A numerical control machining order priority sequencing and production scheduling intelligent management system

Abstract

The application relates to the technical field of intelligent production scheduling, and discloses a numerical control machining order priority sequencing and production scheduling intelligent management system, which comprises the following modules: a data acquisition and time period division module, which is used for acquiring production basic data and equipment resource data and dividing a lock window time period and a rolling time period; a priority evaluation module, which is used for calculating order priority scores in combination with preset weights; an initial scheduling generation module, which is used for constructing a resource process graph and generating an initial feasible schedule; an influence domain generation module, which is used for identifying a process set affected by a production event and forming an influence domain; an optimized scheduling module, which is used for generating a new schedule in the influence domain based on priority scores and a cost matrix; and an execution issuing module, which is used for freezing the lock window time period schedule and issuing a process execution list. The application realizes intelligent sequencing of production tasks and efficient and stable execution of feasible scheduling in a numerical control machining environment.

Description

Technical Field

[0001] This invention belongs to the field of intelligent production scheduling technology, specifically relating to an intelligent management system for prioritizing CNC machining orders and scheduling production. Background Technology

[0002] With the increasing automation in manufacturing, CNC machining has gradually shifted from the traditional single-batch processing model to a more diversified, small-batch, and flexible approach. During this process, order types, delivery deadlines, equipment availability, and material arrival status on the production floor are all subject to frequent changes, significantly increasing the complexity of scheduling management. Traditional scheduling methods typically rely on manual experience or static algorithms. In the event of unexpected situations such as order insertions, equipment failures, or material delays, the plan often cannot respond quickly, easily leading to equipment downtime, resource conflicts, or order postponements.

[0003] While existing scheduling systems have incorporated priority-based scheduling strategies to some extent, most methods only consider single factors such as delivery date or order value, lacking a comprehensive assessment of factors like customer level, process changeover costs, and processing complexity. Furthermore, some systems employ static global optimization methods, lacking rolling optimization and window-locking protection mechanisms to address production disturbances. This results in frequent recalculations of the overall schedule during actual operation, increasing computational burden and impacting the continuity and executability of the production process. Summary of the Invention

[0004] This invention provides an intelligent management system for prioritizing CNC machining orders and scheduling production, which solves the technical problems in related technologies such as single evaluation dimension of CNC machining order priority, slow scheduling response, and insufficient scheduling stability after production disturbance.

[0005] This invention provides an intelligent management system for CNC machining order prioritization and production scheduling, comprising:

[0006] The data acquisition and time period segmentation module is used to acquire the basic production data and equipment resource data of CNC machining orders, and to segment the time period into locked window and rolling time periods based on the current time.

[0007] The priority evaluation module is used to calculate the priority score of each order based on production base data and equipment resource data, combined with preset weights.

[0008] The initial scheduling generation module is used to construct a resource process diagram based on the order set, process set, and equipment resource data, and determine the allocated equipment, start time, and completion time of each process according to the priority score, thereby generating an initial feasible schedule;

[0009] The influence domain generation module is used to identify the set of processes affected by the first production event based on the resource process diagram, form an influence domain, and reschedule within the influence domain;

[0010] The optimization scheduling module is used to perform deterministic optimization within the influence domain based on priority scores, delivery dates, process type switching cost matrices, and initial feasible schedule perturbations to obtain a new scheduling version.

[0011] The execution and distribution module is used to mark feasible scheduling schemes as the current scheduling version, freeze the scheduling results within the locked window period, and distribute the process execution list.

[0012] Furthermore, the basic production data includes: order set, process set, delivery date, order value, customer level, and material readiness status; the material readiness status includes ready and not ready.

[0013] Equipment resource data includes: a set of processing equipment, the processing time of each process on candidate equipment, the available time period of equipment, and a process type switching cost matrix.

[0014] Furthermore, the generation of the lock window period and scrolling period includes:

[0015] Step 11: Take the time when the scheduled task is received as the current time and set it as a unified time base;

[0016] Step 12: Preset the window locking duration and planned duration according to the production management cycle, where the planned duration is longer than the window locking duration;

[0017] Step 13: Starting from the current moment, define the window locking period according to the window locking duration and the rolling period according to the planned duration.

[0018] Furthermore, based on production baseline data and equipment resource data, and combined with preset weights, a priority score is calculated for each order, including:

[0019] Step 21: By obtaining the processing time of all processes in each order on feasible equipment, determine the maximum processing time of all processes, and calculate the average processing complexity of each order by the ratio of the shortest processing time of each process to the maximum processing time.

[0020] Step 22: Preset delivery urgency weight, customer level weight, order value weight, and processing complexity penalty weight. All four weights are non-negative and their sum is one. Use the difference between the delivery date and the current time as the delivery urgency. Standardize the delivery urgency, customer level, and order value according to the delivery date, customer level, and order value of each order.

[0021] Step 23: The standardized delivery urgency, customer level, order value and average processing complexity of each order are weighted and synthesized according to their corresponding weights to obtain the priority score of each order.

[0022] Furthermore, in the resource process graph, nodes represent various processes, and the edges are constructed in the following ways:

[0023] When a subsequent process in the same order can only begin after the previous process is completed, a process sequence edge is constructed between the two processes;

[0024] When different processes share the same equipment, a resource competition edge is constructed between the two processes;

[0025] When adjacent processes processed consecutively on the same equipment have different process types and there is a switching cost, a process switching edge is constructed between the two processes;

[0026] When the execution of a certain process depends on the material output by another process and the material is not ready, a material dependency edge is constructed between the two processes.

[0027] Furthermore, for each node, its processing start time and completion time are determined, satisfying the following constraints:

[0028] Process sequence constraint: The start time of the subsequent process in each order shall not be earlier than the completion time of the preceding process;

[0029] Equipment occupancy constraint: The equipment can only process one operation at any given time;

[0030] Material readiness constraint: The start time of the process is later than the corresponding material readiness time, and the material readiness status is "ready".

[0031] Equipment availability time constraint: The process completion time shall not exceed the equipment availability time.

[0032] The initial feasible schedule includes: the equipment allocated to each process, the start time, the completion time, the order to which it belongs, and the priority score.

[0033] Furthermore, based on the resource process diagram, the set of processes affected by the first production event is identified, forming an influence domain, and the processes are rescheduled within the influence domain, including:

[0034] Step 31: When the first production event is detected, locate the process node directly associated with the event in the resource process diagram to obtain the initial set of affected nodes and form the influence domain; the first production event includes: equipment downtime event, order insertion event, material delay event and processing timeout event;

[0035] Step 32: Using a pulse closure hybrid scheduling method, within the rolling period, based on the priority score of each order, the process type switching cost matrix, the plan disturbance, and the equipment resource constraints, pulse search based on multi-objective dominance pruning is performed on each piece of equipment to generate a process placement sequence; and the minimum cost closure model is used to perform consistency selection on the process placement segments in each sequence to determine the allocated equipment, processing start time, and processing completion time of each process. The placement satisfies the process sequence constraints, equipment occupancy constraints, material ready state constraints, equipment available time period, and process type switching time requirements.

[0036] Step 33: Screen the process nodes within the influence domain, mark the processes within the locked window period as frozen nodes, and retain the processes within the rolling period to form an adjustable node set, thus forming a limited local scheduling range; for processes that cross the boundary between the locked window period and the rolling period, determine the freezing weight according to the cross ratio of their planned completion time and the locked window boundary time. When the freezing weight is lower than the preset threshold, the process is included in the adjustable node set.

[0037] Step 34: Based on the priority score, reallocate the equipment, processing start time and processing completion time of the process within the influence domain. The rearrangement result satisfies the process sequence constraints, equipment occupancy constraints and material ready state constraints, and keeps the scheduling of the frozen nodes unchanged.

[0038] Furthermore, step 32 specifically includes:

[0039] Step 41: Establish a pulse state set for each device. Each pulse state includes device identifier, current available time, process placement sequence, and cumulative cost vector. The cumulative cost vector consists of delivery default cost, process changeover cost, plan disturbance amount, and priority reversal cost.

[0040] Step 42: Select the unscheduled process from the equipment's process set, determine the earliest feasible start time of the process according to the equipment availability time, the completion time of the preceding process, and the material readiness time, and insert the switching time when the process type is switched to expand and form a new pulse state;

[0041] Step 43: In the pulse state set of the same device, perform a multi-dimensional comparison of the cumulative cost vector of the current pulse state and the existing pulse states. If the current pulse state is not better than any existing pulse state in all cost dimensions, remove the pulse state. At the same time, based on the minimum remaining processing time of the unscheduled process, the minimum switching time, the minimum absolute value of the difference between the planned start time and the current available time, and the minimum number of reversals, obtain the lower limit of cost by weighted summation. If the sum of the cumulative cost of the current pulse state and the lower limit of cost is higher than the current known optimal upper limit, then terminate the expansion of the pulse state.

[0042] Step 44: For each piece of equipment, retain the process placement sequence that has not been removed, and construct the process placement segments in each sequence into a directed acyclic graph. Use the minimum cost closure model to select the optimal closed set that satisfies the process sequence constraint, equipment occupancy constraint and material ready state constraint.

[0043] Step 45: Merge the selected processes into segments to form a scheduling plan for each process, including the allocation of equipment, processing start time, and completion time, and mark this plan as the scheduling version for the current rolling period.

[0044] Furthermore, the optimized scheduling module specifically includes:

[0045] Step 51: Based on the initial feasible scheduling scheme, extract the allocated equipment, start time, completion time and priority score of each process in the influence domain. By weighting the delivery failure cost, process changeover cost, plan disturbance amount and priority reversal cost, construct a comprehensive cost function.

[0046] Step 52: Under the conditions of satisfying process sequence constraints, equipment occupancy constraints and material readiness constraints, the deterministic optimization solution method is used to solve the comprehensive cost function. By performing local insertion and exchange operations in each piece of equipment and calculating the value of the comprehensive cost function in real time, the feasible scheduling scheme of the influence domain after the comprehensive cost function converges is obtained. The scheme includes the allocated equipment, processing start time and processing completion time of each process after adjustment.

[0047] Step 53: Compare the feasible scheduling plan with the initial feasible schedule, generate a differential report, record the changes in process adjustment pairs, total changeover time and priority reversal times, and mark the feasible scheduling plan as the new scheduling version.

[0048] Furthermore, the feasible scheduling scheme is constrained and consistent. When a violation of process sequence constraints, equipment occupancy constraints, material readiness constraints, or resource time constraints is detected, a deterministic correction loop is triggered. The process time shift, process sequence adjustment, and equipment reallocation operations are executed sequentially according to fixed correction rules. After each correction, the deterministic optimization is re-executed until the verification result satisfies all constraints and converges to a feasible scheduling scheme.

[0049] The beneficial effects of this invention are as follows: This invention considers factors such as order delivery date, customer level, processing complexity, and process changeover costs simultaneously during the scheduling process, enabling quantitative ranking of different orders and thus improving the rationality of scheduling decisions. Through partitioned control of locked window periods and rolling periods, the system can perform local optimization only on the affected area when production disturbances occur, avoiding the computational overhead of global rescheduling. An algorithm framework combining impulsive closure hybrid scheduling and deterministic optimization is adopted, enabling the scheduling results to achieve multi-objective balance while satisfying constraints on process sequence, equipment occupancy, and material readiness. Furthermore, the system has a scheduling consistency verification and deterministic correction mechanism, which can automatically identify and repair processes that do not meet constraints, ensuring that the results are executable, stable, and traceable. Overall, this invention improves the response speed, scheduling stability, and equipment utilization rate of production scheduling. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of a module of an intelligent management system for CNC machining order priority sorting and production scheduling according to the present invention. Detailed Implementation

[0051] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0052] like Figure 1 As shown, a CNC machining order priority sorting and production scheduling intelligent management system includes:

[0053] The data acquisition and time period segmentation module 1 is used to acquire the production base data and equipment resource data of CNC machining orders, and to segment the time period into locked window time period and rolling time period according to the current time.

[0054] Priority evaluation module 2 is used to calculate the priority score of each order based on production base data and equipment resource data, combined with preset weights.

[0055] The initial scheduling generation module 3 is used to construct a resource process diagram based on the order set, process set and equipment resource data, and determine the allocated equipment, start time and finish time of each process according to the priority score, and generate an initial feasible schedule;

[0056] The influence domain generation module 4 is used to identify the set of processes affected by the first production event based on the resource process diagram, form an influence domain, and reschedule within the influence domain;

[0057] The optimization scheduling module 5 is used to perform deterministic optimization within the influence domain based on priority score, delivery date, process type switching cost matrix and initial feasible schedule perturbation, to obtain a new scheduling version;

[0058] The execution module 6 is used to mark feasible scheduling schemes as the current scheduling version, freeze the scheduling results within the locked window period, and issue the process execution list.

[0059] In one embodiment of the present invention, the production basic data includes: an order set, a process set, a delivery date, an order value, a customer level, and a material readiness status; the material readiness status includes ready and not ready; the order set refers to all orders to be processed within the scheduling cycle; the process set refers to all technological steps to be executed in each order, each process has a unique process identifier, and forms a one-to-one or one-to-many relationship with the corresponding order; the delivery date refers to the latest time required to complete the order; the order value is used to reflect the economic importance of the order; the customer level is used to reflect the service priority level of the order; and the material readiness status is used to describe whether the materials related to the process are ready for processing.

[0060] Equipment resource data includes: a set of processing equipment, the processing time of each process on candidate equipment, the available time period of equipment, and a process type switching cost matrix. The set of processing equipment refers to all CNC machining equipment participating in the scheduling, with each piece of equipment corresponding to a unique equipment identifier; candidate equipment refers to a subset of equipment capable of completing a certain process; the process type switching cost matrix is ​​used to quantify the adjustment time required for equipment to switch between different process types, reflecting the time loss caused by tooling changes, parameter resets, or calibration operations due to process differences in actual production. The rows and columns of the matrix correspond to the process type identifiers, and the matrix elements represent the adjustment time required for equipment to switch between two process types; for the same process type, its matrix element is 0, indicating that no switching is required; specifically, based on the equipment's historical operating data and process parameter records, the average switching time between various process types is statistically analyzed; based on the equipment's processing function, its executable process types are divided into several categories, such as turning, milling, drilling, grinding, etc., and a unique identifier is assigned to each process type; the operation process of each piece of equipment switching between two types of processes is recorded, the timestamps of the start and completion of the switch are obtained, and the switching duration is calculated through the time difference; the standard switching time between each process type is filled into the corresponding matrix element positions to form a complete process type switching cost matrix.

[0061] In one embodiment of the present invention, the generation of the window lock period and the scrolling period includes:

[0062] Step 11: Take the time when the scheduled task is received as the current time and set it as a unified time base;

[0063] Step 12: Preset the window lock duration and the planned duration according to the production management cycle. The planned duration is longer than the window lock duration. The window lock duration is a fixed time length used to limit the generated scheduling results within a certain period of time. The planned duration is the coverage time range of the entire scheduling calculation. The planned duration is longer than the window lock duration.

[0064] Step 13: Starting from the current moment, define the window locking period according to the window locking duration and the rolling period according to the planned duration. The window locking period represents the execution interval that cannot be changed, and the rolling period represents the dynamic interval that can be optimized and adjusted.

[0065] Through the above process, this invention achieves hierarchical control and a dynamic rolling mechanism for scheduling in the time dimension; the locked window period ensures the stable execution of issued tasks, avoiding production interruptions caused by frequent adjustments; the rolling period provides optimization space, enabling the system to dynamically optimize and schedule based on the latest orders, equipment status, and material readiness. While maintaining scheduling stability, it improves the system's real-time response capability to production changes and scheduling flexibility.

[0066] In one embodiment of the present invention, a priority score for each order is calculated based on production base data and equipment resource data, combined with preset weights, including:

[0067] Step 21: By obtaining the processing time of all processes in each order on feasible equipment, determine the maximum processing time of all processes, and calculate the average processing complexity of each order by the ratio of the shortest processing time of each process to the maximum processing time. The shortest processing time of a process refers to the shortest processing time that the process can achieve among all candidate equipment, and the maximum processing time is the maximum processing time among all process processes in all orders. The average processing complexity is used to reflect the proportion of time occupied by the order at the processing resource level. The larger the value, the more complex the order process, the longer the processing cycle, and the higher the resource consumption.

[0068] Step 22: Preset delivery urgency weight, customer level weight, order value weight, and processing complexity penalty weight. All four weights are non-negative and their sum is one. Use the difference between the delivery date and the current time as the delivery urgency. Standardize the delivery urgency, customer level, and order value for each order according to their delivery date, customer level, and order value. The standardization process refers to mapping the above data proportionally to the range of 0 to 1.

[0069] Step 23: Weight the standardized delivery urgency, customer level, order value, and average processing complexity of each order according to their respective weights to obtain a priority score for each order. Specifically, the standardized delivery urgency, customer level, and order value are weighted and summed according to their respective positive weights. The standardized value of the average processing complexity is then multiplied by a penalty weight and subtracted from the weighted sum to obtain the priority score for each order. This priority score comprehensively reflects the order's time urgency, customer importance, economic value, and process complexity. A higher score indicates that the order should be prioritized for processing resources in the current production stage.

[0070] Through the above steps, this invention enables the quantitative calculation of order priorities under multiple factors, avoiding the one-sidedness of traditional manual scheduling which relies solely on delivery dates or human experience for ranking, thereby improving the objectivity and consistency of scheduling decisions. Furthermore, by introducing average processing complexity, the system can automatically balance conflicts between high-value and high-complexity orders when allocating equipment resources, effectively improving equipment utilization and overall production coordination.

[0071] In one embodiment of the present invention, in the resource process diagram, nodes represent various processes, and each node includes attributes such as process identifier, associated order, set of feasible equipment, processing time, and material readiness status; the edge construction method includes:

[0072] When a subsequent process in the same order can only begin after the previous process is completed, a process sequence edge is constructed between the two processes; the process sequence edge is used to describe the dependencies between processes.

[0073] When different processes share the same equipment, a resource contention edge is constructed between the two processes; the resource contention edge is used to express the exclusive constraint at the equipment level.

[0074] When adjacent processes processed consecutively on the same equipment have different process types and there is a switching cost, a process switching edge is constructed between these two processes; the process switching edge is used to reflect the adjustment time cost required for the equipment to switch between different process types;

[0075] When the execution of a certain process depends on the material output of another process and the material readiness status is not met, a material dependency edge is constructed between the two processes; the material dependency edge is used to describe the material supply and demand relationship across orders or across devices.

[0076] In one embodiment of the present invention, for each node, its processing start time and completion time are determined, satisfying the following constraints:

[0077] Process sequence constraint: The start time of the subsequent process in each order shall not be earlier than the completion time of the preceding process;

[0078] Equipment occupancy constraint: The equipment can only process one operation at any given time;

[0079] Material readiness constraint: The start time of the process is later than the corresponding material readiness time, and the material readiness status is "ready".

[0080] Equipment availability time constraint: The process completion time shall not exceed the equipment availability time.

[0081] The initial feasible schedule includes: the equipment allocated to each process, the start time, the completion time, the order to which it belongs, and the priority score.

[0082] Through the above process, the present invention achieves a unified solution for the time allocation and constraints of process nodes. While ensuring the executability of the scheduling results, it improves the utilization rate of equipment resources and the stability of scheduling, and provides a high-quality initial solution for the dynamic adjustment in the subsequent scheduling optimization stage.

[0083] In one embodiment of the present invention, the process set affected by the first production event is identified based on the resource process diagram to form an influence domain, and the process is rescheduled within the influence domain, including:

[0084] Step 31: When the first production event is detected, the process nodes directly associated with the event are located in the resource process diagram to obtain an initial set of affected nodes. The system further expands hierarchically along the directed edges of the resource process diagram to identify related process nodes that have sequential relationships, shared equipment relationships, or material dependencies, forming an influence domain. The first production event includes: equipment downtime event, order insertion event, material delay event, and processing timeout event. Equipment downtime event refers to equipment suspending processing due to malfunction or maintenance. Order insertion event refers to adding a high-priority order in the current scheduling cycle. Material delay event refers to the failure of the materials required for the process to arrive before the planned time. Processing timeout event refers to the processing time of the process exceeding the original planned cycle.

[0085] Step 32: Using a pulse closure hybrid scheduling method, within the rolling period, based on the priority score of each order, the process type switching cost matrix, the plan disturbance, and the equipment resource constraints, pulse search based on multi-objective dominance pruning is performed on each piece of equipment to generate a process placement sequence; and the minimum cost closure model is used to perform consistency selection on the process placement segments in each sequence to determine the allocated equipment, processing start time, and processing completion time of each process. The placement satisfies the process sequence constraints, equipment occupancy constraints, material ready state constraints, equipment available time period, and process type switching time requirements.

[0086] Step 33: Filter the process nodes within the influence domain, marking processes within the locked window period as frozen nodes. This means keeping the current schedule unchanged while retaining processes within the rolling period to form an adjustable node set, creating a restricted local scheduling range. For processes crossing the boundary between the locked window period and the rolling period, determine the freezing weight based on the crossover ratio of its planned completion time to the locked window boundary time. When the freezing weight is lower than a preset threshold, the process is included in the adjustable node set. The formula for calculating the freezing weight is: , Indicates frozen weights. Indicates the planned completion time. Indicates the window lock boundary time. Indicates the start time of the window locking period;

[0087] Step 34: Based on the priority score, reallocate the equipment, processing start time and processing completion time of the process within the influence domain. The rearrangement result satisfies the process sequence constraints, equipment occupancy constraints and material ready state constraints, and keeps the scheduling of the frozen nodes unchanged.

[0088] Through the above process, this invention can quickly identify the affected process range after detecting production disturbances, and use a pulse closure hybrid scheduling model to perform deterministic optimization of locally feasible scheduling. While ensuring stable execution in the locked window area, it achieves dynamic adaptive adjustment of the rolling area. This method reduces the impact of event disturbances on the overall scheduling, improves the system's response speed and recovery capability to sudden events, and increases equipment utilization and on-time order completion rates.

[0089] In one embodiment of the present invention, step 32 specifically includes:

[0090] Step 41: Establish a pulse state set for each device. Each pulse state describes the local scheduling status of the device at a certain moment. Each pulse state includes the device identifier, current available time, process placement sequence, and cumulative cost vector. The cumulative cost vector consists of delivery delay cost, process changeover cost, plan disturbance amount, and priority reversal cost. The delivery delay cost is used to quantify the loss caused by the process completion time exceeding the delivery date. The delivery delay cost for each process is obtained by calculating the product of the difference between the actual completion time and the order's delivery date and a preset delivery delay penalty coefficient. The preset delivery delay penalty coefficient ranges from 1 to 3. The process changeover cost is calculated using the process type changeover cost matrix element. The planned disturbance is used to reflect the degree of deviation of the current scheduling scheme from the initial feasible schedule. The planned disturbance of each process is obtained by calculating the absolute difference between the planned start time of the initial feasible schedule and the planned start time in the adjusted schedule. The priority reversal cost is used to penalize the situation where high-priority orders are delayed due to scheduling changes. When a process has a lower priority score than another process, but its planned start time is earlier than another process in the optimized schedule, it is regarded as a priority reversal time. The priority reversal cost is obtained by calculating the product of the difference in priority scores between the two processes and the preset reversal penalty coefficient. The preset reversal penalty coefficient ranges from 1 to 3.

[0091] Step 42: Select the unscheduled process from the equipment's process set, determine the earliest feasible start time of the process according to the equipment availability time, the completion time of the preceding process, and the material readiness time, and insert the switching time when the process type is switched to expand and form a new pulse state; this process is used to expand the state space so that the scheduling result can take into account the time cost required for process switching.

[0092] Step 43: In the pulse state set of the same device, perform a multi-dimensional comparison of the cumulative cost vector of the current pulse state and the existing pulse states. If the current pulse state is not better than any existing pulse state in all cost dimensions, remove the pulse state. At the same time, based on the minimum remaining processing time of the unscheduled process, the minimum switching time, the minimum absolute value of the difference between the planned start time and the current available time, and the minimum number of reversals, obtain the lower limit of cost by weighted summation. If the sum of the cumulative cost of the current pulse state and the lower limit of cost is higher than the current known optimal upper limit, terminate the expansion of the pulse state to improve search efficiency and prevent invalid traversal. The cumulative cost of the current pulse state is the sum of the cumulative state and cost increment of the previous state under the expansion. The cost increment includes: delivery default cost increment, switching cost increment, planning disturbance increment, and priority reversal cost increment.

[0093] Step 44: For each piece of equipment, independently retain the process placement sequence that has not been eliminated, and construct a directed acyclic graph (DAG) for the process placement segments in each sequence. In this DAG, nodes represent feasible process placement segments, and edges represent the connectivity of different segments in terms of time and resource allocation. Select the optimal closed set that satisfies the process sequence constraints, equipment occupancy constraints, and material readiness constraints using a minimum cost closure model. Specifically, the minimum cost closure model is established by using node costs as weights. Its objective function is to minimize the sum of the cumulative costs of the selected nodes under the conditions of satisfying the process sequence constraints, equipment occupancy constraints, and material readiness constraints.

[0094] Step 45: Merge the selected processes into segments to form a scheduling plan for each process, including the allocation of equipment, processing start time, and completion time, and mark this plan as the scheduling version for the current rolling period.

[0095] Through the above process, this invention achieves an organic combination of impulse search and closure optimization, improving the computational efficiency of local search while ensuring constraint consistency. This method can quickly generate deterministic, feasible and cost-optimal local scheduling schemes within the rolling scheduling framework.

[0096] In one embodiment of the present invention, the optimized scheduling module specifically includes:

[0097] Step 51: Based on the initial feasible scheduling scheme, extract the allocated equipment, start time, completion time and priority score of each process in the influence domain. By weighting the delivery failure cost, process changeover cost, plan disturbance amount and priority reversal cost, construct a comprehensive cost function.

[0098] Step 52: Under the conditions of satisfying process sequence constraints, equipment occupancy constraints, and material readiness constraints, a deterministic optimization method is used to solve the comprehensive cost function. By performing local insertion and exchange operations within each piece of equipment, the processing sequence and equipment allocation of the processes are gradually adjusted, and the value of the comprehensive cost function is calculated in real time to obtain a feasible scheduling scheme with converged influence domain of the comprehensive cost function. That is, during continuous iteration, when the change in the value of the comprehensive cost function is less than the preset convergence threshold, the optimization process is considered to have converged, and the iteration stops. This scheme includes the adjusted equipment allocation, processing start time, and processing completion time for each process.

[0099] Step 53: Compare the feasible scheduling plan with the initial feasible schedule, generate a differential report, record the changes in process adjustment pairs, total changeover time, and priority reversal counts, and mark the feasible scheduling plan as the new scheduling version. The process adjustment pair represents two processes in the feasible scheduling plan that have different execution order or equipment allocation compared to the initial feasible schedule.

[0100] Through the above process, this invention achieves deterministic optimization of scheduling schemes within the influence domain by taking the comprehensive cost function as the objective, while ensuring constraint consistency. This embodiment realizes unified modeling and real-time convergence determination of multi-objective costs at the algorithm level, and can quickly obtain executable optimized scheduling results.

[0101] It should be noted that the influence domain generation module is used for influence domain identification and schedule reconstruction initialization, and only generates the schedule to be optimized. The schedule optimization module is used to optimize the calculation process, that is, to perform optimization solutions within the identified domain.

[0102] In one embodiment of the present invention, a constraint consistency verification is performed on the feasible scheduling scheme. During the verification phase, the system detects the dependencies between each process, equipment time conflicts, material readiness status, and equipment availability time periods. When a violation of process sequence constraints, equipment occupancy constraints, material readiness status constraints, or resource time period constraints is detected, a deterministic correction loop is triggered. The process time shift, process sequence adjustment, and equipment reallocation operations are executed sequentially according to a fixed correction rule. After each correction, deterministic optimization is re-executed until the verification result satisfies all constraints and converges to a feasible scheduling scheme. Specifically, process time shift refers to eliminating time overlaps or dependency conflicts by shifting the start time of conflicting processes backward without changing equipment allocation; process sequence adjustment refers to reordering the processing order based on priority scores, delivery urgency, and material readiness status when multiple processes within the same equipment have logical errors in processing sequence; and equipment reallocation refers to selecting feasible equipment from the candidate equipment set for replacement when a piece of equipment is overloaded or the processing time of a process exceeds the equipment availability time, and synchronously updating the start and finish times of the newly allocated equipment. After each correction operation, the system re-executes the deterministic optimization process, using the comprehensive cost function to quickly determine the convergence of the corrected solution. When the change in the value of the comprehensive cost function is less than a preset convergence threshold, the scheduling solution is considered converged, and the verification result satisfies all constraints. At this point, the corrected solution is determined as the final feasible scheduling solution.

[0103] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0104] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A system for intelligent management of priority ranking and production scheduling of numerical control machining orders, characterized in that, The method comprises the following steps: A data acquisition and time period division module is configured to acquire production basic data and equipment resource data of a numerical control machining order, and divide a lock window time period and a rolling time period according to a current time; A priority evaluation module is configured to calculate a priority score of each order according to the production basic data and the equipment resource data, in combination with a preset weight; An initial scheduling generation module is configured to construct a resource process graph according to an order set, a process set and the equipment resource data, determine an allocated equipment, a start time and a completion time of each process according to the priority score, and generate an initial feasible schedule; An influence domain generation module is configured to identify a process set affected by a first production event based on the resource process graph, form an influence domain, and reschedule in the influence domain, comprising: Step 31, when the first production event is detected, locating a process node directly associated with the event in the resource process graph to obtain an initial influence node set and form an influence domain; the first production event includes a device downtime event, an order insertion event, a material delay event and a machining overtime event; Step 32, using a pulse closure hybrid scheduling method, performing a pulse search based on multi-objective dominance pruning for each device in the rolling time period according to the priority score of each order, a process type switching cost matrix, a planned disturbance quantity and equipment resource constraints, to generate a process placement sequence; and using a minimum cost closure model to select the process placement segments in each sequence for consistency, to determine the allocated equipment, the machining start time and the machining completion time of each process, the placement satisfying process sequence constraints, equipment occupancy constraints, material readiness state constraints, equipment available time period and process type switching time requirements; Step 33, screening the process nodes in the influence domain, marking the processes in the lock window time period as frozen nodes, retaining the processes in the rolling time period to form an adjustable node set, and forming a restricted local scheduling range; for a process that crosses the boundary between the lock window time period and the rolling time period, determining a frozen weight according to the intersection proportion of its planned completion time and the lock window boundary time, and when the frozen weight is lower than a preset threshold, including the process in the adjustable node set; Step 34, reassigning the equipment, the machining start time and the machining completion time of the processes in the influence domain according to the priority score as the basis for sorting, and rearranging the results to satisfy the process sequence constraints, the equipment occupancy constraints and the material readiness state constraints, while keeping the scheduling of the frozen nodes unchanged; The step 32 specifically comprises: Step 41, establishing a pulse state set for each device, each pulse state containing a device identifier, a current available time, a process placement sequence and a cumulative cost vector, the cumulative cost vector consisting of tardiness violation cost, process switching cost, planned disturbance quantity and priority inversion cost; Step 42, selecting a process that has not been scheduled from the process set of the device, determining the earliest feasible start time of the process according to the device available time, the completion time of the previous process and the material readiness time, and inserting a switching time when the process type is switched to expand a new pulse state. Step 43, in the same device pulse state set, the current pulse state and the cumulative cost vector of the stored pulse state are compared in multiple dimensions, when the current pulse state is not better than any stored pulse state in all cost dimensions, the pulse state is eliminated; meanwhile, based on the minimum remaining processing time of the unsorted process, the minimum switching time, the minimum absolute value of the difference between the planned start time and the current available time, and the minimum number of reversals, the cost lower limit is obtained by weighted summation, if the sum of the cumulative cost of the current pulse state and the cost lower limit is higher than the current known optimal upper limit, the expansion of the pulse state is terminated; Step 44, the uneliminated process placement sequence of each device is independently retained, and the process placement fragments in each sequence are constructed into a directed acyclic graph, and the optimal closed set satisfying the process sequence constraint, the device occupation constraint and the material readiness state constraint is selected by the minimum cost closure model; Step 45, the selected process placement fragments are combined to form the scheduling scheme of the allocation device, the processing start time and the completion time of each process, and the scheme is marked as the scheduling version of the current rolling period; The optimization scheduling module is used for deterministic optimization based on priority score, due date, process type switching cost matrix and initial feasible scheduling plan in the influence domain to obtain a new scheduling version; The execution issuing module is used for marking the feasible scheduling scheme as the current scheduling version, freezing the scheduling result in the lock window period, and issuing the process execution list.

2. The intelligent management system for order priority ranking and production scheduling of numerical control machining according to claim 1, characterized in that, The production basic data includes: order set, process set, due date, order value, customer level and material readiness state; the material readiness state includes ready and unready; The device resource data includes: processing device set, processing time of each process on the candidate device, device available period and process type switching cost matrix.

3. The intelligent management system for order priority sequencing and production scheduling of numerical control machining according to claim 1, characterized in that, The generation of the lock window period and the rolling period includes: Step 11, taking the time when the scheduling task is received as the current time, and setting it as the unified time reference; Step 12, according to the production management cycle, presetting the lock window length and the plan length, wherein the plan length is greater than the lock window length; Step 13, taking the current time as the starting point, dividing the lock window period according to the lock window length, and dividing the rolling period according to the plan length.

4. The intelligent management system for order priority sequencing and production scheduling of numerical control machining according to claim 2, characterized in that, According to the production basic data and the device resource data, the priority score of each order is calculated by combining the preset weight, including: Step 21, by obtaining the processing time of all processes in each order on the feasible device, the maximum processing time of all processes is determined, and the average processing complexity of each order is calculated by the ratio of the shortest processing time of the processes of each order to the maximum processing time; Step 22, presetting the due date urgency weight, customer level weight, order value weight and processing complexity penalty weight, all of which are non-negative and the sum of which is one; the difference between the due date and the current time is taken as the due date urgency, and the due date urgency, customer level and order value of each order are standardized according to the due date, customer level and order value of each order; Step 23, the standardized delivery urgency, customer level, order value and average processing complexity of each order are weighted and synthesized according to the corresponding weight to obtain the priority score of each order.

5. The intelligent management system for order prioritization and production scheduling of numerical control machining according to claim 1, characterized in that, In the resource process graph, nodes are each process, and the construction of edges includes: When a subsequent process in the same order must start after the completion of a previous process, a process sequence edge is constructed between the two processes; When different processes share the same equipment, a resource competition edge is constructed between the two processes; When adjacent processes on the same equipment have different process types and switching costs, a process switching edge is constructed between the two processes; When the execution of a process depends on the output material of another process and the material readiness state does not meet the requirements, a material dependency edge is constructed between the two processes.

6. The intelligent management system for order priority sequencing and production scheduling of numerical control machining according to claim 5, characterized in that, For each node, determine its processing start time and completion time, satisfying the following constraints: Process sequence constraint: the start time of each subsequent process of an order is not earlier than the completion time of the previous process; Device occupation constraint: a device processes only one process at any time; Material readiness constraint: the start time of a process is later than the corresponding material readiness time, and the material readiness state is ready; Device available period constraint: the completion time of a process does not exceed the device available period; The initial feasible scheduling includes the assigned device, start time, completion time, order and priority score of each process.

7. The intelligent management system for priority sequencing and production scheduling of NC machining orders according to claim 1, characterized in that, The optimization scheduling module specifically includes: Step 51, based on the initial feasible scheduling scheme, extract the assigned device, start time, completion time and priority score of each process in the influence domain, construct a comprehensive cost function by weighting the delivery violation cost, process switching cost, plan disturbance and priority inversion cost; Step 52, under the conditions of satisfying the process sequence constraint, device occupation constraint and material readiness state constraint, solve the comprehensive cost function by using a deterministic optimization method, by performing local insertion and exchange operations in each device, and calculating the value of the comprehensive cost function in real time, to obtain the influence domain feasible scheduling scheme after the convergence of the comprehensive cost function, which includes the adjusted assigned device, processing start time and processing completion time of each process; Step 53, compare the feasible scheduling scheme with the initial feasible scheduling to generate a difference report, record the process adjustment, total switching time change and priority inversion number change, and mark the feasible scheduling scheme as a new scheduling version.

8. The intelligent management system for order prioritization and production scheduling of numerical control machining according to claim 1, characterized in that, Conduct constraint consistency verification on the feasible scheduling scheme, when detecting that there is a violation of the process sequence constraint, device occupation constraint, material readiness state constraint or resource period constraint, trigger a deterministic correction cycle, sequentially perform process time translation, process sequence adjustment and device reallocation according to the fixed correction rule, and re-execute deterministic optimization after each correction until the verification result satisfies all constraint conditions and converges to a feasible scheduling scheme.

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