CN111080775A - Server routing inspection method and system based on artificial intelligence - Google Patents
Server routing inspection method and system based on artificial intelligence Download PDFInfo
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- 238000013500 data storage Methods 0.000 claims description 12
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- 230000005540 biological transmission Effects 0.000 claims description 10
- 238000012423 maintenance Methods 0.000 claims description 9
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- 230000007175 bidirectional communication Effects 0.000 claims description 3
- 230000003749 cleanliness Effects 0.000 claims description 3
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- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- G—PHYSICS
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- G06T19/00—Manipulating 3D models or images for computer graphics
- G06T19/003—Navigation within 3D models or images
Abstract
The invention discloses a server inspection method and a server inspection system based on artificial intelligence. The invention comprises the following steps: the robot is sent into a server room to be inspected, and the surrounding environment of the robot is reconstructed into a three-dimensional model through a robot binocular vision module; the cloud server processes the acquired pictures, and models and plans a path in a three-dimensional space; collecting an operation panel image of the server, and inputting an image reasoning identification model for identification; the robot collects the state information of the server equipment through the server state collection module and inputs the collected parameters into the intelligent analysis module to analyze and identify the state information of the server. The robot realizes the planning of the motion path of the server room to be inspected by reconstructing the three-dimensional model, respectively acquires the room environment in real time and intelligently acquires the equipment state of the server, and the intelligent analysis module finishes the inspection of the server, thereby improving the inspection efficiency and ensuring the inspection accuracy.
Description
Technical Field
The invention belongs to the technical field of robot inspection, and particularly relates to a server inspection method based on artificial intelligence, and also relates to a server inspection system based on artificial intelligence.
Background
At present, the machine room inspection in the domestic server industry still adopts a manual inspection mode, namely, the state of equipment and the state of the machine room environment are inspected regularly by an operator on duty, and the state of an information system is monitored by monitoring systems such as i6000 and the like, so that the problems of information acquisition lag, poor information sharing performance, easy personnel relaxation, inaccurate manual data recording and the like are obviously caused by the mode. If the neural network constructed by deep learning can be combined with data acquired by a sensor on a sensitive part of equipment during system inspection, the system operation state is monitored, and a fault is analyzed and diagnosed after an abnormal condition is found, so that information and resource sharing is realized, and the solution efficiency of abnormal information can be accelerated. But a corresponding system is not yet available.
Disclosure of Invention
The invention aims to provide a server routing inspection method and system based on artificial intelligence.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a server inspection method based on artificial intelligence, which comprises the following steps:
step S1: the robot is sent into a server room to be inspected, the surrounding environment of the robot is reconstructed into a three-dimensional model through a robot binocular vision module, and the acquired pictures are transmitted to a cloud server;
step S2: the cloud server processes the acquired pictures, models and plans a path of the three-dimensional space, and provides a motion path for the robot;
step S3: in the moving process of the robot, the environment acquisition module acquires environmental data of a server room in real time and transmits the data to the cloud server for comparison and monitoring;
step S4: the robot reads the RFID label of the server through the RFID reader-writer to obtain server information, collects the operation panel image of the server and inputs the image into an image reasoning and identifying model for identification;
step S5: the robot acquires the state information of the server equipment through the server state acquisition module and inputs the acquired parameters into the intelligent analysis module to analyze and identify the server state information;
step S6: when the alarm processing module of the robot finds the abnormality, the fault result analyzed by the intelligent analysis module is transmitted to the cloud server and is transmitted to the handheld equipment of the operation and maintenance personnel by the cloud server;
step S7: the log module records the execution in the above-described step S1 to step S7.
Preferably, in step S1, the step of reconstructing the three-dimensional scene by using the visual module is as follows:
step S11: respectively acquiring a left image and a right image through a camera forming a binocular, then preprocessing the images through a preprocessing submodule, and sending the preprocessed images into a processor for further processing;
step S12: after the preprocessed image data is subjected to deformity elimination and three-dimensional correction processing, Gaussian Laplace filtering is carried out, parallax is obtained through three-dimensional matching, and then three-dimensional coordinate reconstruction is carried out;
step S13: and combining the data obtained after the three-dimensional coordinate reconstruction with the attitude data of the current robot, and updating the global map information in the storage unit through the processor so as to obtain complete map information.
Preferably, in step S2, the ant colony algorithm is used for path planning for the motion path planning of the robot, and when the server room environment is complex, the robot can be operated and patrolled in a manual remote control manner.
Preferably, in step S3, the data collected by the environment collection module includes sensors of temperature, humidity, cleanliness, smoke concentration and airflow speed.
Preferably, in step S4, the server stores in advance an operation panel diagram of the server in a normal operating state, and the operation panel diagram of the server captured by the robot is uploaded to the cloud server to compare with the operation panel diagram of the server in the normal operating state, where the comparison content includes an operating state of the indicator light and a position of the instrument pointer.
Preferably, in step S5, the intelligent analysis module is a patrol data analysis module and includes multiple sub-modules for machine learning, state analysis and historical state comparison.
Preferably, in step S6, the processing flow of the alarm processing module is as follows:
step S61: when the state information of the acquisition server equipment is not in a normal range specified by the trigger item, judging the trigger item as an abnormal trigger item;
step S62: when an abnormal triggering item exists, searching a routing inspection item corresponding to the abnormal triggering item according to a preset triggering item table;
step S63: and the robot carries out related inspection according to the searched inspection items to generate related inspection results.
The invention relates to a server inspection system based on artificial intelligence, which comprises a cloud server, an intelligent robot and a server in a machine room;
the cloud server, the intelligent robot and the server in the machine room are in bidirectional communication connection in sequence;
the cloud server comprises a picture processing module, a three-dimensional modeling module, a path planning module, a data transmission module and a data storage module;
the intelligent robot comprises an environment acquisition module, a data transmission module, an equipment state acquisition module, an RFID read-write module, a log module, a data storage module, a binocular vision module, an alarm processing module, an image reasoning identification module, an intelligent analysis module and a server state acquisition module;
the server in the computer room comprises an RFID label and a data storage module.
Preferably, the environment acquisition module, the data transmission module, the equipment state acquisition module, the RFID read-write module, the log module, the binocular vision module, the alarm processing module, the image reasoning identification module, the intelligent analysis module and the server state acquisition module are all connected with the data storage module, the alarm processing module is used for an alarm device when abnormality is found, and the server is informed of a mobile intelligent terminal of operation and maintenance personnel through short message notification, mail notification and telephone.
The invention has the following beneficial effects:
(1) according to the invention, the robot is sent into a server room to be inspected, the surrounding environment of the robot is processed through the robot binocular vision module, the acquired left image and right image are subjected to image preprocessing through the preprocessing submodule, after deformity elimination and three-dimensional correction processing are carried out, Gaussian Laplace filtering is carried out, parallax is obtained through three-dimensional matching, then three-dimensional coordinate reconstruction is carried out, and finally the robot can freely move in the server room through the path planning module, so that the artificial intelligent robot can replace the traditional manual inspection, the inspection efficiency is improved, and the inspection accuracy is ensured;
(2) according to the robot, the internal environment of the machine room is monitored in real time in the inspection process, various environmental data are collected and compared with the preset trigger item table, once the comparison result is abnormal, the server operation and maintenance personnel are notified by the cloud server through short messages, mails and telephones, the troubleshooting efficiency is improved, and the loss caused by server faults is reduced to the maximum extent;
(3) according to the invention, the robot scans the RFID tag on the server in inspection, the operation panel of the server is photographed, the image reasoning and identification module is used for comparing and identifying with the operation panel under normal conditions, the abnormal indicator lamp and the instrument pointer are rapidly distinguished, the fault reason is intelligently analyzed and pushed to operation and maintenance personnel, and the maintenance efficiency of the operation and maintenance personnel is improved.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings 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 that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of steps of a server inspection method based on artificial intelligence according to the present invention;
fig. 2 is a schematic diagram of a server inspection system structure based on artificial intelligence according to the present invention.
Detailed Description
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 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.
Referring to fig. 1, the present invention is a server inspection method based on artificial intelligence, including the following steps:
step S1: the robot is sent into a server room to be inspected, the surrounding environment of the robot is reconstructed into a three-dimensional model through a robot binocular vision module, and the acquired pictures are transmitted to a cloud server;
step S2: the cloud server processes the acquired pictures, models and plans a path of the three-dimensional space, and provides a motion path for the robot;
step S3: in the moving process of the robot, the environment acquisition module acquires environmental data of a server room in real time and transmits the data to the cloud server for comparison and monitoring;
step S4: the robot reads the RFID label of the server through the RFID reader-writer to obtain server information, collects the operation panel image of the server and inputs the image into an image reasoning and identifying model for identification;
step S5: the robot acquires the state information of the server equipment through the server state acquisition module and inputs the acquired parameters into the intelligent analysis module to analyze and identify the server state information;
step S6: when the alarm processing module of the robot finds the abnormality, the fault result analyzed by the intelligent analysis module is transmitted to the cloud server and is transmitted to the handheld equipment of the operation and maintenance personnel by the cloud server;
step S7: the log module records the execution in the above-described step S1 to step S7.
In step S1, the step of reconstructing the three-dimensional scene using the visual module is as follows:
step S11: respectively acquiring a left image and a right image through a camera forming a binocular, then preprocessing the images through a preprocessing submodule, and sending the preprocessed images into a processor for further processing;
step S12: after the preprocessed image data is subjected to deformity elimination and three-dimensional correction processing, Gaussian Laplace filtering is carried out, parallax is obtained through three-dimensional matching, and then three-dimensional coordinate reconstruction is carried out;
step S13: and combining the data obtained after the three-dimensional coordinate reconstruction with the attitude data of the current robot, and updating the global map information in the storage unit through the processor so as to obtain complete map information.
In step S2, the ant colony algorithm is used for path planning for the movement path planning of the robot, and when the server room environment is complex, the robot can be controlled and patrolled in a manual remote control mode.
In step S3, the data collected by the environment collection module includes sensors of temperature, humidity, cleanliness, smoke concentration, and airflow velocity.
In step S4, the server stores the operation panel diagram of the server in the normal operating state in advance, and the operation panel diagram of the server shot by the robot is uploaded to the cloud server and compared with the operation panel diagram of the server in the normal operating state, where the comparison content includes the operating state of the indicator light and the position of the instrument pointer.
In step S5, the intelligent analysis module is a patrol data analysis module, and the learning of the machine is completed by a plurality of sub-modules of machine learning, state analysis and historical state comparison through deep learning, so as to analyze the operation state of the server.
In step S6, the alarm processing module processes the following steps:
step S61: when the state information of the acquisition server equipment is not in a normal range specified by the trigger item, judging the trigger item as an abnormal trigger item;
step S62: when an abnormal triggering item exists, searching a routing inspection item corresponding to the abnormal triggering item according to a preset triggering item table;
step S63: and the robot carries out related inspection according to the searched inspection items to generate related inspection results.
Referring to fig. 2, the present invention is a server inspection system based on artificial intelligence, including a cloud server, an intelligent robot and a server in a machine room;
the cloud server, the intelligent robot and the server in the machine room are in bidirectional communication connection in sequence;
the cloud server comprises a picture processing module, a three-dimensional modeling module, a path planning module, a data transmission module and a data storage module;
the intelligent robot comprises an environment acquisition module, a data transmission module, an equipment state acquisition module, an RFID read-write module, a log module, a data storage module, a binocular vision module, an alarm processing module, an image reasoning identification module, an intelligent analysis module and a server state acquisition module;
the server in the computer room comprises an RFID label and a data storage module.
The system comprises an environment acquisition module, a data transmission module, an equipment state acquisition module, an RFID read-write module, a log module, a binocular vision module, an alarm processing module, an image reasoning identification module, an intelligent analysis module and a server state acquisition module, wherein the environment acquisition module, the data transmission module, the equipment state acquisition module, the RFID read-write module, the log module, the binocular vision module, the alarm processing module, the image reasoning identification module, the intelligent analysis module and the server state acquisition module are all connected with the data storage module, the alarm processing module is used for alarming when abnormity is found.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A server inspection method based on artificial intelligence is characterized by comprising the following steps:
step S1: the robot is sent into a server room to be inspected, the surrounding environment of the robot is reconstructed into a three-dimensional model through a robot binocular vision module, and the acquired pictures are transmitted to a cloud server;
step S2: the cloud server processes the acquired pictures, models and plans a path of the three-dimensional space, and provides a motion path for the robot;
step S3: in the moving process of the robot, the environment acquisition module acquires environmental data of a server room in real time and transmits the data to the cloud server for comparison and monitoring;
step S4: the robot reads the RFID label of the server through the RFID reader-writer to obtain server information, collects the operation panel image of the server and inputs the image into an image reasoning and identifying model for identification;
step S5: the robot acquires the state information of the server equipment through the server state acquisition module and inputs the acquired parameters into the intelligent analysis module to analyze and identify the server state information;
step S6: when the alarm processing module of the robot finds the abnormality, the fault result analyzed by the intelligent analysis module is transmitted to the cloud server and is transmitted to the handheld equipment of the operation and maintenance personnel by the cloud server;
step S7: the log module records the execution in the above-described step S1 to step S7.
2. The artificial intelligence based server inspection method according to claim 1, wherein in the step S1, the step of reconstructing the three-dimensional scene by using the visual sense module comprises:
step S11: respectively acquiring a left image and a right image through a camera forming a binocular, then preprocessing the images through a preprocessing submodule, and sending the preprocessed images into a processor for further processing;
step S12: after the preprocessed image data is subjected to deformity elimination and three-dimensional correction processing, Gaussian Laplace filtering is carried out, parallax is obtained through three-dimensional matching, and then three-dimensional coordinate reconstruction is carried out;
step S13: and combining the data obtained after the three-dimensional coordinate reconstruction with the attitude data of the current robot, and updating the global map information in the storage unit through the processor so as to obtain complete map information.
3. The server inspection method based on artificial intelligence according to claim 1, wherein in the step S2, the robot motion path planning adopts an ant colony algorithm to perform path planning, and when the server room environment is complex, the robot operation and control can be inspected in a manual remote control mode.
4. The artificial intelligence based server inspection method according to claim 1, wherein in the step S3, the environment acquisition module acquires data including temperature, humidity, cleanliness, smoke concentration and airflow speed.
5. The server inspection method according to claim 1, wherein in step S4, the server stores in advance an operation panel diagram of the server in a normal operating state, and the operation panel diagram of the server photographed by the robot is uploaded to the cloud server and compared with the operation panel diagram of the server in the normal operating state, where the comparison includes an operating state of an indicator light and a position of an instrument pointer.
6. The server inspection method according to claim 1, wherein in the step S5, the intelligent analysis module is an inspection data analysis module and comprises a plurality of sub-modules for machine learning, state analysis and historical state comparison.
7. The server inspection method and system based on artificial intelligence according to claim 1, wherein in step S6, the alarm processing module has the following processing flow:
step S61: when the state information of the acquisition server equipment is not in a normal range specified by the trigger item, judging the trigger item as an abnormal trigger item;
step S62: when an abnormal triggering item exists, searching a routing inspection item corresponding to the abnormal triggering item according to a preset triggering item table;
step S63: and the robot carries out related inspection according to the searched inspection items to generate related inspection results.
8. The utility model provides a server system of patrolling and examining based on artificial intelligence, server in server, its characterized in that in high in the clouds server, intelligent robot and the computer lab:
the cloud server, the intelligent robot and the server in the machine room are in bidirectional communication connection in sequence;
the cloud server comprises a picture processing module, a three-dimensional modeling module, a path planning module, a data transmission module and a data storage module;
the intelligent robot comprises an environment acquisition module, a data transmission module, an equipment state acquisition module, an RFID read-write module, a log module, a data storage module, a binocular vision module, an alarm processing module, an image reasoning identification module, an intelligent analysis module and a server state acquisition module;
the server in the computer room comprises an RFID label and a data storage module.
9. The server inspection system according to claim 8, wherein the environment acquisition module, the data transmission module, the device state acquisition module, the RFID read-write module, the log module, the binocular vision module, the alarm processing module, the image reasoning identification module, the intelligent analysis module and the server state acquisition module are all connected with the data storage module, the alarm processing module is used for an alarm device when an abnormality is found, and the server is informed of a mobile intelligent terminal of a server operation and maintenance worker through short message notification, mail notification and telephone.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111624994A (en) * | 2020-05-08 | 2020-09-04 | 合肥科大智能机器人技术有限公司 | Robot inspection method based on 5G communication |
| CN112115927A (en) * | 2020-11-19 | 2020-12-22 | 北京蒙帕信创科技有限公司 | Intelligent machine room equipment identification method and system based on deep learning |
| CN112364684A (en) * | 2020-09-23 | 2021-02-12 | 国网天津市电力公司电力科学研究院 | Machine room server state acquisition and three-dimensional management and control system and method |
| CN112561870A (en) * | 2020-12-10 | 2021-03-26 | 广东电网有限责任公司 | System and method for identifying panel inspection of distribution network automation terminal |
| CN112600884A (en) * | 2020-12-02 | 2021-04-02 | 武汉育知联信息科技有限公司 | Cloud inspection management system |
| CN112611381A (en) * | 2020-10-29 | 2021-04-06 | 武汉哈船导航技术有限公司 | Artificial intelligence inertial navigation system |
| CN112698618A (en) * | 2020-12-29 | 2021-04-23 | 济南浪潮高新科技投资发展有限公司 | Server alarm recognition system based on machine vision technology |
| CN112757305A (en) * | 2021-01-20 | 2021-05-07 | 济南浪潮高新科技投资发展有限公司 | Intelligent inspection robot and inspection method |
| CN113361953A (en) * | 2021-06-28 | 2021-09-07 | 广东嘉贸通科技有限公司 | Customs port cargo pipe on-site robot inspection method and system |
| CN113776783A (en) * | 2021-08-27 | 2021-12-10 | 同济大学 | Machine room server fault lamp detection method based on inspection robot |
| CN114147740A (en) * | 2021-12-09 | 2022-03-08 | 中科计算技术西部研究院 | Robot patrol planning system and method based on environment state |
| CN116805435A (en) * | 2023-08-23 | 2023-09-26 | 四川川西数据产业有限公司 | Intelligent inspection device for motor room |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102131222A (en) * | 2011-03-23 | 2011-07-20 | 中兴通讯股份有限公司 | Intelligent inspection method, user terminal, server and system |
| CN107992067A (en) * | 2017-11-24 | 2018-05-04 | 贵州电网有限责任公司 | Unmanned plane inspection fault diagnosis system based on integrated gondola and AI technologies |
| CN108490959A (en) * | 2018-05-22 | 2018-09-04 | 国网天津市电力公司 | A kind of artificial intelligence computer room crusing robot for supporting deep learning operation principle |
| CN110286684A (en) * | 2019-07-17 | 2019-09-27 | 国网湖北省电力有限公司检修公司 | A kind of Intelligent Mobile Robot and substation inspection system |
-
2019
- 2019-12-19 CN CN201911317179.2A patent/CN111080775A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102131222A (en) * | 2011-03-23 | 2011-07-20 | 中兴通讯股份有限公司 | Intelligent inspection method, user terminal, server and system |
| CN107992067A (en) * | 2017-11-24 | 2018-05-04 | 贵州电网有限责任公司 | Unmanned plane inspection fault diagnosis system based on integrated gondola and AI technologies |
| CN108490959A (en) * | 2018-05-22 | 2018-09-04 | 国网天津市电力公司 | A kind of artificial intelligence computer room crusing robot for supporting deep learning operation principle |
| CN110286684A (en) * | 2019-07-17 | 2019-09-27 | 国网湖北省电力有限公司检修公司 | A kind of Intelligent Mobile Robot and substation inspection system |
Non-Patent Citations (1)
| Title |
|---|
| 胡伟等: "高压电力廊道自动巡检机器人系统的研制", 《自动化与仪表》, no. 12, pages 13 - 16 * |
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|---|---|---|---|---|
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| CN112364684A (en) * | 2020-09-23 | 2021-02-12 | 国网天津市电力公司电力科学研究院 | Machine room server state acquisition and three-dimensional management and control system and method |
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| CN112115927A (en) * | 2020-11-19 | 2020-12-22 | 北京蒙帕信创科技有限公司 | Intelligent machine room equipment identification method and system based on deep learning |
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| CN112561870A (en) * | 2020-12-10 | 2021-03-26 | 广东电网有限责任公司 | System and method for identifying panel inspection of distribution network automation terminal |
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| CN113361953A (en) * | 2021-06-28 | 2021-09-07 | 广东嘉贸通科技有限公司 | Customs port cargo pipe on-site robot inspection method and system |
| CN113776783A (en) * | 2021-08-27 | 2021-12-10 | 同济大学 | Machine room server fault lamp detection method based on inspection robot |
| CN114147740A (en) * | 2021-12-09 | 2022-03-08 | 中科计算技术西部研究院 | Robot patrol planning system and method based on environment state |
| CN116805435A (en) * | 2023-08-23 | 2023-09-26 | 四川川西数据产业有限公司 | Intelligent inspection device for motor room |
| CN116805435B (en) * | 2023-08-23 | 2023-10-31 | 四川川西数据产业有限公司 | Intelligent inspection device for motor room |
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