CN115338867A - Fault state monitoring method, device and equipment for mobile robot - Google Patents
Fault state monitoring method, device and equipment for mobile robot Download PDFInfo
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Abstract
The invention discloses a fault state monitoring method, a fault state monitoring device and fault state monitoring equipment of a mobile robot. The method comprises the following steps: acquiring at least two state data of each device to be monitored in the mobile robot; inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; and determining a fault device in the mobile robot according to the monitoring result so as to locate the fault reason of the mobile robot. According to the technical scheme, all sensors and/or hardware devices in the mobile robot are monitored in a targeted manner, so that the fault reason of the mobile robot is accurately positioned; according to the scheme, the accuracy of monitoring each device is improved according to the characteristic incidence relation between at least two kinds of state data of each sensor or hardware device, and the influence caused by inaccuracy of single state data is avoided.
Description
Technical Field
The invention relates to the technical field of robot fault monitoring, in particular to a fault state monitoring method, a fault state monitoring device and fault state monitoring equipment for a mobile robot.
Background
With the development of subjects such as computer theory, electronic information technology, automatic control theory, mechanical automation and the like and the application of novel materials, the mobile robot technology has advanced rapidly. The sensor and hardware installed on the mobile robot are used as important components of the mobile robot, and the data accuracy and integrity of the sensor and hardware are of great significance to decision control of the mobile robot. Once the mobile robot sensor is abnormal, the driving safety of the mobile robot is greatly affected.
In the prior art, the whole mobile robot is monitored by acquiring the running state data of the mobile robot, the mobile robot can only be judged to have a fault, the specific position of the mobile robot cannot be accurately positioned, technical personnel are still required to perform troubleshooting, the workload of the technical personnel is greatly increased, the normal operation of the mobile robot is also influenced, and therefore the accurate positioning of the fault position of the mobile robot is very important.
Disclosure of Invention
The invention provides a fault state monitoring method, a fault state monitoring device and fault state monitoring equipment of a mobile robot, which are used for solving the problem that monitoring of monitoring devices of different types is inaccurate, realizing effective and accurate monitoring and prediction of the monitoring devices and further accurately positioning the fault position of the mobile robot.
According to an aspect of the present invention, there is provided a fault state monitoring method of a mobile robot, the method including:
acquiring at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored comprises all sensor devices and/or hardware devices installed in the mobile robot;
inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used for learning a characteristic association relationship between the at least two state data;
and determining a fault device in the mobile robot according to the monitoring result so as to locate the fault reason of the mobile robot.
According to another aspect of the present invention, there is provided a fault state monitoring apparatus of a mobile robot, the apparatus including:
the data acquisition module is used for acquiring at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored comprises all sensor devices and/or hardware devices installed in the mobile robot;
the detection module is used for inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used for learning a characteristic association relationship between the at least two state data;
and the positioning module is used for determining a fault device in the mobile robot according to the monitoring result so as to position the fault reason of the mobile robot.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of fault condition monitoring of a mobile robot according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a fault status monitoring method for a mobile robot according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the fault reason of the mobile robot is accurately positioned by pertinently monitoring all the sensors and/or hardware devices in the mobile robot, and the monitoring accuracy of each device is improved according to the characteristic incidence relation between at least two kinds of state data of each sensor or hardware device, so that the influence caused by the inaccuracy of single state data is avoided.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a fault state monitoring method for a mobile robot according to an embodiment of the present invention;
fig. 2 is a flowchart of a fault state monitoring method for a mobile robot according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a fault state monitoring apparatus for a mobile robot according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the fault state monitoring method for a mobile robot according to the embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "sample," "object," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for monitoring a fault state of a mobile robot according to an embodiment of the present invention, where the embodiment is applicable to monitoring an operation condition of a car of a mobile robot, such as an AGV, and the method can be executed by a fault state monitoring device of the mobile robot, where the fault state monitoring device of the mobile robot can be implemented in a form of hardware and/or software, and the fault state monitoring device of the mobile robot can be configured in an electronic device having the method for monitoring a fault state of the mobile robot. As shown in fig. 1, the method includes:
s110, acquiring at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored includes all sensor devices and/or hardware devices installed in the mobile robot.
The mobile robot senses the environment around the vehicle through each monitoring device, and controls the steering and the speed of the mobile robot according to the road, the position of the mobile robot, obstacle information and the like obtained through sensing, so that the mobile robot can safely and reliably run on the road. An unmanned vehicle, which may also be referred to as an autonomous vehicle, is one type of mobile robot.
The monitoring devices include all sensor devices installed in the mobile robot, for example, sensor devices including but not limited to laser radar sensors, cameras (depth camera, look-around camera), ultrasonic sensors, fall protection sensors, in-place detection sensors, and tray identification sensors, and/or hardware devices including but not limited to motors and navigation module power supplies.
The state data is used for describing data which is obtained by each device to be monitored during the operation process of the mobile robot and is used for describing the operation condition of the robot, such as current, voltage, rotating speed or temperature and the like during the operation of each sensor, and is used for judging whether the operation of each device to be monitored is abnormal or not. In addition, the acquired at least two state data have a certain incidence relation, so that the incidence relation between the data can be conveniently learned through a machine learning method, and whether the device to be monitored is abnormal or not can be further judged.
S120, inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used to learn a feature association relationship between the at least two state data.
The fault monitoring model is trained on the cloud platform in advance, and is deployed at the mobile robot end to be monitored after training is completed.
The characteristic association relationship between at least two types of state data refers to an interaction relationship existing between state data output by each device to be monitored, for example, a linear relationship existing between a voltage value and a current value, or a relationship existing between a voltage value and a temperature value, and if any state data changes, and the change of the corresponding state data with the association relationship does not conform to the association relationship learned in advance, it indicates that the corresponding device has an abnormal phenomenon.
The fault monitoring model learns the characteristic incidence relation between at least two kinds of state data, so that the state change of each device to be monitored can be judged more accurately, and the misjudgment of the device to be monitored caused by the judgment of single state data is avoided. For example, if the failure result of the device is determined directly according to the comparison result of the single status data and the corresponding threshold, the determination of the failure result of the device may be incorrect due to the occasional abnormality of the single status data.
The monitoring result can be the result of whether the running state of each device to be monitored is abnormal or not, and technicians can be guided to maintain the mobile robot in time through the monitoring result.
Specifically, in the operation process of the mobile robot, the state data of each device to be monitored is recorded and uploaded to the cloud platform. Because the mobile robot has a large scale and the data volume is huge, the fault monitoring model runs on the cloud platform, and the efficiency of training the fault monitoring model is improved. And training a fault monitoring model by using large-scale mass data, continuously iterating and converging the model to obtain the trained associated fault monitoring model of each device to be monitored, deploying the trained associated fault monitoring model to the mobile robot end, and monitoring the mobile robot in real time. The correlation fault monitoring model operates at the mobile robot end, data of each device to be monitored can be monitored in real time, the operation state, the life cycle and the like of each device to be monitored are obtained, guidance of technical personnel for operation and maintenance work is facilitated, meanwhile, the delay problem of deployment on a cloud platform is avoided, and the accuracy of the data is ensured.
Optionally, because different to-be-monitored devices have different functions and different real-time performance required by executed tasks, different monitoring frequencies are set for the to-be-monitored devices in order to ensure the full utilization of robot end resources, that is, state data of the to-be-monitored devices are obtained according to different frequencies. For example, lidar sensors like electromechanical sensors may be monitored at high frequencies, and like cameras, some circuit sensors or pure electronic components, data may be monitored at low frequencies. Alternatively, the average value or the mean variance value of the voltage and the current of each device to be monitored in one monitoring period may be monitored. In addition, when the state data is acquired, the outlier points of which the number is less than the preset number are filtered. The outlier points are abnormal numerical points, for example, data exceeding a preset threshold value, when the outlier points are within a preset number, the abnormal data can be considered as accidental data, and the outlier points are filtered out, but when the outlier points exceed the preset number, the influence on the monitoring result exists, and the filtering is not performed, so that the transmission of data volume is reduced, and the accuracy of the monitoring result is ensured.
In one possible embodiment, the training process of the associated fault monitoring model of the device to be monitored includes the following steps A1 to A3:
step A1, classifying the devices to be monitored based on the device types.
And A2, respectively acquiring at least two sample state data and corresponding state results of each type of device to be monitored.
And A3, training the at least two sample state data and the corresponding state results based on a deep learning network to obtain an associated fault monitoring model of each type of device to be monitored.
The sample state data may be all data related to the device to be monitored, for example, physical state values of hardware, such as voltage and current values, monitored by a device such as Pulse Code Modulation (PCM) or Vehicle Communication Unit (VCU); and data sensed by the sensing sensor, such as a temperature value sensed by the temperature sensor. The state result may be a state of the device to be monitored in each sample state data, that is, whether the device to be monitored is in a normal state or an abnormal state in different sample state data.
Specifically, the association relationships among the state data output by different devices are different, and the devices with the same or similar association relationships are positioned in the same type of device, so that the devices to be monitored can be classified. For example, if the monitoring state data of several devices to be monitored in the mobile robot are the same and the characteristic association relationship between the state data is the same, that is, the association relationship between the state data and the monitoring result is also the same, the several devices to be monitored can be determined as devices of the same type, and the devices to be monitored belonging to the same type can be monitored by using the same association fault monitoring model. After the devices to be monitored are classified according to the relation between the state data of the devices to be monitored, the models of each type of devices to be monitored are trained respectively so as to facilitate the monitoring of the devices to be monitored, and at least two types of sample state data and corresponding state results of each type of devices to be monitored are obtained in advance before training, so that the accuracy of the models can be higher. And finally, learning the acquired at least two sample state data and corresponding state results of each type of device to be monitored based on a deep learning network to obtain an accurate associated fault monitoring model of each type of device to be monitored.
In a possible embodiment, training the at least two sample state data and the corresponding state result based on a deep learning network to obtain an associated fault monitoring model of each type of device to be monitored, may include the following steps B1-B3:
b1, learning a feature association relationship between the at least two sample state data based on a deep learning network, and a direct association relationship between the feature association relationship and a corresponding state result.
And B2, learning a characteristic change relation between at least two sample state data under different data acquisition time information based on the deep learning network, and a trend change relation between the characteristic change relation and corresponding state result change information.
And B3, determining the associated fault monitoring model of each type of device to be monitored according to the direct association relation and the trend change relation.
Wherein, the sample state data comprises data acquisition time information. The data acquisition time information refers to the time at which the sample state data is acquired.
Specifically, at least two kinds of sample state data of the device to be monitored are obtained, a feature association relationship between the at least two kinds of sample state data and a direct association relationship between the feature association relationship and a corresponding state result are learned based on a deep learning network, for example, a current value and a voltage value of the device to be monitored are obtained, a feature association relationship between the current value and the voltage value is learned based on the deep learning network, and a relationship between the feature association relationship between the current value and the voltage value and the state result of the device to be monitored is learned based on the deep learning network, so that the direct association relationship between the feature association relationship and the corresponding state result is obtained. In addition, a characteristic change relationship between at least two types of sample state data under different data acquisition time information and a trend change relationship between the characteristic change relationship and corresponding state result change information need to be learned based on a deep learning network, so that the corresponding change condition of the state data in a time period when the device to be monitored is abnormal can be known conveniently, and finally, an associated fault monitoring model of each type of device to be monitored is obtained according to the direct association relationship and the trend change relationship in a training mode, so that the obtained associated fault monitoring model is more accurate, and accurate real-time monitoring and state prediction of the device to be monitored are realized.
According to the technical scheme, the direct association relationship between the characteristic association relationship of at least two types of sample state data and the corresponding state result is learned based on the deep learning network, and the trend change relationship between the characteristic change relationship of at least two types of sample state data and the corresponding state result change information under different data acquisition time information is learned based on the deep learning network, so that the granularity of data in the training process is ensured to be sufficient, the accuracy of the direct association relationship and the trend change relationship is further ensured, the accurate association fault monitoring model of each type of device to be monitored can be obtained through the direct association relationship and the trend change relationship, and the real-time monitoring and prediction of the device to be monitored of the mobile robot are more accurate.
In a possible embodiment, training the at least two types of sample state data and the corresponding state results based on a deep learning network to obtain an associated fault monitoring model of each type of device to be monitored, includes:
training the at least two sample state data and the corresponding state results under different working condition parameters based on a deep learning network to obtain associated fault monitoring models of each type of device to be monitored under different working conditions.
The sample state data comprises sample state data under different working condition parameters.
The operating condition parameters may be parameter information of the device to be monitored in different operating condition environments, for example, parameters of the device to be monitored in abnormal environments such as rain, snow, high temperature, and the like.
Specifically, the monitoring device of mobile robot can produce unusual state data under different operating modes, but does not show under this condition that waiting to monitor the device and having appeared unusually, therefore can not use the associated fault monitoring model based on the state data training that monitoring device produced under the normal environment, need utilize at least two kinds of sample state data and the corresponding state result under the different operating mode parameters to train the fault monitoring model to obtain accurate associated fault monitoring model, avoided because declare monitoring device the abnormal state for the mistake under different operating modes.
According to the technical scheme, at least two sample state data and corresponding state results under different working condition parameters are trained on the basis of the deep learning network, so that associated fault monitoring models of each type of device to be monitored under different working conditions can be obtained, and the problem that when the same associated fault monitoring model is used for monitoring the devices to be monitored under different working conditions, the monitoring results are inaccurate, and the normal work of the mobile robot is influenced is avoided.
S130, determining a fault device in the mobile robot according to the monitoring result so as to locate the fault reason of the mobile robot.
Specifically, the monitoring results of the devices to be monitored are obtained through the pre-trained associated fault monitoring models of the devices to be monitored, the fault devices in the mobile robot can be accurately judged according to the monitoring results, the fault reasons of the mobile robot are accurately known, the fault position of the mobile robot is accurately positioned, and a technician can conveniently maintain the robot.
In a possible embodiment, after determining a faulty device in the mobile robot from the monitoring results, the method further comprises steps C1-C2:
and C1, determining an auxiliary sensor of the fault sensor based on a predetermined mapping relation.
And C2, acting the auxiliary sensor based on a preset auxiliary mechanism so as to supplement the function of the fault sensor.
The auxiliary sensor can be used for ensuring that when the mobile robot has a problem in the sensor, the fault function of the robot can be supplemented by using the function of the auxiliary sensor in time, and the normal operation of the mobile robot is ensured. A corresponding auxiliary sensor is determined for each sensor in the mobile robot in advance according to the function of each sensor.
Specifically, some sensors which are prone to problems can be matched with auxiliary sensors in the mobile robot, so that when the sensors break down, the auxiliary sensors can be timely acted based on a preset auxiliary mechanism, and normal operation of the mobile robot is guaranteed. For example, when the mobile robot operates in abnormal weather (such as rain, snow, haze and the like), and point cloud data collected by the laser radar sensor is abnormal, the mobile robot can be reminded to start an auxiliary sensor through monitoring and early warning, such as an auxiliary positioning sensor, so as to supplement the function of the laser radar sensor with faults.
According to the technical scheme, the auxiliary sensor which has a mapping relation with the fault sensor acts through the preset auxiliary mechanism so as to supplement the function of the fault sensor, and the normal operation of the mobile robot is ensured.
According to the technical scheme of the embodiment of the invention, at least two kinds of state data of each device to be monitored in the mobile robot are input into the pre-trained associated fault monitoring model of each device to be monitored, so that an accurate monitoring result of each device to be monitored is obtained, wherein the associated fault monitoring model is obtained by training at least two kinds of state data of different types of monitoring devices, the monitoring accuracy of each device is improved, and the influence caused by the inaccuracy of single state data is avoided; according to the scheme, all sensors and/or hardware devices in the mobile robot are monitored in a targeted manner, so that fault devices in the mobile robot can be accurately positioned according to monitoring results, the fault reasons of the mobile robot are further determined, the accuracy of monitoring the mobile robot is improved, and accurate and effective real-time monitoring and prediction of the mobile robot are realized.
Example two
Fig. 2 is a flowchart of a fault status monitoring method for a mobile robot according to a second embodiment of the present invention, and this embodiment describes in detail monitoring results of devices to be monitored obtained in the foregoing embodiment. As shown in fig. 2, the method includes:
s210, at least two kinds of state data of each device to be monitored in the mobile robot are obtained.
And S220, determining a target fault monitoring model according to the type of the device to be monitored.
Specifically, the type of the device to be monitored can be determined according to the incidence relation between the state data of the device to be monitored, and the target fault monitoring model is obtained by training according to the incidence relation between the state data of the device to be monitored, so that the target fault monitoring model can be determined according to the type of the monitoring device.
And S230, inputting the at least two state data into the target fault monitoring model.
Specifically, in the operation process of the mobile robot, the acquired at least two state data are input into a target fault monitoring model determined according to the type of the device to be monitored, so as to obtain an accurate monitoring result of the device to be monitored.
In one possible embodiment, inputting the at least two state data into the target fault monitoring model may include the following steps D1-D2:
and D1, determining a current state monitoring result according to the current state data based on the direct incidence relation and the characteristic incidence relation in the target fault monitoring model.
And D2, determining a future state prediction result according to the current state data and the historical state data based on the trend change relationship and the characteristic change relationship in the target fault monitoring model.
Optionally, the monitoring result includes a current state monitoring result and a future state prediction result; the state data includes current state data and historical state data.
The current state data may be data recorded by the mobile robot at the current moment and generated in the operation process of the device to be monitored. The historical state data can be data generated in the historical operation process of the device to be monitored, which is recorded by the mobile robot before the current moment.
Specifically, the current state monitoring result is information obtained by inputting current state data into the target fault monitoring model based on a direct incidence relation and a characteristic incidence relation in the target fault monitoring model, and is used for judging the current state of the mobile robot, so that a technician is guided to maintain the specified fault position of the low mobile robot, and accurate judgment of a device to be monitored is realized.
The future state prediction result is a possible future state change result of the mobile robot determined according to the current state data and the historical state data based on a trend change relation and a characteristic change relation in the target fault monitoring model, and is used for guiding technicians to maintain the mobile robot with problems in advance. Optionally, the future state prediction result refers to the health degree of the robot, that is, the health degree of the robot is determined according to the historical state data and the current state data of the mobile robot, and when the health degree is lower than a preset threshold, it indicates that a hidden danger exists in the health state of the robot, and a fault problem may occur at any time. For example, in a scene of a factory running at night or being unmanned, a technician needs to monitor the running states of devices to be monitored of all mobile robots, and when the health degree of the mobile robots is reduced to be less than a preset value, after-sales or operation and maintenance people need to be dispatched to perform maintenance and detection, so that the mobile robots are kept at a normal health degree. For example, the preset value is 95, and when the health degree of the mobile robot is lower than 95, the monitoring result of the mobile robot is directly sent to a technician for processing. For example, when it is monitored that the motor, the wearing part or the wearing part of the mobile robot is abnormal or the service life of the mobile robot is short due, and the axle-to-axle gap error of the motor is increased, a technician can directly replace a tire or directly replace a transmission device, so that the maintenance is facilitated for the technician, the automation level is improved, and the technical requirements on operation and maintenance personnel are reduced. In addition, by predicting the future running state of the device to be monitored in advance, early warning technicians can monitor or maintain the mobile robot possibly with problems in the future as soon as possible, and accidents are avoided.
According to the technical scheme, the target fault monitoring model is used for monitoring the device to be monitored, so that an accurate current state monitoring result and a future state prediction result are obtained, the fault position of the mobile robot is accurately positioned, the future running state of the mobile robot is accurately predicted, and the robot with a fault and about to have a fault can be timely and accurately maintained.
In a possible embodiment, after obtaining the monitoring result of each device to be monitored, the method further comprises the following steps E1-E2:
and E1, determining devices to be monitored which are mutually interfered according to the monitoring result.
And E2, arranging an independent power supply module for the devices to be monitored, which are mutually interfered.
Specifically, the devices to be monitored that interfere with each other mean that after a failure occurs in a certain device, the state data of another device that has not failed is affected to generate abnormal state data, and if the abnormality of the device to be monitored is simply determined according to the state data, the device that has not failed can be misjudged. The mutual influence of the devices to be monitored is generally the same because the power supplies are the same, so the devices to be monitored which are mutually interfered are determined according to the monitoring result, if a certain device fails, the state data abnormality of the other device is determined as a failure by the model, but the device is determined to be not failed through the investigation, the mutual interference of the two devices is determined, and in order to avoid the mutual interference of the devices to be monitored, an independent power supply module can be arranged, or the judgment of the physical state can be independently carried out, so that the influence between the devices to be monitored which are mutually interfered is eliminated.
For example, the two devices to be monitored have mutual interference, if one of the devices to be monitored fails, the state data output by the other device to be monitored, which does not have the failure, is affected, and it is easy to determine that both the two devices to be monitored fail during monitoring, so that the workload of technicians is increased, and the operation and maintenance efficiency is reduced.
According to the technical scheme, the independent power supply module is arranged for the devices to be monitored, which interfere with each other, so that the condition data acquired due to the interference between the devices to be monitored are prevented from being inaccurate, and the monitoring result is further influenced.
And S240, determining a fault device in the mobile robot according to the monitoring result so as to locate the fault reason of the mobile robot.
According to the technical scheme of the embodiment of the invention, at least two kinds of state data of each device to be monitored in the mobile robot are obtained, the at least two kinds of state data are input into a pre-trained associated fault monitoring model under the current working condition of each device to be monitored according to the current working condition, a previous state monitoring result and a future state prediction result of each device to be monitored are obtained, the accuracy of the monitoring result is ensured, the fault device in the mobile robot is determined according to the current monitoring result so as to accurately position the fault reason of the mobile robot, and meanwhile, whether the device in the mobile robot is about to have a fault or a possible risk is determined according to the future state prediction result, so that a technician can maintain the mobile robot in advance, and accidents are avoided.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a fault state monitoring apparatus for a mobile robot according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the data acquisition module 310 is configured to acquire at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored includes all sensor devices and/or hardware devices installed in the mobile robot.
The detection module 320 is configured to input the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored, so as to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used to learn a feature association relationship between the at least two state data.
And the positioning module 330 is configured to determine a fault device in the mobile robot according to the monitoring result, so as to position a fault cause of the mobile robot.
The monitoring result comprises a current state monitoring result and a future state prediction result; the state data includes current state data and historical state data.
Optionally, the detection module includes a model training unit, and is specifically configured to:
classifying the devices to be monitored based on the device types;
respectively acquiring at least two sample state data and corresponding state results of each type of device to be monitored;
training the at least two sample state data and the corresponding state result based on a deep learning network to obtain an associated fault monitoring model of each type of device to be monitored;
optionally, the detection module is specifically configured to:
determining a target fault monitoring model according to the type of the device to be monitored;
inputting the at least two state data into the target fault monitoring model.
Optionally, the model training unit includes a first type of model training unit, and is specifically configured to:
learning a feature association relation between the at least two sample state data and a direct association relation between the feature association relation and a corresponding state result based on a deep learning network;
learning a characteristic change relation between at least two sample state data under different data acquisition time information and a trend change relation between the characteristic change relation and corresponding state result change information based on the deep learning network;
and determining the associated fault monitoring model of each type of device to be monitored according to the direct association relation and the trend change relation.
The sample state data includes data acquisition time information.
Optionally, the detection module includes a result determination unit, and is specifically configured to:
determining a current state monitoring result according to the current state data based on the direct incidence relation and the characteristic incidence relation in the target fault monitoring model;
and determining a future state prediction result according to the current state data and the historical state data based on the trend change relationship and the characteristic change relationship in the target fault monitoring model.
Optionally, the model training unit includes a second type of model training unit, and is specifically configured to:
training the at least two sample state data and the corresponding state results under different working condition parameters based on a deep learning network to obtain associated fault monitoring models of each type of device to be monitored under different working conditions.
The sample state data comprises sample state data under different working condition parameters.
Optionally, the detection module further includes an interference avoidance unit, specifically configured to:
determining devices to be monitored which are mutually interfered according to the monitoring result;
and arranging an independent power supply module for the devices to be monitored, which are mutually interfered.
Optionally, the positioning module further includes a replacing unit, specifically configured to:
determining an auxiliary sensor of the faulty sensor based on a predetermined mapping relationship;
and acting the auxiliary sensor based on a preset auxiliary mechanism to supplement the function of the fault sensor.
The fault monitoring model is trained in advance on the cloud platform, and is deployed at the mobile robot end to be monitored after training is completed.
The fault state monitoring device of the mobile robot provided by the embodiment of the invention can execute the fault state monitoring method of the mobile robot provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations and do not violate the good custom of the public order.
Example four
Fig. 4 is a schematic structural diagram of an electronic device that can be used to implement the fault status monitoring method for a mobile robot according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The processor 11 performs the various methods and processes described above, such as fault condition monitoring of the mobile robot.
In some embodiments, the method fault condition monitoring of a mobile robot may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of fault condition monitoring of the mobile robot of the method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g. by means of firmware) to perform fault condition monitoring of the mobile robot of the method.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A fault state monitoring method of a mobile robot, comprising:
acquiring at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored comprises all sensor devices and/or hardware devices installed in the mobile robot;
inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used for learning a characteristic association relationship between the at least two state data;
and determining a fault device in the mobile robot according to the monitoring result so as to locate the fault reason of the mobile robot.
2. The method according to claim 1, wherein the training process of the associated fault monitoring model of the device to be monitored is as follows:
classifying the devices to be monitored based on the device types;
respectively acquiring at least two sample state data and corresponding state results of each type of device to be monitored;
training the at least two sample state data and the corresponding state result based on a deep learning network to obtain an associated fault monitoring model of each type of device to be monitored;
correspondingly, inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored, including:
determining a target fault monitoring model according to the type of the device to be monitored;
inputting the at least two state data into the target fault monitoring model.
3. The method of claim 2, wherein the sample state data includes data acquisition time information;
correspondingly, training the at least two sample state data and the corresponding state results based on the deep learning network to obtain the associated fault monitoring model of each type of device to be monitored, including:
learning a feature association relation between the at least two types of sample state data and a direct association relation between the feature association relation and a corresponding state result based on a deep learning network;
learning a characteristic change relation between at least two sample state data under different data acquisition time information and a trend change relation between the characteristic change relation and corresponding state result change information based on the deep learning network;
and determining the associated fault monitoring model of each type of device to be monitored according to the direct association relation and the trend change relation.
4. The method of claim 3, wherein the monitoring results comprise current state monitoring results and future state prediction results; the state data comprises current state data and historical state data;
correspondingly, inputting the at least two state data into the target fault monitoring model includes:
determining a current state monitoring result according to the current state data based on the direct incidence relation and the characteristic incidence relation in the target fault monitoring model;
and determining a future state prediction result according to the current state data and the historical state data based on the trend change relationship and the characteristic change relationship in the target fault monitoring model.
5. The method of claim 2, wherein the sample state data comprises sample state data under different operating condition parameters;
correspondingly, training the at least two sample state data and the corresponding state results based on the deep learning network to obtain the associated fault monitoring model of each type of device to be monitored, including:
training the at least two sample state data and the corresponding state results under different working condition parameters based on a deep learning network to obtain associated fault monitoring models of each type of device to be monitored under different working conditions.
6. The method of claim 1, wherein after obtaining the monitoring results for each device to be monitored, the method further comprises:
determining devices to be monitored which are mutually interfered according to the monitoring result;
and arranging an independent power supply module for the devices to be monitored, which are mutually interfered.
7. The method of claim 1, wherein after determining a faulty device in the mobile robot from the monitoring results, the method further comprises:
determining an auxiliary sensor of the faulty sensor based on a predetermined mapping relationship;
acting the auxiliary sensor based on a pre-set auxiliary mechanism to supplement the functionality of the faulty sensor.
8. The method according to claim 1, wherein the fault monitoring model is pre-trained on a cloud platform and deployed at a mobile robot end for monitoring after training.
9. A fault state monitoring device of a mobile robot, comprising:
the data acquisition module is used for acquiring at least two state data of each device to be monitored in the mobile robot; wherein the device to be monitored comprises all sensor devices and/or hardware devices installed in the mobile robot;
the detection module is used for inputting the at least two state data into a pre-trained associated fault monitoring model of each device to be monitored to obtain a monitoring result of each device to be monitored; wherein the fault monitoring model is used for learning a characteristic association relationship between the at least two state data;
and the positioning module is used for determining a fault device in the mobile robot according to the monitoring result so as to position the fault reason of the mobile robot.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the fault status monitoring method of the mobile robot of any one of claims 1-8.
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Application publication date: 20221115 Assignee: Muyi (Huzhou) Technology Development Co.,Ltd. Assignor: SHANGHAI MOOE-ROBOT TECHNOLOGY Co.,Ltd. Contract record no.: X2024980007103 Denomination of invention: Fault state monitoring methods, devices, and equipment for mobile robots License type: Common License Record date: 20240613 |