Productivity calculation method and device
Through the scalable and configurable capacity calculation method, the problem that the existing technology cannot adapt to flexible production is solved, and flexible and rapid capacity calculation is achieved to meet the lean and agile needs of the manufacturing industry.
Patent Information
- Application Number
- CN202410129542.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-30
- Publication Date
- 2025-08-01
AI Technical Summary
The existing capacity calculation methods cannot adapt to flexible production and cannot meet the lean and agile needs of the manufacturing industry.
It provides a scalable and configurable capacity calculation method. By detecting demand events, matching the model impact factors in the predefined capacity calculation model library, and obtaining raw data for calculation, supporting user-defined models and impact factors, and enriching the model library with cloud storage to achieve fuzzy matching and fast response.
It realizes flexible and fast capacity calculation, improves calculation flexibility and speed, and supports a variety of scenarios and user customized needs.
Smart Images
Figure CN120409878A_ABST
Abstract
Description
Technical Field
[0001] This application mainly relates to the field of industrial digitization, and particularly relates to a production capacity calculation method and device. Background Art
[0002] Production capacity is important information in the manufacturing industry. Currently, the calculation methods for production capacity are fixed, targeting specific scenarios and specific production capacity types. With the lean and agile development of the manufacturing industry, the current production capacity calculation methods cannot adapt to flexible production.
[0003] Disclosure
[0004] To solve the above technical problems, this application provides a production capacity calculation method and device to provide an extensible and configurable production capacity calculation method that can adapt to flexible production.
[0005] To achieve the above object, this application proposes a production capacity calculation method, which includes:
[0006] Detect a demand event, where the demand event includes a target production capacity;
[0007] Match a production capacity calculation model corresponding to the target production capacity from a predefined production capacity calculation model library, parse the production capacity calculation model, and determine predefined model influencing factors included in the production capacity calculation model. The predefined model influencing factors are stored in a model influencing factor library;
[0008] Obtain the original data of the model influencing factors, and calculate the target production capacity according to the original data of the model influencing factors and the production capacity calculation model.
[0009] Therefore, through the production capacity calculation model library and the model influencing factor library, for a demand event including a target production capacity, the corresponding production capacity calculation model and the model influencing factors included therein can be quickly called, the production capacity calculation demand can be quickly responded to, and the production capacity calculation model library and the model influencing factor library are extensible and configurable, thus realizing an extensible and configurable production capacity calculation method.
[0010] Optionally, the method further includes: receiving a user-defined production capacity calculation model, and updating the user-defined production capacity calculation model to the production capacity calculation model library. Therefore, the user can add a newly defined production capacity calculation model to the production capacity calculation model library, making the production capacity calculation model library extensible and configurable, and further improving the flexibility and speed of production capacity calculation.
[0011] Optionally, the method further includes: receiving a user-defined model impact factor and updating the user-defined model impact factor to the model impact factor library. For this purpose, the user can add newly defined model impact factors to the model impact factor library, making the model impact factor library extensible and configurable, and further improving the flexibility and speed of production capacity calculation.
[0012] Optionally, obtaining the original data of the model parameters includes: obtaining the original data of the model parameters from an enterprise resource planning system, an enterprise resource management system, or an office automation system. For this purpose, the integration with existing resources is achieved.
[0013] Optionally, matching the production capacity calculation model corresponding to the target production capacity from a predefined production capacity calculation model library includes: calculating the similarity between the target production capacity and each production capacity calculation model in the production capacity calculation model library, and determining the production capacity calculation model with the largest similarity as the matched production capacity calculation model. For this purpose, the fuzzy matching of the production capacity calculation model is achieved, and the flexibility of production capacity calculation is further improved.
[0014] Optionally, the method includes: the predefined production capacity calculation model library is stored in the cloud, and the production capacity calculation model corresponding to the target production capacity is matched from the predefined production capacity calculation model library in the cloud. For this purpose, by storing the predefined production capacity calculation model library in the cloud, the types of production capacity calculation models can be enriched, and the scenarios of production capacity calculation are extended.
[0015] The present application also proposes a production capacity calculation device, and the device includes:
[0016] A detection module that detects a demand event, where the demand event includes a target production capacity;
[0017] A matching module that matches the production capacity calculation model corresponding to the target production capacity from a predefined production capacity calculation model library, analyzes the production capacity calculation model, and determines the predefined model impact factors included in the production capacity calculation model, where the predefined model impact factors are stored in the model impact factor library;
[0018] A calculation module that obtains the original data of the model impact factors and calculates the target production capacity according to the original data of the model impact factors and the production capacity calculation model.
[0019] The present application also proposes an electronic device, including a processor, a memory, and instructions stored in the memory, where the instructions, when executed by the processor, implement the method described above.
[0020] The present application also proposes a computer-readable storage medium, on which computer instructions are stored, and the computer instructions, when run, execute the method described above.
[0021] The present application also proposes a computer program product, comprising a computer program, which implements the above method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The following drawings are only intended to illustrate and explain the present application and do not limit the scope of the present application.
[0023] Figure 1 is a flow chart of a method for calculating production capacity according to an embodiment of the present application;
[0024] Figure 2 is a schematic diagram of a capacity calculation method according to an embodiment of the present application;
[0025] Figure 3 is a schematic diagram of a production capacity calculation device according to an embodiment of the present application;
[0026] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present application.
[0027] Description of Reference Numerals
[0028] 100 Capacity Calculation Method
[0029] Steps 110-130
[0030] 21 Original Database
[0031] 211 process route data
[0032] 212 Historical Data
[0033] 213 device data
[0034] 214 calendar data
[0035] 22 Model Impact Factor Library
[0036] Flight 221
[0037] 222 Capacity Allocation Ratio
[0038] 223 standard working hours
[0039] 224 devices
[0040] 23Capacity calculation model library
[0041] 231 Limit Capacity Calculation Model
[0042] 232 Maximum Capacity Calculation Model
[0043] 233 Planned Capacity Calculation Model
[0044] 234 Standard Production Capacity Calculation Model
[0045] 24 Production Capacity Calculation Engine
[0046] 241 First Processing Unit
[0047] 242 Second Processing Unit
[0048] 243 Third Processing Unit
[0049] 244 Fourth Processing Unit
[0050] 300 Production Capacity Calculation Device
[0051] 310 Detection Module
[0052] 320 Matching Module
[0053] 330 Calculation Module
[0054] 400 Electronic Device
[0055] 410 Processor
[0056] 420 Memory Specific Embodiment
[0057] For a clearer understanding of the technical features, objectives, and effects of the present application, the specific embodiments of the present application will now be described with reference to the accompanying drawings.
[0058] Many specific details are set forth in the following description in order to provide a thorough understanding of the present application, but the present application may also be implemented in other ways different from those described herein. Therefore, the present application is not limited by the specific embodiments disclosed below.
[0059] As shown in the present application and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.
[0060] The present application proposes a production capacity calculation method. Figure 1 It is a flowchart of a production capacity calculation method 100 according to an embodiment of the present application, as Figure 1 shown, the method 100 includes:
[0061] Step 110, detecting a demand event, where the demand event includes the target production capacity;
[0062] The demand event can be a user-related event or a non-user-related event. User-related events are triggered by users. For example, when the workshop director receives a new order, the standard production capacity is calculated to confirm whether the current production line can meet the order demand. Non-user-related events can be automatically triggered based on processes. For example, when a single production line fails as set in the system, the minimum production capacity is calculated.
[0063] The demand event includes the target production capacity, that is, the production capacity to be calculated. For example, for the demand event of "when the workshop director receives a new order, calculate the standard production capacity", the target production capacity it includes is the standard production capacity. Another example is that for the demand event of "when a single production line fails, calculate the minimum production capacity", the target production capacity it includes is the minimum production capacity. In the embodiments of the present application, the granularity of the production capacity is not limited and can be the production capacity of a process, the production capacity of a product model, or the production capacity of a process route.
[0064] Step 120: Match the production capacity calculation model corresponding to the target production capacity from the predefined production capacity calculation model library, parse the production capacity calculation model, and determine the predefined model influencing factors included in the production capacity calculation model. The predefined model influencing factors are stored in the model influencing factor library;
[0065] The production capacity calculation model library can include multiple production capacity calculation models predefined by users, such as the limit production capacity calculation model, the maximum production capacity calculation model, the planned production capacity calculation model, the standard production capacity calculation model, etc. Match the production capacity calculation model corresponding to the target production capacity from the production capacity calculation model library. For example, for the target production capacity being the standard production capacity, match the standard production capacity calculation model from the production capacity calculation model library. By parsing the production capacity calculation model, the predefined model influencing factors included in the production capacity calculation model can be determined. For example, the predefined model shadow included in the standard production capacity calculation model is the standard working hours of the process.
[0066] Each production capacity calculation model includes model influencing factors, that is, model parameters. Different production capacity calculation models can share model influencing factors. The model influencing factors are stored in the model influencing factor library, and the production capacity calculation model can call the model influencing factors by accessing the model influencing factor library.
[0067] Exemplarily, the production capacity calculation model library can include the process maximum production capacity calculation model, the process route maximum production capacity calculation model, and the product model maximum production capacity calculation model. Among them, the process maximum production capacity represents the maximum production capacity of a certain process (such as the painting process in automobile production), the process route maximum production capacity represents the maximum production capacity of a certain process route (such as the A drug synthesis process route), and the product model maximum production capacity represents the maximum production capacity of a certain model product (such as the A model car).
[0068] The process maximum production capacity calculation model can be represented by the formula C process-max= T Device-max-supply / (T process-standard + T process-allowance ) indicates that, where C process-max represents the maximum production capacity of the process, T Device-max-supply represents the maximum supply man-hours of the equipment, T process-standard represents the standard man-hours of the process, T process-allowance represents the allowance man-hours of the process.
[0069] The calculation model of the maximum production capacity of the process route can be represented by the formula C routing-max = Min(C process-max ) indicates that, where C routing-max represents the maximum production capacity of the process route, C process-max represents the maximum production capacity of the process.
[0070] The calculation model of the maximum production capacity of the product model can be represented by the formula C product-max = ∑C routing-max indicates that, where C product-max represents the maximum production capacity of the product model, C routing-max represents the maximum production capacity of the process route.
[0071] The calculation model of the maximum production capacity of the process, the calculation model of the maximum production capacity of the process route, and the calculation model of the maximum production capacity of the product model share the model influencing factor, the maximum supply man-hours of the equipment T Device-max-supply , the standard man-hours of the process T process-standard , the allowance man-hours of the process T process-allowance , and these model influencing factors can be stored in the model influencing factor library for the calculation model of the maximum production capacity of the process to call.
[0072] Therefore, through the production capacity calculation model library with multiple production capacity calculation models predefined by the user, the corresponding production capacity calculation model can be called according to the current demand, meeting the customized needs of the user and improving the flexibility and speed of production capacity calculation.
[0073] In some embodiments, the method further includes: receiving the production capacity calculation model defined by the user and updating the production capacity calculation model defined by the user to the production capacity calculation model library. Therefore, the user can add the newly defined production capacity calculation model to the production capacity calculation model library, making the production capacity calculation model library extensible and configurable, and further improving the flexibility and speed of production capacity calculation.
[0074] In some embodiments, the method further includes: receiving the model influencing factor defined by the user and updating the model influencing factor defined by the user to the model influencing factor library. Therefore, the user can add the newly defined model influencing factor to the model influencing factor library, making the model influencing factor library extensible and configurable, and further improving the flexibility and speed of production capacity calculation.
[0075] In some embodiments, matching the production capacity calculation model corresponding to the target production capacity from the predefined production capacity calculation model library includes: calculating the similarity between the target production capacity and each production capacity calculation model in the production capacity calculation model library, and determining the production capacity calculation model with the largest similarity as the matched production capacity calculation model. For cases where an exact match cannot be achieved, the production capacity calculation model that is closest can be determined by means of similarity. To this end, fuzzy matching of the production capacity calculation model is realized, further improving the flexibility of production capacity calculation.
[0076] Step 130, obtain the original data of the model influencing factors, and calculate the target production capacity according to the original data of the model influencing factors and the production capacity calculation model.
[0077] The original data of the model influencing factors can be obtained from a database, which can be a system database or a third-party database. The third-party database can call relevant original data through an application programming interface. In some embodiments, obtaining the original data of the model parameters includes: obtaining the original data of the model parameters from an enterprise resource planning system, an enterprise resource management system, or an office automation system. Using the obtained original data of the model influencing factors and the determined production capacity calculation model to calculate the target production capacity, thus realizing the calculation of the target production capacity. So far, through the production capacity calculation model library and the model influencing factor library, for a demand event containing the target production capacity, the corresponding production capacity calculation model and the model influencing factors contained therein can be quickly called, the production capacity calculation demand can be quickly responded to, and the production capacity calculation model library and the model influencing factor library are extensible and configurable, also realizing an extensible and configurable production capacity calculation method.
[0078] In some embodiments, the method includes: storing the predefined production capacity calculation model library in the cloud, and matching the production capacity calculation model corresponding to the target production capacity from the predefined production capacity calculation model library in the cloud. The cloud can provide sufficient capacity and computing power, and production capacity calculation models can be shared and exchanged among different users. By storing the predefined production capacity calculation model library in the cloud, the types of production capacity calculation models can be enriched, and the scenarios of production capacity calculation can be extended.
[0079] Figure 2 is a schematic diagram of a production capacity calculation method according to an embodiment of the present application, as Figure 2As shown in the figure, the production capacity calculation engine 24 can call the production capacity calculation models in the production capacity calculation model library 23. The production capacity calculation model library 23 can call the model impact factors in the model impact factor library 22. The model impact factors in the model impact factor library 22 can call the original data in the original database 21. The production capacity calculation engine 24 includes a first processing unit 241, a second processing unit 242, a third processing unit 243, and a fourth processing unit 244. The production capacity calculation model library 23 includes a limit production capacity calculation model 231, a maximum production capacity calculation model 232, a planned production capacity calculation model 233, and a standard production capacity calculation model 234. The model impact factor library 22 includes an impact factor shift 221, a production capacity allocation ratio 222, a standard working hour 223, and the number of devices 224. The original database 21 includes process route data 211, historical data 212, equipment data 213, and calendar data 214.
[0080] For the demand event of "the user triggers the calculation of the maximum production capacity of the automobile painting process", the first processing unit 241 determines that the target production capacity is the maximum production capacity of the process, and the second processing unit 242 calls the maximum production capacity calculation model 232 of the process from the production capacity calculation model library 23. The third processing unit 243 calls the model impact factor of the maximum supply working hour T of the equipment from the model impact factor library 22 according to the maximum production capacity calculation model 232 of the process Device-max-supply , the standard working hour T of the process process-standard , the relaxation working hour T of the process process-allowance , and calls the maximum supply working hour T of the equipment from the original database 21 Device-max-supply , the standard working hour T of the process process-standard , the relaxation working hour T of the process process-allowance of the original data. The fourth processing unit 244 calculates the maximum production capacity of the vehicle painting process according to the maximum production capacity calculation model 232 obtained by the second processing unit 242 and the data obtained by the third processing unit 243, thus realizing the production capacity calculation demand.
[0081] This application proposes a production capacity calculation method. Through the production capacity calculation model library and the model impact factor library, for a demand event containing the target production capacity, the corresponding production capacity calculation model and the model impact factors contained therein can be quickly called, and the production capacity calculation demand can be quickly responded to. Moreover, the production capacity calculation model library and the model impact factor library are extensible and configurable, and an extensible and configurable production capacity calculation method is also realized.
[0082] This application also proposes a production capacity calculation device. Figure 3 It is a schematic diagram of a production capacity calculation device 300 according to an embodiment of this application. As Figure 3 shown, the device 300 includes:
[0083] A detection module 310 that detects a demand event, and the demand event contains the target production capacity;
[0084] A matching module 320 matches a production capacity calculation model corresponding to a target production capacity from a predefined production capacity calculation model library, parses the production capacity calculation model, and determines predefined model influencing factors included in the production capacity calculation model. The predefined model influencing factors are stored in a model influencing factor library.
[0085] A calculation module 330 obtains original data of the model influencing factors and calculates the target production capacity according to the original data of the model influencing factors and the production capacity calculation model.
[0086] This application also proposes an electronic device 400. Figure 4 It is a schematic diagram of an electronic device 400 according to an embodiment of this application. As Figure 4 shown, the electronic device 400 includes a processor 410 and a memory 420. Instructions are stored in the memory 420, and when the instructions are executed by the processor 410, the method 100 described above is implemented.
[0087] This application also proposes a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are run, the method 100 described above is executed.
[0088] This application also proposes a computer program product, including a computer program. When the computer program is executed by a processor, the method 100 described above is implemented.
[0089] Some aspects of the method and apparatus of this application can be fully executed by hardware, can be fully executed by software (including firmware, resident software, microcode, etc.), or can be executed by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLCs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors or combinations thereof. In addition, aspects of this application may be embodied as a computer product located in one or more computer-readable media, and the product includes computer-readable program codes. For example, the computer-readable media may include, but are not limited to, magnetic storage devices (such as hard disks, floppy disks, magnetic tapes...), optical discs (such as compact discs (CDs), digital versatile discs (DVDs)...), smart cards, and flash memory devices (such as cards, sticks, key drives...).
[0090] Flowcharts are used herein to illustrate the operations performed by the methods according to embodiments of the present application. It should be understood that the foregoing operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously. Also, one or more operations may be added to these processes, or one or more steps may be removed from these processes.
[0091] It should be understood that although this specification is described according to various embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in various embodiments may also be appropriately combined to form other embodiments understandable by those skilled in the art.
[0092] The foregoing is only a schematic specific embodiment of the present application and is not intended to limit the scope of the present application. Any equivalent changes, modifications, and combinations made by those skilled in the art without departing from the concept and principles of the present application shall fall within the scope of protection of the present application.
[0093] In this patent application, nouns and pronouns related to people are not limited to a specific gender.
Claims
1. A production capacity calculation method (100), characterized in that, The method (100) includes: Detecting a demand event, where the demand event includes a target production capacity (110); Matching, from a predefined production capacity calculation model library, a production capacity calculation model corresponding to the target production capacity, parsing the production capacity calculation model, and determining predefined model impact factors included in the production capacity calculation model, where the predefined model impact factors are stored in a model impact factor library (120); Obtaining original data of the model impact factors, and calculating the target production capacity according to the original data of the model impact factors and the production capacity calculation model (130).
2. The method (100) according to claim 1, characterized in that, The method (100) further includes: receiving a user-defined production capacity calculation model, and updating the user-defined production capacity calculation model to the production capacity calculation model library.
3. The method (100) according to claim 1 or 2, characterized in that, The method (100) further includes: receiving user-defined model impact factors, and updating the user-defined model impact factors to the model impact factor library.
4. The method (100) according to claim 1, characterized in that, Obtaining the original data of the model parameters includes: obtaining the original data of the model parameters from an enterprise resource planning system, an enterprise resource management system, or an office automation system.
5. The method (100) according to claim 1, wherein Matching, from a predefined production capacity calculation model library, a production capacity calculation model corresponding to the target production capacity includes: calculating the similarity between the target production capacity and each production capacity calculation model in the production capacity calculation model library, and determining the production capacity calculation model with the maximum similarity as the matched production capacity calculation model.
6. The method (100) according to claim 1, characterized in that The method (100) includes: the predefined production capacity calculation model library is stored in the cloud, and matching, from the predefined production capacity calculation model library in the cloud, a production capacity calculation model corresponding to the target production capacity.
7. A production capacity calculation device (300), characterized in that, The device (300) includes: A detection module (310) for detecting a demand event, where the demand event includes a target production capacity; A matching module (320) for matching, from a predefined production capacity calculation model library, a production capacity calculation model corresponding to the target production capacity, parsing the production capacity calculation model, and determining predefined model impact factors included in the production capacity calculation model, where the predefined model impact factors are stored in a model impact factor library; A calculation module (330) for obtaining original data of the model impact factors, and calculating the target production capacity according to the original data of the model impact factors and the production capacity calculation model.
8. An electronic device (400) includes a processor (410), a memory (420), and instructions stored in the memory (420), where when the instructions are executed by the processor (410), the method (100) according to any one of claims 1-6 is implemented.
9. A computer-readable storage medium, on which computer instructions are stored, and the computer instructions, when running, execute the method (100) according to any one of claims 1-6.
10. A computer program product, characterized in that, Including a computer program, where when the computer program is executed by a processor, the method (100) according to any one of claims 1-6 is implemented.