Adaptive Sequence Control Method and System for Flexible Manufacturing

Abstract

The present invention discloses an adaptive sequence control method and system for flexible manufacturing, which relates to the technical field of automatic control. The method includes: parsing process monitoring data to identify abnormal processes and determining an abnormal time window; determining a constraint time window based on the processing time window relationship of the abnormal processes; combining the abnormal time window and the constraint time window to perform timing analysis on the manufacturing processes to obtain a deployment window period; and implementing sequence control according to the deployment window period and robot control parameters. It solves the technical problem that when abnormal processes occur in the existing flexible manufacturing environment, the production plan cannot be quickly adjusted, resulting in low efficiency in the manufacturing process, and achieves the technical effect of being able to quickly adjust the production plan when abnormal processes occur in the flexible manufacturing environment.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to an adaptive sequence control method and system for flexible manufacturing. Background Art

[0002] With the continuous development of industrial automation and intelligence, flexible manufacturing, as an important form of modern manufacturing system, has played an important role in responding to the dynamic production needs of multiple varieties and small batches. However, the manufacturing processes in the flexible manufacturing environment often have complex time dependencies and resource allocation problems. Once an abnormality occurs in a process (such as processing time limit, equipment failure or resource conflict), it may have a chain effect on the entire production sequence, resulting in obstruction of the production process. Existing technologies usually rely on fixed rules or manual intervention to handle abnormal processes, but this method is often difficult to respond to the impact of abnormalities on the production sequence in a timely manner, and lacks flexibility in a dynamic environment, and cannot adapt to the requirements of flexible manufacturing for real-time adjustment. Especially in scenarios where there is a strict time dependency between processes, existing methods are difficult to effectively coordinate the time constraints and resource conflicts between processes, which can easily lead to production delays or resource waste. How to deal with process abnormalities in a flexible manufacturing environment has become a technical problem that needs to be solved urgently.

[0003] At present, there is a technical problem in the relevant technologies that when abnormalities occur in the process under the flexible manufacturing environment, the production plan cannot be adjusted quickly, resulting in low efficiency of the manufacturing process. Summary of the invention

[0004] The present application solves the technical problem that when abnormalities occur in the process in the existing flexible manufacturing environment, the production plan cannot be adjusted quickly, resulting in low efficiency of the manufacturing process, by providing an adaptive sequence control method and system for flexible manufacturing.

[0005] The present application provides an adaptive sequence control method for flexible manufacturing, including:

[0006] Connect process monitoring data, analyze the process monitoring data, and identify abnormal processes; perform abnormal timing identification based on the abnormal processes to obtain abnormal time windows; based on the abnormal processes, use the processing time window relationship of the manufacturing processes to determine the constraint time window; perform window timing analysis on each manufacturing process based on the abnormal time window and the constraint time window to obtain the allocation window period of each manufacturing process; based on the allocation window period of each manufacturing process, perform sequence control according to the robot control parameters of each manufacturing process.

[0007] The present application provides an adaptive sequence control system for flexible manufacturing, including:

[0008] An abnormal process identification module, which is used to connect process monitoring data, parse the process monitoring data, and identify abnormal processes; an abnormal time window acquisition module, which is used to perform abnormal time series identification based on the abnormal processes to obtain an abnormal time window; a constrained time window module, which is used to determine a constrained time window based on the abnormal processes and using the processing time window relationship of manufacturing processes; a deployment time window acquisition module, which is used to perform window time series parsing on each manufacturing process according to the abnormal time window and the constrained time window to obtain the deployment time window of each manufacturing process; a sequence control module, which is used to perform sequence control according to the deployment time window of each manufacturing process and in accordance with the robot control parameters of each manufacturing process.

[0009] It is intended to propose an adaptive sequence control method and system for flexible manufacturing through the present application. First, process monitoring data is parsed to identify abnormal processes and determine an abnormal time window; based on the processing time window relationship of the abnormal processes, a constrained time window is determined; by combining the abnormal time window and the constrained time window, time series parsing is performed on manufacturing processes to obtain a deployment time window; sequence control is implemented according to the deployment time window and robot control parameters, achieving the technical effect of being able to quickly adjust the production plan when an abnormality occurs in the process in a flexible manufacturing environment. Brief Description of the Drawings

[0010] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the application.

[0011] It should be understood that the operations in front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to needs, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0012] Figure 1 It is a schematic flowchart of the adaptive sequence control method for flexible manufacturing provided by the embodiment of the present application;

[0013] Figure 2 It is a schematic structural diagram of the adaptive sequence control system for flexible manufacturing provided by the embodiment of the present application.

[0014] Description of the reference numerals: abnormal process identification module 10, abnormal time window acquisition module 20, constrained time window module 30, deployment time window acquisition module 40, sequence control module 50. Detailed Embodiments

[0015] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the specific implementation manners of the present application.

[0016] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0017] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0018] The embodiment of the present application provides an adaptive sequence control method for flexible manufacturing, as Figure 1 shown, the method includes:

[0019] Step S100: Connect the process monitoring data, parse the process monitoring data, and identify abnormal processes. Specifically, in a flexible manufacturing environment, the connection and parsing of process monitoring data are key steps in identifying abnormal processes. First, by connecting the monitoring devices of each manufacturing process to the data management system, multi-dimensional data during the processing are collected in real time, including processing time (such as the actual time taken to complete a process), equipment status (such as equipment vibration frequency, temperature change, current fluctuation, etc.), resource utilization (such as raw material consumption, energy use efficiency), and quality indicators of process results (such as dimensional deviation, product defect rate, etc.). For example, if the processing time of a certain process significantly exceeds the standard time, it indicates a decrease in equipment operation efficiency or an unreasonable processing path plan; while an abnormal increase in equipment temperature may indicate component aging or equipment overload. After the collected data passes the integrity check, it will be compared with historical normal data to extract key abnormal factors, such as processing time deviation amount, equipment operation volatility, or resource over-consumption ratio, etc. Through comparative analysis, when the energy consumption of a certain device is significantly higher than the historical average of the same type of process, it indicates energy waste or internal wear of the device; when it is found that the processing time is extremely long and accompanied by unstable equipment operation, it may indicate a latent fault in the equipment. Further, by parsing the abnormal factors and combining multi-factor comprehensive evaluation, it is judged whether the process is abnormal and classified. For example, process abnormality (processing time is extremely long or skip-step processing), equipment abnormality (equipment overheating, shutdown, or overload), and resource abnormality (raw material depletion or excessive consumption). For example, when a production line repeatedly shows significantly insufficient processing time, it means steps are missing or the detection device is faulty, and frequent overloading of a certain device indicates unreasonable equipment load distribution. By clarifying the specific type, cause, and time node of the abnormal process, reliable data support is provided for the subsequent adjustment and optimization of the production sequence, thus ensuring the stable operation of the manufacturing system in a dynamic and complex environment.

[0020] In a possible implementation, connect the process monitoring data, parse the process monitoring data, and identify abnormal processes. Step S100 further includes step S110, which is to identify the abnormal factors of each manufacturing process and deploy process monitoring equipment based on the data attributes of the abnormal factors. Specifically, identifying the abnormal factors of each manufacturing process and deploying monitoring equipment based on their data attributes is the key to achieving efficient monitoring. First, by analyzing the characteristics of each process and historical production data, extract the key factors that may cause abnormalities. For example, the abnormal factor of processing time may be manifested as overtime or early completion, the abnormal factor of equipment status may include too high temperature, too large vibration frequency, abnormal current, etc., and the abnormal factor of resource utilization may involve excessive use or shortage of raw materials. For different process types, the focus of abnormal factors is also different. For example, vibration and processing time are the main factors in precision machining processes, while heat treatment processes focus on the combined abnormality of temperature and time. After identifying the factors, select the corresponding monitoring equipment according to their data attributes. For processing time, a high-precision industrial timer can be deployed; for equipment status, temperature sensors, vibration sensors or current monitoring modules can be installed; for resource consumption, raw material metering devices or energy consumption monitoring modules can be used. If some processes involve multiple abnormal factors, multiple devices need to be jointly deployed. For example, in precision machining processes, vibration sensors and timers are used simultaneously to monitor equipment stability and processing time. On multi-stage production lines, monitoring equipment needs to cover each key process. For example, in high-energy-consuming processes, temperature sensors and energy consumption monitoring equipment are used in combination to track the operating efficiency and energy use in real time. In addition, when the initial monitoring results cannot reflect abnormal characteristics, necessary monitoring modules can be added. For example, when the monitoring of processing time is unclear, high-precision timing equipment or real-time data recording devices can be added. After the deployment is completed, the monitoring effect also needs to be verified through actual operation. For example, compare the monitoring data with historical normal data to check whether the equipment accurately captures the abnormal factors. If there are blind spots or insufficient monitoring accuracy, further optimize the equipment configuration to ensure the effectiveness and stability of the monitoring system.

[0021] Step S120: Identify and determine the processing time characteristics of the process monitoring data for each manufacturing process. When the processing time characteristics cannot be determined, add a processing time monitoring device to the process monitoring equipment. Specifically, in a flexible manufacturing environment, identifying and determining the processing time characteristics of process monitoring data is an important step to ensure the normal operation of the process. First, the process data collected by the monitoring equipment usually includes the processing start time, end time, and equipment operating status. For example, in a machining process, the equipment records that the tool starts at 10:00:00 and ends at 10:05:00, and the calculated processing duration is 5 minutes. If the process runs beyond the expected time (such as the processing duration reaches 7 minutes), it may indicate a decrease in equipment operating efficiency or an unreasonable processing path setting; if the time is insufficient (such as only 2 minutes), there may be missing process steps or the equipment has not completed the full processing. For the collected data, by comparing it with the historical normal processing time, it is possible to determine whether there is an abnormality in the current processing time. However, in some cases, the existing monitoring equipment may not be able to clearly reflect the processing time characteristics, such as missing monitoring data, too low sampling frequency, or excessive noise interference. At this time, it is necessary to add a processing time monitoring module to the process monitoring equipment, such as a high-precision industrial timer, a photoelectric sensor, or a time recording module, to accurately record the start and end times of processing. Taking the assembly line process as an example, when the existing monitoring equipment cannot record the time when the workpiece enters the processing area, a photoelectric sensor can be added to capture this key time node; in the heat treatment process, an independent time monitoring module can be deployed to accurately record the heating and cooling durations. After adding the new equipment, through re-calibration and data verification, ensure that the data collected by the new equipment can accurately reflect the processing time characteristics and the monitoring of the process operating status is more comprehensive. For example, after installing the time monitoring equipment, the data record of a certain process shows that the processing time changes from the previous fuzzy interval to a clear 5 minutes and 20 seconds, thus discovering a slight time delay problem in the processing process.

[0022] Step S130: Connect the process monitoring devices of each manufacturing process to obtain the process monitoring data of each manufacturing process. Specifically, in a flexible manufacturing environment, connecting the monitoring devices of each manufacturing process and obtaining the monitoring data are the basis for achieving efficient monitoring. First, according to the characteristics of different processes, select appropriate monitoring devices. For example, an industrial timer is used to record the processing time, a vibration sensor monitors the equipment stability, a temperature sensor monitors the temperature change in the heat treatment process, and an energy consumption meter tracks the resource consumption, etc. For an assembly line, if multiple parameters need to be monitored simultaneously for some processes, such as processing time, equipment operating status, and raw material usage, then a timer, a vibration sensor, and an energy consumption measurement device need to be jointly deployed. After the device connection is completed, connect the devices to the data management system through industrial Ethernet or a wireless sensor network (such as Wi-Fi, Bluetooth) to achieve real-time data collection. For example, in a machining process, the timer records the tool start time as 10:00:00 and the end time as 10:05:00. At the same time, the vibration sensor detects that the vibration amplitude during operation is within the normal range, and the energy consumption measurement device shows that the power consumption is 5 kW. During the data collection process, it is particularly necessary to synchronously obtain the data of multiple processes to ensure the consistency of the time series. For example, on an assembly line, it is necessary to record the processing time of the assembly process and the quality data of the inspection process simultaneously. In addition, for data noise and anomalies (such as data deviation caused by sensor failures), they can be processed through filtering or correction techniques. The collected data is integrated and uploaded to a centralized data management system, and the system formats, identifies, and cleans the data. For example, convert the data recorded by different sensors into a unified time series format, and at the same time remove duplicate values or missing values. The real-time data is used to monitor the current state of the process. For example, the processing duration and energy consumption of a certain device are displayed as real-time values, while the historical data is archived for subsequent analysis or modeling. For example, retain the processing time data of the past month to establish a process operation benchmark. If inaccuracies are found during data collection, such as the recorded processing duration of a certain process is significantly lower than the manual record, the installation location or accuracy of the collection device can be checked, and the data collection can be optimized by adding monitoring devices or adjusting the sampling frequency. For example, in the heat treatment process, the original temperature sensor was arranged outside the equipment, making it difficult to accurately monitor the internal temperature change. After adding an internal sensor, this problem was successfully solved.

[0023] Step S200: Identify abnormal time sequences based on the abnormal processes to obtain abnormal time windows. Specifically, in flexible manufacturing, identifying abnormal time sequences based on abnormal processes and obtaining abnormal time windows is an important part of accurately adjusting production scheduling. First, by obtaining the monitoring data of abnormal processes, key features are extracted, including information such as processing time, equipment status, and resource consumption. For example, a record shows that the normal processing time of a certain process is 5 minutes, but the actual processing time this time is 8 minutes, and the equipment temperature has been continuously rising and exceeding the set threshold of 70°C since the 3rd minute of operation. By analyzing these data, first calculate the deviation amount, such as the processing time deviation amount is 3 minutes and the equipment temperature deviation is 5°C. Combining historical data and monitoring curves, use time series prediction models (such as linear extrapolation or ARIMA models) to predict the time period during which the abnormal state may persist. For example, an abnormal situation like overtime operation usually lasts for 5 to 7 minutes, and the prediction this time is 6 minutes. At the same time, by comparing the current process data with the historical normal data curve, determine the starting time of the abnormality. For example, when the equipment runs to the 3rd minute, the temperature curve deviates from the normal range and starts to rise rapidly, and it is determined that the starting point of the abnormality is 10:03. Combining the starting point of the abnormality and the predicted duration, determine the abnormal time window as 10:03 to 10:09. If it is found during the monitoring process that the abnormal state extends, such as the equipment temperature has not returned to normal after 6 minutes, then dynamically extend the time window to 10:03 to 10:12; if the state returns to normal in advance, then shorten the time window accordingly. Finally, the determined abnormal time window is recorded in the data management system and marked with process characteristics, such as "processing time overtime" and "equipment temperature abnormality", and the time period is marked as 10:03 to 10:09.

[0024] Step S300: Based on the abnormal process, determine the constraint time window by using the processing time window relationship of the manufacturing process. Specifically, in a flexible manufacturing environment, determining the constraint time window based on the processing time window relationship of the abnormal process is an important step to ensure the continuity of the production plan. First, extract the time characteristics of the abnormal process, including the earliest start time, the latest end time, and the processing time deviation. For example, the standard processing time of an abnormal process is 10 minutes, but the actual processing time is extended to 13 minutes, its earliest start time is 9:00, and the latest end time is 9:13. Then, analyze the time dependence relationship between the abnormal process and the preceding and subsequent processes. For the time window of the preceding process, calculate the interval between the latest completion time of the preceding process and the earliest start time of the abnormal process. For example, if the latest completion time of the preceding process is 8:50 and the earliest start time of the abnormal process is 9:00, the preceding time window is 10 minutes; for the time window of the subsequent process, calculate the interval between the earliest start time of the subsequent process and the latest end time of the abnormal process. For example, if the earliest start time of the subsequent process is 9:20 and the latest end time of the abnormal process is 9:13, the subsequent time window is 7 minutes. On this basis, apply the dynamic moving window mechanism to adjust the time window according to the processing time deviation of the abnormal process. For example, since the abnormal process is extended by 3 minutes, the start time of the subsequent process needs to be postponed to ensure reasonable connection. To evaluate the rationality of the time window adjustment, introduce a penalty mechanism to calculate the adjustment penalty value from three dimensions: time delay, dependence relationship conflict, and resource utilization. For example, if the extension of the abnormal process causes the time overlap of the subsequent process, the penalty value is weighted and calculated according to the severity of the conflict. Finally, determine the reasonable constraint time window by screening the time window with the lowest total penalty value. For example, the adjusted preceding time window is from 8:50 to 9:00, the subsequent time window is from 9:13 to 9:20, and the overall constraint time window is from 8:50 to 9:20.

[0025] In a possible implementation, based on the abnormal process, the constraint time window is determined by using the processing time window relationship of the manufacturing process. Step S300 further includes step S310 of performing abnormal time series prediction according to the abnormal monitoring data attributes and abnormal deviation amounts of the abnormal process to obtain a predicted time period. Specifically, in a flexible manufacturing environment, performing abnormal time series prediction according to the monitoring data attributes and abnormal deviation amounts of the abnormal process is an important link for accurately judging the abnormal duration. First, real-time data of the abnormal process is obtained through monitoring equipment, such as processing time, equipment temperature, vibration frequency, etc. For example, the processing time of a piece of equipment is recorded as 30 minutes (the standard is 20 minutes), and the equipment temperature starts to rise at 10:03 and remains above 75°C (the standard is 70°C). Subsequently, the monitoring data is classified and time features are extracted, including the start time of the abnormal data, the maximum value or the fluctuation range, etc. For example, the normal range of the equipment vibration frequency is ±5%, but the current fluctuation amplitude reaches 20%, and the processing time exceeds the standard by 10 minutes, indicating that the abnormality significantly deviates from the normal state. Then, by comparing the current data with the historical normal data, the abnormal deviation amount is calculated. For example, if the standard processing time is 20 minutes and the current record is 30 minutes, the deviation amount is 10 minutes; the temperature deviation amount is the current 75°C minus the standard 70°C, resulting in 5°C. Combining the analysis of the deviation amount, a time series prediction model (such as the ARIMA model or exponential smoothing method) is used to predict the possible duration period of the abnormality. For example, based on the processing time data of historical similar abnormalities, it is found that such abnormalities usually last for 5 - 8 minutes. Combining the current larger deviation amount (the 10-minute deviation amount is 25% larger than the historical average deviation amount), it is inferred that the abnormality may last for 7 minutes. At the same time, observing the data change trend, it is found that the temperature continues to rise, further verifying that the abnormal state is a gradually accumulating process. Finally, the predicted time period is output as a reference for abnormality management. For example, the abnormal time period is from 10:03 to 10:10, and the total duration is 7 minutes, providing an accurate time basis for subsequent abnormality handling.

[0026] Step S320: Obtain the abnormal starting point based on the comparison curve between the monitoring data and the normal data of the abnormal process. Specifically, in a flexible manufacturing environment, accurately identifying the abnormal starting point by analyzing the comparison curve between the monitoring data of the abnormal process and that of the normal process is a crucial step. First, obtain the real-time monitoring data of the abnormal process, including information such as processing time, equipment status (such as temperature, vibration frequency, current fluctuation, etc.), and resource consumption. For example, the temperature of a certain equipment starts to rise after running for 3 minutes, exceeding the normal range by 70°C and continuing to rise to 75°C. At the same time, establish a baseline curve of the normal process in combination with historical data to reflect the operating state under normal conditions. For example, the temperature fluctuation range is 68°C to 70°C, and the processing time is 18 to 22 minutes. Compare the monitoring data of the abnormal process with the baseline curve to generate a comparison curve to show the deviation trend between the two. Analyze the change point where the abnormal curve first deviates from the normal curve in the comparison curve. For example, when the equipment temperature reaches 72°C at the 3rd minute of operation, breaking through the normal range for the first time, determine the 3rd minute as the change point. Further confirm the starting point of significant deviation. For example, the processing time curve shows that it exceeds the normal range of 22 minutes at the 10th minute, and the equipment temperature continues to rise to 75°C. After comprehensive analysis, determine the abnormal starting point as the 10th minute. In addition, filter out short-term fluctuations through data smoothing technology to avoid misjudgment. For example, the vibration frequency fluctuates briefly by 0.5% at the 2nd minute, but does not continue to exceed the set threshold of 5%, so it is not included in the abnormal starting point. Finally, combine multi-parameter comparison and trend analysis to clarify that the abnormal starting point is the 10th minute of operation, record it in the data management system, and at the same time mark the corresponding abnormal characteristics, such as excessive processing time and over-standard temperature.

[0027] Step S330: Obtain the abnormal time window according to the abnormal starting point and the predicted time period. Specifically, in a flexible manufacturing system, calculating the abnormal time window based on the abnormal starting point and the predicted time period is an important step in abnormality management. First, clarify the abnormal starting point, which is the time point when the monitoring data first significantly deviates from the normal range. For example, in equipment temperature monitoring, the temperature curve first breaks through the normal range of 70°C at 10:03 and reaches 72°C, so 10:03 is determined as the abnormal starting point. At the same time, combined with the multi-dimensional characteristics of the abnormal starting point, label the abnormal processing time, equipment status, and resource consumption. For example, the processing time exceeds the standard range by 22 minutes at the 10th minute, and is accompanied by an abnormal increase in equipment temperature. Subsequently, analyze the possible duration of the abnormality through a time series prediction model (such as the ARIMA model or exponential smoothing method). Combining historical data, it is found that similar temperature abnormalities usually last for 5 to 8 minutes, and the current prediction is 6 minutes. However, due to the large temperature deviation (the current temperature is 75°C, the deviation is 5°C, higher than the historical average deviation), the corrected predicted time period is extended to 7 minutes. Combine the abnormal starting point with the predicted time period to calculate the abnormal time window. For example, if the abnormal starting point is 10:03 and the predicted period is 7 minutes, the abnormal time window is from 10:03 to 10:10. During the monitoring process, dynamically adjust the abnormal time window in real time. For example, if the equipment temperature has not returned to the normal value at 10:10, extend the time window to 10:15; if the temperature has returned to the normal range at 10:08, shorten the time window to from 10:03 to 10:08. Finally, record the determined abnormal time window in the data management system. For example, label the time window as from 10:03 to 10:10, record the abnormal type as "equipment temperature exceeding the standard", and at the same time feedback the time window to the subsequent system for optimization and adjustment. For example, adjust the subsequent process plan to avoid the impact of the abnormality.

[0028] In a possible implementation, based on the abnormal process, the constraint time window is determined by using the processing time window relationship of the manufacturing process. Step S300 further includes step S340 of obtaining the normal preceding process and the normal succeeding process of the abnormal process. Specifically, in a flexible manufacturing environment, obtaining the normal preceding process and the succeeding process of the abnormal process is the basis for analyzing the process dependency relationship. First, by analyzing the position of the abnormal process in the production process, the logical relationship between its preceding process and succeeding process is clarified. For example, engine installation is an abnormal process, and its preceding processes include engine testing and transportation, and the succeeding processes are vehicle assembly and quality inspection. The task of engine testing is to check whether the engine torque is qualified and provide the tested qualified engine to the installation process after transportation. Vehicle assembly completely depends on the result of engine installation. To ensure clear time relationships, it is necessary to record the time parameters of each process. For example, the latest completion time of engine testing is 8:50, the earliest start time of engine installation is 9:00, and the earliest start time of vehicle assembly is 9:25. At the same time, analyze the dependency relationship between the preceding process and the abnormal process to confirm whether the output of the preceding process (such as a qualified engine) meets the input requirements of the abnormal process; for the succeeding process, it is necessary to analyze whether the output conditions of the abnormal process meet the start requirements of the subsequent tasks. For example, after engine installation is completed, an installation result that meets the accuracy requirements must be provided to start vehicle assembly. By integrating the time and task dependency information of the preceding process and the succeeding process, a complete process dependency chain is formed, such as "engine testing (latest completion time 8:50) → engine installation (time window 9:00 to 9:20) → vehicle assembly (earliest start time 9:25)".

[0029] Step S350: Analyze the process dependencies among the regular pre - process, regular post - process, and the abnormal process to determine the processing time window. Specifically, in a flexible manufacturing environment, analyzing the dependencies among the regular pre - process, regular post - process, and the abnormal process is a key step in accurately determining the processing time window. First, it is necessary to clarify the dependency of the pre - process on the abnormal process. For example, the pre - process of engine installation is engine testing, and the qualified result of the test is directly used as the input for the installation process. By analyzing the time parameters of the pre - process, record its earliest start time as 8:00 and the latest completion time as 8:50, and calculate the pre - time window. For example, the latest completion time of engine testing is 8:50, and the earliest start time of the abnormal process of engine installation is 9:00, so the calculated pre - time window is 10 minutes. At the same time, the time dependency of the post - process also needs to be clarified. For example, the post - process of engine installation is vehicle assembly, and the start of the assembly process needs to wait for the completion of engine installation. Record the earliest start time of vehicle assembly as 9:25 and the latest completion time of engine installation as 9:20, and calculate the subsequent time window as 5 minutes. Combine the pre - time window and the subsequent time window, and determine the processing time window of the abnormal process by taking the intersection. For example, the pre - time window is from 8:50 to 9:00, and the subsequent time window is from 9:20 to 9:25, and the final processing time window is from 9:00 to 9:20. During the verification process, it is necessary to ensure that the processing time window is sufficient to meet the execution of the abnormal process. For example, if the actual time required for engine installation is 20 minutes, but the processing time window is only 15 minutes, then the time parameters of the front and back processes need to be readjusted to ensure that their time dependencies are not damaged. In addition, the possibility of dynamic changes needs to be considered. For example, if the pre - process is delayed until 8:55 to complete, then the pre - time window will be shortened to 5 minutes, and the processing time window needs to be recalculated.

[0030] Step S360: Adjust and fit the processing time window through a dynamic moving window to obtain the adjustment penalty value of each moving time window. Specifically, in a flexible manufacturing environment, adjusting and fitting the processing time window through a dynamic moving window is an important method to provide flexible time support for abnormal processes. First, the adjustment of the dynamic moving window includes moving the time window forward or backward. For example, the originally scheduled processing time window for a certain abnormal process is from 9:00 to 9:20. When the processing time is extended by 5 minutes, the time window is adjusted to 9:00 to 9:25; if the processing time is reduced to 15 minutes, the window is narrowed to 9:00 to 9:15. At the same time, it is necessary to dynamically fit the time dependence relationship between the previous process and the subsequent process. For example, the latest completion time of the previous process, engine test, is 8:55, and the earliest start time of the subsequent process, vehicle assembly, is 9:30. The adjusted time window should meet the limit of 8:55 to 9:30 to ensure the logical connection between processes. After the time window adjustment is completed, it is necessary to calculate the adjustment penalty value to evaluate the rationality of the adjustment plan. The adjustment penalty value is calculated through a multi-dimensional function, including time delay penalty, process dependence conflict penalty, and resource conflict penalty. For example, a certain plan adjusts the time window to 9:00 to 9:25, in which the time delay causes the start time of the subsequent process to be delayed to 9:35, resulting in a time delay penalty of 15 minutes; the process dependence conflict increases the penalty by 10 points due to the limitation of the subsequent process; the resource conflict increases the penalty by 5 points due to equipment usage conflicts, and the total penalty value is 30 points. By generating multiple adjustment plans for comparison, such as Plan A with a penalty value of 30 points, Plan B with 25 points, and Plan C with 40 points, select Plan B with the lowest penalty value as the final adjustment plan, determine the time window as 9:00 to 9:25, and record it in the system. The final adjustment plan and its penalty value will be fed back to the production scheduling system to ensure the effective implementation of the dynamic adjustment, while minimizing the negative impact on process connection and resource utilization.

[0031] Step S370: Screen the adjustment penalty values of each moving time window using the penalty constraint threshold to obtain the constrained time window. Specifically, in a flexible manufacturing environment, screening the adjustment penalty values of each moving time window by setting a penalty constraint threshold is an important step in optimizing the time window adjustment. First, the penalty constraint threshold needs to be defined. For example, the maximum allowable penalty value is set to 50 points, and adjustment plans exceeding this value will be directly discarded. At the same time, clarify the weights of different penalty dimensions, such as the time delay weight is 0.5, the process dependency conflict weight is 0.3, and the resource conflict weight is 0.2, which are used to comprehensively evaluate the rationality of the adjustment plan. For example, in time window adjustment plan A, the time delay causes the subsequent process to start late, with a penalty value of 20 points; the process dependency conflict breaks the time connection with the subsequent process, with a penalty value of 15 points; the resource conflict generates an additional burden due to equipment usage conflicts, with a penalty value of 10 points. When calculating the total penalty value, the total penalty value of plan A is 20×0.5 + 15×0.3 + 10×0.2 = 16.5, which is lower than the threshold of 50 points, so this plan is retained. On the contrary, for another adjustment plan B, the time delay penalty value is 30 points, the process dependency conflict penalty value is 20 points, and the resource conflict penalty value is 15 points. The calculated total penalty value is 30×0.5 + 20×0.3 + 15×0.2 = 25.5, but it is excluded after further evaluation due to its excessive resource burden. Among the retained plans, finally select plan A with the lowest total penalty value, determine its adjusted time window as 9:00 to 9:25, and record it as the final constrained time window. In addition, the adjusted time window and penalty value will be fed back to the production scheduling system for optimizing the subsequent execution plan of abnormal processes.

[0032] In a possible implementation, analyze the process dependencies among the regular pre - process, regular post - process, and the abnormal process to determine the processing time window. Step S350 further includes step S351. According to the process dependency between the regular pre - process and the abnormal process, obtain the pre - time window, where the pre - time window represents the relationship between the latest completion time of the pre - process and the earliest start time of the abnormal process. Specifically, in a flexible manufacturing system, calculating the subsequent time window requires clarifying the time dependency between the abnormal process and the regular post - process and conducting precise analysis in combination with specific time parameters. First, the latest completion time of the abnormal process and the earliest start time of the post - process form the boundaries of the subsequent time window. For example, the earliest start time of engine installation (abnormal process) is 9:00, the planned processing time is 20 minutes, and the latest completion time is 9:20; while the earliest start time of vehicle assembly (post - process) is 9:25. Therefore, the initially calculated subsequent time window is [9:20, 9:25]. In the calculation, it is necessary to ensure that the subsequent time window conforms to the dependency relationship, that is, the latest completion time of the abnormal process shall not be later than the earliest start time of the post - process. If the abnormal process is delayed, for example, the engine installation is extended to 9:25 due to equipment failure, then the subsequent time window needs to be dynamically adjusted to [9:25, 9:30]. At the same time, the start conditions of the post - process also need to meet the requirements of resource preparation and time dependency. For example, vehicle assembly needs to ensure that the assembly line is ready and can start at 9:25 at the earliest, and not earlier than the completion time of engine installation. In addition, the rationality of the subsequent time window needs to be further verified to ensure that the window size is sufficient to accommodate the actual completion time of the abnormal process and the start conditions of the post - process. For example, if the subsequent time window is [9:20, 9:25], but the engine installation is actually delayed until 9:25 to complete, then the time window needs to be dynamically extended, or the start time of vehicle assembly is adjusted to 9:30. Through analyzing and adjusting, by dynamically verifying the rationality of the time window, ensure the continuity and logical connection of the processes. Finally, take the subsequent time window as the input of the scheduling system and record it as [9:25, 9:30], which is used as the time basis for subsequent production plan optimization and exception handling.

[0033] Step S352, according to the process dependency relationship between the conventional post-process and the abnormal process, obtain the subsequent time window, which is the relationship between the earliest start time of the subsequent process and the latest end time of the abnormal process. Specifically, in the flexible manufacturing system, the calculation of the subsequent time window depends on the time relationship between the abnormal process and the conventional post-process. The starting point of the subsequent time window is the latest completion time of the abnormal process, and the end point is the earliest start time of the post-process. For example, the earliest start time of the engine installation (abnormal process) is 9:00, the planned duration is 20 minutes, the latest completion time is 9:20, and the earliest start time of the vehicle assembly (post-process) is 9:25. Based on these data, the subsequent time window is initially calculated as [9:20,9:25]. In order to ensure the rationality of the subsequent time window, it is necessary to verify whether its range meets the process connection requirements. If an abnormal process is delayed, for example, the engine installation process is extended to 25 minutes due to equipment problems, and its latest completion time is adjusted to 9:25, the subsequent time window needs to be dynamically adjusted to [9:25,9:30] so that the vehicle assembly can start on time. In addition, the start conditions of the subsequent processes need to be considered, such as whether the assembly line is ready. If the resources for vehicle assembly are not in place, its earliest start time may need to be further delayed. For example, if the resource preparation time is extended to 9:30, the subsequent time window should be adjusted to [9:25,9:30] and recorded in the scheduling system as the time basis for the execution of subsequent processes.

[0034] Step S353: Perform time transfer according to the pre - time window and the post - time window to obtain the processing time window. Specifically, in a flexible manufacturing system, through the intersection calculation of the pre - time window and the post - time window, the processing time window of the abnormal process can be accurately determined. The pre - time window is determined by the latest completion time of the pre - process and the earliest start time of the abnormal process. For example, the latest completion time of engine testing (pre - process) is 8:55, and the earliest start time of engine installation (abnormal process) is 9:00, and the calculated pre - time window is [8:55, 9:00]. The post - time window is determined by the latest completion time of the abnormal process and the earliest start time of the post - process. For example, the latest completion time of engine installation is 9:20, and the earliest start time of vehicle assembly (post - process) is 9:25, and the post - time window is [9:20, 9:25]. By performing the intersection calculation of these two windows, the processing time window can be obtained as [9:00, 9:20]. If the abnormal process is delayed, for example, the engine installation is delayed to 9:25 due to equipment failure, then the post - time window needs to be dynamically adjusted to [9:25, 9:30], and the processing time window needs to be re - evaluated. In addition, it is also necessary to verify whether the processing time window is reasonable to ensure that it can meet the execution requirements of the abnormal process. For example, if the processing time window is [9:00, 9:15], but the engine installation actually requires 20 minutes, then the time window needs to be extended to meet the requirements. During the dynamic adjustment process, it is necessary to ensure the rationality of the time window to avoid affecting the completion time of the pre - process and the start time of the post - process. Finally, the calculated processing time window such as [9:00, 9:20] not only ensures the production connection but also provides a clear time basis for the execution of the abnormal process.

[0035] In a possible implementation manner, the processing time window is adjusted and fitted through a dynamically moving window to obtain the adjustment penalty value of each moving time window. Step S360 further includes step S361 of constructing a penalty function from multiple dimensions of time - window over - boundary delay penalty, process - dependency violation penalty, and production - resource conflict penalty. Specifically, in a flexible manufacturing system, the construction of the penalty function covers three dimensions of time - window over - boundary delay penalty, process - dependency violation penalty, and production - resource conflict penalty to ensure the rationality of time adjustment and scheduling efficiency. First, the time - window over - boundary delay penalty is used to evaluate whether the start time or end time of a process exceeds its allowed time window. For example, if the allowed time window of a certain process is , , and its actual start time is less than or the end time is greater than , then a penalty is required, and the formula is: , where represents the process The time window out-of-bounds delay penalty value, represents the actual start time of the operation and represents the earliest allowable start time of the operation and represents the actual end time of the operation and represents the latest allowable end time of the operation and represents the time window out-of-bounds delay penalty coefficient, which is used to adjust the penalty degree of the delay. For example, if the allowable time window of a certain operation is [9:00, 10:00], but the actual completion time is delayed to 10:15, then the penalty value for the 15-minute delay needs to be calculated according to the formula. In addition, if the delay causes the subsequent operation to fail to start on time, for example, the vehicle assembly is delayed to 10:30, then the delayed part is also included in the penalty. Secondly, the operation dependency violation penalty is used to measure whether the logical sequence between operations is violated. For example, the completion time of the preceding operation is and the start time of the subsequent operation is . If < , then the dependency is violated, and its penalty function is: where represents the penalty value for the violation of the operation dependency between the operation and its preceding operation , and represents the penalty coefficient for the violation of the dependency, which is used to adjust the penalty degree for violating the operation sequence. For example, the completion time of the engine test (preceding operation) is 10:00, and the start time of the engine installation (subsequent operation) is adjusted to 9:55, then =β ⋅ (−5), and the penalty for the violation of the dependency needs to be calculated according to the formula. Finally, the production resource conflict penalty is used to evaluate the conflict situation of resource usage in the adjusted time window. For example, if the operations and need to use the same equipment R and their time windows overlap, it is regarded as a resource conflict, and the penalty function is: where represents the penalty value for the production resource conflict between the operation and the operation , and Represents the resource conflict penalty coefficient, which is used to adjust the penalty degree of resource conflicts. For example, the time window for engine testing is [9:00, 9:30], and the time window for engine installation is [9:15, 9:45]. The overlapping part is 15 minutes. The resource conflict penalty value is calculated according to the formula. To comprehensively evaluate the impacts of multiple dimensions, a combined penalty function is constructed by assigning weights to the above penalty functions. For example, the penalty weight for time window out-of-bounds is 0.5, the penalty weight for dependency violation is 0.3, and the penalty weight for resource conflict is 0.2. The combined formula is: , where represents the total penalty value calculated by combining multi-dimensional penalty functions, which is the basis for comprehensively evaluating the pros and cons of the adjustment plan. Assume that in a certain plan = 10, ， , then the total penalty value is: In this way, the pros and cons of different adjustment plans can be quantified, providing a scientific basis for the time scheduling optimization of flexible manufacturing systems.

[0036] Step S362: Configure the proportionality coefficients for each penalty dimension, and fuse the time window boundary-crossing delay penalty function, the process dependency violation penalty function, and the production resource conflict penalty function according to the proportionality coefficients of the penalty dimensions to construct a fused penalty function. Specifically, in a flexible manufacturing system, by configuring the proportionality coefficients of the penalty dimensions, the time window boundary-crossing delay penalty, the process dependency violation penalty, and the production resource conflict penalty are fused into a unified evaluation criterion, which can comprehensively evaluate the rationality of time adjustment. First, the time window boundary-crossing delay penalty is mainly used to measure whether the start time or end time of a process exceeds the allowed time range. For example, the time window of a certain process is from 9:00 am to 10:00 am, but the actual completion time is delayed to 10:15 am due to adjustment. In this case, the delay penalty needs to be calculated based on the exceeded time and incorporated into the overall evaluation. In addition, if this delay further affects subsequent processes, such as the engine installation delay causing the vehicle assembly to not start as scheduled at 10:20 am, the cascading impact on the overall production rhythm also needs to be considered. Second, the process dependency violation penalty is used to evaluate whether the adjusted time disrupts the logical sequence between processes. For example, the previous process of engine testing is completed at 10:00 am, and the subsequent engine installation is adjusted to start at 9:55 am, which obviously violates the logic of process dependency. Therefore, the penalty needs to be calculated based on the difference in time advance or delay. Finally, the production resource conflict penalty mainly considers the rationality of equipment or resource allocation. For example, engine testing and engine installation use the same equipment, and their time windows are from 9:00 am to 9:30 am and from 9:15 am to 9:45 am respectively. The time overlap will cause equipment scheduling conflicts. Therefore, the penalty value needs to be calculated for the conflicting time part. To comprehensively evaluate the impacts of these dimensions, weights are assigned to the penalties of different dimensions according to their importance. For example, the impact of time window boundary-crossing delay is relatively large and usually given a higher weight, while the impact of resource conflict is relatively small and the weight is lower. In actual calculation, by weighted summing the penalty values of each adjustment plan, the total penalty value of each plan can be obtained, thereby quantifying its optimization degree. For example, in a certain plan, if the impact of time window delay is large, the total penalty value of this plan will also be high; while in another plan, if the time window is adjusted reasonably but there are more resource conflicts, the total penalty value will also be significantly affected. This comprehensive evaluation method can not only identify the best time adjustment plan, but also adapt to the needs of different production scenarios by adjusting the weights, providing a scientific optimization basis for the flexible manufacturing system.

[0037] Step S363: Calculate the adjustment penalty value for each moving time window using the fusion penalty function. Specifically, the process of calculating the penalty value for the moving time window using the fusion penalty function involves multi-dimensional precise evaluation and optimization. First, it is necessary to clarify the adjusted time window and its allowable range for each process. For example, the original time window for a certain process is from 9:00 am to 9:30 am, the adjusted time window is from 9:15 am to 9:45 am, and the allowable range is from 9:00 am to 10:00 am. If the adjusted time exceeds the allowable range, for example, the end time is delayed to 10:15 am, a time window out-of-bounds delay penalty will be triggered, and this delay may also affect subsequent processes. For example, the original plan for vehicle assembly was to start at 10:20 am, but it was forced to be postponed to 10:30 am due to the delay of the previous process. All these will be included in the penalty value calculation. Second, it is necessary to evaluate whether the process dependency is violated. For example, the completion time of engine testing is 10:00 am, and the adjusted time for engine installation is 9:55 am. This situation violates the logical sequence of the processes, and the penalty value needs to be quantified according to the degree of time conflict between the two. At the same time, resource conflict is also an important penalty dimension. For example, engine testing and engine installation require the same equipment, but the overlapping part of their adjusted time windows is from 9:15 am to 9:30 am. This 15-minute resource conflict will be calculated as an additional conflict penalty value. The fusion penalty function weights the penalty values of different dimensions. For example, the weight for time window out-of-bounds delay is set to 0.5, the weight for process dependency is 0.3, and the weight for resource conflict is 0.2. Then, the penalty values of each dimension are summed according to the weights to obtain the total penalty value for each adjustment plan. For example, if the time window out-of-bounds penalty for a certain plan is 10, the penalty for violating the dependency relationship is 5, and the resource conflict penalty is 8, then the total penalty value is calculated as 8.1. By comparing the total penalty values of different plans, the plan with the lowest penalty value can be selected as the optimization recommendation. For example, if the total penalty value of Plan B is 7.5, then this plan will be preferentially adopted. This fusion calculation method can not only quantify the advantages and disadvantages of time window adjustment, but also dynamically adjust the weights according to actual needs, making the evaluation results more in line with the actual production scenario, thereby effectively improving the scheduling efficiency and resource utilization rate of the flexible manufacturing system.

[0038] Step S400, according to the abnormal time window and the constraint time window, the window timing analysis is performed on each manufacturing process to obtain the allocation window period of each manufacturing process. Specifically, in the flexible manufacturing system, according to the abnormal time window and the constraint time window, the timing analysis is performed on each manufacturing process to obtain the allocation window period, which is an important step to ensure the rationality of production scheduling. First, the abnormal time window needs to be clarified. For example, if an abnormal process fails between 10 am and 10:30 am, then this time period is defined as the abnormal time window. At the same time, the constraint time window is determined according to the dependency relationship between the preceding and following processes. For example, the engine test (pre-process) needs to be completed at 9:50, and the vehicle assembly (post-process) needs to start at 11 o'clock, then the constraint time window is [9:50, 11:00]. Next, by constructing a directed graph of process dependency, the temporal relationship between the processes is analyzed, the nodes represent the processes, and the edges are marked with time constraints. For example, the engine installation can only start after the engine test is completed, and the time dependency is [9:50, 11:00]. During the analysis process, the earliest start time of each process needs to be calculated from front to back, for example, the earliest start time of engine testing is 9:00, the completion time is 9:50, and the earliest start time of engine installation is 9:50; at the same time, the latest completion time is calculated from back to front, for example, the latest completion time of vehicle assembly is 12:00, and the latest completion time of engine installation is 11:00. If the abnormal time window conflicts with these process times, it needs to be adjusted dynamically. For example, if the engine test is delayed until 10:30 due to a fault, the deployment window period of engine installation needs to be adjusted to [10:30,11:00], while avoiding a chain effect on the time of vehicle assembly. In addition, if the engine installation and vehicle assembly share the same equipment, it is necessary to ensure the rationality of resource allocation within the time window, such as adjusting the use time of the engine installation on the equipment to 10:30 to 10:50, leaving a 10-minute buffer time to ensure that the vehicle assembly starts on time. Finally, the dispatch window of each process will be dynamically recorded, for example, the dispatch window of engine testing is [9:00, 10:30], and the dispatch window of engine installation is [10:30, 11:00]. Through analysis and dynamic adjustment, the process time allocation can be effectively optimized to ensure the accurate and efficient production scheduling of the flexible manufacturing system.

[0039] In a possible implementation, according to the abnormal time window and the constraint time window, the window time sequence of each manufacturing process is analyzed to obtain the deployment window period of each manufacturing process. Step S400 further includes step S410 of constructing a directed graph structure according to the time window and the dependency relationship between the manufacturing processes. Among them, the graph nodes are manufacturing process nodes, the connecting edges represent the process dependency relationship, the direction of the edges represents the sequence relationship between the processes, and the weight of the edges represents the time window limit. Specifically, in a flexible manufacturing system, constructing a directed graph structure based on the process time window and the dependency relationship is a key step in optimizing scheduling. In this structure, each process is defined as a node. For example, engine testing, engine installation, and vehicle assembly correspond to nodes A, B, and C respectively. The dependency relationship between the processes is connected by directed edges, and the direction of the edges represents the sequential constraint of the processes. For example, engine installation can only be carried out after engine testing is completed, so there is a directed edge from node A to node B. In addition, the weight of the edge is used to represent the time limit. For example, the completion time window of engine testing is from 9:00 am to 10:00 am, and engine installation needs to start within 2 hours after the test is completed, then the edge weight from A to B is 2 hours. By constructing such a directed graph, the dependency relationship and time window limit between the processes can be intuitively represented, thus facilitating optimization of scheduling. For example, vehicle assembly depends on the completion of engine installation. If the engine installation time window is adjusted to 10:30 to 11:30, the start time of vehicle assembly needs to be dynamically adjusted to after 11:00 according to this window. When ensuring the rationality of the directed graph, it is necessary to pay attention to detecting loop problems. If node A points to node B and node B points to node A again, it means a process dependency loop, which needs to be solved by rearranging the order. In addition, the introduction of the abnormal time window enables the directed graph to have the ability of dynamic adjustment. For example, if the engine testing is delayed until 10:30 to complete, the edge weight from A to B needs to be adjusted to 2.5 hours, and the time windows of nodes B and C need to be re-planned. In the finally generated directed graph, the time windows and constraint conditions of all nodes and edges are clearly marked. For example, the time window of node A is [9:00, 10:30], the time window of node B is [10:30, 12:00], and it is ensured that vehicle assembly is completed within the specified time.

[0040] Step S420: Based on the directed graph structure, obtain the earliest start time and the latest start time of each manufacturing process according to the abnormal time window, the constraint time window, and the completion time of the current process. Specifically, in a flexible manufacturing system, based on the directed graph structure and combined with the abnormal time window, the constraint time window, and the completion time of the current process, the earliest start time and the latest start time of each process can be accurately calculated to ensure the rationality and efficiency of production scheduling. The calculation of the earliest start time starts from the completion time of the preceding process. For example, if the processing time of engine testing (node A) is from 9:00 am to 9:50 am, then the earliest start time of engine installation (node B) needs to meet the completion time constraint of node A, that is, it cannot be earlier than 9:50 am. If node B is also restricted by the abnormal time window, for example, it is unavailable during the period from 10:00 am to 10:30 am, then its earliest start time needs to be further adjusted to 10:30 am. In addition, if a process has multiple preceding processes, for example, vehicle assembly (node C) depends on the completion of engine testing and engine installation, then the maximum value of the completion times of all preceding processes needs to be taken as its earliest start time. For example, if the completion time of node A is 9:50 am and the completion time of node B is 10:30 am, then the earliest start time of node C is 10:30 am. For the calculation of the latest start time, it is deduced backward from the earliest start time of the succeeding process. For example, if vehicle assembly needs to be completed at 12:00 noon and its processing time is 1 hour, then the latest start time is 11:00 am. If the processing time of node B is 30 minutes, then its latest completion time needs to be 11:00 am, and the latest start time is deduced backward to 10:30 am. Combining the abnormal time window and the constraint time window, if the processing time of node B must avoid the abnormal time window from 10:00 am to 10:30 am, then its latest start time needs to be between 10:30 am and 11:00 am. Through this recursive calculation and dynamic adjustment, for example, when engine testing is delayed until 10:30 am to complete, the earliest start time and the latest start time of node B are both dynamically adjusted to 10:30 am, and the time window of node C is re-determined according to the adjustment result of node B as 11:00 am to 12:00 noon. Finally, the time results of all processes will be clearly recorded. For example, the time window of node A is [9:00, 9:50], node B is [10:30, 11:00], and node C is [11:00, 12:00], ensuring the scientific nature of time scheduling and the efficient operation of the flexible manufacturing system.

[0041] Step S430: According to the abnormal time window, the constraint time window, the set constraint conditions and the optimization objectives, construct a fitness function to evaluate the fitness of the time window state transition of each process, and iteratively calculate the time windows of all processes until the optimal scheduling sequence is output to obtain the deployment window period of each manufacturing process. Herein, the state transition refers to the process of transferring from one time window configuration to another. Specifically, in a flexible manufacturing system, by combining the abnormal time window, the constraint time window and the completion time of the current process, set the constraint conditions and optimization objectives, and use the fitness function to iteratively optimize the time window state transition, so as to obtain the optimal scheduling plan. This process first needs to clarify the constraint conditions, including the time window range, process dependencies, resource limitations, and the avoidance of abnormal time windows. For example, the constraint time window of a certain process is from 9:00 am to 12:00 pm, and the abnormal time window is from 10:00 to 10:30. The start time of this process needs to be between 9:00 and 12:00 and avoid the abnormal window. At the same time, the process dependencies need to be satisfied. For example, the engine test needs to be completed before the engine installation, and the time window of the engine installation cannot be earlier than the time when the test is completed. In addition, the resource limitation requires avoiding multiple processes from occupying the same equipment at the same time. For example, when the engine installation and the vehicle assembly share equipment, their time windows cannot overlap. Based on the above constraint conditions, construct a fitness function to evaluate the time window configuration. The fitness function comprehensively considers the penalty for time window overrun, the penalty for violating process dependencies, and the penalty for resource conflicts. For example, if the time window of the engine test is adjusted to [9:00, 10:00], and the engine installation is postponed to [10:30, 11:30] due to an abnormality, the fitness function needs to evaluate whether there are resource conflicts or violations of dependencies. The dynamic adjustment of the time window is achieved through state transition. For example, if the engine test is delayed until 10:00 to complete, the subsequent time window of the engine installation will be adjusted accordingly to [10:30, 11:30]. After each state transition, use the fitness function to evaluate the new configuration, and record the configuration with the lowest fitness value as the current optimal solution. Iteratively calculate until the fitness value tends to be stable or reaches the maximum number of iterations, and finally output the optimal scheduling plan. Taking specific results as an example, the time window of the engine test may be determined as [9:00, 10:00], the engine installation is [10:30, 11:30], and the vehicle assembly is [11:30, 12:30]. The entire scheduling plan maximizes the resource utilization rate and production efficiency while satisfying all constraint conditions.

[0042] Step S500: According to the deployment time windows of the respective manufacturing processes, sequence control is performed in accordance with the robot control parameters of each manufacturing process. Specifically, in a flexible manufacturing system, according to the deployment time windows of the respective manufacturing processes, precise sequence control is achieved by binding the robot control parameters to ensure that production tasks are efficiently executed according to the scheduled time. First, the deployment time window for each process is the result of comprehensive adjustment through time-dependent relationships, abnormal time windows, and constraint conditions. For example, the deployment time window for Process A (part installation) is [9:00, 9:30], for Process B (welding operation) is [9:30, 10:00], and for Process C (quality inspection) is [10:00, 10:30]. These time periods determine when the robot starts the action instructions. For example, the gripper completes the part grasping and installation tasks within the time window of Process A. In specific execution, the robot completes the tasks according to the control parameters. For example, the clamping force for Process A is set to 50N, and the moving speed is 1.5m / s to ensure that the part is installed at the specified position; in Process B, the welding robot moves to the specified welding point and completes the welding at a speed of 2.0m / s, ensuring that the welding accuracy is within 0.5mm. If Process A is completed at 9:20 due to part feeding delay, the time window for Process B needs to be dynamically adjusted to [9:50, 10:20], and the welding action is triggered in a timely manner. During the task execution, sensors monitor the robot actions in real time to ensure that the action parameters meet the requirements. For example, when it is detected that the gripper pressure is insufficient, it is automatically increased to 60N to complete the grasping; if the welding position deviation exceeds 1mm, the robot will adjust to the correct position and then perform the welding. In addition, to avoid resource conflicts, processes sharing the same equipment need to be allocated in time. For example, the welding robot and the inspection robot need to use the same track in sequence according to the deployment time window. After each process is completed, the actual execution time is recorded and compared with the deployment time window. For example, Process A is completed 5 minutes ahead of schedule, and Process B is completed 5 minutes behind schedule. The reasons may be equipment performance degradation or material problems. Based on these records, the robot control parameters are further optimized. For example, the welding speed is increased to 2.5m / s or the clamping force of the gripper is increased to 70N to reduce the execution errors and delays of future tasks. Finally, the execution time and sequence of each process are completely recorded. For example, Process A is completed between [9:00, 9:25], Process B is completed between [9:30, 9:55], and Process C is completed between [10:00, 10:25]. The entire production process efficiently and precisely achieves the goal of flexible manufacturing. Even in abnormal situations, reasonable allocation of time and resources can be achieved through dynamic adjustment, ensuring the smooth completion of production tasks.

[0043] In the embodiments of the present application, the parsing process monitoring data is used to identify abnormal processes and determine the abnormal time window; based on the processing time window relationship of the abnormal processes, the constraint time window is determined; combining the abnormal time window and the constraint time window, the time sequence parsing of the manufacturing processes is performed to obtain the deployment window period; according to the deployment window period and the robot control parameter implementation sequence control, the technical effect of being able to quickly adjust the production plan when an abnormality occurs in the flexible manufacturing environment is achieved.

[0044] In the above, reference is made to Figure 1 The adaptive sequence control method for flexible manufacturing according to the embodiments of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the adaptive sequence control system for flexible manufacturing according to the embodiments of the present invention.

[0045] The adaptive sequence control system for flexible manufacturing according to the embodiments of the present invention is used to solve the technical problem that when an abnormality occurs in the process in the existing flexible manufacturing environment, the production plan cannot be quickly adjusted, resulting in low efficiency in the manufacturing process, and achieves the technical effect of being able to quickly adjust the production plan when an abnormality occurs in the flexible manufacturing environment. The adaptive sequence control system for flexible manufacturing includes: an abnormal process identification module 10, an abnormal time window acquisition module 20, a constraint time window module 30, a deployment window period acquisition module 40, and a sequence control module 50.

[0046] The abnormal process identification module 10 is used to connect the process monitoring data, parse the process monitoring data, and identify abnormal processes.

[0047] The abnormal time window acquisition module 20 is used to perform abnormal time sequence identification according to the abnormal process to obtain an abnormal time window.

[0048] The constraint time window module 30 is used to determine the constraint time window based on the abnormal process and using the processing time window relationship of the manufacturing process.

[0049] The deployment window period acquisition module 40 is used to perform window time sequence parsing on each manufacturing process according to the abnormal time window and the constraint time window to obtain the deployment window period of each manufacturing process.

[0050] The sequence control module 50 is used to perform sequence control according to the deployment window period of each manufacturing process and in accordance with the robot control parameters of each manufacturing process.

[0051] Next, the specific configuration of the abnormal process identification module 10 will be described in detail. As described above, by connecting the process monitoring data and analyzing the process monitoring data, abnormal processes are identified. The abnormal process identification module 10 further includes: a process monitoring device deployment unit for identifying the abnormal factors of each manufacturing process and deploying process monitoring devices based on the data attributes of the abnormal factors; a processing time monitoring device adding unit for performing identification and determination of the processing time characteristics of the process monitoring data of each manufacturing process, and adding a processing time monitoring device to the process monitoring device when the processing time characteristics cannot be determined; and a process monitoring data acquisition unit for connecting the process monitoring devices of each manufacturing process and acquiring the process monitoring data of each manufacturing process.

[0052] Next, the specific configuration of the constrained time window module 30 will be described in detail. As described above, based on the abnormal processes, the constrained time window is determined using the processing time window relationship of the manufacturing processes. The constrained time window module 30 further includes: a predicted time period acquisition unit for performing abnormal time series prediction according to the abnormal monitoring data attributes and abnormal deviation amounts of the abnormal processes to obtain a predicted time period; an abnormal starting point acquisition unit for obtaining an abnormal starting point according to the comparison curve of the monitoring data and normal data of the abnormal processes; and an abnormal time window acquisition unit for obtaining the abnormal time window according to the abnormal starting point and the predicted time period.

[0053] Among them, the constrained time window module 30 further includes: a normal process acquisition unit for acquiring the normal pre-processes and normal post-processes of the abnormal processes; a processing time window determination unit for analyzing the process dependency relationships between the normal pre-processes, normal post-processes and the abnormal processes to determine the processing time window; an adjustment and fitting unit for adjusting and fitting the processing time window through dynamic window movement to obtain the adjustment penalty values of each moving time window; and an adjustment penalty value screening unit for screening the adjustment penalty values of each moving time window using a penalty constraint threshold to obtain the constrained time window.

[0054] Among them, analyze the process dependency relationships of the described conventional pre - process, conventional post - process, and the abnormal process to determine the processing time window. The processing time window determination unit further includes: a pre - time window acquisition subunit, which is used to obtain a pre - time window according to the process dependency relationship between the conventional pre - process and the abnormal process. The pre - time window is the relationship between the latest completion time of the pre - process and the earliest start time of the abnormal process; a post - time window acquisition subunit, which is used to obtain a post - time window according to the process dependency relationship between the conventional post - process and the abnormal process. The post - time window is the relationship between the earliest start time of the post - process and the latest end time of the abnormal process; a time transfer analysis subunit, which is used to perform time transfer according to the pre - time window and the post - time window to obtain the processing time window.

[0055] Among them, adjust and fit the processing time window through a dynamic moving window to obtain the adjustment penalty value of each moving time window. The adjustment and fitting unit further includes: a penalty function construction subunit, which is used to construct a penalty function from multiple dimensions such as time window out - of - bounds delay penalty, process dependency relationship violation penalty, and production resource conflict penalty; a proportionality coefficient configuration subunit, which is used to configure the proportionality coefficients of each penalty dimension, and fuse the time window out - of - bounds delay penalty function, process dependency relationship violation penalty function, and production resource conflict penalty function according to the proportionality coefficients of the penalty dimensions to construct a fused penalty function; a penalty value calculation subunit, which is used to calculate the penalty value of the moving time window using the fused penalty function to obtain the adjustment penalty value of each moving time window.

[0056] Next, the specific configuration of the deployment window period acquisition module 40 will be described in detail. As described above, according to the abnormal time window and the constraint time window, window timing analysis is performed on each manufacturing process to obtain the deployment window period of each manufacturing process. The deployment window period acquisition module 40 further includes: a directed graph structure construction unit, which is used to construct a directed graph structure according to the time window and dependency relationship between each process of the manufacturing process, where the graph nodes are manufacturing process nodes, the connection edges represent process dependencies, the direction of the edges represents the sequence relationship between processes, and the weight of the edges represents the time window limit; a manufacturing process time data acquisition unit, which is used to obtain the earliest start time and the latest start time of each manufacturing process based on the directed graph structure, according to the abnormal time window, the constraint time window, and the completion time of the current process; a fitness function construction unit, which is used to set constraint conditions and optimization objectives according to the abnormal time window and the constraint time window, construct a fitness function, perform fitness evaluation on the time window state transition of each process, and iteratively calculate the time windows of all processes until the optimal scheduling sequence is output, to obtain the deployment window period of each manufacturing process, where the state transition refers to the process of transferring from one time window configuration to another time window configuration.

[0057] The adaptive sequence control system for flexible manufacturing provided by the embodiments of the present invention can execute the adaptive sequence control method for flexible manufacturing provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0058] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0059] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An adaptive sequence control method for flexible manufacturing, characterized in that: include: Connecting process monitoring data, analyzing the process monitoring data, and identifying abnormal processes; Perform abnormal timing identification according to the abnormal process to obtain an abnormal time window; Based on the abnormal process, the constraint time window is determined by utilizing the processing time window relationship of the manufacturing process, including: Obtaining the conventional preceding process and the conventional following process of the abnormal process; Analyze the process dependency relationship between the conventional preceding process, the conventional following process and the abnormal process to determine the processing time window; The processing time window is adjusted and fitted by a dynamic moving window to obtain an adjustment penalty value of each moving time window; Using the penalty constraint threshold to screen the adjustment penalty value of each moving time window to obtain the constraint time window; According to the abnormal time window and the constraint time window, the window timing analysis is performed on each manufacturing process to obtain the deployment window period of each manufacturing process; According to the deployment window period of each manufacturing process, sequence control is performed according to the robot control parameters of each manufacturing process.

2. The adaptive sequence control method for flexible manufacturing according to claim 1, characterized in that: The connection process monitoring data includes: Identify abnormal factors in each manufacturing process and deploy process monitoring equipment based on the data attributes of the abnormal factors; Performing processing time feature recognition and determination on the process monitoring data of each manufacturing process, and when the processing time feature cannot be determined, adding a processing time monitoring device to the process monitoring device; Connect the process monitoring equipment of each manufacturing process to obtain the process monitoring data of each manufacturing process.

3. The adaptive sequence control method for flexible manufacturing according to claim 1, characterized in that: Performing abnormal timing identification according to the abnormal process to obtain an abnormal time window includes: Perform abnormal time series prediction based on the abnormal monitoring data attributes and abnormal deviation of the abnormal process to obtain a predicted time period; According to the comparison curve between the monitoring data of the abnormal process and the normal data, the abnormal starting point is obtained; The abnormal time window is obtained according to the abnormal starting point and the predicted time period.

4. The adaptive sequence control method for flexible manufacturing according to claim 1, characterized in that: Analyzing the process dependency relationship between the conventional preceding process, the conventional following process and the abnormal process to determine the processing time window includes: According to the process dependency relationship between the conventional predecessor process and the abnormal process, a lead time window is obtained, where the lead time window is the relationship between the latest completion time of the predecessor process and the earliest start time of the abnormal process; According to the process dependency relationship between the conventional post-process and the abnormal process, a subsequent time window is obtained, where the subsequent time window is the relationship between the earliest start time of the subsequent process and the latest end time of the abnormal process; The processing time window is obtained by performing time transfer according to the preceding time window and the following time window.

5. The adaptive sequence control method for flexible manufacturing according to claim 1, characterized in that: According to the abnormal time window and the constraint time window, the window timing analysis is performed on each manufacturing process to obtain the deployment window period of each manufacturing process, including: According to the time window and dependency relationship between each manufacturing process, a directed graph structure is constructed, in which the graph nodes are manufacturing process nodes, the connecting edges represent the process dependency relationship, the direction of the edge represents the sequence relationship between the processes, and the weight of the edge represents the limitation of the time window; Based on the directed graph structure, the earliest start time and the latest start time of each manufacturing process are obtained according to the abnormal time window, the constraint time window and the completion time of the current process; According to the abnormal time window and the constraint time window, the constraint conditions and the optimization target are set, a fitness function is constructed, the fitness of the time window state transfer of each process is evaluated, and the time windows of all processes are iteratively calculated until the optimal scheduling sequence is output, and the allocation window period of each manufacturing process is obtained, wherein the state transfer refers to the process of transferring from one time window configuration to another time window configuration.

6. The adaptive sequence control method for flexible manufacturing according to claim 1, characterized in that: Get the adjusted penalty value for each moving time window, including: Construct a penalty function from multiple dimensions, including time window out-of-bounds delay penalty, process dependency violation penalty, and production resource conflict penalty; Configure the proportional coefficient of each penalty dimension, and fuse the time window cross-boundary delay penalty function, the process dependency violation penalty function, and the production resource conflict penalty function according to the proportional coefficient of the penalty dimension to construct a fusion penalty function; The fusion penalty function is used to calculate the penalty value of the moving time window to obtain the adjusted penalty value of each moving time window.

7. Adaptive sequence control system for flexible manufacturing, characterized by: The system is used to implement the adaptive sequence control method for flexible manufacturing according to any one of claims 1 to 6, and the system comprises: An abnormal process identification module, the abnormal process identification module is used to connect the process monitoring data, analyze the process monitoring data, and identify abnormal processes; An abnormal time window acquisition module, the abnormal time window acquisition module is used to identify abnormal timing according to the abnormal process and obtain the abnormal time window; A constrained time window module, the constrained time window module is used to determine the constrained time window based on the abnormal process and using the processing time window relationship of the manufacturing process; A deployment window period acquisition module, wherein the deployment window period acquisition module is used to perform window timing analysis on each manufacturing process according to the abnormal time window and the constraint time window to obtain the deployment window period of each manufacturing process; A sequence control module is used to perform sequence control according to the deployment window period of each manufacturing process and the robot control parameters of each manufacturing process.

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