CN116715034A - Digital twinning-based palletizing robot predictive maintenance system and method - Google Patents
Digital twinning-based palletizing robot predictive maintenance system and method Download PDFInfo
- Publication number
- CN116715034A CN116715034A CN202310901889.XA CN202310901889A CN116715034A CN 116715034 A CN116715034 A CN 116715034A CN 202310901889 A CN202310901889 A CN 202310901889A CN 116715034 A CN116715034 A CN 116715034A
- Authority
- CN
- China
- Prior art keywords
- data
- robot
- module
- palletizing
- palletizing robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 230000003993 interaction Effects 0.000 claims abstract description 11
- 238000004891 communication Methods 0.000 claims abstract description 9
- 238000013528 artificial neural network Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 230000001133 acceleration Effects 0.000 claims description 8
- 230000010354 integration Effects 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 5
- 238000013500 data storage Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 238000012549 training Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- 239000003638 chemical reducing agent Substances 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 3
- 230000000737 periodic effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000012636 effector Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G61/00—Use of pick-up or transfer devices or of manipulators for stacking or de-stacking articles not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G47/00—Article or material-handling devices associated with conveyors; Methods employing such devices
- B65G47/74—Feeding, transfer, or discharging devices of particular kinds or types
- B65G47/90—Devices for picking-up and depositing articles or materials
- B65G47/905—Control arrangements
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G47/00—Article or material-handling devices associated with conveyors; Methods employing such devices
- B65G47/74—Feeding, transfer, or discharging devices of particular kinds or types
- B65G47/90—Devices for picking-up and depositing articles or materials
- B65G47/91—Devices for picking-up and depositing articles or materials incorporating pneumatic, e.g. suction, grippers
- B65G47/917—Devices for picking-up and depositing articles or materials incorporating pneumatic, e.g. suction, grippers control arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The embodiment of the specification provides a digital twin-based palletizing robot predictive maintenance system and a digital twin-based palletizing robot predictive maintenance method, wherein the system comprises: the physical space module is used for collecting running state data and robot control data of the palletizing robot; the virtual system module is used for constructing a digital twin virtual environment according to the collected running state data and robot control data of the palletizing robot and constructing a three-dimensional model of the palletizing robot; the communication module is used for connecting the physical space module with the virtual system module, carrying out data interaction and carrying out real-time monitoring on the physical space module through the digital twin virtual environment; and the prediction maintenance module is used for predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment. The invention realizes the real-time monitoring of the stacking operation of the factory, achieves the effect of prediction maintenance and solves the safety problem.
Description
Technical Field
The document relates to the technical field of digital twin robots, in particular to a predictive maintenance system and method for a palletizing robot based on digital twin.
Background
Digital twinning (Digital Twin) refers to converting a physical entity into a Digital form through a digitizing technology, and simulating and optimizing the running condition and performance of the physical entity in a Digital environment, so as to realize effective supervision and management of the physical entity. This concept was proposed in 2002 by the National Aviation Space Agency (NASA) to provide a virtual environment for the design, operation, and maintenance of spacecraft such as space stations and spacecraft. Digital twinning is an important technical support for the development of the fields of intelligent manufacturing, smart cities and the like, and is now becoming a hot spot for the research of the global science and technology field.
In the hot spot period of modern robot development, especially the demand on the robot in the aspect of industry is especially huge, however, the development technology of the present robot still needs very big cost of labor, for example in the aspect of goods stacking, when the palletizing robot stacks goods, if staff is required to observe the scene, the safety problem is easily caused, the damage is caused, and if the palletizing robot has equipment problems, the field operation is easily caused to be dangerous. Therefore, a digital twin-based palletizing robot monitoring system is needed, when palletizing is completed, palletizing operation is monitored in real time, and predictive maintenance of the palletizing robot is performed, so that the fault problem of the palletizing robot is solved to the greatest extent.
Disclosure of Invention
One or more embodiments of the present specification provide a digital twin-based palletizing robot predictive maintenance method, including:
collecting running state data and robot control data of the palletizing robot;
constructing a digital twin virtual environment according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizing robot;
connecting a physical space module with the virtual system module, performing data interaction, and performing real-time monitoring on the physical space module through a digital twin virtual environment;
and predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
One or more embodiments of the present specification provide a digital twinning-based palletizing robot predictive maintenance system, including: the system comprises a physical space module, a virtual system module, a communication module and a prediction maintenance module;
the physical space module: the robot control system is used for collecting running state data and robot control data of the palletizing robot;
the virtual system module: the robot palletizer is used for constructing a digital twin virtual environment according to the running state data of the palletizer and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizer;
the communication module is as follows: the system comprises a virtual system module, a physical space module, a digital twin virtual environment and a virtual system module, wherein the virtual system module is used for carrying out data interaction on the physical space module;
the predictive maintenance module: the method is used for predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
One or more embodiments of the present specification provide an electronic device including:
a processor; the method comprises the steps of,
a memory arranged to store computer executable instructions which, when executed, cause the processor to implement the steps of the above described digital twin based palletising robot predictive maintenance method.
One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed, implement the steps of the digital twinning-based palletizing robot predictive maintenance method described above.
By adopting the embodiment of the invention, the real-time monitoring of the stacking operation of the factory is realized, the real-time observation of the stacking operation by personnel is not needed, the personnel safety is ensured, and the labor cost is reduced; the robot palletizer is predictively maintained based on the RBF neural network and the Bayesian classifier, so that the safety performance of the robot palletizer for palletizing cargoes is improved, the equipment asset loss is reduced, and the safety of staff is improved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow diagram of a method for predictive maintenance of a palletizing robot based on digital twinning, provided in one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a digital twinning-based palletizing robot predictive maintenance system according to one or more embodiments of the present disclosure;
FIG. 3 is a schematic diagram of a digital twinning-based palletizing robot predictive maintenance system building framework provided by one or more embodiments of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
Method embodiment
According to an embodiment of the present invention, a method for predictive maintenance of a palletizing robot based on digital twin is provided, and fig. 1 is a flowchart of the method for predictive maintenance of a palletizing robot based on digital twin provided in one or more embodiments of the present specification, as shown in fig. 1, the method for predictive maintenance of a palletizing robot based on digital twin according to the embodiment of the present invention specifically includes:
s1, acquiring running state data and robot control data of the palletizing robot.
The collecting of the operation state data and the robot control data of the palletizing robot specifically comprises the following steps: the method comprises the steps of collecting data of attributes, dimensions, positions in space and material attributes of the palletizing robot, collecting data of a robot controller, measuring data of a sensor on joint angles of the palletizing robot and image data of a container by a visual sensor.
S2, constructing a digital twin virtual environment according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizing robot.
By acquiring data of a physical space, modeling the palletizing robot, a container and a conveying device by adopting SolidWorks three-dimensional modeling software according to running state data of the palletizing robot and robot control data acquired by a physical space module, the invention is remarkable in that PIXYZ plug-in is adopted, after Solidworks modeling is finished, the model is subjected to light weight processing, the number of triangular surfaces of the model is reduced, the loss of a CPU is reduced, and then the model can be directly guided into Unity3D for building a virtual environment without format conversion of the model built by Solidworks.
S3, connecting the physical space module with the virtual system module, performing data interaction, and monitoring the physical space module in real time through the digital twin virtual environment.
After the digital twin environment is built, data interaction is realized, connection between a physical space and a virtual system is established, connection between the physical space and the virtual system is realized by adopting an MQTT, unity3D is used as a client, a palletizing robot controller is used as a server, on the basis, a plurality of clients are established to be connected with the server, each client receives data from the server and displays the data, the clients are connected with the corresponding server, for example, a client is established by using the clients, for example, the server of the palletizing robot, and a client is established by using an end effector of palletizing.
The method comprises the steps of utilizing Text of a UI component in Unity3D, writing a C# script, utilizing multithreading of an MQTT protocol, wherein the MQTT protocol is a lightweight, small-sized, low-cost and low-broadband communication protocol with little code quantity, establishing a plurality of client IDs and IP addresses through the MQTT protocol, defining the maximum connection quantity of a client, assigning data transmitted by a server to the Text, establishing Button events, wherein the Button events can interrupt working states of a virtual space and a physical space, and can avoid dangers when equipment faults occur.
S4, predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
Writing an RBF neural network and Bayesian classifier integrated algorithm into a Unity3D client, firstly, downloading an RNF algorithm library, introducing the RNF algorithm library into a Unity3D project, creating a neural network model in the Unity, including an input layer, a hidden layer and an output layer, writing codes by using C#, realizing the neural network algorithm in the Unity, updating the weight and deviation of the neural network by using an Update () function of the Unity, carrying out forward and backward propagation in each time step, and training the neural network model by using training data. Asynchronous training procedures are implemented using the Unity Coroutine (Coroutine). The accuracy and performance of the neural network are tested by using the test data, and then the script is integrated into the Untiy client, so that the real-time data and state of the palletizing robot can be observed, the palletizing robot can be predicted and maintained, and the safety is ensured,
Specifically, data collected in a physical space are integrated, the data are preprocessed, such as data cleaning, missing value processing, data conversion and the like, characteristics of a motor of the palletizing robot, such as temperature, humidity, current, voltage and the like are extracted, the data are divided into a test set and a training set, an RBF neural network model is trained by using the training set, model optimization and parameter adjustment are carried out, the data of the training set are input into a Bayesian classifier, and model optimization and parameter adjustment are carried out. The RBF neural network and the Bayesian classifier are combined for integrated learning, the accuracy and the robustness of model prediction are improved, data of a test set are input into an integrated model for prediction, maintenance and maintenance of the palletizing robot are performed according to a prediction result, and then the palletizing robot is monitored in real time, so that the maximum safety performance of the robot and even the whole equipment can be ensured. The specific implementation mode is as follows:
firstly, each hardware of the palletizing robot is subjected to data preprocessing, data such as temperature, humidity, rotating speed, vibration and the like of a speed reducer of the palletizing robot are collected through a sensor, current, voltage, temperature, humidity, vibration and rotating speed of a servo motor, strength of joints and bolts of a mechanical arm of the palletizing robot, the magnitude and materials of pretightening force and the like, joint pose, speed, acceleration, angular speed and the like of the mechanical arm of the robot are collected through a force sensor, moment and torque required by a mechanical arm executor for carrying goods are collected through a force sensor, positions, attitudes and weights of materials are collected through a position sensor, the data are collected, abnormal values and missing values are removed after the data of the palletizing robot are collected, and then useful characteristics of the palletizing robot such as calculation distance, included angle and orientation of the palletizing robot are extracted. The palletizing robot needs to convey the required container to a specified position, and the rotating speed, the temperature, the current and the voltage of the servo motor of the robot are extracted from the extracted characteristics; pose, speed, acceleration, angular velocity, angular acceleration, etc. of the palletizing robot joint; the moment and torque required by the palletizing robot for carrying goods, the temperature, the rotating speed, the vibration and the like of the speed reducer can influence the carrying work of the palletizing robot, and the safety problem occurs.
Further, RBF neural network training is carried out on the characteristics affecting the palletizing robot, the extracted characteristics are used as input, a Bayesian classifier is trained, classification normalization is carried out, and the category with the highest probability is selected as a classification result through a large amount of data analysis and test according to the characteristics of a servo motor, the characteristics of a speed reducer, the characteristics of an actuator, the characteristics of a mechanical arm and the like of the palletizing robot. When the palletizing robot works, whether each hardware of the palletizing robot needs maintenance or not is predicted through the established prediction maintenance system. The specific implementation mode is as follows: when the palletizing robot works. The working state of the palletizing robot is displayed in real time through a digital twin-based visual platform, the periodical and continuous state detection and fault diagnosis activities are carried out on the palletizing robot based on the RBF neural network and the predictive maintenance of the Bayesian classifier, the running state of equipment is judged, the future running state of the palletizing robot is predicted, then a predictive maintenance plan is formulated for the palletizing robot, the time, the mode, the content, the method and the like of the palletizing robot which need to be regularly maintained are determined, the purpose of predictive maintenance is achieved, the equipment is prevented from being broken down, and the safety problem is caused.
In the embodiment of the invention, the running state data and the robot control data of the palletizing robot, the set of twin data and the data generated by the real-time monitoring system can be stored, and the angle, the speed, the acceleration and the moment of the motion joint of the palletizing robot, and the data of the motor, the controller and the sensor are stored.
The method of the present embodiment further comprises: the working data of the physical space is displayed in real time through a control panel of the monitoring system built in the digital twin virtual environment, and the working data comprise the number of palletizing containers, the palletizing completion state and the working state of the palletizing robot.
And establishing a human-computer interaction system, and establishing a monitoring panel to monitor the motion data of the palletizing robot in real time by establishing a UGUI control panel. In the Unity client, the palletizing robot and palletizing conditions are checked by clicking and observing the control panel, the working conditions of the site are observed in real time, the data of each hardware of the palletizing robot are displayed in real time in the control panel, various data are tested and analyzed, periodic diagnosis is carried out on faults of the palletizing robot, such as periodic analysis and test on the temperature of a motor, the problem of temperature faults of the motor before analysis is solved, a predictive maintenance periodic plan is formulated, and the problem that the temperature of the motor is not too high is ensured.
The invention has the following beneficial effects:
by adopting the embodiment of the invention, the real-time monitoring of the stacking operation of the factory is realized, the real-time observation of the stacking operation by personnel is not needed, the personnel safety is ensured, and the labor cost is reduced; the time, the mode, the content, the method and the like of the palletizing robot which need to be regularly maintained are determined based on the RBF neural network and the Bayesian classifier, and the predictive maintenance is regularly carried out, so that the safety performance of the palletizing robot when palletizing cargoes is improved, the equipment cost loss caused by equipment faults in palletizing operation is reduced, and the safety of staff is improved.
System embodiment
According to an embodiment of the present invention, a digital twin-based palletizing robot predictive maintenance system is provided, and fig. 2 is a schematic composition diagram of the digital twin-based palletizing robot predictive maintenance system provided in one or more embodiments of the present specification, as shown in fig. 2, where the digital twin-based palletizing robot predictive maintenance system according to the embodiment of the present invention specifically includes: a physical space module 201, a virtual system module 202, a communication module 203 and a predictive maintenance module 204;
the physical space module 201: the robot palletizer is used for collecting running state data and robot control data of the palletizer.
The physical space module 201 is specifically configured to: the method comprises the steps of collecting data of the attribute, the size, the position in space and the material attribute of the palletizing robot, and collecting data of a robot controller, data of a sensor and image data of a container.
The virtual system module 202: and the digital twin virtual environment is built according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and a three-dimensional model of the palletizing robot is built.
The communication module 203: the system is used for connecting the physical space module with the virtual system module, carrying out data interaction and carrying out real-time monitoring on the physical space module through the digital twin virtual environment.
The predictive maintenance module 204: the method is used for predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
The system further comprises a data storage module 205, configured to store the data collected by the physical space module, the set of twin data, and the data generated by the real-time monitoring system, and store the angle, speed, acceleration, and moment of the motion joint of the palletizing robot, and the data of the motor, the controller, and the sensor.
The system further comprises a display module 206, which comprises a control panel of the monitoring system, is built in the digital twin virtual environment, and displays working data of the physical space on the control panel in real time, including the number of stacking containers, the stacking completion state and the working state of the stacking robot.
Specifically, fig. 3 is a schematic diagram of a building frame of a palletizing robot predictive maintenance system based on digital twinning according to one or more embodiments of the present disclosure, including:
physical hierarchy: the physical space hierarchy includes attributes to physical space, physical device size, data collection by the controller, etc. The palletizing robot is the main body of the control system, so the embodiment comprises data acquisition of the attribute, the size, the position in space and the material attribute of the palletizing robot, data acquisition of a robot controller, data acquisition of a sensor, image acquisition of a container carried by the robot and the like.
Virtual environment hierarchy: the virtual environment level comprises the construction of a virtual environment of a physical space, the construction of a digital twin virtual environment through data acquired by the physical space comprises the construction of a three-dimensional model of the palletizing robot, the position, the geometric dimension, the material, the subordinate relation, the forward and reverse kinematic characteristics and the like of the palletizing robot, the mapping effect is emphasized through real-time data on the physical space, the real-time monitoring on the physical space is realized, the rendering of the physical space is further required to be enhanced, and the real effect is achieved.
Predicting a maintenance level: after the virtual environment is built, the prediction maintenance of the palletizing robot based on the RBF neural network and the Bayesian classifier is provided, and the RBF neural network and the Bayesian classifier integrated algorithm is written into Unity, so that not only can the palletizing condition be monitored in real time through a digital twin monitoring system, but also the running state of equipment can be predicted in advance, and the early maintenance is carried out, thereby not only ensuring the safety of staff, but also ensuring the safety of the equipment.
Data storage hierarchy: the data storage layer comprises data acquisition of a physical space, collection of twin data and storage of data generated by a real-time monitoring system, historical data are reproduced, and angles, speeds, accelerations and moments of motion joints of the palletizing robot, and data of a motor, a controller and a sensor are stored, so that data loss is prevented.
Communication layer: after the digital twin virtual environment is built, the physical space and the digital twin virtual environment are required to be connected, so that the physical space is actually reflected by the virtual space, the consistency of the physical space and the virtual environment is achieved, and the real-time monitoring of the physical space by the virtual system is achieved.
Application hierarchy: the application level comprises the step of building a UGUI control panel of a monitoring system in a virtual environment, wherein working data of a physical space, such as the number of palletizing containers, the palletizing completion state and the working state of a palletizing robot, are displayed on the control panel in real time.
By adopting the embodiment of the invention, the real-time monitoring of the stacking operation of the factory is realized, the real-time observation of the stacking operation by personnel is not needed, the personnel safety is ensured, and the labor cost is reduced; the time, the mode, the content, the method and the like of the palletizing robot which need to be regularly maintained are determined based on the RBF neural network and the Bayesian classifier, and the predictive maintenance is regularly carried out, so that the safety performance of the palletizing robot when palletizing cargoes is improved, the equipment cost loss caused by equipment faults in palletizing operation is reduced, and the safety of staff is improved.
Device embodiment 1
An embodiment of the present invention provides an electronic device, as shown in fig. 4, including: memory 40, processor 42 and a computer program stored on the memory 40 and executable on the processor 42, which when executed by the processor 42 performs the following method steps:
s1, collecting running state data and robot control data of a palletizing robot;
s2, constructing a digital twin virtual environment according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizing robot;
s3, connecting the physical space module with the virtual system module, performing data interaction, and monitoring the physical space module in real time through a digital twin virtual environment;
s4, predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
Device example two
An embodiment of the present invention provides a computer-readable storage medium having stored thereon a program for realizing information transmission, which when executed by a processor 42 realizes the following method steps:
s1, collecting running state data and robot control data of a palletizing robot;
s2, constructing a digital twin virtual environment according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizing robot;
s3, connecting the physical space module with the virtual system module, performing data interaction, and monitoring the physical space module in real time through a digital twin virtual environment;
s4, predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
The computer readable storage medium of the present embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, etc.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A digital twinning-based palletizing robot predictive maintenance system, comprising: the system comprises a physical space module, a virtual system module, a communication module and a prediction maintenance module;
the physical space module: the robot control system is used for collecting running state data and robot control data of the palletizing robot;
the virtual system module: the robot palletizer is used for constructing a digital twin virtual environment according to the running state data of the palletizer and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizer;
the communication module is as follows: the system comprises a virtual system module, a physical space module, a digital twin virtual environment and a virtual system module, wherein the virtual system module is used for carrying out data interaction on the physical space module;
the predictive maintenance module: the method is used for predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
2. The system according to claim 1, wherein the physical space module is specifically configured to: the method comprises the steps of collecting data of the attribute, the size, the position in space and the material attribute of the palletizing robot, and collecting data of a robot controller, data of a sensor and image data of a container.
3. The system of claim 1, further comprising a data storage module for storing data collected by the physical space module, the set of twinning data, and data generated by the real-time monitoring system, and storing data of angles, speeds, accelerations, moments of the motion joints of the palletizing robot, and data of the motors, controllers, and sensors.
4. The system of claim 1, further comprising a display module including a monitoring system control panel configured in a digital twin virtual environment, wherein the control panel displays in real time the working data of the physical space including the number of palletized containers, the completion status of palletizing, and the working status of the palletizing robot.
5. A digital twinning-based palletizing robot predictive maintenance method is characterized by comprising the following steps:
collecting running state data and robot control data of the palletizing robot;
constructing a digital twin virtual environment according to the running state data of the palletizing robot and the robot control data acquired by the physical space module, and constructing a three-dimensional model of the palletizing robot;
connecting a physical space module with the virtual system module, performing data interaction, and performing real-time monitoring on the physical space module through a digital twin virtual environment;
and predicting and maintaining the running state of the palletizing robot in the digital twin virtual environment through an RBF neural network and Bayesian classifier integration algorithm.
6. The method according to claim 5, wherein the collecting of the palletizing robot operation state data and the robot control data comprises: the method comprises the steps of collecting data of the attribute, the size, the position in space and the material attribute of the palletizing robot, and collecting data of a robot controller, data of a sensor and image data of a container.
7. The method according to claim 5, wherein the method further comprises:
and storing the data acquired by the physical space module, the set of twin data and the data generated by the real-time monitoring system, and storing the angle, the speed, the acceleration and the moment of the motion joint of the palletizing robot, and the data of the motor, the controller and the sensor.
8. The method according to claim 5, wherein the method further comprises:
the system comprises a control panel of a monitoring system, wherein the control panel is built in a digital twin virtual environment, and working data of a physical space are displayed on the control panel in real time, and the working data comprise the number of palletizing containers, the palletizing completion state and the working state of a palletizing robot.
9. An electronic device, comprising:
a processor; the method comprises the steps of,
a memory arranged to store computer executable instructions which when executed cause the processor to carry out the steps of a digital twin based palletising robot predictive maintenance method as claimed in any of claims 5 to 8.
10. A storage medium storing computer executable instructions which when executed implement the steps of the digital twin based palletising robot predictive maintenance method as claimed in any one of claims 5 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310901889.XA CN116715034A (en) | 2023-07-20 | 2023-07-20 | Digital twinning-based palletizing robot predictive maintenance system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310901889.XA CN116715034A (en) | 2023-07-20 | 2023-07-20 | Digital twinning-based palletizing robot predictive maintenance system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116715034A true CN116715034A (en) | 2023-09-08 |
Family
ID=87873599
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310901889.XA Pending CN116715034A (en) | 2023-07-20 | 2023-07-20 | Digital twinning-based palletizing robot predictive maintenance system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116715034A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372630A (en) * | 2023-12-07 | 2024-01-09 | 陕西星辰时代科技发展有限公司 | Data visualization system and method based on digital twin technology |
-
2023
- 2023-07-20 CN CN202310901889.XA patent/CN116715034A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117372630A (en) * | 2023-12-07 | 2024-01-09 | 陕西星辰时代科技发展有限公司 | Data visualization system and method based on digital twin technology |
| CN117372630B (en) * | 2023-12-07 | 2024-03-01 | 陕西星辰时代科技发展有限公司 | Data visualization system and method based on digital twin technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111708332B (en) | Digital twin system of production line | |
| Khalil et al. | Dexterous robotic manipulation of deformable objects with multi-sensory feedback-a review | |
| CN111496781B (en) | Mechanical arm modeling, controlling and monitoring integrated system driven by digital twin | |
| CN111633644A (en) | Industrial robot digital twin system combined with intelligent vision and operation method thereof | |
| CN112836404A (en) | Method for constructing digital twin body of structural performance of intelligent excavator | |
| CN116715034A (en) | Digital twinning-based palletizing robot predictive maintenance system and method | |
| US11782427B2 (en) | Augmented reality human machine interface testing | |
| CN110765635A (en) | Collaboration method, system, electronic device, and medium for digital twin system | |
| Liang et al. | Real-time state synchronization between physical construction robots and process-level digital twins | |
| Zong et al. | A multi-robot monitoring system based on digital twin | |
| CN111168660B (en) | Redundant degree of freedom hydraulic heavy load robot arm initiative safety system | |
| EP4336385A1 (en) | Method and apparatus for updating target detection model | |
| CN115408828A (en) | OPC UA protocol-based digital twin workshop virtual-real synchronous operation method and system | |
| CN113868803A (en) | Mechanism model and dynamic data combined driven cloud-edge combined digital twinning method | |
| CN112800606A (en) | Digital twin production line construction method and system, electronic device and storage medium | |
| CN116081488B (en) | Unmanned control method for scene self-adaptive single-rail lifting robot | |
| CN114559435B (en) | Mechanical arm track planning method based on sphere envelope and under optimal performance target | |
| Wang et al. | Digital twin modeling method for container terminal in port | |
| Herbert et al. | Sensor-based interior modeling | |
| Leng et al. | Flexible online planning based residual space object de-spinning for dual-arm space-borne maintenance | |
| Wehe et al. | Intelligent robotics and remote systems for the nuclear industry | |
| Wei et al. | Design of Deep Learning Algorithm in the Control System of Intelligent Inspection Robot of Substation | |
| Chen et al. | Multisensor based autonomous mobile robot through Internet control | |
| Lu et al. | Slam and navigation of electric power intelligent inspection robot based on ROS | |
| Gao et al. | Research on Real-Time Data-Driven Simulation Technology and Application of Digital Workshop |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |