CN116484651B - Digital twinning-based system parameter adjusting method and device and electronic equipment - Google Patents
Digital twinning-based system parameter adjusting method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application discloses a system parameter adjusting method and device based on digital twinning and electronic equipment. The method comprises the following steps: acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production; the method comprises the steps of obtaining twin product data output by a process physical model, and adjusting process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target; judging whether twin product data output by the process physical model reaches an optimization target after each process parameter adjustment; if the optimization target is reached, the parameter values of the current process parameters in the process physical model are displayed in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values. The application utilizes a digital twin process physical model to realize the optimization and adjustment of process parameters under different optimization targets.
Description
Technical Field
The embodiment of the application relates to the technical field of parameter adjustment, in particular to a system parameter adjustment method and device based on digital twinning and electronic equipment.
Background
With the fusion and landing application of new generation information technologies such as cloud computing, internet of things, big data, mobile interconnection, artificial intelligence and the like and industrial manufacturing, the pace of intelligent transformation and upgrading in the coal industry is accelerated. Under the background of rapid development of intelligent manufacturing, digital twin technology is rapidly developed, and a digital twin system combined with industrial production also becomes one of important solution means for process industry intelligence.
At present, the digital twin technology is mainly applied to the industrial intellectualization, namely, each process flow is digitally displayed, but the actual process flow is usually limited to model display, and more actual requirements cannot be met.
Disclosure of Invention
The embodiment of the application provides a system parameter adjusting method and device based on digital twinning and electronic equipment, so as to meet more production requirements.
In a first aspect, an embodiment of the present application provides a method for adjusting a system parameter based on digital twinning, where the method includes:
acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production;
obtaining twin product data output by the process physical model, and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, the twin product data and an optimization target;
judging whether twin product data output by the process physical model reaches the optimization target after each process parameter adjustment;
and if the optimization target is reached, displaying the parameter values of the current process parameters in the process physical model in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values.
In a second aspect, an embodiment of the present application provides a digital twin-based system parameter adjustment device, where the device includes:
the twin simulation module is used for acquiring the original quality data of a product to be processed, and inputting the original quality data into a process physical model of a digital twin system constructed in advance for simulation production;
the parameter adjusting module is used for acquiring twin product data output by the process physical model and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, the twin product data and an optimization target;
the judging module is used for judging whether twin product data output by the process physical model reach the optimization target after the process parameters are regulated each time;
and the display module is used for displaying the parameter values of the current process parameters in the process physical model in a preset mode if the optimization target is reached, so that a user can adjust the actual process parameters according to the displayed parameter values.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a digital twinning-based system parameter tuning method as provided by any of the embodiments of the present application.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement a digital twin-based system parameter adjustment method according to any of the embodiments of the present application.
According to the technical scheme, original quality data of a product to be processed is obtained, and the original quality data is input into a process physical model of a digital twin system constructed in advance for simulation production; obtaining twin product data output by the process physical model, and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, the twin product data and an optimization target; judging whether twin product data output by the process physical model reaches the optimization target after each process parameter adjustment; and if the optimization target is reached, displaying the parameter values of the current process parameters in the process physical model in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values. Based on the method, the digital twin process physical model is utilized to realize the optimization and adjustment of the process parameters under different optimization targets.
Drawings
FIG. 1 is a flow chart of a digital twinning-based system parameter adjustment method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a digital twin system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a digital twin-based system parameter adjusting device according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
Example 1
Fig. 1 is a flow chart of a digital twin-based system parameter adjustment method according to an embodiment of the present application, where the method is applicable to a digital twin-based system parameter adjustment scenario. The method can be executed by a digital twin-based system parameter adjusting device, the device can be realized in a hardware and/or software mode, and can be generally integrated in electronic equipment such as a computer with data operation capability, and the like, and the method specifically comprises the following steps:
and 101, acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production.
In this step, the product to be processed refers to an original product which is not processed yet, and in a specific example, the digital twin system is constructed as a coal preparation plant digital twin system, so that the product to be processed is the excavated raw coal, and the original quality data of the raw coal is coal quality data.
In addition, referring to fig. 2, fig. 2 is a schematic diagram of a digital twin system according to an embodiment of the present application.
As shown in fig. 2, the digital twin system comprises a basic data acquisition layer, a basic model building layer, a production execution layer, an analysis decision layer and a product display layer. The basic data layer mainly realizes the acquisition of bottom basic data required by the construction of the digital twin system, and comprises the acquisition of coal preparation plant quantity data, the acquisition of quality data (comprising the coal quality data), the acquisition of water quantity data and the acquisition of medium quantity data.
The basic model building layer is mainly used for building a bottom model of a digital twin system of a coal preparation plant and comprises object model management, process system management, process flow model building, equipment flow model building, process flow dynamic simulation calculation, equipment flow dynamic simulation calculation, a coal quality feature library and a three-dimensional modeling model (comprising a workshop arrangement model, an equipment simulation model and a pipeline trend model).
The production execution layer mainly comprises various production system control modules, including coal mining management, intelligent start-stop, mao Mei unified allocation, intelligent secret control, intelligent medium feeding, intelligent coarse slime separation, intelligent flotation, intelligent concentration dosing, intelligent filter pressing, a water balance system and the like, and the digital twin system analysis decision result instructions of the coal preparation plant, such as the instructions of various product quality control indexes and the like, are issued to the production execution layer, and the specific control of the production system is realized by the production execution layer module to execute the control instructions.
The analysis decision layer mainly realizes analysis and decision functions of digital twin data, and specifically comprises comparison analysis (comprising quantity comparison analysis, quality comparison analysis, water quantity comparison analysis, medium quantity comparison analysis, consumption comparison analysis and the like) of theoretical calculation data and actual data of a process flow to give out prompt of abnormal production, and analysis of load rate conditions of a system and equipment is realized through analysis of load balancing of a whole production system, so that load balancing of the system and the equipment is realized through adjustment.
The product display layer is mainly used for displaying the product application of the digital twin system for a user, and the display content comprises 2D production process drawings (a production overview chart, a production system overview chart, a dynamic process flow chart and a dynamic equipment flow chart); 3D visual interaction system (including production overview chart, production system overview chart, scene roaming, equipment running simulation, AR support); production anomaly reporting, production scene playback reproduction, presentation of real-time status and parameters of systems and devices, and the like. The display terminal of the product comprises a PC end, a PAD end and a mobile phone end.
Based on the digital twin system, the application adjusts system parameters, mainly actual process parameters. Such as the operating parameters of the various equipment in the process flow, etc.
Because the raw quality data of raw coal may be different in different strata and even in different areas of the same stratum, the process of obtaining the raw quality data of the product to be processed and the subsequent simulation production in this step are all continuously performed, so that the parameter adjustment process is more suitable for the actual coal quality requirement.
In addition, in order to make the step more standardized and improve the efficiency of parameter adjustment, the original quality data can be compared with the quality data in the quality feature library first, and a target quality data sample matched with the original quality data can be determined; then obtaining a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into a process physical model; and finally, carrying out simulation production according to the original quality data and the configured process physical model.
The quality feature library stores various quality data samples, wherein the sources of the quality feature library can be that historical quality data is analyzed every month, quality data meeting classification conditions is determined to be one quality data sample, and corresponding technological parameter mapping of the quality data is stored.
It should be noted that the classification condition may be that the difference between the classification condition and the existing quality data is greater than a preset quality difference threshold.
Based on the setting, in the step, the original quality data and the quality data in the quality feature library can be compared, and a target quality data sample matched with the original quality data is determined. It should be noted that, the target quality data sample matched with the original quality data refers to a quality data sample that is different from the original quality data by less than a preset quality difference threshold.
In addition, after the target quality data sample is determined, the preset process parameter set corresponding to the target quality data sample is configured into the process physical model, so that the subsequent tuning process can be started to be adjusted from a similar process parameter, and compared with the process that the tuning process is started to be adjusted from a fixed process parameter, the method can shorten the iteration times of adjustment, and further improve the parameter adjustment efficiency.
In addition, before the preset process parameter set is configured into the process physical model, comparing the preset process parameter set with the current process parameter in the process physical model, and if the comparison result meets the preset replacement condition, executing the step of configuring the preset process parameter set into the process physical model; and if the comparison result does not meet the preset replacement condition, carrying out simulation production according to the original quality data and the current process physical model.
The preset replacement condition means that the average difference value between the preset process parameter set and the current process parameter in the process physical model is smaller than the preset process parameter difference threshold value. Based on the mode, iteration continuity can be guaranteed to a certain extent, and the phenomenon that similar coal quality data cause larger fluctuation to an iteration process is avoided.
Step 102, twin product data output by the process physical model are obtained, and process parameters in the process physical model are adjusted according to preset process parameter adjustment logic, the twin product data and an optimization target.
In this step, to accommodate different optimization objectives, different process parameter tuning logic may be used according to different optimization objectives. Specifically, if the optimization target is a first optimization target, determining a first process parameter adjusting logic as a preset process parameter adjusting logic; if the optimization target is a second optimization target, determining a second process parameter adjusting logic as a preset process parameter adjusting logic; and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic and twin product data.
The first optimization objective may be an optimization objective centered on the maximum yield principle, and the second optimization objective may be an optimization objective centered on the maximum economic benefit principle.
Either the first process parameter tuning logic or the second process parameter tuning logic may be involved in the respective core process parameters and peripheral process parameters. The core process parameters refer to parameters with larger influence on the optimization target, and the peripheral process parameters refer to parameters with smaller influence on the optimization target, and specifically, the parameters can be customized by operation and maintenance personnel according to the actual process flow.
When the twin product data meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the core process parameters indicated in the preset process parameter adjustment logic; and if the twin product data does not meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the peripheral process parameters indicated in the preset process parameter adjustment logic.
It should be noted that the preset core process parameter adjustment requirement may be that the difference between the twin product data and the expected value specified in the optimization target is greater than the first yield gap threshold.
And step 103, judging whether twin product data output by the process physical model reaches an optimization target after the process parameters are adjusted each time.
In this step, whether the optimization target is reached may be determined that the difference between the twin yield data and the expected value specified in the optimization target is smaller than a second yield difference threshold.
And 104, if the optimization target is reached, displaying the parameter values of the current process parameters in the process physical model in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values.
In this step, the preset mode may be to display the parameter values of each process parameter and the difference between the parameter values of the actual process parameter at preset positions in the pre-constructed production process map. Of course, the parameter values of the process parameters, the parameter values of the actual process parameters and the difference values can be displayed for the user to check.
In addition, the application can balance the load of the equipment in the system, and specifically, the actual dynamic load rate of each equipment in the process physical model is obtained; then, for any one device, if the actual dynamic load rate corresponding to the device is higher than a preset load threshold, acquiring the actual dynamic load rate of the similar device; and finally, adjusting the process parameters in the process physical model according to the actual dynamic load rate of each similar device so as to balance the load of the similar device.
The method is characterized in that the process parameters in the process physical model are adjusted according to the actual dynamic load rate of each similar device so as to balance the load of the similar device, and mainly the part of any device higher than a preset load threshold is distributed to other similar devices.
In the embodiment, original quality data of a product to be processed is obtained, and the original quality data is input into a process physical model of a digital twin system constructed in advance for simulation production; the method comprises the steps of obtaining twin product data output by a process physical model, and adjusting process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target; judging whether twin product data output by the process physical model reaches an optimization target after each process parameter adjustment; if the optimization target is reached, the parameter values of the current process parameters in the process physical model are displayed in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values. Based on the method, the digital twin process physical model is utilized to realize the optimization and adjustment of the process parameters under different optimization targets.
Example two
Fig. 3 is a schematic structural diagram of a system parameter adjusting device based on digital twinning according to a second embodiment of the present application. The system parameter adjusting device based on digital twin provided by the embodiment of the application can execute the system parameter adjusting method based on digital twin provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the executing method. The device can be realized in a software and/or hardware mode, as shown in fig. 3, the system parameter adjusting device based on digital twinning specifically comprises: the system comprises a twinning simulation module 301, a parameter adjustment module 302, a judgment module 303 and a display module 304.
The twin simulation module is used for acquiring the original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for simulation production;
the parameter adjusting module is used for acquiring twin product data output by the process physical model, and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target;
the judging module is used for judging whether twin product data output by the process physical model reach an optimization target after the process parameters are regulated each time;
and the display module is used for displaying the parameter values of the current process parameters in the process physical model in a preset mode if the optimization target is reached, so that a user can adjust the actual process parameters according to the displayed parameter values.
In the embodiment, original quality data of a product to be processed is obtained, and the original quality data is input into a process physical model of a digital twin system constructed in advance for simulation production; the method comprises the steps of obtaining twin product data output by a process physical model, and adjusting process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target; judging whether twin product data output by the process physical model reaches an optimization target after each process parameter adjustment; if the optimization target is reached, the parameter values of the current process parameters in the process physical model are displayed in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values. Based on the method, the digital twin process physical model is utilized to realize the optimization and adjustment of the process parameters under different optimization targets.
Further, the twin simulation module is specifically configured to:
comparing the original quality data with quality data in a quality feature library, and determining a target quality data sample matched with the original quality data;
acquiring a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into a process physical model;
and performing simulation production according to the original quality data and the configured process physical model.
Further, the twin simulation module is specifically further configured to:
comparing the preset process parameter set with the current process parameters in the process physical model, and if the comparison result meets the preset replacement condition, executing the step of configuring the preset process parameter set into the process physical model;
and if the comparison result does not meet the preset replacement condition, carrying out simulation production according to the original quality data and the current process physical model.
Further, the parameter adjusting module is specifically configured to:
if the optimization target is a first optimization target, determining a first process parameter adjusting logic as a preset process parameter adjusting logic;
if the optimization target is a second optimization target, determining a second process parameter adjusting logic as a preset process parameter adjusting logic;
and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic and twin product data.
Further, the parameter adjusting module is specifically configured to:
if the twin product data meets the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the core process parameters indicated in the preset process parameter adjustment logic;
and if the twin product data does not meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the peripheral process parameters indicated in the preset process parameter adjustment logic.
Further, the display module is specifically configured to:
and displaying the parameter values of each process parameter and the difference value between the parameter values of the actual process parameter at preset positions in the pre-built production process diagram.
Further, the device is also used for:
acquiring the actual dynamic load rate of each device in the process physical model;
for any equipment, if the actual dynamic load rate corresponding to the equipment is higher than a preset load threshold, acquiring the actual dynamic load rate of the similar equipment;
and adjusting the process parameters in the process physical model according to the actual dynamic load rate of each similar device so as to balance the load of the similar device.
Example III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present application, as shown in fig. 4, the electronic device includes a processor 410, a memory 420, an input device 430 and an output device 440; the number of processors 410 in the electronic device may be one or more, one processor 410 being taken as an example in fig. 4; the processor 410, memory 420, input device 430, and output device 440 in the electronic device may be connected by a bus or other means, for example in fig. 4.
The memory 420 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the digital twin based system parameter adjustment method in the embodiments of the present application. The processor 410 executes various functional applications and data processing of the electronic device by running software programs, instructions and modules stored in the memory 420, i.e. implementing the digital twinning-based system parameter adjustment method described above:
acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production;
the method comprises the steps of obtaining twin product data output by a process physical model, and adjusting process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target;
judging whether twin product data output by the process physical model reaches an optimization target after each process parameter adjustment;
if the optimization target is reached, the parameter values of the current process parameters in the process physical model are displayed in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values.
Further, inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production, comprising:
comparing the original quality data with quality data in a quality feature library, and determining a target quality data sample matched with the original quality data;
acquiring a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into a process physical model;
and performing simulation production according to the original quality data and the configured process physical model.
Further, before configuring the set of preset process parameters into the process physical model, inputting the raw quality data into the process physical model of the pre-built digital twin system for analog production further comprises:
comparing the preset process parameter set with the current process parameters in the process physical model, and if the comparison result meets the preset replacement condition, executing the step of configuring the preset process parameter set into the process physical model;
and if the comparison result does not meet the preset replacement condition, carrying out simulation production according to the original quality data and the current process physical model.
Further, adjusting the process parameters in the process physical model according to the preset process parameter adjusting logic, the twin product data and the optimization target, including:
if the optimization target is a first optimization target, determining a first process parameter adjusting logic as a preset process parameter adjusting logic;
if the optimization target is a second optimization target, determining a second process parameter adjusting logic as a preset process parameter adjusting logic;
and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic and twin product data.
Further, adjusting the process parameters in the process physical model according to the preset process parameter adjustment logic and twin product data comprises:
if the twin product data meets the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the core process parameters indicated in the preset process parameter adjustment logic;
and if the twin product data does not meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the peripheral process parameters indicated in the preset process parameter adjustment logic.
Further, displaying parameter values of current process parameters in the process physical model in a preset mode includes:
and displaying the parameter values of each process parameter and the difference value between the parameter values of the actual process parameter at preset positions in the pre-built production process diagram.
Further, the method further comprises:
acquiring the actual dynamic load rate of each device in the process physical model;
for any equipment, if the actual dynamic load rate corresponding to the equipment is higher than a preset load threshold, acquiring the actual dynamic load rate of the similar equipment;
and adjusting the process parameters in the process physical model according to the actual dynamic load rate of each similar device so as to balance the load of the similar device.
Memory 420 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 420 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 420 may further include memory remotely located relative to processor 410, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Example IV
A fourth embodiment of the present application also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a digital twinning-based system parameter adjustment method, the method comprising:
acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production;
the method comprises the steps of obtaining twin product data output by a process physical model, and adjusting process parameters in the process physical model according to preset process parameter adjusting logic, twin product data and an optimization target;
judging whether twin product data output by the process physical model reaches an optimization target after each process parameter adjustment;
if the optimization target is reached, the parameter values of the current process parameters in the process physical model are displayed in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values.
Further, inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production, comprising:
comparing the original quality data with quality data in a quality feature library, and determining a target quality data sample matched with the original quality data;
acquiring a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into a process physical model;
and performing simulation production according to the original quality data and the configured process physical model.
Further, before configuring the set of preset process parameters into the process physical model, inputting the raw quality data into the process physical model of the pre-built digital twin system for analog production further comprises:
comparing the preset process parameter set with the current process parameters in the process physical model, and if the comparison result meets the preset replacement condition, executing the step of configuring the preset process parameter set into the process physical model;
and if the comparison result does not meet the preset replacement condition, carrying out simulation production according to the original quality data and the current process physical model.
Further, adjusting the process parameters in the process physical model according to the preset process parameter adjusting logic, the twin product data and the optimization target, including:
if the optimization target is a first optimization target, determining a first process parameter adjusting logic as a preset process parameter adjusting logic;
if the optimization target is a second optimization target, determining a second process parameter adjusting logic as a preset process parameter adjusting logic;
and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic and twin product data.
Further, adjusting the process parameters in the process physical model according to the preset process parameter adjustment logic and twin product data comprises:
if the twin product data meets the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the core process parameters indicated in the preset process parameter adjustment logic;
and if the twin product data does not meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the peripheral process parameters indicated in the preset process parameter adjustment logic.
Further, displaying parameter values of current process parameters in the process physical model in a preset mode includes:
and displaying the parameter values of each process parameter and the difference value between the parameter values of the actual process parameter at preset positions in the pre-built production process diagram.
Further, the method further comprises:
acquiring the actual dynamic load rate of each device in the process physical model;
for any equipment, if the actual dynamic load rate corresponding to the equipment is higher than a preset load threshold, acquiring the actual dynamic load rate of the similar equipment;
and adjusting the process parameters in the process physical model according to the actual dynamic load rate of each similar device so as to balance the load of the similar device.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present application is not limited to the above method operations, but may also perform the related operations in the digital twin-based system parameter adjustment method provided in any embodiment of the present application.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present application.
It should be noted that, in the above-mentioned embodiments of the search apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (9)
1. A digital twinning-based system parameter adjustment method, the method comprising:
acquiring original quality data of a product to be processed, and inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production;
obtaining twin product data output by the process physical model, and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, the twin product data and an optimization target;
judging whether twin product data output by the process physical model reaches the optimization target after each process parameter adjustment;
if the optimization target is reached, displaying the parameter values of the current process parameters in the process physical model in a preset mode, so that a user can adjust the actual process parameters according to the displayed parameter values;
inputting the original quality data into a process physical model of a pre-constructed digital twin system for analog production, wherein the process physical model comprises the following steps of:
comparing the original quality data with quality data in a quality feature library, and determining a target quality data sample matched with the original quality data;
acquiring a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into the process physical model;
and carrying out simulation production according to the original quality data and the configured process physical model.
2. The method of claim 1, wherein said inputting said raw quality data into a pre-built process physical model of a digital twin system for analog production prior to said configuring said set of pre-set process parameters into said process physical model further comprises:
comparing the preset process parameter set with the current process parameters in the process physical model, and if the comparison result meets the preset replacement condition, executing the step of configuring the preset process parameter set into the process physical model;
and if the comparison result does not meet the preset replacement condition, carrying out simulation production according to the original quality data and the current process physical model.
3. The method of claim 1, wherein said adjusting process parameters in said process physical model according to preset process parameter adjustment logic, said twin product data, and optimization objectives comprises:
if the optimization target is a first optimization target, determining a first process parameter adjusting logic as the preset process parameter adjusting logic;
if the optimization target is a second optimization target, determining a second process parameter adjusting logic as the preset process parameter adjusting logic;
and adjusting the process parameters in the process physical model according to the preset process parameter adjusting logic and the twin product data.
4. The method of claim 3, wherein said adjusting process parameters in said process physical model according to said preset process parameter adjustment logic and said twin product data comprises:
if the twin product data meets the preset core process parameter adjustment requirement, carrying out stepwise adjustment on the core process parameters indicated in the preset process parameter adjustment logic;
and if the twin product data does not meet the preset core process parameter adjustment requirement, carrying out stepwise adjustment on peripheral process parameters indicated in preset process parameter adjustment logic.
5. The method of claim 1, wherein displaying parameter values of current process parameters in the process physical model in a preset manner comprises:
and displaying the parameter values of each process parameter and the difference value between the parameter values of the actual process parameter at preset positions in the pre-built production process diagram.
6. The method according to claim 1, wherein the method further comprises:
acquiring the actual dynamic load rate of each device in the process physical model;
for any equipment, if the actual dynamic load rate corresponding to the equipment is higher than a preset load threshold, acquiring the actual dynamic load rate of the similar equipment;
and adjusting the process parameters in the process physical model according to the actual dynamic load rate of each similar device so as to balance the loads of the similar devices.
7. A digital twinning-based system parameter adjustment device, the device comprising:
the twin simulation module is used for acquiring the original quality data of a product to be processed, and inputting the original quality data into a process physical model of a digital twin system constructed in advance for simulation production;
the parameter adjusting module is used for acquiring twin product data output by the process physical model and adjusting the process parameters in the process physical model according to preset process parameter adjusting logic, the twin product data and an optimization target;
the judging module is used for judging whether twin product data output by the process physical model reach the optimization target after the process parameters are regulated each time;
the display module is used for displaying the parameter values of the current process parameters in the process physical model in a preset mode if the optimization target is reached, so that a user can adjust the actual process parameters according to the displayed parameter values;
the twin simulation module is specifically used for:
comparing the original quality data with quality data in a quality feature library, and determining a target quality data sample matched with the original quality data;
acquiring a preset process parameter set corresponding to the target quality data sample, and configuring the preset process parameter set into the process physical model;
and carrying out simulation production according to the original quality data and the configured process physical model.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the digital twinning-based system parameter tuning method of any one of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a digital twinning based system parameter tuning method according to any one of claims 1-6.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112417619A (en) * | 2020-11-23 | 2021-02-26 | 江苏大学 | Pump unit optimal operation adjusting system and method based on digital twinning |
| CN113656971A (en) * | 2021-08-18 | 2021-11-16 | 中国石油化工股份有限公司 | Digital twin system establishing method and device, electronic equipment and readable storage medium |
| CN114423473A (en) * | 2019-09-20 | 2022-04-29 | 美敦力迷你公司 | Situation personalized closed loop adjustment method and system |
| CN115098472A (en) * | 2022-06-30 | 2022-09-23 | 北京亚控科技发展有限公司 | Digital twinning method, device and equipment for full life cycle of physical entity |
| CN115577641A (en) * | 2022-11-14 | 2023-01-06 | 成都飞机工业（集团）有限责任公司 | Training method, device, equipment and medium for digital twin model |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114423473A (en) * | 2019-09-20 | 2022-04-29 | 美敦力迷你公司 | Situation personalized closed loop adjustment method and system |
| CN112417619A (en) * | 2020-11-23 | 2021-02-26 | 江苏大学 | Pump unit optimal operation adjusting system and method based on digital twinning |
| CN113656971A (en) * | 2021-08-18 | 2021-11-16 | 中国石油化工股份有限公司 | Digital twin system establishing method and device, electronic equipment and readable storage medium |
| CN115098472A (en) * | 2022-06-30 | 2022-09-23 | 北京亚控科技发展有限公司 | Digital twinning method, device and equipment for full life cycle of physical entity |
| CN115577641A (en) * | 2022-11-14 | 2023-01-06 | 成都飞机工业（集团）有限责任公司 | Training method, device, equipment and medium for digital twin model |
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