Optimized scheduling method and system for hardware mold production process

Abstract

The invention discloses an optimal scheduling method and system for a hardware mold production process, and the method comprises the steps: obtaining order data, decomposing a whole production task according to the order data and a mold production technology, forming a plurality of subtasks, carrying out the priority sorting of each subtask, and generating a task priority table; obtaining all resource states in the current workshop, and determining a resource scheduling scheme according to the task priority table and the resource states; analyzing the dependency relationship of each subtask, generating a task dependency graph, and determining parallel tasks based on the task dependency graph; the execution sequence of all the parallel tasks is optimized, and an optimal parallel scheduling scheme is determined; determining an initial scheduling scheme of the hardware mold production process according to the resource scheduling scheme and the parallel scheduling scheme; and optimizing the process time and the interval time of the initial scheduling scheme, and outputting an optimized scheduling scheme of the hardware mold production process. The production process is optimized, and the production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent optimization scheduling of production workshops, and in particular to an optimization scheduling method and system for a hardware mold production process. Background Art

[0002] As an important basic equipment in the manufacturing industry, hardware molds usually include design, rough machining, fine machining, heat treatment, surface treatment, assembly and debugging, mold trial and mold repair, mass production and other steps in the processing and manufacturing process. It requires multiple production processes to work together and involves the scheduling management of different equipment and materials.

[0003] However, traditional production scheduling methods often arrange each process in a fixed order, without fully considering the optimal allocation and dynamic adjustment of production resources. Due to the lack of comprehensive consideration of factors such as production processes, equipment utilization, and process priorities, the equipment utilization rate is low, the production cycle is too long, and there is an increase in production costs due to waste of resources. Summary of the invention

[0004] In order to solve the above-mentioned technical problems of low equipment utilization, long production cycle, increased production cost due to waste of resources due to lack of comprehensive consideration of factors such as production process, equipment utilization, process priority, etc., the present invention provides an optimization scheduling method and system for the hardware mold production process.

[0005] In a first aspect, the present invention provides a method for optimizing the scheduling of a metal mold production process, the method comprising:

[0006] Obtain order data, decompose the entire production task into multiple subtasks according to the order data and mold production process, and prioritize each subtask to generate a task priority table;

[0007] Obtaining the status of all resources in the current workshop, and determining a resource scheduling plan according to the task priority table and the resource status;

[0008] Analyze the dependency relationship of each of the subtasks, generate a task dependency graph, and determine parallelizable tasks based on the task dependency graph;

[0009] Optimizing the execution order of all the parallelizable tasks to determine the optimal parallel scheduling solution;

[0010] Determine an initial scheduling plan for the metal mold production process according to the resource scheduling plan and the parallel scheduling plan;

[0011] Optimize the process time and interval time of the initial scheduling plan, and output an optimized scheduling plan for the metal mold production process;

[0012] According to the optimization scheduling scheme, task allocation instructions are sent to each production unit to execute the hardware mold production process.

[0013] Preferably, each subtask is prioritized to generate a task priority table, including:

[0014] Determine a multidimensional factor according to the order data and the real-time production status, and determine a priority weight of each of the subtasks according to the multidimensional factor;

[0015] The subtasks are sorted based on the priority weights to generate the task priority table.

[0016] Preferably, determining the priority weight of each of the subtasks according to the multidimensional factors includes:

[0017]

[0018] Where P j Represents subtask t i，j The priority of w k represents the weight of the priority factor k, f k (t i，j ) represents subtask t i，j The attribute value at priority factor k, T d (t i，j ) represents the priority adjustment amount based on real-time dynamic changes, and n represents the total number of priority influencing factors.

[0019] Preferably, determining a resource scheduling scheme according to the task priority table and the resource status includes:

[0020] Determine a multi-objective optimization problem, and establish a multi-objective resource scheduling model based on the multi-objective optimization problem;

[0021] Based on the multi-objective resource scheduling model, combined with the task priority table and the resource status, the resource scheduling solution is output.

[0022] Preferably, the execution order of all the parallelizable tasks is optimized, including:

[0023] Dividing all the parallelizable tasks into several groups using a parallel computing model;

[0024] Prioritize the subtasks of each group based on the status of the device, and dynamically adjust the scheduling order according to the priority score;

[0025] Use simulation tools to perform scheduling simulation and obtain simulation results;

[0026] According to the simulation results, the task grouping and scheduling order are repeatedly adjusted to determine the optimal parallel scheduling solution.

[0027] Preferably, the process time and the interval time of the initial scheduling scheme are optimized, including:

[0028] Analyze historical production data and estimate the execution time of each process;

[0029] Dynamically adjust the time interval for process connection according to the real-time status of the equipment and the execution time;

[0030] The resource allocation state is dynamically adjusted according to the adjusted time interval.

[0031] Preferably, the optimization scheduling method further includes:

[0032] Obtain real-time production data of the hardware mold production process;

[0033] determining a current operating status of the equipment based on the real-time production data;

[0034] When the current operating state of the device is an abnormal state, the abnormal level of the device is determined, corresponding abnormal information is sent to a user matching the abnormal level, and a maintenance plan determined by the user based on the abnormal information is obtained, and the resource scheduling plan is adjusted according to the maintenance plan.

[0035] Preferably, the optimization scheduling method further comprises: executing a corresponding display strategy for the real-time production data according to the current operating state and user level of the equipment.

[0036] Preferably, detecting whether the optimized scheduling scheme reaches a preset satisfaction level;

[0037] If the preset satisfaction level is reached, production scheduling is planned according to the optimized scheduling plan;

[0038] If the preset satisfaction level is not reached, the deviation between the satisfaction level of the optimized scheduling solution and the preset satisfaction level is obtained to determine whether the deviation level is greater than the preset deviation level; wherein the preset deviation level is a user-defined setting or is determined based on the resource status;

[0039] If the deviation is not greater than the preset deviation, the resource scheduling scheme is re-optimized, and a re-optimization request is sent to the corresponding management position based on the re-optimized resource scheduling scheme. When the re-optimization grant request returned by the corresponding management position is obtained, a new optimized scheduling scheme is generated again according to the re-optimized resource scheduling scheme; wherein the satisfaction of the new optimized scheduling scheme reaches the preset satisfaction;

[0040] If the deviation is greater than the preset deviation, an optimization failure notification and an optimization scheduling plan customization request are sent to the corresponding management position. When the customized optimization scheduling plan determined by the corresponding management position based on the optimization scheduling plan customization request is obtained, the hardware mold production process is executed according to the customized optimization scheduling plan.

[0041] In a second aspect, the present invention further provides an optimization scheduling system for a hardware mold production process, the system comprising:

[0042] A generation module is used to obtain order data, decompose the entire production task into multiple subtasks according to the order data and the mold production process, and prioritize each subtask to generate a task priority table;

[0043] A first determination module is used to obtain the status of all resources in the current workshop and determine a resource scheduling plan according to the task priority table and the resource status;

[0044] A second determination module is used to analyze the dependency relationship of each of the subtasks, generate a task dependency graph, and determine parallelizable tasks based on the task dependency graph;

[0045] A third determination module is used to optimize the execution order of all the parallelizable tasks and determine a parallel scheduling scheme;

[0046] A fourth determination module, used to determine an initial scheduling plan for the metal mold production process according to the resource scheduling plan and the parallel scheduling plan;

[0047] An output module is used to optimize the process time and interval time of the initial scheduling plan and output an optimized scheduling plan for the metal mold production process;

[0048] The instruction sending module is used to send the task allocation instructions to each production unit according to the optimization scheduling plan to execute the hardware mold production process.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention decomposes a production task into multiple subtasks, prioritizes the multiple subtasks, obtains a resource scheduling scheme according to the resource status in the workshop and the task priority, determines parallel tasks according to the dependency relationship of the subtasks, and optimizes the execution order of the parallel tasks to obtain a parallel scheduling scheme. The initial scheduling scheme of the hardware mold production process after preliminary optimization is obtained by combining the parallel scheduling scheme and the resource scheduling scheme, and the process time and interval time of the initial scheduling scheme are optimized to obtain the final optimized scheduling scheme for the hardware mold production process, which realizes comprehensive consideration of factors such as production process, equipment utilization, process priority, etc., improves equipment utilization as a whole, shortens the production cycle, and avoids the problem of increased production costs due to waste of resources.

[0051] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0053] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0054] Figure 1 A schematic diagram of a process flow of an optimization scheduling method for a hardware mold production process provided by an embodiment of the present invention;

[0055] Figure 2 A schematic structural diagram of an optimization scheduling system for a hardware mold production process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0057] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0060] It should be noted that the production process of hardware molds mainly includes the following main links:

[0061] Design and development: Design is the first critical step before production. Mold design engineers will use CAD and other software to design the 3D model and processing drawings of the mold according to the customer's product requirements. This process requires precise dimensional and functional analysis to ensure the correct manufacture of the mold.

[0062] Mold material selection: According to the characteristics of the product and production requirements, select the appropriate mold material. Commonly used materials include carbon steel, alloy steel, stainless steel, etc. The choice of material directly affects the durability and precision of the mold.

[0063] Rough machining: In this stage, the mold blank is rough machined by milling machines, lathes, drilling machines, etc. Rough machining aims to remove most of the excess material and lay a good foundation for subsequent fine machining.

[0064] Finishing: After rough machining is completed, fine machining is carried out. Commonly used equipment includes CNC machine tools (CNC), electric discharge machines (EDM), wire cutting machines, etc. At this time, the size and shape of the mold will be strictly controlled within the tolerance range required by the design.

[0065] Heat treatment: Heat treatment is to improve the hardness and wear resistance of the mold material. In this process, the mold will be heated to a specific temperature and then cooled quickly to make the material achieve the desired mechanical properties.

[0066] Surface treatment: After finishing, the mold may need to undergo surface treatment processes such as polishing, chrome plating, and nitriding. This step can improve the mold's finish, wear resistance, and corrosion resistance, ensuring that it maintains a long service life in high-intensity production.

[0067] Assembly and debugging: After the mold parts are processed, they are assembled together and debugged. During debugging, the mold will be installed on the stamping machine or injection molding machine, and trial production will be carried out to check whether the mold can accurately produce hardware parts that meet the requirements.

[0068] Mold trial and mold repair: During the mold trial process, if any size or appearance is found to be unsatisfactory, the mold will be repaired, that is, the mold size, shape or structure will be adjusted to achieve the best condition.

[0069] Mass production: After the mold is debugged and corrected, it will enter the formal production stage. At this time, the mold will be installed on the production line to produce large quantities of hardware parts.

[0070] Mold maintenance: In order to ensure the life and accuracy of the mold, it is necessary to clean, lubricate and repair it regularly during use to avoid mold wear or damage.

[0071] At present, the traditional production scheduling method often arranges each process in a fixed order, without fully considering the optimal allocation and dynamic adjustment of production resources. Due to the lack of comprehensive consideration of factors such as production process, equipment utilization, and process priority, the utilization rate of equipment is not high, the production cycle is too long, and there is an increase in production costs due to waste of resources. To this end, the present invention provides an optimization scheduling method for the production process of hardware molds, which realizes multi-process parallel processing by dynamically adjusting production resources and task priorities, reduces the idle time of equipment, and thus greatly improves production efficiency. By optimizing the time arrangement and equipment scheduling of each process, the waiting time between each process can be effectively compressed, thereby shortening the overall production cycle. Through dynamic resource allocation, the efficient use of equipment and materials is ensured, unnecessary waste is avoided, and production costs are reduced.

[0072] See also Figure 1 , Figure 1 The present invention provides a schematic flow chart of an optimization scheduling method for a hardware mold production process. Figure 1 As shown, an optimization scheduling method for a hardware mold production process includes the following steps:

[0073] S10, obtaining order data, decomposing the entire production task according to the order data and the mold production process to form multiple subtasks, and prioritizing each subtask to generate a task priority table.

[0074] In this embodiment, after receiving the customer order, the order is first parsed to confirm the type, quantity, process requirements and other information of the hardware molds to be produced. According to the order requirements and the mold production process, the production task is decomposed into specific subtasks of each process, such as design, rough processing, fine processing, heat treatment, surface treatment, assembly, etc. The weighted analytic hierarchy process (AHP) is used to comprehensively consider factors such as order urgency, task complexity, and process time consumption to determine the priority of each subtask and form a task priority table to ensure that high-priority tasks are processed first and reduce production delays. Specifically:

[0075] First, get the order data (O i ), and then parse it into subtasks (t i，j ), let the order set be O = {O 1 , O 2 , O 3 , …, O n}, where each order O i Contains order attributes A(O i ), for example: material type, processing accuracy, construction period, etc., define an analytical function P, and convert the order O i Convert to task set T(O i ), that is: T(O i )=P(O i )={(D i，j，1 , D i，j，2 , …, D i，j，k )|1≤j≤m};

[0076] T(O i ) indicates order O i The task set;

[0077] P(O i ) indicates order O i The attribute set of

[0078] m represents order O i The total number of subtasks. This formula represents the detailed decomposition of tasks from multiple dimensions (such as resources, equipment, and processes), which helps to perform fine-grained scheduling of each subtask in actual production.

[0079] Break down orders into multi-dimensional tasks for more comprehensive scheduling and optimization, namely:

[0080] t i，j =(D i，j，1 , Di，j，2 , …, D i，j，k )

[0081] t i , j represents the jth subtask in the i-th order;

[0082] D i , j, k represents the attributes of the jth subtask in the i-th order in the k-th dimension, such as resource requirements, process requirements, time constraints, etc.

[0083] In one embodiment, each subtask is prioritized to generate a task priority table, including:

[0084] S11, determining multi-dimensional factors according to the order data and the real-time production status, and determining the priority weight of each subtask according to the multi-dimensional factors; including:

[0085]

[0086] Where P j Represents subtask t i，j The priority of w k represents the weight of the priority factor k, f k (t i，j ) represents subtask t i，j The attribute value at priority factor k, T d (t i，j ) represents the priority adjustment amount based on real-time dynamic changes, and n represents the total number of priority influencing factors.

[0087] In this embodiment, by combining static factors and dynamic factors, the priority of each task is determined, which is suitable for complex and real-time changing production environments. The execution order of tasks is determined by calculating the priority. The priority of tasks is set according to multidimensional factors (for example, resource tension, task urgency), and each task is placed in the scheduling queue after the priority is calculated according to the current conditions.

[0088] S12. Sort the subtasks based on the priority weights to generate the task priority table.

[0089] S20, obtaining the status of all resources in the current workshop, and determining a resource scheduling plan according to the task priority table and the resource status;

[0090] In this embodiment, MES (Manufacturing Execution System) is used to obtain the status of all resources (equipment, manpower, materials, etc.) in the current workshop, including equipment availability, current task load, remaining working hours and other information, and genetic algorithm or particle swarm optimization algorithm is used to dynamically allocate equipment, manpower and materials according to task priority and resource status. To ensure the best match and efficient use of resources, the system will automatically adjust resources dynamically according to equipment status and task progress. If task delays or equipment failures are found, resources will be automatically reallocated to keep production smooth.

[0091] In one embodiment, determining a resource scheduling scheme according to the task priority table and the resource status includes:

[0092] S21, determining a multi-objective optimization problem, and establishing a multi-objective resource scheduling model based on the multi-objective optimization problem;

[0093] Specifically, resources are allocated according to the priority and start time of the task. Ensure that resources are available at the specified time and there will be no conflicts, as shown in the following formula:

[0094] S(R i，j )=Available stC seq (R i，j , t i，j )≤T start (t i，j )

[0095] In the formula, R i，j represents the jth sub-resource in the i-th order, S(R i，j ) indicates sub-resource R i，j Status, C seq (R i，j , t i，j ) is the resource R i，j The timing constraint of subtask t i，j availability before the start time (or the resource cannot be occupied by other tasks before the current time), T start (t i，j ) represents subtask t i，j The start time of the

[0096] Through the above formula, it is guaranteed that the sub-resource R i，j In subtask t i，j It is available before the start, avoiding resource conflicts. By analyzing the historical usage of resources (such as materials, equipment, space, operating steps, etc.), the available time of resources (such as materials, equipment, space, operating steps, etc.) is predicted, and the subtask t is used to i，j Adjust the time according to the requirements to prevent resource conflicts.

[0097] When allocating resources, we must not only consider the completion time, but also take into account multi-objective optimization such as cost and energy consumption, as shown in the following formula:

[0098]

[0099] Where, t i，j represents the jth subtask in the i-th order, T end (t i，j ) represents subtask t i，j The end time, R i，j represents the jth sub-resource in the i-th order, E usage (R i，j ) is the sub-resource R i，j In subtask t i，j Energy consumption in cost (R i，j ) indicates sub-resource R i，j In subtask t i，j The usage cost in , m represents the total number of tasks.

[0100] The above formula describes a multi-objective optimization problem, covering multiple objectives such as time, energy consumption, and cost, that is, time, energy consumption, and cost need to be balanced in production. This formula can ensure that production management balances resources between multiple optimization objectives, and selects the optimal resource allocation plan by evaluating the resource requirements of each task and combining the energy consumption and cost control requirements of the enterprise.

[0101] S22. Based on the multi-objective resource scheduling model, combined with the task priority table and the resource status, output the resource scheduling solution.

[0102] S30, analyzing the dependency relationship of each of the subtasks, generating a task dependency graph, and determining parallelizable tasks based on the task dependency graph;

[0103] In this embodiment, all tasks are drawn into a graph and the dependencies of the tasks are marked. Any task without a pre-dependency can be considered for parallel processing, such as an isolated node or an independent subgraph in the figure. Dependency analysis is performed on all tasks to identify task groups that can be processed in parallel. For processes without sequential dependencies, such as surface treatment and assembly debugging, they can be processed in parallel. The analysis tool displays the task dependencies through a task dependency graph or a directed acyclic graph (DAG graph), clarifying which tasks can be executed independently and which need to wait.

[0104] Among them, the task dependency graph can clearly show the dependency relationship between tasks. Each task is a node, and the dependency relationship between tasks is connected by edges. For example, if task A must be completed after task B, then the node of task A will point to the node of task B;

[0105] DAG: Directed acyclic graph, commonly used to represent task dependencies. Since DAG has no loops, it ensures that tasks do not form interdependent cycles, and is a core tool for analyzing parallel execution.

[0106] Specifically, the dependency between tasks is represented by a directed acyclic graph (DAG): G = (V, E), where V represents the task set and E represents the dependency between subtasks. The parallel execution conditions of tasks are:

[0107] IF(t i ,j,t k ,l)∈E, then cannot be parallelized (cannot be parallelized);

[0108] If needed, you can also use the critical path method (CPM) to calculate the earliest start time and the latest end time of the tasks on the critical path. The tasks on the critical path determine the shortest duration of the project or production. These tasks need to be executed in sequence, while tasks not on the critical path may be executed in parallel.

[0109] S40, optimizing the execution order of all the parallelizable tasks to determine an optimal parallel scheduling solution;

[0110] In this implementation, according to the dependency, process requirements and equipment conditions, the parallel order of tasks is optimized to ensure the reasonable arrangement of parallel tasks and avoid resource waste. Tasks that can be executed in parallel are automatically assigned to different groups, and the execution order is set according to priority. At the same time, for task groups with tight time, critical path resources can be prioritized, and other groups can flexibly adjust the time window, and the execution of parallel tasks can be monitored in real time through MES. If a task is delayed, other tasks can be rearranged or the priority can be adjusted to reduce interference with overall production.

[0111] In one embodiment, the execution order of all the parallelizable tasks is optimized, including:

[0112] S41, dividing all the parallelizable tasks into several groups using a parallel computing model;

[0113] After completing the task dependency analysis, identify which tasks can be processed in parallel and group them;

[0114] Specifically: Use the parallel computing model to group tasks, and dynamically put tasks that can be executed simultaneously into the same group according to the real-time status of the tasks to ensure that there are no conflicts. Dynamic programming or graph theory algorithms can be used to optimize the grouping and scheduling of tasks; among them, the optimization formula for parallel task grouping is:

[0115] G p =Gdynamic (t i，j , S(t i，j ), R i,j )

[0116] In the formula, G p represents a parallel task group, G dynamic represents the dynamic grouping algorithm, S(t i，j ) represents subtask t i，j The real-time status of R i，j Represents subtask t i，j Required resources.

[0117] S42, performing a priority score on each group of subtasks based on the status of the device, and dynamically adjusting the scheduling order according to the priority score; including:

[0118] Determine the scheduling order based on a comprehensive evaluation of task priority, equipment utilization, and estimated completion time. Specifically: rate each task for priority, dynamically adjust the scheduling order based on the equipment status, and use algorithms based on polling scheduling or dynamic priority scheduling to ensure that high-priority tasks are executed as quickly as possible while maximizing equipment utilization;

[0119] It should be noted that if multiple tasks require the use of the same device, the tasks with lower device load should be prioritized for execution to reduce waiting time and improve device utilization.

[0120] S43, using a simulation tool to perform scheduling simulation and obtain simulation results;

[0121] S44. According to the simulation results, repeatedly adjust the task grouping and scheduling order to determine the optimal parallel scheduling solution.

[0122] In this embodiment, scheduling simulation is performed by using a simulation tool to repeatedly adjust task grouping and scheduling order until the optimal parallel solution is found; and based on the simulation results, the scheduling solution is continuously optimized until the feasibility and efficiency of parallel execution are optimal, thereby ensuring the rationality of task grouping and scheduling order.

[0123] It should be noted that simulation tools such as Simul8 and AnyLogic can evaluate the effectiveness of the scheduling scheme by simulating the parallel execution of tasks, and analyze the execution of parallel task groups through simulation results. If there are delays in tasks or excessive load on equipment, it is necessary to adjust the scheduling order or task grouping scheme. In the simulation, identify the bottleneck tasks that affect the scheduling efficiency, and optimize their execution order or resource allocation. Among them, the main indicators of the simulation process include:

[0124] Task completion time: whether the actual execution time of each task group meets the expected time.

[0125] Equipment utilization: whether there is equipment idle time or equipment resource conflicts.

[0126] Parallel execution results: Coordination and efficiency of parallel execution task groups.

[0127] S50, determining an initial scheduling plan for the metal mold production process according to the resource scheduling plan and the parallel scheduling plan;

[0128] In this embodiment, the initial scheduling plan of the hardware mold production process is determined by combining the resource scheduling plan and the parallel scheduling plan obtained by the above-mentioned optimization calculation, thereby obtaining a preliminary optimized scheduling plan for the hardware mold production process, ensuring the stability and reliability of the work, and providing an accurate and reasonable optimization basis for subsequent further optimization and scheduling, thereby achieving refined, precise and efficient scheduling and execution effects of the production process.

[0129] S60, optimizing the process time and interval time of the initial scheduling plan, and outputting an optimized scheduling plan for the hardware mold production process;

[0130] In this embodiment, the execution time of each process is estimated through historical data analysis. According to the real-time status of the equipment, the time interval of the process connection is dynamically adjusted to avoid waiting time, and the materials and equipment required for the next process are prepared in advance to ensure seamless connection of the processes. For example, before the heat treatment process, the personnel and equipment are scheduled in advance to reduce waiting, and a Gantt chart is generated to show the time schedule of all processes to ensure that the tasks are seamlessly connected in the set time sequence. The operator of each workstation views the schedule through the MES interface and knows the next work arrangement. In actual operation, the workers can provide feedback through the MES. If problems are encountered (such as equipment failure, missing materials), the system will automatically perform scheduling optimization.

[0131] In one embodiment, optimizing the process time and the interval time of the initial scheduling scheme includes:

[0132] S61. Analyze historical production data and estimate the execution time of each process;

[0133] S62, dynamically adjusting the time interval for connecting the processes according to the real-time status of the equipment and the execution time;

[0134] In this embodiment, the waiting time in the process connection is dynamically adjusted according to the actual production line configuration, the influence of material flow and equipment conversion time, and the task start time and end time, specifically, including:

[0135] min W ij =min(T start (t i，j )-Tend (t i-1，j )+T transition (E i，j ))

[0136] Where W ij Indicates the waiting time between subtasks (or processes), T start (t i，j ) represents the current subtask t i，j The start time, T end (t i-1，j ) represents the previous subtask t i-1，j (or process) end time, T transition (E i，j ) indicates device E i，j The conversion time is the device commissioning time from one task to the next.

[0137] S63. Dynamically adjust the resource allocation state according to the adjusted time interval.

[0138] In this embodiment, by introducing the load balancing factor λ, the load balancing of the equipment in a complex production environment with multiple processes is ensured, thereby adjusting the task allocation of the equipment, ensuring the workload of each equipment is balanced, avoiding overloading of some equipment, and improving the overall production efficiency; specifically, including:

[0139]

[0140] In the formula, L(E i ) indicates device E i The load balancing degree, λ represents the load balancing factor, T usage (E i ) means E i The total usage time, n represents the number of devices, Indicates the average usage time of all devices.

[0141] S70. Sending task allocation instructions to each production unit according to the optimized scheduling plan to execute the hardware mold production process.

[0142] In one embodiment, the optimization scheduling method further includes:

[0143] Obtain real-time production data of the hardware mold production process;

[0144] determining a current operating status of the equipment based on the real-time production data;

[0145] When the current operating state of the device is an abnormal state, the abnormal level of the device is determined, corresponding abnormal information is sent to a user matching the abnormal level, and a maintenance plan determined by the user based on the abnormal information is obtained, and the resource scheduling plan is adjusted according to the maintenance plan.

[0146] In this embodiment, when an abnormality occurs in the equipment, by determining the abnormality level, the severity of the problem can be accurately classified, so that appropriate countermeasures can be taken quickly, and corresponding abnormal information is sent to matching users (such as operators, maintenance personnel, managers) according to the abnormality level to ensure that relevant personnel are aware of and take action in a timely manner. The response speed is effectively improved, and the loss caused by delayed information transmission is avoided. After receiving the abnormal information, the user formulates a corresponding maintenance plan. And upload the system, by automatically adjusting the resource scheduling plan according to the maintenance plan formulated by the user, such as adjusting the equipment usage time or arranging spare equipment, to minimize downtime, improve production efficiency, reduce equipment downtime caused by failure, and then improve the overall production efficiency, realize intelligent equipment monitoring and maintenance, can effectively improve the operating efficiency in the production process of hardware molds, reduce the risk of downtime caused by equipment abnormalities, and optimize the use of resources.

[0147] In one of the embodiments, the optimization scheduling method further includes: executing a corresponding display strategy for the real-time production data according to the current operating status and user level of the equipment.

[0148] In this embodiment, different display strategies are executed according to the current operating state of the device and the user level, thereby greatly improving the versatility of the data display effect and the interactivity with the host computer side.

[0149] In one embodiment, according to the current operating state of the device and the user level, a corresponding display strategy is executed on the real-time production data, including:

[0150] When the current operating state of the equipment is the normal state, dynamically updating the real-time production data;

[0151] When the current operating state of the equipment is the abnormal state, the dynamic update of the real-time production data is stopped, and the real-time production data is displayed through the first display screen. The preset screen is displayed on the second display screen according to the user level so that the corresponding user level can accurately see the information they want to see. For example, corresponding to the level of maintenance personnel, the screen displayed on the second display screen is fault diagnosis information and corresponding guidance action identifiers, so as to help maintenance personnel to troubleshoot the problem in a timely and accurate manner; corresponding to the level of team leader, the screen displayed on the second display screen is fault diagnosis information, maintenance time and maintenance personnel information, so as to help the team leader to grasp the production status in a timely and accurate manner. According to needs, display screens of corresponding levels such as supervisors and factory managers can also be set accordingly.

[0152] In one of the embodiments, detecting whether the optimized scheduling scheme reaches a preset satisfaction level;

[0153] If the preset satisfaction level is reached, production scheduling is planned according to the optimized scheduling plan;

[0154] If the preset satisfaction level is not reached, the deviation between the satisfaction level of the optimized scheduling solution and the preset satisfaction level is obtained to determine whether the deviation level is greater than the preset deviation level; wherein the preset deviation level is a user-defined setting or is determined based on the resource status;

[0155] If the deviation is not greater than the preset deviation, the resource scheduling scheme is re-optimized, and a re-optimization request is sent to the corresponding management position based on the re-optimized resource scheduling scheme. When the re-optimization grant request returned by the corresponding management position is obtained, a new optimized scheduling scheme is generated again according to the re-optimized resource scheduling scheme; wherein the satisfaction of the new optimized scheduling scheme reaches the preset satisfaction;

[0156] If the deviation is greater than the preset deviation, an optimization failure notification and an optimization scheduling plan customization request are sent to the corresponding management position. When the customized optimization scheduling plan determined by the corresponding management position based on the optimization scheduling plan customization request is obtained, the hardware mold production process is executed according to the customized optimization scheduling plan.

[0157] In this embodiment, since the optimized scheduling scheme is obtained by further optimizing the initial scheduling scheme, and the initial scheduling scheme is obtained based on the optimized resource scheduling scheme and the parallel scheduling scheme, it is obvious that the optimized scheduling scheme is the best scheme obtained under the current state. However, in order to meet the actual production needs, after obtaining the optimized scheduling scheme, it is also necessary to evaluate the optimized scheduling scheme to detect whether it reaches the preset satisfaction level. If it does not reach the preset satisfaction level, it is necessary to optimize the optimized scheduling scheme for a second time to effectively ensure the stability and reliability of production.

[0158] It should be noted that the user's customized setting of the preset deviation degree can improve the user's operability of the production process, further ensure the reliability and stability of production, and determine the preset deviation degree based on the resource status to ensure that the optimized scheduling through the optimized resource status can meet the implementation of the optimized scheduling plan. For example, due to the current shortage of materials or the equipment being in maintenance status, the deviation degree of the optimized scheduling plan does not meet the preset deviation degree. By sending this situation and the secondary optimized plan to the corresponding job management user (such as supervisor or team leader), after the user's confirmation, the user can be prompted to handle the resource (material or equipment) in a timely manner, thereby ensuring the implementation of the plan.

[0159] The present invention provides a reasonable basis for subsequent dynamic resource allocation and task parallelization through order parsing, task refinement and priority sorting. And real-time scheduling is performed according to task priority and current resource status. Reasonable matching of resources affects the parallel processing of tasks and the time connection between processes. Production efficiency is improved through parallel processing, and resource utilization is maximized by analyzing task dependencies to ensure that parallel tasks do not conflict. By refining the time schedule of each process, it is ensured that each task is seamlessly connected in time. It is a further refinement of resource allocation and parallel processing. The dynamic adjustment capability in the production process is ensured through feedback and adjustment mechanisms, and the timeliness of the feedback system directly affects the continuity of the overall process. The effectiveness of equipment failure or other abnormal processing determines the continuity of production. Efficient collaboration of each step is achieved to ensure dynamic scheduling of resources, parallel execution of tasks and real-time feedback adjustment. The flexibility and efficiency of the overall production system are enhanced.

[0160] In summary, the present invention realizes parallel processing of multiple processes by dynamically adjusting production resources and task priorities, reduces the idle time of equipment, and thus greatly improves production efficiency. By optimizing the time arrangement and equipment scheduling of each process, it can effectively compress the waiting time between processes, thereby shortening the overall production cycle. Through dynamic resource allocation, efficient utilization of equipment and materials is ensured, unnecessary waste is avoided, and production costs are reduced.

[0161] See also Figure 2In some embodiments, the present invention further provides an optimization scheduling system for a hardware mold production process, the system comprising:

[0162] The generation module 100 is used to obtain order data, decompose the entire production task into multiple subtasks according to the order data and the mold production process, and prioritize each subtask to generate a task priority table;

[0163] The first determination module 200 is used to obtain the status of all resources in the current workshop and determine the resource scheduling plan according to the task priority table and the resource status;

[0164] A second determination module 300 is used to analyze the dependency relationship of each of the subtasks, generate a task dependency graph, and determine parallelizable tasks based on the task dependency graph;

[0165] The third determination module 400 is used to optimize the execution order of all the parallelizable tasks and determine a parallel scheduling scheme;

[0166] The fourth determination module 500 is used to determine the initial scheduling plan of the metal mold production process according to the resource scheduling plan and the parallel scheduling plan;

[0167] Output module 600, used to optimize the process time and interval time of the initial scheduling plan, and output the optimized scheduling plan for the hardware mold production process;

[0168] The instruction sending module 700 is used to send the task allocation instructions to each production unit according to the optimization scheduling plan to execute the hardware mold production process.

[0169] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0170] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0171] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0172] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0173] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0174] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0175] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0177] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. An optimization scheduling method for a hardware mold production process, characterized in that: The method comprises: Obtain order data, decompose the entire production task into multiple subtasks according to the order data and mold production process, and prioritize each subtask to generate a task priority table; Obtaining the status of all resources in the current workshop, and determining a resource scheduling plan according to the task priority table and the resource status; Analyze the dependency relationship of each of the subtasks, generate a task dependency graph, and determine parallelizable tasks based on the task dependency graph; Optimizing the execution order of all the parallelizable tasks to determine the optimal parallel scheduling solution; Determine an initial scheduling plan for the metal mold production process according to the resource scheduling plan and the parallel scheduling plan; Optimize the process time and interval time of the initial scheduling plan, and output an optimized scheduling plan for the metal mold production process; According to the optimization scheduling scheme, task allocation instructions are sent to each production unit to execute the hardware mold production process.

2. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: And prioritize each subtask to generate a task priority table, including: Determine a multidimensional factor according to the order data and the real-time production status, and determine a priority weight of each of the subtasks according to the multidimensional factor; The subtasks are sorted based on the priority weights to generate the task priority table.

3. The optimization scheduling method for the hardware mold production process according to claim 2 is characterized in that: And determining the priority weight of each of the subtasks according to the multi-dimensional factors, including: Where P j Represents subtask t i,j The priority of w k represents the weight of the priority factor k, f k (t i,j ) represents subtask t i,j The attribute value at priority factor k, T d (t i,j ) represents the priority adjustment amount based on real-time dynamic changes, and n represents the total number of priority influencing factors.

4. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: Determining a resource scheduling scheme according to the task priority table and the resource status includes: Determine a multi-objective optimization problem, and establish a multi-objective resource scheduling model based on the multi-objective optimization problem; Based on the multi-objective resource scheduling model, combined with the task priority table and the resource status, the resource scheduling solution is output.

5. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: The execution order of all the parallelizable tasks is optimized, including: Dividing all the parallelizable tasks into several groups using a parallel computing model; Prioritize the subtasks of each group based on the status of the device, and dynamically adjust the scheduling order according to the priority score; Use simulation tools to perform scheduling simulation and obtain simulation results; According to the simulation results, the task grouping and scheduling order are repeatedly adjusted to determine the optimal parallel scheduling solution.

6. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: Optimizing the process time and interval time of the initial scheduling plan includes: Analyze historical production data and estimate the execution time of each process; Dynamically adjust the time interval for process connection according to the real-time status of the equipment and the execution time; The resource allocation state is dynamically adjusted according to the adjusted time interval.

7. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: Optimized scheduling methods also include: Obtain real-time production data of the hardware mold production process; determining a current operating status of the equipment based on the real-time production data; When the current operating state of the device is an abnormal state, the abnormal level of the device is determined, corresponding abnormal information is sent to a user matching the abnormal level, and a maintenance plan determined by the user based on the abnormal information is obtained, and the resource scheduling plan is adjusted according to the maintenance plan.

8. The optimization scheduling method for the hardware mold production process according to claim 7 is characterized in that: Optimized scheduling methods also include: According to the current operating state of the device and the user level, a corresponding display strategy is executed on the real-time production data.

9. The optimization scheduling method for the hardware mold production process according to claim 1 is characterized in that: Optimized scheduling methods also include: Detecting whether the optimized scheduling scheme reaches a preset satisfaction level; If the preset satisfaction level is reached, production scheduling is planned according to the optimized scheduling plan; If the preset satisfaction level is not reached, the deviation between the satisfaction level of the optimized scheduling solution and the preset satisfaction level is obtained to determine whether the deviation level is greater than a preset deviation level; wherein the preset deviation level is a user-defined setting or is determined based on the resource status; If the deviation is not greater than the preset deviation, the resource scheduling scheme is re-optimized, and a re-optimization request is sent to the corresponding management position based on the re-optimized resource scheduling scheme. When the re-optimization grant request returned by the corresponding management position is obtained, a new optimized scheduling scheme is generated again according to the re-optimized resource scheduling scheme; wherein the satisfaction of the new optimized scheduling scheme reaches the preset satisfaction; If the deviation is greater than the preset deviation, an optimization failure notification and an optimization scheduling plan customization request are sent to the corresponding management position. When the customized optimization scheduling plan determined by the corresponding management position based on the optimization scheduling plan customization request is obtained, the hardware mold production process is executed according to the customized optimization scheduling plan.

10. An optimization scheduling system for the production process of hardware molds, characterized in that: The system comprises: A generation module is used to obtain order data, decompose the entire production task into multiple subtasks according to the order data and the mold production process, and prioritize each subtask to generate a task priority table; A first determination module is used to obtain the status of all resources in the current workshop and determine a resource scheduling plan according to the task priority table and the resource status; A second determination module is used to analyze the dependency relationship of each of the subtasks, generate a task dependency graph, and determine parallelizable tasks based on the task dependency graph; A third determination module is used to optimize the execution order of all the parallelizable tasks and determine a parallel scheduling scheme; A fourth determination module, used to determine an initial scheduling scheme for the metal mold production process according to the resource scheduling scheme and the parallel scheduling scheme; An output module is used to optimize the process time and interval time of the initial scheduling plan and output an optimized scheduling plan for the metal mold production process; The instruction sending module is used to send the task allocation instructions to each production unit according to the optimization scheduling plan to execute the hardware mold production process.

Images