Productivity optimization method, apparatus and device for distributed factory, and medium

Abstract

The embodiment of the invention discloses a productivity optimization method and device for a distributed factory, equipment and a medium. The method comprises the following steps: establishing a productivity evaluation model corresponding to the distributed factory; acquiring assembly stations on the assembly line corresponding to the distributed factory and an assembly resource quantity corresponding to each assembly station, and inputting the assembly resource quantities into the productivity evaluation model for simulation to obtain the expected productivity of the assembly line; if the expected capacity is smaller than the preset target capacity, the maximum capacity values corresponding to all the assembly stations on the assembly line are obtained, and a bottleneck station is determined according to the comparison result of all the maximum capacity values; and increasing the assembly resource quantity corresponding to the bottleneck station, and returning to execute the operation of inputting the assembly resource quantity into the productivity evaluation model for simulation until the expected productivity is greater than or equal to the preset target productivity, and configuring the distributed factory according to the assembly resource quantity corresponding to the expected productivity. And the capacity and the production efficiency of the distributed factory can be optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production management, and particularly relates to a distributed factory capacity optimization method, device, equipment and medium. BACKGROUND

[0002] With the development of global economy and the continuous growth of market demand, the demand of manufacturing industry for improving production efficiency and reducing cost is more and more urgent. At present, due to the characteristics of large production scale, complex operation and difficult scheduling of the central centralized factory mode, the resource utilization rate of the factory is low, the production cycle is long and the transportation cost is high, so a manufacturing mode based on distributed factory is proposed. Among them, the manufacturing mode based on distributed factory refers to dispersing different links in the manufacturing process to multiple locations for production, and improving the capacity of distributed factory through reasonable resource allocation.

[0003] In the prior art, the production resources of the distributed factory are usually scheduled by manual combination with historical experience. However, due to the complexity and flexibility of the distributed factory, the method of scheduling resources by manual combination with historical experience often fails to accurately allocate appropriate assembly resource quantity for each assembly station, resulting in low capacity and production efficiency of the distributed factory. SUMMARY

[0004] The present application provides a distributed factory capacity optimization method, device, equipment and medium, which can allocate the optimal assembly resource quantity for each assembly station in the distributed factory, thereby improving the capacity and production efficiency of the distributed factory.

[0005] In the first aspect, a distributed factory capacity optimization method is provided, which comprises: establishing a capacity evaluation model corresponding to the distributed factory according to each assembly station in the distributed factory and the constraint relationship between the assembly stations; for each assembly line corresponding to the distributed factory, performing the following capacity optimization operation: obtaining the assembly stations corresponding to the assembly line and the assembly resource quantity corresponding to each assembly station respectively, and inputting the assembly resource quantity into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantity; if the expected capacity is less than the preset target capacity, obtaining the maximum capacity value corresponding to each assembly station on the assembly line respectively, and determining the bottleneck station according to the comparison result of the maximum capacity values; increasing the assembly resource quantity corresponding to the bottleneck station, and returning to perform the operation of inputting the assembly resource quantity into the capacity evaluation model for simulation until the expected capacity is greater than or equal to the preset target capacity; if the expected capacity is greater than or equal to the preset target capacity, configuring the distributed factory according to the assembly resource quantity corresponding to the expected capacity.

[0006] Optionally, the assembly resource quantity comprises a number of non-shared tool sets and a number of workers per group; obtaining the number of non-shared tool sets corresponding to each assembly station comprises: obtaining an upper limit value of the capacity of a set of non-shared tools corresponding to the assembly station, and determining the number of non-shared tool sets of the assembly station according to the preset demand output of the assembly station and the upper limit value of the capacity of the non-shared tool set.

[0007] Optionally, obtaining the upper limit value of the capacity of the non-shared tool set corresponding to the assembly station comprises: obtaining assembly man-hours corresponding to the assembly station and a maximum number of workers corresponding to the non-shared tool set, and dividing the assembly man-hours by the maximum number of workers to obtain a minimum assembly cycle corresponding to the assembly station; dividing the preset simulation time by the minimum assembly cycle to obtain the upper limit value of the capacity of the non-shared tool set corresponding to the assembly station.

[0008] Optionally, obtaining the number of workers per group corresponding to each assembly station comprises: determining an expected capacity of each set of non-shared tools according to the division result of the preset demand output and the number of non-shared tool sets; dividing the preset simulation time by the expected capacity of each set of non-shared tools to obtain a maximum assembly cycle of each set of non-shared tools; determining a lower limit value of the number of workers per group corresponding to each set of non-shared tools according to the division result of the assembly man-hours and the maximum assembly cycle; comparing the lower limit value of the number of workers per group with a preset minimum number of workers to obtain the number of workers per group corresponding to the assembly station.

[0009] Optionally, determining the bottleneck station according to the comparison result of the maximum capacity values comprises: comparing the maximum capacity values to obtain a minimum value among the maximum capacity values, and taking the assembly station corresponding to the minimum value among the maximum capacity values as the bottleneck station.

[0010] Optionally, increasing the assembly resource quantity corresponding to the bottleneck station comprises: if the number of workers per group corresponding to the bottleneck station is less than a preset maximum number of workers per group, increasing the number of workers per group corresponding to the bottleneck station; if the number of workers per group corresponding to the bottleneck station is greater than or equal to the preset maximum number of workers per group, increasing the number of non-shared tool sets corresponding to the bottleneck station, and updating the number of workers per group corresponding to the bottleneck station according to the updated number of non-shared tool sets.

[0011] Optionally, after the simulation of inputting the assembly resource quantity into the capacity evaluation model to obtain the expected capacity of the assembly line under the assembly resource quantity, further comprising: determining the theoretical production cycle corresponding to each assembly station on the assembly line according to the division result of the preset simulation time and each maximum capacity value, and determining the maximum theoretical production cycle in each theoretical production cycle; obtaining the number of assembly stations on the assembly line, and calculating a first multiplication result obtained by multiplying the maximum theoretical production cycle by the number of assembly stations; calculating a first addition result obtained by adding each theoretical production cycle, calculating a first subtraction result obtained by subtracting the first addition result from the first multiplication result, and taking a first division result obtained by dividing the first subtraction result by the first multiplication result as the balance performance evaluation value of the assembly line.

[0012] In a second aspect, a capacity optimization device for a distributed factory is provided, which comprises: a capacity evaluation model establishing module configured to establish a capacity evaluation model corresponding to the distributed factory according to each assembly station in the distributed factory and constraint relationships between the assembly stations; a repeated execution module configured to execute, for each assembly line corresponding to the distributed factory, an expected capacity determining module, a bottleneck station determining module, a cycle adjusting module and a factory configuring module respectively; the expected capacity determining module is configured to obtain assembly stations corresponding to the assembly line and assembly resource quantities corresponding to each assembly station respectively, and simulate inputting the assembly resource quantity into the capacity evaluation model to obtain the expected capacity of the assembly line under the assembly resource quantity; the bottleneck station determining module is configured to obtain maximum capacity values corresponding to each assembly station on the assembly line if the expected capacity is less than a preset target capacity, and determine the bottleneck station according to a comparison result of the maximum capacity values; the cycle adjusting module is configured to increase the assembly resource quantity corresponding to the bottleneck station, and return to execute the operation of simulating inputting the assembly resource quantity into the capacity evaluation model until the expected capacity is greater than or equal to the preset target capacity; and the factory configuring module is configured to configure the distributed factory according to the assembly resource quantity corresponding to the expected capacity if the expected capacity is greater than or equal to the preset target capacity.

[0013] In a third aspect, an electronic device is provided, which comprises: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores a computer program which can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the capacity optimization method for a distributed factory according to any one of the embodiments of the present application.

[0014] In a fourth aspect, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the capacity optimization method for a distributed factory according to any one of the embodiments of the present application when the computer instructions are executed by the processor.

[0015] The technical scheme of the embodiment of the present application simulates the expected capacity of each assembly line corresponding to the distributed factory through the capacity evaluation model, and adds the technical means of the amount of assembly resources to the bottleneck station in the distributed factory when the expected capacity of the assembly line is less than the preset target capacity, solves the problem that the prior art of configuring resources for the distributed factory through manual work combined with historical experience often cannot accurately allocate appropriate assembly resource amount to each assembly station, and can realize optimal allocation of assembly resource amount to each assembly station in the distributed factory, thereby improving the capacity and production efficiency of the distributed factory.

[0016] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a flowchart of a capacity optimization method of a distributed factory according to the first embodiment of the present application;

[0019] Figure 2 is a schematic diagram of a capacity evaluation model according to the present application;

[0020] Figure 3 is a flowchart of a capacity optimization method performed for each assembly line corresponding to the distributed factory according to the first embodiment of the present application;

[0021] Figure 4 is a flowchart of another capacity optimization method of a distributed factory according to the second embodiment of the present application;

[0022] Figure 5 is a flowchart of a preferred capacity optimization method of a distributed factory according to the present application;

[0023] Figure 6 is a structural schematic diagram of a capacity optimization device of a distributed factory according to the third embodiment of the present application;

[0024] Figure 7 is a structural schematic diagram of an electronic device for implementing the capacity optimization method of the distributed factory according to the present application. DETAILED DESCRIPTION

[0025] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of the present application.

[0026] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described accompanying drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0027] Embodiment one

[0028] Figure 1 is a flowchart of a distributed factory capacity optimization method according to the first embodiment of the present application. The present embodiment can be applicable to the case of optimizing the capacity of a distributed factory. The method can be executed by a distributed factory capacity optimization device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device such as a computer. As shown in the figure, the method comprises: Figure 1

[0029] S110, according to each assembly station in the distributed factory and the constraint relationship between each assembly station, a capacity evaluation model corresponding to the distributed factory is established.

[0030] In the present embodiment, the distributed factory can be composed of multiple factories that participate in production and manufacturing at the same time. The assembly station can be used to perform one production and manufacturing link in the distributed factory.

[0031] In this step, specifically, after obtaining each assembly station in the distributed factory and the constraint relationship between each assembly station, a capacity evaluation model corresponding to the distributed factory can be established according to the above-mentioned each assembly station and the constraint relationship between each assembly station through a pre-defined modeling simulation software.

[0032] ​For example, assuming that the distributed factory is used for aircraft manufacturing, and the distributed factory is composed of a front fuselage factory, a middle and rear fuselage factory, and an assembly factory, a plurality of front fuselage stations corresponding to the front fuselage factory, a plurality of middle and rear fuselage stations corresponding to the middle and rear fuselage factory, and a plurality of assembly stations corresponding to the assembly factory can be obtained. Then, according to the front fuselage stations, the middle and rear fuselage stations, the assembly stations, and the constraint relationship between the stations, a capacity evaluation model as shown in Figure 2 may be established by using a modeling simulation software.

[0033] In S120, a capacity optimization operation is performed for each assembly line corresponding to the distributed factory.

[0034] In this embodiment, the assembly line can be a flow line in the distributed factory for implementing a specific production task. At least one assembly line can be included in the distributed factory, and at least one assembly station can be arranged on each assembly line.

[0035] As shown in Figure 2 , the distributed factory for aircraft manufacturing can include at least one front fuselage assembly line and at least one middle and rear fuselage assembly line. The front fuselage assembly line includes front fuselage stations and assembly stations, and the middle and rear fuselage assembly line includes middle and rear fuselage stations and assembly stations.

[0036] In one specific embodiment, as shown in Figure 3 , S120 can specifically include:

[0037] In S121, assembly stations corresponding to the assembly line and assembly resource amounts corresponding to each assembly station are obtained.

[0038] In this embodiment, the assembly resource amount can be used to reflect the number of assembly resources provided for the assembly station. The assembly resources can be various, such as tooling and workers, etc.

[0039] In this step, specifically, the assembly resource amount corresponding to each assembly station can be obtained according to a pre-stored assembly resource table. The assembly resource table includes the corresponding relationship between the assembly station and the assembly resource amount. Alternatively, the assembly resource amount of the assembly station can be determined according to the preset demand output of the assembly station and the limit capacity value of the assembly resource.

[0040] In S122, the assembly resource amount is input into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource amount.

[0041] The expected capacity can be the capacity obtained by configuring the assembly line according to the assembly resource amount.

[0042] Exemplarily, Table 1 is an input and output data table of a capacity evaluation model provided according to an embodiment of the present application. As shown in Table 1, the constraint relationship between each assembly station, the tooling required by each assembly station, the assembly man-hour of each assembly station, the worker group number of each assembly station, the number of workers in each group, the number of each type of tooling, and the preset simulation duration can be input into the capacity evaluation model, and the expected capacity of the assembly line under the assembly resource amount, the bottleneck station requiring the assembly resource amount to be updated, the simulation capacity of each assembly station, the non-shared tooling resource utilization rate of each assembly station, the maximum capacity value of each assembly station under full load, the production rhythm of each station under full load, and the balance performance evaluation value of the assembly line can be obtained by the capacity evaluation model according to the input data.

[0043] Table 1

[0044]

[0045] The advantage of such a setting is that the expected capacity of the assembly line under the assembly resource amount is estimated by the capacity evaluation model, so that the assembly resource amount corresponding to each assembly station can be updated in time when the expected capacity does not meet the user's requirements, so as to improve the capacity and production efficiency of the distributed factory.

[0046] S123, if the expected capacity is less than the preset target capacity, the maximum capacity value corresponding to each assembly station on the assembly line is obtained, and the bottleneck station is determined according to the comparison result of each maximum capacity value.

[0047] In this embodiment, the preset target capacity can be the capacity required by the user for the distributed factory. The maximum capacity value can be the capacity of the assembly station when the assembly material corresponding to the assembly station is always sufficient during the simulation process of the capacity evaluation model. The assembly material can be a material that needs to be processed on the assembly station, such as an aircraft front fuselage. The bottleneck station can be an assembly station that can improve the expected capacity of the assembly line by increasing the assembly resource amount.

[0048] In this step, specifically, if the expected capacity is less than the preset target capacity input into the capacity evaluation model by the user, the maximum capacity value corresponding to each assembly station on the assembly line is compared, and the bottleneck station is determined according to the comparison result.

[0049] S124, increase the assembly resource amount corresponding to the bottleneck station, and return to execute the operation of inputting the assembly resource amount into the capacity evaluation model for simulation.

[0050] At this step, specifically, the assembly resource quantity corresponding to the bottleneck station can be increased according to a predefined resource quantity addition rule. The predefined resource quantity addition rule can be to add the same resource quantity each time. Then, the operation of inputting the assembly resource quantity into the capacity evaluation model for simulation can be performed until the expected capacity is greater than or equal to the preset target capacity.

[0051] S125, if the expected capacity is greater than or equal to the preset target capacity, configuring the distributed factory according to the assembly resource quantity corresponding to the expected capacity.

[0052] At this step, specifically, when the expected capacity of the assembly line is greater than or equal to the preset target capacity, the respective assembly resource quantities corresponding to the expected capacity of the assembly line can be obtained, and the respective assembly stations on the assembly line can be configured with resources according to the respective assembly resource quantities.

[0053] The advantage of such a setting is that by respectively configuring resources for each assembly line in the distributed factory, optimal configuration of capacity can be achieved, production costs can be reduced, delivery cycles can be shortened, product quality and customer satisfaction can be improved.

[0054] The technical scheme of the embodiment, by establishing a capacity evaluation model corresponding to the distributed factory according to the respective assembly stations in the distributed factory and the constraint relationship between the respective assembly stations, performing the following capacity optimization operation for each assembly line corresponding to the distributed factory: obtaining the assembly stations corresponding to the assembly line and the respective assembly resource quantities corresponding to each assembly station, and inputting the assembly resource quantities into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantities; if the expected capacity is less than the preset target capacity, obtaining the maximum capacity values respectively corresponding to the respective assembly stations on the assembly line, and determining the bottleneck station according to the comparison result of the respective maximum capacity values; increasing the assembly resource quantity corresponding to the bottleneck station, and returning to perform the operation of inputting the assembly resource quantity into the capacity evaluation model for simulation until the expected capacity is greater than or equal to the preset target capacity; if the expected capacity is greater than or equal to the preset target capacity, configuring the distributed factory according to the assembly resource quantity corresponding to the expected capacity, solves the problem that the prior art of configuring resources for the distributed factory by manual work combined with historical experience often cannot accurately allocate appropriate assembly resource quantities for the respective assembly stations, can achieve optimal allocation of assembly resource quantities for the respective assembly stations in the distributed factory, and thus improves the capacity and production efficiency of the distributed factory.

[0055] Embodiment Two

[0056] Figure 4is a flow chart of another distributed factory capacity optimization method provided according to the second embodiment of the present invention. This embodiment is a further optimization and extension of the above embodiments, wherein the bottleneck position can be determined based on the comparison results of the maximum capacity values, specifically:

[0057] The maximum capacity values ​​are compared to obtain the minimum value among the maximum capacity values, and the assembly station corresponding to the minimum value among the maximum capacity values ​​is used as the bottleneck station.

[0058] The amount of assembly resources corresponding to bottleneck stations will be increased, specifically:

[0059] If the number of workers in each group corresponding to the bottleneck station is less than the preset maximum number of workers in each group, increase the number of workers in each group corresponding to the bottleneck station;

[0060] If the number of workers in each group corresponding to the bottleneck station is greater than or equal to the preset maximum number of workers in each group, the number of non-shared tooling sets corresponding to the bottleneck station is increased, and the number of workers in each group corresponding to the bottleneck station is updated according to the updated number of non-shared tooling sets.

[0061] like Figure 4 As shown, this embodiment provides a method for optimizing the production capacity of a distributed factory, which specifically includes:

[0062] S210: Establish a production capacity evaluation model corresponding to the distributed factory based on the assembly stations in the distributed factory and the constraint relationships between the assembly stations.

[0063] Specifically, in this step, the source module in the simulation modeling software can first simulate the initial material generation stage of the assembly process to provide raw materials for the assembly module in the simulation modeling software. During the material generation stage, the source module can generate intelligent entities, such as the aircraft fuselage, and set the generation method and type of the intelligent entities. There are various ways to generate intelligent entities, such as by rate, by interval, by arrival table in a database, by rate schedule, by arrival schedule, and through dependency injection function calls. The generation type of intelligent entities can be customized according to user needs.

[0064] The assembly module in the simulation modeling software can then be used to simulate the work of each assembly station in the distributed factory, and the assembly resource quota for each assembly station can be set within the assembly module. Optionally, the priority of each assembly station can be pre-set within the assembly module, and assembly stations with higher priorities can preempt assembly resources from lower-priority stations.

[0065] Finally, since the assembly module involves the use of assembly resources, a resource pool module needs to be introduced. Specifically, the resource pool module in the simulation modeling software can be used to store the assembly resource quantity (such as various types of tooling and workers) in the distributed factory, and the types and quantities of assembly resources can be set in the resource pool module. Optionally, the maintenance time and maintenance cycle of the tooling resource can be set, and the random distribution mode of the failure occurrence event and the failure occurrence cycle can be recorded.

[0066] S220, for each assembly line corresponding to the distributed factory, the following capacity optimization operation is performed: obtaining the assembly station corresponding to the assembly line, and the assembly resource quantity corresponding to each assembly station.

[0067] Among them, the assembly resource quantity includes the number of non-shared tooling sets and the number of workers in each group.

[0068] Optionally, obtaining the number of non-shared tooling sets corresponding to each assembly station includes: obtaining the capacity upper limit value of a set of non-shared tooling corresponding to the assembly station, and determining the number of non-shared tooling sets of the assembly station according to the preset demand output of the assembly station and the capacity upper limit value of the non-shared tooling.

[0069] Among them, the capacity upper limit value of the non-shared tooling can be the maximum capacity under the non-shared tooling.

[0070] In one specific embodiment, the assembly time corresponding to the assembly station can be divided by the maximum number of workers corresponding to the non-shared tooling to obtain the shortest assembly cycle corresponding to the assembly station, and the preset simulation time can be divided by the shortest assembly cycle to obtain the capacity upper limit value of the non-shared tooling corresponding to the assembly station. Wherein, the maximum number of workers can be the upper limit of the number of personnel operating a set of non-shared tooling. Alternatively, the capacity upper limit value of the non-shared tooling corresponding to the assembly station can be obtained according to the pre-stored capacity upper limit table. Among them, the capacity upper limit table contains the correspondence between non-shared tooling and capacity upper limit value.

[0071] Then, the number of non-shared tooling sets of the assembly station can be determined according to the division result between the preset demand output of the assembly station and the capacity upper limit value of the non-shared tooling. Among them, the number of non-shared tooling sets of the assembly station can be the minimum number of non-shared tooling sets required by the assembly station.

[0072] For example, the minimum number of non-shared tooling sets required by the assembly station can be determined by the following specific calculation formula:

[0073]

[0074] Among them, Smin is the minimum number of non-shared tooling sets required for the assembly station, ceil() indicates rounding up, d is the user's required output for the assembly station, floor() indicates rounding down, t is the preset simulation time, T is the assembly working hours of the assembly station, and rmax is the maximum number of workers corresponding to each set of non-shared tooling at the assembly station.

[0075] It should be noted that since shared tooling is generally not a critical tooling, the technical solution of this embodiment assumes that the number of shared tooling is a relatively large number (that is, there is no shortage of shared tooling), so there is no need to consider adjusting the number of shared tooling.

[0076] Optionally, the number of workers in each group corresponding to each assembly station is obtained, including: determining the expected production capacity of each set of non-shared tooling based on the division result of the preset demand output and the number of non-shared tooling sets; dividing the preset simulation time by the expected production capacity of each set of non-shared tooling to obtain the longest assembly cycle of each set of non-shared tooling; determining the lower limit of the number of workers in each group corresponding to each set of non-shared tooling based on the division result of the assembly working hours and the longest assembly cycle; comparing the lower limit of the number of workers in each group with the preset minimum number of workers to obtain the number of workers in each group corresponding to the assembly station.

[0077] The number of workers in each group corresponding to the assembly station may be the minimum number of workers in each group under the current minimum number of non-shared tooling sets required for the assembly station.

[0078] In a specific embodiment, the result of dividing the assembly working hours by the longest assembly cycle may be rounded up to obtain a lower limit value of the number of workers in each group corresponding to each set of non-shared tooling.

[0079] For example, the minimum number of workers per group at the assembly station under the current minimum number of non-shared tooling sets required can be determined by the following specific calculation formula:

[0080]

[0081] Among them, rmin is the preset minimum number of workers (that is, the minimum number of workers corresponding to each set of non-shared tooling at the assembly station), T is the assembly working hours of the assembly station, and t is the preset simulation time. The production capacity that users need to complete with each set of non-shared tooling, is the longest acceptable assembly cycle, s is the formula Smin in.

[0082] S230: Input the assembly resource quantity into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantity.

[0083] Table 2 is a basic input information table provided according to an embodiment of the present application, Table 3 is a station input information table provided according to an embodiment of the present application, Table 4 is a tooling input information table provided according to an embodiment of the present application, and Table 5 is a model output information table provided according to an embodiment of the present application. Taking the front fuselage assembly line of an aircraft as an example, after the basic input information in Table 2 is input into the capacity evaluation model, the station input information in Table 3 and the tooling input information in Table 4 can be obtained, and the station input information and the tooling input information are input into the capacity evaluation model as assembly resource quantity for simulation, and the model output information shown in Table 5 is obtained. The basic input information in Table 2 can include preset target capacity, assembly station quantity and tooling quantity; the station input information in Table 3 can include station serial number, station name, worker group number, worker number per group, assembly man-hour, minimum worker number per group, maximum worker number per group and preset simulation time; the tooling input information in Table 4 can include tooling serial number, tooling name, tooling quantity, whether shared, first maintenance time, next maintenance time and maintenance duration; and the model output information in Table 5 can include station name, simulation capacity of the assembly station under the preset simulation time, non-shared tooling utilization rate, maximum capacity value, theoretical production cycle, expected capacity of the assembly line under the assembly resource quantity and balance performance evaluation value of the assembly line.

[0084] Table 2

[0085]

[0086] Table 3

[0087]

[0088]

[0089] Table 4

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] Table 5

[0096]

[0097] The advantage of such arrangement is that, by inputting the data in Tables 2, 3 and 4 into the productivity evaluation model for simulation, the productivity of the distributed factory with productivity ramp-up requirements can be estimated, and the assembly resource quantity of each assembly station can be adjusted in time according to the expected productivity obtained by estimation.

[0098] In S240, if the expected productivity is less than the preset target productivity, the maximum productivity values corresponding to each assembly station on the assembly line are compared to obtain the minimum value among the maximum productivity values, and the assembly station corresponding to the minimum value among the maximum productivity values is taken as the bottleneck station.

[0099] In the embodiment, the maximum productivity value of the assembly station can be determined according to the simulation productivity value of the assembly station and the ratio of the non-shared tool utilization rate corresponding thereto.

[0100] In the above example, since the expected productivity of the front fuselage assembly line is 19 aircrafts / 7200h, the expected productivity is less than the preset target productivity of 30 aircrafts / 7200h, and thus the maximum productivity values corresponding to each assembly station on the front fuselage assembly line can be obtained in Table 5. Then, the maximum productivity values are compared to obtain the minimum value of 21.57671 among the maximum productivity values, and the door frame wallboard station corresponding to 21.57671 is taken as the bottleneck station.

[0101] In S250, if the number of workers in each group corresponding to the bottleneck station is less than the preset maximum number of workers in each group, the number of workers in each group corresponding to the bottleneck station is increased.

[0102] In this step, the number of workers in each group corresponding to the bottleneck station can be increased according to a predefined worker number increasing rule. The predefined worker number increasing rule can be various, for example, a first preset number of workers is added to each worker group each time, and the first preset number can be one or more.

[0103] In S260, if the number of workers in each group corresponding to the bottleneck station is greater than or equal to the preset maximum number of workers in each group, the number of non-shared tool sets corresponding to the bottleneck station is increased, and the number of workers in each group corresponding to the bottleneck station is updated according to the updated number of non-shared tool sets.

[0104] In this step, the number of non-shared tool sets corresponding to the bottleneck station can be increased according to a predefined tool set number increasing rule. The predefined tool set number increasing rule can be various, for example, a second preset number of non-shared tool sets is added to each worker group each time, and the second preset number can be one or more, and the first preset number and the second preset number can be the same or different. Then, the updated number of non-shared tool sets is substituted into the formula The updated number of workers corresponding to each group of the bottleneck station is calculated, and the updated assembly resource quantity (i.e., the updated number of non-shared tool sets and the number of workers in each group) is input into the capacity evaluation model to perform simulation until the expected capacity is greater than or equal to the preset target capacity. In actual application, the assembly resource quantity of the assembly station can be directly updated in the assembly module of the capacity evaluation model.

[0105] In the above example, since the number of workers in each group of the door frame wallboard station (i.e., 8) is greater than or equal to the preset maximum number of workers in each group (i.e., 12), the number of door frame wallboard assembly racks corresponding to the door frame wallboard station is increased by one, and one corresponding group of workers is added.

[0106] The advantage of such an arrangement is that, by modeling and simulating each link of the distributed factory, predicting the capacity performance of the distributed factory under different configuration resource quantities, and automatically adjusting the number of non-shared tool sets and the number of workers in each group according to the capacity performance, the problem of insufficient assembly resource quantity leading to low capacity of the distributed factory can be avoided, and optimal production of the distributed factory is achieved.

[0107] S270, if the expected capacity is greater than or equal to the preset target capacity, the distributed factory is configured according to the assembly resource quantity corresponding to the expected capacity.

[0108] In the above example, Table 6 is an updated model output information table provided by an embodiment of the present application. As shown in Table 6, after adding one front cargo door frame wallboard assembly rack and one group of workers, the expected capacity of the front fuselage assembly line is 30 units / 7200h, which reaches the preset target capacity.

[0109] Table 6

[0110]

[0111]

[0112] Optionally, after inputting the assembly resource quantity into the capacity evaluation model to perform simulation and obtaining the expected capacity of the assembly line under the assembly resource quantity, the method further includes: determining a theoretical production cycle corresponding to each assembly station on the assembly line according to a result of dividing the preset simulation time by each maximum capacity value, and determining a maximum theoretical production cycle in the theoretical production cycles; obtaining the number of assembly stations on the assembly line, and calculating a first multiplication result obtained by multiplying the maximum theoretical production cycle by the number of assembly stations; calculating a first addition result obtained by adding the theoretical production cycles, calculating a first subtraction result obtained by subtracting the first addition result from the first multiplication result, and taking a first division result obtained by dividing the first subtraction result by the first multiplication result as the balance performance evaluation value of the assembly line.

[0113] The theoretical production cycle can be a production cycle corresponding to the maximum capacity value of the assembly station. The balance performance evaluation value can be used to reflect the balance performance of the assembly line. The closer the balance performance evaluation value is to 0, the better the balance performance of the assembly line.

[0114] In one specific embodiment, the balance performance evaluation value of the assembly line can be determined by the following specific calculation formula:

[0115]

[0116] wherein max(Ti) is the maximum theoretical cycle time, N is the total number of assembly stations on the assembly line, and Ti represents the theoretical production cycle corresponding to each assembly station on the assembly line.

[0117] The advantage of such a setting is that by determining the balance performance evaluation value of the assembly line, the amount of assembly resources and assembly materials allocated to each assembly station on the assembly line can be adjusted in a timely manner to improve the capacity and production efficiency of the distributed factory.

[0118] The above technical solution first forms a virtual factory by digitally modeling the physical system and data of the factory, which can simulate and predict the operation of the factory. Secondly, by calculating the number of non-shared tool sets and the number of workers in each group corresponding to each assembly station before executing the capacity simulation, the number of simulation times of the capacity evaluation model can be reduced, and the capacity optimization efficiency can be improved. Finally, the technical solution of the present embodiment proposes a capacity configuration optimization scheme based on bottleneck stations, which evaluates the capacity performance of the current assembly line, identifies possible bottlenecks and improvement spaces, optimizes the assembly line for bottlenecks and improvement points, and effectively improves the capacity and efficiency of the distributed factory.

[0119] To explain in detail the capacity optimization method for each assembly line in the distributed factory in the present scheme, a most detailed embodiment is described as follows:

[0120] As Figure 5As shown, the assembly stations corresponding to the assembly line can be obtained, as well as the number of non-shared tool sets, the number of workers in each group and the rest of the production data corresponding to each assembly station. The rest of the production data can include the assembly station name, the number of assembly stations, the tool name, the number of tools, the tool type (including shared and non-shared), the number of worker groups corresponding to each assembly station, the assembly time, the minimum number of workers in each group and the maximum number of workers in each group, and the first maintenance time, the next maintenance time and the maintenance duration corresponding to each tool, etc. Then, the capacity of the assembly line can be evaluated according to the number of non-shared tool sets, the number of workers in each group and the rest of the production data through the capacity evaluation model to obtain the expected capacity of the assembly line. Then, the capacity of the assembly line can be optimized through the pre-designed capacity optimization algorithm. The pseudo code of the capacity optimization algorithm is as follows:

[0121]

[0122] Call the capacity evaluation model;

[0123] EndWhile.

[0124] Finally, if the expected capacity of the assembly line is greater than or equal to the preset target capacity, the distributed factory can be configured according to the number of non-shared tool sets and the number of workers in each group corresponding to the expected capacity.

[0125] Embodiment three

[0126] Figure 6 is a structural schematic diagram of a capacity optimization device of a distributed factory according to embodiment three of the present application. As shown in Figure 6As shown, the device comprises: a capacity evaluation model establishing module 61, a repeated execution module 62, an expected capacity determining module 63, a bottleneck station determining module 64, a cycle adjustment module 65, and a factory configuration module 66. The capacity evaluation model establishing module 61 is configured to establish a capacity evaluation model corresponding to the distributed factory according to each assembly station in the distributed factory and the constraint relationship between each assembly station. The repeated execution module 62 is configured to execute the expected capacity determining module, the bottleneck station determining module, the cycle adjustment module, and the factory configuration module for each assembly line corresponding to the distributed factory, respectively. The expected capacity determining module 63 is configured to obtain the assembly station corresponding to the assembly line and the assembly resource amount corresponding to each assembly station, respectively, and input the assembly resource amount into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource amount. The bottleneck station determining module 64 is configured to obtain the maximum capacity value corresponding to each assembly station on the assembly line if the expected capacity is less than the preset target capacity, and determine the bottleneck station according to the comparison result of each maximum capacity value. The cycle adjustment module 65 is configured to increase the assembly resource amount corresponding to the bottleneck station and return to the operation of inputting the assembly resource amount into the capacity evaluation model for simulation until the expected capacity is greater than or equal to the preset target capacity. The factory configuration module 66 is configured to configure the distributed factory according to the assembly resource amount corresponding to the expected capacity if the expected capacity is greater than or equal to the preset target capacity.

[0127] The technical scheme provided by the embodiment of the application solves the problem that the prior art of configuring resources for a distributed factory by manual work combined with historical experience often cannot accurately allocate appropriate assembly resource amounts for each assembly station, and can realize optimal allocation of assembly resource amounts for each assembly station in a distributed factory, thereby improving the capacity and production efficiency of the distributed factory.

[0128] Optionally, the assembly resource amount comprises the number of non-shared tool sets and the number of workers in each group.

[0129] Optionally, the expected capacity determination module 63 comprises: a non-shared tool set number determination unit configured to obtain an upper limit value of capacity of a set of non-shared tools corresponding to the assembly station, and determine the number of non-shared tool sets of the assembly station according to the preset demand output of the assembly station and the upper limit value of capacity of the non-shared tools; an each-group worker number determination unit configured to determine the expected capacity of each set of non-shared tools according to a division result of the preset demand output and the number of non-shared tool sets; divide the preset simulation time by the expected capacity of each set of non-shared tools to obtain the longest assembly cycle of each set of non-shared tools; determine a lower limit value of the number of each group of workers corresponding to each set of non-shared tools according to a division result of the assembly man-hours and the longest assembly cycle; and compare the lower limit value of the number of each group of workers with the preset minimum number of workers to obtain the number of each group of workers corresponding to the assembly station.

[0130] Optionally, the non-shared tool set number determination unit comprises: a shortest assembly cycle determination subunit configured to obtain the assembly man-hours corresponding to the assembly station and the maximum number of workers corresponding to the non-shared tools, and divide the assembly man-hours by the maximum number of workers to obtain the shortest assembly cycle corresponding to the assembly station; and an upper limit value of capacity determination subunit configured to divide the preset simulation time by the shortest assembly cycle to obtain the upper limit value of capacity of the non-shared tools corresponding to the assembly station.

[0131] Optionally, the bottleneck station determination module 64 is specifically configured to: compare the maximum capacity values to obtain a minimum value in the maximum capacity values, and take the assembly station corresponding to the minimum value in the maximum capacity values as the bottleneck station.

[0132] Optionally, the cycle adjustment module 65 is specifically configured to: if the number of each group of workers corresponding to the bottleneck station is less than the preset maximum number of each group of workers, increase the number of each group of workers corresponding to the bottleneck station; and if the number of each group of workers corresponding to the bottleneck station is greater than or equal to the preset maximum number of each group of workers, increase the number of non-shared tool sets corresponding to the bottleneck station, and update the number of each group of workers corresponding to the bottleneck station according to the updated number of non-shared tool sets.

[0133] Optionally, the device further comprises an evaluation value determination module, which is specifically configured to: determine theoretical production cycles respectively corresponding to each assembly station on the assembly line according to a division result of the preset simulation time and the maximum capacity values, and determine a maximum theoretical production cycle in the theoretical production cycles; obtain the number of assembly stations on the assembly line, and calculate a first multiplication result obtained by multiplying the maximum theoretical production cycle by the number of assembly stations; calculate a first addition result obtained by adding the theoretical production cycles, calculate a first subtraction result obtained by subtracting the first addition result from the first multiplication result, and take a first division result obtained by dividing the first subtraction result by the first multiplication result as an evaluation value of the balance performance of the assembly line.

[0134] The distributed factory capacity optimization device provided by the embodiment of the present application can execute the distributed factory capacity optimization method provided by any embodiment of the present application, has the function modules and beneficial effects corresponding to the execution method.

[0135] Embodiment four

[0136] Figure 7 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the applications described and / or claimed in this document.

[0137] As shown in Figure 7 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0138] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a loudspeaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0139] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the throughput optimization method of a distributed factory.

[0140] In some embodiments, the throughput optimization method of a distributed factory can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the throughput optimization method of a distributed factory described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the throughput optimization method of a distributed factory by any other suitable means, such as by means of firmware.

[0141] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0142] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0143] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0145] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0146] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0147] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0148] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the production capacity of a distributed factory, characterized in that: The method comprises: Based on the assembly stations in the distributed factory and the constraints between them, a capacity evaluation model corresponding to the distributed factory is established; For each assembly line corresponding to the distributed factory, perform the following capacity optimization operations: Obtaining assembly stations corresponding to the assembly line and assembly resource quantities corresponding to each assembly station, and inputting the assembly resource quantities into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantities; If the expected production capacity is less than the preset target production capacity, obtaining the maximum production capacity values ​​corresponding to each assembly station on the assembly line, and determining the bottleneck station according to the comparison results of the maximum production capacity values; Increasing the amount of assembly resources corresponding to the bottleneck station, and returning to execute the operation of inputting the amount of assembly resources into the capacity evaluation model for simulation until the expected capacity is greater than or equal to the preset target capacity; If the expected production capacity is greater than or equal to the preset target production capacity, the distributed factory is configured according to the assembly resource quantity corresponding to the expected production capacity.

2. The method according to claim 1, characterized in that The assembly resource quantity includes the number of non-shared tooling sets and the number of workers in each group; Obtain the number of non-shared tooling sets corresponding to each assembly station, including: Obtain the production capacity upper limit of a set of non-shared tooling corresponding to the assembly station, and determine the number of non-shared tooling sets for the assembly station based on the preset required output of the assembly station and the production capacity upper limit of the non-shared tooling.

3. The method according to claim 2, characterized in that Get the production capacity upper limit of the non-shared tooling corresponding to the assembly station, including: Obtaining the assembly working hours corresponding to the assembly station and the maximum number of workers corresponding to the non-shared tooling, and dividing the assembly working hours by the maximum number of workers to obtain the shortest assembly cycle corresponding to the assembly station; The preset simulation time is divided by the shortest assembly cycle to obtain the upper limit of the production capacity of the non-shared tooling corresponding to the assembly station.

4. The method according to claim 3, characterized in that Get the number of workers in each group corresponding to each assembly station, including: Determining the expected production capacity of each set of non-shared tooling based on the result of dividing the preset required output by the number of non-shared tooling sets; Divide the preset simulation time by the expected production capacity of each non-shared tooling to obtain the longest assembly cycle of each non-shared tooling; Determining a lower limit of the number of workers in each group corresponding to each set of non-shared tooling based on the result of dividing the assembly working hours by the longest assembly cycle; The lower limit of the number of workers in each group is compared with a preset minimum number of workers to obtain the number of workers in each group corresponding to the assembly station.

5. The method according to claim 1, wherein Determining the bottleneck location based on the comparison results of the maximum production capacities includes: The maximum capacity values ​​are compared to obtain a minimum value among the maximum capacity values, and the assembly station corresponding to the minimum value among the maximum capacity values ​​is used as the bottleneck station.

6. The method according to claim 2, characterized in that Increase the amount of assembly resources corresponding to the bottleneck station, including: If the number of workers in each group corresponding to the bottleneck station is less than the preset maximum number of workers in each group, increase the number of workers in each group corresponding to the bottleneck station; If the number of workers in each group corresponding to the bottleneck station is greater than or equal to the preset maximum number of workers in each group, the number of non-shared tooling sets corresponding to the bottleneck station is increased, and the number of workers in each group corresponding to the bottleneck station is updated according to the updated number of non-shared tooling sets.

7. The method according to claim 1, characterized in that After inputting the assembly resource quantity into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantity, the method further includes: Determine the theoretical production cycle corresponding to each assembly station on the assembly line according to the result of dividing the preset simulation time by each maximum production capacity value, and determine the maximum theoretical production cycle among the theoretical production cycles; Obtaining the number of assembly stations on the assembly line, and calculating a first multiplication result obtained by multiplying the maximum theoretical production cycle by the number of assembly stations; Calculate a first addition result obtained by adding each of the theoretical production cycles, calculate a first subtraction result obtained by subtracting the first addition result from the first multiplication result, and divide the first subtraction result by the first multiplication result to obtain a first division result as a balance performance evaluation value of the assembly line.

8. A distributed factory production capacity optimization device, characterized in that: include: A capacity assessment model establishment module is used to establish a capacity assessment model corresponding to the distributed factory based on the assembly stations in the distributed factory and the constraint relationships between the assembly stations; A repeated execution module is used to execute the expected capacity determination module, the bottleneck station determination module, the cycle adjustment module and the factory configuration module for each assembly line corresponding to the distributed factory; an expected capacity determination module, configured to obtain assembly stations corresponding to the assembly line and the assembly resource quantities corresponding to each assembly station, and input the assembly resource quantities into the capacity evaluation model for simulation to obtain the expected capacity of the assembly line under the assembly resource quantities; a bottleneck station determination module, configured to obtain the maximum capacity values ​​corresponding to the respective assembly stations on the assembly line if the expected capacity is less than the preset target capacity, and determine the bottleneck station according to the comparison results of the respective maximum capacity values; a loop adjustment module, configured to increase the amount of assembly resources corresponding to the bottleneck station and return to executing an operation of inputting the amount of assembly resources into the capacity evaluation model for simulation until the expected capacity is greater than or equal to a preset target capacity; A factory configuration module is configured to configure the distributed factory according to the amount of assembly resources corresponding to the expected production capacity if the expected production capacity is greater than or equal to a preset target production capacity.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method for optimizing the production capacity of a distributed plant according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distributed factory production capacity optimization method according to any one of claims 1 to 7 when executed.

Images