CN115730114A - Equipment data visualization method based on digital twinning - Google Patents
Equipment data visualization method based on digital twinning Download PDFInfo
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- CN115730114A CN115730114A CN202211662354.3A CN202211662354A CN115730114A CN 115730114 A CN115730114 A CN 115730114A CN 202211662354 A CN202211662354 A CN 202211662354A CN 115730114 A CN115730114 A CN 115730114A
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Abstract
The invention relates to the technical field of equipment monitoring, in particular to an equipment data visualization method based on digital twins, which comprises the following steps: s1, obtaining modeling data of equipment, and establishing a corresponding equipment model; s2, acquiring attribute data of the equipment, adding the attribute data into an equipment model, and constructing a digital twin model of the equipment; s3, acquiring running data of the equipment through the IOT sensor based on the service requirement; s4, processing and analyzing the operation data of the equipment to obtain the operation state of the equipment; and S5, displaying the attribute data and the running state of the equipment in real time by combining the digital twin model of the equipment. The invention can quickly and accurately know specific abnormal contents when equipment is abnormal, and becomes a problem to be solved urgently at present.
Description
Technical Field
The invention relates to the technical field of equipment monitoring, in particular to an equipment data visualization method based on digital twins.
Background
In order to ensure the smooth production, enterprises need to know the conditions of equipment in place. If people are sent to check and maintain only regularly, the equipment is still largely used for a long time when the state is abnormal but the equipment does not break down, so that the fault occurs in the production process and the normal production of enterprises is influenced.
Therefore, a plurality of enterprises can collect the data of the equipment and send the data to the monitoring end, so that the equipment supervision personnel can know the running condition of the equipment at any time. However, the types of the devices of the enterprises are often complicated and the number of the devices is large, which results in very large and messy collected data, and when facing the messy data with large number, the device supervisor is easy to miss or misjudge. Based on the method, some enterprises customize data analysis software, and judge whether the equipment has the abnormality or the fault by analyzing the collected data, so that the equipment supervision personnel can relatively clearly know the equipment with the abnormality or the fault in the current equipment. However, the equipment supervisor only knows that there is an abnormality or malfunction in the equipment, and the specific location where there is a malfunction or abnormality still needs to be known by careful inspection before the corresponding equipment. Moreover, due to the fact that the equipment is more and complicated, most workers cannot comprehensively master the characteristics of all the equipment, and therefore when one equipment is abnormal, the specific abnormality of the equipment is still difficult to lock quickly and accurately, and the follow-up maintenance still consumes time and labor.
Therefore, how to quickly and accurately know the specific abnormal content when the equipment is abnormal becomes a problem to be solved urgently at present.
Disclosure of Invention
In view of the above deficiencies of the prior art, the present invention provides a method for visualizing device data based on digital twin, which can quickly and accurately understand specific abnormal content when a device is abnormal, and thus, the method becomes a problem to be solved urgently.
In order to solve the technical problems, the invention adopts the following technical scheme:
a digital twin-based device data visualization method comprises the following steps:
s1, obtaining modeling data of equipment, and establishing a corresponding equipment model;
s2, acquiring attribute data of the equipment, adding the attribute data into an equipment model, and constructing a digital twin model of the equipment;
s3, acquiring running data of the equipment through the IOT sensor based on the service requirement;
s4, processing and analyzing the operation data of the equipment to obtain the operation state of the equipment;
and S5, displaying the attribute data and the running state of the equipment in real time by combining the digital twin model of the equipment.
Preferably, S1 comprises:
s101, obtaining modeling data required by modeling according to equipment to be monitored;
and S102, constructing a three-dimensional entity model of each component of the equipment through modeling software according to modeling data, and assembling to obtain an equipment model.
Preferably, S2 comprises:
s201, acquiring attribute data of equipment, and attaching the attribute data to an equipment model through a secondary development interface;
s202, importing the equipment model and the attribute data thereof into a model library of the digital twin system, and binding the unique codes of the equipment to form a one-to-one correspondence relationship between the twin model and the equipment data to obtain the digital twin model of the equipment.
Preferably, in S201, the attribute data includes name, number, type, outer size, and security.
Preferably, S3 comprises:
s301, selecting operation data to be monitored based on the service scene requirements of the equipment;
s302, selecting a corresponding IOT sensor based on the operation data to be monitored, and binding each data in the operation data to be monitored with at least one IOT sensor; and the operation data corresponding to the equipment is collected through the IOT sensor.
Preferably, S4 comprises:
s401, preprocessing the operation data of the equipment collected in the S3 to obtain available operation data of the equipment;
s402, analyzing the available operation data according to the service scene requirements to obtain corresponding state information.
Preferably, in S5, the attribute data of the device is displayed in a manner of visual pop-up window.
Preferably, in S5, the operation data and the operation state of the device are displayed in a visual pop-up window manner; the operation state comprises a normal state, a fault abnormal state, an early warning state and a fault state; if the operation state of a certain part of the equipment is abnormal, the color of the part is displayed as an abnormal color.
Compared with the prior art, the invention has the following beneficial effects:
1. after a digital twin model of the equipment is constructed, the corresponding IOT sensor is selected based on specific service requirements to collect the required operation data of the equipment, whether the equipment is abnormal or failed is analyzed, and fusion display is carried out by matching with the digital twin model. The relevant staff can know the real-time condition of each part of the equipment through the digital twin model.
In the prior art, the use of a digital twin model is basically used for the simulation of the equipment. The real-time specific abnormal display only appears on the monitoring of address disaster detection (such as earthquake), and can only display a certain area. The invention creatively provides a new using method of a digital twin model, and the abnormal monitoring is carried out on the equipment at the microscopic level of the level of parts.
By the mode, once a certain part of the equipment is abnormal or fails, displayed contents are not limited to the abnormal early warning or the written description, specific abnormal parts can be displayed through the digital twin model, and even if workers are not familiar with the equipment, the specific abnormal contents can be locked quickly and accurately through the display and the written description.
In summary, the present invention can quickly and accurately know specific abnormal content when an abnormality occurs in a device, and becomes a problem to be solved at present.
2. When the operation is abnormal, the digital twin model of the equipment displays the abnormal part of the equipment by using abnormal colors, compared with a simple text description, the colors are distinguished more obviously and easily perceived by workers, the workers can quickly and clearly know whether each part of the equipment is normal or not, the abnormal part can be locked at the first time when the equipment is abnormal, and the targeted processing can be quickly carried out.
3. Besides displaying the running state of the device in real time, the invention also displays the attribute data (name, number, type, external dimension and the like) of the equipment in a visual popup mode. On one hand, when the equipment is abnormal or has faults, the attribute information can be timely, conveniently and accurately known without independently calling data information; on the other hand, the specific attributes of the equipment can be conveniently known by workers in daily life.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a flow chart in the embodiment;
FIG. 2 is a schematic representation of a CAD drawing of an engine in an example of an embodiment;
FIG. 3 is a diagrammatic illustration of the effect of an exemplary engine of an embodiment;
FIG. 4 shows a schematic diagram of states of an exemplary engine of an embodiment.
Detailed Description
The following is further detailed by the specific embodiments:
example (b):
as shown in fig. 1, the present embodiment discloses a device data visualization method based on digital twins, which includes the following steps:
s1, obtaining modeling data of equipment, and establishing a corresponding equipment model.
In specific implementation, S1 includes:
s101, obtaining modeling data required by modeling according to equipment to be monitored; the modeling data includes CAD drawings, process data and effect maps of the equipment.
And S102, constructing a three-dimensional entity model of each component of the equipment through modeling software according to the modeling data, and assembling to obtain an equipment model.
And S2, acquiring attribute data of the equipment, adding the attribute data into the equipment model, and constructing a digital twin model of the equipment.
In specific implementation, S2 includes:
s201, acquiring attribute data of equipment, and attaching the attribute data to an equipment model through a secondary development interface; wherein the attribute data includes name, number, type, form factor, and security.
S202, importing the equipment model and the attribute data thereof into a model library of the digital twin system, and forming a one-to-one correspondence relationship between the twin model and the equipment data through binding of the unique code of the equipment to obtain the digital twin model of the equipment;
and S3, acquiring the operation data of the equipment through the IOT sensor based on the service requirement.
In specific implementation, S3 includes:
s301, selecting operation data to be monitored based on the service scene requirements of the equipment;
s302, selecting a corresponding IOT sensor based on the operation data to be monitored, and binding each data in the operation data to be monitored with at least one IOT sensor; and the operation data corresponding to the equipment is collected through the IOT sensor.
And S4, processing and analyzing the operation data of the equipment to obtain the operation state of the equipment.
In specific implementation, S4 includes:
s401, preprocessing the operation data of the equipment collected in the S3 to obtain available operation data of the equipment;
s402, analyzing the available operation data according to the service scene requirements to obtain corresponding state information.
And S5, displaying the attribute data and the running state of the equipment in real time by combining the digital twin model of the equipment.
In specific implementation, the attribute data of the equipment is displayed in a visual popup mode. Moreover, the running data and running state of the equipment are displayed in a visual pop-up window mode; the running state comprises a normal state, a fault abnormal state, an early warning state and a fault state; if the operation state of a certain part of the equipment is abnormal, the color of the part is displayed as an abnormal color.
After a digital twin model of the equipment is constructed, the corresponding IOT sensor is selected based on specific service requirements to acquire the required operation data of the equipment, whether the equipment is abnormal or failed is analyzed, and fusion display is carried out by matching with the digital twin model. The related staff can know the real-time conditions of all parts of the equipment through the digital twin model. In the prior art, the use of a digital twin model is basically used for the simulation of the equipment. The real-time specific abnormal display only appears on the monitoring of address disaster detection (such as earthquake), and can only display a certain area. The invention creatively provides a new using method of a digital twin model, and the abnormal monitoring is carried out on the equipment at the microscopic level of the level of parts. By the mode, once a certain part of the equipment is abnormal or has faults, displayed contents are not limited to abnormal early warning or word explanation, specific abnormal parts can be displayed through the digital twin model, and even if workers are not familiar with the equipment, the specific abnormal contents can be locked quickly and accurately through the display and the word explanation.
And when the operation is abnormal, the digital twin model of the equipment displays the abnormal part of the equipment by using abnormal color, compared with a simple text description, the color distinction is more striking and easy to perceive for workers, the workers can quickly and clearly know whether each part of the equipment is normal or not, the abnormal part can be locked at the first time when the equipment is abnormal, and the targeted treatment can be quickly carried out. Besides displaying the running state of the device in real time, the invention also displays the attribute data (name, number, type, external dimension and the like) of the equipment in a visual popup mode. On one hand, when the equipment is abnormal or has faults, the attribute information can be timely, conveniently and accurately known without independently calling data information; on the other hand, the specific attributes of the equipment can be conveniently known by workers in daily life.
To facilitate a clearer understanding of the details of the present invention, a description is given by way of a specific example. In this example, the device is an engine of some type.
First, the CAD drawings of the engine shown in fig. 2 and the effect diagrams shown in fig. 3 are collected.
And then, establishing a three-dimensional entity model of the target equipment by using modeling software such as u3d and ue4 through the collected model data materials, and carrying out type division on the model to obtain a model group.
And determining basic attribute information of the engine, including name, number, type, overall dimension, safety and the like, through an actual service scene, and adding data to the established three-dimensional entity model through a secondary development interface.
Then, the engine model operation data is obtained in real time through the data transmission mode of the IOT sensor, for example: VALTRANIC absolute pressure 950bar, relative pressure 50bar, air flow meter 13kg/h, oil injection time 2.72ms.
Analyzing and processing the engine attribute data and the real-time data, marking the engine state of the acquired data by setting an abnormal state threshold, namely, knowing that the oil injection of an oil injection nozzle is abnormal by the oil injection time being more than 1.6ms, and transmitting the state in real time. Then, as shown in fig. 4, in the digital twin model of the engine, static information such as the name and the number of the engine can be displayed through model attribute data transmission visualization pop-up windows, and in the running process of the engine, the static information is displayed in a real-time dynamic data exception pop-up window visualization mode aiming at the abnormal condition of the oil injection data.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (8)
1. A digital twin-based device data visualization method is characterized by comprising the following steps:
s1, obtaining modeling data of equipment, and establishing a corresponding equipment model;
s2, acquiring attribute data of the equipment, adding the attribute data into an equipment model, and constructing a digital twin model of the equipment;
s3, acquiring running data of the equipment through the IOT sensor based on the service requirement;
s4, processing and analyzing the operation data of the equipment to obtain the operation state of the equipment;
and S5, displaying the attribute data and the running state of the equipment in real time by combining the digital twin model of the equipment.
2. The digital twin-based device data visualization method as set forth in claim 1, wherein: s1 comprises the following steps:
s101, obtaining modeling data required by modeling according to equipment to be monitored;
and S102, constructing a three-dimensional entity model of each component of the equipment through modeling software according to modeling data, and assembling to obtain an equipment model.
3. A method for visualization of device data based on digital twinning as claimed in claim 1, wherein: s2 comprises the following steps:
s201, acquiring attribute data of equipment, and attaching the attribute data to an equipment model through a secondary development interface;
s202, importing the equipment model and the attribute data thereof into a model library of the digital twin system, and forming a one-to-one correspondence relationship between the twin model and the equipment data through the binding of the unique codes of the equipment to obtain the digital twin model of the equipment.
4. A method for visualization of data for a device based on digital twins as claimed in claim 3, characterized in that: in S201, the attribute data includes name, number, type, outer dimension, and security.
5. The digital twin-based device data visualization method as set forth in claim 4, wherein: s3 comprises the following steps:
s301, selecting operation data to be monitored based on the service scene requirements of the equipment;
s302, selecting a corresponding IOT sensor based on the operation data to be monitored, and binding each data in the operation data to be monitored with at least one IOT sensor; and the operation data corresponding to the equipment is collected through the IOT sensor.
6. The digital twin-based device data visualization method as set forth in claim 5, wherein: s4 comprises the following steps:
s401, preprocessing the operation data of the equipment collected in the S3 to obtain available operation data of the equipment;
s402, analyzing the available operation data according to the service scene requirements to obtain corresponding state information.
7. The digital twin-based device data visualization method as set forth in claim 6, wherein: and S5, displaying the attribute data of the equipment in a visual popup mode.
8. A method for visualization of device data based on digital twinning as claimed in claim 7, wherein: s5, displaying the running data and running state of the equipment in a visual popup mode; the running state comprises a normal state, a fault abnormal state, an early warning state and a fault state; if the operation state of a certain part of the equipment is abnormal, the color of the part is displayed as an abnormal color.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202211662354.3A CN115730114A (en) | 2022-12-23 | 2022-12-23 | Equipment data visualization method based on digital twinning |
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| CN202211662354.3A CN115730114A (en) | 2022-12-23 | 2022-12-23 | Equipment data visualization method based on digital twinning |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117352118A (en) * | 2023-12-06 | 2024-01-05 | 惠医(天津)健康科技有限公司 | Multi-terminal diagnosis and treatment interaction method and device based on mobile internet |
| CN117404767A (en) * | 2023-12-14 | 2024-01-16 | 深圳市伟昊净化设备有限公司 | Intelligent perception-based filter differential pressure safety monitoring method and system |
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2022
- 2022-12-23 CN CN202211662354.3A patent/CN115730114A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117352118A (en) * | 2023-12-06 | 2024-01-05 | 惠医(天津)健康科技有限公司 | Multi-terminal diagnosis and treatment interaction method and device based on mobile internet |
| CN117352118B (en) * | 2023-12-06 | 2024-03-08 | 惠医(天津)健康科技有限公司 | Multi-terminal diagnosis and treatment interaction method and device based on mobile internet |
| CN117404767A (en) * | 2023-12-14 | 2024-01-16 | 深圳市伟昊净化设备有限公司 | Intelligent perception-based filter differential pressure safety monitoring method and system |
| CN117404767B (en) * | 2023-12-14 | 2024-03-22 | 深圳市伟昊净化设备有限公司 | Intelligent perception-based filter differential pressure safety monitoring method and system |
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