Tippler robot cooperative control method and system for train operation line

Abstract

The invention provides a car dumper robot cooperative control method and system for a train operation line, and relates to the technical field of robot control. Task allocation analysis of the turnover robot is carried out by taking the task as a constraint, and task collaborative planning is obtained and comprises unhooking planning of an unhooking robot, positive hooking planning of a positive hooking robot and re-hooking planning of a re-hooking robot; the sensor assembly is activated to monitor the cooperative control process of the to-be-overturned operation, and real-time overturning information is obtained; and dynamically adjusting the task collaborative planning based on the real-time rollover information according to a predetermined collaborative strategy. The technical problems that in the prior art, due to the fact that a fine-grained collaborative planning mechanism for the to-be-overturned operation is lacked, operation of multiple robots is serial, scheduling is rigid, and task efficiency is low in the overturning operation are solved, and the technical effects that robot task intelligent distribution and dynamic collaborative control are achieved, and the overturning operation efficiency is improved are achieved.

Description

Technical Field

[0001] This application relates to the technical field of robot control, and particularly to a collaborative control method and system for a dumper robot used in a train operation line. Background Art

[0002] In the railway transportation system, as a key device for unloading bulk materials, the dumper is widely used in train operation lines. With the development of automation technology, robot systems have gradually been introduced into dumper operations, such as hook-unhooking robots, hook-aligning robots, and hook-reconnecting robots, to replace manual labor in performing heavy and dangerous tasks such as disassembling and assembling couplers.

[0003] However, existing dumper robot control methods generally have problems such as high structural rigidity, weak collaborative ability, and inflexible response. Most systems adopt a preset static operation process and lack the state perception of the dumper operation to be performed, resulting in unreasonable task allocation for robots and being unable to flexibly adjust the operation plan according to the state of the actual operation object (such as train formation mode, coupler type, etc.). In addition, the degree of collaboration between robots is relatively low, and the operation process is mostly executed serially, causing time redundancy and low overall efficiency. Once a deviation or abnormality occurs during the operation process, it is also difficult for the system to adjust the task plan in a timely manner, relying on manual intervention and affecting the continuity and intelligent level of the operation. Summary of the Invention

[0004] This application provides a collaborative control method and system for a dumper robot used in a train operation line, which solves the technical problem that in the prior art, due to the lack of a fine-grained collaborative planning mechanism for the dumper operation to be performed, multiple robots operate serially in the dumper operation, resulting in rigid scheduling and low task efficiency, and achieves the technical effect of realizing intelligent task allocation and dynamic collaborative control of robots and improving the dumper operation efficiency.

[0005] In view of the above problems, on the one hand, this application provides a collaborative control method for a dumper robot used in a train operation line. The method includes: collecting multi-dimensional features of the dumper operation to be performed to obtain a task feature set; performing task allocation analysis of the dumper robot with the task feature set as a constraint to obtain a task collaborative plan, where the task collaborative plan includes a hook-unhooking plan for the hook-unhooking robot, a hook-aligning plan for the hook-aligning robot, and a hook-reconnecting plan for the hook-reconnecting robot; activating a sensor component to monitor the collaborative control process of the dumper operation to be performed based on the task collaborative plan to obtain real-time dumper information; reading a predetermined collaborative strategy, and based on the predetermined collaborative strategy, dynamically adjusting the task collaborative plan based on the real-time dumper information, including dynamically adjusting the hook-unhooking plan and / or the hook-aligning plan and / or the hook-reconnecting plan.

[0006] Preferably, the task assignment analysis of the tipping robot is performed with the task feature set as a constraint to obtain a task collaborative plan, including: extracting a train list in the to-be-tipped operation, where the train list includes multiple trains with model numbers and position identifiers; performing clustering analysis on multiple trains with the model number and the position as clustering constraints to obtain a clustering result, where the clustering result includes a first clustering cluster; analyzing first information of a first train in the first clustering cluster to obtain a first priority of the first clustering cluster; generating a collaborative operation sequence of the to-be-tipped operation according to the first priority; analyzing the collaborative operation sequence to obtain a collaborative operation path of the tipping robot, and forming the task collaborative plan with the collaborative operation sequence.

[0007] Preferably, the first information includes a first model number, a first position, and a first tipping limit of the first train. Analyzing the first information of the first train in the first clustering cluster to obtain the first priority of the first clustering cluster includes: matching a first uncoupling duration of the first model number; combining the real-time position of the uncoupling robot with the first position to obtain a second uncoupling duration; adding the first uncoupling duration and the second uncoupling duration to obtain a predicted uncoupling duration, and combining the first tipping limit to obtain a first priority index; taking the maximum value in the first priority index as the first priority of the first clustering cluster.

[0008] Preferably, analyzing the collaborative operation sequence to obtain a collaborative operation path of the tipping robot, and forming the task collaborative plan with the collaborative operation sequence includes: generating an uncoupling collaborative operation sequence of the uncoupling robot in the to-be-tipped operation according to the first priority; analyzing the uncoupling collaborative operation sequence to obtain an uncoupling collaborative operation path of the uncoupling robot, and forming an uncoupling plan with the uncoupling collaborative operation sequence; generating the coupling plan of the coupling robot with the predicted uncoupling duration as a constraint; obtaining a predicted coupling duration of the coupling robot, and combining the coupling plan to obtain a recoupling plan of the recoupling robot; the uncoupling plan, the coupling plan, and the recoupling plan form the task collaborative plan.

[0009] Preferably, a predetermined collaborative strategy is read, and based on the predetermined collaborative strategy, the task collaborative plan is dynamically adjusted according to the real-time tipping information, including: dynamically monitoring the uncoupling robot in the task collaborative plan through the sensor assembly to obtain real-time uncoupling information; analyzing the real-time uncoupling position and real-time uncoupling state in the real-time uncoupling information to obtain a real-time uncoupling progress; comparing the real-time uncoupling progress with the real-time predetermined uncoupling progress in the task collaborative plan to obtain a real-time uncoupling deviation; if the real-time uncoupling deviation reaches a predetermined deviation constraint, the coupling plan and the recoupling plan are dynamically adjusted in sequence.

[0010] Preferably, if the real-time unhooking deviation reaches a predetermined deviation constraint, the forward hooking plan and the rehooking plan are dynamically adjusted in sequence, and it further includes: obtaining the real-time operation state parameters of the unhooking robot through the sensor assembly; obtaining the predetermined operation state parameters of the unhooking robot, comparing them with the real-time operation state parameters, and obtaining the real-time operation health degree of unhooking; obtaining the real-time operation abnormality degree of unhooking based on the real-time operation health degree of unhooking, and dynamically adjusting the unhooking plan, the forward hooking plan, and the rehooking plan in sequence with the real-time operation abnormality degree of unhooking as the weight.

[0011] Preferably, after comparing the real-time unhooking progress with the real-time predetermined unhooking progress in the task collaborative plan to obtain the real-time unhooking deviation, it further includes: if the real-time unhooking deviation does not reach the predetermined deviation constraint, obtaining the real-time forward hooking deviation of the forward hooking robot; if the real-time forward hooking deviation reaches the predetermined deviation constraint, dynamically adjusting the rehooking plan.

[0012] Preferably, after obtaining the real-time forward hooking deviation of the forward hooking robot when the real-time unhooking deviation does not reach the predetermined deviation constraint, it further includes: if the real-time forward hooking deviation does not reach the predetermined deviation constraint, obtaining the real-time operation abnormality degree of the forward hooking robot; dynamically adjusting the forward hooking plan and the rehooking plan in sequence with the real-time operation abnormality degree of the forward hooking robot as the weight.

[0013] Preferably, the method further includes: obtaining the real-time rehooking deviation, and dynamically adjusting the rehooking plan when the real-time rehooking deviation reaches the predetermined deviation constraint.

[0014] On the other hand, the present application also provides a dumper robot collaborative control system for a train operation line, and the system includes: a task feature collection module, configured to perform multi-dimensional feature collection on the to-be-dumped operation to obtain a task feature set; a task collaborative planning module, configured to perform task assignment analysis of the dumper robot with the task feature set as a constraint to obtain a task collaborative plan, where the task collaborative plan includes an unhooking plan of the unhooking robot, a forward hooking plan of the forward hooking robot, and a rehooking plan of the rehooking robot; a collaborative control monitoring module, configured to activate the sensor assembly to monitor the collaborative control process of the to-be-dumped operation based on the task collaborative plan to obtain real-time dumper information; a collaborative strategy adjustment module, configured to read a predetermined collaborative strategy, and based on the predetermined collaborative strategy, dynamically adjust the task collaborative plan based on the real-time dumper information, including dynamically adjusting the unhooking plan and / or the forward hooking plan and / or the rehooking plan.

[0015] One or more technical solutions provided in the present application have at least the following beneficial effects:

[0016] By collecting multi-dimensional characteristics of the tipping operation, a comprehensive task feature set is formed, providing a decision-making basis for subsequent task planning to ensure the pertinence and adaptability of task allocation. Through the task allocation analysis of the tipping robot with the task feature set as a constraint, corresponding operation plans are formulated for the unhooking robot, the right-hooking robot, and the re-hooking robot respectively, obtaining a task collaboration plan to ensure the reasonable division of labor and cooperation of each robot in space and time, and realizing parallel and efficient collaboration. By activating the sensor components to monitor the collaborative control process, real-time tipping information is obtained, a perception feedback mechanism is constructed to provide data support for dynamic adjustment, and the operation controllability is enhanced. By reading the predetermined collaborative strategy and dynamically adjusting the task collaboration plan based on the real-time tipping information, the task allocation and operation process between robots are optimized to adapt to the actual change situation.

[0017] In summary, this application realizes the intelligent division of labor, efficient collaboration, and flexible adjustment of the roles of robots in the tipping operation by establishing a multi-robot collaborative planning mechanism based on task characteristics and combining real-time perception and dynamic feedback control, significantly improving the automation level, execution efficiency, and operation stability of the tipping operation system.

[0018] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically illustrates the specific implementation manners of this application. Brief Description of the Drawings

[0019] Figure 1 It is a schematic flow chart of the collaborative control method for the tipping machine robot used in the train operation line provided by the embodiment of this application.

[0020] Figure 2 It is a schematic flow chart of obtaining the task collaboration plan in the collaborative control method for the tipping machine robot used in the train operation line provided by the embodiment of this application.

[0021] Figure 3 It is a schematic structural diagram of the collaborative control system for the tipping machine robot used in the train operation line provided by the embodiment of this application.

[0022] Description of the reference numerals: Task feature collection module 10, Task collaboration planning module 20, Collaborative control monitoring module 30, Collaborative strategy adjustment module 40. Detailed Description of the Invention

[0023] By providing a collaborative control method and system for a dumper robot used in a train operation line, the embodiment of the present application solves the technical problem in the prior art that due to the lack of a fine-grained collaborative planning mechanism for the dumper operation, the operation of multiple robots is in series during the dumper operation, the scheduling is rigid, and the task efficiency is low, and achieves the technical effect of realizing the intelligent allocation and dynamic collaborative control of robot tasks and improving the dumper operation efficiency.

[0024] Embodiment 1, as Figure 1 shown, the embodiment of the present application provides a collaborative control method for a dumper robot used in a train operation line, and the method includes:

[0025] Step S100: Collect multi-dimensional features of the dumper operation to obtain a task feature set.

[0026] Specifically, the dumper operation to be performed refers to the train operation object that needs to be unloaded currently, and usually includes multiple train units to be dumped. The multi-dimensional features refer to multiple attribute dimensions used to describe the state of the dumper operation, covering information such as vehicle model, coupler type, position, dumper limit (deadline), historical operation data, weather and track status. The task feature set is a structured data set aggregated from the above multi-dimensional features and is used to guide the subsequent robot task planning.

[0027] Through the perception devices (such as high-definition cameras, lidars, RFID readers, geomagnetic sensors, etc.) deployed around the train operation line, comprehensive data collection is carried out on the current dumper operation object to identify the positions of each train, extract the vehicle models, and judge the coupler types (rotary couplers, semi-automatic couplers, etc.) used. Further, the dumper deadline (i.e., the dumper limit) corresponding to the train in the scheduling plan is obtained. At the same time, the state parameters such as the current positions and operable capabilities of the uncoupling, coupling, and recoupling robots can also be read. All these information are integrated into a set of structured data to form a task feature set, providing an input basis for the task planning in the subsequent steps.

[0028] Step S200: Perform task assignment analysis of the dumper robots with the task feature set as a constraint to obtain a task collaborative plan, where the task collaborative plan includes an uncoupling plan of the uncoupling robot, a coupling plan of the coupling robot, and a recoupling plan of the recoupling robot.

[0029] Specifically, the task assignment analysis refers to the process of specifically dividing tasks for different types of robots (uncoupling, coupling, recoupling) according to the task feature set. The task collaborative plan refers to the overall scheduling plan including the collaborative operation paths and operation sequences of multiple robots. The uncoupling plan, coupling plan, and recoupling plan respectively correspond to the operation timings and collaborative operation path designs of the operations undertaken by the uncoupling robot, coupling robot, and recoupling robot in the current operation task.

[0030] Using the task feature set obtained in step S100 as a constraint condition, intelligent allocation analysis is carried out. First, all coupler operation units to be processed are identified, and then the trains are clustered or sorted according to features such as train type, position, coupler status, and derailment priority. For example, trains with an earlier derailment limit time and a shorter estimated operation time are given priority for processing. Subsequently, combining the current position of the uncoupling robot, historical operation efficiency, etc., the execution order and path planning of the uncoupling task are generated, and the operation plans of the coupling and recoupling robots are derived in sequence, so as to form a complete closed loop of time coordination and continuous operation among the three robots.

[0031] Through multi-robot task collaborative planning with the task feature set as a constraint, intelligent division of labor and operation coordination among the robots are achieved, the efficiency bottleneck brought by traditional serial operations is solved, and the overall collaborative intelligence level of the robots and the throughput capacity of the operation system are improved.

[0032] Step S300: Activate the sensor component to monitor the collaborative control process of the to-be-overturned operation based on the task collaborative planning, and obtain real-time derailment information.

[0033] Specifically, the sensor component refers to the hardware unit used to monitor the robot state and the operation site conditions, including position sensors, visual recognition modules, motor state monitors, etc. The collaborative control process refers to the operation process in which multiple robots synchronously carry out operations according to the preset task collaborative planning. The real-time derailment information refers to the operation status feedback dynamically collected by the sensor component, including the current position, operation progress, execution deviation, fault alarm, etc.

[0034] While executing the task collaborative planning, activate various sensor components deployed on the robot body and the operation site to achieve real-time perception of the operation status. The interactive robot control system obtains information such as the position coordinates, operation progress, and execution action status of each robot during operation, and at the same time collects visual or data feedback on the on-site environment (such as changes in coupler status, availability of the operation line, etc.) to judge the accuracy, timeliness, and abnormal status of the collaborative operation. These information are integrated into real-time derailment information for use in the next dynamic adjustment. For example, during the execution of the uncoupling robot, its motor torque sensor detects abnormal hook head resistance, and the visual module identifies that the hook is not completely detached. Based on this, the control system generates an "uncoupling failure" alarm and records its current position and abnormal parameters as real-time derailment information.

[0035] Step S400: Read the predetermined collaborative strategy, and based on the predetermined collaborative strategy, dynamically adjust the task collaborative planning based on the real-time derailment information, including dynamically adjusting the uncoupling plan and / or the coupling plan and / or the recoupling plan.

[0036] Specifically, the predetermined collaboration strategy refers to the pre-set rules and methods for the collaborative work of the car dumper robots, including task adjustment strategies, priority allocation strategies, exception handling strategies, etc. under different circumstances, which are used to guide the adjustment of the task collaborative planning when the real-time car dumping information changes. The dynamic adjustment refers to the online modification behavior of the original task collaborative planning based on the real-time feedback information during the operation execution. The dynamic adjustment of the unhooking plan, hooking plan, and rehooking plan refers to the real-time optimization and update of the robot operation paths, operation sequences, or participating roles in the three stages.

[0037] After the real-time car dumping information is fed back to the control system, the configured predetermined collaboration strategy is immediately called to compare the difference between the actual operation status and the original plan. For example, if there is a delay at a certain task point of the unhooking robot, the impact range of the delay on the hooking and rehooking operations will be analyzed, and based on this, the operation sequences and time points of the subsequent robots will be adjusted, or even the standby robot will be used to take over the task. The dynamic adjustment is not only applicable to the progress deviation, but also applicable to the situations such as abnormal robot operation health and action failure. The adjustment process specifically includes recalculating and planning parameters such as the unhooking sequence and time interval in the unhooking plan, the hooking force and position accuracy in the hooking plan, and the rehooking timing in the rehooking plan. The adjusted task collaborative planning will be redistributed to each robot to guide them to continue to execute the operation tasks according to the new plan, ensuring that the entire car dumping operation process can adapt to the changes in the actual situation and successfully complete the predetermined goals.

[0038] Furthermore, as Figure 2 shown, step S200 includes:

[0039] Step S210: Extract the train list in the to-be-car-dumped operation, where the train list includes multiple trains with model and position identifications.

[0040] Step S220: Perform clustering analysis on multiple trains with the model and the position as the clustering constraints to obtain a clustering result, where the clustering result includes a first clustering cluster.

[0041] Step S230: Analyze the first information of the first train in the first clustering cluster to obtain the first priority of the first clustering cluster.

[0042] Step S240: Generate the collaborative operation sequence of the to-be-car-dumped operation according to the first priority.

[0043] Step S250: Analyze the collaborative operation sequence to obtain the collaborative operation path of the car dumper robot, and form the task collaborative planning with the collaborative operation sequence.

[0044] Furthermore, the first information includes the first model, the first position, and the first car dumping limit of the first train.

[0045] Specifically, the train list refers to the information set of all trains involved in the current train tipping operation. The model identifier refers to the structural and performance categories of the train, reflecting the complexity of the tipping process. The position identifier refers to the actual position coordinates of the train on the operation line, such as the track station position and the train serial number. Based on the multi-dimensional feature data obtained in step S100, the structural information of the current operation object is extracted therefrom to generate a train list including all trains. This list not only records the model of each train but also includes the physical positions of these trains on the track. These information will be used as key features for clustering and scheduling analysis later.

[0046] The clustering constraint is the grouping basis adopted during the clustering analysis, which here is the model and position of the train. The first clustering cluster refers to any one of several subsets divided according to the clustering result, containing a group of adjacent and similar-type trains. Using clustering algorithms (such as K-Means, DBSCAN, or density-based hierarchical clustering, etc.), with the train model and position as input features, perform clustering analysis to group trains with the same model at adjacent positions into the same group, obtaining a clustering result containing multiple first clustering clusters. This clustering result helps to integrate similar operation objects into a unified scheduling unit and improve the collaborative operation efficiency.

[0047] The first train refers to any train in the first clustering cluster, and the first information is the key features corresponding to the first train, including the model, position, and tipping deadline (the last time limit to complete the operation). The first priority is the execution priority level assigned to the first clustering cluster, usually the more urgent ones are ranked higher. Select any train in the first clustering cluster, extract its model (the first model), position information (the first position), and tipping deadline time (the first tipping deadline), estimate the time cost required for this task in combination with the current state of the uncoupling robot (such as position, execution efficiency, etc.), and then compare it with the tipping deadline to generate a priority index indicating the urgency of the task. The higher this priority index, the more this task should be given priority.

[0048] The collaborative operation order refers to the robot operation sequence plan generated according to the priorities of each clustering cluster, that is, which trains operate first and which operate later. Summarize the first priorities of all first clustering clusters and use them as the sorting basis to generate the collaborative operation order. This order will be used as the basis for subsequent robot path planning, enabling the robot to complete the operation of multiple clusters in sequence according to the urgency of the tasks.

[0049] The collaborative operation path refers to the moving route and operation sequence of the robot on the operation line according to the operation order. Based on the collaborative operation order, combined with the current position, operation ability of each robot and the on-site operation conditions, the path planning algorithm (such as A-STAR algorithm, Dijkstra algorithm, etc.) is used to calculate the optimal path of the robot from the current position to the target operation position. At the same time, consider the collaborative requirements between robots, such as avoiding path crossing and collision, ensuring the coherence of operations, etc. Combine the planned collaborative operation path with the collaborative operation order to form a set of operation blueprints containing the order and path scheduling information of multiple robots, that is, task collaborative planning, and clarify the specific operation steps, execution time, moving path, etc. of each robot in each operation task, providing comprehensive guidance for the actual control and operation execution of the robot.

[0050] Further, step S230 includes:

[0051] Step S231: Match the first uncoupling duration of the first model.

[0052] Step S232: Combine the real-time position of the uncoupling robot with the first position to obtain the second uncoupling duration.

[0053] Step S233: Add the first uncoupling duration and the second uncoupling duration to obtain the predicted uncoupling duration, and combine the first tipping limit to obtain the first priority index.

[0054] Step S234: Take the maximum value in the first priority index as the first priority of the first cluster.

[0055] Specifically, the first uncoupling duration refers to the duration required for a typical uncoupling operation standardized according to the train model. The model determines the coupler structure, fastening method, etc., and is a key parameter affecting the complexity of the uncoupling operation. First, identify the model of the first train in the first cluster (i.e., the first model), and then look up the standard uncoupling duration corresponding to this model in the preset model - operation time database, denoted as the first uncoupling duration. This first uncoupling duration is used for subsequent combination with the dynamic path time to form a more comprehensive task duration estimate.

[0056] The second uncoupling duration refers to the travel time required for the uncoupling robot to move from the current position to the first train. The real-time position of the current uncoupling robot is obtained through the sensor component or positioning module, and then the time required for it to reach the position where the first train is located is calculated. This time depends on factors such as the current coordinates of the uncoupling robot, running speed, and path smoothness. Combining the train position coordinates, the shortest path algorithm (such as A-STAR algorithm, Dijkstra algorithm, etc.) can be used to calculate the moving distance, and the travel time is estimated by combining the current speed to obtain the second uncoupling duration.

[0057] The predicted unhooking duration is the estimated total time required for the unhooking robot to complete the target unhooking task. Add the first unhooking duration and the second unhooking duration to obtain the predicted unhooking duration, and then compare and calculate it with the first tipping limit to obtain the first priority index. This first priority index is a quantitative indicator generated based on the task urgency, and the larger the value, the higher the priority. The calculation example of the first priority index is as follows: Subtract the predicted unhooking duration from the first tipping limit to obtain the remaining buffer time. The smaller this time, the more urgent the task. After normalizing and inverting the remaining buffer time, a priority index represented positively is obtained. For example: S = 1 / (1 + ΔT), where ΔT = T1 - T2. Here, S is the first priority index, ΔT is the remaining buffer time, T1 is the first tipping limit, and T2 is the predicted unhooking duration.

[0058] After calculating the first priority indices of each first train in the first clustering cluster, compare these first priority indices to find the maximum value among them. This maximum value is the first priority of this clustering cluster. In this way, in the subsequent overall operation plan, according to the first priorities of each first clustering cluster, the operation tasks of different clustering clusters can be sorted and arranged, and the first clustering cluster with a higher priority is processed first to ensure the risk sensitivity of task scheduling.

[0059] Further, step S250 includes:

[0060] Step S251: Generate the unhooking collaborative operation order of the unhooking robot in the to-be-tipped operation according to the first priority.

[0061] Step S252: Analyze the unhooking collaborative operation order to obtain the unhooking collaborative operation path of the unhooking robot, and form the unhooking plan together with the unhooking collaborative operation order.

[0062] Step S253: Generate the hooking plan of the hooking robot with the predicted unhooking duration as a constraint.

[0063] Step S254: Obtain the predicted hooking duration of the hooking robot, and combine it with the hooking plan to obtain the rehooking plan of the rehooking robot.

[0064] Step S255: The unhooking plan, the hooking plan, and the rehooking plan form the task collaborative plan.

[0065] Specifically, the unhooking collaborative operation order refers to the operation order formed by sorting the to-be-tipped operation according to the priorities of different clustering clusters. After completing the priority evaluation of multiple first clustering clusters, sort these first clustering clusters from high to low according to the priority to form the order of tasks that the unhooking robot needs to execute. The sorting result is the collaborative operation order, which serves as the main framework for the task scheduling of the unhooking robot.

[0066] According to the determined uncoupling collaborative operation sequence, call the track topology data, train position coordinates, traffic rules, and the current position of the uncoupling robot, and comprehensively use path planning algorithms (such as A-STAR algorithm, Dijkstra algorithm, etc.) to generate the shortest path or optimal route for the uncoupling robot. After combining the path with the task sequence, an executable uncoupling plan is formed and pushed to the robot control system.

[0067] Since the coupling operation depends on the completion of uncoupling, taking the predicted uncoupling duration as the time constraint condition, set the earliest start time for the coupling robot. According to this time window and the status of track resources, combined with the list of trains to be coupled, use similar priority evaluation and path planning algorithms to formulate the coupling task sequence and path for the coupling robot, and obtain the coupling plan.

[0068] The predicted coupling duration is the estimated value of the time required for the coupling robot to complete the coupling task. After the coupling plan is completed, according to the number of tasks, task distribution, and historical data of the coupling robot, estimate the predicted coupling duration of each task. Subsequently, taking these predicted coupling durations as time constraints, combined with the requirements of train recoupling and path status, arrange the specific operation sequence and travel path for the recoupling robot, and this path will avoid the working area of the coupling robot. This process ensures that the recoupling task can be started orderly immediately after the completion of the coupling task, realizing seamless connection of processes. Finally, integrate the generated uncoupling plan, coupling plan, and recoupling plan into a unified task collaboration plan, and construct a unified task control framework. This task collaboration plan not only includes task sequence, path arrangement, and time synchronization information, but also can embed dynamic adjustment interfaces for easy update after real-time monitoring and feedback. This task collaboration plan is sent to each robot through the network or control bus to achieve collaborative operation.

[0069] Further, step S400 includes:

[0070] Step S410: Dynamically monitor the uncoupling robot in the task collaboration plan through the sensor component to obtain real-time uncoupling information.

[0071] Step S420: Analyze the real-time uncoupling position and real-time uncoupling status in the real-time uncoupling information to obtain the real-time uncoupling progress.

[0072] Step S430: Compare the real-time uncoupling progress with the real-time scheduled uncoupling progress in the task collaboration plan to obtain the real-time uncoupling deviation.

[0073] Step S440: If the real-time uncoupling deviation reaches the predetermined deviation constraint, dynamically adjust the coupling plan and the recoupling plan in sequence.

[0074] Specifically, during the tipping operation, the sensor assembly is continuously activated to collect the movement trajectory and execution actions of the unhooking robot. The raw data generated by the sensors is processed by the edge computing device or the main control server, denoised, and key features are extracted to form a standardized real-time unhooking information packet, including position coordinates, current operation stage, execution speed, load status, etc., which serves as the input basis for subsequent task adjustment. The information collection cycle is usually in milliseconds to ensure the continuity of monitoring and the timeliness of response.

[0075] The real-time unhooking progress is a quantitative representation of the current execution degree of the unhooking task of the unhooking robot, and describes the task completion situation in terms of percentage or stage identification. After receiving the real-time unhooking information, the current physical position and the operation stage (such as approaching, aligning, executing, withdrawing) of the unhooking robot are extracted and matched with the standard stages in the task plan to calculate the current progress. For example, the path from the task start point to the end point is divided into several sub-stages, and the proportion of the current stage in the total task is the real-time progress. In addition, the progress analysis can also integrate action timing information to enhance the accuracy of judging the execution rhythm. Exemplarily, the unhooking task is divided into 5 unhooking sub-stages. If the current execution reaches the 3rd stage and the running time meets the expectation, the output real-time unhooking progress is 60%.

[0076] According to the progress schedule set in the task collaborative plan, the expected unhooking progress corresponding to the current moment is retrieved and numerically compared with the actual unhooking progress collected in real time to calculate the difference. This difference is the real-time unhooking deviation, which can be positive (behind), negative (ahead), or zero (synchronized). A preset deviation constraint threshold, that is, a predetermined deviation constraint (such as ±10%), is set to trigger anomaly judgment and strategy adjustment. When it is detected that the real-time unhooking deviation of the unhooking task exceeds the predetermined deviation constraint, the dynamic adjustment mechanism is immediately triggered. First, the current plan of the positive hook robot is delayed or re-planned to adapt to the new time window, and the path is re-optimized to avoid resource conflicts or waiting for idling. Immediately afterwards, the double-hook plan is adjusted to ensure that the overall process is not amplified by local anomalies. At the same time, if the unhooking is seriously lagging behind, spare robots can also be dispatched or emergency strategies (such as skipping some tasks, parallel execution, etc.) can be enabled to ensure the smoothness of the entire tipping operation process.

[0077] Furthermore, step S440 further includes:

[0078] Step S441: Obtain the real-time operating state parameters of the unhooking robot through the sensor assembly.

[0079] Step S442: Obtain the predetermined operating state parameters of the unhooking robot, and compare them with the real-time operating state parameters to obtain the real-time unhooking operation health.

[0080] Step S443: Obtain the real-time unhooking operation abnormality degree based on the real-time unhooking operation health degree, and dynamically adjust the unhooking plan, the normal-hooking plan, and the rehooking plan in sequence with the real-time unhooking operation abnormality degree as the weight.

[0081] Specifically, the real-time operation state parameters refer to the key operation index data of the unhooking robot during the current operation, such as motor current, joint temperature, vibration intensity, etc. The sensor assembly mainly includes vibration sensors, current sensors, temperature sensors, etc. here, and is installed at key parts of the robot. While the unhooking robot is performing the unhooking task, its operation state parameters are periodically collected. The collection frequency can be set to several times per second to ensure the real-time and integrity of the state data. These data will be filtered and normalized by the edge computing device to form the real-time operation state parameters for health diagnosis.

[0082] The predetermined operation state parameters refer to the reference values of the operation indexes of the unhooking robot under normal working conditions, which can be obtained through offline modeling, experimental sampling, or historical data statistics. The operation health degree is an evaluation index of the similarity between the current operation state and the predetermined state, represented by a normalized value (such as 0 to 1), and the higher the value, the more normal the operation. Retrieve the operation benchmark parameters of this type of unhooking robot in similar tasks and environments, including joint temperature, motor current, vibration intensity, etc. Then, compare the real-time operation parameters with the corresponding predetermined operation state parameters one by one, calculate the deviation degree of each parameter, and output the current real-time unhooking operation health degree according to the preset health degree evaluation function (such as weighted average, maximum deviation, etc.) to assist in judging whether a failure or efficiency reduction may occur in the current operation.

[0083] The real-time unhooking operation abnormality degree is an inverse index of the real-time unhooking operation health degree, which can be defined as: abnormality degree = 1 - health degree. This real-time unhooking operation abnormality degree is used to evaluate whether the robot is in an abnormal trend and its severity. Convert the real-time unhooking operation health degree into the real-time unhooking operation abnormality degree, and then dynamically adjust the unhooking plan, the normal-hooking plan, and the rehooking plan with the real-time unhooking operation abnormality degree as the weight. The specific adjustment methods include: increasing the task interval time of the unhooking robot according to the real-time unhooking operation abnormality degree, optimizing the task path to avoid potential fault points, reserving more buffer time for the normal-hooking and rehooking robots, etc. For example, if the real-time unhooking operation abnormality degree is relatively high, the task time of the unhooking robot at each operation point can be extended, and at the same time, the plans of the normal-hooking and rehooking robots are adjusted so that their operation times are postponed accordingly, and their paths are optimized to reduce waiting time or avoid conflicts. The task replanning will be automatically generated based on the current scheduling diagram and path planning algorithm, enhancing the adaptability and fault tolerance to equipment failures, reducing the chain problems caused by single-point abnormalities, and ensuring the continuous and stable operation of the multi-robot system under complex working conditions.

[0084] Further, after step S430, the following steps are also included:

[0085] If the real-time unhooking deviation does not reach the predetermined deviation constraint, obtain the real-time hooking deviation of the hooking robot; if the real-time hooking deviation reaches the predetermined deviation constraint, dynamically adjust the rehooking plan.

[0086] Specifically, when the real-time unhooking deviation does not reach the predetermined deviation constraint, that is, the actual operation progress of the unhooking robot is basically consistent with the expectation, continue to monitor the execution of the hooking robot. Obtain information such as the real-time position, operating state parameters, and task progress of the hooking robot through the sensor component, and compare the real-time hooking task progress with the predetermined hooking progress in the task coordination plan to calculate the real-time hooking deviation. The calculation process is similar to the real-time unhooking deviation and will not be elaborated here. If the real-time hooking deviation exceeds the predetermined deviation constraint, immediately activate the adjustment process of the rehooking plan and dynamically adjust the rehooking plan, including re-evaluating the rehooking start time, obstacle avoidance path, grasping parameters, etc., to ensure that the rehooking robot will not misoperate or cause station congestion when the hooking is not fully ready. The adjustment result will be updated to the robot control system. For example, the hooking robot B is supposed to complete the hooking of the 5th carriage at 10:05 as planned, but the sensor feedback shows that its operation progress lags behind by 1 minute and the spatial positioning error reaches 12 cm, exceeding the predetermined deviation tolerance. After detecting this deviation, the start time of the rehooking robot C is postponed from 10:06 to 10:08, and the path is re-planned to avoid the area where the hooking operation has not been completed yet to prevent misoperation.

[0087] Further, if the real-time unhooking deviation does not reach the predetermined deviation constraint, obtain the real-time hooking deviation of the hooking robot, and the following steps are also included:

[0088] If the real-time hooking deviation does not reach the predetermined deviation constraint, obtain the real-time abnormal operation degree of the hooking robot; use the real-time abnormal operation degree of the hooking robot as the weight to dynamically adjust the hooking plan and the rehooking plan in sequence.

[0089] Specifically, the real-time abnormal degree of the forward hook operation is an indicator that measures the deviation of the current operating state of the forward hook robot from the normal value, reflecting the degree of abnormality of the forward hook robot in an abnormal working state. When the current operation deviation of the forward hook robot does not exceed the set tolerance, the real-time operating state parameters of the forward hook robot are retrieved and compared with the preset operating state parameters during normal operation to calculate the real-time abnormal degree of the forward hook operation. The specific calculation process can refer to the calculation process of the real-time abnormal degree of the unhooking operation. Using the real-time abnormal degree of the forward hook operation as the weight, a warning adjustment is made to the current forward hook plan, such as reducing the load, extending the operation time, optimizing the path to avoid high-risk areas, etc.; at the same time, the re-hooking plan is also adjusted so that it can flexibly wait or adapt to the new completed state of the forward hook, avoid relying on risky nodes, and improve the stability of the dumper operation process.

[0090] Furthermore, the method further includes:

[0091] Obtain the real-time re-hooking deviation, and when the real-time re-hooking deviation reaches the predetermined deviation constraint, perform dynamic adjustment on the re-hooking plan.

[0092] Specifically, the real-time re-hooking deviation refers to the difference value between the actual operation progress or state of the re-hooking robot and the re-hooking plan in the task coordination plan during the dumper operation. During the dumper operation process, the re-hooking robot is continuously monitored through the sensor component to collect its real-time position information, task progress, and operating state parameters, and the real-time re-hooking progress is compared with the predetermined re-hooking task progress in the task coordination plan, thereby calculating the real-time re-hooking deviation. The calculation process can refer to the calculation process of the real-time unhooking deviation. If the real-time re-hooking deviation exceeds the predetermined deviation constraint, it indicates that there is a risk of serious lag, deviation, or execution error in the re-hooking task. At this time, a dynamic adjustment mechanism is triggered to optimize the re-hooking plan, including but not limited to: recalculating the path obstacle avoidance strategy, reallocating the re-hooking start time, adjusting the execution sequence, or arranging for a backup robot to intervene when necessary to restore the coordination stability and prevent the system efficiency from decreasing.

[0093] In summary, the dumper robot cooperative control method provided by the embodiments of the present application for the train operation line has the following beneficial effects:

[0094] An embodiment of the present application proposes a collaborative control method for a dumper robot in a train operation line, which systematically models and dynamically schedules the efficiency and stability issues in multi-robot collaborative operations. First, by collecting multi-dimensional characteristics of the operation to be dumped, a task feature set is established to provide a data basis for subsequent task planning. Then, in the task assignment analysis, the operation units are divided with the train model and position as clustering constraints, and the collaborative operation sequence is constructed through priority calculation. Combining the timing and path requirements of various robots such as uncoupling, hooking, and rehooking, a task collaborative plan is generated, thus realizing fine-grained task parallel scheduling. In the execution stage, the real-time status of each robot is monitored through the sensor component to dynamically obtain the actual operation information. According to the preset collaborative strategy, the actual progress of the uncoupling, hooking, and rehooking operations is compared with the predetermined progress to determine in real time whether there is a deviation. When the deviation exceeds the tolerance threshold, combined with the running status and health, the relevant sub-plans are adjusted according to the abnormality weight to realize intelligent deviation correction of the collaborative path. In addition, the rehooking task is also incorporated into the independent monitoring and dynamic response logic to ensure stable connection of subsequent operation links. Through the above method, the embodiment of the present application effectively solves the problems of rough task arrangement, serial robot collaboration, and slow abnormal response in the prior art, and significantly improves the collaborative intelligence level, scheduling flexibility, and dumper operation efficiency of the dumper robot system.

[0095] Embodiment 2, as Figure 3 shown, based on the same inventive concept as the foregoing Embodiment 1, an embodiment of the present application provides a collaborative control system for a dumper robot in a train operation line, and the system includes:

[0096] A task feature collection module 10, configured to collect multi-dimensional characteristics of the operation to be dumped to obtain a task feature set.

[0097] A task collaborative planning module 20, configured to perform task assignment analysis of the dumper robot with the task feature set as a constraint to obtain a task collaborative plan, where the task collaborative plan includes an uncoupling plan of an uncoupling robot, a hooking plan of a hooking robot, and a rehooking plan of a rehooking robot.

[0098] A collaborative control monitoring module 30, configured to activate the sensor component to monitor the collaborative control process of the operation to be dumped based on the task collaborative plan to obtain real-time dumper information.

[0099] A collaborative strategy adjustment module 40, configured to read a predetermined collaborative strategy, and based on the predetermined collaborative strategy, dynamically adjust the task collaborative plan based on the real-time dumper information, including dynamically adjusting the uncoupling plan and / or the hooking plan and / or the rehooking plan.

[0100] Further, the task collaborative planning module 20 of the embodiment of the present application is further configured to execute the following steps:

[0101] Extract the train list in the to-be-rolled-over operation. The train list includes multiple trains with model numbers and position identifiers. Perform clustering analysis on the multiple trains with the model number and the position as clustering constraints to obtain a clustering result, where the clustering result includes a first clustering cluster. Analyze the first information of the first train in the first clustering cluster to obtain the first priority of the first clustering cluster. Generate a collaborative operation sequence for the to-be-rolled-over operation according to the first priority. Analyze the collaborative operation sequence to obtain the collaborative operation path of the roll-over robot, and form the task collaborative plan with the collaborative operation sequence.

[0102] Further, the first information includes the first model number, the first position, and the first roll-over limit of the first train. The task collaborative planning module 20 in the embodiment of the present application is further configured to perform the following steps:

[0103] Match the first uncoupling duration of the first model number. Combine the real-time position of the uncoupling robot with the first position to obtain a second uncoupling duration. Sum the first uncoupling duration and the second uncoupling duration to obtain a predicted uncoupling duration, and combine the first roll-over limit to obtain a first priority index. Take the maximum value in the first priority index as the first priority of the first clustering cluster.

[0104] Further, the task collaborative planning module 20 in the embodiment of the present application is further configured to perform the following steps:

[0105] Generate an uncoupling collaborative operation sequence for the uncoupling robot in the to-be-rolled-over operation according to the first priority. Analyze the uncoupling collaborative operation sequence to obtain the uncoupling collaborative operation path of the uncoupling robot, and form the uncoupling plan with the uncoupling collaborative operation sequence. Generate the hooking plan for the hooking robot with the predicted uncoupling duration as a constraint. Obtain the predicted hooking duration of the hooking robot, and combine the hooking plan to obtain the re-hooking plan for the re-hooking robot. The uncoupling plan, the hooking plan, and the re-hooking plan form the task collaborative plan.

[0106] Further, the module name in the embodiment of the present application is further configured to perform the following steps:

[0107] Dynamically monitor the uncoupling robot in the task collaborative plan through the sensor component to obtain real-time uncoupling information. Analyze the real-time uncoupling position and the real-time uncoupling state in the real-time uncoupling information to obtain the real-time uncoupling progress. Compare the real-time uncoupling progress with the real-time predetermined uncoupling progress in the task collaborative plan to obtain a real-time uncoupling deviation. If the real-time uncoupling deviation reaches a predetermined deviation constraint, dynamically adjust the hooking plan and the re-hooking plan in sequence.

[0108] Further, the collaborative strategy adjustment module 40 in the embodiments of the present application is further configured to perform the following steps:

[0109] Obtain the real-time operation state parameters of the unhooking robot through the sensor assembly; obtain the predetermined operation state parameters of the unhooking robot, and compare them with the real-time operation state parameters to obtain the real-time unhooking operation health degree; based on the real-time unhooking operation health degree, obtain the real-time unhooking operation abnormality degree, and use the real-time unhooking operation abnormality degree as a weight to dynamically adjust the unhooking plan, the normal hooking plan, and the rehooking plan in sequence.

[0110] Further, the collaborative strategy adjustment module 40 in the embodiments of the present application is further configured to perform the following steps:

[0111] If the real-time unhooking deviation does not reach the predetermined deviation constraint, obtain the real-time normal hooking deviation of the normal hooking robot; if the real-time normal hooking deviation reaches the predetermined deviation constraint, dynamically adjust the rehooking plan.

[0112] Further, the collaborative strategy adjustment module 40 in the embodiments of the present application is further configured to perform the following steps:

[0113] If the real-time normal hooking deviation does not reach the predetermined deviation constraint, obtain the real-time normal hooking operation abnormality degree of the normal hooking robot; use the real-time normal hooking operation abnormality degree as a weight to dynamically adjust the normal hooking plan and the rehooking plan in sequence.

[0114] Further, the collaborative strategy adjustment module 40 in the embodiments of the present application is further configured to perform the following steps:

[0115] Obtain the real-time rehooking deviation, and when the real-time rehooking deviation reaches the predetermined deviation constraint, dynamically adjust the rehooking plan.

[0116] Through the foregoing detailed description of the collaborative control method for the car dumper robot used in the train operation line in this specification, those skilled in the art can clearly know the collaborative control system for the car dumper robot used in the train operation line in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0117] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A collaborative control method for a car dumper robot used in a train operation line, characterized in that: include: Perform multi-dimensional feature collection on the rollover operation to obtain a task feature set; The task allocation analysis of the tipping robot is performed with the task feature set as a constraint to obtain task coordination planning, wherein the task coordination planning includes hook removal planning of the hook removal robot, hook positioning planning of the hook positioning robot, and hook re-positioning planning of the hook re-positioning robot; Activating the sensor component to monitor the collaborative control process of the to-be-rolled vehicle operation based on the task collaborative planning to obtain real-time rollover information; Read a predetermined coordination strategy, and according to the predetermined coordination strategy, dynamically adjust the task coordination plan based on the real-time rollover information, including dynamically adjusting the unhooking plan and / or the hooking plan and / or the re-hooking plan.

2. The collaborative control method of a car dumper robot for a train operation line according to claim 1, characterized in that: The task feature set is used as a constraint to perform task allocation analysis of the tipping robot, and obtain task collaborative planning, including: Extracting a list of trains to be turned over, wherein the list of trains includes a plurality of trains with model and location identifiers; Performing cluster analysis on a plurality of trains with the model and the location as clustering constraints to obtain a clustering result, wherein the clustering result includes a first clustering cluster; analyzing first information of a first train in the first cluster to obtain a first priority of the first cluster; Generating a collaborative operation order of the to-be-turned operation according to the first priority; The collaborative operation sequence is analyzed to obtain the collaborative operation path of the tipping robot, and the collaborative operation path and the collaborative operation sequence form the task collaborative planning.

3. The collaborative control method of a car dumper robot for a train operation line according to claim 2, characterized in that: The first information includes a first model, a first position, and a first rollover limit of the first train, and analyzing the first information of the first train in the first cluster to obtain a first priority of the first cluster includes: Match the first unhooking duration of the first model; Combining the real-time position of the unhooking robot with the first position, obtaining a second unhooking time; The predicted hook-removing time is obtained by adding the first hook-removing time and the second hook-removing time, and the first rollover limit is combined to obtain a first priority index; The maximum value of the first priority indexes is taken as the first priority of the first cluster.

4. The method for collaborative control of a car dumper robot for a train operation line according to claim 3, characterized in that: Analyzing the collaborative operation sequence to obtain the collaborative operation path of the tipping robot, and forming the task collaborative planning with the collaborative operation sequence, including: Generating a hook-removing collaborative operation order of the hook-removing robot in the operation to be turned over according to the first priority; Analyze the hook-removing collaborative operation sequence to obtain the hook-removing collaborative operation path of the hook-removing robot, and form the hook-removing plan together with the hook-removing collaborative operation sequence; Taking the predicted hook removal time as a constraint, generating the hook planning of the hook robot; Obtaining the predicted hooking time of the positive hook robot, and obtaining the double hooking plan of the double hook robot in combination with the positive hooking plan; The unhooking plan, the hooking plan and the re-hooking plan constitute the task coordination plan.

5. The method for collaborative control of a car dumper robot for a train operation line according to claim 1, characterized in that: Reading a predetermined coordination strategy, and dynamically adjusting the task coordination planning based on the predetermined coordination strategy and the real-time rollover information, including: Dynamically monitoring the dehooking robot in the task collaborative planning through the sensor component to obtain real-time dehooking information; Analyze the real-time hook removal position and the real-time hook removal status in the real-time hook removal information to obtain the real-time hook removal progress; Comparing the real-time hook removal progress with the real-time scheduled hook removal progress in the task collaborative planning to obtain a real-time hook removal deviation; If the real-time hook removal deviation reaches a predetermined deviation constraint, the positive hook plan and the re-hook plan are dynamically adjusted in sequence.

6. The collaborative control method of a car dumper robot for a train operation line according to claim 5, characterized in that: If the real-time hook removal deviation reaches a predetermined deviation constraint, the positive hook plan and the re-hook plan are dynamically adjusted in sequence, further comprising: Acquiring real-time operating status parameters of the unhooking robot through the sensor component; Obtaining predetermined operating status parameters of the unhooking robot, and comparing them with the real-time operating status parameters to obtain real-time unhooking operating health; The real-time hook removal operation abnormality is obtained based on the real-time hook removal operation health, and the real-time hook removal operation abnormality is used as a weight to dynamically adjust the hook removal plan, the positive hook plan and the re-hook plan in sequence.

7. The method for collaborative control of a car dumper robot for a train operation line according to claim 6, characterized in that: After comparing the real-time hook removal progress with the real-time scheduled hook removal progress in the task collaborative planning to obtain the real-time hook removal deviation, the method further includes: If the real-time hook removal deviation does not reach the predetermined deviation constraint, obtaining the real-time hook deviation of the hook robot; If the real-time positive hook deviation reaches the predetermined deviation constraint, the complex hook planning is dynamically adjusted.

8. The collaborative control method of a car dumper robot for a train operation line according to claim 7, characterized in that: If the real-time hook-off deviation does not reach the predetermined deviation constraint, after obtaining the real-time hook-off deviation of the hook-off robot, the method further includes: If the real-time positive hook deviation does not reach the predetermined deviation constraint, obtaining the real-time positive hook operation abnormality of the positive hook robot; The real-time normal hook operation abnormality degree is used as a weight to dynamically adjust the normal hook planning and the double hook planning in turn.

9. The method for collaborative control of a car dumper robot for a train operation line according to claim 8, characterized in that: include: A real-time re-hook deviation is obtained, and when the real-time re-hook deviation reaches the predetermined deviation constraint, the re-hook planning is dynamically adjusted.

10. A car dumper robot collaborative control system for a train operation line, characterized in that: The system is used to execute the collaborative control method of a car dumper robot for a train operation line according to any one of claims 1 to 9, comprising: A task feature collection module is used to collect multi-dimensional features of the rollover operation to obtain a task feature set; A task collaborative planning module is used to perform task allocation analysis of the tipping robot with the task feature set as a constraint to obtain task collaborative planning, wherein the task collaborative planning includes hook removal planning of the hook removal robot, hook positioning planning of the hook positioning robot, and hook re-hook planning of the hook re-hook robot; A collaborative control monitoring module is used to activate the sensor component to monitor the collaborative control process of the to-be-rolled operation based on the task collaborative planning to obtain real-time rollover information; The collaborative strategy adjustment module is used to read the predetermined collaborative strategy and dynamically adjust the task collaborative plan based on the predetermined collaborative strategy and the real-time rollover information, including dynamically adjusting the unhooking plan and / or the hooking plan and / or the re-hooking plan.

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