CN112346440A - Robot health monitoring method, device, equipment and readable storage medium - Google Patents
Robot health monitoring method, device, equipment and readable storage medium Download PDFInfo
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/24065—Real time diagnostics
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Abstract
The invention discloses a robot health monitoring method, which comprises the following steps: acquiring control parameters for driving a monitored component of the robot and operation data generated by the monitored component based on the control parameters; sending the control parameters to a preset simulation model, and receiving virtual operation data returned by the preset simulation model based on the control parameters; and comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored part of the robot according to the comparison result. The invention also discloses a robot health monitoring device, equipment and a readable storage medium. According to the invention, whether the monitored component needs to be maintained is evaluated by comparing the operation data with the virtual operation data, so that the prediction of whether the monitored component is damaged is realized, and the labor maintenance cost is saved.
Description
Technical Field
The invention relates to the field of robot monitoring, in particular to a robot health monitoring method, a device, equipment and a readable storage medium.
Background
With the rapid development of scientific technology, robots are widely visible in the lives of people. In some robotic manufacturers, the buyer is provided with an annual warranty service.
At present, in the product guarantee period, after a purchaser finds a robot fault, the purchaser can inform related personnel of home maintenance, or the purchaser sends the fault robot to a specified maintenance point for maintenance, so that not only much manpower is consumed, but also the occurrence of the robot fault can only be known later and the robot part to be damaged cannot be predicted in advance.
Disclosure of Invention
The invention mainly aims to provide a robot health monitoring method, a device, equipment and a readable storage medium, and aims to solve the technical problem that a robot part to be damaged cannot be predicted in advance in the prior art.
In order to achieve the above object, the present invention further provides a robot health monitoring method, including the steps of:
acquiring control parameters for driving a monitored component of the robot and operation data generated by the monitored component based on the control parameters;
sending the control parameters to a preset simulation model, and receiving virtual operation data returned by the preset simulation model based on the control parameters;
and comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored part of the robot according to the comparison result.
Optionally, the acquiring control parameters of a monitored component for driving the robot and operation data generated by the monitored component based on the control parameters includes:
acquiring the control parameters when the control parameters are sent to a monitored part of the robot;
and acquiring operation data generated by the monitored component executing the control parameters in the current environment. .
Optionally, the comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored component of the robot according to the comparison result includes:
comparing the operation data with the virtual operation data to obtain a current data average difference, and taking the current data average difference as a comparison result;
if the comparison result is greater than or equal to the first preset threshold, judging that the health condition of the monitored part of the robot is not good;
and if the comparison result is smaller than the first preset threshold value, judging that the health condition of the monitored part of the robot is good.
Optionally, the method further comprises:
acquiring real-time environment information of the current position when the monitored component generates operation data based on the control parameters;
determining target environment information influencing the operation data in the real-time environment information according to a difference value between the operation data and the virtual operation data;
and correcting the evaluation result of the health condition of the monitored part of the robot according to the target environment information.
Optionally, the determining, according to a difference between the operation data and the virtual operation data, target environment information that affects the operation data in the real-time environment information includes:
and determining real-time environment information in a preset time period to screen out the target environment information when the difference value between the operation data and the virtual operation data is greater than or equal to a preset value in the preset time period according to the change curve of the operation data and the change curve of the virtual operation data.
Optionally, the comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored component of the robot according to the comparison result includes:
comparing the operating data with the virtual operating data to obtain a difference value;
determining the influence factor of real-time environment information on the operation of the monitored component according to the difference value;
and compensating the influence factors to the comparison result, and evaluating the health condition of the monitored part of the robot according to the compensated comparison result.
Optionally, the sending the control parameter to a preset simulation model and receiving virtual operation data returned by the preset simulation model based on the control parameter includes:
sending the control parameters to a preset simulation model, wherein the preset simulation model determines a simulation component corresponding to the monitored component in the preset simulation model according to the control parameters, inputs the control parameters to the simulation component, acquires simulation data generated by the simulation component, and takes the simulation data as virtual operation data;
the robot receives the virtual operating data.
In order to achieve the above object, the present invention also provides a robot health monitoring apparatus configured to a robot, the robot health monitoring apparatus including:
the acquisition module is used for acquiring control parameters of a monitored component for driving the robot and operation data generated by the monitored component based on the control parameters;
the sending module is used for sending the control parameters to a preset simulation model and receiving virtual operation data returned by the preset simulation model based on the control parameters;
and the evaluation module is used for comparing the operation data with the virtual operation data and evaluating the health condition of the monitored part of the robot according to the comparison result.
In addition, to achieve the above object, the present invention also provides a robot health monitoring apparatus including: the robot health monitoring system comprises a memory, a processor and a robot health monitoring program stored on the memory and capable of running on the processor, wherein the robot health monitoring program realizes the steps of the robot health monitoring method when being executed by the processor.
In addition, to achieve the above object, the present invention further provides a readable storage medium, wherein the readable storage medium stores a robot health monitoring program, and the robot health monitoring program, when executed by a processor, implements the steps of the robot health monitoring method as described above.
The embodiment of the invention provides a robot health monitoring method, a device, equipment and a readable storage medium. The robot health monitoring method comprises the steps of obtaining control parameters and operation data of a monitored part of the robot, and comparing the operation data with virtual operation data of a preset simulation model, so that the health condition of the monitored part of the robot is evaluated, whether maintenance is needed or not is judged, the damage of the monitored part is predicted in advance, and the labor maintenance cost is saved.
Drawings
Fig. 1 is a schematic hardware structure diagram of an implementation manner of a robot health monitoring apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of a robot health monitoring method according to the present invention;
fig. 3 is a functional block diagram of a robot health monitoring apparatus according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The robot health monitoring terminal (also called terminal, equipment or terminal equipment) in the embodiment of the invention can be a PC, or terminal equipment with a data processing function such as a smart phone, a tablet computer, a portable computer and the like, or a robot.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. Such as light sensors, motion sensors, and other sensors. Of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a robot health monitoring program may be included in a memory 1005, which is a kind of readable storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a robot health monitoring program stored in the memory 1005, and the robot health monitoring program, when executed by the processor, implements the operations in the robot health monitoring method provided by the embodiments described below.
Based on the hardware structure of the equipment, the embodiment of the robot health monitoring method is provided.
Referring to fig. 2, in the first embodiment of the robot health monitoring method of the present invention, the robot health monitoring method includes steps S10 to S30:
step S10, acquiring control parameters for driving a monitored component of the robot and operation data generated by the monitored component based on the control parameters.
The robot health monitoring method in this embodiment is applied to a robot, where the robot includes a power supply system (e.g., a battery), a driving system (e.g., a motor), and driven components (e.g., a rotating shaft and wheels), and each component of the robot may be correspondingly equipped with a sensor or an acquisition circuit, which may monitor and acquire control parameters and operation data of each component of the robot. Specifically, the control parameter in this embodiment refers to a parameter input to a monitored component of the robot and used for driving and controlling the monitored component, and the operation data in this embodiment refers to data output by the monitored component of the robot after receiving the control parameter, for example, if the monitored component of the robot is a motor, the control parameter refers to a parameter (including voltage, current, and the like) used for controlling the power of the motor, and the operation data refers to data such as the rotating speed, temperature, and the like of the motor. Therefore, the monitored part of the robot acquires the control parameters and the operation data of the monitored part in the operation process.
Step S20, sending the control parameter to a preset simulation model, and receiving virtual operation data returned by the preset simulation model based on the control parameter.
The preset simulation model in this embodiment is a data simulation model constructed based on the robot in this embodiment, and includes each simulation component of the robot and a connection relationship between each simulation component. The preset simulation model may be installed in a target terminal, and the target terminal is a terminal for establishing a communication connection with the robot, such as a mobile phone, a computer, a server or a computer device with data processing capability. Or, the preset simulation model is set in a control system of the robot, and at this time, the robot is used as a target terminal. The robot acquires the control parameters and the operation data of the monitored component, and then sends the control parameters of the monitored component to the target terminal so that the target terminal can analyze the control parameters of the monitored component, the target terminal can generate virtual operation data after analyzing the control parameters of the monitored component and send the virtual operation data to the robot, namely the robot receives the virtual operation data, and specifically, after receiving the control parameters of the monitored component, the target terminal analyzes the control parameters, which will be detailed below.
And step S30, comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored component of the robot according to the comparison result.
After receiving the virtual operation data sent by the target terminal, the robot compares the operation data of the monitored component with the virtual operation data, and specifically, the virtual operation data received by the robot and the operation data of the monitored component are the same in data type and data number, which also provides convenience for comparing the two data. It can be known that the comparison result in this embodiment reflects the difference between the operation data and the virtual operation data, specifically, if the data types of the operation data and the virtual operation data are more, the comparison result may be an average value of the differences between the corresponding data between the operation data and the virtual operation data, and this value may reflect the difference between the operation data and the virtual operation data to a certain extent, and if the difference between the operation data and the virtual operation data is larger, it may be stated that the health condition of the monitored component is not good and needs to be maintained; if the difference between the operation data and the virtual operation data is not large, the health condition of the monitored component is good, and maintenance is not needed.
Specifically, in step S30, comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored component of the robot according to the comparison result, the step of refining includes steps a 1-a 3:
step a1, comparing the operation data with the virtual operation data to obtain a current data average difference, and taking the current data average difference as a comparison result;
a2, if the comparison result is greater than or equal to the first preset threshold, determining that the health condition of the monitored component of the robot is not good;
step a3, if the comparison result is less than the first preset threshold, determining that the health condition of the monitored component of the robot is good.
As can be known, the current data average difference can be obtained by comparing the operation data with the virtual operation data, where the current data average difference in this embodiment refers to an average value of differences between corresponding data between the operation data and the virtual operation data, for example, there are three operation data and three virtual operation data, where the three operation data are a1, a2 and a3, and the three virtual operation data are b1, b2 and b3, respectively, the current data average difference is [ | (a1-b1) | + | (a2-b2) | + | (a3-b3) | ]/3, and the obtained current data average difference is used as a comparison result to determine whether the comparison result is greater than a first preset threshold, where the first preset threshold is an upper limit value obtained according to multiple experiments or long-term tests, that is, if the data average difference between the operation data and the virtual operation data is greater than or equal to the first preset threshold, it may be determined that the monitored component needs to be repaired, and if the data difference between the operation data and the virtual operation data is less than the first preset threshold, it may be determined that the monitored component does not need to be repaired.
The robot health monitoring method further comprises the steps of b 1-b 3:
step b1, acquiring real-time environment information of the current position when the monitored component generates operation data based on the control parameters;
b2, determining target environment information influencing the operation data in the real-time environment information according to the difference value between the operation data and the virtual operation data;
and b3, correcting the evaluation result of the health condition of the monitored part of the robot according to the target environment information.
Specifically, the step b2 for refining further comprises:
step c1, according to the variation curve of the operation data and the variation curve of the virtual operation data, when the difference value between the operation data and the virtual operation data in a preset time period is greater than or equal to a preset value, determining that the target environment information is screened out from the real-time environment information in the preset time period.
In this embodiment, the real-time environment information is introduced into a second preset threshold, where the second preset threshold is smaller than the first preset threshold, when the comparison result is smaller than or equal to the first preset threshold, it may be further determined whether the comparison result is greater than the second preset threshold, and when the comparison result is greater than the second preset threshold and smaller than or equal to the first preset threshold, it indicates that there is a certain problem in the operation data of the monitored component, but it is not enough to directly determine whether the monitored component needs to be maintained, in which case the current working state of the robot needs to be obtained, because the special real-time environment information may cause different degrees of influence on the operation of the monitored component of the robot, for example, the monitored component is a motor, the operation data is a rotation speed and a temperature, the rotation speed in the operation data is relatively slow, and may also be caused by the robot being loaded or crawling, that is, since the robot is in a loaded state, if the current operating state of the robot is in a loaded state, it is determined that the maintenance of the monitored component is not necessary, and if the current operating state of the robot is in a non-loaded state, it is determined that the maintenance of the monitored component is necessary.
In the present embodiment, when the health of the robot is good, the difference between the operation data of the robot and the virtual operation data is relatively small. At this time, in the operation process of the robot, if the difference value between the operation data and the virtual operation data of the robot is suddenly larger in a preset time period and is larger than a preset value, it is indicated that the current real-time environment information has a relatively large influence on the operation of the controlled component, and the environment information is screened by obtaining the real-time environment information and screening out the target environment information which causes the sudden larger difference value between the operation data and the virtual operation data in the real-time environment information according to the comparison.
The robot health monitoring method further comprises:
step d1, comparing the operation data with the virtual operation data to obtain a difference value;
step d2, determining the influence factor of the real-time environment information on the operation of the monitored component according to the difference value;
and d3, compensating the influence factors to the comparison result, and evaluating the health condition of the monitored part of the robot according to the compensated comparison result.
In this embodiment, a third preset threshold is introduced into the historical data, where the third preset threshold is smaller than the second preset threshold, when the comparison result is smaller than or equal to the second preset threshold, it is further necessary to further determine whether the comparison result is larger than the third preset threshold, and when the comparison result is larger than the third preset threshold and smaller than or equal to the second preset threshold, it indicates that the operation data of the monitored component is slightly problematic, but it is still insufficient to directly determine that the monitored component needs to be maintained, and in this case, it is necessary to query the pre-stored historical data for average difference. Optionally, the historical data average difference is a data average difference obtained and stored in advance, after the historical data average difference is inquired, the robot health monitoring program will draw a data average difference historical graph according to the historical data average difference, the current data average difference and a preset time period, for example, the data average difference historical graph is drawn in a two-dimensional coordinate system, wherein a horizontal axis of the two-dimensional coordinate system represents a time point, and a vertical axis represents a value of the data average difference, so that it can be known that the preset time period is a period of time selected on the horizontal axis of the two-dimensional coordinate system, the end of the period of time is a time for obtaining the current data average difference, for example, the frequency for obtaining the control parameters and the operation data of the monitored component is once a day, and the preset time period is a month, then the drawn data average difference historical graph is drawn by all data average differences in a month before the time point for obtaining the current data average difference, after a data average difference historical curve graph is obtained, the number of target data average differences which are the same as the current data average difference and are less than or equal to a second preset threshold and greater than a third preset threshold in the data average difference historical curve graph is obtained, if the number of the target data average differences is greater than or equal to a fourth preset threshold, the monitored part is judged to be required to be maintained, if the comparison result is less than or equal to the third preset threshold, or the number of the target data average differences is less than the fourth preset threshold, the monitored part is judged not to be required to be maintained, wherein the fourth preset threshold is determined by a preset time period, control parameters of the monitored part and the frequency of operation data, namely the preset time period and the frequency are larger, the fourth preset threshold is larger, the application scene is that the obtained current data average difference has a certain problem, and the data average difference with the size appears for a plurality of times in the past time period, intermittent faults may be determined to exist with the monitored component.
Further, target environment information is marked, a compensation value of an influence factor is made for each target environment information, when the robot is in a scene of the target environment information, compensation is performed through the influence factor to obtain a corrected comparison result, for example, if the slope is a preset angle, under the same voltage and current values (control parameters), however, the electromechanical rotating speed and temperature (operation data) of the robot are different if the slope is a preset angle, so that when the robot climbs the slope suddenly, a difference value between the operation data and the virtual operation data is suddenly larger, and therefore, the slope of the preset angle is screened out as the target environment information through the real-time environment information, and the influence of the target environment information on a controlled component is marked. Meanwhile, an influence back factor compensation value is made for the influence caused by the target environment information, and the compensation value is added to the comparison result, so that the difference value in the comparison result is smaller than a first preset threshold value, and the misjudgment is avoided.
In the robot health monitoring method in this embodiment, the control parameters and the operation data of the monitored component of the robot are acquired, and the operation data is compared with the virtual operation data of the preset simulation model, so that the health condition of the monitored component of the robot is evaluated, whether maintenance is needed is judged, the damage of the monitored component is predicted in advance, and the labor maintenance cost is saved.
Further, in a second embodiment of the robot health monitoring method of the present invention, the robot health monitoring method includes:
step S40, sending the control parameters to a preset simulation model, wherein the preset simulation model determines a simulation component corresponding to the monitored component in the preset simulation model according to the control parameters, inputs the control parameters to the simulation component, obtains simulation data generated by the simulation component, and takes the simulation data as virtual operation data.
Step S50, the robot receives the virtual operation data.
The preset simulation model in this embodiment is installed at the target terminal or installed in the simulation software of the robot control system, and is capable of simulating the operation of all components of the robot, where the preset simulation model is equivalent to a complete robot without any problem, and when the target terminal receives the control parameters sent by the robot, the target terminal first obtains the monitored component corresponding to the control parameters, and then determines, according to the monitored component, the simulation component corresponding to the monitored component in the preset robot simulation model, for example, the monitored component is a motor, the simulation component is also a motor, and only the motor of the simulation component is a virtual motor, and only can receive data and simulate the operation of the motor, and then outputs simulated operation data. It is known that the simulation part corresponds to a perfect monitored part, and if it is assumed that the control parameter is the input power of the motor of 120 watts and the operation data is the rotation speed of the motor of 30 revolutions per second, the power of 120 watts is input to the simulation part, and the simulation part obtains the rotation speed of the motor of 35 revolutions per second by simulating the operation of the motor, which results in a difference between the simulated operation data and the operation data of the real monitored part, and it is known that the simulated data will be sent to the robot as virtual operation data.
In the embodiment, the operation of the robot which is complete and has no problem is simulated through the preset simulation model installed on the target terminal, so that the virtual operation data is obtained, and data support is provided for the comparison of the subsequent operation data and the virtual operation data.
In addition, referring to fig. 3, an embodiment of the present invention further provides a robot health monitoring apparatus, where the robot health monitoring apparatus includes:
an obtaining module 10, configured to obtain a control parameter of a monitored component for driving the robot and operation data generated by the monitored component based on the control parameter;
a sending module 20, configured to send the control parameter to a preset simulation model, and receive virtual operation data returned by the preset simulation model based on the control parameter;
and the evaluation module 30 is used for comparing the operation data with the virtual operation data and evaluating the health condition of the monitored part of the robot according to the comparison result.
Optionally, the obtaining module 10 includes:
a control parameter acquisition unit for acquiring the control parameter when transmitting the control parameter to a monitored component of the robot;
and the operation data acquisition unit is used for acquiring operation data generated by the monitored component executing the control parameters in the current environment.
Optionally, the evaluation module 30 includes:
the comparison unit is used for comparing the operation data with the virtual operation data to obtain a current data average difference and taking the current data average difference as a comparison result;
a first determination unit, configured to determine that a health condition of a monitored component of the robot is not good if the comparison result is greater than or equal to the first preset threshold;
and the second judging unit is used for judging that the health condition of the monitored part of the robot is good if the comparison result is smaller than the first preset threshold value.
Optionally, the robot health monitoring apparatus further includes:
the real-time environment information acquisition module is used for acquiring the real-time environment information of the current position when the monitored component generates the operation data based on the control parameters;
the target environment information determining module is used for determining target environment information which influences the operation data in the real-time environment information according to a difference value between the operation data and the virtual operation data;
and the correction module is used for correcting the evaluation result of the health condition of the monitored part of the robot according to the target environment information.
Optionally, the target environment information determining module includes:
and the screening module is used for determining that the target environment information is screened out from the real-time environment information in a preset time period when the difference value between the operation data and the virtual operation data is greater than or equal to a preset value in the preset time period according to the change curve of the operation data and the change curve of the virtual operation data.
Optionally, the evaluation module 30 includes:
the first comparison unit is used for comparing the operation data with the virtual operation data to obtain a difference value;
the determining unit is used for determining the influence factor of the real-time environment information on the operation of the monitored component according to the difference value;
and the evaluation unit is used for compensating the influence factors to the comparison result and evaluating the health condition of the monitored part of the robot according to the compensated comparison result.
In addition, referring to fig. 3, an embodiment of the present invention further provides a robot health monitoring apparatus, where the robot health monitoring apparatus includes:
a control parameter sending module 40, configured to send the control parameter to a preset simulation model, where the preset simulation model determines, according to the control parameter, a simulation component in the preset simulation model corresponding to the monitored component, inputs the control parameter to the simulation component, obtains simulation data generated by the simulation component, and uses the simulation data as virtual operating data;
a virtual operation data receiving module 50, configured to receive the virtual operation data by the robot.
In addition, an embodiment of the present invention further provides a readable storage medium, where a robot health monitoring program is stored on the readable storage medium, and when the robot health monitoring program is executed by a processor, the robot health monitoring program implements operations in the robot health monitoring method provided in the foregoing embodiment.
The method executed by each program module can refer to each embodiment of the method of the present invention, and is not described herein again.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity/action/object from another entity/action/object without necessarily requiring or implying any actual such relationship or order between such entities/actions/objects; the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
For the apparatus embodiment, since it is substantially similar to the method embodiment, it is described relatively simply, and reference may be made to some descriptions of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, in that elements described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, where the software product is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above, and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the robot health monitoring method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A robot health monitoring method is applied to a robot, and comprises the following steps:
acquiring control parameters for driving a monitored component of the robot and operation data generated by the monitored component based on the control parameters;
sending the control parameters to a preset simulation model, and receiving virtual operation data returned by the preset simulation model based on the control parameters;
and comparing the operation data with the virtual operation data, and evaluating the health condition of the monitored part of the robot according to the comparison result.
2. The robot health monitoring method of claim 1, wherein the acquiring control parameters for driving a monitored component of the robot and the operating data generated by the monitored component based on the control parameters comprises:
acquiring the control parameters when the control parameters are sent to a monitored part of the robot;
and acquiring operation data generated by the monitored component executing the control parameters in the current environment.
3. The robot health monitoring method of claim 1, wherein comparing the operational data with the virtual operational data and assessing the health of the monitored component of the robot based on the comparison comprises:
comparing the operation data with the virtual operation data to obtain a current data average difference, and taking the current data average difference as a comparison result;
if the comparison result is greater than or equal to the first preset threshold, judging that the health condition of the monitored part of the robot is not good;
and if the comparison result is smaller than the first preset threshold value, judging that the health condition of the monitored part of the robot is good.
4. The robotic health monitoring method of claim 1, wherein the method further comprises:
acquiring real-time environment information of the current position when the monitored component generates operation data based on the control parameters;
determining target environment information influencing the operation data in the real-time environment information according to a difference value between the operation data and the virtual operation data;
and correcting the evaluation result of the health condition of the monitored part of the robot according to the target environment information.
5. The robot health monitoring method of claim 4, wherein the determining target environmental information affecting the operational data from the real-time environmental information according to a difference between the operational data and the virtual operational data comprises:
and determining real-time environment information in a preset time period to screen out the target environment information when the difference value between the operation data and the virtual operation data is greater than or equal to a preset value in the preset time period according to the change curve of the operation data and the change curve of the virtual operation data.
6. The robot health monitoring method of claim 1, wherein comparing the operational data with the virtual operational data and assessing the health of the monitored component of the robot based on the comparison comprises:
comparing the operating data with the virtual operating data to obtain a difference value;
determining the influence factor of real-time environment information on the operation of the monitored component according to the difference value;
and compensating the influence factors to the comparison result, and evaluating the health condition of the monitored part of the robot according to the compensated comparison result.
7. The robot health monitoring method of claim 1, wherein the sending the control parameters to a preset simulation model and receiving virtual operating data returned by the preset simulation model based on the control parameters comprises:
sending the control parameters to a preset simulation model, wherein the preset simulation model determines a simulation component corresponding to the monitored component in the preset simulation model according to the control parameters, inputs the control parameters to the simulation component, acquires simulation data generated by the simulation component, and takes the simulation data as virtual operation data;
the robot receives the virtual operating data.
8. A robot health monitoring device, configured to be disposed on a robot, the device comprising:
the acquisition module is used for acquiring control parameters of a monitored component for driving the robot and operation data generated by the monitored component based on the control parameters;
the sending module is used for sending the control parameters to a preset simulation model and receiving virtual operation data returned by the preset simulation model based on the control parameters;
and the evaluation module is used for comparing the operation data with the virtual operation data and evaluating the health condition of the monitored part of the robot according to the comparison result.
9. A robotic health monitoring device, comprising: a memory, a processor and a robot health monitoring program stored on the memory and executable on the processor, the robot health monitoring program when executed by the processor implementing the steps of the robot health monitoring method according to any of claims 1 to 7.
10. A readable storage medium having stored thereon a robot health monitoring program which, when executed by a processor, implements the steps of the robot health monitoring method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113776791A (en) * | 2021-08-04 | 2021-12-10 | 深圳优地科技有限公司 | Method and device for monitoring health state of robot, robot and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103853133A (en) * | 2012-11-29 | 2014-06-11 | 发纳科机器人美国公司 | Robot system calibration method |
| CN104317637A (en) * | 2014-10-16 | 2015-01-28 | 安徽理工大学 | Multi-agent-based virtual miner safety behavior modeling and emergency simulation system |
| CN107450351A (en) * | 2017-09-01 | 2017-12-08 | 上海贝乐文化传播有限公司 | A kind of wheeled robot Control System Imitation system and emulation mode |
| WO2019075925A1 (en) * | 2017-10-20 | 2019-04-25 | 深圳市前海安测信息技术有限公司 | Health monitoring robot having family affection interaction function and health monitoring method |
| CN109815557A (en) * | 2018-12-31 | 2019-05-28 | 深圳市越疆科技有限公司 | A kind of methods of exhibiting of robot model, device and intelligent terminal |
| KR20190073118A (en) * | 2017-12-18 | 2019-06-26 | (주)휴레이포지티브 | Monitoring Robot for health care of user |
| CN111046587A (en) * | 2019-12-27 | 2020-04-21 | 上海节卡机器人科技有限公司 | Robot simulation method and device, electronic equipment and storage medium |
| CN111230887A (en) * | 2020-03-10 | 2020-06-05 | 合肥学院 | Industrial gluing robot running state monitoring method based on digital twin technology |
| CN112101662A (en) * | 2020-09-16 | 2020-12-18 | 珠海格力电器股份有限公司 | Equipment health condition and life cycle detection method, storage medium and electronic equipment |
-
2020
- 2020-11-20 CN CN202011318383.9A patent/CN112346440B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103853133A (en) * | 2012-11-29 | 2014-06-11 | 发纳科机器人美国公司 | Robot system calibration method |
| CN104317637A (en) * | 2014-10-16 | 2015-01-28 | 安徽理工大学 | Multi-agent-based virtual miner safety behavior modeling and emergency simulation system |
| CN107450351A (en) * | 2017-09-01 | 2017-12-08 | 上海贝乐文化传播有限公司 | A kind of wheeled robot Control System Imitation system and emulation mode |
| WO2019075925A1 (en) * | 2017-10-20 | 2019-04-25 | 深圳市前海安测信息技术有限公司 | Health monitoring robot having family affection interaction function and health monitoring method |
| KR20190073118A (en) * | 2017-12-18 | 2019-06-26 | (주)휴레이포지티브 | Monitoring Robot for health care of user |
| CN109815557A (en) * | 2018-12-31 | 2019-05-28 | 深圳市越疆科技有限公司 | A kind of methods of exhibiting of robot model, device and intelligent terminal |
| CN111046587A (en) * | 2019-12-27 | 2020-04-21 | 上海节卡机器人科技有限公司 | Robot simulation method and device, electronic equipment and storage medium |
| CN111230887A (en) * | 2020-03-10 | 2020-06-05 | 合肥学院 | Industrial gluing robot running state monitoring method based on digital twin technology |
| CN112101662A (en) * | 2020-09-16 | 2020-12-18 | 珠海格力电器股份有限公司 | Equipment health condition and life cycle detection method, storage medium and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113776791A (en) * | 2021-08-04 | 2021-12-10 | 深圳优地科技有限公司 | Method and device for monitoring health state of robot, robot and storage medium |
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