WO2024065779A1

WO2024065779A1 - Method for manufacturing operation management, edge device, cloud device, and storage medium

Info

Publication number: WO2024065779A1
Application number: PCT/CN2022/123517
Authority: WO
Inventors: Ling Fu; Yanbin Yu
Original assignee: Siemens Aktiengesellschaft; Siemens Ltd., China
Priority date: 2022-09-30
Filing date: 2022-09-30
Publication date: 2024-04-04

Abstract

A method for manufacturing operation management, an edge device a cloud device, and a storage medium are provided. In the method for manufacturing operation management, the edge device located on a shopfloor acquires operation data of a factory, determines whether a manufacturing operation status of the factory is abnormal based on the operation data, and sends, if the manufacturing operation status of the factory is abnormal, a request message for acquiring a suggestion on manufacturing operation management of the factory to the cloud device; and the cloud device, after receiving the request message, acquires the operation data, and updates a model of the factory based on the operation data. The cloud device generates a resource scheduling solution based on the operation data, simulates the resource scheduling solution on the updated model of the factory; and sends a response message to the edge device, wherein the message includes information for describing the resource scheduling solution and information for describing a simulation result. The edge device determines an instruction for manufacturing operation management of the factory based on the response message.

Description

METHOD FOR MANUFACTURING OPERATION MANAGEMENT, EDGE DEVICE, CLOUD DEVICE, AND STORAGE MEDIUM

TECHNICAL FIELD

The present disclosure relates to the technical field of manufacturing operation management (MOM) , and particularly relates to a method for manufacturing operation management, an edge device, a cloud device, and a storage medium.

BACKGROUND

A factory may synergize production resources (including workers, machines, materials, and the like) on a shopfloor using a MOM technology, to manage a manufacturing process.

At present, implementations of the MOM technology in the factory usually rely on commercial software. The commercial software acquires a manufacturing operation status of the factory, and provides the manufacturing operation status to a manager of manufacturing operation management in the factory, such that the manager gives an instruction based on the manufacturing operation status to manage the manufacturing process. The quality of the instruction strongly depends on the human experience and ability.

Therefore, a solution capable of generating a precise instruction to scientifically manage the manufacturing process is urgently needed.

SUMMARY

In order to solve the above technical problems, embodiments of the present disclosure provide a method for manufacturing operation management, an edge device, a cloud device, and a storage medium, and synergize the cloud device and the edge device on a shopfloor, thereby automatically generating an instruction based on a manufacturing operation status on the shopfloor, and reducing the reliance on the human experience and ability.

According to a first aspect of the embodiments of the present disclosure, a method for manufacturing operation management is provided. In the method, an edge device acquires operation data of a factory, and sends a request message to a cloud device when determining that a manufacturing operation status of the factory is abnormal based on the operation data, where the request message is used for acquiring a suggestion on manufacturing operation management of the factory; the cloud device, after receiving the request message, acquires the operation data, updates a model of the factory based on the operation data, generates a resource scheduling solution for manufacturing operation management of the factory, and simulates the resource scheduling solution on the updated model of the factory; the cloud device generates a response message and sends the response message to the edge device, where the response message includes information for describing the resource scheduling solution and information for describing a simulation result; and the edge device determines an instruction for manufacturing operation management of the factory based on the response message.

According to a second aspect of the embodiments of the present disclosure, another method for manufacturing operation management is provided, which may be executed by an edge device located on a shopfloor, and includes the steps executed by the edge device in the first aspect.

According to a third aspect of the embodiments of the present disclosure, still another method for manufacturing operation management is provided, which may be executed by a cloud device, and includes the steps executed by the cloud device in the first aspect.

According to a fourth aspect of the embodiments of the present disclosure, an edge device is provided. The edge device is runnable on a shopfloor of a factory, and may include at least one memory for storing computer readable instructions; and at least one processor coupled to at least one processor for invoking the computer readable instructions to execute the method for manufacturing operation management provided in the second aspect.

According to a fifth aspect of the embodiments of the present disclosure, a cloud device is provided, including at least one memory for storing computer readable instructions; and at least one processor coupled to at least one processor for invoking the computer readable instructions to execute the method for manufacturing operation management provided in the third aspect.

According to a sixth aspect of the embodiments of the present disclosure, a computer readable storage medium is provided. The computer readable storage medium stores computer readable instructions thereon, where the computer readable instructions, when executed by at least one processor, cause the at least one processor to execute the method provided in the first aspect or the second aspect.

According to a seventh aspect of the embodiments of the present disclosure, a system for manufacturing operation management is provided, including: a cloud device and an edge device located on a shopfloor of a factory where the edge device is configured to execute the steps executed by the edge device in the method provided in the first aspect; and the cloud device is configured to execute the steps executed by the cloud device in the method provided in the first aspect.

According to an eighth aspect of the embodiments of the present disclosure, a computer program product is provided. The computer program product is tangibly stored on a computer readable medium, and includes computer readable instructions, where the computer readable instructions, when executed, cause at least one processor to execute the method provided in the second aspect or the third aspect.

According to a ninth aspect of the embodiments of the present disclosure, an apparatus for manufacturing operation management is provided, including a computer program module for executing the steps of the method provided in the second aspect.

According to a tenth aspect of the embodiments of the present disclosure, an apparatus for manufacturing operation management is provided. The apparatus is runnable in the cloud, and includes a computer program module for executing the steps of the method provided in the third aspect.

In the above technical solutions, the cloud device and the edge device are synergized to implement manufacturing operation management of the factory. Operation data of the factory is acquired to determine an updated manufacturing operation status on a shopfloor. The edge device located on the shopfloordetermines whether the manufacturing operation status is abnormal in time, and requests, when the manufacturing operation status is abnormal, the cloud device for a suggestion on manufacturing operation management. Compared with the edge device, the cloud device has more abundant processing resources and storage capabilities, may update a model of the factory based on the operation data, may generate a resource scheduling solution for manufacturing operation management based on the operation data, and may simulate the resource scheduling solution on the updated model to obtain a simulation result. These operations are completed based on the operation data, such that the processing by the cloud device can reflect an updated status of the shopfloor, thereby obtaining a more accurate resource scheduling solution and a more accurate simulation result. The cloud device sends the simulation result together with the resource scheduling solution to the edge device as the suggestion on manufacturing operation management, and the edge device determines an instruction for manufacturing operation management of the factory based on the suggestion, thereby achieving precise manufacturing operation management of the shopfloor, and reducing the reliance on the human experience and ability.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 shows a system for manufacturing operation management provided in an embodiment of the present disclosure.

Fig. 2 shows a method for manufacturing operation management provided in an embodiment of the present disclosure.

Fig. 3 shows another method for manufacturing operation management provided in an embodiment of the present disclosure.

Fig. 4 shows still another method for manufacturing operation management provided in an embodiment of the present disclosure.

Fig. 5 shows an edge device provided in an embodiment of the present disclosure.

Fig. 6 shows a cloud device provided in an embodiment of the present disclosure.

Fig. 7 shows two apparatuses for manufacturing operation management provided in an embodiment of the present disclosure.

List of reference numerals in the figures:

1000: System for manufacturing operation management

100: Edge device 200: Cloud device 300: Data acquisition system 400: Manager of manufacturing operation management

501: Method for manufacturing operation management, executed synergistically by a cloud device 200 and an edge device 100

502: Method for manufacturing operation management, executed by a cloud device 200

503: Method for manufacturing operation management, executed by an edge device 100

S5011-S5022: Steps of the method for manufacturing operation management

1001: Memory 1002: Processor 1003: Communication module

2001: Memory 2002: Processor 2003: Communication module

11: Operation data 12: Static data 21: Request message 22: Response message

30: Model of a factory 40: Resource scheduling solution 50: Simulation result 60: Instruction for manufacturing operation management

70: Optimization objective item 80: Constraint

111: Data acquiring module 112: Abnormality detecting module 113: Communication module 114: Instruction generating module

211: Communication module 212: Model updating module 213: Resource scheduling solution generating module

214: Simulating module 215: Executing module 216: Objective setting module

217: Constraint managing module

DETAILED DESCRIPTION OF EMBODIMENTS

In order to make the content of the present disclosure more easily understood, relevant concepts are explained, but these explanations should not be regarded as limitations to the scope of protection of the present disclosure.

1. Manufacturing operation management: This means to monitor a manufacturing operation status of a factory, and coordinate production resources (including persons, production devices, materials, and the like) on a shopfloor of the factory, to manage a manufacturing process.

2. Static data of the factory

The static data is used for describing a production line design (including a structure and a layout of the production line) , a production resource, a manufacturingprocess, and the like in the factory, irrespective of whether the production line is operating, may include: machines included on the production line, machine capabilities, factory layout, bill of materials (BOM) , bill of process (BOP) , logistics design, and the like, and may further include other data related to manufacturing operation management.

3. Operation data of the factory

Once the production line in the factory operates, operation data is generated. The manufacturing operation status of the production line may be known based on the operation data. The operation data includes: data in an industrial controller (for example: data of a programmable logic controller (PLC) ) , sensor data (for example: ambient temperature) , data for indicating starting time and ending time of the industrial controller, and the like. The operation data of the factory may be acquired from a data acquisition system on the shopfloor (for example, a Supervisory Control and Data Acquisition (SCADA) system) .

In the embodiment of the present disclosure, the operation data of the factory may be acquired from the data acquisition system located on the shopfloor, and the operation data is sent from the shopfloor to the cloud, such that the cloud device generates a suggestion on manufacturing operation management based on the operation data, and sends the suggestion back to the shopfloor, thereby achieving synergy between the cloud and an edge side of the shopfloor, and generating the suggestion on manufacturing operation management by a computer, instead of making a decision entirely by a manager of manufacturing operation management of the factory.

In some embodiments of the present disclosure, updated operation data may be acquired. The "updated" here means to exclude historical operation data, because, on the one hand, the operation data needs to be sent from the shopfloor to the cloud, and only the updated operation data is sent to reduce the data size, reduce the bandwidth occupancy, and realize better real-time performance; and on the other hand, a decision on manufacturing operation management depends more on an updated manufacturing operation status of the factory, and the historical data has less important reference significance. For example, it may be necessary to only consider which machines are currently runnable on the production line, without considering the assessment on the possibility of occurrence of failure at a moment in the future based on historical service data of these machines.

4. Model of the factory

The above static data may be pre-configured in the model of the factory. The model may be updated based on the above operation data. The resource scheduling solution of manufacturing operation management may be pre-simulated on the model before execution on the shopfloor to acquire a simulation result, which is provided to the shopfloor as the suggestion on manufacturing operation management. In the embodiment of the present disclosure, the above functions may be implemented by factory simulation software (for example, Tecnomatix Plant Simulation) . The cloud device generates the resource scheduling solution based on the manufacturing operation status of the factory. The resource scheduling solution simulates an implementation result on the model of the factory. The resource scheduling solution is sent back to the shopfloor from the cloud together with the simulation result as the suggestion on manufacturing operation management.

5. Production resource

An important task of manufacturing operation management is the production resource allocation. The production resource may include a machine, a material, a person on the shopfloor, and the like.

6. Constraint on production resource allocation

In a process of manufacturing operation management, the production resource allocation needs to satisfy certain constraints, for example: the number of machines, and a processing capability; and for another example: a job can only be assigned to one machine at a given moment, and a job can be started only after another job is completed. The constraint may come from, e.g., the BOP, the BOM, and the machine capabilities. The vast majority of constraints are static, and may be inferred from the above static data. The constraint may be used in the generation of an instruction for manufacturing operation management.

In the embodiment of the present disclosure, the cloud may generate a new constraint based on the operation data from the shopfloor. This means that the cloud acquires the operation data on the shopfloor, and can generate the new constraint based on actual status on the shopfloor, while these updated constraints are used for making a decision on manufacturing operation management, thereby making a decision based on the operation data on the shopfloor in the cloud, and using the decision for manufacturing operation management on the shopfloor. The constraint generated based on the operation data may include: some machines are unavailable, some jobs are short of a material, and the like. In addition, in order to guarantee that some running jobs can continuously run in a preset period of time, some constraints may be generated to guarantee that these jobs can be changed only after the preset period of time, and the settings of the preset period of time can be determined based on the production flexibility level. This gives the manager of manufacturing operation management, and persons and machines on the shopfloor enough time to make adjustment to respond to the production resource reallocation. In addition, the constraint may also come from other systems, for example: a new job generated by software for creating a job for the shopfloor.

As mentioned above, in the embodiment of the present disclosure, the cloud needs to generate the resource scheduling solution for manufacturing operation management. An alternative implementation is to obtain an optimized resource scheduling solution using a mathematical method. In this case, the constraint may be expressed by the following mathematical symbols and formulas.

The definitions of the symbols in the above formulas are as follows:

Symbol	Definition
n	Number of machines
m	Number of jobs
M _i	the ith machine, 1≤i≤n
J _j	the jth job, 1≤j≤m
l	Number of operations of J _j
O _jk	the kth operation of J _j, 1≤k≤l
T	Scheduling horizon
t	A time point belonged to T
D _j	Due date of J _j
C _j	Completion time of J _j in rescheduling
S _jk′	Starting time of O _jk in initial scheduling plan
S _jk	Starting time of O _jk in rescheduling
P _jk	Processing time of O _jk in rescheduling
U _i	Utility rate of M _i
U	Lower bound of average utility rate
E _i	Energy consumed by M _i
E	Upper bound of total energy consumption
D	Upper bound of total tardiness
X _ij	1, if J _j is assigned on M _i; 0, otherwise
Y _ijt	1, if J _j is assigned on M _i at time t; 0, otherwise

7: Optimization objective

In some embodiments of the present disclosure, an optimization objective item for production resource allocation may be set, and the cloud device generates a resource scheduling solution for manufacturing operation management to optimize the optimization objective item. The optimization objective item may be an output profit or completion time, or may be a weighted value of a plurality of key performance indicators (KPIs) of production.

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic structural diagram of a system 1000 for manufacturing operation management provided in an embodiment of the present disclosure. As shown in Fig. 1, the system 1000 may include: a cloud device 200 and an edge device 100 located on a shopfloor of a factory. The edge device 100 and the cloud device 200 may be implemented by at least one hardware device (e.g., a server) , respectively, or may be implemented by at least one software program module running on a hardware device. The edge device 100 is deployed on the shopfloor, and at least one edge device 100 may be deployed in a factory. When a plurality of edge devices 100 is deployed, these devices are distributed on different production lines of the factory respectively, collect data of each production line respectively, and then synergize with each other to complete interaction with the cloud device 200 and generation of a final instruction for manufacturing operation management.

The edge device 100 may acquire operation data 11 of the factory, determine whether a manufacturing operation status of the factory is abnormal based on the operation data 11, and requests, when the manufacturing operation status of the factory is abnormal, the cloud device 200 for a suggestion on manufacturing operation management. The cloud device 200 updates a model 30 of the factory based on the operation data 11, generates a resource scheduling solution 40 for manufacturing operation management of the factory, simulates the resource scheduling solution 40 on the updated model 30 of the factory, and finally sends the resource scheduling solution 40 and a simulation result 50 back to the edge device 100 as a suggestion on manufacturing operation management of the factory, such that the edge device 100 generates a final instruction 60.

The edge device 100 may acquire the operation data 11 from a data acquisition system 300 of the factory, and then send a request message 21 to the cloud device 200 for requesting the suggestion on manufacturing operation management of the factory, where the edge device carries the operation data 11 in the request message 21. Alternatively, the edge device 100 does not carry the operation data 11 in the request message 21, and the cloud device 200 acquires the operation data from the data acquisition system 300 after receiving the request message 21.

After receiving a response message 22 from the cloud device 200, the edge device 100 acquires the resource scheduling solution 40 and the simulation result 50 therefrom, and generates the final instruction 60 based on the resource scheduling solution 40 and the simulation result 50. The instruction 60 may be sent to a manager 400 of manufacturing operation management, and may be executed after being confirmed by the manager.

Operations of the edge device 100 and the cloud device 200 in the system 1000 are described in detail below with reference to Fig. 2. Fig. 2 shows a method 501 for manufacturing operation management provided in an embodiment of the present disclosure. The method may include the following steps:

S5011: acquiring, by an edge device 100, operation data of a factory.

S5012: determining, by the edge device 100, whether a manufacturing operation status of the factory is abnormal based on the operation data 11. The edge device 100 may periodically analyze the operation data 11 of the shopfloor at certain time intervals (e.g., several seconds or minutes) , and determine whether the manufacturing operation status of the factory is abnormal in accordance with a certain rule, i.e., whether it is necessary to interfere with existing manufacturing operation management. The rule may include, but is not limited to:

1) amachine operates abnormally;

2) aproduction schedule deviates significantly from a plan (e.g., 10 workpieces were scheduled to be completed at 10: 30, but only 5 pieces were actually completed) ;

3) quality problems frequently occur (for example: a test bench found that the quality of a few consecutive workpieces was below standard; and for another example: tool wear occurred, and a preorder process went wrong) ; and

4) work-in-process accumulation, etc.

As long as one of the preset rules is satisfied, a cloud device 200 determines that the manufacturing operation status is abnormal.

S5013: sending, if the manufacturing operation status of the factory is abnormal, a request message 21 from the edge device 100 to the cloud device 200, where the request message 21 is used for acquiring a suggestion on manufacturing operation management of the factory.

S5014: acquiring the operation data 11 by the cloud device 200 after receiving the request message 21.

Two alternative implementations are available for acquiring the operation data 11 by the cloud device 200. Accordingly, as shown in Fig. 4, step S5014 executed by the cloud device 200 may include step S5014a or step S5014b.

Specifically, in the first implementation, the request message 21 includes the operation data 11, and the cloud device 200 acquires the operation data 11 from the request message 21 through step S5014a; in the second implementation, the request message 21 does not include the operation data 11, and the cloud device 200 acquires, after receiving the request message 21, the operation data 11 from a data acquisition system 300 of the factory through step S5014b. In the first implementation, the cloud device 200 may acquire the operation data 11 directly from the request message 21; however, a relatively large transmission bandwidth is occupied by the message sending; and in the second implementation, the cloud device 200 needs to receive the request message 21 and then obtain the operation data 11 from the data acquisition system 300, thereby resulting in certain time delay.

S5015: updating, by the edge device 200, a model 30 of the factory based on the operation data 11.

S5016: generating, by the cloud device 200, a resource scheduling solution 40 for manufacturing operation management of the factory based on the operation data 11.

The cloud device 200 may generate the resource scheduling solution 40 after receiving the request message 21 from the cloud device 200, or based on a request from other systems, e.g., from an enterprise resource planning (ERP) system or a request for an unscheduled new job of other planning software. Alternatively, the resource scheduling solution 40 may include a job list, where each job has a parameter to indicate an assigned machine, expected starting time, expected completion time, and the like.

S5017: simulating, by the cloud device 200, the resource scheduling solution 40 on the updated model 30 of the factory. Operations of simulating the resource scheduling solution 40 may be executed by a computer program module, e.g., a simulating module 214 shown in Fig. 7. Alternatively, the simulating module 214 may be implemented by commercial software, for example: Tecnomatix Plant Simulation software.

S5018: generating a response message 22 by the cloud device 200, where the response message 22 includes information for describing the resource scheduling solution 40 and information for describing a simulation result 50.

S5019: sending the response message 22 from the cloud device 200 to the edge device 100, where the resource scheduling solution 40 and the simulation result 50 are sent to the edge device 100 as the suggestion on manufacturing operation management. Here, the resource scheduling solution 40 is sent because a final instruction 60 for manufacturing operation management is generated by the edge device 100, the cloud device 200 only gives the suggestion, and the edge device 100 may assess the resource scheduling solution 40 based on the simulation result 50, to determine the final instruction 60.

S5020: determining, by the edge device 100, an instruction 60 for manufacturing operation management of the factory based on the response message 22.

As shown in Fig. 3, a plurality of alternative implementations is available for determining the instruction 60 by the edge device 100 based on the response message 22. In an alternative implementation, the edge device 100 may further determine whether the resource scheduling solution 40 conflicts with an updated manufacturing operation status of the factory. Therefore, step S5020 may further include the following sub-steps:

S5020a: determining, by the edge device 100, whether the resource scheduling solution 40 conflicts with an updated manufacturing operation status of the factory, executing sub-step S5020b if the resource scheduling solution does not conflict with the updated manufacturing operation status of the factory, and executing sub-step S5020c if the resource scheduling solution conflicts with the updated manufacturing operation status of the factory.

S5020b: determining, by the edge device 100, the instruction 60 based on the resource scheduling solution 40 and the simulation result 50.

S5020c: notifying, by the edge device 100, a manager 400 of manufacturing operation management of the factory to adjust the instruction 60 based on the resource scheduling solution 40 and the simulation result 50.

Alternatively, if the edge device 100 determines that the resource scheduling solution 40 does not conflict with the updated manufacturing operation status of the factory, the method may further include step S5023: notifying the manager 400 of manufacturing operation management of the factory to confirm the instruction 60. If the manager 400 of manufacturing operations management does not agree with the resource scheduling solution 40, he or she may generate a new resource scheduling solution and issue an instruction.

The edge device 100 may judge a feasibility of the resource scheduling solution 40 through the conflict detecting mechanism discussed above. The edge device 100 may determine whether the resource scheduling solution 40 conflicts with an updated production status on the shopfloor, so as to guarantee that the new scheduling solution will not affect the ongoing production process, for example: a started job should not be reassigned.

Alternatively, before generating the resource scheduling solution 40, the cloud device 200 may determine an optimization objective item 70 of manufacturing operation of the factory through step S5021, and may optimize the production resource allocation based on the optimization objective item 70. Operations of generating the resource scheduling solution 40 may be implemented by a computer program module (e.g., a resource scheduling solution generating module 213 in Fig. 7) . The task of the objective is to find a resource scheduling solution to make the optimization objective item 70 reach an optimized value. When the cloud device 200 generates the resource scheduling solution 40 based on the operation data 11 in step S5016, specifically, the cloud device may generate the resource scheduling solution 40 to optimize the optimization objective item 70. In this case, the simulation result 50 may include an optimization result of the optimization objective item 70 obtained by running the resource scheduling solution 40 on the updated model 30 of the factory. A possible situation is failure to obtain the optimized value of the optimization objective item 70 in an iterative process. In this case, a current value of the optimization objective item 70 obtained during the simulation may be used as an input for computing the resource scheduling solution 40, and a next iteration is performed to obtain the optimized value of the optimization objective term 70.

Alternatively, in step S5022, the cloud device 200 may further update constraints 80 to be followed for manufacturing operation management of the factory based on the operation data 11. In this case, in step S5016, the cloud device 200 may generate the resource scheduling solution 40 based on the operation data 11 with the constraints 80 as an input, where the generated resource scheduling solution 40 should satisfy these constraints 80. Alternatively, the constraints 80 may be coded into a software module configured to generate the resource scheduling solution 40 (e.g., a constraint managing module 217 shown in Fig. 7) . The software module may be implemented by commercial software, for example: Simulation tool HEEDS.

Fig. 3 shows another method 502 for manufacturing operation management provided in an embodiment of the present disclosure. The method is executed on the above edge device 100, and may include the steps executed by the edge device 100 in the method 501. The description will not be repeated here.

Fig. 4 shows still another method 503 for manufacturing operation management provided in an embodiment of the present disclosure. The method is executed by the above cloud device 200, and may include the steps executed by the cloud device 200 in the method 501. The description will not be repeated here.

Fig. 5 is a schematic structural diagram of an edge device 100 provided in an embodiment of the present disclosure. As shown in Fig. 5, the edge device 100 may include: at least one memory 1001 for storing computer readable instructions; and at least one processor 1002 coupled to at least one memory1001 for invoking the computer readable instructions to execute the method 502 for manufacturing operation management shown in Fig. 3. In addition, the edge device 100 may further include a communication interface 1003, which may be used for sending a request message 21 and receiving a response message 22.

Alternatively, the at least one processor 1002, the at least one memory 1001, and the communication interface 1003 may be connected through a bus.

The at least one processor 1002 may be a central processing unit (CPU) , or an Application Specific Integrated Circuit (ASIC) , or one or more integrated circuits configured to implement the embodiments of the present disclosure. One or more processors included in a smart device may be processors of a given type, e.g., one or more CPUs; or may be processors of different types, e.g., one or more CPUs and one or more ASICs.

The at least one memory 1001 may include a high-speed RAM memory, and may further include a non-volatile memory, e.g., at least one disk memory.

Fig. 6 is a schematic structural diagram of a cloud device 200 provided in an embodiment of the present disclosure. As shown in Fig. 6, the cloud device 200 may include: at least one memory 2001 for storing computer readable instructions; and at least one processor 2002 coupled to the at least one memory 2001 for invoking the computer readable instructions to execute the method 503 for manufacturing operation management shown in Fig. 4. In addition, the cloud device 200 may further include a communication interface 2003, which may be used for receiving a request message 21 and sending a response message 22.

Alternatively the at least one processor 2002, the at least one memory 2001, and the communication interface 2003 may be connected through a bus.

The at least one processor 2002 may be a CPU, or an ASIC, or one or more integrated circuits configured to implement the embodiments of the present disclosure. One or more processors included in a smart device may be processors of a given type, e.g., one or more CPUs; or may be processors of different types, e.g., one or more CPUs and one or more ASICs.

The at least one memory 2001 may include a high-speed RAM memory, and may further include a non-volatile memory, e.g., at least one disk memory.

An apparatus 110 for manufacturing operation management is deployed on a shopfloor of a factory, and may include:

a data acquiring module 111 configured to acquire operation data 11 of the factory;

an abnormality detecting module 112 configured to determinewhether a manufacturing operation status of the factory is abnormal based on the operation data 11;

a communication module 113 configured to:

send, when the abnormality detecting module 112 determines that the manufacturing operation status of the factory is abnormal, a request message 21 to a cloud device 200, where the request message 21 is used for acquiring a suggestion on manufacturing operation management of the factory and includes the operation data 11, or acquire the operation data 11 from a data acquisition system 300 of the factory after the cloud device 200 receives the request message 21; and

receive a response message 22 from the cloud device 200, where the response message 22 includes information for describing a resource scheduling solution 40 for manufacturing operation management of the factory and information for describing a simulation result 50 of simulating the resource scheduling solution 40 on a model 30 of the factory; and

an instruction generating module 114 configured to determine an instruction 60 for manufacturing operation management of the factory based on the response message 22.

Alternatively, the operation data 11 is updated.

Alternatively, the instruction generating module 114 is, when determining the instruction 60 for manufacturing operation management of the factory based on the response message 22, specifically configured to: determine whether the resource scheduling solution 40 conflicts with an updated manufacturing operation status of the factory, and determine, if the resource scheduling solution does not conflict with the updated manufacturing operation status of the factory, the instruction 60 based on the resource scheduling solution 40 and the simulation result 50.

Alternatively, the instruction generating module 114 is, when determining the instruction 60 for manufacturing operation management of the factory based on the response message 22, further configured to: notify, if determining that the resource scheduling solution 40 conflicts with the updated manufacturing operation status of the factory, a manager 400 of manufacturing operation management of the factory to adjust the instruction 60 based on the resource scheduling solution 40 and the simulation result 50.

Alternatively, the instruction generating module 114 is further configured to: notify, if determining that the resource scheduling solution 40 does not conflict with the updated manufacturing operation status of the factory, the manager 400 of manufacturing operation management of the factory to confirm the instruction 60.

An apparatus 210 for manufacturing operation management is located in the cloud, and may include:

a communication module 211 configured to: receive a request message 21 from an edge device 100 on a shopfloor of a factory, where the request message 21 is used by the edge device 100 for acquiring a suggestion on manufacturing operation management the factory; and acquire operation data 11 of the factory;

a model updating module 212 configured to update a model 30 of the factory based on the operation data 11, where static data 12 is pre-configured in the model 30;

a resource scheduling solution generating module 213 configured to generate a resource scheduling solution 40 for manufacturing operation management of the factory based on the operation data 11;

a simulating module 214 configured to simulate the resource scheduling solution 40 on the updated model 30 of the factory; and

an executing module 215 configured to generate a response message 22, where the response message 22 includes information for describing the resource scheduling solution 40 and information for describing a simulation result 50, and is used by the edge device 100 for determining an instruction 60 for manufacturing operation management of the factory; and

the communication module 211 being further configured to: send the response message 22 to the edge device 100.

Alternatively, the request message 21 includes the operation data 11, and the communication module 211 is, when acquiring the operation data 11, specifically configured to: acquire the operation data 11 from the request message 21; or the communication module 211 is, when acquiring the operation data 11, specifically configured to: acquire the operation data 11 from a data acquisition system 300 of the factory after receiving the request message 21.

Alternatively, the apparatus 210 may further include: an objective setting module 216 configured to determine an optimization objective item 70 for manufacturing operation of the factory. The resource scheduling solution generating module 213 is specifically configured to: generate the resource scheduling solution 40 to optimize the optimization objective item 70; and the simulation result 50 includes: an optimization result of the optimization objective item 70 obtained by running the resource scheduling solution 40 on the updated model 30 of the factory.

Alternatively, the operation data 11 is updated.

Alternatively, the apparatus 210 further includes: a constraint managing module 217 configured to update a constraint 80 to be followed when performing manufacturing operation management of the factory based on the operation data 11; and the resource scheduling solution generating module 213 is specifically configured to: generate the resource scheduling solution 40 satisfying the updated constraint 80.

A typical application scenario of the embodiments of the present disclosure is that a machine downtime occurs on a shopfloor. Considering that an orderusuallyinvolves a plurality of machineson the shopfloor, one of the machines maybreaks down during production. An abnormal signal generated by the downtime is acquired by a SCADA system. When finding that a job is assigned to the machine, the abnormality detecting module 112 of the edge device 100 will immediately send the request message 21 to the cloud device 200 through the communication module 113, indicating that the machine goes down.

The constraint managing module 217 in the cloud device 200 updates the constraint 80 based on the request message 21 indicating that the machine is unavailable, and locks an ongoing job to be completed by the machine within the next half hour, thus giving the shopfloor enough adjustment time. At the same time, the model updating module 212 will update the model 30 of the factory to reflect a job execution status on the shopfloor. The resource scheduling solution generating module 213 searches an alternative job assignment strategy satisfying the constraint 80 within a space of the resource scheduling solution, and sends a searched resource scheduling solution 40 to the simulating module 214 for simulation. The simulating module 214 returns completion time obtained by the simulation to the resource scheduling solution generating module 213 to obtain a further job assignment strategy, until a minimum value of the completion time is obtained.

Once an optimized solution is obtained, the communication module 211 in the cloud device 200 sends the new suggestion on manufacturing operation management to the edge device 100, including predicted starting time and completion time of each job. The instruction generating module 114 in the edge device 100 detects the resource scheduling solution to determine whether the resource scheduling solution conflicts with a started job. If the resource scheduling solution does not conflict with a started job, a new job will be distributed for execution.

In addition, the embodiments of the present disclosure further provide a computer readable storage medium storing instructions for causing a machine to execute the method for manufacturing operation management as described herein. Specifically, a system or apparatus equipped with a storage medium may be provided, where the storage medium stores a software program code for implementing the functions of any one embodiment among the above embodiments, and makes a computer (or CPU or MPU) of the system or apparatus to read and execute the program code stored in the storage medium.

In this case, the program code read from the storage medium itself can implement the functions of any one embodiment among the above embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present disclosure.

Examples of storage mediums for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, or DVD+RW) , a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer through a communication network.

In addition, it should be clear that a part or all of the actual operations may be completed not only by executing the program code read by the computer, but also by, e.g., an operating system operating on the computer based on instructions of the program code, thereby realizing the functions of any one embodiment among the above embodiments.

In addition, it is understandable that the program code read from the storage medium is written into a memory provided in an expansion board inserted into the computer or written into a memory provided in an expansion module connected to the computer, and then a part and all of the actual operations are executed by, e.g., a CPU installed on the expansion board or the expansion module based on the instructions of the program code, thereby realizing the functions of any one embodiment among the above embodiments.

In addition, the embodiments of the present disclosure further provide a computer program product. The computer program product is tangibly stored on a computer readable medium, and includes computer executable instructions, where the computer executable instructions, when executed, cause at least one processor to execute the method for manufacturing operation management provided in the above embodiments. It should be understood that each solution in the present embodiment has corresponding technical effects in the above method embodiments. The description will not be repeated here.

It should be noted that not all the steps and modules in the above processes and structural diagrams of the system are necessary. Some steps or modules may be omitted based on actual requirements. The execution sequence of the steps is not constant, and may be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, i.e., some modules may be implemented by a given physical entity, or may be implemented by a plurality of physical entities, or may be implemented together by some components in a plurality of standalone devices.

In the above embodiments, hardware modules may be implemented mechanically or electrically. For example, a hardware module may include a permanent dedicated circuit or logic (e.g., a dedicated processor, FPGA, or ASIC) to complete corresponding operations. The hardware module may further include a programmable logic or circuit (e.g., a general-purpose processor or other programmable processors) , which may be temporarily set by software to complete corresponding operations. Specific implementations (mechanical or dedicated permanent circuit, or temporarily provided circuit) may be determined based on costs and time.

The present disclosure is shown and described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present disclosure is not limited to these disclosed embodiments. Based on the above plurality of embodiments, those skilled in the art can know that the code review means in the above different embodiments may be combined to obtain more embodiments of the present disclosure. These embodiments are also encompassed within the scope of protection of the present disclosure.

Claims

A method (501) for manufacturing operation management, comprising:

acquiring (S5011) , by an edge device (100) located on a shopfloor of a factory, operation data (11) of the factory, wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

determining (S5012) , by the edge device (100) , whether the manufacturing operation status of the factory is abnormal based on the operation data (11) ;

sending (S5013) , if the manufacturing operation status of the factory is abnormal, a request message (21) from the edge device (100) to a cloud device (200) , wherein the request message (21) is used for acquiring a suggestion on manufacturing operation management of the factory;

acquiring (S5014) the operation data (11) by the cloud device (00) after receiving the request message (21) ;

updating (S5015) , by the cloud device (200) , a model (30) of the factory based on the operation data (11) , wherein static data (12) of the factory is pre-configured in the model (30) , and the static data (12) includes information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating;

generating (S5016) , by the cloud device (200) , a resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) ;

simulating (S5017) , by the cloud device (200) , the resource scheduling solution (40) on the updated model (30) of the factory;

generating (S5018) a response message (22) by the cloud device (200) , wherein the response message (22) comprises information for describing the resource scheduling solution (40) and information for describing a simulation result (50) ;

sending (S5019) the response message (22) from the cloud device (200) to the edge device (100) , and

determining (S5020) , by the edge device (100) , an instruction (60) for manufacturing operation management of the factory based on the response message (22) .
The method according to claim 1, wherein

the request message (21) comprises the operation data (11) , and the acquiring (S5014) the operation data (11) by the cloud device (200) comprises: acquiring (S5014a) the operation data (11) by the cloud device (200) from the request message (21) ; or

the acquiring (S5014) the operation data (11) by the cloud device (200) comprises: acquiring (S5014b) the operation data (11) by the cloud device (200) from a data acquisition system (300) of the factory after receiving the request message (21) .
The method according to claim 1, wherein the method further comprises: determining (S5021) , by the cloud device (200) , an optimization objective item (70) for manufacturing operation of the factory;

the generating (S5016) , by the cloud device (200) , the resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) comprises: generating, by the cloud device (200) , the resource scheduling solution (40) to optimize the optimization objective item (70) ; and

the simulation result (50) comprises: an optimization result of the optimization objective item (70) obtained by running the resource scheduling solution (40) on the updated model (30) of the factory.
The method according to claim 1, wherein the operation data (11) is updated.
The method according to claim 1, wherein the method further comprises: updating (S5022) , by the cloud device (200) , a constraint (80) to be followed when performing manufacturing operation management of the factory based on the operation data (11) ; and

the generating (S5016) , by the cloud device (200) , the resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) comprises: generating, by the cloud device (200) , the resource scheduling solution (40) satisfying the updated constraint (80) .
The method according to claim 1, wherein the determining (S5020) , by the edge device (100) , the instruction (60) for manufacturing operation management of the factory based on the response message (22) comprises:

determining (S5020a) , by the edge device (100) , whether the resource scheduling solution (40) conflicts with an updated manufacturing operation status of the factory, and determining (S5020b) , if the resource scheduling solution does not conflict with the updated manufacturing operation status of the factory, the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The method according to claim 6, wherein the determining (S5020) , by the edge device (100) , the instruction (60) for manufacturing operation management of the factory based on the response message (22) further comprises:

notifying (S5020c) , if the edge device (100) determines that the resource scheduling solution (40) conflicts with the updated manufacturing operation status of the factory, a manager (400) of manufacturing operation management of the factory to adjust the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The method according to claim 6, wherein the method further comprises:

notifying (S5023) , if the edge device (100) determines that the resource scheduling solution (40) does not conflict with the updated manufacturing operation status of the factory, the manager (400) of manufacturing operation management of the factory to confirm the instruction (60) .
A method (502) for manufacturing operation management, comprising:

acquiring (S5011) , by an edge device (100) located on a shopfloor of a factory, operation data (11) of the factory, wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

determining (S5012) , by the edge device (100) , whether the manufacturing operation status of the factory is abnormal based on the operation data (11) ;

sending (S5013) , if the manufacturing operation status of the factory is abnormal, a request message (21) from the edge device (100) to a cloud device (200) , wherein the request message (21) is used for acquiring a suggestion on manufacturing operation management of the factory and comprises the operation data (11) , or acquiring the operation data (11) by the cloud device (200) from a data acquisition system (300) of the factory after receiving the request message (21) ;

receiving (S5019) , by the edge device (100) , a response message (22) from the cloud device (200) , wherein the response message (22) comprises information for describing a resource scheduling solution (40) for manufacturing operation management of the factory and information for describing a simulation result (50) of simulating the resource scheduling solution (40) on a model (30) of the factory, wherein static data (12) of the factory is pre-configured in the model (30) , the model (30) is updated by the cloud device (200) based on the operation data (11) , and the static data (12) comprises information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating;

determining (S5020) , by the edge device (100) , an instruction (60) for manufacturing operation management of the factory based on the response message (22) .
The method according to claim 9, wherein the operation data (11) is updated.
The method according to claim 9, wherein the determining (S5020) , by the edge device (100) , the instruction (60) for manufacturing operation management of the factory based on the response message (22) comprises:

determining (S5020a) , by the edge device (100) , whether the resource scheduling solution (40) conflicts with an updated manufacturing operation status of the factory, and determining (S5020b) , if the resource scheduling solution does not conflict with the updated manufacturing operation status of the factory, the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The method according to claim 11, wherein the determining (S5020) , by the edge device (100) , the instruction (60) for manufacturing operation management of the factory based on the response message (22) further comprises:

notifying (S5020c) , if the edge device (100) determines that the resource scheduling solution (40) conflicts with the updated manufacturing operation status of the factory, a manager (400) of manufacturing operation management of the factory to adjust the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The method according to claim 11, wherein the method further comprises:

notifying (S5022) , if the edge device (100) determines that the resource scheduling solution (40) does not conflict with the updated manufacturing operation status of the factory, the manager (400) of manufacturing operation management of the factory to confirm the instruction (60) .
A method (503) for manufacturing operation management, comprising:

receiving (S5013) , by a cloud device (200) , a request message (21) from an edge device (100) on a shopfloor of a factory, wherein the request message (21) is used by the edge device (100) for acquiring a suggestion on manufacturing operation management the factory;

acquiring (S5014) , by the cloud device (200) , operation data (11) of the factory after receiving the request message (21) , wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

updating (S5015) , by the cloud device (200) , a model (30) of the factory based on the operation data (11) , wherein static data (12) of the factory is pre-configured in the model (30) , and the static data (12) comprises information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating;

generating (S5016) , by the cloud device (200) , a resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) ;

simulating (S5017) , by the cloud device (200) , the resource scheduling solution (40) on the updated model (30) of the factory;

generating (S5018) a response message (22) by the cloud device (200) , wherein the response message (22) comprises information for describing the resource scheduling solution (40) and information for describing a simulation result (50) , and is used by the edge device (100) for determining an instruction (60) for manufacturing operation management of the factory; and

sending (S5019) the response message (22) from the cloud device (200) to the edge device (100) .
The method according to claim 14, wherein

the request message (21) comprises the operation data (11) , and the acquiring (S5014) the operation data (11) by the cloud device (200) comprises: acquiring (S5014a) the operation data (11) by the cloud device (200) from the request message (21) ; or

the acquiring (S5014) the operation data (11) by the cloud device (200) comprises: acquiring (S5014b) the operation data (11) by the cloud device (200) from a data acquisition system (300) of the factory after receiving the request message (21) .
The method according to claim 14, wherein the method further comprises: determining (S5021) , by the cloud device (200) , an optimization objective item (70) for manufacturing operation of the factory;

the generating (S5016) , by the cloud device (200) , the resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) comprises: generating, by the cloud device (200) , the resource scheduling solution (40) to optimize the optimization objective item (70) ; and

the simulation result (50) comprises: an optimization result of the optimization objective item (70) obtained by running the resource scheduling solution (40) on the updated model (30) of the factory.
The method according to claim 14, wherein the operation data (11) is updated.
The method according to claim 14, wherein the method further comprises: updating (S5022) , by the cloud device (200) , a constraint (80) to be followed when performing manufacturing operation management of the factory based on the operation data (11) ; and

the generating (S5016) , by the cloud device (200) , the resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) comprises: generating, by the cloud device (200) , the resource scheduling solution (40) satisfying the updated constraint (80) .
An edge device (100) , comprising:

at least one memory (1001) configured to store computer readable instructions; and

at least one processor (1002) coupled to the at least one memory (1001) for invoking the computer readable instructions to execute the method (502) for manufacturing operation management according to any one of claims 9-13.
A cloud device (200) , comprising:

at least one memory (2001) configured to store computer readable instructions; and

at least one processor (2002) coupled to the at least one memory (2001) for invoking the computer readable instructions to execute the method (503) for manufacturing operation management according to any one of claims 14-18.
A computer readable storage medium, storing computer readable instructions thereon, wherein the computer readable instructions, when executed by at least one processor, cause the at least one processor to execute the method according to any one of claims 9-18.
A system (2000) for manufacturing operation management, comprising: a cloud device (200) and an edge device (100) located on a shopfloor of a factory, wherein

the edge device (100) is configured to:

acquire operation data (11) of the factory, wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

send, when determining that the manufacturing operation status of the factory is abnormal based on the operation data (11) , a request message (21) to the cloud device (200) , wherein the request message (21) is used for acquiring a suggestion on manufacturing operation management of the factory; and

the cloud device (200) is configured to:

acquire the operation data (11) after receiving the request message (21) ;

update a model (30) of the factory based on the operation data (11) , wherein static data (12) of the factory is pre-configured in the model (30) , and the static data (12) comprises information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating;

generate a resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) ;

simulate the resource scheduling solution (40) on the updated model (30) of the factory;

generate a response message (22) , wherein the response message (22) comprises information for describing the resource scheduling solution (40) and information for describing a simulation result (50) ; and

send the response message (22) to the edge device (100) , and

the edge device (100) is further configured to:

determine an instruction (60) for manufacturing operation management of the factory based on the response message (22) .
A computer program product, being tangibly stored on a computer readable medium and comprising computer readable instructions, wherein the computer readable instructions, when executed, cause at least one processor to execute the method according to any one of claims 9-18.
An apparatus (110) for manufacturing operation management, being deployed on a shopfloor of a factory, and comprising:

a data acquiring module (111) configured to acquire operation data (11) of the factory, wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

an abnormality detecting module (112) configured to determine whether the manufacturing operation status of the factory is abnormal based on the operation data (11) ;

a communication module (113) configured to:

send, when the abnormality detecting module (112) determines that the manufacturing operation status of the factory is abnormal, a request message (21) to a cloud device (200) , wherein the request message (21) is used for acquiring a suggestion on manufacturing operation management of the factory and comprises the operation data (11) , or acquire the operation data (11) from a data acquisition system (300) of the factory after the cloud device (200) receives the request message (21) ; and

receive a response message (22) from the cloud device (200) , wherein the response message (22) comprises information for describing a resource scheduling solution (40) for manufacturing operation management of the factory and information for describing a simulation result (50) of simulating the resource scheduling solution (40) on a model (30) of the factory, wherein static data (12) of the factory is pre-configured in the model (30) , the model is updated by the cloud device (200) based on the operation data (11) , and the static data (12) comprises information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating; and

an instruction generating module (114) configured to determine an instruction (60) for manufacturing operation management of the factory based on the response message (22) .
The apparatus according to claim 24, wherein the operation data (11) is updated.
The apparatus according to claim 24, wherein the instruction generating module (114) is, when determining the instruction (60) for manufacturing operation management of the factory based on the response message (22) , specifically configured to:

determining whether the resource scheduling solution (40) conflicts with an updated manufacturing operation status of the factory, and determine (S5019b) , if the resource scheduling solution does not conflict with the updated manufacturing operation status of the factory, the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The apparatus according to claim 26, wherein the instruction generating module (114) is, when determining the instruction (60) for manufacturing operation management of the factory based on the response message (22) , further configured to:

notify, if determining that the resource scheduling solution (40) conflicts with the updated manufacturing operation status of the factory, a manager (400) of manufacturing operation management of the factory to adjust the instruction (60) based on the resource scheduling solution (40) and the simulation result (50) .
The apparatus according to claim 26, wherein the instruction generating module (114) is further configured to:

notify, if determining that the resource scheduling solution (40) does not conflict with the updated manufacturing operation status of the factory, the manager (400) of manufacturing operation management of the factory to confirm the instruction (60) .
An apparatus (210) for manufacturing operation management, being located in the cloud, and comprising:

a communication module (211) configured to:

receive a request message (21) from an edge device (100) on a shopfloor of a factory, wherein the request message (21) is used by the edge device (100) for acquiring a suggestion on manufacturing operation management the factory; and

acquire operation data (11) of the factory, wherein the operation data (11) comprises data that is generated only after a production line in the factory operates and is usable for ascertaining a manufacturing operation status of the factory;

a model updating module (212) configured to update a model (30) of the factory based on the operation data (11) , wherein static data (12) of the factory is pre-configured in the model (30) , and the static data (12) comprises information for describing a production line design, a production resource, and a manufacturingprocess in the factory, irrespective of whether the production line is operating;

a resource scheduling solution generating module (213) configured to generate a resource scheduling solution (40) for manufacturing operation management of the factory based on the operation data (11) ;

a simulating module (214) configured to simulate the resource scheduling solution (40) on the updated model (30) of the factory; and

an executing module (215) configured to generate a response message (22) , wherein the response message (22) comprises information for describing the resource scheduling solution (40) and information for describing a simulation result (50) , and is used by the edge device (100) for determining an instruction (60) for manufacturing operation management of the factory; and

the communication module (211) being further configured to: send the response message (22) to the edge device (100) .
The apparatus according to claim 29, wherein

the request message (21) comprises the operation data (11) , and the communication module (211) is, when acquiring the operation data (11) , specifically configured to: acquire the operation data (11) from the request message (21) ; or

the communication module (211) is, when acquiring the operation data (11) , specifically configured to: acquire the operation data (11) from a data acquisition system (300) of the factory after receiving the request message (21) .
The apparatus according to claim 29, wherein the apparatus further comprises: an objective setting module (216) configured to determine an optimization objective item (70) for manufacturing operation of the factory;

the resource scheduling solution generating module (213) is specifically configured to: generate the resource scheduling solution (40) to optimize the optimization objective item (70) ; and

the simulation result (50) comprises: an optimization result of the optimization objective item (70) obtained by running the resource scheduling solution (40) on the updated model (30) of the factory.
The apparatus according to claim 29, wherein the operation data (11) is updated.
The apparatus according to claim 29, wherein the apparatus further comprises: a constraint managing module (217) configured to update a constraint (80) to be followed when performing manufacturing operation management of the factory based on the operation data (11) ; and

the resource scheduling solution generating module (213) is specifically configured to: generate the resource scheduling solution (40) satisfying the updated constraint (80) .