Production scheduling optimization method and device fusing equipment health degree

Abstract

The present application relates to the technical field of production line automation, and particularly relates to a production scheduling optimization method and device fusing equipment health degree, which evaluates equipment health degree by real-time collection of welding production line equipment operation parameters, and quantifies the process complexity of the to-be-scheduled vehicle model in combination with the welding process characteristics of the to-be-scheduled vehicle model; then a matching relationship between the equipment and the vehicle model is established by using an adaptability scoring model; finally, optimization matching is performed with the goal of maximizing the overall adaptability, a dynamic vehicle model-station production scheduling scheme is generated, and the scheme is updated according to the real-time state in the production process. The scheme matches and optimizes by constructing the adaptability scoring model of the two, so that the production scheduling scheme can dynamically adapt to the actual capacity of the equipment and the process requirements, thereby improving the rationality of equipment utilization and the stability of the production rhythm; in combination with the rolling update mechanism, the adaptability of the production scheduling system to changes is enhanced, and the optimization level of production efficiency and resource allocation is improved.

Description

Technical Field

[0001] This invention relates to the field of production line automation technology, and in particular to a production scheduling optimization method and apparatus that integrates equipment health. Background Technology

[0002] With increasingly fierce market competition, the manufacturing industry faces an urgent need to transform and upgrade towards intelligent and flexible operations. The core element in achieving this goal lies in optimizing the production scheduling system. Efficient and accurate production scheduling is the cornerstone for ensuring production efficiency, reducing production costs, and responding quickly to the market.

[0003] In the automotive manufacturing industry, the welding production line, as a crucial link in the body-in-white forming process, presents particularly complex and critical challenges in production scheduling optimization. Welding production lines typically consist of various automated equipment (such as welding robots, welding torches, and fixtures) and are responsible for welding different vehicle models. Due to design differences, each vehicle model exhibits significantly different welding process characteristics (such as the number of weld points, welding path complexity, and sheet metal thickness), resulting in varying technical requirements and load capacities for the equipment. Simultaneously, under long-term high-load operation, the performance of the production line equipment inevitably undergoes dynamic degradation, manifesting as decreased accuracy, increased failure rates, and other health deterioration phenomena.

[0004] Therefore, how to build a method that can integrate equipment health and vehicle process complexity in real time and optimize production scheduling dynamically and accurately has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0005] The main objective of this invention is to provide a production scheduling optimization method and apparatus that integrates equipment health, aiming to solve the technical problem in the prior art of how to construct a method that can integrate equipment health and vehicle process complexity in real time and perform dynamic and accurate production scheduling optimization accordingly.

[0006] To achieve the above objectives, the present invention provides a production scheduling optimization method that integrates equipment health, the method comprising the following steps:

[0007] The health status of each piece of equipment is obtained based on the real-time collected operating parameters of the welding production line equipment. Based on the welding process characteristics of the models to be produced, the process complexity of each model to be produced is obtained. Based on the equipment health and process complexity, a compatibility score is obtained for each piece of equipment and each model to be produced, using a compatibility scoring model. Based on the compatibility score, the matching is optimized with the goal of maximizing the overall compatibility, resulting in a vehicle model-workstation production scheduling plan.

[0008] Optionally, the step of obtaining the equipment health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment includes: Based on the equipment operating parameters, calculate the deviation rate between the real-time values ​​and standard values ​​of the equipment for preset key performance indicators; A comprehensive evaluation value is obtained by weighting and fusing the deviation rate with the number of historical equipment failures. Based on the preset health grading threshold, the comprehensive evaluation value is mapped to the corresponding health status level; The device health level is obtained based on the health status level.

[0009] Optionally, the step of obtaining the process complexity of each vehicle model to be produced based on its welding process characteristics includes: Based on the production task of the vehicle model to be produced, key process indicators are obtained. The key process indicators include at least the number of weld points, average process time, and material thickness factor. Based on the influence weights of the key process indicators, the values ​​of the key process indicators are weighted and summed to obtain the complexity index of the model to be produced. Based on a preset complexity grading range, the complexity index is mapped to a corresponding complexity level, and the complexity level is used as the process complexity of the model to be produced.

[0010] Optionally, the step of obtaining a one-to-one compatibility score between each piece of equipment and each model to be produced, based on the equipment health and the process complexity, using a compatibility scoring model, includes: Based on the real-time health status of the device, determine the current health level of the device; Based on the process complexity level of the vehicle model to be produced and the preset complexity level correspondence rules, determine the cycle time requirement level of the vehicle model; Based on the task cycle range allowed by the health level of the device and the cycle requirement level of the vehicle model, an initial matching score is obtained by matching through the adaptability scoring model; Based on the influence weights of health and process complexity, a weighted fusion calculation is performed on the initial matching score to obtain the compatibility score between the device and the vehicle model.

[0011] Optionally, the step of performing a weighted fusion calculation on the initial matching score based on the influence weight of health and the influence weight of process complexity to obtain the suitability score includes: Obtain the dynamic adjustment coefficient of the influence weight of the device's health status, wherein the dynamic adjustment coefficient is determined based on the trend slope of the device's current health status level and historical health status changes; Obtain the vehicle model priority factor that influences the weight of the process complexity, wherein the vehicle model priority factor is determined based on order urgency and configuration change flag; The weighted fusion result is processed by linear normalization using a weighted fusion algorithm to obtain a standardized fit score. Based on the scoring correction rule set corresponding to the device type, a typological correction calculation is performed on the standardized fit score to obtain the fit score.

[0012] Optionally, the step of optimizing the matching based on the adaptability score with the goal of maximizing the total adaptability to obtain a vehicle model-workstation production scheduling plan includes: Based on the compatibility score between each device and each model to be produced, a many-to-many matching relationship modeling operation is performed to obtain an initial set of matching relationships. Based on the preset optimization objective of maximizing the overall compatibility, a global optimization calculation operation is performed on the initial matching relationship set to obtain the optimized device-vehicle model correspondence. Based on the optimized equipment-vehicle model correspondence, a workstation-level task mapping operation is performed to obtain the workstation production scheduling and allocation relationship; Based on the workstation production allocation relationship, a production scheduling scheme is constructed to obtain the vehicle model-workstation production scheduling scheme.

[0013] Optionally, the production scheduling optimization method for the health status of the integrated equipment further includes: Based on the preset rolling update cycle trigger conditions, the real-time operating status data of the equipment is collected periodically; Based on the real-time operating status data, the order insertion command, device standby signal and alarm information are obtained; Based on the order insertion command, equipment standby signal and alarm information, perform deviation analysis and calculation on the current production cycle to obtain the average deviation of the actual cycle. When the average deviation exceeds the preset cycle deviation threshold, a workstation-level cycle allocation instruction is generated. Based on the cycle time allocation instruction, a dynamically updated vehicle model-workstation production scheduling scheme is obtained.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a production scheduling optimization device that integrates equipment health, the production scheduling optimization device integrating equipment health comprising: The health assessment module is used to obtain the health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment. The complexity quantification module is used to obtain the process complexity of each model to be produced based on the welding process characteristics of the models to be produced. The compatibility calculation module is used to obtain the compatibility score of each device and each model to be produced one by one based on the equipment health and the process complexity through the compatibility scoring model. The production scheduling optimization module is used to optimize the matching based on the adaptability score, with the goal of maximizing the total adaptability, to obtain the vehicle model-workstation production scheduling plan.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a production scheduling optimization device that integrates equipment health, the production scheduling optimization device integrating equipment health includes: a memory, a processor, and a production scheduling optimization program integrating equipment health stored in the memory and executable on the processor, the production scheduling optimization program integrating equipment health is configured to implement the steps of the production scheduling optimization method integrating equipment health as described above.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a production scheduling optimization program for the health status of fusion equipment, wherein when the production scheduling optimization program for the health status of fusion equipment is executed by a processor, the steps of the production scheduling optimization method for the health status of fusion equipment as described above are implemented.

[0017] The proposed technical solutions in this application have at least the following technical effects: This method dynamically evaluates and classifies the health of key equipment in the welding production line by collecting historical data and current status of operating parameters; at the same time, it comprehensively analyzes the welding process characteristics of the models to be produced and quantifies their process complexity level; based on this, it constructs a matching score model between equipment health and model complexity, and performs global optimization matching based on the score results to generate a dynamically adjustable model-workstation allocation scheme, which can be continuously updated based on real-time production data during operation. Because this solution comprehensively considers the continuously changing characteristics of equipment operating status and the differentiated requirements of different vehicle models for equipment capabilities, it establishes a two-way mapping and adaptation evaluation mechanism between equipment health and process complexity. This enables production scheduling decisions to more accurately reflect the matching relationship between actual production capacity and production tasks, thereby effectively avoiding quality and efficiency risks caused by equipment operating with defects or insufficient capacity during the planning stage. At the same time, the dynamic optimization and rolling update mechanism adopted by the solution enhances the system's ability to cope with uncertainties on the production site, helping to achieve a smooth overall production rhythm and optimized resource allocation while ensuring stable equipment operation, ultimately improving the overall operational efficiency and intelligence level of the production line. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the production scheduling optimization method integrating equipment health of the present invention; Figure 2 This is a flowchart illustrating the second embodiment of the production scheduling optimization method integrating equipment health of the present invention; Figure 3 This is a flowchart illustrating the third embodiment of the production scheduling optimization method integrating equipment health of the present invention; Figure 4 This is a structural block diagram of the first embodiment of the production scheduling optimization device that integrates equipment health status according to the present invention; Figure 5 This is a schematic diagram of the structure of the production optimization equipment for the integrated equipment health of the hardware operating environment involved in the embodiments of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods. The main solution of this application embodiment is as follows: based on the real-time collected operating parameters of the welding production line equipment, the equipment health of each piece of equipment is obtained; based on the welding process characteristics of the models to be produced, the process complexity of each model to be produced is obtained; based on the equipment health and the process complexity, a matching score is obtained for each piece of equipment and each model to be produced through a matching score model; based on the matching score, optimization matching is performed with the goal of maximizing the total matching score to obtain the model-workstation scheduling scheme.

[0024] It should be noted that the executing entity of this invention can be a scheduling optimization device that integrates equipment health, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a thermal management device that integrates equipment health and optimizes production, capable of achieving the above functions. This embodiment does not specifically limit this. The following uses a scheduling optimization device that integrates equipment health as the executing entity as an example to describe this embodiment and the following embodiments.

[0025] Based on this, embodiments of this application provide a production scheduling optimization method that integrates equipment health, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the production scheduling optimization method that integrates equipment health in this application.

[0026] In this embodiment, the production scheduling optimization method that integrates equipment health includes steps S10 to S40: Step S10: Obtain the equipment health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment.

[0027] It should be noted that the equipment operating parameters in this step refer to a series of quantifiable data that objectively reflect the current working status and performance degradation of key physical equipment in the welding production line, such as welding robots, servo welding guns, and fixtures. These parameters are typically derived from equipment controllers, sensors, and maintenance logs, and may include, but are not limited to, continuous trouble-free operation time, historical maintenance records, number of actions of core components, real-time current and temperature fluctuations of motors, and positioning accuracy deviations. These dynamic and static data together constitute the original basis for assessing the health status of the equipment.

[0028] Understandably, equipment health is a comprehensive quantitative indicator. It doesn't simply refer to the binary state of equipment being "intact" or "faulty," but rather to the equipment's ability to continuously, stably, and accurately complete specific welding processes relative to its brand-new state. For example, if a robot has slight wear on its mechanical structure, resulting in a slight decrease in repeatability, even if it doesn't trigger a shutdown alarm, its health is no longer optimal. If assigned to a vehicle model requiring extremely high welding precision, this could lead to quality risks.

[0029] It should be understood that obtaining health status based on operating parameters is a model-based processing procedure that transforms multi-source heterogeneous data into a unified and comparable evaluation result. This embodiment employs rule-based empirical models, statistical analysis models based on historical data, or more advanced machine learning prediction models. Its core lies in using preset or trained algorithms or rule sets to weightedly fuse and normalize various parameters, ultimately outputting a standardized health status score or level that is easily used in production scheduling logic. For example, the health status score can be set from zero to one, with a higher score representing a better equipment condition and a stronger ability to handle highly complex production tasks.

[0030] In one embodiment, obtaining the equipment health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment includes: calculating the deviation rate between the real-time value and the standard value of the equipment in a preset key performance indicator based on the equipment operating parameters; weighting and fusing the deviation rate with the number of historical equipment failures to obtain a comprehensive evaluation value; mapping the comprehensive evaluation value to a corresponding health status level based on a preset health status grading threshold; and obtaining the equipment health status of the equipment based on the health status level.

[0031] Step S20: Based on the welding process characteristics of the models to be produced, obtain the process complexity of each model to be produced.

[0032] It should be noted that the welding process characteristics in this step refer to a set of key attributes extracted from vehicle product data that can affect the difficulty and resource consumption level of welding operations. Specifically, these include the total number of weld points involved in the body-in-white welding process of this vehicle model, the total length of welds, the number of different types of welding guns or fixtures required, the accessibility level of the welding operation, and the complexity of the sheet metal thickness and material combination. These characteristics collectively define the technical requirements and workload required to complete the welding task of this vehicle model.

[0033] Understandably, process complexity is used to quantify and compare the varying requirements of different vehicle models for welding production line equipment capabilities, operation time, and technical precision. This complexity specifically refers to the manufacturing difficulty presented in a particular production stage. For example, a vehicle model with extremely densely distributed weld points and welding operations in multiple narrow spaces will obviously have a higher process complexity compared to a vehicle model with evenly distributed weld points and a spacious operating area.

[0034] It should be understood that obtaining process complexity involves aggregating the aforementioned multi-dimensional process characteristics into a single indicator or graded result that can be used for production scheduling decisions, using a set of established evaluation criteria or quantitative models. This process may involve assigning weights to each characteristic and performing weighted calculations, ultimately classifying vehicle models into different complexity levels such as high, medium, and low, thereby providing a clear and actionable basis for subsequent matching with equipment health.

[0035] In one embodiment, obtaining the process complexity of each vehicle model to be produced based on its welding process characteristics includes: obtaining key process indicators based on the production tasks of the vehicle models to be produced, wherein the key process indicators include at least the number of weld points, average process time, and material thickness factor; weighting and summing the values ​​of the key process indicators according to their influence weights to obtain a complexity index of the vehicle models to be produced; mapping the complexity index to a corresponding complexity level according to a preset complexity grading interval, and using the complexity level as the process complexity of the vehicle models to be produced.

[0036] Step S30: Based on the equipment health and the process complexity, obtain the compatibility score of each piece of equipment and each model to be produced through the compatibility scoring model.

[0037] It should be noted that the fit score model is a pre-defined computational logic or rule engine. Its core function is to construct a quantitative matching relationship between equipment capabilities and production task requirements. In practice, any two indicators—equipment health representing the equipment status and process complexity representing the task requirements—are used as input parameters. Through the mapping and calculation rules defined in the model, a numerical result that can objectively measure the expected performance of the robot at that workstation when executing the production task is output, namely the fit score.

[0038] Understandably, the suitability score aims to assess the appropriateness of a specific piece of equipment for undertaking welding tasks for a specific vehicle model. A high suitability score means that the equipment's current health condition is sufficient to meet the process complexity requirements of that vehicle model, and stable production with high quality and efficiency is expected. Conversely, a low score indicates a risk of mismatch; for example, assigning a vehicle model with high process complexity to equipment with poor health may lead to quality problems or equipment overload.

[0039] It should be understood that calculating the fit score is a correlation analysis aimed at achieving global optimization. The model calculates for each possible pair of equipment and vehicle models, generating a global score matrix. This matrix reveals the inherent potential and risks of different matching schemes, providing a crucial data foundation for subsequent production scheduling optimization.

[0040] Step S40: Based on the adaptability score, optimize the matching with the goal of maximizing the total adaptability to obtain the vehicle model-workstation production scheduling plan.

[0041] It should be noted that optimal matching refers to a process of systematically finding the best combination of solutions under given constraints. Its input "fitness scores" form a matrix reflecting all potential matching relationships, and the optimization objective is to maximize the sum of the scores of all determined matches. This process is often constrained by resources in actual production, such as specific car models only being produced at compatible workstations, a workstation only being able to process one car model at a time, and the need to meet production plan requirements for the number of each car model.

[0042] Understandably, the objective function of "maximizing overall fit" aims to improve the overall operational efficiency and robustness of the welding production line. In practice, this requires a holistic consideration of the combination of all equipment and all vehicle models to strive for the optimal overall system state. For example, sometimes assigning a high-healthy piece of equipment to a vehicle model of moderate complexity, rather than forcing it to match the most complex model, can free up resources for other critical tasks, thereby achieving a more stable and lower-risk globally optimal solution throughout the entire production cycle.

[0043] It should be understood that the final "vehicle model-workstation production scheduling plan" is the output of this optimization calculation. It clarifies which welding station or which piece of equipment should be responsible for each vehicle model to be produced in the upcoming production cycle. This plan is an executable production instruction derived after comprehensively considering equipment status and process requirements. Its core value lies in effectively reducing the risk of quality fluctuations, efficiency losses, or abnormal equipment downtime caused by mismatch between equipment capabilities and task requirements through scientific decision-making methods.

[0044] In one embodiment, the production scheduling optimization method based on the integrated equipment health status further includes: periodically collecting real-time operating status data of the equipment according to a preset rolling update cycle trigger condition; obtaining order insertion instructions, equipment standby signals, and alarm information based on the real-time operating status data; performing deviation analysis calculation on the current production cycle based on the order insertion instructions, equipment standby signals, and alarm information to obtain the average deviation of the actual cycle; generating a workstation-level cycle allocation instruction when the average deviation exceeds a preset cycle deviation threshold; and obtaining a dynamically updated vehicle model-workstation production scheduling scheme based on the cycle allocation instruction.

[0045] It should be noted that this embodiment introduces a dynamic update mechanism. The system automatically collects the latest operating status data of the equipment according to a preset rolling update cycle, such as at fixed time intervals or when specific events occur. Based on this, it can analyze various dynamic events such as new production order insertion instructions, equipment entering standby mode due to material shortages, and alarm information caused by equipment failures. Then, by comparing the difference between the current production rhythm and the original planned rhythm, an index representing the average deviation of the actual rhythm is calculated. This process can aggregate scattered abnormal events into an objective benchmark for measuring the overall stability of the production line, thereby determining whether the current production scheduling plan is still applicable.

[0046] It should be understood that when the average deviation exceeds a preset reasonable threshold, it indicates that the original production scheduling plan has become seriously out of sync with the actual situation. The system will then automatically generate workstation-level cycle time allocation instructions. Based on the latest equipment health status and production task list, the system will re-run the adaptability scoring and optimization matching process, ultimately outputting a dynamically updated vehicle model-workstation production scheduling plan. This mechanism ensures that the production schedule can not only be optimized "statically" but also adapted "dynamically," always maintaining the production line in an efficient manner at its optimal or near-optimal state.

[0047] In this embodiment, a quantitative adaptation relationship is constructed between the health status of the welding production line equipment and the process complexity of the vehicle model to be produced by real-time assessment. Based on this, an optimized matching is performed to generate an initial production scheduling plan. Furthermore, the method also designs a dynamic update mechanism, which monitors abnormal events and cycle time deviations during the production process and triggers a re-optimization of the production scheduling plan when necessary, thereby ensuring that the production plan can adapt to dynamic changes on site.

[0048] In summary, this technical solution incorporates equipment health, a key intrinsic factor, into production scheduling decisions, making capacity allocation more aligned with actual equipment capabilities. This helps reduce the risk of quality defects and production interruptions caused by operating faulty equipment or mismatched capabilities. Simultaneously, by establishing a suitability scoring model and performing global optimization, it promotes the scientific allocation of production line resources across different vehicle models, thereby improving overall production efficiency and equipment utilization. Furthermore, the introduced dynamic response mechanism gives the scheduling system resilience to handle unforeseen circumstances, enabling timely adjustments to plans to mitigate disturbances, ultimately achieving a synergistic improvement in the stability and flexibility of welding production.

[0049] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 In the production scheduling optimization method that integrates equipment health, step S30 includes steps S301 to S304: Step S301: Determine the current health level of the device based on the real-time health status of the device.

[0050] It should be noted that real-time equipment health status refers to dynamic data reflecting the equipment's performance degradation, potential failure risks, and overall stability at the current moment or in the near future, obtained through comprehensive evaluation by sensor monitoring, equipment operation log analysis, and predictive maintenance models. Transforming this continuous, multi-dimensional status data into discrete health levels simplifies subsequent matching logic, making the correspondence between equipment capabilities and task requirements clearer and easier to operate. Level classification is typically based on preset thresholds; for example, equipment status can be divided into a finite number of levels such as Excellent, Good, Average, and Poor.

[0051] Step S302: Determine the cycle time requirement level of the vehicle model to be produced based on the process complexity level of the vehicle model to be produced and the preset complexity level correspondence rules.

[0052] It should be noted that the process complexity level of the vehicle to be produced refers to the classification result obtained after a preliminary quantitative assessment of various technical parameters such as the number of welding points in the body design, the characteristics of the materials used, and assembly precision requirements. The complexity level correspondence rule is a clear mapping table or transformation logic, whose function is to transform the abstract process complexity level into a specific, operable cycle time requirement level. The cycle time requirement level is directly related to the standard time range allowed for the production line to complete the welding operation of a single vehicle, and is a key indicator for measuring production efficiency and resource requirements.

[0053] Understandably, the core task of this step is to translate the design and manufacturing characteristics of the vehicle model into direct time constraints on the production execution system. For example, a vehicle model with a complex body structure and extensive use of high-strength steel and aluminum hybrid connection technology has a high level of manufacturing complexity. According to preset rules, this level might be mapped to a "tense" cycle time requirement, meaning the production line needs to operate at a high speed to meet the delivery cycle. Conversely, for conventional vehicles with simple structures and standard welding points, the complexity level is lower, and the corresponding cycle time requirement might be "standard" or "relaxed."

[0054] It should be understood that the takt time requirement level is a crucial element in achieving refined production scheduling. It ensures that the production scheduling plan not only matches the vehicle's manufacturing process in terms of equipment capacity but also meets the efficiency targets of the production plan in terms of time. By uniformly converting complexity levels into takt time requirement levels, the system can incorporate the differentiated production efficiency needs of different vehicle models into the optimization model, thereby formulating a scientific production scheduling plan that guarantees both quality and efficiency.

[0055] Step S303: Based on the task cycle range allowed by the health level of the device and the cycle requirement level of the vehicle model, match them using the adaptability scoring model to obtain an initial matching score.

[0056] It should be noted that the permissible task cycle range of the equipment's health level refers to the range of production efficiency that the equipment can maintain stably based on a scientific assessment of its current operating status. For example, equipment with an excellent health level may be allowed to perform all cycle tasks from fast to standard, while equipment with a level of only qualified may only be recommended to undertake standard or more relaxed tasks to avoid overload operation that could lead to failure.

[0057] Understandably, the fit scoring model acts as a function to quantify the degree of matching. This model receives the device's allowed clock speed range and the clock speed requirement level of the vehicle model as input, and outputs a score through built-in matching logic. For example, when a device that allows fast clock speeds is matched with a vehicle model that requires fast clock speeds, the model will give a high score; if it is matched with a vehicle model that only requires standard clock speeds, the score may be moderate due to performance redundancy; and if an attempt is made to force a device that only allows standard clock speeds to match a vehicle model with fast clock speeds, the model will give a low score or even a negative score to indicate the risk of mismatch.

[0058] It should be understood that the initial matching score generated in this step is used to quantify the matching degree between a single piece of equipment and a single vehicle model in terms of cycle time capability requirements. This score is used to construct a comprehensive scoring matrix for the next step of global optimization. This matrix covers all possible combinations of equipment and vehicle models and serves as the data foundation for the system to find the optimal production scheduling solution.

[0059] Step S304: Based on the influence weight of health and the influence weight of process complexity, perform a weighted fusion calculation on the initial matching score to obtain the compatibility score between the equipment and the vehicle model.

[0060] It should be noted that the weights of health and process complexity reflect the different emphases placed on equipment operational stability and product quality process requirements in the current production scheduling strategy, respectively. The setting of these two weight values ​​should be based on actual production experience and optimization goals. For example, in production cycles where extremely high product quality is pursued, the weight of process complexity will be set higher; while during critical equipment maintenance periods, the weight of health may be appropriately increased to ensure the long-term stability of the production line.

[0061] Understandably, performing a weighted fusion calculation on the initial matching score is a multi-factor decision-making process. The initial matching score only reflects the degree of matching between equipment production capacity and vehicle cycle time requirements, while the weighted fusion further introduces the bias of production scheduling strategies. The calculation logic integrates the initial scores according to the proportions of the two weights, thereby obtaining a more comprehensive and instructive final fit score. This score comprehensively considers two dimensions: production efficiency matching and technical risk control.

[0062] In one embodiment, the step of performing a weighted fusion calculation on the initial matching score based on the health influence weight and the process complexity influence weight to obtain the suitability score includes: obtaining a dynamic adjustment coefficient for the health influence weight of the device, wherein the dynamic adjustment coefficient is determined based on the trend slope of the current health level and historical health changes of the device; obtaining a vehicle model priority factor for the process complexity influence weight, wherein the vehicle model priority factor is determined based on order urgency and configuration change flags; performing a linear normalization operation on the weighted fusion result through a weighted fusion algorithm to obtain a standardized suitability score; and performing a typological correction calculation on the standardized suitability score according to the scoring correction rule set corresponding to the device type to obtain the suitability score.

[0063] It should be noted that the dynamic adjustment coefficient aims to make the weight values ​​no longer fixed, but rather responsive to changes in the real-time status of the device. For example, when historical data indicates that a device's health is declining rapidly and continuously, even if the current level is acceptable, the trend slope will trigger the adjustment coefficient to increase the health weight, in order to mitigate potential risks in advance.

[0064] Understandably, the introduction of the vehicle model priority factor integrates external demands at the production planning level into the technology matching calculation. Order urgency determines the priority of a vehicle model, while configuration change flags indicate potential additional process challenges due to design tweaks. For example, for an expedited order involving a new configuration, the priority factor will have a higher weighting for process complexity to ensure high-quality completion. The linear normalization operation of the weighted fusion results aims to standardize the scores of all equipment within a comparable range of values.

[0065] It should be understood that the scoring correction rules are used to quantify respect for the differentiated requirements of different types of equipment. For example, the rules may be stricter for precision robotic welding stations, where even slight mismatches can result in significant deductions; while for more versatile workstations, the rules are relatively lenient. This typological correction ensures that the fit score not only reflects general matching logic but also accurately matches the technical characteristics of specific equipment and its actual role in the production process.

[0066] This embodiment quantifies the dynamic matching relationship between equipment health and vehicle model process complexity, and introduces weight adjustment and global optimization to construct a vehicle model-workstation scheduling scheme that comprehensively considers production efficiency, equipment stability, and process quality. The scheme sequentially completes the classification of equipment and vehicle models, initial matching score, multi-factor weighted fusion, and global optimization matching, ultimately generating executable and refined scheduling instructions.

[0067] In summary, this embodiment dynamically and quantitatively matches the real-time health status of equipment with the process requirements of vehicle models, thereby proactively mitigating the risk of equipment overload and ensuring the process quality of highly complex vehicle models. By introducing weighting factors and global optimization, production scheduling decisions not only meet cycle time requirements but also flexibly adapt to changes in production strategy emphasis and order urgency. Therefore, this method significantly improves the scientific rigor and robustness of production scheduling schemes, effectively increases the overall utilization rate of the production line and product qualification rate, and enhances the production system's ability to cope with equipment status fluctuations and order changes.

[0068] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 In the production scheduling optimization method for the health of the integrated equipment, step S40 includes steps S401 to S404: Step S401: Based on the compatibility score between each device and each model to be produced, perform a many-to-many matching relationship modeling operation to obtain an initial matching relationship set.

[0069] It should be noted that many-to-many matching relationship modeling refers to constructing a mathematical model that describes all possible pairings between each available piece of equipment on the production line and each vehicle model to be produced, along with their corresponding fit scores. This step aims to transform the real-world resource allocation problem into a computable optimization problem framework.

[0070] Understandably, the initial set of matching relationships is the direct output of this modeling operation, containing all theoretically feasible equipment and vehicle model pairings. This set may be very large because it does not yet consider the actual capacity constraints of the production line and the production plan's requirements for the number of vehicle models. Its purpose is to provide a comprehensive and complete search space for subsequent global optimization.

[0071] It should be understood that this step is a crucial bridge connecting the micro-level individual fit assessment with the macro-level overall production scheduling planning. It systematically integrates the matching assessment results of individual equipment and individual vehicle models from the previous steps into a global, structured data model, laying a solid foundation for finding the optimal production line resource allocation scheme.

[0072] Step S402: Based on the preset optimization objective of maximizing the overall fit, perform a global optimization calculation operation on the initial matching relationship set to obtain the optimized device-vehicle correspondence.

[0073] It should be noted that the preset optimization goal of maximizing the overall compatibility score refers to the ultimate indicator pursued by the production scheduling system. This means that the final equipment and vehicle model pairing scheme should maximize the sum of compatibility scores for all pairings. This ensures that production line resources are utilized most effectively overall.

[0074] Understandably, global optimization computation is a complex decision-making process that requires the application of specific optimization algorithms, such as constrained programming or integer programming, to select the optimal pairing from a massive number of initial matching relationships, under practical constraints such as each vehicle model must be produced and each piece of equipment has limited capacity. This process aims to solve the problem of optimal allocation between equipment resources and production tasks.

[0075] It should be understood that the optimized equipment-vehicle model correspondence is the core outcome of this step. It is no longer all possible combinations, but rather a specific and feasible allocation scheme that achieves optimal overall system efficiency under numerous practical constraints. This correspondence clarifies the specific vehicle model that each particular piece of equipment is responsible for producing within this production cycle.

[0076] Step S403: Based on the optimized equipment-vehicle model correspondence, perform a workstation-level task mapping operation to obtain the workstation production scheduling and allocation relationship.

[0077] It should be noted that the workstation-level task mapping operation refers to further specifying the abstract "equipment-vehicle model" correspondence to each physical workstation on the production line. Because a piece of equipment may serve one or more workstations, and a workstation may contain multiple pieces of equipment, this operation aims to accurately assign the production tasks of vehicle models to each smallest executable work unit.

[0078] Understandably, the resulting workstation scheduling and allocation relationship is more granular than the equipment-vehicle model correspondence. It clearly defines which specific workstation, which specific equipment, and which process for which vehicle model is being performed. For example, if the optimization result determines that "robot welding equipment A" produces "vehicle model X", then after mapping, it is clearly defined as "robot welding equipment A at workstation 3" being responsible for "the welding process of the left door of vehicle model X".

[0079] It should be understood that this step is crucial for transforming the production scheduling plan from the decision-making level to the executable level. It ensures that the macro-level resource allocation plan derived from optimized calculations can be decomposed and implemented precisely and without loss to the actual operational units on the production floor, thus preparing for the generation of final production scheduling instructions that can guide production.

[0080] Step S404: Based on the workstation production allocation relationship, perform a production scheduling scheme construction operation to obtain the vehicle model-workstation production scheduling scheme.

[0081] It should be noted that the production scheduling scheme construction operation is an information integration and formatting process. It combines the workstation scheduling allocation relationship with information such as production cycle time and time sequence to construct a complete production plan with time attributes.

[0082] Understandably, the final vehicle model-workstation production scheduling plan is a detailed execution guide. It not only includes when each workstation will begin producing which vehicle model, but may also include the production sequence, expected duration, and the logical connections between workstations. This plan serves as the basis for final instructions that can be directly issued to the manufacturing execution system.

[0083] It should be understood that this step marks the completion of the entire intelligent scheduling process. The generated vehicle model-workstation scheduling plan is the culmination of all the aforementioned analysis, matching, and optimization work. It comprehensively considers multiple factors such as equipment status, process requirements, and production targets, aiming to ensure efficient, stable, and high-quality production processes.

[0084] In this embodiment, an initial matching relationship set between equipment and vehicle models is constructed, and global optimization is performed based on the goal of maximizing overall adaptability. The optimization results are then mapped to specific workstations, ultimately generating an accurate vehicle model-workstation production scheduling plan. This solution realizes a systematic construction process from theoretical matching to an executable plan.

[0085] In summary, this embodiment ensures the integrity of the optimization foundation by systematically modeling all possible equipment-vehicle combinations. Then, by solving the optimization problem under constraints, it achieves globally efficient allocation of production line resources. Finally, the macro-level optimization results are refined layer by layer to the workstation-level tasks, ensuring the executability and accuracy of the production scheduling plan. Therefore, this method can effectively improve the overall rationality of resource allocation, ensure a better balance between production efficiency and resource utilization in the production plan, and enhance the guidance accuracy and operability of production scheduling instructions for actual operations on the production floor.

[0086] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the production scheduling optimization method for integrating equipment health in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0087] This application also provides a production scheduling optimization device that integrates equipment health information; please refer to [reference needed]. Figure 4 The production scheduling optimization device for the health status of the integrated equipment includes: The health assessment module 10 is used to obtain the health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment. Complexity quantification module 20 is used to obtain the process complexity of each model to be produced based on the welding process characteristics of the models to be produced. The compatibility calculation module 30 is used to obtain the compatibility score of each device and each model to be produced one by one based on the equipment health and the process complexity through the compatibility scoring model. The production scheduling optimization module 40 is used to optimize the matching based on the adaptability score, with the goal of maximizing the total adaptability, to obtain a vehicle model-workstation production scheduling plan.

[0088] The production scheduling optimization device for integrating equipment health provided in this application, employing the production scheduling optimization method for integrating equipment health in the above embodiments, can solve the technical problem of how to construct a method that can integrate equipment health and vehicle process complexity in real time and perform dynamic and accurate production scheduling optimization accordingly. Compared with the prior art, the beneficial effects of the production scheduling optimization device for integrating equipment health provided in this application are the same as those of the production scheduling optimization method for integrating equipment health provided in the above embodiments, and other technical features in the production scheduling optimization device for integrating equipment health are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0089] This application provides a production scheduling optimization device for integrating equipment health, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the production scheduling optimization method for integrating equipment health in the above embodiment 1.

[0090] The following is for reference. Figure 5 This document illustrates a structural diagram of a scheduling optimization device suitable for implementing the health status of converged devices in the embodiments of this application. The scheduling optimization device for the health status of converged devices in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The production scheduling optimization equipment shown for the health of the fusion equipment is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0091] like Figure 5As shown, the production scheduling optimization equipment for integrating equipment health may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the production scheduling optimization equipment for integrating equipment health. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the scheduling optimization equipment for fusion device health to exchange data wirelessly or via wired communication with other devices. Although the figure shows a scheduling optimization equipment for fusion device health with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0092] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0093] The production scheduling optimization equipment based on integrated equipment health provided in this application, employing the production scheduling optimization method based on integrated equipment health in the above embodiments, can solve the technical problem of how to construct a method that can dynamically and accurately optimize production scheduling by integrating equipment health and vehicle process complexity in real time. Compared with the prior art, the beneficial effects of the production scheduling optimization equipment based on integrated equipment health provided in this application are the same as those of the production scheduling optimization method based on integrated equipment health provided in the above embodiments, and other technical features in this production scheduling optimization equipment based on integrated equipment health are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0094] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0096] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the production scheduling optimization method for the health of fusion equipment in the above embodiments.

[0097] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0098] The aforementioned computer-readable storage medium may be included in the production optimization equipment for fusion equipment health; or it may exist independently and not be assembled into the production optimization equipment for fusion equipment health.

[0099] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the production scheduling optimization equipment that integrates equipment health, the equipment scheduling optimization equipment performs the following: Based on real-time collected operating parameters of the welding production line equipment, it obtains the equipment health of each piece of equipment; based on the welding process characteristics of the models to be produced, it obtains the process complexity of each model; based on the equipment health and the process complexity, it obtains a one-to-one matching score between each piece of equipment and each model to be produced through a matching score model; based on the matching score, it optimizes the matching with the goal of maximizing the total matching score, and obtains a model-workstation production scheduling scheme.

[0100] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0103] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described production scheduling optimization method that integrates equipment health. This solves the technical problem of how to construct a method that can dynamically and accurately optimize production scheduling by integrating equipment health and vehicle model process complexity in real time. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the production scheduling optimization method that integrates equipment health provided in the above embodiments, and will not be repeated here.

[0104] The computer program product provided in this application can solve the technical problem of production scheduling optimization for the health status of converged equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the production scheduling optimization method for the health status of converged equipment provided in the above embodiments, and will not be repeated here.

[0105] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for production scheduling optimization with fusion of equipment health, characterized in that, The method includes: The health status of each piece of equipment is obtained based on the real-time collected operating parameters of the welding production line equipment. Based on the welding process characteristics of the models to be produced, the process complexity of each model to be produced is obtained. Based on the equipment health and process complexity, a compatibility score is obtained for each piece of equipment and each model to be produced, using a compatibility scoring model. Based on the compatibility score, the matching is optimized with the goal of maximizing the overall compatibility, resulting in a vehicle model-workstation production scheduling plan.

2. The method of claim 1, wherein, The health status of each piece of equipment is obtained based on the real-time collected operating parameters of the welding production line equipment, including: Based on the equipment operating parameters, calculate the deviation rate between the real-time values ​​and standard values ​​of the equipment for preset key performance indicators; A comprehensive evaluation value is obtained by weighting and fusing the deviation rate with the number of historical equipment failures. Based on the preset health grading threshold, the comprehensive evaluation value is mapped to the corresponding health status level; The device health level is obtained based on the health status level.

3. The method of claim 1, wherein, The process complexity of each vehicle model to be produced is obtained based on its welding process characteristics, including: Based on the production task of the vehicle model to be produced, key process indicators are obtained. The key process indicators include at least the number of weld points, average process time, and material thickness factor. Based on the influence weights of the key process indicators, the values ​​of the key process indicators are weighted and summed to obtain the complexity index of the model to be produced. Based on a preset complexity grading range, the complexity index is mapped to a corresponding complexity level, and the complexity level is used as the process complexity of the model to be produced.

4. The production scheduling optimization method for integrating equipment health according to claim 1, characterized in that, The process involves using a compatibility scoring model, based on the equipment health and process complexity, to obtain a one-to-one compatibility score between each piece of equipment and each vehicle model to be produced. This includes: Based on the real-time health status of the device, determine the current health level of the device; Based on the process complexity level of the vehicle model to be produced and the preset complexity level correspondence rules, determine the cycle time requirement level of the vehicle model; Based on the task cycle range allowed by the health level of the device and the cycle requirement level of the vehicle model, an initial matching score is obtained by matching through the adaptability scoring model; Based on the influence weights of health and process complexity, a weighted fusion calculation is performed on the initial matching score to obtain the compatibility score between the device and the vehicle model.

5. The production scheduling optimization method for integrating equipment health according to claim 4, characterized in that, The initial matching score is weighted and fused based on the influence weights of health and process complexity to obtain the suitability score, including: Obtain the dynamic adjustment coefficient of the influence weight of the device's health status, wherein the dynamic adjustment coefficient is determined based on the trend slope of the device's current health status level and historical health status changes; Obtain the vehicle model priority factor that influences the weight of the process complexity, wherein the vehicle model priority factor is determined based on order urgency and configuration change flag; The weighted fusion result is processed by linear normalization using a weighted fusion algorithm to obtain a standardized fit score. Based on the scoring correction rule set corresponding to the device type, a typological correction calculation is performed on the standardized fit score to obtain the fit score.

6. The production scheduling optimization method for integrating equipment health according to claim 1, characterized in that, The process of optimizing the matching based on the compatibility score, with the goal of maximizing the overall compatibility, to obtain a vehicle model-workstation production scheduling plan includes: Based on the compatibility score between each device and each model to be produced, a many-to-many matching relationship modeling operation is performed to obtain an initial set of matching relationships. Based on the preset optimization objective of maximizing the overall compatibility, a global optimization calculation operation is performed on the initial matching relationship set to obtain the optimized device-vehicle model correspondence. Based on the optimized equipment-vehicle model correspondence, a workstation-level task mapping operation is performed to obtain the workstation production scheduling and allocation relationship; Based on the workstation production allocation relationship, a production scheduling scheme is constructed to obtain the vehicle model-workstation production scheduling scheme.

7. The production scheduling optimization method for integrating equipment health according to claim 1, characterized in that, The production scheduling optimization method for the health status of the integrated equipment also includes: Based on the preset rolling update cycle trigger conditions, the real-time operating status data of the equipment is collected periodically; Based on the real-time operating status data, the order insertion command, device standby signal and alarm information are obtained; Based on the order insertion command, equipment standby signal and alarm information, perform deviation analysis and calculation on the current production cycle to obtain the average deviation of the actual cycle. When the average deviation exceeds the preset cycle deviation threshold, a workstation-level cycle allocation instruction is generated. Based on the cycle time allocation instruction, a dynamically updated vehicle model-workstation production scheduling scheme is obtained.

8. A production scheduling optimization device integrating equipment health, characterized in that, The production scheduling optimization device for the health status of the integrated equipment includes: The health assessment module is used to obtain the health status of each piece of equipment based on the real-time collected operating parameters of the welding production line equipment. The complexity quantification module is used to obtain the process complexity of each model to be produced based on the welding process characteristics of the models to be produced. The compatibility calculation module is used to obtain the compatibility score of each device and each model to be produced one by one based on the equipment health and the process complexity through the compatibility scoring model. The production scheduling optimization module is used to optimize the matching based on the adaptability score, with the goal of maximizing the total adaptability, to obtain the vehicle model-workstation production scheduling plan.

9. A production scheduling optimization device that integrates equipment health, characterized in that, The production scheduling optimization device for the health status of the integrated equipment includes: a memory, a processor, and a production scheduling optimization program for the health status of the integrated equipment stored in the memory and executable on the processor. The production scheduling optimization program for the health status of the integrated equipment is configured to implement the production scheduling optimization method for the health status of the integrated equipment as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a production scheduling optimization program for the health status of the fusion equipment. When the production scheduling optimization program for the health status of the fusion equipment is executed by the processor, it implements the production scheduling optimization method for the health status of the fusion equipment as described in any one of claims 1 to 7.

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