CN113836755A - Control method and device based on digital twin model - Google Patents
Control method and device based on digital twin model Download PDFInfo
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- CN113836755A CN113836755A CN202111427678.4A CN202111427678A CN113836755A CN 113836755 A CN113836755 A CN 113836755A CN 202111427678 A CN202111427678 A CN 202111427678A CN 113836755 A CN113836755 A CN 113836755A
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Abstract
The application discloses a control method and a device based on a digital twin model, wherein the method comprises the following steps: acquiring attribute information, at least one first operation scheme and actual operation data of first equipment in a real space; constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operating plan, and the actual operating data; acquiring a target operation scheme by utilizing the digital twin model based on set target operation data; and controlling the first equipment to operate based on the target operation scheme so that first operation data generated in the operation process of the first equipment conforms to the target operation data. The method can assist in finding the debugging or optimizing control strategy, shorten the debugging or optimizing period of the equipment and reduce the debugging or optimizing cost of the equipment.
Description
Technical Field
The application relates to the technical field of production equipment control, in particular to a control method and device based on a digital twin model.
Background
After the production equipment is installed or upgraded, the target operation performance of the design can be achieved through the processes of equipment debugging and optimization control. However, debugging a real production device usually requires repeated adjustment, and tests the adjusted operation performance, such as production efficiency, product quality, energy consumption, etc., to determine whether the adjustment mode can achieve the intended purpose, which not only consumes time, but also causes high debugging and optimization costs due to unstable production efficiency, product quality and energy consumption in the debugging process. Therefore, how to improve the debugging and optimizing efficiency of the production equipment and reduce the debugging and optimizing cost becomes a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The application provides a control method and a control device based on a digital twin model, and the technical scheme adopted by the embodiment of the application is as follows:
one aspect of the present application provides a control method based on a digital twin model, including:
acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, wherein the actual operation data is data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first equipment;
constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operating plan, and the actual operating data;
acquiring a target operation scheme by utilizing the digital twin model based on set target operation data; wherein the target operational data characterizes at least one target operational property of the digital twin model, the target operational scenario being for controlling the digital twin model to operate and conforming the digital twin model to the target operational property;
and controlling the first equipment to operate based on the target operation scheme so that first operation data generated in the operation process of the first equipment conforms to the target operation data.
In some embodiments, the obtaining a target operating scenario using the digital twin model based on the set target operating data includes:
based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme;
controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model;
and under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
In some embodiments, the mechanism model is formed by training through an established model architecture, wherein the training process comprises:
and training the model architecture by taking the actual operation data as input data and the first operation scheme as output data.
In some embodiments, said constructing a digital twin model of said first device in virtual space based on said attribute information, said first operating scenario and said actual operating data comprises:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
In some embodiments, the controlling the operation of the first device based on the target operation scheme so that the first operation data generated during the operation of the first device conforms to the target operation data includes:
generating control instructions for controlling each operation process of the first equipment based on the target operation scheme, and forming an instruction set containing each control instruction;
and sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
Another aspect of the present application provides a control apparatus based on a digital twin model, including:
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, the actual operation data is data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first equipment;
a construction module for constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operating scenario, and the actual operating data;
the optimizing module is used for acquiring a target operation scheme by utilizing the digital twin model based on set target operation data; wherein the target operational data characterizes at least one target operational performance of the digital twin model, the target operational scenario being for controlling the digital twin model to operate and to comply with the target operational performance;
and the control module is used for controlling the first equipment to operate based on the target operation scheme so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
In some embodiments, the optimizing module is specifically configured to:
based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme;
controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model;
and under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
In some embodiments, the mechanism model is formed by training through an established model architecture, wherein the training process comprises:
and training the model architecture by taking the actual operation data as input data and the first operation scheme as output data.
In some embodiments, the building module is specifically configured to:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
In some embodiments, the control module is specifically configured to:
generating control instructions for controlling each operation process of the first equipment based on the target operation scheme, and forming an instruction set containing each control instruction;
and sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
According to the control method based on the digital twin model, the digital twin model which is twin with the first equipment is constructed in the virtual enough space, the digital twin model is used for debugging based on the set target operation data, or the operation process of the digital twin model is optimized and adjusted, a target operation scheme which can enable simulation operation data of the digital twin model to accord with the target operation data is obtained, the first equipment in the real space is controlled based on the target operation scheme, the operation performance of the first equipment can reach the target operation performance with high probability, the debugging or optimizing period of the first equipment in the real space can be shortened, and the debugging or optimizing cost is reduced.
Drawings
Fig. 1 is a flowchart of a control method according to a first embodiment of the present application;
fig. 2 is a flowchart of step S102 of the control method according to the embodiment of the present application;
FIG. 3 is a flowchart of a control method according to a second embodiment of the present application;
fig. 4 is a block diagram showing a control apparatus according to a third embodiment of the present application;
fig. 5 is a block diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the drawings.
It will be understood that various modifications may be made to the embodiments of the present application. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the application.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application of unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the application.
Fig. 1 is a flowchart of a digital twin model-based control method according to a first embodiment of the present disclosure, and referring to fig. 1, the digital twin model-based control method according to the embodiment of the present disclosure may specifically include the following steps:
s101, acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, wherein the actual operation data are data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represent at least one actual operation performance of the first equipment.
The real space is the physical space where the real first device is located. The first apparatus may comprise a single apparatus, for example, a single production processing apparatus. The first apparatus may also be a production line system composed of a plurality of apparatuses, for example, a chemical product production system composed of a plurality of apparatuses. In practical applications, the first device may be a plurality of types of devices, and the type of the first device is not limited herein.
The attribute information of the first device may include three-dimensional size information, process parameter information, action process information, and the like of the first device. The first operation scheme is a scheme for controlling the actual operation of the first device, and may be a historical scheme for controlling the actual operation of the first device, or may be an operation scheme of other similar first devices. The actual operational data can characterize at least one actual operational property of the first device, such as production efficiency, product quality, energy consumption, safety factor, and the like. The actual operation data may be historical data generated during operation of the first device based on the first operation scheme or historical data generated during operation of other similar first devices.
S102, constructing a digital twin model of the first device in the virtual space based on the attribute information, the first operation scheme and the actual operation data.
The virtual space is a digital space for constructing the digital twin model, and the virtual space can be constructed by hardware and software of the electronic device together.
The digital twin model is a digital clone body of the first equipment, which is constructed in a virtual space based on the attribute information, the first operation scheme and the actual operation data of the first equipment, has the same three-dimensional size and the same operation mechanism as the first equipment, and even triggers the same alarm or prompt because of the same event or state. The purpose is to make the digital twin model infinitely consistent with the first device so as to simulate the operation process of the first device through the digital twin model.
And S103, acquiring a target operation scheme by using the digital twin model based on the set target operation data. The target operation data represents at least one target operation performance of the digital twin model, and the target operation scheme is used for controlling the digital twin model to operate and enabling the digital twin model to accord with the target operation performance.
The target operation data is a set debugging target or an optimization target, and can represent at least one target operation performance of the digital twin model, such as target productivity, target product quality, target safety factor and the like. In specific implementation, the digital twin model can be debugged or the operation process of the digital twin model can be optimized and adjusted based on the set target operation data, so that the simulation operation performance of the digital twin model conforms to the target operation performance. For example, the production efficiency of the digital twin model is made to reach a given target production efficiency, or the target safety factor of the digital twin model is made to reach a given target safety factor, or the digital twin model is made to occur or a specific event is avoided.
The target operation scheme is a simulation operation scheme for debugging the digital twin model or optimizing and adjusting the operation process of the digital twin model so that the simulation operation performance of the digital twin model conforms to the target operation performance. The target operating scheme may include a full process control scheme of the digital twin model, for example, when the digital twin model is a chemical production system model, the target operating scheme may include control schemes of various links of various equipment of the chemical production line. The target operation scheme may also include only a control scheme for one or more links of the digital twin model, for example, an operation control scheme for a certain device in the production line, or even a control scheme for a specific operation process of a certain device.
And S104, controlling the first equipment to operate based on the target operation scheme so that the first operation data generated in the operation process of the first equipment conforms to the target operation data.
The digital twin model is a twin body of the first equipment in a digital space, and the digital twin model is controlled based on the target operation scheme, so that the digital twin model can accord with the target operation performance.
Of course, it should be noted that the first operation data described herein corresponds to the target operation data, and does not mean that the first operation data completely reaches the target operation data, but is understood to be that the first operation data approaches the target operation data or reaches the target operation data.
According to the control method based on the digital twin model, the digital twin model which is twin with the first equipment is constructed in the virtual enough space, the digital twin model is used for debugging based on the set target operation data, or the operation process of the digital twin model is optimized and adjusted, a target operation scheme which can enable simulation operation data of the digital twin model to accord with the target operation data is obtained, the first equipment in the real space is controlled based on the target operation scheme, the operation performance of the first equipment can reach the target operation performance with high probability, the debugging or optimizing period of the first equipment in the real space can be shortened, and the debugging or optimizing cost is reduced.
As shown in fig. 2, in some embodiments, the step S101, constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operation scheme and the actual operation data, includes:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
Alternatively, a digital twin model of the first device may be constructed in the virtual space based on the three-dimensional size information and the operation mechanism information of the first device. And controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data in the operation process of the digital twin model. And then, comparing the virtual operation data with the actual operation data, and judging whether the error between the virtual operation data and the actual operation data is smaller than a first threshold value, wherein the first threshold value is an acceptable error. If the error between the virtual operation data and the real operation data is smaller than the first threshold, the consistency between the digital twin model and the first equipment is determined to meet the requirement, if the error between the virtual operation data and the real operation data is larger than the first threshold, the consistency between the digital twin model and the first equipment is considered to be not met the requirement, the digital twin model needs to be adjusted, and then the consistency between the digital twin model and the real operation data is judged again until the consistency between the digital twin model and the first equipment meets the requirement. The higher the consistency between the first device and the digital twin model, the higher the probability that the obtained target operation scheme will bring the first device to the target operation performance.
Fig. 3 is a flowchart of a digital twin model-based control method according to a second embodiment of the present disclosure, and referring to fig. 3, the digital twin model-based control method according to the embodiment of the present disclosure may specifically include the following steps:
s201, acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, wherein the actual operation data is data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first equipment.
S202, constructing a digital twin model of the first device in the virtual space based on the attribute information, the first operation scheme and the actual operation data.
The process of acquiring the attribute information, the first operation scheme and the actual operation data, and the process of constructing the digital twin model of the first device in the virtual space are similar to those in the first embodiment, and are not described herein again.
S203, constructing a mechanism model based on the first operation scheme and the actual operation data, wherein the mechanism model can represent the operation mechanism of the digital twin model.
The mechanism model may be a machine learning model, and the machine learning model may be a supervised learning model, an unsupervised learning model, a semi-supervised learning model or a reinforcement learning model, such as a neural network model, and the like, where the type of the machine learning model is not limited.
The mechanism model can be formed by training through the established model architecture. For example, after the first operating scenario and the actual operating data are obtained, a training data set and a validation data set may be constructed based on the first operating scenario and the actual operating data, the training data set and the validation data set each including input data and output data. The input data is formed from the selected actual operating data and the output data is formed from the first operating profile.
When the mechanism model is constructed, firstly, a model architecture can be constructed, then, the model architecture is trained on the basis of input data and output data in a training data set, namely, actual operation data is used as input data, a first operation scheme is used as output data, and the model architecture is trained. And then, inputting the input data in the verification data set into the trained model architecture, comparing the output data of the model architecture with the output data in the verification data set, and determining that the mechanism model training is completed if the output data meets the end condition.
And S204, based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme.
When the target operation data is set, the target operation data may be input to the mechanism model as input data, and at least one second operation scenario output by the mechanism model may be acquired. Since the mechanism model is capable of characterizing the digital twin model and the operating mechanism of the first plant, target operating data is input into the mechanism model, which speculates on one or more second operating scenarios that may be consistent with the target operating data.
S205, controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model.
And when the second operation scheme is obtained, the first equipment is not directly controlled based on the second operation scheme, the digital twin model can be controlled to operate based on each second operation scheme, and second operation data generated in the process that the digital twin model operates based on each second operation scheme is obtained, wherein the second operation data can represent the operation performance of the digital twin model in the process that the digital twin model operates based on the second operation scheme.
S206, under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
In the case that the second operation data is obtained, the second operation data may be compared with the target operation data to check whether each of the second operation schemes enables the digital twin model to achieve the target operation performance. For example, the digital twin model can achieve a predetermined productivity and a predetermined production efficiency. And if the second operation data are in accordance with the target operation data, determining the corresponding second operation scheme as the target operation scheme, and if the second operation data are not in accordance with the target operation data, iteratively obtaining the second operation scheme by using the mechanism model until the second operation data generated by the digital twin model based on at least one second operation scheme are in accordance with the target operation data. Of course, when the second operation data does not meet the target operation data, a scheme that the second operation data is closest to the target operation data can be selected from the at least one second operation scheme as the target operation scheme.
And S207, generating control instructions for controlling the operation processes of the first equipment based on the target operation scheme, and forming an instruction set containing the control instructions.
The target operation scheme is a control scheme capable of characterizing a process or an action process of the first device, and when the target operation scheme is obtained, a control instruction for controlling each operation process of the first device may be generated based on the target operation scheme, for example, a control instruction for controlling each component or assembly in the first device, and executing a start, a shut, or a specific operation at a specific time node, where the control instruction may include, for example, time information and operation information, the time information is used to indicate a time for executing the control instruction, and the time information may be absolute time information or relative time information, and the operation information is used to indicate a specific operation performed by a certain component or assembly of the first device. Then, an instruction set that can be recognized by the edge controller is formed based on these control instructions.
And S208, sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set, so that first operation data generated in the operation process of the first equipment conforms to the target operation data.
Alternatively, the instruction set may be sent to an edge controller via a communication link, the edge controller being disposed on a side of the first device, and the edge controller controlling the first device to operate based on a control instruction in the instruction set. By separating the optimizing equipment from the control equipment, the data processing capacity of the optimizing equipment and the timeliness of the control equipment can be considered.
According to the control method based on the digital twin model, at least one second operation scheme is output by using a mechanism model, the digital twin model is controlled to operate based on each second operation scheme, second operation data generated in the operation process of the digital twin model is obtained, the second operation data is compared with target operation data to judge the operation performance of the digital twin model in the operation process based on each second operation scheme, the second operation scheme with the second operation data conforming to the target operation data is determined as the target operation scheme, and the first equipment is controlled to operate based on the target operation scheme.
Fig. 4 is a block diagram of a digital twin model-based control device according to a third embodiment of the present disclosure, and referring to fig. 4, the digital twin model-based control device according to the embodiment of the present disclosure may specifically include:
an obtaining module 301, configured to obtain attribute information of a first device in a real space, at least one first operation scheme, and actual operation data, where the actual operation data is data generated by controlling operation of the first device based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first device;
a building module 302 configured to build a digital twin model of the first device in a virtual space based on the attribute information, the first operating scenario, and the actual operating data;
the optimizing module 303 is configured to obtain a target operation scheme by using the digital twin model based on set target operation data; wherein the target operational data characterizes at least one target operational performance of the digital twin model, the target operational scenario being for controlling the digital twin model to operate and to comply with the target operational performance;
a control module 304, configured to control the first device to operate based on the target operation scheme, so that the first operation data generated during the operation of the first device conforms to the target operation data.
In some embodiments, the optimizing module 303 is specifically configured to:
based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme;
controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model;
and under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
In some embodiments, the mechanism model is formed by training through an established model architecture, wherein the training process comprises:
and training the model architecture by taking the actual operation data as input data and the first operation scheme as output data.
In some embodiments, the building module 302 is specifically configured to:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
In some embodiments, the control module 304 is specifically configured to:
generating control instructions for controlling each operation process of the first equipment based on the target operation scheme, and forming an instruction set containing each control instruction;
and sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
Referring to fig. 5, an electronic device according to an embodiment of the present application further includes at least a memory 401 and a processor 402, where the memory 401 stores a program, and the processor 402 implements the control method according to any of the above embodiments when executing the program on the memory 401.
It will be apparent to one skilled in the art that embodiments of the present application may be provided as methods, electronic devices, computer-readable storage media, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
The processor may be a general purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof. A general purpose processor may be a microprocessor or any conventional processor or the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
The readable storage medium may be a magnetic disk, an optical disk, a DVD, a USB, a Read Only Memory (ROM), a Random Access Memory (RAM), etc., and the specific form of the storage medium is not limited in this application.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A control method based on a digital twin model is characterized by comprising the following steps:
acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, wherein the actual operation data is data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first equipment;
constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operating plan, and the actual operating data;
acquiring a target operation scheme by utilizing the digital twin model based on set target operation data; wherein the target operational data characterizes at least one target operational property of the digital twin model, the target operational scenario being for controlling the digital twin model to operate and conforming the digital twin model to the target operational property;
and controlling the first equipment to operate based on the target operation scheme so that first operation data generated in the operation process of the first equipment conforms to the target operation data.
2. The method of claim 1, wherein the obtaining a target operating scenario using the digital twin model based on the set target operating data comprises:
based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme;
controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model;
and under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
3. The method of claim 2, wherein the mechanistic model is formed by training an established model architecture, wherein training the established model architecture comprises:
and training the model architecture by taking the actual operation data as input data and the first operation scheme as output data.
4. The method of claim 1, wherein constructing a digital twin model of the first device in virtual space based on the attribute information, the first operational scenario, and the actual operational data comprises:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
5. The method of claim 1, wherein said controlling the operation of the first plant based on the target operation scheme to conform first operational data generated during operation of the first plant to the target operational data comprises:
generating control instructions for controlling each operation process of the first equipment based on the target operation scheme, and forming an instruction set containing each control instruction;
and sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
6. A control apparatus based on a digital twin model, comprising:
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring attribute information of first equipment in a real space, at least one first operation scheme and actual operation data, the actual operation data is data generated by controlling the first equipment to operate based on the first operation scheme, and the actual operation data represents at least one actual operation performance of the first equipment;
a construction module for constructing a digital twin model of the first device in a virtual space based on the attribute information, the first operating scenario, and the actual operating data;
the optimizing module is used for acquiring a target operation scheme by utilizing the digital twin model based on set target operation data; wherein the target operational data characterizes at least one target operational performance of the digital twin model, the target operational scenario being for controlling the digital twin model to operate and to comply with the target operational performance;
and the control module is used for controlling the first equipment to operate based on the target operation scheme so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
7. The apparatus of claim 6, wherein the optimizing module is specifically configured to:
based on the target operation data, utilizing a mechanism model capable of representing the operation mechanism of the digital twin model to obtain at least one second operation scheme;
controlling the digital twin model to operate based on each second operation scheme, and acquiring second operation data generated in the operation process of the digital twin model;
and under the condition that the second operation data accord with the target operation data, determining the corresponding second operation scheme as the target operation scheme.
8. The apparatus of claim 7, wherein the mechanistic model is formed by training an established model architecture, wherein training the established model architecture comprises:
and training the model architecture by taking the actual operation data as input data and the first operation scheme as output data.
9. The apparatus of claim 6, wherein the building module is specifically configured to:
building a data twin model of the first device in a virtual space based on the attribute information;
controlling the digital twin model to operate based on the first operation scheme, and acquiring virtual operation data generated in the operation process of the digital twin model;
determining that the digital twin model is built completely if an error between the virtual operating data and the actual operating data is less than a first threshold.
10. The apparatus of claim 6, wherein the control module is specifically configured to:
generating control instructions for controlling each operation process of the first equipment based on the target operation scheme, and forming an instruction set containing each control instruction;
and sending the instruction set to an edge controller, wherein the instruction set is used for enabling the edge controller to control the first equipment to operate based on the instruction set so as to enable first operation data generated in the operation process of the first equipment to accord with the target operation data.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114153482A (en) * | 2022-02-09 | 2022-03-08 | 深圳市爱云信息科技有限公司 | Deep learning programming method and system based on digital twin DaaS platform |
| CN115164378A (en) * | 2022-06-09 | 2022-10-11 | 中国电子工程设计院有限公司 | Fresh air handling unit regulation and control method based on digital twins and related device |
| CN116088325A (en) * | 2022-12-05 | 2023-05-09 | 广州视声智能股份有限公司 | Digital twinning-based household equipment control method and device and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3376325A1 (en) * | 2017-03-16 | 2018-09-19 | Siemens Aktiengesellschaft | Development of control applications in augmented reality environment |
| CN113657031A (en) * | 2021-08-12 | 2021-11-16 | 杭州英集动力科技有限公司 | Digital twin-based heat supply scheduling automation realization method, system and platform |
-
2021
- 2021-11-29 CN CN202111427678.4A patent/CN113836755A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3376325A1 (en) * | 2017-03-16 | 2018-09-19 | Siemens Aktiengesellschaft | Development of control applications in augmented reality environment |
| CN113657031A (en) * | 2021-08-12 | 2021-11-16 | 杭州英集动力科技有限公司 | Digital twin-based heat supply scheduling automation realization method, system and platform |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114153482A (en) * | 2022-02-09 | 2022-03-08 | 深圳市爱云信息科技有限公司 | Deep learning programming method and system based on digital twin DaaS platform |
| CN115164378A (en) * | 2022-06-09 | 2022-10-11 | 中国电子工程设计院有限公司 | Fresh air handling unit regulation and control method based on digital twins and related device |
| CN116088325A (en) * | 2022-12-05 | 2023-05-09 | 广州视声智能股份有限公司 | Digital twinning-based household equipment control method and device and storage medium |
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