A method for managing information of a storage location of an automated three-dimensional warehouse

Abstract

The application discloses a kind of automatic stereoscopic warehouse's goods location information management method, it is related to warehouse management technical field, the multidimensional basic data of stereoscopic warehouse is collected and constructs goods location virtual mapping system, multidimensional basic data covers the key data category of influence goods location management, establishes the real-time correspondence and data interaction relationship of virtual scene with physical scene.The automatic stereoscopic warehouse's goods location information management method described in the application constructs goods location virtual mapping system, complete copy the core features and operating process of physical warehouse, all goods location management strategies are verified by virtual system before landing execution, in multiple scene simulation.The potential problems such as device operation path conflict, process connection is not smooth, etc., are found and avoided in advance by simulating the cross-region operation coordination effect of equipment, the rationality of goods location cluster division and operation path smoothness, to ensure that the strategy can quickly adapt to the actual operation scene, improve the execution efficiency.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology, and in particular to a method for managing the location information of an automated three-dimensional warehouse. Background Technology

[0002] With the rapid development of the logistics industry, automated storage and retrieval systems (AS / RS) have been widely used in manufacturing, retail, cold chain logistics and other fields due to their advantages such as high space utilization, high degree of automation, and low labor costs. As the core link of AS / RS operation, location information management directly affects warehousing efficiency, inventory turnover rate and equipment operation stability.

[0003] However, existing methods for managing warehouse location information have many shortcomings: First, existing solutions often directly implement management strategies in physical warehouses without establishing a linkage system between physical and virtual scenarios. During the strategy formulation process, the feasibility of the solution cannot be verified through simulation, which can easily lead to problems such as conflicting equipment operation paths, unreasonable division of warehouse location clusters, and poor connection of operation processes after the strategy is implemented. This not only affects operational efficiency but also increases equipment wear and tear and trial and error costs.

[0004] Secondly, warehousing operations have multiple core objectives, such as space utilization, operational efficiency, cost control, and risk prevention. Existing solutions lack quantifiable conflict coordination mechanisms and often focus on a single objective while sacrificing others. For example, adopting a dense storage model to improve space utilization can lead to longer work paths and reduced sorting efficiency; reducing equipment maintenance frequency to lower operating costs can increase the risk of equipment failure and thus affect operational continuity; and centralizing goods in high-frequency work areas to quickly respond to sorting needs can result in insufficient space utilization.

[0005] Third, the optimization of existing solutions is mostly an ex-post adjustment, that is, it is only passively corrected after the strategy is implemented based on operational feedback such as inefficiency or cost overruns. It cannot make forward-looking optimizations based on trend analysis of historical operational data and prediction of future business needs. When faced with sudden operational needs such as batch temporary warehousing or scenario changes such as emergency outbound of special goods, existing solutions are difficult to adapt quickly, which can easily lead to operational congestion, inventory backlog or service delays. To address this, we propose a method for managing the location information of automated warehouses. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a method for managing storage location information in automated storage and retrieval systems (AS / RS), solving the technical problems of static storage location management, lack of virtual simulation verification, inability to coordinate multi-objective conflicts, and lagging optimization in existing technologies.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for managing the location information of an automated storage and retrieval system (AS / RS), comprising the following steps:

[0011] Step 1: Collect multi-dimensional basic data of the automated warehouse and build a virtual location mapping system. The multi-dimensional basic data covers key data categories that affect location management, including data related to location space layout, cargo characteristics, inventory turnover, warehouse equipment operation, and business scenario requirements. The virtual location mapping system establishes a real-time correspondence and data interaction relationship between the physical scene and the virtual scene by replicating the core characteristics and operation process of the physical warehouse.

[0012] Step 2: Based on spatial association rules, multi-factor dynamic association prediction mechanism and virtual mapping system of cargo location, multi-scenario simulation analysis is performed to systematically integrate and process multi-dimensional basic data to generate cargo location suitability assessment results that include adaptation suggestions for a future operation cycle.

[0013] Step 3: Based on the results of the warehouse location suitability assessment, differentiated business objectives, and multi-objective conflict coordination mechanisms, formulate targeted warehouse location management strategies with forward-looking optimization capabilities;

[0014] Step 4: Implement the storage location management strategy and synchronize the dynamic data of the entire process during the strategy execution in real time through the storage location virtual mapping system to build a two-way data interaction loop between the physical scene and the virtual scene;

[0015] Step 5: Through quantitative indicator verification, deviation attribution analysis, and forward-looking optimization mechanism, evaluate the effectiveness of strategy implementation and dynamically iterate the storage location management strategy to achieve accurate cyclical management of storage location information. The forward-looking optimization mechanism is based on trend analysis of historical operating data and prediction of future business needs to adjust management parameters in advance to adapt to subsequent operating scenarios.

[0016] Preferably, the specific method for constructing the virtual mapping system for cargo locations in step one includes:

[0017] Based on the relevant data of the warehouse space layout, a three-dimensional space replica carrier is built to fully restore the spatial location parameters, area division attributes, relationship between adjacent warehouses and the topology of the inbound and outbound paths of the warehouse, ensuring that the size error between the virtual space and the physical space is controlled within the preset accuracy threshold.

[0018] Cargo characteristic data is collected through cargo feature identification equipment and a unique association is established with the virtual storage location in the virtual mapping system. The cargo characteristic data includes the physical attribute parameters of the cargo, storage environment requirements, shelf life, circulation priority level and batch specification parameters.

[0019] The inventory monitoring system captures real-time inventory turnover data and synchronizes it to the virtual mapping system. The inventory turnover data includes records of the frequency of goods entering the warehouse, records of the frequency of goods leaving the warehouse, inventory turnover cycle duration, and the continuous occupancy time of a single storage location.

[0020] The system collects relevant data on the operation of warehousing equipment through the equipment status monitoring module and maps it to virtual equipment in the virtual mapping system. The relevant data on the operation of warehousing equipment includes the actual operating efficiency of warehousing equipment, maximum load limit value, failure occurrence time record and regular maintenance cycle duration.

[0021] The business scenario requirement data is collected through the relevant interface and associated with the scenario configuration module of the virtual mapping system. The business scenario requirement data includes batch storage requirement standards, rapid sorting requirement indicators, special goods storage requirement conditions, and peak operation response requirement parameters.

[0022] Establish a dedicated data transmission channel between the physical entity and the virtual mapping system, and configure a real-time data synchronization and verification mechanism to ensure that the synchronization delay between the state of the virtual mapping system and the actual state of the physical warehouse does not exceed a preset time threshold.

[0023] Preferably, the method for constructing spatial association rules in step two is as follows:

[0024] Based on the virtual mapping system for cargo locations, a preset number of virtual simulations of business scenarios are conducted. Operational efficiency data is collected in each round of simulation. Cargo location areas with different operational frequency levels are divided according to the distribution of operational efficiency values. The accessibility weight of cargo locations in different areas is determined through statistical analysis of simulation data.

[0025] Establish dynamic spatial adaptation rules for storage locations and goods, and combine key attributes of goods characteristics that change over time, including the change range of remaining shelf life and the changing trend of storage environment requirements, to dynamically update the matching standards of goods and storage space parameters and carrying capacity.

[0026] Construct a model linking equipment operating range with storage area, simulate the collaborative effect of equipment operating across regions through a virtual mapping system, record operation time and energy consumption data under different collaborative modes, clarify the optimal service storage area cluster range for each equipment and the collaborative rules for cross-regional operations, and avoid cross-conflicts in equipment operating paths in advance.

[0027] Preferably, the multi-factor dynamic correlation prediction mechanism in step two specifically includes:

[0028] Standardized preprocessing is performed on the collected multi-dimensional basic data. Abnormal data exceeding the preset range is identified and removed through data rationality verification. Missing data is supplemented based on the statistical distribution pattern of similar data. Duplicate records are identified and deleted through data uniqueness verification.

[0029] A dynamic correlation model is constructed, which includes factors related to changes in cargo characteristics, factors related to inventory turnover trends, and factors related to equipment failure prediction. The factors related to changes in cargo characteristics are calculated based on the changing patterns of key parameters of cargo characteristics. The factors related to inventory turnover trends predict the turnover frequency of future operating cycles through trend deduction methods based on historical inbound and outbound data. The factors related to equipment failure prediction deduce the probability of failure based on comprehensive analysis of key parameters of equipment operation.

[0030] We will analyze the real-time compatibility between cargo characteristics and storage conditions, the matching degree between predicted inventory turnover frequency and storage area attributes, and the compatibility between equipment failure prediction results and storage load.

[0031] A weighted statistical method is used to quantify the impact of each dynamic factor on the efficiency of warehouse management. The weights are calibrated through multiple simulation iterations of the virtual mapping system to ensure that the adaptability assessment results include predictive adaptability suggestions for a future continuous operating cycle.

[0032] Based on the dynamic correlation prediction results, the current availability of the storage location, the types of goods that can be adapted in the future, the priority of operations, and the potential risk warning information are integrated to form a complete storage location adaptability assessment result.

[0033] Preferably, the multi-objective conflict coordination mechanism in step three specifically includes:

[0034] The system systematically outlines the core objectives of warehousing operations, including space utilization, operational efficiency, cost control, and risk prevention.

[0035] The conflict scenarios of different target dimensions are simulated through a virtual mapping system of cargo locations, and the conflict level is determined by a method for quantifying the degree of conflict impact.

[0036] Establish a dynamic allocation method for conflict coordination weights. Based on relevant data and predictive adaptation suggestions for business scenario requirements, adjust the priority weights of each objective dimension in real time. In specific business scenarios, the weight of the core objective is higher than that of other objectives by a preset ratio.

[0037] Set clear quantitative target values ​​and conflict tolerance ranges for different business scenarios to form a standardized and differentiated business target system that takes into account both conflict coordination and scenario adaptation, and ensure that the degree of conflict in each target dimension does not exceed the preset tolerance range.

[0038] Preferably, the specific method for generating targeted storage location management strategies in step three is as follows:

[0039] For batch storage scenarios, based on the cluster simulation results of the virtual mapping system of storage locations, a "centralized allocation + flexible space reservation" scheme is adopted to centrally allocate the same type of goods to the same storage location cluster, while reserving a preset proportion of flexible storage locations in the cluster to cope with sudden inbound demand, thereby improving space utilization while ensuring operational flexibility.

[0040] For rapid sorting scenarios, based on the accessibility weight of the storage location and the predicted inventory turnover trend, the current high-frequency turnover goods and the predicted high-frequency turnover goods are jointly allocated to the high-operation frequency area near the inbound and outbound ports. The time consumption of different operation paths is simulated through the virtual mapping system, and the sorting order is optimized so that the operation path is shortened by more than the preset percentage on average.

[0041] For special cargo storage scenarios, based on the real-time adaptability of storage conditions to cargo characteristics, an independent dedicated storage location cluster is allocated. In the virtual mapping system, a dedicated storage environment monitoring threshold is set for this cluster, and a dedicated monitoring device linked with the virtual system is configured to provide real-time feedback on changes in storage environment parameters. When the parameters exceed the threshold, an early warning is automatically triggered and the storage conditions are adjusted.

[0042] By integrating storage location allocation rules, regional operation rules, equipment scheduling rules, and conflict coordination solutions, a personalized storage location management strategy that is adaptable to different business scenarios and has predictive capabilities is formed. Through a virtual storage location mapping system, a preset number of pre-run verifications are conducted to ensure that the achievement rate of core objectives is not lower than a preset percentage.

[0043] Preferably, the method for constructing the bidirectional data interaction loop in step four includes:

[0044] The occupancy status of the storage location, the storage time of the goods, and the changes in environmental parameters are collected in real time by the storage location status sensing equipment. The data is then transmitted in encrypted form to the virtual storage location mapping system to update the corresponding status of the virtual storage location.

[0045] The actual operating time, operating path trajectory, energy consumption value and fault occurrence time data of the warehousing equipment are collected by the equipment operation monitoring module. The data are compared in real time with the preset operating parameters in the virtual mapping system to generate parameter deviation data.

[0046] The system records the time spent on each step of the goods entering and leaving the warehouse and the data update delay through the work process traceability system, forming a timeline data of the entire work cycle, which is then synchronized to the process traceability module of the virtual mapping system.

[0047] By collecting data on target achievement in different scenarios through business performance feedback modules, and combining it with simulation data from the virtual mapping system, a dynamic data set for two-way verification is formed, combining physical measurement data and virtual simulation data, to ensure the integrity and accuracy of data interaction.

[0048] Preferably, the quantitative indicator verification method in step five is as follows:

[0049] Core validation metrics are extracted from dynamic datasets. These core validation metrics include storage space utilization, goods inbound and outbound operation efficiency, equipment unit operation energy consumption, inventory data accuracy, scenario target achievement rate, and prediction adaptation accuracy.

[0050] The calculation logic of each indicator is clearly explained using textual descriptions. The storage space utilization rate is calculated by the ratio of the number of occupied effective storage locations to the total number of effective storage locations. The efficiency of goods inbound and outbound operations is determined by the amount of qualified operations completed per unit time. The energy consumption per unit of equipment operation is calculated by the ratio of the total energy consumption of operations to the total amount of operations completed. The inventory data accuracy rate is determined by the ratio of the number of storage locations with consistent data to the total number of storage locations. The scenario target achievement rate is determined by the ratio of the number of scenarios that achieve quantitative targets to the total number of business scenarios. The prediction adaptation accuracy rate is determined by the ratio of the number of storage locations that are successfully predicted to the total number of predicted storage locations.

[0051] The actual calculation results of each core verification indicator are compared with the quantitative target value and conflict tolerance range in the differentiated business target system to classify the degree of fit. Each core indicator must reach a specified proportion of the corresponding preset target value to be judged as a high degree of fit.

[0052] Preferably, the deviation attribution analysis method in step five specifically includes:

[0053] For the core verification indicators that fail to meet the matching level, the operation process is backtracked and simulated through the virtual mapping system of the storage location. The root causes of the positioning deviation include unreasonable division of storage location clusters, deviation of dynamic correlation prediction factor weights, improper allocation of multi-objective conflict coordination weights, mismatch between equipment scheduling rules and storage location area association, insufficient data collection accuracy or failure to update prediction model parameters in a timely manner.

[0054] Correlation analysis was used to clarify the impact of each deviation cause on the indicators, and a correspondence model between deviation causes and core indicators was established. The accuracy of cause determination was verified through the parameter adjustment function of the virtual mapping system to ensure that the cause location accuracy rate is not lower than the preset percentage.

[0055] Based on the correspondence model and virtual debugging results, the deviation correction direction with the highest priority is determined according to the degree of impact, providing a precise basis for subsequent strategy adjustments.

[0056] Preferably, the look-ahead optimization and strategy iteration method in step five is as follows:

[0057] Based on the direction of deviation correction and combined with the forward-looking optimization mechanism, the corresponding management parameters are adjusted in a targeted manner, including re-optimizing the scope of the cargo location cluster, calibrating the weight of the dynamic correlation prediction factor, correcting the multi-objective conflict coordination weight allocation logic, adjusting the association rules between equipment scheduling and cargo location areas, and upgrading the accuracy of data acquisition equipment or updating the prediction model parameters.

[0058] The adjusted parameters are integrated into the original storage location management strategy. A virtual simulation of multiple scenarios is conducted through the storage location virtual mapping system. The simulation results are compared with the preset target values ​​to ensure that the core indicators are improved to a high degree of consistency.

[0059] Develop an iterative strategy that adapts to changes in scenarios, data feedback, and future trends. Apply the iterative strategy to subsequent storage location management processes, repeating steps four and five to build a forward-looking cyclical management model that iterates from prediction to optimization. Compared to the initial strategy, this will improve storage location space utilization and operational efficiency by more than a preset percentage, reduce equipment energy consumption by more than a preset percentage, and stabilize the prediction accuracy rate at more than a specified percentage.

[0060] (III) Beneficial Effects

[0061] 1. By collecting multi-dimensional basic data such as warehouse space layout, cargo characteristics, inventory turnover, equipment operation, and business needs, a multi-factor dynamic correlation prediction mechanism is constructed. This mechanism comprehensively considers the dynamic changes in cargo characteristics over time, fluctuations in inventory turnover trends, and the decline in equipment operating status, overcoming the limitations of existing solutions that rely on historical data or single indicators. By dynamically updating the matching standards between warehouse locations and cargo and quantifying the impact weight of each factor on management efficiency, precise matching between warehouse locations, cargo, and equipment is achieved. This significantly reduces problems such as cargo storage risks and uneven equipment workload caused by insufficient compatibility, while improving the effective utilization rate of warehouse resources. Secondly, a virtual mapping system for warehouse locations is constructed to fully replicate the core characteristics and operating processes of a physical warehouse. All warehouse location management strategies are simulated and verified in multiple scenarios through the virtual system before implementation. By simulating the cross-regional collaborative effects of equipment operations, the rationality of warehouse location cluster division, and the smoothness of operation paths, potential problems such as equipment operation path conflicts and inefficient process connections are identified and avoided in advance, ensuring that strategies can quickly adapt to actual operating scenarios and improve execution efficiency.

[0062] 2. Establish a multi-objective conflict coordination mechanism, systematically sort out core objectives such as space utilization, operational efficiency, cost control, and risk prevention, quantify the degree of conflict between different objectives through virtual simulation, and dynamically adjust the priority weight of each objective. For different scenarios such as batch storage, rapid sorting, and special goods storage, formulate differentiated strategies to ensure the precise implementation of core objectives in each scenario; that is, in the batch storage scenario, combine centralized allocation with flexible space reservation to improve space utilization while ensuring operational flexibility; in the rapid sorting scenario, optimize storage location allocation and operation sequence to shorten operation paths; in the special goods storage scenario, strengthen risk prevention and control through dedicated storage location clusters and linked monitoring equipment, solving the problem of existing solutions focusing on one objective at the expense of others.

[0063] 3. By analyzing historical operational data trends and predicting future business needs, a forward-looking optimization mechanism is built, ensuring that strategy adjustments are no longer limited to reactive corrections. A virtual location mapping system synchronizes dynamic data across the entire strategy execution process in real time, combined with quantitative indicator verification and deviation attribution analysis, accurately identifying strategy deficiencies and enabling rapid iteration. When faced with sudden operational demands or scenario changes, management parameters can be adjusted in advance based on predictive adaptation suggestions to quickly adapt to new requirements, avoiding operational congestion, inventory backlog, or service delays. This leads to continuous improvement in location management efficiency and helps automated warehouses steadily develop towards precision, efficiency, and intelligence. Attached Figure Description

[0064] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0065] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0066] Figure 2 This is a flowchart of the conflict quantification analysis sub-process in an embodiment of the present invention;

[0067] Figure 3 This is a flowchart of the dynamic weight allocation sub-process in an embodiment of the present invention;

[0068] Figure 4 This is a flowchart of a bidirectional data interaction loop in an embodiment of the present invention;

[0069] Figure 5 This is a flowchart of the deviation attribution and optimization iteration sub-process in an embodiment of the present invention. Detailed Implementation

[0070] This application provides a method for managing storage location information in an automated storage and retrieval system (AS / RS), addressing the technical problems of static storage location management, lack of virtual simulation verification, inability to coordinate multi-objective conflicts, and delayed optimization in existing technologies. It constructs a virtual storage location mapping system that fully replicates the core characteristics and operational processes of a physical warehouse. All storage location management strategies undergo multi-scenario simulation verification through this virtual system before implementation. By simulating the cross-regional collaborative effects of equipment operations, the rationality of storage location cluster division, and the smoothness of operational paths, potential problems such as equipment operational path conflicts and inefficient process connections are identified and avoided in advance, ensuring that strategies can quickly adapt to actual operational scenarios and improve execution efficiency.

[0071] Example: Refer to Figures 1 to 5 As shown, the technical solution in this application embodiment addresses the technical problems of static storage location management, lack of virtual simulation verification, inability to coordinate multi-objective conflicts, and lagging optimization in the prior art. The overall approach is as follows:

[0072] To address the problems existing in the prior art, this invention provides a method for managing the location information of an automated storage and retrieval system (AS / RS). The steps of this management method are as follows:

[0073] The first step is to collect multi-dimensional basic data and build a virtual mapping system for warehouse locations. This mainly involves comprehensively collecting five key data categories: warehouse space layout, cargo characteristics, inventory turnover, equipment operation, and business needs. These data cover the core dimensions of warehouse operations and form the basis for all subsequent analysis and decision-making. At the same time, a real-time linkage platform between physical and virtual scenarios is built to support data simulation and trend prediction.

[0074] The second step is to integrate and process multi-dimensional data and assess the suitability of storage locations. This involves sorting out the spatial relationships between data through spatial association rules, using a multi-factor dynamic association prediction mechanism to uncover the changing trends behind the data, and combining virtual simulation technology to simulate the operating effects of different suitability schemes. Finally, an assessment result containing suitability suggestions for the future period is generated.

[0075] The third step is to develop targeted storage location management strategies. This involves combining the results of the storage location suitability assessment with differentiated business objectives. By using a multi-objective conflict coordination mechanism to balance the contradictions between different objectives, a personalized strategy with forward-looking optimization capabilities is developed. This ensures that the strategy meets actual business needs while also taking into account efficiency, cost, and risk control.

[0076] The fourth step involves the construction of a loop for strategy execution and two-way data interaction. The established strategies are implemented in the physical scenario, while the entire process operation data is synchronized in real time through the virtual mapping system of the cargo location. This forms a two-way flow mechanism for data transmission from the physical scenario to the virtual scenario, and for the virtual scenario to provide feedback on optimization directions through simulation analysis, ensuring the real-time performance and integrity of the data.

[0077] The fifth step is forward-looking optimization and strategy iteration. Based on the verification results of quantitative indicators, the effectiveness of strategy execution is judged. Deviation attribution analysis is used to locate the root cause of the problem. With the help of forward-looking optimization mechanism, combined with historical data trends and future business predictions, strategy parameters are dynamically adjusted to achieve continuous improvement in management effectiveness.

[0078] In the above, the virtual location mapping system, as the core carrier throughout the entire process, needs to be able to perform data synchronization, simulation verification, trend prediction, and deviation backtracking, and then use digital mirroring to accurately control the physical warehouse; the forward optimization core solves the traditional ex-post correction method, and based on in-depth analysis of historical data and scientific prediction of future business needs, it can adjust management parameters in advance to ensure that the strategy always adapts to the dynamic changes in warehouse operations.

[0079] Taking an automated storage and retrieval system (AS / RS) in the manufacturing industry as an example, this warehouse has a total of 8,000 storage locations, equipped with 6 stacker cranes and 8 conveyor belts. Its business covers batch storage of raw materials, rapid sorting of semi-finished products, and special storage of precision parts, with an average daily inbound and outbound volume of 1,200 items. During implementation, a comprehensive collection of multi-dimensional basic data is required. The storage location layout data needs to clearly define the three-dimensional coordinate range of all storage locations, the boundary of area division, the physical relationship between adjacent storage locations, and the specific direction of inbound and outbound paths. The cargo characteristic data needs to record in detail the key attributes of various goods, such as weight, storage requirements, and shelf life. The inventory turnover data needs to statistically analyze the recent frequency of goods entering and leaving the warehouse, the inventory turnover cycle, and other circulation patterns. The equipment operation data needs to cover the operating parameters of stacker cranes and conveyors, such as operating efficiency, load capacity, maintenance cycle, and failure occurrence. The business requirement data needs to clearly define the specific operational requirements and quality standards under various scenarios.

[0080] Subsequently, a virtual mapping system for warehouse locations was constructed. Professional 3D modeling tools were used to accurately replicate the physical structure of the warehouse and an efficient real-time data synchronization channel was established to ensure that the state synchronization delay between the virtual scene and the physical scene was controlled within a reasonable range, while the size error was controlled at a small level.

[0081] Next, data integration and warehouse location suitability assessment are carried out. Through a multi-factor dynamic correlation prediction mechanism, we conduct in-depth analysis of the changes in cargo characteristics over time, the fluctuation trend of inventory turnover, the special storage environment requirements of precision parts, and the degradation of equipment operating status, and form a clear conclusion on warehouse location suitability.

[0082] Then, based on the needs of different business scenarios, targeted storage location management strategies are formulated. For batch storage scenarios, a combination of centralized allocation and flexible storage location reservation is adopted. For rapid sorting scenarios, the operation time is shortened by optimizing the storage location allocation order and operation process. For special storage scenarios, dedicated storage locations and linkage monitoring equipment are configured to ensure the safety of goods. During the strategy execution phase, various operation data need to be collected simultaneously, including key indicators such as storage location occupancy status, equipment operation time, and energy consumption. Finally, based on data feedback and future business predictions, forward-looking optimization is carried out to adjust the storage location allocation scheme and equipment scheduling parameters, thereby continuously improving operational efficiency.

[0083] The method for constructing a virtual location mapping system achieves accurate linkage between physical and virtual scenes through a progressive process at each stage; the details are as follows:

[0084] Firstly, a three-dimensional spatial replica is constructed, mainly based on the warehouse space layout data, to create a virtual space that is completely consistent with the physical warehouse in terms of size, layout, and paths. This is the foundation for ensuring that the virtual scene can accurately reflect the operating status of the physical scene. The construction process must follow a three-step logic of actual measurement, modeling and restoration, and laser calibration. First, the precise size data of the physical warehouse is obtained through professional measurement tools. Then, professional modeling tools are used for digital restoration. Finally, laser calibration technology is used to correct model errors, ensuring that the error between the virtual space and the physical space is controlled within the allowable error range.

[0085] Secondly, data association mapping aims to establish a unique correspondence between physical entities and virtual entities, ensuring that all types of data are clearly attributed and accurately synchronized. Specifically, each physical storage location and piece of equipment needs to be assigned a unique virtual ID. All storage location layout data, cargo characteristic data, equipment operation data, and business requirement data related to that physical entity are bound to the corresponding virtual ID, forming a one-item-one-code-one-data-chain association mode to avoid data confusion or incorrect attribution.

[0086] Thirdly, the construction of data transmission and verification mechanisms mainly ensures efficient, secure, and accurate data transmission between physical and virtual scenarios. The transmission channel adopts a dual-link design to ensure the continuity of data transmission even if a single link fails. Transmitted data must be processed through encryption algorithms to prevent data from being tampered with or leaked during transmission. At the same time, a high-frequency data verification mechanism is established to promptly detect and correct errors in the data transmission process by comparing the consistency between physical and virtual data. A synchronization delay monitoring module is set up to ensure the real-time synchronization of data.

[0087] The key to the above technology lies in three aspects: 3D replication accuracy control, data association uniqueness, and data verification logic. Among them, 3D replication accuracy control directly determines the reliability of the virtual scene, and multiple rounds of measurement and calibration ensure that the dimensional error meets the requirements. Data association uniqueness is the prerequisite for achieving accurate data synchronization. The design of a unique virtual ID ensures that various types of data can be accurately associated with the corresponding physical entities. The data verification logic comprehensively ensures the security and accuracy of data transmission through a triple mechanism of encryption before transmission, verification during transmission, and comparison after reception.

[0088] During implementation, the construction of the 3D spatial replication platform requires the use of professional 2D drawing tools to create warehouse floor plans, which are then imported into 3D modeling tools to construct a three-dimensional model. The location of all storage locations, area boundaries, and turning angles and lengths of inbound and outbound paths are recreated according to the actual dimensions of the physical warehouse. Precise calibration is performed area by area using laser rangefinders to ensure that the coordinate errors of the storage locations are controlled within a minimal range. Simultaneously, the operating tracks of stacker cranes and conveyors are recreated to clarify the correspondence between equipment travel routes and storage locations. Data association and mapping requires assigning a continuous virtual ID to each physical storage location, binding the storage location spatial layout data with the corresponding virtual storage location, and using RFID technology to link cargo characteristic data. According to the written tags, after scanning, they are automatically associated with the corresponding virtual storage location. The equipment operation data is synchronized to the corresponding virtual equipment in real time through a dedicated control interface. Business requirement data is imported into the scenario configuration module of the virtual system through the warehouse management system interface. The data transmission and verification mechanism adopts a dual-link transmission mode of industrial Ethernet and 5G backup network. The transmitted data is encrypted using the DES encryption algorithm, and a data hash verification is performed periodically to compare the consistency between physical data and virtual data. If the deviation exceeds the preset standard, the data retransmission is automatically triggered. When the synchronization delay exceeds the set threshold, it automatically switches to the 5G backup network to ensure uninterrupted real-time data synchronization.

[0089] The spatial association rule construction method aims to improve operational efficiency and space utilization by designing rules to streamline the compatibility between storage space, goods, and equipment; specifically as follows:

[0090] The division of storage locations and determination of accessibility weights are based on the virtual simulation operation efficiency data of the storage location virtual mapping system. Storage locations are scientifically classified and their accessibility is quantified. The division process uses operation efficiency as the core indicator. Multiple rounds of virtual simulation are used to eliminate the influence of accidental factors and ensure the rationality of the division. Storage locations with shorter operation times are divided into high-frequency operation areas, those with medium operation times are divided into medium-frequency operation areas, and those with longer operation times are divided into low-frequency operation areas. Then, statistical analysis is used to quantify the accessibility weight of each area to provide a basis for subsequent storage location allocation.

[0091] The establishment of dynamic matching rules between storage locations and goods involves dynamically updating the matching standards between storage locations and goods based on changes in the characteristics of goods over time, thus avoiding rigid matching rules. For different types of goods, clear trigger conditions are set. When the characteristics of goods change according to preset conditions, the type of storage location to which they are matched is automatically adjusted. For example, goods with a shelf life shortened to a certain period are automatically matched to high-frequency operation areas, and goods with changes in storage environment requirements are automatically matched to storage locations with corresponding adjustment functions. At the same time, a reasonable ratio range between the volume of goods and the volume of storage locations is set to ensure a balance between space utilization and operational convenience.

[0092] The core of building the equipment and storage area association model is to simulate the collaborative operation effect of equipment through virtual simulation, clarify the service range of equipment and cross-regional operation rules, balance equipment utilization and operation efficiency, and avoid operation conflicts. Based on the performance parameters and operation capabilities of the equipment, a fixed service area is assigned to each piece of equipment, and a load threshold is set for the equipment. When the load of equipment in a certain area reaches the threshold, a collaborative request is automatically sent to equipment in adjacent areas. During collaborative operation, tasks are assigned according to the principle of responding to the nearest location. At the same time, the equipment operation trajectory is monitored in real time through the virtual mapping system of storage locations, and the risk of path intersection is predicted in advance and the operation sequence is adjusted.

[0093] In the above, the zoning is primarily based on operational efficiency, with multiple rounds of simulation ensuring objective results; dynamic adaptation uses changes in cargo characteristics as triggering conditions to flexibly adjust adaptation rules; equipment association ensures smooth operation by simulating collaborative effects and predicting conflicts; during implementation, the zoning of storage locations and the determination of accessibility weights require 60 rounds of operational simulation based on a virtual mapping system for storage locations, comprehensively recording the average duration of each single operation for each storage location. Based on the duration distribution, storage locations are divided into high-frequency, medium-frequency, and low-frequency operation zones. Accessibility weights for each zone are calculated through statistical analysis, with high-frequency zones having the highest weight, followed by medium-frequency zones, and low-frequency zones having the lowest. Dynamic adaptation of storage locations and cargo... The matching rules need to be established separately for raw materials, semi-finished products, and precision parts. The shelf life is the core trigger condition for raw materials, the turnover frequency is the core trigger condition for semi-finished products, and the storage environment parameters are the core trigger condition for precision parts. At the same time, the corresponding storage space volume ratio requirements for each type of goods should be clearly defined. The construction of the equipment and storage space area association model should allocate service areas according to the performance differences of stacker cranes and conveyors. High-frequency and medium-frequency operation areas are handled by equipment with better performance, and low-frequency and special storage areas are handled by dedicated equipment. Reasonable equipment load coordination thresholds should be set, and the request process and task allocation principles for cross-regional operations should be clearly defined. Conflicts should be avoided in advance through real-time trajectory monitoring.

[0094] A multi-factor dynamic correlation prediction mechanism is used to scientifically predict the demand for warehouse space matching, providing data support for strategy formulation; specifically as follows:

[0095] First, data preprocessing is performed, specifically through operations such as anomaly removal, missing data completion, and deduplication to improve data quality and ensure the accuracy of subsequent analysis results. Anomaly removal adopts the 3σ principle to remove outliers that exceed the data distribution range, avoiding interference from abnormal data with the analysis results. Missing data completion uses the mean of similar data to fill in missing data, ensuring data integrity. Deduplication is performed by verifying the uniqueness of data IDs to delete duplicate records, reducing data redundancy.

[0096] After data processing, a dynamic correlation model is constructed, mainly to build three types of factors: changes in cargo characteristics, inventory turnover trends, and equipment failure prediction. The calculation methods for each factor are clarified, and the factor calculations are all based on the actual data change patterns to ensure the objectivity of the prediction. Specifically, the cargo characteristic change factor is calculated by the ratio of the change rate of cargo characteristic parameters to the remaining term; the inventory turnover trend factor uses historical turnover data to predict future turnover frequency using the moving average method; and the equipment failure prediction factor combines parameters such as equipment runtime and load fluctuation amplitude to comprehensively deduce the probability of failure.

[0097] After the model is built, multi-factor correlation analysis can be performed. In order to facilitate the quantification of the fit between goods and storage locations, inventory and regions, and equipment and load, a weighted calculation method is adopted to combine the importance of various factors to obtain the fit value, avoid the bias caused by subjective judgment, and ensure that the analysis results are verifiable.

[0098] The system generates impact weight quantification and location suitability assessment results. First, it calibrates the weights of various factors through multiple rounds of simulation iterations to ensure that the weights are highly matched with the actual operational effects, thereby improving the accuracy of the location suitability assessment results. Finally, it integrates multi-dimensional information to clarify key conclusions such as suitable location areas and operational priorities for various types of goods.

[0099] In the above, factor calculation is based on actual data patterns to ensure objective predictions; correlation analysis uses quantitative methods to ensure verifiable results; weight quantification is calibrated through simulation iterations to ensure matching with actual operations. During implementation, data preprocessing requires comprehensive cleaning of collected equipment operation data, cargo storage data, etc., removing abnormal data that exceeds equipment load capacity or cargo storage standards, filling in missing cargo characteristic data, and deleting duplicate equipment operation records to ultimately obtain high-quality, effective data; dynamic correlation model construction requires calculating characteristic change factors, inventory turnover trend factors, and fault prediction factors for each type of cargo, ensuring that the calculation logic of each factor matches the actual business scenario; multi-factor correlation analysis requires setting reasonable weight allocation ratios based on the characteristics of cargo type and storage area, and obtaining the compatibility values ​​of various cargo types with different storage areas and the matching values ​​of equipment with operating areas through weighted calculations; the generation of influence weight quantification and storage location suitability assessment results requires multiple rounds of simulation iterations through a storage location virtual mapping system, continuously calibrating the weights of various factors based on simulation results, and finally generating clear storage location suitability conclusions, clarifying the suitable storage locations and operational priorities for various types of cargo, providing a direct basis for subsequent strategy formulation.

[0100] The multi-objective conflict coordination mechanism aims to achieve a balanced optimization of multiple core objectives in warehouse operations; specifically as follows:

[0101] First, we need to clarify the core objective dimensions and define and quantify the four core objectives: space utilization, operational efficiency, cost control, and risk prevention. Space utilization is quantified by the ratio of the actual number of occupied storage locations to the total number of available storage locations, with the goal of maximizing utilization. Operational efficiency is quantified by the amount of qualified work completed per unit of time, with a clear minimum standard set. Cost control is quantified by the sum of total energy consumption and equipment maintenance costs, with a maximum control threshold set. Risk prevention is quantified by the abnormal rate of special cargo storage, with a minimum control standard set. These four objectives together constitute the core evaluation system for warehouse operations.

[0102] Conflict scenario simulation and level determination: Through the virtual mapping system of cargo locations, conflict scenarios between different objectives are recreated, the degree of conflict is quantified, and the conflict level is divided according to the proportion of a certain objective not being achieved. Mild conflict is ≤5% of the objective not being achieved, moderate conflict is 5% to 10%, and severe conflict is more than 10%. The quantification of conflict level provides a clear basis for subsequent coordination.

[0103] Dynamic weight allocation is essentially about adjusting the weight ratio of the four objectives based on the core needs of different business scenarios, ensuring that the core objectives are prioritized while also taking other objectives into account. The core objective of the batch storage scenario is space utilization, so it is given the highest weight. The core objective of the rapid sorting scenario is operational efficiency, so the weight is tilted towards operational efficiency. The core objective of the special goods storage scenario is risk control, so risk control has the highest weight. Differentiated weight allocation balances the conflict of objectives in different scenarios.

[0104] Quantitative targets and conflict tolerance ranges are set, with clear quantitative values ​​and acceptable conflict ranges for the four major targets in each scenario. Quantitative targets should be formulated based on historical operational data and the bottom line of business needs, making them both challenging and achievable. Conflict tolerance ranges are set according to the importance of the targets, with stricter tolerance ranges for core targets and more lenient ranges for non-core targets, ensuring that conflicts are kept within a controllable range and do not affect the overall operational results.

[0105] The key lies in the conflict quantification logic, the core of dynamic weight adjustment, and the basis for setting tolerance ranges. Conflict quantification clarifies the degree of conflict through specific proportions, avoiding ambiguous judgments; dynamic weight adjustment is scenario-driven, achieving flexible adaptation to target priorities; tolerance ranges are set based on historical data and business bottom lines, ensuring the rationality of conflict control; during implementation, the core target dimensions need to clarify the specific quantification methods and core requirements of each target, maximizing space utilization, achieving an operational efficiency of over 100 items per hour per day, controlling costs to within 500 yuan per day, and ensuring that the abnormality rate of special goods storage does not exceed 0.3%; conflict scenario simulation and level determination need to recreate target conflicts under different scenarios through a virtual location mapping system, such as batch storage of raw materials. Dense allocation can improve space utilization but reduce operational efficiency; the conflict level is determined when the computational efficiency fails to meet the target. Adding monitoring equipment to precision parts can reduce the anomaly rate but increase costs; the conflict level is determined by the cost overrun ratio. Dynamic weight allocation should be based on the different needs of the three core scenarios, and the weight of each target should be reasonably allocated to ensure that the core targets are given priority protection. The setting of quantitative targets and conflict tolerance ranges should be combined with the characteristics of the scenarios. Quantitative targets and conflict tolerance ranges that meet the business needs should be set for raw material storage, semi-finished product sorting, and precision parts storage respectively. For example, the space utilization rate of raw material storage should reach more than 90%, and the conflict tolerance range should not exceed 10%; the anomaly rate of precision parts storage should be controlled within 0.3%, and the conflict tolerance range should not exceed 5%.

[0106] The targeted storage location management strategy generation method focuses on scenario differentiation, formulating personalized strategies for three core business scenarios to ensure a high degree of adaptation between the strategies and scenario requirements. Simultaneously, the feasibility of the strategies is verified through virtual simulation. For the batch storage scenario, the core requirement is to balance space utilization and operational flexibility. Therefore, a strategy combining centralized allocation and flexible space reservation is adopted. Centralized allocation maximizes the use of storage space and improves space utilization, while flexible space reservation reserves a certain proportion of storage locations to handle sudden inbound demand, avoiding operational congestion caused by centralized allocation and ensuring smooth workflow. When implementing the strategy, the centralized allocation area and quantity of storage locations must be determined based on the total volume of batch goods and storage capacity. At the same time, a fixed proportion of flexible storage locations must be reserved. This reservation proportion must be set based on historical sudden inbound data and business growth expectations to ensure that space is not wasted while meeting sudden demand.

[0107] The core requirement of rapid sorting scenarios is to improve operational efficiency. The strategy revolves around optimizing the accessibility of storage locations and the flow trend. It concentrates the current high-frequency flow goods and the goods predicted to be high-frequency flow in the future to high-frequency operation areas with high accessibility, shortens the goods handling distance, and optimizes the sorting order by virtual simulation of different operation paths, following the principle of sorting from near to far and from light to heavy, reducing equipment travel time and waiting time, and further improving sorting efficiency.

[0108] The core requirement for special cargo storage scenarios is risk control. The strategy focuses on the configuration of dedicated storage location clusters and the deployment of linked monitoring equipment. Independent dedicated storage areas are allocated for special cargo to ensure the storage environment is not affected by other goods. The spacing between storage locations is appropriately increased according to the characteristics of the cargo to avoid physical collision damage. Simultaneously, environmental monitoring equipment linked to the virtual storage location mapping system is installed to monitor key parameters such as temperature and humidity in real time, setting clear threshold standards. When parameters exceed the thresholds, the adjustment equipment is automatically activated and an early warning is triggered, achieving real-time risk control. After all strategies are formulated, multiple rounds of simulation and rehearsal are required through the virtual storage location mapping system to simulate various scenarios such as normal operations, peak inbound traffic, and equipment failures. This verifies the achievement rate of the core objectives of the strategies under different conditions. Only when the achievement rate reaches the preset standard can the strategies be implemented. Through rehearsals, strategy loopholes are identified in advance, reducing the cost of trial and error in physical scenarios.

[0109] In the above, the scenario differentiation logic designs strategies based on the core needs of different scenarios, avoiding a one-size-fits-all, extensive management approach; the flexible reservation core scientifically reserves storage space to cope with sudden demands, balancing space utilization and operational flexibility; the pre-implementation verification role uses virtual simulation technology to predict the effect of the strategy in advance, ensuring the reliability of the strategy implementation. During implementation, using the aforementioned manufacturing warehouse scenario, the batch storage scenario for raw material goods determines the number of centrally allocated medium-frequency operation area storage locations based on their single inbound quantity, reserving flexible storage locations at a rate of 10%, and verifying through virtual simulation whether the space utilization rate and operational efficiency meet the quantitative targets; rapid sorting... In the picking scenario, for semi-finished goods, based on the predicted future outbound frequency and current circulation status, high-frequency goods are centrally allocated to high-frequency operation areas. The sorting sequence is optimized through virtual simulation, shortening the operation path and improving sorting efficiency. In the special goods storage scenario, for precision parts, a dedicated constant temperature storage area is designated, the spacing between storage locations is expanded, temperature and humidity monitoring equipment is installed and linked with the virtual system, threshold standards and early warning mechanisms are set, and the storage anomaly rate is verified through simulation. Finally, various emergencies are simulated through multi-scenario hybrid simulation to ensure that the core objectives of the three scenarios are met before the strategy is officially implemented.

[0110] The bidirectional data interaction loop construction method, with real-time data flow and bidirectional verification as its core, synchronizes and deeply integrates data from physical and virtual scenarios, providing complete data support for subsequent evaluation and optimization; specifically as follows:

[0111] The system collects and synchronizes data on the status of storage locations, capturing key operational data in real time, including occupancy status, storage duration, and environmental parameters. By installing specialized equipment such as weight sensors, temperature and humidity sensors, and timers in each storage location, the system accurately detects various data. The data collected by the sensors is transmitted in real time to the virtual storage location mapping system via an industrial bus. A fixed update frequency is set to ensure data real-time performance, and the synchronization delay is controlled at an extremely low level to ensure that the virtual storage location status is completely consistent with the physical storage location.

[0112] Equipment operation data collection and comparison: By installing operation monitoring modules on operating equipment such as stacker cranes and conveyors, the system collects operating parameters such as operating time, path trajectory, energy consumption, and fault time. At the same time, standard operating parameters of the equipment are preset in the virtual mapping system of the storage location. The collected actual data is compared with the preset standards in real time to generate a deviation data record table, which clarifies the difference between the actual operation and the standard parameters, and provides a basis for equipment status assessment and fault diagnosis.

[0113] Work process data recording and synchronization: The warehouse management system automatically records the time spent at each stage of the entire lifecycle of goods from inbound to outbound, including waiting time, sorting time, handling time, data update delay time, etc., forming a complete work timeline data. This data is synchronized in real time to the process traceability module of the virtual location mapping system, supporting full process backtracking of any operation, which is convenient for analyzing work bottlenecks and process optimization directions.

[0114] A dynamic dataset is formed, integrating daily collected physical measurement data and virtual simulation data. The physical measurement data includes data on the status of storage locations, equipment operation data, and operational processes, while the virtual simulation data includes the results of multiple rounds of scenario simulation. The two types of data form a dynamic dataset for two-way verification between physical and virtual data, ensuring that the data covers all dimensions of warehouse operations and providing a complete and reliable data foundation for subsequent quantitative indicator verification and deviation attribution analysis.

[0115] The two-way interactive logic enables a cyclical flow of physical data to the virtual scene and virtual data to the physical scene, providing feedback on optimization directions. Data integrity is ensured by covering four dimensions: storage location, equipment, process, and business, guaranteeing no data omissions. Deviation data value is directly reflected in the strategy execution effect and equipment operating status through the comparison of actual data with standard parameters, providing direct evidence for problem localization. During implementation, storage location status data collection requires configuring corresponding sensor devices for each storage location to ensure accurate data collection. Transmission is achieved in real-time synchronization via an industrial bus, with update frequency and latency controlled within preset standards. Equipment operation data collection requires installing monitoring modules on all operating equipment, clearly defining preset standard parameters, and generating deviation data through real-time comparison. Operation process data recording is automatically completed by the warehouse management system, forming detailed timeline data and synchronizing it to the virtual system. Dynamic data sets are integrated on a daily basis to ensure that the total amount of data and coverage dimensions meet the needs of subsequent analysis, achieving data integrity and traceability.

[0116] The quantitative indicator verification method is based on objective quantification and provides a clear basis for strategy optimization by comprehensively evaluating the effectiveness of strategy implementation; the details are as follows:

[0117] The core verification indicators are extracted in accordance with the principle of comprehensive coverage of the core dimensions of management. Six core indicators are selected: storage space utilization rate, goods inbound and outbound operation efficiency, equipment unit operation energy consumption, inventory data accuracy, scenario target achievement rate, and prediction adaptation accuracy. These indicators correspond to the six key dimensions of warehouse operation: space utilization, operation efficiency, cost control, data quality, scenario adaptation, and prediction accuracy, to ensure a comprehensive and thorough evaluation.

[0118] The calculation logic of the indicators is clear, and the simplified calculation method ensures that implementers can operate quickly. The storage space utilization rate is calculated by the ratio of the number of occupied effective storage locations to the total number of effective storage locations. The efficiency of goods inbound and outbound operations is the amount of qualified operations completed per unit time. The energy consumption per unit of equipment operation is calculated by the ratio of the total energy consumption of operations to the total amount of operations completed. The inventory data accuracy rate is the ratio of the number of storage locations with consistent data to the total number of storage locations. The scenario target achievement rate is the ratio of the number of scenarios that achieve the quantitative target to the total number of business scenarios. The prediction adaptation accuracy rate is the ratio of the number of storage locations that are successfully predicted to the total number of predicted storage locations. All calculation logic is intuitive and easy to understand, avoiding calculation errors caused by complex formulas.

[0119] The alignment level is determined based on whether the preset target value has been achieved. It is divided into three levels: high, medium, and low, in combination with the conflict tolerance range. The high alignment level requires all indicators to reach the preset target value. The medium alignment level allows some non-core indicators to fail to meet the target within the conflict tolerance range. The low alignment level means that the core indicators fail to meet the target or multiple indicators exceed the tolerance range. The level determination clarifies the quality of the strategy implementation and provides direction for subsequent optimization.

[0120] The selection of indicators comprehensively covers core dimensions to ensure objective evaluation; the simplified calculation logic reduces operational difficulty and ensures accurate results; the level determination is based on preset targets to ensure clear conclusions. During implementation, the aforementioned manufacturing warehouse scenario is used. The extraction of core verification indicators requires clear definitions and statistical ranges for the six major indicators to avoid errors caused by inconsistent statistical standards; indicator calculations must be strictly performed according to the established logic, based on real data in a dynamic dataset to ensure reliable data sources; the degree of fit determination requires comparing the calculation results of each indicator with preset target values ​​and conflict tolerance ranges to ultimately determine the overall degree of fit. If the degree of fit is determined to be high, it indicates that the strategy has been implemented effectively; if it is medium or low, deviation attribution analysis needs to be initiated.

[0121] Deviation attribution analysis is based on accurately locating the root cause of a problem and provides targeted direction through strategy optimization, avoiding blind adjustments; specifically as follows:

[0122] To pinpoint the cause of deviations, the backtracking simulation function of the virtual location mapping system is used to fully recreate the entire operational scenario in which the deviation occurred, including the flow path of goods, the operating status of equipment, and the data transmission status. Factors that may cause deviations are investigated from multiple dimensions such as process, rules, equipment, and data. The focus is on key nodes where core indicators are not met. For example, when the operational efficiency is not up to standard, it is necessary to investigate whether the location allocation is reasonable, whether there are conflicts in equipment scheduling, and whether there are bottlenecks in the operation process, so as to ensure that the root cause is located rather than the surface phenomenon.

[0123] Impact degree analysis: Correlation analysis is used to quantify the correlation strength between each identified cause and the deviation. By calculating the correlation coefficient, the influence weight of different causes on the deviation is clarified. The higher the correlation coefficient, the greater the influence. Based on this, the main causes, secondary causes and auxiliary causes are divided to avoid confusion of multiple causes and unclear optimization direction, and to ensure that the main and secondary causes are clearly distinguished.

[0124] Once the direction of correction is determined, the causes should be prioritized based on their degree of impact. Corrective measures should be developed to address the primary causes first, followed by secondary and auxiliary causes. The corrective measures must be targeted and feasible. For example, if the lack of equipment scheduling rules leads to efficiency deviations, the scheduling rules need to be optimized; if the concentrated allocation of storage locations leads to congestion, the storage location allocation plan needs to be adjusted to avoid resource waste and new problems caused by comprehensive adjustments.

[0125] Backtracking simulation fully recreates the scenario to pinpoint the root cause; quantifying the impact clarifies the primary and secondary relationships through correlation analysis; targeted correction ensures precise and effective optimization measures; during implementation, assuming the efficiency of goods inbound and outbound operations does not meet the preset target, the cause of the deviation needs to be identified by backtracking the entire process of this batch of operations through the virtual mapping system of storage locations, discovering multiple possible causes such as missing equipment scheduling rules, concentrated storage location allocation, and unadjusted conveyor speed; impact analysis uses Pearson correlation analysis to calculate the correlation coefficient between each cause and the efficiency deviation, determining that the missing equipment scheduling rules are the primary cause; the correction direction should prioritize optimizing the equipment scheduling rules, setting limits on the number of devices operating simultaneously, and then adjusting the storage location allocation scheme and conveyor operating speed to ensure that the corrective measures directly address the core of the problem.

[0126] The forward-looking optimization and strategy iteration method focuses on continuously improving management effectiveness. It achieves dynamic strategy optimization through three major steps, breaking through the limitations of static management; specifically as follows:

[0127] Management parameter adjustments are based on the root causes identified through deviation attribution analysis and combined with future business forecasts. These adjustments must be targeted, for example, adjusting equipment coordination thresholds and scheduling logic to address deviations caused by missing equipment scheduling rules; and adjusting the number of storage locations and the flexible reservation ratio in advance to anticipate future business growth, ensuring that parameter adjustments not only solve current problems but also adapt to future business changes.

[0128] Strategy pre-run verification involves multi-scenario simulation using a virtual location mapping system. This simulates various possible scenarios, such as different workloads, equipment failures, and business peaks, to verify the effectiveness of the adjusted parameters and strategies under different scenarios. The focus is on whether the core indicators meet the preset standards and whether new deviations or conflicts occur. By conducting the pre-run, potential problems are eliminated, ensuring that the optimized strategy has stability and reliability.

[0129] The implementation of iterative strategies involves formally applying the optimized strategies, which have been validated through pre-runs, to actual operations in physical scenarios. During the implementation process, continuous data collection throughout the entire process is conducted, and changes in key indicators are monitored in real time to form a new dynamic data set. Based on this data, the actual effectiveness of the iterative strategies is evaluated. At the same time, combined with future business forecasts for the next round, a data foundation is accumulated for subsequent optimizations, forming a cycle of strategy iteration and achieving continuous improvement in management effectiveness.

[0130] Parameter adjustments are based on the root causes of problems and future predictions to ensure accuracy and effectiveness; pre-implementation verification eliminates potential risks through multi-scenario simulations to ensure strategy stability; iterative cycles achieve dynamic optimization through continuous data feedback to ensure continuous improvement in management effectiveness; during implementation, the aforementioned manufacturing warehouse scenario is used, and for deviations in operational efficiency that do not meet standards, management parameter adjustments need to optimize equipment scheduling rules, adjust the storage location allocation scheme, and increase conveyor operating speed, while also adding flexible storage locations in advance in conjunction with future production plans; strategy pre-implementation verification needs to simulate different operational loads and equipment failure scenarios to ensure that optimized operational efficiency meets standards and there are no new conflicts; after the iterative strategy is implemented, operational data is continuously collected, changes in core indicators are monitored, and new optimization points are discovered based on feedback, such as the problem of frequent start-ups of dehumidification equipment in the precision parts storage area, and the plan is to adjust the humidity threshold and linkage logic in the next round to further reduce energy consumption and achieve cyclical improvement in storage location management efficiency.

[0131] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for managing the location information of an automated storage and retrieval system (AS / RS), characterized in that, The management method involves the following steps: Collect multi-dimensional basic data of automated warehouses and build a virtual location mapping system. The multi-dimensional basic data covers key data categories that affect location management, including location space layout, goods characteristics, inventory turnover, warehouse equipment operation and business scenario requirements data. The virtual location mapping system establishes a real-time correspondence and data interaction relationship between physical and virtual scenarios by replicating the core characteristics and operation process of physical warehouses. Based on spatial association rules, multi-factor dynamic association prediction mechanism and virtual mapping system of cargo location, multi-scenario simulation analysis is carried out to systematically integrate and process multi-dimensional basic data to generate cargo location suitability assessment results that include adaptation suggestions for a future operation cycle. Based on the results of the warehouse location suitability assessment, differentiated business objectives, and multi-objective conflict coordination mechanisms, develop targeted warehouse location management strategies with forward-looking optimization capabilities. Implement the storage location management strategy and synchronize the dynamic data of the entire process during the strategy execution in real time through the storage location virtual mapping system to build a two-way data interaction loop between the physical scene and the virtual scene; Through quantitative indicator verification, deviation attribution analysis, and forward-looking optimization mechanisms, the effectiveness of strategy implementation is evaluated and the warehouse management strategy is dynamically iterated. The forward-looking optimization mechanism adjusts management parameters to adapt to subsequent operational scenarios based on trend analysis of historical operational data and prediction of future business needs. Based on the virtual mapping system of storage locations, virtual simulation of business scenarios is carried out, and the operation efficiency data in each round of simulation is collected. Storage location areas with different operation frequency levels are divided according to the distribution of operation efficiency values. The accessibility weight of storage locations in different areas is determined through statistical analysis of simulation data. Establish dynamic spatial adaptation rules for storage locations and goods, and combine key attributes of goods characteristics that change over time, including the change range of remaining shelf life and the changing trend of storage environment requirements, to dynamically update the matching standards of goods and storage space parameters and carrying capacity. Construct a model relating equipment operating range to storage area, simulate the collaborative effect of equipment operating across regions through a virtual mapping system, record operation time and energy consumption data under different collaborative methods, and obtain the optimal service storage area cluster range for each equipment and the collaborative rules for cross-regional operations. Standardized preprocessing is performed on the collected multi-dimensional basic data. Abnormal data exceeding the preset range is identified and removed through data rationality verification. Missing data is supplemented based on the statistical distribution pattern of similar data. Duplicate records are identified and deleted through data uniqueness verification. A dynamic correlation model is constructed, which includes factors related to changes in cargo characteristics, factors related to inventory turnover trends, and factors related to equipment failure prediction. The factors related to changes in cargo characteristics are calculated based on the changing patterns of key parameters of cargo characteristics. The factors related to inventory turnover trends predict the turnover frequency of future operating cycles through trend deduction methods based on historical inbound and outbound data. The factors related to equipment failure prediction deduce the probability of failure based on comprehensive analysis of key parameters of equipment operation. We will analyze the real-time compatibility between cargo characteristics and storage conditions, the matching degree between predicted inventory turnover frequency and storage area attributes, and the compatibility between equipment failure prediction results and storage load. The weighted statistical method is used to quantify the impact of each dynamic factor on the efficiency of warehouse management. The weights are calibrated through multiple simulation iterations of the virtual mapping system. The adaptability assessment results include predictive adaptation suggestions for a future continuous operation cycle. Based on the dynamic correlation prediction results, the current availability of the storage location, the types of goods that can be adapted in the future, the priority of operations, and the potential risk warning information are integrated to form the storage location adaptability assessment results. The system systematically outlines the core objectives of warehousing operations, including space utilization, operational efficiency, cost control, and risk prevention. The conflict scenarios of different target dimensions are simulated through a virtual mapping system of cargo locations, and the conflict level is determined by a method for quantifying the degree of conflict impact. Establish a dynamic allocation method for conflict coordination weights. Based on relevant data and predictive adaptation suggestions for business scenario requirements, adjust the priority weights of each objective dimension in real time. In specific business scenarios, the weight of the core objective is higher than that of other objectives by a preset ratio.

2. The method according to claim 1, characterized in that, The specific process of constructing a virtual cargo location mapping system is as follows: Based on the relevant data of the warehouse space layout, a three-dimensional space replica is built to restore the spatial location parameters, area division attributes, relationship between adjacent warehouses and topology of inbound and outbound paths of the warehouse. Cargo characteristic data is collected through cargo feature identification equipment and a unique association is established with the virtual storage location in the virtual mapping system. The cargo characteristic data includes the physical attribute parameters of the cargo, storage environment requirements, shelf life, circulation priority level and batch specification parameters. The inventory monitoring system captures real-time inventory turnover data and synchronizes it to the virtual mapping system. The inventory turnover data includes records of the frequency of goods entering the warehouse, records of the frequency of goods leaving the warehouse, inventory turnover cycle duration, and the continuous occupancy time of a single storage location. The system collects relevant data on the operation of warehousing equipment through the equipment status monitoring module and maps it to virtual equipment in the virtual mapping system. The relevant data on the operation of warehousing equipment includes the actual operating efficiency of warehousing equipment, maximum load limit value, failure occurrence time record and regular maintenance cycle duration. The business scenario requirement data is collected through the relevant interface and associated with the scenario configuration module of the virtual mapping system. The business scenario requirement data includes batch storage requirement standards, rapid sorting requirement indicators, special goods storage requirement conditions, and peak operation response requirement parameters. Establish a dedicated data transmission channel between the physical entity and the virtual mapping system, configure a real-time data synchronization and verification mechanism, and control the synchronization delay between the state of the virtual mapping system and the actual state of the physical warehouse to not exceed a preset time threshold.

3. The method according to claim 2, characterized in that, The specific methods for generating targeted storage location management strategies are as follows: For batch storage scenarios, based on the cluster simulation results of the virtual location mapping system, similar types of goods are centrally allocated to the same location cluster, while reserving a preset proportion of flexible locations in the cluster. For rapid sorting scenarios, based on the accessibility weight of the storage location and the predicted inventory turnover trend, the current high-frequency turnover goods and the predicted high-frequency turnover goods are jointly allocated to the high-operation frequency area near the inbound and outbound ports. The time consumption of different operation paths is simulated through the virtual mapping system, and the sorting order is optimized so that the operation path is shortened by more than the preset percentage on average. For special cargo storage scenarios, based on the real-time adaptability of storage conditions to cargo characteristics, an independent dedicated storage location cluster is allocated. In the virtual mapping system, a dedicated storage environment monitoring threshold is set for this cluster, and a dedicated monitoring device linked with the virtual system is configured to provide real-time feedback on changes in storage environment parameters. When the parameters exceed the threshold, an early warning is automatically triggered and the storage conditions are adjusted. By integrating storage location allocation rules, regional operation rules, equipment scheduling rules, and conflict coordination solutions, a personalized storage location management strategy that is adaptable to different business scenarios and has predictive capabilities is formed. Through a virtual storage location mapping system, a preset number of pre-run verifications are conducted to ensure that the achievement rate of core objectives is not lower than a preset percentage.

4. The method according to claim 1, characterized in that, Methods for constructing bidirectional data interaction loops include: The occupancy status of the storage location, the storage time of the goods, and the changes in environmental parameters are collected in real time by the storage location status sensing equipment. The data is then transmitted in encrypted form to the virtual storage location mapping system to update the corresponding status of the virtual storage location. The actual operating time, operating path trajectory, energy consumption value and fault occurrence time data of the warehousing equipment are collected by the equipment operation monitoring module. The data are compared in real time with the preset operating parameters in the virtual mapping system to generate parameter deviation data. The system records the time spent on each step of the goods entering and leaving the warehouse and the data update delay through the work process traceability system, forming a timeline data of the entire work cycle, which is then synchronized to the process traceability module of the virtual mapping system. Data on goal achievement in different scenarios is collected through business performance feedback modules, combined with simulation data from the virtual mapping system.

5. The method according to claim 4, characterized in that, The steps for verifying quantitative indicators are as follows: Core validation metrics are extracted from dynamic datasets. These core validation metrics include storage space utilization, goods inbound and outbound operation efficiency, equipment unit operation energy consumption, inventory data accuracy, scenario target achievement rate, and prediction adaptation accuracy. The calculation logic of each indicator is clearly explained using textual descriptions. The storage space utilization rate is calculated by the ratio of the number of occupied effective storage locations to the total number of effective storage locations. The efficiency of goods inbound and outbound operations is determined by the amount of qualified operations completed per unit time. The energy consumption per unit of equipment operation is calculated by the ratio of the total energy consumption of operations to the total amount of operations completed. The inventory data accuracy rate is determined by the ratio of the number of storage locations with consistent data to the total number of storage locations. The scenario target achievement rate is determined by the ratio of the number of scenarios that achieve quantitative targets to the total number of business scenarios. The prediction adaptation accuracy rate is determined by the ratio of the number of storage locations that are successfully predicted to the total number of predicted storage locations. The actual calculation results of each core verification indicator are compared with the quantitative target value and conflict tolerance range in the differentiated business target system to classify the degree of fit. Each core indicator must reach a specified proportion of the corresponding preset target value to be judged as a high degree of fit.

6. The method according to claim 5, characterized in that, Deviation attribution analysis methods specifically include: For the core verification indicators that fail to meet the matching level, the operation process is backtracked and simulated through the virtual mapping system of the storage location. The root causes of the positioning deviation include unreasonable division of storage location clusters, deviation of dynamic correlation prediction factor weights, improper allocation of multi-objective conflict coordination weights, mismatch between equipment scheduling rules and storage location area association, insufficient data collection accuracy or failure to update prediction model parameters in a timely manner. Correlation analysis was used to clarify the impact of each deviation cause on the indicators, and a correspondence model between deviation causes and core indicators was established. The accuracy of cause determination was verified by the parameter adjustment function of the virtual mapping system. Based on the correspondence model and virtual debugging results, the deviation correction direction with the highest priority is determined according to the degree of impact.

7. The method according to claim 6, characterized in that, The forward-looking optimization and strategy iteration method is as follows: Based on the direction of deviation correction and combined with the forward-looking optimization mechanism, the corresponding management parameters are adjusted in a targeted manner, including re-optimizing the scope of the cargo location cluster, calibrating the weight of the dynamic correlation prediction factor, correcting the multi-objective conflict coordination weight allocation logic, adjusting the association rules between equipment scheduling and cargo location areas, and upgrading the accuracy of data acquisition equipment or updating the prediction model parameters. The adjusted parameters are integrated into the original storage location management strategy. A virtual simulation of multiple scenarios is conducted through a storage location virtual mapping system, and the degree of fit between the simulation results and the preset target values ​​is compared. Develop iterative strategies that adapt to changes in scenarios, data feedback, and future trends.

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