A shuttle garage automation scheduling control optimization method and system

Abstract

The application provides a shuttle garage automatic scheduling control optimization method and system, relates to the shuttle garage scheduling control technical field, receives the operation information of the equipment unit, calibrates the operation state of the equipment unit according to the event report, and actively speculates the estimated operation state of the equipment unit according to the state updated after the last calibration, the current task instruction and the preset kinematics model during the two event reports. Finally, according to the calibrated operation state and / or the estimated operation state, the subsequent task allocation, path planning and timing coordination planning of the equipment unit are adjusted, so that the intermittent delay or data packet loss of the local wireless communication link during the high-load operation in the prior art is effectively solved. The time difference between the equipment state information received by the central control system and the actual situation exists, and then the problems of suboptimal scheduling decision, increased equipment idling, task conflict and even system 'negative optimization' are caused.

Description

Technical Field

[0001] This application relates to the field of shuttle parking garage scheduling and control technology, and more specifically, to an optimization method and system for automated scheduling and control of shuttle parking garages. Background Technology

[0002] With the rapid development of intelligent parking systems, automated shuttle parking garages, as an important component of modern urban parking solutions, face increasingly severe challenges in terms of operational efficiency and reliability. Especially when multiple transporters, elevators, and a large number of vehicles need to be accessed simultaneously within the garage, traditional control logic often struggles to achieve real-time optimization of equipment action sequences. This frequently leads to increased equipment idling time and even task conflicts. The root cause lies in the possibility of intermittent delays or data packet loss in local wireless communication links during high-load operation, resulting in a slight time lag between the equipment status information received by the central control system and the actual situation of the equipment. This inconsistency can mislead the central system into making suboptimal scheduling decisions, or even cause its optimization modules to produce counterproductive effects, thereby impacting the overall operational efficiency of the parking garage and the user experience.

[0003] During peak operation of automated shuttle parking garages, the central dispatch system, when handling high-concurrency tasks, experiences a slight time lag between the received status information of some devices (such as shuttle cars) and their actual status due to intermittent delays or packet loss in local wireless communication links. This data inconsistency causes the central system to allocate tasks and plan routes based on an inaccurate "global status table," frequently resulting in suboptimal scheduling decisions, such as assigning vehicles to suboptimal parking spaces or sending instructions to devices that do not match their actual status. Existing error handling mechanisms often attribute such problems to momentary response delays and perform simple retries or fine-tuning. However, due to fundamental flaws in the decision-making basis, these measures fail to effectively solve the problem and may instead lead to ineffective loops, consuming computational resources and communication bandwidth. Even worse, the system's built-in adaptive optimization module may make "negative optimization" decisions when receiving biased input data, further reducing the utilization rate of devices and task completion efficiency in local areas, leading to a decrease in overall throughput, and making it difficult to accurately diagnose the root cause of the problem. Summary of the Invention

[0004] This application provides an automated scheduling and control optimization method and system for shuttle parking garages, aiming to solve the problems of inaccurate equipment status information caused by communication delays or data loss during peak operation of existing automated shuttle parking garages, which in turn leads to suboptimal scheduling decisions, increased equipment idling, task conflicts, and system "negative optimization".

[0005] On the one hand, this application provides an optimization method for automated scheduling and control of shuttle parking garages, including:

[0006] The system receives operational information from equipment units within the shuttle parking garage. These equipment units are automated execution devices that perform vehicle handling, lifting and conveying, and parking space management within the shuttle parking garage. The operational information includes event reports sent by the equipment units when they identify critical event nodes and online signals sent by the equipment units when they do not identify critical event nodes. The online signals are used to characterize the normal communication and online status of the equipment units. The event reports include event type, timestamp, and critical status information.

[0007] Based on the event reports in the received operational information, the operational status of the device unit is calibrated; the operational status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit.

[0008] During the period between two event reports, based on the operating status after the last calibration update, the currently executing task instructions, and the preset kinematic model, the operating status of the device unit at the current moment is actively inferred to obtain the estimated operating status;

[0009] Based on the calibrated operating status and / or the estimated operating status, adjust the subsequent task allocation, path planning, and timing coordination planning of the equipment unit.

[0010] Optionally, the key event nodes are the completion of the vehicle storage and retrieval task performed by the equipment unit, the state switching, and the abnormal operation triggering nodes; the event types are arrival events, vehicle storage and retrieval events, start-stop events, fault events, and parking space occupancy / release events.

[0011] Optionally, the step of calibrating the operating status of the device unit based on the event reports in the received operating information includes:

[0012] The system continuously monitors and parses event reports in the operation information through the wireless communication interface. Using the timestamps and key status information carried in the event reports as the reference data source, it performs overlay calibration and updates on the locally maintained operation status corresponding to the device unit.

[0013] Optionally, the step of speculating on the operating state of the device unit at the current moment to obtain the estimated operating state includes:

[0014] The state estimator is activated. It uses the operating state of the device unit after the last calibration update as the initial benchmark, combines the expected motion trend implied by the task instruction currently being executed by the device unit, and the preset kinematic model used to reflect the physical motion capability of the device unit, and recursively calculates the operating state of the device unit at the current moment to obtain the estimated operating state.

[0015] Optionally, after the step of adjusting the subsequent task allocation, path planning, and timing coordination planning of the equipment unit based on the calibrated operating status and / or the estimated operating status, the following steps are included:

[0016] If no operational information is received from the monitoring equipment unit within a preset time, or if the deviation between the estimated operational status of the equipment unit and the preset expected status exceeds a preset threshold, an abnormal handling process is triggered.

[0017] Optionally, if the monitoring device unit does not receive operational information from the device unit within a preset time, or if the deviation between the estimated operational status of the device unit and the preset expected status exceeds a preset threshold, the steps for triggering the abnormal handling process include:

[0018] Each device unit is configured with a report receiving timer and a status deviation evaluator, the status deviation evaluator being used to evaluate the deviation between the estimated operating status of the device unit and the preset expected status;

[0019] Based on the current task type and physical area of ​​the device unit, the timeout threshold of the report receiving timer and the deviation judgment threshold of the state deviation evaluator are dynamically adjusted.

[0020] Based on the dynamically adjusted timeout threshold and deviation judgment threshold, the reception status of the operation information and the deviation between the estimated operation status of the device unit and the preset expected status are classified to obtain deviation classification results. The deviation classification results include communication abnormality, motion abnormality or composite abnormality.

[0021] Based on the deviation classification results and the current task priority of the device unit, the priority of deviation processing is determined;

[0022] Based on the priority of the deviation handling, the corresponding handling strategy is executed. The handling strategy includes adjusting the scheduling safety margin, activating the backup plan, or requesting the equipment unit to immediately report the detailed status.

[0023] Optionally, the step of determining the priority of deviation processing based on the deviation classification result and the current task priority of the device unit includes:

[0024] The system receives deviation classification results from multiple device units, performs correlation analysis on the deviation classification results of multiple device units, and obtains correlation analysis results. The correlation analysis includes identifying whether there is shared resource competition, temporal dependence, or spatial proximity relationship between deviations.

[0025] Based on the correlation analysis results, a deviation influence network is constructed, which describes the degree of mutual influence and potential chain reactions between different deviations;

[0026] Based on the deviation impact network, the impact weight of each deviation on the overall scheduling efficiency and security is evaluated, and the impact weight takes into account both the direct impact of the deviation and the indirect impact generated through chain reactions.

[0027] The priority of deviation processing is determined based on the influence weight and the current task priority of the device unit.

[0028] Optionally, after the step of determining the priority of deviation processing based on the influence weight and the current task priority of the device unit, the method further includes:

[0029] Identify competition for limited resources within the garage from multiple high-priority deviation handling tasks, including alternative routes, specific elevators, or critical scheduling command channels;

[0030] Based on the competition relationship between the high-priority deviation processing tasks and resources, a resource contention graph is constructed. The nodes in the resource contention graph represent the high-priority deviation processing tasks, and the edges represent the competition relationship between the tasks for shared resources.

[0031] Based on the resource contention graph, resource conflict detection is performed on the high-priority deviation processing task to obtain the resource conflict detection result;

[0032] Based on the resource conflict detection results, a resource allocation strategy is initiated, which combines the priority, influence weight, and resource availability of each task.

[0033] Based on the resource allocation strategy, the execution order of tasks is adjusted or alternative resources are allocated to ensure the priority execution of critical tasks and minimize the negative impact of resource competition on overall scheduling efficiency.

[0034] Optionally, after the step of adjusting the execution order of tasks or allocating alternative resources according to the resource allocation strategy to ensure the priority execution of critical tasks and minimize the negative impact of resource contention on overall scheduling efficiency, the following steps are included:

[0035] Continuously monitor the operating status of equipment units and the quality of communication links to obtain monitoring results;

[0036] Based on the monitoring results, new equipment failures, communication link degradation, or abnormal task progress are identified, and the identification results are obtained.

[0037] Based on the identification results, the effectiveness of the allocated resources is reassessed to obtain the assessment results;

[0038] Based on the assessment results, the resource allocation strategy will be restarted.

[0039] On the other hand, this application provides an automated scheduling and control optimization system for shuttle parking garages, the system comprising:

[0040] The information receiving module is used to receive the operation information of the equipment units in the shuttle garage. The equipment units are automated execution equipment that performs vehicle handling, lifting and conveying, and parking space management in the shuttle garage. The operation information includes event reports sent by the equipment units when key event nodes are identified and online signals sent by the equipment units when key event nodes are not identified. The event reports include event type, timestamp, and key status information.

[0041] The status calibration module is used to calibrate the operating status of the device unit based on the event reports in the received operating information; the operating status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit.

[0042] The state prediction module is used to actively predict the operating state of the device unit at the current moment between two event reports, based on the operating state after the last calibration update, the currently executed task instructions, and the preset kinematic model, and obtain the estimated operating state.

[0043] The scheduling and adjustment module is used to adjust the subsequent task allocation, path planning, and timing coordination planning of the equipment unit based on the calibrated operating status and / or the estimated operating status.

[0044] This application discloses an automated scheduling and control optimization method and system for shuttle parking garages. It receives operational information from equipment units and calibrates their operational status based on event reports. Simultaneously, between two event reports, it proactively infers the estimated operational status of the equipment units based on the previously calibrated and updated status, current task instructions, and a preset kinematic model. Finally, based on the calibrated and / or estimated operational status, it adjusts the subsequent task allocation, path planning, and timing coordination planning for the equipment units. This method effectively solves the problem in existing technologies where intermittent delays or data packet loss in local wireless communication links during high-load operation lead to a time lag between the equipment status information received by the central control system and the actual situation, resulting in suboptimal scheduling decisions, increased equipment idling, task conflicts, and even system "negative optimization." Through real-time calibration and proactive inference mechanisms, this application ensures that the scheduling system always makes decisions based on data closest to the actual equipment status, significantly improving the accuracy and real-time performance of scheduling decisions. This effectively reduces equipment idling time, minimizes task conflicts, improves the overall operational efficiency and reliability of the shuttle parking garage, and optimizes the user experience. Attached Figure Description

[0045] To illustrate this application more clearly, the accompanying drawings used in the embodiments will be briefly described below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0046] Figure 1 The diagram above illustrates a flowchart of an optimization method for automated scheduling and control of a shuttle parking garage.

[0047] Figure 2 The diagram above illustrates a structural schematic of an automated scheduling and control optimization system for a shuttle parking garage.

[0048] Figure reference numerals: 100, Shuttle Garage Automated Scheduling Control Optimization System; 10, Information Receiving Module; 20, Status Calibration Module; 30, Status Inference Module; 40, Scheduling Adjustment Module. Detailed Implementation

[0049] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0050] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] Traditional automated shuttle parking garages, when handling high-concurrency tasks, experience a slight time lag between the device status information received by the central dispatch system and the actual device status due to intermittent delays or packet loss in local wireless communication links. This inconsistency can mislead the central system into making suboptimal dispatching decisions, or even cause its optimization modules to have adverse effects, thereby impacting the overall operational efficiency and user experience of the parking garage.

[0052] like Figure 1 The diagram illustrates a flowchart of an automated scheduling and control optimization method for shuttle parking garages. This application proposes an automated scheduling and control optimization method for shuttle parking garages, comprising:

[0053] S10, Receive the operation information of the equipment unit in the shuttle garage. The equipment unit is an automated execution device in the shuttle garage that performs vehicle handling, lifting and conveying, and parking space management. The operation information includes an event report sent by the equipment unit when a key event node is identified and an online signal sent by the equipment unit when no key event node is identified. The online signal is used to characterize the normal communication and online status of the equipment unit. The event report includes the event type, timestamp, and key status information.

[0054] In this context, "equipment unit" refers to all the hardware devices within the shuttle parking garage that perform automated tasks. These include shuttles responsible for horizontal vehicle transport, elevators for vertical transport, and sensors and actuators for monitoring and controlling parking space status. These equipment units work together to complete vehicle storage, retrieval, and scheduling. Operational information is the data stream of information about the equipment unit's status that it sends to the central control system. It includes two main forms: event reports and online signals. Event reports are detailed information proactively reported by the equipment unit when it completes a specific action, experiences a significant change in status, or detects an anomaly, such as vehicle arrival, task completion, or fault occurrence. Online signals are short heartbeat packets periodically sent by the equipment unit to indicate that its communication link is normal and it is online, even without critical events.

[0055] S20, calibrate the operating status of the device unit based on the event reports in the received operating information; the operating status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit.

[0056] The operating status is a comprehensive description of the current state of the equipment unit, including its precise spatial pose within the garage (e.g., X, Y, Z coordinates and attitude), motion parameters (e.g., speed, acceleration), the progress of the current task, the communication status with the central system (e.g., signal strength, connection stability), and whether any faults exist.

[0057] S30, during the period between two event reports, based on the operating status after the last calibration update, the currently executing task instructions, and the preset kinematic model, actively infers the operating status of the device unit at the current moment and obtains the estimated operating status;

[0058] The preset kinematic model is a pre-established mathematical model that describes the physical motion characteristics of the equipment unit. This model includes parameters such as the equipment unit's maximum speed, acceleration, deceleration, and turning radius, as well as its motion patterns under different loads and operating conditions. Using this model, the equipment unit's trajectory and state changes over a future period can be predicted based on its task instructions.

[0059] S40, based on the calibrated operating status and / or the estimated operating status, adjust the subsequent task allocation, path planning, and timing coordination planning of the equipment unit.

[0060] The automated scheduling and control optimization method for shuttle parking garages in this application first requires receiving operational information from the equipment units within the shuttle parking garage. These equipment units can be shuttle cars, elevators, transport robots, etc., responsible for performing automated tasks such as vehicle handling, lifting and conveying, and parking space management. Operational information consists of data about the equipment unit's own status sent to the central control system. This information can include event reports sent by the equipment unit when a critical event node is detected, and online signals sent by the equipment unit when no critical event node is detected. Online signals primarily characterize whether the equipment unit's communication is normal and whether it is online. Event reports are more detailed, typically including the event type (e.g., arrival event, vehicle retrieval event, fault event), timestamp (time of event occurrence), and key status information (e.g., current equipment location, task completion status). For example, when a shuttle car moves a vehicle to the target parking space and completes parking, it generates an event report containing the "arrival event" type, the current timestamp, and the precise parking location, and sends it to the central system.

[0061] Upon receiving operational information, the system calibrates the operational status of the equipment unit based on the event reports within that information. The operational status is a comprehensive description of the equipment unit's current condition, including its spatial pose, motion parameters, task execution progress, communication status, and fault status. For example, if the central system previously recorded a shuttle's position as point A, but receives an event report that the shuttle completed its task at point B, it will immediately calibrate the shuttle's position to point B. This calibration mechanism ensures the central system's accurate understanding of the actual status of the equipment unit.

[0062] During the period between two event reports, the state of the equipment unit may continuously change due to ongoing movement or task execution. To bridge the information gap between event reports, this application proactively infers the operating state of the equipment unit at the current moment based on the operating state after the last calibration update, the currently executing task instructions, and a preset kinematic model, thus obtaining an estimated operating state. For example, if a shuttle reports an event at time T1, its state is calibrated. Between T1 and T2, the shuttle is performing a transport task from point C to point D. Using the calibrated state at time T1 as a starting point, combined with the transport task instructions (the target is point D) and a preset shuttle kinematic model (e.g., maximum speed, acceleration), the application infers the possible position and speed of the shuttle at time T2, thereby obtaining an estimated operating state.

[0063] Finally, based on the calibrated and / or predicted operating status, the subsequent task allocation, path planning, and timing coordination planning of the equipment units will be adjusted. For example, if calibration or prediction indicates that a lift is about to become idle and its location is suitable for lifting the next vehicle, the task will be prioritized for that lift. Simultaneously, the travel path of the equipment unit will be dynamically adjusted based on its real-time status to avoid potential congestion areas, and timing coordination with other equipment units will be implemented to ensure the smooth operation of the entire garage.

[0064] The automated scheduling and control optimization method for shuttle parking garages proposed in this application effectively overcomes the decision-making defects caused by information lag and inaccuracy in traditional scheduling systems by introducing a real-time calibration and proactive prediction mechanism for the operating status of equipment units. Traditional methods often rely on limited status information periodically reported by equipment units, or only update the status when critical events occur. This can easily lead to a discrepancy between the central control system's perception of the actual equipment status and the actual situation during high-load operation. This discrepancy causes the central system to make task allocation and path planning based on an inaccurate "global status table," resulting in frequent suboptimal scheduling decisions, such as assigning vehicles to non-optimal parking spaces or sending instructions to equipment that do not match their actual status, thereby affecting the overall operating efficiency and user experience of the parking garage.

[0065] This application achieves refined management of equipment status information by receiving operational information from equipment units and distinguishing between event reports and online signals. Event reports provide accurate status snapshots at the time of critical events, used for comprehensive calibration updates of the equipment unit's operational status, ensuring the accuracy of core status information. Meanwhile, online signals are used to continuously monitor the communication status of equipment units and promptly detect communication anomalies. More importantly, between two event reports, this application introduces an active status inference mechanism based on the operational status after the previous calibration update, current task instructions, and a preset kinematic model. This mechanism enables the central system to obtain the estimated operational status of equipment units even without real-time event reports, thus bridging information gaps and providing a more continuous and real-time view of equipment status.

[0066] By combining calibration and prediction, the central dispatch system can obtain more accurate and real-time equipment operating status. Based on this high-precision status information, the system can more accurately adjust subsequent task allocation, path planning, and timing coordination planning for equipment units. For example, during task allocation, the system can select the optimal equipment to execute tasks based on the real-time location and estimated idle time of each equipment unit, avoiding idle time and resource waste. During path planning, the system can dynamically adjust driving paths based on the real-time movement status of equipment units and the surrounding environment, avoiding potential conflict points. During timing coordination planning, the system can optimize the execution sequence of tasks based on the estimated arrival time of each equipment unit, ensuring the smooth operation of the entire garage.

[0067] Compared with existing technologies, the core innovation of this application lies in its ability to "real-time perceive" and "proactively predict" the operating status of equipment units. Traditional systems, when faced with intermittent delays or packet loss in local wireless communication links, often can only perform simple retries or fine-tuning. However, due to fundamental flaws in the decision-making basis, these measures cannot effectively solve the problem and may even fall into ineffective loops. This application, by combining state calibration and state inference, constructs a more robust and accurate equipment state management mechanism. This not only improves the accuracy and efficiency of scheduling decisions but also effectively avoids the generation of "negative optimization" decisions, thereby significantly improving the overall throughput and operational reliability of the shuttle garage.

[0068] In some embodiments, the key event nodes are the completion of the device unit's action to perform the vehicle storage and retrieval task, the state switching, and the abnormal operation triggering node; the event types are arrival events, vehicle storage and retrieval events, start-stop events, fault events, and parking space occupancy / release events.

[0069] Specifically, critical event nodes refer to specific moments or conditions during the execution of a device's core tasks that significantly impact its operational status, task progress, or system decisions. These nodes include, but are not limited to, the completion of an action by the device (e.g., a vehicle being moved to its designated location, an elevator reaching a specified floor), the switching of the device from one operating state to another (e.g., from idle to task execution, or from running to stopped), and any unexpected or abnormal situations that occur during the device's operation (e.g., sensor failure, communication interruption, mechanical jamming). By identifying these critical event nodes, the system can ensure that event reports are received when status information is most needed, thereby improving the timeliness and accuracy of status calibration.

[0070] The event types categorize the specific situations represented by these key event nodes. A arrival event typically refers to a device unit completing a predetermined movement or operation and reaching its target location; a vehicle storage / retrieval event specifically refers to a vehicle successfully stored or retrieved from a parking space; a start / stop event reflects a change in the operating state of a device unit from stationary to moving or from moving to stationary; a fault event indicates that a device unit detects an abnormality in itself or its environment, which may affect its normal operation; and a parking space occupancy / release event is directly related to the management of parking space resources, indicating changes in the parking space status. These clearly defined event types enable the system to perform refined processing of received event reports, understand the nature of the events, and take appropriate scheduling or control measures accordingly.

[0071] Through the above technical solution, this application can more accurately and timely grasp the operating status of equipment units within the shuttle garage. Clearly defined key event nodes and event types allow the system to focus on information crucial for scheduling decisions, avoiding invalid or redundant data processing and improving information acquisition efficiency. This not only enhances the system's ability to perceive the actual operating status of equipment units and reduces the risk of scheduling errors due to delayed or inaccurate information, but also provides strong support for early detection and rapid response to abnormal situations, thereby significantly improving the reliability and safety of the entire shuttle garage's automated scheduling and control.

[0072] In some embodiments, the step of calibrating the operating state of the device unit based on the event reports in the received operating information includes:

[0073] The system continuously monitors and parses event reports in the operation information through the wireless communication interface. Using the timestamps and key status information carried in the event reports as the reference data source, it performs overlay calibration and updates on the locally maintained operation status corresponding to the device unit.

[0074] The system continuously monitors and parses event reports from operational information via a wireless communication interface. The scheduling and control system, through its built-in wireless communication module, continuously receives event reports from various equipment units within the shuttle garage. These event reports are generated and sent instantly when a equipment unit identifies a critical event node, ensuring real-time data transmission. Upon receiving the report, the system parses it, extracting the event type, timestamp, and key status information.

[0075] Furthermore, using the timestamps and key status information carried in the event reports as the baseline data source means that the system regards this data, which is directly obtained from the device units and has clear time stamps and specific status descriptions, as the most authoritative and accurate current status information. This information directly reflects the actual physical state and task execution status of the device unit at a specific moment, such as its spatial pose, motion parameters, task execution progress, communication status, and fault status.

[0076] Therefore, a comprehensive calibration update is performed on the locally maintained operating status corresponding to the equipment unit. After receiving and parsing a new event report, the scheduling and control system uses the baseline data source in the report to directly replace or update the old operating status data maintained internally for that equipment unit. This comprehensive update mechanism ensures that the system's understanding of the equipment unit's operating status is always consistent with the latest actual situation of the equipment unit, avoiding scheduling decision deviations caused by data lag or inaccuracy.

[0077] Through the aforementioned technical solutions, the dispatch and control system can achieve precise and real-time calibration of the operating status of equipment units within the shuttle depot. This event-report-based overlay update mechanism significantly improves the accuracy and response speed of the system's perception of equipment unit status, effectively avoiding dispatching errors and safety hazards caused by delayed or inconsistent status information. Therefore, it provides more reliable and efficient data support for the automated dispatch and control of the entire shuttle depot, thereby improving overall operational efficiency and safety.

[0078] In some embodiments, the step of inferring the operating state of the device unit at the current moment to obtain the estimated operating state includes:

[0079] The state estimator is activated. It uses the operating state of the device unit after the last calibration update as the initial benchmark, combines the expected motion trend implied by the task instruction currently being executed by the device unit, and the preset kinematic model used to reflect the physical motion capability of the device unit, and recursively calculates the operating state of the device unit at the current moment to obtain the estimated operating state.

[0080] The state predictor can be understood as a set of software modules or algorithms specifically designed to predict the future or current state of a device unit. This predictor is designed to provide high-precision estimates of the device unit's operating state using limited real-time data and a pre-defined physical model.

[0081] In practical applications, the operating status of the device unit after the last calibration update is used as the initial baseline to provide a reliable starting point for status prediction. Since the calibrated operating status is corrected based on actual event reports, it has high accuracy and can effectively avoid the accumulation of errors during the prediction process.

[0082] Furthermore, by incorporating the expected movement trends implied by the task instructions currently being executed by the device unit, the system considers the type of task currently assigned to the device unit and its corresponding standard operating procedures when performing state inference. For example, if the device unit is performing an "inbound" task, its expected movement trends might include a series of actions such as acceleration, constant speed driving, deceleration, and precise stopping. This expected movement trend provides important contextual information for state inference, helping to narrow down the inference range and improve accuracy.

[0083] Furthermore, the pre-defined kinematic model used to reflect the physical motion capability of the equipment unit can be understood as a set of mathematical equations or algorithms that describe how the spatial pose, velocity, acceleration, and other motion parameters of the equipment unit change over time under different control commands. This model is usually established based on the physical properties of the equipment unit, such as its mechanical structure, drive system characteristics, and load conditions, with the aim of accurately simulating the actual motion behavior of the equipment unit.

[0084] Through recursive calculation, the state predictor can continuously and iteratively update the operating state of the device unit within the time interval between two event reports, based on the initial baseline, expected motion trend, and kinematic model, thereby obtaining the predicted operating state at any current moment.

[0085] Through the above technical solution, this application can significantly improve the real-time performance and accuracy of estimating the operating status of equipment units within a shuttle parking garage. Between two event reports, the system is no longer in a "blind spot" but can proactively predict based on multi-source information fusion, effectively bridging the gap in data updates. This allows the scheduling system to detect potential anomalies earlier, such as deviations from expected equipment trajectories or task delays, providing a valuable time window for timely intervention and adjustment. Furthermore, the high-precision estimated operating status provides more refined and reliable input for subsequent task allocation, path planning, and time-series collaborative planning, thereby improving the overall operational efficiency and safety of the shuttle parking garage system.

[0086] In some embodiments, after the step of adjusting the subsequent task allocation, path planning, and timing coordination planning of the device unit based on the calibrated operating state and / or the estimated operating state, the following is included:

[0087] If no operational information is received from the monitoring equipment unit within a preset time, or if the deviation between the estimated operational status of the equipment unit and the preset expected status exceeds a preset threshold, an abnormal handling process is triggered.

[0088] Specifically, monitoring the operational information of equipment units involves continuously monitoring the communication status of each unit and its reported data stream. Event reports are key information proactively sent by equipment units when completing specific actions or switching states, while online signals are used to periodically confirm the normality and online status of the equipment unit's communication links. The preset time can be understood as the maximum interval at which the system expects to receive equipment unit operational information under normal circumstances. This can be set based on the equipment unit's task cycle, communication protocol timeout settings, or empirical values. If no event report or online signal is received within this preset time, it indicates a possible communication failure or equipment unit downtime.

[0089] Furthermore, the estimated operating state of a device unit is the current state inferred by the system based on kinematic models and task instructions, while the preset expected state is the ideal state that the system expects the device unit to reach based on the current task instructions and scheduling plan. For example, if a shuttle is instructed to move to a parking space, its expected state is to arrive at the parking space and complete the storage / retrieval action. A deviation exceeding a preset threshold means there is a significant difference between the actual operation of the device unit (reflected by the estimated state) and the expected operation planned by the system, which may indicate that the device unit has experienced motion abnormalities, jamming, or malfunctions. The preset threshold can be dynamically configured according to the type of device unit, motion accuracy requirements, and the fault tolerance of the task. Once any of the above conditions is met, an anomaly handling process is triggered. This process aims to classify detected anomalies, prioritize them, and execute corresponding processing strategies.

[0090] Through the above technical solution, this application can significantly improve the robustness and safety of the automated scheduling and control system for shuttle parking garages. By introducing a proactive anomaly monitoring mechanism, this solution can promptly detect communication or motion anomalies in equipment units, avoiding scheduling errors and potential risks caused by missing information or state deviations. Compared to the traditional method that relies solely on equipment units actively reporting faults, this solution can identify potential problems earlier, thus gaining valuable response time for the system. This allows the scheduling system to adjust strategies, activate backup plans, or intervene more quickly, effectively reducing the probability of equipment conflicts, task failures, and safety incidents, ensuring the stable and efficient operation of the shuttle parking garage.

[0091] In some embodiments, if the monitoring device unit does not receive operational information from the device unit within a preset time or the deviation between the estimated operational state of the device unit and the preset expected state exceeds a preset threshold, the steps for triggering the abnormal handling process include:

[0092] Each device unit is configured with a report receiving timer and a status deviation evaluator, the status deviation evaluator being used to evaluate the deviation between the estimated operating status of the device unit and the preset expected status;

[0093] Based on the current task type and physical area of ​​the device unit, the timeout threshold of the report receiving timer and the deviation judgment threshold of the state deviation evaluator are dynamically adjusted.

[0094] Based on the dynamically adjusted timeout threshold and deviation judgment threshold, the reception status of the operation information and the deviation between the estimated operation status of the device unit and the preset expected status are classified to obtain deviation classification results. The deviation classification results include communication abnormality, motion abnormality or composite abnormality.

[0095] Based on the deviation classification results and the current task priority of the device unit, the priority of deviation processing is determined;

[0096] Based on the priority of the deviation handling, the corresponding handling strategy is executed. The handling strategy includes adjusting the scheduling safety margin, activating the backup plan, or requesting the equipment unit to immediately report the detailed status.

[0097] Specifically, a report receiving timer and a state deviation evaluator are configured for each device unit. The report receiving timer is used to continuously track the time interval between the device unit sending event reports or online signals to determine whether there is a communication interruption or delay; the state deviation evaluator is responsible for comparing the difference between the estimated operating state of the device unit and the system's preset expected state in real time, such as spatial pose, motion parameters, etc., and quantifying this deviation.

[0098] Furthermore, the timeout threshold of the report receiving timer and the deviation judgment threshold of the status deviation evaluator are dynamically adjusted based on the current task type and physical area of ​​the device unit. For example, when the device unit is performing a high-priority task (such as emergency vehicle retrieval) or is in a critical area (such as a main road or elevator entrance), the timeout threshold and deviation judgment threshold are set to more stringent values ​​to ensure a rapid response to potential anomalies; while when the device unit is performing a low-priority task or is in a non-critical area, these thresholds can be appropriately relaxed to reduce unnecessary alarms.

[0099] Therefore, based on the dynamically adjusted timeout threshold and deviation judgment threshold, the reception status of operational information and the deviation between the estimated operating status of the device unit and the preset expected status are classified to obtain deviation classification results. These deviation classification results can include communication anomalies (e.g., no online signal or event report received for an extended period), motion anomalies (e.g., excessive deviation between estimated pose and expected pose), or combined anomalies (e.g., simultaneous existence of communication and motion anomalies). This classification helps the system more accurately identify the nature of the anomalies.

[0100] Based on this, the priority of deviation handling is determined according to the deviation classification results and the current task priority of the device unit. For example, for device units located in critical areas and performing high-priority tasks, the priority of their communication or motion abnormalities will be higher than that of device units located in non-critical areas and performing low-priority tasks.

[0101] Finally, based on the priority of deviation handling, the corresponding processing strategy is executed. These processing strategies may include adjusting the scheduling safety margin, such as reserving a larger safety distance or time window for the affected equipment unit or its surrounding area; activating backup plans, such as switching to a backup path, activating backup equipment, or adjusting task allocation; or requesting the equipment unit to immediately report detailed status to obtain more comprehensive diagnostic information.

[0102] Through the above technical solutions, this application significantly improves the ability of the automated scheduling and control system for shuttle parking garages to detect, classify, and handle equipment unit anomalies. Dynamically adjusted timeout and deviation judgment thresholds allow the system to adaptively adjust anomaly judgment criteria based on the specific operating conditions (task type, physical area) of the equipment unit, effectively reducing false alarm rates and ensuring timely response to critical anomalies. The introduction of deviation classification results enables the system to more accurately identify the nature of anomalies, providing a basis for subsequent targeted processing. The deviation processing priority determination mechanism, combined with task priority, ensures that limited system resources are prioritized for resolving anomalies that have the greatest impact on overall scheduling efficiency and safety, avoiding resource waste. Finally, diversified processing strategies (adjusting scheduling safety margins, activating backup plans, requesting detailed status) allow the system to take flexible and efficient countermeasures based on the type and priority of anomalies, thereby improving the system's robustness, reliability, and overall operating efficiency, ensuring the smooth and safe operation of the shuttle parking garage.

[0103] In some embodiments, the step of determining the priority of deviation processing based on the deviation classification result and the current task priority of the device unit includes:

[0104] The system receives deviation classification results from multiple device units, performs correlation analysis on the deviation classification results of multiple device units, and obtains correlation analysis results. The correlation analysis includes identifying whether there is shared resource competition, temporal dependence, or spatial proximity relationship between deviations.

[0105] Based on the correlation analysis results, a deviation influence network is constructed, which describes the degree of mutual influence and potential chain reactions between different deviations;

[0106] Based on the deviation impact network, the impact weight of each deviation on the overall scheduling efficiency and security is evaluated, and the impact weight takes into account both the direct impact of the deviation and the indirect impact generated through chain reactions.

[0107] The priority of deviation processing is determined based on the influence weight and the current task priority of the device unit.

[0108] The process of receiving deviation classification results from multiple equipment units involves continuously collecting abnormal information reported by all automated execution equipment in the shuttle garage through the scheduling and control system. After preliminary classification, these classification results are aggregated for subsequent comprehensive analysis.

[0109] Furthermore, correlation analysis aims to identify whether there are inherent connections between different deviation events. Specifically, this can include:

[0110] Identify shared resource contention: For example, multiple device units may attempt to use the same path, the same elevator, or the same charging station simultaneously, but if one or more device units deviate from their intended path, it may lead to resource contention or congestion.

[0111] Identify timing dependencies: For example, a deviation in one device unit causes its task to be delayed, which in turn affects the startup time or path planning of other device units that depend on it to complete preceding tasks.

[0112] Identify spatial proximity relationships: For example, if two or more device units that are physically close to each other deviate at the same time, it may indicate the presence of local environmental problems (such as obstacles, signal interference) or potential collision risks.

[0113] Building upon this, "constructing an deviation impact network" involves visualizing or modeling the results of correlation analysis. This network can be a directed graph where nodes represent different deviation events, and edges represent the influence relationships between deviations—for example, how one deviation causes or exacerbates another, or how the resolution of one deviation affects the state of other deviations. This network can clearly depict the propagation path of deviations and potential chain reactions, thereby helping the scheduling system understand the global impact of anomalous events.

[0114] Subsequently, the impact weight of each deviation on the overall scheduling efficiency and safety is assessed by calculating the combined impact on the overall shuttle garage system's operational efficiency and safety for each deviation node in the network. This impact weight considers not only the direct severity of the deviation itself (e.g., a complete shutdown of a device has a greater direct impact than a minor positioning deviation) but also the indirect impacts revealed by the cascading effects of the deviation on the network. For example, a seemingly minor deviation, if located on the critical path and potentially causing widespread traffic congestion, will have its indirect impact weight significantly increased.

[0115] Through the aforementioned technical solution, this application enables more intelligent and refined management of complex anomalies within shuttle parking garages. Specifically, by introducing correlation analysis and deviation impact networks, the system can identify and quantify the interactions between multiple deviations, thereby avoiding suboptimal decisions or cascading failures that may result from handling deviations in isolation. Consequently, during anomaly handling, the system can more accurately identify the critical deviations with the greatest impact on the overall system and assign them higher processing priorities, ensuring that limited scheduling resources are efficiently used to address the most pressing issues. This significantly improves the robustness and resilience of shuttle parking garages in the face of complex anomalies, ensuring the continuity and safety of core operations such as vehicle handling, lifting and conveying, and parking space management, and effectively reducing operational risks and economic losses caused by abnormal events.

[0116] As a specific implementation, suppose that in a shuttle parking garage, the following two deviations occur simultaneously: when the first shuttle is driving toward the designated parking space, its motion sensor reports a slight positioning deviation (motion anomaly); at the same time, when the second shuttle is waiting for the elevator, its wireless communication module reports intermittent signal loss (communication anomaly).

[0117] First, the system receives the two deviation classification results. Next, a correlation analysis is performed, revealing that both the first and second shuttles need to use the same elevator (shared resource contention), and the positioning deviation of the first shuttle may prevent it from arriving at the elevator on time, thus delaying the use of the second shuttle (time dependence).

[0118] Based on this correlation analysis, a deviation impact network is constructed. In this network, the motion anomaly node of the first shuttle is connected to the elevator resource node, and further connected to the task node of the second shuttle, indicating that it may cause a delay in the second shuttle's task. The communication anomaly node of the second shuttle directly affects the execution of its own task.

[0119] The system then assesses the impact weight of each deviation. If the first shuttle has only a slight positioning deviation, but its task is an emergency outbound operation, and its deviation may occupy a critical elevator for an extended period, disrupting the entire outbound process, then its impact weight will be assessed as high. Conversely, if the second shuttle experiences a communication anomaly, but its task priority is low and a backup communication link exists or can be restored, its impact weight may be relatively low.

[0120] Ultimately, based on the assessed impact weights and the current task priorities of the equipment units, the system determines the priority of deviation handling. For example, the system may prioritize handling the positioning deviation of the first shuttle, adjusting its path or guiding it to a safe area to release elevator resources as quickly as possible, ensure the smooth execution of the emergency outbound mission, and avoid causing a wider chain reaction on subsequent vehicle scheduling.

[0121] In some embodiments, after determining the priority of deviation processing based on the influence weight and the current task priority of the device unit, the method further includes:

[0122] Identify competition for limited resources within the garage from multiple high-priority deviation handling tasks, including alternative routes, specific elevators, or critical scheduling command channels;

[0123] Based on the competition relationship between the high-priority deviation processing tasks and resources, a resource contention graph is constructed. The nodes in the resource contention graph represent the high-priority deviation processing tasks, and the edges represent the competition relationship between the tasks for shared resources.

[0124] Based on the resource contention graph, resource conflict detection is performed on the high-priority deviation processing task to obtain the resource conflict detection result;

[0125] Based on the resource conflict detection results, a resource allocation strategy is initiated, which combines the priority, influence weight, and resource availability of each task.

[0126] Based on the resource allocation strategy, the execution order of tasks is adjusted or alternative resources are allocated to ensure the priority execution of critical tasks and minimize the negative impact of resource competition on overall scheduling efficiency.

[0127] Specifically, identifying the competition for limited resources within the parking garage by multiple high-priority deviation handling tasks involves the system proactively analyzing the shared resources that these tasks might need to utilize during execution when it identifies multiple high-priority anomalies requiring immediate attention. These resources can be understood as a limited number of physical or logical entities within the parking garage that are crucial to the system's operation. For example, when a fault occurs in a certain area, a specific alternative route may be needed for detour; when a shuttle vehicle malfunctions and requires emergency repair, a specific elevator may need to be used for transfer; or when handling certain emergencies, a critical dispatch command channel may need to be exclusively used to ensure the timely issuance and execution of commands.

[0128] The resource contention graph is constructed based on the resource competition relationships among high-priority deviation processing tasks. This is done by treating the identified high-priority deviation processing tasks as nodes in the graph and the competition between these tasks for the same limited resources as edges. For example, if two high-priority tasks need to use the same backup path, there will be an edge between these two task nodes in the resource contention graph, indicating that they are competing for resources. This resource contention graph visually displays the resource dependencies and conflicts among all current high-priority deviation processing tasks.

[0129] In practical applications, resource conflict detection for high-priority deviation processing tasks, based on the resource contention graph, involves analyzing the graph's topology and node connections to identify which tasks have direct or indirect resource conflicts. For example, it can detect loop contention or situations where multiple tasks simultaneously request a resource. This yields resource conflict detection results, clearly indicating which tasks are competing for resources and the specific types of resources being contested.

[0130] Furthermore, based on the resource conflict detection results, the resource allocation strategy is activated. This is achieved by the system activating a set of preset or dynamically generated resource allocation rules after a resource conflict is identified. This resource allocation strategy does not simply allocate resources according to priority order, but rather comprehensively considers the priority of each task, its impact weight on overall scheduling efficiency and security, and the real-time availability of various resources within the garage. For example, even if a task has a slightly lower priority, if its impact weight is extremely high and the required resources are currently available, the strategy may prioritize allocating resources to it.

[0131] Ultimately, based on the resource allocation strategy, the system optimizes the execution plans of high-priority tasks by adjusting the task execution order or allocating alternative resources. This may include rescheduling task start times to avoid conflicting with shared resources; or, in some cases, allocating different but functionally equivalent alternative resources to tasks, such as using another available backup path or another elevator. The goal is to ensure that critical tasks essential to garage operation receive priority resources and execute smoothly, while minimizing waiting time or efficiency losses due to resource contention, thereby guaranteeing the stable operation of the overall scheduling system.

[0132] Through the above technical solution, this application can significantly improve the system's decision-making ability and execution efficiency when handling complex anomalies in automated shuttle parking garage scheduling. Compared to solutions that only handle a single priority, this application can proactively identify and resolve potential resource contention issues between multiple high-priority deviation processing tasks. This avoids task blocking or inefficiency caused by resource conflicts, ensuring the priority execution of critical tasks under resource-constrained conditions. Furthermore, by constructing a resource contention graph and implementing intelligent resource allocation strategies, the system can more comprehensively assess and manage risks, minimizing the negative impact of resource contention on overall scheduling efficiency and security, thereby improving the stability and reliability of the entire automated shuttle parking garage scheduling system, especially its performance under high-load or multi-fault concurrent scenarios.

[0133] In some embodiments, after the step of adjusting the execution order of tasks or allocating alternative resources according to the resource allocation strategy to ensure the priority execution of critical tasks and minimize the negative impact of resource contention on overall scheduling efficiency, the following steps are included:

[0134] Continuously monitor the operating status of equipment units and the quality of communication links to obtain monitoring results;

[0135] Based on the monitoring results, new equipment failures, communication link degradation, or abnormal task progress are identified, and the identification results are obtained.

[0136] Based on the identification results, the effectiveness of the allocated resources is reassessed to obtain the assessment results;

[0137] Based on the assessment results, the resource allocation strategy will be restarted.

[0138] Specifically, continuous monitoring of the operational status of equipment units and the quality of communication links involves the uninterrupted collection of real-time data from each equipment unit, including but not limited to spatial pose, motion parameters, task execution progress, communication status, and fault status. Simultaneously, it evaluates the stability, bandwidth, latency, and other quality indicators of the communication link between the equipment unit and the scheduling system. Through this continuous monitoring, the real-time operational status and communication health of all automated execution equipment within the garage can be comprehensively assessed. Identifying new equipment faults, communication link degradation, or abnormal task progress based on monitoring results involves analyzing the operational status and communication link quality data obtained from continuous monitoring. Using pre-defined rules, models, or machine learning algorithms, the system detects any anomalies that deviate from expected behavior. Examples include a sudden deviation of equipment unit motion parameters from the normal range, a significant increase in communication data packet loss rate, and task execution time far exceeding expectations. Once these new anomalies are identified, corresponding identification results are generated, clearly defining the type and location of the anomaly. In practical applications, reassessing the effectiveness of allocated resources based on the identification results means that after identifying new anomalies, the scheduling system will re-examine whether the resources currently allocated for high-priority deviation handling tasks are still applicable and effective. For example, if a critical elevator is assigned to a high-priority task, but monitoring reveals a new malfunction in the elevator, the original allocation scheme may become invalid. The evaluation process considers the impact of the anomaly on resource availability, task execution paths, and timing coordination. Therefore, restarting the resource allocation strategy based on the evaluation results means that when the evaluation finds that the allocated resources are no longer effective or a better allocation scheme exists, the scheduling system will trigger the re-execution of the resource allocation strategy. This means that the system will combine the latest equipment status, communication quality, task progress anomalies, and resource availability information to run the resource allocation algorithm again to generate a new resource allocation scheme that is more adapted to the current garage conditions, thereby dynamically adjusting the task execution order or allocating alternative resources to ensure the priority execution of critical tasks and the maintenance of overall scheduling efficiency.

[0139] Through the above technical solution, this application can significantly improve the robustness and adaptability of the automated scheduling and control system for shuttle parking garages. This solution enables the system not only to effectively resolve resource contention in the initial stage but also to continuously respond to dynamic changes during subsequent operation, promptly identifying and correcting resource allocation failures caused by new anomalies. This ensures that even in unforeseen circumstances such as equipment failure, communication interruption, or abnormal task progress, critical tasks can still be prioritized, and resources can be efficiently and flexibly reconfigured. This minimizes the negative impact of abnormal events on overall scheduling efficiency and safety, maintaining the continuity and stability of garage operation.

[0140] This application also proposes an automated scheduling and control optimization system for shuttle parking garages, such as... Figure 2 As shown, a shuttle parking garage automated scheduling and control optimization system 100 includes:

[0141] The information receiving module 10 is used to receive the operation information of the equipment units in the shuttle garage. The equipment units are automated execution equipment that performs vehicle handling, lifting and conveying, and parking space management in the shuttle garage. The operation information includes event reports sent by the equipment units when key event nodes are identified and online signals sent by the equipment units when key event nodes are not identified. The event reports include event type, timestamp, and key status information.

[0142] The status calibration module 20 is used to calibrate the operating status of the device unit based on the event reports in the received operating information; the operating status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit.

[0143] The state prediction module 30 is used to actively predict the operating state of the device unit at the current moment based on the operating state after the last calibration update, the currently executed task instructions, and the preset kinematic model during the period between two event reports, so as to obtain the estimated operating state.

[0144] The scheduling adjustment module 40 is used to adjust the subsequent task allocation, path planning and timing coordination planning of the equipment unit according to the calibrated operating status and / or the estimated operating status.

[0145] This application presents an automated scheduling and control optimization system for shuttle parking garages. Addressing the problem that traditional automated shuttle parking garages suffer from inaccurate equipment status information due to communication delays or data loss when handling high-concurrency tasks, leading to suboptimal scheduling decisions or even "negative optimization," this application proposes an innovative solution. Existing technologies often rely on limited status information periodically reported by equipment, or update the status only when critical events occur. This can easily lead to discrepancies between the central control system's perception of the actual equipment status and the actual situation during high-load operation.

[0146] Compared with existing technologies, the core innovation of this application lies in its collaborative construction of a more robust and accurate equipment status management mechanism through the collaboration of an information receiving module, a status calibration module, and a status prediction module. The information receiving module can finely distinguish between event reports and online signals, ensuring the accurate acquisition of key status information. The status calibration module uses event reports for overlay updates, guaranteeing the accuracy of core statuses. More importantly, the status prediction module can proactively predict equipment status based on kinematic models between two event reports, effectively filling information gaps and providing a continuous and real-time view of equipment status. This combination of "real-time perception" and "proactive prediction" capabilities enables the scheduling and adjustment module to make decisions based on high-precision equipment operating status, thereby significantly improving the accuracy and efficiency of task allocation, path planning, and time-series collaborative planning. Therefore, the system of this application can effectively avoid the situation where traditional systems fall into ineffective loops or generate "negative optimization" due to deficiencies in decision-making basis, thus significantly improving the overall throughput, operational reliability, and safety of the shuttle garage.

[0147] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing the automated scheduling and control of a shuttle parking garage, characterized in that, include: The system receives operational information from equipment units within the shuttle parking garage. These equipment units are automated execution devices that perform vehicle handling, lifting and conveying, and parking space management within the shuttle parking garage. The operational information includes event reports sent by the equipment units when they identify critical event nodes and online signals sent by the equipment units when they do not identify critical event nodes. The online signals are used to characterize the normal communication and online status of the equipment units. The event reports include event type, timestamp, and critical status information. The operating status of the device unit is calibrated based on the event reports received in the operating information. The operating status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit. During the period between two event reports, based on the operating status after the last calibration update, the currently executing task instructions, and the preset kinematic model, the operating status of the device unit at the current moment is actively inferred to obtain the estimated operating status; Based on the calibrated operating status and / or the estimated operating status, adjust the subsequent task allocation, path planning, and timing coordination planning of the equipment unit; If no operational information is received from the monitoring equipment unit within a preset time, or if the deviation between the estimated operational status of the equipment unit and the preset expected status exceeds a preset threshold, an abnormal handling process is triggered, which specifically includes: Each device unit is configured with a report receiving timer and a status deviation evaluator, the status deviation evaluator being used to evaluate the deviation between the estimated operating status of the device unit and the preset expected status; Based on the current task type and physical area of ​​the device unit, the timeout threshold of the report receiving timer and the deviation judgment threshold of the state deviation evaluator are dynamically adjusted. Based on the dynamically adjusted timeout threshold and deviation judgment threshold, the reception status of the operation information and the deviation between the estimated operation status of the device unit and the preset expected status are classified to obtain deviation classification results. The deviation classification results include communication abnormality, motion abnormality or composite abnormality. Based on the deviation classification results and the current task priority of the device unit, the priority of deviation processing is determined; Based on the priority of the deviation handling, the corresponding handling strategy is executed. The handling strategy includes adjusting the scheduling safety margin, activating the backup plan, or requesting the equipment unit to immediately report the detailed status.

2. The shuttle garage automated scheduling and control optimization method according to claim 1, characterized in that, The key event nodes are the completion of the equipment unit's vehicle storage and retrieval task, state switching, and abnormal operation triggering nodes; the event types are arrival events, vehicle storage and retrieval events, start-stop events, fault events, and parking space occupancy / release events.

3. The automated scheduling and control optimization method for shuttle parking garages according to claim 1, characterized in that, The step of calibrating the operating status of the device unit based on the event reports in the received operating information includes: The system continuously monitors and parses event reports in the operation information through the wireless communication interface. Using the timestamps and key status information carried in the event reports as the reference data source, it performs overlay calibration and updates on the locally maintained operation status corresponding to the device unit.

4. The optimized method for automated scheduling and control of shuttle parking garages according to claim 1, characterized in that, The step of inferring the operating state of the device unit at the current moment and obtaining the estimated operating state includes: The state estimator is activated. It uses the operating state of the device unit after the last calibration update as the initial benchmark, combines the expected motion trend implied by the task instruction currently being executed by the device unit, and the preset kinematic model used to reflect the physical motion capability of the device unit, and recursively calculates the operating state of the device unit at the current moment to obtain the estimated operating state.

5. The automated scheduling and control optimization method for shuttle parking garages according to claim 1, characterized in that, The step of determining the priority of deviation processing based on the deviation classification result and the current task priority of the device unit includes: The system receives deviation classification results from multiple device units, performs correlation analysis on the deviation classification results of multiple device units, and obtains correlation analysis results. The correlation analysis includes identifying whether there is shared resource competition, temporal dependence, or spatial proximity relationship between deviations. Based on the correlation analysis results, a deviation influence network is constructed, which describes the degree of mutual influence and potential chain reactions between different deviations; Based on the deviation impact network, the impact weight of each deviation on the overall scheduling efficiency and security is evaluated, and the impact weight takes into account both the direct impact of the deviation and the indirect impact generated through chain reactions. The priority of deviation processing is determined based on the influence weight and the current task priority of the device unit.

6. The shuttle garage automated scheduling and control optimization method according to claim 5, characterized in that, After the step of determining the priority of deviation processing based on the influence weight and the current task priority of the device unit, the following steps are included: Identify competition for limited resources within the garage from multiple high-priority deviation handling tasks, including alternative routes, specific elevators, or critical scheduling command channels; Based on the competition relationship between the high-priority deviation processing tasks and resources, a resource contention graph is constructed. The nodes in the resource contention graph represent the high-priority deviation processing tasks, and the edges represent the competition relationship between the tasks for shared resources. Based on the resource contention graph, resource conflict detection is performed on the high-priority deviation processing task to obtain the resource conflict detection result; Based on the resource conflict detection results, a resource allocation strategy is initiated, which combines the priority, influence weight, and resource availability of each task. Based on the resource allocation strategy, the execution order of tasks is adjusted or alternative resources are allocated to ensure the priority execution of critical tasks and minimize the negative impact of resource competition on overall scheduling efficiency.

7. The automated scheduling and control optimization method for shuttle parking garages according to claim 6, characterized in that, The step following the step of adjusting the execution order of tasks or allocating alternative resources according to the resource allocation strategy to ensure the priority execution of critical tasks and minimize the negative impact of resource contention on overall scheduling efficiency includes: Continuously monitor the operating status of equipment units and the quality of communication links to obtain monitoring results; Based on the monitoring results, new equipment failures, communication link degradation, or abnormal task progress are identified, and the identification results are obtained. Based on the identification results, the effectiveness of the allocated resources is reassessed to obtain the assessment results; Based on the assessment results, the resource allocation strategy will be restarted.

8. An automated scheduling and control optimization system for a shuttle parking garage, characterized in that, The system includes: The information receiving module is used to receive the operation information of the equipment units in the shuttle garage. The equipment units are automated execution equipment that performs vehicle handling, lifting and conveying, and parking space management in the shuttle garage. The operation information includes event reports sent by the equipment units when key event nodes are identified and online signals sent by the equipment units when key event nodes are not identified. The event reports include event type, timestamp, and key status information. The status calibration module is used to calibrate the operating status of the device unit based on the event reports in the received operating information; the operating status includes the spatial pose, motion parameters, task execution progress, communication status, and fault status of the device unit. The state prediction module is used to actively predict the operating state of the device unit at the current moment between two event reports, based on the operating state after the last calibration update, the currently executed task instructions, and the preset kinematic model, and obtain the estimated operating state. The scheduling adjustment module is used to adjust the subsequent task allocation, path planning, and timing coordination planning of the equipment unit based on the calibrated operating status and / or the estimated operating status. Afterwards, it also performs the following steps: If no operational information is received from the monitoring equipment unit within a preset time, or if the deviation between the estimated operational status of the equipment unit and the preset expected status exceeds a preset threshold, an abnormal handling process is triggered, which specifically includes: Each device unit is configured with a report receiving timer and a status deviation evaluator, the status deviation evaluator being used to evaluate the deviation between the estimated operating status of the device unit and the preset expected status; Based on the current task type and physical area of ​​the device unit, the timeout threshold of the report receiving timer and the deviation judgment threshold of the state deviation evaluator are dynamically adjusted. Based on the dynamically adjusted timeout threshold and deviation judgment threshold, the reception status of the operation information and the deviation between the estimated operation status of the device unit and the preset expected status are classified to obtain deviation classification results. The deviation classification results include communication abnormality, motion abnormality or composite abnormality. Based on the deviation classification results and the current task priority of the device unit, the priority of deviation processing is determined; Based on the priority of the deviation handling, the corresponding handling strategy is executed. The handling strategy includes adjusting the scheduling safety margin, activating the backup plan, or requesting the equipment unit to immediately report the detailed status.

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