A goods location management method and system of an intelligent stereoscopic warehouse and a storage medium

Abstract

The present application relates to the technical field of goods location management, and particularly relates to a goods location management method and system of an intelligent stereoscopic warehouse and a storage medium, the method comprising: configuring an edge computing unit for each four-way shuttle vehicle, the four-way shuttle vehicle being used for performing various transportation tasks of a warehouse system; reconstructing the edge computing unit into an independent edge intelligent agent with task data collection and edge intelligent analysis; when performing a transportation task, each independent edge intelligent agent autonomously collects associated traceability data of a whole link of the task, the associated traceability data at least comprising a trigger type of a task and traceability binding data, whole-time sequence operation data of a transportation path, and binding and association data of a related warehouse task and a main task; and based on the associated traceability data, the independent edge intelligent agent of the four-way shuttle vehicle completes multi-dimensional autonomous attribution analysis of a single task at an edge end. The present application effectively solves the problem of lack of real-time attribution capability of goods location management, and realizes real-time and accurate attribution of the edge end of goods location management.

Description

Technical Field

[0001] This invention relates to the field of warehouse location management technology, and in particular to warehouse location management methods, systems and storage media for intelligent automated warehouses. Background Technology

[0002] Four-way shuttle automated warehouses are high-density automated storage and retrieval systems. The core equipment, the four-way shuttle, can travel independently along the aisle and laterally within the racking plane, and has the ability to change direction, lift, and reload goods to realize the storage, retrieval, and handling of goods. Therefore, it is necessary to monitor and optimize the storage location status, operating efficiency, and path planning in the automated warehouse for storage location management. In existing systems, each four-way shuttle is usually equipped with an edge computing unit to collect its own operating data and interact with the warehouse control system.

[0003] Currently, the storage location management of four-way shuttle automated warehouses generally adopts a centralized architecture. The warehouse control system issues tasks to each shuttle, which is responsible for executing the tasks and transmitting the operational data back to the central server for storage and analysis. When it is necessary to analyze a single operation, all related data is transmitted to the central side for centralized processing via the network. At this time, the binding relationship between the full-time data and tasks during the operation execution process is prone to delays, packet loss, or timing errors during transmission, resulting in a lack of real-time attribution capability for a single operation. The central system often only knows the macro results of operation timeouts or frequent relocations, but cannot immediately locate the specific links of efficiency loss after the operation is completed, distinguish between normal congestion and occasional avoidance, determine whether relocation is ineffective, and the triggering cause.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] In view of at least one of the above technical problems, the present invention provides a method, system and storage medium for intelligent automated warehouse location management. It adopts edge intelligence to collect data from the entire operation chain and complete multi-dimensional attribution analysis to solve the problems of lack of real-time attribution capability and difficulty in locating anomalies in location management.

[0006] This invention provides a method for managing the storage locations of an intelligent automated warehouse, applicable to a four-way shuttle automated warehouse system. The method includes the following steps: Each four-way shuttle is equipped with an edge computing unit, and the four-way shuttle is used to perform various transportation operations of the warehousing system; The edge computing unit is reconstructed into an independent edge agent capable of acquiring job data and performing edge intelligent analysis; When each independent edge agent performs transportation operations, it autonomously collects the associated traceability data of its own entire operation chain. The associated traceability data includes at least the trigger type and traceability binding data of the operation task, the full time-series operation data of the transportation path, and the binding association data between the related warehouse relocation operation and the main task. Based on the associated source data, the independent edge agent of the four-way shuttle completes multi-dimensional autonomous attribution analysis for a single operation at the edge.

[0007] Furthermore, the independent edge agent includes: The associated traceability layer is used to bind and mark transportation operations at the task dimension and associate them with traceability, forming the traceability binding data, and establishing the full-link structure relationship between the main transportation task and the associated warehouse transfer sub-tasks. The data acquisition layer collects the full-time operation data of the four-way shuttle along the transportation path; The edge analysis layer performs the autonomous attribution analysis on the multi-dimensional aspects of a single operation at the edge. The communication link layer is used to establish a two-way real-time communication link between the edge computing unit and the original vehicle controller, vehicle-mounted sensing components, lane edge gateway, and warehousing system of the four-way shuttle.

[0008] Furthermore, the autonomous attribution analysis includes at least attribution of operational efficiency loss, identification of path congestion bottlenecks, and determination of the effectiveness of reversing operations.

[0009] Furthermore, the multi-dimensional autonomous attribution analysis for completing a single task at the edge includes: Break down the deviation between the total time of a single operation and the benchmark time of similar operations, locate the specific links in the efficiency loss, quantify the loss ratio of each link, and clarify the causes and links of the efficiency loss. Locate congested nodes, ineffective turns, and unnecessary detours in a single operation path; distinguish between occasional temporary avoidances and regular road bottlenecks; quantify the additional time and mileage loss caused by congestion and ineffective driving; and determine the causes of path loss. Determine whether the associated relocation operation is invalid, quantify the additional time consumption, energy consumption and path loss generated by the associated relocation operation, trace the triggering cause of invalid relocation, and clarify the link of relocation loss.

[0010] Furthermore, when performing transportation operations, the independent edge agent autonomously collects relevant traceability data across the entire operational chain, including: After receiving the transportation operation instruction, a data interaction link is established through the communication link layer; The data acquisition layer collects and processes the job data, and the association and tracing layer generates a traceable structured dataset. The autonomous attribution analysis is completed through the edge analysis layer, and the analysis results are reported by issuing instructions through the communication link layer.

[0011] Furthermore, it also includes multiple four-way shuttles in the same lane, which establish a data interaction channel through the edge gateway of the lane via the communication link layer of their respective independent edge agents.

[0012] Furthermore, the edge computing unit is divided into a data acquisition zone and an analysis zone. The data acquisition zone is used to receive raw data from the vehicle-mounted sensing components in real time and to complete preprocessing and storage. The analysis zone deploys lightweight analysis models.

[0013] Furthermore, the independent edge agent has a built-in dynamic resource scheduling mechanism. During the execution of the job, resources are prioritized to the collection area to ensure the real-time performance and integrity of the full-time data collection. During job intervals, the resource allocation ratio is automatically adjusted to schedule redundant resources to the analysis area.

[0014] The present invention also provides a storage location management system for an intelligent automated warehouse, the system comprising: An edge configuration module is provided to each four-way shuttle, which is used to perform various transportation operations of the warehousing system. The data acquisition and analysis module reconstructs the edge computing unit into an independent edge intelligent agent capable of acquiring operational data and performing edge intelligent analysis. The associated traceability module allows each independent edge agent to autonomously collect associated traceability data across the entire operational chain when performing transportation operations. The attribution analysis module allows the independent edge agent of the four-way shuttle to perform multi-dimensional autonomous attribution analysis for a single operation at the edge based on the associated source data.

[0015] The present invention also provides a storage medium storing computer instructions for causing the computer to execute the storage location management method.

[0016] The technical solution of this invention can achieve the following technical effects: By equipping each four-way shuttle with an independent edge agent, the vehicle can autonomously collect the associated traceability data of the entire chain during the operation, and directly complete the multi-dimensional attribution analysis of a single operation at the edge. This significantly improves the real-time performance of operation attribution, avoids the impact of data transmission delay and timing disorder on the analysis accuracy, and improves the accuracy of locating abnormal operations.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a logical diagram of the storage location management method of the intelligent automated warehouse in an embodiment of the present invention; Figure 2 This is a logical schematic diagram of an independent edge agent in an embodiment of the present invention; Figure 3 This is a logical diagram of autonomous attribution analysis in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for performing a multi-dimensional autonomous attribution analysis of a single task at the edge in an embodiment of the present invention; Figure 5 This is a schematic diagram of the process for autonomously collecting associated traceability data of the entire operation chain in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] This invention provides a method such as Figures 1 to 5 The storage location management method for the intelligent automated warehouse shown is applied to the storage system of a four-way shuttle automated warehouse. The method includes the following steps: Each four-way shuttle is equipped with an edge computing unit, and the four-way shuttle is used to perform various transportation operations in the warehousing system; Reconstruct edge computing units into independent edge agents capable of acquiring operational data and performing edge intelligent analysis; When each independent edge agent performs transportation operations, it autonomously collects the associated traceability data of its own entire operation chain. The associated traceability data includes at least the trigger type and traceability binding data of the operation task, the full time-series operation data of the transportation route, and the binding and association data between the related warehouse relocation operation and the main task. The independent edge agent of the four-way shuttle completes multi-dimensional autonomous attribution analysis of a single operation at the edge based on the associated source data.

[0023] The working principle of this invention is as follows: The warehousing system includes multiple rows of high-rise racks, several four-way shuttles, and a warehousing control system. The four-way shuttles are used to perform transportation operations such as picking, delivering, and retrieving goods. First, each four-way shuttle is equipped with an edge computing unit, which can be an embedded industrial control board. It connects to the shuttle's original vehicle controller and onboard sensors via CAN bus or Ethernet. The edge computing unit is pre-installed with a lightweight operating system and data processing framework.

[0024] Then, the edge computing unit is reconstructed into an independent edge agent. The process includes deploying data acquisition services, data association services, and edge analysis services in the edge computing unit, thereby enabling autonomous decision-making. The independent edge agent can actively identify the start and end of the job, can locally mark and store the collected data, and can call the built-in analysis algorithm to make judgments on the data, significantly improving the real-time performance of job attribution.

[0025] When a four-way shuttle begins a transportation operation, the independent edge agent autonomously collects at least three types of related traceability data throughout its entire operation. The first type is the trigger type and traceability binding data of the task. For example, the independent edge agent records whether the task is a main task actively issued by the warehouse control system or a sub-task triggered by other vehicles occupying space or the system automatically balancing the load. At the same time, it generates a unique task identifier for this operation and establishes a connection with the previous level task. The second type is the full-time operation data of the transportation path. Independent edge agents record information such as the shuttle's position, speed, direction, motor current, and stopping time at a fixed frequency, forming a complete temporal and spatial trajectory that reflects the vehicle's operating status at any given moment. The third type is the data associated with the reversing operation and the main task. If, during the execution of the main task, the shuttle performs a reversing operation due to the target storage location being occupied, the path being blocked, or the instruction being changed, the independent edge agent automatically records the start time, end time, reversing path length, and positional changes before and after the reversing operation. It also uniquely binds the reversing sub-task to the main task, ensuring that subsequent analysis can clearly identify which main task triggered the reversing operation, thus guaranteeing the integrity and accuracy of the associated traceability data.

[0026] After the task is completed, the edge analysis module compares the total time of this task with the historical average time of similar tasks stored locally. If a significant deviation is found, it further breaks down the actual time of each stage, such as driving, waiting, and reversing, and compares it with the baseline value to determine which stage the efficiency loss mainly occurs in. At the same time, the analysis module also checks for abnormal low speeds or stopping points in the path, and whether the reversing operation is reasonable. All of these analyses are completed locally on the shuttle, without transmitting the raw data to the central server.

[0027] In some embodiments of the present invention, such as Figure 2 As shown, independent edge agents include: The association and traceability layer is used to bind and associate transportation operations with the task dimension, forming traceability binding data and establishing a full-link structure relationship between the main transportation task and the associated warehouse relocation sub-tasks. When the four-way shuttle receives a new transportation task, the association and traceability layer generates a globally unique task identifier for the transportation task and determines the trigger type according to the source of the task. The trigger type is recorded in the form of an enumeration value. For the hierarchical relationship between tasks, the association and traceability layer adopts a tree data structure, forming a full-link structure relationship between the main transportation task and the associated warehouse relocation sub-tasks. This allows subsequent analysis to clearly trace which main task triggered any warehouse relocation operation under what conditions.

[0028] The data acquisition layer collects real-time operational data of the four-way shuttle along its transport path. It can periodically read data from the original vehicle controller and onboard sensing components via the communication interface provided by the communication link layer. This data includes real-time position coordinates from the encoder, instantaneous speed and acceleration from the drive motor, attitude angles from the inertial measurement unit, fork status from photoelectric sensors, and voltage and current from the battery management system. The data acquisition layer adds the task identifier and timestamp to this data and writes it to the local circular buffer of the edge computing unit, ensuring the integrity of the entire data chain.

[0029] The edge analysis layer performs multi-dimensional autonomous attribution analysis on a single task at the edge. It first acquires stored full-time-series runtime data from the data acquisition layer and obtains the structural relationships between tasks from the correlation and tracing layer. Then, it runs pre-defined analysis algorithms locally. For example, the efficiency analysis module breaks down the total time of the task into several stages and compares them with local historical statistics; the path analysis module scans for speed anomalies and stop points in the location sequence to identify abnormal driving sections; and the correlation analysis module combines the binding relationship between the main task and sub-tasks to determine whether the reversing operation occurred under reasonable triggering conditions. Autonomous attribution analysis is completed locally on the edge computing unit, achieving real-time multi-dimensional autonomous attribution at the edge.

[0030] The communication link layer establishes a two-way real-time communication link between the edge computing unit and the original vehicle controller, onboard sensing components, lane edge gateway, and warehousing system of the four-way shuttle. Specifically, the communication link layer uses different physical interfaces and protocols for different communication objects to ensure the real-time and deterministic nature of data exchange and achieve low-latency data sharing among multiple vehicles. Furthermore, the communication link layer provides a unified application programming interface for the correlation and tracing layer, data acquisition layer, and edge analysis layer, shielding the differences in underlying protocols and allowing upper-layer modules to transparently invoke communication functions.

[0031] In some embodiments of the present invention, such as Figure 3 As shown, autonomous attribution analysis includes at least the attribution of operational efficiency loss, identification of path congestion bottlenecks, and determination of the effectiveness of reversing operations.

[0032] Operational efficiency loss attribution: The edge analysis layer divides the total time of this operation into stages such as picking up goods, straight-line movement, reversing direction, waiting, transferring goods to warehouse, and unloading, and compares them one by one with the historical average time of the same type stored locally. If the time of a certain stage significantly exceeds the benchmark, it is determined that there is an efficiency loss in the current stage.

[0033] Path congestion bottleneck identification: Extract location and speed sequences from full-time running data, set low-speed thresholds and stopping duration thresholds, mark abnormal points, and quantify additional time consumption and mileage loss.

[0034] Validity determination of reverse parking operation: First, determine whether the main task was successfully completed after the reverse parking. If successful, it is a valid reverse parking; if the main task fails after the reverse parking, it is an invalid reverse parking. Obtain the reason for the reverse parking trigger from the associated tracing layer and output a comprehensive evaluation.

[0035] In some embodiments of the present invention, such as Figure 4 As shown, the multi-dimensional autonomous attribution analysis for completing a single task at the edge includes: The process involves breaking down the total time of a single operation into deviations from the baseline time for similar operations. This identifies the specific steps leading to efficiency losses, quantifies the loss percentage of each step, and clarifies the causes and specific steps contributing to efficiency losses. The edge analysis layer further breaks down the total time of the current operation, categorizing it by steps such as picking time, driving time, reversing time, waiting time, and warehouse relocation time. Historical baseline times for similar operations are retrieved from local storage, such as the average of each step in the last 100 similar tasks. The deviation between the current operation's total time and the baseline time is calculated. If a positive deviation exists, the actual time of each step is compared with the baseline time, identifying one or more steps with the largest deviation. The loss percentage of the current step is calculated, thus clarifying the specific steps and causes of efficiency losses. This precise identification and quantification of the specific steps contributing to efficiency losses avoids vague attributions.

[0036] The system identifies congested nodes, ineffective turnarounds, and unnecessary detours along a single work path, distinguishing between occasional temporary yielding and persistent road bottlenecks. It quantifies the additional time and mileage loss caused by congestion and ineffective travel, determining the causes of path loss. The edge analysis layer extracts location and speed sequences from full-time operational data, scans for anomalies in the path, sets low-speed thresholds and stopping duration thresholds, marks locations with speeds consistently below the thresholds as congested nodes, marks trajectories with short-term round trips without effective work as ineffective turnarounds, and marks road sections with actual travel distances exceeding 1.5 times the theoretical shortest path as unnecessary detours. Furthermore, by querying historical traffic records of locations: if the same location frequently experiences low speeds or stops in recent operations, it is identified as a persistent road bottleneck; if it only occurs in the current operation and other vehicles are detected nearby, it is identified as an occasional temporary yielding. This effectively distinguishes between persistent bottlenecks and occasional yielding, and outputs the causes of path loss based on the additional time and mileage loss.

[0037] To determine whether the current associated reversing operation is invalid, the additional time, energy consumption, and path loss generated by the operation are quantified. The triggering cause of invalid reversing is traced, and the link in the reversing loss is identified. The edge analysis layer obtains the binding relationship between the main task and the reversing sub-task in this operation. First, it determines whether the reversing is invalid. If the main task fails to complete successfully after the reversing, it is directly determined as invalid. If the main task completes successfully after the reversing, it is determined as valid. The additional time, energy consumption, and path loss generated by the reversing are further quantified. This can be done by comparing the time, motor current integral value, and travel distance of this reversing with the average value of historical reversing in the same lane. The part exceeding the threshold is the additional loss. At the same time, the triggering cause of the reversing is obtained from the correlation tracing layer to trace the root cause of invalid or high-cost reversing and to identify at which link the reversing loss occurs.

[0038] In some embodiments of the present invention, such as Figure 5 As shown, when an independent edge agent performs transportation operations, it autonomously collects relevant traceability data across the entire operational chain, including: After receiving the transportation operation instructions issued by the warehousing system, the independent edge agent establishes a data interaction link through the communication link layer. This includes establishing bidirectional real-time communication links with the original vehicle controller of the four-way shuttle, the on-board sensing components, the lane edge gateway, and the warehousing system. The communication link layer can connect to the controller via CAN bus, to the lane edge gateway via wireless protocol, and to the warehousing system via Ethernet. A handshake confirmation is then completed to ensure smooth subsequent data transmission channels, guaranteeing the real-time performance and reliability of data transmission.

[0039] The data acquisition layer collects and processes the operation data, and the association and traceability layer generates a traceable structured dataset. The data acquisition layer reads raw operation data such as position, speed, and status from the original vehicle controller and sensing components at a fixed frequency, and performs preprocessing such as noise reduction and timestamp alignment. The association and traceability layer generates a unique task identifier for the current operation, records the trigger type, and binds the preprocessed operation data with the task identifier to form a traceable structured dataset.

[0040] Autonomous attribution analysis is performed through the edge analysis layer, and instructions are issued and analysis results are reported through the communication link layer. By reading the structured dataset, pre-set analysis algorithms are called to complete multi-dimensional autonomous attribution analysis, including at least efficiency comparison, path scanning and reversing judgment algorithms. After the analysis is completed, the communication link layer issues control instructions to the original vehicle controller and reports the attribution analysis results to the warehouse system or lane edge gateway, realizing closed-loop feedback of operations and shortening the anomaly response time.

[0041] In some embodiments of the present invention, multiple four-way shuttles in the same lane are also included, which establish a data interaction channel through the lane's edge gateway via the communication link layer of their respective independent edge agents. The lane edge gateway is an industrial-grade embedded device deployed at the end or side of the lane, which has the ability to aggregate and forward data.

[0042] Through data exchange channels, each vehicle shares operation-related data, including at least current task priority, real-time location, driving direction, speed, estimated time to pass key nodes, and destination cargo location. Based on this shared data, multiple vehicles can collaboratively adjust their operation sequence: when a high-priority vehicle approaches an intersection, a low-priority vehicle automatically calculates its waiting time and adjusts its speed or pauses to allow the high-priority vehicle to pass first; path avoidance: when two vehicles plan to enter the same narrow side alley, they exchange driving plans through the edge gateway and negotiate which vehicle enters first, while the other vehicle waits at the entrance or chooses an alternative route; regional load balancing: when the edge gateway detects excessively high vehicle density in a certain section of the alley, it sends instructions to subsequent vehicles, suggesting they postpone entry or detour through other alleys to avoid localized congestion.

[0043] Collaborative decision-making is completed independently by the edge agents of each vehicle based on shared data, without the need for intervention from a central server. It relies solely on the lane edge gateway as a data exchange intermediary, which significantly improves the passage efficiency and system stability within the lane, while reducing the real-time dependence on the central dispatch system.

[0044] In some embodiments of the present invention, the edge computing unit is divided into a data acquisition zone and an analysis zone. The data acquisition zone is used to receive raw data from the vehicle-mounted sensing components in real time and to complete preprocessing and storage. The analysis zone deploys lightweight analysis models.

[0045] The data acquisition zone is equipped with independent buffer resources and real-time data processing threads. It is directly connected to vehicle-mounted sensing components such as encoders, LiDAR, and photoelectric sensors through the communication link layer to receive raw data in real time at a high frequency. After receiving the data, the acquisition zone immediately performs preprocessing operations, including noise removal, aligning data from different sensors with a unified timestamp, and unit conversion. The preprocessed data is stored in the local memory of the acquisition zone in a circular buffer to ensure that the latest data overwrites the oldest data, and the writing process will not be blocked due to the storage being full. This ensures the real-time performance and integrity of the data acquisition throughout the entire operation and prevents data loss due to resource consumption by analysis tasks.

[0046] The analysis zone is equipped with a lightweight analysis model, which can use a decision tree model based on a rule engine or an anomaly detection model based on statistical thresholds. The analysis zone reads preprocessed data from the collection zone and then calls the lightweight analysis model to perform analysis tasks such as efficiency attribution and path identification, thereby improving the overall reliability and execution efficiency of the edge computing unit.

[0047] In some embodiments of the present invention, the independent edge agent has a built-in dynamic resource scheduling mechanism. During the execution of the job, resources are prioritized to the collection area to ensure the real-time performance and integrity of the data collection throughout the entire time series. During the job interval, the resource allocation ratio is automatically adjusted to schedule redundant resources to the analysis area.

[0048] When the four-way shuttle is performing transportation operations, the edge computing unit monitors the operation status in real time. When the operation is in progress, the dynamic resource scheduling mechanism prioritizes allocating most of the computing resources, such as CPU time slices, memory bandwidth, and storage I / O, to the data acquisition zone. At the same time, it appropriately reduces the resource quota of the analysis zone, retaining a minimum operating capacity. This ensures that the data acquisition zone can receive the raw data from the vehicle-mounted sensing components in real time with the highest priority, complete preprocessing and storage, and avoid data frame loss or delays caused by resource contention.

[0049] When the four-way shuttle completes a task and has not yet received a new task, the edge computing unit detects a task gap. At this time, the dynamic resource scheduling mechanism automatically adjusts the resource allocation ratio, scheduling redundant computing resources to the analysis zone. The analysis zone uses these released resources to perform more complex analysis tasks, such as in-depth mining of historical data, updating lightweight analysis model parameters, or recalculating baseline time statistics. When a new task instruction arrives, the scheduling mechanism switches back to prioritizing the data acquisition zone, improving the resource utilization efficiency of the edge computing unit. The system can flexibly balance real-time data acquisition and in-depth analysis, taking into account both data quality and analytical capabilities.

[0050] Based on the same inventive concept as the storage location management method for an intelligent automated warehouse in the foregoing embodiments, the present invention also provides a storage location management system for an intelligent automated warehouse, the system comprising: The edge configuration module equips each four-way shuttle with an edge computing unit, and the four-way shuttle is used to perform various transportation operations in the warehousing system. The data acquisition and analysis module reconstructs the edge computing unit into an independent edge intelligent agent capable of acquiring operational data and performing edge intelligent analysis. The correlation and traceability module allows each independent edge agent to autonomously collect correlation and traceability data across the entire operational chain when performing transportation operations. The attribution analysis module enables the independent edge agents of the four-way shuttle to perform multi-dimensional autonomous attribution analysis for a single operation at the edge based on associated source data.

[0051] The above-mentioned storage location management system in this invention can effectively realize the storage location management method of intelligent automated warehouse, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0052] The present invention also provides a storage medium storing computer instructions for causing a computer to execute a location management method.

[0053] Similarly, the storage medium described above in this invention can effectively enable a computer to execute a location management method, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0054] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for managing the storage locations of an intelligent automated warehouse, characterized in that, The method is applied to the storage system of a four-way shuttle automated warehouse, and includes the following steps: Each four-way shuttle is equipped with an edge computing unit, and the four-way shuttle is used to perform various transportation operations of the warehousing system; The edge computing unit is reconstructed into an independent edge agent capable of acquiring job data and performing edge intelligent analysis; When each independent edge agent performs transportation operations, it autonomously collects the associated traceability data of its own entire operation chain. The associated traceability data includes at least the trigger type and traceability binding data of the operation task, the full time-series operation data of the transportation path, and the binding association data between the related warehouse relocation operation and the main task. Based on the associated source data, the independent edge agent of the four-way shuttle completes multi-dimensional autonomous attribution analysis for a single operation at the edge.

2. The method for managing the storage location of an intelligent automated warehouse according to claim 1, characterized in that, The independent edge agent includes: The associated traceability layer is used to bind and mark transportation operations at the task dimension and associate them with traceability, forming the traceability binding data, and establishing the full-link structure relationship between the main transportation task and the associated warehouse transfer sub-tasks. The data acquisition layer collects the full-time operation data of the four-way shuttle along the transportation path; The edge analysis layer performs the autonomous attribution analysis on the multi-dimensional aspects of a single operation at the edge. The communication link layer is used to establish a two-way real-time communication link between the edge computing unit and the original vehicle controller, vehicle-mounted sensing components, lane edge gateway, and warehousing system of the four-way shuttle.

3. The method for managing the storage location of an intelligent automated warehouse according to claim 1 or 2, characterized in that, The autonomous attribution analysis includes at least the attribution of operational efficiency loss, identification of path congestion bottlenecks, and determination of the effectiveness of reversing operations.

4. The method for managing the storage location of an intelligent automated warehouse according to claim 3, characterized in that, The multi-dimensional autonomous attribution analysis for completing a single task at the edge includes: Break down the deviation between the total time of a single operation and the benchmark time of similar operations, locate the specific links in the efficiency loss, quantify the loss ratio of each link, and clarify the causes and links of the efficiency loss. Locate congested nodes, ineffective turns, and unnecessary detours in a single operation path; distinguish between occasional temporary avoidances and regular road bottlenecks; quantify the additional time and mileage loss caused by congestion and ineffective driving; and determine the causes of path loss. Determine whether the associated relocation operation is invalid, quantify the additional time consumption, energy consumption and path loss generated by the associated relocation operation, trace the triggering cause of invalid relocation, and clarify the link of relocation loss.

5. The method for managing the storage location of an intelligent automated warehouse according to claim 2, characterized in that, When performing transportation operations, the independent edge agent autonomously collects relevant traceability data across the entire operational chain, including: After receiving the transportation operation instruction, a data interaction link is established through the communication link layer; The data acquisition layer collects and processes the job data, and the association and tracing layer generates a traceable structured dataset. The autonomous attribution analysis is completed through the edge analysis layer, and the analysis results are reported by issuing instructions through the communication link layer.

6. The method for managing the storage location of an intelligent automated warehouse according to claim 2, characterized in that, It also includes multiple four-way shuttles in the same lane, which establish a data interaction channel through the edge gateway of the lane via the communication link layer of their respective independent edge agents.

7. The method for managing the storage location of an intelligent automated warehouse according to claim 1, characterized in that, The edge computing unit is divided into a data acquisition zone and an analysis zone. The data acquisition zone is used to receive raw data from the vehicle-mounted sensing components in real time and to perform preprocessing and storage. The analysis zone deploys lightweight analysis models.

8. The method for managing the storage location of an intelligent automated warehouse according to claim 7, characterized in that, The independent edge agent has a built-in dynamic resource scheduling mechanism. During the execution of the job, resources are prioritized to the collection area to ensure the real-time performance and integrity of the full-time data collection. During job intervals, the resource allocation ratio is automatically adjusted to schedule redundant resources to the analysis area.

9. A storage location management system for an intelligent automated warehouse, characterized in that, Using the storage location management method as described in any one of claims 1-8, the system comprises: An edge configuration module is provided to each four-way shuttle, which is used to perform various transportation operations of the warehousing system. The data acquisition and analysis module reconstructs the edge computing unit into an independent edge intelligent agent capable of acquiring operational data and performing edge intelligent analysis. The associated traceability module allows each independent edge agent to autonomously collect associated traceability data across the entire operational chain when performing transportation operations. The attribution analysis module allows the independent edge agent of the four-way shuttle to perform multi-dimensional autonomous attribution analysis for a single operation at the edge based on the associated source data.

10. A storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the storage location management method according to any one of claims 1-8.

Images