Assembly production line dynamic reconstruction and scheduling method under high-frequency disturbance

Abstract

The invention relates to the technical field of production line management, in particular to an assembly production line dynamic reconstruction and scheduling method under high-frequency disturbance, which comprises the following steps: selecting a first production task by acquiring order task information and determining a time margin coefficient, dividing production equipment in an assembly production line into production units, the method comprises the following steps: selecting an order task under high-frequency disturbance, identifying inertial production time of the order task under different production units, selecting a collaborative task based on a time margin coefficient, dividing a front production unit and a rear production unit according to a shared production unit, and calculating a priority proportion in combination with a target optimization formula. The layout and the production plan of the production line are dynamically adjusted, the adaptability and the anti-interference capability of the production line to the external environment change are enhanced, the idle time of equipment and the waiting time in the production process are reduced by optimizing production scheduling, and the production cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of production line management technology, and in particular to a method for dynamic reconstruction and scheduling of assembly production lines under high-frequency disturbances. Background Technology

[0002] Matrix assembly lines are a flexible production layout model. Their core feature is that the production line is divided into multiple independent "production units". Each production unit is responsible for a specific assembly task or product module. The production units are connected to each other through a logistics system (such as conveyor belts, AGVs, etc.) to form an efficient and collaborative production system.

[0003] The prior art CN104571007A discloses an optimization scheduling method for the production assembly process of a final assembly line in automobile manufacturing. The method includes determining the scheduling model and optimization objective of the final assembly line production assembly process, and using a hybrid distribution estimation algorithm to optimize the optimization objective. The scheduling model is established based on the processing completion time of each body module on each machine, and the optimization objective is to minimize the maximum completion time.

[0004] However, with increasingly fierce market competition and diversified customer needs, the high-frequency disturbance factors faced by assembly production lines have increased significantly, such as sudden changes in order demand, equipment failure, and delays in raw material supply. Traditional matrix assembly production line scheduling methods are usually based on static production plans and fixed production unit divisions, lacking the ability to dynamically respond to high-frequency disturbances. When disturbances occur, the original production plans and production line layouts are difficult to adapt to changes, which can easily lead to problems such as order delivery delays, decreased production efficiency, and low resource utilization. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing a dynamic reconstruction and scheduling method for assembly lines under high-frequency disturbances.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances, which specifically includes the following steps:

[0008] Step 1: Obtain all order tasks and task information in the system within the current time period, identify the deadline in the task information, analyze the deadline and the corresponding order quantity, determine the time margin coefficient, and select the first production task based on the time margin coefficient.

[0009] Step 2: Obtain all order tasks again. First, divide the production equipment in the assembly line into several production units according to the production process. Then, based on historical production information, identify the inertial production time of each order task in different production units.

[0010] Step 3: Using the first production task as the baseline task, select collaborative tasks from the remaining order tasks based on the time margin coefficient, identify the shared production units between the collaborative tasks and the first production task, divide the assembly line into front-end production units and back-end production units according to the location of the shared production units, obtain the inertial production time, and calculate the front-end time and back-end time of the front-end production units and back-end production units respectively. Then, based on the target optimization formula, calculate the priority ratio of the unit production quantity of the first production task and the collaborative tasks, and then determine the maximum value of the unit production quantity according to the priority ratio. Split the corresponding order quantity according to the maximum value of the unit production quantity and start production simultaneously.

[0011] As a further aspect of the present invention, the method for selecting the first production task includes:

[0012] Based on the order placement time in the task information, the order tasks are arranged according to their time positions to obtain the order sequence. Using the current time as the base time node, the deadline of the order task is obtained. The deadline is subtracted from the base time node to obtain the remaining deadline value. Here, the deadline refers to the agreed delivery time of the corresponding order task.

[0013] Extract the number of orders from the order task, divide the remaining deadline of the order task by the number of orders in the order task, and obtain the unit production time Td;

[0014] Obtain the production products and historical production information of the assembly line for this order task. In the historical production information, obtain the actual production time of a single production process for this product type. Take n actual production times and average them. Mark the average result as the production characteristic time, where n is the standard sample size.

[0015] Divide the unit production time Td by the corresponding production characteristic time of the product, and mark the result as the time margin coefficient.

[0016] Obtain the time margin coefficients for all order tasks, identify the minimum value among all time margin coefficients, and mark the order task corresponding to the minimum time margin coefficient as the first production task.

[0017] As a further aspect of the present invention, when calculating the remaining time limit, only the working time of the assembly line is calculated, and the downtime of the assembly line is not included in the calculation of the remaining time limit.

[0018] As a further aspect of the present invention, the method for dividing production units includes:

[0019] Obtain the complete assembly line, identify the production function of each production equipment in the assembly line, and at the same time, arbitrarily select an order task and mark this order task as the target task. Obtain the production process of the target task, which includes multiple process operations. The multiple process operations constitute the total production process of the target task in the assembly line.

[0020] According to the workflow of the target task, the production functions of the production equipment in the assembly line are matched with the workflow. The production equipment corresponding to one workflow is marked as a production unit. Furthermore, there are one or more production equipment in a production unit, and if there are multiple production equipment in a production unit, the positional relationship between the multiple production equipment is an adjacent positional relationship.

[0021] As a further aspect of the present invention, the method for obtaining inertial production time includes:

[0022] Meanwhile, in the historical production data, the single-process time of each product when it is processed in a production unit is identified in turn. The single-process time refers to the operation time required for the target task to perform a single independent process operation. That is, the time when the production product in the target task enters the corresponding production unit is taken as the start time. Then, the production product is tracked in real time until the production product is completed in the production unit. At the same time, the completion time is marked as the end time. At this time, the single-process time is the difference obtained by subtracting the start time from the end time.

[0023] Arbitrarily select a production unit, identify all single-process times of this production unit in the historical production data, calculate the average of the single-process times of this production unit, and mark the average calculation result as the inertial production time of the target task in this process.

[0024] Using the method described above, the inertial production time of all production units in the target task is calculated sequentially from historical production data.

[0025] As a further aspect of the present invention, before calculating the inertial production time of the production unit in the target task, it is necessary to identify and delete abnormal data in the single-process time, and then calculate the inertial production time based on the remaining single-process time. The method for identifying abnormal data includes:

[0026] In the production data, select all single-process times corresponding to a production unit. Using a normal distribution algorithm, first average all single-process times to obtain the time mean Tz. Then, use the formula... The standard deviation σ is obtained, where m = 1, 2, ..., M, indicating that there are a total of M single-process times;

[0027] Based on the Tz±k×σ interval, data in the single process time Tm that do not belong to the Tz±k×σ interval are marked as abnormal data, and the value of k is set to 2.

[0028] As a further aspect of the present invention, the method for selecting collaborative tasks includes:

[0029] Obtain the first production task and simultaneously retrieve the assembly line corresponding to the first production task. At the same time, using the production unit as the node unit, identify the order tasks among all order tasks that have one or more production units that are the same as the production unit of the first production task, and mark this order task as a similar order Li, where i represents the label of different similar orders.

[0030] Identify the time margin coefficient of all order tasks within the same type of order, and select the order task corresponding to the minimum time margin coefficient, marking this order task as a collaborative task.

[0031] As a further aspect of the present invention, the method for obtaining the pre-time and post-time includes:

[0032] Identify production units that are the same as the first production task and mark them as shared production units. Using shared production units as the dividing point, divide production units into front-end production units and back-end production units according to the production sequence. The front-end production units include synchronous front-end production units and mainline front-end production units, and the back-end production units include synchronous back-end production units and mainline back-end production units.

[0033] Specifically, according to the production order of the collaborative task, the production units before the shared production unit are merged and marked as synchronous pre-production units, and the production units after the shared production unit are marked as synchronous post-production units. Then, according to the production order of the first production task, the production units before the shared production unit are merged and marked as main line pre-production units, and the production units after the shared production unit are merged and marked as main line post-production units.

[0034] The inertial production time of each production unit in the collaborative task and the first production task is obtained separately. The inertial production time of all production units in the preceding production units in the same production task is accumulated and marked as the synchronization preceding time T21 and the main line preceding time T11. The inertial production time of all production units in the following production units in the same production task is accumulated and marked as the synchronization following time T22 and the main line following time T12. At the same time, the inertial production time of the shared production unit in the first production task is marked as the main line intersection time T10, and the inertial production time of the shared production unit in the collaborative task is marked as the synchronization intersection time T20. Further, the time value corresponding to the first production task includes the main line preceding time T11, the main line following time T12, and the main line intersection time T10, and the time value of the collaborative task includes the synchronization preceding time T21, the synchronization following time T22, and the synchronization intersection time T20.

[0035] As a further aspect of the present invention, the method for splitting the corresponding order quantity according to the maximum unit production quantity includes:

[0036] Let the unit production quantity in the first production task be x, and the unit production quantity in the collaborative task be y. Then, based on the objective optimization formula... Calculate the balance ratio PH = x:y in each sub-formula, where x and y are both greater than or equal to 0 and are integers. Then, select the priority ratio PHb from all balance ratios so that x + y is the maximum value.

[0037] Obtain the order quantity in the first production task and the order quantity in the collaborative task respectively. Based on the priority ratio PHb, within the corresponding order quantity, obtain the maximum value CZmax and CXmax of the unit production quantity of the first production task and the collaborative task respectively, where CZmax / CXmax=PHb;

[0038] The order quantity is decomposed according to the maximum production quantity of the first production task and the collaborative task, CZmax and CXmax respectively. At the same time, CZmax and CXmax are used to produce the first production task and the collaborative task simultaneously.

[0039] Compared with existing technologies, the advantages of this invention are:

[0040] This invention selects the first production task by acquiring order task information and determining the time margin coefficient, prioritizing time-sensitive tasks, effectively ensuring on-time order delivery, and improving customer satisfaction. It then divides the production equipment in the assembly line into production units and identifies the inertial production time of order tasks in different production units. Based on the time margin coefficient, collaborative tasks are selected, and pre-production units and post-production units are divided according to shared production units. The priority ratio is calculated using the objective optimization formula, enabling the reasonable splitting and parallel production of multiple tasks, fully utilizing production resources, and improving the overall production efficiency of the production line. Furthermore, this method can dynamically adjust the layout and production plan of the production line according to changes in order tasks under high-frequency disturbances, enhancing the production line's adaptability and anti-interference capabilities to changes in the external environment. Finally, by optimizing production scheduling, it reduces equipment idle time and waiting time during production, lowers production costs, and improves the company's economic benefits and market competitiveness. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0043] Reference Figure 1 A method for dynamic reconstruction and scheduling of assembly lines under high-frequency disturbances, which specifically includes the following steps:

[0044] Step 1: Collect basic information about the assembly line. In this embodiment, the assembly line is a matrix assembly line, which means that the assembly line is divided into multiple independent production units. Each production unit is responsible for a specific assembly task or product module. Furthermore, the basic information refers to the production or assembly task of each device in the assembly line.

[0045] Step 2: Obtain all order tasks and task information in the system within the current time period. The task information includes the product to be produced, the order time, and the order quantity.

[0046] Based on the order placement time in the task information, the order tasks are arranged according to their time position to obtain an order sequence. Then, time analysis is performed on all order tasks in the order sequence to obtain time margin coefficients. Based on the time margin coefficients, the first production task is selected. Specifically, the methods for obtaining the time margin coefficients include:

[0047] Using the current time as the base time node, obtain the deadline of the order task, subtract the base time node from the deadline to obtain the remaining deadline value, where the deadline refers to the agreed delivery time of the corresponding order task;

[0048] Extract the number of orders from the order task, divide the remaining deadline of the order task by the number of orders in the order task, and obtain the unit production time Td;

[0049] Obtain the production products and historical production information of the assembly line for this order task. In the historical production information, obtain the actual production time of a single production process for this product type. Take n actual production times and average them. Mark the average result as the production characteristic time. Here, n is the standard sample size value. The specific standard sample data size value is set by those skilled in the art based on big data experience.

[0050] Then, divide the unit production time Td by the corresponding production characteristic time of the product, and mark the result as the time margin coefficient.

[0051] It should be further noted that when calculating the remaining time limit, only the working time of the assembly line is calculated, and the downtime of the assembly line is not included in the calculation of the remaining time limit.

[0052] Obtain the time margin coefficients for all order tasks, identify the minimum value among all time margin coefficients, and mark the order task corresponding to the minimum time margin coefficient as the first production task;

[0053] Step 3: After the first production task is determined, all order tasks are retrieved again, and based on historical production information, the inertial production time of each order task under different production units is identified. The specific methods for determining the inertial production time include:

[0054] S1: Obtain the complete assembly line, identify the production function of each production equipment in the assembly line, and at the same time, arbitrarily select an order task from all order tasks and mark this order task as the target task. Taking the target task as an example, obtain the production process of the target task. The production process includes multiple process operations, such as cleaning, polishing, welding, etc. The multiple process operations constitute the total production process of the target task in the assembly line.

[0055] According to the workflow of the target task, the production functions of the production equipment in the assembly line are matched with the workflow. The production equipment corresponding to one workflow is marked as a production unit. Furthermore, there are one or more production equipment in a production unit, and if there are multiple production equipment in a production unit, the positional relationship between the multiple production equipment is an adjacent positional relationship.

[0056] S2: Extract the production data of the target task from the historical production information. In this embodiment, the production data refers to the most recent production record of the production task that is consistent with the production products of the same type as the target task.

[0057] Meanwhile, in the historical production data, the single-process time of each product when it is processed in a production unit is identified in turn. Specifically, the single-process time refers to the operation time required for the target task to perform a single independent process operation. That is, the time when the production product in the target task enters the corresponding production unit is taken as the start time. Then, the production product is tracked in real time until the production product is completed in the production unit. At the same time, the completion time is marked as the end time. At this time, the single-process time is the difference between the end time and the start time.

[0058] S3: Select any production unit and take this production unit as an example. Identify all single-process times of this production unit in the historical production data, calculate the average of the single-process times of this production unit, and mark the average calculation result as the inertial production time of the target task in this process.

[0059] Using the method described above, the inertial production time of all production units in the target task is calculated sequentially from the historical production data.

[0060] It should be further explained that before calculating the inertial production time of the production unit in the target task, it is necessary to identify and delete abnormal data in the single-process time. Based on the remaining single-process time, the inertial production time is then calculated. Furthermore, the methods for identifying abnormal data include:

[0061] In the production data, select all single-process times corresponding to a production unit. Using a normal distribution algorithm, first average all single-process times to obtain the time mean Tz. Then, use the formula... The standard deviation σ is obtained, where m = 1, 2, ..., M, indicating that there are a total of M single-process times;

[0062] Then, based on the Tz±k×σ interval, data in the single process time Tm that do not belong to the Tz±k×σ interval are marked as abnormal data. In this embodiment, the value of k is set to 2.

[0063] Step 4: Based on the inertial production time of each order task in different production units, and combined with the time slack coefficient of the order task, the remaining order tasks are scheduled and analyzed, taking the first production task as the baseline task, to balance the allocation of order tasks and thus improve the production efficiency of the assembly line. Specifically, the methods for scheduling and analyzing order tasks include:

[0064] SS1: Obtain the first production task and simultaneously retrieve the assembly line corresponding to the first production task. At the same time, using the production unit as the node unit, identify the order tasks that have one or more production units that are the same as the first production task among all order tasks, and mark this order task as a similar order Li, where i represents the label of different similar orders.

[0065] Identify the time margin coefficient of all order tasks in the same type of order, and select the order task corresponding to the minimum time margin coefficient, and mark this order task as a collaborative task;

[0066] SS2: Identify production units that are the same as the first production task and mark them as shared production units. Using shared production units as the dividing point, divide production units into front-end production units and back-end production units according to the production sequence. The front-end production units include synchronous front-end production units and mainline front-end production units, and the back-end production units include synchronous back-end production units and mainline back-end production units.

[0067] Furthermore, according to the production order of the collaborative task, the production units before the shared production unit are merged and marked as synchronous pre-production units, and the production units after the shared production unit are marked as synchronous post-production units. Then, according to the production order of the first production task, the production units before the shared production unit are merged and marked as main line pre-production units, and the production units after the shared production unit are merged and marked as main line post-production units.

[0068] SS3: Obtain the inertial production time of each production unit in the collaborative task and the first production task respectively. Accumulate the inertial production time of all production units in the preceding production units in the same production task and mark the accumulated value as the synchronization preceding time T21 and the main line preceding time T11. Accumulate the inertial production time of all production units in the following production units in the same production task and mark the accumulated value as the synchronization following time T22 and the main line following time T12. At the same time, mark the inertial production time of the shared production unit in the first production task as the main line intersection time T10, and mark the inertial production time of the shared production unit in the collaborative task as the synchronization intersection time T20. Further, the time value corresponding to the first production task includes the main line preceding time T11, the main line following time T12 and the main line intersection time T10, and the time value of the collaborative task includes the synchronization preceding time T21, the synchronization following time T22 and the synchronization intersection time T20.

[0069] SS4: Set the unit production quantity in the first production task as x, and the unit production quantity in the collaborative task as y, then optimize based on the objective formula. Calculate the balance ratio PH = x:y in each sub-formula, where x and y are both greater than or equal to 0 and are integers. Then, select the priority ratio PHb from all balance ratios so that x + y is the maximum value.

[0070] SS5: Obtain the order quantity in the first production task and the order quantity in the collaborative task respectively. Based on the priority ratio PHb, within the corresponding order quantity, obtain the maximum value CZmax and CXmax of the unit production quantity of the first production task and the collaborative task respectively, where CZmax / CXmax = PHb.

[0071] The order quantity is decomposed according to the maximum production quantity of the first production task and the collaborative task, CZmax and CXmax respectively. At the same time, CZmax and CXmax are used to produce the first production task and the collaborative task simultaneously, so that the load of each production unit in the matrix assembly line is balanced, and production efficiency is further improved.

[0072] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances, characterized in that, The method specifically includes the following steps: Step 1: Obtain all order tasks and task information in the system within the current time period, identify the deadline in the task information, analyze the deadline and the corresponding order quantity, determine the time margin coefficient, and select the first production task based on the time margin coefficient. Step 2: Obtain all order tasks again. First, divide the production equipment in the assembly line into several production units according to the production process. Then, based on historical production information, identify the inertial production time of each order task in different production units. Step 3: Using the first production task as the baseline task, select collaborative tasks from the remaining order tasks based on the time margin coefficient, identify the shared production units between the collaborative tasks and the first production task, divide the assembly line into front-end production units and back-end production units according to the location of the shared production units, obtain the inertial production time, and calculate the front-end time and back-end time of the front-end production units and back-end production units respectively. Then, based on the target optimization formula, calculate the priority ratio of the unit production quantity of the first production task and the collaborative tasks, and then determine the maximum value of the unit production quantity according to the priority ratio. Split the corresponding order quantity according to the maximum value of the unit production quantity and start production simultaneously.

2. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 1, characterized in that, The methods for selecting the first production task include: Based on the order placement time in the task information, the order tasks are arranged according to their time positions to obtain the order sequence. Using the current time as the base time node, the deadline of the order task is obtained. The deadline is subtracted from the base time node to obtain the remaining deadline value. Here, the deadline refers to the agreed delivery time of the corresponding order task. Extract the number of orders from the order task, divide the remaining deadline of the order task by the number of orders in the order task, and obtain the unit production time Td; Obtain the production products and historical production information of the assembly line for this order task. In the historical production information, obtain the actual production time of a single production process for this product type. Take n actual production times and average them. Mark the average result as the production characteristic time, where n is the standard sample size. Divide the unit production time Td by the corresponding production characteristic time of the product, and mark the result as the time margin coefficient. Obtain the time margin coefficients for all order tasks, identify the minimum value among all time margin coefficients, and mark the order task corresponding to the minimum time margin coefficient as the first production task.

3. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 2, characterized in that, When calculating the remaining time limit, only the working time of the assembly line is calculated; the downtime of the assembly line is not included in the calculation of the remaining time limit.

4. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 1, characterized in that, Methods for dividing production units include: Obtain the complete assembly line, identify the production function of each production equipment in the assembly line, and at the same time, arbitrarily select an order task and mark this order task as the target task. Obtain the production process of the target task, which includes multiple process operations. The multiple process operations constitute the total production process of the target task in the assembly line. According to the workflow of the target task, the production functions of the production equipment in the assembly line are matched with the workflow. The production equipment corresponding to one workflow is marked as a production unit. There are one or more production equipment in a production unit. If there are multiple production equipment in a production unit, the positional relationship between the multiple production equipment is an adjacent positional relationship.

5. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 4, characterized in that, Methods for obtaining inertial production time include: Meanwhile, in the historical production data, the single-process time of each product when it is processed in a production unit is identified in turn. The single-process time refers to the operation time required for the target task to perform a single independent process operation. That is, the time when the production product in the target task enters the corresponding production unit is taken as the start time. Then, the production product is tracked in real time until the production product is completed in the production unit. At the same time, the completion time is marked as the end time. At this time, the single-process time is the difference obtained by subtracting the start time from the end time. Arbitrarily select a production unit, identify all single-process times of this production unit in the historical production data, calculate the average of the single-process times of this production unit, and mark the average calculation result as the inertial production time of the target task in this process. Using the method described above, the inertial production time of all production units in the target task is calculated sequentially from historical production data.

6. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 5, characterized in that, Before calculating the inertial production time of the production unit in the target task, it is necessary to identify and delete abnormal data in the single-process time. Based on the remaining single-process time, the inertial production time is then calculated. The methods for identifying abnormal data include: In the production data, select all single-process times corresponding to a production unit. Using a normal distribution algorithm, first average all single-process times to obtain the time mean Tz. Then, use the formula... The standard deviation σ is obtained, where m = 1, 2, ..., M, indicating that there are a total of M single-process times; Based on the Tz±k×σ interval, data in the single process time Tm that do not belong to the Tz±k×σ interval are marked as abnormal data, and the value of k is set to 2.

7. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 1, characterized in that, Methods for selecting collaborative tasks include: Obtain the first production task and simultaneously retrieve the assembly line corresponding to the first production task. At the same time, using the production unit as the node unit, identify the order tasks among all order tasks that have one or more production units that are the same as the production unit of the first production task, and mark this order task as a similar order Li, where i represents the label of different similar orders. Identify the time margin coefficient of all order tasks within the same type of order, and select the order task corresponding to the minimum time margin coefficient, marking this order task as a collaborative task.

8. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 1, characterized in that, Methods for obtaining the pre-time and post-time include: Identify production units that are the same as the first production task and mark them as shared production units. Using shared production units as the dividing point, divide production units into front-end production units and back-end production units according to the production sequence. The front-end production units include synchronous front-end production units and mainline front-end production units, and the back-end production units include synchronous back-end production units and mainline back-end production units. Specifically, according to the production order of the collaborative task, the production units before the shared production unit are merged and marked as synchronous pre-production units, and the production units after the shared production unit are marked as synchronous post-production units. Then, according to the production order of the first production task, the production units before the shared production unit are merged and marked as main line pre-production units, and the production units after the shared production unit are merged and marked as main line post-production units. The inertial production time of each production unit in the collaborative task and the first production task is obtained separately. The inertial production time of all production units in the preceding production units in the same production task is accumulated and marked as the synchronization preceding time T21 and the main line preceding time T11. The inertial production time of all production units in the following production units in the same production task is accumulated and marked as the synchronization following time T22 and the main line following time T12. At the same time, the inertial production time of the shared production unit in the first production task is marked as the main line intersection time T10, and the inertial production time of the shared production unit in the collaborative task is marked as the synchronization intersection time T20. The time values ​​corresponding to the first production task include the main line preceding time T11, the main line following time T12, and the main line intersection time T10. The time values ​​of the collaborative task include the synchronization preceding time T21, the synchronization following time T22, and the synchronization intersection time T20.

9. The method for dynamic reconfiguration and scheduling of assembly lines under high-frequency disturbances according to claim 8, characterized in that, Methods for splitting the corresponding order quantity based on the maximum unit production quantity include: Let the unit production quantity in the first production task be x, and the unit production quantity in the collaborative task be y. Then, based on the objective optimization formula... Calculate the balance ratio PH = x:y in each sub-formula, where x and y are both greater than or equal to 0 and are integers. Then, select the priority ratio PHb from all balance ratios so that x + y is the maximum value. Obtain the order quantity in the first production task and the order quantity in the collaborative task respectively. Based on the priority ratio PHb, within the corresponding order quantity, obtain the maximum value CZmax and CXmax of the unit production quantity of the first production task and the collaborative task respectively, where CZmax / CXmax=PHb; The order quantity is decomposed according to the maximum production quantity of the first production task and the collaborative task, CZmax and CXmax respectively. At the same time, CZmax and CXmax are used to produce the first production task and the collaborative task simultaneously.

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