Production scheduling method, device, equipment and medium

Abstract

The invention discloses a production scheduling method and device, equipment and a medium. The method comprises the steps of obtaining order information of a production order and equipment information of production equipment; determining at least one target device in the at least one production device based on the order information of the production order and the device information of the production device, and determining an initial production process; acquiring state information of the at least one target device in a production process according to the initial production process; and determining an optimized production process according to the order information, the initial production process and the state information of the at least one piece of target equipment, thereby effectively solving the problems that the progress is influenced by frequent faults of part of equipment, resource waste is caused by low-load operation of part of equipment, and the production efficiency is improved due to the fact that the actual state and performance difference of the equipment are not fully considered in the existing production arrangement mode. The production efficiency is difficult to reach the optimum, the cost is increased, and the delivery is influenced.

Description

Technical Field

[0001] This invention belongs to the field of industrial production, and specifically relates to a production scheduling method, apparatus, equipment, and medium. Background Technology

[0002] In the manufacturing process, the effective execution of production orders depends on the rational allocation of production equipment and the scientific arrangement of production processes. Currently, when faced with production orders, companies typically assign them to corresponding production equipment according to a pre-set fixed production process. However, in actual production scenarios, due to differences in the performance of production equipment, different pieces of equipment exhibit varying performance in terms of production efficiency, stability, and failure rate when handling the same production task. Furthermore, the operating status of equipment changes dynamically over time during production, and performance fluctuations may occur due to equipment aging, component wear, and other factors.

[0003] Existing production scheduling methods often fail to adequately consider the actual condition and performance differences of equipment. This leads to situations where some equipment may frequently malfunction due to overload, impacting production progress, while others may operate at low capacity, resulting in resource waste. Such irrational production scheduling makes it difficult to achieve optimal production efficiency, hindering the timely and efficient completion of production orders. Consequently, it increases production costs, delays product delivery, and severely impacts a company's market competitiveness and economic benefits. Therefore, how to dynamically and rationally arrange production processes based on production order demands and the actual condition of production equipment has become a critical issue that urgently needs to be addressed in the manufacturing sector. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a production scheduling method, apparatus, equipment and medium that overcomes or at least partially solves the problem of how to dynamically and reasonably arrange production processes according to production order requirements and the actual state of production equipment.

[0005] In a first aspect, embodiments of the present invention provide a production scheduling method, the method comprising: Obtain order information for production orders and equipment information for production equipment; Based on the order information of the production order and the equipment information of the production equipment, at least one target equipment is determined among the at least one production equipment, and an initial production process is determined; Obtain the status information of the at least one target device during the production process according to the initial production procedure; Based on the order information, the initial production process, and the status information of the at least one target device, an optimized production process is determined.

[0006] Optionally, the order information includes production complexity, and the status information includes equipment operating status, equipment utilization rate, processing capacity data, and current task progress. Determining the optimized production process based on the order information, the initial production process, and the status information of the at least one target device includes: Based on the status information, the production complexity, the initial production process, and the preset knowledge base, process optimization rules are determined, and the process optimization rules are used to specify the optimized process; the status information includes at least one of equipment utilization rate, processing capacity data, and current task progress data. Based on the process optimization rules, the optimized production process is determined.

[0007] Optionally, the status information includes processing capacity data, the processing capacity data includes production quality yield, the process optimization rules include rules for adding new processes, and the step of determining the process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base includes: If the production quality yield is less than or equal to a preset production quality yield threshold, a new process rule is determined based on the production complexity, the initial production process, and the preset knowledge base. The production quality yield refers to the proportion of qualified products to the total production quantity during the production process. The new process rule is used to guide the insertion of new processes into existing processes.

[0008] Optionally, the status information includes processing capacity data and current task progress data, the processing capacity data includes production efficiency, the process optimization rules include moving process rules, and determining the process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base includes: If the production efficiency is less than or equal to a preset production efficiency threshold, and / or the current task progress data is less than or equal to a preset task progress threshold, a process movement rule is determined based on the production complexity, the initial production process, and the preset knowledge base. The process movement rule is used to guide the adjustment of the sequence arrangement between existing processes.

[0009] Optionally, the process optimization rules include cloning process rules, and determining the process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base includes: Based on the production complexity, determine whether there are any high-risk processes in the initial production process; In the event that there are high-risk processes in the initial production process, a cloning process rule is determined based on the status information, the initial production process, and the preset knowledge base. The cloning process rule is used to guide the replication of processes in the existing process.

[0010] Optionally, the status information includes equipment utilization rate, the process optimization rules include process deletion rules, and determining the process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base includes: Based on the production complexity, determine whether there are redundant processes in the initial production process; If there are redundant processes in the initial production process, and / or the equipment utilization rate is greater than or equal to a preset equipment utilization rate threshold, a process deletion rule is determined based on the status information, the initial production process, and the preset knowledge base. The process deletion rule is used to guide the deletion of processes in the existing process.

[0011] Optionally, determining at least one target device from the at least one production device based on the order information of the production order and the equipment information of the production equipment includes: Based on the order information of the production order, determine the production priority of the production order; Based on the production priority and the equipment information of the at least one production device, at least one target device is determined among the at least one device.

[0012] Optionally, the order information includes order urgency, customer level, production complexity, and inventory cost. Determining the production priority of a production order based on its order information includes: The production priority of a production order is determined based on the order urgency, the customer level, the production complexity, and the inventory cost.

[0013] Secondly, embodiments of the present invention provide a production scheduling device, the device comprising: The information acquisition module is used to acquire order information for production orders and equipment information for production equipment; The equipment and process determination module is used to determine at least one target equipment among the at least one production equipment, and to determine the initial production process, based on the order information of the production order and the equipment information of the production equipment; The status information acquisition module is used to acquire the status information of the at least one target device during the production process according to the initial production procedure; The optimized process determination module is used to determine the optimized production process based on the order information, the initial production process, and the status information of the at least one target device.

[0014] Optionally, the order information includes production complexity, the status information includes equipment operating status, equipment utilization rate, processing capacity data, and current task progress, and the optimized process determination module includes: The optimization rule determination submodule is used to determine process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base. The process optimization rules are used to specify the optimized process. The status information includes at least one of equipment utilization rate, processing capacity data, and current task progress data. The rule optimization process determination module determines the optimized production process based on the process optimization rules.

[0015] Optionally, the status information includes processing capacity data, the processing capacity data includes production quality yield, the process optimization rules include rules for adding new processes, and the optimization rule determination submodule includes: A new process unit is added to determine the rules for adding a new process when the production quality yield is less than or equal to a preset production quality yield threshold, based on the production complexity, the initial production process, and a preset knowledge base. The production quality yield refers to the proportion of qualified products to the total production quantity during the production process. The rules for adding a new process are used to guide the insertion of new processes into existing processes.

[0016] Optionally, the status information includes processing capacity data and current task progress data, the processing capacity data includes production efficiency, the process optimization rules include process movement rules, and the optimization rule determination submodule includes: A moving process unit is used to determine moving process rules based on the production complexity, the initial production process, and a preset knowledge base when the production efficiency is less than or equal to a preset production efficiency threshold, and / or the current task progress data is less than or equal to a preset task progress threshold. The moving process rules are used to guide the adjustment of the sequence arrangement between existing processes.

[0017] Optionally, the process optimization rules include cloning process rules, and the optimization rule determination sub-module includes: A high-risk process determination unit is used to determine whether there is a high-risk process in the initial production process based on the production complexity. The replication process unit is used to determine cloning process rules based on the status information, the initial production process, and a preset knowledge base when there are high-risk processes in the initial production process. The cloning process rules are used to guide the replication of processes in the existing process.

[0018] Optionally, the status information includes equipment utilization, the process optimization rules include process deletion rules, and the optimization rule determination submodule includes: A redundant process determination unit is used to determine whether there are redundant processes in the initial production process based on the production complexity. The process deletion unit is used to determine process deletion rules based on the status information, the initial production process, and a preset knowledge base when there are redundant processes in the initial production process and / or the equipment utilization rate is greater than or equal to a preset equipment utilization rate threshold. The process deletion rules are used to guide the deletion of processes in the existing process.

[0019] Optionally, the equipment and process determination module includes: The priority determination submodule is used to determine the production priority of the production order based on the order information of the production order; The target equipment determination submodule is used to determine at least one target equipment among the at least one equipment based on the production priority and the equipment information of the at least one production equipment.

[0020] Optionally, the order information includes order urgency, customer level, production complexity, and inventory cost, and the priority determination submodule includes: The production priority determination unit is used to determine the production priority of the production order based on the order urgency, the customer level, the production complexity, and the inventory cost.

[0021] Thirdly, embodiments of the present invention provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0022] Fourthly, embodiments of the present invention provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0023] In this embodiment of the invention, by acquiring order information of production orders and equipment information of production equipment, at least one target device and an initial production process are determined based on this information. Then, the status information of at least one target device during the production process according to the initial production process is acquired. Based on the order information, the initial production process, and the status information of at least one target device, an optimized production process is determined. This enables dynamic and precise arrangement of production tasks and processes according to the actual status and performance of the equipment, making reasonable and efficient use of equipment resources and ensuring a smooth and efficient production process. It effectively solves the technical problems of existing production arrangement methods that do not fully consider the actual status and performance differences of equipment, resulting in frequent failures of some equipment affecting progress, low-load operation of some equipment causing resource waste, difficulty in achieving optimal production efficiency, increased costs, and impact on delivery. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the steps of an embodiment of the production scheduling method of the present invention; Figure 2 This is a schematic diagram of a newly added process in an embodiment of the production scheduling method of the present invention; Figure 3 This is a schematic diagram of a moving process in an embodiment of a production scheduling method of the present invention; Figure 4 This is a schematic diagram of the cloning process in an embodiment of the production scheduling method of the present invention; Figure 5 This is a schematic diagram of a deletion process in an embodiment of the production scheduling method of the present invention; Figure 6 This is a flowchart illustrating an embodiment of the production scheduling method of the present invention; Figure 7 This is a structural block diagram of an embodiment of a production scheduling device according to the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0027] The production scheduling method, apparatus, equipment, and medium provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0028] Reference Figure 1 The diagram illustrates a flowchart of an embodiment of the production scheduling method of the present invention, which may specifically include the following steps: Step 101: Obtain the order information for the production order and the equipment information for the production equipment; Production orders refer to production instructions issued by the enterprise's production management department, which include requirements such as product specifications, quantity, and delivery time. Order information refers to various business data related to production orders, including basic order attribute information (such as product type, order quantity, and delivery deadline), order commercial value information (such as customer importance and order urgency), order technical requirements information (such as process complexity and quality standards), and order resource usage information (such as inventory costs and capital tied up). Production equipment refers to various manufacturing equipment used to perform production tasks, including but not limited to CNC machine tools, machining centers, testing equipment, assembly equipment, and other industrial production equipment. Equipment information refers to various technical data related to production equipment, including basic equipment attribute information (such as equipment model, technical parameters, and processing capacity), current equipment status information (such as operating status, load, and maintenance records), equipment performance indicators (such as production efficiency, processing accuracy, and quality capability), and equipment availability information (such as idle time and fault history). In practice, order information can be automatically synchronized through an interface with the enterprise resource planning system or obtained through manual entry. Equipment information is acquired in real time through industrial IoT technology using various sensors installed on the equipment. This invention is primarily applied in the field of modern intelligent manufacturing, and is particularly suitable for industrial scenarios requiring personalized production based on order demands. Typical application scenarios include various automated production lines, flexible manufacturing systems, smart factories, and other manufacturing environments, involving the collaborative operation and dynamic scheduling of multiple production devices.

[0029] Step 102: Based on the order information of the production order and the equipment information of the production equipment, determine at least one target equipment among the at least one production equipment, and determine the initial production process; After obtaining order and equipment information, it is necessary to determine the target equipment and initial production processes based on this information. Target equipment refers to the specific equipment or equipment combination determined to execute the production task for this order after matching and optimizing according to order requirements and equipment capabilities. Initial production processes refer to the preliminary production process route plan formulated for this order based on product process requirements, standard operating procedures, and equipment capability matching results. This includes the execution sequence of each process, the logical relationships between processes, the allocation of execution equipment for each process, and the time arrangement between processes. Specifically, by analyzing the technical requirements and business priorities in the order information, combined with the capability parameters and current status in the equipment information, an optimization matching algorithm is used to determine the optimal equipment allocation scheme. Based on the product's standard process specifications and the equipment allocation results, a preliminary process execution plan is generated.

[0030] Step 103: Obtain the status information of the at least one target device during the production process according to the initial production procedure; After identifying the target equipment and initial production process, it is necessary to acquire the status information of the target equipment during the production process according to the initial production process. This status information refers to various real-time operating data and performance indicators of the production equipment during the actual execution of production tasks. This includes, but is not limited to, equipment operating status data (such as whether the equipment is operating normally, whether it is in a faulty state, or whether maintenance is required), equipment workload data (such as equipment utilization rate, task queuing status, and resource occupancy rate), equipment production performance data (such as production efficiency, processing speed, and capacity achievement rate), equipment quality performance data (such as production quality yield, defect rate, and rework rate), and production progress data (such as the completion rate of the current process, overall task progress, and estimated completion time), among other multi-dimensional real-time monitoring information. This status information is acquired in real time through various sensors, monitoring devices, and data acquisition systems deployed on the production equipment, and transmitted to the central data processing system for storage and analysis via industrial communication networks.

[0031] Step 104: Determine the optimized production process based on the order information, the initial production process, and the status information of the at least one target device.

[0032] Finally, based on order information, initial production processes, and target equipment status information, the optimized production processes are determined. Optimizing production processes refers to the intelligent and dynamic adjustment of the initial production processes based on the analysis of real-time monitoring data, combined with order requirements and production process knowledge, resulting in an improved process execution plan. This optimization process is a dynamic and continuous decision-making process that can automatically identify efficiency bottlenecks, quality risks, or unreasonable resource allocation in the production process based on real-time changes in the production site, and generate corresponding process adjustment strategies. Optimizing production processes can be achieved through various means, including but not limited to: adding new process nodes to existing processes to compensate for technical deficiencies or strengthen quality control; adjusting the execution order of existing processes to optimize production efficiency; replicating key processes to enhance risk control capabilities; and removing redundant or inefficient processes to improve overall efficiency. In practice, the system uses a built-in intelligent decision-making algorithm to perform in-depth analysis of the real-time status data, combined with historically accumulated process knowledge and experience data, to identify optimization opportunities in the production process, automatically generate corresponding process optimization suggestions, and adjust the original production process arrangement accordingly to achieve the optimization goals of continuously improving production efficiency, ensuring product quality, reducing resource consumption, and shortening delivery cycles.

[0033] In one embodiment, the order information includes production complexity, and the status information includes equipment operating status, equipment utilization rate, processing capacity data, and current task progress. Step 104 may include the following sub-steps: Sub-step S11: Based on the status information, the production complexity, the initial production process, and the preset knowledge base, determine the process optimization rules, which are used to specify the optimized process; the status information includes at least one of equipment utilization rate, processing capacity data, and current task progress data; First, real-time operational data of the equipment during the production process is collected, including equipment status, resource utilization, processing performance, and task execution progress. Simultaneously, the system acquires the technical complexity indicators of the current production order and the pre-defined process flow arrangement. By cross-referencing and intelligently analyzing this real-time data with the system's built-in historical experience database, the system can identify potential problems or optimization opportunities in the current production process and generate corresponding process optimization guidance rules based on preset decision logic. These rules are essentially a set of conditional triggering mechanisms, defining what type of process adjustment strategy should be adopted under specific circumstances. Production complexity refers to a quantitative indicator of the technical difficulty and process complexity involved in the production order, reflecting the technical capabilities and resource input level required to complete the order. Initial production process refers to the preliminary production process flow arrangement formulated based on the matching of order requirements and equipment capabilities. The preset knowledge base refers to the system's built-in historical experience data set, including structured knowledge such as past successful optimization cases, process standards, and equipment performance databases. Process optimization rules refer to guiding decision-making criteria generated based on data analysis results. These rules indicate under what conditions what type of process adjustment measures should be taken. For example, in addition to combining a pre-set knowledge base, the following methods can be used: Machine learning algorithms: such as supervised learning methods. For instance, historical production data can be collected, including equipment status information, order complexity, initial process arrangements, and the final optimization measures and their effects, to construct a training dataset. By training classification models such as support vector machines, random forests, and neural networks, the system can learn what type of process optimization strategy should be adopted under specific conditions. Unsupervised learning methods: The system can use clustering algorithms such as K-means and hierarchical clustering to group historical production status data and identify production patterns with similar characteristics. For each production pattern, the system can statistically analyze its most common optimization strategies and directly recommend the corresponding optimization scheme when encountering new similar states. Reinforcement learning methods: The system can model the process optimization process as a Markov decision process, where the state space includes equipment status, order information, etc., the action space contains four types of process optimization, and the reward function is based on the optimized production effect (such as efficiency improvement, cost reduction, etc.). Through continuous interaction with the production environment, the system can learn the policy function that selects the optimal optimization strategy under different states.

[0034] Furthermore, rule-based reasoning mechanisms can be integrated: for example, the experience and knowledge of domain experts can be integrated to build an expert system containing hundreds of if-then rules. Each rule corresponds to a specific production scenario and optimization suggestion. When the system detects a state that meets the preconditions of a certain rule, the corresponding conclusion is automatically triggered. Fuzzy logic processing: Considering that many state parameters in the production environment are fuzzy, fuzzy logic can be used to handle this uncertainty. By defining membership functions and fuzzy reasoning rules, the system can make more reasonable optimization decisions under boundary conditions. A case-based reasoning mechanism can be established. When encountering a new production state, similarity calculations are used to find the most similar cases in the historical case library, and successful optimization solutions from these cases are referenced. This method is particularly suitable for handling complex optimization problems with multiple coupled factors, such as those using knowledge graphs or graph theory algorithms.

[0035] For example, the system receives a batch of parts orders with high production complexity, involving precision machining and special heat treatment processes. During production, it is monitored that the equipment utilization rate for performing the heat treatment process consistently exceeds 90%, and the current batch's production progress is 15% behind schedule. By querying the processing experience of similar complex orders in the preset knowledge base, it is found that high-complexity orders are prone to quality problems when critical equipment is overloaded. Therefore, an optimization rule can be generated to "add an online quality inspection process after the heat treatment process".

[0036] Sub-step S12: Determine the optimized production process according to the process optimization rules.

[0037] Based on the optimization rules generated in the previous step, and considering the current production environment and resource constraints, the original production process is adjusted accordingly. This adjustment may involve various methods such as adding or deleting processes, rearranging sequences, and replicating key steps. By executing these adjustments, the system generates an improved production process plan. This plan, while meeting product quality requirements, better adapts to current production conditions, achieving optimized resource allocation and improved production efficiency. Optimized production processes refer to the improved production flow formed after intelligent adjustments based on the initial production processes. The aim is to improve production efficiency, ensure product quality, or reduce production costs. By comprehensively considering factors such as equipment status information, production complexity, and initial processes, more reasonable process optimization rules can be formulated, thereby improving overall production efficiency. Adjusting production processes according to these optimization rules better matches equipment capabilities and process requirements, contributing to improved product processing accuracy and quality stability.

[0038] The following four exemplary embodiments are given to illustrate the process optimization rules in detail. In the first exemplary embodiment, the status information includes processing capacity data, the processing capacity data includes production quality yield, and the process optimization rules include new process rules. When the production quality yield is less than or equal to a preset production quality yield threshold, sub-step S12 may include the following: Based on the production complexity, the initial production process, and the preset knowledge base, rules for adding new processes are determined. The production quality yield refers to the proportion of qualified products to the total production quantity during the production process. The rules for adding new processes are used to guide the insertion of new processes into existing processes.

[0039] A process optimization strategy driven by quality indicators is adopted. This embodiment focuses on quality control issues in the production process, triggering corresponding process optimization mechanisms by real-time monitoring of the key indicator of product yield. Quality inspection data is continuously collected during the production process to calculate the yield of the current process or product batch. Yield refers to the proportion of qualified products to the total production quantity, directly reflecting the quality control level of the production process. A preset yield control threshold (e.g., 95%) is established. When the real-time monitored production yield is less than or equal to this preset threshold, a quality risk is identified in the current production process, requiring corresponding optimization measures. When the triggering condition is met, the system enters the stage of generating new process rules: comprehensively analyzing the production complexity of the current order, the existing initial process arrangement, and historical experience data in the preset knowledge base. Based on the production complexity information, the system assesses whether the current process flow has sufficient quality control capabilities. For high-complexity orders, more stringent quality inspection procedures can be implemented. Successful handling cases of similar complex orders with low yield are retrieved from the preset knowledge base to extract effective optimization strategies. Based on the above analysis, the system generates a new process rule. This rule explicitly indicates the location where a new quality control process needs to be inserted into the existing process flow. The new process rule guides the insertion of new processes into existing ones, with the core objective of improving overall production quality by increasing quality control points.

[0040] For example, the type of new process (such as online inspection, full inspection, functional testing, etc.) can be determined first, then the insertion position of the new process can be determined (usually after the critical processing process or before the quality risk point). Finally, the execution standards and quality requirements of the new process can be determined according to business needs. For example, based on the aforementioned optimization rule of "adding an online quality inspection process after the heat treatment process", the system can adjust the original "rough processing → heat treatment → fine processing → final inspection" process to an optimized production process of "rough processing → heat treatment → online inspection → fine processing → final inspection", thereby reducing the risk of batch scrap by adding process quality control points.

[0041] Reference Figure 2 The diagram illustrates a newly added process in an embodiment of the production scheduling method of the present invention, the details of which are as follows: The original production process before adding a process step was: material preparation → horizontal lathe → five-axis machining → vertical machining → coordinate measuring machine (CMM). The process after adding a process step is: material preparation → horizontal lathe → cutting line → electrical discharge machining (EDM) → five-axis machining → vertical machining → CMM. Generally, when there are technical gaps or quality bottlenecks in the production process, it is necessary to add processes to fill the gaps in key capabilities. For example, due to technological upgrades, two processes—cutting line and EDM—may need to be added after the horizontal lathe. The system supports dynamic process addition to meet the needs of new processes during production. Through this process addition rule generation mechanism based on quality yield, preventative measures can be taken at the initial stage of quality problems. By combining order complexity and historical experience, quality risk points can be accurately located, and the process flow can be dynamically adjusted based on real-time quality data. Early intervention can prevent large-scale quality accidents, effectively improving product quality stability and customer satisfaction.

[0042] In a second exemplary embodiment, the status information includes processing capacity data and current task progress data, the processing capacity data includes production efficiency, the process optimization rules include moving process rules, and sub-step S12 may include the following: If the production efficiency is less than or equal to a preset production efficiency threshold, and / or the current task progress data is less than or equal to a preset task progress threshold, a process movement rule is determined based on the production complexity, the initial production process, and the preset knowledge base. The process movement rule is used to guide the adjustment of the sequence arrangement between existing processes.

[0043] Based on a process optimization strategy driven by both production efficiency and task progress, this embodiment focuses on efficiency bottlenecks and schedule delays in the production process. It triggers corresponding process optimization mechanisms by real-time monitoring of these two key performance indicators (KPIs). Efficiency data is continuously collected during the production process, including indicators such as equipment output per unit time, process cycle time, and resource utilization. Simultaneously, the system tracks the completion status of current production tasks in real time, including progress data such as the number of completed processes, the number of remaining processes, and the comparison between estimated and planned completion times. The system presets production efficiency and task progress thresholds. When production efficiency is less than or equal to the preset production efficiency threshold, current task progress is less than or equal to the preset task progress threshold, or both conditions are met simultaneously, the system determines that there is an efficiency or schedule problem in the current production process and requires corresponding optimization measures.

[0044] When the triggering conditions are met, the system enters the process move rule generation phase. First, it analyzes the bottlenecks in the current process flow based on production complexity information, as high-complexity processes often require more time and resources and are prone to becoming efficiency bottlenecks. Next, based on the initial process arrangement, it identifies dependencies and parallelization possibilities between processes, seeking opportunities to improve overall efficiency by adjusting the process sequence. Simultaneously, the system retrieves successful processing cases of similar complex orders inefficient or delayed situations from a pre-set knowledge base, extracts effective process sequence adjustment strategies, and generates process move rules based on the above comprehensive analysis results. These rules explicitly indicate the need to adjust the order of existing processes. The process move rules guide the adjustment of the order of existing processes, with the core purpose of improving production efficiency and accelerating task progress by optimizing the process execution sequence. In practice, the rule determines the processes to be moved and their target locations, analyzes the impact of the process move on preceding and following processes, assesses the expected efficiency improvement after the process move, and confirms the feasibility and constraints of the process move. Typical scenarios for moving processes include bringing forward time-consuming and complex processes to avoid subsequent processes waiting, adjusting processes that can be executed in parallel to be executed simultaneously, reordering processes with weak dependencies to balance equipment load, and interspersing simple processes between complex processes to improve equipment utilization.

[0045] Reference Figure 3 The diagram illustrates a moving process according to an embodiment of the production scheduling method of the present invention, and the specific details are as follows: The process before the process relocation was: material preparation → horizontal lathe → 5-axis machining → vertical machining → coordinate measuring machine (CMM); correspondingly, the process after the relocation was: material preparation → vertical machining → 5-axis machining → horizontal lathe → CMM. Generally, when it's necessary to identify bottlenecks or inefficient steps in the current process, adjusting the process sequence can be considered to improve equipment utilization. In the original process, 5-axis machining is usually complex and time-consuming, causing the subsequent vertical machining center to frequently wait, resulting in low equipment utilization. Identifying the 5-axis machining center as a bottleneck and analyzing that the output status of the preceding process (horizontal lathe) might be detrimental to efficient 5-axis operation was identified. By moving the vertical machining center before the horizontal lathe, the state of the workpiece entering the bottleneck process was optimized, significantly improving bottleneck efficiency. This ultimately led to positive effects such as shortened cycle time, increased equipment utilization, stable quality, and reduced work-in-process inventory. This efficiency and schedule-based process relocation rule generation mechanism can eliminate efficiency bottlenecks. By optimizing the process sequence, efficiency bottlenecks in the production process can be eliminated, ensuring on-time delivery, timely adjustments to process arrangements to guarantee order completion on schedule, and improving resource utilization.

[0046] In a third exemplary embodiment, the process optimization rule includes a cloning process rule, and sub-step S12 may include the following: Based on the production complexity, determine whether there are any high-risk processes in the initial production process; In the event that there are high-risk processes in the initial production process, a cloning process rule is determined based on the status information, the initial production process, and the preset knowledge base. The cloning process rule is used to guide the replication of processes in the existing process.

[0047] This embodiment of a process optimization strategy based on risk identification and control focuses on quality risks and critical process failures in the production process. It triggers corresponding process optimization mechanisms by identifying high-risk links in the process flow. First, a comprehensive risk assessment of the initial production process is conducted based on the order's production complexity information to identify potentially high-risk processes. These processes typically have strict quality requirements, high technological difficulty, and severe consequences if they fail. If a high-risk process is identified in the initial production process, the process for generating cloned process rules can be initiated. At this point, the system comprehensively analyzes the current status information, the initial process arrangement, and historical risk management experience from a pre-set knowledge base. Status information includes factors that may affect the execution of high-risk processes, such as equipment operational stability, operator skill levels, and environmental conditions. The initial production process provides the position and role of the risky process within the overall process, while the pre-set knowledge base contains successful management cases and best practices for similar risky processes. Based on this deep fusion analysis, cloned process rules can be generated. These rules are specifically designed to guide the replication of critical processes in existing processes, reducing the risk of single-process failure through process redundancy.

[0048] The core function of cloning process rules is to enhance the fault tolerance and risk resistance of the entire production process by replicating critical processes. When the original high-risk process fails due to equipment malfunction, operational errors, or other unforeseen factors, the cloned process can serve as a backup or supplement, ensuring that product quality is not affected or that the impact is minimized. The implementation of such rules typically involves determining the number of cloned processes, their insertion positions, and the execution logic relationship between the cloned and original processes. For example, parallel execution can be used to improve detection reliability, or serial execution can be used for multiple verifications.

[0049] Reference Figure 4 The diagram illustrates the cloning process of an embodiment of the production scheduling method of the present invention, and the specific details are as follows: The processes before cloning are: material preparation → horizontal lathe → five-axis machining → vertical machining → coordinate measuring machine (CMM) inspection; the processes after cloning are: material preparation → horizontal lathe → five-axis machining → vertical machining → CMM inspection → CMM inspection. Generally, when high-risk processes (such as critical quality checks or error-prone steps) exist in the production process, failure of these high-risk processes may lead to batch rework or scrap. In this case, cloning can strengthen risk control. The original process only has one CMM inspection (after vertical machining); if a defect is missed and enters the market, the losses will be huge. Cloning first identifies high-risk processes with low fault tolerance and serious consequences, then adds the same process at the same or different nodes, and finally completes the cloning process. This reduces quality risk and improves the process's fault tolerance.

[0050] In a fourth exemplary embodiment, the status information includes equipment utilization, the process optimization rule includes deleting process rules, and sub-step S12 may include the following: Based on the production complexity, determine whether there are redundant processes in the initial production process; If there are redundant processes in the initial production process, and / or the equipment utilization rate is greater than or equal to a preset equipment utilization rate threshold, a process deletion rule is determined based on the status information, the initial production process, and the preset knowledge base. The process deletion rule is used to guide the deletion of processes in the existing process.

[0051] Based on resource efficiency and process value assessment, this embodiment focuses on resource waste and process redundancy in the production process. It triggers corresponding process optimization mechanisms by comprehensively evaluating the value contribution of processes and equipment utilization efficiency. The system first performs a comprehensive value analysis of the initial production processes based on the order's production complexity information, identifying potentially redundant processes. These processes are typically characterized by technological substitution, functional duplication, minimal quality contribution, or can be completed by merging with other processes. When redundant processes are identified in the initial production process, or when equipment utilization is detected to be greater than or equal to a preset equipment utilization threshold, the system initiates the process of generating deletion rules. High equipment utilization often indicates tight equipment resources, potentially due to overuse or improper resource allocation. Deleting unnecessary processes can free up valuable equipment resources. The system comprehensively analyzes current status information, initial process arrangements, and historical optimization experience in a preset knowledge base. Status information, especially equipment utilization data, provides crucial real-time evidence for deletion decisions. The initial production processes reveal the logical relationships and dependencies between processes, while the preset knowledge base provides successful deletion cases and technical verification data from similar past situations.

[0052] Based on in-depth analysis of this information, the system generates deletion rules. These rules are specifically designed to guide the removal of specific processes from existing workflows, optimizing resource allocation and improving production efficiency by eliminating valueless or low-value steps. The implementation of these deletion rules involves a rigorous feasibility verification process, including assessing the impact of deletion on product quality, analyzing the integrity of the process flow after deletion, and confirming the cascading effects of deletion on upstream and downstream processes. This ensures that the deletion operation does not compromise the final product quality and technical requirements.

[0053] Reference Figure 5 The diagram illustrates a deletion process according to an embodiment of the production scheduling method of the present invention, and the specific details are as follows: The process before deletion was: material preparation → horizontal lathe → 5-axis machining → vertical machining → coordinate measuring machine (CMM). The process after deletion is: material preparation → horizontal lathe → 5-axis machining → vertical machining → CMM. Generally, when there are redundant processes, technological substitutions, or resource optimization needs in the production process, unnecessary steps can be removed to reduce costs and increase efficiency. In the deleted process, 5-axis machining has high costs and long cycles, and the vertical machining equipment has been upgraded to complete some 5-axis tasks. Therefore, when redundant 5-axis processes are identified, the redundant processes will be deleted. The deletion process first identifies redundant points, then conducts a risk assessment, and finally executes the deletion of redundant processes. This shortens the production cycle, reduces workpiece costs, and improves equipment utilization.

[0054] In one embodiment, step 102 may include the following sub-steps: Sub-step S21: Determine the production priority of the production order based on the order information of the production order; The acquired production order information undergoes a multi-dimensional comprehensive analysis, transforming the originally qualitative order importance into comparable numerical priority indicators. This process considers several key attributes of the order, including its urgency, customer importance, production technical complexity, and inventory costs. The system establishes a scientific evaluation model to calculate a corresponding priority score for each order. This score reflects the order's relative importance and urgency in the current production environment, providing crucial information for subsequent equipment allocation decisions.

[0055] Sub-step S22: Based on the production priority and the equipment information of the at least one production device, determine at least one target device among the at least one device.

[0056] Based on the order priorities calculated in the previous step, and combined with the actual status and capacity information of the production equipment, the system intelligently matches and selects target equipment. This sub-step considers not only whether the basic technical parameters of the equipment meet the order requirements, but also comprehensively evaluates factors such as the equipment's current workload, operating status, maintenance plan, and compatibility with other orders. For high-priority orders, the system tends to allocate equipment with better performance and more stable status, even if this means interrupting or postponing the execution of some low-priority orders. Simultaneously, the system considers the overall utilization efficiency of equipment resources to avoid idle equipment due to over-concentration. Through this hierarchical decision-making mechanism, the system can achieve optimal matching between order importance and equipment capacity, ensuring that important orders are prioritized while maximizing the overall utilization efficiency of equipment resources.

[0057] The order information includes order urgency, customer level, production complexity, and inventory cost. Sub-step S21 may include the following sub-steps: Sub-step S211: Determine the production priority of the production order based on the order urgency, customer level, production complexity, and inventory cost.

[0058] The four core indicators—order urgency, customer level, production complexity, and inventory cost—are uniformly quantified. By establishing reasonable weighting relationships among these indicators, priority values ​​that accurately reflect the relative importance of orders are calculated. Order urgency reflects the time sensitivity of an order, typically calculated as the ratio of remaining available time to the standard production cycle. Higher urgency indicates that the order needs to be prioritized to avoid delivery delays. Customer level reflects the importance of a customer to the company. Customers are categorized into different levels based on factors such as past cooperation, order size, and payment history. Higher-level customers naturally have higher importance and priority. Production complexity measures the technical difficulty and resource requirements of order production. Higher complexity often means more time, technology, and equipment resources are needed. Inventory cost considers the economic impact of orders on warehousing resources during the production process, including the storage costs of raw materials and work-in-process, capital tied up in inventory, and inventory management costs. In the specific calculation process, each indicator can be assigned a corresponding weight coefficient. For example, the weight for order urgency is 0.50, customer level is 0.20, production complexity is 0.15, and inventory cost is 0.15. The final order priority score is obtained by weighted summation. This multi-factor comprehensive evaluation method can comprehensively consider various factors affecting the importance of orders, avoiding the one-sidedness that may result from single-indicator decision-making, and ensuring the scientific and reasonable nature of the priority evaluation results. Through this priority determination mechanism based on multi-factor comprehensive evaluation, the system can accurately quantify the importance of different orders, providing a reliable decision-making basis for subsequent equipment allocation and production scheduling. It is particularly suitable for enterprise production environments with diverse order types and complex customer needs, and can achieve a balance between ensuring customer service quality and enterprise economic benefits.

[0059] Reference Figure 6 The diagram illustrates a flow chart of an embodiment of the production scheduling method of the present invention, the details of which are as follows: First, the system automatically synchronizes the remaining orders, or administrators can manually insert emergency orders. Then, the system calculates the priority of each production order and schedules its production. The first step is to determine if there is equipment available for production on the production floor. If so, the corresponding order is assigned to the corresponding production equipment. If there is no equipment available, the corresponding order will temporarily enter the waiting queue. After the equipment is assigned, the system can determine whether the process needs to be adjusted based on the order information and the operating status of the corresponding equipment. If so, it can further determine which type of adjustment should be made, including adding, moving, deleting, and cloning. If no adjustment is needed, production continues according to the original process until the order is completed and the finished product is put into storage.

[0060] By acquiring order information from production orders and equipment information from production equipment, at least one target device and an initial production process are identified from at least one production device. Then, the status information of at least one target device during the production process according to the initial production process is acquired. Based on the order information, the initial production process, and the status information of at least one target device, an optimized production process is determined. This enables dynamic and precise scheduling of production tasks and processes based on the actual status and performance of the equipment. This allows for the rational and efficient use of equipment resources and a smooth and efficient production process. It effectively solves the technical problems of existing production scheduling methods that do not fully consider the actual status and performance differences of equipment, resulting in frequent failures of some equipment affecting progress, low-load operation of some equipment causing resource waste, difficulty in achieving optimal production efficiency, increased costs, and impact on delivery.

[0061] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0062] Reference Figure 7 The diagram shows a structural block diagram of an embodiment of the production scheduling device of the present invention, the details of which are as follows: The information acquisition module 201 is used to acquire order information of production orders and equipment information of production equipment; Equipment and process determination module 202 is used to determine at least one target equipment among the at least one production equipment based on the order information of the production order and the equipment information of the production equipment, and to determine the initial production process; Status information acquisition module 203 is used to acquire status information of the at least one target device during the production process according to the initial production procedure; The optimized process determination module 204 is used to determine the optimized production process based on the order information, the initial production process, and the status information of the at least one target device.

[0063] In one embodiment, the order information includes production complexity, the status information includes equipment operating status, equipment utilization rate, processing capacity data, and current task progress, and the optimized process determination module includes: The optimization rule determination submodule is used to determine process optimization rules based on the status information, the production complexity, the initial production process, and the preset knowledge base. The process optimization rules are used to specify the optimized process. The status information includes at least one of equipment utilization rate, processing capacity data, and current task progress data. The rule optimization process determination module determines the optimized production process based on the process optimization rules.

[0064] In one embodiment, the status information includes processing capacity data, the processing capacity data includes production quality yield, the process optimization rules include rules for adding new processes, and the optimization rule determination submodule includes: A new process unit is added to determine the rules for adding a new process when the production quality yield is less than or equal to a preset production quality yield threshold, based on the production complexity, the initial production process, and a preset knowledge base. The production quality yield refers to the proportion of qualified products to the total production quantity during the production process. The rules for adding a new process are used to guide the insertion of new processes into existing processes.

[0065] In one embodiment, the status information includes processing capacity data and current task progress data, the processing capacity data includes production efficiency, the process optimization rules include process movement rules, and the optimization rule determination submodule includes: A moving process unit is used to determine moving process rules based on the production complexity, the initial production process, and a preset knowledge base when the production efficiency is less than or equal to a preset production efficiency threshold, and / or the current task progress data is less than or equal to a preset task progress threshold. The moving process rules are used to guide the adjustment of the sequence arrangement between existing processes.

[0066] In one embodiment, the process optimization rule includes a cloning process rule, and the optimization rule determining submodule includes: A high-risk process determination unit is used to determine whether there is a high-risk process in the initial production process based on the production complexity. The replication process unit is used to determine cloning process rules based on the status information, the initial production process, and a preset knowledge base when there are high-risk processes in the initial production process. The cloning process rules are used to guide the replication of processes in the existing process.

[0067] In one embodiment, the status information includes equipment utilization, the process optimization rules include process deletion rules, and the optimization rule determination submodule includes: A redundant process determination unit is used to determine whether there are redundant processes in the initial production process based on the production complexity. The process deletion unit is used to determine process deletion rules based on the status information, the initial production process, and a preset knowledge base when there are redundant processes in the initial production process and / or the equipment utilization rate is greater than or equal to a preset equipment utilization rate threshold. The process deletion rules are used to guide the deletion of processes in the existing process.

[0068] In one embodiment, the equipment and process determination module includes: The priority determination submodule is used to determine the production priority of the production order based on the order information of the production order; The target equipment determination submodule is used to determine at least one target equipment among the at least one equipment based on the production priority and the equipment information of the at least one production equipment.

[0069] In one embodiment, the order information includes order urgency, customer level, production complexity, and inventory cost; the priority determination submodule includes: The production priority determination unit is used to determine the production priority of the production order based on the order urgency, the customer level, the production complexity, and the inventory cost.

[0070] The production scheduling device in this embodiment of the invention can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This embodiment of the invention does not impose specific limitations.

[0071] The production scheduling device in this embodiment of the invention can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this embodiment of the invention does not impose specific limitations.

[0072] The production scheduling device provided in this embodiment of the invention can achieve Figures 1 to 6 The various processes implemented by the production scheduling device in the method embodiment will not be described again here to avoid repetition.

[0073] By acquiring order information from production orders and equipment information from production equipment, at least one target device and an initial production process are identified from at least one production device. Then, the status information of at least one target device during the production process according to the initial production process is acquired. Based on the order information, the initial production process, and the status information of at least one target device, an optimized production process is determined. This enables dynamic and precise scheduling of production tasks and processes based on the actual status and performance of the equipment. This allows for the rational and efficient use of equipment resources and a smooth and efficient production process. It effectively solves the technical problems of existing production scheduling methods that do not fully consider the actual status and performance differences of equipment, resulting in frequent failures of some equipment affecting progress, low-load operation of some equipment causing resource waste, difficulty in achieving optimal production efficiency, increased costs, and impact on delivery.

[0074] This invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described production scheduling method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0075] This invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the various processes of the above-described production scheduling method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0077] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0078] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0079] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, 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 under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A production scheduling method characterized by, The method comprises: obtaining order information of a production order and equipment information of a production device; determining at least one target device in the at least one production device based on the order information of the production order and the equipment information of the production device, and determining an initial production procedure; obtaining state information of the at least one target device in a production process according to the initial production procedure; determining an optimized production procedure according to the order information, the initial production procedure and the state information of the at least one target device.

2. The production scheduling method according to claim 1, characterized by, The order information comprises production complexity, and the state information comprises at least one of equipment running state, equipment utilization, processing capacity data and current task progress, and the determination of the optimized production procedure according to the order information, the initial production procedure and the state information of the at least one target device comprises: determining a procedure optimization rule according to the state information, the production complexity, the initial production procedure and a preset knowledge base, the procedure optimization rule being used to specify an optimized procedure; and the state information comprising at least one of equipment utilization, processing capacity data and current task progress data; determining the optimized production procedure according to the procedure optimization rule.

3. The production scheduling method according to claim 2, characterized by, The state information comprises processing capacity data, the processing capacity data comprises production quality yield, and the procedure optimization rule comprises an added procedure rule, and the determination of the procedure optimization rule according to the state information, the production complexity, the initial production procedure and the preset knowledge base comprises: in a case where the production quality yield is less than or equal to a preset production quality yield threshold, determining the added procedure rule according to the production complexity, the initial production procedure and the preset knowledge base, the production quality yield being a proportion of the number of qualified products to the total number of products in the production process, and the added procedure rule being used to guide insertion of a new procedure in an existing procedure.

4. The production scheduling method according to claim 2, characterized by, The state information comprises processing capacity data and current task progress data, the processing capacity data comprises production efficiency, and the procedure optimization rule comprises a moved procedure rule, and the determination of the procedure optimization rule according to the state information, the production complexity, the initial production procedure and the preset knowledge base comprises: in a case where the production efficiency is less than or equal to a preset production efficiency threshold and / or the current task progress data is less than or equal to a preset task progress threshold, determining the moved procedure rule according to the production complexity, the initial production procedure and the preset knowledge base, the moved procedure rule being used to guide adjustment of the order arrangement between existing procedures.

5. The production scheduling method according to claim 2, characterized by, The procedure optimization rule comprises a cloned procedure rule, and the determination of the procedure optimization rule according to the state information, the production complexity, the initial production procedure and the preset knowledge base comprises: determining whether there is a high-risk procedure in the initial production procedure according to the production complexity; in a case where there is a high-risk procedure in the initial production procedure, determining the cloned procedure rule according to the state information, the initial production procedure and the preset knowledge base, the cloned procedure rule being used to guide copying of a procedure in an existing procedure.

6. The production scheduling method according to claim 2, characterized by, The state information comprises a device utilization rate, and the process optimization rule comprises a deletion process rule. The process optimization rule is determined according to the state information, the production complexity, the initial production process and a preset knowledge base, and the determination comprises: According to the production complexity, it is determined whether the initial production process has a redundant process. In a case where the initial production process has a redundant process and / or the device utilization rate is greater than or equal to a preset device utilization rate threshold, a deletion process rule is determined according to the state information, the initial production process and a preset knowledge base, and the deletion process rule is used to guide the deletion of a process in an existing process.

7. The production scheduling method according to claim 1, characterized by, The at least one target device is determined in the at least one production device based on order information of the production order and device information of the production device, and the determination comprises: According to the order information of the production order, a production priority of the production order is determined. According to the production priority and the device information of the at least one production device, at least one target device is determined in the at least one device.

8. The production scheduling method according to claim 7, characterized by, The order information comprises an order urgency, a customer level, a production complexity and a stock cost, and the production priority of the production order is determined according to the order information of the production order, and the determination comprises: According to the order urgency, the customer level, the production complexity and the stock cost, the production priority of the production order is determined.

9. A production scheduling apparatus characterized by comprising: The device comprises: An information acquisition module is configured to acquire order information of a production order and device information of a production device. A device and process determination module is configured to determine at least one target device in at least one production device based on the order information of the production order and the device information of the production device, and determine an initial production process. A state information acquisition module is configured to acquire state information of the at least one target device in a production process according to the initial production process. An optimized process determination module is configured to determine an optimized production process according to the order information, the initial production process and the state information of the at least one target device.

10. An electronic device, comprising: The device comprises a processor, a memory and a program or instruction stored on the memory and executable on the processor, and the program or instruction is executed by the processor to implement the steps of the production scheduling method as claimed in claims 1-8.

11. A readable storage medium, characterized by, The readable storage medium stores a program or instruction, and the program or instruction is executed by the processor to implement the steps of the production scheduling method as claimed in claims 1-8.

Images