Intelligent multi-station clamping machining control system

Abstract

The invention relates to the technical field of machine tool automatic control, in particular to an intelligent multi-station clamping machining control system which comprises an event monitoring module, a state analysis module, a decision logic module and a dynamic scheduling module. According to the method, the monitoring and analysis capability of the production process is improved by capturing and calculating the time point data of the multi-station clamping machining equipment, so that the evaluation of the production efficiency is more accurate and timely, and an instant data processing and evaluation mechanism allows an enterprise to dynamically adjust the production strategy and optimize the scheduling and priority of workpieces, so that the production efficiency is improved. Therefore, waiting time and machining delay in production are reduced, particularly, in multi-station operation, efficient resource allocation and smooth conversion between procedures are guaranteed through real-time adjustment and optimization functions, machining precision is improved, energy consumption is reduced, the production cost is further reduced through an energy consumption management strategy, and the machining efficiency is improved by accurately controlling the machining speed and path. The purpose of saving energy is achieved, the sustainability of enterprises is improved, and the environmental influence is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of machine tool automatic control, and in particular to an intelligent multi-station clamping processing control system. Background Art

[0002] The field of machine tool automatic control technology involves the use of automated systems to manage and control the operation of machine tools, including automated programming, motion control, workpiece positioning and clamping. The core technology in this field is to improve the processing accuracy and efficiency of machine tools and reduce the need for manual operation. Automatic control systems can automatically perform complex processing tasks based on preset programs, such as numerical control programming (CNC), which guides machine tools to perform precise cutting, drilling, milling and other operations. The technical field also includes real-time monitoring and adjustment of machine tool operating parameters to ensure stability during processing and product quality.

[0003] Among them, the intelligent multi-station clamping processing control system is an automated control system designed specifically for multi-station machine tools. It aims to optimize the processing flow of machine tools and improve production efficiency. The system adjusts the clamping force and position through intelligent algorithms to ensure the stability and precise alignment of each workpiece during the processing process, thereby adapting to workpieces of different shapes and sizes. The main uses of this system include high-efficiency processing in automated production lines, and it is particularly suitable for occasions that require diversified and high-precision processing, such as the automotive, aviation and precision equipment manufacturing industries. By implementing such a system, companies can reduce manual intervention, shorten production cycles, reduce costs, and improve product consistency and quality.

[0004] The existing intelligent multi-station clamping processing control system supports complex processing tasks and achieves a certain degree of automation, but lacks sufficient flexibility and adaptability to respond to actual changes in the production line in real time. The fixed programs and parameter settings in the traditional system are difficult to adapt to the rapidly changing market demands and diversified production tasks. This lack of flexibility leads to limited processing accuracy and production efficiency of machine tools, especially when processing multi-station and high-variability workpieces, which leads to unreasonable resource allocation and unoptimized production scheduling. The existing technology also shows shortcomings in energy consumption control. Many systems do not implement effective energy consumption management strategies, which not only increases operating costs, but also increases the burden on the environment. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an intelligent multi-station clamping processing control system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent multi-station clamping processing control system comprises: The event monitoring module captures the data of the multi-station clamping processing equipment, including the time point data of machine tool startup, workpiece clamping, processing start, processing end, and workpiece unloading, calculates the time interval of the workflow, generates time interval data, uses the time interval data to evaluate production efficiency, and generates production efficiency evaluation results; The status analysis module analyzes the queue length, estimated completion time, and workpiece type of the current multiple workstations based on the generation efficiency evaluation result, generates a workstation status result, adjusts the production strategy according to the workstation status result, and generates a workstation adjustment strategy; The decision logic module sorts and prioritizes workpieces based on the workstation adjustment strategy and real-time data, selects workpieces to be processed, generates a workpiece scheduling plan, analyzes key factors causing delays based on the workpiece scheduling plan, formulates preventive measures, and generates an optimized scheduling strategy; The dynamic scheduling module adjusts the machine tool processing sequence, allocates resources, optimizes the conversion between processes, and generates sequence optimization results based on the optimized scheduling strategy. Based on the sequence optimization results, the dynamic scheduling module adjusts the processing speed, selects the matching processing path, implements the energy consumption management strategy, and generates the energy-saving control result.

[0007] As a further solution of the present invention, the step of acquiring the time interval data is specifically as follows: Capture the data of multi-station clamping processing equipment, record the exact time points of machine startup, workpiece clamping, processing start, processing end, and workpiece unloading, store the time points in an array, and generate a time point array; Based on the time point array, by calculating the difference between the time points of consecutive events, apply the formula:

[0008] Determine the time interval of each workflow and obtain a time interval array, where and are the timestamps of two consecutive events, is the adjusted time interval, is the flexibility parameter for time interval adjustment; Using the time interval array, calculate the overall average time interval using the formula:

[0009] Reflecting the criticality of the differentiated workflow and the signal strength at the differentiated time points, calculate the weighted average time interval and obtain the time interval data, where: represents a single time interval, is the total number of time intervals, is the weight of the time interval, is the weighted average time interval.

[0010] As a further solution of the present invention, the step of obtaining the generation efficiency evaluation result is specifically as follows: Based on the time interval data, the duration of multiple stages was preliminarily analyzed using the formula:

[0011] Calculate the time interval of each stage and generate standardized time interval data, where represents the time interval of a single stage, is the total number of stages, is a small positive number used to stabilize calculations. is the standardized time interval data; According to the standardized time interval data, the time efficiency of each stage is calculated using the formula:

[0012] Get the stage efficiency data, where is the standardized time interval of the ith stage, is the efficiency of the stage, is the adjustment factor for efficiency calculation, is a stabilizing factor; Using the efficiency data of the above stages, the efficiency of the entire production process is evaluated by the weighted average method, using the formula:

[0013] The production efficiency evaluation results are calculated, where: represents the efficiency of the i-th stage, is the weight of the ith stage, is a fine-tuning parameter, It is the result of production efficiency evaluation.

[0014] As a further solution of the present invention, the step of obtaining the workstation adjustment strategy is specifically as follows: Based on the efficiency evaluation results, collect the queue lengths, estimated completion times, and workpiece types of the current multiple workstations, evaluate the current status of the workstations, and use the formula:

[0015] Generate a workstation status score, where represents the efficiency evaluation result, is the queue length of the ith station, is the estimated completion time of the ith workstation, are the weight coefficients, is the workstation status score; According to the workstation status scores, the workstation needs are analyzed and prioritized, and the adjustment needs are determined by using the threshold comparison method. The formula is applied:

[0016] Generate decision results, where is the workstation status score, is the threshold value, is the adjustment factor, It is the result of decision making; Based on the decision results, an adjustment strategy is formulated, and the influencing factors are used to analyze the workstations using the formula:

[0017] Combined with the criticality and urgency of multi-station adjustment, the station adjustment strategy is calculated, among which, is the result of decision making. is the impact factor, It is a workstation adjustment strategy.

[0018] As a further solution of the present invention, the step of obtaining the workpiece scheduling plan is specifically as follows: According to the analysis of the workstation adjustment strategy, combined with the data of machine status and current workpiece queue length, the formula is adopted:

[0019] Get the initial priority of the workpiece processing, where is the workstation adjustment strategy score of the i-th workpiece, is the position of the workpiece in the queue, represents the machine state of the i-th workpiece corresponding to the workstation, are weight parameters, For small amount, is the priority score.

[0020] The initial priority of the workpiece processing is screened to determine the workpieces to be processed first, using the formula:

[0021] Adjust the priority of each workpiece and decide whether to process it, and get the processing decision weight of the workpiece, where: is the priority score of the artifact, is the adjustment parameter, is the decision weight of each artifact.

[0022] According to the , arrange the scheduling of workpieces to be processed, using the formula:

[0023] Generate a workpiece scheduling plan, where is the decision weight of each artifact, is the influence coefficient of the workpiece, It is the workpiece scheduling plan.

[0024] As a further solution of the present invention, the step of obtaining the optimized scheduling strategy is specifically: Based on the workpiece scheduling plan, real-time production data and historical delay records are collected and analyzed, key delay factors are identified, and the quantitative impact value of each factor is calculated using the formula:

[0025] Get the quantified delay impact value, where Represents the quantized delay impact value, is the influence weight of factor j on i, It is real-time data. is the adjustment factor, is the normalization constant, is the stability constant; Based on the quantified delay impact value, develop targeted preventive measures and evaluate the potential effect, using the formula:

[0026] Get the priority of preventive measures, where is the priority for the implementation of preventive measures, , , , , represents the adjustment factor, Indicates the quantized delay impact value.

[0027] Integrate the priorities of preventive measures and optimize preventive measures using the formula:

[0028] The optimized scheduling strategy is formed, where is the priority for the implementation of preventive measures, is the impact weight of the measure, and is the adjustment factor, Indicates the optimized scheduling strategy.

[0029] As a further solution of the present invention, the step of obtaining the sequence optimization result is specifically: Based on the optimized scheduling strategy, the cumulative time of the clamping processing tasks on each machine tool is calculated to generate a set of estimated completion times using the formula:

[0030] in, Indicates the estimated completion time. Indicates the start time of the processing task. Indicates the processing time, represents the priority coefficient, Indicates the number of tasks; Based on the estimated completion time set, the processing efficiency of each task is calculated using the efficiency calculation formula:

[0031] Generate a set of task efficiencies, where Indicates the efficiency of the task, Indicates the critical weight of the product. Indicates the estimated completion time. Indicates the contribution of the product. Indicates the quantity of products. Indicates a product.

[0032] According to the task efficiency set, the clamping processing sequence of the machine tool station is readjusted, the tasks are arranged according to the efficiency, and the sequence optimization result is obtained.

[0033] As a further solution of the present invention, the step of obtaining the energy-saving control result is specifically as follows: Based on the sequence optimization results, the optimal processing speed on each machine tool is calculated, through the physical length of the task and the machine tool performance, using the formula:

[0034] The processing speed adjustment result is calculated, where: represents the task length, is the available time of the machine tool, is the efficiency factor, is the speed adjustment factor, Indicates the processing speed adjustment result; According to the processing speed adjustment result and the machine tool characteristics, the optimal processing path is selected, and the path selection is optimized through the formula;

[0035] Generate the optimal path selection result, where are the coordinates of the starting and ending points of the path, is the path optimization coefficient, Indicates the optimal path selection result; According to the optimal path selection result, the energy consumption management strategy is implemented, the processing parameters are adjusted, and the energy consumption management formula is adopted:

[0036] The energy-saving control result is calculated, where: is the unit cost of energy consumption, is the energy consumption adjustment factor, Indicates the energy-saving control result. Indicates the optimal path selection result.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by capturing and calculating the time point data of the multi-station clamping processing equipment, the monitoring and analysis capabilities of the production process are improved, making the evaluation of production efficiency more accurate and timely. This real-time data processing and evaluation mechanism allows enterprises to dynamically adjust production strategies, optimize the scheduling and priority of workpieces, thereby reducing waiting time and processing delays in production. Especially in multi-station operations, the real-time adjustment and optimization functions of the system ensure efficient resource allocation and smooth conversion between processes, which not only improves processing accuracy, but also reduces energy consumption. The system's energy consumption management strategy further reduces production costs, and achieves the purpose of energy saving by accurately controlling the processing speed and path, improving the sustainability of the enterprise and reducing environmental impact. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the time interval data in the present invention; Figure 3 A flow chart for generating efficiency evaluation results in the present invention; Figure 4 It is a flow chart of the workstation adjustment strategy in the present invention; Figure 5 A flowchart of the workpiece scheduling plan in the present invention; Figure 6 It is a flow chart of the optimized scheduling strategy in the present invention; Figure 7 A flow chart of sequence optimization results in the present invention; Figure 8 It is a flow chart of the energy-saving control result in the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Embodiment 1

[0041] See also Figure 1 , an intelligent multi-station clamping processing control system includes: The event monitoring module captures the data of multi-station clamping processing equipment, including the time point data of machine tool startup, workpiece clamping, processing start, processing end, and workpiece unloading, calculates the time interval of the workflow, generates time interval data, uses the time interval data to evaluate production efficiency, and generates production efficiency evaluation results; The status analysis module analyzes the queue length, estimated completion time, and workpiece type of the current multi-station based on the generation efficiency evaluation results, generates station status results, adjusts the production strategy according to the station status results, and generates a station adjustment strategy; The decision logic module sorts and prioritizes workpieces based on the workstation adjustment strategy and real-time data, selects workpieces to be processed, generates a workpiece scheduling plan, analyzes the key factors causing delays based on the workpiece scheduling plan, formulates preventive measures, and generates an optimized scheduling strategy; The dynamic scheduling module adjusts the machine tool processing sequence, allocates resources, optimizes the conversion between processes, and generates sequence optimization results based on the optimized scheduling strategy. Based on the sequence optimization results, it adjusts the processing speed, selects the matching processing path, implements the energy consumption management strategy, and generates energy-saving control results.

[0042] The time interval data includes start-up time, clamping time, processing start time, processing end time, and unloading time. The production efficiency evaluation results include workflow efficiency, production rate, and time utilization. The workstation status results include queue length, estimated completion time, and workpiece type. The workstation adjustment strategy includes production rhythm adjustment, resource reallocation, and priority redefinition. The workpiece scheduling plan includes workpiece sequence, workpiece priority, and workpiece processing time. The optimized scheduling strategy includes delay prevention measures, key factor response, and scheduling priority setting. The sequence optimization results include the adjusted processing sequence, resource allocation status, and process conversion efficiency. The energy-saving control results include speed optimization, path selection, and energy consumption management.

[0043] See also Figure 2, the specific steps for obtaining time interval data are: Capture the data of multi-station clamping processing equipment, record the exact time points of machine startup, workpiece clamping, processing start, processing end, and workpiece unloading, store the time points in an array, and generate a time point array; Based on the array of time points, by calculating the difference between the time points of consecutive events, apply the formula:

[0044] Determine the time interval of each workflow and obtain a time interval array, where and are the timestamps of two consecutive events, is the adjusted time interval, is the flexibility parameter for time interval adjustment; Using the time interval array, calculate the overall average time interval using the formula:

[0045] Reflecting the criticality of the differentiated workflow and the signal strength at the differentiated time points, calculate the weighted average time interval and obtain the time interval data, where: represents a single time interval, is the total number of time intervals, is the weight of the time interval, is the weighted average time interval.

[0046] Adjusted time interval formula:

[0047] Assume that the timestamps of two consecutive events are Seconds and seconds, choose a suitable parameter To adjust the sensitivity of the time interval; Compute the difference between two time points:

[0048] Substituting the difference into the exponential part of the formula:

[0049] According to the formula, calculate the adjusted time interval:

[0050] This result indicates that after taking into account the nonlinear adjustment of the time point interval, the actual effective time interval is 49.67 seconds.

[0051] The weighted average time interval formula is:

[0052] Assume there are three time intervals Second, Second, seconds, and the corresponding weights ; Calculate the weighted time interval:

[0053]

[0054]

[0055] Calculate the sum of weights:

[0056] Calculate the weighted average time interval:

[0057] This result indicates that the weighted average time interval is 15.04 seconds, taking into account the importance of different time intervals.

[0058] See also Figure 3 , the specific steps for obtaining the efficiency evaluation results are: Based on the time interval data, the duration of multiple stages was preliminarily analyzed using the formula:

[0059] Calculate the time interval of each stage and generate standardized time interval data, where represents the time interval of a single stage, is the total number of stages, is a small positive number used to stabilize calculations. is the standardized time interval data; According to the standardized time interval data, the time efficiency of each stage is calculated using the formula:

[0060] Get the stage efficiency data, where is the standardized time interval of the ith stage, is the efficiency of the stage, is the adjustment factor for efficiency calculation, is a stabilizing factor; Using the stage efficiency data, the efficiency of the entire production process is evaluated by the weighted average method, using the formula:

[0061] The production efficiency evaluation results are calculated, where: represents the efficiency of the i-th stage, is the weight of the ith stage, is a fine-tuning parameter, It is the result of production efficiency evaluation.

[0062] Formula for normalizing time interval data:

[0063] : The time interval of the i-th stage; : The total number of stages; : A very small positive number used to stabilize the division operation and prevent the denominator from being zero; Assume that there is a production process consisting of three stages with time intervals of 30 minutes, 45 minutes, and 15 minutes respectively. The parameter setting is minute, ; Compute the sum of all phase time intervals:

[0064] Calculate the normalized time interval

[0065]

[0066]

[0067]

[0068] The values ​​={0.333, 0.555, 0.167} represent the proportion of each stage to the total time.

[0069] The efficiency formula of the stage is:

[0070] : The standardized time interval of the ith stage; : Adjustment factor, assumed to be 2; : small positive number, to prevent zero errors, set to 0.05; Calculate the efficiency of each stage :

[0071]

[0072]

[0073] The efficiency value reflects the time efficiency of each stage. Leading to higher efficiency values.

[0074] Production efficiency evaluation result formula:

[0075] : The efficiency of the i-th stage; : Weights, assumed equal, each is 1 / 3; : small deviation, set to 0.1; Calculate overall production efficiency

[0076]

[0077] This value It represents the overall production efficiency evaluation result, reflecting the weighted average efficiency of the three stages.

[0078] See also Figure 4 , the specific steps for obtaining the workstation adjustment strategy are: Based on the efficiency evaluation results, collect the queue lengths, estimated completion times, and workpiece types of the current multiple workstations, evaluate the current status of the workstations, and use the formula:

[0079] Generate a workstation status score, where represents the efficiency evaluation result, is the queue length of the ith station, is the estimated completion time of the ith workstation, are the weight coefficients, is the workstation status score; Analyze the workstation needs and prioritize them according to the workstation status scores, and use the threshold comparison method to determine the adjustment needs. The formula is:

[0080] Generate decision results, where is the workstation status score, is the threshold value, is the adjustment factor, It is the result of decision making; Based on the decision results, formulate adjustment strategies and use influencing factors to analyze the workstations using the formula:

[0081] Combined with the criticality and urgency of multi-station adjustment, the station adjustment strategy is calculated, among which, is the result of decision making. is the impact factor, It is a workstation adjustment strategy.

[0082] Workstation status score formula:

[0083] : Weight factor, set according to the actual needs of the production environment, assuming , ; : Efficiency evaluation result, assuming that the efficiency evaluation obtained from the system is 0.8; : The queue length of the i-th station is assumed to be 3; : The estimated completion time of the i-th station (hours), assumed to be 2; Calculate the impact of queue length:

[0084] Calculate the impact of estimated completion time:

[0085]

[0086] Calculate the total status score:

[0087] Represents the status score of the i-th workstation. This score is used to evaluate the current performance and urgency of the workstation. A lower score indicates improvement measures that need to be prioritized.

[0088] Decision result formula:

[0089] : Decision threshold, assumed to be 0.6; : Adjustment coefficient, assumed to be 1.5; : Fine-tuning parameter, assumed to be 0.05; Calculate the decision result:

[0090]

[0091] Indicates the specific adjustment needs of the workstation. This value indicates the degree of difference relative to the threshold and the urgency of the adjustment.

[0092] Workstation adjustment strategy formula:

[0093] : Impact factor, assumed to be 2; Calculation adjustment strategy:

[0094]

[0095] Represents the overall workstation adjustment strategy. This value reflects the overall effectiveness of the adjustment and the breadth of its impact.

[0096] See also Figure 5 ,The specific steps for obtaining the workpiece scheduling plan are: According to the workstation adjustment strategy, combined with the data of machine status and current workpiece queue length, the formula is adopted:

[0097] Get the initial priority of the workpiece processing, where is the workstation adjustment strategy score of the i-th workpiece, is the position of the workpiece in the queue, represents the machine state of the i-th workpiece corresponding to the workstation, are weight parameters, For small amount, is the priority score.

[0098] Screen the initial priority of workpiece processing and determine the workpieces to be processed first, using the formula:

[0099] Adjust the priority of each workpiece and decide whether to process it, and get the processing decision weight of the workpiece, where: is the priority score of the artifact, is the adjustment parameter, is the decision weight of each artifact.

[0100] according to , arrange the scheduling of workpieces to be processed, using the formula:

[0101] Generate a workpiece scheduling plan, where is the decision weight of each artifact, is the influence coefficient of the workpiece, It is the workpiece scheduling plan.

[0102] Priority scoring formula:

[0103] : The workstation adjustment strategy score is assumed to be obtained through historical data analysis, for example ; : The position of the workpiece in the queue is obtained through the real-time monitoring system, e.g. ; : Machine status index, reflecting the current workload or maintenance status of the machine, assuming ; : Weight coefficient, set based on historical data and expert experience, e.g. ; : A small amount to ensure the stability of calculation. ; Calculate the square root of the queue length and add one:

[0104] Calculate the numerator:

[0105] Calculate the denominator:

[0106] This evaluates the entire expression:

[0107] What you get The value represents the priority score of artifact i, and the higher the value, the higher the priority.

[0108] The decision weight formula for each artifact is:

[0109] : The priority score calculated in the previous step, for example ; : New weight coefficient, adjusted according to the optimization goal and actual needs, for example .

[0110] calculate And add 2:

[0111] Compute the square root portion of the denominator:

[0112] This evaluates the entire expression:

[0113] The value represents the processing decision weight of workpiece i. The larger the value, the higher the possibility that the workpiece will be selected.

[0114] Workpiece scheduling plan formula:

[0115] : The workpiece selection weight calculated in the previous step, for example ; : The impact factor of the artifact, reflecting the urgency and importance of the artifact, assuming ; Calculate the score:

[0116] If there are multiple workpieces, the calculation results of all workpieces are accumulated to determine the scheduling plan.

[0117] The value represents the scheduling priority of all workpieces to be processed, which is used for the final production scheduling decision.

[0118] See also Figure 6 , the specific steps for obtaining the optimized scheduling strategy are: Based on the workpiece scheduling plan, collect and analyze real-time production data and historical delay records, identify key delay factors, and calculate the quantitative impact value of each factor using the formula:

[0119] Get the quantified delay impact value, where Represents the quantized delay impact value, is the influence weight of factor j on i, It is real-time data. is the adjustment factor, is the normalization constant, is the stability constant; Based on the quantified delay impact value, develop targeted preventive measures and evaluate the potential effect, using the formula:

[0120] Get the priority of preventive measures, where is the priority for the implementation of preventive measures, , , , , represents the adjustment factor, Indicates the quantized delay impact value.

[0121] Integrate the priorities of preventive measures and optimize preventive measures using the formula:

[0122] The optimized scheduling strategy is formed, where is the priority for the implementation of preventive measures, is the impact weight of the measure, and is the adjustment factor, Indicates the optimized scheduling strategy.

[0123] The formula for quantifying the delay impact value is:

[0124] Assumptions and examples: : Indicates that there are two factors affecting the delay; : Weight coefficient, the influence weight of the first factor is 0.5, and the influence weight of the second factor is 0.3; : Actual data value, such as machine downtime (minutes); : Adjustment coefficient, taking into account the variability of the data; : normalization constant; : Stability constant, ensuring that the denominator is not zero; Calculate the adjusted factor values:

[0125]

[0126] Calculate the denominator:

[0127] Since this is an example, simplify and use the denominator of the first factor for demonstration; calculate :

[0128] It represents the quantitative impact value of the i-th factor on delay after combining all factors.

[0129] Formula for the priority of implementing preventive measures:

[0130] Assumptions and examples: : obtained from the previous formula; : are adjustment parameters; Calculate the numerator:

[0131]

[0132] Calculate the denominator:

[0133] calculate

[0134]

[0135] Indicates the priority of the preventive measure. The higher the value, the higher the priority.

[0136] The optimized scheduling strategy formula is:

[0137] Assumptions and examples: Assume two precautions, so ; : Obtained from the previous calculation , Assume priority for another preventive measure; : Weight coefficient, reflecting the importance of different measures; : Basic offset, ensure that the denominator is non-zero; : Adjustment coefficient, the impact of adjusting the priority of preventive measures; Calculate the strategy score for each measure: For measure 1: Denominator

[0138] Fraction

[0139] For measure 2: Denominator

[0140] Fraction

[0141] Calculate the overall optimal scheduling strategy

[0142]

[0143] It represents the total score of the optimized scheduling strategy generated after integrating all measures. This score reflects the comprehensive effect of preventive measures and the adjustment of their priority. The higher the value, the more conducive the strategy is to reducing delays.

[0144] See also Figure 7 , the specific steps for obtaining sequence optimization results are: Based on the optimized scheduling strategy, the cumulative time of the clamping processing tasks on each machine tool is calculated to generate a set of estimated completion times using the formula:

[0145] in, Indicates the estimated completion time. Indicates the start time of the processing task. Indicates the processing time, represents the priority coefficient, Indicates the number of tasks; Based on the estimated completion time set, calculate the processing efficiency of each task using the efficiency calculation formula:

[0146] Generate a set of task efficiencies, where Indicates the efficiency of the task, Indicates the critical weight of the product. Indicates the estimated completion time. Indicates the contribution of the product. Indicates the quantity of products. Indicates a product.

[0147] According to the task efficiency set, the clamping processing sequence of the machine tool station is readjusted, and the tasks are arranged according to efficiency to obtain the sequence optimization result.

[0148] The formula for estimated completion time is:

[0149] : The estimated completion time of the i-th machine tool; : The start time of the processing task; : The processing time of the jth task on the i-th machine tool; : The priority coefficient of the jth task on the i-th machine tool; : Number of tasks.

[0150] Assume there is a machine tool (i=1) with start time hours, handling three tasks , the processing time and priority coefficient of each task are as follows: Task 1: Hour, ; Task 2: Hour, ; Task 3: Hour, ; According to the formula:

[0151] This result Hours means that the machine tool is expected to complete all tasks in 4.7 hours.

[0152] The efficiency formula of the task is:

[0153] : The efficiency of the i-th task; : The importance weight of the i-th task to the k-th product; : The contribution of task i to product k; : The estimated completion time of the i-th machine tool obtained from step 1; : product quantity; Using the previous step hours, set three products And the corresponding importance weights and contributions are as follows: Product 1: ; Product 2: ; Product 3: ; According to the formula:

[0154] This result The efficiency of the representative machine tool in processing the task is evaluated as 0.553, taking into account the product importance and contribution, relative to its total processing time.

[0155] See also Figure 8 , the specific steps for obtaining energy-saving control results are: Based on the sequence optimization results, the optimal processing speed on each machine tool is calculated, through the physical length of the task and the performance of the machine tool, using the formula:

[0156] The processing speed adjustment result is calculated, where: represents the task length, is the available time of the machine tool, is the efficiency factor, is the speed adjustment factor, Indicates the processing speed adjustment result; According to the processing speed adjustment results and machine tool characteristics, the optimal processing path is selected and the path selection is optimized through the formula;

[0157] Generate the optimal path selection result, where are the coordinates of the starting and ending points of the path, is the path optimization coefficient, Indicates the optimal path selection result; According to the optimal path selection results, implement energy consumption management strategy, adjust processing parameters, and use energy consumption management formula:

[0158] The energy-saving control result is calculated, where: is the unit cost of energy consumption, is the energy consumption adjustment factor, Indicates the energy-saving control result. Indicates the optimal path selection result.

[0159] Processing speed adjustment result formula:

[0160] : The length or complexity of the task, assuming the task length is 1000mm; : The available time of machine tool i, assuming that the available time is 8 hours (480 minutes); : The efficiency factor of the machine tool, assuming the efficiency factor is 0.85; : Speed ​​adjustment coefficient, assuming the adjustment coefficient is 1.1; Substitute the above parameters into the formula to calculate the optimal processing speed:

[0161] : The optimal processing speed of the machine tool. The calculated result is 2.7mm / min, which means the optimal processing speed that the machine tool should be set to under the current conditions.

[0162] The optimal path selection result formula is:

[0163] : The starting point coordinates of the path; : The coordinates of the end point of the path; : Path optimization coefficient; Assumption: Starting point coordinates , end point coordinates , path optimization coefficient ; Substitute the above parameters into the formula to calculate the optimal processing path:

[0164]

[0165] : The optimal processing path. The calculated result is 339.41, which means the optimal processing path length selected under the current conditions.

[0166] Energy-saving control result formula:

[0167] : Unit cost of energy consumption per machine tool; : The optimal path selection result for each machine tool; : Energy consumption adjustment factor; ; Number of machine tools; Assumption: Unit cost of energy consumption kWh / mm, optimal path selection results mm, energy consumption adjustment factor , number of machine tools tower; Substitute the above parameters into the formula to calculate the total energy consumption:

[0168] : Energy-saving control results, the calculated result is 188.56kWh, which represents the total energy consumption of the entire processing process under the current conditions.

[0169] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent multi-station clamping processing control system, characterized in that: The system comprises: The event monitoring module captures the data of the multi-station clamping processing equipment, including the time point data of machine tool startup, workpiece clamping, processing start, processing end, and workpiece unloading, calculates the time interval of the workflow, generates time interval data, uses the time interval data to evaluate production efficiency, and generates production efficiency evaluation results; The status analysis module analyzes the queue length, estimated completion time, and workpiece type of the current multiple workstations based on the generation efficiency evaluation result, generates a workstation status result, adjusts the production strategy according to the workstation status result, and generates a workstation adjustment strategy; The decision logic module sorts and prioritizes workpieces based on the workstation adjustment strategy and real-time data, selects workpieces to be processed, generates a workpiece scheduling plan, analyzes key factors causing delays based on the workpiece scheduling plan, formulates preventive measures, and generates an optimized scheduling strategy; The dynamic scheduling module adjusts the machine tool processing sequence, allocates resources, optimizes the conversion between processes, and generates sequence optimization results based on the optimized scheduling strategy. Based on the sequence optimization results, the dynamic scheduling module adjusts the processing speed, selects the matching processing path, implements the energy consumption management strategy, and generates the energy-saving control result.

2. The intelligent multi-station clamping processing control system according to claim 1 is characterized in that: The steps for obtaining the time interval data are specifically as follows: Capture the data of multi-station clamping processing equipment, record the exact time points of machine startup, workpiece clamping, processing start, processing end, and workpiece unloading, store the time points in an array, and generate a time point array; Based on the time point array, by calculating the difference between the time points of consecutive events, apply the formula: ； Determine the time interval of each workflow and obtain a time interval array, where and are the timestamps of two consecutive events, is the adjusted time interval, is the flexibility parameter for time interval adjustment; Using the time interval array, calculate the overall average time interval using the formula: ； Reflecting the criticality of the differentiated workflow and the signal strength at the differentiated time points, calculate the weighted average time interval and obtain the time interval data, where: represents a single time interval, is the total number of time intervals, is the weight of the time interval, is the weighted average time interval.

3. The intelligent multi-station clamping processing control system according to claim 2 is characterized in that: The steps for obtaining the generation efficiency evaluation result are specifically as follows: Based on the time interval data, the duration of multiple stages was preliminarily analyzed using the formula: ； Calculate the time interval of each stage and generate standardized time interval data, where represents the time interval of a single stage, is the total number of stages, is a small positive number used to stabilize calculations. is the standardized time interval data; According to the standardized time interval data, the time efficiency of each stage is calculated using the formula: ； Get the stage efficiency data, where is the standardized time interval of the ith stage, is the efficiency of the stage, is the adjustment factor for efficiency calculation, is a stabilizing factor; Using the efficiency data of the above stages, the efficiency of the entire production process is evaluated by the weighted average method, using the formula: ； The production efficiency evaluation results are calculated, where: represents the efficiency of the i-th stage, is the weight of the ith stage, is a fine-tuning parameter, It is the result of production efficiency evaluation.

4. The intelligent multi-station clamping processing control system according to claim 3 is characterized in that: The steps for obtaining the workstation adjustment strategy are specifically as follows: Based on the efficiency evaluation results, collect the queue lengths, estimated completion times, and workpiece types of the current multiple workstations, evaluate the current status of the workstations, and use the formula: ； Generate a workstation status score, where represents the efficiency evaluation result, is the queue length of the ith station, is the estimated completion time of the ith workstation, are the weight coefficients, is the workstation status score; According to the workstation status scores, the workstation needs are analyzed and prioritized, and the adjustment needs are determined by using the threshold comparison method. The formula is applied: ； Generate decision results, where is the workstation status score, is the threshold value, is the adjustment factor, It is the result of decision making; Based on the decision results, an adjustment strategy is formulated, and the influencing factors are used to analyze the workstations using the formula: ； Combined with the criticality and urgency of multi-station adjustment, the station adjustment strategy is calculated, among which, is the result of decision making. is the impact factor, It is a workstation adjustment strategy.

5. The intelligent multi-station clamping processing control system according to claim 4 is characterized in that: The steps for obtaining the workpiece scheduling plan are specifically as follows: According to the analysis of the workstation adjustment strategy, combined with the data of machine status and current workpiece queue length, the formula is adopted: ； Get the initial priority of the workpiece processing, where is the workstation adjustment strategy score of the i-th workpiece, is the position of the workpiece in the queue, represents the machine state of the i-th workpiece corresponding to the workstation, are weight parameters, For small amount, is the priority score; The initial priority of the workpiece processing is screened to determine the workpieces to be processed first, using the formula: ； Adjust the priority of each workpiece and decide whether to process it, and get the processing decision weight of the workpiece, where: is the priority score of the artifact, is the adjustment parameter, is the decision weight of each artifact; According to the , arrange the scheduling of workpieces to be processed, using the formula: ； Generate a workpiece scheduling plan, where is the decision weight of each artifact, is the influence coefficient of the workpiece, It is the workpiece scheduling plan.

6. The intelligent multi-station clamping processing control system according to claim 5 is characterized in that: The steps for obtaining the optimized scheduling strategy are specifically as follows: Based on the workpiece scheduling plan, real-time production data and historical delay records are collected and analyzed, key delay factors are identified, and the quantitative impact value of each factor is calculated using the formula: ； Get the quantified delay impact value, where Represents the quantized delay impact value, is the influence weight of factor j on i, It is real-time data. is the adjustment factor, is the normalization constant, is the stability constant; Based on the quantified delay impact value, develop targeted preventive measures and evaluate the potential effect, using the formula: ； Get the priority of preventive measures, where is the priority for the implementation of preventive measures, , , , , represents the adjustment factor, represents the quantized delay impact value; Integrate the priorities of preventive measures and optimize preventive measures using the formula: ； The optimized scheduling strategy is formed, where is the priority for the implementation of preventive measures, is the impact weight of the measure, and is the adjustment factor, Indicates the optimized scheduling strategy.

7. The intelligent multi-station clamping processing control system according to claim 6 is characterized in that: The steps for obtaining the sequence optimization results are specifically as follows: Based on the optimized scheduling strategy, the cumulative time of the clamping processing tasks on each machine tool is calculated to generate a set of estimated completion times using the formula: ； in, Indicates the estimated completion time. Indicates the start time of the processing task. Indicates the processing time, represents the priority coefficient, Indicates the number of tasks; Based on the estimated completion time set, the processing efficiency of each task is calculated using the efficiency calculation formula: ； Generate a set of task efficiencies, where Indicates the efficiency of the task, Indicates the critical weight of the product. Indicates the estimated completion time. Indicates the contribution of the product. Indicates the quantity of products. Indicates a product; According to the task efficiency set, the clamping processing sequence of the machine tool station is readjusted, the tasks are arranged according to the efficiency, and the sequence optimization result is obtained.

8. The intelligent multi-station clamping processing control system according to claim 7 is characterized in that: The steps for obtaining the energy-saving control result are specifically as follows: Based on the sequence optimization results, the optimal processing speed on each machine tool is calculated, through the physical length of the task and the machine tool performance, using the formula: ； The processing speed adjustment result is calculated, where: represents the task length, is the available time of the machine tool, is the efficiency factor, is the speed adjustment factor, Indicates the processing speed adjustment result; According to the processing speed adjustment result and the machine tool characteristics, the optimal processing path is selected, and the path selection is optimized through the formula; ； Generate the optimal path selection result, where are the coordinates of the starting and ending points of the path, is the path optimization coefficient, Indicates the optimal path selection result; According to the optimal path selection result, the energy consumption management strategy is implemented, the processing parameters are adjusted, and the energy consumption management formula is adopted: ； The energy-saving control result is calculated, where: is the unit cost of energy consumption, is the energy consumption adjustment factor, Indicates the energy-saving control result. Indicates the optimal path selection result.

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