CN116579164A - Digital twin intelligent manufacturing system - Google Patents
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Abstract
The invention discloses a digital twin intelligent manufacturing system, relates to the technical field of intelligent manufacturing, and solves the technical problems that the consistency of a digital twin model and an actual scene is difficult to be completely ensured in the prior art, and the production process of enterprises is influenced; according to the invention, whether the loss occurs in a plurality of simulation objects is judged by combining the historical production information; if yes, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database; the invention can detect the consistency of the virtual and the reality in real time, and update the digital twin model in time according to the detection result, thereby improving the production process of enterprises; when the simulation physical object is damaged, the basic parameters of the simulation physical object are collected again, and the corresponding relation between the loss and the basic parameters is established; constructing a loss mapping model according to the corresponding relation between the loss and the basic parameters; the invention can obtain the corresponding adjustment information according to the damage of a plurality of simulation objects, and realize the rapid update of the digital twin model.
Description
Technical Field
The invention belongs to the field of intelligent manufacturing, and particularly relates to a digital twin intelligent manufacturing system.
Background
Smart manufacturing is a manufacturing process with smart sensing and communication capabilities that is capable of carrying the information needed throughout the supply chain and product lifecycle. The core problem of intelligent manufacturing is to achieve the integration of the physical world and the information world, so digital twin technology is also gradually applied to the field of intelligent manufacturing.
At present, a digital twin technology is applied to an intelligent manufacturing process by a plurality of enterprises, simulation entities corresponding to the enterprise production line are mapped into a digital twin space, adjustment parameters are generated through standard operation ranges of the simulation entities, and operation parameters of each simulation entity in the production line are adjusted. In the prior art, a simulation body is built only at the beginning of the building according to the acquired data, and the simulation body is difficult to update in time in the long-term production and manufacturing process, so that the digital twin model is not completely consistent with an actual scene, and the production process of an enterprise is influenced.
The invention provides a digital twin intelligent manufacturing system to solve the problems.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a digital twin intelligent manufacturing system which is used for solving the technical problem that the consistency of a digital twin model and an actual scene is difficult to be completely ensured in the prior art, and the production process of an enterprise is influenced.
To achieve the above object, a first aspect of the present invention provides a digital twin intelligent manufacturing system, including a hub control module, and a data interaction module connected thereto; the central control module acquires basic information of a plurality of simulation objects through a database connected with the data interaction module; mapping the basic information to a plurality of simulation entities in a digital twin space to generate a digital twin model; the central control module adjusts the operation of a plurality of simulation objects based on the digital twin model, and acquires real-time production information through a data sensor connected with the data interaction module in the adjusting process; comparing the real-time production information with the analog production information of the digital twin model to judge whether the real-time production information is normal or not; when the real-time production information is abnormal, judging whether the simulation objects are worn or not according to the historical production information; and if so, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database.
Applying digital twinning techniques to smart manufacturing, problems that may occur in smart manufacturing can be simulated and predicted by digital twinning models. Various losses of each simulation entity (namely production equipment) in the manufacturing industry occur in a long-term production process, and the information is not updated in the digital twin model, so that the simulation result of the digital twin model cannot be applied to actual production, and the intelligent manufacturing process is affected.
Comparing actual production information with simulated production information obtained by simulating a digital twin model, and judging whether the real-time production information is normal or not; if the simulation object is abnormal, whether the simulation object is caused by loss is further judged. If the abnormality is caused by the loss, the simulation entity in the digital twin model can be updated according to the loss, and the consistency of the digital twin model and the actual situation is maintained.
The basic information of the present invention includes basic parameters and scene information. The basic parameters are the appearance characteristics and the physical characteristics of the simulation physical object, and the physical characteristics can be directly extracted from the database according to the appearance characteristics; the physical characteristics mainly comprise information such as size, function, loss degree and the like. The scene information comprises the position relation among the simulation objects and is used for simulating the sequence relation among the simulation objects.
The central control module is communicated and/or electrically connected with the data interaction module; the data interaction module is respectively communicated and/or electrically connected with the database and the data sensor; the database is used for storing basic information and loss mapping models of the simulation objects, and the data sensor is used for collecting production information of the simulation objects. The central control module is responsible for data processing, and the data interaction module is responsible for the collection of various data.
Preferably, the mapping the basic information to a plurality of simulation entities in the digital twin space to create a digital twin model includes: extracting basic parameters and scene information in the basic information; constructing a plurality of simulation entities based on the basic parameters, and constructing a simulation scene based on scene information; combining a plurality of simulation entities with a simulation scene to generate a digital twin model.
After all simulation objects are arranged according to the production flow, basic parameters and scene information can be extracted. And mapping the basic parameters and the scene information into a digital twin space by utilizing a digital twin technology, constructing a simulation entity and a simulation scene, and further generating a digital twin model. The simulation objects in the invention are equivalent to intelligent production equipment on the production line, and a plurality of simulation objects can simulate and construct the production line; the simulation entity is the mapping of the simulation entity in the digital twin space.
According to the invention, the digital twin model is constructed and obtained according to the simulation entity and the simulation reality, so that not only can the actual working state of the simulation entity be simulated, but also the linkage relation among the simulation entities can be accurately expressed. That is to say, the process of product production by simulating real objects can be restored as far as possible through the constructed digital twin model, once simulation is inconsistent with reality, abnormality can be rapidly positioned, and the influence on the production process is reduced.
Preferably, the central control module adjusts the operation of a plurality of simulation objects based on a digital twin model, and the central control module comprises: inputting the acquired production planning information into a digital twin model to acquire simulation parameters of a plurality of simulation entities; and carrying out dynamic parameter adjustment on the plurality of simulation objects forming a mapping relation with the plurality of simulation entities based on the simulation parameters, and controlling the plurality of simulation objects to run according to the dynamically adjusted parameters.
According to the invention, the working processes of a plurality of simulation entities are simulated through a digital twin model according to the production planning information as input, and the optimal simulation parameters of each simulation entity are obtained. And dynamically adjusting the corresponding simulation objects according to the simulation parameters to realize that each simulation object on the production line works in an optimal state. The production planning information comprises raw material information, time limit requirements and the like, the data of the raw material information, the time limit requirements and the like are input into the digital twin model, and the digital twin model carries out simulation planning on the production process according to the production planning information, so that the optimization of the production process can be realized.
Preferably, the comparing the real-time production information with the analog production information of the digital twin model to determine whether the real-time production information is normal includes: extracting real-time production information, and extracting analog production information corresponding to the real-time production information from the digital twin model; judging whether the difference between the real-time production information and the simulated production information exceeds a set threshold value; if yes, judging that the real-time production information is abnormal; if not, judging that the real-time production information is normal, and recording the real-time production information.
The invention obtains real-time production information after dynamically adjusting the working parameters of each simulation object. Comparing the real-time production information with the analog production information of the digital twin model, and judging that the actual production process has a problem if the difference between the real-time production information and the analog production information is large. There are two main problems raised here, one is production error due to the loss of the simulation real object, and the other is production error due to emergency. Aiming at production errors caused by the loss of the simulation real object, the loss is recorded and uploaded to the digital twin model, and the simulation real object is subjected to parameter adjustment after the digital twin model is optimized, so that the production errors can be reduced. Aiming at production errors caused by emergency, the emergency needs to be identified and processed in time; the emergency situations are various, such as improper intervention operation of staff, structural damage of simulation objects, and the like.
The real-time production information in the present invention includes product parameters and efficiency parameters. The product parameters refer to the size, the precision and the like of the product and are used for evaluating whether the produced product is qualified or not; the efficiency parameter refers to the production efficiency of the product, which affects the delivery of the product. It is noted that the real-time production information can be obtained from the terminal of the whole production line, and the product which can be delivered is obtained at the moment, and whether the whole production line meets the requirement is judged through the product parameters and the efficiency parameters; semi-finished product information can be obtained from adjacent simulation objects, and whether part of production lines meet requirements is judged through product parameters and efficiency parameters.
Preferably, the determining whether the plurality of simulation objects have loss by combining the historical production information includes: extracting historical production information of a plurality of simulation objects; judging whether the real-time production information is changed or not according to the change trend of the gap between the historical production information and the corresponding simulated production information; if yes, judging that the simulation physical object has faults; and if not, judging that the simulation object has loss.
The mapped analog product information exists in the digital twin model, whether the real-time production information corresponding to the semi-finished product or the final product. And comparing the two parameters to judge whether the product parameters or the production efficiency meet the requirements. Once the real-time production information is changed, if the information is changed, the information belongs to a fault, and the information cannot be adjusted through a digital twin model; otherwise, the loss can be adjusted by a digital twin model.
The invention judges whether the real-time production information is changed based on the historical production information, and the thinking is mainly to acquire the change trend of the difference between the historical production information and the corresponding simulated production information and judge whether the difference between the real-time production information and the corresponding simulated production information is above the change trend. If yes, the difference caused by the loss of the simulation objects can be judged; otherwise, judging the difference caused by the fault of the simulation object.
Preferably, the updating the several simulation entities in the digital twin model according to the loss mapping model stored in the database includes: extracting initial simulation physical losses and simulation physical losses when abnormality occurs, and integrating according to the differences of the initial simulation physical losses and the simulation physical losses to obtain loss differences; and inputting the loss gap into a loss mapping model to obtain adjustment information of a plurality of simulation entities, and updating the plurality of simulation entities based on the adjustment information.
When a plurality of simulation objects just start to produce, the influence of the loss on the product parameters and the efficiency parameters can be ignored. With the progress of production work, the loss of the simulation real object is gradually increased, and the real-time production information is abnormal. The difference obtained by comparing the real-time production information at the time of abnormality with the initial real-time production information (the initial simulated production information is also possible) is the loss difference. And searching and matching in the loss mapping model according to the loss gap to obtain adjustment information, so as to update a plurality of simulation entities in the digital twin model and ensure consistency of virtual and reality.
It should be noted that, the adjustment information is consistent with the content attribute of the basic information, and includes the basic parameter and the scene information. In general, scene information in adjustment information changes little, and urgency can be ignored. And updating a plurality of simulation entities in the digital twin model based on the adjustment information, wherein the main content of the update is the loss degree of the plurality of simulation entities. The updated digital twin model is basically consistent with the actual situation, so that real data can be obtained when analog production is performed, and a data base is laid for subsequent production planning.
Preferably, the loss mapping model is constructed based on loss experimental data of a simulation entity, and comprises: simulating the actual production process of a plurality of simulation objects; when the simulation object is damaged, the basic parameters of the simulation object are collected again, and the corresponding relation between the loss and the basic parameters is established; and constructing a loss mapping model according to the corresponding relation between the loss and the basic parameters.
The simulation objects in the invention belong to the same production line, so that the loss of any simulation object can influence the real-time production information. The method sequentially sets the loss of different degrees for the simulation objects, combines the loss of a plurality of simulation objects, and generates a loss group; and acquiring basic parameters corresponding to a plurality of simulation objects under each loss group (the basic parameters are the basic parameters of the simulation objects). And taking the loss group as standard input data, taking basic parameters corresponding to a plurality of simulation objects as standard output data, and training the artificial intelligent model to obtain a loss mapping model. The artificial intelligence model includes a BP neural network model or an RBF neural network model.
Compared with the prior art, the invention has the beneficial effects that:
1. when the real-time production information is abnormal, the invention combines the history production information to judge whether the simulation objects have loss; if yes, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database; the invention can detect the consistency of the virtual and the reality in real time, and update the digital twin model in time according to the detection result, thereby improving the production process of enterprises.
2. The invention simulates the actual production process of a plurality of simulation objects; when the simulation object is damaged, the basic parameters of the simulation object are collected again, and the corresponding relation between the loss and the basic parameters is established; constructing a loss mapping model according to the corresponding relation between the loss and the basic parameters; the invention can obtain the corresponding adjustment information according to the damage of a plurality of simulation objects, and realize the rapid update of the digital twin model.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system principle of the present invention;
FIG. 2 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, an embodiment of a first aspect of the present invention provides a digital twin intelligent manufacturing system, including a hub control module, and a data interaction module connected thereto; the central control module acquires basic information of a plurality of simulation objects through a database connected with the data interaction module; mapping the basic information to a plurality of simulation entities in a digital twin space to generate a digital twin model; the central control module adjusts the operation of a plurality of simulation objects based on the digital twin model, and acquires real-time production information through a data sensor connected with the data interaction module in the adjusting process; comparing the real-time production information with the analog production information of the digital twin model to judge whether the real-time production information is normal or not; when the real-time production information is abnormal, judging whether the simulation objects are worn or not according to the historical production information; and if so, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database.
The first step of the embodiment is that a central control module obtains basic information of a plurality of simulation objects through a database connected with a data interaction module; and mapping the basic information to a plurality of simulation entities corresponding to the digital twin space to generate a digital twin model.
Basic information of a plurality of simulation objects on a production line is extracted from a database, mapping is carried out in a digital twin space according to the basic information, and a digital twin model is obtained. The digital twin model can simulate the actual process of the production line through necessary physical input. On the basis of physical input, the digital twin model is continuously simulated and optimized, and each simulation entity on the production line is ensured to run in an optimal state, so that simulation parameters of each simulation entity can be extracted. In reality, the simulation objects corresponding to the simulation object phase mapping are adjusted according to the simulation parameters, so that the production line can be ensured to be in the optimal running state.
However, in the actual production process, the abrasion of the simulation objects and the connection problem of the adjacent simulation objects exist, which causes various problems in the actual production process, and the problems cannot be reflected in the digital twin model in time, so that the digital twin model cannot maintain higher mapping precision.
The second step of the embodiment is that the central control module adjusts the operation of a plurality of simulation objects based on a digital twin model, and in the adjusting process, real-time production information is obtained through a data sensor connected with the data interaction module; comparing the real-time production information with the analog production information of the digital twin model to judge whether the real-time production information is normal or not; when the real-time production information is abnormal, judging whether the simulation objects are worn or not according to the historical production information.
After the optimal simulation parameters are obtained according to the digital twin model simulation, the simulation physical object of the production line is regulated according to the simulation parameters, and the theoretical simulation physical object is in an optimal running state, so that the obtained production information is consistent with the simulation situation.
And acquiring real-time production information of the products of the production line in the actual running process, wherein the real-time production information is mainly used for judging whether each quality inspection standard and production efficiency of the products meet the requirements. The reference standard is the analog production information of the digital twin model.
When judging whether the real-time production information is normal, the real-time production information is mainly judged according to the difference between the real-time production information and the simulated production information, specifically, if the product specification, the production efficiency and the like are large, the real-time production information is judged to be abnormal. When the real-time production information is abnormal, further judging the reason of the abnormality is needed; and when the real-time production information is normal, recording and storing.
The specific steps for judging the cause of the abnormality are as follows: acquiring historical production information, calculating the difference between the historical production information and corresponding simulated production information, and fitting and establishing a difference change curve according to the difference at a plurality of moments; when the difference between the real-time production information and the corresponding simulation production information is on a difference change curve, judging that the difference is caused by the loss of the simulation object; otherwise, it is determined that the gap is caused by an emergency. The gap change curve is established based on single data, such as any one of quality inspection data and production efficiency data; the emergency refers to the abnormality of the simulation physical object caused by external force factors, such as the fault of the simulation physical object caused by forced intervention of staff. The simulation object can be understood as various production devices on a production line.
The third step in this embodiment is to update several simulation entities in the digital twin model according to the loss mapping model stored in the database.
When the abnormality of the real-time production information is judged to be due to loss, the difference between the loss and the loss of a plurality of simulation objects when the abnormality occurs is taken as the loss difference. And inputting the loss gap into the loss mapping model to acquire adjustment information of a plurality of simulation entities, and updating the plurality of simulation entities in the digital twin model.
The loss mapping model is actually built in one-time simulation, loss values are set for a plurality of simulation objects in a loss range, and a plurality of loss groups can be obtained by permutation and combination. And simulating optimal basic parameters of a plurality of simulation objects corresponding to each loss group, and training an artificial intelligent model by combining the loss groups to obtain a loss mapping model. According to the loss mapping model, basic parameters corresponding to the loss group in the current state can be obtained, and then updating of a plurality of simulation entities in the digital twin model is achieved, so that consistency of the virtual and reality is guaranteed.
The working principle of the invention is as follows: acquiring basic information of a plurality of simulation objects; and mapping the basic information to a plurality of simulation entities corresponding to the digital twin space to generate a digital twin model. Adjusting the operation of a plurality of simulation objects based on a digital twin model, and acquiring real-time production information through a data sensor connected with a data interaction module in the adjusting process; and comparing the real-time production information with the analog production information of the digital twin model to judge whether the real-time production information is normal or not. When the real-time production information is abnormal, judging whether a plurality of simulation objects are worn or not according to the historical production information; and if so, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. A digital twin intelligent manufacturing system comprises a central control module and a data interaction module connected with the central control module; the method is characterized in that:
the central control module acquires basic information of a plurality of simulation objects through a database connected with the data interaction module; mapping the basic information to a plurality of simulation entities in a digital twin space to generate a digital twin model; wherein the basic information comprises basic parameters and scene information;
the central control module adjusts the operation of a plurality of simulation objects based on the digital twin model, and acquires real-time production information through a data sensor connected with the data interaction module in the adjusting process; comparing the real-time production information with the analog production information of the digital twin model to judge whether the real-time production information is normal or not; and
when the real-time production information is abnormal, judging whether a plurality of simulation objects are worn or not according to the historical production information; if yes, updating a plurality of simulation entities in the digital twin model according to the loss mapping model stored in the database; the loss mapping model is constructed based on the loss experimental data of the simulation object.
2. The digital twin intelligent manufacturing system according to claim 1, wherein the mapping of the base information to the digital twin space creates a corresponding plurality of simulation entities, and generating the digital twin model comprises:
extracting basic parameters and scene information in the basic information; the basic parameters comprise the appearance characteristics and the physical characteristics of the simulation physical objects, and the scene information comprises the positions of the simulation physical objects and the connection modes of the simulation physical objects;
constructing a plurality of simulation entities based on the basic parameters, and constructing a simulation scene based on scene information; combining a plurality of simulation entities with a simulation scene to generate a digital twin model.
3. The digital twin smart manufacturing system of claim 1, wherein the hub control module adjusts the operation of the plurality of simulated entities based on the digital twin model, comprising:
inputting the acquired production planning information into a digital twin model to acquire simulation parameters of a plurality of simulation entities; wherein the production planning information comprises raw material information and time limit requirements;
and carrying out dynamic parameter adjustment on the plurality of simulation objects forming a mapping relation with the plurality of simulation entities based on the simulation parameters, and controlling the plurality of simulation objects to run according to the dynamically adjusted parameters.
4. The digital twin intelligent manufacturing system according to claim 1, wherein comparing the real-time production information with the analog production information of the digital twin model to determine whether the real-time production information is normal comprises:
extracting real-time production information, and extracting analog production information corresponding to the real-time production information from the digital twin model; wherein the real-time production information comprises product parameters and efficiency parameters;
judging whether the difference between the real-time production information and the simulated production information exceeds a set threshold value; if yes, judging that the real-time production information is abnormal; if not, judging that the real-time production information is normal, and recording the real-time production information.
5. The digital twin intelligent manufacturing system according to claim 1, wherein the determining whether a plurality of simulation entities are worn in combination with the historical production information comprises:
extracting historical production information of a plurality of simulation objects;
judging whether the real-time production information is changed or not according to the change trend of the gap between the historical production information and the corresponding simulated production information; if yes, judging that the simulation physical object has faults; and if not, judging that the simulation object has loss.
6. The digital twin intelligent manufacturing system according to claim 1, wherein updating the plurality of simulation entities in the digital twin model according to the database stored loss map model comprises:
extracting initial simulation physical losses and simulation physical losses when abnormality occurs, and integrating according to the differences of the initial simulation physical losses and the simulation physical losses to obtain loss differences;
inputting the loss gap into a loss mapping model to obtain adjustment information of a plurality of simulation entities, and updating the plurality of simulation entities based on the adjustment information; wherein the adjustment information is consistent with the content attribute of the base information.
7. The digital twin intelligent manufacturing system according to claim 1, wherein the loss mapping model is constructed based on loss experimental data of simulation objects, comprising:
simulating the actual production process of a plurality of simulation objects; when the simulation object is damaged, the basic parameters of the simulation object are collected again, and the corresponding relation between the loss and the basic parameters is established;
and constructing a loss mapping model according to the corresponding relation between the loss and the basic parameters.
8. A digital twin smart manufacturing system as defined in claim 1, wherein the hub control module is in communication and/or electrical connection with the data interaction module; the data interaction module is respectively communicated and/or electrically connected with the database and the data sensor;
the database is used for storing basic information and loss mapping models of the simulation objects, and the data sensor is used for collecting production information of the simulation objects.
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| CN202310557581.8A CN116579164A (en) | 2023-05-17 | 2023-05-17 | Digital twin intelligent manufacturing system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116954180A (en) * | 2023-09-21 | 2023-10-27 | 广东鑫钻节能科技股份有限公司 | Multi-station cooperative control system and method based on digital energy blasting station |
| CN117289624A (en) * | 2023-09-20 | 2023-12-26 | 广东省电信规划设计院有限公司 | Data acquisition method and system applied to data twinning technology |
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2023
- 2023-05-17 CN CN202310557581.8A patent/CN116579164A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117289624A (en) * | 2023-09-20 | 2023-12-26 | 广东省电信规划设计院有限公司 | Data acquisition method and system applied to data twinning technology |
| CN117289624B (en) * | 2023-09-20 | 2024-03-15 | 广东省电信规划设计院有限公司 | Data acquisition method and system applied to data twinning technology |
| CN116954180A (en) * | 2023-09-21 | 2023-10-27 | 广东鑫钻节能科技股份有限公司 | Multi-station cooperative control system and method based on digital energy blasting station |
| CN116954180B (en) * | 2023-09-21 | 2023-12-12 | 广东鑫钻节能科技股份有限公司 | Multi-station cooperative control system and method based on digital energy blasting station |
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