CN114330026A - Digital twin system simulation method and device - Google Patents
Digital twin system simulation method and device Download PDFInfo
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- CN114330026A CN114330026A CN202210078728.0A CN202210078728A CN114330026A CN 114330026 A CN114330026 A CN 114330026A CN 202210078728 A CN202210078728 A CN 202210078728A CN 114330026 A CN114330026 A CN 114330026A
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Abstract
The invention provides a digital twin system simulation method and a device, wherein the digital twin system simulation method comprises the following steps: carrying out data acquisition on the production and manufacturing process to acquire data information of a production workshop, wherein the data information comprises: dynamic data information and static data information; constructing a digital twin model according to the static data information to obtain a digital twin model of the production workshop; performing a digital twinning simulation test by using a digital twinning model of the production workshop and combining dynamic data information to generate twinning data of the production workshop; analyzing equipment operation and production flow aiming at twin data of a production workshop to obtain prediction information of the production workshop; and controlling and managing the production and manufacturing process by referring to the forecast information of the production workshop. The invention simulates the production workshop in the production and manufacturing process through the digital twin technology, and can better manage the production workshop.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a method and a device for simulating a digital twinning system.
Background
The digital twin is an beyond-reality concept, can be regarded as a digital mapping system of one or more important equipment systems which are dependent on each other, and is a simulation process integrating multidisciplinary, multi-physical quantity, multi-scale and multi-probability by fully utilizing data such as physical models, sensor updating, operation history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected, and the problem of interaction and fusion of an information space and a physical space is realized.
The digital twin is widely applied to intelligent factory construction, intelligent manufacturing, production workshops, distributed intelligent workshops, workshop production processes and intelligent production management and control. The invention provides a digital twin system simulation method and a digital twin system simulation device, which are used for simulating a production workshop in a production and manufacturing process by adopting a digital twin technology and can better manage the production workshop.
Disclosure of Invention
The present invention is directed to a method and an apparatus for simulating a digital twin system, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a digital twin system simulation method, comprising:
carrying out data acquisition on the production and manufacturing process to acquire data information of a production workshop, wherein the data information comprises: dynamic data information and static data information;
constructing a digital twin model according to the static data information to obtain the digital twin model of the production workshop;
performing a digital twinning simulation test by using the digital twinning model of the production workshop and combining the dynamic data information to generate twinning data of the production workshop;
performing equipment operation analysis and production flow analysis on twin data of the production workshop to obtain prediction information of the production workshop;
and controlling and managing the production and manufacturing process by referring to the forecast information of the production workshop.
Preferably, when data acquisition is performed in a production and manufacturing process, comprehensive data acquisition is performed for a production workshop, the static data information is data that does not change in the production and manufacturing process, and is acquired only once in the data acquisition process, the dynamic data information is data that changes at any time in the production and manufacturing process, real-time dynamic data acquisition is performed in the data acquisition process, and data preprocessing is performed for the dynamic data information when the dynamic data information is acquired, and the data preprocessing includes: redundant data removal, data statistical fitting and abnormal data analysis.
Preferably, constructing a digital twin model from the static data information comprises:
constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model; when a space geometric model is constructed, performing space mapping on the production workshop according to the static data information, and constructing the space distribution of the production workshop to form a space geometric module; when an attribute feature model is constructed, sequentially extracting data information of each article or person in the production workshop from the static data information, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, performing space contour construction according to the appearance feature information and the article or person to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the function feature information, and giving the space model of the article or person a function attribute to obtain the attribute feature model; when a production algorithm model is constructed, analyzing the production and manufacturing process, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
and after the space geometric model, the attribute characteristic model and the production algorithm model are obtained, model fusion is carried out on the space geometric model, the attribute characteristic model and the production algorithm model to obtain a digital twin model.
Preferably, the digital twin simulation test is performed by using the digital twin model of the production plant in combination with the dynamic data information, and comprises the following steps: extracting production parameter information from the dynamic data information, wherein the production parameter information comprises: workshop environment information, operator information and product ingredient information; and updating the state of the digital twin model of the production workshop according to the production parameter information, and performing a full-true production simulation test on the digital twin model after the state is updated to generate twin data of the production workshop.
Preferably, when performing equipment operation analysis and production flow analysis on twin data of the production plant, the method comprises the following steps: analyzing the running state of the equipment in the production workshop according to the twin data of the production workshop to obtain the running state data of the equipment, and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information; analyzing the twin data of the production workshop according to the twin data of the production workshop, and optimizing the production process to obtain a production optimization scheme; and carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
A digital twin system simulation apparatus comprising: the system comprises a data acquisition module, a model building module, a digital twin module, a data analysis module and a simulation application module;
the data acquisition module is used for acquiring data in the manufacturing process and acquiring data information of a production workshop, and the acquired data information of the production workshop comprises: dynamic data information and static data information;
the model establishing module is used for establishing a digital twin model according to the static data information acquired by the data acquisition module to obtain the digital twin model of the production workshop;
the digital twin module is used for performing a digital twin simulation test by combining the dynamic data information with the digital twin model of the production workshop established by the model establishing module to generate twin data of the production workshop;
the data analysis module is used for carrying out equipment operation analysis and production flow analysis aiming at twin data of the production workshop to obtain the prediction information of the production workshop;
and the simulation application module is used for controlling and managing the generation and manufacturing process by referring to the prediction information of the production workshop.
Preferably, the data acquisition module performs comprehensive data acquisition for a production workshop when performing data acquisition on a production and manufacturing process, the static data information is data which does not change in the production and manufacturing process, and the dynamic data information is data which changes at any time in the production and manufacturing process; the data acquisition module comprises: the device comprises a static data acquisition unit, a dynamic data acquisition unit and a data preprocessing unit; the static data acquisition unit is used for acquiring data which does not change in the production and manufacturing process to obtain static data information and acquiring the data only once in the data acquisition process;
the dynamic data acquisition unit is used for acquiring real-time dynamic data of data which changes at any time in the production and manufacturing process to obtain dynamic data information;
the data preprocessing unit is used for preprocessing the dynamic data information acquired by the dynamic data acquisition unit in real time, and the data preprocessing comprises the following steps: redundant data removal, data statistical fitting and abnormal data analysis.
Preferably, the model building module when building the digital twin model from the static data information comprises: the method comprises the following steps of constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model, wherein the model establishing module comprises the following steps: the system comprises a first model building unit, a second model building unit, a third model building unit and a model fusion unit;
the first model building unit is used for building a space geometric model, and when the space geometric model is built, space mapping is carried out on the production workshop according to the static data information, and space distribution of the production workshop is built to form a space geometric module;
the second model building unit is used for building an attribute feature model, sequentially extracting data information of each article or person in the production workshop from the static data information when the attribute feature model is built, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, wherein the feature information comprises appearance feature information and functional feature information, performing space contour building on the article or person according to the appearance feature information to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the functional feature information, and giving the space model of the article or person functional attributes to obtain the attribute feature model;
the third model building unit is used for building a production algorithm model, analyzing the production and manufacturing process when the production algorithm model is built, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
the model fusion unit is used for carrying out model fusion on the space geometric model, the attribute feature model and the production algorithm model which are constructed by the first model construction unit, the second model construction unit and the third model construction unit to obtain the digital twin model.
Preferably, the digital twinning module comprises: an information extraction unit and a digital twinning unit;
the information extraction unit is configured to extract information from the dynamic data information to obtain production parameter information, where the production parameter information includes: workshop environment information, operator information and product ingredient information;
and the digital twin unit is used for updating the state of the digital twin model of the production workshop according to the production parameter information obtained by the information extraction unit, and enabling the digital twin model after state updating to perform a full-true production and manufacturing simulation test to generate twin data of the production workshop.
Preferably, the data analysis module comprises: a state analysis unit, an optimization analysis unit and a production analysis unit;
the state analysis unit is used for analyzing the running state of the equipment in the production workshop according to twin data of the production workshop to obtain running state data of the equipment and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information;
the optimization analysis unit is used for analyzing the production and manufacturing process of the twin data of the production workshop according to the twin data of the production workshop, optimizing the production and manufacturing process and obtaining a production optimization scheme;
and the production analysis unit is used for carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram illustrating steps of a digital twin system simulation method according to the present invention;
FIG. 2 is a diagram illustrating a second step in a simulation method of a digital twin system according to the present invention;
FIG. 3 is a schematic diagram of a third step in a simulation method of a digital twin system according to the present invention;
FIG. 4 is a schematic diagram of a digital twin system simulation apparatus according to the present invention;
FIG. 5 is a schematic diagram of a data acquisition module in the digital twin system simulation apparatus according to the present invention;
FIG. 6 is a schematic diagram of a model building module in the digital twin system simulation apparatus according to the present invention;
FIG. 7 is a diagram of a digital twinning module in the digital twinning system simulation apparatus according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, an embodiment of the present invention provides a digital twin system simulation method, including:
step one, carrying out data acquisition on a production and manufacturing process, and acquiring data information of a production workshop, wherein the data information comprises: dynamic data information and static data information;
secondly, constructing a digital twin model according to the static data information to obtain the digital twin model of the production workshop;
thirdly, performing a digital twinning simulation test by using the digital twinning model of the production workshop and combining the dynamic data information to generate twinning data of the production workshop;
fourthly, analyzing equipment operation and production flow aiming at twin data of the production workshop to obtain prediction information of the production workshop;
and fifthly, controlling and managing the generated manufacturing process by referring to the predicted information of the production workshop.
According to the digital twin system simulation method provided by the technical scheme, firstly, data acquisition is carried out on a production and manufacturing process, all information in a production workshop during production and manufacturing is acquired, and data information of the production workshop is obtained, wherein the data information of the production workshop comprises the following steps: dynamic data information and static data information; then, the static data information is used for constructing a production workshop, and a digital twin model is established to obtain the digital twin model of the production workshop; then combining the obtained digital twin model of the production workshop with dynamic data information, and performing a digital twin simulation experiment to generate twin data of the production workshop; then analyzing twin data of the production workshop, and analyzing the running condition of equipment in the production and manufacturing process and the production condition of products in the production and manufacturing process so as to obtain the prediction information of the production workshop; and finally, combining the prediction information of the production workshop obtained through the digital twin system with the actual production and manufacturing process, and controlling and managing the production and manufacturing process by referring to the prediction information of the production workshop.
According to the technical scheme, the digital twinning technology is used for carrying out digital twinning on the actual production and manufacturing process, so that the simulation and prediction can be carried out on the production and manufacturing process, a solution can be found in time when a fault or a problem occurs in the production and manufacturing process, the influence of the fault on the production and manufacturing is reduced, the loss caused by the fault is reduced, the production and manufacturing process can be optimized and managed, the production and manufacturing efficiency is improved, and the unnecessary cost consumption of the production and manufacturing is reduced. And the data information of the production workshop is acquired by data acquisition and divided into dynamic data information and static data information, so that the data acquisition of the static data information for a plurality of times can be effectively avoided, the effectiveness of the data acquisition is improved, the time waste caused by the repeated acquisition of the static data information is avoided, the applicability of a digital twin model constructed according to the static data information can be improved, the digital twin simulation test process can be repeatedly used in the digital twin simulation test process, the digital twin simulation test can be realized for a plurality of times only by combining different dynamic data information, the efficiency of the digital twin simulation test is improved, in addition, the control and management of the production process by referring to the prediction information of the production workshop not only can optimize the production process and improve the production process, but also can predict equipment, therefore, the device fault in the production and manufacturing process is avoided and prevented, and the fault loss is reduced.
In one embodiment of the present invention, when data is collected in a manufacturing process, data is collected comprehensively for a manufacturing shop, static data information is data that does not change in the manufacturing process, and is collected only once in the data collection process, dynamic data information is data that changes at any time in the manufacturing process, real-time dynamic data collection is performed in the data collection process, and data preprocessing is performed for the dynamic data information when the dynamic data information is collected, the data preprocessing includes: redundant data removal, data statistical fitting and abnormal data analysis.
Above-mentioned technical scheme carries out data acquisition to the manufacturing process when, carries out comprehensive data acquisition to the workshop through data acquisition device to obtain the data information of workshop, wherein, data information includes: the dynamic data information is data which can not change in the production and manufacturing process, so that the data acquisition is only needed to be carried out once, the dynamic data information is data which can change at any time in the production and manufacturing process, so that the real-time information acquisition is carried out on the dynamic data during the data acquisition, the data preprocessing is also carried out on the dynamic data information after the dynamic data information is acquired, redundant data removal, data statistics fitting and abnormal data analysis are carried out on the dynamic data information during the data preprocessing, irrelevant information in the dynamic data information is removed through the redundant data removal, the irrelevant information refers to information factors which change in the data information but can not influence the production and manufacturing, and the dynamic data information is fitted and predicted through the data statistics fitting, the statistics of the dynamic data information is realized, and the abnormal data analysis is used for determining the abnormal data information in the dynamic data information, so that the preprocessed dynamic data information is combined with a digital twin model of a production workshop.
The technical scheme can improve the effectiveness of data acquisition and avoid time waste caused by acquiring static data information for a plurality of times during data acquisition, can enable a digital twin simulation test to be more consistent with an actual production manufacturing process by acquiring real-time information of dynamic data information, effectively reduces errors of the digital twin simulation test and the actual production manufacturing process, improves the accuracy of the digital twin simulation test, reduces surplus of irrelevant information in the dynamic data information by preprocessing the dynamic data information after the dynamic data information is acquired, reduces the complexity of the dynamic data information, can reflect fluctuation trend and change of the dynamic data information, finds abnormality of the dynamic data information in time, and avoids producing defective products, thereby improving the quality of production and manufacture.
In one embodiment provided by the present invention, constructing a digital twin model according to the static data information includes:
constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model; when a space geometric model is constructed, performing space mapping on the production workshop according to the static data information, and constructing the space distribution of the production workshop to form a space geometric module; when an attribute feature model is constructed, sequentially extracting data information of each article or person in the production workshop from the static data information, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, performing space contour construction according to the appearance feature information and the article or person to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the function feature information, and giving the space model of the article or person a function attribute to obtain the attribute feature model; when a production algorithm model is constructed, analyzing the production and manufacturing process, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
and after the space geometric model, the attribute characteristic model and the production algorithm model are obtained, model fusion is carried out on the space geometric model, the attribute characteristic model and the production algorithm model to obtain a digital twin model.
According to the technical scheme, at least a space geometric model, an attribute characteristic model and a production algorithm model are constructed when the digital twin model is constructed, and space mapping is carried out on a production workshop according to static data information when the space geometric model is constructed, so that the space distribution of the production workshop is constructed to form a space geometric module; when an attribute feature model is constructed, sequentially extracting data information of each article or person in a production workshop from static data information, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, wherein the feature information comprises appearance feature information and functional feature information, performing space contour construction according to the appearance feature information and the article or person to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the functional feature information, and giving the space model of the article or person functional attributes to obtain the attribute feature model; when the production algorithm model is constructed, analyzing the production and manufacturing process, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model; and after the space geometric model, the attribute characteristic model and the production algorithm model are obtained, model fusion is carried out on the space geometric model, the attribute characteristic model and the production algorithm model, so that the digital twin model is obtained.
According to the technical scheme, the production workshop is mapped by constructing a space geometric model, constructing an attribute characteristic model and constructing a production algorithm model, so that the digital twin model can highly restore the production workshop, and the accuracy of digital twin simulation is improved.
In an embodiment of the present invention, the performing a digital twin simulation test by using the digital twin model of the production shop and combining the dynamic data information includes: extracting production parameter information from the dynamic data information, wherein the production parameter information comprises: workshop environment information, operator information and product ingredient information; and updating the state of the digital twin model of the production workshop according to the production parameter information, and performing a full-true production simulation test on the digital twin model after the state is updated to generate twin data of the production workshop.
In the technical scheme, in the process of carrying out a digital twin simulation test by combining a digital twin model of a production workshop with dynamic data information, production parameter information is firstly extracted from the dynamic data information, wherein the production parameter information at least comprises: workshop environment information, operator information and product ingredient information; and then, updating the state of the digital twin model of the production workshop according to the workshop environment information, the operator information and the product ingredient information, so that a full-true production and manufacturing simulation test is performed according to the product ingredient information through the digital twin model of the production workshop under the workshop environment information according to the operator information, and twin data of the production workshop are generated.
According to the technical scheme, production parameter information is extracted from dynamic data information, so that surplus irrelevant information in the dynamic data information can be removed, key factor information influencing production products in the production and manufacturing processes is determined, the production workshop and the information space for carrying out the digital twin simulation test can be completely integrated, the complexity of combination of the digital twin model and the dynamic data information of the production workshop is reduced, the accuracy of combination of the digital twin model and the dynamic data information of the production workshop is improved, and the accuracy of the full-true production and manufacturing simulation test is further improved.
In an embodiment of the present invention, when performing equipment operation analysis and production flow analysis on twin data of the production plant, the method includes: analyzing the running state of the equipment in the production workshop according to the twin data of the production workshop to obtain the running state data of the equipment, and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information; analyzing the twin data of the production workshop according to the twin data of the production workshop, and optimizing the production process to obtain a production optimization scheme; and carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
When the technical scheme is used for carrying out equipment operation analysis and production flow analysis on twin data of a production workshop, the method at least comprises the following steps: analyzing the running state of equipment in the production workshop according to twin data of the production workshop to obtain running state data of the equipment, and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information; analyzing the production and manufacturing process of the twin data of the production workshop according to the twin data of the production workshop, and optimizing the production and manufacturing process to obtain a production optimization scheme; and carrying out production energy analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
When the capacity efficiency of the production workshop is obtained, the calculation is carried out by the following formula:
in the above formula, D table type capacity efficiency, Y represents capacity in twin data of a production plant, UiData representing the ith ingredient in the product ingredient information, CiShowing the conversion rate of the ith ingredient in the production manufacturing process, T showing the predicted time to complete the production, TjThe time consumed in the jth production step in the twin data simulation test process is represented, alpha represents the mapping ratio of the twin data simulation test to the actual production process, n represents the total time consumed in the production and the manufacture of the twin data simulation test, and l represents the average production in the twin data simulation testThe time consumed by one product, N represents the number of products obtained by a twin data simulation test, and c represents the product percent of pass.
According to the technical scheme, the equipment failure prediction is realized by analyzing the running state of the equipment in the production workshop according to twin data in the production workshop, so that the loss caused by equipment failure can be reduced in the production and manufacturing process, and the failure evasion can be effectively performed in the production and manufacturing process by predicting the equipment failure; the productivity efficiency of a production workshop can be determined by carrying out productivity analysis, and the production and manufacturing progress can be known; in addition, the production and manufacturing process analysis can find out the place where the production and manufacturing process can be improved, so that the production scheme is optimized aiming at the production process, the production and manufacturing process is better, and the benefit of a production manufacturer is improved.
The invention provides a digital twin system simulation device, which comprises: the system comprises a data acquisition module, a model building module, a digital twin module, a data analysis module and a simulation application module;
the data acquisition module is used for acquiring data in the manufacturing process and acquiring data information of a production workshop, and the acquired data information of the production workshop comprises: dynamic data information and static data information;
the model establishing module is used for establishing a digital twin model according to the static data information acquired by the data acquisition module to obtain the digital twin model of the production workshop;
the digital twin module is used for performing a digital twin simulation test by combining the dynamic data information with the digital twin model of the production workshop established by the model establishing module to generate twin data of the production workshop;
the data analysis module is used for carrying out equipment operation analysis and production flow analysis aiming at twin data of the production workshop to obtain the prediction information of the production workshop;
and the simulation application module is used for controlling and managing the generation and manufacturing process by referring to the prediction information of the production workshop.
The digital twin system simulation device improved by the technical scheme comprises: the system comprises a data acquisition module, a model building module, a digital twin module, a data analysis module and a simulation application module; when the digital twin system simulation device carries out the digital twin system simulation, firstly, data acquisition is carried out on the production and manufacturing process through a data acquisition module, all information in a production workshop during the production and manufacturing is acquired, and the data information of the production workshop is obtained, wherein the data information of the production workshop comprises the following steps: dynamic data information and static data information; then the model building module is used for building a production workshop by using the static data information, and a digital twin model is built to obtain the digital twin model of the production workshop; then combining the obtained digital twin model of the production workshop with dynamic data information by a digital twin module, and carrying out a digital twin simulation experiment so as to generate twin data of the production workshop; then, the data analysis module analyzes twin data of the production workshop, and analyzes the running condition of equipment in the production and manufacturing process and the production condition of products in the production and manufacturing process so as to obtain the prediction information of the production workshop; and finally, the simulation application module combines the prediction information of the production workshop obtained through the digital twin system with the actual production and manufacturing process, and controls and manages the production and manufacturing process by referring to the prediction information of the production workshop.
According to the technical scheme, the digital twin technology is adopted by the digital twin system simulation device to carry out digital twin aiming at the actual production and manufacturing process, so that simulation and prediction can be carried out aiming at the production and manufacturing process, a solution can be found in time when a fault or a problem occurs in the production and manufacturing process, the influence of the fault on the production and manufacturing is reduced, the loss caused by the fault is reduced, the production and manufacturing process can be optimized and managed, the production and manufacturing efficiency is improved, and the unnecessary cost consumption of the production and manufacturing is reduced. And the data acquisition module is used for acquiring data information of the production workshop and dividing the data information into dynamic data information and static data information, so that the data acquisition of the static data information for multiple times can be effectively avoided, the effectiveness of the data acquisition is improved, the time waste caused by the multiple acquisition of the static data information is avoided, the model establishment module is used for establishing a digital twin model according to the static data information, the applicability of the model can be improved, the model can be repeatedly used in the process of a digital twin simulation test, the digital twin simulation test can be realized by only combining different dynamic data information in the digital twin module, the efficiency of the digital twin simulation test is improved, in addition, the simulation application module controls and manages the production process according to the forecast information of the production workshop, the production and manufacturing process can be optimized, the production and manufacturing process is improved, and equipment can be predicted, so that equipment faults in the production and manufacturing process are avoided and prevented, and fault loss is reduced.
In one embodiment provided by the invention, the data acquisition module performs comprehensive data acquisition aiming at a production workshop when performing data acquisition on the production and manufacturing process, the static data information is data which does not change in the production and manufacturing process, and the dynamic data information is data which changes at any time in the production and manufacturing process; the data acquisition module comprises: the device comprises a static data acquisition unit, a dynamic data acquisition unit and a data preprocessing unit; the static data acquisition unit is used for acquiring data which does not change in the production and manufacturing process to obtain static data information and acquiring the data only once in the data acquisition process;
the dynamic data acquisition unit is used for acquiring real-time dynamic data of data which changes at any time in the production and manufacturing process to obtain dynamic data information;
the data preprocessing unit is used for preprocessing the dynamic data information acquired by the dynamic data acquisition unit in real time, and the data preprocessing comprises the following steps: redundant data removal, data statistical fitting and abnormal data analysis.
Data acquisition module among the above-mentioned technical scheme carries out comprehensive data acquisition to the workshop when carrying out data acquisition to the manufacturing process, and data acquisition module includes moreover: the device comprises a static data acquisition unit, a dynamic data acquisition unit and a data preprocessing unit; the dynamic data acquisition unit is connected with the data preprocessing unit, and the data acquisition module acquires data which does not change in the production and manufacturing process through the static data acquisition unit when acquiring data to obtain static data information, wherein the static data information is data which does not change in the production and manufacturing process and is acquired only once in the data acquisition process; the dynamic data information is data which changes at any time in the production and manufacturing process and needs to be acquired in real time, the dynamic data information is acquired by acquiring the dynamic data information in real time through a dynamic data acquisition unit, the dynamic data information acquired in real time by the dynamic data acquisition unit is preprocessed by a data preprocessing unit after the dynamic data information is acquired, wherein the data preprocessing at least comprises redundant data removing, data statistics fitting and abnormal data analysis, irrelevant information in the dynamic data information is removed through the redundant data removing, the irrelevant information refers to information factors which change but do not influence the production and manufacturing, the dynamic data information is fitted and predicted through the data statistics fitting, and the statistics of the dynamic data information is realized, and determining abnormal data information in the dynamic data information through abnormal data analysis, so that the preprocessed dynamic data information is combined with a digital twin model of the production workshop.
The technical scheme can improve the effectiveness of data acquisition and avoid time waste caused by acquiring static data information for once by the static data acquisition unit when the data is acquired, can make a digital twin simulation test more consistent with an actual production and manufacturing process by acquiring the real-time information of the dynamic data information by the dynamic data acquisition unit, effectively reduces the error between the digital twin simulation test and the actual production and manufacturing process, improves the accuracy of the digital twin simulation test, can reduce the surplus of irrelevant information in the dynamic data information by preprocessing the data of the dynamic data information by the data preprocessing unit after the dynamic data information is acquired, reduces the complexity of the dynamic data information, can reflect the fluctuation trend and change of the dynamic data information, and finds the abnormity of the dynamic data information in time, the product produced is not defective, thereby improving the quality of production.
In one embodiment of the present invention, the model building module when building the digital twin model according to the static data information includes: the method comprises the following steps of constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model, wherein the model establishing module comprises the following steps: the system comprises a first model building unit, a second model building unit, a third model building unit and a model fusion unit;
the first model building unit is used for building a space geometric model, and when the space geometric model is built, space mapping is carried out on the production workshop according to the static data information, and space distribution of the production workshop is built to form a space geometric module;
the second model building unit is used for building an attribute feature model, sequentially extracting data information of each article or person in the production workshop from the static data information when the attribute feature model is built, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, wherein the feature information comprises appearance feature information and functional feature information, performing space contour building on the article or person according to the appearance feature information to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the functional feature information, and giving the space model of the article or person functional attributes to obtain the attribute feature model;
the third model building unit is used for building a production algorithm model, analyzing the production and manufacturing process when the production algorithm model is built, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
the model fusion unit is used for carrying out model fusion on the space geometric model, the attribute feature model and the production algorithm model which are constructed by the first model construction unit, the second model construction unit and the third model construction unit to obtain the digital twin model.
The technical scheme at least comprises the steps of constructing a space geometric model, constructing an attribute characteristic model and constructing a production algorithm model when constructing the digital twin model, and the model establishing module comprises the following steps: the system comprises a first model building unit, a second model building unit, a third model building unit and a model fusion unit; the model building module builds a space geometric model through the first model building units respectively when building a digital twin model according to the static data information, and performs space mapping on a production workshop according to the static data information when building the space geometric model to build the space distribution of the production workshop to form a space geometric module; constructing an attribute feature model through a second model construction unit, sequentially extracting data information of each article or person in the production workshop from the static data information when the attribute feature model is constructed, extracting features according to the attributes of the articles or persons to obtain feature information of the articles or persons, wherein the feature information comprises appearance feature information and functional feature information, constructing a spatial contour of the articles or persons according to the appearance feature information to obtain a spatial model of the articles or persons, writing the spatial model of the articles or persons into the spatial model of the articles or persons through coding according to the functional feature information, and giving functional attributes to the spatial model of the articles or persons to obtain the attribute feature model; a production algorithm model is built through a third model building unit, when the production algorithm model is built, the production manufacturing process is analyzed, the step flow of production manufacturing is determined, and a production algorithm is generated according to the step flow of production manufacturing to obtain a production algorithm model; and then carrying out model fusion on the space geometric model, the attribute characteristic model and the production algorithm model through a model fusion unit to obtain a digital twin model.
According to the technical scheme, the production workshop is mapped by the first model building unit, the second model building unit and the third model building unit through respectively building a space geometric model, a property characteristic model and a production algorithm model, so that the production workshop is highly reduced by the digital twin model, the accuracy of digital twin simulation is improved, the pattern distribution of the production workshop is completely mapped by building the space geometric model, objects and characters in the production workshop are highly reduced under the production and manufacturing effects by building the property characteristic model, the difference between the digital twin simulation and the actual production workshop is reduced by the production algorithm model repeated-carving production and manufacturing method, the accuracy of the digital twin is improved, and the space geometric model building unit, the second model building unit and the third model building unit can simultaneously carry out the space geometric model, And (3) constructing an attribute characteristic model and a production algorithm model, so that the efficiency of constructing the digital twin model by the model construction module is improved.
In one embodiment, the digital twinning module comprises: an information extraction unit and a digital twinning unit;
the information extraction unit is configured to extract information from the dynamic data information to obtain production parameter information, where the production parameter information includes: workshop environment information, operator information and product ingredient information;
and the digital twin unit is used for updating the state of the digital twin model of the production workshop according to the production parameter information obtained by the information extraction unit, and enabling the digital twin model after state updating to perform a full-true production and manufacturing simulation test to generate twin data of the production workshop.
The digital twin module in the technical scheme comprises: an information extraction unit and a digital twinning unit; in the process of carrying out a digital twin simulation test by combining a digital twin model of a production workshop with dynamic data information, a digital twin module firstly extracts production parameter information from the dynamic data information through an information extraction unit, wherein the production parameter information at least comprises: workshop environment information, operator information and product ingredient information; and then, updating the state of the digital twin model of the production workshop in the digital twin unit according to the workshop environment information, the operator information and the product ingredient information, so that a full-true production and manufacturing simulation test is performed according to the product ingredient information through the digital twin model of the production workshop under the workshop environment information and according to the operator information, and twin data of the production workshop are generated.
According to the technical scheme, the production parameter information is extracted from the dynamic data information through the information extraction unit, the surplus of irrelevant information in the dynamic data information can be removed, the key factor information influencing the production products in the production and manufacturing processes is determined, the production workshop and the information space for carrying out the digital twin simulation test can be completely integrated, the complexity of combining the digital twin model and the dynamic data information of the production workshop is reduced, the accuracy of combining the digital twin model and the dynamic data information of the production workshop is improved, and the accuracy of carrying out the full-true production and manufacturing simulation test by the digital twin unit is improved.
In one embodiment of the present invention, the data analysis module includes: a state analysis unit, an optimization analysis unit and a production analysis unit;
the state analysis unit is used for analyzing the running state of the equipment in the production workshop according to twin data of the production workshop to obtain running state data of the equipment and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information;
the optimization analysis unit is used for analyzing the production and manufacturing process of the twin data of the production workshop according to the twin data of the production workshop, optimizing the production and manufacturing process and obtaining a production optimization scheme;
and the production analysis unit is used for carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
The data analysis module in the above technical solution at least includes: a state analysis unit, an optimization analysis unit and a production analysis unit; when the data analysis module analyzes equipment operation and production flow of twin data in the production workshop, the state analysis unit analyzes the operation state of the equipment in the production workshop according to the twin data in the production workshop to obtain the operation state data of the equipment, and performs fault prediction on the equipment according to the operation state data of the equipment to obtain equipment fault prediction information; analyzing the production and manufacturing process of twin data of the production workshop through an optimization analysis unit according to the twin data of the production workshop, and optimizing the production and manufacturing process to obtain a production optimization scheme; and (4) carrying out production energy analysis on the twin data of the production workshop through the production analysis unit according to the twin data of the production workshop, and obtaining the capacity efficiency of the production workshop.
When the capacity efficiency of the production workshop is obtained, the calculation is carried out by the following formula:
in the above formula, D table type capacity efficiency, Y represents capacity in twin data of a production plant, UiData representing the ith ingredient in the product ingredient information, CiShowing the conversion rate of the ith ingredient in the production manufacturing process, T showing the predicted time to complete the production, TjThe time consumed in the jth production step in the twin data simulation test process is represented, alpha represents the mapping ratio of the twin data simulation test to the actual production process, N represents the total time consumed in the twin data simulation test for production and manufacturing, l represents the time consumed for averagely producing a product in the twin data simulation test, N represents the number of products obtained in the twin data simulation test, and c represents the product yield.
According to the technical scheme, the state analysis unit analyzes the running state of the equipment in the production workshop according to twin data in the production workshop so as to realize fault prediction of the equipment, further reduce loss caused by equipment faults in the production and manufacturing process, and effectively avoid faults in the production and manufacturing process by performing fault prediction on the equipment; the productivity analysis is carried out through the production analysis unit, so that the productivity efficiency of a production workshop can be determined, and the production and manufacturing progress can be known; in addition, the optimization analysis unit can be used for analyzing the production and manufacturing process to find out the place where the production and manufacturing process can be improved, so that the production scheme is optimized aiming at the production process, the production and manufacturing process is better, and the benefit of a production manufacturer is improved.
It will be understood by those skilled in the art that the first, second and third references in the present invention are merely different stages of application.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A digital twin system simulation method, characterized in that the digital twin system simulation method comprises:
carrying out data acquisition on the production and manufacturing process to acquire data information of a production workshop, wherein the data information comprises: dynamic data information and static data information;
constructing a digital twin model according to the static data information to obtain the digital twin model of the production workshop;
performing a digital twinning simulation test by using the digital twinning model of the production workshop and combining the dynamic data information to generate twinning data of the production workshop;
performing equipment operation analysis and production flow analysis on twin data of the production workshop to obtain prediction information of the production workshop;
and controlling and managing the production and manufacturing process by referring to the forecast information of the production workshop.
2. The simulation method of a digital twin system according to claim 1, wherein when data collection is performed on a production process, data collection is performed on a production shop in a comprehensive manner, the static data information is data that does not change during the production process, the static data information is collected only once during the data collection, the dynamic data information is data that changes at any time during the production process, real-time dynamic data collection is performed during the data collection, and when the dynamic data information is collected, data preprocessing is performed on the dynamic data information, the data preprocessing includes: redundant data removal, data statistical fitting and abnormal data analysis.
3. The digital twin system simulation method of claim 1, wherein constructing a digital twin model from the static data information comprises:
constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model; when a space geometric model is constructed, performing space mapping on the production workshop according to the static data information, and constructing the space distribution of the production workshop to form a space geometric module; when an attribute feature model is constructed, sequentially extracting data information of each article or person in the production workshop from the static data information, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, performing space contour construction according to the appearance feature information and the article or person to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the function feature information, and giving the space model of the article or person a function attribute to obtain the attribute feature model; when a production algorithm model is constructed, analyzing the production and manufacturing process, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
and after the space geometric model, the attribute characteristic model and the production algorithm model are obtained, model fusion is carried out on the space geometric model, the attribute characteristic model and the production algorithm model to obtain a digital twin model.
4. The digital twin system simulation method of claim 1, wherein performing a digital twin simulation test using a digital twin model of the production plant in combination with the dynamic data information comprises: extracting production parameter information from the dynamic data information, wherein the production parameter information comprises: workshop environment information, operator information and product ingredient information; and updating the state of the digital twin model of the production workshop according to the production parameter information, and performing a full-true production simulation test on the digital twin model after the state is updated to generate twin data of the production workshop.
5. The digital twin system simulation method according to claim 1, wherein when performing equipment operation analysis and production flow analysis on the twin data of the production plant, the method comprises: analyzing the running state of the equipment in the production workshop according to the twin data of the production workshop to obtain the running state data of the equipment, and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information; analyzing the twin data of the production workshop according to the twin data of the production workshop, and optimizing the production process to obtain a production optimization scheme; and carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
6. A digital twin system simulation apparatus, characterized in that the digital twin system simulation apparatus comprises: the system comprises a data acquisition module, a model building module, a digital twin module, a data analysis module and a simulation application module;
the data acquisition module is used for acquiring data in the manufacturing process and acquiring data information of a production workshop, and the acquired data information of the production workshop comprises: dynamic data information and static data information;
the model establishing module is used for establishing a digital twin model according to the static data information acquired by the data acquisition module to obtain the digital twin model of the production workshop;
the digital twin module is used for performing a digital twin simulation test by combining the dynamic data information with the digital twin model of the production workshop established by the model establishing module to generate twin data of the production workshop;
the data analysis module is used for carrying out equipment operation analysis and production flow analysis aiming at twin data of the production workshop to obtain the prediction information of the production workshop;
and the simulation application module is used for controlling and managing the generation and manufacturing process by referring to the prediction information of the production workshop.
7. The digital twin system simulation device according to claim 6, wherein the data collection module collects data for a production workshop comprehensively when collecting data in a production and manufacturing process, the static data information is data which does not change in the production and manufacturing process, and the dynamic data information is data which changes at any time in the production and manufacturing process; the data acquisition module comprises: the device comprises a static data acquisition unit, a dynamic data acquisition unit and a data preprocessing unit; the static data acquisition unit is used for acquiring data which does not change in the production and manufacturing process to obtain static data information and acquiring the data only once in the data acquisition process;
the dynamic data acquisition unit is used for acquiring real-time dynamic data of data which changes at any time in the production and manufacturing process to obtain dynamic data information;
the data preprocessing unit is used for preprocessing the dynamic data information acquired by the dynamic data acquisition unit in real time, and the data preprocessing comprises the following steps: redundant data removal, data statistical fitting and abnormal data analysis.
8. The digital twin system simulation apparatus of claim 6, wherein the model building module when building a digital twin model from the static data information comprises: the method comprises the following steps of constructing a space geometric model, constructing an attribute feature model and constructing a production algorithm model, wherein the model establishing module comprises the following steps: the system comprises a first model building unit, a second model building unit, a third model building unit and a model fusion unit;
the first model building unit is used for building a space geometric model, and when the space geometric model is built, space mapping is carried out on the production workshop according to the static data information, and space distribution of the production workshop is built to form a space geometric module;
the second model building unit is used for building an attribute feature model, sequentially extracting data information of each article or person in the production workshop from the static data information when the attribute feature model is built, performing feature extraction according to the attribute of the article or person to obtain feature information of the article or person, wherein the feature information comprises appearance feature information and functional feature information, performing space contour building on the article or person according to the appearance feature information to obtain a space model of the article or person, writing the space model of the article or person into the space model of the article or person through coding according to the functional feature information, and giving the space model of the article or person functional attributes to obtain the attribute feature model;
the third model building unit is used for building a production algorithm model, analyzing the production and manufacturing process when the production algorithm model is built, determining the step flow of production and manufacturing, and generating a production algorithm according to the step flow of production and manufacturing to obtain the production algorithm model;
the model fusion unit is used for carrying out model fusion on the space geometric model, the attribute feature model and the production algorithm model which are constructed by the first model construction unit, the second model construction unit and the third model construction unit to obtain the digital twin model.
9. The digital twinning system simulation device of claim 6, wherein the digital twinning module comprises: an information extraction unit and a digital twinning unit;
the information extraction unit is configured to extract information from the dynamic data information to obtain production parameter information, where the production parameter information includes: workshop environment information, operator information and product ingredient information;
and the digital twin unit is used for updating the state of the digital twin model of the production workshop according to the production parameter information obtained by the information extraction unit, and enabling the digital twin model after state updating to perform a full-true production and manufacturing simulation test to generate twin data of the production workshop.
10. The digital twin system simulation apparatus of claim 6, wherein the data analysis module comprises: a state analysis unit, an optimization analysis unit and a production analysis unit;
the state analysis unit is used for analyzing the running state of the equipment in the production workshop according to twin data of the production workshop to obtain running state data of the equipment and predicting the fault of the equipment according to the running state data of the equipment to obtain equipment fault prediction information;
the optimization analysis unit is used for analyzing the production and manufacturing process of the twin data of the production workshop according to the twin data of the production workshop, optimizing the production and manufacturing process and obtaining a production optimization scheme;
and the production analysis unit is used for carrying out capacity analysis on the twin data of the production workshop according to the twin data of the production workshop to obtain the capacity efficiency of the production workshop.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114707428A (en) * | 2022-06-01 | 2022-07-05 | 中科航迈数控软件(深圳)有限公司 | Method, device, terminal and storage medium for simulating unobservable links of numerical control machine tool |
| CN114777651A (en) * | 2022-05-05 | 2022-07-22 | 东北大学 | Airplane surface assembly quality detection method based on digital twinning |
| CN114926583A (en) * | 2022-05-09 | 2022-08-19 | 北京工业大学 | Site simulation management method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114777651A (en) * | 2022-05-05 | 2022-07-22 | 东北大学 | Airplane surface assembly quality detection method based on digital twinning |
| CN114777651B (en) * | 2022-05-05 | 2024-01-26 | 东北大学 | Digital twinning-based aircraft surface assembly quality detection method |
| CN114926583A (en) * | 2022-05-09 | 2022-08-19 | 北京工业大学 | Site simulation management method and device, electronic equipment and storage medium |
| CN114707428A (en) * | 2022-06-01 | 2022-07-05 | 中科航迈数控软件(深圳)有限公司 | Method, device, terminal and storage medium for simulating unobservable links of numerical control machine tool |
| CN114707428B (en) * | 2022-06-01 | 2022-09-02 | 中科航迈数控软件(深圳)有限公司 | Method, device, terminal and storage medium for simulating unobservable links of numerical control machine tool |
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