CN114872086A - Inspection robot state detection method and system - Google Patents
Inspection robot state detection method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/06—Safety devices
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
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- H—ELECTRICITY
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- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/128—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment involving the use of Internet protocol
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Abstract
The invention discloses a state detection method of an inspection robot, which comprises the following steps: judging the running state of the inspection robot through the signal receiving state of the detection node; if the inspection robot runs abnormally, a path from the current detection node to the next detection node is sent to the management terminal; if the inspection robot runs normally, the running grade, the power grid environment and the detection node environment of the inspection robot are judged, and the running state of the inspection robot can be reversely monitored.
Description
Technical Field
The invention belongs to the technical field of transformer substation inspection, and particularly relates to a method and a system for detecting the state of an inspection robot.
Background
With the rapid development of the internet technology, in the application of the current network technology, the big data analysis is performed on various data by accessing data of different products, so that the operation of various indexes and data is realized, which is already an important content applied by the internet; in order to perform big data analysis, basic data related to access from each product becomes an important content, and the efficiency and quality of accessing the basic data directly affect the efficiency and quality of subsequent analysis processing and other processes.
In the field of power grid monitoring, an inspection robot is relied on to collect data, and the inspection robot intervenes the collected data into an intranet through a power data security access technology to store the data; however, the navigation technology of the conventional inspection robot is not mature enough, and the data receiving module cannot reversely monitor the running state of the inspection robot through the data access condition, so that the running state of the inspection robot cannot be guaranteed, and the normal access of electric power data is influenced.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method and a system for detecting the state of an inspection robot, which can reversely monitor the running state of the inspection robot.
The technical problem to be solved by the invention is realized by the following technical scheme:
in a first aspect, a method for detecting the state of an inspection robot is provided, which includes:
judging the running state of the inspection robot through the signal receiving state of the detection node;
if the inspection robot runs abnormally, a path from the current detection node to the next detection node is sent to the management terminal;
and if the inspection robot operates normally, judging the operation level, the power grid environment and the detection node environment of the inspection robot.
With reference to the first aspect, further, the determining the operation state of the inspection robot according to the signal receiving state of the detection node includes:
when the system time is advanced to the expected time for receiving the inspection robot to finish the detection of a certain detection node, judging whether the inspection information from the detection node is received, if so, judging that the inspection robot operates normally, otherwise, judging that the inspection robot operates abnormally.
With reference to the first aspect, further, when the detection time of the inspection robot to complete a certain detection node is received, the following formula is obtained:
JS＝KS+α1×LC/YS (1)
and KS is the starting time, alpha 1 is a proportionality coefficient, LC is the path length from the current node to the next node, YS is the current running speed of the inspection robot, and JS is the detection time of the inspection robot for completing a certain detection node.
With reference to the first aspect, further, the determining the operation level of the inspection robot includes:
comparing the operation coefficient of the inspection robot with the maximum operation threshold and the minimum operation threshold according to the given maximum operation threshold and the given minimum operation threshold, and if the operation coefficient is less than or equal to the minimum operation threshold, judging that the operation level of the inspection robot is three levels; if the operation coefficient is between the maximum operation threshold value and the minimum operation threshold value, judging that the operation grade of the inspection robot is in a second grade; and if the operation coefficient is greater than or equal to the maximum operation threshold value, judging that the operation grade of the inspection robot is one grade.
With reference to the first aspect, further, the operation coefficient is obtained by the following formula
YX＝PC/(JS-KS)
The system comprises a PC, a JS and a KS, wherein YX is an operation coefficient, the PC is a difference value between the time of actually receiving information of a certain detection node and the detection time of the polling robot for completing the certain detection node, the JS is the detection time of the polling robot for completing the certain detection node, and the KS is the current time of the system.
Combine first aspect, it is further, to patrolling and examining robot operation electric wire netting environment and detecting node environment and judging and include:
if the abnormal ratio is larger than or equal to the abnormal threshold value and the environmental performance value is larger than or equal to the environmental performance threshold value, judging that both the power grid environment and the detection node environment need to be adjusted;
if the abnormal ratio is larger than or equal to the abnormal threshold and the environmental performance value is smaller than the environmental performance threshold, determining that the power grid environment needs to be adjusted;
if the anomaly ratio is smaller than the anomaly threshold value and the environmental performance value is larger than or equal to the environmental performance threshold value, judging that the detection node needs environmental regulation;
and if the abnormal ratio is smaller than the abnormal threshold and the environment performance value is smaller than the environment performance threshold, judging that the power grid environment and the detection node environment both meet the requirements.
In a second aspect, a system for detecting the state of a patrol robot is provided, which comprises:
the robot running state judging module is used for judging the running state of the inspection robot according to the signal receiving state of the detection node;
the abnormality processing module is used for sending a path from the current detection node to the next detection node to the management terminal if the inspection robot runs abnormally;
and the environment judgment module is used for judging the operation level, the power grid environment and the detection node environment of the inspection robot if the inspection robot operates normally.
The invention has the following beneficial effects:
1. the access analysis module can monitor the time and the state of the inspection robot reaching the detection node each time, so that the inspection robot is prevented from generating operation faults due to immaturity of a navigation technology, the phenomenon that electric power data cannot be normally accessed due to abnormal operation of the inspection robot is prevented, in addition, the operation grade analysis is carried out on the inspection robot which normally operates, and the operation grade of the inspection robot is judged according to the timeliness that the inspection robot reaches the next detection node;
2. and judging whether the detection nodes are abnormal nodes or not according to the numerical value of the environment coefficient, and further obtaining the occupation ratio of the abnormal nodes in the detection nodes and the environment coefficient deviation degree of each detection node, so that the environment of the power grid and each detection node is monitored.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a block diagram of the system modules involved in determining the operational grade of the present invention;
FIG. 3 is a flow chart of the present invention in determining the operation level;
FIG. 4 is a block diagram of the system modules involved in determining the environment according to the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For better understanding of the present invention, the related art in the technical solution of the present invention is explained below.
Example 1
As shown in fig. 1 to 4, the invention provides a method for detecting the state of an inspection robot, which comprises the following steps:
step one, judging the running state of the inspection robot
In order to judge the running state of the inspection robot, the data collected by the inspection robot needs to be acquired, a camera used for image shooting is arranged on the inspection robot, the position of the inspection robot for data collection is marked as a detection node i, i is 1, 2, …, n is a positive integer, and the inspection robot responds to a signal sent by an intelligent access platform after running to the detection node and sends the collected data to an access analysis module.
It should be noted that the inspection robot is divided into a magnetic navigation robot and a laser navigation robot according to different navigation modes, a magnetic track needs to be arranged in advance for magnetic navigation (namely, a permanent magnet is buried on an inspection route of the robot), the robot moves forward or backward along the magnetic track, an electronic tag RFID needs to be additionally buried beside the electromagnetic track for each point needing to be stopped for detection or for actions such as turning, speed regulation and the like, information injected during debugging is provided in the electronic tag, such as detection, speed regulation, turning and the like, and the robot performs corresponding actions after scanning the tags. Six magnetic inductors are arranged below the robot, so that whether the robot moves along a preset magnetic track or not can be ensured, if the robot slightly deviates, the robot can be adjusted, and when the next label is detected, the robot can know the position of the robot to continuously execute the current task; the navigation of the laser navigation robot is laser, and compared with magnetic navigation, the laser navigation is more advanced, a magnetic track and an electronic tag are not required to be arranged in advance, and the hardware is saved and mainly analyzed by software. Although the magnetic guide rails are not required to be arranged, a two-dimensional plane graph of the laser map needs to be established in the first step, the two-dimensional plane graph comprises XY coordinates, so that each point in the power station corresponds to the XY coordinates, and only one action (detection, turning and speed regulation) is set for the robot when the robot reaches each point in advance, so that the electronic tag is not needed, and the robot moves by setting the coordinate points, so that the magnetic guide rails are not needed. One problem of the laser navigation robot is the problem of the laser matching rate, namely, the scene actually scanned by the robot is matched with a previously set electronic map, and if the matching rate is not good at the position and the matching rate is too low, the robot cannot judge the position of the robot and stops. The establishment of the electronic map can push the robot to patrol the power station once or scan the power station once by using a special laser scanner, and the height of the laser scanner is required to be set to be consistent with the height of a laser navigator carried by the robot.
It can be understood that, no matter the magnetic navigation robot or the laser navigation robot, the detection node of the present application is set at the position where the inspection robot performs the operations of detection, turning and speed regulation.
The method comprises the steps that after an access analysis module receives data collected by an inspection robot, the current position of the inspection robot is obtained, the current position of the inspection robot and the collected data are sent to an abnormality analysis module, the current system time is marked as a start time KS, and the path length from a current node to a next node is obtained and marked as a path length LC; acquiring the current running speed of the inspection robot, marking the current running speed as YS, and obtaining the finish time JS through a formula JS ═ KS + alpha 1 × LC/YS, namely when the detection time of the inspection robot for finishing the next detection node is expected to be received, wherein alpha 1 is a proportional coefficient (set according to an empirical value), and alpha 1 is more than or equal to 1 and less than or equal to 1.15; when the system time is up to the end time, whether the access analysis module receives the collected data of the inspection robot again is judged: if the data are received, the inspection robot is judged to normally operate, and the received data are sent to an abnormality analysis module; if not, judging that the inspection robot runs abnormally.
Step two, exception handling
When the running state of the inspection robot is judged to be abnormal, the paths of the current node and the next node are sent to the intelligent access platform, the intelligent access platform sends the received paths to the mobile phone terminal of a manager, and whether intervention is carried out or not can be determined through artificial judgment.
Step three, normal processing
When the inspection robot operates normally, marking the time when the access analysis module receives the data collected by the inspection robot again as marking time (actually receiving information time of a certain detection node), marking the time difference between the ending time and the marking time as a deviation value PC, and obtaining an operation coefficient YX of the inspection robot through a formula YX (PC/(JS-KS), wherein the operation coefficient is a numerical value reflecting the operation state of the inspection robot, and the larger the numerical value of the operation coefficient is, the better the operation state of the inspection robot is; empirically setting a minimum operating threshold YXmin, a maximum operating threshold YXmax, stored in a memory module, comparing the operating coefficient YX of the inspection robot with the operating thresholds YXmin, YXmax: if YX is less than or equal to YXmin, judging that the operation level of the inspection robot is a third level; if YXmin is less than YX and less than YXmax, judging that the operation level of the inspection robot is a second level; if YX is larger than or equal to YXmax, the operation level of the inspection robot is judged to be one level; the access analysis module sends the operation level of the inspection robot to the intelligent access platform; the invention can monitor the time and the state of the inspection robot reaching the detection node each time through the arranged access analysis module, thereby avoiding the operation fault of the inspection robot due to the immature navigation technology and preventing the phenomenon that the electric power data cannot be normally accessed due to the abnormal operation of the inspection robot.
When the inspection robot operates normally, the power grid environment and the detection node environment also need to be judged.
The data that inspection robot gathered include temperature data WDi, humidity data SDi and the dust data HCi of detection node i, and the acquisition process of temperature data WDi includes: the temperature value and the suitable temperature range collected by the inspection robot are obtained, the temperature value is directly obtained by a temperature sensor, and the temperature sensor can sense the temperature and convert the temperature into a usable output signal. The temperature sensor is the core part of a temperature measuring instrument, has various varieties, can be divided into two types of contact and non-contact according to the measuring mode, and is divided into two types of thermal resistance and thermocouple according to the characteristics of sensor materials and electronic elements; marking the average value of the maximum value and the minimum value of the suitable temperature range as a temperature standard value, and marking the absolute value of the difference value of the temperature value and the temperature standard value as temperature data WDi; the acquisition process of the humidity data SDi includes: the method comprises the steps of obtaining a humidity value and a proper humidity range collected by the inspection robot, wherein the humidity value is directly obtained by a humidity-sensitive sensor, the humidity-sensitive sensor can sense external humidity change, and converts the humidity into a useful signal through the physical or chemical property change of a device material, and the humidity detection is difficult compared with the detection of other physical quantities, firstly, because the content of water vapor in the air is much less than that of the air; in addition, some high molecular materials and electrolyte materials are dissolved by liquid water, and a part of water molecules are ionized and then combined with impurities dissolved in air in the water to form acid or alkali, so that the humidity-sensitive material is corroded and aged to different degrees, and the original properties of the humidity-sensitive material are lost; furthermore, the transmission of the moisture information must be completed by direct contact of water to the moisture sensitive device, so that the moisture sensitive device can only be directly exposed to the environment to be measured and cannot be sealed. Generally, the following requirements are placed on moisture sensitive devices: the stability is good, the response time is short, the service life is long, the interchangeability is realized, the pollution resistance is realized, the influence of temperature is small and the like under various gas environments; marking the average value of the maximum value and the minimum value of the suitable humidity range as a humidity standard value; marking the absolute value of the difference value between the humidity value and the humidity standard value as humidity data SDi; the dust data HCi acquisition process includes: the method comprises the following steps of obtaining an air dust concentration value acquired by an inspection robot and marking the air dust concentration value as dust data HCi, wherein the dust concentration value is directly obtained by a dust detector, the dust detector is an instrument for detecting the dust content in air, the basic principle of the method is that detection laser of an optical sensor is received by a photosensitive element after being scattered by dust particles and generates a pulse signal, the pulse signal is output and amplified, then digital signal processing is carried out, and a comparison result is expressed by different parameters through comparison with a standard particle signal; an environment coefficient HJi is obtained by a formula HJi ═ β 1 × WDi + β 2 × SDi + β 3 × HCi, where it should be noted that the environment coefficient is a numerical value that reflects how good the environment of the detection node is, and the larger the numerical value of the environment coefficient is, the worse the environment of the detection node is; wherein the beta 1, the beta 2 and the beta 3 are proportionality coefficients and are obtained according to the following method: collecting a plurality of groups of sample data and setting a corresponding environment coefficient for each group of sample data; substituting the set environmental coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of beta 1, beta 2 and beta 3, wherein the values of beta 1, beta 2 and beta 3 are respectively 5.27, 2.54 and 1.25, and beta 1 is more than beta 2 and more than beta 3; obtaining an environment threshold HJmax through a data storage module, and comparing an environment coefficient HJi of the detection node i with the environment threshold HJmax (HJmax is used for measuring whether the environment is abnormal or not, and is set according to an empirical value): if the environment coefficient HJi is smaller than the environment threshold HJmax, the environment of the detection node i is judged to be normal, and the corresponding detection node is marked as a normal node; if the environment coefficient HJi is greater than or equal to the environment threshold HJmax, judging that the environment of the detection node i is abnormal, and marking the corresponding detection node as an abnormal node; marking the number of abnormal nodes as m, marking the ratio of m to n (n represents the number of detection points) as an abnormal ratio, establishing an environment set { HJ1, HJ2, … and HJn } for the environment coefficient of the detection nodes, carrying out variance calculation on the environment set to obtain an environment expression value, obtaining an abnormal threshold value and an environment expression threshold value through a data storage module, and comparing the abnormal ratio and the environment expression value with the abnormal threshold value and the environment expression threshold value respectively: if the anomaly ratio is greater than or equal to the anomaly threshold value and the environmental expression value is greater than or equal to the environmental expression threshold value, judging that the power grid and the detection node both need environmental regulation, sending a network node regulation signal to the intelligent access platform by the anomaly analysis module, and sending the network node regulation signal to a mobile phone terminal of a manager by the intelligent access platform after receiving the network node regulation signal; if the abnormal ratio is greater than or equal to the abnormal threshold value and the environmental expression value is smaller than the environmental expression threshold value, judging that the power grid needs environmental regulation, sending a power grid regulation signal to the intelligent access platform by the abnormal analysis module, and sending the power grid regulation signal to a mobile phone terminal of a manager by the intelligent access platform after receiving the power grid regulation signal; if the anomaly ratio is smaller than the anomaly threshold value and the environmental expression value is larger than or equal to the environmental expression threshold value, judging that the detection node needs environmental regulation, sending a node regulation signal to the intelligent access platform by the anomaly analysis module, and sending the node regulation signal to a mobile phone terminal of a manager by the intelligent access platform after receiving the node regulation signal; if the anomaly ratio is smaller than the anomaly threshold and the environment expression value is smaller than the environment expression threshold, judging that the environments of the power grid and the detection node meet the requirements, and sending an environment normal signal to the intelligent access platform by the anomaly analysis module; the abnormity analysis module sends data and environmental coefficients collected by the inspection robot to the data storage module, the temperature data, the humidity data and the dust data of the detection nodes are processed through the set abnormity analysis module to obtain the environmental coefficients, whether the detection nodes are abnormal nodes is judged through the numerical value of the environmental coefficients, the occupation ratio of the abnormal nodes in the detection nodes and the environmental coefficient deviation degree of each detection node are further obtained, and therefore the power grid and each detection node are subjected to environment monitoring.
The machine inspection data intelligent access system based on the electric power data safety access technology is characterized in that during work, an inspection robot receives a switching signal sent by an intelligent access platform after running to a detection node and sends acquired data to an access analysis module, the access analysis module receives the data acquired by the inspection robot and analyzes the data to obtain end time JS, when the system time reaches the end time JS, whether the access analysis module receives the data acquired by the inspection robot again is judged, and an anomaly analysis module receives the data acquired by the inspection robot and then processes and analyzes the data and judges whether the environment of a power grid and the detection node is qualified.
Example 2
The invention also provides a system for detecting the state of the inspection robot, which comprises:
the robot running state judging module is used for judging the running state of the inspection robot according to the signal receiving state of the detection node;
the exception handling module is used for sending a path from the current detection node to the next detection node to the management terminal if the inspection robot runs abnormally;
and the environment judgment module is used for judging the operation level, the power grid environment and the detection node environment of the inspection robot if the inspection robot operates normally.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (7)
1. A state detection method for an inspection robot is characterized by comprising the following steps:
judging the running state of the inspection robot through the signal receiving state of the detection node;
if the inspection robot runs abnormally, a path from the current detection node to the next detection node is sent to the management terminal;
and if the inspection robot operates normally, judging the operation level, the power grid environment and the detection node environment of the inspection robot.
2. The inspection robot state detection method according to claim 1, wherein the judging the operation state of the inspection robot through the signal receiving state of the detection node comprises:
when the system time is advanced to the expected time for receiving the inspection robot to finish the detection of a certain detection node, judging whether the inspection information from the detection node is received, if so, judging that the inspection robot operates normally, otherwise, judging that the inspection robot operates abnormally.
3. The inspection robot state detection method according to claim 2, further comprising: when the detection time of the inspection robot for completing a certain detection node is expected to be received, the detection time is obtained according to the following formula:
JS＝KS+α1×LC/YS (1)
and KS is the starting time, alpha 1 is a proportionality coefficient, LC is the path length from the current node to the next node, YS is the current running speed of the inspection robot, and JS is the detection time of the inspection robot for completing a certain detection node.
4. The inspection robot state detection method according to claim 1, wherein determining the operation level of the inspection robot includes:
comparing the operation coefficient of the inspection robot with the maximum operation threshold and the minimum operation threshold according to the given maximum operation threshold and the given minimum operation threshold, and if the operation coefficient is less than or equal to the minimum operation threshold, judging that the operation level of the inspection robot is three levels; if the operation coefficient is between the maximum operation threshold value and the minimum operation threshold value, judging the operation level of the inspection robot to be two levels; and if the operation coefficient is greater than or equal to the maximum operation threshold value, judging that the operation grade of the inspection robot is one grade.
5. The inspection robot status detection method according to claim 1, wherein the operation coefficients are derived from the following equation
YX＝PC/(JS-KS)
The system comprises a PC, a JS and a KS, wherein YX is an operation coefficient, the PC is a difference value between the time of actually receiving information of a certain detection node and the detection time of the polling robot for completing the certain detection node, the JS is the detection time of the polling robot for completing the certain detection node, and the KS is the current time of the system.
6. The inspection robot state detection method according to claim 1, wherein the determining of the inspection robot operating grid environment and the detection node environment comprises:
if the abnormal ratio is larger than or equal to the abnormal threshold value and the environmental performance value is larger than or equal to the environmental performance threshold value, judging that both the power grid environment and the detection node environment need to be adjusted;
if the abnormal ratio is larger than or equal to the abnormal threshold and the environmental performance value is smaller than the environmental performance threshold, determining that the power grid environment needs to be adjusted;
if the anomaly ratio is smaller than the anomaly threshold value and the environmental performance value is larger than or equal to the environmental performance threshold value, judging that the detection node needs environmental regulation;
and if the abnormal ratio is smaller than the abnormal threshold and the environment performance value is smaller than the environment performance threshold, judging that the power grid environment and the detection node environment both meet the requirements.
7. The utility model provides a patrol and examine robot state detecting system which characterized in that includes:
the robot running state judging module is used for judging the running state of the inspection robot according to the signal receiving state of the detection node;
the abnormality processing module is used for sending a path from the current detection node to the next detection node to the management terminal if the inspection robot runs abnormally;
and the environment judgment module is used for judging the operation level, the power grid environment and the detection node environment of the inspection robot if the inspection robot operates normally.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210580838.7A CN114872086B (en) | 2022-05-26 | 2022-05-26 | Inspection robot state detection method and system |
Applications Claiming Priority (1)
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115781697A (en) * | 2023-02-06 | 2023-03-14 | 山东协和学院 | Industrial robot control system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106695741A (en) * | 2017-02-10 | 2017-05-24 | 中国东方电气集团有限公司 | Method for system state detection and initial work of mobile robot |
| CN110821560A (en) * | 2019-10-18 | 2020-02-21 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Tunnel inspection system |
| CN112348433A (en) * | 2020-10-28 | 2021-02-09 | 北京京东乾石科技有限公司 | Exception handling method and system and scheduling system under storage system operation mode |
| CN112486175A (en) * | 2020-12-01 | 2021-03-12 | 南京科瑞电力科技有限公司 | Intelligent sensing robot applied to power equipment monitoring |
| CN112562280A (en) * | 2020-12-08 | 2021-03-26 | 广州赛特智能科技有限公司 | Robot alarm method and system |
| CN113867181A (en) * | 2021-09-27 | 2021-12-31 | 北京声智科技有限公司 | Robot cooperation method and device and first robot |
| CN114290329A (en) * | 2021-12-13 | 2022-04-08 | 库卡机器人制造（上海）有限公司 | Calibration control method and system for robot, storage medium and robot assembly |
-
2022
- 2022-05-26 CN CN202210580838.7A patent/CN114872086B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106695741A (en) * | 2017-02-10 | 2017-05-24 | 中国东方电气集团有限公司 | Method for system state detection and initial work of mobile robot |
| CN110821560A (en) * | 2019-10-18 | 2020-02-21 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Tunnel inspection system |
| CN112348433A (en) * | 2020-10-28 | 2021-02-09 | 北京京东乾石科技有限公司 | Exception handling method and system and scheduling system under storage system operation mode |
| CN112486175A (en) * | 2020-12-01 | 2021-03-12 | 南京科瑞电力科技有限公司 | Intelligent sensing robot applied to power equipment monitoring |
| CN112562280A (en) * | 2020-12-08 | 2021-03-26 | 广州赛特智能科技有限公司 | Robot alarm method and system |
| CN113867181A (en) * | 2021-09-27 | 2021-12-31 | 北京声智科技有限公司 | Robot cooperation method and device and first robot |
| CN114290329A (en) * | 2021-12-13 | 2022-04-08 | 库卡机器人制造（上海）有限公司 | Calibration control method and system for robot, storage medium and robot assembly |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115781697A (en) * | 2023-02-06 | 2023-03-14 | 山东协和学院 | Industrial robot control system |
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