Flexible intelligent processing production line multi-online cooperative scheduling method and system

Abstract

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop scheduling, and in particular to a multi-machine collaborative scheduling method and system for a flexible intelligent processing production line. Background Art

[0002] With the rapid development of intelligent manufacturing, flexible intelligent production lines are increasingly being used in high-end manufacturing sectors such as automotive parts, aerospace, wind power equipment, and medical devices. These production lines typically incorporate various types of processing equipment, including lathes, grinders, and machining centers, as well as auxiliary equipment such as gantry manipulators and articulated robots. These devices possess diverse operating characteristics, processing capabilities, and control systems, creating a typical heterogeneous environment.

[0003] In existing technologies, most control methods either optimize for a single type of device or simplify the processing of heterogeneous devices. These methods fail to fully account for the differences in the characteristics of various devices, making it difficult to achieve truly efficient collaborative operations. In particular, there are significant deficiencies in the following areas: First, the lack of a unified representation of heterogeneous device capabilities makes it impossible to accurately assess the matching of tasks and devices; second, the traditional centralized control model suffers from poor system flexibility and limited adaptability to dynamically changing production environments; and finally, insufficient spatiotemporal coordination between devices leads to conflicting actions and wasted resources.

[0004] To this end, a multi-machine collaborative scheduling method and system for flexible intelligent processing production lines are proposed. Summary of the Invention

[0005] The present invention aims to provide a multi-machine collaborative scheduling method and system for a flexible intelligent processing production line, enabling flexible collaborative control of heterogeneous equipment. The method includes: establishing a numerical control parameter model for the processing equipment, decomposing the processing program into basic process instruction units, analyzing the compatibility between the basic process instruction units and the equipment by combining the numerical control parameter model with the equipment's real-time operating status, and generating a preliminary task allocation plan; constructing a distributed control network between the various equipment, generating a local task sequence for each equipment based on the preliminary task allocation plan and the compatibility, and obtaining a final task allocation plan and task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling plan within the framework; establishing a heterogeneous equipment motion collaborative control model, performing timeline control and dynamic safety zone management, and performing multi-level regulation through event-driven feedback control.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-machine collaborative scheduling method for a flexible intelligent processing production line, comprising:

[0008] Establish a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, collect real-time equipment operating status and processing result data, and update the CNC parameter model;

[0009] Decompose the machining program into basic process instruction units, analyze the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generate a preliminary task allocation plan;

[0010] A distributed control network is built between each device. Based on the preliminary task allocation plan and fitness, a task evaluation function is constructed for each device. The local task sequence of each device is generated according to the global constraints. The final task allocation plan and task execution plan set are obtained based on the contract network protocol algorithm.

[0011] Generate collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework;

[0012] Establish a collaborative control model for the motion of heterogeneous devices, perform predictive timeline control and dynamic safety area management based on forward-looking motion envelopes, and perform multi-level adjustments through event-driven feedback control, including adjustment of underlying motion parameters and correction of upper-level model parameters.

[0013] Preferably, establishing a CNC parameter model specifically includes: obtaining basic equipment parameter data, historical processing data and process specification requirements, extracting equipment geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, and integrating them into a CNC parameter model using a multi-dimensional tensor representation method; geometric capabilities include workspace, travel range and maximum load; dynamic performance includes maximum speed, acceleration, deceleration characteristics and emergency stop distance; precision parameters include positioning accuracy, repeat positioning accuracy and trajectory accuracy; process capabilities include executable process types, process quality indicators and production efficiency; control characteristics include command response delay, communication interface type and supported instruction sets.

[0014] Preferably, the process of generating a preliminary task allocation plan specifically includes: obtaining and parsing the processing program and product process requirements, decomposing the processing program into basic process instruction units, and determining the dependency relationship between the basic process instruction units; based on the CNC parameter model and the real-time operating status of the equipment, evaluating the process matching, capability compliance and expected execution results of each basic process instruction unit with each processing equipment, and calculating the adaptability; generating a preliminary task allocation plan based on the adaptability and preset allocation rules.

[0015] Preferably, the process of obtaining the final task allocation plan and task execution plan set specifically includes: in the distributed control network, configuring a decision unit for each device, each decision unit generates and optimizes a local task sequence under global constraints based on the preliminary task allocation plan, fitness and its own task evaluation function; each decision unit declares, bids and negotiates according to the basic process instruction unit based on the contract network protocol algorithm; through multiple rounds of negotiation and evaluation, the final task allocation plan for each device and the task execution plan set including task sequence and time arrangement are formed.

[0016] Preferably, the process of generating a collaborative production scheduling plan specifically includes: the multi-constraint collaborative framework is composed of process constraints, resource constraints, time constraints and space constraints; within the multi-constraint collaborative framework, the final task allocation plan and task execution plan set are used as input, and an optimization algorithm is used to solve a scheduling solution that meets all constraints and optimizes preset production goals; the scheduling solution is converted into an executable collaborative production scheduling plan, and the collaborative production scheduling plan clearly defines the start and end time of each task on each equipment and the required resources.

[0017] Preferably, the heterogeneous device motion collaborative control model specifically includes: integrating the kinematic characteristics, motion timing logic and spatial geometric information of each heterogeneous device; performing time axis control through the heterogeneous device motion collaborative control model includes: setting a global unified virtual time base, and planning the start and stop times and synchronization nodes of each device's actions; performing dynamic safety area management includes: calculating and updating the safe working area and non-safe working area of ​​each device based on the real-time motion status data of the device.

[0018] Preferably, the process of multi-level adjustment through event-driven feedback control specifically includes: real-time monitoring of the production process, when the dynamic safety area management based on the forward-looking motion envelope predicts that there will be a risk of motion interference in the future, the underlying feedback control mechanism is triggered, and the underlying feedback control mechanism adjusts the speed, acceleration and motion path of the relevant equipment in real time according to the risk level and interference type to dynamically avoid potential collisions; after the task is executed, the actual motion trajectory and completion time of the equipment collected by the sensor are compared with the predicted value of the heterogeneous equipment motion collaborative control model; when the deviation between the two exceeds a preset threshold, the upper-level feedback control mechanism is triggered, and the upper-level feedback control mechanism uses the deviation data to reversely correct and optimize the dynamic performance parameters in the CNC parameter model to improve the model accuracy and the accuracy of future decisions.

[0019] A multi-machine collaborative scheduling system for a flexible intelligent processing production line is used to execute a multi-machine collaborative scheduling method for a flexible intelligent processing production line, comprising:

[0020] The equipment capability characterization module establishes a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities, and control characteristics. It collects real-time equipment operating status and processing result data and updates the CNC parameter model.

[0021] The task decomposition and matching module decomposes the machining program into basic process instruction units, analyzes the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generates a preliminary task allocation plan;

[0022] The distributed negotiation module builds a distributed control network between various devices. Based on the preliminary task allocation plan and fitness, it constructs a task evaluation function for each device, generates a local task sequence for each device according to the global constraints, and obtains the final task allocation plan and task execution plan set based on the contract network protocol algorithm.

[0023] Multi-constraint optimization module generates collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework;

[0024] The motion coordination module establishes a heterogeneous equipment motion coordination control model, performs timeline control and dynamic safety area management, and performs multi-level adjustment through event-driven feedback control.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The capabilities of different types of processing equipment are uniformly characterized across five dimensions: geometric capability, precision parameters, dynamic performance, process capability, and control characteristics. The model can also predict the actual processing capability of the equipment based on its real-time status. The model introduces an adaptive update mechanism that collects data on equipment operating status and processing results, compares the difference between predicted and actual measured values, and dynamically adjusts model parameters to form a closed-loop optimization. This unified characterization mechanism significantly improves the accuracy of heterogeneous equipment capability assessment, provides a reliable basis for subsequent task allocation, and more accurately matches process requirements with equipment capabilities, providing the fundamental support for achieving efficient collaboration among heterogeneous equipment.

[0027] 2. A distributed autonomous negotiation mechanism is used for task allocation. Each device is endowed with independent decision-making capabilities and connected by a distributed control network to form a negotiation system. A preliminary task allocation plan is first generated and its suitability is calculated. Then, a decision-making unit is configured for each device, and a multi-objective task evaluation function is constructed. Based on its own status, capabilities, and task requirements, each device bids for tasks and negotiates resources through a contract network protocol algorithm to achieve optimal resource allocation. This distributed negotiation model improves the adaptability and robustness of collaborative control. It can flexibly adjust task allocation based on the real-time status of devices, effectively responding to dynamic changes in the production environment, improving the overall efficiency of the production line, reducing waiting times, and enabling heterogeneous devices to form an organic whole for collaborative operation.

[0028] 3. A method for coordinating the motion of heterogeneous devices based on spatiotemporal coupling solves the problem of precise coordination of heterogeneous devices at the motion level. This method establishes a coordinating control model for the motion of heterogeneous devices that integrates kinematic characteristics, motion timing logic, and spatial geometric information. Through a virtual timeline, a globally unified time base is achieved, accurately controlling the start and stop times and synchronization nodes of each device's motion. At the same time, based on the real-time motion status of the device, the safe and unsafe working areas are dynamically calculated and updated to avoid spatial interference and collisions. The device status, task progress, and abnormal events are monitored in real time. When the preset conditions are triggered, feedback control is initiated, the motion parameters are adjusted, and the results are fed back to the CNC parameter model. The coordinating control of spatiotemporal coupling significantly improves the accuracy and stability of the motion coordination of heterogeneous devices, ensuring the continuity and safety of the production process.

[0029] 4. A multi-level, dual-loop feedback control architecture was established, combining immediate physical risk avoidance with long-term model adaptive optimization. This architecture uses a bottom-level real-time safety loop to proactively adjust motion parameters based on motion prediction to avoid potential collisions. Simultaneously, through a higher-level model cognitive loop, after task completion, it reversely corrects and optimizes the CNC parameter model based on actual execution deviations. This dual-loop collaborative mechanism not only ensures physical safety during production execution but also empowers the entire system to learn from experience and continuously evolve, significantly improving the system's long-term stability and decision-making accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a multi-machine collaborative scheduling method for a flexible intelligent processing production line according to the present invention;

[0031] Figure 2 A schematic diagram of the process of obtaining the final task allocation plan and task execution plan set in the present invention;

[0032] Figure 3 This is a schematic diagram of the dynamic security zone management process of the present invention;

[0033] Figure 4 This is a structural diagram of a multi-machine collaborative scheduling system for a flexible intelligent processing production line according to the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] See also Figures 1 to 4 The present invention provides a method and system for multi-machine collaborative scheduling of a flexible intelligent processing production line. The technical solution is as follows:

[0036] Example 1:

[0037] This embodiment is applied to the cylinder head production line of a certain automobile parts manufacturing company. The production line includes 5 CNC machine tools (3 five-axis machining centers, 1 horizontal boring and milling machine and 1 precision grinder), 2 industrial robots and 1 automatic guided vehicle (AGV), which are mainly responsible for the production of V6 engine cylinder heads. Due to the complex structure of cylinder head products, many processing steps, and the need for multiple equipment to work together, the use of traditional control methods has problems such as unstable production rhythm, low equipment utilization, and long waiting time between processes. In order to achieve efficient collaborative control of heterogeneous equipment, a flexible intelligent processing production line multi-line collaborative scheduling method is implemented, such as Figure 1 As shown, including:

[0038] Establish a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, collect real-time equipment operating status and processing result data, and update the CNC parameter model;

[0039] Decompose the machining program into basic process instruction units, analyze the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generate a preliminary task allocation plan;

[0040] A distributed control network is built between each device. Based on the preliminary task allocation plan and fitness, a task evaluation function is constructed for each device. The local task sequence of each device is generated according to the global constraints. The final task allocation plan and task execution plan set are obtained based on the contract network protocol algorithm.

[0041] Generate collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework;

[0042] Establish a collaborative control model for the motion of heterogeneous devices, perform predictive timeline control and dynamic safety area management based on forward-looking motion envelopes, and perform multi-level adjustments through event-driven feedback control, including adjustment of underlying motion parameters and correction of upper-level model parameters.

[0043] Furthermore, establishing a NC parameter model specifically includes: obtaining basic equipment parameter data, historical processing data and process specification requirements, extracting equipment geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, and integrating them into a NC parameter model using a multi-dimensional tensor representation method;

[0044] The numerical control parameter model is a third-order tensor M ijk , where the first dimension i represents the device number on the production line, the second dimension j represents the parameter category (for example, j = 1 corresponds to geometric capability, j = 2 corresponds to precision parameter, etc.), and the third dimension k corresponds to the specific parameter value. Non-numeric parameters, such as "communication interface type," are digitized using one-hot encoding and stored in tensors.

[0045] Geometric capabilities include working space, travel range and maximum load; dynamic performance includes maximum speed, acceleration, deceleration characteristics and emergency stop distance; precision parameters include positioning accuracy, repeatability and trajectory accuracy; process capabilities include executable process types, process quality indicators and production efficiency; control characteristics include command response delay, communication interface type and supported instruction sets.

[0046] Through five-dimensional parameter representation and multidimensional tensor integration, a standardized description and unified assessment of the capabilities of different types of devices are achieved. The detailed parameter classification ensures that the model covers all aspects of device characteristics, from spatial capabilities to control interfaces. This unified representation provides a scientific basis for comparing capabilities and matching tasks between devices, eliminating the inconsistent description of device capabilities in traditional methods.

[0047] Updating the CNC parameter model includes: setting an update cycle, triggering an update when the cycle is reached, updating the equipment's geometric capabilities, precision parameters, dynamic performance, and control characteristics based on the collected equipment operating status data, and updating the equipment's process capability-related parameters based on the latest collected processing result data.

[0048] Table 1 Examples of basic process instruction units

[0049]

[0050] For engine cylinder head machining, the complete machining program must first be broken down into several basic process instruction units. This embodiment obtains the complete cylinder head machining program through a production management system. The machining program is typically provided in the form of G-code or a process file in a specific format. Using a combination of syntax analysis and semantic understanding, the G-code or process file is parsed and broken down into basic process instruction units. Table 1 shows an example of basic process instruction units for an engine cylinder head machining program. Ra is a surface roughness evaluation indicator, referring to the arithmetic mean deviation of the profile. Ra3.2 indicates an upper limit of Ra of 3.2.

[0051] Furthermore, the process of generating a preliminary task allocation plan specifically includes: obtaining and parsing the processing program and product process requirements, decomposing the processing program into basic process instruction units, and determining the dependency relationship between each basic process instruction unit; based on the CNC parameter model and the real-time operating status of the equipment, evaluating the process matching, capability compliance and expected execution results of each basic process instruction unit with each processing equipment, and calculating the adaptability; generating a preliminary task allocation plan based on the adaptability and preset allocation rules.

[0052] Through the process of process decomposition and adaptability evaluation, an accurate mapping relationship between process requirements and equipment capabilities was established. By comprehensively considering process matching, capability compliance and expected execution results, the optimal allocation of preliminary tasks was achieved, providing a solid foundation for subsequent distributed negotiation and avoiding waste of resources.

[0053] Specifically, the current production target mode set by the upper-level production management system is first obtained, such as efficiency priority or quality priority. When in efficiency priority mode, this method aggregates multiple non-critical processes that can be executed continuously on the same device into a coarser-grained basic process instruction unit to reduce the communication overhead and task switching time of subsequent distributed negotiations. On the contrary, when in quality priority mode, each independent process, especially those involving critical dimensions or precision, is fine-grained decomposition to ensure that each key step can be independently evaluated and assigned to the optimal device. This decomposition logic further integrates the real-time health status of the equipment to achieve more refined dynamic adjustments.

[0054] By adopting a "dynamic granularity" process decomposition approach, process decomposition is transformed from a static preprocessing step into the first link in the entire collaborative scheduling optimization chain, equipped with intelligent perception and adaptive capabilities. This enables the generated task units to better serve macro-production goals, significantly improving the flexibility and purposefulness of production scheduling. Whether pursuing maximum output or ensuring ultimate quality, adaptation can be achieved from the source of the task.

[0055] In this embodiment, process compatibility evaluates whether the equipment has the basic ability to execute the process; capability compliance evaluates the degree of match between equipment performance and process requirements, such as whether the equipment's precision parameters and dynamic performance meet the process's requirements for precision and efficiency; and the expected execution result comprehensively evaluates the quality level, completion time, and resource consumption that can be achieved by executing the basic process instruction unit under the current equipment state. A weighted scoring method is used, and different weights are assigned to each evaluation dimension based on expert experience. The comprehensive score is calculated to obtain the degree of compatibility. In this embodiment, the weights are preferably 0.3, 0.3, and 0.4. Specifically, the comprehensive score is calculated by multiplying the normalized score of each factor (such as compatibility, time cost, and resource consumption) by its corresponding weight coefficient, and then adding all the products together. The CNC parameter model and the real-time operating status of the equipment are received through a built-in model to obtain the expected execution result. The built-in model can be an empirical formula obtained through a large number of experiments, a statistical regression model, or a machine learning model. In this embodiment, a random forest regression model is used for prediction. The model takes the equipment's dynamic performance parameters and task characteristics as input and historical processing time and quality as output. The preset allocation rules include priority allocation rules, load balancing rules, process concentration rules and emergency task priority rules; the priority allocation rule means that the basic process instruction unit is allocated to the equipment with the highest adaptability first; the load balancing rule takes into account the equipment load situation to avoid overloading of certain equipment; the process concentration rule is to allocate similar processes to the same equipment first to reduce switching costs; the emergency task priority rule ensures that emergency tasks are allocated first.

[0056] Further, Figure 2 The present invention provides a flow chart for obtaining the final task allocation scheme and task execution plan set, and the process of obtaining the final task allocation scheme and task execution plan set specifically includes: in the distributed control network, configuring a decision unit for each device, each decision unit generates and optimizes a local task sequence under global constraints based on the preliminary task allocation scheme, fitness and its own task evaluation function; each decision unit declares, bids and negotiates according to the basic process instruction unit based on the contract network protocol algorithm; through multiple rounds of negotiation and evaluation, the final task allocation scheme for each device and the task execution plan set including task sequence and time arrangement are formed.

[0057] The bidding information of each equipment decision unit is a data packet containing [task ID, equipment ID, task evaluation value V ij , estimated start time, estimated completion time]. After the Task Manager receives all bids, for tasks without resource conflicts, select V ijThe highest-ranking device wins the bid. When multiple winning tasks conflict on the same device or shared resources, a conflict resolution process is initiated. This process employs a priority-based, iterative reallocation strategy: a) The highest-priority conflicting task allocation is retained. b) The remaining conflicting tasks are reset to "pending," their priority scores in the evaluation function of the original winning device are deducted, and a new, smaller-scale bidding and evaluation process is initiated. To prevent the negotiation process from becoming trapped in an endless loop, the system sets a maximum number of negotiation rounds. For example, a maximum of three re-bidding and evaluation rounds are allowed for the same conflicting task group. If the conflict remains unresolved, the task is transferred to an exception handling queue for manual intervention or the execution of a pre-set default allocation strategy.

[0058] Dynamic task allocation is achieved through decision-making units and contract network protocols. Multiple rounds of negotiation and evaluation effectively resolve resource conflicts, forming execution plans that both meet global constraints and optimize local resource utilization, improving responsiveness and adaptability to production changes.

[0059] Table 2 shows the changes in some task allocations before and after the negotiation. While most task allocations remain unchanged in the final plan, the allocations of some tasks are adjusted due to real-time status changes (e.g., M003's high temperature causing T005 to be reassigned to M001) and resource conflicts.

[0060] Table 2 Comparison of task allocation plans before and after negotiation

[0061]

[0062] In this embodiment, global constraints primarily include delivery constraints (overall production must be completed within a specified time window), resource constraints (restrictions on the use of shared resources across multiple devices), process continuity constraints (specific processes must be completed continuously without interruption), and safe operation constraints (spatial safety constraints for multi-device collaborative operations). Based on the preliminary task allocation plan, fitness, and its own task evaluation function, each device decision-making unit generates a local task sequence within the boundaries of the global constraints. This generation process employs an optimization algorithm to maximize the score of the task evaluation function while simultaneously satisfying all global constraints.

[0063] Based on the distributed control network, a task evaluation function is constructed for each device. The core of the task evaluation function is to provide a scientific and quantitative decision-making basis for the equipment during the "bidding" stage. The construction and calculation process of this function includes three key steps: First, the system will comprehensively evaluate factors in six dimensions, including: a compatibility factor that reflects the degree of match between the task and the equipment process; a time factor that considers the expected execution time of the task and the current equipment load; a resource factor that focuses on the availability of resources such as tools and fixtures required to perform the task; a quality factor that evaluates the expected process quality level (such as tolerance and surface roughness); an energy consumption factor that calculates the energy consumption required to perform the task; and a priority factor that reflects the urgency and importance of the task. Secondly, before performing the calculation, the function will normalize the raw data of the above six dimensions and convert them into a unified and comparable score value. Most importantly, the weight coefficient of each factor is not fixed, but can be dynamically adjusted according to real-time production goals. For example, when the production mode is set to "Quality First," the system automatically increases the weight coefficient of the quality factor; when it is set to "Efficiency First," the weight coefficient of the time factor is increased; and when it is set to "Energy Priority," the weight coefficient of the energy factor is increased accordingly. Finally, the function multiplies the normalized scores of all factors by their corresponding dynamic weight coefficients, and then weights and sums all these products to generate a weighted score for the basic factors that comprehensively and objectively reflects the execution of the task under the current production target.

[0064] Calculate the synergistic benefit term, which is determined by proactively analyzing the correlation between the task to be bid and the previous and next tasks immediately adjacent in the local task sequence of the equipment. The correlation evaluation includes at least: process continuity (for example, the new task uses the same tool or fixture as the previous and next tasks, which can reduce the change time) and state inheritance (for example, the processing parameters of the new task are similar to those of the previous task, which can reduce the equipment state adjustment time). If the new task has high synergy with the context task, the synergistic benefit term is positive (reward); conversely, if a large number of production changes and adjustments are required, it is negative (penalty). Ultimately, the comprehensive evaluation value of the task evaluation function is determined by the weighted score of the basic factors and the synergistic benefit term.

[0065] This makes equipment "bidding" decisions more far-sighted and holistic, significantly improving the micro-beat and macro-efficiency of the production line. First, by introducing the "synergistic benefit term," the evaluation function no longer shortsightedly judges the pros and cons of individual tasks. Instead, it intelligently assesses the "smoothness" of how tasks fit into the existing production rhythm from a **process chain" perspective. This context-based evaluation mechanism can spontaneously guide tasks toward equipment that can best achieve continuous processing and reduce waiting time for production changeovers. This effectively reduces the "hidden" time costs incurred by frequently changing tools, fixtures, or adjusting equipment status within the framework of distributed decision-making. Ultimately, this method not only optimizes the allocation of individual tasks, but also optimizes the local "rhythm" of the equipment task sequence, making the operating rhythm of the entire production line more stable and smooth, thereby improving overall production efficiency and equipment utilization.

[0066] A contract network protocol algorithm is used to implement task negotiation and allocation among multiple devices, including the task declaration phase, bidding phase, bid evaluation phase, award phase, and conflict resolution phase. In the task declaration phase, the basic process instruction units to be assigned are declared as tasks in the network. In the bidding phase, each device's decision-making unit calculates a task evaluation function based on the preliminary task allocation plan and the optimized local task sequence. In the bid evaluation phase, a unified assessment is performed based on the results of the task evaluation functions of all devices, selecting the optimal bid. In the award phase, the task is formally assigned to the winning device (i.e., the device with the highest calculated task evaluation function), which then incorporates the task into its local task sequence. The conflict resolution phase addresses issues such as multiple devices bidding on the same task or resource conflicts.

[0067] In this embodiment, the local task sequence refers to the task execution plan optimized and generated for each device by the decision-making unit of each device, based on the preliminary task allocation plan and fitness data, within the decision boundary set by the global constraints. The local task sequence primarily contains information such as the priority, expected execution time, and resource requirements of the basic process instruction units that the device needs to process, and is an important basis for equipment bidding decisions. The task execution plan set, on the other hand, is a collection of detailed execution plans for each device that have been negotiated and determined after multiple rounds of contract network agreement negotiations. It includes the specific start and end times, required resources, and execution order of tasks on each device.

[0068] Furthermore, the process of generating a collaborative production scheduling plan specifically includes: the multi-constraint collaborative framework is composed of process constraints, resource constraints, time constraints and space constraints; within the multi-constraint collaborative framework, the final task allocation plan and task execution plan set are used as input, and an optimization algorithm is used to solve a scheduling solution that meets all constraints and optimizes preset production goals; the scheduling solution is converted into an executable collaborative production scheduling plan, and the collaborative production scheduling plan clearly defines the start and end time of each task on each equipment and the required resources.

[0069] A multi-constraint collaborative framework that includes process, resource, time and space constraints is constructed. The optimal scheduling plan that meets multiple constraints is solved through optimization algorithms. Production goals are optimized while ensuring feasibility. The generated scheduling plan has clear time arrangements and resource allocation, which improves the certainty of execution and the overall efficiency of the production line.

[0070] In this embodiment, process constraints focus on the logical sequence and process requirements of the processing steps, including the dependencies between the previous and next steps, process parameter restrictions, and quality control requirements. Resource constraints focus on the use restrictions of various resources in the production process, including equipment resources, tooling resources, human resources, and material resources. Time constraints focus on the timing of task execution, including deadline constraints, release time constraints, time window constraints, and task duration constraints. Spatial constraints focus on the spatial coordination of multiple devices in a shared workspace, including device workspace division, dynamic collision avoidance zones, safe distance maintenance requirements, and mutually exclusive access control of shared areas.

[0071] Based on a multi-constraint collaborative framework, this embodiment adopts a multi-objective optimization algorithm to solve a scheduling solution that satisfies all constraints and optimizes preset production targets: the optimization objective function is set according to production needs, and can comprehensively consider factors such as the total time to complete all tasks, total energy consumption, resource utilization balance, and expected product quality index, and perform a weighted combination of these factors through weight coefficients; the weight coefficients can be dynamically adjusted according to the production focus to meet the needs of different production scenarios; the solution algorithm adopts a hybrid optimization strategy, including using an improved genetic algorithm to generate an initial feasible solution set, using a simulated annealing algorithm for local search optimization, using a taboo search algorithm to jump out of the local optimum, and finally screening the non-dominated solution set through Pareto analysis; the penalty function method is used for constraint processing, imposing high penalty values ​​on violations of hard constraints such as process sequence and resource capacity, and imposing relatively low penalty values ​​on violations of soft constraints such as balanced load.

[0072] In this embodiment, the process of optimizing the scheduling scheme is as follows:

[0073] A genetic algorithm performs a global search. Its chromosomes use a process-based encoding scheme, with each individual representing a complete scheduling solution. After 100 generations of evolution, the five individuals with the highest fitness are selected as candidate solutions. A simulated annealing algorithm is then applied to each of these five candidate solutions for local optimization. Neighborhood solutions are generated by swapping the order of two tasks on the same device or moving a task to another available device. The initial temperature is set to 100, and the annealing coefficient is 0.98. The best solution among all local optimization results is ultimately selected as the scheduling solution.

[0074] F(x)=C max +λ1·∑V proc+λ2·∑V res ,

[0075] The objective function is to minimize the maximum completion time and add penalty terms, including V proc , the penalty for violating the process dependency constraint, if violated, it is 1, otherwise it is 0; V res , a penalty for exceeding resource capacity. λ1 and λ2 are sufficiently large penalty coefficients, and 10000 is used in this embodiment.

[0076] The scheduling solution obtained through optimization is converted into an executable collaborative production scheduling plan, specifying the specific start and end times and required resources for each task on each device. The data structure of the collaborative production scheduling plan includes global information, device control information, and shared resource information. Global information includes the overall start and end time, expected output, and quality goals; device control information details the task list, resource allocation, and status change points for each device; and shared resource information records the type, capacity, and allocation timeline of each shared resource. The collaborative production scheduling plan conversion process includes time point conversion, task refinement, and resource allocation. Time point conversion converts relative time into absolute timestamps; task refinement breaks down abstract tasks into specific device operation instructions; and resource allocation converts resource requirements into specific resource allocation plans. Finally, the scheduling plan is distributed to each device via a distributed control network.

[0077] Furthermore, the heterogeneous equipment motion collaborative control model specifically includes:

[0078] Integrate the kinematic characteristics, motion timing logic and spatial geometry information of various heterogeneous devices; specifically, establish a global world coordinate system within the system. The spatial geometry information of all devices is loaded through a unified data structure (a triangular mesh model is used in this embodiment) and registered in the world coordinate system. The kinematic model of each device is encapsulated as a standard interface, which receives motion instructions in a unified format (target pose, speed, timestamp) and can calculate its envelope in the global coordinate system in real time. Timeline control through the heterogeneous device motion collaborative control model includes: setting a globally unified virtual time base, and planning the start and stop times and synchronization nodes of each device's actions;

[0079] The establishment and synchronization process of the global unified virtual time base is as follows: in the production line control system, a central scheduling controller is preset as the time master. When the system is initialized or a new device is connected to the network, each device controller executes an internal time calibration program. This program is based on the principle of the Network Time Protocol (NTP) or the Precision Time Protocol (PTP) to communicate with the time master to achieve precise synchronization of their respective local clocks; during system operation, all motion instructions issued by the controller and status information reported by the device are forced to be attached with a synchronization timestamp based on the unified time, providing a unified time reference base for all heterogeneous devices, so that the timing relationship of cross-device collaborative actions (such as start, synchronization, delay, etc.) can be defined and executed accurately to the millisecond level.

[0080] Dynamic safety zone management involves calculating and updating each device's safe and unsafe operating areas based on its real-time motion status data. This management is based on a forward-looking motion envelope. This forward-looking motion envelope predicts the spatial range that all device components may occupy, based on the device's current state and motion instructions for the next t seconds (e.g., t = 0.5s). The calculation method involves taking the device's geometric model at the current moment and interpolating its pose at N key time points in the next t seconds based on the motion instructions. The geometric models at these N+1 poses are then unioned to form an envelope. The system periodically calculates the forward-looking motion envelope for each device (e.g., every 100ms) and uses the GJK algorithm to detect interference between these future envelopes. The GJK algorithm is then used to calculate the shortest distance between any two non-fixed links. When this distance falls below a preset safety threshold (e.g., 200mm), the system immediately halts the motion of the relevant device through an event-driven feedback control mechanism, thus achieving predictive obstacle avoidance.

[0081] The collaborative control model that integrates device motion characteristics, timing logic, and spatial information realizes precise motion control and dynamic safety zone management under a unified time base, significantly reduces the risk of interference between devices, ensures the safety of simultaneous operation of multiple devices, and improves stability and reliability.

[0082] Furthermore, the process of multi-level adjustment through event-driven feedback control specifically includes: real-time monitoring of equipment status changes, task execution progress and abnormal events during the production process; when the monitoring information triggers the preset conditions, starting the feedback control mechanism; the feedback control mechanism adjusts the motion parameters of related equipment based on the specific event type and deviation data through the heterogeneous equipment motion collaborative control model, and the motion parameters include speed, acceleration and motion path, and the adjustment results and actual execution results are fed back to update the CNC parameter model.

[0083] When a task is completed, its actual processing accuracy P is collected. actual and the accuracy parameter P in the model model Compare. The parameter update adopts the exponential moving average method:

[0084] P new =α·P actual +(1-α)·P model ,

[0085] Where α is the learning rate, which ranges from (0 to 1) and is set to 0.1 in this embodiment. This update formula feeds back the latest processing results to the model to achieve adaptive adjustment of parameters.

[0086] The event-driven feedback control mechanism can respond to production anomalies in real time and dynamically adjust action parameters. It can not only handle immediate anomalies, but also improve adaptive capabilities and ensure long-term stable operation.

[0087] Example 2:

[0088] A flexible intelligent processing production line multi-line collaborative scheduling system, such as Figure 3 As shown, it includes device capability characterization module, task decomposition and matching module, distributed negotiation module, multi-constraint optimization module and action coordination module; specifically, it includes:

[0089] The equipment capability characterization module establishes a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities, and control characteristics. It collects real-time equipment operating status and processing result data and updates the CNC parameter model.

[0090] Furthermore, establishing a CNC parameter model specifically includes: obtaining basic equipment parameter data, historical processing data and process specification requirements, extracting equipment geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, and integrating them into a CNC parameter model using a multi-dimensional tensor representation method; geometric capabilities include workspace, travel range and maximum load; dynamic performance includes maximum speed, acceleration, deceleration characteristics and emergency stop distance; precision parameters include positioning accuracy, repeat positioning accuracy and trajectory accuracy; process capabilities include executable process types, process quality indicators and production efficiency; control characteristics include command response delay, communication interface type and supported instruction sets.

[0091] The task decomposition and matching module decomposes the machining program into basic process instruction units, analyzes the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generates a preliminary task allocation plan;

[0092] Furthermore, the process of generating a preliminary task allocation plan specifically includes: obtaining and parsing the processing program and product process requirements, decomposing the processing program into basic process instruction units, and determining the dependency relationship between each basic process instruction unit; based on the CNC parameter model and the real-time operating status of the equipment, evaluating the process matching, capability compliance and expected execution results of each basic process instruction unit with each equipment, and calculating the adaptability; generating a preliminary task allocation plan based on the adaptability and preset allocation rules.

[0093] The distributed negotiation module builds a distributed control network between various devices. Based on the preliminary task allocation plan and fitness, it constructs a task evaluation function for each device, generates a local task sequence for each device according to the global constraints, and obtains the final task allocation plan and task execution plan set based on the contract network protocol algorithm.

[0094] Furthermore, the process of obtaining the final task allocation plan and task execution plan set specifically includes: in the distributed control network, a decision unit is configured for each device, each decision unit generates and optimizes a local task sequence under global constraints based on the preliminary task allocation plan, fitness and its own task evaluation function; each decision unit declares, bids and negotiates according to the basic process instruction unit based on the contract network protocol algorithm; through multiple rounds of negotiation and evaluation, the final task allocation plan for each device and the task execution plan set including task sequence and time arrangement are formed.

[0095] Multi-constraint optimization module generates collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework;

[0096] Furthermore, the process of generating a collaborative production scheduling plan specifically includes: the multi-constraint collaborative framework is composed of process constraints, resource constraints, time constraints and space constraints; within the multi-constraint collaborative framework, the final task allocation plan and task execution plan set are used as input, and an optimization algorithm is used to solve a scheduling solution that meets all constraints and optimizes preset production goals; the scheduling solution is converted into an executable collaborative production scheduling plan, and the collaborative production scheduling plan clearly defines the start and end time of each task on each equipment and the required resources.

[0097] The motion coordination module establishes a heterogeneous equipment motion coordination control model, performs timeline control and dynamic safety area management, and performs multi-level adjustment through event-driven feedback control.

[0098] Furthermore, the heterogeneous device motion collaborative control model specifically includes: integrating the kinematic characteristics, motion timing logic, and spatial geometry information of each heterogeneous device; performing timeline control through the heterogeneous device motion collaborative control model includes: setting a global unified virtual time base and planning the start and stop times and synchronization nodes of each device's motion; performing dynamic safety zone management includes: calculating and updating the safe working area and unsafe working area of ​​each device based on the real-time motion status data of the device;

[0099] The process of multi-level adjustment through event-driven feedback control specifically includes: real-time monitoring of equipment status changes, task execution progress and abnormal events during the production process; when the monitoring information triggers the preset conditions, the feedback control mechanism is activated; the feedback control mechanism adjusts the motion parameters of related equipment based on the specific event type and deviation data through the heterogeneous equipment motion collaborative control model, and the motion parameters include speed, acceleration and motion path, and the adjustment results and actual execution results are fed back to update the CNC parameter model.

[0100] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-machine collaborative scheduling method for a flexible intelligent processing production line, characterized in that: include: Establish a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, collect real-time equipment operating status and processing result data, and update the CNC parameter model; Decompose the machining program into basic process instruction units, analyze the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generate a preliminary task allocation plan; A distributed control network is built between each device. Based on the preliminary task allocation plan and fitness, a task evaluation function is constructed for each device. The local task sequence of each device is generated according to the global constraints. The final task allocation plan and task execution plan set are obtained based on the contract network protocol algorithm. Generate collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework; Establish a collaborative control model for the motion of heterogeneous devices, perform predictive timeline control and dynamic safety area management based on forward-looking motion envelopes, and perform multi-level adjustments through event-driven feedback control, including adjustment of underlying motion parameters and correction of upper-level model parameters.

2. A multi-machine collaborative scheduling method for a flexible intelligent processing production line according to claim 1, characterized in that: Establishing a CNC parameter model specifically includes: obtaining basic equipment parameter data, historical processing data and process specification requirements, extracting the equipment's geometric capabilities, precision parameters, dynamic performance, process capabilities and control characteristics, and integrating them into a CNC parameter model using a multi-dimensional tensor representation method; geometric capabilities include workspace, travel range and maximum load; dynamic performance includes maximum speed, acceleration, deceleration characteristics and emergency stop distance; precision parameters include positioning accuracy, repeat positioning accuracy and trajectory accuracy; process capabilities include executable process types, process quality indicators and production efficiency; control characteristics include command response delay, communication interface type and supported instruction sets.

3. The method for multi-machine collaborative scheduling of a flexible intelligent processing production line according to claim 1 is characterized in that: The process of generating a preliminary task allocation plan specifically includes: obtaining and parsing the processing program and product process requirements, decomposing the processing program into basic process instruction units, and determining the dependency relationship between each basic process instruction unit; based on the CNC parameter model and the real-time operating status of the equipment, evaluating the process matching, capability compliance and expected execution results of each basic process instruction unit with each equipment, and calculating the adaptability; generating a preliminary task allocation plan based on the adaptability and preset allocation rules.

4. The method for multi-machine collaborative scheduling of a flexible intelligent processing production line according to claim 1 is characterized in that: The process of obtaining the final task allocation plan and task execution plan set specifically includes: in the distributed control network, configuring a decision unit for each device, each decision unit generates and optimizes a local task sequence under global constraints based on the preliminary task allocation plan, fitness and its own task evaluation function; each decision unit declares, bids and negotiates according to the basic process instruction unit based on the contract network protocol algorithm; through multiple rounds of negotiation and evaluation, the final task allocation plan for each device and the task execution plan set including task sequence and time arrangement are formed.

5. The method for multi-machine collaborative scheduling of a flexible intelligent processing production line according to claim 1 is characterized in that: The process of generating a collaborative production scheduling plan specifically includes: the multi-constraint collaborative framework is composed of process constraints, resource constraints, time constraints and space constraints; within the multi-constraint collaborative framework, the final task allocation plan and task execution plan set are used as input, and an optimization algorithm is used to solve a scheduling solution that meets all constraints and optimizes preset production goals; the scheduling solution is converted into an executable collaborative production scheduling plan, and the collaborative production scheduling plan clearly defines the start and end time of each task on each equipment and the required resources.

6. The method for multi-machine collaborative scheduling of a flexible intelligent processing production line according to claim 1 is characterized in that: The heterogeneous device motion collaborative control model specifically includes: integrating the kinematic characteristics, motion timing logic and spatial geometric information of each heterogeneous device; timeline control through the heterogeneous device motion collaborative control model includes: setting a global unified virtual time base, and planning the start and stop times and synchronization nodes of each device's actions; dynamic safety area management includes: calculating and updating the safe working area and unsafe working area of ​​each device based on the real-time motion status data of the device.

7. The method for multi-machine collaborative scheduling of a flexible intelligent processing production line according to claim 1 is characterized in that: The process of multi-level adjustment through event-driven feedback control specifically includes: real-time monitoring of the production process, when the dynamic safety area management based on the forward-looking motion envelope predicts that there will be a risk of motion interference in the future, the underlying feedback control mechanism is triggered, and the underlying feedback control mechanism adjusts the speed, acceleration and motion path of the relevant equipment in real time according to the risk level and interference type to dynamically avoid potential collisions; after the task is executed, the actual motion trajectory and completion time of the equipment collected by the sensor are compared with the predicted value of the heterogeneous equipment motion collaborative control model; when the deviation between the two exceeds the preset threshold, the upper-level feedback control mechanism is triggered, and the upper-level feedback control mechanism uses the deviation data to reversely correct and optimize the dynamic performance parameters in the CNC parameter model to improve the model accuracy and the accuracy of future decisions.

8. A multi-machine collaborative scheduling system for a flexible intelligent processing production line, which executes a multi-machine collaborative scheduling method for a flexible intelligent processing production line according to claim 1, characterized in that: include: The equipment capability characterization module establishes a CNC parameter model for the processing equipment, including geometric capabilities, precision parameters, dynamic performance, process capabilities, and control characteristics. It collects real-time equipment operating status and processing result data and updates the CNC parameter model. The task decomposition and matching module decomposes the machining program into basic process instruction units, analyzes the compatibility between the basic process instruction units and the equipment by combining the CNC parameter model and the real-time operation status of the equipment, and generates a preliminary task allocation plan; The distributed negotiation module builds a distributed control network between various devices. Based on the preliminary task allocation plan and fitness, it constructs a task evaluation function for each device, generates a local task sequence for each device according to the global constraints, and obtains the final task allocation plan and task execution plan set based on the contract network protocol algorithm. Multi-constraint optimization module generates collaborative production scheduling solutions based on the final task allocation plan and task execution plan set under a multi-constraint collaborative framework; The motion coordination module establishes a motion coordination control model for heterogeneous devices, performs predictive timeline control and dynamic safety area management based on forward-looking motion envelopes, and performs multi-level adjustment through event-driven feedback control.

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