Intelligent scheduling system for dynamic coordination control of full-automatic and semi-automatic die-cutting machines

Abstract

The invention discloses an intelligent scheduling system for dynamic coordination control of full-automatic and semi-automatic die-cutting machines, which relates to the technical field of dynamic coordination control of die-cutting machines and comprises a task structure analysis module, a beat adaptation modeling module, a beat coordination scheduling module and a beat feedback optimization module. The rhythm adaptation modeling module is used for acquiring rhythm control attribute information of the full-automatic die-cutting machine and the semi-automatic die-cutting machine in the task section propelling process, and generating a rhythm adaptation mapping relation graph between each task section and equipment based on a corresponding relation between a task rhythm structure model and the rhythm control attribute information; and the rhythm coordinated scheduling module is used for determining the inconsistency degree of task execution rhythm structures when the multiple sections of tasks are simultaneously scheduled to the full-automatic die-cutting machine and the semi-automatic die-cutting machine based on the generated rhythm adaptive mapping relation graph. According to the method, the problem of difficulty in task propulsion rhythm coordination of heterogeneous die cutting equipment is solved, and inconsistency evaluation and classified scheduling control before scheduling are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic coordination control of die-cutting machines, and particularly to an intelligent scheduling system for dynamic coordination control of fully automatic and semi-automatic die-cutting machines. Background Art

[0002] The dynamic coordination control of fully automatic and semi-automatic die-cutting machines refers to a coordination mechanism that maximizes the overall operating efficiency of the system by dynamically adjusting the working rhythm and task allocation of each device through real-time monitoring of the operating status, task progress, and load conditions of various die-cutting devices, using a centralized or distributed control method. In actual production, die-cutting devices usually include both fully automatic and semi-automatic devices, which have significant differences in working ability, response speed, and degree of manual dependence. If traditional static or manual scheduling methods are used, it is very easy to cause waste of resources, production capacity bottlenecks, or equipment idleness. Therefore, building a system with intelligent scheduling capabilities that can dynamically optimize task allocation and execution order based on multi-dimensional factors such as device capabilities, task priorities, and real-time status can not only improve the coordination efficiency of the entire die-cutting production line, but also effectively relieve the burden of manual intervention, enhance the system's ability to respond to abnormal conditions, and thus achieve the goals of efficient, flexible, and intelligent die-cutting production management.

[0003] The existing intelligent scheduling technologies for dynamic coordination control of fully automatic and semi-automatic die-cutting machines mainly include four core links: device status collection, task information modeling, scheduling algorithm decision-making, and instruction issuance and execution feedback. First, the working status data of each die-cutting machine, such as running, standby, faulty, and current task progress, are collected in real time through sensors, PLCs, or industrial communication interfaces. Secondly, the die-cutting tasks to be processed are standardized and modeled according to process parameters, production priorities, and time limit requirements. Then, the scheduling system uses optimization algorithms (such as rule engines, heuristic algorithms, reinforcement learning, or hybrid intelligent algorithms) to match and allocate tasks to fully automatic and semi-automatic die-cutting machines, comprehensively considering factors such as device capabilities, current load, switching costs, and time efficiency, and generating an optimal or sub-optimal scheduling plan. The scheduling results are sent to each device control unit through an industrial bus or protocol interface to guide it to execute operations according to the scheduling strategy. Finally, through a feedback mechanism, the task execution situation and device response data are continuously monitored, and the scheduling strategy is dynamically adjusted to cope with emergencies such as device failures, task changes, or manual interventions, forming a closed-loop control, so as to achieve an efficient and stable production rhythm.

[0004] The existing technologies have the following deficiencies: In the case where a multi - segment task containing multiple consecutive process segments is assigned to be jointly executed by a fully automatic die - cutting machine and a semi - automatic die - cutting machine, if some process segments of the task have high requirements for beat connection, there will be a problem of inconsistent advancement rhythms between task segments. Since the fully automatic die - cutting machine has the ability to run continuously at a fixed beat, while the semi - automatic die - cutting machine needs to wait for an external trigger signal before continuing to execute each segment of the task. Therefore, when such heterogeneous devices cooperate to execute a beat - compact task, if the scheduling method only allocates task beats according to the target execution time period of the task without recognizing the rhythm differences in the execution logic structures of different devices, it will lead to the problem of asynchronous advancement of task segments. The existing intelligent scheduling technology for dynamic coordination control of fully automatic and semi - automatic die - cutting machines cannot execute corresponding scheduling measures according to the degree of inconsistency in the task execution beat structure when a multi - segment task is simultaneously scheduled to a fully automatic device and a semi - automatic device, which will cause a situation where some parts of the task are completed in advance and waiting, while some parts are stuck and stagnant, and then lead to the breakage of the task chain and the disordered update of the scheduling table, ultimately resulting in the failure of overall task execution and the chaos of resource scheduling.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent scheduling system for dynamic coordination control of fully automatic and semi - automatic die - cutting machines to solve the problems in the above - mentioned background art.

[0007] To achieve the above object, the present invention provides the following technical solution: An intelligent scheduling system for dynamic coordination control of fully automatic and semi - automatic die - cutting machines, including a task structure analysis module, a beat adaptation modeling module, a beat coordination scheduling module, and a beat feedback optimization module; The task structure analysis module performs a structural analysis on the multi - segment task to be scheduled, identifies the process sequence relationship and beat coupling requirements between each task segment, and constructs a task beat structure model; The beat adaptation modeling module obtains the beat control attribute information of the fully automatic die - cutting machine and the semi - automatic die - cutting machine during the process of advancing task segments, and generates a beat adaptation mapping relationship graph between each task segment and the device based on the corresponding relationship between the task beat structure model and the beat control attribute information; The beat coordination scheduling module, based on the generated beat adaptation mapping relationship graph, determines the degree of inconsistency in the task execution beat structure when the multi - segment task is simultaneously scheduled to the fully automatic die - cutting machine and the semi - automatic die - cutting machine, and executes corresponding scheduling measures according to the determination result; The beat feedback optimization module, after the task is completed, collects the actual progress data of the task segment and updates the beat adaptation mapping relationship graph for correcting the task beat structure model and the device beat control attribute information.

[0008] Preferably, in the task structure analysis module, the structural analysis of the multi-segment tasks to be scheduled is carried out to identify the technological sequence relationship and beat coupling requirements between each task segment, and a task beat structure model is constructed, specifically as follows: Extract the inter-segment dependency information between each task segment in the multi-segment tasks to be scheduled; According to the inter-segment dependency information, construct a directed sequence graph for representing the technological sequence relationship; In the directed sequence graph, add the maximum time interval threshold mark and the inter-segment buffer allowance mark between adjacent task segments for identifying the beat coupling requirements; Combine the directed sequence graph, the maximum time interval threshold mark and the inter-segment buffer allowance mark to generate a task beat structure model.

[0009] Preferably, in the beat adaptation modeling module, based on the correspondence between the task beat structure model and the beat control attribute information, a beat adaptation mapping relationship graph between each task segment and the device is generated, specifically as follows: Traverse the connection relationship between each task segment and the adjacent task segment in the task beat structure model, and extract the corresponding maximum time interval threshold and buffer allowance mark; For each candidate full-automatic die cutter and semi-automatic die cutter, extract its beat control attribute information during the task segment switching process. The beat control attribute information includes the propulsion mode type, the response mode identifier and the inter-segment control structure; Perform a structural matching between the beat requirements included in the task beat structure model and the beat control attribute information. For each combination of a task segment and a device, establish a beat matching relationship item, and generate the connection relationship in the graph based on this; Structurally organize the beat matching relationships corresponding to all combinations of task segments and devices to form a beat adaptation mapping relationship graph between task segments and devices for representing the matching situation between the task beat structure model and the beat control attribute information.

[0010] Preferably, in the beat coordination scheduling module, the beat adaptation structured input information is extracted from the generated beat adaptation mapping relationship graph, and preprocessing is performed after extraction; the task beat coupling structure information and the device propulsion behavior feature information are extracted from the preprocessed beat adaptation structured input information, and they are analyzed to generate a beat consistency index and a beat structure difference index respectively; an inconsistency degree determination model is constructed for the generated beat consistency index and beat structure difference index, and an inconsistency coefficient is generated through weighted summation; a preset inconsistency coefficient threshold interval is determined, and after determination, it is compared with the generated inconsistency coefficient, and according to the comparison result, the inconsistency degree of the task execution beat structure when multiple segments of tasks are simultaneously scheduled to the full-automatic die cutter and the semi-automatic die cutter is determined, and corresponding scheduling measures are executed according to the determination result.

[0011] Preferably, the acquisition logic of the beat consistency index is specifically as follows: The task beat coupling structure information is extracted from the preprocessed beat adaptation structured input information, specifically including the structure dependence level, the beat buffer tolerance mark value, and the scheduling window overlap rate of each pair of adjacent task segments in the beat adaptation mapping relationship graph, and they are sequentially marked as , and , , , where the structure dependence level represents the hierarchical difference of the process dependence paths identified by the front and rear task segments in the task beat structure model for the th pair of adjacent task segments; the beat buffer tolerance mark value represents whether a buffer time period is allowed to be inserted in the scheduling between the front and rear task segments for the th pair of adjacent task segments. If not allowed, then , and if allowed, then ; the scheduling window overlap rate represents the time window overlap ratio value of the front and rear task segments in the scheduling time configuration for the th pair of adjacent task segments; Calculate the beat consistency index , and the specific calculation method is as follows: Add the exponential value of the structure dependence level of each pair of adjacent task segments, the beat buffer tolerance mark value , and the natural logarithm value after adding one to the scheduling window overlap rate , and take the arithmetic mean among all task segment pairs to obtain the beat consistency index .

[0012] Preferably, the acquisition logic of the beat structure difference index is specifically as follows: Extract the device propulsion behavior feature information from the preprocessed beat adaptation structured input information, specifically including the difference in the length of the propulsion operation sequence, the difference in the propulsion mode number, and the ratio of the control frequency difference in each task segment in the beat adaptation mapping relationship graph, and respectively label them as , and , , where the difference in the length of the propulsion operation sequence represents the difference in the number of operation steps included when the th task segment is propelled on the fully automatic die-cutting machine and the semi-automatic die-cutting machine; the difference in the propulsion mode number represents the difference in the propulsion mode number of the th task segment on the fully automatic die-cutting machine and the semi-automatic die-cutting machine. The numbering rule is: 0 represents continuous automatic propulsion, 1 represents control trigger propulsion, and 2 represents manual confirmation propulsion; the ratio of the control frequency difference represents the difference ratio of the control action occurrence frequency during the propulsion of the th task segment on the fully automatic die-cutting machine and the semi-automatic die-cutting machine; Calculate the beat structure difference index , and the specific calculation method is as follows: Sum the e-th power of the difference in the propulsion mode number in each task segment, the square root value of the ratio of the control frequency difference , and the natural logarithm value of one plus the difference in the length of the propulsion operation sequence , and then calculate the arithmetic mean of all task segments to obtain the beat structure difference index .

[0013] Preferably, construct a non-uniformity degree determination model for the generated beat consistency index and the beat structure difference index , and generate a non-uniformity coefficient by weighted summation.

[0014] Preferably, determine the preset non-uniformity coefficient threshold interval , and after determination, compare it with the generated non-uniformity coefficient , and determine the non-uniformity degree of the task execution beat structure when multiple segments of tasks are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine according to the comparison result. The specific comparison and analysis are as follows: If , when multiple tasks are simultaneously scheduled to a fully automatic die-cutting machine and a semi-automatic die-cutting machine, the degree of inconsistency in the task execution beat structure is at a low level; If , when multiple tasks are simultaneously scheduled to a fully automatic die-cutting machine and a semi-automatic die-cutting machine, the degree of inconsistency in the task execution beat structure is at a medium level; If , when multiple tasks are simultaneously scheduled to a fully automatic die-cutting machine and a semi-automatic die-cutting machine, the degree of inconsistency in the task execution beat structure is at a severe level.

[0015] Preferably, corresponding scheduling measures are executed according to the determination result, specifically: If the determination result is at a low level, the specific scheduling measure to be executed is: the multiple tasks are respectively assigned to the fully automatic die-cutting machine and the semi-automatic die-cutting machine according to the original task segment division order, and no beat buffer time period is inserted, and the equipment independently advances its respective task segments according to its own advancement rhythm; If the determination result is at a medium level, the specific scheduling measure to be executed is: after the task segments are assigned to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, a beat buffer time period with a preset length is inserted at the boundary of the task segments with beat differences, and signal synchronization processing is performed on the equipment startup timing before the task segments are executed; If the determination result is at a severe level, the specific scheduling measure to be executed is: it is prohibited to split and execute the multiple tasks between the fully automatic die-cutting machine and the semi-automatic die-cutting machine, and all task segments are integrally scheduled to the equipment category with consistent beat advancement attributes for continuous execution, and equipment type switching is not allowed during the scheduling process.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. By constructing a task beat structure model and establishing a mapping relationship between it and the equipment advancement attributes, the present invention realizes for the first time the quantitative expression of the beat structure adaptation ability of a fully automatic die-cutting machine and a semi-automatic die-cutting machine in the scenario of jointly executing multiple tasks. The system models through two indicators, namely the beat consistency index and the beat structure difference index, accurately evaluates the sensitivity of the task itself to beat connection and the difference in the advancement logic between equipment, enabling the scheduling system to identify potential beat conflict risks before task allocation, thereby significantly improving the foresight and accuracy of scheduling decisions.

[0017] 2. The present invention adopts an inconsistency coefficient determination mechanism, simplifies the complex structure adaptation problem into a single numerical comparison, and combines a preset threshold range to achieve a hierarchical judgment of the degree of inconsistency. Based on this, the system formulates differential scheduling measures, ranging from allowing independent advancement of the beat, buffer coordination to isolation and execution of equipment types, ensuring that scheduling strategies with matching response accuracies can be obtained for different levels of inconsistency risks, effectively avoiding abnormal phenomena such as asynchronous task advancement, chain interruption, or equipment idling, and improving the stability of task execution and the efficiency of system scheduling.

[0018] 3. The present invention also introduces a feedback correction mechanism after task execution to dynamically update the beat adaptation map, supporting the continuous optimization of the task beat structure model and the equipment advancement attributes. This closed-loop self-learning ability enables the scheduling system to continuously enhance its adaptability to actual tasks and equipment characteristics during long-term operation, avoiding misjudgments in structure matching caused by template solidification or assumption deviation, and thus having good system scalability, scheduling robustness, and industrial scenario adaptation capabilities, with significant application and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0020] Figure 1 It is a schematic diagram of the modules of the intelligent scheduling system for dynamic coordinated control of the full-automatic and semi-automatic die-cutting machines of the present invention.

[0021] Figure 2 It is a system mind map of the intelligent scheduling system for dynamic coordinated control of the full-automatic and semi-automatic die-cutting machines of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Now, the exemplary embodiments will be described more comprehensively with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] The present invention provides an intelligent scheduling system for dynamic coordinated control of full-automatic and semi-automatic die-cutting machines as shown in Figure 1 and Figure 2 , which includes a task structure analysis module, a beat adaptation modeling module, a beat coordination scheduling module, and a beat feedback optimization module; The task structure analysis module performs a structural analysis on the multi-segment tasks to be scheduled, identifies the technological sequence relationship and beat coupling requirements among the task segments, and constructs a task beat structure model; In this embodiment, in the task structure analysis module, a structural analysis is performed on the multi-segment tasks to be scheduled, the technological sequence relationship and beat coupling requirements among the task segments are identified, and a task beat structure model is constructed. Specifically: Extract the inter-segment dependency information between each task segment in the multi-segment tasks to be scheduled; During the task structure analysis process, in order to extract the inter-segment dependency information between each task segment in the multi-segment tasks to be scheduled, it can be completed by combining static analysis based on the task data structure and scheduling rule template matching. Specifically, first, a structured analysis can be performed on the multi-segment task configuration file or database table recorded in the task management system. This data source usually contains information such as the unique identifier of the task segment, process number, task segment execution sequence number, upstream task segment , trigger condition fields, etc. By constructing a dependency relationship parsing logic, the program can automatically identify whether a certain task segment depends on other task segments to be completed before it can be executed, and parse it into a "predecessor-successor" relationship chain. In addition, a preset process flow template library can be combined to match the process type to which the task belongs, and it can be judged whether there is a mandatory sequence dependency or an optional dependency between task segments under a certain type of process flow. For example, if a certain task belongs to the "pre-cutting - fine trimming - forming" process path, the system can automatically establish a strong dependency relationship chain for these three segments according to the template rules. Through the above method, the logical connection and scheduling dependency path of task segments in the process flow can be automatically extracted, and a preliminary inter-segment dependency graph can be constructed. This method can not only improve the accuracy of task parsing, but also provide basic input for the subsequent generation of a process directed graph, so as to realize the ability to automatically identify the process sequence in the software system.

[0024] Construct a directed sequence diagram for representing the technological sequence relationship according to the inter-segment dependency information; In order to construct a directed sequence diagram for representing the process sequence relationship based on the inter-segment dependency information, the execution relationship between task segments can be expressed in software by means of graph data structure modeling. Specifically, after the system extracts the task segment dependency information, a graph node can be generated for each task segment, and this node contains basic information such as the task segment number, process type, execution order, etc.; subsequently, according to the front-to-back execution dependency relationship between task segments, a directed edge pointing from the predecessor node to the successor node is created in the graph to represent the process execution order. If a certain task segment must start after another task segment is completed, a directed edge will be established between the two to represent a strict sequential dependency; if a certain task segment has no strong dependency requirement on the upstream task segment, no connection will be constructed or it will be marked as an optional edge. The entire graph construction process can be achieved by traversing the task segment data structure and dynamically calling the edge connection rule function for judgment and edge generation, realizing the automatic generation of the graph structure. The purpose of constructing this directed sequence diagram is to transform the linear or non-linear scheduling information of the original task segments into a visual, traversable, and analyzable structural model, enabling the system to perform graph algorithm operations such as task segment sorting analysis, path judgment, and beat connection recognition in the subsequent process, thereby supporting the scheduling optimization and coordinated control under complex process flows, and it is the prerequisite structure for implementing intelligent scheduling strategies.

[0025] In the directed sequence diagram, a maximum time interval threshold mark and an inter-segment buffer allowance mark are added between adjacent task segments to identify the beat coupling requirements; In order to add the maximum time interval threshold mark and the inter-segment buffer allowance mark between adjacent task segments in a directed sequence diagram to identify the beat coupling requirements, it can be implemented in software by jointly modeling the scheduling policy rules and historical execution data between task segments. The specific implementation method is that when the system constructs a directed edge, it not only establishes the sequential connection relationship between the front and back task segments, but also needs to attach two key attributes to each edge: one is the maximum time interval threshold, which is used to indicate how long the successor task segment must start execution after the predecessor task segment is completed, and the other is the inter-segment buffer allowance mark, which is used to identify whether a scheduling buffer segment or an idle cycle is allowed to be inserted between the front and back task segments. The setting methods of these two attributes can be based on process template rules (such as the specified maximum waiting time between segments in a preset process standard flow), or by analyzing the average waiting time and fluctuation range between adjacent segments in historical task execution records to dynamically calculate reasonable thresholds. The inter-segment buffer allowance can be judged by task segment characteristics, such as whether there are process coherence requirements, whether there are flag fields such as allowing equipment to wait for signals, etc. The system automatically generates the above marks through rule matching and data analysis and binds them to each directed edge in the diagram, so as to realize the structured expression of the beat coupling strength between task segments. The fundamental purpose of doing this is to enable the scheduling system to identify which task segment combinations have strict beat continuity requirements and must maintain the same advancement rhythm, and which can insert beat buffers in the scheduling, so as to provide a basic judgment basis for the beat coordination scheduling module and is the core input of the beat adaptation evaluation logic.

[0026] Generate a task beat structure model by combining a directed sequence diagram, a maximum time interval threshold mark, and an inter-segment buffer allowance mark.

[0027] In order to combine the directed sequence diagram, the maximum time interval threshold flag, and the inter-segment buffer permission flag to generate a task beat structure model, it can be implemented in software by constructing a unified data structure model, integrating the graphical structure and scheduling constraint attributes into a callable beat control unit. Specifically, in the directed sequence diagram that has been constructed in the early stage of the system, each graph node represents a task segment, and each directed edge represents the technological sequence connection relationship between task segments. On this basis, the software further binds two scheduling constraint attributes to each edge: the maximum time interval threshold and the inter-segment buffer permission flag, and writes them into the data structure of the graph as extended fields of the edge, forming a data model that can be parsed by the scheduling engine. Subsequently, the system traverses the entire graph structure, extracts all the edges containing constraint attributes, and integrates them with the execution order and path of the task segments into a task beat structure model object. This object can be stored in the form of a graph database, nested dictionary, or relational structure, and has capabilities such as structure parsing, path reasoning, and scheduling parameter query. The role of this structure model is not only to express the technological execution order of task segments, but also to integrate scheduling timing requirements and beat continuity constraints, enabling the scheduling system to quickly call the beat dependence characteristics between tasks during subsequent beat adaptation evaluation and strategy classification, thereby supporting intelligent coordinated control in a heterogeneous device environment. In this way, the originally discrete structural relationships and scheduling rules are uniformly abstracted into a callable, updatable, and feedbackable scheduling core structure, providing a basic semantic carrier for the entire intelligent scheduling process.

[0028] The beat adaptation modeling module obtains the beat control attribute information of the full-automatic die-cutting machine and the semi-automatic die-cutting machine during the process of advancing task segments, and generates a beat adaptation mapping relationship graph between each task segment and the device based on the corresponding relationship between the task beat structure model and the beat control attribute information. In order to obtain the beat control attribute information of the full-automatic die-cutting machine and the semi-automatic die-cutting machine during the process of advancing task segments, it can be achieved by combining the establishment of a device control attribute data structure and an interface call mechanism. The system first extracts the basic static configuration of each device from the device management module or the process parameter database, including features such as whether it supports continuous segment automatic advancement, whether there is an intermediate waiting mechanism, and whether it depends on external trigger signals, as the source of the advancement mode type and response mode identifier. At the same time, the system collects the dynamic behavior data of the device during the task segment switching process through the device communication interface or the scheduling execution log, such as the inter-segment response delay time, the task segment switching confirmation behavior, and the sequence of behaviors such as whether there is a controllable buffer between segments. These dynamic data are structured processed by the behavior classification function in the software, and finally generate unified format beat control attribute information, including the inter-segment advancement logic structure, control signal type, advancement continuity characteristics, etc., and establish a device capability attribute table indexed by the device identifier for subsequent scheduling modules to call.

[0029] The reason for generating the beat adaptation mapping relationship graph between each task segment and the device based on the correspondence between the task beat structure model and the beat control attribute information is that the task segment itself has clear time continuity requirements for beat advancement, while the device has different capabilities in actual operation for beat advancement. Only by structurally matching the task beat requirements and the device advancement capabilities item by item can it be scientifically judged whether the task segment can be smoothly advanced on a certain device. By constructing this mapping relationship graph in the software system, the beat adaptation situation between each task segment and all candidate devices is structurally connected, and attributes such as matching level and beat conflict flag are added, thus forming a queryable and traversable decision-making basic structure. This graph is the pre-input for the scheduling system to achieve intelligent matching, evaluate the feasibility of task advancement, and hierarchical and classified scheduling strategies, and is also the core intermediate structure for calculating the evaluation coefficient and generating the scheduling path, which can significantly improve the determination accuracy and adaptability of the scheduling system under the condition of heterogeneous device collaboration.

[0030] In this embodiment, in the beat adaptation modeling module, based on the correspondence between the task beat structure model and the beat control attribute information, the beat adaptation mapping relationship graph between each task segment and the device is generated, specifically as follows: Traverse the connection relationship between each task segment and the adjacent task segment in the task beat structure model, and extract the corresponding maximum time interval threshold and buffer allowance flag; To traverse the connection relationship between each task segment and the adjacent task segment in the task beat structure model and extract the corresponding maximum time interval threshold and buffer allowance flag, it can be realized by the cooperation of the graph structure traversal and attribute reading mechanism in the software system. Specifically, the task beat structure model is usually represented in the form of a directed graph, each node represents a task segment, and each directed edge represents the technological sequence connection relationship between task segments. When the software performs traversal, it can use graph algorithms (such as depth-first or breadth-first traversal) to access each node and its outgoing edges, and perform structured attribute extraction operations on each directed edge. When the system constructs the graph structure, the maximum time interval threshold and buffer allowance flag have been stored as additional attributes of the edge. Therefore, during the traversal process, the program can call the edge attribute parsing function to directly extract the scheduling control parameters corresponding to each pair of front and back task segments. In addition, to ensure data integrity, the software can also call the rule completion module when the edge attribute is missing, and automatically assign default thresholds and buffer marks according to the process flow type to which the task segment belongs. This traversal and extraction process can not only run as a pre-processing process in the scheduling engine, but also provide accurate and structured task beat control requirement input for the subsequent beat matching logic, enabling the system to have the ability to perform refined beat evaluation and control according to the timing and buffer tolerance degree between task segments.

[0031] For each candidate fully automatic die-cutting machine and semi-automatic die-cutting machine, extract the beat control attribute information during the task segment switching process. The beat control attribute information includes the type of propulsion mode, response mode identifier, and inter-segment control structure; In order to extract the beat control attribute information of each candidate fully automatic die-cutting machine and semi-automatic die-cutting machine during the task segment switching process, it can be realized by constructing an equipment behavior description model and combining with the equipment control interface data acquisition mechanism in the software system. The specific implementation method is that the system first maintains a set of static configuration tables and dynamic behavior data sets for each die-cutting machine. The static configuration tables record parameter information such as equipment type, supported propulsion control mode, whether continuous segment execution is supported, and whether mid-segment waiting is allowed, which serves as the basic source of the type of propulsion mode and response mode identifier. The dynamic behavior data set is collected in real-time or periodically through the equipment communication interface, including the response time during task segment switching, inter-segment control status, whether manual confirmation or signal triggering is required, and whether there is a beat compensation logic, etc., which is used to determine the inter-segment control structure. The system can call the equipment attribute acquisition module during the scheduling preparation stage, combine the static configuration and dynamic data, generate the beat control attribute information object of the equipment during the task segment switching process, and assign a unified data structure label to each equipment. The type of propulsion mode can be classified according to whether the equipment supports fixed beat propulsion and whether intermittent signals are required for driving; the response mode identifier can be marked according to whether the equipment provides synchronous feedback during execution switching and whether it has an interrupt waiting mechanism; the inter-segment control structure is identified and classified into a standard mode based on the behavior state sequence during the segment switching process. After the above information extraction is completed in the software system, a complete set of beat control attribute information can be constructed for each equipment, which can be used for subsequent adaptation analysis and matching evaluation with the task beat structure model. This process not only realizes the structural modeling of the equipment behavior ability but also provides a data support basis for the scheduling system to intelligently identify the equipment beat adaptation ability.

[0032] Perform a structural match between the beat requirements included in the task beat structure model and the beat control attribute information. For each combination of a task segment and an equipment, establish a beat matching relationship item, and generate the connection relationship in the graph based on this; In order to structurally match the beat requirements included in the task beat structure model with the beat control attribute information, and establish a beat matching relationship item for each combination of a task segment and a device, it can be achieved by constructing a task-device adaptation scoring logic and a matching rule engine in the software system. Specifically, the system first extracts the requirement parameters for beat advancement of each task segment according to the connection relationship between the task segments in the task beat structure model, such as the maximum time interval threshold, whether buffering is allowed, the tightness level of inter-segment dependencies, etc.; at the same time, the system extracts the advancement mode type, response mode identifier, and inter-segment control structure of the corresponding device from the beat control attribute information, which are used to express the beat ability performance of the device during the advancement of the task segment. The software matches the task beat requirements with the device control capabilities one by one by defining a set of structure matching rules. For example, when a task segment requires bufferless advancement and the device control structure supports fixed-beat continuous execution, the matching level is high; conversely, if the device has an obvious response lag or does not support beat-locked execution, the matching level is low. The matching rules can be defined in the system in the form of a scoring matrix or a mapping table, and the adaptation degree of each task segment and each candidate device is evaluated item by item through the matching engine to form a beat matching relationship item, which contains information such as the matching level, conflict flag, and recommended scheduling mode. The system then stores the matching relationship items of all task segments and devices in a structured manner, and connects the task segment nodes and device nodes by constructing the edge relationship in the connection graph to form a beat adaptation map with matching attributes, which is used to support subsequent evaluation calculations and scheduling strategy selections. This process realizes the structural linkage expression between the beat requirements and the device capabilities, enabling the scheduling system to have the ability to make scheduling decisions based on the structural adaptation accuracy.

[0033] Structurally organize the beat matching relationships corresponding to all combinations of task segments and devices to form a beat adaptation mapping relationship graph between task segments and devices, which is used to represent the matching situation between the task beat structure model and the beat control attribute information.

[0034] To structurally organize the beat matching relationships corresponding to all task segments and device combinations, and ultimately form a beat adaptation mapping relationship graph between task segments and devices, it can be achieved by constructing a graph structure storage model for task-device binary tuples in the software system. Specifically, after the software system calculates the beat matching relationship for each pair of task segments and devices, this combination is regarded as an independent adaptation relationship unit and represented as an edge with attributes in the internal data structure. The starting point of the edge is the task segment node, the ending point is the device node, and structured attributes such as beat matching score, matching level, conflict flag, and scheduling suggestion type are attached to the edge. In software implementation, this mapping relationship graph can be organized through a graph database, nested dictionary mapping, or two-dimensional matrix structure. The system traverses all valid task segment-device combinations one by one, maps the matching results into this graph structure, and dynamically records the relationship between nodes and the content of matching attributes in the graph. In addition, to improve the retrieval and decision-making efficiency, the system can also build an indexing mechanism on this graph, enabling the scheduling engine to quickly obtain the beat matching situations corresponding to each task segment on multiple devices when executing scheduling decisions, and comparing the adaptation structure differences of different device combinations. The key purpose of forming this graph is to represent the corresponding relationship between the task beat structure model and the device beat control attribute information in a structured, traversable, and queryable manner, thereby providing a unified data basis for the subsequent generation of evaluation coefficients, distribution of scheduling classification strategies, and judgment of scheduling feasibility, and it is one of the core intermediate expression structures for implementing intelligent scheduling logic.

[0035] The beat coordination scheduling module, based on the generated beat adaptation mapping relationship graph, determines the degree of inconsistency in the task execution beat structure when multiple segments of tasks are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, and executes corresponding scheduling measures according to the determination result; In this embodiment, in the beat coordination scheduling module, beat adaptation structured input information is extracted from the generated beat adaptation mapping relationship graph and preprocessed after extraction; task beat coupling structure information and device advancement behavior feature information are extracted from the preprocessed beat adaptation structured input information and analyzed to generate a beat consistency index and a beat structure difference index respectively; an inconsistency degree determination model is constructed for the generated beat consistency index and beat structure difference index, and an inconsistency coefficient is generated through weighted summation; a preset inconsistency coefficient threshold interval is determined and compared with the generated inconsistency coefficient after determination, and according to the comparison result, the degree of inconsistency in the task execution beat structure when multiple segments of tasks are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine is determined, and corresponding scheduling measures are executed according to the determination result.

[0036] In the beat coordination scheduling module, to extract the beat adaptation structured input information from the generated beat adaptation mapping relation graph, the system can be implemented in software through a graph structure-oriented traversal and field parsing method. Specifically, the beat adaptation mapping relation graph is stored in the form of a graph database or a nested mapping structure, where each edge connects a task segment node and a device node, and is attached with multiple structured fields, including task beat coupling structure information and device advancement behavior feature information. The software system traverses all edge relations in the graph, automatically identifies the field labels attached to the edges, and classifies and summarizes them into two major information sets: one is the numerical fields related to the beat advancement requirements between task segments, such as dependency level, scheduling overlap rate, buffer tolerance flag, etc., which constitute the task beat coupling structure information; the other is the fields related to the device behavior advancement structure, such as the number of advancement steps, control mode number, control action frequency, etc., which constitute the device advancement behavior feature information. During the extraction process, based on the field naming rules and data type recognition mechanism, the system automatically filters the numerical fields, and establishes a one-to-one mapping structure according to the task segment-device combination, and outputs it to the parameter analysis module in a standardized format.

[0037] The purpose of preprocessing is to convert the originally extracted structured input information into a high-quality data set that can directly participate in mathematical calculations and modeling analysis, and avoid the distortion of the evaluation model caused by data anomalies, missing values, or inconsistent formats. The preprocessing process can be completed in three steps in software: data cleaning, format standardization, and anomaly filling. First, in the data cleaning stage, the system automatically identifies the fields with missing or illegal numerical values (such as negative numbers, null values, overflow values, etc.) in the extracted information, and performs deletion or replacement processing; second, in the format standardization stage, all percentage, number, boolean value, etc. fields are uniformly numerically converted. For example, the percentage field is converted to a floating point number between 0 and 1, the boolean value is converted to an integer flag of 0 or 1, and the classification number field is mapped to a preset integer value; finally, in the anomaly filling stage, if some key fields are missing for a certain device or task segment, the system can fill them based on the historical data mean or the preset value of the process template to ensure the integrity and consistency of the data dimensions. Through this preprocessing process, the beat adaptation structured input information is converted into a set of standardized inputs with clear structure, unified numerical values, and can participate in calculations, providing a solid data foundation for subsequent parameter generation and inconsistency coefficient calculation.

[0038] To extract task beat coupling structure information and device advancement behavior feature information from the preprocessed beat adaptation structured input information, it can be implemented in software through field classification mapping and label-driven structure deconstruction. Specifically, after preprocessing, the beat adaptation structured input information has been standardized into a set of structures with unified field names and data formats. The system can automatically classify and identify the information based on field label or field prefix naming rules. During the extraction process, the system first identifies the set of fields related to the advancement structure between task segments, such as dependency level, buffer tolerance mark, scheduling overlap ratio, etc. These fields are uniformly classified into the task beat coupling structure information category. Subsequently, the system identifies the set of fields related to the device advancement process control structure, such as the number of operation steps, advancement mode number, control frequency value, etc. These are classified into the device advancement behavior feature information category. During the extraction process, the system uses the mapping relationship between each task segment and the target device as the key index, and assembles the corresponding task attribute values and device attribute values into two independent data vector sets respectively, and outputs them to the parameter generation module to ensure that the input for model calculation has clear structure separation and logical attribution. This method not only realizes the accurate extraction of parameter subsets from structured input, but also supports subsequent independent calculation and combined modeling of each type of information.

[0039] To determine the preset inconsistency coefficient threshold interval, it can be completed in software by combining statistical analysis of historical scheduling data and process task feature clustering modeling. Specifically, the system first extracts a large number of representative multi-segment task execution samples from historical task scheduling records, and calculates the corresponding beat consistency index and beat structure difference index for each sample, so as to obtain the corresponding inconsistency coefficient distribution data. The software system then uses clustering analysis methods (such as mean or density clustering) to cluster and classify these inconsistency coefficients, and automatically identifies the typical distribution intervals of different beat structure adaptation states such as "highly matched", "coordinatable", "incompatible" in actual execution. Based on the clustering results, the system calculates the inconsistency coefficient boundary values corresponding to each adaptation category, and uses these boundary values as the threshold interval for beat inconsistency degree classification. In addition, the system can also introduce conditional variables such as the process path type to which the task belongs and the device combination characteristics to refine and adjust the threshold, forming a dynamic threshold template library for specific task types. At runtime, the scheduling module can select the matching threshold configuration file according to the task characteristics to achieve personalized, data-driven determination of the inconsistency coefficient threshold interval, thereby improving the accuracy of beat structure coordination judgment and decision-making adaptability.

[0040] In this embodiment, the acquisition logic of the beat consistency index is specifically as follows: Extract the task beat coupling structure information from the pre - processed beat adaptation structured input information, specifically including the structure dependence level, beat buffer tolerance flag value, and scheduling window overlap rate of each pair of adjacent task segments in the beat adaptation mapping relationship graph, and calibrate them respectively as , and , , where is a positive integer; The structure dependence level represents the process dependence path level difference between the front and back task segments in the th pair of adjacent task segments in the task beat structure model. According to the formula: In the formula, represents the nd task segment in the th pair of adjacent task segments, represents the st task segment in the th pair of adjacent task segments, represents the hierarchical depth of the task segment in the task beat structure diagram, and also represents the number of hops in the longest path from the starting task segment to the task segment , represents the hierarchical depth of the task segment in the task beat structure diagram, and also represents the number of hops in the longest path from the starting task segment to the task segment ; To calculate the structure dependence level in the software system, the system needs to perform graph structure traversal and path analysis on the directed dependence relationship between task segments based on the task beat structure model. First, in the modeling stage, the system abstracts the structural sequence relationship of multiple segments of tasks into a directed acyclic graph (DAG). Each node in the graph represents a task segment, and the edge represents the sequential execution dependence in the process. Subsequently, the system identifies all nodes with an in - degree of zero as the starting task segment nodes, and starting from this point, performs a depth - first traversal (DFS) or topological sorting on the entire graph, so as to record the structural hierarchical depth of each task segment node in the graph, that is, the maximum number of hops in the reachable path from the starting point. The system dynamically maintains a depth value field for each task segment node during the traversal process, and updates the maximum depth value of the current node whenever the path extends until all nodes are traversed. For any th pair of adjacent task segments, the system searches for the node identifiers of its front and back task segments in the model graph and reads their corresponding depth values This value reflects the degree of structural nesting of task segments in the process execution sequence. The system can write the calculation result as a numerical attribute into the structured input information for beat adaptation, which can be used for subsequent calculation of the beat consistency index. The entire calculation process can be automatically completed by the pre-scheduling processing module without manual intervention, and has high generality and scalability.

[0041] The hierarchical depth in the task beat structure diagram refers to the structural level position of a certain task segment node in the entire task execution flow chart, which is specifically calculated by the system according to the directed dependency relationship between task segments. The system first constructs the entire multi-segment task into a directed acyclic graph (DAG), where each node represents a task segment and the edge represents the process sequence dependency before and after; then the system identifies all starting task segment nodes (i.e., nodes without predecessors) as root nodes, and starts from these root nodes to traverse the entire graph along the dependency path. During the traversal process, the system records the longest number of hops that each task segment node can reach from the starting node path, that is, the number of edges included in the longest path from the root node to the task segment node. This number of hops is the hierarchical depth of the task segment. The larger the value of the hierarchical depth, the later the execution stage of the task segment in the process flow, the more dependent on the previous tasks, and it usually has a stronger structural dependency during the scheduling process. This hierarchical depth value is automatically generated by the system through graph algorithms when constructing the task beat structure model, and is the core index reflecting the logical nesting relationship of task segments.

[0042] Beat buffer tolerance flag value Indicates the Whether it is allowed to insert a buffer time period between adjacent task segments in the scheduling. If not, then If so, then ; In order to implement the extraction and assignment of the beat buffer tolerance flag value in the software system, the system first needs to identify whether there is an allowable condition for beat buffer between each pair of adjacent task segments from the task beat structure model. Specifically, during the modeling stage, the system will attach a scheduling control attribute field to the directed connection edge between task segments to identify whether it is allowed to insert a non-consecutive execution buffer time period or an intermediate idle segment between two task segments. This identifier can be derived from user-defined task templates, process rule libraries, or automatically generated structure constraint configurations. During actual processing, the system will traverse all pairs of adjacent task segments in the beat adaptation mapping relationship graph, and read the value of the "buffer tolerance" field for each edge. If the field is marked as "non-bufferable" or defined as rigid sequential execution in the matching rule, the system will automatically set the It is assigned a value of 1; conversely, if the field is marked as "buffered" or there is no restriction on buffering, the system assigns it a value of 0. All marker values are ultimately uniformly stored as standard numerical fields in the beat adaptation structured input information for subsequent calculation of the beat consistency index. In this process, the system can integrate a consistency verification mechanism to ensure that each pair of task segments has a clear buffering marker and support dynamic judgment rules based on task type, process level, or equipment characteristics, making this assignment logic scalable and adaptable.

[0043] Scheduling window overlap rate Indicates the Time window overlap ratio value of the front and rear task segments in the adjacent task segments in the scheduling time configuration. The specific calculation method is: divide the length of the intersection of the scheduling time windows of each pair of adjacent task segments by the total span length to obtain this ratio value. The result is a floating-point number, and the value range is ; the closer the value is to 1, the stronger the time continuity of the two task segments.

[0044] To calculate the scheduling window overlap rate in the software system, the system needs to automatically identify the intersection relationship in the time dimension of each pair of adjacent task segments based on the time window definitions of each task segment in the scheduling plan. Specifically, the system first extracts the time window information of each task segment from the scheduling database or the task description model. This window is usually represented by the start time and the end time . For each pair of adjacent task segments (denoted as task segment and ), the system reads their time windows and respectively, calculates the length of the intersection interval through time interval operations, that is, the time amount of the overlap of the two task windows; then calculates the total span of their time windows, that is, the overall interval length covered from the earlier start time to the later end time. Subsequently, the system divides the intersection length by the total span length to obtain the overlap rate , and its result is a floating-point number with a value range between . The closer the value is to 1, the closer the execution time arrangements of the two tasks are, and they can be regarded as almost continuous or seamlessly connected; the closer the value is to 0, the greater the scheduling interval between task segments. This calculation logic can be uniformly completed by a dedicated calculation module in the structural evaluation stage before scheduling, and record the value corresponding to each pair of task segments in the beat adaptation structured input information for subsequent invocation of the evaluation model of the beat consistency index. The entire process can be automatically completed by software without relying on manual input and is applicable to batch analysis of large-scale task scheduling graphs.

[0045] Calculate the beat consistency index , the specific calculation method is as follows: Add the natural logarithm values after adding 1 to the exponential values and beat buffer tolerance flag values of each pair of adjacent task segments and the overlapping rate with the scheduling window , and take their arithmetic mean among all task segment pairs to obtain the beat consistency index ; The specific calculation formula is as follows:

[0046] In the formula, is the beat consistency index.

[0047] The reason why the calculation formula of this beat consistency index adopts this formula is to comprehensively evaluate the beat coupling strength between task segments from multiple dimensions, and strengthen the influence degree of key structural features through a reasonable mathematical operation form. Among them, the exponential function is used to amplify the influence of the structural dependence level on the beat advancement. Because in the case of deeper nesting and greater hierarchical differences in the task segment structure, the requirement for beat continuity in the scheduling advancement process is higher, and this structural sensitivity should increase exponentially; the term is a binary flag, which is used to directly reflect the rigid limit of the beat buffer. When the buffer is prohibited, it is set to 1, and the requirement for advancement connection is rigid, and its influence is linearly included, reflecting the strong scheduling constraint; while this logarithmic function processes the overlapping rate of the scheduling window, and can provide a decreasing amplification effect during the process of the overlapping ratio increasing from 0 to 1, so that the contribution of task segment pairs with windows close to continuity to the index gradually saturates, avoiding the overlapping rate from dominating the evaluation result. The overall calculation formula sums up the three items of data and then takes the average to realize the overall quantification of the beat consistency requirements of the entire task segment sequence, which not only retains the independence of each field in the structural logic, but also improves the discrimination and model expressiveness of the evaluation through non-linear functions.

[0048] The size of the beat consistency index directly reflects the consistency requirements of the multi-segment task on the continuity of the advancement beat in its internal structure, thereby determining whether the task is easily inconsistent due to the difference in equipment beat control when it is simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine. Specifically, the larger the beat consistency index, the stronger the process dependency, the stricter the time continuity requirements and the less buffer tolerance space between the task segments. At this time, the overall task has higher requirements for the synchronization of the beat advancement rhythm. Under the conditions of joint scheduling of heterogeneous equipment, if there are differences in the equipment advancement mode, control logic or response mechanism, it is more likely to have structural inconsistencies such as beat breakpoints and advancement imbalance. Therefore, the larger the beat consistency index, the stronger the task's sensitivity to beat coordination, the higher the requirements for the matching degree of the equipment advancement structure, and the corresponding inconsistency risk also increases. As an indicator for evaluating the strictness of the task beat before scheduling, this index can be used to determine in advance whether the scheduling plan has the ability to coordinate the beat across equipment, and then guide the inconsistency risk classification and scheduling plan optimization.

[0049] In this embodiment, the logic for obtaining the beat structure difference index is as follows: The equipment propulsion behavior feature information is extracted from the preprocessed beat adaptation structured input information, including the propulsion operation sequence length difference, propulsion mode number difference and control frequency difference ratio of each task segment in the beat adaptation mapping relationship map, and calibrated as , and , , is a positive integer; Difference in length of push operation sequence Indicates The difference in the number of operating steps involved in each task segment when it is advanced on a fully automatic die-cutting machine and a semi-automatic die-cutting machine; In order to implement the push operation sequence length difference in the software system In order to extract and calculate, the system needs to analyze the advancement operation sequences involved in the full-automatic die-cutting machine and the semi-automatic die-cutting machine when executing the same task segment, and compare the number of operation steps. In the specific implementation process, the system will call the operation event sequence recorded in the task execution behavior log or the equipment operation model. These sequences usually include each step of the control command, state transition, sensor response or execution action and other information, and sequentially form the advancement operation chain of the task segment on a certain device. The system first parses the operation chain of the full-automatic die-cutting machine to execute the task segment, counts the number of operation nodes, and records it as the operation sequence length of the device; then performs the same processing on the operation chain of the semi-automatic die-cutting machine to execute the same task segment, and obtains the corresponding operation length. Subsequently, the system subtracts the number of operation steps between the two and takes the absolute value to obtain the first The length difference of the advancement operation sequence for each task segment This value can reflect the difference in process complexity of the execution structures of the two types of devices, and is written as an important component field in the beat adaptation structured input information as device advancement behavior feature information for subsequent calls by the beat structure difference index calculation module. The entire process can be automatically executed in software through preset behavior sequence parsing logic and field mapping rules, and is applicable to parallel analysis and structure comparison of batch task segments.

[0050] The difference in advancement mode numbers Indicates the difference in advancement mode numbers of the th task segment on the fully automatic die-cutting machine and the semi-automatic die-cutting machine. The numbering rule is: 0 represents continuous automatic advancement, 1 represents control trigger advancement, and 2 represents manual confirmation advancement; According to the formula: wherein, represents the fully automatic die-cutting machine, represents the semi-automatic die-cutting machine, indicates the th task segment on the fully automatic die-cutting machine for the advancement mode number value, indicates the th task segment on the semi-automatic die-cutting machine for the advancement mode number value; In order to implement the extraction and calculation of the difference in advancement mode numbers in the software system, the system first needs to establish a correspondence rule table between the device advancement control mode and the numbers. This rule can be preset as: 0 represents continuous automatic advancement, 1 represents control trigger advancement, and 2 represents manual confirmation advancement. Before task scheduling, the system analyzes the advancement control method of each device when executing a certain task segment based on the device capability model or behavior template library. In the specific implementation process, the system calls the task segment and device combination information recorded in the beat adaptation mapping relationship graph, and determines whether the advancement process of the current task segment on the target device is continuous automatic execution, requires external triggering, or requires manual confirmation by querying the device configuration file or scheduling rule template, and accordingly matches the corresponding advancement mode number value. For example, if a certain task segment is set to a continuous advancement mode on the fully automatic die-cutting machine, the system numbers it as 0; if it is required to be advanced by signal triggering on the semi-automatic die-cutting machine, the number is 1. Subsequently, the system calculates the difference between these two number values and takes the absolute value to obtain the difference in advancement mode numbers of the th task segment. This value is written as a numerical field into the device advancement behavior feature information for subsequent use in calculating the beat structure difference index. The entire process can be automatically executed in software through logic such as rule mapping, field extraction, and template comparison, without manual intervention, and has universality and configurability.

[0051] Control frequency difference ratio Indicates the difference ratio of the control action occurrence frequencies during the advancement of the th task segment on a fully automatic die cutter and a semi-automatic die cutter; In order to implement the extraction and calculation of the control frequency difference ratio in the software system, the system needs to obtain from the task execution log or device behavior data the number of control actions triggered during the advancement of the th task segment on the fully automatic die cutter and the semi-automatic die cutter. First, the system retrieves the control event sequence generated during the advancement of this task segment on different devices. Each control action includes operation commands with clear identifications such as start, stop, confirmation, waiting for signals, etc. The system filters out the records belonging to the "control action" category according to the preset event type list and conducts statistics in chronological order. Calculate the total number of control actions that occurred during the advancement of this task segment on the fully automatic die cutter and the semi-automatic die cutter respectively, denoted as and and . Then, the system calculates the absolute value of the difference between the two divided by the average or maximum value of the two (configurable selection), thereby obtaining the control frequency difference ratio . For example, if a certain task segment requires 2 control actions on the fully automatic device and 6 control actions on the semi-automatic device, then can be calculated as . This ratio reflects the relative difference in the operation control complexity of the two devices when executing the same task segment. The system can write it as a standardized floating-point field into the device advancement behavior feature information for use in the subsequent model of the beat structure difference index. This calculation process is implemented in the software through log analysis, event classification statistics, and difference normalization logic, and has the ability of batch processing and strong scalability.

[0052] Calculate the beat structure difference index , and the specific calculation method is as follows: Sum the e-power of the difference in the advancement mode numbers in each task segment, the square root value of the control frequency difference ratio , and the natural logarithm value of one plus the difference in the advancement operation sequence lengths , and then calculate the arithmetic average of all task segments to obtain the beat structure difference index ; the specific calculation formula is as follows:

[0053] In the formula, is the beat structure difference index.

[0054] The reason for using this formula for the calculation formula of the beat structure difference index is to quantify the difference degree of the propulsion behavior structure of the device when performing multi-segment tasks from three dimensions, and to differentially enhance the sensitivity of the differences in each dimension through different mathematical operation methods. Among them, the exponential function acts on the difference in the propulsion mode numbers, and is used to amplify the influence of the structural differences between the device propulsion control methods. Because the larger the difference in the propulsion mode numbers, the more fundamental the difference in the device control logic, and the more significant the impact on the task beat coordination; the square root function acts on the ratio of the control frequency differences, aiming to smooth the contribution of the control frequency differences to the overall index, so that it will not expand excessively in the high-frequency difference scenario and retain the difference sensitivity in the low-frequency difference scenario; the natural logarithm function processes the difference in the length of the propulsion operation sequence, effectively avoiding the invalidation of the index when the difference in the number of operation steps is too small, and suppressing the dominant role of extreme values in special cases in the overall calculation. By performing normalization enhancement on the three types of structural difference data and taking the arithmetic mean, the system can accurately evaluate the structural behavior deviation of the fully automatic and semi-automatic die-cutting machines during the specific task propulsion process, providing a reliable quantitative basis for subsequent beat coordination matching.

[0055] The size of the beat structure difference index directly reflects the difference degree between the propulsion behavior structures of the fully automatic die-cutting machine and the semi-automatic die-cutting machine when performing the same task segment, thus determining whether it is easy to generate inconsistencies in the task beat execution structure due to different propulsion methods, control complexities, and operation paths when multi-segment tasks are simultaneously scheduled to the two types of devices. When the beat structure difference index is larger, it indicates that the two types of devices have more differences in the number of operation steps required for propelling this task segment, more significant differences in the control response methods, and more obvious differences in the control action frequencies, representing that there are large behavioral deviations between these two devices in the coordinated propulsion of the task beat, which is extremely likely to lead to phenomena such as inconsistent propulsion paces, asynchronous beats, or logical conflicts of the task segment, thus causing the breakage of the scheduling chain or the failure of the task. Therefore, the larger this index is, the more the system needs to be vigilant against the risk of mismatch in the propulsion structure between devices, and its value can be used as an important basis for evaluating the feasibility of beat coordination when tasks are executed across devices.

[0056] In this embodiment, for the generated beat consistency index and the beat structure difference index an inconsistency degree determination model is constructed, and an inconsistency coefficient is generated through weighted summation. The specific calculation formula is as follows:

[0057] In the formula, is the inconsistency coefficient, and are respectively the beat consistency index and the beat structure difference index with non - zero weight coefficients, and .

[0058] To generate the inconsistency coefficient in the software system, the system needs to perform a weighted combination of the calculated beat consistency index and the beat structure difference index . In the specific implementation process, the system first calls the and values output by the pre - module as the input variables of the inconsistency degree model. Subsequently, according to the preset weight configuration file, the system reads the corresponding weight coefficients and respectively. Both of these weight coefficients are floating - point numbers greater than zero, representing and 's relative importance in the final inconsistency judgment; among them, is usually used to emphasize the requirements of the task itself for beat continuity, is used to measure the impact of the equipment propulsion structure difference on the coordination difficulty. The system requires to ensure that the inconsistency coefficient after weighted summation maintains a standardized proportional output. This configuration can be set by domain experts based on experience or automatically adjusted through training with historical scheduling samples. When the two indices and weights are determined, the system performs simple floating - point multiplication and addition operations to generate the value in real - time, and uses it as the core judgment basis for subsequent scheduling classification and control strategy selection. This process can be automatically completed by the model calculation engine module, with configurability and scalability, supporting dynamic weight strategy switching according to task types, equipment combinations, or production scenarios.

[0059] In this embodiment, a pre - set inconsistency coefficient threshold interval is determined, and after determination, it is compared with the generated inconsistency coefficient . According to the comparison result, the inconsistency degree of the task execution beat structure when multiple segments of tasks are simultaneously scheduled to the full - automatic die - cutting machine and the semi - automatic die - cutting machine is determined. The specific comparison and analysis are as follows: If , when multiple segments of tasks are simultaneously scheduled to the full - automatic die - cutting machine and the semi - automatic die - cutting machine, the inconsistency degree of the task execution beat structure is low; This situation indicates that the multi-segment task has relatively low requirements for beat coordination in terms of structure. At the same time, the structural behaviors of the fully automatic die-cutting machine and the semi-automatic die-cutting machine during the advancement process show relatively small differences. The task advancement beat has high fault tolerance and scheduling flexibility. In such scenarios, the two types of equipment can seamlessly cooperate to complete the task segment advancement under the conventional scheduling strategy without the need for additional synchronization control or beat correction mechanisms. The system can preferentially adopt an efficient parallel scheduling strategy to improve production capacity utilization.

[0060] If , when the multi-segment task is simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, the degree of inconsistency in the task execution beat structure is at a medium level; This situation indicates that the task structure has certain coupling requirements for beat advancement. At the same time, there are perceptible differences in the advancement control methods or operation frequencies of the equipment. At this time, the system needs to introduce a beat buffer coordination mechanism or a semi-synchronous advancement strategy during the scheduling process. By inserting buffer time slots, pre-aligning control signals, etc., to bridge the beat differences between the equipment and ensure that the continuity between task segments will not be broken due to inconsistent beat paces. This is a key test interval for the system's scheduling adjustment ability.

[0061] If , when the multi-segment task is simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, the degree of inconsistency in the task execution beat structure is at a severe level.

[0062] This situation indicates that the task structure highly depends on beat advancement, and there are significant differences in the equipment advancement behaviors in terms of control logic, operation complexity, and response mode, resulting in a high degree of mismatch in the advancement beat of this task under the current equipment combination. In such cases, if forced to schedule for joint execution on fully automatic and semi-automatic equipment, it is extremely likely to cause asynchronous advancement of task segments, breakage of the scheduling chain, or overall failure of the task. The system should immediately trigger a structure avoidance strategy, prohibit splitting and scheduling of this task between heterogeneous equipment, or force priority allocation to similar types of equipment with similar beat attributes for execution to ensure the coherence of the overall task advancement and scheduling stability.

[0063] In this embodiment, corresponding scheduling measures are executed according to the determination result. Specifically: If the determination result is at a low level, the specific scheduling measure to be executed is: The multi-segment task is separately assigned to the fully automatic die-cutting machine and the semi-automatic die-cutting machine for execution according to the original task segment division order, and no beat buffer time period is inserted. The equipment independently advances its respective task segments according to its own advancement rhythm; To implement the scheduling measures corresponding to the low degree of inconsistency in the software system, the system first needs to read and maintain the original task segment division order of multiple segments of tasks, that is, not to reconstruct or rearrange the task structure. The scheduling module traverses the task segment topological order in the task beat structure model, and combines the information on the types of task segments that each device can execute in the device capability map, and maps the task segments to compatible fully automatic die-cutting machines or semi-automatic die-cutting machines in the original order of the task segments. In this process, the system does not introduce a beat buffer slot field, nor does it perform beat alignment or timing interpolation operations, so as to ensure that the task segments are directly executed independently relying on the device propulsion logic. In addition, after the task segments are scheduled to the target devices, the system calls the propulsion parameter configuration files of each device, reads the propulsion mode, operation sequence and response mechanism, and schedules their respective execution plans according to the self-propulsion rhythm of the devices, without implementing a unified synchronization instruction. The reason for adopting this method is that under the low degree of inconsistency, the task beat structure has a high fault tolerance space, and the propulsion behaviors of the two types of devices do not constitute a logical conflict under this task type, allowing each device to independently complete the task under its own beat control, so as to maximize the utilization of scheduling concurrency and system execution efficiency. This process is implemented in the system by the cooperation of the scheduling mapping module and the propulsion control module, and has high efficiency and automatic adaptation ability.

[0064] If the determined result is medium, the specific scheduling measures to be executed are as follows: after the task segments are allocated to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, a beat buffer time period of a preset length is inserted at the boundary of the task segments with beat differences, and signal synchronization processing is performed on the device startup timing before the task segments are executed; To implement the scheduling measures corresponding to a medium degree of inconsistency in the software system, the system first, after completing the task assignment between the task segments and the fully automatic die-cutting machine and the semi-automatic die-cutting machine, based on the beat control attributes recorded in the beat adaptation mapping relationship graph, automatically identifies the task segment boundaries with advancing beat differences. The system determines the nodes with offset beat connections by comparing the advancing mode numbers, control frequencies, and operation sequence lengths of the front and rear task segments on different devices, and inserts a beat buffer time period of a preset length between these nodes. The length of this buffer period can be determined by the beat tolerance range parameter corresponding to the task segment type and is inserted into the scheduling plan as an additional field of the scheduling time window to ensure that the task segments can still be transitioned in sequence even when the beats are not completely consistent. In addition, to avoid propulsion breaks caused by the starting time offset of task segments on different devices during actual propulsion, the system sets a starting signal synchronization mechanism for the task segments to be jointly propelled in the scheduling plan, that is, a unified starting trigger signal is issued before the task segment starts, and the devices are coordinated to enter the execution state through the central control instruction. This approach can achieve the "soft alignment" of the task segment scheduling time at the software layer, neither forcing the rearrangement of the original task structure nor improving the collaborative stability of device propulsion, thus ensuring the continuity and reliability of task scheduling in the scenario of medium-degree structural inconsistency.

[0065] If the determination result is the severity level, the specific scheduling measures to be implemented are as follows: It is prohibited to split and execute this multi-segment task between the fully automatic die-cutting machine and the semi-automatic die-cutting machine. All task segments are scheduled to be continuously executed in the device category with consistent beat propulsion attributes, and device type switching is not allowed during the scheduling process.

[0066] To implement scheduling measures corresponding to a severe inconsistency level in a software system, after generating an inconsistency coefficient and determining that it is higher than the maximum threshold, the system needs to automatically invoke a scheduling restriction policy to prohibit the splitting and allocation of this multi-segment task to different types of devices. In specific implementation, the system first identifies all task segment numbers corresponding to the task and establishes a complete task segment sequence binding relationship; then, in the task-device mapping link, by analyzing the beat advancement attributes of each device in the device capability map, it filters out a set of similar devices with highly consistent beat control methods, operation complexities, and response mechanisms, such as all fully automatic die-cutting machines or all semi-automatic die-cutting machines. Based on this, the system enables the "task segment non-splitting" rule, forcibly maps the entire task segment sequence to one or more consecutive resources in this set of consistent devices, and locks its scheduling path to prohibit the configuration of device type switching fields. In addition, the system performs consistency verification on this task segment combination during the scheduling constraint verification phase. If it is found that there is an execution path across device types in the scheduling plan, the scheduling plan is immediately rolled back and the device reallocation is performed again. The reason for adopting this scheduling strategy is that when the beat structure inconsistency has reached a severe level, if it is still allowed for task segments to flow between devices with significantly different beat advancement mechanisms, it will directly cause beat misalignment, control conflicts, or task interruptions. Therefore, it is necessary to ensure the advancement coherence of the task structure among similar devices to ensure the reliability of the overall system operation and the stability of task execution. This scheduling control strategy is uniformly executed by the scheduling constraint engine module during the scheduling diagram generation phase and has a high degree of regularization and configurability.

[0067] The beat feedback optimization module, after the task execution is completed, collects the actual advancement process data of the task segments and updates the beat adaptation mapping relationship map for correcting the task beat structure model and device beat control attribute information.

[0068] To implement the function of the beat feedback optimization module in a software system, after the execution of the multi-segment task is completed, the system needs to automatically collect the actual operation data during the advancement process of each task segment on different devices, including the actual start and end times of the task segments, the control response duration of each step, the actual performance of the advancement mode, the number of control signal triggers, and the advancement interval between task segments, etc. These data can be parsed and extracted from device control logs, event trigger records, sensor data, and execution status codes, and are structured and stored with the task segment number and device number as indexes. After the collection is completed, the system uses these data as the "actual advancement behavior characteristics" of the task segments under specific devices and compares them with the "expected advancement attribute information" recorded in the beat adaptation mapping relationship map before task scheduling to identify whether there are deviations in the number of advancement steps, control frequency, response mode, etc.

[0069] After the system identifies the differences, it automatically corrects and updates the fields in the beat adaptation mapping relationship graph according to the offset between the actual advancement process and the original model. For example, it adjusts the beat control attributes between task segments and devices, recalculates the beat adaptation score, modifies the advancement mode number, etc. At the same time, the system writes the execution feedback of this round of tasks into the data cache for model training, providing a data basis for subsequent construction of a dynamic beat learning model or generation of beat prediction parameters. The purpose of this process is to enhance the adaptive ability of the scheduling system, so that the beat coordination control no longer depends on static templates or manual settings, but continuously optimizes the matching accuracy between the task beat structure model and the device advancement attributes based on actual execution behaviors, thereby improving the scheduling accuracy and the stability of task execution. This mechanism can be continuously and automatically executed in the background through the collaborative operation of the task feedback analysis module and the graph update module, without manual intervention, and has real-time performance and scalability.

[0070] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0071] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0072] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0073] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0074] In several embodiments provided by this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical or other form.

[0075] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0077] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An intelligent scheduling system for dynamic coordinated control of fully automatic and semi-automatic die-cutting machines, characterized in that, It includes a task structure analysis module, a beat adaptation modeling module, a beat coordination scheduling module, and a beat feedback optimization module; The task structure analysis module conducts a structural analysis on the multi-segment tasks to be scheduled, identifies the technological sequence relationship and beat coupling requirements between each task segment, and constructs a task beat structure model; The beat adaptation modeling module obtains the beat control attribute information of the fully automatic die-cutting machine and the semi-automatic die-cutting machine during the process of advancing the task segments. Based on the corresponding relationship between the task beat structure model and the beat control attribute information, it generates a beat adaptation mapping relationship graph between each task segment and the equipment; The beat coordination scheduling module, based on the generated beat adaptation mapping relationship graph, determines the degree of inconsistency in the task execution beat structure when the multi-segment tasks are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, and executes corresponding scheduling measures according to the determination result; The beat feedback optimization module, after the task execution is completed, collects the actual advancement process data of the task segments, and updates the beat adaptation mapping relationship graph for correcting the task beat structure model and the equipment beat control attribute information.

2. The intelligent scheduling system for dynamic coordination control of a fully automatic and semi-automatic die-cutting machine according to claim 1, wherein In the task structure analysis module, when conducting a structural analysis on the multi-segment tasks to be scheduled, identifying the technological sequence relationship and beat coupling requirements between each task segment, and constructing a task beat structure model, specifically: Extract the inter-segment dependency information between each task segment in the multi-segment tasks to be scheduled; According to the inter-segment dependency information, construct a directed sequence diagram for representing the technological sequence relationship; In the directed sequence diagram, add a maximum time interval threshold mark and an inter-segment buffer allowance mark between adjacent task segments for identifying the beat coupling requirements; Combine the directed sequence diagram, the maximum time interval threshold mark, and the inter-segment buffer allowance mark to generate a task beat structure model.

3. The intelligent scheduling system for dynamic coordination control of a fully automatic and semi-automatic die cutting machine according to claim 2, wherein In the beat adaptation modeling module, based on the corresponding relationship between the task beat structure model and the beat control attribute information, generating a beat adaptation mapping relationship graph between each task segment and the equipment, specifically: Traverse the connection relationship between each task segment and its adjacent task segment in the task beat structure model, and extract the corresponding maximum time interval threshold and buffer allowance mark; For each candidate fully automatic die-cutting machine and semi-automatic die-cutting machine, extract its beat control attribute information during the task segment switching process. The beat control attribute information includes the advancement mode type, the response mode identifier, and the inter-segment control structure; Conduct a structural matching between the beat requirements included in the task beat structure model and the beat control attribute information. For each combination of a task segment and an equipment, establish a beat matching relationship item, and generate the connection relationship in the graph based on this; Structurally organize the beat matching relationships corresponding to all combinations of task segments and equipment to form a beat adaptation mapping relationship graph between task segments and equipment, which is used to represent the matching situation between the task beat structure model and the beat control attribute information.

4. The intelligent scheduling system for dynamic coordination control of a fully automatic and semi-automatic die cutting machine according to claim 3, wherein In the beat coordination scheduling module, the beat adaptation structured input information is extracted from the generated beat adaptation mapping relationship graph, and preprocessing is performed after extraction; the task beat coupling structure information and the device propulsion behavior feature information are extracted from the preprocessed beat adaptation structured input information, and they are analyzed to generate a beat consistency index and a beat structure difference index respectively; an inconsistency degree determination model is constructed for the generated beat consistency index and beat structure difference index, and an inconsistency coefficient is generated through weighted summation; a preset inconsistency coefficient threshold interval is determined, and after determination, it is compared with the generated inconsistency coefficient. According to the comparison result, the inconsistency degree of the task execution beat structure is determined when multiple task segments are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, and corresponding scheduling measures are executed according to the determination result.

5. The intelligent scheduling system for dynamic coordination control of fully automatic and semi-automatic die-cutting machines according to claim 4, characterized in that, The acquisition logic of the beat consistency index is specifically as follows: Extract task beat coupling structure information from the preprocessed beat adaptation structured input information, specifically including the structural dependence level, beat buffer tolerance marker value, and scheduling window overlap rate of each pair of adjacent task segments in the beat adaptation mapping relationship graph, and calibrate them respectively as , and , , where is a positive integer; Structure Dependence Level Indicating the Level difference of the process dependence path identified in the task beat structure model between the previous and subsequent task segments in adjacent task segments; Beat buffer tolerance flag value Indicates the Whether a buffer time period is allowed to be inserted in the scheduling between the previous and the next task segments in adjacent task segments. If not allowed, then If allowed, then ; Scheduling window overlap rate Indicates the Time window overlap ratio value of the front and back task segments in adjacent task segments in the scheduling time configuration; Calculate the beat consistency index , and the specific calculation method is as follows: Add the exponential values of the structural dependency levels of each pair of adjacent task segments , the beat buffer tolerance flag values and the overlapping rate of the scheduling window after adding one, take the natural logarithm of the sum, and then take the arithmetic mean among all task segment pairs to obtain the beat consistency index .

6. The intelligent scheduling system for dynamic coordination control of a full-automatic and semi-automatic die-cutting machine according to claim 5, characterized in that, The acquisition logic of the beat structure difference index is specifically as follows: Extract device advancement behavior feature information from the preprocessed beat adaptation structured input information, specifically including the difference in the advancement operation sequence lengths of each task segment in the beat adaptation mapping relationship graph, the difference in the advancement mode numbers, and the ratio of the control frequency differences, and calibrate them respectively as 、 and , , where \(n\) is a positive integer; Difference in the length of the advancing operation sequence Indicates the difference in the number of operation steps included when the th task segment is advanced on a fully automatic die cutter and a semi-automatic die cutter; Advancement mode number difference Indicates the advancement mode number difference of the th task segment on the fully automatic die cutting machine and the semi-automatic die cutting machine. The numbering rule is: 0 indicates continuous automatic advancement, 1 indicates controlled trigger advancement, and 2 indicates manual confirmation advancement; Control frequency difference ratio Indicates the difference ratio of the occurrence frequency of control actions during the advancement of the th task segment on a fully automatic die cutter and a semi-automatic die cutter; Calculate the beat structure difference index , and the specific calculation method is as follows: Take the e-power of the difference in the propulsion mode numbers in each task segment, the square root value of the ratio of the control frequency differences , and the natural logarithm value of one plus the difference in the lengths of the propulsion operation sequences , sum them up, and then calculate the arithmetic mean for all task segments to obtain the beat structure difference index .

7. The intelligent scheduling system for dynamic coordination control of a fully automatic and semi-automatic die cutting machine according to claim 6, characterized in that, For the generated beat consistency index and the beat structure difference index Construct an inconsistency degree determination model to generate an inconsistency coefficient through weighted summation .

8. The intelligent scheduling system for dynamic coordination control of a full-automatic and semi-automatic die-cutting machine according to claim 7, characterized in that, Determine the pre-set threshold interval of the inconsistency coefficient , and after determination, compare it with the generated inconsistency coefficient , and determine the degree of inconsistency in the task execution beat structure when multiple tasks are simultaneously scheduled to the fully automatic die-cutting machine and the semi-automatic die-cutting machine according to the comparison result. The specific comparison and analysis are as follows: If , when multiple tasks are simultaneously scheduled to the fully automatic die cutter and the semi-automatic die cutter, the degree of inconsistency in the task execution beat structure is low; If , when multiple tasks are simultaneously scheduled to the fully automatic die cutter and the semi-automatic die cutter, the degree of inconsistency in the task execution beat structure is at a medium level; If , when multiple tasks are simultaneously scheduled to the fully automatic die cutter and the semi-automatic die cutter, the degree of inconsistency in the task execution beat structure is the severity level.

9. The intelligent scheduling system for dynamic coordination control of a full-automatic and semi-automatic die-cutting machine according to claim 8, wherein Executing corresponding scheduling measures according to the determination result specifically means: If the determination result is a low degree, the specific scheduling measure to be executed is: The multiple task segments are respectively assigned to the fully automatic die-cutting machine and the semi-automatic die-cutting machine according to the original task segment division order, and no beat buffer time period is inserted, and the devices independently propel their respective task segments according to their respective propulsion rhythms; If the determination result is a medium degree, the specific scheduling measure to be executed is: After the task segments are assigned to the fully automatic die-cutting machine and the semi-automatic die-cutting machine, a beat buffer time period with a preset length is inserted at the boundary of the task segments with beat differences, and signal synchronization processing is performed on the device startup timing before the task segments are executed; If the determination result is a severe degree, the specific scheduling measure to be executed is: It is prohibited to split and execute the multiple task segments between the fully automatic die-cutting machine and the semi-automatic die-cutting machine, and all task segments are scheduled to be continuously executed in the device category with consistent beat propulsion attributes, and device type switching is not allowed during the scheduling process.

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