] A three-dimensional warehouse of the present invention can implement the operating method of any intelligent three-dimensional warehouse of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete an operating method of an intelligent three-dimensional warehouse. The modules inside the warehouse cooperate with each other, thereby improving the operating efficiency and flexibility of the three-dimensional warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0088] Figure 1 It is a schematic diagram of the steps of the three-dimensional warehouse and the operation method thereof of the present invention;

[0089] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0090] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0091] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0092] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0093] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and / or processor methods and / or microcontroller methods.

[0094] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0095] To achieve this, please refer to Figures 1 to 3 The present invention provides an operation method of an intelligent stereoscopic warehouse, the method comprising the following steps:

[0096] Step S1: obtaining automated stereoscopic warehouse design data, and extracting stereoscopic warehouse layout features based on the automated stereoscopic warehouse design data, thereby obtaining stereoscopic warehouse layout data; performing logistics process design based on the stereoscopic warehouse layout data, thereby obtaining logistics process data;

[0097] In this embodiment, in the automated three-dimensional warehouse, the design data of the system is first obtained, including the shelf layout, channel planning, equipment configuration and other information of the three-dimensional warehouse. The building information modeling (BIM) technology is used to extract the layout features of the three-dimensional warehouse, and the layout data such as the number, size, and location of the shelves are obtained. Subsequently, the logistics process is designed based on these layout data, and the path of the goods from entering the warehouse to leaving the warehouse is defined, including the processes of picking, sorting, and handling. For example, a warehouse adopts a highly optimized shelf layout and designs the path of the automated handling equipment to ensure that the goods can be efficiently transported from the receiving area to the storage area and then to the shipping area. Through the logistics process design, the best picking area and handling path are determined to achieve the best logistics efficiency.

[0098] Step S2: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data; performing logistics emergency situation analysis according to the logistics operation simulation data, thereby obtaining logistics emergency situation data;

[0099] In this embodiment, after obtaining the logistics process data, an operation simulation is performed to analyze the performance of the system in actual operation. For example, simulation software is used to simulate order processing, assuming different order volumes, categories and flow conditions, and generate simulation data. Based on these data, analyze emergencies that occur, such as equipment failures, order delays, etc. For example, during the simulation process, it was found that the cargo handling time during peak hours increased significantly, resulting in delays in shipping. After analyzing the simulation data, identify emergencies that are prone to occur under high equipment load conditions, such as overload of the automated picking system, thereby providing a basis for subsequent optimization.

[0100] Step S3: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse; performing storage density analysis according to the structural data of the stereoscopic warehouse, thereby obtaining the storage density data of the stereoscopic warehouse; performing cargo turnover rate analysis according to the logistics process data, thereby obtaining the cargo turnover rate data;

[0101] In this embodiment, after obtaining the structural design data of the automated stereoscopic warehouse, the structural features, such as the specific size and layout of the shelves, are extracted. These data are used to calculate the total area and total height of the shelves, and the storage density of each shelf is further calculated. Assuming that a warehouse design data includes multiple high-bay shelves, data processing is performed to obtain a storage density of 100 pieces of goods per cubic meter. At the same time, the cargo turnover rate is calculated based on the logistics process data. For example, by comparing the inbound and outbound data, the monthly turnover rate of the cargo is 30 times / piece. Through these analyses, the storage efficiency and cargo flow speed of the warehouse are evaluated, providing data support for subsequent optimization.

[0102] Step S4: Acquire the warehouse automation equipment data, and perform equipment utilization analysis based on the warehouse automation equipment data, thereby obtaining equipment utilization data; perform warehouse flexibility calculation based on the warehouse storage density data and cargo turnover rate data, thereby obtaining warehouse flexibility data;

[0103] In this embodiment, the data of automated equipment in the stereoscopic warehouse, such as the actual use time and idle time of stackers, conveyor belts, etc., are collected. Data analysis tools are used to calculate equipment utilization. For example, the actual utilization rate of a stacker is 75%, while the expected utilization rate is 90%. At the same time, the flexibility of the warehouse is calculated by combining storage density and turnover rate data. For example, if a warehouse with high-density storage has low flexibility under high turnover rate, the flexibility is low. Through these data, the adaptability of the warehouse under different operating conditions is determined, and its responsiveness to changing needs is evaluated.

[0104] Step S5: Perform logistics process scheduling on the logistics emergency data according to the equipment utilization rate data, so as to obtain logistics process scheduling data; perform warehouse operation process efficiency analysis according to the warehouse flexibility data and the logistics process scheduling data, so as to obtain warehouse operation process efficiency data;

[0105] In this embodiment, logistics process scheduling optimization is performed based on equipment utilization data and emergency analysis results. For example, it is found that the utilization rate of stackers is insufficient during high-demand periods, and the equipment scheduling strategy is adjusted based on these data, such as increasing the equipment operating time during peak hours or adding spare equipment. Combined with the flexibility data of the stereoscopic warehouse, process efficiency analysis is performed to evaluate the impact of the adjusted scheduling plan on the overall logistics process. For example, analysis of the optimized scheduling data found that the processing time was reduced from an average of 4 hours to 3 hours, improving the overall operational efficiency.

[0106] Step S6: Optimize the three-dimensional warehouse process according to the three-dimensional warehouse operation process efficiency data, so as to obtain the three-dimensional warehouse process optimization data, and upload it to the three-dimensional warehouse operation management platform to execute the three-dimensional warehouse process operation optimization task.

[0107] In this embodiment, the process of the three-dimensional warehouse is optimized according to the results of the operation process efficiency analysis. For example, the bottlenecks in the picking area and the transportation area are identified, and the shelf layout and equipment scheduling plan are adjusted to improve the processing efficiency. After the optimization is completed, the optimization data is uploaded to the operation management platform of the three-dimensional warehouse, and the system performs the optimization task according to the new process parameters. For example, through the process data updated by the platform, the equipment configuration and personnel arrangement are adjusted in real time to ensure the effective implementation of the optimization measures, thereby improving the operating efficiency and response speed of the entire warehouse.

[0108] Optionally, step S1 specifically includes:

[0109] Step S11: acquiring the design data of the automated stereoscopic warehouse, and extracting the layout features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the layout data of the stereoscopic warehouse;

[0110] In this embodiment, the design data of the automated stereoscopic warehouse is obtained, including information such as the specific location of the shelf, channel layout, and equipment location. These data usually exist in the form of CAD files or BIM models. Using feature extraction tools (such as AutoCAD or Revit), the design data is analyzed to extract layout features such as the size, layout form, and channel width of the shelf. For example, in the design data of a certain warehouse, a layout with a shelf width of 2 meters and a height of 10 meters, arranged in 10 rows and 20 columns per row, is extracted. Through these feature data, the layout data of the stereoscopic warehouse is generated, the specific location and quantity of the shelf are clarified, and a basis is provided for the subsequent functional area division.

[0111] Step S12: dividing the three-dimensional warehouse into functional areas according to the three-dimensional warehouse layout data, thereby obtaining the three-dimensional warehouse functional area data;

[0112] In this embodiment, functional area division is performed based on the extracted stereoscopic warehouse layout data. This involves dividing the warehouse space into different areas, such as receiving areas, storage areas, picking areas, and shipping areas. Use spatial analysis software (such as 3DStudioMax or SketchUp) to define the boundaries and functions of each area based on the layout data. For example, in a warehouse with 5,000 square meters, the first 1,000 square meters are allocated as the receiving area, the next 2,000 square meters are allocated as the storage area, and the remaining area is divided into the picking area and the shipping area. After the division is completed, the stereoscopic warehouse functional area data is generated, including the area and location of each area.

[0113] Step S13: designing the warehouse cargo flow direction according to the functional area data of the three-dimensional warehouse, thereby obtaining the warehouse cargo flow direction data;

[0114] In this embodiment, the cargo flow direction is designed according to the functional area data of the stereoscopic warehouse. This involves determining the flow path of the cargo from receiving, storing, picking to shipping. Use logistics simulation software (such as FlexSim or AnyLogic) to simulate the flow process of the cargo according to the functional area data. The design should take into account the optimization of the path to avoid bottlenecks and cross interference. For example, the receiving area and the storage area are set to be connected by an automatic conveyor belt, and the picking area and the shipping area are set to be an efficient handling system. Finally, the warehouse cargo flow data is generated, including the cargo flow path and transportation method.

[0115] Step S14: Acquire the location data of the goods in the three-dimensional warehouse and the data of goods entering and leaving the three-dimensional warehouse;

[0116] In this embodiment, the cargo location data and in-and-out data of the stereoscopic warehouse are obtained and sorted. The location and in-and-out status of the cargo are monitored in real time through a warehouse management system (WMS) or a sensor network. For example, a warehouse is equipped with RFID tags and scanners to record the specific location of the cargo in real time. The entry time, location and exit time of each cargo are recorded to generate detailed cargo location data and in-and-out data. These data will be used for subsequent heat map drawing and logistics process analysis.

[0117] Step S15: drawing a cargo location heat map according to the cargo location data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, thereby obtaining a cargo location heat map;

[0118] In this embodiment, the cargo location data and in-and-out data of the stereoscopic warehouse are used to draw a cargo location heat map. Use data visualization tools (such as Tableau or QlikView) to draw a heat map based on the frequency of cargo in and out of the warehouse. For example, if the cargo in and out of a certain shelf area is frequent, the area will be displayed in red on the heat map, representing a high-heat area; otherwise, it will be displayed in blue. The heat map helps identify the most active and idle areas in the warehouse, providing a basis for logistics process design.

[0119] Step S16: Design the logistics process according to the cargo location heat map and the warehouse cargo flow data, so as to obtain the logistics process data.

[0120] In this embodiment, the optimized logistics process is designed based on the cargo heat map and the warehouse cargo flow data. Use process optimization tools (such as Visio or Lucidchart) to redesign the logistics path and operation sequence in combination with the high-heat areas identified in the heat map. For example, prioritize the cargo picking tasks in high-heat areas to reduce picking time. Design a new flow chart to optimize the path of goods from the receiving area to the shipping area to improve the overall warehouse operation efficiency. The generated logistics process data will include the time, path, and required equipment for each step, providing detailed guidance for subsequent implementation.

[0121] Optionally, step S2 specifically includes:

[0122] Step S21: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data;

[0123] In this embodiment, a detailed operation simulation is performed using logistics simulation software (such as AnyLogic or FlexSim) based on the logistics process data extracted from the stereoscopic warehouse. First, the layout, equipment configuration, cargo type and flow direction of the warehouse are input into the simulation system. For example, the order processing process in the simulation is set up, including the receipt, storage, picking and delivery of goods. The simulation system is run for several hours to record the operation time, equipment usage and personnel operation of each link. The generated logistics operation simulation data includes indicators such as equipment operation time, picking efficiency, and overall operation cycle time for subsequent analysis and optimization.

[0124] Step S22: performing logistics equipment failure analysis according to the logistics operation simulation data, thereby obtaining logistics equipment failure data;

[0125] In this embodiment, logistics operation simulation data is used to perform equipment failure analysis. The data includes equipment failure events, failure time points, and related operation conditions. Use data analysis tools (such as MATLAB or the pandas library in Python) to analyze the frequency and type of equipment failures. For example, the failure data is classified into mechanical failures, electrical failures, and software failures, and the number of occurrences and duration of each type are counted. The generated logistics equipment failure data includes the incidence rate of each failure type, the mean repair time, and the impact of the failure on the overall operation. These data help identify potential equipment problems and formulate maintenance strategies.

[0126] Step S23: performing logistics order load analysis according to the logistics operation simulation data, thereby obtaining logistics order load data;

[0127] In this embodiment, logistics order load analysis is performed based on logistics operation simulation data. The input data includes the size of each order, processing time, required equipment and space, etc. Use order analysis tools (such as R language or Pivot Table in Excel) to analyze the processing load of the order. For example, calculate the number of orders in each processing stage, the average processing time of each order, and resource consumption. The generated logistics order load data includes the processing capacity of the order, the equipment required, and the processing time. These data are used to evaluate whether the system can meet the processing requirements of high-load orders and make optimization adjustments.

[0128] Step S24: Integrate logistics emergency data according to logistics equipment failure data and logistics order carrying data, so as to obtain logistics emergency data.

[0129] In this embodiment, the logistics equipment failure data and the logistics order carrying data are integrated to perform logistics emergency analysis. Use data integration tools (such as Tableau or PowerBI) to cross-analyze the failure data and order data. For example, analyze whether the order processing capacity is affected during the equipment failure, and the impact of the failure on the order processing time. The generated logistics emergency data includes the actual impact of equipment failure on order carrying capacity, system response time and recovery capability, etc. These data are used to formulate emergency plans and improve system reliability to reduce the impact of emergencies on warehouse operations.

[0130] Optionally, step S22 is specifically:

[0131] Step S221: extracting logistics operation simulation fault features according to the logistics operation simulation data, thereby obtaining logistics operation simulation fault data;

[0132] In this embodiment, after completing the logistics operation simulation, a fault detection algorithm is used to extract fault features in the logistics operation simulation data. Assume that the simulation software generates a log data file containing equipment failures, which records the time, duration, equipment type, and fault description of the failure. Use data mining technology (such as the anomaly detection algorithm in Python's scikit-learn library) to analyze the log data to extract the characteristics of various types of failures. For example, identify which failures occur frequently and which failures are more likely to occur under specific operating conditions. The extracted logistics operation simulation fault data includes fault type, fault mode, and the specific impact of the fault on the operation.

[0133] Step S222: classifying the logistics operation simulation fault data into the types of logistics operation equipment faults, thereby obtaining the automation control system fault data and the equipment network communication fault data;

[0134] In this embodiment, the fault feature data is classified. Assume that the data set contains descriptions and timestamps of device faults, where the faults include automated control system faults and device network communication faults. The fault data is classified using a classification algorithm (such as a support vector machine (SVM) or a decision tree). For example, using the classification algorithm in Python's scikit-learn library, the data is divided into "automated control system faults" and "device network communication faults" according to the fault features. The generated automated control system fault data and device network communication fault data respectively contain information such as the number of occurrences, impact, and processing time of each fault type.

[0135] Step S223: performing automatic control system fault frequency statistics on the automatic control system fault data, thereby obtaining high-frequency automatic control system fault data and low-frequency automatic control system fault data;

[0136] In this embodiment, the frequency statistics of the automation control system fault data are performed. It is assumed that the fault data includes the time and type of each fault. Use statistical analysis tools (such as pivot tables in Excel or pandas libraries in Python) to count the frequency of occurrence of each fault type. For example, the number of high-frequency faults (faults that occur more than 5 times) and low-frequency faults (faults that occur less than 5 times) is calculated within a month. The generated high-frequency automation control system fault data and low-frequency automation control system fault data include the frequency of occurrence of each fault type and its impact on system operation.

[0137] Step S224: performing statistics on the device network communication failure time for the device network communication failure data, thereby obtaining the device network communication failure data during the peak period and the device network communication failure data during the non-peak period;

[0138] In this embodiment, the device network communication fault data is subjected to time statistics. It is assumed that the fault data includes the specific time and duration of the fault. Use a time series analysis tool (such as the statsmodels library in Python or a chart tool in Excel) to count the fault time, distinguishing between peak periods (such as business peak hours) and non-peak periods (such as nighttime). For example, the frequency and duration of faults during the peak period of working days (such as 9:00-17:00 every day) and non-peak period (such as 22:00-06:00) are counted. The generated peak period device network communication fault data and non-peak period device network communication fault data include fault frequency and impact analysis.

[0139] Step S225: performing an intersection operation of high-risk logistics equipment failures according to the high-frequency automation control system failure data and the peak period equipment network communication failure data, thereby obtaining high-risk logistics equipment failure data;

[0140] In this embodiment, it is assumed that both high-frequency automation control system failure data and peak equipment network communication failure data have been counted. Use intersection analysis methods (such as set operations in Python) to find out the equipment that has both high-frequency failures and peak failures. The generated high-risk logistics equipment failure data includes the specific failure types of these equipment, the time of occurrence, and the risks caused to the logistics process.

[0141] Step S226: performing an intersection operation of low-risk logistics equipment failures according to the low-frequency automation control system failure data and the off-peak equipment network communication failure data, thereby obtaining low-risk logistics equipment failure data;

[0142] In this embodiment, it is assumed that the low-frequency automation control system fault data and the off-peak equipment network communication fault data have been classified. The intersection analysis is used to find the equipment that has both low-frequency faults and off-peak faults. The generated low-risk logistics equipment fault data includes the equipment's fault type, frequency of occurrence, and impact assessment on the overall logistics system.

[0143] Step S227: Integrate the logistics equipment failure data according to the high-risk logistics equipment failure data and the low-risk logistics equipment failure data, so as to obtain the logistics equipment failure data.

[0144] In this embodiment, the high-risk and low-risk logistics equipment failure data are integrated. It is assumed that the high-risk and low-risk equipment failure types have been identified respectively. Use data integration tools (such as the pandas library in Python for merging operations) to merge the two types of failure data into a comprehensive data set. The generated logistics equipment failure data includes all failure types, their frequency of occurrence, time distribution, and specific impact on logistics operations. These integrated data are used to formulate maintenance and optimization strategies to improve the overall reliability and efficiency of the system.

[0145] Optionally, step S23 is specifically:

[0146] Step S231: extracting logistics order features according to the logistics operation simulation data, thereby obtaining logistics order data;

[0147] In this embodiment, logistics operation simulation data is obtained, which includes information such as order number, product type, quantity, weight, volume, etc. Data processing tools (such as the pandas library in Python) are used to extract features from the order data. For example, the total weight, total volume, and proportion of various products of each order are extracted. Assuming that the data set contains 1,000 order records, the logistics order data obtained after feature extraction includes the total weight, volume, and product type and quantity of each order. These data are used to analyze the complexity of orders and logistics requirements.

[0148] Step S232: performing logistics order fluctuation statistics according to the logistics order data, thereby obtaining large fluctuation order data;

[0149] In this embodiment, fluctuation statistics are performed on logistics order data. The standard of fluctuation (such as the average daily change in the number of orders) is determined. The order data is analyzed using statistical analysis tools (such as the data analysis toolkit in Excel or the numpy library in Python). For example, the standard deviation of the number of orders is calculated to identify large fluctuation orders. The statistical results show that orders whose order quantity fluctuations exceed a certain threshold (such as 1.5 times the standard deviation) are classified as large fluctuation orders. The generated large fluctuation order data includes the numbers of these orders, the fluctuation range, and information on the relevant time periods.

[0150] Step S233: extracting the picking area features of the stereoscopic warehouse and the transport area features of the stereoscopic warehouse according to the functional area data of the stereoscopic warehouse, thereby obtaining the picking area data of the stereoscopic warehouse and the transport area data of the stereoscopic warehouse;

[0151] In this embodiment, the functional area data of the stereoscopic warehouse is obtained, including the layout diagram and equipment configuration of the picking area and the transportation area. The feature data of the picking area and the transportation area are extracted using a spatial analysis tool (such as CAD software or the Shapely library in Python). For example, the area of ​​the picking area, the equipment configuration (such as the number and type of automated picking equipment) and its operating efficiency are extracted; similarly, the same analysis is performed on the transportation area. The generated stereoscopic warehouse picking area data and transportation area data include area size, equipment type, quantity and its configuration.

[0152] Step S234: extracting the maximum picking data of the automated equipment in the picking area according to the picking area data of the stereoscopic warehouse, thereby obtaining the maximum picking data of the automated equipment in the picking area;

[0153] In this embodiment, the maximum picking capacity of the automated equipment in the picking area is extracted based on the picking area data. Assume that the picking area is equipped with multiple automated picking equipment, and the picking capacity of each equipment is known in the equipment specification. Use a data recording system (such as a warehouse management system (WMS)) to collect the actual picking capacity data of the equipment under different loads. Extract the maximum picking capacity of each equipment under peak load, and calculate the total maximum picking capacity data of the equipment in the picking area.

[0154] Step S235: extracting the maximum transportation data of the transportation area automation equipment according to the transportation area data of the stereoscopic warehouse, thereby obtaining the maximum transportation data of the transportation area automation equipment;

[0155] In this embodiment, the transport area data is obtained to extract the maximum transport capacity of the automated equipment in the transport area. Assume that a plurality of automated conveying equipment are configured in the transport area. The maximum transport capacity of each equipment under full load is calculated using the technical specifications and actual operation data provided by the equipment manufacturer. By statistically analyzing the performance data of these equipment, the total maximum transport capacity data in the transport area is obtained.

[0156] Step S236: performing order picking overload calculation on the large fluctuation order data according to the maximum picking data of the automated equipment in the picking area, thereby obtaining order picking overload data;

[0157] In this embodiment, the maximum picking data and large fluctuation order data are used to calculate the picking overload. It is assumed that the picking demand of some large fluctuation orders exceeds the maximum picking capacity of the automated equipment in the picking area. The order picking overload data is calculated by comparing the order picking demand with the maximum picking capacity of the equipment using an optimization algorithm (such as linear programming). The generated order picking overload data includes the specific value of each large fluctuation order exceeding the picking capacity and its impact on the picking process.

[0158] Step S237: performing order transportation overload calculation on the large fluctuation order data according to the maximum transportation data of the transportation area automation equipment, thereby obtaining order transportation overload data;

[0159] In this embodiment, the transportation overload calculation is performed based on the maximum transportation data and the large fluctuation order data. It is assumed that when the automated equipment in the transportation area processes orders during the peak period, the transportation demand exceeds the maximum transportation capacity of the equipment. Use a transportation demand and equipment capacity comparison analysis tool (such as a regression analysis model in the scikit-learn library in Python) to calculate the overload of each large fluctuation order in the transportation stage. The generated order transportation overload data includes the transportation overload of each order and its potential impact on the transportation process.

[0160] Step S238: Integrate the logistics order carrying data according to the order picking overload data and the order transportation overload data, so as to obtain the logistics order carrying data.

[0161] In this embodiment, the order picking overload data and the order transportation overload data are integrated. The two types of overload data are merged using data integration tools (such as the pandas library in Python) to generate comprehensive logistics order carrying data. The integrated data includes the overload situation of each large fluctuation order in the picking and transportation stages, as well as the overall carrying capacity analysis. These data help optimize warehouse resource allocation and improve logistics processing efficiency.

[0162] Optionally, step S3 specifically includes:

[0163] Step S31: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse;

[0164] In this embodiment, the structural features of the stereoscopic warehouse are extracted from the design data of the automated stereoscopic warehouse. These data include the layout diagram of the warehouse, the shelf configuration, the equipment installation location, etc. The design data is converted into structural feature information using CAD software tools. Assuming that the design data includes the number of layers of the warehouse, the number of shelves on each layer and their arrangement, this information is extracted to generate the structural data of the stereoscopic warehouse. This data includes the detailed specifications of each shelf (such as length, width, height of each shelf), the total volume of the warehouse and the configuration of the shelves, and this information is used for subsequent analysis and optimization.

[0165] Step S32: Calculating the shelf area and the shelf height of the stereoscopic warehouse according to the stereoscopic warehouse structure data, thereby obtaining the shelf area data and the shelf height data of the stereoscopic warehouse;

[0166] In this embodiment, the area and height of the shelves in the warehouse are calculated based on the stereoscopic warehouse structure data. Using a data calculation tool (such as Excel or the NumPy library in Python), the length and width of the shelf are calculated to obtain the total area of ​​each shelf. The height of the shelf is extracted from the design data and used to calculate the height and total height of each layer. Assuming that the height of each shelf is 2.5 meters, the total number of shelves is 50 layers, and the total height is calculated to be 125 meters. The shelf area calculation result is the area of ​​each shelf multiplied by the number of shelves, and the generated stereoscopic warehouse shelf area data and height data are used for further storage density calculation.

[0167] Step S33: Calculate the total weight of the order goods according to the logistics order data, so as to obtain the total weight data of the order goods;

[0168] In this embodiment, the total weight of goods for each order is extracted from the logistics order data. The order data includes the type, quantity and weight of each item of goods. The total weight of goods for each order is calculated using a data processing tool (such as the pandas library in Python). For example, for an order containing 10 products, where the quantity and unit price of each product are known, the total weight of each product is calculated and summarized to obtain the total weight of the order. If an order contains 500 kg of goods, the total weight of the order goods generated is 500 kg, which is used for subsequent storage density calculations.

[0169] Step S34: Calculate the storage density of the three-dimensional warehouse according to the total weight data of the ordered goods, the area data of the three-dimensional warehouse shelves, and the height data of the three-dimensional warehouse shelves, so as to obtain the storage density data of the three-dimensional warehouse;

[0170] In this embodiment, the total weight data of the ordered goods, the shelf area data and the height data are used to calculate the storage density of the stereoscopic warehouse. The storage density can be calculated by dividing the total weight of the goods by the total effective storage area of ​​the warehouse. Assuming that the total shelf area of ​​the warehouse is 5,000 square meters, calculate the weight of the goods per square meter. For example, if the total weight of the goods is 200,000 kilograms, the storage density is 200,000 kilograms / 5,000 square meters=40 kilograms / square meter. The storage density data is used to analyze the space utilization of the warehouse and optimize the cargo storage plan.

[0171] Step S35: Calculate the quarterly average inventory of the three-dimensional warehouse according to the in-and-out data of the three-dimensional warehouse goods, so as to obtain the quarterly average inventory data of the three-dimensional warehouse;

[0172] In this embodiment, the quarterly average inventory is calculated based on the cargo in-and-out data of the stereoscopic warehouse. Collect monthly inventory data and calculate the monthly average inventory. Assuming that the monthly inventory is 10,000, 12,000, 11,000, and 13,000 kg respectively, the quarterly average inventory is (10,000+12,000+11,000+13,000) / 4=11,500 kg. Use data analysis tools (such as SQL query or Excel pivot table) to calculate and generate the quarterly average inventory data of the stereoscopic warehouse.

[0173] Step S36: Calculate the cargo turnover rate based on the quarterly average inventory data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, so as to obtain cargo turnover rate data.

[0174] In this embodiment, the cargo turnover rate is calculated using the quarterly average inventory data and the cargo in-and-out data of the stereoscopic warehouse. The turnover rate calculation formula is the total in-and-out volume of the quarter divided by the quarterly average inventory. For example, if the total in-and-out volume in the quarter is 460,000 kg, and the quarterly average inventory is 11,500 kg, the cargo turnover rate is 460,000 kg / 11,500 kg=40. Use data analysis tools (such as the SciPy library in Python) to calculate and generate cargo turnover rate data. This data is used to evaluate the operational efficiency of the warehouse and provide a basis for inventory management and warehouse layout optimization.

[0175] Optionally, step S5 specifically includes:

[0176] Step S51: extracting the real-time utilization rate feature of the stacker according to the equipment utilization rate data, thereby obtaining the real-time utilization rate data of the stacker;

[0177] In this embodiment, the real-time utilization characteristics of the stacker are extracted from the equipment utilization data. These data include the stacker's operating time, downtime, workload, etc. The real-time utilization of each stacker is calculated using data processing tools (such as the pandas library in Python). For example, if the stacker works for a total of 45 minutes in an hour and the total available time of the equipment is 60 minutes, the real-time utilization is 75% (45 minutes / 60 minutes). This real-time utilization data can be used to monitor the use of the stacker in real time and help optimize equipment scheduling.

[0178] Step S52: extracting the stacker sudden failure data according to the logistics emergency situation data, thereby obtaining the stacker sudden failure data;

[0179] In this embodiment, the sudden failure data of the stacker is extracted based on the logistics emergency data. This includes the failure event records of the stacker, such as the time of failure and the type of failure (e.g., electrical failure, mechanical failure, etc.). These failure data are extracted using a fault management system or log analysis tool (e.g., ELKStack). For example, it is extracted from the log that the stacker had 5 electrical failures and 3 mechanical failures in the past week. This data helps to analyze the reliability of the equipment and predict failures.

[0180] Step S53: performing utilization statistics according to the real-time utilization data of the stacker, thereby obtaining low utilization data of the stacker;

[0181] In this embodiment, utilization statistics are performed based on the real-time utilization data of the stacker to obtain low utilization data of the stacker. The utilization of each stacker over a period of time is considered when performing statistics. For example, a stacker with a utilization rate of less than 50% will be marked as a low utilization stacker. Statistical analysis tools (such as the Matplotlib library in R language or Python) are used to summarize and analyze these data to generate a low utilization data report of the stacker in order to identify and improve the utilization efficiency of the equipment.

[0182] Step S54: performing stacker equipment allocation on the stacker sudden failure data according to the stacker low utilization rate data, thereby obtaining stacker equipment allocation data;

[0183] In this embodiment, equipment allocation is performed on the stacker sudden failure data according to the stacker low utilization data. The failure records of low-utilization stackers are analyzed, and these devices are reallocated to areas with high demand. For example, if the failure frequency of stackers in a certain area is high and the utilization rate is low, some low-utilization equipment can be deployed to this area to improve the overall equipment efficiency. Use equipment scheduling tools (such as operations optimization software) to optimize the equipment allocation plan to obtain stacker equipment allocation data.

[0184] Step S55: performing a three-dimensional warehouse operation process efficiency analysis based on the three-dimensional warehouse flexibility data and the logistics process scheduling data, thereby obtaining the three-dimensional warehouse operation process efficiency data.

[0185] In this embodiment, the efficiency of the three-dimensional warehouse operation process is analyzed based on the three-dimensional warehouse flexibility data and the logistics process scheduling data. The flexibility data includes the layout variability of the warehouse, the cargo handling capacity, etc., while the logistics process scheduling data includes the operation time and processing efficiency of each process. Use process optimization tools (such as Simul8 or AnyLogic) to perform simulation analysis to identify bottlenecks and improvement points. For example, the analysis found that the efficiency of the cargo sorting process was lower than expected. By adjusting the process and optimizing the equipment layout, the overall operation efficiency can be improved. The generated three-dimensional warehouse operation process efficiency data helps warehouse managers develop optimization plans.

[0186] Optionally, step S54 is specifically:

[0187] Step S541: classifying the stacker sudden fault data by fault type, thereby obtaining the stacker electrical fault data and the stacker electrical software data;

[0188] In this embodiment, the stacker's sudden fault data is classified into fault types, with the goal of dividing the fault data into electrical fault data and electrical software data. The stacker's fault records are collected, including fault description, fault occurrence time, affected equipment components, etc. The faults are classified using data classification algorithms (such as decision trees, support vector machines, etc.). For example, by analyzing the fault logs, it is found that all faults described as "electrical short circuit" or "power supply abnormality" are classified as electrical faults, while "software crash" or "control system error" are classified as electrical software faults. This classification helps to focus on solving specific types of fault problems and improve maintenance efficiency.

[0189] Step S542: performing fault time statistics according to the stacker electrical fault data and the stacker electrical software data, thereby obtaining the long-term stacker electrical fault data and the long-term stacker electrical software data;

[0190] In this embodiment, the fault time statistics are performed on the electrical fault data and electrical software fault data of the stacker to obtain the long-term electrical fault data and electrical software fault data of the stacker. First, the start time and end time of each fault are extracted from the fault record. Then, a time analysis tool (such as a pivot table in Excel or a time series analysis library in Python) is used to calculate the duration of each fault type. For example, statistics show that the average duration of electrical faults is 2 hours, while the average duration of electrical software faults is 1 hour. Based on these data, long-term fault modes can be identified and the fault handling process can be improved.

[0191] Step S543: Draw an operation flow chart according to the logistics process data, thereby obtaining an operation flow chart;

[0192] In this embodiment, a work flow chart is drawn based on the logistics process data. First, all relevant work flow data is collected, including task steps, dependencies between tasks, task execution time, etc. A flowchart is created using a flowchart drawing tool (such as Microsoft Visio, Lucidchart, etc.). For example, a flowchart containing the four main work steps of warehousing, inspection, sorting, and outbound is drawn, and the order and relationship of each step are marked. This helps to visualize the process, thereby optimizing the work order and improving operational efficiency.

[0193] Step S544: performing a work flow sequence analysis according to the work flow chart, thereby obtaining work flow sequence data;

[0194] In this embodiment, the operation process sequence analysis is performed according to the operation flow chart to obtain the operation process sequence data. First, the sequence of each operation step in the flow chart and its time consumption are analyzed. The process is analyzed using a process optimization tool (such as a BPMN (Business Process Model and Notation) analysis tool). For example, the delay of the "sorting" step between "warehouse entry" and "warehouse exit" is identified, and the time consumption and dependency of each step are calculated. By optimizing the step sequence, the overall operation time can be reduced and the warehouse operation efficiency can be improved.

[0195] Step S545: performing operation delay calculation on the operation process sequence data according to the long-term stacker electrical fault data and the long-term stacker electrical software data, thereby obtaining the stacker electrical fault operation delay data and the stacker electrical software operation delay data;

[0196] In this embodiment, the operation delay calculation is performed on the operation process sequence data based on the long-term stacker electrical fault data and electrical software fault data. First, the specific impact of each fault on the operation process is calculated in combination with the operation process sequence data and the fault data. For example, if an electrical fault causes a step to be delayed for 2 hours, the delay is marked in the operation flow chart. By analyzing the impact of different fault types on the process, the stacker electrical fault operation delay data and electrical software operation delay data are obtained. These data are used to optimize the operation process and reduce delays caused by faults.

[0197] Step S546: Obtain order priority data;

[0198] In this embodiment, order priority data is obtained, including information such as the priority of each order, order urgency, customer demand, etc. First, the data of all orders are sorted out, and the priority (such as high, medium, low) and urgency of each order are marked. These data can be exported using an order management system (such as an ERP system). For example, through system query, priority data of 100 orders are obtained. These data are used to optimize the order of order processing and improve customer satisfaction and response speed.

[0199] Step S547: Optimizing order scheduling according to the order priority data, the stacker electrical fault operation delay data, and the stacker electrical software operation delay data, thereby obtaining order scheduling optimization data;

[0200] In this embodiment, order scheduling optimization is performed based on order priority data, stacker electrical failure operation delay data, and electrical software operation delay data. A scheduling optimization algorithm (such as linear programming or heuristic algorithm) is used to optimize the processing order of orders. For example, high-priority orders are arranged to be processed in time periods with fewer equipment failures to avoid delays during periods of frequent failures. Through simulation optimization, order scheduling optimization data is generated, which can improve the overall order processing efficiency and ensure that important orders are completed first.

[0201] Step S548: Perform stacker equipment allocation on the order scheduling optimization data according to the stacker low utilization data, thereby obtaining stacker equipment allocation data.

[0202] In this embodiment, stacker equipment is allocated to order scheduling optimization data based on stacker low utilization data. First, the utilization and task load data of each stacker are analyzed to identify low-utilization equipment. Then, equipment scheduling algorithms (such as genetic algorithms or simulated annealing algorithms) are used to reallocate equipment. For example, stackers with low utilization are allocated to areas that require high-frequency operations, thereby improving equipment utilization. Stacker equipment allocation data is generated to ensure optimal use of equipment and improve the overall work efficiency of the warehouse.

[0203] Optionally, step S55 is specifically:

[0204] Step S551: classifying the storage methods according to the high-bay warehouse flexibility data, thereby obtaining pallet storage method data and bulk storage method data;

[0205] In this embodiment, storage methods are classified according to the flexibility data of the stereoscopic warehouse (such as the type of goods, storage density, frequency of entry and exit, etc.) to obtain pallet storage method data and bulk storage method data. First, collect relevant data on different storage methods in the warehouse, including the type of goods, handling methods and storage requirements for pallet storage and bulk storage. Use data classification techniques (such as K-means clustering) to analyze these data. For example, through analysis, it is found that some goods are suitable for pallet storage (such as large quantities, standardized goods), while other goods are suitable for bulk storage (such as bulk raw materials). The results will be classified into pallet storage method and bulk storage method data, which will help optimize warehouse space and improve storage efficiency.

[0206] Step S552: Perform process scheduling efficiency statistics based on the logistics process scheduling data, thereby obtaining low-efficiency process scheduling data;

[0207] In this embodiment, process scheduling efficiency statistics are performed based on logistics process scheduling data to obtain inefficient process scheduling data. First, logistics process scheduling data is collected, including the time, resource allocation and actual operation of each operation step. Statistical analysis tools (such as Python's Pandas library or R language) are used to calculate the efficiency of each process step. For example, statistics show that some process steps (such as cargo sorting) take a long time, resulting in low overall scheduling efficiency. By analyzing inefficient data, process bottlenecks can be identified, scheduling strategies can be improved, and overall efficiency can be improved.

[0208] Step S553: ​​Calculating the order processing time according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage order processing time data and the bulk storage order processing time data;

[0209] In this embodiment, the order processing time is calculated based on the pallet storage method data and the bulk storage method data to obtain the pallet storage order processing time data and the bulk storage order processing time data. First, the order processing time under each storage method is analyzed, including the picking, packaging and transportation time. For example, for orders stored in pallets, the average processing time data from order placement to delivery is collected, and for orders stored in bulk, the processing time data is also collected. The calculation results show that the order processing time for pallet storage is 2 hours, while the order processing time for bulk storage is 3 hours. Through these data, the storage method can be optimized and the processing efficiency can be improved.

[0210] Step S554: Calculating the space utilization rate according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage space utilization rate data and the bulk storage space utilization rate data;

[0211] In this embodiment, the space utilization is calculated based on the pallet storage mode data and the bulk storage mode data to obtain the pallet storage space utilization data and the bulk storage space utilization data. First, the total area and the actual occupied area of ​​the pallet storage area and the bulk storage area in the warehouse are calculated. The space utilization is calculated using the formula (actual occupied area / total area). For example, if the total area of ​​the pallet storage area is 5,000 square meters and the actual occupied area is 4,000 square meters, the pallet storage space utilization is 80%. Similarly, the space utilization of the bulk storage area is calculated. These data are used to evaluate the space efficiency of different storage methods.

[0212] Step S555: Draw a pallet storage value flow diagram according to the pallet storage order processing time data and the pallet storage space utilization rate data, thereby obtaining a pallet storage value flow diagram;

[0213] In this embodiment, a pallet storage value flow diagram is drawn based on the pallet storage order processing time data and the pallet storage space utilization data. Combined with the order processing time and space utilization data of the pallet storage, a value flow diagram is created using a flowchart drawing tool (such as Microsoft Visio). For example, the high-efficiency area and low-efficiency area of ​​the pallet storage are marked in the drawing, as well as the corresponding processing time. The flow chart can clearly show the value performance of the pallet storage and provide a reference for further optimization.

[0214] Step S556: Draw a bulk storage value flow diagram according to the bulk storage order processing time data and the bulk storage space utilization rate data, thereby obtaining a bulk storage value flow diagram;

[0215] In this embodiment, a bulk storage value flow diagram is drawn based on bulk storage order processing time data and bulk storage space utilization data. First, the order processing time and space utilization data of bulk storage are integrated, and a bulk storage value flow diagram is created using a flowchart drawing tool (such as Lucidchart). For example, the flowchart shows high utilization areas and low utilization areas of bulk storage, as well as the corresponding order processing time. This information helps identify the advantages and disadvantages of bulk storage and optimizes storage layout and operation processes.

[0216] Step S557: Perform a three-dimensional warehouse process efficiency analysis based on the pallet storage value flow diagram and the bulk storage value flow diagram to obtain the three-dimensional warehouse operation process efficiency data.

[0217] In this embodiment, the efficiency analysis of the stereoscopic warehouse process is performed based on the pallet storage value flow diagram and the bulk storage value flow diagram to obtain the stereoscopic warehouse operation process efficiency data. First, the value flow diagrams of pallet storage and bulk storage are comprehensively analyzed to compare the processing time, space utilization and efficiency performance. For example, by comparison, it is found that pallet storage is better than bulk storage in processing speed, but the space utilization is lower. Use these data for comprehensive evaluation, identify the optimal storage method and improvement points, and thus formulate a strategy to improve the overall operation efficiency of the stereoscopic warehouse.

[0218] Optionally, the present specification further provides an intelligent stereoscopic warehouse, which includes a stereoscopic warehouse main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the stereoscopic warehouse main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the stereoscopic warehouse main body and control the stereoscopic warehouse main body, and the electrical control unit includes:

[0219] Warehouse logistics process design module: used to obtain automated warehouse design data, and extract warehouse layout features based on the automated warehouse design data, thereby obtaining warehouse layout data; perform logistics process design based on warehouse layout data, thereby obtaining logistics process data;

[0220] Warehouse logistics operation simulation module: used to simulate logistics operations according to logistics process data, so as to obtain logistics operation simulation data; analyze logistics emergencies according to logistics operation simulation data, so as to obtain logistics emergencies data;

[0221] Warehouse density analysis module: used to extract the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, so as to obtain the structural data of the stereoscopic warehouse; to perform storage density analysis according to the structural data of the stereoscopic warehouse, so as to obtain the storage density data of the stereoscopic warehouse; to perform cargo turnover rate analysis according to the logistics process data, so as to obtain the cargo turnover rate data;

[0222] Warehouse flexibility evaluation module: used to obtain warehouse automation equipment data, and perform equipment utilization analysis based on warehouse automation equipment data to obtain equipment utilization data; perform warehouse flexibility calculation based on warehouse storage density data and cargo turnover rate data to obtain warehouse flexibility data;

[0223] Warehouse efficiency analysis module: used to schedule logistics processes based on equipment utilization data and logistics emergency data, thereby obtaining logistics process scheduling data; to analyze the efficiency of the warehouse operation process based on the warehouse flexibility data and logistics process scheduling data, thereby obtaining the warehouse operation process efficiency data;

[0224] Warehouse optimization design module: used to optimize the warehouse process according to the warehouse operation process efficiency data, so as to obtain the warehouse process optimization data, and upload it to the warehouse operation management platform to perform the warehouse process operation optimization task.

[0225] A three-dimensional warehouse of the present invention can implement the operating method of any intelligent three-dimensional warehouse of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete an operating method of an intelligent three-dimensional warehouse. The modules inside the warehouse cooperate with each other, thereby improving the operating efficiency and flexibility of the three-dimensional warehouse.

[0226] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0227] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for operating an intelligent high-bay warehouse, characterized in that: The following steps are involved: Step S1: obtaining automated stereoscopic warehouse design data, and extracting stereoscopic warehouse layout features based on the automated stereoscopic warehouse design data, thereby obtaining stereoscopic warehouse layout data; performing logistics process design based on the stereoscopic warehouse layout data, thereby obtaining logistics process data; Step S2: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data; Perform logistics emergency analysis based on logistics operation simulation data to obtain logistics emergency data; Step S3: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse; performing storage density analysis according to the structural data of the stereoscopic warehouse, thereby obtaining the storage density data of the stereoscopic warehouse; Cargo turnover rate analysis is performed based on logistics process data to obtain cargo turnover rate data; Step S4: Acquire the data of the automated equipment of the three-dimensional warehouse, and perform equipment utilization analysis based on the data of the automated equipment of the three-dimensional warehouse, so as to obtain the equipment utilization data; The flexibility of the three-dimensional warehouse is calculated based on the storage density data and the cargo turnover rate data of the three-dimensional warehouse, so as to obtain the flexibility data of the three-dimensional warehouse; Step S5: Perform logistics process scheduling on the logistics emergency data according to the equipment utilization data, thereby obtaining logistics process scheduling data; Conduct warehouse operation process efficiency analysis based on warehouse flexibility data and logistics process scheduling data, thereby obtaining warehouse operation process efficiency data; Step S6: Optimize the warehouse process according to the warehouse operation process efficiency data, so as to obtain warehouse process optimization data, and upload it to the warehouse operation management platform to execute the warehouse process operation optimization task.

2. The operation method of the intelligent stereoscopic warehouse according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring the design data of the automated stereoscopic warehouse, and extracting the layout features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the layout data of the stereoscopic warehouse; Step S12: dividing the three-dimensional warehouse into functional areas according to the three-dimensional warehouse layout data, thereby obtaining the three-dimensional warehouse functional area data; Step S13: designing the warehouse cargo flow direction according to the functional area data of the three-dimensional warehouse, thereby obtaining the warehouse cargo flow direction data; Step S14: Acquire the location data of the goods in the three-dimensional warehouse and the data of goods entering and leaving the three-dimensional warehouse; Step S15: drawing a cargo location heat map according to the cargo location data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, thereby obtaining a cargo location heat map; Step S16: Design the logistics process according to the cargo location heat map and the warehouse cargo flow data, so as to obtain the logistics process data.

3. The operation method of the intelligent stereoscopic warehouse according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: Performing logistics operation simulation according to the logistics process data, thereby obtaining logistics operation simulation data; Step S22: performing logistics equipment failure analysis according to the logistics operation simulation data, thereby obtaining logistics equipment failure data; Step S23: performing logistics order load analysis according to the logistics operation simulation data, thereby obtaining logistics order load data; Step S24: Integrate logistics emergency data according to logistics equipment failure data and logistics order carrying data, so as to obtain logistics emergency data.

4. The operation method of the intelligent stereoscopic warehouse according to claim 3 is characterized in that: Step S22 is specifically as follows: Step S221: extracting logistics operation simulation fault features according to the logistics operation simulation data, thereby obtaining logistics operation simulation fault data; Step S222: classifying the logistics operation simulation fault data into the types of logistics operation equipment faults, thereby obtaining the automation control system fault data and the equipment network communication fault data; Step S223: performing automatic control system fault frequency statistics on the automatic control system fault data, thereby obtaining high-frequency automatic control system fault data and low-frequency automatic control system fault data; Step S224: performing statistics on the device network communication failure time for the device network communication failure data, thereby obtaining the device network communication failure data during the peak period and the device network communication failure data during the non-peak period; Step S225: performing an intersection operation of high-risk logistics equipment failures according to the high-frequency automation control system failure data and the peak period equipment network communication failure data, thereby obtaining high-risk logistics equipment failure data; Step S226: performing an intersection operation of low-risk logistics equipment failures according to the low-frequency automation control system failure data and the off-peak equipment network communication failure data, thereby obtaining low-risk logistics equipment failure data; Step S227: Integrate the logistics equipment failure data according to the high-risk logistics equipment failure data and the low-risk logistics equipment failure data, so as to obtain the logistics equipment failure data.

5. The operation method of the intelligent stereoscopic warehouse according to claim 3 is characterized in that: Step S23 is specifically as follows: Step S231: extracting logistics order features according to logistics operation simulation data, thereby obtaining logistics order data; Step S232: performing logistics order fluctuation statistics according to the logistics order data, thereby obtaining large fluctuation order data; Step S233: extracting the picking area features of the stereoscopic warehouse and the transport area features of the stereoscopic warehouse according to the functional area data of the stereoscopic warehouse, thereby obtaining the picking area data of the stereoscopic warehouse and the transport area data of the stereoscopic warehouse; Step S234: extracting the maximum picking data of the automated equipment in the picking area according to the picking area data of the stereoscopic warehouse, thereby obtaining the maximum picking data of the automated equipment in the picking area; Step S235: extracting the maximum transportation data of the transportation area automation equipment according to the transportation area data of the stereoscopic warehouse, thereby obtaining the maximum transportation data of the transportation area automation equipment; Step S236: performing order picking overload calculation on the large fluctuation order data according to the maximum picking data of the automated equipment in the picking area, thereby obtaining order picking overload data; Step S237: performing order transportation overload calculation on the large fluctuation order data according to the maximum transportation data of the transportation area automation equipment, thereby obtaining order transportation overload data; Step S238: Integrate the logistics order carrying data according to the order picking overload data and the order transportation overload data, so as to obtain the logistics order carrying data.

6. The operation method of the intelligent stereoscopic warehouse according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, thereby obtaining the structural data of the stereoscopic warehouse; Step S32: Calculating the shelf area and the shelf height of the stereoscopic warehouse according to the stereoscopic warehouse structure data, thereby obtaining the shelf area data and the shelf height data of the stereoscopic warehouse; Step S33: Calculate the total weight of the order goods according to the logistics order data, so as to obtain the total weight data of the order goods; Step S34: Calculate the storage density of the three-dimensional warehouse according to the total weight data of the ordered goods, the area data of the three-dimensional warehouse shelves, and the height data of the three-dimensional warehouse shelves, so as to obtain the storage density data of the three-dimensional warehouse; Step S35: Calculate the quarterly average inventory of the three-dimensional warehouse according to the in-and-out data of the three-dimensional warehouse goods, so as to obtain the quarterly average inventory data of the three-dimensional warehouse; Step S36: Calculate the cargo turnover rate based on the quarterly average inventory data of the three-dimensional warehouse and the cargo in-and-out data of the three-dimensional warehouse, so as to obtain cargo turnover rate data.

7. The operation method of the intelligent stereoscopic warehouse according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: extracting the real-time utilization rate feature of the stacker according to the equipment utilization rate data, thereby obtaining the real-time utilization rate data of the stacker; Step S52: extracting the stacker sudden failure data according to the logistics emergency situation data, thereby obtaining the stacker sudden failure data; Step S53: performing utilization statistics according to the real-time utilization data of the stacker, thereby obtaining low utilization data of the stacker; Step S54: performing stacker equipment allocation on the stacker sudden failure data according to the stacker low utilization rate data, thereby obtaining stacker equipment allocation data; Step S55: performing a three-dimensional warehouse operation process efficiency analysis based on the three-dimensional warehouse flexibility data and the logistics process scheduling data, thereby obtaining the three-dimensional warehouse operation process efficiency data.

8. The method for operating the intelligent high-bay warehouse according to claim 7, characterized in that: Step S54 is specifically as follows: Step S541: classifying the stacker sudden fault data by fault type, thereby obtaining the stacker electrical fault data and the stacker electrical software data; Step S542: performing fault time statistics according to the stacker electrical fault data and the stacker electrical software data, thereby obtaining the long-term stacker electrical fault data and the long-term stacker electrical software data; Step S543: Draw an operation flow chart according to the logistics process data, thereby obtaining an operation flow chart; Step S544: performing a work flow sequence analysis according to the work flow chart, thereby obtaining work flow sequence data; Step S545: performing operation delay calculation on the operation process sequence data according to the long-term stacker electrical fault data and the long-term stacker electrical software data, thereby obtaining the stacker electrical fault operation delay data and the stacker electrical software operation delay data; Step S546: Obtain order priority data; Step S547: Optimizing order scheduling according to the order priority data, the stacker crane electrical fault operation delay data, and the stacker crane electrical software operation delay data, thereby obtaining order scheduling optimization data; Step S548: Perform stacker equipment allocation on the order scheduling optimization data according to the stacker low utilization data, thereby obtaining stacker equipment allocation data.

9. The method for operating the intelligent high-bay warehouse according to claim 7, characterized in that: Step S55 is specifically as follows: Step S551: classifying the storage methods according to the high-bay warehouse flexibility data, thereby obtaining pallet storage method data and bulk storage method data; Step S552: Perform process scheduling efficiency statistics based on the logistics process scheduling data, thereby obtaining low-efficiency process scheduling data; Step S553: ​​Calculating the order processing time according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage order processing time data and the bulk storage order processing time data; Step S554: Calculating the space utilization rate according to the pallet storage mode data and the bulk storage mode data, thereby obtaining the pallet storage space utilization rate data and the bulk storage space utilization rate data; Step S555: Draw a pallet storage value flow diagram according to the pallet storage order processing time data and the pallet storage space utilization rate data, thereby obtaining a pallet storage value flow diagram; Step S556: Draw a bulk storage value flow diagram according to the bulk storage order processing time data and the bulk storage space utilization rate data, thereby obtaining a bulk storage value flow diagram; Step S557: Perform a three-dimensional warehouse process efficiency analysis based on the pallet storage value flow diagram and the bulk storage value flow diagram to obtain the three-dimensional warehouse operation process efficiency data.

10. An intelligent three-dimensional warehouse, characterized in that: It includes a stereoscopic warehouse main body, a power supply unit and an electrical control unit. The power supply unit is installed inside the stereoscopic warehouse main body. The electrical control unit is electrically connected to the power supply unit. The electrical control unit is used to charge the stereoscopic warehouse main body and control the stereoscopic warehouse main body. The electrical control unit includes: Warehouse logistics process design module: used to obtain automated warehouse design data, and extract warehouse layout features based on the automated warehouse design data, thereby obtaining warehouse layout data; perform logistics process design based on warehouse layout data, thereby obtaining logistics process data; Warehouse logistics operation simulation module: used to simulate logistics operations according to logistics process data, so as to obtain logistics operation simulation data; analyze logistics emergencies according to logistics operation simulation data, so as to obtain logistics emergencies data; Warehouse density analysis module: used to extract the structural features of the stereoscopic warehouse according to the design data of the automated stereoscopic warehouse, so as to obtain the structural data of the stereoscopic warehouse; to perform storage density analysis according to the structural data of the stereoscopic warehouse, so as to obtain the storage density data of the stereoscopic warehouse; to perform cargo turnover rate analysis according to the logistics process data, so as to obtain the cargo turnover rate data; Warehouse flexibility evaluation module: used to obtain warehouse automation equipment data, and perform equipment utilization analysis based on warehouse automation equipment data to obtain equipment utilization data; perform warehouse flexibility calculation based on warehouse storage density data and cargo turnover rate data to obtain warehouse flexibility data; Warehouse efficiency analysis module: used to schedule logistics processes based on equipment utilization data and logistics emergency data, thereby obtaining logistics process scheduling data; to analyze the efficiency of the warehouse operation process based on the warehouse flexibility data and logistics process scheduling data, thereby obtaining the warehouse operation process efficiency data; Warehouse optimization design module: used to optimize the warehouse process according to the warehouse operation process efficiency data, so as to obtain the warehouse process optimization data, and upload it to the warehouse operation management platform to perform the warehouse process operation optimization task.

Images