Flexible manufacturing-oriented adaptive sequence control method and system

Abstract

The invention discloses a self-adaptive sequence control method and system for flexible manufacturing, and relates to the technical field of automatic control, and the method comprises the steps: analyzing process monitoring data, recognizing an abnormal process, and determining an abnormal time window; determining a constraint time window based on the processing time window relationship of the abnormal process; in combination with the abnormal time window and the constraint time window, performing time sequence analysis on the manufacturing process to obtain a deployment window period; and implementing sequence control according to the deployment window period and the robot control parameters. The technical problem that the manufacturing process is low in efficiency due to the fact that the production plan cannot be rapidly adjusted when the working procedure is abnormal in the existing flexible manufacturing environment is solved, and the technical effect that the production plan can be rapidly adjusted when the working procedure is abnormal in the flexible manufacturing environment is achieved.

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: 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.

[0006] The present application provides an adaptive sequence control system for flexible manufacturing, including: 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 the abnormal process; an abnormal time window acquisition module, the abnormal time window acquisition module is used to perform abnormal timing identification according to the abnormal process and obtain the abnormal time window; a constraint time window module, the constraint time window module is used to determine the constraint time window based on the abnormal process and the processing time window relationship of the manufacturing process; an allocation window period acquisition module, the allocation 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, and obtain the allocation window period of each manufacturing process; a sequence control module, the sequence control module is used to perform sequence control according to the robot control parameters of each manufacturing process according to the allocation window period of each manufacturing process.

[0007] The adaptive sequence control method and system for flexible manufacturing proposed in this application first analyzes the process monitoring data to identify abnormal processes and determine the abnormal time window; based on the processing time window relationship of the abnormal process, the constraint time window is determined; combining the abnormal time window and the constraint time window, the manufacturing process is time-series analyzed to obtain the deployment window period; sequence control is implemented according to the deployment window period and robot control parameters, thereby 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

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the drawings of the embodiment 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 embodiment of the present application.

[0009] It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0010] Figure 1 A schematic diagram of a flow chart of an adaptive sequence control method for flexible manufacturing provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an adaptive sequence control system for flexible manufacturing provided in an embodiment of the present application.

[0011] Description of reference numerals: abnormal process identification module 10 , abnormal time window acquisition module 20 , constraint time window module 30 , deployment window period acquisition module 40 , sequence control module 50 . DETAILED DESCRIPTION

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0014] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0015] The present application embodiment provides an adaptive sequence control method for flexible manufacturing, such as Figure 1 As shown, the method includes: Step S100, connect the process monitoring data, analyze the process monitoring data, and identify abnormal processes. Specifically, in a flexible manufacturing environment, the connection and analysis of process monitoring data is a key step in identifying abnormal processes. First, by connecting the monitoring equipment of each manufacturing process with the data management system, multi-dimensional data in the processing process is collected in real time, including processing time (such as the actual time taken to complete the process), equipment status (such as equipment vibration frequency, temperature change, current fluctuation, etc.), resource utilization (such as raw material consumption, energy 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 that the equipment operation efficiency has decreased or the processing path planning is unreasonable; and an abnormal increase in equipment temperature may indicate that the components are aging or the equipment is overloaded. After the integrity check of the collected data, it will be compared with the historical normal data to extract key abnormal factors, such as processing time deviation, equipment operation fluctuation rate or resource overconsumption ratio. Through comparative analysis, when the energy consumption of a certain equipment is significantly higher than the historical average of the same type of process, it indicates that there is energy waste or internal loss of the equipment; when it is found that the processing time is too long and accompanied by unstable equipment operation, it may indicate that the equipment has hidden faults. Further, by analyzing the abnormal factors and combining multi-factor comprehensive evaluation, it is judged whether the process is abnormal and classified, such as process abnormalities (excessive processing time or skipped processing), equipment abnormalities (equipment overheating, shutdown or overload) and resource abnormalities (raw material exhaustion or excessive consumption). For example, when a production line has repeatedly experienced significantly insufficient processing time, it means that steps are missed or the detection device fails, and frequent overload operation of a certain equipment indicates that the equipment load distribution is unreasonable. By clarifying the specific type, cause and time node of the abnormal process, reliable data support is provided for the adjustment and optimization of the subsequent production sequence, thereby ensuring the stable operation of the manufacturing system in a dynamic and complex environment.

[0016] In a possible implementation, the process monitoring data is connected, the process monitoring data is parsed, and the abnormal process is identified. Step S100 further includes step S110, identifying the abnormal factors of each manufacturing process, and deploying the process monitoring equipment based on the data attributes of the abnormal factors. Specifically, identifying the abnormal factors of each manufacturing process and deploying the monitoring equipment based on its data attributes is the key to achieving efficient monitoring. First, by analyzing the characteristics of each process and historical production data, the key factors that may cause abnormalities are extracted. For example, the abnormal factor of processing time may be manifested as timeout or early completion, the abnormal factor of equipment status may include excessive temperature, excessive 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 processing processes, while heat treatment processes focus on the combined abnormality of temperature and time. After identifying the factors, the corresponding monitoring equipment is selected according to their data attributes. For processing time, a high-precision industrial timer can be deployed; for equipment status, a temperature sensor, a vibration sensor or a current monitoring module can be installed; for resource consumption, a raw material metering device or an energy consumption monitoring module can be used. If some processes involve multiple abnormal factors, it is necessary to jointly deploy multiple devices, such as using vibration sensors and timers to monitor equipment stability and processing time in precision machining processes. In multi-stage production lines, monitoring equipment needs to cover each key process, such as combining temperature sensors and energy consumption monitoring equipment in high-energy consumption processes to track 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 processing time monitoring is unclear, high-precision timing equipment or real-time data recording devices can be added. After the deployment is completed, the monitoring effect must be verified through actual operation, such as comparing the monitoring data with historical normal data, checking whether the equipment accurately captures abnormal factors, and if there are blind spots or insufficient monitoring accuracy, further optimize the equipment configuration to ensure the effectiveness and stability of the monitoring system.

[0017] Step S120, the process monitoring data of each manufacturing process is subjected to processing time feature recognition and determination. When the processing time feature cannot be determined, a processing time monitoring device is added to the process monitoring device. Specifically, in a flexible manufacturing environment, processing time feature recognition and determination of the process monitoring data is an important step to ensure the normal operation of the process. First, the process data collected by the monitoring device usually includes the processing start time, the end time and the equipment operation status. For example, in a mechanical processing process, the equipment records the start time of the tool as 10:00:00 and the end time as 10:05:00, and the processing time is calculated to be 5 minutes. If the process operation exceeds the expected time (such as the processing time reaches 7 minutes), it may indicate that the equipment operation efficiency is reduced or the processing path setting is unreasonable; if the time is insufficient (such as only 2 minutes), there may be omissions in the process steps or the equipment has not completed the complete processing. For the collected data, by comparing it with the historical normal processing time, it can be judged whether the current processing time is abnormal. 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 too much 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, photoelectric sensor or time recording module, to accurately record the start and end time of the 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 critical time node; in the heat treatment process, an independent time monitoring module can be deployed to accurately record the heating and cooling time. After the new equipment is added, recalibration and data verification are carried out to ensure that the data collected by the new equipment can accurately reflect the processing time characteristics, and the monitoring of the process operation status is more comprehensive. For example, after the time monitoring equipment is installed, the data record of a certain process shows that the processing time has changed from the previous vague interval to a clear 5 minutes and 20 seconds, thereby discovering that there is a slight time delay problem in the processing process.

[0018] Step S130, connecting the process monitoring equipment of each manufacturing process to obtain the process monitoring data of each manufacturing process. Specifically, in a flexible manufacturing environment, connecting the monitoring equipment of each manufacturing process and obtaining the monitoring data is the basis for achieving efficient monitoring. First, according to the characteristics of different processes, select appropriate monitoring equipment, such as industrial timers for recording processing time, vibration sensors for monitoring equipment stability, temperature sensors for monitoring temperature changes in heat treatment processes, and energy consumption meters for tracking resource consumption. For an assembly line, if some processes need to monitor multiple parameters at the same time, such as processing time, equipment operating status, and raw material usage, timers, vibration sensors, and energy consumption metering devices need to be deployed jointly. After completing the equipment connection, connect the equipment to the data management system through industrial Ethernet or wireless sensor networks (such as Wi-Fi, Bluetooth) to realize real-time data collection. For example, in a mechanical processing 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 metering device displays the power consumption as 5kW. During the data collection process, it is particularly necessary to synchronously obtain data from multiple processes to ensure the consistency of the time series. For example, on the assembly line, the processing time of the assembly process and the quality data of the inspection process need to be recorded simultaneously. In addition, data noise and anomalies (such as data deviation caused by sensor failure) can be processed by filtering or correction technology. The collected data is integrated and uploaded to the centralized data management system. The system formats, identifies and cleans the data, such as converting the data recorded by different sensors into a unified time series format, while removing duplicate or missing values. Real-time data is used to monitor the current status of the process, such as the processing time and energy consumption of a certain device are displayed as real-time values, while historical data is archived for subsequent analysis or modeling, such as retaining the processing time data of the past month to establish a process operation benchmark. If inaccuracies are found in data collection, such as the processing time record of a certain process is significantly lower than the manual record, the installation position or accuracy of the collection equipment can be checked, and data collection can be optimized by adding monitoring equipment 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 changes. The addition of an internal sensor successfully solved this problem.

[0019] Step S200, performing abnormal timing identification according to the abnormal process and obtaining an abnormal time window. Specifically, in flexible manufacturing, performing abnormal timing identification according to the abnormal process and obtaining an abnormal time window is an important link in accurately adjusting production scheduling. First, by acquiring the monitoring data of the abnormal process, 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 process is 5 minutes, but the actual processing time is 8 minutes, and the equipment temperature continues to rise after the 3rd minute of operation and exceeds the set threshold of 70°C. By analyzing these data, the deviation is first calculated, such as the processing time deviation is 3 minutes and the equipment temperature deviation is 5°C. Combined with historical data and monitoring curves, a time series prediction model (such as linear extrapolation or ARIMA model) is used to predict the time period that the abnormal state may last. For example, an abnormality similar to overtime operation usually lasts for 5 to 7 minutes, and this prediction is 6 minutes. At the same time, by comparing the current process data with the historical normal data curve, the start time of the abnormality is determined. For example, when the equipment is running for 3 minutes, the temperature curve deviates from the normal range and begins to rise rapidly, and the abnormal starting point is determined to be 10:03. Combining the abnormal starting point and the predicted duration, the abnormal time window is determined to be 10:03 to 10:09. If the abnormal state is found to be prolonged during the monitoring process, for example, the equipment temperature has not returned to normal after 6 minutes, the time window is dynamically extended and adjusted to 10:03 to 10:12; if the state is restored in advance, the time window is shortened accordingly. Finally, the determined abnormal time window is recorded in the data management system and marked with process characteristics, such as "processing time timeout" and "equipment temperature abnormality", and the time period is marked as 10:03 to 10:09.

[0020] Step S300, based on the abnormal process, the constraint time window is determined 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, the time characteristics of the abnormal process are extracted, 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, with the earliest start time of 9:00 and the latest end time of 9:13. Next, the time dependency of the abnormal process and the preceding and subsequent processes is analyzed. For the time window of the predecessor process, the interval between the latest completion time of the predecessor process and the earliest start time of the abnormal process is calculated. For example, if the latest completion time of the predecessor process is 8:50 and the earliest start time of the abnormal process is 9:00, the predecessor time window is 10 minutes; for the time window of the subsequent process, the interval between the earliest start time of the subsequent process and the latest end time of the abnormal process is calculated. 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, the dynamic moving window mechanism is applied 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. In order to evaluate the rationality of the time window adjustment, a penalty mechanism is introduced to calculate the adjustment penalty value from three dimensions: time delay, dependency conflict and resource utilization. For example, if the abnormal process extension causes the time overlap of the subsequent process, the penalty value is weighted according to the severity of the conflict. Finally, by screening the time window with the lowest total penalty value, a reasonable constraint time window is determined. For example, the adjusted lead time window is 8:50 to 9:00, the follow-up time window is 9:13 to 9:20, and the overall constraint time window is 8:50 to 9:20.

[0021] In a possible implementation, based on the abnormal process, the processing time window relationship of the manufacturing process is used to determine the constraint time window, and step S300 further includes step S310, performing abnormal time series prediction according to the abnormal monitoring data attributes and abnormal deviation amount 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 amount of the abnormal process is an important link for accurately judging the duration of the abnormality. First, the real-time data of the abnormal process, such as processing time, equipment temperature, vibration frequency, etc., is obtained through monitoring equipment. For example, the processing time of a device is recorded as 30 minutes (the standard is 20 minutes), and the device temperature starts to rise from 10:03 and continues to be above 75°C (the standard is 70°C). Subsequently, the monitoring data is classified and time features are extracted, including the start time, maximum value or fluctuation range of the abnormal data. For example, the normal range of the vibration frequency of the device is ±5%, but the current fluctuation range is 20%, and the processing time exceeds the standard by 10 minutes, indicating that the abnormality is significantly deviated 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 is 10 minutes; the temperature deviation is 5°C minus the standard 70°C from the current 75°C. Combined with the deviation analysis, a time series prediction model (such as the ARIMA model or exponential smoothing method) is used to predict the possible duration of the abnormality. For example, based on the historical processing time data of similar abnormalities, it is found that such abnormalities usually last for 5-8 minutes. Combined with the current large deviation (the 10-minute deviation is 25% larger than the historical average deviation), 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 gradual accumulation process. Finally, the output prediction time period is used as a reference for abnormal management. For example, the abnormal time period is from 10:03 to 10:10, and the total duration is 7 minutes, which provides an accurate time basis for subsequent abnormal processing.

[0022] Step S320, according to the comparison curve of the monitoring data of the abnormal process and the normal data, the abnormal starting point is obtained. Specifically, in a flexible manufacturing environment, accurately identifying the abnormal starting point by analyzing the comparison curve of the monitoring data of the abnormal process and the normal process is a key 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 device starts to rise after 3 minutes of operation, exceeds the normal range of 70°C, and continues to rise to 75°C. At the same time, a baseline curve of the normal process is established in combination with historical data to reflect the operating status under normal conditions, such as a temperature fluctuation range of 68°C to 70°C and a processing time of 18 to 22 minutes. The monitoring data of the abnormal process is compared with the baseline curve, and a comparison curve is generated to show the deviation trend of the two. Analyze the change point where the abnormal curve deviates from the normal curve for the first time in the comparison curve. For example, the equipment temperature reaches 72°C in the 3rd minute of operation, breaking through the normal range for the first time, and the 3rd minute is determined to be the change point. The starting point of significant deviation is further confirmed. 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, it is determined that the abnormal starting point is the 10th minute. In addition, data smoothing technology is used to filter short-term fluctuations to avoid misjudgment. For example, the vibration frequency briefly fluctuates by 0.5% in the 2nd minute, but does not continue to exceed the set threshold of 5%, so it is not counted as the abnormal starting point. Finally, combined with multi-parameter comparison and trend analysis, it is clear that the abnormal starting point is the 10th minute of operation, and it is recorded in the data management system, and the corresponding abnormal features are marked, such as excessive processing time and excessive temperature.

[0023] Step S330, according to the abnormal starting point and the predicted time period, the abnormal time window is obtained. 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 abnormal management. First, the abnormal starting point is clarified, that is, the time point when the monitoring data deviates significantly from the normal range for the first time. For example, in the equipment temperature monitoring, the temperature curve breaks through the normal range of 70°C for the first time at 10:03 and reaches 72°C, and 10:03 is clearly the abnormal starting point. At the same time, combined with the multi-dimensional characteristics of the abnormal starting point, the abnormal processing time, equipment status and resource consumption are marked, for example, the processing time exceeds the standard range of 22 minutes in the 10th minute, and the equipment temperature is abnormally increased. Subsequently, the possible duration period of the abnormality is analyzed by a time series prediction model (such as an ARIMA model or an exponential smoothing method). Combined with historical data, it is found that similar temperature anomalies usually last for 5 to 8 minutes, and the current prediction is 6 minutes, but due to the large temperature deviation (currently 75°C, the deviation is 5°C, which is higher than the historical average deviation), the revised prediction 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 10:03 to 10:10. During the monitoring process, the abnormal time window is adjusted dynamically in real time. For example, if the equipment temperature has not returned to the normal value at 10:10, the time window is extended to 10:15; if the temperature has returned to the normal range at 10:08, the time window is shortened to 10:03 to 10:08. Finally, the determined abnormal time window is recorded in the data management system, for example, the time window is marked as 10:03 to 10:10, and the abnormal type is recorded as "equipment temperature exceeds the standard". At the same time, the time window is fed back to the subsequent system for optimization and adjustment, such as adjusting the subsequent process plan to avoid the impact of the abnormality.

[0024] 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, and step S300 further includes step S340, obtaining the conventional preceding process and conventional following process of the abnormal process. Specifically, in a flexible manufacturing environment, obtaining the conventional preceding process and following 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 following process is clarified. For example, engine installation is an abnormal process, and its preceding process includes engine testing and transportation, and the following process is 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 the transportation is completed. The assembly of the whole vehicle is completely dependent on the result of the completion of the engine installation. In order to ensure that the time relationship is clear, it is necessary to record the time parameters of each process, for example, the latest completion time of the engine test is 8:50, the earliest start time of the engine installation is 9:00, and the earliest start time of the vehicle assembly is 9:25. At the same time, the dependency relationship between the preceding process and the abnormal process is analyzed to confirm whether the output of the preceding process (such as a qualified engine) meets the input requirements of the abnormal process; for the post-process, it is necessary to analyze whether the output conditions of the abnormal process meet the start-up requirements of the subsequent tasks. For example, after the engine is installed, the installation results that meet the accuracy requirements must be provided before the vehicle assembly can be started. By integrating the time and task dependency information of the preceding process and the post-process, a complete process dependency chain is formed, such as "engine test (latest completion time 8:50) → engine installation (time window 9:00 to 9:20) → vehicle assembly (earliest start time 9:25)".

[0025] Step S350, analyzing the process dependency between the conventional pre-process, conventional post-process and the abnormal process, and determining the processing time window. Specifically, in a flexible manufacturing environment, analyzing the dependency between the conventional pre-process, conventional 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 of 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 lead time window. For example, the latest completion time of the engine test is 8:50, and the earliest start time of the abnormal process engine installation is 9:00, and the calculated lead 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, the latest completion time of engine installation as 9:20, and calculate the subsequent time window as 5 minutes. Combine the leading time window and the following time window, and determine the processing time window of the abnormal process by taking the intersection. For example, the leading time window is 8:50 to 9:00, the following time window is 9:20 to 9:25, and the final processing time window is 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 engine installation actually takes 20 minutes, but the processing time window is only 15 minutes, the time parameters of the previous and next processes need to be readjusted to ensure that their time dependency is not destroyed. In addition, the possibility of dynamic changes must also be considered. For example, if the leading process is delayed until 8:55, the leading time window will be shortened to 5 minutes, and the processing time window needs to be recalculated.

[0026] Step S360, adjusting and fitting 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 original processing time window of an abnormal process is 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 reduced to 9:00 to 9:15. At the same time, it is necessary to dynamically fit the time dependency between the preceding process and the following process. For example, the latest completion time of the preceding process engine test is 8:55, and the earliest start time of the following 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 the processes. After completing the time window adjustment, it is necessary to calculate the adjustment penalty value to evaluate the rationality of the adjustment plan. The adjustment penalty value is calculated through multi-dimensional functions, including time delay penalty, process dependency conflict penalty and resource conflict penalty. For example, a plan adjusts the time window to 9:00 to 9:25, where the time delay causes the start time of the subsequent process to be delayed to 9:35, resulting in a 15-minute time delay penalty; the process dependency conflict increases the penalty by 10 points due to the restriction of the subsequent process; the resource conflict increases the penalty by 5 points due to the equipment use conflict, and the total penalty value is 30 points. By generating multiple adjustment plans for comparison, for example, the penalty value of plan A is 30 points, plan B is 25 points, and plan C is 40 points, plan B with the lowest penalty value is selected as the final adjustment plan, and the time window is determined to be 9:00 to 9:25, and recorded in the system. The final adjustment plan and its penalty value will be fed back to the production scheduling system to ensure that the dynamic adjustment can be effectively implemented while minimizing the negative impact on process connection and resource utilization.

[0027] Step S370, using the penalty constraint threshold to filter the adjustment penalty values ​​of each moving time window, and obtain the constrained time window. Specifically, in a flexible manufacturing environment, filtering the adjustment penalty values ​​of each moving time window by setting the penalty constraint threshold is an important step in optimizing the time window adjustment. First, it is necessary to define the penalty constraint threshold, for example, set the maximum allowable penalty value to 50 points, and the adjustment scheme exceeding this value will be directly discarded. At the same time, the weights of different penalty dimensions are clarified, 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 is used to comprehensively evaluate the rationality of the adjustment scheme. For example, in the time window adjustment scheme A, the time delay causes the subsequent process to start late, and the penalty value is 20 points; the process dependency conflict destroys the time connection with the subsequent process, and the penalty value is 15 points; the resource conflict generates additional burden due to the equipment use conflict, and the penalty value is 10 points. When the total penalty value is calculated comprehensively, the total penalty value of scheme A is 20×0.5+15×0.3+10×0.2=16.5, which is lower than the threshold of 50 points, so the scheme is retained. On the contrary, the time delay penalty value of another adjustment plan B is 30 points, the process dependency conflict penalty value is 20 points, and the resource conflict penalty value is 15 points. The total penalty value is calculated to be 30×0.5+20×0.3+15×0.2=25.5, but it is excluded from further evaluation due to its excessive resource burden. Among the retained plans, plan A with the lowest total penalty value is finally selected, and its adjusted time window is determined to be 9:00 to 9:25, and it is recorded as the final constraint time window. In addition, the adjusted time window and penalty value will be fed back to the production scheduling system to optimize the subsequent execution plan of the abnormal process.

[0028] In a possible implementation, the process dependency relationship between the conventional preceding process, the conventional following process and the abnormal process is analyzed to determine the processing time window. Step S350 further includes step S351, obtaining the leading time window according to the process dependency relationship between the conventional preceding process and the abnormal process, wherein the leading time window is the relationship between the latest completion time of the preceding process and the earliest start time of the abnormal process. Specifically, in a flexible manufacturing system, the calculation of the subsequent time window requires the clarification of the time dependency relationship between the abnormal process and the conventional following process, and accurate analysis is performed in combination with specific time parameters. First, the latest completion time of the abnormal process and the earliest start time of the following process constitute the boundary of the subsequent time window. For example, the earliest start time of the 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 the vehicle assembly (follow-up process) is 9:25. Therefore, the preliminary calculated subsequent time window is [9:20,9:25]. In the calculation, it is necessary to ensure that the subsequent time window meets the dependency relationship, that is, the latest completion time of the abnormal process must not be later than the earliest start time of the subsequent process. If the abnormal process is delayed, for example, the engine installation is extended to 9:25 due to equipment failure, the subsequent time window needs to be dynamically adjusted to [9:25,9:30]. At the same time, the start conditions of the subsequent process must also meet the requirements of resource preparation and time dependency. For example, the assembly of the whole vehicle needs to ensure that the assembly line is ready and can start at 9:25 at the earliest, but not earlier than the time when the engine installation is completed. 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 subsequent process. For example, if the subsequent time window is [9:20,9:25], but the engine installation is actually delayed to 9:25, the time window needs to be dynamically extended, or the start time of the whole vehicle assembly needs to be adjusted to 9:30. The analysis and adjustment dynamically verifies the rationality of the time window to ensure the continuity and logical connection of the process. Finally, the subsequent time window is used as the input of the scheduling system and recorded as [9:25,9:30], which is used as the time basis for subsequent production plan optimization and exception handling.

[0029] 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.

[0030] Step S353, time transfer is performed according to the leading time window and the subsequent time window to obtain the processing time window. Specifically, in the flexible manufacturing system, the processing time window of the abnormal process can be accurately determined by calculating the intersection of the leading time window and the subsequent time window. The leading time window is determined by the latest completion time of the leading process and the earliest start time of the abnormal process. For example, the latest completion time of the engine test (leading process) is 8:55, and the earliest start time of the engine installation (abnormal process) is 9:00. The leading time window is calculated to be [8:55,9:00]. The subsequent time window is determined by the latest completion time of the abnormal process and the earliest start time of the subsequent process. For example, the latest completion time of the engine installation is 9:20, and the earliest start time of the vehicle assembly (subsequent process) is 9:25. The subsequent time window is [9:20,9:25]. The intersection of these two windows is calculated to obtain the processing time window [9:00,9:20]. If an abnormal process is delayed, for example, the engine installation is delayed until 9:25 due to equipment failure, the subsequent 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 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 takes 20 minutes, the time window needs to be expanded to meet the demand. During the dynamic adjustment process, it is necessary to ensure the rationality of the time window to avoid affecting the completion time of the preceding process and the start time of the following process. In the end, the calculated processing time window, such as [9:00,9:20], not only ensures production connection, but also provides a clear time basis for the execution of abnormal processes.

[0031] In one possible implementation, the processing time window is adjusted and fitted by a dynamic moving window to obtain an adjusted penalty value for each moving time window, and step S360 further includes step S361, constructing a penalty function from multiple dimensions including time window out-of-bounds 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: time window out-of-bounds 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 out-of-bounds 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 process is [ , ], its actual start time Less than or end time Greater than , then a penalty is required, the formula is: ,in Indicates the process The time window out-of-bounds delay penalty value, Indicates the process The actual start time of Indicates the process The earliest allowed start time, Indicates the process The actual end time of Indicates the process The latest end time allowed, It indicates the penalty coefficient for delays beyond the time window, which is used to adjust the penalty for delays. For example, if the allowed time window for a process is [9:00, 10:00], but the actual completion time is delayed to 10:15, then the penalty value for a delay of 15 minutes needs to be calculated according to the formula. In addition, if the delay causes the subsequent process to be unable to start on time, such as the vehicle assembly is delayed to 10:30, the delay is also included in the penalty. Secondly, the penalty for process dependency violation is used to measure whether the logical order between processes is destroyed. For example, the predecessor process The completion time is , subsequent processes The start time is ,like < , then the dependency is violated, and the penalty function is: , Indicates the process Its predecessor The penalty value for violating the process dependency relationship between them, The penalty coefficient for dependency violation is used to adjust the penalty for violating the process sequence. For example, if the completion time of engine testing (pre-process) is 10:00, and the start time of engine installation (post-process) is adjusted to 9:55, then =β⋅(−5), the dependency violation penalty needs to be calculated according to the formula. Finally, the production resource conflict penalty is used to evaluate the conflict of resource usage in the adjusted time window. For example, if the process and If the same device R needs to be used and their time windows overlap, it is considered a resource conflict and the penalty function is: , Indicates the process and process The penalty value for conflicts in production resources between Represents the resource conflict penalty coefficient, which is used to adjust the degree of penalty for resource conflicts. For example, the time window for engine testing is [9:00, 9:30], the time window for engine installation is [9:15, 9:45], and the overlap is 15 minutes. The resource conflict penalty value is calculated according to the formula. In order to comprehensively evaluate the impact of multiple dimensions, a fusion penalty function is constructed by assigning weights to the above penalty functions. For example, the time window out-of-bounds penalty weight is 0.5, the dependency violation weight is 0.3, and the resource conflict penalty weight is 0.2. The fusion formula is: ,in It represents the total penalty value calculated by integrating multi-dimensional penalty functions, which is the basis for comprehensively evaluating the pros and cons of adjustment plans. =10, ， , then the total penalty value is: In this way, the advantages and disadvantages of different adjustment schemes can be quantified, providing a scientific basis for the time scheduling optimization of flexible manufacturing systems.

[0032] Step S362, configure the proportional coefficient of each penalty dimension, and fuse the time window out-of-bounds delay penalty function, the process dependency violation penalty function, and the production resource conflict penalty function according to the penalty dimension proportional coefficient to construct a fusion penalty function. Specifically, in the flexible manufacturing system, by configuring the proportional coefficient of the penalty dimension, the time window out-of-bounds delay penalty, the process dependency violation penalty, and the production resource conflict penalty are integrated into a unified evaluation standard, so that the rationality of the time adjustment can be comprehensively evaluated. First, the time window out-of-bounds delay penalty is mainly used to measure whether the start time or end time of the process exceeds the allowed time range. For example, the time window of a process is from 9 am to 10 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 time of the excess part and included in the total evaluation. In addition, if this delay further affects the subsequent processes, such as the delay in engine installation causing the vehicle assembly to fail to start at 10:20 as planned, it is also necessary to consider its chain effect on the overall production rhythm. Secondly, the penalty for process dependency violation is used to evaluate whether the adjusted time destroys the logical order between processes. For example, the preceding process engine test is completed at 10 o'clock, and the subsequent engine installation is adjusted to start at 9:55. This obviously violates the logic of process dependency, so it is necessary to calculate the penalty 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, the engine test and engine installation use the same equipment, and their time windows are 9:00 to 9:30 am and 9:15 to 9:45 am respectively. The overlap in time will lead to equipment scheduling conflicts, so it is necessary to calculate the penalty value for the conflicting part of the time. In order to comprehensively evaluate the impact of these dimensions, the penalties of different dimensions are weighted according to their importance. For example, the impact of time window out-of-bounds delay is greater, and it is usually given a higher weight, while the impact of resource conflict is relatively small, so it is given a lower weight. In actual calculations, by weighting and summarizing 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 one solution, if the time window delay has a greater impact, the total penalty value of the solution will also be higher; in another solution, if the time window adjustment is reasonable 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.

[0033] Step S363, using the fusion penalty function to calculate the penalty value of the moving time window, and obtain the adjusted penalty value of each moving time window. Specifically, the process of calculating the penalty value of the moving time window using the fusion penalty function involves accurate evaluation and optimization in multiple dimensions. First, it is necessary to clarify the adjustment time window and its allowed range for each process. For example, the original time window of a process is from 9:00 to 9:30 in the morning, the adjusted time window is from 9:15 to 9:45, and the allowed range is from 9:00 to 10:00. If the adjusted time exceeds the allowed range, for example, the end time is delayed to 10:15, it will trigger the time window out-of-bounds delay penalty. This delay may also affect subsequent processes. For example, the assembly of the whole vehicle was originally planned to start at 10:20, but was forced to be postponed to 10:30 due to the delay of the previous process. These will be included in the penalty value calculation. Secondly, it is necessary to evaluate whether the process dependency is destroyed. For example, the completion time of the engine test is 10 o'clock, while the adjustment time of the engine installation is 9:55. This situation violates the logical order of the process, 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, the engine test and engine installation require the same equipment, but the overlap of the adjusted time windows of the two is from 9:15 to 9:30. The 15-minute resource conflict will be calculated as an additional conflict penalty value. The fusion penalty function weights the penalty values ​​of different dimensions by setting weights. For example, the weight of the time window out-of-bounds delay is set to 0.5, the weight of the process dependency is 0.3, and the weight of the resource conflict is 0.2. Then the penalty values ​​of each dimension are summed up according to the weight to obtain the total penalty value of each adjustment plan. For example, if the time window out-of-bounds penalty of a certain plan is 10, the dependency violation penalty is 5, and the resource conflict penalty is 8, then the total penalty value is calculated to be 8.1. By comparing the total penalty values ​​of different solutions, the solution with the lowest penalty value can be selected as the optimization recommendation. For example, if the total penalty value of solution B is 7.5, this solution is preferred. This fusion calculation method can not only quantify the pros and cons of time window adjustment, but also dynamically adjust the weight according to actual needs, so that the evaluation results are more in line with the actual production scenario, thereby effectively improving the scheduling efficiency and resource utilization of the flexible manufacturing system.

[0034] 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.

[0035] In a possible implementation, 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. Step S400 further includes step S410, according to the time window and dependency relationship between each manufacturing process, a directed graph structure is constructed, wherein 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. Specifically, in a flexible manufacturing system, constructing a directed graph structure based on the process time window and 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 processes is connected by directed edges, and the direction of the edge represents the sequential constraint of the process. For example, the engine test must be completed before the engine can be installed, so there is a directional edge from node A to node B. In addition, the edge weight is used to represent the time limit. For example, the completion time window of the engine test is from 9:00 to 10:00 in the morning, and the engine installation needs to start within 2 hours after the test is completed. The edge weight from A to B is 2 hours. By constructing such a directed graph, the dependencies between processes and the time window restrictions can be intuitively represented, which facilitates the optimization of scheduling. For example, the assembly of the whole vehicle depends on the completion of the engine installation. If the engine installation time window is adjusted to 10:30 to 11:30, the start time of the whole vehicle assembly needs to be dynamically adjusted to after 11 o'clock according to the window. When ensuring the rationality of the directed graph, attention should be paid to detecting loop problems. If node A points to node B, and node B points to node A, it indicates a process dependency cycle, which needs to be solved by rearranging the order. In addition, the introduction of abnormal time windows enables the directed graph to have dynamic adjustment capabilities. For example, if the engine test is delayed to 10:30, 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 replanned. In the final directed graph, the time windows and constraints of all nodes and edges are clearly marked. For example, the time window of node A is [9:00, 10:30], and the time window of node B is [10:30, 12:00], ensuring that the assembly of the entire vehicle is completed within the specified time.

[0036] Step S420, based on the directed graph structure, according to 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 manufacturing process are obtained. 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 first starts from the completion time of the preceding process. For example, the processing time of the engine test (node ​​A) is from 9:00 to 9:50 am, then the earliest start time of the engine installation (node ​​B) needs to meet the completion time constraint of node A, that is, it must not be earlier than 9:50. If node B is also restricted by the abnormal time window, for example, it is unavailable from 10:00 to 10:30 am, then its earliest start time needs to be further adjusted to 10:30. In addition, if a process has multiple predecessor processes, such as vehicle assembly (node ​​C) depends on engine testing and engine installation, the maximum completion time of all predecessor processes needs to be taken as its earliest start time. For example, if the completion time of node A is 9:50 and the completion time of node B is 10:30, then the earliest start time of node C is 10:30. For the calculation of the latest start time, reverse deduction is made from the earliest start time of the subsequent process. For example, if the vehicle assembly needs to be completed at 12 noon and its processing time is 1 hour, then the latest start time is 11 am. If the processing time of node B is 30 minutes, then its latest completion time needs to be 11 am, and the latest start time is reversed to 10:30. Combined with the abnormal time window and the constraint time window, if the processing time of node B must avoid the abnormal time window from 10 am to 10:30 am, then its latest start time needs to be between 10:30 and 11 am. Through this recursive calculation and dynamic adjustment, for example, when the engine test is delayed to 10:30, the earliest start time and the latest start time of node B are dynamically adjusted to 10:30, and the time window of node C is re-determined to 11:00 to 12:00 based on the adjustment result of node B. Ultimately, 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.

[0037] Step S430, according to the abnormal time window and the constraint time window, set the constraint conditions and optimization goals, construct the fitness function, evaluate the fitness of the time window state transfer of each process, iteratively calculate the time windows of all processes, until the optimal scheduling sequence is output, and obtain the deployment window period of each manufacturing process, wherein the state transfer refers to the process of transferring from one time window configuration to another time window configuration. Specifically, in the 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 goals, and use the fitness function to iteratively optimize the time window state transfer, so as to obtain the optimal scheduling scheme. This process first needs to clarify the constraint conditions, including the time window range, process dependency, resource restrictions and avoidance of abnormal time windows. For example, the constraint time window of a process is from 9 am to 12 pm, and the abnormal time window is from 10 am to 10:30 am. The start time of the process must be between 9 am and 12 pm and avoid the abnormal window. At the same time, the dependencies between processes must be satisfied. For example, the engine test must be completed before the engine is installed, and the time window for engine installation must not be earlier than the time when the test is completed. In addition, resource constraints require that multiple processes should not occupy the same equipment at the same time. For example, when engine installation and vehicle assembly share equipment, their time windows must not overlap. Based on the above constraints, a fitness function is constructed to evaluate the time window configuration. The fitness function comprehensively considers the time window out-of-bounds penalty, process dependency violation penalty, and resource conflict penalty. 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 is a resource conflict or dependency violation problem. The dynamic adjustment of the time window is achieved through state transfer. For example, if the engine test is delayed to 10 o'clock, the time window of the subsequent engine installation will be adjusted to [10:30, 11:30] accordingly. After each state transfer, the new configuration is evaluated using the fitness function, and the configuration with the lowest fitness value is recorded as the current optimal solution. Iterative calculation is performed until the fitness value tends to be stable or the maximum number of iterations is reached, and the optimal scheduling solution is finally output. Taking the specific results as an example, the time window for engine testing may be determined as [9:00, 10:00], engine installation as [10:30, 11:30], and vehicle assembly as [11:30, 12:30]. The entire scheduling plan optimizes resource utilization and production efficiency to the greatest extent while satisfying all constraints.

[0038] Step S500, according to the deployment window period of each manufacturing process, the sequence control is performed according to the robot control parameters of each manufacturing process. Specifically, in the flexible manufacturing system, according to the deployment window period of each manufacturing process, accurate sequence control is achieved by binding the robot control parameters to ensure that the production tasks are efficiently executed according to the scheduled time. First, the deployment window period of each process is the result of comprehensive adjustment through time dependency, abnormal time window and constraint conditions. For example, the deployment window period of process A (parts installation) is [9:00,9:30], process B (welding operation) is [9:30,10:00], and process C (quality inspection) is [10:00,10:30]. These time periods determine when the robot starts the action instruction, such as the gripper completes the part grabbing and installation tasks within the window period of process A. In the specific execution, the robot completes the task according to the control parameters. For example, the clamping force of process A is set to 50N and the moving speed is 1.5m / s to ensure that the parts are 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, and ensures that the welding accuracy is within 0.5mm. If process A is delayed to 9:20 due to part feeding, the time window of process B needs to be dynamically adjusted to [9:50,10:20], and the welding action is triggered in time. During the execution of the task, the sensor monitors the robot action in real time to ensure that the action parameters meet the requirements. For example, when the clamping force is detected to be insufficient, it is automatically increased to 60N to complete the clamping; if the welding position deviation exceeds 1mm, the robot will adjust to the correct position before performing welding. In addition, to avoid resource conflicts, the 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 window period. After each process is completed, the actual execution time is recorded and compared with the deployment window period. For example, process A is completed 5 minutes ahead of schedule, and process B is completed 5 minutes late. The reason may be equipment performance degradation or material problems. Based on these records, the robot control parameters are further optimized, such as increasing the welding speed to 2.5m / s or increasing the clamping force of the gripper to 70N, to reduce the execution error and delay of future tasks. Finally, the execution time and sequence of each process are fully recorded. For example, process A is completed at [9:00,9:25], process B is completed at [9:30,9:55], and process C is completed at [10:00,10:25]. The entire production process efficiently and accurately achieves the goal of flexible manufacturing. Even in abnormal situations, it can achieve reasonable allocation of time and resources through dynamic adjustment, ensuring the smooth completion of production tasks.

[0039] The embodiment of the present application uses analysis of process monitoring data to identify abnormal processes and determine abnormal time windows; based on the processing time window relationship of the abnormal process, a constraint time window is determined; combining the abnormal time window and the constraint time window, a timing analysis of the manufacturing process is performed to obtain an adjustment window period; sequence control is implemented according to the adjustment window period and robot control parameters, thereby 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.

[0040] In the above, refer to Figure 1 The adaptive sequence control method for flexible manufacturing according to an embodiment of the present invention is described in detail. Figure 2 An adaptive sequence control system for flexible manufacturing according to an embodiment of the present invention is described.

[0041] The adaptive sequence control system for flexible manufacturing according to the embodiment of the present invention is used to solve the technical problem that when an abnormality occurs in a process in an existing flexible manufacturing environment, the production plan cannot be quickly adjusted, resulting in low efficiency of the manufacturing process, and achieves the technical effect of being able to quickly adjust the production plan when an abnormality occurs in a process in a 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, an allocation window period acquisition module 40, and a sequence control module 50.

[0042] The abnormal process identification module 10 is used to connect the process monitoring data, analyze the process monitoring data, and identify the abnormal process.

[0043] The abnormal time window acquisition module 20 is used to identify the abnormal time sequence according to the abnormal process and obtain the abnormal time window.

[0044] The constrained time window module 30 is used to determine the constrained time window based on the abnormal process and using the processing time window relationship of the manufacturing process.

[0045] The allocation window period acquisition module 40 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 allocation window period of each manufacturing process.

[0046] The sequence control module 50 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.

[0047] The specific configuration of the abnormal process identification module 10 will be described in detail below. As described above, the process monitoring data is connected, the process monitoring data is parsed, and the abnormal process is identified. The abnormal process identification module 10 further includes: a process monitoring equipment deployment unit, the process monitoring equipment deployment unit is used to identify the abnormal factors of each manufacturing process, and deploy the process monitoring equipment based on the data attributes of the abnormal factors; a processing time monitoring equipment adding unit, the processing time monitoring equipment adding unit is used to identify and determine the processing time characteristics of the process monitoring data of each manufacturing process, and when the processing time characteristics cannot be determined, the processing time monitoring equipment is added to the process monitoring equipment; a process monitoring data acquisition unit, the process monitoring data acquisition unit is used to connect the process monitoring equipment of each manufacturing process and acquire the process monitoring data of each manufacturing process.

[0048] The specific configuration of the constrained time window module 30 will be described in detail below. As described above, based on the abnormal process, the constrained time window is determined by using the processing time window relationship of the manufacturing process, and the constrained time window module 30 further includes: a predicted time period acquisition unit, the predicted time period acquisition unit is used to perform abnormal time series prediction according to the abnormal monitoring data attributes and abnormal deviation of the abnormal process, and obtain the predicted time period; an abnormal starting point acquisition unit, the abnormal starting point acquisition unit is used to obtain the abnormal starting point according to the comparison curve of the monitoring data of the abnormal process and the normal data; an abnormal time window acquisition unit, the abnormal time window acquisition unit is used to obtain the abnormal time window according to the abnormal starting point and the predicted time period.

[0049] Among them, the constraint time window module 30 further includes: a regular process acquisition unit, which is used to acquire the regular preceding process and the regular following process of the abnormal process; a processing time window determination unit, which is used to analyze the process dependency relationship between the regular preceding process, the regular following process and the abnormal process, and determine the processing time window; an adjustment fitting unit, which is used to adjust and fit the processing time window through a dynamic moving window to obtain an adjustment penalty value of each moving time window; an adjustment penalty value screening unit, which is used to screen the adjustment penalty value of each moving time window using a penalty constraint threshold to obtain the constraint time window.

[0050] Among them, the process dependency relationship between the conventional predecessor process, the conventional post-process and the abnormal process is analyzed to determine the processing time window, and the processing time window determination unit further includes: a leading time window acquisition subunit, the leading time window acquisition subunit is used to obtain the leading time window according to the process dependency relationship between the conventional predecessor process and the abnormal process, and the leading time window is the relationship between the latest completion time of the predecessor process and the earliest start time of the abnormal process; a subsequent time window acquisition subunit, the subsequent time window acquisition subunit is used to obtain the subsequent time window according to the process dependency relationship between the conventional post-process and the abnormal process, and 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; a time transfer analysis subunit, the time transfer analysis subunit is used to perform time transfer according to the leading time window and the subsequent time window to obtain the processing time window.

[0051] Among them, the processing time window is adjusted and fitted by a dynamic moving window to obtain the adjusted penalty value of each moving time window, and the adjustment fitting unit further includes: a penalty function construction subunit, the penalty function construction subunit is used to construct a penalty function from multiple dimensions of time window out-of-bounds delay penalty, process dependency violation penalty, and production resource conflict penalty; a proportional coefficient configuration subunit, the proportional coefficient configuration subunit is used to configure the proportional coefficient of each penalty dimension, and the time window out-of-bounds delay penalty function, the process dependency violation penalty function, and the production resource conflict penalty function are merged according to the penalty dimension proportional coefficient to construct a fused penalty function; a penalty value calculation subunit, the penalty value calculation subunit is used to use the fused penalty function to calculate the penalty value of the moving time window to obtain the adjusted penalty value of each moving time window.

[0052] The specific configuration of the allocation window period acquisition module 40 will be described in detail below. As described above, 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. The allocation 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 windows and dependencies between the various manufacturing processes, wherein the graph nodes are manufacturing process nodes, the connecting edges represent the process dependencies, the directions of the edges represent the sequence relationships between the processes, and the weights of the edges represent the restrictions of the time windows; a manufacturing process time data acquisition unit, which is used to obtain the manufacturing process time data based on the time windows and dependencies between the various manufacturing processes. A directed graph structure is provided, which obtains 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; a fitness function construction unit is used to set constraints and optimization goals according to the abnormal time window and the constraint time window, construct a fitness function, evaluate the fitness of the time window state transition of each process, iteratively calculate the time windows of all processes until the optimal scheduling sequence is output, and obtain the allocation window period of each manufacturing process, wherein the state transition refers to the process of transferring from one time window configuration to another time window configuration.

[0053] The adaptive sequence control system for flexible manufacturing provided by the embodiment 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 of the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0055] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this 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, a constraint time window is determined using a processing time window relationship of the manufacturing process; 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: 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; The penalty constraint threshold is used to screen the adjustment penalty value of each moving time window to obtain the constraint time window.

5. The adaptive sequence control method for flexible manufacturing according to claim 4, 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 subsequent time window.

6. The adaptive sequence control method for flexible manufacturing according to claim 4, 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.

7. The adaptive sequence control method for flexible manufacturing according to claim 4, 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.

8. 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 7, 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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