A parallel processing scheduling method and system of a multi-station laser cutting machine and a medium

Abstract

The application provides a parallel processing scheduling method and system of a multi-station laser cutting machine and a medium, and relates to the field of laser processing scheduling and production optimization control. The method comprises the following steps: collecting graphic information of a to-be-processed task, extracting path structure features, and assigning path segment numbers; constructing a subtask feature vector and a station capacity score vector; and performing preliminary scheduling of the subtask based on a matching degree value; evaluating thermal influence conflicts and performing scheduling optimization in combination with spatial distance and processing time period overlap rate; and monitoring station abnormalities and dynamically rescheduling remaining path segments during execution. The problems of coarse multi-station laser cutting task scheduling granularity, unreasonable resource allocation, failure to consider thermal influence interference between stations, and abnormal response lag in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of laser processing scheduling and production optimization control technology, and in particular to a parallel processing scheduling method, system and medium for a multi-station laser cutting machine. Background Technology

[0002] With the widespread application of laser cutting technology in metal processing, sheet metal manufacturing, and automotive parts production, multi-station laser cutting machines are gradually becoming key equipment for improving production capacity and flexible manufacturing efficiency. However, existing laser cutting scheduling methods have several problems. First, multi-station laser cutting tasks typically employ coarse-grained scheduling strategies based on whole-image or whole-part partitioning, lacking detailed analysis of the internal structural characteristics of the task. This leads to uneven allocation of station resources, significant differences in processing time, and reduced overall equipment utilization. Second, existing methods often ignore potential thermal interference between stations during task allocation. If multiple stations process simultaneously in spatially adjacent locations, heat diffusion may cause a decrease in cutting accuracy or equipment damage. Furthermore, if a station malfunctions or is interrupted during task execution, existing methods struggle to effectively and dynamically schedule and quickly restore the remaining processing based on the task execution status, impacting overall production cycle and delivery time.

[0003] Therefore, there is an urgent need for an intelligent scheduling method that can perform task division at the path segment level and comprehensively consider task structure complexity, station capacity and thermal impact constraints, in order to improve the processing efficiency of multi-station laser cutting machines. Summary of the Invention

[0004] The purpose of this application is to provide a parallel processing scheduling method, system, and medium for multi-station laser cutting machines. This addresses the problems in existing multi-station laser cutting technologies, such as coarse-grained task scheduling, unreasonable resource allocation, failure to consider inter-station thermal interference, and delayed abnormal response.

[0005] In view of the above technical problems, this application provides a parallel processing scheduling method, system and medium for multi-station laser cutting machines.

[0006] A first aspect of this application provides a parallel processing scheduling method for a multi-station laser cutting machine, the method comprising:

[0007] The graphical information of the tasks to be processed by the multi-station laser cutting machine is obtained from the task file. The material type, material thickness parameters, task urgency and scheduling priority are obtained through the production scheduling management system. The graphical information is used to represent the vector map data of the cutting trajectory of the area to be processed. The target completion time of the task to be processed is obtained according to the task urgency and scheduling priority.

[0008] Based on the graphic information, the structural features of the cutting path are extracted, and a path complexity score is calculated to measure the difficulty of the task to be processed.

[0009] The task to be processed is divided into multiple subtasks, the estimated processing time of each subtask is estimated, and the urgency score of each subtask is calculated by combining the path complexity score and the target completion time. A subtask feature vector is constructed, which includes the path complexity score, the urgency score and the estimated processing time of the subtask.

[0010] Obtain the current task queue number, historical average processing time, historical failure count, processing power level, and station location coordinates for each laser cutting station, and construct a station capability scoring vector;

[0011] Based on the feature vector of each subtask and the capability score vector of the workstation, the matching degree value between each subtask and each laser cutting workstation is calculated by summing the absolute values ​​of the vector differences. Based on the matching degree value, laser cutting workstations are assigned to each subtask, and a subtask allocation table is output.

[0012] Based on the target station identifier and estimated processing time of each subtask in the subtask allocation table, and combined with the position coordinates of each laser cutting station, thermal impact conflicts between stations are determined by the overlap rate of spatial distance and processing time period. Conflicting subtasks are scheduled to stations without thermal impact conflicts according to the order of processing start time to eliminate the conflicts, and the subtask allocation table is updated.

[0013] The multi-station laser cutting machine is controlled to execute cutting tasks according to the updated subtask allocation table. If a station abnormality is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

[0014] A second aspect of this application provides a parallel processing scheduling system for a multi-station laser cutting machine, the system comprising:

[0015] The task and parameter acquisition module is used to obtain graphic information of the tasks to be processed by the multi-station laser cutting machine from the task file, and to obtain material type, material thickness parameters, task urgency and scheduling priority through the production scheduling management system. The graphic information is used to represent vector map data of the cutting trajectory of the area to be processed. Based on the task urgency and scheduling priority, the target completion time of the task to be processed is obtained.

[0016] The path structure feature extraction and complexity calculation module is used to extract the structural features of the cutting path based on the graphic information and calculate the path complexity score to measure the difficulty of the task structure execution.

[0017] The subtask construction and feature vector generation module is used to divide the task to be processed into multiple subtasks, estimate the expected processing time of each subtask, calculate the urgency score of each subtask by combining the path complexity score and the target completion time, and construct a subtask feature vector. The subtask feature vector includes the path complexity score, the urgency score and the expected processing time of the subtask.

[0018] The station capability scoring vector construction module is used to obtain the current task queue number, historical average processing time, historical failure number, processing power level and station location coordinates of each laser cutting station, and construct the station capability scoring vector.

[0019] The subtask and workstation matching and allocation module is used to calculate the matching degree value between each subtask and each laser cutting workstation based on the subtask feature vector and the workstation capability score vector, using the sum of the absolute values ​​of the vector differences. The module then allocates laser cutting workstations to each subtask according to the matching degree value and outputs a subtask allocation table.

[0020] The subtask thermal impact conflict avoidance module is used to determine the thermal impact conflict between workstations based on the target workstation identifier and the estimated processing time of each subtask in the subtask allocation table, combined with the position coordinates of each laser cutting workstation, by the overlap rate of spatial distance and processing time period, and to schedule the conflicting subtasks to workstations without thermal impact conflict according to the order of processing start time to eliminate the conflict, and update the subtask allocation table.

[0021] The anomaly recovery and subtask rescheduling module is used to control the multi-station laser cutting machine to execute cutting tasks according to the updated subtask allocation table. If an anomaly is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

[0022] A third aspect of this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the parallel processing scheduling method for a multi-station laser cutting machine as described above.

[0023] One or more technical solutions provided in this application have at least the following technical effects or advantages: By introducing a subtask partitioning method based on the structural characteristics of the cutting path, complex processing tasks can be decomposed into independently schedulable subtasks, ensuring uniform task granularity and facilitating parallel allocation; by constructing a subtask feature vector containing indicators such as path complexity, urgency, and estimated time consumption, combined with a workstation capability scoring vector considering the number of tasks queuing, estimated processing time, historical failure count, and processing power level, quantitative matching and allocation of subtasks and workstations are achieved, avoiding the arbitrariness of traditional experience-based scheduling; the workstation with the smallest matching degree value is prioritized for scheduling, achieving precise adaptation between subtasks and workstation capabilities, significantly improving workstation utilization and task execution efficiency; based on this, combined with a dual-threshold judgment rule of spatial distance and processing time overlap rate, automatic detection and elimination of thermal impact conflicts under high-density multi-workstation conditions are achieved, ensuring the stability and safety of the cutting process; when a workstation experiences a running failure or abnormal interruption, the system can obtain processing status feedback information in real time, automatically identify the remaining path segments, and trigger a rescheduling mechanism to ensure continuous task execution and stable production rhythm. Therefore, this application not only improves the overall production efficiency of multi-station laser cutting machines, but also enhances the intelligence level of scheduling and the robustness of the processing process through quantitative matching, automatic conflict avoidance, and dynamic rescheduling for anomalies. It solves the problems of coarse granularity in multi-station laser cutting task scheduling, unreasonable resource allocation, failure to consider inter-station thermal interference, and delayed anomaly response in existing technologies.

[0024] The above description is merely an overview of the technical solution of this application. In order to more clearly explain the technical means of this application, and to enable its implementation in accordance with the contents of the specification, and to make the above and other objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application are described below. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0026] Figure 1 A flowchart illustrating a parallel processing scheduling method for a multi-station laser cutting machine provided in this application embodiment;

[0027] Figure 2 A schematic diagram of the structure of a parallel processing scheduling system for a multi-station laser cutting machine provided in this application embodiment;

[0028] Figure 3 This is a schematic diagram of the structure of the medium in Embodiment 3 of the present invention.

[0029] Figure labeling: 10 Task and parameter acquisition module, 20 Path structure feature extraction and complexity calculation module, 30 Subtask construction and feature vector generation module, 40 Workstation capability score vector construction module, 50 Subtask and workstation matching and allocation module, 60 Subtask thermal impact conflict avoidance module, 70 Anomaly recovery and subtask rescheduling module. Detailed Implementation

[0030] This application provides a parallel processing scheduling method, system, and medium for multi-station laser cutting machines, which solves the problems of coarse granularity of multi-station laser cutting task scheduling, unreasonable resource allocation, failure to consider thermal interference between stations, and delayed abnormal response in the prior art.

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0032] It should be noted that the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0033] Example 1, as Figure 1 As shown, this application provides a parallel processing scheduling method for a multi-station laser cutting machine, wherein the method includes:

[0034] The graphical information of the tasks to be processed by the multi-station laser cutting machine is obtained from the task file. The material type, material thickness parameters, task urgency and scheduling priority are obtained through the production scheduling management system. The graphical information is used to represent vector map data of the cutting trajectory of the area to be processed. Based on the task urgency and scheduling priority, the target completion time of the task to be processed is obtained.

[0035] Specifically, the process begins by parsing the task file to obtain the graphical information of the task to be processed. This graphical information is vector data in DXF format, representing the cutting trajectory of the area to be processed, and accurately describes the geometric structure and connectivity of the path to be processed. The geometric structure of the vector data specifically includes the starting point of each path segment. , endpoint and Coordinate parameters, specifically the connection order of path segments, provide the foundational data for extracting the number of cutting path segments and the number of direction changes. The estimation of the target completion time needs to consider the total time consumed by all currently pending sub-tasks and the remaining capacity of each workstation to ensure the estimation results match the actual production cycle. Subsequently, through data interaction with the Production Scheduling Management System (MES), the system obtains the material type, material thickness parameters, task urgency, and scheduling priority associated with the pending tasks. The system jointly determines the target completion time of the pending tasks based on the task urgency and scheduling priority: if specified in the task file, it is directly extracted; otherwise, it is estimated and generated by the scheduling system based on task priority, current workstation load, and delivery deadline factors.

[0036] Based on the graphic information, the structural features of the cutting path are extracted, and a path complexity score is calculated to measure the difficulty of the task to be processed.

[0037] Furthermore, the path complexity score includes:

[0038] Based on the vector map data of the cutting trajectory of the area to be processed as described in the graphic information, the number of segments of the cutting path of the task to be processed is extracted. The total length of the cutting path The This indicates the number of consecutive line segments in the cutting path. This is the sum of the lengths of all path segments;

[0039] Based on the direction vector difference between adjacent path segments in the cutting path, the number of direction changes is counted. The Used to reflect the degree of tortuosity of the cutting path;

[0040] Calculate the path complexity score according to the preset complexity weighted scoring formula. The pre-defined complexity-weighted scoring formula is:

[0041] ;

[0042] in, , , For the pre-set weighting coefficients, To adjust the weighting coefficient of the number of path segments, The weighting coefficient for the total path length. The weighting coefficient is the number of directional changes. The path complexity score represents the structural execution difficulty of the cutting path to be processed.

[0043] Specifically, the structural features of the cutting path include the number of segments, the total path length, and the number of direction changes. A precise association between features and the path can be achieved by assigning a unique path segment number to each segment. The path complexity score is calculated using a preset weighted scoring formula (combining weight coefficients for the number of segments, total path length, and number of direction changes), ensuring that the score objectively reflects the structural execution difficulty of the task to be processed. After collecting the graphic information of the task to be processed, the system performs structural analysis on each path based on the graphic information. The graphic information is vector data of the area to be processed in DXF format, containing the geometric coordinates, connection order, and layer attribute fields of multiple path segments. The system performs structured parsing of the vector data, extracting the number of segments, total path length, and number of direction changes of the cutting path. The number of segments refers to the number of consecutive line segments in the cutting path, and the total path length is the sum of the cumulative lengths of all path segments. Subsequently, for each path segment, the system assigns a unique path segment number based on its geometric connection order. To quantify the structural execution difficulty of the task to be processed, the system calculates the path complexity score according to a preset weighted complexity scoring formula. The scoring formula calculates the number of direction changes based on the difference in direction vectors between adjacent path segments, used to measure the path's tortuosity. Specifically, the statistical rule involves calculating the direction vector of each path segment based on the coordinate information of the vector map data. For points... and The defined path segment, its direction vector Then, the direction vectors of adjacent path segments are calculated. and The angle between The calculation formula is: Set a threshold for an angle (e.g.) ).when When this occurs, it is determined to be a valid change of direction. The count increases by 1; if If the direction has not changed significantly, it is considered not counted. The preset complexity-weighted scoring formula is: ； in, 、 、 The weighting coefficients are pre-defined and satisfy the following conditions: + + = 1 constraint condition, To adjust the weighting coefficient of the number of path segments to be cut (initially 0.4). This is the weighting coefficient for the total path length (initially 0.3). The weighting coefficient for the number of directional changes (initially 0.3). The path complexity score represents the structural execution difficulty of the cutting path to be processed. To ensure the consistency of the scoring logic and the interpretability of the parameters, a regression analysis based on the scheduling success rate and path structure characteristics in historical processing tasks is used. The system also supports user-customized adjustments based on different workpiece types, structural complexity, and processing efficiency targets. The adjustment range is limited to the [0,1] interval and satisfies... + + The constraint condition = 1, the path complexity score In subsequent steps, the urgency score and the estimated processing time of subtasks will be processed using a minimum-maximum normalization method to eliminate dimensional differences, ultimately transforming them into a normalized path complexity score. It is used for constructing feature vectors for subtasks and calculating the matching degree of subsequent workstations.

[0044] The task to be processed is divided into multiple subtasks, the estimated processing time of each subtask is estimated, and the urgency score of each subtask is calculated by combining the path complexity score and the target completion time. A subtask feature vector is constructed, which includes the path complexity score, the urgency score and the estimated processing time of the subtask.

[0045] Furthermore, the construction of the subtask feature vector includes:

[0046] Based on the structural characteristics of the cutting path, the task to be processed is divided into multiple cutting path segments, and a unique path segment number is assigned to each path segment. Several adjacent path segments are combined into a subtask. The subtask is a processing unit that can be independently scheduled and can be allocated and executed in parallel among multiple laser cutting stations.

[0047] Obtain the average processing time for each path in historical processing records that have the same material type and thickness parameters as the current one, and record it as the historical average processing time. Combined with the number of segments of the cutting path in the current subtask Calculate the estimated processing time for each subtask. The calculation formula is:

[0048] ;

[0049] Based on the path complexity score With the target completion time Calculate the urgency score for each subtask. The urgency score represents the time pressure intensity of the subtask's estimated processing time relative to the target completion time, and is calculated using the following formula:

[0050] ;

[0051] The path complexity score Urgency score Compared with the expected processing time The numerical range is normalized using a minimum-maximum normalization method, which linearly maps each parameter value to the [0,1] interval. The normalization formula is as follows:

[0052] ;

[0053] in, ∈{ ， ， }, normalized parameters 、 These are the minimum and maximum values ​​of the corresponding feature terms among all the subtasks to be processed.

[0054] Normalized features Subtask feature vectors are constructed by combining elements in a fixed order.

[0055] Specifically, the task to be processed is divided into multiple cutting path segments based on the structural features of the cutting path. These structural features include, but are not limited to, the geometric length of the path, the number of direction changes, the spatial relationship between the start and end points of the path, and corner density factors. By analyzing the ratio of the angle of direction change to the relative length between continuous path segments, natural segment boundaries within the path are identified. The rule for determining the angle of direction change is to traverse the cutting path and calculate the angle of direction change between adjacent path segments based on the coordinate information of the vector map data. θ .when θ When a preset angle threshold (e.g., 15°) is set, path segments are divided at that location. The rule for determining the relative length ratio is to calculate the average length of all path segments. ( =Total path length / If the length of a certain path segment is greater than a preset proportion threshold (e.g., twice the average length) ), then the position of the change in direction of the path itself (i.e. θThe system divides the task into multiple subtasks based on the path segment numbers (at a preset angle threshold). Each subtask consists of several consecutive path segments, each corresponding to a unique set of path segment numbers. Each subtask, as an independently schedulable processing unit, can be allocated and executed in parallel across different laser cutting stations. After task division, the system retrieves historical processing task records that match the material type and thickness parameters of the current task, calculates the average processing time for each path segment (recorded as the historical average processing time), and estimates the expected processing time using the following formula, combined with the number of path segments contained in each current subtask: ,in, This indicates the estimated processing time for the subtask. This indicates the number of path segments contained in the subtask. The historical average processing time is used, specifically the average processing time of a single path segment. Then, the system combines the path complexity score for each subtask with the target completion time of the entire task. Calculate the urgency score for this subtask. This reflects the urgency level of the time allocation. The urgency score is calculated using the following formula: ; must meet ≤ (Based on production scheduling logic, the processing time is expected to be no more than the task target completion time. If the processing time is affected by historical data deviations...) > Then force setting =1 indicates that this subtask is extremely urgent and needs to be scheduled first; if =0 indicates no time pressure. The path complexity is scored. Urgency score Estimated processing time for subtasks A minimum-maximum normalization process is performed. This normalization process involves scaling the current task queue count, historical average processing time, historical failure count, and processing power level proportionally based on the maximum and minimum values ​​of the corresponding fields in all laser cutting stations. This compresses the original values ​​of each field into the [0,1] range to ensure the comparability of weights for different fields when constructing the station capability scoring vector. The normalization formula is as follows: ： ;in, ∈{ ， ， } Normalized parameters 、 The minimum and maximum values ​​of the corresponding feature terms in all current subtasks to be processed; the normalized features Subtask feature vectors are constructed by combining elements in a fixed order. The subtask feature vector serves as the basic input for matching workstation capabilities in subsequent scheduling algorithms, comprehensively representing the structural complexity, time pressure, and resource consumption requirements of the current subtask.

[0056] The current task queue number, historical average processing time, historical failure count, processing power level, and station location coordinates of each laser cutting station are obtained to construct a station capability scoring vector.

[0057] Furthermore, including:

[0058] The production scheduling and management system obtains the current task queue number for each laser cutting station. and workstation location coordinates ,in Indicates the first One laser cutting station;

[0059] Based on historical processing task records, the number of historical failures for each workstation is counted. ;

[0060] The processing power level is obtained based on the workstation configuration parameters. ;

[0061] By combining the historical average processing time of each workstation for handling similar sub-tasks, the historical average processing time of each workstation is obtained. ;

[0062] The historical average processing time of the workstation Current task queue count Historical number of failures With processing power level Normalization is performed, and the score is calculated according to the preset workstation weighting formula. The workstation capability score is calculated using the following weighted formula:

[0063] ;

[0064] in For the normalized first The number of tasks currently queued at each workstation. This represents the normalized historical average processing time for each workstation. The normalized number of historical faults. This is the normalized processing power level. For the first Capability rating for each laser cutting station. 、 、 、 Preset weighting coefficients for each scoring item are used to reflect the degree of influence of each indicator on the workstation's capability, satisfying... + + + =1;

[0065] The ability scores corresponding to each workstation Workstation capability scoring vectors are constructed in order of workstation number. = , This represents the total number of laser cutting stations.

[0066] Specifically, the production scheduling management system queries the current task queue count for each laser cutting station in real time, reflecting the current task congestion level at each station, and obtains the spatial coordinates of each laser cutting station. These coordinates are pre-set layout parameters, read and stored by the production scheduling management system during the initialization phase. Secondly, based on historical processing task records, the number of failures at each station over a past period is statistically analyzed. The processing power level refers to the maximum output power parameter of the laser configured at each station, usually measured in watts (W), reflecting its processing capability for materials of different thicknesses and complexities. This can be extracted based on parameters recorded in the equipment specifications for each station. Subsequently, the current task queue count, historical average processing time, historical failure count, and processing power level for each station are normalized using a minimum-maximum normalization formula. ： ;in, ∈{ , , , } Normalized parameters 、 Given the minimum and maximum values ​​of the corresponding feature items among all currently pending subtasks, the system assigns weights to each parameter according to a preset workstation scoring weighting formula and sums them to obtain the capability score for each workstation: ； in For the normalized first The number of tasks currently queued at each workstation. This represents the normalized historical average processing time for each workstation. The normalized number of historical faults. This is the normalized processing power level. For the first Capability rating for each laser cutting station. 、 、 、 Preset weighting coefficients for each scoring item are used to reflect the degree of influence of each indicator on the workstation's capability, satisfying... + + + =1, its initial setting: task queuing weight =0.3, weight of historical average processing time of the workstation =0.3, weight of historical failure count =0.2, weight of processing power level =0.2. The weighting coefficient supports online dynamic adjustment. Finally, the capability scores of each laser cutting station are arranged in order of station number to form a station capability score vector: = ,in, n The total number of laser cutting stations currently participating in the scheduling is represented by the station's capability score vector, which serves as one of the input parameters for the scheduling matching process. This vector is used to calculate the matching degree with the sub-task feature vector, thereby guiding the task scheduling priority and station allocation decisions.

[0067] Based on the feature vector of each subtask and the capability score vector of the workstation, the matching degree value between each subtask and each laser cutting workstation is calculated by summing the absolute values ​​of the vector differences. Laser cutting workstations are assigned to each subtask according to the matching degree value, and a subtask allocation table is output.

[0068] Furthermore, the matching degree value between each subtask and each laser cutting station is calculated by summing the absolute values ​​of the vector differences between the subtask feature vector and the station capability score vector, including:

[0069] Based on the subtask feature vector Workstation Capability Rating Vector = { ， ， ， } The matching degree value between each subtask and each laser cutting station is calculated using the sum of the absolute values ​​of vector differences. The formula for calculating the matching degree value is as follows:

[0070] ;

[0071] in, For the first Sub-tasks For the first One laser cutting station Indicates the first Sub-tasks and the first Matching degree value between individual workstations Score the path complexity of the normalized subtasks. To score the urgency of the subtasks after normalization, The normalized estimated processing time for the subtask. This represents the current task queue size at the laser cutting station after normalization. This represents the normalized historical number of failures at the laser cutting station. This represents the normalized processing power level of the laser cutting station. This represents the normalized historical average processing time for laser cutting stations. , 、 、 、 The preset weighting coefficients correspond to the adjustment weights of path matching difference, urgency difference, time difference, and power adaptability, respectively, to satisfy... + + + =1;

[0072] Matching each subtask with each workstation. The matching degree value is sorted, and the smaller the matching degree value, the closer the current workstation is to the subtask characteristics. Each subtask is assigned to the laser cutting workstation with the smaller matching degree value and that has not yet been occupied, and a subtask allocation table is generated. The subtask allocation table includes the subtask number, the task identifier, the estimated processing time of the subtask, the planned start time, the matching degree value, the target workstation identifier, and the preliminary scheduling status information.

[0073] Specifically, the subtask feature vector includes a normalized path complexity score, urgency score, and estimated processing time; the workstation capability score vector includes a normalized current task queue count, historical failure count, processing power level, and average processing time. The matching degree value is calculated using the following formula: ;in, For the first Sub-tasks For the first One laser cutting station Indicates the first Sub-tasks and the first Matching degree value between individual workstations For the normalized first The path complexity score of each subtask represents the degree to which the subtask requires complex processing capabilities at the workstation (the higher the value, the more complex the path features). For the normalized first The current number of tasks in the queue for each laser cutting station represents the "current workload of complex tasks at the station" (the larger the value, the more tasks the station has backlogged and the less room it has to take on new complex tasks). The absolute value of the difference between the two is used to quantify the difference between the "complex path requirements of subtasks" and the "complex processing capacity margin under the current load of the workstation"—the smaller the difference, the more the remaining resources of the workstation can meet the complex path processing requirements of the subtasks under the current queuing state (such as avoiding problems such as trajectory connection errors and speed fluctuations caused by excessive load). After normalization The urgency score of each subtask represents the requirement for "timeliness of response of the subtask to the workstation task" (the higher the value, the more urgent the task, the more urgent it is, the more it needs to be completed in a short time, and the higher the requirements for the response speed and stability of the workstation). For the normalized first The historical number of failures for each laser cutting station represents the "degree of interference from station failures" (the higher the value, the more frequent the station has experienced failures in the past, which means that the probability of interruption and restart due to failures during processing is greater, which may lead to task delays). The absolute value of the difference between the two is used to quantify the difference between the "urgent needs of the subtask" and the "risk level of workstation failure"—the smaller the difference, the better the low failure characteristics of the workstation can match the high timeliness requirements of the subtask (i.e., workstations with fewer failures can better ensure the timely completion of urgent tasks). For the normalized first The estimated processing time for each subtask is determined by the value; the higher the value, the more time-consuming the task itself will be. For the normalized first The historical average processing time of each laser cutting station is the value. The smaller the value, the higher the station's processing efficiency and the shorter the time required to complete similar tasks. The absolute value of the difference between the two is used to quantify the matching difference between the "subtask time requirement" and the "workstation time supply capacity". The smaller the difference, the better the workstation's time capacity to process the task matches the time requirement of the subtask itself (it will not cause task delays due to the workstation being too slow, nor will it cause resource idleness due to the workstation being too fast). For the normalized first The processing power level of each laser cutting station is... ∈[0,1], The higher the value, the more sufficient the power supply of the workstation (more redundant power can better ensure the stability of energy output during the cutting process and cope with energy demand fluctuations caused by material thickness fluctuations or trajectory changes), and the stronger the power supply capability of the workstation. The physical meaning is power adequacy gap. The smaller the gap value, the more abundant the power capacity of the workstation, and the better it can guarantee the stable output of cutting energy. Therefore, the matching degree... The smaller the value, the higher the matching degree. It should be noted that the definition of "matching degree value" is essentially the comprehensive difference between the sub-task requirements and the workstation capabilities. This value is obtained by calculating the weighted sum of the differences between the components of the feature vector. Therefore, the smaller the "matching degree value," the smaller the difference between the sub-task and the workstation, and the higher the degree of adaptation. This definition contradicts the common convention that "a larger matching degree value represents better adaptability." This is explicitly stated to ensure consistency in understanding and reproducibility of the technical solution in this application, and this definition is used throughout the entire document. , 、 、 、 These are preset weighting coefficients, with default values ​​of 0.3, 0.3, 0.2, and 0.2, respectively, corresponding to the adjustment weights for path matching difference, urgency difference, time difference, and power adaptability, to satisfy... + + + =1; Based on the matching degree value between all subtasks and all workstations. The system calculates the matching degree value between each subtask and all workstations, and sorts them in ascending order of matching degree value. The smaller the matching degree value, the more closely the workstation matches the subtask's characteristics. Subtasks are preferentially assigned to the laser cutting workstation with the lowest matching degree value that is also available, ensuring that the workstation is not repeatedly occupied during scheduling. If multiple workstations have the same matching degree value, the system compares the current task queue count, processing power level, and historical stability score, prioritizing the assignment. The final output is a subtask allocation table, which includes the subtask number, its associated task identifier, estimated processing time, planned start time, matching degree value, target workstation identifier, and preliminary scheduling status information.

[0074] Based on the target station identifier and estimated processing time of each subtask in the subtask allocation table, and combined with the position coordinates of each laser cutting station, thermal impact conflicts between stations are determined by the overlap rate of spatial distance and processing time. Conflicting subtasks are then scheduled to stations without thermal impact conflicts according to the order of processing start time to eliminate the conflicts, and the subtask allocation table is updated.

[0075] Furthermore, including:

[0076] Combined with workstation location coordinates and Calculate any two target workstations, i.e., workstations m With workstation n The spatial distance between them is calculated using the following formula: ;

[0077] Based on subtasks and Processing time period [ , ][ , ] Calculate the overlap length of the processing time period The calculation formula is:

[0078] ;

[0079] Based on the time period overlap length Calculate the overlap rate of time periods The calculation formula is: ;

[0080] in , Subtasks and The estimated processing time for the subtasks mentioned above;

[0081] When the spatial distance ,in The preset safe distance threshold, and the overlap rate of the time periods. ,in If the preset time overlap rate threshold is used, then the subtask is determined. sub-tasks There is a conflict of thermal effects;

[0082] For subtasks with the aforementioned thermal impact conflict According to subtasks Plan start time The conflicting subtasks will be processed in order from morning to night. Dispatch to a spatial distance greater than And the target workstation has no thermal impact conflict with the subtasks already assigned in the current subtask allocation table;

[0083] Update the target workstation identifier, planned start time, and preliminary scheduling status information fields in the subtask allocation table.

[0084] Specifically, firstly, based on the planned start time and estimated processing time of each subtask, its processing end time is calculated, thereby determining the processing time period of the subtask. The processing time period refers to the closed interval bounded by the planned start time and estimated processing time of the subtask, denoted as […]. , Then, after completing the initial matching and allocation of subtasks, based on the target workstation identifier, planned start time, and estimated processing time of each subtask in the subtask allocation table, combined with the spatial coordinates of the laser cutting workstation... Calculate any two workstations m and n European spatial distance between The distance is calculated using the Euclidean distance formula, which is: Next, determine whether there is any overlap in time between the subtasks being processed simultaneously at the two workstations, using the overlap length of the processing time interval. The calculation formula is: ; Calculate the overlap rate of time periods ;in 、 Subtasks and The estimated processing time for the sub-tasks mentioned above; when the workstation space distance ,in The preset safe distance threshold (set to 400mm) and the time period overlap rate ,in A preset time overlap rate threshold (set to 0.3) was established. Experiments showed that for a 3kW laser cutting machine, when the station spacing is less than 400mm and the time overlap rate exceeds 30%, the workpiece cut quality significantly decreases. Therefore, in this embodiment, a safe distance threshold is preferably set. =400mm, time overlap rate threshold =0.3). Then determine the subtask. sub-tasks Thermal impact conflicts exist. For subtasks with thermal impact conflicts, each conflicting subtask is processed sequentially from earliest to latest, according to their planned start times. For each conflicting subtask, a target workstation meeting the following conditions is selected as the new scheduling location: ① Its spatial distance from the current subtask is greater than a preset safety distance threshold. ② The overlap rate between this task and all currently assigned subtasks in terms of time period does not exceed the preset time overlap rate threshold. ③ The workstation is available and not occupied. If multiple workstations meet the conditions, priority is given to those with the lowest matching degree. If the matching degrees are the same, priority is given to the workstation with the smaller number. After the scheduling adjustment is completed, the target workstation identifier, planned start time, matching degree value and preliminary scheduling status fields in the subtask allocation table are updated to ensure that all thermal impact conflicts are effectively eliminated, thereby achieving thermal interference control and stable allocation of processing paths under high-density workstation layout.

[0085] The multi-station laser cutting machine is controlled to execute cutting tasks according to the updated subtask allocation table. If a station abnormality is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

[0086] Furthermore, including:

[0087] The production scheduling and management system obtains real-time processing status feedback information during task execution, obtains the completed path segment number of the interrupted subtask, calculates the length of the processed path, and the length of the processed path is the sum of the cumulative lengths of the completed path segments.

[0088] Based on the number of segments of the cutting path of the interrupted subtask, the total path length, the number of direction changes, and the length of the processed path, the remaining path segments are determined, and the estimated processing time of the subtasks of the remaining path segments is estimated.

[0089] Determine whether the interruption event is an abnormal event. The abnormal events include: laser cutting station operation failure, equipment processing time exceeding the preset tolerance range of the expected processing time of the sub-task, abnormal station status signal, or other abnormal operation information that causes the task to be unable to continue execution.

[0090] After detecting the abnormal event, based on the estimated processing time of the remaining path segment, the path complexity score and the urgency score, a subtask feature vector corresponding to the remaining path segment is constructed. Combined with the workstation capability score vector of each workstation, the matching degree value is calculated, and target workstations that meet the following conditions are selected: the matching degree value is not higher than a preset threshold, there is no thermal impact conflict with other workstations in the remaining processing time period, and the status is available.

[0091] The remaining path segment of the interrupted subtask is assigned to the target workstation, and the subtask assignment table is updated. The update includes the target workstation identifier, planned start time, matching degree value, and preliminary scheduling status information fields.

[0092] Specifically, the production scheduling and management system (MES) acquires real-time processing status feedback information of the laser cutting equipment during task execution, identifies abnormal workstations and their corresponding interrupted subtask numbers, and collects a set of completed path segment numbers for the interrupted subtask. These path segment numbers are uniquely assigned according to the preset path segment sequence of the processing task, and the length of the processed path is calculated. The formula for the total cumulative length of all completed path segments is: ;in This is the set of completed path segment numbers. For the number k The length of the path segment. Estimate the estimated processing time for the remaining path segment subtasks. ;in and β Obtained by fitting historical processing data. The processing time constant per unit length. β The additional adjustment time for a unit change in direction. The number of direction changes in the remaining path segment, and the total length of the interrupted subtask path. , The total length of the path segments has been calculated. Then, it is determined whether the interruption event is an abnormal event. If the subtask is determined to be interrupted due to a workstation malfunction, a rescheduling mechanism is immediately triggered. Abnormal events include laser cutting workstation malfunctions, equipment processing time exceeding the preset tolerance range (10%) of the subtask's estimated processing time, and abnormal workstation status signals, all of which prevent the subtask from continuing execution. The system first constructs a new subtask feature vector based on the remaining path segments and calculates its matching degree value by combining it with the capability score vector of each workstation. During scheduling, only workstations that simultaneously meet the following conditions are selected as target workstations: ① The matching degree value is not higher than the preset threshold of 0.25; ② There is no thermal impact conflict between workstations processing subtasks simultaneously. The determination method is: when the spatial distance is less than the recommended safe distance threshold of 400mm and the processing time overlap rate exceeds the time overlap rate threshold. When the score is 0.3, a thermal impact conflict is considered to exist; ③ The workstation is currently available. If there are multiple candidate workstations, the one with the lowest matching score is selected first. If the matching scores are the same, they are selected in ascending order of workstation number. After determining the target workstation, the remaining path segment is assigned to that workstation as a new subtask, and the subtask allocation table is updated, including fields such as target workstation identifier, planned start time, matching score, and scheduling status, to achieve orderly recovery and continuous execution of interrupted subtasks.

[0093] Example 2, based on the same inventive concept as the parallel processing scheduling method for a multi-station laser cutting machine in the foregoing examples, such as... Figure 2 As shown, this application provides a parallel processing scheduling system for a multi-station laser cutting machine. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0094] The task and parameter acquisition module 10 is used to obtain graphic information of the processing tasks of the multi-station laser cutting machine from the task file, obtain material type, material thickness parameters, task urgency and scheduling priority through the production scheduling management system, the graphic information is used to represent vector map data of the cutting trajectory of the processing area, and obtain the target completion time of the processing tasks according to the task urgency and scheduling priority.

[0095] The path structure feature extraction and complexity calculation module 20 is used to extract the structural features of the cutting path based on the graphic information and calculate the path complexity score used to measure the difficulty of the task structure execution.

[0096] The subtask construction and feature vector generation module 30 is used to divide the task to be processed into multiple subtasks, estimate the expected processing time of each subtask, calculate the urgency score of each subtask by combining the path complexity score and the target completion time, and construct a subtask feature vector. The subtask feature vector includes the path complexity score, the urgency score and the expected processing time of the subtask.

[0097] The station capability rating vector construction module 40 is used to obtain the current task queue number, historical average processing time, historical failure number, processing power level and station position coordinates of each laser cutting station, and construct the station capability rating vector.

[0098] The subtask and workstation matching and allocation module 50 is used to calculate the matching degree value between each subtask and each laser cutting workstation based on the subtask feature vector and the workstation capability score vector, using the sum of the absolute values ​​of the vector differences. The module then allocates laser cutting workstations to each subtask according to the matching degree value and outputs a subtask allocation table.

[0099] The subtask thermal impact conflict avoidance module 60 is used to determine the thermal impact conflict between workstations based on the target workstation identifier and the estimated processing time of each subtask in the subtask allocation table, combined with the position coordinates of each laser cutting workstation, by the overlap rate of spatial distance and processing time period, and to schedule the conflicting subtasks to workstations without thermal impact conflict according to the order of processing start time to eliminate the conflict, and update the subtask allocation table.

[0100] The anomaly recovery and subtask rescheduling module 70 is used to control the multi-station laser cutting machine to execute cutting tasks according to the updated subtask allocation table. If an anomaly is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

[0101] Example 3, as Figure 3 As shown, Embodiment 3 of the present invention provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements a parallel processing scheduling method for a multi-station laser cutting machine as described in Embodiment 1.

[0102] The computer storage medium provided in this embodiment is used to implement a parallel processing scheduling method for a multi-station laser cutting machine. Therefore, the computer storage medium also possesses the technical effects of the parallel processing scheduling method based on a multi-station laser cutting machine, and will not be repeated here.

[0103] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A parallel processing scheduling method for a multi-station laser cutting machine, characterized in that, include: The graphical information of the tasks to be processed by the multi-station laser cutting machine is obtained from the task file. The material type, material thickness parameters, task urgency and scheduling priority are obtained through the production scheduling management system. The graphical information is used to represent the vector map data of the cutting trajectory of the area to be processed. The target completion time of the task to be processed is obtained according to the task urgency and scheduling priority. Based on the graphic information, the structural features of the cutting path are extracted, and a path complexity score is calculated to measure the difficulty of the task to be processed. The task to be processed is divided into multiple subtasks, the estimated processing time of each subtask is estimated, and the urgency score of each subtask is calculated by combining the path complexity score and the target completion time. A subtask feature vector is constructed, which includes the path complexity score, the urgency score and the estimated processing time of the subtask. Obtain the current task queue number, historical average processing time, historical failure count, processing power level, and station location coordinates for each laser cutting station, and construct a station capability scoring vector; Based on the feature vector of each subtask and the capability score vector of the workstation, the matching degree value between each subtask and each laser cutting workstation is calculated by summing the absolute values ​​of the vector differences. Based on the matching degree value, laser cutting workstations are assigned to each subtask, and a subtask allocation table is output. Based on the target station identifier and estimated processing time of each subtask in the subtask allocation table, and combined with the position coordinates of each laser cutting station, thermal impact conflicts between stations are determined by the overlap rate of spatial distance and processing time period. Conflicting subtasks are scheduled to stations without thermal impact conflicts according to the order of processing start time to eliminate the conflicts, and the subtask allocation table is updated. The multi-station laser cutting machine is controlled to execute cutting tasks according to the updated subtask allocation table. If a station abnormality is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

2. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 1, characterized in that, The path complexity score includes: Based on the vector map data of the cutting trajectory of the area to be processed as described in the graphic information, the number of segments of the cutting path of the task to be processed is extracted. The total length of the cutting path The This indicates the number of consecutive line segments in the cutting path. This is the sum of the lengths of all path segments; Based on the direction vector difference between adjacent path segments in the cutting path, the number of direction changes is counted. The Used to reflect the degree of tortuosity of the cutting path; Calculate the path complexity score according to the preset complexity-weighted scoring formula. The pre-defined complexity-weighted scoring formula is: ； in, , , For the pre-set weighting coefficients, To adjust the weighting coefficient of the number of path segments, The weighting coefficient for the total path length. The weighting coefficient is the number of directional changes. The path complexity score represents the structural execution difficulty of the cutting path to be processed.

3. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 1, characterized in that, The construction of the subtask feature vector includes: Based on the structural characteristics of the cutting path, the task to be processed is divided into multiple cutting path segments, and a unique path segment number is assigned to each path segment. Several adjacent path segments are combined into a subtask. The subtask is a processing unit that can be independently scheduled and can be allocated and executed in parallel among multiple laser cutting stations. Obtain the average processing time for each path in the historical processing records that have the same material type and material thickness parameters as the current one, and record it as the historical average processing time. Combined with the number of segments of the cutting path in the current subtask Calculate the estimated processing time for each subtask. The calculation formula is: ； Based on the path complexity score With the target completion time Calculate the urgency score for each subtask. The urgency score represents the time pressure intensity of the subtask's estimated processing time relative to the target completion time, and is calculated using the following formula: ； The path complexity score Urgency score Estimated processing time for subtasks The numerical range is normalized using a minimum-maximum normalization method, which linearly maps each parameter value to the interval [0,1]. The normalization formula is as follows: ； in, ∈{ ， ， }, normalized parameters 、 These are the minimum and maximum values ​​of the corresponding feature terms among all the subtasks to be processed. Normalized features Subtask feature vectors are constructed by combining elements in a fixed order. .

4. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 1, characterized in that, The process involves acquiring the current task queue number, historical average processing time, historical failure count, processing power level, and station location coordinates for each laser cutting station, and constructing a station capability scoring vector, including: The production scheduling and management system obtains the current task queue number for each laser cutting station. and workstation location coordinates ,in Indicates the first One laser cutting station; Based on historical processing task records, the number of historical failures for each workstation is counted. ; The processing power level is obtained based on the workstation configuration parameters. ; By combining the historical average processing time of each workstation for handling similar sub-tasks, the historical average processing time of each workstation is obtained. ; The historical average processing time of the workstation Current task queue count Historical number of failures With processing power level Normalization is performed, and the score is calculated according to the preset workstation weighting formula. The workstation capability score is calculated using the following weighted formula: ； in For the normalized first The number of tasks currently queued at each workstation. The normalized historical average processing time for each workstation. The normalized number of historical faults. This is the normalized processing power level. For the first Capability rating for each laser cutting station. 、 、 、 The preset weighting coefficients for each scoring item are used to reflect the degree of influence of each indicator on the workstation's capability, and to meet the following requirements. + + + =1; The ability scores corresponding to each workstation Workstation capability scoring vectors are constructed in order of workstation number. = This represents the total number of laser cutting stations.

5. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 1, characterized in that, The matching degree value between each sub-task and each laser cutting station is calculated by summing the absolute values ​​of the vector differences between the sub-task feature vector and the station capability score vector, including: Based on the subtask feature vector Workstation Capability Rating Vector = { ， ， ， } The matching degree value between each subtask and each laser cutting station is calculated using the sum of the absolute values ​​of vector differences. The formula for calculating the matching degree value is as follows: ； in, For the first Sub-tasks For the first One laser cutting station Indicates the first Sub-tasks and the first Matching degree value between individual workstations Score the path complexity of the normalized subtasks. To score the urgency of the subtasks after normalization, The normalized estimated processing time for the subtask. This represents the current task queue size at the laser cutting station after normalization. This represents the normalized historical number of failures at the laser cutting station. This represents the normalized processing power level of the laser cutting station. This represents the normalized historical average processing time for laser cutting stations. , 、 、 、 The preset weighting coefficients correspond to the adjustment weights of path matching difference, urgency difference, time difference, and power adaptability, respectively, to satisfy... + + + =1; Matching each subtask with each workstation. The matching degree value is sorted, and the smaller the matching degree value, the closer the current workstation is to the subtask characteristics. Each subtask is assigned to the laser cutting workstation with the smaller matching degree value and that has not yet been occupied, and a subtask allocation table is generated. The subtask allocation table includes the subtask number, the task identifier, the estimated processing time of the subtask, the planned start time, the matching degree value, the target workstation identifier, and the preliminary scheduling status information.

6. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 1, characterized in that, The process involves using the target workstation identifier and estimated processing time of each subtask in the subtask allocation table, combined with the coordinates of each laser cutting workstation, to determine thermal impact conflicts between workstations based on spatial distance and the overlap rate of processing time periods. Conflicting subtasks are then reassigned to workstations without thermal impact conflicts according to their processing start times to eliminate the conflicts, and the subtask allocation table is updated. This includes: Based on each subtask in the subtask allocation table Corresponding planned start time Estimated processing time for subtasks Calculation subtask Processing end time Determine the processing time period. , ]; Combined with workstation location coordinates and Calculate any two target workstations, i.e., workstations m With workstation n The spatial distance between them is calculated using the following formula: ; Based on subtasks and Processing time period [ , ][ , ] Calculate the overlap length of the processing time period The calculation formula is: ； Based on the time period overlap length Calculate the overlap rate of time periods The calculation formula is: ; in , Subtasks and The estimated processing time for the subtasks is as follows; When the spatial distance ,in The preset safe distance threshold, and the overlap rate of the time periods. ,in If the preset time overlap rate threshold is used, then the subtask is determined. sub-tasks There is a conflict of thermal effects; For subtasks with the aforementioned thermal impact conflict According to subtasks Plan start time The conflicting subtasks will be processed in order from morning to night. Dispatch to a spatial distance greater than And the target workstation has no thermal impact conflict with the subtasks already assigned in the current subtask allocation table; Update the target workstation identifier, planned start time, and preliminary scheduling status information fields in the subtask allocation table.

7. The parallel processing scheduling method for a multi-station laser cutting machine according to claim 2, characterized in that, The controlled multi-station laser cutting machine executes cutting tasks according to the updated subtask allocation table. If a station abnormality is detected during execution, causing a subtask interruption, the subtask is rescheduled to another station with a matching degree value not exceeding a preset threshold, no thermal impact conflicts, and an available state, based on the incomplete path segment and the currently processed length of the subtask. The subtask allocation table is then updated, including: The production scheduling and management system obtains real-time processing status feedback information during task execution, obtains the completed path segment number of the interrupted subtask, calculates the length of the processed path, and the length of the processed path is the sum of the cumulative lengths of the completed path segments. Based on the number of segments of the cutting path of the interrupted subtask, the total path length, the number of direction changes, and the length of the processed path, the remaining path segments are determined, and the estimated processing time of the subtasks of the remaining path segments is estimated. Determine whether the interruption event is an abnormal event. The abnormal events include: laser cutting station operation failure, equipment processing time exceeding the preset tolerance range of the expected processing time of the sub-task, abnormal station status signal, or other abnormal operation information that causes the task to be unable to continue execution. After detecting the abnormal event, based on the estimated processing time of the remaining path segment, the path complexity score and the urgency score, a subtask feature vector corresponding to the remaining path segment is constructed. Combined with the workstation capability score vector of each workstation, the matching degree value is calculated, and target workstations that meet the following conditions are selected: the matching degree value is not higher than a preset threshold, there is no thermal impact conflict with other workstations in the remaining processing time period, and the status is available. The remaining path segment of the interrupted subtask is assigned to the target workstation, and the subtask assignment table is updated. The update includes the target workstation identifier, planned start time, matching degree value, and preliminary scheduling status information fields.

8. A parallel processing scheduling system for a multi-station laser cutting machine, characterized in that, The system includes: The task and parameter acquisition module is used to obtain graphic information of the tasks to be processed by the multi-station laser cutting machine from the task file, and to obtain material type, material thickness parameters, task urgency and scheduling priority through the production scheduling management system. The graphic information is used to represent vector map data of the cutting trajectory of the area to be processed. Based on the task urgency and scheduling priority, the target completion time of the task to be processed is obtained. The path structure feature extraction and complexity calculation module is used to extract the structural features of the cutting path based on the graphic information and calculate the path complexity score to measure the difficulty of the task structure execution. The subtask construction and feature vector generation module is used to divide the task to be processed into multiple subtasks, estimate the expected processing time of each subtask, calculate the urgency score of each subtask by combining the path complexity score and the target completion time, and construct a subtask feature vector. The subtask feature vector includes the path complexity score, the urgency score and the expected processing time of the subtask. The station capability scoring vector construction module is used to obtain the current task queue number, historical average processing time, historical failure number, processing power level and station location coordinates of each laser cutting station, and construct the station capability scoring vector. The subtask and workstation matching and allocation module is used to calculate the matching degree value between each subtask and each laser cutting workstation based on the subtask feature vector and the workstation capability score vector, using the sum of the absolute values ​​of the vector differences. The module then allocates laser cutting workstations to each subtask according to the matching degree value and outputs a subtask allocation table. The subtask thermal impact conflict avoidance module is used to determine the thermal impact conflict between workstations based on the target workstation identifier and the estimated processing time of each subtask in the subtask allocation table, combined with the position coordinates of each laser cutting workstation, by the overlap rate of spatial distance and processing time period, and to schedule the conflicting subtasks to workstations without thermal impact conflict according to the order of processing start time to eliminate the conflict, and update the subtask allocation table. The anomaly recovery and subtask rescheduling module is used to control the multi-station laser cutting machine to execute cutting tasks according to the updated subtask allocation table. If an anomaly is detected during the execution process, causing the subtask to be interrupted, the subtask is rescheduled to other stations with a matching degree value not higher than a preset threshold, no thermal impact conflict, and an available state based on the incomplete path segment and the current subtask's processed length, and the subtask allocation table is updated.

9. A computer storage medium storing a computer program thereon, characterized in that: When the computer program is executed by the processor, it implements a parallel processing scheduling method for a multi-station laser cutting machine as described in any one of claims 1-7.

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