CN116246366A - Unmanned aerial vehicle healthy operation assessment method based on digital twinning - Google Patents
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Abstract
The invention discloses a digital twin-based unmanned aerial vehicle healthy operation assessment method, which comprises the steps of firstly obtaining unmanned aerial vehicle related data, constructing an unmanned aerial vehicle digital twin model, then carrying out cross fusion and iterative feedback on the unmanned aerial vehicle related data, fusing the unmanned aerial vehicle related data with the unmanned aerial vehicle digital twin model, and constructing an unmanned aerial vehicle digital twin health management and control platform; performing fault diagnosis and prediction on the unmanned aerial vehicle operation process based on the platform, outputting health assessment parameters, analyzing, visualizing the unmanned aerial vehicle operation state in the unmanned aerial vehicle digital twin health management and control platform, and making a decision on unmanned aerial vehicle health assessment; according to the unmanned aerial vehicle health operation assessment method, the unmanned aerial vehicle with the physical environment is truly displayed in the virtual environment, the unmanned aerial vehicle health operation condition can be rapidly and intuitively displayed, so that health assessment can be timely made, corresponding loss is reduced, the real condition of the unmanned aerial vehicle is accurately displayed through data interaction feedback, and maintenance cost is saved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle operation safety, in particular to a digital twinning-based unmanned aerial vehicle healthy operation assessment method.
Background
Currently, the industry scale of global unmanned aerial vehicles is rapidly growing. Unmanned aerial vehicle fleet size, unmanned aerial vehicle participants, unmanned aerial vehicle operation volume, etc. are all increasing. The rapid development and popularization of unmanned aerial vehicles enable the unmanned aerial vehicles to be widely applied in the fields of agricultural plant protection, homeland resource planning, emergency rescue, forest fire prevention, smart cities, military politics, leisure tourism and the like. Unmanned aerial vehicles of different models are different in size, performance, tasks and the like, so that faults and health conditions of the unmanned aerial vehicles are different. The large-scale development of unmanned aerial vehicles increasingly exposes the problem of unmanned aerial vehicle operation safety, provides new requirements and challenges for unmanned aerial vehicle's health management, and is also the key of guarantee aviation safety simultaneously.
Along with the deep integration of the emerging communication technologies such as artificial intelligence technology, internet of things technology and 5G, the real-time monitoring of the running state of the unmanned aerial vehicle can be realized, unmanned aerial vehicle faults can be found in time, and the health condition of the unmanned aerial vehicle can be evaluated more accurately, more efficiently and intelligently. Meanwhile, the operation process information of the unmanned aerial vehicle is realized through a digital twin technology and is fed back to the development process and the maintenance process of the unmanned aerial vehicle, so that the cost of the whole life cycle of the unmanned aerial vehicle is reduced, and meanwhile, the operation reliability of the unmanned aerial vehicle is improved. Therefore, the method has important significance for operation entry health assessment of the unmanned aerial vehicle.
Disclosure of Invention
The invention aims to: aiming at the problems in the background art, the invention provides the unmanned aerial vehicle healthy operation assessment method based on digital twinning, which reduces the unmanned aerial vehicle operation risk, improves the unmanned aerial vehicle operation safety, improves the unmanned aerial vehicle healthy operation and solves the unmanned aerial vehicle healthy assessment problem.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
a unmanned aerial vehicle healthy operation assessment method based on digital twinning comprises the following steps:
s1, collecting related data of an unmanned aerial vehicle, and constructing a digital twin model of the unmanned aerial vehicle;
s2, carrying out cross fusion and iterative feedback on the related data of the unmanned aerial vehicle, so that the related data of the unmanned aerial vehicle is fused with the digital twin model of the unmanned aerial vehicle; constructing a digital twin health management and control platform of the unmanned aerial vehicle based on the fusion result, and realizing supervision of the health state of the unmanned aerial vehicle;
s3, performing fault diagnosis and prediction on the operation process of the unmanned aerial vehicle based on the digital twin health management and control platform of the unmanned aerial vehicle, and outputting health evaluation parameters;
and S4, analyzing based on the health evaluation parameters, visualizing the running state of the unmanned aerial vehicle in the unmanned aerial vehicle digital twin health management and control platform, and deciding the health evaluation of the unmanned aerial vehicle.
Further, in step S1, the unmanned aerial vehicle related data includes operation state data, operation environment data, and geometric physical behavior rules of the unmanned aerial vehicle;
the operation state data comprise unmanned aerial vehicle component connection state data, unmanned aerial vehicle activity state data and unmanned aerial vehicle operation posture data;
the operation environment data comprise 5G radio transmission signal data, ground auxiliary equipment information data, meteorological environment data and geographic environment data;
the geometric physical behavior rules of the unmanned aerial vehicle comprise unmanned aerial vehicle shape parameters, size parameters, performance parameters, material characteristics and a movable range;
the unmanned aerial vehicle digital twin model establishment comprises an unmanned aerial vehicle physical entity, an unmanned aerial vehicle virtual entity and a connection relation between the unmanned aerial vehicle physical entity and the virtual entity; firstly, according to geometric data of a physical entity of the unmanned aerial vehicle, completing three-dimensional modeling of a geometric model and a physical model of the unmanned aerial vehicle in a CATIA; and then integrating the unmanned aerial vehicle running state data, running environment data and unmanned aerial vehicle geometric physical behavior rule data into a Simulink environment in MATLAB, completing the construction of an unmanned aerial vehicle behavior model and an unmanned aerial vehicle rule model, and realizing the digital mapping of the unmanned aerial vehicle virtual entity and the physical entity. The unmanned plane physical entity and the virtual entity adopt an embedded system and a plurality of sensors to complete data exchange.
Further, in step S3, fault diagnosis and prediction are performed on the unmanned aerial vehicle operation process based on the deep learning technology; the method specifically comprises the following steps:
s3.1, acquiring unmanned aerial vehicle running state data and running environment data as original input data;
s3.2, preprocessing original input data, and extracting features of the preprocessed data;
and S3.3, inputting the data after feature extraction into a deep learning model for training, performing fault diagnosis and prediction on the unmanned aerial vehicle operation process, and finally outputting health evaluation parameters.
Further, the unmanned aerial vehicle health assessment parameters include: unmanned aerial vehicle fault parameters, unmanned aerial vehicle part reliability information parameters, unmanned aerial vehicle key part stress state parameters and fatigue strength parameters.
Further, in step S4, according to the unmanned aerial vehicle health evaluation parameters, the influence of the unmanned aerial vehicle running state is analyzed, the unmanned aerial vehicle running state is visualized in the unmanned aerial vehicle health management and control platform, and a health evaluation decision is made in a targeted manner; in particular, the method comprises the steps of,
according to the deep learning model, unmanned aerial vehicle running state influence analysis is carried out, wherein the unmanned aerial vehicle running state influence analysis comprises unmanned aerial vehicle fault propagation influence analysis and unmanned aerial vehicle function influence analysis, the unmanned aerial vehicle fault propagation influence analysis mainly relates to whether other structural members are influenced or not, and the unmanned aerial vehicle function influence analysis mainly relates to whether functions necessary for normal running of the unmanned aerial vehicle are influenced or not;
the visual unmanned aerial vehicle running state is a virtual control platform of the unmanned aerial vehicle digital twin body, and the unmanned aerial vehicle digital twin body is connected with a platform display through a server to monitor the unmanned aerial vehicle running health status in real time;
the health evaluation decision is to evaluate the health state of the unmanned aerial vehicle and the task capacity of the unmanned aerial vehicle according to the health evaluation parameters, so as to make an unmanned aerial vehicle maintenance decision.
The fault diagnosis parameters, the health evaluation parameters and the health evaluation decision data information are fed back to the physical entity unmanned aerial vehicle database; the physical world unmanned aerial vehicle feeds back the unmanned aerial vehicle related data to the digital twin unmanned aerial vehicle, so as to continuously and iteratively update the digital twin unmanned aerial vehicle related data, and monitor the operation health state of the unmanned aerial vehicle.
The beneficial effects are that:
according to the unmanned aerial vehicle healthy operation method provided by the invention, the digital twin technology is adopted, so that the cooperation of safe operation and cost reduction of the unmanned aerial vehicle is effectively realized. And by combining a 5G technology, the communication problem of the unmanned aerial vehicle is effectively improved. And adopting a finite element technology or an unmanned aerial vehicle digital twin body model constructed in a Simulink environment, and truly displaying the unmanned aerial vehicle in a physical environment in a virtual environment. The constructed unmanned aerial vehicle visual management and control platform can quickly and intuitively display the healthy running condition of the unmanned aerial vehicle so as to make health assessment in time and reduce corresponding loss. Through interactive feedback of unmanned aerial vehicle data in virtual environment and the real environment, accurate efficient shows unmanned aerial vehicle real condition in virtual environment, provides support for unmanned aerial vehicle's development, operation, maintenance.
Drawings
Fig. 1 is a flowchart of a digital twinning-based unmanned aerial vehicle health operation assessment method provided by the invention;
fig. 2 is a schematic diagram of acquiring relevant data of an unmanned aerial vehicle according to the present invention;
FIG. 3 is a schematic diagram of the digital twin model construction of the unmanned aerial vehicle provided by the invention;
FIG. 4 is a flow chart of fault diagnosis and prediction provided by the present invention;
fig. 5 is a schematic diagram of a health assessment decision provided by the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings. This description is not to be taken as limiting the invention, but rather as a more detailed description of certain aspects, features and embodiments of the invention.
As shown in fig. 1, the method for evaluating the healthy operation of the unmanned aerial vehicle based on digital twinning provided by the invention comprises the following steps:
s1, collecting related data of the unmanned aerial vehicle, and constructing a digital twin model of the unmanned aerial vehicle.
Specifically, as shown in fig. 2, the unmanned plane related data includes: operating state data, operating environment data and geometric physical behavior rules of the unmanned aerial vehicle;
the operation state data comprises, but is not limited to, unmanned aerial vehicle component connection state data, unmanned aerial vehicle activity state data and unmanned aerial vehicle operation posture data;
the operational environment data includes, but is not limited to, 5G radio transmit signal data, ground assistance device information data, weather environment data, and geographic environment data.
Meteorological environment data includes, but is not limited to, wind speed, rain and snow, fog, air density, and atmospheric temperature. The geographical environment data includes data of unmanned aerial vehicle flying in mountain areas, canyons, woodland, roads, etc.
The geometric and physical behavior rules of the unmanned aerial vehicle include, but are not limited to, unmanned aerial vehicle shape parameters, dimension parameters, performance parameters, material characteristics and activity ranges.
The unmanned aerial vehicle digital twin model comprises an unmanned aerial vehicle physical entity, an unmanned aerial vehicle virtual entity and a connection relation between the unmanned aerial vehicle physical entity and the virtual entity. As shown in fig. 3, firstly, according to the geometric data of the physical entity of the unmanned aerial vehicle, completing the three-dimensional modeling of the geometric model and the physical model of the unmanned aerial vehicle in the CATIA; and then integrating the unmanned aerial vehicle running state data, running environment data and unmanned aerial vehicle geometric physical behavior rule data into a Simulink environment in MATLAB, completing the construction of an unmanned aerial vehicle behavior model and an unmanned aerial vehicle rule model, and realizing the digital mapping of the unmanned aerial vehicle virtual entity and the physical entity. The unmanned plane physical entity and the virtual entity adopt an embedded system and a plurality of sensors to complete data exchange.
S2, carrying out cross fusion and iterative feedback on the related data of the unmanned aerial vehicle, so that the related data of the unmanned aerial vehicle is fused with the digital twin model of the unmanned aerial vehicle; and constructing a digital twin health management and control platform of the unmanned aerial vehicle based on the fusion result, and realizing the supervision of the health state of the unmanned aerial vehicle.
And S3, performing fault diagnosis and prediction on the unmanned aerial vehicle operation process based on the unmanned aerial vehicle digital twin health management and control platform, and outputting health evaluation parameters. As shown in fig. 4:
s3.1, acquiring unmanned aerial vehicle running state data and running environment data as original input data;
step S3.2, preprocessing the original input data, including but not limited to filtering, noise reduction and time-frequency conversion. Feature extraction is performed on the preprocessed data, including but not limited to extracting mean values, standard deviations.
And S3.3, inputting the data after feature extraction into a deep learning model for training, performing fault diagnosis and prediction on the unmanned aerial vehicle operation process, and finally outputting health evaluation parameters.
The unmanned aerial vehicle health assessment parameters include: unmanned aerial vehicle fault parameters, unmanned aerial vehicle part reliability information parameters, unmanned aerial vehicle key part stress state parameters and fatigue strength parameters.
And S4, analyzing based on the health evaluation parameters, visualizing the running state of the unmanned aerial vehicle in the unmanned aerial vehicle digital twin health management and control platform, and deciding the health evaluation of the unmanned aerial vehicle.
Performing unmanned aerial vehicle running state influence analysis according to a fault diagnosis model, wherein the unmanned aerial vehicle running state influence analysis comprises unmanned aerial vehicle fault propagation influence analysis and unmanned aerial vehicle function influence analysis, and the unmanned aerial vehicle fault propagation influence analysis mainly relates to whether other structural members are influenced or not, and the unmanned aerial vehicle function influence analysis mainly relates to whether functions necessary for normal running of the unmanned aerial vehicle are influenced or not;
the visual unmanned aerial vehicle running state is a virtual control platform of the unmanned aerial vehicle digital twin body, and the unmanned aerial vehicle digital twin body can be connected with the platform through a server, further connected with a display, and used for monitoring the unmanned aerial vehicle running health status in real time;
the health evaluation decision is to evaluate the health state of the unmanned aerial vehicle and the task capacity of the unmanned aerial vehicle according to the health evaluation parameters, so as to make unmanned aerial vehicle maintenance decision in a targeted manner. The fault diagnosis parameters, the health evaluation decision data information and the like are continuously fed back to the physical world unmanned aerial vehicle database, and the physical world unmanned aerial vehicle data are fed back to the digital twin unmanned aerial vehicle, so that the digital twin unmanned aerial vehicle parameters are continuously and iteratively updated, and the operation health state of the unmanned aerial vehicle is monitored more scientifically.
The embodiment also provides an unmanned aerial vehicle healthy operation evaluation system based on digital twinning, which comprises a state acquisition module, an unmanned aerial vehicle digital twinning body module and a health management and control platform module;
status acquisition mainly collects data from sensors distributed on the unmanned fuselage, such as: the system comprises a current sensor, a magnetic sensor, a tilt sensor, an engine air inlet flow sensor, an acceleration sensor, an inertial measurement unit and other sensors, wherein the data collected by the sensors are communicated with a server through a 5G network;
the unmanned aerial vehicle digital twin body is used for determining the real running state of the unmanned aerial vehicle according to the collected state data, and is also used for determining a fault diagnosis model and health assessment of the unmanned aerial vehicle;
the health management and control platform module is mainly used for continuously feeding back the fault diagnosis parameters, the health evaluation decision data information and the like into the physical world unmanned aerial vehicle database, and feeding back the physical world unmanned aerial vehicle data into the digital twin unmanned aerial vehicle so as to continuously and iteratively update the digital twin unmanned aerial vehicle parameters and help more scientifically monitor the operation health state of the unmanned aerial vehicle.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (5)
1. The unmanned aerial vehicle healthy operation assessment method based on digital twinning is characterized by comprising the following steps of:
s1, collecting related data of an unmanned aerial vehicle, and constructing a digital twin model of the unmanned aerial vehicle;
s2, carrying out cross fusion and iterative feedback on the related data of the unmanned aerial vehicle, so that the related data of the unmanned aerial vehicle is fused with the digital twin model of the unmanned aerial vehicle; constructing a digital twin health management and control platform of the unmanned aerial vehicle based on the fusion result, and realizing supervision of the health state of the unmanned aerial vehicle;
s3, performing fault diagnosis and prediction on the operation process of the unmanned aerial vehicle based on the digital twin health management and control platform of the unmanned aerial vehicle, and outputting health evaluation parameters;
and S4, analyzing based on the health evaluation parameters, visualizing the running state of the unmanned aerial vehicle in the unmanned aerial vehicle digital twin health management and control platform, and deciding the health evaluation of the unmanned aerial vehicle.
2. The unmanned aerial vehicle healthy operation assessment method based on digital twinning according to claim 1, wherein the unmanned aerial vehicle related data in the step S1 comprises operation state data, operation environment data and unmanned aerial vehicle geometric physical behavior rules;
the operation state data comprise unmanned aerial vehicle component connection state data, unmanned aerial vehicle activity state data and unmanned aerial vehicle operation posture data;
the operation environment data comprise 5G radio transmission signal data, ground auxiliary equipment information data, meteorological environment data and geographic environment data;
the geometric physical behavior rules of the unmanned aerial vehicle comprise unmanned aerial vehicle shape parameters, size parameters, performance parameters, material characteristics and a movable range;
the unmanned aerial vehicle digital twin model establishment comprises an unmanned aerial vehicle physical entity, an unmanned aerial vehicle virtual entity and a connection relation between the unmanned aerial vehicle physical entity and the virtual entity; firstly, according to geometric data of a physical entity of the unmanned aerial vehicle, completing three-dimensional modeling of a geometric model and a physical model of the unmanned aerial vehicle in a CATIA; and then integrating the unmanned aerial vehicle running state data, running environment data and unmanned aerial vehicle geometric physical behavior rule data into a Simulink environment in MATLAB, completing the construction of an unmanned aerial vehicle behavior model and an unmanned aerial vehicle rule model, and realizing the digital mapping of the unmanned aerial vehicle virtual entity and the physical entity. The unmanned plane physical entity and the virtual entity adopt an embedded system and a plurality of sensors to complete data exchange.
3. The unmanned aerial vehicle healthy operation assessment method based on digital twinning according to claim 2, wherein in the step S3, fault diagnosis and prediction are performed on the unmanned aerial vehicle operation process based on a deep learning technology; the method specifically comprises the following steps:
s3.1, acquiring the unmanned aerial vehicle running state data and the running environment data as original input data;
s3.2, preprocessing original input data, and extracting features of the preprocessed data;
and S3.3, inputting the data after feature extraction into a deep learning model for training, performing fault diagnosis and prediction on the unmanned aerial vehicle operation process, and finally outputting health evaluation parameters.
4. A method of evaluating the health operation of a drone based on digital twinning according to claim 3, wherein the drone health evaluation parameters include: unmanned aerial vehicle fault parameters, unmanned aerial vehicle part reliability information parameters, unmanned aerial vehicle key part stress state parameters and fatigue strength parameters.
5. The unmanned aerial vehicle health operation assessment method based on digital twinning according to claim 4, wherein in step S4, according to the unmanned aerial vehicle health assessment parameters, unmanned aerial vehicle operation state influences are analyzed, unmanned aerial vehicle operation states are visualized in an unmanned aerial vehicle health management and control platform, and health assessment decisions are made in a targeted manner; in particular, the method comprises the steps of,
performing unmanned aerial vehicle running state influence analysis according to the deep learning model, wherein the unmanned aerial vehicle running state influence analysis comprises unmanned aerial vehicle fault propagation influence analysis and unmanned aerial vehicle function influence analysis, the unmanned aerial vehicle fault propagation influence analysis comprises judging whether other structural members are influenced, and the unmanned aerial vehicle function influence analysis comprises judging whether functions necessary for the normal running of the unmanned aerial vehicle are influenced;
the visual unmanned aerial vehicle running state is a virtual management and control platform of a digital twin body of the unmanned aerial vehicle, and the digital twin body of the unmanned aerial vehicle is connected with a platform display through a server to monitor the running health condition of the unmanned aerial vehicle in real time;
the health evaluation decision is to evaluate the health state of the unmanned aerial vehicle and the task capacity of the unmanned aerial vehicle according to the health evaluation parameters so as to make an unmanned aerial vehicle maintenance decision;
the fault diagnosis parameters, the health evaluation parameters and the health evaluation decision data information are fed back to the physical entity unmanned aerial vehicle database; the physical world unmanned aerial vehicle feeds back the unmanned aerial vehicle related data to the digital twin unmanned aerial vehicle, so as to continuously and iteratively update the digital twin unmanned aerial vehicle related data, and monitor the operation health state of the unmanned aerial vehicle.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957361A (en) * | 2023-07-27 | 2023-10-27 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
| CN117131712A (en) * | 2023-10-26 | 2023-11-28 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
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- 2021-09-18 CN CN202111109826.8A patent/CN116246366A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957361A (en) * | 2023-07-27 | 2023-10-27 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
| CN116957361B (en) * | 2023-07-27 | 2024-02-06 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
| CN117131712A (en) * | 2023-10-26 | 2023-11-28 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
| CN117131712B (en) * | 2023-10-26 | 2024-01-16 | 南开大学 | Virtual-real combined emergency rescue simulation system and method |
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