CN117217020A - Industrial model construction method and system based on digital twin - Google Patents
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Abstract
The application relates to the technical field of digital twinning and industrial Internet, and discloses an industrial model construction method and system based on digital twinning, wherein the method comprises the following steps: acquiring regular entity data, application scene environment data and condition matching data in an industrial system, preprocessing the acquired data, and storing the acquired data in a database; converting entity data in a database to form a digital model, and unifying the digital model into a digital twin model; performing design optimization on the digital twin model, performing management test, performing simulation test on the digital twin model to obtain a test result, judging faults and performing virtual maintenance; acquiring irregular change data in an industrial system from a monitoring system by detecting performance and data indexes of the digital twin model, updating and maintaining a database, and improving the data model; inputting the application scene environment data into a digital twin model, operating, detecting faults and vulnerabilities, and repairing and perfecting.
Description
Technical Field
The application relates to the technical fields of digital twinning and industrial Internet, in particular to an industrial model construction method and system based on digital twinning.
Background
Digital twinning is the modeling of physical equipment, products, factories, etc. in the real world by digital technology and integration of the model with sensing and control systems for simulation and optimization of production and manufacturing processes, product design and management. And extracting data from the physical entity model to form a virtual model, and carrying out simulation and analysis based on the virtual model. Through digital twin science and technology, we can more accurately describe object behavior characteristics, better realize equipment sustainability, create more imagination and work efficiency. The industrial model construction is a technology for modeling and displaying objects in the real world by various means, has a wide application range, can be used for simulating and predicting the processes of design, production, operation, maintenance and the like of industrial products, and can also help people to better understand and display the information of the appearance, structure, internal constitution, performance characteristics and the like of the objects, thereby helping people to carry out the works of design, research, development, analysis and the like.
In existing conventional industrial model building methods and systems, repeated inspection and design modification are required, production efficiency is low, and economy of scale cannot be achieved when small batches of models are manufactured. In order to solve the problems, the acceleration of the construction of an industrial model based on digital twinning is a necessary trend, the realization of high-efficiency manufacturing production and the guarantee of reliability and safety.
Disclosure of Invention
The application provides an industrial model construction method and system based on digital twinning, which aims to solve the problem of low production efficiency caused by poor industrial model construction effect in the prior art.
The aim of the application can be achieved by the following technical scheme:
an industrial model construction method based on digital twinning comprises the following steps:
acquiring regular entity data, application scene environment data and condition matching data in an industrial system, preprocessing the acquired data, and storing the acquired data in a database;
converting entity data in a database to form a digital model, and unifying the digital model into a digital twin model through a digital twin platform;
carrying out design optimization on the digital twin model, carrying out management test according to the condition matching data, carrying out simulation test on the digital twin model, and obtaining a test result, thereby judging faults in the test result and carrying out virtual maintenance;
acquiring irregular change data in an industrial system from a monitoring system by detecting performance and data indexes of the digital twin model, updating and maintaining a database, and improving and adjusting the digital twin model by using the updated data;
inputting the application scene environment data into a digital twin model for operation, detecting faults and vulnerabilities, repairing and perfecting the faults and the vulnerabilities to achieve a stable operation state, and further performing simulation prediction on the digital twin model for stable operation.
Preferably, the preprocessing comprises the steps of screening various acquired data, eliminating incomplete data and re-acquiring the incomplete data; the data is filtered, compensation optimized to ensure availability and accuracy of the data.
Preferably, the process of transforming the entity data in the database to form the digitized model further includes moderately simplifying the steps of:
performing sparsification treatment on the entity data, and performing knowledge mechanism collaborative modeling, wherein compressed sensing is common;
classifying and storing data according to a certain rule, and accessing the data and the chemical equation data according to a catalogue form;
deleting big data and repeating data of knowledge mechanism.
Preferably, the method for constructing the industrial model based on digital twin further comprises the following steps:
according to the data in the real-time collection, screening and matching industrial environment, updating the existing entity data in the database;
calculating model data in real time to perfect a digital twin model of the industrial system;
and (3) intelligently optimizing data in the mobilized digital twin platform, and correcting the existing digital twin model.
Preferably, the acquiring entity data of the industrial system includes sensor data, 3D scan data and image data; the application scene environment data comprise external environment data such as temperature, humidity and air pressure; the condition matching data includes athletic performance and load data.
Preferably, the step of optimizing the design of the digital twin model further comprises comparing and verifying the digital twin model with an actual industrial system entity.
Preferably, the simulation prediction refers to analysis of influencing factors which will occur in an entity industrial system, carrying into a digital twin model for risk prediction, and carrying out structural improvement on the industrial system so as to obtain result prediction.
An industrial model construction system based on digital twin performs the industrial model construction method based on digital twin, and comprises a data acquisition end, a model construction module, a simulation optimization module and a function realization module;
the data acquisition end is a sensor network and a 3D scanning system and is used for scanning to acquire data, acquiring real-time environment data through a sensor and preprocessing the data;
the model building module is a data twin platform and is used for optimizing data, moderately simplifying twin system data and building a digital twin model;
the simulation optimization module is used for comparing and supplementing the constructed digital twin model with an industrial system entity and performing simulation test;
the function implementation module is used for carrying out risk prediction and structure adjustment prediction on the updated and optimized digital twin model.
Preferably, the sensor network comprises a sensor terminal and a plurality of sensor nodes.
The beneficial effects of the application are as follows: according to the scheme, the method for constructing the industrial model and the system for constructing the industrial model are optimized, when the industrial model is constructed, a digital twin model is established by combining a digital twin technology, data are obtained from physical entities to form the industrial model, in the construction process, preprocessing is carried out on the data, further processing is carried out on the physical data and scene application data, simplification is carried out, historical data which have no reference meaning are deleted, and improvement and optimization on the digital twin model are achieved on the data. Through digital twinning, a physical model can be completely simulated in a digital environment and subjected to system testing, verification and optimization before the model is created and assembled. The method has the advantages that the efficiency of model construction is improved, the production speed is improved, meanwhile, the constructed industrial model is simulated through data twinning, the industrial model can be more similar to an entity industrial system, factors affecting the industrial system can be tested through the digital twinning model, meanwhile, simulation prediction and evaluation can be carried out on results of changing the system structure, and the entity industrial system improvement is more efficient and high in accuracy.
Drawings
The present application is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a block diagram showing the steps of a method for constructing an industrial model based on digital twinning according to one embodiment of the present application;
FIG. 2 is a simplified illustration of the specific steps in forming a digitized model in a digital twinning-based industrial model building method provided in one embodiment of the present application;
FIG. 3 is a block diagram of a digital twinning-based industrial model building system provided in one embodiment of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the application with reference to the attached drawings and the preferred embodiment.
Digital twinning is a technique that combines a real world physical system with its digital simulation model. The digital twin can also dynamically track and monitor the running state of the physical model and provide the services of prediction, early warning, optimization and the like. The use of digital twinning technology has the advantage of greatly improving the efficiency, reliability and safety of the manufacturing and production fields. In order to better apply the digital twin technology to create an excellent industrial model, an industrial model construction method and system based on digital twin needs to be established to help simulate and coordinate workflow, management data, design, construction, test and optimization, thereby being beneficial to planning large-scale industrial construction, optimizing design efficiently and improving industrial construction efficiency.
An industrial model construction method based on digital twinning, as shown in figure 1, comprises the following steps:
in step S1, entity data, application scene environment data, and condition matching data of rules in an industrial system are acquired, and the acquired data are preprocessed and stored in a database. The entity data acquisition utilizes a 3D scanning technology to acquire data of entities such as equipment in an industrial system, and the image acquisition result comprises the spatial appearance and structure of an object and color data, and is a model building technology. Meanwhile, the sensor is used for collecting data of the environment and the running condition of the industrial system, and the data are stored in the data collecting end for subsequent processing. The method comprises the steps of obtaining historical data parameters from an industrial system, screening collected entity data and environmental and behavior condition data through the historical data parameters in a preprocessing process, removing the part with errors of the data, removing the data with incomplete information, sending the data with the defects collected again to a sensor network, and supplementing the removed data.
In the step S2, the entity data in the database are converted to form a digital model, and the digital model is unified into a digital twin model through a digital twin platform; in step S3, design optimization is carried out on the digital twin model, management testing is carried out according to the condition matching data, simulation testing is carried out on the digital twin model, a test result is obtained, and therefore faults existing in the test result are judged and virtual maintenance is carried out.
In step S4, the irregular change data in the industrial system is obtained from the monitoring system by detecting the performance and the data index of the digital twin model, the database is updated and maintained, and the digital twin model is improved and adjusted by using the updated data; in step S5, the application scene environment data is input into a digital twin model and operated, faults and vulnerabilities are detected, repair is completed, a stable operation state is achieved, and further simulation prediction is performed on the stable operation digital twin model.
Further, in a preferred embodiment of the present application, the preprocessing includes screening the acquired various types of data, removing incomplete data, and re-acquiring the incomplete data; the data is filtered, compensation optimized to ensure availability and accuracy of the data. The situation that clutter exists in data or data information is incomplete exists in the data acquisition process, the defect data is unfavorable for building a model according to the data, the built model can have detail problems in various aspects, adjustment and modification are required continuously, and cost is consumed. The acquired data then needs to be first preprocessed. According to the integrity detection of the scanned image, if the image is incomplete, blurred or blocked by pollution and cannot display clear image information, the image needs to be corrected and supplemented or removed, and the image is collected again until the image can clearly reflect the information.
Further, in a preferred embodiment of the present application, the process of transforming the entity data in the database to form the digitized model further includes moderate simplification, and in general, the model is constructed by a large amount of data, so that the large amount of data includes the entity data, external environmental influencing factors, conditional data and historical data, and an orderly data storage form needs to be established from the large amount of data, which is beneficial to improving the efficiency of model construction. As shown in fig. 2, the simplified specific steps include:
in step S11, the entity data is subjected to sparsification processing, knowledge mechanism collaborative modeling is performed, and compressed sensing is commonly performed; in step S12, data are stored according to a certain rule classification, and the data and the chemical equation data are accessed in a catalog form; in step S13, the big data and the knowledge mechanism repeated data are deleted. The signals are represented mathematically, typically using vector or matrix form. In this process, the data needs to be subjected to dimension reduction processing to meet the requirement of matrix compression. The values in the matrix are encoded and quantized and converted into binary format for efficient compression during transmission. The matrix is compressed by using a sparse representation method, most of the values in the matrix are set to be zero, and only a small number of non-zero values are reserved, so that the data volume can be greatly reduced.
When the data is needed to be processed, the compressed data is decoded and dequantized to restore the original matrix form. The knowledge mechanism collaborative modeling is carried out by inputting the data subjected to the sparsification processing and decoding recovery into a knowledge mechanism model. By simplifying data processing and modeling, the data volume is reduced, the data processing efficiency can be improved, modeling can be cooperated with knowledge mechanism, and possible influences and results of model prediction can be accurately performed.
Further, in a preferred embodiment of the present application, creation of a digital twin model in an industrial manufacturing process may enhance management of the manufacturing process. The digital twin-based industrial model construction is characterized in that various influencing factors are required to be controlled in a manufactured model when each module in an industrial system changes, and the model needs to be adjusted to the environment changing in real time in the process of simulating an industrial production system, so that the established model not only can be similar to entity equipment or a system in appearance, but also can be functionally attached to the entity equipment. In industrial systems where the equipment is the main part, the coordination between the individual equipment is very important, and the information collected from it also needs to contain information on the cooperation between the equipment.
And (3) data collection is carried out on the industrial system of the entity by constructing a sensor network. According to the data in the real-time collection, screening and matching industrial environment, updating the existing entity data in the database;
the digital twin can acquire and monitor the industrial system in real time, and calculate model data in real time to perfect a digital twin model of the industrial system;
and (3) intelligently optimizing data in the mobilized digital twin platform, and correcting the existing digital twin model. Digital twinning can carry out real-time data acquisition and monitoring on industrial system
Further, in a preferred embodiment of the present application, the acquiring entity data of the industrial system includes sensor data, 3D scan data and image data; the application scene environment data comprise external environment data such as temperature, humidity and air pressure; the condition matching data includes athletic performance and load data. The industrial model is constructed by aiming at the problems and influencing factors which are faced by the existing entity industrial system, and the digital twin model can have the same risk resistance and system regulation capability for coping with the influence of external factors as the entity industrial system by bringing in data of various influences.
Further, in a preferred embodiment of the present application, the design optimization of the digital twin model further comprises comparing the digital twin model with actual industrial system entities for verification. The digital twin can comprehensively analyze and optimize the industrial system, and can acquire the running states and problems of all the devices in the industrial system by carrying out big data analysis and artificial intelligence processing on the acquired data, and based on the analysis results, a manager or an operator can prepare a response optimization scheme according to different requirements. And (3) performing a drilling test on the model by using the optimization scheme, subsequently, performing a test adjustment scheme, and finally, improving or adjusting the equipment of the entity to achieve the required production effect. Thus, operators can know possible problems or parts of reduced production efficiency in advance before making development plans and production scales, thereby reducing production cost and waste and improving economic benefit.
Furthermore, in a preferred embodiment of the present application, the digital twin technology not only can efficiently build an intelligent model, but also can predict the result generated by the structural change of the model by means of the digital twin platform, aiming at the limited application scenario and difficult expansion of the existing industrial system entity model. The simulation prediction refers to analyzing influencing factors which will appear in an entity industrial system, carrying the influencing factors into a digital twin model to perform risk prediction, and structurally improving the industrial system to obtain result prediction.
An industrial model construction system based on digital twin performs the aforementioned industrial model construction method based on digital twin, as shown in fig. 3, and includes a data acquisition end, a model construction module, a simulation optimization module and a function implementation module. The model construction system realizes the construction from entity to model as efficiently as possible through the cooperation of each module and the terminal, wherein the model construction system also has a plurality of data screening processes, eliminates or supplements and perfects various problems frequently occurring in the construction of the model in advance, improves the model construction efficiency of the system, constructs a high-quality model for the simulation adjustment of industrial production, and better develops an industrial system.
The data acquisition end is a sensor network and a 3D scanning system, wherein the sensor network is internally provided with a plurality of sensors, and different nodes acquire data at different positions or different angles; the 3D scanning system not only can identify spatial information such as the shape and structure of equipment in an industrial system, but also can accurately acquire color information and reflectance values. Through the interaction of the two, the data information of the industrial system can be quickly and well collected to construct a model system. The data acquisition end is used for scanning to acquire data, acquiring real-time environment data through the sensor, and preprocessing the data.
The distribution of the sensor nodes forms a sensor network with the sensor terminals in a net structure or a radial structure; in the network structure, the sensor nodes can directly communicate with other adjacent nodes to form an interconnected network; in the radial structure, the sensor terminals are located at the center and are directly connected with each sensor node, and the sensor terminals receive data information collected by all the sensor nodes in the divergent distribution.
The model construction module is a data twin platform and is used for optimizing data, moderately simplifying twin system data and constructing a digital twin model. The model construction module comprises a simplification system, the simplification system performs important screening work on model establishment, and the simplification system performs denoising, normalization and standardization processing on data. The data is mapped to a specified range for processing, so that the process can be fast and convenient; the data standardization method has various linear methods, broken line type methods and curve type methods, the model is more scientific to construct through standardized data processing, the requirements of some industrial systems with specified production standards are met, after the standardized processing, the convergence speed of the model can be improved, the precision of the model is improved, meanwhile, the calculation is simplified, the requirements on processing equipment are flexible, and the applicability is strong.
The simulation optimization module is used for comparing and supplementing the constructed digital twin model with an industrial system entity and performing simulation test. The data in the model program written in the simulation test is complemented and perfected according to the updated data, so that the formed model is very similar to an industrial entity. Meanwhile, when the model program is written, corresponding data is written according to the aspect needing improvement, so that obvious advantages and disadvantages of the improved equipment or system can be seen in the operation of the model, the test is used for adjusting, and the loopholes of the written program can be repaired.
The function implementation module is used for carrying out risk prediction and structure adjustment prediction on the updated and optimized digital twin model. The function implementation module in the model building system determines some requirements of the model on functions and subsequent improvements to new functions of the entity industrial system through final testing. Whether the model test function can be efficiently realized or not is performed, so that the problems existing in the actual entity industrial system are improved, the problems of incomplete function test existing in the existing model construction are solved, and the quality and efficiency of model construction are improved.
The present application is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present application.
Claims (9)
1. The industrial model construction method based on digital twinning is characterized by comprising the following steps of:
acquiring regular entity data, application scene environment data and condition matching data in an industrial system, preprocessing the acquired data, and storing the acquired data in a database;
converting entity data in a database to form a digital model, and unifying the digital model into a digital twin model through a digital twin platform;
carrying out design optimization on the digital twin model, carrying out management test according to the condition matching data, carrying out simulation test on the digital twin model, and obtaining a test result, thereby judging faults in the test result and carrying out virtual maintenance;
acquiring irregular change data in an industrial system from a monitoring system by detecting performance and data indexes of the digital twin model, updating and maintaining a database, and improving and adjusting the digital twin model by using the updated data;
inputting the application scene environment data into a digital twin model for operation, detecting faults and vulnerabilities, repairing and perfecting the faults and the vulnerabilities to achieve a stable operation state, and further performing simulation prediction on the digital twin model for stable operation.
2. The method for constructing an industrial model based on digital twinning according to claim 1, wherein the preprocessing includes screening various acquired data, eliminating incomplete data, and re-acquiring; the data is filtered, compensation optimized to ensure availability and accuracy of the data.
3. The method for constructing an industrial model based on digital twinning according to claim 1, wherein the process of transforming the entity data in the database to form the digitized model further comprises a moderate simplification, and the specific steps of simplifying include:
performing sparsification treatment on the entity data, and performing knowledge mechanism collaborative modeling, wherein compressed sensing is common;
classifying and storing data according to a certain rule, and accessing the data and the chemical equation data according to a catalogue form;
deleting big data and repeating data of knowledge mechanism.
4. The digital twinning-based industrial model building method of claim 1, further comprising:
according to the data in the real-time collection, screening and matching industrial environment, updating the existing entity data in the database;
calculating model data in real time to perfect a digital twin model of the industrial system;
and (3) intelligently optimizing data in the mobilized digital twin platform, and correcting the existing digital twin model.
5. The method for constructing a digital twin based industrial model according to claim 1, wherein the acquiring entity data of the industrial system includes sensor data, 3D scan data and image data; the application scene environment data comprise external environment data such as temperature, humidity and air pressure; the condition matching data includes athletic performance and load data.
6. The method of claim 1, wherein the step of optimizing the design of the digital twin model further comprises comparing the digital twin model with actual industrial system entities.
7. The method for constructing a digital twin based industrial model according to claim 1, wherein the simulation prediction comprises:
analyzing influencing factors which will appear in the entity industrial system;
carrying out risk prediction by taking the digital twin model;
and structurally improving the industrial system to obtain a result prediction.
8. An industrial model construction system based on digital twin is characterized in that the method for constructing the industrial model based on digital twin according to any one of claims 1 to 7 is executed and comprises a data acquisition end, a model construction module, a simulation optimization module and a function realization module;
the data acquisition end is a sensor network and a 3D scanning system and is used for scanning to acquire data, acquiring real-time environment data through the sensor network and preprocessing the data;
the model building module is a data twin platform and is used for optimizing data, moderately simplifying twin system data and building a digital twin model;
the simulation optimization module is used for comparing and supplementing the constructed digital twin model with an industrial system entity and performing simulation test;
the function implementation module is used for carrying out risk prediction and structure adjustment prediction on the updated and optimized digital twin model.
9. The digital twinning-based industrial model system of claim 8, wherein the sensor network comprises a sensor terminal and a number of sensor nodes.
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