Task flexible scheduling method for multiple automatic lines based on dynamic measurement data

Abstract

The invention relates to a task flexible scheduling method for multiple automatic lines based on dynamic measurement data, and belongs to the field of workshop scheduling. The method comprises the following steps: setting different acquisition frequencies based on different parameter characteristics to perform parameter acquisition, and performing data cleaning on different types of acquired parameters by different data cleaning methods; building an equipment operation, product quality and material supply analysis model based on the data after data cleaning; based on equipment operation, product quality and material supply analysis models, a scheduling model including multi-objective function optimization and multi-constraint conditions is constructed, and an optimal solution in a complex task allocation combination is solved. According to the method, flexible task scheduling of multiple automatic lines can be realized, a task scheduling plan more conforming to a complex production scene is realized, effective task allocation is provided for actual production, and the task scheduling efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of workshop scheduling, and particularly relates to a task flexible scheduling method for multiple automatic lines based on dynamic measurement data. Background Art

[0002] The workshop flexible scheduling method refers to a way to achieve efficient production by flexibly adjusting production tasks and resource allocation in the workshop production process to cope with various uncertainties and changes. Common methods include rule-based scheduling methods, which formulate rules based on production characteristics and goals for quick decision-making, but lack flexibility. Heuristic algorithms such as genetic algorithms search for approximate optimal solutions in the solution space through heuristic rules and search strategies, which can handle complex problems but cannot guarantee global optimality. The simulated annealing algorithm starts from an initial solution and searches for the optimal solution through random perturbations and acceptance criteria. It has strong global search ability but large computational complexity and slow convergence. The constraint satisfaction-based scheduling method transforms the problem into a constraint satisfaction problem to accurately handle complex constraints, but it is difficult to solve and has low efficiency. Agent technology abstracts workshop entities as agents and realizes scheduling through the interaction and cooperation of agents. It has good flexibility and adaptability, but the design and coordination mechanisms are complex.

[0003] There are many deficiencies in the existing technology in workshop scheduling, which urgently need to be solved. Taking CN112183817A as an example, it uses particle coding combined with genetic algorithm and simulated annealing algorithm for scheduling. Although there are optimizations, there are still limitations. The main limitations are as follows:

[0004] Limitations in data collection and processing: The existing technology is not comprehensive and accurate enough in data collection, lacking systematic collection of dynamic measurement data in multiple aspects such as equipment operation, product quality, and material supply, and it is difficult to comprehensively reflect the actual situation of the production process. For example, when monitoring the equipment status, only some key parameters may be concerned, ignoring other factors affecting production; the collection frequency is fixed and cannot adapt to the dynamic change characteristics of parameters, resulting in untimely acquisition of key information. In data processing, the methods for dealing with outliers are relatively single, and it is difficult to effectively cope with data noise in a complex production environment, affecting the accuracy and reliability of subsequent analysis.

[0005] Analysis model is imperfect: The analysis models in the existing technology mostly focus on a single production factor and fail to comprehensively construct a comprehensive analysis model covering equipment failure prediction, the relationship between product quality and production parameters, and material supply trends. For example, when predicting equipment failures, there is a lack of in-depth mining and effective integration of equipment operation parameters, and the prediction accuracy is insufficient; the correlation analysis between product quality and production parameters is not deep enough to provide accurate guidance for production parameter adjustment; in terms of material supply, it is difficult to accurately predict the inventory consumption trend and replenishment time point, which is likely to lead to material shortages or overstocking, affecting production continuity.

[0006] The scheduling model is not optimized: The existing scheduling models are not comprehensive and flexible enough in setting the objective function and constraints. The objective function often only focuses on a single indicator, such as production cycle or equipment utilization rate, and cannot comprehensively balance multiple production objectives; the constraints are not considered comprehensively enough, and the complex constraints in actual production, such as equipment maintenance, task sequence, and material supply, are not handled carefully enough, making it difficult to adapt to complex and changing production scenarios, resulting in low production efficiency and resource waste.

[0007] The scheduling adjustment mechanism lacks flexibility: The existing technologies lack an effective scheduling adjustment mechanism when facing dynamic situations such as equipment failures and order changes. Once an equipment failure occurs, tasks cannot be quickly and reasonably reallocated, resulting in an extended production interruption time; when an order changes, it is difficult to adjust task allocation and production plans in a timely manner according to the new requirements, affecting the order delivery time and customer satisfaction, and reducing the adaptability and robustness of the production system.

[0008] The efficiency and quality of the solution algorithm need to be improved: The solution algorithms adopted by the existing technologies, such as the combination of genetic algorithm and simulated annealing algorithm, have deficiencies in search efficiency and solution quality. The genetic algorithm is prone to falling into local optimal solutions, and the simulated annealing algorithm has a large amount of calculation and slow convergence, making it difficult to quickly search for better solutions in complex task allocation combinations, unable to meet the requirements of efficient scheduling in actual production, and affecting production efficiency and enterprise competitiveness. Summary of the Invention

[0009] The object of the present invention is to provide a task flexible scheduling method for multiple automatic lines based on dynamic measurement data, which can realize the task flexible scheduling of multiple automatic lines, realize a task scheduling plan more in line with complex production scenarios, provide effective task allocation for actual production, and improve task scheduling efficiency.

[0010] To achieve the above object, the technical solution of the present invention is: A task flexible scheduling method for multiple automatic lines based on dynamic measurement data, including the following steps:

[0011] S1. Parameter acquisition: The acquisition terminal obtains the first parameter text according to the set acquisition frequency, and the first parameter text includes equipment operation parameters, product quality parameters, and material supply parameters;

[0012] S2. Data cleaning: Perform data cleaning operations on the equipment operation parameters, product quality parameters, and material supply parameters to obtain a parameter processing text;

[0013] S3. Data analysis: Based on the parameter processing text, construct an equipment operation analysis model, a product quality analysis model, and a material supply analysis model;

[0014] S4. Scheduling Model Construction: Based on the analysis results of the equipment operation analysis model, product quality analysis model, and material supply analysis model, optimize the objective function, and set constraint conditions according to the objective function to construct a task scheduling model;

[0015] S5. Scheduling Model Solving: Use the genetic algorithm to solve the task scheduling model and obtain the optimal task allocation plan.

[0016] Furthermore, in S1, the equipment operation parameters include rotational speed, temperature, and pressure; the product quality parameters include dimensions; and the material supply parameters include inventory quantity and material conveying speed.

[0017] Furthermore, in S1, the method for setting the acquisition frequency is as follows:

[0018] For the equipment operation parameters, set the acquisition frequency to 5 Hz;

[0019] For the product quality parameters, the acquisition frequency is determined according to the production beat. The production beat means producing 1 product every N seconds, and set the acquisition frequency = 1 / N Hz;

[0020] For the material supply parameters, set the acquisition frequency to 1 / 60 Hz.

[0021] Furthermore, in S2, the data cleaning operation is as follows:

[0022] For the equipment operation parameters, determine the outliers by setting the upper and lower limit thresholds, and clean the outliers using the moving average method;

[0023] For the product quality parameters, determine the outliers by setting the standard threshold, and clean the outliers by comparing with the data of similar products;

[0024] For the material supply parameters, determine the outliers by setting the standard threshold or upper and lower limit thresholds, and clean the outliers by comparing with the data of the previous moment or using the weighted average method of data in adjacent time periods.

[0025] Furthermore, in S3, the data analysis is specifically implemented as follows:

[0026] The equipment operation analysis model uses the ARIMA(p, d, q) model to predict the equipment failure time. For the time series data {y T} of the equipment operation parameters after being processed in S2, y t is the data at the t-th moment in {y T}, and the model expression is:

[0027]

[0028] Among them, the autoregressive order p = 2, the differencing order d = 1, and the moving average order q = 1. and are model parameters, y t-i is the data at the (t - i)-th moment in {y T}, B is the backshift operator, μ is the constant term, is the white noise at the t-th moment, is the white noise at the (t - j)-th moment; the model output is the predicted value of the equipment failure time t fault .

[0029] The product quality analysis model refers to constructing a linear regression model that correlates product quality with production parameters. The expression of the model is as follows:

[0030] Y = β0 + β1X1 + β2X2 + β3X3 +

[0031] Among them, the qualified rate of product size in the product quality parameters is Y, and the production parameters are the equipment rotation speed X1, the equipment temperature X2, and the material conveying speed X3. is a constant, and the regression coefficients β0, β1, β2, and β3 are estimated by the least squares method to determine the quantitative relationship between product quality and production parameters.

[0032] The material supply analysis model refers to using time series analysis to predict the material inventory consumption trend and the replenishment time point. Let the time series data of the material inventory quantity be {I T}, I t is the data at the t-th moment in {I T}. By analyzing the historical data trend and seasonal characteristics, a time series prediction model I t+1 = αI t + (1 - α)I t-1 is established, where α is the smoothing coefficient, and its value range is between 0 and 1. I t-1 is the data at the (t - 1)-th moment in {I T}. The material inventory quantity I t+k at the future k-th moment is predicted to determine the replenishment time point t replenish . At the same time, the stability of the material conveying speed is analyzed, and the standard deviation σ speed of the material conveying speed fluctuation is calculated.

[0033] S4, Construction of the scheduling model: Based on the analysis results of the equipment operation analysis model, the product quality analysis model, and the material supply analysis model, the objective function is optimized, and the constraint conditions are set according to the objective function to construct the task scheduling model.

[0034] S5, Solving the scheduling model: The genetic algorithm is used to solve the task scheduling model, and the optimal task allocation scheme is obtained.

[0035] Further, in S4, the construction of the scheduling model is specifically implemented as follows:

[0036] The objective function includes the optimization of the production cycle minimization function, the optimization of the equipment utilization rate maximization function, and the optimization of the order delivery time satisfaction function;

[0037] The construction process of the production cycle minimization function optimization is as follows:

[0038] On the basis of considering the equipment failure time t fault and the impact of changes in product quality requirements on task processing time, the production cycle objective function is further optimized. Since equipment failures may lead to task reallocation, let the set of tasks that need to be reallocated due to equipment failures be R. For task i ∈ R, its new processing time t ij,new needs to comprehensively consider the processing capacity c of the new equipment j,new and the factor of task priority. Assuming the task priority coefficient is π i , then the adjusted production cycle objective function is expressed as , n represents the number of tasks, and m represents the number of equipment; for normal tasks, the processing time , w i is the workload of task i, and c j,normal is the normal processing capacity of the equipment; for task i ∈ R, ;

[0039] The construction process of the equipment utilization rate maximization function optimization is as follows:

[0040] Combined with the changes in processing time caused by equipment failures and the task reallocation situation, the equipment utilization rate objective function is improved. When equipment fails, the available working time T of the equipment j needs to be redefined. Let the working time before equipment j fails be T j,used , and the failure repair time be T j,repair , then the new available working time T j,new =T j -T j,used -T j,repair , and the equipment utilization rate , where t ij is calculated according to the actual situation after equipment failures and task reallocations;

[0041] The construction process of the order delivery time satisfaction function optimization is as follows:

[0042] On the basis of considering the change in the start time s of task i caused by material supply constraints i and the processing time t of task i iWhen the factors affecting the material transportation speed change, the objective function of the order delivery time is adjusted. Let the time delay of task i due to material shortage be Δt delay , and the increased part of the processing time of the task due to unstable material transportation speed be Δt increase , then the order delivery time is adjusted to (s i + Δt delay ) + (t i + Δt increase ) ≤ D k , where D k is the order delivery time. Through this adjustment, it is ensured that in actual production, it can be accurately judged whether the order can be delivered on time;

[0043] The constraint conditions include equipment processing capacity constraints, task precedence constraints, and material supply constraints;

[0044] Equipment processing capacity constraints:

[0045] Suppose equipment j has a maintenance plan during the time period [t1, t2]. During this period, the equipment processing capacity c j,t = 0. For task i, its processing time t ij needs to satisfy that within the normal processing time period of the equipment, that is, t start,i ≥ t2 and t start,i + t ij ≤ t end,j , where t start,i is the start time of task i, t end,j is the end working time of equipment j in the current production cycle, and at the same time, w i / t ij ≤ c j,t ;

[0046] Task precedence constraints:

[0047] Suppose task groups G1 and G2. If G1 needs to be completed before G2, for task i ∈ G1 and task l ∈ G2, it is necessary to satisfy , and at the same time, for the tasks within the task group, follow the original task precedence constraint s l ≥ s i + t i ;

[0048] Material supply constraints:

[0049] If there is a problem with the quality of a certain batch of materials, let the affected material be r′, and the set of tasks involved be M. For task i ∈ M, the task needs to be suspended or the task assignment needs to be adjusted until the material quality problem is solved. Let the time to solve the material quality problem be t resolve , then for task i ∈ M, if t < t resolve, task i cannot start or needs to be re - planned. At the same time, considering the unstable material conveying speed, the safe range of the material conveying speed is set as [v min , v max . If the actual conveying speed , it is necessary to adjust the material waiting time in the task processing time according to the speed fluctuation situation.

[0050] Furthermore, in S5, the genetic algorithm is used to solve the task scheduling model. The iteration termination condition is set that the fitness value does not increase significantly for N = 10 consecutive generations. When the termination condition is met, the chromosome with the highest fitness value is output, that is, the optimal task allocation scheme is obtained.

[0051] Furthermore, the specific implementation of the genetic algorithm is as follows:

[0052] Set the initial population size P = 50, that is, there are initially 50 chromosomes. Each chromosome represents a task allocation scheme. The chromosome coding adopts integer coding. For n tasks and m devices, the chromosome is represented as [j1, j2, ……, j n , where j i represents the equipment number to which task i is assigned;

[0053] The roulette wheel selection method is adopted to calculate the selection probability of each chromosome according to the fitness value of the chromosome. Let the fitness value of chromosome k be F k , then its selection probability , is the fitness value of chromosome ;

[0054] Set the crossover probability Pc = 0.6. For the selected chromosome pairs for crossover, the single - point crossover method is adopted. For chromosome A = [a1, a2, ……, a n and chromosome B = [b1, b2, ……, b n , a random crossover point p’ is selected, 1 < p’ < n. After crossover, new chromosomes A′ = [a1, ……, a p’ , b p’+1 , ……, b n and B′ = [b1, ……, b p’ , a p’+1 , ……, a n are generated;

[0055] Set the mutation probability Pm = 0.01. For the mutated chromosome, a random gene position is selected for mutation. For chromosome C = [c1, c2, ……, c n , if the gene c q’ is selected for mutation, a new automatic line number j new, where 1 ≤ j new ≤ m, the mutated chromosome C′ = [c1, ……, c q’-1 , j new , c q’+1 , ……, c n ;

[0056] After selection, crossover, and mutation operations, a new population is generated;

[0057] Repeat the above process for multiple generations of iterative solution; set the iteration termination condition as the fitness value not significantly improving for N = 10 consecutive generations. When the termination condition is met, output the chromosome with the highest fitness value, i.e., obtain the optimal task allocation scheme.

[0058] The present invention also provides a computer-readable storage medium, on which computer program instructions capable of being run by a processor are stored. When the processor runs the computer program instructions, the method steps as described in any of the above can be implemented.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. More comprehensive and accurate data collection: The present invention collects dynamic measurement data in multiple aspects such as equipment operation, product quality, and material supply, and sets different collection frequencies according to the parameter change characteristics, which can more comprehensively and accurately reflect the actual situation of the production process. For example, high-frequency collection is performed on parameters with rapid changes in equipment operation status, and low-frequency collection is performed on parameters with slow changes, which not only ensures timely acquisition of key information but also avoids data redundancy.

[0061] 2. More effective data processing: The present invention adopts specific data cleaning methods for different types of parameters, such as threshold judgment and moving average method for equipment operation parameters, comparison method of data of similar products for product quality parameters, and verification and correction method for material supply parameters, etc. Compared with the prior art, it can more effectively process outliers, improve data quality, and provide a reliable basis for subsequent analysis and scheduling decisions.

[0062] 3. More perfect analysis model: A multi-dimensional analysis model is constructed, including an equipment operation analysis model to predict the equipment failure time to affect the equipment processing capacity in task scheduling, a product quality analysis model to establish a quantitative relationship between production parameters and product quality and then affect the task processing time, and a material supply analysis model to predict the material inventory consumption and replenishment time points and consider the impact of material conveying speed on task scheduling. While the prior art has single data in evaluating the email classification model, the present invention can more deeply and comprehensively analyze the production process and provide a more scientific basis for task scheduling.

[0063] 4. More optimized scheduling model: A scheduling model including multi-objective function optimization and multiple constraints is established. Considering actual factors such as production cycle, equipment utilization rate, order delivery time, equipment processing capacity, task sequence, and material supply, compared with the prior art that can only perform simple email classification and single-dimensional evaluation, the present invention can achieve more complex and practical production demand-oriented task scheduling planning, improving production efficiency and resource utilization rate.

[0064] 5. More flexible scheduling adjustment: It has a scheduling adjustment mechanism for equipment failures and order changes, and can timely adjust task allocation when dynamic changes occur during production, ensuring the continuity and stability of production. In contrast, the prior art lacks an effective adjustment mechanism for similar complex change situations, and the present invention makes the production system more adaptable and robust.

[0065] 6. More efficient solution method: The genetic algorithm is used to solve the scheduling model. By reasonably setting genetic operation parameters and iteration termination conditions, it can quickly search for better solutions in complex task allocation combinations. Compared with some traditional scheduling methods, it can more efficiently provide high-quality task allocation solutions for actual production, improving scheduling efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0067] The technical solutions of the present invention will be specifically described below with reference to the drawings.

[0068] The present invention provides a task flexible scheduling method for multiple automatic lines based on dynamic measurement data, including:

[0069] S1. Parameter acquisition: The acquisition terminal obtains the first parameter text according to the set acquisition frequency, and the first parameter text includes equipment operation parameters, product quality parameters, and material supply parameters;

[0070] S2. Data cleaning: Perform data cleaning operations on the equipment operation parameters, product quality parameters, and material supply parameters to obtain a parameter processing text;

[0071] S3. Data analysis: Based on the parameter processing text, establish an equipment operation analysis model, a product quality analysis model, and a material supply analysis model;

[0072] S4. Scheduling model construction: Based on the analysis results of the equipment operation analysis model, the product quality analysis model, and the material supply analysis model, optimize the objective function, and set constraints according to the objective function to construct a task scheduling model;

[0073] S5. Scheduling model solution: The genetic algorithm is used to solve the task scheduling model, and the optimal task allocation plan is obtained.

[0074] The following is the specific implementation process of the present invention.

[0075] As Figure 1 shown, an embodiment of the present invention is a task flexible scheduling method for multiple automatic lines based on dynamic measurement data, and the specific steps include:

[0076] S1. Parameter acquisition: The first parameter text is obtained by the acquisition terminal according to the set acquisition frequency;

[0077] Specifically, the first parameter text includes equipment operation parameters, product quality parameters, and material supply parameters;

[0078] The equipment operation parameters include:

[0079] Rotation speed: For various rotating equipment, such as motors, lathe spindles, etc., the rotation speed data is collected. The rotation speed directly reflects the operating speed of the equipment and has an important impact on production efficiency and product quality. For example, unstable motor rotation speed may lead to a decrease in product processing accuracy.

[0080] Temperature: Monitor the temperature of key parts of the equipment, such as the temperature of the engine block, transformer winding, etc. Excessive temperature may indicate equipment failure. For example, long-term high-temperature operation of a motor may burn out the winding.

[0081] Pressure: For equipment involving pressure control, such as hydraulic presses, compressors, etc., collect pressure data. Abnormal pressure may affect the normal operation of the equipment. For example, insufficient pressure in a hydraulic press will result in incomplete workpiece processing.

[0082] The product quality parameters include:

[0083] Dimensions: Use equipment such as laser measuring instruments and calipers to measure the dimensions of the product. Non-compliance of the product dimensions with the standards will directly affect the assembly and use performance of the product. For example, dimensional deviations of automotive parts may lead to difficulties in vehicle assembly.

[0084] The material supply parameters include:

[0085] Inventory quantity: With the help of the inventory management system and sensors, the inventory quantities of raw materials and semi-finished products are obtained in real time. Insufficient inventory may lead to production interruption. For example, the production line may be forced to stop due to the lack of a certain component.

[0086] Material conveying speed: Install speed sensors on the material conveying line to monitor the conveying speed of the material. Unstable conveying speed may affect the production rhythm. For example, too fast or too slow material conveying speed will cause material accumulation or shortage on the production line.

[0087] The method for setting the acquisition frequency is as follows:

[0088] For parameters such as rotational speed, temperature, and pressure whose changes in the operating state of the equipment are relatively fast, the acquisition frequency is set to 5 Hz, that is, 5 times per second. According to the formula N fast =f fast ×Δt (where the acquisition frequency f fast =5 Hz, Δt = 1 s), it can be calculated that within a 1-second time interval, the number of acquisitions N fast = 5 times. High-frequency acquisition can promptly capture the instantaneous changes in the equipment state and provide timely data support for equipment fault prediction and production process adjustment.

[0089] For product size parameters, since products are produced and inspected one by one, the acquisition frequency is determined according to the production rhythm. Assuming that the production rhythm is to produce one product every 10 seconds, then the acquisition frequency of product quality parameters is 0.1 Hz. Within a 10-second time interval, the number of acquisitions Nquality = 1 time, which can ensure that the quality data of each product is accurately acquired.

[0090] For parameters such as the material inventory quantity that change relatively slowly, the acquisition frequency is set to 1 / 60 Hz, that is, 1 time per minute. Within a 60-second time interval, through the formula N slow =f slow ×Δt (the acquisition frequency f slow =1 / 60 Hz, Δt = 60 s), the number of acquisitions N slow = 1 time. For the material conveying speed, if its fluctuation has a greater impact on production, the acquisition frequency can be set to 1 Hz, that is, 1 time per second, so as to promptly grasp the material conveying situation.

[0091] The acquisition terminal includes sensors for equipment operating parameters, product quality detection sensors, and material supply sensors;

[0092] Sensors for equipment operating parameters include temperature, pressure, and rotational speed sensors, which are used to monitor the operating state of the equipment, judge whether the equipment is working properly, and predict equipment faults;

[0093] Product quality detection sensors refer to size measurement sensors that are used to obtain product quality data in real time, so as to promptly adjust production parameters or process unqualified products;

[0094] Material supply sensors include material inventory sensors and material conveying speed sensors, which are used to master the material supply situation and avoid production interruption caused by material shortage;

[0095] S2: Data cleaning: Perform data cleaning operations on equipment operating parameters, product quality parameters, and material supply parameters to obtain parameter processing texts;

[0096] Specifically, the data cleaning operation of the equipment operation parameters is as follows:

[0097] Taking the temperature data as an example, for the equipment temperature data sequence {Tn} transmitted from the acquisition module, due to factors such as environmental interference, there may be outliers. Based on the normal working characteristics of the equipment, the normal temperature lower limit Tmin = 50°C and the upper limit Tmax = 80°C are determined. Using the outlier judgment expression, if the temperature value Ti collected at the i-th moment is Ti < Tmin or Ti > Tmax, then Ti is determined as an outlier. For other equipment operation parameters such as rotational speed and pressure, in a similar manner, thresholds are set according to the normal operation range of the equipment to identify and clean outliers. For example, the normal rotational speed range of a certain motor is 1400 - 1500 revolutions per minute, and the rotational speed data outside this range can be determined as outliers;

[0098] Then, the sliding average method is used to clean the outliers. Set the sliding window size w = 10. When the data point serial number k ≥ w, if the k-th data point is an outlier, its cleaned value ; when k < w, if the k-th data point is an outlier, its cleaned value .

[0099] The data cleaning operation of the product quality parameters is as follows:

[0100] For the product dimension data, if the measured value exceeds the tolerance range of the product standard dimension, it is determined as an outlier. For example, the standard length of a certain product is 100 mm, and the tolerance range is ±0.5 mm. The data with a measured value less than 99.5 mm or greater than 100.5 mm is an outlier.

[0101] The comparison method of the data of the same type of products can be used to clean the outliers of the product quality parameters. For the dimension data, the average value and standard deviation of the dimensions of the products in the same batch can be calculated. The data that deviates from the average value by more than 3 times the standard deviation is regarded as an outlier and corrected. The correction method is to replace the outlier with the median of the dimensions of the products in the same batch to ensure the accuracy and reliability of the data and provide effective data for subsequent product quality analysis and production parameter adjustment

[0102] The data cleaning operation of the material supply parameters is as follows:

[0103] If the material inventory quantity data appears as a negative number, it is determined as an outlier. If the material conveying speed data appears as zero instantaneously or a value far beyond the normal conveying speed range, it is also regarded as an outlier. For example, the normal conveying speed range of a certain material is 0.5 - 1 m / s. The data with a speed value less than 0.5 m / s or greater than 1 m / s and a duration less than 1 second may be an outlier.

[0104] The abnormal values of the cleaning material inventory quantity can be checked against the inventory quantity at the previous moment and the material in-out records to find out the reasons for errors and make corrections. For the abnormal values of the material conveying speed, the weighted average of the speed values in adjacent time periods is used for correction, and the weights are determined according to the proximity of the time intervals. The closer the time interval, the greater the weight, so as to ensure that the corrected data can truly reflect the material conveying situation.

[0105] S3: Data analysis: Based on the parameter-processed text, construct an equipment operation analysis model, a product quality analysis model, and a material supply analysis model;

[0106] Specifically, the equipment operation analysis model uses the ARIMA(p,d,q) model to predict the equipment failure time. For the time series data {y T} of the equipment operation parameters (such as rotation speed, temperature, and pressure) after being processed by S2, y t is the data at the t-th moment in {y T}, and the model expression is:

[0107] The equipment operation analysis model uses the ARIMA(p,d,q) model to predict the equipment failure time. For the time series data {y T} of the equipment operation parameters after being processed by S2, y t is the data at the t-th moment in {y T}, and the model expression is:

[0108]

[0109] Among them, the autoregressive order p = 2, the differencing order d = 1, and the moving average order q = 1. 、 are the model parameters, y t-i is the data at the (t - i)-th moment in {y T}, B is the backshift operator, μ is the constant term, is the white noise at the t-th moment, is the white noise at the (t - j)-th moment; through the processing of historical data, the model outputs the predicted value of the equipment failure time t fault .

[0110] It should be further noted that in the task scheduling model, the equipment processing capacity constraint is affected by the equipment failure time. Let the actual processing capacity of equipment j at time t be c j,t . If t ≥ t fault , then c j,t = 0; if t < t fault , c j,t is determined according to the normal operation parameters of the equipment. In the objective function of minimizing the production cycle , the processing time t of task i on equipment jij It will be adjusted according to the change of the equipment processing capacity. Here, n is the total number of tasks and m is the total number of equipment. When the equipment is normal , where w i is the workload of task i, c j,normal is the normal processing capacity of the equipment. When a equipment failure is predicted, t ij needs to be reassigned to other equipment to calculate the processing time. In maximizing the equipment utilization rate , , T j is the available working time of equipment j. The equipment failure causes a change in t ij , which in turn affects the calculation of the equipment utilization rate.

[0111] The product quality analysis model refers to constructing a linear regression model that correlates product quality with production parameters. Let the qualified rate of product size be Y, and the production parameters be X1 (equipment rotation speed), X2 (equipment temperature), X3 (parameters related to product quality such as material conveying speed, etc.). The model expression is Y = β0 + β1X1 + β2X2 + β3X3 + . The regression coefficients β0, β1, β2, and β3 are estimated by the least squares method, and the quantitative relationship between product quality and production parameters is determined through a large amount of data;

[0112] It should be further noted that when the product quality requirement is improved, such as the product size tolerance range is reduced, then the new quality requirement is set as Y new . According to the linear regression model, to meet Y new , the production parameters need to be adjusted, which in turn affects the task processing time. Assuming that the qualified rate of product size is related to the equipment rotation speed X1 and the equipment temperature X2, to achieve Y new , X1 needs to be adjusted to X 1,new , and X2 needs to be adjusted to X 2,new . The changes in the equipment rotation speed and equipment temperature will change the equipment processing capacity. Let the equipment processing capacity become c j,new , then the processing time t ij of task i on equipment j becomes t ij,new = w i / c j,new . This will affect the calculation of the objective function T for minimizing the production cycle and the calculation of the equipment utilization rate U for maximizing, because the change in t ij,new will change value;

[0113] The material supply analysis model refers to using time series analysis to predict the material inventory consumption trend and the replenishment time point. Let the time series data of the material inventory quantity be {I T}, and I t is {I TThe data at the t-th moment in {I t+1 = αI t +(1 - α)I t-1 , where α is the smoothing coefficient with a value range between 0 and 1, and I t-1 is the data at the (t - 1)-th moment in {I T}, to predict the material inventory quantity I t+k at the future k-th moment, determine the replenishment time point t replenish . Meanwhile, analyze the stability of the material conveying speed and calculate the standard deviation σ of the material conveying speed fluctuation speed ;

[0114] It should be further noted that in the material supply constraint of the task scheduling model, assume the demand for material r by task i is m ir , and the inventory quantity of material r at moment t is I r,t . If I r,t < m ir and t < t replenish , the task scheduling needs to be adjusted. Prioritize arranging tasks with smaller demand for the corresponding material. Assume that originally task i was planned to start at moment t and needs to be postponed to moment t + Δt due to material shortage. This will change the task start time s i , and further affect the judgment of the objective function s i + t i ≤ D k . Meanwhile, the material conveying speed is unstable. Assume the original material waiting time in the task processing time t i is t wait . The material conveying speed fluctuation causes the material waiting time to become t wait,new = t wait + Δt wait , where Δt wait is determined by σ speed . Then t i becomes t i,new = t i - t wait + t wait,new , affecting the calculation of minimizing the production cycle T and meeting the order delivery time;

[0115] The determination method of Δt wait is as follows:

[0116] Assume the average speed of material conveying is v. When the standard deviation of material conveying speed fluctuation is σ speed , the increment Δt wait of the material waiting time is calculated by the following formula:

[0117]

[0118] where t i is the estimated value of the material waiting time in the original task processing time, which is determined according to historical data. The value of γ is generally determined by statistical analysis and actual verification of past production data. For example, through a large amount of data statistics and actual production observations, it is found that when σ speed increases by a certain proportion, the material waiting time will increase by a specific proportion accordingly. The value of γ is obtained by fitting methods such as regression analysis.

[0119] S4: Scheduling model construction: Based on the analysis results of the equipment operation analysis model, product quality analysis model, and material supply analysis model, optimize the objective function, and set constraint conditions according to the objective function;

[0120] Specifically, the objective function includes the optimization of the minimum production cycle function, the optimization of the maximum equipment utilization rate function, and the optimization of the function to meet the order delivery time;

[0121] The constraint conditions include equipment processing capacity constraints, task precedence constraints, and material supply constraints;

[0122] The construction process of the optimization of the minimum production cycle function is as follows:

[0123] On the basis of considering the equipment failure time t fault and the impact of changes in product quality requirements on the task processing time, further optimize the production cycle objective function. Since equipment failures may lead to task reallocation, let the set of tasks that need to be reallocated due to equipment failures be R. For task i ∈ R, its new processing time t ij,new needs to comprehensively consider the processing capacity c j,new of the new equipment and factors such as task priority. Assume the task priority coefficient is π i , then the adjusted production cycle objective function can be expressed as , where for normal tasks t ij is calculated according to the original rules. For task i ∈ R, . In this way, when minimizing the production cycle, high-priority tasks are arranged first to reduce the impact of equipment failures on the overall production schedule.

[0124] The construction process of the optimization of the maximum equipment utilization rate function is as follows:

[0125] Combined with the changes in processing time caused by equipment failures and the task reallocation situation, improve the equipment utilization rate objective function. When the equipment fails, the available working time T j of the equipment needs to be redefined. Let the working time before the failure of equipment j be T j,used , and the failure repair time be T j,repair , then the new available working time Tj,new =T j -T j,used -T j,repair , equipment utilization rate , where t ij needs to be calculated according to the actual situation after equipment failure and task reallocation. Through this optimization, it can more accurately reflect the actual utilization efficiency of the equipment in a complex production environment and provide more accurate guidance for improving equipment utilization rate.

[0126] The optimization construction process of the order delivery time function is as follows:

[0127] When considering the change in the task start time s i caused by material supply constraints and the change in the task processing time t i due to factors such as unstable material conveying speed, the order delivery time objective function is adjusted. Let the time delay in the start of task i due to material shortage be Δt delay , and the increased part of the task processing time due to unstable material conveying speed be Δt increase , then the order delivery time is adjusted to (s i + Δt delay ) + (t i + Δt increase ) ≤ D k , through this adjustment, it is ensured that in actual production, even in the face of problems such as material supply, it can accurately judge whether the order can be delivered on time and provide more realistic time constraints for task scheduling.

[0128] The equipment processing capacity constraint means:

[0129] Suppose equipment j has a maintenance plan during the time period [t1, t2]. During this period, the equipment processing capacity c j,t = 0. For task i, its processing time t ij needs to meet the condition that within the normal processing time period of the equipment, that is, when t start,i ≥ t2 and t start,i + t ij ≤ t end,j , where t start,i is the start time of task i, t end,j is the end working time of equipment j in the current production cycle, and at the same time w i / t ij ≤ c j,t , in this way, the actual situation such as equipment maintenance is fully considered to ensure that the task allocation is within the allowable range of the equipment processing capacity.

[0130] The task precedence constraint means:

[0131] Set task groups G1 and G2. If G1 needs to be completed before G2, for task i ∈ G1 and task l ∈ G2, it is necessary to satisfy , and at the same time, for the tasks within the task group, follow the original task sequence constraint s l ≥ s i + t i . Through this hierarchical task sequence constraint setting, it is more in line with the logical relationship between tasks in complex production processes and ensures the orderly progress of production.

[0132] The material supply constraint means that:

[0133] If there is a problem with the quality of a certain batch of materials, let the affected material be r′, and the set of tasks involved be M. For task i ∈ M, it is necessary to suspend the task or adjust the task assignment until the material quality problem is solved. Let the material quality problem solving time be t resolve , then for task i ∈ M, if t < t resolve , task i cannot start or needs to be re - planned. At the same time, considering the unstable material conveying speed situation, let the safe range of the material conveying speed be [v min , v max . If the actual conveying speed , it is necessary to adjust the material waiting time in the task processing time according to the speed fluctuation situation to ensure the stability of the material supply link and not affect the normal progress of the task.

[0134] S5: Solving the scheduling model: Use the genetic algorithm to solve the task scheduling model. Set the iteration termination condition as that the fitness value does not increase significantly for N = 10 consecutive generations. When the termination condition is met, output the chromosome with the highest fitness value.

[0135] Specifically, the genetic algorithm is as follows:

[0136] Set the initial population size P = 50, that is, there are initially 50 chromosomes, and each chromosome represents a task assignment scheme. The chromosome encoding adopts the integer encoding method. For example, for n tasks and m automatic lines, the chromosome can be expressed as [j1, j2, ……, j n , where ji represents the automatic line number to which task i is assigned;

[0137] Adopt the roulette wheel selection method to calculate the probability of each chromosome being selected according to the fitness value of the chromosome; Let the fitness value of chromosome k be F k , then its selection probability , is the fitness value of chromosome . Through roulette wheel selection, chromosomes with higher fitness values have a greater probability of being selected into the next generation, realizing the retention and inheritance of excellent task assignment schemes;

[0138] Set the crossover probability Pc = 0.6. For the selected chromosome pairs to perform crossover, the single-point crossover method is adopted. For example, for chromosome A = [a1, a2, ……, a n and chromosome B = [b1, b2, ……, b n , randomly select a crossover point p’ (1 < p’ < n). After crossover, generate new chromosomes A′ = [a1, ……, a p’ , b p’+1 , ……, b n and B′ = [b1, ……, b p’ , a p’+1 , ……, a n . Through the crossover operation, realize the gene exchange between different task assignment schemes and explore better task assignment combinations;

[0139] Set the mutation probability Pm = 0.01. For the mutated chromosomes, randomly select a gene position for mutation. For example, for chromosome C = [c1, c2, ……, c n , if the gene c q ’ is selected for mutation, randomly generate a new automatic line number j new (1 ≤ j new ≤ m). After mutation, the chromosome C′ = [c1, ……, c q’-1 , j new , c q’+1 , ……, c n . Through the mutation operation, introduce new genes into the population to prevent the algorithm from falling into a local optimal solution;

[0140] After the selection, crossover, and mutation operations, generate a new population. Repeat the above steps of fitness function calculation, genetic operations, etc., and perform multi-generation iterative solution. Set the iteration termination condition as the fitness value not increasing significantly for N = 10 consecutive generations. When the termination condition is met, output the chromosome with the highest fitness value, that is, obtain the optimal task assignment scheme to guide the actual task scheduling of multiple automatic lines.

[0141] The fitness calculation method is as follows:

[0142] For each chromosome, that is, the task assignment scheme, calculate its corresponding production cycle T, equipment utilization rate U, and whether it meets the order delivery time constraint. Let the weight coefficients be w T , w U , w D and w T + w U + w D = 1. The fitness function F can be expressed as F = w T × T1 + w U × U + w D×δ, where δ is an indicator variable for the order delivery time compliance. If all orders meet the delivery time, then δ = 1; otherwise, δ = 0. Through this fitness function, the three objectives of production cycle, equipment utilization rate, and order delivery time are balanced during the solution process;

[0143] S6: Scheduling adjustment: When equipment failure triggers scheduling adjustment, remove the faulty equipment from the task scheduling model, recalculate the processing time of each task on other available equipment, and reassign the tasks originally assigned to the faulty equipment according to the remaining processing capacity of other equipment and task priorities. When the order changes, update the order delivery time in the objective function of the task scheduling model. If the order increases in production volume, under the condition of sufficient material supply, preferentially assign the new tasks to the automatic line with a relatively low current load and matching processing capacity, and recalculate the scheduling plan.

[0144] S7: Task execution: After receiving the scheduling instruction, the automatic line control terminal parses the instruction content, converts the task assignment information into equipment control signals, controls the startup, stop, and adjustment of processing parameters of the equipment, and carries out production in an orderly manner according to the new task assignment plan.

[0145] The automatic line control terminal includes a central processing unit, a storage module, and a communication interface;

[0146] The central processing unit has powerful data processing and computing capabilities, which are used to quickly parse the received scheduling instructions, efficiently process complex task assignment information, and lay a foundation for subsequent conversion into equipment control signals. For example, in the multi-variety and small-batch production mode, in the face of frequently changing task scheduling instructions, the CPU can complete a large amount of data parsing and computing in a short time, ensuring the response speed of the control terminal and avoiding production delays.

[0147] The storage module is used to store various programs and data required for the operation of the control terminal. It includes both the basic software for system operation, equipment control programs, and historical production data, equipment parameter configuration information, etc. After receiving the scheduling instruction, the control terminal can quickly retrieve relevant equipment parameters and control logic from the storage module to accurately convert the task assignment information into equipment control signals. For example, when it is necessary to adjust the equipment processing parameters, the control terminal can quickly read the best parameter configuration of the corresponding equipment under different tasks from the storage module to achieve precise adjustment of equipment parameters.

[0148] The communication interface is used for high-speed data transmission with the server where the task scheduling model is located, the enterprise management information system, etc., to receive the latest scheduling instructions and feedback production status information. At the same time, it is also equipped with serial ports (such as RS - 232, RS - 485), fieldbus interfaces (such as Profibus, CANopen), etc., for connecting various sensors, actuators, and lower-level device controllers. Through these communication interfaces, the control terminal can establish stable and reliable communication links with various devices in the automatic line to ensure the accurate transmission of data.

[0149] The above are the preferred embodiments of the present invention. All changes made according to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention in terms of the functions and effects produced belong to the protection scope of the present invention.

Claims

1. A task flexible scheduling method for multiple automatic lines based on dynamic measurement data, characterized in that It includes the following steps: S1. Parameter acquisition: The acquisition terminal obtains the first parameter text according to the set acquisition frequency. The first parameter text includes equipment operation parameters, product quality parameters, and material supply parameters; S2. Data cleaning: Perform data cleaning operations on the equipment operation parameters, product quality parameters, and material supply parameters to obtain the parameter processing text; S3. Data analysis: Based on the parameter processing text, construct an equipment operation analysis model, a product quality analysis model, and a material supply analysis model; The specific implementation is as follows: The device operation analysis model uses the ARIMA(p,d,q) model to predict the device failure time. For the time series data {y T} of the device operation parameters after being processed by S2, y t is the data at the t-th moment in {y T}, and the model expression is: Among them, the autoregressive order p = 2, the differencing order d = 1, and the moving average order q = 1. , are model parameters, y t-i is the data at the (t - i)-th moment in {y T}, B is the backward shift operator, μ is the constant term, is the white noise at the t-th moment, is the white noise at the (t - j)-th moment; the model output is the predicted value of the equipment failure time t fault . The product quality analysis model refers to constructing a linear regression model that correlates product quality with production parameters; The material supply analysis model refers to using time series analysis to predict the material inventory consumption trend and replenishment time point. Let the time series data of the material inventory quantity be {I T}, I t being the data at the t-th moment in {I T}. By analyzing the historical data trend and seasonal characteristics, a time series prediction model I t+1 = αI t + (1 - α)I t-1 is established, where α is the smoothing coefficient with a value range between 0 and 1, I t-1 being the data at the (t - 1)-th moment in {I T}. Predict the material inventory quantity I t+k at the future k-th moment, determine the replenishment time point t replenish . At the same time, analyze the stability of the material conveying speed and calculate the standard deviation σ speed of the material conveying speed fluctuation; S4. Scheduling model construction: Based on the analysis results of the equipment operation analysis model, the product quality analysis model, and the material supply analysis model, optimize the objective function, and set constraint conditions according to the objective function to construct a task scheduling model; S5. Scheduling model solution: Use the genetic algorithm to solve the task scheduling model, and obtain the optimal task allocation plan; 2. The task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that In S1, the equipment operation parameters include rotational speed, temperature, and pressure; the product quality parameters include dimensions; the material supply parameters include inventory quantity and material conveying speed.

3. The task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that In S1, the method for setting the acquisition frequency is as follows: For the equipment operation parameters, set the acquisition frequency to 5Hz; For the product quality parameters, the acquisition frequency is determined according to the production beat. The production beat means producing 1 product every N seconds, and set the acquisition frequency = 1 / N Hz; For the material supply parameters, set the acquisition frequency to 1 / 60Hz.

4. A task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that In S2, the data cleaning operations are specifically as follows: For the equipment operation parameters, set upper and lower limit thresholds to identify outliers, and use the moving average method to clean the outliers; For the product quality parameters, set standard thresholds to identify outliers, and use the method of comparing with data of similar products to clean the outliers; For the material supply parameters, set standard thresholds or upper and lower limit thresholds to identify outliers, and use the method of comparing with the data of the previous moment or the weighted average method of data in adjacent time periods to clean the outliers.

5. A task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that The expression of the linear regression model that correlates product quality with production parameters is as follows: Y = β0 + β1X1 + β2X2 + β3X3 + Among them, the qualified rate of product size in product quality parameters is Y, and the production parameters are the equipment rotation speed X1, the equipment temperature X2, and the material conveying speed X3. β0, β1, β2, and β3 are constants. The least squares method is used to estimate the regression coefficients β0, β1, β2, and β3 to determine the quantitative relationship between product quality and production parameters.

6. The task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that, In S4, the specific implementation of the scheduling model construction is as follows: The objective function includes minimizing the production cycle function optimization, maximizing the equipment utilization rate function optimization, and meeting the order delivery time function optimization; The construction process of the minimizing production cycle function optimization is as follows: Considering the equipment failure time t fault and the influence of changes in product quality requirements on task processing time, the production cycle objective function is optimized. Let the set of tasks that need to be reallocated due to equipment failure be R. For task i ∈ R, its new processing time t ij,new needs to comprehensively consider the processing capacity c of the new equipment j,new and the factor of task priority. Assuming the task priority coefficient is π i , then the adjusted production cycle objective function is expressed as , n represents the number of tasks, and m represents the number of equipment; for normal tasks, the processing time , w i is the workload of task i, and c j,normal is the normal processing capacity of the equipment; for task i ∈ R, ; The construction process of the maximizing equipment utilization rate function optimization is as follows: Combined with the change in processing time caused by equipment failures and the task reassignment situation, the equipment utilization objective function is improved. When the equipment fails, the available working time T of the equipment j needs to be redefined. Let the working time of equipment j before the failure be T j,used , and the failure repair time be T j,repair , then the new available working time T j,new = T j - T j,used - T j,repair . The equipment utilization rate , where t ij is calculated according to the actual situation after equipment failure and task reassignment; The construction process of the meeting order delivery time function optimization is as follows: Considering the change in the start time s of task i due to material supply constraints i and the change in the processing time t of task i i due to the unstable material conveying speed, the objective function of the order delivery time is adjusted. Let the delay start time of task i due to material shortage be Δt delay , and the increased part of the task processing time due to the unstable material conveying speed be Δt increase . Then the order delivery time is adjusted to (s i + Δt delay ) + (t i + Δt increase ) ≤ D k , where D k is the order delivery time. Through this adjustment, it is ensured that in actual production, it can be accurately judged whether the order can be delivered on time; The constraint conditions include equipment processing capacity constraints, task precedence constraints, and material supply constraints; Equipment processing capacity constraints: Suppose device j has a maintenance plan during the time period [t1, t2], and during this period, the processing capacity c of the device j,t = 0. For task i, its processing time t ij needs to satisfy that it is within the normal processing time period of the device, that is, t start,i ≥ t2 and t start,i + t ij ≤ t end,j , where t start,i is the start time of task i, t end,j is the end working time of device j in the current production cycle. At the same time, w i / t ij ≤ c j,t ; Task precedence constraints: Set task groups G1 and G2. If G1 needs to be completed before G2, for task i ∈ G1 and task l ∈ G2, it is necessary to satisfy , and at the same time, for the tasks within the task group, follow the original task sequence constraint s l ≥ s i + t i ; Material supply constraints: If there are quality problems with a certain batch of materials, let the affected materials be r′, and the set of tasks involved be M. For task i ∈ M, the task needs to be suspended or the task assignment adjusted until the material quality problem is resolved. Let the time to resolve the material quality problem be t resolve , then for task i ∈ M, if t < t resolve , task i cannot start or needs to be replanned. At the same time, considering the unstable material conveying speed, let the safe range of the material conveying speed be [v min , v max . If the actual conveying speed , it is necessary to adjust the material waiting time in the task processing time according to the speed fluctuation situation.

7. A task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1, characterized in that In S5, use the genetic algorithm to solve the task scheduling model, and set the iteration termination condition as the fitness value not significantly improving for N = 10 consecutive generations. When the termination condition is met, output the chromosome with the highest fitness value, that is, obtain the optimal task allocation plan.

8. A task flexible scheduling method for multiple automatic lines based on dynamic measurement data according to claim 1 or 7, characterized in that The specific implementation of the genetic algorithm is as follows: Set the initial population size P = 50, that is, there are initially 50 chromosomes, and each chromosome represents a task allocation scheme. The chromosome encoding adopts integer encoding. For n tasks and m devices, the chromosome is represented as [j1, j2, ……, j n , where j i represents the device number to which task i is assigned; The roulette wheel selection method is adopted to calculate the probability of each chromosome being selected according to the fitness value of the chromosome. Let the fitness value of chromosome k be F k , then the probability of its being selected , is the fitness value of chromosome . Set the crossover probability Pc = 0.

6. For the selected chromosome pairs to perform crossover, the single-point crossover method is adopted. For chromosome A = [a1, a2, ……, a n and chromosome B = [b1, b2, ……, b n , randomly select a crossover point p’, where 1 < p’ < n. After crossover, generate new chromosomes A’ = [a1, ……, a p’ , b p’+1 , ……, b n and B’ = [b1, ……, b p’ , a p’+1 , ……, a n ; Set the mutation probability Pm = 0.

01. For the mutated chromosome, randomly select a gene position for mutation. For chromosome C = [c1, c2, ……, c n , if gene c q’ is selected for mutation, then randomly generate a new automatic line number j new , 1 ≤ j new ≤ m, and the mutated chromosome C′ = [c1, ……, c q’-1 , j new , c q’+1 , ……, c n ; After selection, crossover, and mutation operations, generate a new population; Repeat the above process for multiple generations of iterative solution; set the iteration termination condition as that the fitness value does not increase significantly for N = 10 consecutive generations. When the termination condition is met, output the chromosome with the highest fitness value, that is, obtain the optimal task allocation scheme.

9. A computer-readable storage medium, on which computer program instructions capable of being run by a processor are stored. When the processor runs the computer program instructions, the steps of a task flexible scheduling method for multiple automatic lines based on dynamic measurement data as described in any one of claims 1-8 can be implemented.

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