A method and system for multi-lane rail transfer of a stacker

Abstract

The present application relates to the technical field of stacker rail changing scheduling, and discloses a stacker multi-lane rail changing scheduling method and system, which comprises the following steps: collecting motor current and vibration signals of a stacker rail changing mechanism in real time, and evaluating the real-time health state grade of the stacker; setting a differentiated task load upper limit for each stacker according to the health state grade, generating an initial rail changing path and a task allocation scheme; simulating the scheme, if positioning inaccuracy or action timeout caused by performance decline is identified, automatically reducing the task load of the corresponding stacker and re-planning the path, and iteratively generating a robust scheduling sequence; executing the sequence, monitoring the operation data in real time, if action lag or execution time continuously deviates from the expectation, immediately triggering strategy adjustment, dynamically reallocating tasks or switching to a backup path to ensure smooth scheduling. The present application can realize dynamic task planning and forward-looking fault-tolerant scheduling based on the health state of the equipment, effectively improving the system operation efficiency and reliability.

Description

Technical Field

[0001] This invention relates to the field of stacker crane track changing scheduling technology, and in particular to a stacker crane multi-lane track changing scheduling method and system. Background Technology

[0002] In modern warehousing and logistics systems, stacker cranes, as the core equipment of automated storage and retrieval systems (AS / RS), directly determine the throughput capacity of the entire system through their operational efficiency and reliability. Stacker cranes with multi-aisle track-changing capabilities, in particular, can overcome the limitations of fixed aisles, greatly improving equipment utilization and system layout flexibility. With the rapid development of e-commerce, intelligent manufacturing, and other industries, higher demands are placed on the efficiency and response speed of warehousing and logistics. High-frequency, high-intensity operation of stacker cranes has become the norm. However, this long-term, uninterrupted operation subjectes the mechanical and electrical components of the stacker crane, especially the track-changing mechanism responsible for aisle switching, to continuous wear and aging stress, posing a potential threat to the stable operation of the system.

[0003] Currently, most stacker crane scheduling strategies used in the industry rely on preset fixed logic or static algorithms based on task priority. These methods work effectively when the equipment is new or in a stable state, but their core flaw is that they treat all stacker cranes as equipment with uniform performance, ignoring the dynamic differentiation of the health status of individual equipment after long-term operation. For example, a stacker crane whose positioning accuracy has begun to decline due to guide rail wear is often assigned the same intensity and accuracy of tasks as a brand new stacker crane in the existing system. This leads to a serious problem: when equipment with degraded performance is assigned tasks that exceed its current capabilities, it is very prone to motion lag, inaccurate positioning, or even task failure. This not only interrupts the current operation process but may also cause secondary failures, thereby reducing the throughput efficiency of the entire system and even causing safety accidents.

[0004] Existing technologies attempt to address this through regular manual inspections or post-failure repairs, but this is a passive and reactive approach that cannot proactively address and mitigate problems before they occur, by which time losses have already been incurred. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the defects of the existing stacker crane scheduling strategy, which cannot adapt to the dynamic changes in the individual health status, resulting in low operating efficiency and insufficient stability. The present invention provides a stacker crane multi-lane track-changing scheduling method and system, which can perform dynamic task planning and forward-looking fault-tolerant scheduling based on the real-time health status of the stacker crane, thereby effectively improving the overall operating efficiency and reliability of the system.

[0006] To solve the above-mentioned technical problems, the present invention provides a multi-lane track-changing scheduling method for stacker cranes, comprising the following steps:

[0007] The motor current and vibration signals of the stacker crane's track-changing mechanism are collected in real time. The signals are transformed in the time and frequency domains to extract features, and a comprehensive performance degradation index is calculated to evaluate the real-time health status level of the stacker crane.

[0008] Based on the real-time health status level, set a differentiated task load limit for each stacker crane. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology, generate an initial track change path and task allocation scheme.

[0009] The initial trajectory change path and task allocation scheme are simulated. In the simulation, if any stacker is found to be inaccurate in positioning or timed out due to performance degradation, the task load of the stacker is automatically reduced and its path is replanned. This process is iterated until a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stackers is generated.

[0010] A robust scheduling sequence is issued to the corresponding stacker crane for execution. During execution, the actual operating data of the stacker crane is monitored in real time. If a delay or a task execution time that deviates from the expected time is detected, the strategy is immediately adjusted to dynamically reallocate the current task or switch to a backup path to ensure the smoothness of the overall scheduling process.

[0011] In one embodiment of the present invention, the motor current and vibration signals of the stacker crane's track-changing mechanism are collected in real time, the signals are transformed in the time and frequency domains to extract features, and a comprehensive performance degradation index is calculated, including the following steps:

[0012] The collected motor current and vibration signals are decomposed into a series of independent modal components reflecting different physical sources. Noise modes that are unrelated to the mechanical motion of the track-changing mechanism are eliminated, and the remaining effective modes are reconstructed into a denoised core signal.

[0013] The time-domain and frequency-domain features of the core signal are matched with a pre-generated standard state feature library corresponding to different health stages of the stacker crane. By calculating the deviation between the core signal and each standard state feature vector in the feature library, a set of multi-dimensional degradation quantification values ​​are obtained.

[0014] The arithmetic mean of a set of preliminary degradation metrics across multiple dimensions is calculated, and the resulting average value is used as an indicator of overall performance degradation.

[0015] In one embodiment of the present invention, based on the task load limit, the current inbound and outbound task queues of each lane, and the warehouse track topology, an initial track-changing path and task allocation scheme are generated, including the following steps:

[0016] For each task in the current inbound / outbound task queue, calculate the path cost between its target location and each aisle entrance based on the warehouse track topology map. Combine the current health status level of each stacker crane and the remaining task load capacity to generate a matching sequence between tasks and stacker cranes.

[0017] Based on the matching degree sequence, the stacker crane with the best health status is given priority to be assigned the most remote task with the highest path cost, while the stacker crane with the second best health status is assigned the medium-range task with the moderate path cost, so as to ensure that the estimated task execution time of each stacker crane tends to be balanced.

[0018] In one embodiment of the present invention, when generating the initial track-changing path, the spatiotemporal overlap of the planned paths of each stacker crane at track intersections and track-changing sections is detected in real time. By inserting a waiting time window or adjusting the path passage order, a conflict-free track-changing path sequence is generated.

[0019] In one embodiment of the present invention, during the task allocation process, an emergency capacity of no less than 30% of the upper limit of the task load is reserved for stacker cranes with a health status level of severe aging. This portion of the capacity is only activated when an emergency task occurs in the aisle where the stacker crane is located.

[0020] In one embodiment of the present invention, the simulation of the initial trajectory change path and task allocation scheme includes the following steps:

[0021] Based on the real-time health status level of the stacker crane, a corresponding motion accuracy attenuation model and response delay model are established. The stacker crane with a lower health status level is given a larger positioning error range and a longer motion execution time in the simulation.

[0022] A random disturbance field is constructed in the simulation environment. When the stacker crane performs key actions, random disturbance factors that conform to the actual working conditions are injected into the disturbance field, including instantaneous communication delay, friction coefficient changes caused by track surface attachments, and power supply voltage fluctuations.

[0023] The simulation was run with performance degradation and random disturbances applied, and the state parameters of each stacker crane during task execution were monitored. When a positioning deviation or action completion timeout occurs, the path point is marked as a fault tolerance boundary point.

[0024] For the identified fault-tolerant boundary points, the original path is softened, including inserting pre-deceleration points in sections with high positioning accuracy requirements, adding attitude calibration positions before complex orbital changes, and configuring parallel backup paths for critical tasks.

[0025] In one embodiment of the present invention, during the stacker crane's task execution, the following is also included:

[0026] By collecting position feedback signals and drive motor torque data in real time through monitoring units deployed at key track nodes, a high-frequency execution state trajectory is established.

[0027] The real-time collected execution state trajectory is compared with the expected state trajectory generated by the simulation. When multiple consecutive trajectory points are detected to deviate from the simulation position or torque fluctuations show a specific abnormal pattern, it is determined to be a potential action stutter.

[0028] A tiered response mechanism is activated based on the severity of the identified abnormal patterns: for minor deviations, compensation is provided via speed fine-tuning commands; for persistent deviations, collaborative avoidance commands are sent to other stacker cranes in the relevant area, and a new local path is replanned for the current stacker crane.

[0029] After the strategy is adjusted, the status recovery of the stacker crane is continuously monitored. Once it is confirmed that it has returned to a stable operating range, the cooperative avoidance constraints are gradually lifted, and the abnormal data and processing effect are fed back to the health status assessment model for model update.

[0030] In one embodiment of the present invention, the method further includes: recording the execution log and performance indicators of each scheduling process, using time series analysis to predict the future performance degradation trend of each stacker crane's track-changing mechanism, and updating the health status level in advance based on the prediction results, so as to realize the scheduling system's autonomous adaptation to long-term changes in equipment performance.

[0031] In one embodiment of the present invention, predicting the future performance degradation trend of each stacker crane's trajectory-changing mechanism includes:

[0032] Record the comprehensive performance degradation index obtained in each scheduling evaluation, and construct a time series of the performance degradation index of each stacker crane in chronological order;

[0033] By performing trend analysis on the time series of performance degradation indicators, the current degradation stage of the equipment can be identified based on its rate of change and acceleration.

[0034] Based on the identified current degradation stage, the system matches the change patterns of performance degradation indicators within the same stage from historical data, combines the subsequent development trajectory of the pattern with the total runtime of the device, and predicts the evolution range of performance degradation indicators within a specific future period.

[0035] To solve the above-mentioned technical problems, the present invention also provides a multi-lane track-changing scheduling system for stacker cranes, comprising:

[0036] The status monitoring module is used to collect the motor current and vibration signals of the stacker crane's track-changing mechanism in real time, perform time-frequency domain transformation on the signals to extract features, and calculate a comprehensive performance degradation index to evaluate the real-time health status level of the stacker crane.

[0037] The scheduling and planning module is connected to the status monitoring module and is used to set a differentiated task load limit for each stacker crane according to the real-time health status level. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology map, it generates an initial track change path and task allocation scheme.

[0038] The simulation verification module is connected to the scheduling and planning module and is used to simulate the initial trajectory change path and task allocation scheme. In the simulation, if any stacker crane is found to have inaccurate positioning or timeout due to performance degradation, the scheduling and planning module is automatically triggered to re-plan. Through the iterative process, a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stacker cranes is finally generated.

[0039] The execution control module is communicatively connected to the simulation verification module and the status monitoring module. It is used to send the robust scheduling sequence to the corresponding stacker crane for execution. During the execution process, if the status monitoring module detects an action lag or a continuous deviation of the task execution time from the expected time, it immediately triggers a strategy adjustment to dynamically reallocate the current task or switch to a backup path.

[0040] The technical solution of the present invention has the following advantages compared with the prior art:

[0041] The stacker crane multi-lane track-changing scheduling method and system described in this invention integrates real-time perception of equipment health status, state-based differentiated task planning, forward-looking verification of scheduling schemes, and closed-loop feedback of the execution process to construct an intelligent scheduling system that can proactively adapt to equipment performance fluctuations.

[0042] This invention effectively transforms equipment failure from "post-event remediation" to "pre-event prevention," significantly improving the success rate and reliability of scheduling operations in complex warehousing environments. Furthermore, by avoiding unplanned downtime and reducing task failures, it ensures the overall operational efficiency and stability of the warehousing system, while also relatively extending the service life of the equipment. Attached Figure Description

[0043] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0044] Figure 1 This is a flowchart of the steps of the stacker crane multi-lane track-changing scheduling method of the present invention;

[0045] Figure 2 This is a flowchart of the steps for calculating the performance degradation index in this invention;

[0046] Figure 3This is a flowchart of the steps in this invention to generate the initial trajectory change path and task allocation scheme;

[0047] Figure 4 This is a flowchart illustrating the steps of simulating the initial trajectory change path and task allocation scheme according to the present invention.

[0048] Figure 5 This is a flowchart of the steps for triggering strategy adjustment during the execution of the trajectory change path in this invention;

[0049] Figure 6 This is a flowchart of the steps involved in long-term performance prediction and model adaptation in this invention;

[0050] Figure 7 This is a structural framework diagram of the stacker crane multi-lane track-changing scheduling system of the present invention. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0052] Reference Figure 1 As shown, the present invention provides a multi-lane track-changing scheduling method for stacker cranes, comprising the following steps:

[0053] First, the motor current and vibration signals of the stacker crane's track-changing mechanism are collected in real time. The signals are then transformed in the time and frequency domains to extract features, and a comprehensive performance degradation index is calculated to evaluate the real-time health status level of the stacker crane.

[0054] In this embodiment, the health status of the stacker crane is quantified by analyzing the motor current and vibration signals in real time. This is equivalent to establishing a dynamic health record for each piece of equipment, making it possible to clearly identify which equipment is "sub-healthy" or "fatigued".

[0055] Secondly, based on the real-time health status level, a differentiated task load limit is set for each stacker crane. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology, an initial track change path and task allocation scheme are generated.

[0056] When generating scheduling schemes, tasks are no longer allocated in a "one-size-fits-all" manner. Instead, a task load limit that matches the health level of each device is set, which prevents overload operation from the source.

[0057] Furthermore, the initial trajectory change path and task allocation scheme are simulated. In the simulation, if any stacker is found to be inaccurate in positioning or timed out due to performance degradation, the task load of the stacker is automatically reduced and its path is replanned. This process is iterated until a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stackers is generated.

[0058] In this embodiment, the simulation step is like a "sand table exercise" before actual action. It can expose potential risks that are difficult to find in theoretical planning in advance. For example, whether an aging stacker crane will fail in a complex continuous track change task due to slow response. Through this simulation, the plan can be adjusted in advance to replace the equipment with a simpler and more reliable route and task, thereby ensuring the robustness of the entire scheduling sequence before actual execution.

[0059] Finally, the robust scheduling sequence is sent to the corresponding stacker crane for execution. During the execution process, the actual operating data of the stacker crane is monitored in real time. If the action is slowed down or the task execution time deviates from the expected time, the strategy adjustment is triggered immediately to dynamically reallocate the current task or switch to the backup path to ensure the smoothness of the overall scheduling process.

[0060] Introducing a real-time feedback mechanism during execution means that if unexpected situations arise during execution, the real-time feedback control mechanism can act as an alert escort, quickly intervening and resolving crises through dynamic adjustments to ensure the smooth operation of the entire process.

[0061] Furthermore, referring to Figure 2 As shown in the figure, this embodiment discloses a method for assessing the real-time health status level of a stacker crane, including the following steps: First, the collected mixed signal is decomposed into a series of independent modal components that can reflect different physical sources. This step is similar to classifying a mixed signal, aiming to separate the signals generated by the operation of specific mechanical components such as bearings, guide rails, and gears, while eliminating irrelevant signals from electrical interference or environmental vibration. Subsequently, these valuable modal components are reconstructed into a denoised core signal. The significance of this technical operation is that it improves the signal-to-noise ratio from the data source, ensuring that the subsequent analysis relies on the pure information that best characterizes the health status of the machine body, thereby laying a reliable data foundation for accurate health assessment. Its technical effect is to significantly improve the anti-interference capability and accuracy of condition monitoring.

[0062] After obtaining the clean core signal, the system enters the state comparison stage. The time and frequency domain characteristics of the core signal are precisely matched against a pre-generated standard state feature library encompassing all health stages of the stacker crane, from brand new to severely aged. By calculating the deviation between the current signal and the feature vectors of each standard state in the library, the abstract degree of "unhealthiness" is transformed into a set of specific, quantifiable, multi-dimensional degradation values. This step technically establishes an objective metric, making "health status" no longer a vague qualitative concept, but an indicator that can be precisely graded and compared. Its technical effect lies in achieving refined and digital management of equipment health status, providing a precise decision-making basis for subsequent differentiated scheduling.

[0063] Ultimately, to form a single, clear comprehensive performance degradation indicator to drive scheduling decisions, an arithmetic average was used to fuse the aforementioned multi-dimensional degradation metrics. The mechanism behind this approach is that it follows the principle of "overall trend assessment," assuming that the overall health level of the stacker crane is a concentrated reflection of the state of all its components. By averaging the degradation levels across all dimensions, the potential for random fluctuations in a single indicator can be smoothed out, thus more stably reflecting the overall decline trend of equipment performance. This provides a robust estimate of the equipment's macroscopic health status, making it particularly suitable for assessing the gradual, overall aging of equipment. It avoids overreactions caused by momentary false alarms from individual sensors, ensuring the stability of the scheduling system's decision-making basis.

[0064] In order to transform abstract scheduling principles into executable computational logic, this embodiment refers to... Figure 3 As shown, this paper further discloses a method for generating initial track-changing paths and task allocation schemes based on the task load limit, the current inbound and outbound task queues of each lane, and the warehouse track topology. This includes the following steps: For each independent task in the current inbound and outbound task queue, a path cost analysis is performed based on the warehouse track topology. This cost comprehensively considers key factors affecting efficiency, such as travel distance and the number of switch changes, thereby quantifying the basic resource cost required to execute the task. This path cost is then coupled with the stacker crane's own state attributes—namely, its real-time health status level and current remaining task load capacity—to generate a matching degree sequence between tasks and stacker cranes. The technical mechanism of this step lies in establishing a multi-dimensional decision-making model. It no longer views task requirements or equipment status in isolation but strives to find the optimal combination point between task characteristics and equipment capabilities. Its direct technical effect is to achieve precise and rational task allocation, avoiding the allocation of complex tasks beyond the capabilities of poorly performing stacker cranes, thereby improving the scientific nature and success rate of task scheduling.

[0065] After obtaining the matching sequence that characterizes the task-equipment compatibility, the load balancing decision-making phase begins. Based on this sequence, a differentiated task allocation strategy is implemented: remote tasks with the highest path cost are prioritized for assignment to the stacker crane with the best health status, while medium-range tasks with moderate path cost are assigned to the next best health status. This strategy operates by acknowledging the inherent capability differences between different devices due to varying health statuses. The most powerful devices are assigned the most time-consuming remote tasks, fully utilizing their high reliability to ensure efficient completion of these long-cycle tasks. Meanwhile, moderately difficult tasks are assigned to slightly less powerful devices, effectively utilizing their remaining capacity while avoiding pushing them to potentially malfunctioning extreme conditions. This differentiated approach aims not merely for task completion, but by guiding devices in different states to execute tasks matching their capabilities, the estimated task execution time of all stacker cranes tends to be balanced. This eliminates the risk of individual devices becoming efficiency bottlenecks due to task overload at the system level, ensuring the smoothness and stability of the overall workflow and maximizing system throughput efficiency.

[0066] Specifically, in the process of implementing track change scheduling, on the one hand, the safety of path execution needs to be considered. Running in a shared track network inevitably faces resource conflicts, especially in critical bottleneck areas such as track intersections and track switching sections. In this embodiment, to solve this problem, conflict detection is proactively performed when generating the initial track change path. The possibility of temporal and spatial overlap between the planned paths of each stacker crane is analyzed. Instead of simply prohibiting passage, two flexible strategies are intelligently adopted: inserting waiting time windows or adjusting the path passage order. The technical mechanism combines abstract path planning with concrete spatiotemporal resource management. By introducing a short time delay or optimizing the passage sequence, potential physical collision risks are transformed into controllable time scheduling problems. This ensures that even under optimal task allocation, the entire scheduling scheme is physically executable, generating a conflict-free track change path sequence. This directly avoids serious operational failures such as system deadlock and equipment collisions, ensuring the continuous and safe operation of the system.

[0067] On the other hand, the system's emergency response capability also needs to be considered. Therefore, this embodiment introduces the concept of emergency capacity. During task allocation, a portion of the load capacity is forcibly reserved for equipment in poor health. The technical mechanism of this design is based on a preventative resource reservation strategy. It acknowledges that in complex warehousing operations, sudden emergency tasks (such as urgent production line orders or high-priority orders) are unavoidable. If the capacity of all stacker cranes, including aging equipment, is fully utilized, then when an emergency task happens to occur in its designated aisle, there will be a lack of rapid localized response capability, potentially requiring complex global rescheduling and causing response delays. Therefore, by reserving a portion of emergency capacity in advance, it is equivalent to setting up strategic reserves at critical locations, ensuring that in the event of an emergency, even if the performance of the stacker crane in this aisle degrades, it can immediately initiate a response within its safe load range. The resulting technical effect is a significant improvement in the system's agility and reliability in responding to emergencies, avoiding global scheduling chaos caused by local emergencies, and achieving a balance between normal high efficiency and emergency response capability.

[0068] Reference Figure 4 As shown, this invention further discloses the process of simulating the initial trajectory change path and task allocation scheme, including the following steps: based on the real-time health status level of the stacker crane, establishing a corresponding motion accuracy attenuation model and response delay model. The technical mechanism lies in quantifying the relatively abstract assessment conclusion of "health status" into specific parameters that directly affect the operating results in the simulation environment—that is, the worse the health status of the stacker crane, the larger the positioning error range and the longer the motion execution time are assigned in the simulation. The technical effect of this step is to break the limitations of traditional simulation that treats all equipment as ideal models, enabling the simulation environment to realistically reflect the inherent tendency of sluggish motion and inaccurate positioning of aging stacker cranes, providing a realistic basis for subsequent risk assessment.

[0069] After establishing a non-idealized equipment model, the simulation environment further simulates the uncertainties of the external environment by constructing a random disturbance field. This disturbance field injects random interferences, such as instantaneous communication delays, changes in the friction coefficient caused by adhesions on the track surface, and power supply voltage fluctuations, into the stacker crane when it performs critical actions, reflecting actual operating conditions. The technical mechanism of this step is to acknowledge that the real operating environment is not a vacuum and contains a large number of unpredictable but persistent microscopic disturbances. By actively introducing these random factors, the consistency between the simulation environment and actual operating conditions is significantly enhanced, thereby exposing potential risks that cannot be detected under ideal conditions.

[0070] Next, simulations were run under stringent conditions, simultaneously applying internal performance degradation and external random disturbances, while continuously monitoring the status parameters of each stacker crane. When a positioning deviation exceeded the safety tolerance or an action completion time exceeded a predetermined standard, the system marked that path point as a fault-tolerant boundary point. The technical mechanism of this step lies in actively exploring the safety boundary of the scheduling scheme under the dual pressure of insufficient equipment performance and external interference through stress testing. Its technical effect is to accurately identify the weakest link in the entire scheduling chain, visualizing these key points that may meet the standards in theoretical calculations but are extremely prone to failure in the real world, providing clear targets for subsequent optimization.

[0071] Finally, for the identified fault-tolerant boundary points, the original path is actively softened, including: inserting pre-deceleration points in sections with high positioning accuracy requirements to sacrifice a small amount of time for higher stability; adding attitude calibration positions before complex trajectory changes to ensure the equipment enters critical operations in optimal condition; and configuring parallel backup paths for critical tasks to provide contingency solutions in case of unforeseen events. The technical mechanism of this step is to shift from a failure-prevention mindset to a fluctuation-tolerant mindset, increasing the flexibility and robustness of the path to accommodate uncertainties in equipment and the environment. The ultimate technical effect is to transform a rigid scheduling scheme that might fail due to minor disturbances into a resilient scheme that can adapt to performance degradation and external interference, thereby significantly improving the success rate and reliability of the scheduling strategy in actual deployment.

[0072] Reference Figure 5 As shown, this invention further discloses an adjustment strategy for the stacker crane during task execution, including the following steps: By deploying monitoring units at key nodes of the track, the stacker crane's position feedback signals and drive motor torque data can be collected in real time, thereby constructing a high-frequency execution state trajectory. Through real-time data acquisition, a data foundation capable of accurate comparison with simulation expectations is created, transforming the actual behavior of the stacker crane into a quantifiable digital trajectory, thus ensuring that any deviation from expectations is detected.

[0073] After obtaining the actual operating trajectory, the system enters the intelligent diagnostic phase, continuously comparing the real-time trajectory with the simulated expected trajectory, which serves as an ideal blueprint. The key technology lies not in capturing deviations at a single moment, but in identifying specific abnormal patterns exhibited by "multiple consecutive trajectory points," such as continuously increasing positional deviations or torque fluctuations at specific frequencies. This pattern-based, rather than single-point threshold-based, judgment mechanism can distinguish between transient disturbances and genuine performance degradation trends, enabling early and accurate warnings of potential operational stutters and preventing system misjudgments and overreactions.

[0074] Once an anomaly is confirmed, a tiered response mechanism is immediately activated. Differentiated handling strategies are adopted based on the severity of the anomaly, reflecting an optimal balance between resource consumption and response effectiveness. Specifically: for minor deviations, only a speed fine-tuning command is issued for compensation—a low-cost online correction aimed at restoring stability without disrupting the overall workflow. For persistent deviations, indicating the problem cannot be resolved by simple fine-tuning, the response level is immediately escalated. On one hand, coordinated avoidance commands are sent to other stacker cranes in the relevant area to create safe operating space for the faulty equipment and prevent cascading accidents. On the other hand, the local path of the current stacker crane is replanned to avoid high-difficulty maneuvers that could exacerbate the fault. The technical advantage of this tiered response is that it effectively isolates risks with minimal system disturbance while ensuring the continuous progress of the overall workflow.

[0075] Finally, after the strategy was adjusted, the recovery status of the stacker crane was continuously monitored. Once it was confirmed that the equipment had returned to a stable operating range, the previously imposed constraints such as cooperative avoidance were gradually lifted, and normal operation was restored. At the same time, all data and handling effects of this anomaly were fed back to the health status assessment model, completing the knowledge loop of learning from an anomaly event. This enabled the continuous iteration and optimization of the health status assessment model, making its predictions of similar anomalies more accurate and its response faster.

[0076] Building upon the aforementioned embodiments, this embodiment further elevates the intelligence level of the track-changing scheduling method from responding to the "current state" to anticipating and adapting to "future changes." This constitutes a complete cycle from data accumulation and trend insight to forward-looking decision-making. By recording the execution logs and performance indicators of each scheduling process, and utilizing time series analysis, the future performance degradation trajectory of each stacker crane's track-changing mechanism is predicted. Each real-time scheduling is treated as a valuable data acquisition experiment, stringing together these discrete operational data into a continuous dataset describing the evolution of equipment health status. This transcends static assessments of the current health status, allowing for insights into the direction and speed of dynamic changes. The management perspective of track-changing scheduling extends from passive response to proactive anticipation, making the health status level no longer merely a description of the current situation, but a predictive indicator that can be updated in advance, thus laying a data foundation for the long-term autonomous adaptation of scheduling strategies.

[0077] Reference Figure 6As shown, to enable the orbital scheduling method to have predictive capabilities, this embodiment records the comprehensive performance degradation index obtained from each evaluation and constructs a time series in chronological order. The technical effect of this step is to transform the originally isolated evaluation points into a continuous data stream that reflects trends, providing structured input for subsequent analysis. Subsequently, a deep trend analysis is performed on this time series, focusing not only on its rate of change (first derivative, i.e., the speed of performance degradation) but also on its acceleration (second derivative, i.e., whether the degradation is accelerating or decelerating). By analyzing these two key dynamic parameters, it is possible to accurately identify whether the equipment is currently in a stable "plateau period," a "turning point" where performance is accelerating, or a "deterioration period" where it is rapidly declining. This dynamic-based stage identification mechanism can more profoundly reveal the essential process of equipment aging, rather than merely remaining at the level of observing surface numerical values.

[0078] After identifying the current degradation stage of the equipment, a crucial operation was initiated: matching performance change patterns within the same stage from historical data. This involved identifying equipment that had previously been at the same stage and analyzing how their performance indicators subsequently evolved. These empirical patterns were then used as a blueprint to predict the future trajectory of the current equipment. Finally, the matched patterns were combined with the equipment's total runtime, as cumulative runtime is a key macroscopic parameter for measuring overall wear and fatigue. Through this dual calibration of the "current stage pattern" and "total service time," the evolution range of performance degradation indicators within a specific future period can ultimately be predicted.

[0079] The technical effect of the above-mentioned technical solution is that it no longer generates an isolated prediction point, but a more reliable prediction range that takes into account individual differences and historical patterns. It can prepare in advance for further performance degradation, dynamically adjust the health status level and corresponding scheduling strategies, thereby achieving true autonomous adaptation to long-term changes in equipment performance, and significantly improving the predictability of the entire warehousing system planning and the stability of operation.

[0080] Reference Figure 7 As shown, in order to implement the above-mentioned track-changing scheduling method, this embodiment also discloses a multi-lane track-changing scheduling system for stacker cranes, characterized in that it includes:

[0081] The status monitoring module is used to collect the motor current and vibration signals of the stacker crane's track-changing mechanism in real time, perform time-frequency domain transformation on the signals to extract features, and calculate a comprehensive performance degradation index to evaluate the real-time health status level of the stacker crane.

[0082] The scheduling and planning module is connected to the status monitoring module and is used to set a differentiated task load limit for each stacker crane according to the real-time health status level. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology map, it generates an initial track change path and task allocation scheme.

[0083] The simulation verification module is connected to the scheduling and planning module and is used to simulate the initial trajectory change path and task allocation scheme. In the simulation, if any stacker crane is found to have inaccurate positioning or timeout due to performance degradation, the scheduling and planning module is automatically triggered to re-plan. Through the iterative process, a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stacker cranes is finally generated.

[0084] The execution control module is communicatively connected to the simulation verification module and the status monitoring module. It is used to send the robust scheduling sequence to the corresponding stacker crane for execution. During the execution process, if the status monitoring module detects an action lag or a continuous deviation of the task execution time from the expected time, it immediately triggers a strategy adjustment to dynamically reallocate the current task or switch to a backup path.

[0085] Specifically, the system also includes a long-term performance prediction module, which records the execution logs and performance indicators of each scheduling process, uses time series analysis methods to predict the future performance degradation trend of each stacker crane's track-changing mechanism, and updates the health status level in advance based on the prediction results, so as to enable the scheduling system to autonomously adapt to long-term changes in equipment performance.

[0086] Obviously, the above embodiments are merely illustrative examples for clear explanation 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 here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A multi-lane track-changing scheduling method for stacker cranes, characterized in that: Includes the following steps: The motor current and vibration signals of the stacker crane's track-changing mechanism are collected in real time. The signals are transformed in the time and frequency domains to extract features, and a comprehensive performance degradation index is calculated to evaluate the real-time health status level of the stacker crane. Based on the real-time health status level, set a differentiated task load limit for each stacker crane. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology, generate an initial track change path and task allocation scheme. The initial trajectory change path and task allocation scheme are simulated. In the simulation, if any stacker crane is found to have inaccurate positioning or timeout due to performance degradation, the task load of the stacker crane is automatically reduced and its path is replanned. This process is iterated until a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stackers is generated. The initial trajectory change path and task allocation scheme are simulated, including the following steps: Based on the real-time health status level of the stacker crane, a corresponding motion accuracy attenuation model and response delay model are established. The stacker crane with a lower health status level is given a larger positioning error range and a longer motion execution time in the simulation. A random disturbance field is constructed in the simulation environment. This disturbance field injects random interference factors that conform to actual working conditions when the stacker crane performs key actions, including instantaneous communication delay, friction coefficient changes caused by track surface attachments, and power supply voltage fluctuations. The simulation is run on the basis of applying performance attenuation and random disturbances to monitor the status parameters of each stacker crane during task execution. When positioning deviation or action completion time exceeds the limit, the path is marked as a fault tolerance boundary point. For the identified fault tolerance boundary points, the original path is softened, including inserting pre-deceleration points in sections with high positioning accuracy requirements, adding attitude calibration positions before complex trajectory change actions, and configuring parallel backup paths for key tasks. A robust scheduling sequence is issued to the corresponding stacker crane for execution. During execution, the actual operating data of the stacker crane is monitored in real time. If a delay or a continuous deviation from the expected task execution time is detected, a strategy adjustment is immediately triggered to dynamically reallocate the current task or switch to a backup path to ensure the smoothness of the overall scheduling process.

2. The stacker crane multi-lane track-changing scheduling method according to claim 1, characterized in that: The system collects motor current and vibration signals of the stacker crane's track-changing mechanism in real time, performs time-frequency domain transformation on the signals to extract features, and calculates a comprehensive performance degradation index, including the following steps: The collected motor current and vibration signals are decomposed into a series of independent modal components reflecting different physical sources. Noise modes that are unrelated to the mechanical motion of the track-changing mechanism are eliminated, and the remaining effective modes are reconstructed into a denoised core signal. The time-domain and frequency-domain features of the core signal are matched with a pre-generated standard state feature library corresponding to different health stages of the stacker crane. By calculating the deviation between the core signal and each standard state feature vector in the feature library, a set of multi-dimensional degradation quantification values ​​are obtained. The arithmetic mean of a set of preliminary degradation metrics across multiple dimensions is calculated, and the resulting average value is used as an indicator of overall performance degradation.

3. The stacker crane multi-lane track-changing scheduling method according to claim 1, characterized in that: Based on the task load limit, the current inbound and outbound task queues of each lane, and the warehouse track topology, an initial track change path and task allocation scheme are generated, including the following steps: For each task in the current inbound / outbound task queue, calculate the path cost between its target location and each aisle entrance based on the warehouse track topology map. Combine the current health status level of each stacker crane and the remaining task load capacity to generate a matching sequence between tasks and stacker cranes. Based on the matching degree sequence, the stacker crane with the best health status is given priority to be assigned the most remote task with the highest path cost, while the stacker crane with the second best health status is assigned the medium-range task with the moderate path cost, so as to ensure that the estimated task execution time of each stacker crane tends to be balanced.

4. The stacker crane multi-lane track-changing scheduling method according to claim 3, characterized in that: When generating the initial track-changing path, the spatiotemporal overlap of the planned paths of each stacker crane at track intersections and track-changing sections is detected in real time. By inserting waiting time windows or adjusting the path passage order, a conflict-free track-changing path sequence is generated.

5. The stacker crane multi-lane track-changing scheduling method according to claim 3, characterized in that: During task allocation, a reserve of no less than 30% of the task load limit is reserved for stacker cranes with a health status level of severe aging. This reserve is only activated when an emergency task occurs in the aisle in which the stacker crane is located.

6. The stacker crane multi-lane track-changing scheduling method according to claim 1, characterized in that: The stacker crane's task execution process also includes: By collecting position feedback signals and drive motor torque data in real time through monitoring units deployed at key track nodes, a high-frequency execution state trajectory is established. The real-time collected execution state trajectory is compared with the expected state trajectory generated by the simulation. When multiple consecutive trajectory points are detected to deviate from the simulation position or torque fluctuations show a specific abnormal pattern, it is determined to be a potential action stutter. A tiered response mechanism is activated based on the severity of the identified abnormal patterns: for minor deviations, compensation is provided via speed fine-tuning commands; for persistent deviations, collaborative avoidance commands are sent to other stacker cranes in the relevant area, and a new local path is replanned for the current stacker crane. After the strategy is adjusted, the status recovery of the stacker crane is continuously monitored. Once it is confirmed that it has returned to a stable operating range, the cooperative avoidance constraints are gradually lifted, and the abnormal data and processing effect are fed back to the health status assessment model for model update.

7. The stacker crane multi-lane track-changing scheduling method according to claim 1, characterized in that: Also includes: Record the execution logs and performance indicators of each scheduling process, use time series analysis to predict the future performance degradation trend of each stacker crane's track-changing mechanism, and update the health status level in advance based on the prediction results, so as to enable the scheduling system to autonomously adapt to long-term changes in equipment performance.

8. The stacker crane multi-lane track-changing scheduling method according to claim 7, characterized in that: Predicting the future performance degradation trend of various stacker crane trajectory changing mechanisms, including: Record the comprehensive performance degradation index obtained in each scheduling evaluation, and construct a time series of the performance degradation index of each stacker crane in chronological order; By performing trend analysis on the time series of performance degradation indicators, the current degradation stage of the equipment can be identified based on its rate of change and acceleration. Based on the identified current degradation stage, the system matches the change patterns of performance degradation indicators within the same stage from historical data, combines the subsequent development trajectory of the pattern with the total runtime of the device, and predicts the evolution range of performance degradation indicators within a specific future period.

9. A stacker crane multi-lane track-changing scheduling system, characterized in that: include: The status monitoring module is used to collect the motor current and vibration signals of the stacker crane's track-changing mechanism in real time, perform time-frequency domain transformation on the signals to extract features, and calculate a comprehensive performance degradation index to evaluate the real-time health status level of the stacker crane. The scheduling and planning module is connected to the status monitoring module and is used to set a differentiated task load limit for each stacker crane according to the real-time health status level. Based on the task load limit, the current inbound and outbound task queues of each aisle, and the warehouse track topology map, it generates an initial track change path and task allocation scheme. The simulation verification module is connected to the scheduling and planning module and is used to simulate the initial track change path and task allocation scheme. In the simulation, if any stacker crane is found to have inaccurate positioning or timeout due to performance degradation, the scheduling and planning module is automatically triggered to re-plan. Through the iterative process, a conflict-free and robust scheduling sequence that satisfies the capability constraints of all stacker cranes is finally generated. The initial trajectory change path and task allocation scheme are simulated, including the following steps: Based on the real-time health status level of the stacker crane, a corresponding motion accuracy attenuation model and response delay model are established. The stacker crane with a lower health status level is given a larger positioning error range and a longer motion execution time in the simulation. A random disturbance field is constructed in the simulation environment. This disturbance field injects random interference factors that conform to actual working conditions when the stacker crane performs key actions, including instantaneous communication delay, friction coefficient changes caused by track surface attachments, and power supply voltage fluctuations. The simulation is run on the basis of applying performance attenuation and random disturbances to monitor the status parameters of each stacker crane during task execution. When positioning deviation or action completion time exceeds the limit, the path is marked as a fault tolerance boundary point. For the identified fault tolerance boundary points, the original path is softened, including inserting pre-deceleration points in sections with high positioning accuracy requirements, adding attitude calibration positions before complex trajectory change actions, and configuring parallel backup paths for key tasks. The execution control module is communicatively connected to the simulation verification module and the status monitoring module. It is used to send the robust scheduling sequence to the corresponding stacker crane for execution. During the execution process, if the status monitoring module detects an action lag or a continuous deviation of the task execution time from the expected time, it immediately triggers a strategy adjustment to dynamically reallocate the current task or switch to a backup path.

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