CN111844029A - Robot early warning monitoring method and device - Google Patents
Robot early warning monitoring method and device Download PDFInfo
- Publication number
- CN111844029A CN111844029A CN202010656755.2A CN202010656755A CN111844029A CN 111844029 A CN111844029 A CN 111844029A CN 202010656755 A CN202010656755 A CN 202010656755A CN 111844029 A CN111844029 A CN 111844029A
- Authority
- CN
- China
- Prior art keywords
- fault
- robot
- type
- early warning
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a robot early warning monitoring method, which is used for early warning fault information of a robot and comprises the following steps: acquiring a preset early warning rule; the early warning rules comprise fault types possibly occurring in the robot and SOP schemes after the fault types occur; receiving a fault report sent by a robot; analyzing the fault report, and judging the type and specific fault information of the robot; and implementing a corresponding SOP scheme according to the fault type and the specific fault information. According to the invention, the fault type information of the robot is obtained by analyzing after the fault report sent by the robot is received through the preset early warning rule, and then the corresponding SOP scheme is rapidly obtained through the fault type information to correspondingly process the robot, so that the function of modular processing is realized, and the efficiency of solving the robot fault is improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of robots, in particular to a robot early warning monitoring method and device.
[ background of the invention ]
During the operation of the robot at an actual station, various unexpected conditions including hardware damage, hardware failure, program error, environmental predicament and the like may occur, so that the state of the robot needs to be pre-warned and monitored. However, in the prior art, there is no fast and modularized solution for the fault report sent by the robot, so that the fault of the robot cannot be handled in time.
In view of the above, it is desirable to provide a method and an apparatus for monitoring a robot to overcome the above-mentioned drawbacks.
[ summary of the invention ]
The invention aims to provide a robot early warning monitoring method and device, and aims to solve the problem that a fault report sent by a robot does not have a quick and modularized coping scheme, and improve the efficiency of solving the robot fault.
In order to achieve the above object, an aspect of the present invention provides a robot early warning monitoring method for early warning fault information of a robot, including the following steps:
acquiring a preset early warning rule; the early warning rules comprise fault types which can possibly occur to the robot and SOP schemes after the fault types occur;
receiving a fault report sent by the robot;
analyzing the fault report, and judging the type and specific fault information of the robot;
and implementing a corresponding SOP scheme according to the fault type and the specific fault information.
In a preferred embodiment, the method further comprises the steps of:
analyzing and counting the fault reports sent by all the robots;
and counting the occurrence frequency of each fault type, and generating a quality report of the robot.
In a preferred embodiment, the step of obtaining the preset early warning rule includes:
acquiring preset fault type information of the robot; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type;
acquiring error code definition corresponding to each fault type;
obtaining different levels of error grading on the error code definition;
and acquiring different alarm levels and different notification modes which are set according to different error grades.
In a preferred embodiment, the step of receiving the fault information sent by the robot includes:
acquiring preset statistical time interval information;
receiving an alarm record sent by the robot;
and counting the alarm records at regular intervals according to the counting time interval information and generating a fault report.
In a preferred embodiment, said step of implementing a corresponding SOP scheme according to said fault type comprises:
obtaining a judgment result of the fault type; when the judgment result is that the software program fails, resetting and recovering the state according to the specific failure information and by combining the current state of the robot; and when the judgment result is that the hardware module is in fault, sending the specific fault information to a worker.
Another aspect of the present invention provides a robot early warning monitoring apparatus, including:
the acquisition module is used for acquiring a preset early warning rule; the early warning rules comprise fault types which can possibly occur to the robot and SOP schemes after the fault types occur;
the receiving module is used for receiving a fault report sent by the robot;
the analysis module is used for analyzing the fault report and judging the fault type and specific fault information of the robot;
and the processing module is used for implementing a corresponding SOP scheme according to the fault type and the specific fault information.
In a preferred embodiment, the method further comprises:
the statistical module is used for analyzing and counting the fault reports sent by all the robots;
and the generating module is used for counting the occurrence frequency of each fault type and generating a quality report of the robot.
In a preferred embodiment, the obtaining module includes:
the first acquisition unit is used for acquiring preset fault type information of the robot; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type;
A second obtaining unit, configured to obtain an error code definition corresponding to each fault type;
a third obtaining unit, configured to obtain different levels of error classification for the error code definition;
and the fourth acquisition unit is used for acquiring different alarm levels and different notification modes which are set according to different error grades.
In a preferred embodiment, the receiving module comprises:
the time acquisition unit is used for acquiring preset statistical time interval information;
the record receiving unit is used for receiving the alarm record sent by the robot;
and the report generating unit is used for counting the alarm records at regular intervals according to the counting time interval information and generating fault reports.
In a preferred embodiment, the processing module comprises:
a result obtaining unit, configured to obtain a determination result of the fault type;
the recovery unit is used for resetting and recovering the state according to the specific fault information and by combining the current state of the robot when the judgment result is that the software program fails;
and the notification unit is used for sending the specific fault information to a worker when the judgment result is that the hardware module has a fault.
According to the invention, the fault type information of the robot is obtained by analyzing after the fault report sent by the robot is received through the preset early warning rule, and then the corresponding SOP scheme is rapidly obtained through the fault type information to correspondingly process the robot, so that the function of modular processing is realized, and the efficiency of solving the robot fault is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a robot early warning monitoring method provided by the present invention;
FIG. 2 is a flow chart of another embodiment of the robot early warning monitoring method shown in FIG. 1;
fig. 3 is a flowchart illustrating sub-steps of step S101 of the robot early warning and monitoring method shown in fig. 1;
fig. 4 is a flowchart illustrating sub-steps of step S102 of the robot early warning and monitoring method shown in fig. 1;
fig. 5 is a flowchart illustrating sub-steps of step S104 of the robot early warning and monitoring method shown in fig. 1;
Fig. 6 is a block diagram of an architecture of a robot early warning and monitoring device provided by the present invention;
FIG. 7 is a block diagram of another embodiment of the robot early warning and monitoring device shown in FIG. 6;
fig. 8 is a block diagram of an architecture of an acquisition module of the robot early warning and monitoring device shown in fig. 6;
fig. 9 is a block diagram of an architecture of a receiving module of the robot early warning and monitoring device shown in fig. 6;
fig. 10 is a block diagram of the processing module of the robot early warning and monitoring device shown in fig. 6.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantageous effects of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a robot early warning monitoring method which is used for early warning fault information of a robot, remotely monitoring the robot running at a site, establishing a background of an early warning system and conveniently and timely processing various faults of the robot.
As shown in fig. 1, the method comprises the following steps S101-S104.
In step S101, a preset early warning rule is acquired; the early warning rules include fault types that may occur to the robot and SOP (Standard Operating Procedure) schemes after each fault type occurs.
In the step, a classification is performed on various fault types which may occur to the robot, and a corresponding targeted solution is preset for each classification. For example, the types of faults that the robot may generate include hardware device faults, system software errors, task response timeouts, etc. of the robot. The hardware equipment failure can be repaired manually by workers for example; remote debugging and repairing can be performed aiming at system software error faults. Sending a confirmation request instruction to the robot at intervals, and if the robot normally feeds back the confirmation request instruction, determining that the robot normally runs; if the robot does not feed back the confirmation request instruction or the feedback time is overtime, the robot is tried to be controlled to upload the current states of each hardware module, system software and task execution, and if the robot does not upload the states, the states can be regarded as hardware faults, so that the state of the robot can be clearly known in a remote mode, and targeted troubleshooting is facilitated.
Specifically, as shown in FIG. 3, step S101 includes sub-steps S1011-S1014.
In step S1011, the preset fault type information of the robot is acquired; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type. Specifically, the faults of the robot are classified, the fault processing modes of the same type are the same, and a modular processing mode is formed, namely each type of fault corresponds to one SOP scheme. It can be understood that after the fault is divided into a plurality of large classes, each large class is internally subdivided into a plurality of small classes, so that accurate statistics on the type of the fault is facilitated.
In step S1012, an error code definition corresponding to each fault type is acquired. For each major fault, error code encoding is performed, for example, the error code of the software system is 01, the error code of the motherboard module is 02, and the error code of the sensor module is 03; then, each subclass of the software system class continues to encode, for example, the error code for the os failure is 011, the error code for the communication system failure is 012, and so on. Therefore, fault information uploaded by the follow-up robot is sent in the form of an error code, and background statistics is facilitated.
In step S1013, error classifications for different levels of error code definitions are obtained. Where the importance of the impact of the fault on the operation of the robot can be prioritized. For example, a failure in a master module is the most important first level, and a failure in a sensor module is the second level of secondary importance. By carrying out priority classification on fault types related to importance, the state of the robot can be flexibly monitored and processed by workers on the premise of ensuring the execution of tasks of the robot.
In step S1014, different alarm levels and different notification manners according to different error classifications are acquired. Specifically, for example, for the first level with the highest priority, an alarm mode with the maximum intensity is correspondingly set, such as a combination of light, sound and vibration to give an alarm, so as to remind a worker to maintain in the shortest time; for the second level of next highest priority, a combination of sound and vibration is used to alert the worker that treatment must be performed within a certain time.
Therefore, through error code encoding and grading of fault types, working personnel can reasonably plan the processing progress of the robot or carry out priority sequencing on processing when a plurality of robots simultaneously have faults, and smooth execution of tasks of the robots is ensured.
In step S102, the fault report transmitted by the robot is received. Wherein, after the robot breaks down, the self error reporting information is sent to the background. The robot sends the current positioning information, task execution information, data information detected by each sensor module and the like to the background at intervals, when abnormal data occur to a certain module,
specifically, as shown in FIG. 4, step S102 includes S1021-S1023.
In step S1021, preset statistical time interval information is acquired. The background is preset with a statistic time interval, and the error report sent by the robot is counted every other time interval and recorded in the database.
In step S1022, the alarm record transmitted by the robot is received. The background is in a state of receiving the alarm report of the robot at any time. And after receiving an alarm report sent by the robot, carrying out corresponding error code coding and grading on fault information in the alarm report according to a preset early warning rule.
In step S1023, the alarm records are counted at regular intervals according to the counted time interval information, and a fault report is generated. In the time interval, the background classifies, counts and sorts the faults in all the alarm reports sent by all the robots according to error code codes and classification, automatically marks out the faults needing to be solved in sequence according to the classification priority, and facilitates corresponding processing by workers. Meanwhile, the occurrence frequency of each type of error codes is counted, and if the occurrence frequency of the type of error codes is high, it indicates that the equipment or the software system of the robot, which may have the type of fault, needs to be upgraded and optimized.
It can be appreciated that each robot has a unique code, and therefore, the fault sent by each robot is traceable. The fault report comprises the statistical records of all faults of each robot, and also comprises the statistical records of the occurrence frequency of all robots in error codes of various types, so that the problems in robot design can be more easily caused by workers, and the workers can perform subsequent improvement in a targeted manner.
In step S103, the fault report is analyzed to determine the type and specific fault information of the fault occurring in the robot.
In this step, analysis is performed based on alarm information sent by the robot. For the fault of the system software class, the method mainly finds out a program in which data is abnormal or fails to operate normally, determines the fault type of the robot and the specific processing module or applet in the type, including but not limited to system file missing, configuration file failure and the like. For the faults of the hardware modules, the background can judge the connection state of each hardware by sending a confirmation instruction aiming at the main board module, the motion assembly and the sensor assembly or by detecting the current or level state of each assembly, so as to accurately position the fault occurrence area.
In step S104, a corresponding SOP scheme is implemented according to the fault type and the specific fault information.
After the fault information of the robot is determined, the staff carries out corresponding processing according to the SOP scheme. The method optimizes the working process, realizes the modularized processing of the robot process, reasonably arranges the fault processing flows of a plurality of robots, and improves the efficiency of robot fault removal.
Specifically, as shown in FIG. 5, step S104 includes sub-steps S1041-S1042.
In step S1041, a determination result of the determination type is acquired. And receiving the analyzed specific fault information with the attached error code so as to determine the fault type.
In step S1042, when the determination result is that the software program has a fault, a state reset recovery is performed according to the specific fault information and the current state of the robot. At this time, the system can be restored by reloading the system software, adding missing configuration files and the like; or checking the real-time state and the running state of the current robot according to the alarm content sent by the robot, and recovering the current state and the running state by combining the current sensing data, camera data, task progress, historical track and the like of the robot. When the robot recovers the action capability, if the robot has a task, the corresponding task is executed.
And when the judgment result is that the hardware module has a fault, sending specific fault information to a worker. The treatment is carried out manually.
Further, in one embodiment, as shown in fig. 2, the present invention further includes a step S105.
Analyzing and counting fault reports sent by all robots; and counting the occurrence frequency of each fault type to generate a quality report of the robot.
Specifically, all fault reports sent by each robot are counted, a fault list is generated in the form of error codes, and a region with high fault frequency of the robot is judged, so that the robot is correspondingly maintained. In addition, the fault reports of all robots are counted, fault lists are counted and generated in the form of error codes, the frequency of occurrence of each error code is included, and the operation conditions of all parts of the robots in the batch are obtained through data analysis, so that improvement is performed on parts with high fault rates, for example, software which frequently reports errors is optimized, BUGs are cleaned, and the like, or suppliers of sensors are replaced. And combining the final fault list with the analysis report to generate a quality report of the single robot or the batch-changed robot, so that the follow-up improvement is facilitated.
Therefore, the method classifies the faults sent by the robot and adopts the corresponding SOP scheme for processing, so that the processing efficiency is improved. Meanwhile, the occurrence frequency of each type of fault is counted to judge the high-occurrence area of the fault of the robot, so that designers can correspondingly improve in subsequent development, and the quality of the subsequent development is improved.
Another aspect of the present invention provides a robot early-warning monitoring apparatus 100 for performing remote monitoring on a robot, and the specific implementation and principle are the same as those of the robot early-warning monitoring method, so that the detailed description is omitted.
As shown in fig. 6, the robot early warning monitoring apparatus 100 includes:
the acquisition module 10 is used for acquiring a preset early warning rule; the early warning rules comprise fault types possibly occurring in the robot and SOP schemes after the fault types occur;
a receiving module 20, configured to receive a fault report sent by the robot;
the analysis module 30 is used for analyzing the fault report and judging the fault type and specific fault information of the robot;
and the processing module 40 is used for implementing a corresponding SOP scheme according to the fault type and the specific fault information.
Further, in one embodiment, as shown in fig. 7, the robot early warning and monitoring apparatus 100 further includes:
the 50 statistic module is used for analyzing and counting fault reports sent by all the robots;
and 60, a generation module for counting the occurrence frequency of each fault type and generating a quality report of the robot.
As shown in fig. 8, the robot early warning and monitoring apparatus 100 includes an acquisition module 10:
The first acquiring unit 11 is used for acquiring preset fault type information of the robot; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type;
a second obtaining unit 12, configured to obtain an error code definition corresponding to each fault type;
a third obtaining unit 13, configured to obtain different levels of error classification for error code definitions;
a fourth obtaining unit 14, configured to obtain different alarm levels and different notification manners according to different error classifications.
Further, in one embodiment, as shown in fig. 9, the receiving module 20 includes:
a time obtaining unit 21, configured to obtain preset statistical time interval information;
the record receiving unit 22 is used for receiving the alarm record sent by the robot;
and the report generating unit 23 is configured to count the alarm records at regular intervals according to the counting time interval information and generate a fault report.
Further, in one embodiment, as shown in fig. 10, the processing module 40 includes:
a result acquisition unit 41 for acquiring a judgment result of the type of the failure;
the recovery unit 42 is used for performing state resetting and recovery according to specific fault information and by combining the current state of the robot when the judgment result is that the software program fails;
And the notification unit 43 is configured to send specific fault information to the staff when the determination result is that the hardware module is faulty.
The invention provides a terminal, which comprises a memory, a processor and a robot early warning monitoring program stored in the memory and capable of running on the processor, wherein when the robot early warning monitoring program is executed by the processor, the steps of the robot early warning monitoring method are realized.
In another aspect of the present invention, a readable storage medium stores a robot early-warning monitoring program, and the robot early-warning monitoring program, when executed by a processor, implements the steps of the robot early-warning monitoring method as described above.
In summary, according to the invention, the fault type information of the robot is obtained by analyzing after the fault report sent by the robot is received through the preset early warning rule, and then the corresponding SOP scheme is rapidly obtained through the fault type information to perform corresponding processing on the robot, so that the function of modular processing is realized, and the efficiency of solving the robot fault is improved.
The invention is not limited solely to that described in the specification and embodiments, and additional advantages and modifications will readily occur to those skilled in the art, so that the invention is not limited to the specific details, representative apparatus, and illustrative examples shown and described herein, without departing from the spirit and scope of the general concept as defined by the appended claims and their equivalents.
Claims (10)
1. A robot early warning monitoring method is used for early warning fault information of a robot and is characterized by comprising the following steps:
acquiring a preset early warning rule; the early warning rules comprise fault types which can possibly occur to the robot and SOP schemes after the fault types occur;
receiving a fault report sent by the robot;
analyzing the fault report, and judging the type and specific fault information of the robot;
and implementing a corresponding SOP scheme according to the fault type and the specific fault information.
2. The robot early warning monitoring method of claim 1, further comprising the steps of:
analyzing and counting the fault reports sent by all the robots;
and counting the occurrence frequency of each fault type, and generating a quality report of the robot.
3. The robot early warning monitoring method according to claim 1, wherein the step of obtaining the preset early warning rule comprises:
acquiring preset fault type information of the robot; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type;
Acquiring error code definition corresponding to each fault type;
obtaining different levels of error grading on the error code definition;
and acquiring different alarm levels and different notification modes which are set according to different error grades.
4. The robot early warning monitoring method according to claim 1, wherein the step of receiving the fault information sent by the robot comprises:
acquiring preset statistical time interval information;
receiving an alarm record sent by the robot;
and counting the alarm records at regular intervals according to the counting time interval information and generating a fault report.
5. The robot early warning monitoring method of claim 1, wherein the step of implementing a corresponding SOP scheme according to the fault type comprises:
obtaining a judgment result of the fault type; when the judgment result is that the software program fails, resetting and recovering the state according to the specific failure information and by combining the current state of the robot; and when the judgment result is that the hardware module is in fault, sending the specific fault information to a worker.
6. A robot early warning monitoring device, comprising:
The acquisition module is used for acquiring a preset early warning rule; the early warning rules comprise fault types which can possibly occur to the robot and SOP schemes after the fault types occur;
the receiving module is used for receiving a fault report sent by the robot;
the analysis module is used for analyzing the fault report and judging the fault type and specific fault information of the robot;
and the processing module is used for implementing a corresponding SOP scheme according to the fault type and the specific fault information.
7. The robotic early warning monitoring device of claim 6, further comprising:
the statistical module is used for analyzing and counting the fault reports sent by all the robots;
and the generating module is used for counting the occurrence frequency of each fault type and generating a quality report of the robot.
8. The robotic pre-warning monitoring device of claim 6, wherein the acquisition module comprises:
the first acquisition unit is used for acquiring preset fault type information of the robot; the fault types comprise a software system type, a task response timeout type, a main board module type and a sensor module type;
A second obtaining unit, configured to obtain an error code definition corresponding to each fault type;
a third obtaining unit, configured to obtain different levels of error classification for the error code definition;
and the fourth acquisition unit is used for acquiring different alarm levels and different notification modes which are set according to different error grades.
9. The robotic pre-warning monitoring device of claim 6, wherein the receiving module comprises:
the time acquisition unit is used for acquiring preset statistical time interval information;
the record receiving unit is used for receiving the alarm record sent by the robot;
and the report generating unit is used for counting the alarm records at regular intervals according to the counting time interval information and generating fault reports.
10. The robotic pre-warning monitoring device of claim 6, wherein the processing module comprises:
a result obtaining unit, configured to obtain a determination result of the fault type;
the recovery unit is used for resetting and recovering the state according to the specific fault information and by combining the current state of the robot when the judgment result is that the software program fails;
And the notification unit is used for sending the specific fault information to a worker when the judgment result is that the hardware module has a fault.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010656755.2A CN111844029A (en) | 2020-07-09 | 2020-07-09 | Robot early warning monitoring method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010656755.2A CN111844029A (en) | 2020-07-09 | 2020-07-09 | Robot early warning monitoring method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111844029A true CN111844029A (en) | 2020-10-30 |
Family
ID=73152653
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010656755.2A Pending CN111844029A (en) | 2020-07-09 | 2020-07-09 | Robot early warning monitoring method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111844029A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113848812A (en) * | 2021-09-03 | 2021-12-28 | 珠海格力智能装备有限公司 | Robot control method, control device and control system |
| CN114367983A (en) * | 2021-12-29 | 2022-04-19 | 北京旷视机器人技术有限公司 | Abnormality processing method, robot, conveyance system, program product, and storage medium |
| CN114770509A (en) * | 2022-05-05 | 2022-07-22 | 新代科技(苏州)有限公司 | Fault diagnosis method applied to welding robot system |
| CN114833817A (en) * | 2021-02-01 | 2022-08-02 | 丰田自动车株式会社 | Robot control system, robot control method, and computer-readable medium |
| CN117278805A (en) * | 2023-11-17 | 2023-12-22 | 广州市千钧网络科技有限公司 | Information prompting method, device, electronic equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7641107B1 (en) * | 1996-11-29 | 2010-01-05 | Diebold, Incorporated | Fault monitoring and notification system for automated banking machines |
| CN102733152A (en) * | 2012-05-15 | 2012-10-17 | 海尔集团公司 | Washing machine alarm device and system and washing machine alarm processing method |
| CN102759411A (en) * | 2012-07-16 | 2012-10-31 | 力帆实业(集团)股份有限公司 | Temperature and humidity monitoring system of battery pack |
| CN103647662A (en) * | 2013-12-06 | 2014-03-19 | 北京奇虎科技有限公司 | Fault monitoring alarm method and apparatus |
| CN104548259A (en) * | 2015-01-15 | 2015-04-29 | 深圳市科曼医疗设备有限公司 | Alarming method of injection pump and injection pump |
| CN106154906A (en) * | 2016-05-03 | 2016-11-23 | 北京光年无限科技有限公司 | Fault reporting methods and robot operating system towards intelligent operating system |
| CN108989132A (en) * | 2018-08-24 | 2018-12-11 | 深圳前海微众银行股份有限公司 | Fault warning processing method, system and computer readable storage medium |
| CN109204189A (en) * | 2018-09-07 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Automated driving system, fault alarm method and device |
| CN109491875A (en) * | 2018-11-09 | 2019-03-19 | 浙江国自机器人技术有限公司 | A kind of robot information display method, system and equipment |
| CN109581994A (en) * | 2017-09-28 | 2019-04-05 | 深圳市优必选科技有限公司 | Robot fault diagnosis method and system and terminal equipment |
| US20190227915A1 (en) * | 2019-03-29 | 2019-07-25 | Intel Corporation | Programmatically generating software test libraries for functional safety |
-
2020
- 2020-07-09 CN CN202010656755.2A patent/CN111844029A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7641107B1 (en) * | 1996-11-29 | 2010-01-05 | Diebold, Incorporated | Fault monitoring and notification system for automated banking machines |
| CN102733152A (en) * | 2012-05-15 | 2012-10-17 | 海尔集团公司 | Washing machine alarm device and system and washing machine alarm processing method |
| CN102759411A (en) * | 2012-07-16 | 2012-10-31 | 力帆实业(集团)股份有限公司 | Temperature and humidity monitoring system of battery pack |
| CN103647662A (en) * | 2013-12-06 | 2014-03-19 | 北京奇虎科技有限公司 | Fault monitoring alarm method and apparatus |
| CN104548259A (en) * | 2015-01-15 | 2015-04-29 | 深圳市科曼医疗设备有限公司 | Alarming method of injection pump and injection pump |
| CN106154906A (en) * | 2016-05-03 | 2016-11-23 | 北京光年无限科技有限公司 | Fault reporting methods and robot operating system towards intelligent operating system |
| CN109581994A (en) * | 2017-09-28 | 2019-04-05 | 深圳市优必选科技有限公司 | Robot fault diagnosis method and system and terminal equipment |
| CN108989132A (en) * | 2018-08-24 | 2018-12-11 | 深圳前海微众银行股份有限公司 | Fault warning processing method, system and computer readable storage medium |
| CN109204189A (en) * | 2018-09-07 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Automated driving system, fault alarm method and device |
| CN109491875A (en) * | 2018-11-09 | 2019-03-19 | 浙江国自机器人技术有限公司 | A kind of robot information display method, system and equipment |
| US20190227915A1 (en) * | 2019-03-29 | 2019-07-25 | Intel Corporation | Programmatically generating software test libraries for functional safety |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114833817A (en) * | 2021-02-01 | 2022-08-02 | 丰田自动车株式会社 | Robot control system, robot control method, and computer-readable medium |
| CN113848812A (en) * | 2021-09-03 | 2021-12-28 | 珠海格力智能装备有限公司 | Robot control method, control device and control system |
| CN113848812B (en) * | 2021-09-03 | 2023-12-08 | 珠海格力智能装备有限公司 | Robot control method, control device and control system |
| CN114367983A (en) * | 2021-12-29 | 2022-04-19 | 北京旷视机器人技术有限公司 | Abnormality processing method, robot, conveyance system, program product, and storage medium |
| CN114770509A (en) * | 2022-05-05 | 2022-07-22 | 新代科技(苏州)有限公司 | Fault diagnosis method applied to welding robot system |
| CN117278805A (en) * | 2023-11-17 | 2023-12-22 | 广州市千钧网络科技有限公司 | Information prompting method, device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111844029A (en) | Robot early warning monitoring method and device | |
| CN113614666A (en) | System and method for detecting and predicting faults in an industrial process automation system | |
| CN117215940A (en) | Intelligent operation and maintenance emergency processing system | |
| CN111932077A (en) | TPM (trusted platform Module) whole-person production maintenance platform and method based on Internet of things | |
| CN113946499A (en) | Micro-service link tracking and performance analysis method, system, equipment and application | |
| CN110597196A (en) | Data acquisition system and data acquisition method | |
| CN113077065A (en) | Method, device and equipment for processing faults of vehicle production line and storage medium | |
| CN115174639A (en) | Industrial equipment management method and system based on Internet of things | |
| WO2023185061A1 (en) | Function security system detection method and system, electronic device, and storage medium | |
| CN115158086A (en) | Unattended operation method and system for battery replacement station and unattended battery replacement station | |
| CN111708654A (en) | Method and equipment for repairing virtual machine fault | |
| KR101748282B1 (en) | Plant diagnosis system and diagnosis method using the same | |
| CN115329877A (en) | Battery replacement station fault information pushing method | |
| CN110597198B (en) | Quality control device, quality control system and quality control method for TFT substrate glass | |
| CN117724882A (en) | Work order generation method, device and equipment of heat pump machine and storage medium | |
| CN106557839B (en) | Equipment maintenance strategy optimization method and system based on big data | |
| CN117194154A (en) | APM full-link monitoring system and method based on micro-service | |
| EP4246264A1 (en) | Analytical system for surface mount technology (smt) and method thereof | |
| CN116506278A (en) | Abnormal monitoring platform based on zabbix | |
| CN115824687A (en) | Equipment fault early warning method, device, equipment and storage medium | |
| CN115130692A (en) | Equipment cloud monitoring system | |
| CN114356460A (en) | Medical equipment health real-time acquisition monitoring method and system | |
| CN114372500A (en) | Intelligent factory control system based on big data | |
| CN114693051A (en) | On-duty monitoring method, system, equipment and medium for power change station | |
| CN111309537A (en) | Method and equipment for detecting error report of server diagnosis system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201030 |
|
| RJ01 | Rejection of invention patent application after publication |