Production optimization scheduling method and system of computer case

Abstract

The invention discloses a production optimization scheduling method and system for a computer case, and relates to the related technical field of production optimization scheduling, and the method comprises the steps: taking each production station of the computer case as a data collection node, and executing data perception; performing tension state evaluation of multi-dimensional analysis by utilizing the sensing data set; establishing a scheduling action interval of a production station, taking the station tension score as input data, and executing scheduling decision optimization based on global station tension relief in the scheduling action interval; and performing fitting verification on the scheduling decision optimization result, and if the fitting verification is passed, performing production optimization scheduling management according to the scheduling decision optimization result. The technical problems of unreasonable resource allocation and low production efficiency caused by static optimization scheduling of computer case production, uneven station load and insufficient dynamic response capability in the prior art are solved, and the technical effects of optimizing production resource allocation in real time, balancing station tension and improving the overall production efficiency are achieved.

Description

Technical Field

[0001] This application relates to the technical field of production optimization scheduling, specifically to a production optimization scheduling method and system for computer chassis. Background Technology

[0002] Production scheduling optimization is key to improving manufacturing efficiency, reducing costs, and enhancing market competitiveness. As an important component of electronic equipment, computer chassis production involves the collaborative operation of multiple workstations, including stamping, welding, assembly, and testing. Traditional production scheduling mainly relies on static scheduling, which is difficult to cope with dynamic changes in the production process, such as equipment failure, material delays, and uneven workstation loads. The production capacity and real-time load of different workstations vary, making it difficult to establish a reasonable evaluation model. It also lacks consideration of physical limits such as the maximum capacity of workstations and equipment compatibility, as well as related constraints such as process dependencies and material flow, which in turn affects the rational allocation of resources, leading to decreased production efficiency, low resource utilization, and even affecting the overall delivery cycle.

[0003] Therefore, current technologies suffer from problems such as static scheduling of computer chassis production optimization, uneven workstation load, and insufficient dynamic response capabilities, leading to unreasonable resource allocation and low production efficiency. Summary of the Invention

[0004] This application provides a production optimization scheduling method and system for computer chassis, which solves the technical problems in the prior art such as static production optimization scheduling, uneven workstation load, and insufficient dynamic response capability, which lead to unreasonable resource allocation and low production efficiency. It achieves the technical effects of real-time optimization of production resource allocation, balanced workstation tension, and improved overall production efficiency.

[0005] This application provides a production optimization scheduling method for computer chassis. The method includes: using each production workstation of the computer chassis as a data acquisition node, performing data sensing, and establishing a sensing dataset, the sensing dataset including workstation status and equipment status; using the sensing dataset to perform multi-dimensional analysis of tension status evaluation and establish a workstation tension score; establishing a scheduling action range for the production workstation, the scheduling action range being constructed based on the ultimate bearing capacity of the production workstation and related action constraints, using the workstation tension score as input data, performing scheduling decision optimization based on global workstation tension relief within the scheduling action range, and establishing a scheduling decision optimization result; performing fitting verification on the scheduling decision optimization result, and if the fitting verification is successful, performing production optimization scheduling management based on the scheduling decision optimization result.

[0006] In a possible implementation, the production optimization scheduling method for computer chassis further performs the following processing: parsing the sensing dataset to obtain data on task queuing quantity, job waiting time, equipment load rate, equipment historical failure rate, and process complexity; activating a first-dimensional analysis window and using it to perform waiting congestion analysis based on the task queuing quantity and job waiting time to establish a first tension factor; activating a second-dimensional analysis window and using it to perform dynamic pressure resistance assessment based on the equipment load rate and equipment historical failure rate to establish a second tension factor; activating a third-dimensional analysis window and using it to evaluate the workstation status under job switching frequency and historical intervention frequency based on the process complexity data to establish a third tension factor; and constructing a workstation tension score based on the first tension factor, the second tension factor, and the third tension factor.

[0007] In a possible implementation, the production optimization scheduling method for computer chassis further performs the following processing: activating a fourth-dimensional analysis window, using the fourth-dimensional analysis window to predict the tension evolution trend of the task queue based on the perception dataset, and establishing a fourth tension factor. Specifically, this includes: extracting the task queue of the production workstation, performing an analysis of the expected task duration, expected load impact, and historical similar task intervention probability for each task in the task queue, and establishing a task tension disturbance sequence; calling the equipment performance degradation curve and runtime of the equipment to construct an equipment tension evolution function; using the equipment tension evolution function and the task tension disturbance sequence to predict the workstation tension evolution trend and establish a fourth tension factor; and constructing a workstation tension score based on the first tension factor, the second tension factor, the third tension factor, and the fourth tension factor.

[0008] In a possible implementation, the production optimization scheduling method for computer chassis further performs the following processing: performing tension abnormality threshold trigger analysis on the second tension factor; if the result is an abnormal threshold trigger, reporting an abnormal equipment status and generating an equipment replacement instruction; stopping and replacing the workstation equipment according to the equipment replacement instruction, and reporting an equipment abnormality warning.

[0009] In a possible implementation, the production optimization scheduling method for computer chassis further performs the following processing: configuring an objective function for scheduling decision optimization, wherein the objective function is constructed as follows: establishing a tension balance evaluation item, which is used to evaluate the tension range of production stations after scheduling; establishing a task migration intervention cost evaluation item, which is used to evaluate the cost loss of cross-station migration; establishing a predicted tension evolution trend suppression evaluation item, which is used to evaluate the tension evolution curvature suppression effect of high-tension stations; establishing a process continuity maintenance evaluation item, and constructing the objective function based on the tension balance evaluation item, the task migration intervention cost evaluation item, the predicted tension evolution trend suppression evaluation item, and the process continuity maintenance evaluation item; using the objective function to perform scheduling decision optimization, and establishing the scheduling decision optimization result.

[0010] In a possible implementation, the production optimization scheduling method for computer chassis further performs the following processing: after configuring a random factor, selecting scheduling actions within the scheduling action range based on the random factor and the workstation tension score to construct an initial action set; using the objective function to evaluate the fitness of the initial action set and establishing a fitness evaluation result; using the fitness evaluation result to perform optimization iteration of the initial action set to complete the scheduling decision optimization.

[0011] In a possible implementation, the computer chassis production optimization scheduling method further performs the following processing: retrieving historical tasks similar to the tension structure and equipment status of production workstations from historical data; reading migration operations and production performance based on similar historical tasks to establish reading results; and verifying the positive effect of scheduling decisions for each production workstation based on the reading results to complete the fitting verification.

[0012] This application also provides a production optimization scheduling system for computer chassis, the system comprising: a data sensing module, used to perform data sensing and establish a sensing dataset by using each production workstation of the computer chassis as a data acquisition node, the sensing dataset including workstation status and equipment status; a tension status evaluation module, used to perform multi-dimensional tension status evaluation using the sensing dataset and establish a workstation tension score; a scheduling decision optimization module, used to establish a scheduling action range for the production workstation, the scheduling action range being constructed based on the ultimate bearing capacity and associated action constraints of the production workstation, using the workstation tension score as input data, and performing scheduling decision optimization based on global workstation tension relief within the scheduling action range to establish a scheduling decision optimization result; and a fitting verification module, used to perform fitting verification on the scheduling decision optimization result, and if the fitting verification is successful, then performing production optimization scheduling management based on the scheduling decision optimization result.

[0013] This application proposes a production optimization scheduling method and system for computer chassis. Each production workstation is used as a data acquisition node for data sensing. The system utilizes the sensed dataset for multi-dimensional tension state evaluation. A scheduling action range for each production workstation is established, using workstation tension scores as input data. Within this range, a scheduling decision optimization based on global workstation tension relief is performed. The optimization results are then fitted and validated. If the fitting validation is successful, production optimization scheduling management is implemented based on the optimization results. This method solves the technical problems in existing computer chassis production optimization scheduling, such as static scheduling, uneven workstation load, and insufficient dynamic response capabilities, leading to unreasonable resource allocation and low production efficiency. It achieves the technical effects of real-time optimization of production resource allocation, balanced workstation tension, and improved overall production efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 This is a schematic diagram of a production optimization scheduling method for computer chassis provided in an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of a production optimization scheduling system for computer chassis provided in an embodiment of this application.

[0017] Figure labeling: Data sensing module 10, tension state evaluation module 20, scheduling decision optimization module 30, fitting verification module 40. Detailed Implementation

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0021] This application provides a method for optimizing the production scheduling of computer chassis, such as... Figure 1 As shown, the method includes: Step S100: Using each production workstation of the computer chassis as a data acquisition node, perform data sensing and establish a sensing dataset, which includes workstation status and equipment status.

[0022] Preferably, on the computer chassis production line, each production station is equipped with sensors, IoT devices, or production management units as real-time data acquisition nodes. These production stations may include stamping stations, welding stations, assembly stations, and inspection stations. For example, the stamping station collects operating parameters such as the pressure and speed of the stamping machine and the status of the mold; the assembly station records the motion trajectory of the robotic arm, the screw tightening torque, and the assembly time; and the inspection station obtains quality inspection results such as dimensional errors and surface defects, as well as the health status of the inspection equipment. Data perception is achieved through data acquisition nodes, utilizing various types of sensors, RFID tags, PLC / SCADA systems, and Manufacturing Execution System (MES) to acquire workstation and equipment status. Workstation status reflects the current production operation of the workstation, specifically including: task queues (e.g., number of pending work orders, task priority, estimated completion time); job waiting time (duration of tasks in the queue, e.g., a 30-minute wait for an assembly task); process complexity (number of steps in the current process, frequency of tool changes, e.g., changing fixtures when assembling different chassis models); and manual intervention records (whether frequent manual adjustments are needed, e.g., fault reset, material replenishment). Equipment status reflects the real-time operational health of production equipment, such as equipment load rate (e.g., CPU utilization of CNC machine tools, motor current values); fault indicators (e.g., historical fault counts and current alarm signals for overheating and abnormal vibration); performance degradation data (e.g., wear level of key components, e.g., 80% remaining lifespan of stamping dies); and energy consumption data (e.g., real-time power consumption, percentage of idle time). Finally, the workstation status and equipment status are combined to form a perception dataset, which can be used to dynamically assess workstation pressure and predict potential problems with production equipment.

[0023] Step S200: Use the sensor dataset to perform multidimensional analysis of the tension status evaluation and establish a workstation tension score.

[0024] Step S200 further includes step S210, parsing the sensing dataset to obtain data on task queuing quantity, job waiting time, equipment load rate, equipment historical failure rate, and process complexity; step S220, activating the first-dimensional analysis window, using the first-dimensional analysis window to perform waiting congestion analysis based on the task queuing quantity and job waiting time, and establishing a first tension factor; step S230, activating the second-dimensional analysis window, using the second-dimensional analysis window to perform dynamic pressure resistance assessment based on the equipment load rate and equipment historical failure rate, and establishing a second tension factor; step S240, activating the third-dimensional analysis window, using the third-dimensional analysis window to perform workstation status evaluation under job switching frequency and historical intervention frequency based on the process complexity data, and establishing a third tension factor; step S250, constructing a workstation tension score based on the first tension factor, the second tension factor, and the third tension factor.

[0025] Preferably, the perception dataset is parsed to transform the raw data into quantifiable metrics, obtaining data such as task queue count, job waiting time, equipment load rate, historical equipment failure rate, and process complexity. Specifically, task queue count refers to the number of jobs pending at the current workstation, such as 5 chassis waiting to be welded; job waiting time refers to the longest or average waiting time for a task in the queue, such as a task waiting for 20 minutes; equipment load rate refers to the current resource utilization rate of the equipment, such as a CNC machine tool CPU utilization rate of 90%; historical equipment failure rate refers to the frequency of equipment failures over a past period, such as 3 material jam alarms last week; and process complexity refers to the complexity of the current process, which may include the number of process steps, switching frequency, etc., such as a chassis model requiring additional drilling, increasing the number of steps by 30%.

[0026] Preferably, three analysis windows are used to evaluate different pressure sources at the computer chassis production station and generate corresponding tension factors. Specifically, the first analysis window is activated, and the number of tasks in the queue and the waiting time are used as input data for congestion analysis. If there are many queued tasks and long waiting times, it indicates severe congestion at the station. A first tension factor is generated through weighted calculation or threshold comparison. The higher the value, the greater the congestion pressure, in order to identify bottleneck stations in the production process. The second analysis window is activated, and the equipment load rate and historical equipment failure rate are used as input data for dynamic pressure capacity assessment. If a high load rate (e.g., >80%) is combined with a high failure rate (e.g., >2 times per week), it indicates that the equipment is on the verge of overload or its reliability is declining. Based on equipment performance curves, such as the relationship model between load rate and failure probability, a second tension factor is generated. A higher value indicates a greater risk of equipment failure, used to predict production equipment failure risk and trigger predictive maintenance. A third-dimensional analysis window is activated, using job switching frequency and historical intervention frequency as input data to evaluate workstation status. Historical intervention frequency refers to the number of manual adjustments. Complex processes (such as frequent fixture changes) or high intervention requirements (such as manual reset once per hour) increase workstation pressure. A third tension factor is generated through a complexity weight table to assess workstation flexibility and avoid production efficiency decline due to process variations. The complexity weight table is a mapping table storing the number of process steps and their corresponding tension weights. Finally, the first, second, and third tension factors are weighted and integrated to construct a workstation tension score. For example, the weights for the first, second, and third tension factors are 0.4, 0.3, and 0.3, respectively, with the weights adjusted according to actual production line needs. By transforming abstract production states into calculable workstation tension scores, some tasks can be diverted to idle workstations, or the workstation can be paused for equipment maintenance.

[0027] Furthermore, step S250 also includes activating the fourth-dimensional analysis window, using the fourth-dimensional analysis window to predict the tension evolution trend of the task queue based on the perception dataset, and establishing a fourth tension factor. Specifically, this includes: step S251, extracting the task queue of the production workstation, performing an analysis of the expected task duration, expected load impact, and historical similar task intervention probability for each task in the task queue, and establishing a task tension disturbance sequence; step S252, calling the equipment performance degradation curve and runtime of the equipment, and constructing an equipment tension evolution function; step S253, using the equipment tension evolution function and the task tension disturbance sequence to predict the workstation tension evolution trend and establish a fourth tension factor; step S254, constructing a workstation tension score based on the first tension factor, the second tension factor, the third tension factor, and the fourth tension factor.

[0028] Preferably, the fourth-dimensional analysis window is used to predict the pressure change trend of the workstation over a future period. Activating the fourth-dimensional analysis window allows the sensor dataset to be used as input data to predict the tension evolution trend of the task queue. By combining the potential disturbances in the task queue with the degradation patterns of equipment performance, potential production bottlenecks or equipment risks can be identified in advance, thereby generating a fourth tension factor. Specifically, the task queue of the production workstation is extracted and each task in the current workstation task queue is analyzed to predict its impact on future tension. This includes analyzing the expected task duration, expected load impact, and the probability of intervention for similar historical tasks. The expected task duration refers to estimating the time consumed by each task based on historical data or process standards; for example, assembling a chassis requires 15 minutes. The expected load impact refers to assessing the degree of equipment resource occupancy by the task; for example, a welding task requires 100% power operation, indicating a high load impact. The probability of intervention for similar historical tasks refers to determining whether similar tasks require manual intervention based on past records; for example, the screw hole positions of a certain model of chassis are prone to errors, with an intervention probability of 30%. This generates a task tension disturbance sequence, quantifying the cumulative pressure of future tasks on the workstation, including the tension status of each task in the task queue.

[0029] Preferably, the system retrieves the equipment performance degradation curve and runtime of the equipment. The equipment performance degradation curve refers to the curve showing the change of various performance degradations of the production equipment over time; for example, the bearing wear rate of a stamping press is 5% efficiency reduction every 100 hours of operation. The current runtime refers to the cumulative usage time of the equipment since the last maintenance, such as 80 consecutive hours of operation. Then, based on the equipment performance degradation curve, the equipment degradation coefficient is calculated to construct the equipment tension evolution function. This involves calculating the product of the current runtime and the equipment degradation coefficient to update the equipment's basic tension and predict the impact of performance degradation over time on the tension. Next, the system uses the equipment tension evolution function and the task tension disturbance sequence to predict the workstation tension evolution trend. This involves superimposing the task disturbance sequence with the equipment evolution function to simulate and predict future tension changes, including short-term and long-term predictions. If there are three high-load tasks in the queue and the equipment is nearing its performance inflection point, the tension may increase sharply. The long-term prediction considers the cumulative effect of equipment degradation, such as efficiency decline leading to task backlog. Finally, the system synthesizes the workstation tension evolution trend prediction results and outputs the predicted future tension growth rate, generating a fourth tension factor. Finally, the first tension factor, the second tension factor, the third tension factor and the fourth tension factor are weighted and integrated to construct the workstation tension score. Among them, the fourth tension factor emphasizes forward-looking production control and significantly improves the intelligent level of computer chassis production optimization scheduling.

[0030] Furthermore, step S230 also includes step S231, performing tension abnormality threshold trigger analysis on the second tension factor; step S232, if the result is an abnormal threshold trigger, reporting an abnormal equipment status and generating an equipment replacement instruction; step S233, stopping and replacing the workstation equipment according to the equipment replacement instruction, and reporting an equipment abnormality warning.

[0031] Preferably, the second tension factor is used to assess the dynamic pressure-bearing capacity of the equipment, quantify the real-time health status and collapse risk of the equipment, and perform tension anomaly threshold trigger analysis on the second tension factor. Specifically, a tension anomaly threshold is set for the equipment based on the equipment type and historical data to determine whether the equipment is in a dangerous state. For example, the tension anomaly threshold is set to 8.0. When the second tension factor exceeds the tension anomaly threshold, it is determined to be an abnormal threshold trigger result, and an abnormal equipment status is reported, for example through on-site warning lights, buzzers, or other audible and visual alarms, or MES system pop-ups, SMS / email pushes. The abnormal equipment status alarm content may include the production equipment ID, anomaly type, and the number of faults. The system generates two tension factor values ​​and corresponding recommended measures; simultaneously, it generates equipment replacement instructions, including the spare equipment number, replacement operation process, and downtime window, such as immediate shutdown or shutdown after completing the current task; finally, it performs workstation equipment shutdown and replacement according to the equipment replacement instructions, that is, saving the current task progress, cutting off the equipment power or switching to safe mode, disassembling the faulty equipment, calling the spare equipment for installation, calibration and testing, reassigning the task queue to the new equipment, and linking to report equipment abnormality warnings, such as automatically recording fault details and triggering preventive maintenance plans, marking high-frequency abnormal equipment, avoiding sudden failures caused by equipment overload, thereby ensuring the continuity and reliability of computer chassis generation.

[0032] Step S300: Establish the scheduling action range of the production workstation. The scheduling action range is constructed based on the ultimate bearing capacity of the production workstation and the associated action constraints. The workstation tension score is used as input data. Within the scheduling action range, scheduling decision optimization based on global workstation tension relief is performed, and scheduling decision optimization results are established.

[0033] Preferably, the ultimate carrying capacity of the production workstation and the associated action constraints construct the scheduling action range of the production workstation, i.e., the dynamically feasible operational range, used to define the boundaries of scheduling operations that can be executed on a specific production workstation. The ultimate carrying capacity of the production workstation includes physical limits such as the maximum capacity, speed, and precision of the equipment, such as a stamping machine's maximum stamping speed of 30 times per minute, and long-term operational safety and stability limits, such as the possibility of equipment failure if the load rate exceeds 85%. Associated action constraints include process dependencies, resource constraints, and time windows. Process dependencies refer to tasks that must be performed in a fixed order, such as welding before painting; resource constraints include material supply, personnel skills, and tool availability, such as the inability to assemble a specific chassis due to the lack of a certain type of fixture; and time windows include order delivery deadlines and maintenance plans.

[0034] Preferably, the workstation tension score is used as input data to perform global workstation tension relief-based scheduling decision optimization within the scheduling action range. This involves identifying high-pressure workstations and quantifying adjustment needs. Specifically, within the scheduling action range, multi-dimensional optimization objectives such as tension balance, intervention cost, trend suppression, and process continuity are combined to perform global workstation tension relief scheduling decision optimization, generating candidate scheduling schemes as the results of the scheduling decision optimization. These may include task reallocation, migrating some tasks from high-tension workstations to low-tension workstations; equipment reassignment, activating backup equipment or adjusting production rhythm; and process adjustment, simplifying highly complex processes, such as merging inspection steps. By linking all workstations through tension scores, overall efficiency is improved. The range and decisions are updated in real time according to changes in production status such as equipment failure and order insertion, ensuring scientific scheduling in complex computer-controlled production environments, balancing efficiency, reliability, and flexibility.

[0035] Furthermore, step S300 also includes configuring an objective function for scheduling decision optimization, the objective function being constructed as follows: Step S310, establishing a tension balance evaluation item, which is used to evaluate the tension range of production stations after scheduling; Step S320, establishing a task migration intervention cost evaluation item, which is used to evaluate the cost loss of cross-station migration; Step S330, establishing a predicted tension evolution trend suppression evaluation item, which is used to evaluate the tension evolution curvature suppression effect of high-tension stations; Step S340, establishing a process continuity maintenance evaluation item, the objective function being constructed based on the tension balance evaluation item, the task migration intervention cost evaluation item, the predicted tension evolution trend suppression evaluation item, and the process continuity maintenance evaluation item; Step S350, using the objective function to perform scheduling decision optimization, and establishing the scheduling decision optimization result.

[0036] Preferably, an objective function for optimizing scheduling decisions is configured to quantitatively evaluate the merits of different scheduling schemes. This aims to determine a scheduling scheme that maximizes the balance of load across workstations, minimizes scheduling costs, mitigates future risks, and ensures production continuity, all while meeting production constraints. Specifically, the range (maximum minus minimum) or standard deviation of the tension scores for each workstation after scheduling is statistically analyzed to measure the balance of tension scores across all workstations. A smaller range indicates better balance, thus establishing a tension balance evaluation item. The cost of cross-workstation migration is also considered, including the time cost of handling and readjusting equipment, and the additional material costs. The costs of resources such as energy or manpower, as well as the losses caused by the relocation leading to delays in other tasks, are used to quantify the cost of task relocation across workstations and serve as an evaluation item for task relocation intervention costs. Based on the fourth dimension of tension evolution prediction, the effect of the scheduling scheme on suppressing the future deterioration of pressure at high-tension workstations is evaluated, i.e., the predicted tension growth rate before scheduling minus the predicted tension growth rate after scheduling, which serves as an evaluation item for suppressing the predicted tension evolution trend. The logical relationships between processes are evaluated, including whether the sequence is violated, such as painting must be done before assembly, or whether unskippable workstations are crossed, such as mandatory quality inspection links, to establish a process continuity maintenance evaluation item and ensure that scheduling does not disrupt the logical relationships of production processes.

[0037] Preferably, the evaluation items of tension balance, task migration intervention cost, predicted tension evolution trend suppression, and process continuity maintenance are weighted and integrated to obtain an objective function. The scheduling decision optimization aims to minimize the tension balance and task migration intervention cost, maximize the predicted tension evolution trend suppression effect, and minimize the number of process continuity violations. Specifically, within the scheduling action interval, multiple feasible scheduling schemes (i.e., task migration combinations) are generated randomly or heuristically. The comprehensive objective value of each feasible scheduling scheme is calculated using the objective function, and the evaluation items of tension balance, task migration intervention cost, predicted tension evolution trend suppression, and process continuity maintenance are evaluated. The optimal solution is gradually approximated through iterations such as genetic algorithms and gradient descent. Finally, the feasible scheduling scheme with the smallest objective function value is selected as the output of the scheduling decision optimization result, realizing scientific and intelligent multi-objective optimization scheduling that takes into account the production efficiency, cost, and reliability of computer chassis.

[0038] Furthermore, step S350 also includes step S351, after configuring the random factor, selecting scheduling actions within the scheduling action interval according to the random factor and the workstation tension score to construct an initial action set; step S352, using the objective function to perform a fitness evaluation on the initial action set and establishing a fitness evaluation result; step S353, using the fitness evaluation result to perform optimization iteration on the initial action set to complete the scheduling decision optimization.

[0039] Preferably, a variety of scheduling schemes are generated by introducing randomness, and the optimal solution is selected iteratively using the objective function. Specifically, a random factor is configured to introduce controllable randomness and avoid the algorithm from getting trapped in local optima. This may include the station selection probability for task migration, such as a 70% probability that tasks at high-tension stations will be migrated; random fluctuations in the number of migrated tasks, such as ±2 tasks; and then, scheduling actions are selected within the scheduling action range based on the random factor and station tension score. That is, within the scheduling action range, multiple candidate scheduling actions are generated by combining the station tension score and the random factor. For example, action 1 is to migrate any number of tasks from the welding station with a tension score of 8.5 to the inspection station; action 2 is to activate a random number of backup devices to share the tasks at high-load stations.

[0040] Preferably, a fitness evaluation is performed on each candidate scheduling action in the initial action set using an objective function to calculate a comprehensive score. This score evaluates the candidate scheduling scheme's tension balance, task migration intervention cost, predicted tension evolution trend suppression, and process continuity maintenance. The fitness score is then input into the objective function to obtain the fitness evaluation result. The smaller the value, the better the candidate scheduling scheme. Finally, based on the fitness evaluation result, the initial action set is iteratively optimized using a genetic algorithm or simulated annealing. This iteratively improves the scheduling scheme, including retaining candidate scheduling schemes with high fitness and generating updated candidate scheduling schemes through crossover and mutation combinations or fine-tuning. When the maximum number of iterations is reached or the fitness evaluation is stable, the final optimization result of the scheduling decision is output to ensure that it meets the needs of complex computer chassis production environments.

[0041] Step S400: The optimization result of the scheduling decision is fitted and verified. If the fitting verification is successful, production optimization scheduling management is carried out based on the optimization result of the scheduling decision.

[0042] Step S400 further includes step S410, retrieving historical tasks similar to the tension structure and equipment status of the production station from historical data; step S420, reading migration operations and production performance based on similar historical tasks, and establishing reading results; step S430, verifying the positive effect of scheduling decisions for each production station based on the reading results, in order to complete the fitting verification.

[0043] Preferably, by retrieving data from similar historical scenarios, the optimization results of scheduling decisions are fitted and verified to validate the expected effect of the current scheduling scheme, ensuring its feasibility and effectiveness in actual production, and avoiding production interruptions or efficiency declines due to decision-making errors. Specifically, similarity metrics such as Euclidean distance and cosine similarity are used to quantify the degree of matching between the current scenario and the production workstation tension structure and equipment status in historical data. The production workstation tension structure refers to the tension score distribution of each workstation when the task occurs, and the equipment status includes the equipment load rate, failure rate, and performance degradation degree of historical tasks, thereby filtering out similar historical tasks with high matching degrees.

[0044] Preferably, based on similar historical tasks, migration operations and production performance are read from MES system logs, equipment operation records, and manual intervention reports. This includes specific actions taken in historical scheduling, such as migrating two welding tasks to the inspection station, and the actual production effects after migration, including improvements in tension balance, changes in task completion time, and whether secondary problems are caused, such as overloading of the inspection station due to migration. The results are then obtained. Finally, the positive effects of the scheduling decision for each production station are verified based on the reading results. This involves comparing the migration operations of the current scheduling plan with those of historical plans to verify the expected scheduling decision effect, i.e., production performance. If the migration operation significantly reduces tension and has no side effects in similar historical cases, the current decision is verified. If historical data shows that migration causes overloading of the inspection station, the current plan needs to be adjusted, such as reducing the number of migration tasks.

[0045] Preferably, once the scheduling decision scheme is validated through fitting historical data, the scheme is formally implemented. This involves dynamically adjusting workstation tasks, equipment resources, and production rhythm on the production line to alleviate workstation overload pressure, improve overall production efficiency, and ensure the continuity of computer chassis production. Specifically, tasks are redistributed based on the optimization results of the scheduling decision, transferring some tasks from high-tension workstations to low-tension workstations and dynamically modifying the task queue order, such as handling urgent orders in advance. Equipment resources are allocated, including calling on idle equipment to share the load and optimizing equipment operating parameters, such as increasing the speed of the stamping press to clear backlogged tasks. Production rhythm is controlled, including adjusting the production speed of upstream and downstream workstations to avoid bottlenecks, such as slowing down assembly workstations to match welding speeds, and conducting preventative maintenance on high-risk production equipment, such as replacing machine tools with excessive second tension factors. Process adaptation modifications are made, such as merging inspection steps or reducing the number of fixture changes. Ultimately, production resource allocation is optimized in real time, workstation tension is balanced, and efficient, stable, and flexible production management is achieved, improving overall production efficiency.

[0046] In the above text, refer to Figure 1 A method for optimizing the production scheduling of computer chassis according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2This invention describes a production optimization scheduling system for computer chassis according to an embodiment of the present invention.

[0047] According to an embodiment of the present invention, a production optimization scheduling system for computer chassis is provided to solve the technical problems existing in the prior art, such as static production optimization scheduling, uneven workstation load, and insufficient dynamic response capability, leading to unreasonable resource allocation and low production efficiency. The system achieves the technical effects of real-time optimization of production resource allocation, balanced workstation workload, and improved overall production efficiency. Figure 2 As shown, a production optimization scheduling system for computer chassis includes: a data sensing module 10, a tension state evaluation module 20, a scheduling decision optimization module 30, and a fitting verification module 40.

[0048] The data sensing module 10 is used to perform data sensing and establish a sensing dataset by using each production workstation of the computer chassis as a data acquisition node. The sensing dataset includes workstation status and equipment status. The tension status evaluation module 20 is used to perform multi-dimensional analysis of tension status evaluation using the sensing dataset and establish a workstation tension score. The scheduling decision optimization module 30 is used to establish a scheduling action range for the production workstation. The scheduling action range is constructed based on the ultimate bearing capacity of the production workstation and related action constraints. The workstation tension score is used as input data, and scheduling decision optimization based on global workstation tension relief is performed within the scheduling action range to establish a scheduling decision optimization result. The fitting verification module 40 is used to perform fitting verification on the scheduling decision optimization result. If the fitting verification is successful, production optimization scheduling management is performed based on the scheduling decision optimization result.

[0049] The specific configuration of the tension status evaluation module 20 will be described in detail below. The tension status evaluation module 20 further includes: parsing the sensing dataset to obtain data on task queuing quantity, job waiting time, equipment load rate, historical equipment failure rate, and process complexity; activating a first-dimensional analysis window to perform congestion analysis based on the task queuing quantity and job waiting time, and establishing a first tension factor; activating a second-dimensional analysis window to perform dynamic pressure-bearing capacity assessment based on the equipment load rate and historical equipment failure rate, and establishing a second tension factor; activating a third-dimensional analysis window to perform workstation status evaluation based on the process complexity data under job switching frequency and historical intervention frequency, and establishing a third tension factor; and constructing a workstation tension score based on the first tension factor, the second tension factor, and the third tension factor.

[0050] The specific configuration of the tension status evaluation module 20 will be described in detail below. The tension status evaluation module 20 further includes: activating a fourth-dimensional analysis window, using the fourth-dimensional analysis window to predict the tension evolution trend of the task queue based on the perception dataset, and establishing a fourth tension factor. Specifically, this includes: extracting the task queue of the production workstation; performing an analysis of the expected task duration, expected load impact, and historical similar task intervention probability for each task in the task queue to establish a task tension disturbance sequence; calling the equipment performance degradation curve and runtime of the equipment to construct an equipment tension evolution function; using the equipment tension evolution function and the task tension disturbance sequence to predict the workstation tension evolution trend and establish a fourth tension factor; and constructing a workstation tension score based on the first tension factor, the second tension factor, the third tension factor, and the fourth tension factor.

[0051] The specific configuration of the tension status evaluation module 20 will be described in detail below. The tension status evaluation module 20 further includes: performing tension anomaly threshold trigger analysis on the second tension factor; if the result is an anomaly threshold trigger, reporting an equipment status anomaly and generating an equipment replacement instruction; stopping and replacing the workstation equipment according to the equipment replacement instruction, and reporting an equipment anomaly warning.

[0052] The specific configuration of the scheduling decision optimization module 30 will be described in detail below. The scheduling decision optimization module 30 further includes: configuring an objective function for scheduling decision optimization, the objective function being constructed as follows: establishing a tension balance evaluation item, which is used to evaluate the tension range of production stations after scheduling; establishing a task migration intervention cost evaluation item, which is used to evaluate the cost loss of cross-station migration; establishing a predicted tension evolution trend suppression evaluation item, which is used to evaluate the tension evolution curvature suppression effect of high-tension stations; establishing a process continuity maintenance evaluation item, and constructing the objective function based on the tension balance evaluation item, the task migration intervention cost evaluation item, the predicted tension evolution trend suppression evaluation item, and the process continuity maintenance evaluation item; using the objective function to perform scheduling decision optimization, and establishing the scheduling decision optimization result.

[0053] The specific configuration of the scheduling decision optimization module 30 will be described in detail below. The scheduling decision optimization module 30 further includes: configuring a random factor, selecting scheduling actions within the scheduling action range based on the random factor and the workstation tension score, and constructing an initial action set; using the objective function to evaluate the fitness of the initial action set and establishing a fitness evaluation result; and using the fitness evaluation result to perform optimization iterations on the initial action set to complete the scheduling decision optimization.

[0054] The specific configuration of the fitting verification module 40 will be described in detail below. The fitting verification module 40 further includes: retrieving historical tasks similar to the tension structure and equipment status of the production station from historical data; reading migration operations and production performance based on similar historical tasks to establish reading results; and verifying the positive effect of scheduling decisions for each production station based on the reading results to complete the fitting verification.

[0055] The computer chassis production optimization scheduling system provided in this embodiment of the invention can execute the computer chassis production optimization scheduling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0056] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0057] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing scheduling of production of computer cases, characterized by, The method comprises: Taking each production station of a computer chassis as a data acquisition node, performing data sensing, and establishing a sensing data set, the sensing data set comprising station state and device state; Performing multi-dimensional analysis of the sensing data set to evaluate tension state, and establishing a station tension score; Establishing a scheduling action interval for the production station, the scheduling action interval being constructed based on the limit bearing capacity of the production station and associated action constraints, taking the station tension score as input data, performing scheduling decision optimization based on global station tension relief in the scheduling action interval, and establishing a scheduling decision optimization result; Fitting and verifying the scheduling decision optimization result, and if the fitting and verification is passed, performing production optimization scheduling management according to the scheduling decision optimization result.

2. The method of claim 1, wherein the computer case production optimization scheduling method is characterized by, The multi-dimensional analysis of the sensing data set to evaluate tension state and establish a station tension score comprises: Performing data analysis on the sensing data set to obtain task queuing quantity, job waiting time, device load rate, device historical failure rate, and process complexity data; Activating a first-dimensional analysis window, using the first-dimensional analysis window to perform waiting congestion analysis based on the task queuing quantity and the job waiting time, and establishing a first tension factor; Activating a second-dimensional analysis window, using the second-dimensional analysis window to perform dynamic pressure bearing capacity evaluation based on the device load rate and the device historical failure rate, and establishing a second tension factor; Activating a third-dimensional analysis window, using the third-dimensional analysis window to perform station state evaluation under job switching frequency and historical intervention frequency based on the process complexity data, and establishing a third tension factor; Constructing a station tension score according to the first tension factor, the second tension factor, and the third tension factor.

3. The method of claim 2, wherein the computer case production optimization scheduling is performed by a computer system. The construction of the station tension score according to the first tension factor, the second tension factor, and the third tension factor further comprises: Activating a fourth-dimensional analysis window, using the fourth-dimensional analysis window to perform tension evolution trend prediction of a task queue based on the sensing data set, and establishing a fourth tension factor, specifically comprising: Extracting a task queue of a production station, performing predicted task duration, predicted load impact, and historical similar task intervention probability analysis on each task in the task queue, and establishing a task tension disturbance sequence; Calling a device performance degradation curve and a running time of the device to construct a device tension evolution function; Using the device tension evolution function and the task tension disturbance sequence to perform station tension evolution trend prediction, and establishing a fourth tension factor; Constructing a station tension score according to the first tension factor, the second tension factor, the third tension factor, and the fourth tension factor.

4. The method of claim 2, wherein the computer case production optimization scheduling method is characterized by, After the second tension factor is established, it comprises: Performing tension abnormal threshold triggering analysis on the second tension factor; If it is an abnormal threshold triggering result, reporting device state abnormality and generating a device replacement instruction; Performing station device shutdown replacement according to the device replacement instruction, and reporting device abnormality warning.

5. The method of claim 1, wherein the computer case production optimization scheduling method is characterized by, The work station tension score is taken as input data to perform scheduling decision optimization based on global work station tension relief in the scheduling action interval, and scheduling decision optimization results are established, including: A target function for scheduling decision optimization is configured, and the target function is constructed as follows: A tension balance degree evaluation item is established, which is used to evaluate the tension range of the production work station after scheduling; A task migration intervention cost evaluation item is established, which is used to evaluate the cost loss of cross-work station migration; A predicted tension evolution trend suppression evaluation item is established, which is used to evaluate the tension evolution curvature suppression effect of the high tension work station; A process continuity maintenance evaluation item is established, and the target function is constructed according to the tension balance degree evaluation item, the task migration intervention cost evaluation item, the predicted tension evolution trend suppression evaluation item, and the process continuity maintenance evaluation item; Scheduling decision optimization is performed using the target function, and scheduling decision optimization results are established.

6. The method of Claim 5, wherein the computer case production optimization scheduling method is characterized by, The target function performs scheduling decision optimization, and scheduling decision optimization results are established, including: After the random factor is configured, a scheduling action is selected in the scheduling action interval according to the random factor and the work station tension score, and an initial action set is constructed; The fitness evaluation results are established by using the target function to evaluate the fitness of the initial action set; Optimization iteration of the initial action set is performed using the fitness evaluation results to complete scheduling decision optimization.

7. The method of claim 1, wherein the computer case production optimization scheduling method is characterized by, The fitting verification of the scheduling decision optimization results includes: Similar historical tasks are retrieved in the historical data according to the production work station tension structure and the equipment state; Migration operations and production performance are read according to similar historical tasks to establish read results; The scheduling decision positive effect verification of each production work station is performed according to the read results to complete the fitting verification.

8. A production optimization scheduling system for computer chassis, characterized by, The system is used to implement the production optimization scheduling method of a computer case according to any one of claims 1 to 7, and the system includes: A data perception module is used to take each production work station of a computer case as a data collection node to perform data perception and establish a perception data set, and the perception data set includes work station state and equipment state; A tension state evaluation module is used to perform multi-dimensional analysis of tension state evaluation using the perception data set to establish a work station tension score; A scheduling decision optimization module is used to establish a scheduling action interval of a production work station, and the scheduling action interval is constructed based on the limit bearing capacity of the production work station and the associated action constraints, and the work station tension score is taken as input data to perform scheduling decision optimization based on global work station tension relief in the scheduling action interval, and scheduling decision optimization results are established; A fitting verification module is used to perform fitting verification on the scheduling decision optimization results, and if the fitting verification is passed, production optimization scheduling management is performed according to the scheduling decision optimization results.

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