CN112100039B - Equipment fault alarm method and system - Google Patents
Equipment fault alarm method and system Download PDFInfo
- Publication number
- CN112100039B CN112100039B CN202011281343.1A CN202011281343A CN112100039B CN 112100039 B CN112100039 B CN 112100039B CN 202011281343 A CN202011281343 A CN 202011281343A CN 112100039 B CN112100039 B CN 112100039B
- Authority
- CN
- China
- Prior art keywords
- information
- equipment
- picture
- indicator light
- indicator
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/325—Display of status information by lamps or LED's
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
- G06K17/0025—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device the arrangement consisting of a wireless interrogation device in combination with a device for optically marking the record carrier
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Computer Networks & Wireless Communication (AREA)
- Life Sciences & Earth Sciences (AREA)
- Manipulator (AREA)
Abstract
The invention relates to an equipment fault alarm method, which comprises the following steps: acquiring a picture containing equipment, wherein information patterns which can be identified are arranged on the equipment; identifying an information pattern in the picture to obtain equipment information, and detecting an indicator light in the picture to obtain indicator light information; judging the working state of an indicator lamp on the equipment according to the information pattern and the indicator lamp information; determining whether the device is malfunctioning based on the determination result. The invention can replace manual work to judge and process information, overcomes the problems of low manual monitoring efficiency and the like, solves the problem of positioning of a plurality of devices in the machine room, and improves the automatic monitoring capability of the machine room.
Description
Technical Field
The invention belongs to the field of equipment fault identification, and particularly relates to an equipment fault alarm method and system.
Background
The machine room inspection is an important system for guaranteeing the safe operation of the machine room. In view of the problems that the traditional manual inspection has large workload, is greatly influenced by subjective factors such as the experience of inspectors, is difficult to store manual records and the like, more and more intelligent inspection robots are practically applied in a machine room, the automatic equipment identification and fault alarm efficiency is effectively improved, the labor intensity of operation and maintenance personnel is reduced, and powerful technical support is provided for unattended operation of the machine room.
Although the existing inspection robot generates massive visible light images when inspecting in a machine room, the basis for monitoring and analyzing the appearance characteristics of key equipment of the machine room is provided; however, the current equipment fault detection mainly adopts a manual analysis mode, so that the workload is high, serious detection misjudgment or misjudgment conditions are easy to occur during fault detection, and the fault is difficult to be accurately and timely found. In addition, the number of devices in the machine room is various, and it is difficult to determine the types and positions of the devices and the corresponding relationship between the devices and the indicator lights on the pictures according to the pictures taken by the robot.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the equipment fault alarm method and the equipment fault alarm system, which can replace manual work to judge and process information, overcome the problems of low manual monitoring efficiency and the like, solve the problem of positioning of a plurality of equipment in a machine room and improve the automatic monitoring capability of the machine room.
In a first aspect, the present invention provides an apparatus failure alarm method, including the following steps:
acquiring a picture containing equipment, wherein information patterns which can be identified are arranged on the equipment;
identifying information patterns in the picture to obtain equipment information, and detecting an indicator light in the picture to obtain indicator light information, wherein the method comprises the following steps:
-constructing a training sample set of information patterns and indicator lights, the training sample set comprising a plurality of pictures of marked information patterns and indicator light positions, the pictures being pre-collected by the robot in the tour inspection;
-training a plurality of pictures in the training sample set using a yolo algorithm, resulting in respective model parameters of a deep learning algorithm model;
-identifying an information pattern and indicator light information of a picture containing the device from the obtained deep learning algorithm model;
judging the working state of an indicator lamp on the equipment according to the information pattern and the indicator lamp information;
determining whether the device is malfunctioning based on the determination result.
Wherein the equipment information comprises equipment name information and relative position information of the equipment in the machine room.
The identifying information patterns in the picture to obtain device information, and detecting the indicator light in the picture to obtain indicator light information specifically include:
identifying an information pattern on the picture by using a deep learning algorithm model to obtain an equipment name and the equipment position information;
and detecting the indicator lights on the pictures by using a deep learning algorithm model to obtain indicator light information containing the positions of the indicator lights.
Wherein, judge the pilot lamp operating condition on the equipment according to information pattern and pilot lamp information, specifically include:
correspondingly converting coordinate position information of the equipment on the picture according to the equipment position information;
and judging the working state of the indicator light on the equipment according to the detected indicator light information and the coordinate position of the equipment on the picture.
Wherein the information pattern is disposed at an upper left position and a lower right position of the apparatus.
The correspondingly converting the coordinate information of the device on the picture according to the device position information specifically includes:
identifying information patterns based on a deep learning algorithm model;
judging whether the corresponding information pattern is positioned at the upper left position or the lower right position;
and determining the position and the coordinates of the equipment on the picture according to the corresponding position of the identified information pattern on the picture.
Wherein, the judging the working state of the indicator light on the equipment according to the detected indicator light information and the coordinate position of the equipment on the picture comprises the following steps:
according to the indicator light information detected by the deep learning algorithm model, obtaining the position information and the working state of the indicator light;
judging whether the position of the indicator light is within the position range of the equipment or not according to the position information of the indicator light and the coordinate position of the equipment on the picture;
and if the position of the indicator light is within the position range of the equipment, outputting the working state of the indicator light on the corresponding equipment.
Wherein, the information pattern is a two-dimensional code or a bar code.
In a second aspect, the present invention further provides an equipment failure alarm system, including:
the image acquisition module is used for acquiring an image containing the equipment;
the identification detection module is used for identifying the information pattern in the picture to obtain equipment information and detecting the indicator lamp in the picture to obtain indicator lamp information;
the state judgment module is used for judging the working state of the indicator lamp on the equipment according to the information pattern and the indicator lamp information;
a status output module for determining whether the device is malfunctioning based on the determination result.
Wherein the identification detection module comprises:
the construction module is used for constructing a training sample set of the information patterns and the indicating lamps, the training sample set comprises a plurality of marked information patterns and pictures of the positions of the indicating lamps, and the pictures are collected by the robot in the inspection process in advance;
the training module trains a plurality of pictures in the training sample set by adopting a yolo algorithm to obtain each model parameter of the deep learning algorithm model;
and the equipment identification module is used for identifying the information pattern and the indicator light information of the picture containing the equipment according to the obtained deep learning algorithm model.
Compared with the prior art, the method is based on the deep learning YOLO target detection algorithm, the inspection robot can be used for collecting the characteristics of the two-dimensional codes and the indicator lamps in the machine room, then the positions of the two-dimensional codes and the indicator lamps are detected, the corresponding equipment names are judged according to the two-dimensional codes, the equipment models and the indicator lamp detection results on the equipment are judged and generated on the basis, and finally the alarm signals of the equipment faults are output. Therefore, the invention can replace manual work to judge and process information, overcomes the problems of low efficiency of manual monitoring and the like, can solve the problem of positioning of a plurality of devices in the machine room, simultaneously corresponds different devices and the states of the indicator lights on the devices one by one, and improves the automatic monitoring capability of the machine room.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:
FIG. 1 is a flow chart illustrating a method of device fault alerting according to an embodiment of the present invention;
FIG. 2 is a flow diagram illustrating a method of device fault alerting, according to an embodiment of the present invention;
FIG. 3 is a flow diagram illustrating a method of device fault alerting, according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a machine room equipment fault alarm method based on two-dimensional codes and deep learning according to an embodiment of the present invention;
FIG. 5 is a block diagram illustrating the structure of an equipment malfunction alerting system according to an embodiment of the present invention;
fig. 6 is a schematic diagram showing an electronic apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, 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.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and "a plurality" typically includes at least two.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in the article or device in which the element is included.
Alternative embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Example one
Referring to fig. 1, an embodiment of the present invention provides an apparatus fault alarm method, including the following steps:
acquiring a picture containing equipment, wherein information patterns which can be identified are arranged on the equipment;
identifying information patterns in the picture based on a deep learning algorithm model to obtain equipment information, and detecting an indicator light in the picture to obtain indicator light information;
judging the working state of an indicator lamp on the equipment according to the information pattern and the indicator lamp information;
determining whether the device is malfunctioning based on the determination result.
Wherein, pilot lamp information can include pilot lamp position, colour and quantity, and the different kinds of bright lamp quantity judgement operating condition that different equipment corresponds is counted. The indicator operating status may include whether the indicator is detected to be lit, and the color of the indicator (e.g., the color may be red, yellow, blue, green).
Example two
On the basis of the first embodiment, the present embodiment may include the following:
in the embodiment of the present invention, the information pattern arranged on the device may be an identifiable information pattern such as a two-dimensional code or a barcode. In addition, the mode of arranging the information pattern on the equipment can be selected according to the requirement, in one application scene, the information pattern is directly pasted on the equipment, and in another application scene, the information pattern is sprayed or engraved on the equipment. Further, in order to avoid the occurrence of a situation where the information pattern on the device is damaged and cannot be recognized, a plurality of information patterns may be arranged on each device, and the plurality of information patterns may be one or both of a two-dimensional code and a barcode. In addition, the device information obtained by recognizing the information pattern in the picture may include device name information and device relative position information within the room.
Referring to fig. 2, in some application scenarios, the identifying an information pattern in the picture to obtain device information, and detecting an indicator light in the picture to obtain indicator light information may include:
identifying an information pattern on the picture by using a deep learning algorithm model to obtain an equipment name and the equipment position information;
and detecting the indicator lights on the pictures by using a deep learning algorithm model to obtain indicator light information containing the positions of the indicator lights.
Further, the determining the operating state of the indicator light on the device according to the information pattern and the indicator light information according to the embodiment of the present invention may include:
correspondingly converting coordinate position information of the equipment on the picture according to the equipment position information;
and judging the working state of the indicator light on the equipment according to the detected indicator light information and the coordinate position of the equipment on the picture.
The detected indicating lamp information comprises the coordinate position of the indicating lamp on the picture, and the number and the color of the indicating lamps on the equipment can be obtained by judging whether the position of the indicating lamp is in the coordinate position of the equipment or not and the detected indicating lamp information.
In the embodiment of the present invention, the position where the information pattern is arranged on the device may be selected as needed, and in one application scenario, the information pattern is arranged at the upper left position and/or the lower right position of the device. In another application scenario, the information patterns are disposed on a plurality of corners of the photographed side of the device. The embodiment of the invention can directly determine the position and the coordinate of the equipment on the picture through the arrangement position of the information pattern when the information pattern is identified through the deep learning algorithm model. Correspondingly, the correspondingly converting the coordinate information of the device on the picture according to the device location information may include:
identifying information patterns based on a deep learning algorithm model;
judging whether the corresponding information pattern is positioned at the upper left position or the lower right position;
and determining the position and the coordinates of the equipment on the picture according to the corresponding position of the identified information pattern on the picture.
Referring to fig. 3, after the coordinate position information of the device on the picture is correspondingly calculated according to the device position information, the determining the operating state of the indicator light on the device according to the detected indicator light information and the coordinate position of the device on the picture in the embodiment of the present invention may specifically include:
according to the indicator light information detected by the deep learning algorithm model, obtaining the position information and the working state of the indicator light;
judging whether the position of the indicator light is within the position range of the equipment or not according to the position information of the indicator light and the coordinate position of the equipment on the picture;
and if the position of the indicator light is within the position range of the equipment, outputting the working state of the indicator light on the corresponding equipment.
In the embodiment of the present invention, after obtaining a picture including a device, identifying an information pattern in the picture based on a deep learning algorithm model to obtain device information, and detecting an indicator light in the picture to obtain indicator light information, which may specifically include:
constructing a training sample set of information patterns and indicator lights, wherein the sample set comprises a plurality of marked information patterns and images of the positions of the indicator lights, and the images are collected by the robot in the inspection process in advance;
training a plurality of pictures in the training sample set by adopting a yolo algorithm (deep learning algorithm) to obtain each model parameter of a deep learning algorithm model for equipment identification;
and identifying the information pattern and the indicator light information of the picture containing the equipment according to the obtained deep learning algorithm model.
The training process of the YOLO algorithm may include:
collecting a plurality of (for example 7000) pictures containing equipment information patterns and indicator lights in a machine room, and marking the information patterns and the indicator lights in the pictures in a manual mode;
establishing a folder, and putting the labeled files and pictures into the folder according to the training requirements;
downloading a pre-training weight file yolov3.weights in a YoLO official website, converting a marking file into a file in a YOLO format according to a conversion script, and dividing the file into a training set, a testing set and a verification set;
and executing the training script to start training, testing the generated deep learning algorithm model after the training is finished, and obtaining the information patterns of the equipment in the machine room and all the parameters of the deep learning algorithm model for identifying the indicator light after the identification accuracy is determined to meet the requirement.
EXAMPLE III
On the basis of the above embodiment, the present embodiment may include the following:
referring to fig. 4, an embodiment of the present invention takes an information pattern arranged on a device as a two-dimensional code for example, and provides a machine room device fault alarm method based on a two-dimensional code and deep learning, which may include the following steps:
attaching two-dimensional codes at the upper left position and the lower right position of the key equipment of the machine room and writing corresponding equipment names and relative position information of the equipment in the machine room;
when the robot patrols and examines in the machine room, pictures containing key equipment of the machine room are shot;
detecting a two-dimensional code and an indicator light on a picture by using a deep learning algorithm;
identifying the two-dimensional code information to obtain the name of the equipment and the corresponding position information of the equipment in the picture;
correspondingly converting coordinate information of the equipment on the picture according to the position information of the equipment in the picture;
judging the working state of the indicator light on the equipment according to the detected indicator light information and the coordinate position of the equipment on the picture;
and outputting whether the corresponding equipment in the machine room has a fault according to the working state of the indicator lamp.
In some application scenarios, detecting the two-dimensional code and the indicator light on the picture using the deep learning algorithm may include:
constructing a training sample set of the two-dimensional codes and the indicating lamps, wherein the sample set comprises a plurality of pictures marked with the two-dimensional codes and the indicating lamps, and the pictures are collected by the robot in the inspection process in advance;
training the multiple pictures in the training sample set by adopting a yolo algorithm to obtain each model parameter of the machine room key equipment recognition deep learning algorithm model, and then detecting the two-dimensional codes and the indicator light information of the corresponding pictures by using the obtained deep learning algorithm model.
In addition, in the embodiment of the present invention, the converting the coordinate information of the device on the picture according to the position information of the device in the picture may include:
recognizing the two-dimensional code information, and reading whether the corresponding information is the upper left position or the lower right position;
and according to the corresponding position of the two-dimensional code identified by the deep learning algorithm model on the picture, the position and the coordinates of the equipment on the picture are sketched.
The embodiment of the invention is based on the current mainstream deep learning YOLO target detection algorithm, uses an inspection robot to carry out feature acquisition on a two-dimensional code and an indicator lamp in a machine room, then detects the positions of the two-dimensional code and the indicator lamp, judges the corresponding equipment name according to the two-dimensional code, judges and generates the equipment model and the indicator lamp detection result on the equipment on the basis, and finally outputs the alarm signal of the equipment with fault.
Example four
On the basis of the above embodiment, the present embodiment may include the following:
referring to fig. 5, an embodiment of the present invention provides an apparatus failure alarm system, including:
the image acquisition module is used for acquiring an image containing the equipment;
the identification detection module is used for identifying the information pattern in the picture to obtain equipment information and detecting the indicator lamp in the picture to obtain indicator lamp information;
the state judgment module is used for judging the working state of the indicator lamp on the equipment according to the information pattern and the indicator lamp information;
a status output module for determining whether the device is malfunctioning based on the determination result.
Wherein the identification detection module may include:
the construction module is used for constructing a training sample set of the information patterns and the indicating lamps, the training sample set comprises a plurality of marked information patterns and pictures of the positions of the indicating lamps, and the pictures are collected by the robot in the inspection process in advance;
the training module trains a plurality of pictures in the training sample set by adopting a yolo algorithm to obtain each model parameter of the deep learning algorithm model;
and the equipment identification module is used for identifying the information pattern and the indicator light information of the picture containing the equipment according to the obtained deep learning algorithm model.
EXAMPLE five
On the basis of the above embodiment, the present embodiment may include the following:
the embodiment of the invention provides a machine room equipment intelligent identification and fault alarm system based on two-dimensional codes and deep learning, which comprises:
the acquisition module is used for acquiring the image characteristics of the two-dimensional code and the indicator lamp in the machine room by adopting the inspection robot according to the preset coordinate position;
the recognition module is used for recognizing equipment pictures collected by the robot by adopting a pre-trained machine room two-dimensional code and a depth learning algorithm model recognized by an indicator lamp and outputting the coordinate positions of the two-dimensional code and the indicator lamp in the pictures;
and the judging module is used for comparing the identified positions of the indicating lamps with the coordinate position of the equipment and judging the number of the working indicating lamps on the equipment.
EXAMPLE six
As shown in fig. 6, this embodiment further provides an electronic device 600, where the electronic device 600 includes: at least one processor 601; and a memory 602 communicatively coupled to the at least one processor 601; wherein the content of the first and second substances,
the memory 602 stores instructions executable by the one processor 601 to be executed by the at least one processor 601 to enable the at least one processor 601 to perform the method steps as described in the above embodiments.
EXAMPLE seven
The disclosed embodiments provide a non-volatile computer storage medium having stored thereon computer-executable instructions that may perform the method steps as described in the embodiments above.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local Area Network (AN) or a Wide Area Network (WAN), or the connection may be made to AN external computer (for example, through the internet using AN internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The foregoing describes preferred embodiments of the present invention, and is intended to provide a clear and concise description of the spirit and scope of the invention, and not to limit the same, but to include all modifications, substitutions, and alterations falling within the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. An equipment fault alarm method is characterized by comprising the following steps:
acquiring a picture containing equipment, wherein information patterns which can be identified are arranged on the equipment;
identifying information patterns in the picture to obtain equipment information, and detecting an indicator light in the picture to obtain indicator light information, wherein the method comprises the following steps:
-constructing a training sample set of information patterns and indicator lights, the training sample set comprising a plurality of pictures of marked information patterns and indicator light positions, the pictures being pre-collected by the robot in the tour inspection;
-training a plurality of pictures in the training sample set using a yolo algorithm, resulting in respective model parameters of a deep learning algorithm model;
-identifying an information pattern and indicator light information of a picture containing the device from the obtained deep learning algorithm model;
the equipment information comprises equipment name information and relative position information of the equipment in the machine room;
according to information pattern and pilot lamp information judge the pilot lamp operating condition on the equipment, include:
-correspondingly converting coordinate position information of the device on the picture according to the relative position information;
-determining the operating state of the indicator light on the device based on the detected information of the indicator light and the coordinate position of the device on the picture;
determining whether the device is malfunctioning based on the determination result.
2. The method of claim 1, wherein the identifying the information pattern in the picture to obtain device information and detecting the indicator light in the picture to obtain indicator light information comprises:
identifying an information pattern on the picture by using a deep learning algorithm model to obtain an equipment name and the relative position information;
and detecting the indicator lights on the pictures by using a deep learning algorithm model to obtain indicator light information containing the positions of the indicator lights.
3. The method of claim 1, wherein the information pattern is disposed at an upper left position and a lower right position of the device.
4. The method of claim 3, wherein the converting the coordinate information of the device on the picture according to the relative position information specifically comprises:
identifying information patterns based on a deep learning algorithm model;
judging whether the corresponding information pattern is positioned at the upper left position or the lower right position;
and determining the position and the coordinates of the equipment on the picture according to the corresponding position of the identified information pattern on the picture.
5. The method of claim 1, wherein said determining the operating status of the indicator light on the device based on the detected information of the indicator light and the coordinate position of the device on the picture comprises:
according to the indicator light information detected by the deep learning algorithm model, obtaining the position information and the working state of the indicator light;
judging whether the position of the indicator light is within the position range of the equipment or not according to the position information of the indicator light and the coordinate position of the equipment on the picture;
and if the position of the indicator light is within the position range of the equipment, outputting the working state of the indicator light on the corresponding equipment.
6. The method of claim 1, wherein the information pattern is a two-dimensional code or a bar code.
7. An equipment malfunction alerting system that implements the method of any one of claims 1 to 6, comprising:
the image acquisition module is used for acquiring an image containing the equipment;
the identification detection module is used for identifying the information pattern in the picture to obtain equipment information and detecting the indicator lamp in the picture to obtain indicator lamp information;
the state judgment module is used for judging the working state of the indicator lamp on the equipment according to the information pattern and the indicator lamp information;
a status output module for determining whether the device is malfunctioning based on the determination result.
8. The system of claim 7, wherein the identification detection module comprises:
the construction module is used for constructing a training sample set of the information patterns and the indicating lamps, the training sample set comprises a plurality of marked information patterns and pictures of the positions of the indicating lamps, and the pictures are collected by the robot in the inspection process in advance;
the training module trains a plurality of pictures in the training sample set by adopting a yolo algorithm to obtain each model parameter of the deep learning algorithm model;
and the equipment identification module is used for identifying the information pattern and the indicator light information of the picture containing the equipment according to the obtained deep learning algorithm model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011281343.1A CN112100039B (en) | 2020-11-17 | 2020-11-17 | Equipment fault alarm method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011281343.1A CN112100039B (en) | 2020-11-17 | 2020-11-17 | Equipment fault alarm method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112100039A CN112100039A (en) | 2020-12-18 |
| CN112100039B true CN112100039B (en) | 2021-07-13 |
Family
ID=73785628
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011281343.1A Active CN112100039B (en) | 2020-11-17 | 2020-11-17 | Equipment fault alarm method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112100039B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284271B (en) * | 2021-04-28 | 2023-06-20 | 中国工商银行股份有限公司 | Machine room inspection method, device, electronic equipment and computer readable storage medium |
| CN113763651A (en) * | 2021-08-31 | 2021-12-07 | 远光软件股份有限公司 | Intensive cabinet file box storage state management method, computer device and storage medium |
| CN113938409A (en) * | 2021-09-24 | 2022-01-14 | 五邑大学 | Switch state monitoring method and device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108520567A (en) * | 2018-03-26 | 2018-09-11 | 云南电网有限责任公司丽江供电局 | A kind of auxiliary method for inspecting identified by capture apparatus operation image |
| CN111292327A (en) * | 2020-05-08 | 2020-06-16 | 成都派沃特科技股份有限公司 | Machine room inspection method, device, equipment and storage medium |
| CN111368735A (en) * | 2020-03-04 | 2020-07-03 | 中国联合网络通信有限公司广州市分公司 | Network equipment self-help troubleshooting method based on image recognition |
| CN111626139A (en) * | 2020-04-30 | 2020-09-04 | 上海允登信息科技有限公司 | Accurate detection method for fault information of IT equipment in machine room |
| CN111696286A (en) * | 2020-04-21 | 2020-09-22 | 国网浙江桐乡市供电有限公司 | Power distribution room alarm positioning monitoring system and method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9454853B2 (en) * | 2013-03-13 | 2016-09-27 | Centric Group Llc | Electronic security patrol compliance systems and methods for institutional facility |
-
2020
- 2020-11-17 CN CN202011281343.1A patent/CN112100039B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108520567A (en) * | 2018-03-26 | 2018-09-11 | 云南电网有限责任公司丽江供电局 | A kind of auxiliary method for inspecting identified by capture apparatus operation image |
| CN111368735A (en) * | 2020-03-04 | 2020-07-03 | 中国联合网络通信有限公司广州市分公司 | Network equipment self-help troubleshooting method based on image recognition |
| CN111696286A (en) * | 2020-04-21 | 2020-09-22 | 国网浙江桐乡市供电有限公司 | Power distribution room alarm positioning monitoring system and method |
| CN111626139A (en) * | 2020-04-30 | 2020-09-04 | 上海允登信息科技有限公司 | Accurate detection method for fault information of IT equipment in machine room |
| CN111292327A (en) * | 2020-05-08 | 2020-06-16 | 成都派沃特科技股份有限公司 | Machine room inspection method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112100039A (en) | 2020-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112100039B (en) | Equipment fault alarm method and system | |
| CN112115927B (en) | Intelligent machine room equipment identification method and system based on deep learning | |
| CN111259892B (en) | Inspection method, inspection device, inspection equipment and inspection medium for state of indicator lamp | |
| KR102022496B1 (en) | Process management and monitoring system using vision image detection and a method thereof | |
| CN112395928A (en) | Method for automatically detecting equipment state operation | |
| CN111044149A (en) | Method and device for detecting temperature abnormal point of voltage transformer and readable storage medium | |
| CN111753712A (en) | Method, system and equipment for monitoring safety of power production personnel | |
| CN116363125B (en) | Deep learning-based battery module appearance defect detection method and system | |
| CN112734394A (en) | Project supervision information management method based on Internet of things | |
| CN112687022A (en) | Intelligent building inspection method and system based on video | |
| CN115035328A (en) | Converter image increment automatic machine learning system and establishment training method thereof | |
| CN117114420B (en) | Image recognition-based industrial and trade safety accident risk management and control system and method | |
| CN103412256B (en) | A kind of thin film switch AOI detector and AOI detection method thereof | |
| CN113778091A (en) | Method for inspecting equipment of wind power plant booster station | |
| CN113297945A (en) | Indoor equipment inspection auxiliary method and system based on mixed reality space positioning | |
| CN113065462A (en) | Monitoring method, device, equipment and storage medium for power grid overhead line | |
| CN116363397A (en) | Equipment fault checking method, device and inspection system | |
| CN115187880A (en) | Communication optical cable defect detection method and system based on image recognition and storage medium | |
| KR20240064037A (en) | How to identify and characterize surface defects of objects and cracks in brake discs by artificial intelligence through fatigue testing | |
| CN113709065A (en) | Automatic detection method, system, equipment and storage medium for panel light of switch | |
| CN114966631A (en) | Fault diagnosis and processing method and device for vehicle-mounted laser radar, medium and vehicle | |
| CN113776783A (en) | Machine room server fault lamp detection method based on inspection robot | |
| CN207728381U (en) | Shield duct piece automatic assembling system | |
| CN111861282A (en) | Method and device for evaluating environmental assessment results and computer-readable storage medium | |
| CN116563770B (en) | Method, device, equipment and medium for detecting vehicle color |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210811 Address after: 200120 unit 706, building 6, hongqiaohui, Lane 990, Shenchang Road, Minhang District, Shanghai Patentee after: Shanghai mengpa Information Technology Co.,Ltd. Address before: 1110, 1 / F, building a, 98 Guangqu Road, Chaoyang District, Beijing 100022 Patentee before: Beijing mengpa Xinchuang Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| CP03 | Change of name, title or address |
Address after: 200137 room 108, block a, building 8, No. 1879, jiangxinsha Road, Pudong New Area, Shanghai Patentee after: Shanghai mengpa Intelligent Technology Co.,Ltd. Address before: 200120 unit 706, building 6, hongqiaohui, Lane 990, Shenchang Road, Minhang District, Shanghai Patentee before: Shanghai mengpa Information Technology Co.,Ltd. |
|
| CP03 | Change of name, title or address |