CN116599857A - Digital twin application system suitable for multiple scenes of Internet of things - Google Patents
Digital twin application system suitable for multiple scenes of Internet of things Download PDFInfo
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
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- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a digital twin application system suitable for multiple scenes of the Internet of things, which comprises the following steps: a basic frame model is built through preset scene data; separating the basic frame models, mapping the basic frame models, and constructing a corresponding number of basic frame models according to the number of scenes; classifying the corresponding basic frame model according to the scene data, collecting the corresponding scene data of the classified basic frame model, and importing the collected data into a classification group to construct a digital twin simulation model; performing intensive training on the digital twin simulation model, and performing parameter matrix analysis on equipment operation parameters, equipment morphological parameters and equipment environment parameters in a scene to map feature vectors in a parameter matrix into the digital twin simulation model, and establishing the feature parameters according to feedback coefficients of the digital twin simulation model; and (3) corresponding the characteristic parameters to the scene data, and importing the corresponding data into the basic framework model.
Description
Technical Field
The invention belongs to the field of Internet of things, and particularly relates to a digital twin application system suitable for multiple scenes of the Internet of things.
Background
At present, digital twinning is a technology suitable for multiple scenes of the Internet of things, and can digitize the real state of a physical system or equipment and simulate and optimize in a virtual space. The background technology of the technology mainly comprises the following aspects:
sensor technology: the digital twin application system needs a large number of sensors to collect data, so that a sensor technology suitable for multiple scenes of the internet of things is needed, such as a sensor network, an internet of things module and the like. Cloud computing technology: the digital twin application system requires a large amount of computing resources to process a large amount of data, and thus a cloud computing technology suitable for multiple scenes of the internet of things, such as cloud computing, big data technology, and the like, is required. Real-time control algorithm: the digital twin application system needs to realize real-time control over a physical system or equipment, so that a real-time control algorithm suitable for multiple scenes of the internet of things is needed. Multidisciplinary fusion: the digital twin application system relates to a plurality of subjects such as physics, computer science, mathematics and the like, and a multidisciplinary fusion algorithm suitable for multiple scenes of the Internet of things is needed. This technique requires the integrated use of a variety of techniques and algorithms to achieve efficient, reliable and high quality applications.
The digital twin application system is suitable for multiple scenes of the Internet of things, and converts the actual physical world into a virtual digital world by utilizing a digital model and a computer technology, so that the monitoring, the control and the optimization of the actual physical system are realized. The internet of things refers to that data is transmitted to a digital twin system through internet of things equipment and a sensor, so that real world monitoring and control are realized. The digital twin application system is a technology for converting a digital model and an actual physical system into a virtual digital model, and can be used for realizing the applications of monitoring, controlling, optimizing and the like of the actual physical system.
Therefore, there is a need for a digital twin application system suitable for multiple scenarios of the internet of things.
Disclosure of Invention
The invention provides a digital twin application system suitable for multiple scenes of the Internet of things, which solves the problems that in the prior art, the digital twin application scenes of the Internet of things are quite many, but the data acquisition of different scenes and the creation of a digital twin model need to be modeled again, so that the data resources are quite wasted and the efficiency is quite low.
The technical scheme of the invention is realized as follows: a digital twin application system suitable for multiple scenarios of the internet of things, comprising:
a basic frame model is built through preset scene data;
separating the basic frame models, mapping the basic frame models, and constructing a corresponding number of basic frame models according to the number of scenes;
classifying the corresponding basic frame model according to the scene data, collecting the corresponding scene data of the classified basic frame model, and importing the collected data into a classification group to construct a digital twin simulation model;
performing intensive training on the digital twin simulation model, and performing parameter matrix analysis on equipment operation parameters, equipment morphological parameters and equipment environment parameters in a scene to map feature vectors in a parameter matrix into the digital twin simulation model, and establishing the feature parameters according to feedback coefficients of the digital twin simulation model;
and (3) corresponding the characteristic parameters to the scene data, importing the corresponding data into a basic framework model, then correlating the operation parameters, the equipment form parameters and the equipment environment parameters with parameter matrixes in the basic framework model, and adjusting the digital twin simulation model according to standard data difference values between the correlation parameters and the basic model to enable the correlation parameters to approach to the standard data parameters.
The system is based on a digital twin technology, and different base frame models are correspondingly constructed according to data under different scenes by constructing the base frame models and separating and mapping the base frame models. And then classifying according to the scene data corresponding to the basic framework model, and collecting data to establish a digital twin simulation model. And mapping the feature vector into a digital twin simulation model by carrying out matrix analysis on the operation parameters, the morphological parameters and the environmental parameters of the equipment in the scene, and establishing the feature parameters. And finally, the characteristic parameters are corresponding to the scene data, the scene data are imported into a basic framework model, and the digital twin simulation model is adjusted by utilizing the standard data difference value between the associated parameters and the basic model, so that the associated parameters are close to the standard data parameters.
As a preferred implementation manner, the basic framework model is a standard parameter model, the parameters in the model are set with standard weight values, the parameter data are mapped into the basic framework model to obtain characteristic data equal to the standard weight values, and the characteristic data are imported into the digital twin simulation model for corresponding adaptation.
As a preferred embodiment, when the basic frame model is divided, each element in the basic frame model is defined as a basic matrix, and after the digital twin simulation model is subjected to intensive training, the characteristic parameters are imported, and the elements in the basic frame model are correspondingly replaced.
As a preferred embodiment, the elements in each row and each column of the base matrix form element groups, and each element in the matrix has corresponding attributes and values, and is analyzed and processed individually as needed.
As a preferred embodiment, the change data of each parameter data is obtained during the reinforcement training, and then repeated attention calculations are performed on the digital twin simulation model to capture the change parameters in the parameter data.
As a preferred embodiment, before the feature parameters correspond to the scene data, the original data needs to be cleaned and preprocessed, and the data with different features is ensured to be processed by cleaning, de-duplication and de-noising the original data.
After the technical scheme is adopted, the invention has the beneficial effects that: the system can realize virtual simulation through a digital twin technology and simulate the running state and the environmental condition of the actual equipment, thereby helping management personnel to better know the working state and the performance of the equipment and improving the running efficiency and the reliability of the equipment. Secondly, the system can realize data acquisition and transmission through the internet of things technology, so that real-time monitoring and control of various parameters and states in an actual scene are realized. The digital twin application system can help a manager to better know the running condition of the equipment, discover problems in time and process the problems, so that the reliability and stability of the equipment are improved. In addition, the system can realize intelligent operation and optimization through a digital twin technology, and the service life and efficiency of equipment are improved. Through the automatic control of the digital twin application system, the efficient equipment maintenance and management can be realized, and errors and risks caused by manual operation are reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, a digital twin application system suitable for multiple scenes of the internet of things includes:
a basic frame model is built through preset scene data;
separating the basic frame models, mapping the basic frame models, and constructing a corresponding number of basic frame models according to the number of scenes;
classifying the corresponding basic frame model according to the scene data, collecting the corresponding scene data of the classified basic frame model, and importing the collected data into a classification group to construct a digital twin simulation model;
performing intensive training on the digital twin simulation model, and performing parameter matrix analysis on equipment operation parameters, equipment morphological parameters and equipment environment parameters in a scene to map feature vectors in a parameter matrix into the digital twin simulation model, and establishing the feature parameters according to feedback coefficients of the digital twin simulation model;
and (3) corresponding the characteristic parameters to the scene data, importing the corresponding data into a basic framework model, then correlating the operation parameters, the equipment form parameters and the equipment environment parameters with parameter matrixes in the basic framework model, and adjusting the digital twin simulation model according to standard data difference values between the correlation parameters and the basic model to enable the correlation parameters to approach to the standard data parameters.
The working flow of the whole system is as follows: presetting scene data and constructing a basic frame model; separating the basic frame models, mapping the basic frame models, and constructing a corresponding number of basic frame models according to the number of scenes; classifying the corresponding basic frame model according to the scene data, collecting the corresponding scene data of the classified basic frame model, and importing the collected data into a classification group to construct a digital twin simulation model; performing intensive training on the digital twin simulation model, and performing parameter matrix analysis on equipment operation parameters, equipment morphological parameters and equipment environment parameters in a scene to map feature vectors in a parameter matrix into the digital twin simulation model, and establishing the feature parameters according to feedback coefficients of the digital twin simulation model; and (3) corresponding the characteristic parameters to the scene data, importing the corresponding data into a basic framework model, then correlating the operation parameters, the equipment form parameters and the equipment environment parameters with parameter matrixes in the basic framework model, and adjusting the digital twin simulation model according to standard data difference values between the correlation parameters and the basic model to enable the correlation parameters to approach to the standard data parameters.
The basic framework model is a standard parameter model, the parameters in the model are set with standard weight values, the parameter data are mapped into the basic framework model to obtain characteristic data equal to the standard weight values, and the characteristic data are imported into the digital twin simulation model for corresponding adaptation. The foundation framework model is suitable for multiple scenes of the Internet of things and comprises a foundation framework body, a sensor and a group of connecting devices. The sensor transmits the data acquired by the equipment to the base frame body, and then the data are processed and analyzed through the characteristic data in the base frame body, so that a target result needing to be monitored and controlled is obtained. Setting the standard weight value refers to the process of mapping the parameter data into the infrastructure model. This process requires the use of certain calculation methods, such as feature extraction algorithms or machine learning algorithms, to calculate the standard weight values of the feature data. Then, the parameter data is imported into the basic framework model, so that a target result which needs to be monitored and controlled is obtained. Computing the standard weight values and importing the underlying framework model are two different processes, but there is some correlation between them. Setting the standard weight value can help us calculate the standard weight value of the feature data, and then the corresponding adaptation is realized by importing the feature data into the digital twin simulation model.
As a preferred embodiment, when the basic frame model is divided, each element in the basic frame model is defined as a basic matrix, and after the digital twin simulation model is subjected to intensive training, the characteristic parameters are imported, and the elements in the basic frame model are correspondingly replaced. The original data is mapped into a new basic matrix, and then imported and replaced through a digital twin simulation model. The digital twin simulation model realizes the corresponding replacement of elements in the basic framework model by learning and training the data. The method has the advantages that the data can be efficiently utilized and converted, and the automation application of the system can be better realized. It should be noted that when using this method, reasonable definition and partitioning of the base matrix are required, which can help to achieve efficient management of the base framework model, and does not affect the functions and effects of the digital twin simulation model. In practical application, the method can flexibly carry out separation processing according to requirements.
The elements in each row and each column of the basic matrix form element groups, and each element in the matrix has corresponding attribute and value and is analyzed and processed independently as required. The basic matrix is a tool for describing a data structure in the scene of the Internet of things, and consists of two parts, namely a row and a column, wherein each row and each column contain a certain number of elements. The elements in the base matrix may be data records, digital codes, or other types of data structures. Each element has corresponding attributes and values, which means that in the base matrix we can analyze and process each element individually as needed to meet different needs. For example, for a digital code element, we can look up a key or function in a digital code line to obtain information about the function and parameters of the element. For a data record element, we can search the corresponding data record row to obtain the information such as the storage position and storage mode of the element. The method for describing the data structure of the Internet of things by using the basic matrix can help us to better understand and apply data, and can improve the efficiency and accuracy of data processing. Meanwhile, the method has certain flexibility and expandability, and can be used for combining and dividing the basic matrix according to actual requirements so as to adapt to different application scenes and requirements.
And when the reinforcement training is carried out, the change data of each parameter data are obtained, and then repeated attention calculation is carried out on the digital twin simulation model so as to capture the change parameters in the parameter data. Based on the attention mechanism in the deep learning algorithm. The attention mechanism is a machine learning algorithm, which can identify key features and relations in data through processing and analyzing the data, so as to realize learning and prediction of unknown data. In the training process of the digital twin simulation model, the model can be helped to learn the relation and the change rule among the parameters by collecting the change data of each parameter data, so that the prediction precision and the adaptability of the model are improved. In particular, this process requires attention calculations on the digital twin simulation model to capture the changing parameters in the parameter data. In particular, this process may include feature extraction, feature selection, feature fusion, and other methods of attention computation on the model to extract key features and relationships that the model needs to be focused on. The variation parameters in the parameter data are then captured by attention calculations and finally these parameter data are input into a digital twin simulation model for training and evaluation.
Before the characteristic parameters correspond to the scene data, the original data needs to be cleaned and preprocessed, and the data with different characteristics is ensured to be processed by cleaning, de-duplication and de-noising the original data. These operations include cleaning and deduplicating the raw data, feature extraction of the data, denoising, etc., to ensure that the processed data meets the needs of the deep learning model. The cleaning and preprocessing steps may help remove redundant or irrelevant data so that the model can better extract and analyze different characteristic parameters. The deduplication operation may reduce the repeatability of the data so that different samples have similar characteristic parameters. The denoising operation can eliminate noise or interference data, so that the model can more accurately fit the data.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. Digital twin application system suitable for thing networking multiple scenario, characterized by comprising:
a basic frame model is built through preset scene data;
separating the basic frame models, mapping the basic frame models, and constructing a corresponding number of basic frame models according to the number of scenes;
classifying the corresponding basic frame model according to the scene data, collecting the corresponding scene data of the classified basic frame model, and importing the collected data into a classification group to construct a digital twin simulation model;
performing intensive training on the digital twin simulation model, and performing parameter matrix analysis on equipment operation parameters, equipment morphological parameters and equipment environment parameters in a scene to map feature vectors in a parameter matrix into the digital twin simulation model, and establishing the feature parameters according to feedback coefficients of the digital twin simulation model;
and (3) corresponding the characteristic parameters to the scene data, importing the corresponding data into a basic framework model, then correlating the operation parameters, the equipment form parameters and the equipment environment parameters with parameter matrixes in the basic framework model, and adjusting the digital twin simulation model according to standard data difference values between the correlation parameters and the basic model to enable the correlation parameters to approach to the standard data parameters.
2. The digital twin application system applicable to multiple scenes of the internet of things as recited in claim 1, wherein: the basic framework model is a standard parameter model, the parameters in the model are set with standard weight values, the parameter data are mapped into the basic framework model to obtain characteristic data equal to the standard weight values, and the characteristic data are imported into the digital twin simulation model for corresponding adaptation.
3. The digital twin application system applicable to multiple scenes of the internet of things as recited in claim 1, wherein: when the basic frame model is divided, each element in the basic frame model is defined as a basic matrix, and after the digital twin simulation model is subjected to reinforcement training, characteristic parameters are imported to correspondingly replace the elements in the basic frame model.
4. A digital twin application system adapted for multiple scenarios in the internet of things as claimed in claim 3, wherein: the elements in each row and each column of the basic matrix form element groups, and each element in the matrix has corresponding attribute and value and is analyzed and processed independently as required.
5. The digital twin application system applicable to multiple scenes of the internet of things as recited in claim 1, wherein: and when the reinforcement training is carried out, the change data of each parameter data are obtained, and then repeated attention calculation is carried out on the digital twin simulation model so as to capture the change parameters in the parameter data.
6. The digital twin application system applicable to multiple scenes of the internet of things as recited in claim 1, wherein: before the characteristic parameters correspond to the scene data, the original data needs to be cleaned and preprocessed, and the data with different characteristics is ensured to be processed by cleaning, de-duplication and de-noising the original data.
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