A timing synchronization control method and system for taking and placing materials by an overhead track robot

Abstract

The application discloses a kind of sky rail robot transfer's pick-and-place timing synchronization control method and system, it is related to robot control technical field.Acquire pick-and-place state data to construct the to-be-executed process timing chain of to-be-processed workpiece, then generate dynamic task graph, then the dynamic task graph is carried out rolling optimization analysis, generate candidate task execution sequence, and carry out verification optimization processing to obtain the candidate task execution sequence after verification optimization processing, obtain historical pick-and-place operation data, carry out timing analysis to the candidate task execution sequence after verification optimization processing, obtain the execution timeline of candidate task execution sequence, finally to the execution timeline is carried out synchronous risk analysis, obtain timing synchronization comprehensive information, then select the task execution sequence of sky rail robot transfer, and control sky rail robot executes the task in this task execution sequence, the adaptive scheduling of sky rail robot is realized, and the transfer efficiency, motion stability and process cooperativity of system are improved.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, specifically to a method and system for synchronous control of material handling timing in a ceiling-mounted robot. Background Technology

[0002] With the rapid development of intelligent manufacturing and production technologies, overhead rail robots are playing an increasingly important role in complex production scenarios involving multiple processes, equipment, and workpieces. In the field of high-precision processing of optical components, such as zoom lenses and crystals, overhead rail robots need to precisely coordinate workpiece transfer between multiple processes, including rough grinding, fine grinding, and polishing. Synchronous control of the transfer sequence of overhead rail robots is the key to achieving precise coordination of workpiece transfer. Its core task is to plan a precise and conflict-free motion sequence for overhead rail robots that perform material transfer in complex production scenarios involving multiple processes, equipment, and workpieces, and to ensure that the sequence is strictly matched with the cycle time of upstream and downstream processing equipment.

[0003] Existing technologies, such as Chinese invention patent with authorization announcement number CN120326642B, disclose a task scheduling method, a robot control method, and a robot control system. By scheduling tasks in a control interface with task tracks, each robot is controlled to perform tasks according to the task tracks in the scheduled control interface.

[0004] In the material handling scenarios of overhead rail robots, traditional production methods often rely on independent operation of single machines and manual loading and unloading, which leads to problems such as high labor intensity, low production efficiency, and easy introduction of operational errors that result in unstable processing accuracy. Even when some automated equipment introduces robotic arms, the lack of optimized buffer layout and cycle time balancing design often leads to chaotic material flow paths and waiting backlogs between processes. At the same time, existing scheduling methods usually adopt fixed cycle time models or simple first-in-first-out queues. These queues assume that the production line state remains static and require pre-programming complete action chains for the robots. However, when dynamic disturbances occur, the execution premises of some nodes in the pre-programmed static timing chain are disrupted. However, due to the lack of real-time perception and dynamic reconstruction capabilities of the scheduling system, the robot will continue to execute the invalid instruction sequence, which will lead to process blockage, cycle time chaos, and even production line shutdown and large backlog of work-in-process. In addition, frequent emergency stops and restarts may introduce additional mechanical stress and accuracy drift risks, further affecting processing quality and equipment lifespan. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for synchronous control of material handling timing in a ceiling-mounted robot, which can effectively solve the problems mentioned in the background section.

[0006] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solution: a method for synchronous control of material handling timing in a ceiling-mounted robot, comprising:

[0007] S1: Obtain the material handling status data of the overhead rail robot, and construct the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot. Analyze the execution sequence chain of all workpieces to be processed and generate a dynamic task diagram of the overhead rail robot.

[0008] S2: Within the preset synchronization optimization time window, perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer, generate candidate task execution sequences for the overhead rail robot transfer, and perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer to obtain the verified and optimized candidate task execution sequences for the overhead rail robot transfer.

[0009] S3: Obtain historical material handling and loading operation data of the overhead rail robot, perform time-series analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot.

[0010] S4: Perform a synchronization risk analysis on the execution timeline of the candidate task execution sequence for the overhead rail robot transfer to obtain comprehensive temporal synchronization information of the candidate task execution sequence.

[0011] S5: Based on the comprehensive information of the timing synchronization of the candidate task execution sequence, select the task execution sequence for the transfer of the overhead rail robot, control the overhead rail robot to execute the tasks in the task execution sequence, and after the task is executed, return to step S1 to reconstruct the dynamic task graph of the transfer of the overhead rail robot and restart the optimization of the candidate task execution sequence.

[0012] Furthermore, the method for constructing the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot is as follows: obtain the material handling status data of the overhead rail robot, which includes the identification of the workpiece to be processed, the emergency status attribute of the workpiece to be processed, the process node where the workpiece to be processed is located, the process bottleneck status attribute of the process node, the processing equipment status of the processing equipment corresponding to the process node, the spatial location of the processing equipment, and the spatial location of the buffer area between process nodes.

[0013] Iterate through each workpiece to be processed, determine the starting point for constructing the execution sequence chain based on the processing equipment status corresponding to the process node where the workpiece is located, and construct the execution sequence chain of the workpiece to be processed by sequentially expanding to the subsequent process nodes according to the logical order between process nodes in the predefined complete process route.

[0014] The execution sequence chain of the workpiece to be processed uses process nodes as elements and the logical sequence relationship between process nodes as connections. All process nodes in the execution sequence chain are bound to the identity identifier of the workpiece to be processed.

[0015] Furthermore, the method for generating the dynamic task graph for the overhead rail robot transfer is as follows: all process nodes in the sequence chain of the processes to be executed for all workpieces to be processed are uniformly parsed and mapped as task nodes for the overhead rail robot transfer.

[0016] Based on the material handling status data of the overhead rail robot, the attribute information of the task nodes and the constraint relationships between the task nodes are analyzed. The attribute information of the task nodes includes the process type, spatial location and task priority value of the task nodes. The constraint relationships between the task nodes include the temporal logic constraints and physical reachability constraints between the task nodes.

[0017] Based on the task nodes of the overhead rail robot, the attribute information of the task nodes, and the constraints between the task nodes, a dynamic task diagram for the overhead rail robot's transportation is generated.

[0018] Furthermore, the method for performing rolling optimization analysis on the dynamic task graph of the overhead rail robot is as follows: within a preset synchronous optimization time window, based on the spatial position and task priority value of the task nodes in the dynamic task graph, multiple rounds of iterative search are performed on the task nodes in the dynamic task graph to generate a candidate task execution sequence.

[0019] Furthermore, the method for verifying and optimizing the candidate task execution sequence of the overhead rail robot is as follows: receive the generated candidate task execution sequence one by one, and perform timing logic constraint verification and resource conflict verification on each candidate task execution sequence in sequence.

[0020] The temporal logic constraint verification is performed sequentially on the candidate task execution sequence to obtain the temporal logic constraint verification result of the candidate task execution sequence. The temporal logic constraint verification result of the candidate task execution sequence includes those that fail the temporal logic constraint and those that pass the temporal logic constraint.

[0021] For candidate task execution sequences that pass the timing logic constraint verification, a resource conflict verification is performed to obtain the resource conflict verification result of the candidate task execution sequence. The resource conflict verification result of the candidate task execution sequence includes whether there is a resource conflict or not.

[0022] Based on the resource conflict verification results of the candidate task execution sequences, the conflict resolution program is started, and all the candidate task execution sequences for the transfer of the sky-rail robot after verification and optimization are output.

[0023] Furthermore, the historical material handling data of the overhead rail robot includes historical task execution time data and historical movement time data between nodes.

[0024] Furthermore, the method for performing time-series analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization is as follows: according to the arrangement order of the task nodes in the candidate task execution sequence, read the process type and spatial position of each task node in sequence.

[0025] Based on historical task execution time data of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the execution time of the task node is determined.

[0026] Based on the historical movement time data between nodes of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the movement time between adjacent task nodes is determined.

[0027] The execution time of each task node in the candidate task execution sequence is sequentially accumulated with the movement time between adjacent task nodes to generate a continuous time-stamped sequence from the starting task node to the ending task node of the candidate task execution sequence, thus forming the execution timeline of the candidate task execution sequence for the overhead rail robot transfer.

[0028] Furthermore, the method for synchronously analyzing the execution timeline of the candidate task execution sequence for the transfer of the sky-rail robot is as follows: receiving the candidate task execution sequence and the execution timeline of the candidate task execution sequence for the transfer of the sky-rail robot; for each candidate task execution sequence, parsing the execution timeline of the candidate task execution sequence, and extracting the predicted start time and predicted end time of all task nodes from it.

[0029] Based on the predicted start and end times of all extracted task nodes, the execution efficiency index, attitude stability index, and timing coordination index of the candidate task execution sequence are analyzed.

[0030] The execution efficiency index, attitude stability index, and timing coordination index of the candidate task execution sequence obtained from the analysis are combined and encapsulated into a comprehensive timing synchronization information of the candidate task execution sequence.

[0031] Furthermore, the method for selecting the task execution sequence for the overhead rail robot is as follows: extract the temporal synchronization comprehensive information of the candidate task execution sequence, determine the qualification of the execution task sequence, and determine that the candidate task execution sequence is a qualified candidate sequence when the execution efficiency index of the candidate task execution sequence is higher than or equal to the preset execution efficiency index threshold, the attitude stability index is lower than or equal to the preset attitude stability index threshold, and the temporal coordination index is lower than or equal to the preset temporal coordination index threshold.

[0032] The best candidate task execution sequence is selected from all qualified candidate sequences to obtain the task execution sequence for the transfer of the overhead rail robot. The task execution sequence is then output to the overhead rail robot controller to drive its execution, and the overhead rail robot is controlled to execute the tasks in the task execution sequence.

[0033] A second aspect of the present invention provides a timing synchronization control system for material handling and loading of a ceiling-mounted robot, comprising: a task graph generation module, used to acquire material handling and loading status data of the ceiling-mounted robot, construct a time sequence chain of pending processes for the workpieces to be processed based on the material handling and loading status data of the ceiling-mounted robot, analyze the time sequence chain of pending processes for all workpieces to be processed, and generate a dynamic task graph for the ceiling-mounted robot.

[0034] The verification and optimization module is used to perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer within a preset synchronous optimization time window, generate candidate task execution sequences for the overhead rail robot transfer, and perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer to obtain the verified and optimized candidate task execution sequences for the overhead rail robot transfer.

[0035] The timing analysis module is used to acquire historical material handling and loading operation data of the overhead rail robot, perform timing analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot.

[0036] The synchronous risk analysis module is used to perform synchronous risk analysis on the execution timeline of the candidate task execution sequence of the overhead rail robot transfer, and obtain comprehensive temporal synchronization information of the candidate task execution sequence.

[0037] The task execution module is used to select the task execution sequence for the overhead rail robot based on the comprehensive information of the timing synchronization of the candidate task execution sequences, control the overhead rail robot to execute the tasks in the task execution sequence, reconstruct the dynamic task graph of the overhead rail robot transfer after the task is executed, and restart the optimization of the candidate task execution sequence.

[0038] The present invention has the following beneficial effects:

[0039] (1) This invention transforms the real-time status of the production line overhead robot transfer into a dynamic graph containing task nodes, multi-dimensional attributes and complex constraints. By adopting a rolling optimization combined with a double verification mechanism, it can quickly generate feasible task sequences that conform to process rules and satisfy resource exclusivity within a limited synchronous optimization time window. This dynamic modeling and real-time optimization capability enables the system to respond to disturbances such as sudden changes in the buffer area and temporary equipment stalls in real time. It effectively avoids robot idling, resource conflicts or production process interruptions caused by the disconnect between the plan and the actual situation in traditional static scheduling, and improves the operational stability of the production system.

[0040] (2) By introducing a multi-dimensional quantitative synchronous risk analysis mechanism, this invention elevates scheduling optimization from a single pursuit of efficiency to the level of ensuring production quality and stability. By performing time-series analysis on candidate plans to obtain the execution timeline, and analyzing execution efficiency indicators, attitude stability indicators, and time-series coordination indicators, this invention achieves accurate assessment of the inherent risks of the execution sequence. This multi-dimensional risk assessment system enhances the ability to predict and control production quality risks and process coordination, and provides accurate data basis for selecting the comprehensive optimal solution.

[0041] (3) This invention integrates dynamic programming and multi-objective optimization in decision-making, and restarts the entire optimization cycle based on the latest production line status. This architecture makes the scheduling strategy no longer fixed, but can continuously learn and adjust in real time to cope with continuously changing production tasks and equipment status. Therefore, this invention not only improves the scientific nature and comprehensive performance of single scheduling decisions, but also endows the overhead rail robot system with the ability to maintain a long-term efficient, stable and collaborative production state through self-adaptation and self-optimization, thereby improving overall production efficiency and product quality. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0043] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: a method for synchronous control of material handling timing of overhead rail robot transfer, including S1, acquiring material handling status data of overhead rail robot transfer, constructing a time sequence chain of pending processes for workpieces to be processed based on the material handling status data of overhead rail robot transfer, analyzing the time sequence chain of pending processes for all workpieces to be processed, and generating a dynamic task diagram of overhead rail robot transfer.

[0046] The method for constructing the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot is as follows:

[0047] The system acquires the material handling status data of the overhead rail robot, which includes the identification of the workpiece to be processed, the emergency status attribute of the workpiece to be processed, the process node where the workpiece to be processed is located, the process bottleneck status attribute of the process node, the status of the processing equipment corresponding to the process node, the spatial location of the processing equipment, and the spatial location of the buffer area between process nodes.

[0048] In this embodiment, the processing equipment status corresponding to the process node includes completed operation and incomplete operation. The processing equipment includes a coarse grinding mill, a fine grinding mill, and a polishing machine. The buffer area between process nodes includes a waiting area, a coarse grinding finished product buffer area, a fine grinding waiting area, a fine grinding finished product buffer area, and a finished product area.

[0049] In this embodiment, the workpiece to be processed is a zoom lens blank that needs to undergo multiple processes such as rough grinding, fine grinding and polishing to form a high-precision optical surface.

[0050] Each workpiece to be processed is traversed, and the starting point for constructing the execution sequence chain is determined based on the processing equipment status corresponding to the process node of the workpiece. Based on the starting point, the execution sequence chain of the workpiece is constructed sequentially towards the next process node according to the logical order between process nodes in the predefined complete process route. This process continues until the last process node of the complete process route is included, thus generating an ordered chain with process nodes as elements and logical order between them. This ordered chain is defined as the execution sequence chain of the workpiece to be processed. All process nodes in the execution sequence chain are bound to the workpiece's identity identifier. The actual progress point of the process is intelligently determined based on the processing equipment status, and completed process history is dynamically trimmed, generating only the actual execution sequence chain for the future. This avoids redundancy and invalid tasks generated when constructing based on static, fixed process routes, allowing subsequent scheduling optimization to focus on solving the real bottlenecks and needs at the current moment.

[0051] The method for determining the starting point of the sequence chain of the process to be executed based on the status of the processing equipment corresponding to the process node of the workpiece to be processed is as follows: if the status of the processing equipment corresponding to the process node of the workpiece to be processed is "completed", then the next process node of the process node of the workpiece to be processed is taken as the starting point of the sequence chain of the process to be executed; if the status of the processing equipment corresponding to the process node of the workpiece to be processed is "incomplete", then the process node of the workpiece to be processed is taken as the starting point of the sequence chain of the process to be executed.

[0052] In one specific embodiment, the acquired material handling status data shows that there are currently two workpieces to be processed, identified as lens A and lens B respectively. Lens A is located in the rough grinding process, and the processing equipment corresponding to the rough grinding process is in the state of completed operation. Lens B is located in the fine grinding process, and the processing equipment corresponding to the fine grinding process is in the state of incomplete operation. The predefined complete process route is as follows: rough grinding process, rough grinding post-buffer, fine grinding process, fine grinding post-buffer, and polishing process.

[0053] First, we iterate through lens A. Since the processing equipment corresponding to the rough grinding process of lens A is in the completed state, we determine the next node after the rough grinding process node, i.e., the rough grinding post-cache node, as the starting point for constructing the execution sequence chain of lens A. Based on the complete process route, we start from this construction starting point and proceed sequentially to the subsequent process nodes, generating an ordered chain in the order of rough grinding post-cache, fine grinding process, fine grinding post-cache, and polishing process. This ordered chain is defined as the execution sequence chain of lens A. Each process node in this sequence chain is bound to the identity identifier lens A and labeled with the process name. The state of the sequence node is pending execution. Then, lens B is traversed. Since the processing equipment corresponding to the fine grinding process of lens B is in the state of incomplete operation, the fine grinding process node of lens B is determined as the starting point for constructing the pending execution process sequence chain of lens B. According to the complete process route, starting from this construction starting point, the construction is carried out sequentially towards the subsequent process nodes to generate an ordered chain in the order of fine grinding process, fine grinding post-buffering and polishing process. This ordered chain is defined as the pending execution process sequence chain of lens B. Each process node in this sequence chain is bound to the identity identifier lens B and marked as pending execution.

[0054] The method for generating a dynamic task diagram for the overhead rail robot transfer is as follows:

[0055] All process nodes in the sequence chain of all pending workpieces are uniformly parsed and mapped to task nodes for transport by the overhead rail robot. At the same time, a set of mapping relationships from process nodes to task nodes is collected. The set of mapping relationships from process nodes to task nodes is a data list, in which each record contains a process node, the identity identifier of the pending workpiece to which the process node belongs, and the task node mapped from the process node. The abstract process requirements are concretized into actions that the overhead rail robot can execute, and all actions and their relationships are gathered in one place to establish a complete task pool for subsequent unified scheduling.

[0056] In one specific embodiment, all process nodes in the time sequence chain of the processes to be executed for lens A and lens B are uniformly mapped to task nodes for transfer by the overhead rail robot. The process nodes in the time sequence chain of the processes to be executed for lens A are the rough grinding buffer, fine grinding process, fine grinding buffer, and polishing process; the process nodes in the time sequence chain of the processes to be executed for lens B are the fine grinding process, fine grinding buffer, and polishing process. Specifically, the rough grinding buffer process node is mapped to the unloading task node that moves lens A from the rough grinding machine to the rough grinding buffer area, and the fine grinding process node is mapped to the task node that moves lens A from the rough grinding buffer area to the fine grinding machine. The task nodes for picking up materials and the subsequent task nodes for placing lens A into the fine grinding machine are mapped to the workpieces to be processed. The fine grinding buffer process nodes are mapped to the task nodes for moving lens A from the fine grinding machine to the fine grinding buffer area and the task nodes for moving lens B from the fine grinding machine to the fine grinding buffer area, respectively. The polishing process nodes are mapped to the workpieces to be processed to the task nodes for picking up lens A from the fine grinding buffer area to the polishing machine, the task nodes for placing lens A into the polishing machine, the task nodes for picking up lens B from the fine grinding buffer area to the polishing machine, and the task nodes for placing lens B into the polishing machine, respectively. The corresponding mapping relationships are recorded in the mapping relationship set.

[0057] Based on the material handling status data of the overhead rail robot, the attribute information of the task nodes and the constraint relationships between them are analyzed. The task nodes of the overhead rail robot represent the material handling tasks to be executed. The attribute information of the task nodes includes the process type, spatial location and task priority value. The process type of the task node includes material handling tasks and material handling tasks. The constraint relationships between the task nodes include the temporal logic constraints and physical reachability constraints between them. The task nodes are given the multi-dimensional attributes required for decision-making, and the hard rules that the task nodes must follow are clarified. The complex physical world and process logic are transformed into standardized constraints that can be processed by the algorithm. This is a key prerequisite for realizing automated and intelligent scheduling.

[0058] Based on the material handling status data of the overhead rail robot, the method for analyzing the attribute information of the task nodes and the constraint relationships between the task nodes is as follows:

[0059] Step a1: Based on the spatial location of the processing equipment associated with the task node transferred by the overhead rail robot and the spatial location of the buffer area between the process nodes associated with the task node, the spatial location of the task node is obtained by analysis.

[0060] In one specific embodiment, the method for analyzing and obtaining the spatial position of a task node based on the spatial position of the processing equipment associated with the task node transferred by the overhead rail robot and the spatial position of the buffer area between the process nodes associated with the task node is as follows: if the process type of the task node is a material picking task and it is associated with a coarse grinding mill, then the spatial position of the task node is the spatial position of the material picking station of the coarse grinding mill; if the task node is a material unloading task and it is associated with a fine grinding mill, then the spatial position of the task node is the spatial position of the inlet of the fine grinding mill; if the task node is associated with a buffer area, then the spatial position is set according to the preset spatial position of the buffer area. The spatial position of the processing equipment includes the spatial position of the inlet of the processing equipment and the spatial position of the outlet of the processing equipment. For example, the spatial position of the material unloading task node associated with the coarse grinding mill is set as the fixed three-dimensional coordinates [x1, y1, z1] of the inlet of the coarse grinding mill, and the spatial position of the material picking task node associated with the buffer area after coarse grinding is set as the fixed three-dimensional coordinates [x2, y2, z2] of the buffer area station.

[0061] Step a2: Analyze the emergency status attributes of the workpieces to be processed associated with the task nodes of the overhead rail robot and the bottleneck status attributes of the corresponding process nodes to obtain the task priority values ​​of the task nodes.

[0062] Calculate a specific priority value for each task node transferred by the overhead rail robot. This priority value consists of a preset base priority value based on historical task data analysis, an emergency status bonus value determined by the emergency status attribute of the workpiece associated with the task node, and a bottleneck status bonus value determined by the bottleneck status attribute of the process node associated with the task node. The emergency status attribute is divided into three levels: high, medium, and low, each corresponding to a different emergency status bonus value, with the highest level corresponding to the highest bonus value. If the bottleneck status attribute of the process node associated with the task node is a bottleneck process, the preset bottleneck status bonus value is added to the base priority value; otherwise, no addition is made. The priority value of a task node is determined by the base priority value. The priority value is obtained by adding the emergency status bonus value and the bottleneck status bonus value. The larger the priority value, the higher the priority. The overhead rail robot sorts and selects tasks based on this value when making scheduling decisions. The specific setting method of the basic priority value, emergency status bonus value and bottleneck status bonus value is as follows: the basic priority value is set to a unified benchmark value, such as 50; the three levels of emergency status attributes, high, medium and low, correspond to bonus values ​​of 30, 15 and 0 respectively; the bottleneck status bonus value is set to 20. These values ​​are initial parameters determined by those skilled in the art after statistical analysis of the average processing time of tasks, the impact factor of order delays and the degree of constraint of bottleneck processes on overall capacity in historical production data, through normalization and experience optimization. The system can be continuously fine-tuned and optimized according to actual performance during operation.

[0063] Step a3: Based on the logical sequence relationship between the process nodes associated with the task nodes and the set of mapping relationships from process nodes to task nodes, analyze and obtain the temporal logical constraints between task nodes.

[0064] For each workpiece to be processed, based on the logical sequence relationship between the process nodes in the execution sequence chain of the workpiece to be processed, and combined with the specific task node mapped to each process node, a directed constraint edge is established from the material picking task node of the workpiece to its subsequent material placement task node. This directed constraint edge restricts that in the final generated task execution sequence, the placement task node must be located after its corresponding material picking task node. For example, for workpiece lens A, given that the rough grinding buffer process node is mapped to the material placement task and the fine grinding process node is mapped to the material picking and placement tasks, a time-series logical constraint directed edge is created between the material picking task node of lens A from the rough grinding buffer area and the material placement task node of lens A to the fine grinding machine. This transforms the irreversibility of the process route into a mandatory sorting rule in the task sequence, ensuring that the scheduling result does not violate the basic process flow, thereby fundamentally avoiding production logic errors caused by disordered task order.

[0065] Step a4: Based on the spatial location of the task nodes, process and obtain the physical reachability constraints between the task nodes.

[0066] For any two task nodes, based on their spatial locations, the historical average moving speed of the corresponding overhead rail robot is extracted, and the estimated moving time required for the overhead rail robot to move from the previous task node position to the next task node position is calculated. If the estimated moving time is less than or equal to the preset maximum allowable moving time threshold, a physically reachable constraint undirected edge is established connecting the two task nodes. This physically reachable constraint undirected edge indicates that, during the scheduling optimization process, the two task nodes are allowed to be arranged in the same continuously executable task sequence. If the estimated moving time is greater than the preset maximum allowable moving time threshold, no physically reachable constraint undirected edge is established. The maximum allowable moving time threshold is determined by those skilled in the art based on statistical analysis of historical moving time data between nodes. For example, by calculating the average value and standard deviation of historical moving time, the maximum allowable moving time threshold is set to the sum of the average value and twice the standard deviation to cover most typical moving scenarios and reserve a reasonable safety margin.

[0067] In a specific embodiment, the maximum allowable movement time threshold is set to 15 seconds. When the estimated time for moving from the task node of feeding material from lens A to the buffer area after coarse grinding and then to the task node of picking up material from lens B after fine grinding is calculated to be 10 seconds, since 10 seconds is less than the threshold of 15 seconds, a physically reachable undirected edge will be created between the two task nodes. Conversely, if the estimated movement time exceeds 15 seconds, a physically reachable undirected edge will not be created, indicating that the two task nodes cannot be arranged as a sequential task pair for consecutive execution within the current scheduling optimization cycle.

[0068] Based on the task nodes, attribute information, and constraints between the task nodes of the overhead rail robot, a dynamic task graph for the overhead rail robot's transportation is generated. This dynamic task graph is a data structure containing a set of task nodes, a set of attribute information for the task nodes, a set of directed edges with temporal logical constraints, and a set of undirected edges with physical reachability constraints. This creates a comprehensive decision-making task graph model that integrates all tasks, attributes, and complex constraints. This task graph model abstracts the dynamic production site into a computable graph network, enabling subsequent path search and optimization to be performed under conditions that fully consider efficiency, rules, and physical limitations. This provides a core data foundation for breaking the static scheduling deadlock and achieving adaptive optimization.

[0069] S2. Within the preset synchronization optimization time window, perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer, generate candidate task execution sequences for the overhead rail robot transfer, and perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer to obtain the verified and optimized candidate task execution sequences for the overhead rail robot transfer.

[0070] The method for performing rolling optimization analysis on the dynamic task diagram of the overhead rail robot's transfer is as follows:

[0071] Within the preset synchronization optimization time window, based on the spatial location and priority value of the task nodes in the dynamic task graph, the optimization objective is to maximize the total priority value of completing the task and minimize the estimated total empty running distance of the overhead rail robot. The task nodes in the dynamic task graph are searched in multiple rounds.

[0072] In each round of search, all starting task nodes that meet the execution conditions are selected from the current dynamic task graph. Meeting the execution conditions means that the task node does not have any incomplete preceding task nodes defined by directed edges constrained by temporal logic. Based on a preset heuristic rule, a task node is selected from the task nodes that meet the execution conditions and added to the currently constructed task sequence. The heuristic rule is to prioritize task nodes with higher task priority values. When there are multiple task nodes with the same task priority value, the task node with the shorter estimated empty travel distance required to move from the current position of the overhead rail robot to the task node is selected.

[0073] Each time a task node is added to the sequence, the task node is marked as planned in the dynamic task graph, and the estimated position of the overhead robot is updated. Then, the above filtering and selection steps are repeated until the length of the task sequence under construction reaches the preset maximum sequence length or there are no task nodes in the dynamic task graph that meet the execution conditions. At this time, a candidate task execution sequence is generated, which is recorded as the candidate task execution sequence for the overhead robot transfer.

[0074] The method for verifying and optimizing the candidate task execution sequence of the overhead rail robot is as follows:

[0075] Receive the generated candidate task execution sequences one by one, and perform timing logic constraint verification and resource conflict verification on each candidate task execution sequence in turn.

[0076] The temporal logic constraint verification is performed sequentially on the candidate task execution sequence to obtain the temporal logic constraint verification result of the candidate task execution sequence. The temporal logic constraint verification result of the candidate task execution sequence includes those that fail the temporal logic constraint verification and those that pass the temporal logic constraint verification. Since some complex constraints may be temporarily ignored in order to pursue efficiency goals during the previous fast search, the verification optimization process can systematically identify and correct the logical contradictions and resource competition problems in the solution, ensuring that each candidate sequence in the final output is theoretically strictly feasible and conflict-free, thereby unifying the optimization process and feasibility assurance.

[0077] The timing logic constraint verification of the candidate task execution sequence is performed sequentially. Specifically, each task node in the candidate task execution sequence is traversed sequentially. The execution sequence chain of the workpiece to be processed to which the task node belongs is checked. All logical preceding task nodes of the task node in the execution sequence chain are verified to see if they are all in the candidate task execution sequence and arranged before the task node. If any logical preceding task node is arranged after the task node or is not included in the candidate task execution sequence, the timing logic constraint verification result of the candidate task execution sequence is marked as failing the timing logic constraint and the candidate task execution sequence is marked as invalid. Otherwise, the timing logic constraint verification result of the candidate task execution sequence is marked as passing the timing logic constraint.

[0078] For candidate task execution sequences that pass the timing logic constraint verification, resource conflict verification is then performed to obtain the resource conflict verification results of the candidate task execution sequences. The resource conflict verification results of the candidate task execution sequences include whether resource conflicts exist or not.

[0079] Next, resource conflict verification is performed. The specific method is as follows: based on the processing equipment status update logic of the task node, the execution process of each task node in the candidate task execution sequence is deduced in sequence, and the resource status mapping table is updated synchronously to dynamically reflect the status of the relevant processing equipment after the execution of each task node. During this deduction process, if the resource status mapping table shows that the same related processing equipment is arranged to execute two or more different task nodes at the same time, the resource conflict verification result of the candidate task execution sequence is marked as having a resource conflict, and the task node with the resource conflict is recorded as the conflict task node. At the same time, the conflict time point of the first resource conflict and the corresponding conflict resource identifier are recorded. Otherwise, the resource conflict verification result of the candidate task execution sequence is marked as not having a resource conflict.

[0080] The resource status mapping table records the occupancy status of each related processing equipment on the timeline. Each status record includes the related processing equipment identifier, occupancy start time, expected release time, and the task node corresponding to the related processing equipment.

[0081] For candidate task execution sequences marked as having no resource conflicts by the resource conflict verification result, they are directly determined as candidate task execution sequences that have passed the verification and have undergone verification optimization.

[0082] For candidate task execution sequences with resource conflicts, a conflict resolution procedure is initiated. Specifically, the order of conflicting task nodes is adjusted based on their priority values, placing conflicting task nodes with higher priority values ​​before those with lower priority values. If the priority values ​​of conflicting task nodes are the same, the conflicting task node belonging to the bottleneck process is placed first. The task nodes are rearranged according to the adjusted execution order to generate a new candidate task execution sequence. The timing logic constraint verification and resource conflict verification are then re-executed on this newly generated candidate task sequence. Each adjustment and re-verification is counted as one deep conflict resolution attempt. This process continues until a candidate task execution sequence that satisfies both timing logic constraints and has no resource conflicts is generated, or until the preset maximum number of conflict resolution attempts is reached. The processing of the candidate task execution sequence is then terminated, and all verified and optimized candidate task execution sequences for the overhead rail robot transfer are output.

[0083] S3. Obtain historical material handling data of the overhead rail robot, perform time-series analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot.

[0084] In this embodiment, the static task sorting is transformed into a dynamic execution timeline that includes accurate time prediction. By performing statistical analysis on historical data, a reliable time estimate is given to each operation. Thus, a complete and time-quantified work process can be deduced before the plan is executed, providing an important time dimension basis for subsequent evaluation of the matching degree between the plan and the production rhythm and identification of potential waiting or conflict periods.

[0085] Historical data on material handling operations of the overhead rail robot includes historical task execution time data and historical movement time data between nodes.

[0086] The historical task execution time data of the overhead rail robot includes the process type identifier of the task node, the associated processing equipment identifier of the task node, and the task execution time value of the task node. The historical task execution time data is classified and stored according to the process type identifier and the processing equipment identifier to support the statistical analysis of the execution time of different types of task nodes.

[0087] The historical movement time data between the nodes transported by the sky-rail robot includes the spatial location identifier of the starting task node, the spatial location identifier of the target task node, and the movement time value between the historical nodes. The historical movement time data is categorized and stored according to spatial location to reflect the actual movement time characteristics under different combinations of task node spatial locations.

[0088] The method for performing time-series analysis on the candidate task execution sequences of the overhead rail robot after verification and optimization is as follows:

[0089] According to the order of task nodes in the candidate task execution sequence, read the process type and spatial location of each task node in turn.

[0090] Based on the historical task execution time data of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the execution time of the task node is determined. For the currently read task node, according to the process type identifier and associated processing equipment identifier of the task node, the task execution time value of historical task nodes with the same process type and the same associated processing equipment is retrieved from the historical task execution time data. The average value of the task execution time values ​​of all task nodes is taken, and this average value is determined as the execution time of the task node.

[0091] Based on the historical movement time data between nodes of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the movement time between adjacent task nodes is determined. Specifically, according to the spatial location identifiers of the starting task node and the target task node corresponding to the adjacent task nodes, the historical movement time values ​​between historical nodes with the same spatial location identifiers of the starting task node and the target task node are retrieved from the historical movement time data, statistically processed, and averaged to obtain the average movement time value between nodes. This average movement time value between nodes is then determined as the movement time between adjacent task nodes.

[0092] Under the premise of satisfying the temporal logic constraints and physical reachability constraints between task nodes, the execution time of task nodes in the candidate task execution sequence and the movement time between adjacent task nodes are accumulated sequentially to generate a continuous time stamp sequence from the starting task node to the ending task node of the candidate task execution sequence, thereby forming the execution timeline of the candidate task execution sequence of the overhead rail robot transfer.

[0093] In this embodiment, the process of generating the execution timeline is not a simple accumulation of time. Its core principle lies in establishing a deterministic time projection model. This model takes a verified, logically and resource-feasible task sequence as input and reliable time units derived from historical statistics as its foundation. Through rigorous time-series accumulation, it simulates the precise operation process of the sky-rail robot over a period of time in the future. The output of this model is not a single point in time, but a complete time-event sequence. It concretizes the abstract task sequence into a measurable and analyzable timeline, enabling the subsequent evaluation of the plan's merits to be upgraded from qualitative comparison to quantitative analysis based on time indicators. This lays the data foundation for selecting the optimal solution among multiple feasible options.

[0094] S4. Perform a synchronization risk analysis on the execution timeline of the candidate task execution sequence for the overhead rail robot transfer to obtain comprehensive temporal synchronization information of the candidate task execution sequence.

[0095] The method for synchronous risk analysis of the execution timeline of candidate task execution sequences for overhead rail robot transportation is as follows:

[0096] Receive the execution sequence of each candidate task and the execution timeline of the candidate task execution sequence output by the overhead rail robot in step S3; for each candidate task execution sequence, first parse the execution timeline of the candidate task execution sequence and extract the predicted start time and predicted end time of all task nodes; based on the extracted predicted start time and predicted end time of all task nodes, analyze and obtain the execution efficiency index, attitude stability index and timing coordination index of the candidate task execution sequence.

[0097] The task completion rate of the candidate task execution sequence is obtained by statistically analyzing the total number of task nodes whose predicted end time is no later than the closing time of the preset synchronization optimization time window within the execution timeline of the candidate task execution sequence, and dividing this total number of task nodes by the total number of task nodes in the sequence. This rate is used as the execution efficiency index of the candidate task execution sequence. The execution efficiency index is used to characterize the task throughput capacity of the skyrail robot within the execution timeline of the candidate task execution sequence, so as to measure the task execution sequence rhythm matching and overall transfer efficiency.

[0098] The arrangement order and spatial position relationship of task nodes in the execution timeline of the candidate task execution sequence are analyzed. Based on the spatial position of two adjacent task nodes in the candidate task execution sequence, and combined with the pre-calibrated joint angles of the end effector of the overhead track robot at the spatial position of the two task nodes, the maximum absolute value of the change in each joint angle is taken as the theoretical attitude angle change for that movement. If the theoretical attitude angle change exceeds the preset attitude change threshold, it is counted as a valid attitude switch. The total number of valid attitude switches in the entire candidate task execution sequence is accumulated and recorded as the attitude switch frequency of the candidate task execution sequence, which is used as the attitude stability index of the candidate task execution sequence. The attitude change threshold is determined experimentally by those skilled in the art based on the joint motion characteristics and machining accuracy requirements of the overhead track robot. For example, the attitude change threshold can be set at the 90th percentile by analyzing the distribution of attitude angle changes in historical stable operation data. The attitude stability index is used to characterize the smoothness and intensity of the overhead track robot's movement during the transfer process, so as to measure the impact risk of the movement process on the stability and safety of high-precision workpiece positioning.

[0099] Analyze the execution timeline of candidate task execution sequences for material placement-retrieving task node pairs that belong to the same workpiece and have a direct logical connection. For each pair of material placement-retrieving task node pairs, calculate the time gap between the predicted end time of the preceding material placement task node and the predicted start time of the subsequent material retrieval task node. Count the number of node pairs whose time gaps exceed the preset time gap length, and record this number as the lag connection point count. This count serves as the temporal coordination index of the candidate task execution sequence. The temporal coordination index is used to characterize the tightness and continuity of the connection between upstream and downstream processes, to measure the coordination efficiency of the task execution sequence and the ability to prevent process disconnection. The specific method for determining the preset time gap length is as follows: those skilled in the art statistically analyze the actual time gaps of all process connections that did not cause downstream processing equipment to be idle during historical normal production cycles, calculate the average value of these time gaps, and set this average value as the preset time gap length. For example, if the average time gap is 8 seconds, the preset time gap length can be set to 10 seconds to provide a reasonable buffer margin.

[0100] The execution efficiency index, attitude stability index, and timing coordination index of the candidate task execution sequence obtained from the analysis are combined and encapsulated into a comprehensive timing synchronization information of the candidate task execution sequence.

[0101] In this embodiment, forward-looking time-series forecasts are transformed into multi-dimensional, quantifiable risk assessments. By introducing composite indicators representing efficiency, stability, and synergy, early identification and quantitative comparison of the inherent risks of candidate plans are achieved, providing a scientific decision-making basis for selecting high-quality solutions that are not only efficient but also stable and highly synergistic from multiple feasible options.

[0102] S5. Based on the comprehensive information of the timing synchronization of the candidate task execution sequence, select the task execution sequence for the transfer of the overhead rail robot, control the overhead rail robot to execute the tasks in the task execution sequence, and after the task is executed, return to step S1 to reconstruct the dynamic task graph of the transfer of the overhead rail robot and restart the optimization of the candidate task execution sequence.

[0103] The method for selecting the task execution sequence for the overhead rail robot transfer is as follows:

[0104] The temporal synchronization information of candidate task execution sequences is extracted. A qualified candidate task sequence is determined when its execution efficiency index is higher than or equal to a preset execution efficiency index threshold, its attitude stability index is lower than or equal to a preset attitude stability index threshold, and its temporal coordination index is lower than or equal to a preset temporal coordination index threshold. The preset execution efficiency index threshold is determined by those skilled in the art through analyzing the task completion rate distribution of all candidate task execution sequences within multiple historical synchronization optimization time windows. For example, the threshold can be set at the 75th percentile of the historical task completion rate distribution to filter out task execution sequences with efficiency higher than the historical average. The preset attitude stability index threshold is determined by those skilled in the art based on a comprehensive analysis of the joint motion characteristics, machining accuracy requirements, and historical stable operation data of the overhead rail robot. For example, by statistically analyzing the attitude switching frequency distribution of historically successfully executed task sequences that did not affect machining accuracy, the threshold is set at its 85th percentile to achieve effective control over the robot's motion stability. The preset timing coordination index threshold is determined by those skilled in the art by analyzing the impact of process connection delays on production cycle time in historical production data. For example, by statistically analyzing the maximum lag number of connection points in historical data that did not cause subsequent process equipment to idle, this is set as the timing coordination index threshold to ensure the tightness of process flow.

[0105] The system performs a merit-based selection process for all candidate task execution sequences deemed as qualified candidate sequences. This merit-based selection follows a preset priority rule: the qualified candidate sequence with the highest execution efficiency index is selected first; if multiple sequences have the highest execution efficiency index, the sequence with the higher attitude stability index is further selected from these sequences; if the attitude stability index is also the same, the sequence with the lower timing coordination index is finally selected. Based on the preset priority rule, the system selects the final candidate task execution sequence for the overhead rail robot transfer from all qualified candidate sequences, records the final candidate task execution sequence as the task execution sequence for the overhead rail robot transfer, and outputs the task execution sequence to the overhead rail robot controller to drive execution, while simultaneously controlling the overhead rail robot to execute the tasks in the task execution sequence.

[0106] In this embodiment, the final decision-making and control from multi-sequence evaluation to single-sequence execution is realized. Based on the quantitative risk assessment results, an optimal task execution sequence is selected and sent to the physical device for execution through qualification screening and multi-objective optimization. The sensing and optimization cycle is restarted immediately after execution, thus forming an adaptive and self-optimizing control system that can continuously respond to changes in the production environment.

[0107] Please see Figure 2As shown, a second aspect of the present invention provides a timing synchronization control system for material handling by a ceiling-mounted robot, comprising a task diagram generation module, a verification and optimization module, a timing analysis module, a synchronization risk analysis module, and a task execution module, wherein each module is connected via wired and / or wireless means.

[0108] The task graph generation module is used to acquire the material handling status data of the overhead rail robot, construct the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot, analyze the execution sequence chain of all workpieces to be processed, and generate a dynamic task graph of the overhead rail robot.

[0109] The verification and optimization module is used to perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer within a preset synchronous optimization time window, generate candidate task execution sequences for the overhead rail robot transfer, and perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer to obtain the verified and optimized candidate task execution sequences for the overhead rail robot transfer.

[0110] The timing analysis module is used to acquire historical material handling and loading operation data of the overhead rail robot, perform timing analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot.

[0111] The synchronous risk analysis module is used to perform synchronous risk analysis on the execution timeline of the candidate task execution sequence of the overhead rail robot transfer, and obtain comprehensive temporal synchronization information of the candidate task execution sequence.

[0112] The task execution module is used to select the task execution sequence for the overhead rail robot based on the comprehensive information of the timing synchronization of the candidate task execution sequences, control the overhead rail robot to execute the tasks in the task execution sequence, reconstruct the dynamic task graph of the overhead rail robot transfer after the task is executed, and restart the optimization of the candidate task execution sequence.

[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0114] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for synchronous control of material handling timing in a ceiling-mounted robot, characterized in that, include: S1: Obtain the material handling status data of the overhead rail robot, and construct the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot, analyze the execution sequence chain of all workpieces to be processed, and generate the dynamic task diagram of the overhead rail robot. S2: Within the preset synchronization optimization time window, perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer, generate candidate task execution sequences for the overhead rail robot transfer, perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer, and obtain the verified and optimized candidate task execution sequences for the overhead rail robot transfer. S3: Obtain historical material handling and loading operation data of the overhead rail robot, perform time series analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot. S4: Perform a synchronization risk analysis on the execution timeline of the candidate task execution sequence for the overhead rail robot transfer to obtain comprehensive temporal synchronization information of the candidate task execution sequence; S5: Based on the comprehensive information of the timing synchronization of the candidate task execution sequence, select the task execution sequence for the transfer of the overhead rail robot, control the overhead rail robot to execute the tasks in the task execution sequence, and after the task is executed, return to step S1 to reconstruct the dynamic task graph of the transfer of the overhead rail robot and restart the optimization of the candidate task execution sequence.

2. The method for synchronous control of material handling timing for a ceiling-mounted robot as described in claim 1, characterized in that, The method for constructing the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot is as follows: The material handling status data of the overhead rail robot is obtained. The material handling status data of the overhead rail robot includes the identification of the workpiece to be processed, the emergency status attribute of the workpiece to be processed, the process node where the workpiece to be processed is located, the process bottleneck status attribute of the process node, the processing equipment status of the process node, the spatial position of the processing equipment, and the spatial position of the buffer area between process nodes. Iterate through each workpiece to be processed, determine the starting point for constructing the execution sequence chain based on the processing equipment status corresponding to the process node where the workpiece is located, and construct the execution sequence chain of the workpiece to be processed by expanding to the subsequent process nodes according to the logical order between process nodes in the predefined complete process route based on the starting point for constructing the execution sequence chain of the workpiece to be processed. The execution sequence chain of the workpiece to be processed uses process nodes as elements and the logical sequence relationship between process nodes as connections. All process nodes in the execution sequence chain are bound to the identity identifier of the workpiece to be processed.

3. The method for synchronous control of material handling sequence of a ceiling-mounted robot as described in claim 2, characterized in that, The method for generating the dynamic task diagram for the transfer of the sky-rail robot is as follows: All process nodes in the sequence chain of all workpieces to be processed are uniformly parsed and mapped to task nodes for transfer by the overhead rail robot. Based on the material handling status data of the overhead rail robot, the attribute information of the task nodes and the constraint relationship between the task nodes are analyzed. The attribute information of the task nodes includes the process type, spatial location and task priority value of the task nodes. The constraint relationship between the task nodes includes the temporal logic constraint and physical reachability constraint between the task nodes. Based on the task nodes of the overhead rail robot, the attribute information of the task nodes, and the constraints between the task nodes, a dynamic task diagram for the overhead rail robot's transportation is generated.

4. The method for synchronous control of material handling sequence of a ceiling-mounted robot as described in claim 3, characterized in that, The method for performing rolling optimization analysis on the dynamic task diagram of the overhead rail robot's transfer is as follows: Within a preset synchronization optimization time window, based on the spatial location and priority value of the task nodes in the dynamic task graph, multiple rounds of iterative search are performed on the task nodes in the dynamic task graph to generate a candidate task execution sequence.

5. The method for synchronous control of material handling timing for a ceiling-mounted robot as described in claim 4, characterized in that, The method for verifying and optimizing the candidate task execution sequence for the overhead rail robot transfer is as follows: Receive the generated candidate task execution sequences one by one, and perform timing logic constraint verification and resource conflict verification on each candidate task execution sequence in turn; The temporal logic constraint verification is performed sequentially on the candidate task execution sequence to obtain the temporal logic constraint verification result of the candidate task execution sequence. The temporal logic constraint verification result of the candidate task execution sequence includes those that fail the temporal logic constraint and those that pass the temporal logic constraint. For candidate task execution sequences that pass the timing logic constraint verification, a resource conflict verification is performed to obtain the resource conflict verification result of the candidate task execution sequence. The resource conflict verification result of the candidate task execution sequence includes whether there is a resource conflict or not. Based on the resource conflict verification results of the candidate task execution sequences, the conflict resolution program is started, and all the candidate task execution sequences for the transfer of the sky-rail robot after verification and optimization are output.

6. The method for synchronous control of material handling timing for a ceiling-mounted robot as described in claim 1, characterized in that, The historical material handling data of the overhead rail robot includes historical task execution time data and historical movement time data between nodes.

7. The method for synchronous control of material handling timing for a ceiling-mounted robot as described in claim 6, characterized in that, The method for performing time-series analysis on the candidate task execution sequences of the overhead rail robot after verification and optimization is as follows: According to the order of task nodes in the candidate task execution sequence, read the process type and spatial location of each task node in turn; Based on the historical task execution time data of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the execution time of the task node is determined. Based on the historical movement time data between nodes of the overhead rail robot, and combined with the process type and spatial location of adjacent task nodes, the movement time between adjacent task nodes is determined. The execution time of each task node in the candidate task execution sequence is sequentially accumulated with the movement time between adjacent task nodes to generate a continuous time-stamped sequence from the starting task node to the ending task node of the candidate task execution sequence, thus forming the execution timeline of the candidate task execution sequence for the overhead rail robot transfer.

8. The method for synchronous control of material handling timing for a ceiling-mounted robot as described in claim 7, characterized in that, The method for synchronously analyzing the execution timeline of candidate task execution sequences for overhead rail robot transfer is as follows: Receive the candidate task execution sequence and the execution timeline of the candidate task execution sequence for the transfer of the sky-rail robot. For each candidate task execution sequence, parse the execution timeline of the candidate task execution sequence and extract the predicted start time and predicted end time of all task nodes. Based on the predicted start and end times of all extracted task nodes, the execution efficiency index, attitude stability index, and timing coordination index of the candidate task execution sequence are analyzed. The execution efficiency index, attitude stability index, and timing coordination index of the candidate task execution sequence obtained from the analysis are combined and encapsulated into a comprehensive timing synchronization information of the candidate task execution sequence.

9. The method for synchronous control of material handling sequence of a ceiling-mounted robot as described in claim 8, characterized in that, The method for selecting the task execution sequence for the overhead rail robot transfer is as follows: Extract the temporal synchronization comprehensive information of the candidate task execution sequence, and determine the qualification of the execution task sequence. When the execution efficiency index of the candidate task execution sequence is higher than or equal to the preset execution efficiency index threshold, the attitude stability index is lower than or equal to the preset attitude stability index threshold, and the temporal coordination index is lower than or equal to the preset temporal coordination index threshold, the candidate task execution sequence is determined to be a qualified candidate sequence. The best candidate task execution sequence is selected from all qualified candidate sequences to obtain the task execution sequence for the transfer of the overhead rail robot. The task execution sequence is then output to the overhead rail robot controller to drive its execution, and the overhead rail robot is controlled to execute the tasks in the task execution sequence.

10. A timing synchronization control system for material handling by a ceiling-mounted robot, characterized in that, include: The task graph generation module is used to acquire the material handling status data of the overhead rail robot, and construct the execution sequence chain of the workpiece to be processed based on the material handling status data of the overhead rail robot, analyze the execution sequence chain of all workpieces to be processed, and generate a dynamic task graph of the overhead rail robot. The verification and optimization module is used to perform rolling optimization analysis on the dynamic task graph of the overhead rail robot transfer within a preset synchronous optimization time window, generate candidate task execution sequences for the overhead rail robot transfer, and perform verification and optimization processing on the candidate task execution sequences for the overhead rail robot transfer to obtain the verification and optimization processed candidate task execution sequences for the overhead rail robot transfer. The timing analysis module is used to acquire historical material handling and loading operation data of the overhead rail robot, perform timing analysis on the candidate task execution sequence of the overhead rail robot after verification and optimization, and obtain the execution timeline of the candidate task execution sequence of the overhead rail robot. The synchronous risk analysis module is used to perform synchronous risk analysis on the execution timeline of the candidate task execution sequence of the overhead rail robot transfer, and obtain comprehensive temporal synchronization information of the candidate task execution sequence. The task execution module is used to select the task execution sequence for the overhead rail robot based on the comprehensive information of the timing synchronization of the candidate task execution sequences, control the overhead rail robot to execute the tasks in the task execution sequence, reconstruct the dynamic task graph of the overhead rail robot transfer after the task is executed, and restart the optimization of the candidate task execution sequence.

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