CN113313428A - AI fault monitoring method and system of smart mine based on big data - Google Patents
AI fault monitoring method and system of smart mine based on big data Download PDFInfo
- Publication number
- CN113313428A CN113313428A CN202110726252.2A CN202110726252A CN113313428A CN 113313428 A CN113313428 A CN 113313428A CN 202110726252 A CN202110726252 A CN 202110726252A CN 113313428 A CN113313428 A CN 113313428A
- Authority
- CN
- China
- Prior art keywords
- real
- video
- time
- acquiring
- fault event
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004891 communication Methods 0.000 claims abstract description 90
- 238000001514 detection method Methods 0.000 claims abstract description 19
- 230000001976 improved effect Effects 0.000 claims abstract description 19
- 238000007726 management method Methods 0.000 claims abstract description 18
- 230000006872 improvement Effects 0.000 claims abstract description 16
- 238000003860 storage Methods 0.000 claims abstract description 15
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000011897 real-time detection Methods 0.000 claims abstract description 9
- 238000013136 deep learning model Methods 0.000 claims description 34
- 239000003245 coal Substances 0.000 claims description 29
- 238000005065 mining Methods 0.000 claims description 28
- 238000004458 analytical method Methods 0.000 claims description 22
- 238000005516 engineering process Methods 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 12
- 230000005540 biological transmission Effects 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 238000013527 convolutional neural network Methods 0.000 claims description 8
- 238000002372 labelling Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 6
- 230000011664 signaling Effects 0.000 claims description 6
- 238000009423 ventilation Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 2
- 238000013523 data management Methods 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063114—Status monitoring or status determination for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Software Systems (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Development Economics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an AI fault monitoring method and system of an intelligent mine based on big data, which are characterized in that a fault event is identified from an intercepted picture of a real-time video through deep learning network learning, and the fault event is detected and obtained through a detection device in a combined manner, so that the detection accuracy of the fault event is improved; and the comprehensive monitoring, remote control and data management of the mine are realized by combining the real-time improvement of a communication system, the real-time detection of a management center and the large data monitoring and storage of an intelligent management platform.
Description
Technical Field
The invention relates to the technical field of coal mines, in particular to an AI fault monitoring method and system of an intelligent mine based on big data.
Background
Coal is one of the main energy sources at present, and the coal mining work is an important link before the coal is used. Because of the particularity of the coal mining industry, accidents happen in the mining process, and therefore monitoring and alarming on the mining environment are an important way for reducing the accident rate and the accident loss.
At present, with the development of science and technology, the number of underground workers is greatly reduced. However, the underground fully mechanized mining equipment of the coal mine is mainly operated on the manual site. The underground coal face is an accident high-occurrence area, the production environment of the coal face is severe and variable, the accidents occur frequently, the personal safety of production personnel is seriously threatened, and the coal production efficiency is reduced. Therefore, if the labor intensity of the coal face is reduced and the safety factor and the coal mining efficiency of workers are improved, the automation and the less humanization of the coal face must be realized.
The intelligent mine is an unmanned mine which can actively sense, automatically analyze and rapidly process production, occupational health and safety, technology, logistics support and the like. Smart mines are essential; safe mines, efficient mines and clean mines, and the digitization and the informatization of the mines are the premise and the foundation of the construction of the smart mines. In recent years, with the development of science and technology, all large mines organize the key scientific and technological links of the smart mine, build the demonstration engineering of the smart mine, popularize the idea and technology of the smart mine, establish the standard of the smart mine, promote the transformation and upgrade of the manufacturing industry of mining equipment, and realize the intrinsic safety, high yield, high efficiency and environmental protection of the mine.
Disclosure of Invention
In order to overcome the technical defects, the invention aims to provide the AI fault monitoring method and the AI fault monitoring system based on the big data for the intelligent mine, which have high safety, high operation efficiency and high adaptability.
The invention discloses an AI fault monitoring method of an intelligent mine based on big data, which comprises the following steps:
acquiring a real-time picture of a target area, establishing a deep learning model based on a convolutional neural network, and predicting a fault event of the target area where the real-time picture is located and the occurrence position of the fault event by using the deep learning model; and/or detecting and acquiring a fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event;
the operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system;
the detection device includes: the system comprises an electric power parameter acquisition device for acquiring related electric power parameters of a high-voltage switch of an underground substation, a gas sensor for acquiring underground gas concentration, a pressure sensor for acquiring pressure of a working face hydraulic support, a vibration analysis device for acquiring underground geological vibration information, a flowmeter for acquiring pipeline water flow in an underground drainage system, a personnel positioning device for positioning underground personnel in real time, and an equipment sensor for acquiring state information of corresponding underground equipment;
carrying out visual display and auditory broadcast on the fault event and the position information thereof as well as the real-time video data, and transmitting the fault event and the position information thereof as well as the real-time video data to a management center through a communication channel;
the management center comprises an intelligent management platform, wherein the intelligent management platform monitors the fully mechanized mining working condition, stores various real-time working parameter information, analyzes and calculates the various real-time working parameter information, and acquires operation guidance and safety guidance;
acquiring a working video of a target area, acquiring an enhanced video from the working video by adopting an AR (augmented reality) technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video and generates an operation instruction for the enhanced video; the marked enhanced video and the operation instruction are transmitted back to the target area;
monitoring the communication channel and setting an improvement node, monitoring in real time when the communication channel is used, and improving the communication quality by adjusting the improvement node; and monitoring and recording the communication quality of the improved communication channel, and analyzing the recorded problems to obtain the reason of the reduction of the communication quality of the communication channel.
Preferably, the safety guidance comprises escape route guidance; acquiring an operation video of a target area, acquiring an enhanced video from the operation video by adopting an AR technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video and generates an operation instruction for the enhanced video; transmitting the annotated enhanced video and the operation instructions back to the target area comprises:
the operation video is an underground live video, an AR technology is adopted for the underground live video to obtain an enhanced video, and the enhanced video is transmitted to a management center; and the management center marks an escape path on the enhanced video and generates an escape instruction, and the marked enhanced video and the escape instruction are transmitted back to the underground live video sending point.
Preferably, the enhanced video includes video picture data and voice data, and the operation instruction includes the voice data and the picture data; the remote command center marks the enhanced video, and the operation instruction for generating the enhanced video comprises the following steps: and the remote command center acquires the target frames of the video picture data, performs graphic annotation and character annotation on each target frame, and generates voice data and annotated picture data for the enhanced video.
Preferably, the acquiring the job video of the target area, acquiring the enhanced video from the job video by using an AR technology, and transmitting the enhanced video to the management center further includes: storing the enhanced video and generating a prompt signal associated with the enhanced video; and the management center acquires the stored enhanced video through the prompt signal.
Preferably, the visually displaying and audibly broadcasting the fault event and the location information thereof and the real-time video data and transmitting the fault event and the location information to the management center through a communication channel further comprises: and when the occurrence duration time of the fault event exceeds a second preset time or the distance between the position of the fault event and the real-time picture acquisition point is less than a second preset distance, automatically stopping the work of the work platform.
Preferably, the acquiring a real-time image of a target area, establishing a deep learning model based on a convolutional neural network, and predicting a fault event of the target area where the real-time image is located and an occurrence position of the fault event by using the deep learning model includes: collecting real-time video data in a target area by taking a first preset distance as an interval; collecting a plurality of source picture frames of the real-time video data at intervals of first preset time, inputting the source picture frames into a detection network based on a convolutional neural network, carrying out fault event parameter labeling on an interested region of the source picture frames to obtain a material set, and dividing the material set into a training set, a verification set and a test set; establishing a deep learning model of the source picture frame, inputting the training set and the verification set into the deep learning model for training, learning, identifying and judging the type of the fault event; inputting the test set into the trained deep learning model for testing to obtain a fault event of the real-time video data, and connecting the fault event with the source picture frame where the fault event is located to obtain a fault event occurring in the fault event picture frame; and obtaining the distance between the fault event and the real-time video data acquisition point according to the projection relation between the real three-dimensional coordinates in the target area and the image plane of the fault event picture frame and the coordinates of the fault event occurrence point in the fault event picture frame.
Preferably, the establishing of the deep learning model of the source picture frame, where the deep learning model includes a segmentation task and a classification task, further includes: the interesting regions comprise a plurality of regions with different content parameters, a channel-based attention mechanism is introduced into the deep learning model, and the weights of the different interesting regions of the source picture frame are adjusted through the channel-based attention mechanism, so that the weights of the different interesting regions in different fault events are different.
Preferably, the visually displaying and audibly broadcasting the fault event and the location information thereof and the real-time video data and transmitting the fault event and the location information to a management center includes: the positions of visual display and auditory broadcast are as follows: the real-time video data acquisition points and/or the management center and/or the coal mining machine and/or the top of the target area and/or the entrance of the coal mining well are/is arranged at intervals of a second preset distance.
Preferably, the intelligent management platform monitors the fully mechanized coal mining working condition, stores various real-time working parameter information, analyzes and calculates the various real-time working parameter information, and obtains the operation guidance and the safety guidance, including: carrying out standardization processing on the stored real-time working parameter information; transmitting the standardized data information to a cloud for storage and encryption; calculating and acquiring decision content aiming at the standardized data information according to a preset decision guiding algorithm, wherein the decision content comprises an operation decision and a safety guidance decision; and a large data analysis systematization environment is constructed by using a data warehouse technology, and data are stored, processed and updated in real time.
The invention also discloses an AI fault monitoring system of the intelligent mine based on the big data, which comprises a terminal, a management center, a display unit and an audio playing unit, wherein the terminal comprises a camera module, a deep learning module, a detection device, a processing module and a communication module;
the method comprises the steps that a real-time picture of a target area is collected through a camera module, a deep learning model is established through a convolution neural network based on the deep learning module, and a fault event of the target area where the real-time picture is located and the occurrence position of the fault event are predicted through the deep learning model; and/or detecting and acquiring a fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event;
the operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system;
the detection device includes: the system comprises an electric power parameter acquisition device for acquiring related electric power parameters of a high-voltage switch of an underground substation, a gas sensor for acquiring underground gas concentration, a pressure sensor for acquiring pressure of a working face hydraulic support, a vibration analysis device for acquiring underground geological vibration information, a flowmeter for acquiring pipeline water flow in an underground drainage system, a positioning device for positioning underground personnel in real time and an equipment sensor for acquiring state information of corresponding underground equipment;
the terminal establishes a communication channel through the communication module, visually displays the fault event and the position information thereof as well as the real-time video data on the display unit, auditorily broadcasts the fault event and the position information thereof on the audio playing unit, and transmits the fault event and the position information thereof as well as the real-time video data to a management center through the communication channel;
the management center comprises an intelligent management platform, wherein the intelligent management platform comprises a monitoring platform for monitoring the fully mechanized coal mining working condition, a storage platform for storing various real-time working parameter information, and an analysis platform for analyzing and calculating the various real-time working parameter information to obtain operation guidance and safety guidance;
the terminal also comprises an AR processing module, a signaling transmission module and a data labeling module; acquiring a working video of a target area through the camera module, acquiring an enhanced video from the working video by the AR processing module through an AR technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video through the data marking module and generates an operation instruction for the enhanced video through the signaling transmission module; the marked enhanced video and the operation instruction are transmitted back to the terminal of the target area;
monitoring the communication channel and setting an improvement node, wherein the communication module comprises a monitoring unit, the monitoring unit is used for monitoring in real time when the communication channel is used, and the communication quality is improved by adjusting the improvement node; the communication module further comprises a storage unit, the monitoring unit monitors the improved communication quality of the communication channel and records the communication quality through the storage unit, and the recorded problems are analyzed to obtain the reason for the reduction of the communication quality of the communication channel.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
1. identifying a fault event from an intercepted picture of a real-time video through deep learning network learning, and detecting and acquiring the fault event through a detection device in a combined manner, so that the detection accuracy of the fault event is improved;
2. the mine comprehensive monitoring system combines real-time improvement of a communication system, real-time detection of a management center and large data monitoring and storage of an intelligent management platform, and realizes comprehensive monitoring, remote regulation and control and data management of a mine.
Drawings
FIG. 1 is a flow chart of an AI fault monitoring method for a big data based smart mine according to the present invention;
fig. 2 is a flowchart for predicting a failure event of a target area in which a real-time frame is located and an occurrence location of the failure event by using a deep learning model according to the present invention.
Detailed Description
The advantages of the invention are further illustrated in the following description of specific embodiments in conjunction with the accompanying drawings.
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure 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. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
Referring to the attached drawing 1, the invention discloses an AI fault monitoring method of an intelligent mine based on big data, which comprises the following steps:
acquiring a real-time picture of a target area, establishing a deep learning model based on a convolutional neural network, and predicting a fault event of the target area where the real-time picture is located and the occurrence position of the fault event by using the deep learning model; and/or detecting and acquiring a fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event;
carrying out visual display and auditory broadcast on the fault event, the position information of the fault event and the real-time video data, and transmitting the fault event, the position information and the real-time video data to a management center through a communication channel;
the management center comprises an intelligent management platform, the intelligent management platform monitors the fully mechanized mining working condition, stores various real-time working parameter information, analyzes and calculates the various real-time working parameter information, and acquires operation guidance and safety guidance;
acquiring a working video of a target area, acquiring an enhanced video from the working video by adopting an AR (augmented reality) technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video and generates an operation instruction for the enhanced video; the marked enhanced video and the operation instruction are transmitted back to the target area;
monitoring a communication channel, setting an improvement node, monitoring in real time when the communication channel is used, and improving the communication quality by adjusting the improvement node; and monitoring and recording the communication quality of the improved communication channel, and analyzing the recorded problems to obtain the reason of the reduction of the communication quality of the communication channel.
According to the method, the fault event is identified from the captured pictures of the real-time video through deep learning network learning, the fault event is detected and obtained through the detection device, and the detection accuracy of the fault event is improved; and the comprehensive monitoring, remote control and data management of the mine are realized by combining the real-time improvement of a communication system, the real-time detection of a management center and the large data monitoring and storage of an intelligent management platform.
Preferably, the operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system, but is not limited to the above.
Preferably, the detection device includes a power parameter acquisition device for acquiring related power parameters of a high-voltage switch of the underground substation, a gas sensor for acquiring a gas concentration in the underground, a pressure sensor for acquiring a pressure of a hydraulic support of the working surface, a vibration analysis device for acquiring geological vibration information in the underground, a flowmeter for acquiring a pipe water flow in the underground drainage system, a personnel positioning device for positioning personnel in the underground in real time, and an equipment sensor for acquiring status information of corresponding equipment in the underground, but is not limited thereto.
Preferably, the safety guidance comprises escape route guidance. The management center can remotely command the escape route of the mine site, specifically, the operation video is an underground live video, the underground live video is subjected to AR technology to obtain an enhanced video, and the enhanced video is transmitted to the management center; the management center marks an escape path on the enhanced video and generates an escape instruction, the marked enhanced video and the escape instruction are transmitted back to a downhole live video sending point, and the escape personnel finish escape according to the enhanced video and the escape instruction, so that the production safety is improved.
Preferably, in order to more specifically convey the command of the commander, the enhanced video includes video picture data and voice data, and the operation command includes voice data and picture data. The remote command center obtains the target frames of the video image data, carries out graphic labeling and character labeling on each target frame, generates voice data and labeled image data for the enhanced video, and mine field personnel can understand commands of commanders jointly according to the videos, the labeled graphics, the labeled characters and the like to complete remote guidance.
Preferably, the situation that the personnel on the mine site are on-line and the commander at the management center is not on-line sometimes exists, so that the management center needs to be reminded in time to prevent important information on the mine site from being omitted. Specifically, the enhanced video is stored, a prompt signal related to the enhanced video is generated, and after a commander is on line in the management center, the stored enhanced video can be acquired through the prompt signal.
Preferably, the mine itself has a greater safety risk, so that for an abnormal situation, it is necessary to take care of the accident to prevent the loss of personnel and property caused by the accident, specifically, when the duration of the fault event exceeds a second preset time, or the distance between the position of the fault event and the real-time image acquisition point is less than a second preset distance, the work of the work platform is automatically stopped, that is, when the duration of the fault event is too long, or the location of the fault event is specific (such as being close to an important area), it is automatically determined that the fault event has a greater risk, and at this time, the work of the work platform is automatically stopped, so as to eliminate hidden danger and ensure safety.
Referring to fig. 2, the predicting the fault event of the target area where the real-time image is located and the occurrence position of the fault event by using the deep learning model specifically includes:
s1, collecting real-time video data in the target area by taking the first preset distance as an interval;
s2, collecting a plurality of source picture frames of real-time video data at intervals of first preset time, inputting the source picture frames into a detection network based on a convolutional neural network, carrying out fault event parameter labeling on an interested region of the source picture frames to obtain a material set, and dividing the material set into a training set, a verification set and a test set;
s3, establishing a deep learning model of the source picture frame, inputting a training set and a verification set into the deep learning model for training, learning, identifying and judging the type of the fault event;
s4, inputting the test set into the trained deep learning model for testing to obtain a fault event of the real-time video data, connecting the fault event with a source picture frame where the fault event is located, and obtaining the fault event occurring in the fault event picture frame;
and S5, obtaining the distance between the fault event and the real-time video data acquisition point according to the projection relation between the real three-dimensional coordinates in the target area and the image plane of the fault event picture frame and the coordinates of the fault event occurrence point in the fault event picture frame.
Preferably, the region of interest includes several regions with different content parameters, a channel-based attention mechanism is introduced into the deep learning model, and the weights of the several different regions of interest of the source picture frame are adjusted by the channel-based attention mechanism, so that the weights of the different regions of interest in different fault events are different, and thus the different fault events can be identified more accurately.
Preferably, the positions of the visual display and the audible broadcast are: the real-time video data acquisition points and/or the management center and/or the coal mining machine and/or the top of the target area and/or the entrance of the coal mining well are/is arranged at intervals of a second preset distance, so that the visual display and the auditory broadcast points can be arranged at all the places simultaneously, and the visual display and the auditory broadcast points can also be independently arranged at a certain position.
Preferably, the intelligent management platform monitors the fully mechanized coal mining working condition, stores various real-time working parameter information, analyzes and calculates the various real-time working parameter information, and obtains the operation guidance and the safety guidance, including: carrying out standardization processing on each item of stored real-time working parameter information; transmitting the standardized data information to a cloud for storage and encryption; calculating and acquiring decision content aiming at the standardized data information according to a preset decision guiding algorithm, wherein the decision content comprises an operation decision and a safety guidance decision; and a large data analysis systematization environment is constructed by using a data warehouse technology, and data are stored, processed and updated in real time.
In order to monitor and improve the communication quality of a command mine, a terminal is interfered with a management center through a communication channel to achieve the purpose of communication, the communication channel is monitored in real time, and an improvement node is set at an access point of the terminal when the terminal joins the communication channel; once the communication quality of the terminal is monitored to be reduced and signals are fuzzy, the maintenance node at the position is improved, the node sends information of the position of the terminal to be transmitted to a local communication information base station, then the base station strengthens communication signals at the position of the terminal, and then the communication environment established by the terminal is strengthened, so that the transmission of two communication parties is faster, the information consumed in the transmission is greatly reduced, when extreme geographical conditions and signal isolation are large, the information sent by one party is recorded to obtain a communication data packet, the communication data packet is transmitted to the terminal or a management center through a cloud transmission server, and the terminal or the management center analyzes the communication data packet to obtain the content of the communication data packet.
The condition that the information transmission is interrupted due to the difference of the communication signals is effectively avoided, data can be transmitted even if the communication signals are completely interrupted, the data which are wanted to be expressed can be transmitted even if the two ends of the communication are positioned at the positions where the signals are interrupted, and the use is convenient.
Data can be extracted for problem analysis, specifically, the improved result and operation of the node are recorded, the recording structure is uploaded to a cloud server, the improved communication quality is detected, and the improved effect is detected; the cloud server analyzes the received improvement information, extracts reasons and problems of poor communication quality and transmits the analyzed problems to the management center.
And after receiving the problems, the management center repairs the problems and accurately maintains the node position through the acquired position information when subsequently maintaining the communication channel until the problems are solved, thereby achieving the purpose of continuously improving the optimization.
The invention also discloses an AI fault monitoring system of the intelligent mine based on the big data, which comprises a terminal, a management center, a display unit and an audio playing unit, wherein the terminal comprises a camera module, a deep learning module, a detection device, a processing module and a communication module. The display unit is a large screen, the audio playing unit is a microphone, and the input module is a camera.
The method comprises the steps that a camera module is used for collecting a real-time picture of a target area, a deep learning model is established through a convolution neural network based deep learning module, and a fault event of the target area where the real-time picture is located and the occurrence position of the fault event are predicted by using the deep learning model; and/or detecting and acquiring the fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event, so that the detection accuracy of the fault event is improved.
The operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system, but is not limited to the above.
The detection device comprises: the system comprises a power parameter acquisition device for acquiring related power parameters of a high-voltage switch of an underground substation, a gas sensor for acquiring underground gas concentration, a pressure sensor for acquiring pressure of a working face hydraulic support, a vibration analysis device for acquiring underground geological vibration information, a flowmeter for acquiring pipeline water flow in an underground drainage system, a positioning device for positioning underground personnel in real time, and an equipment sensor for acquiring state information of corresponding underground equipment, but is not limited to the above.
The terminal establishes a communication channel through the communication module, visually displays the fault event and the position information thereof as well as the real-time video data on the display unit, audibly broadcasts the fault event and the position information thereof on the audio playing unit, and transmits the fault event and the real-time video data to the management center through the communication channel.
The management center comprises an intelligent management platform, wherein the intelligent management platform comprises a monitoring platform for monitoring the fully mechanized coal mining working condition, a storage platform for storing various real-time working parameter information, and an analysis platform for analyzing and calculating the various real-time working parameter information to obtain operation guidance and safety guidance.
The terminal also comprises an AR processing module, a signaling transmission module and a data labeling module; the method comprises the steps that a working video of a target area is obtained through a camera module, an AR processing module obtains an enhanced video from the working video through an AR technology, and the enhanced video is transmitted to a management center; the management center marks the enhanced video through the data marking module and generates an operation instruction for the enhanced video through the signaling transmission module; and transmitting the marked enhanced video and the operation instruction back to the terminal of the target area. The AR processing module is typically AR glasses.
The communication module comprises a monitoring unit, and when the communication channel is used, the monitoring unit is used for monitoring in real time, and the communication quality is improved by adjusting the improvement node; the communication module further comprises a storage unit, the communication quality of the improved communication channel is monitored through the monitoring unit, the recording is carried out through the storage unit, and the recorded problems are analyzed to obtain the reason for the reduction of the communication quality of the communication channel.
It should be noted that the embodiments of the present invention have been described in terms of preferred embodiments, and not by way of limitation, and that those skilled in the art can make modifications and variations of the embodiments described above without departing from the spirit of the invention.
Claims (10)
1. An AI fault monitoring method of an intelligent mine based on big data is characterized by comprising the following steps:
acquiring a real-time picture of a target area, establishing a deep learning model based on a convolutional neural network, and predicting a fault event of the target area where the real-time picture is located and the occurrence position of the fault event by using the deep learning model;
and/or detecting and acquiring a fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event;
the operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system;
the detection device includes: the system comprises an electric power parameter acquisition device for acquiring related electric power parameters of a high-voltage switch of an underground substation, a gas sensor for acquiring underground gas concentration, a pressure sensor for acquiring pressure of a working face hydraulic support, a vibration analysis device for acquiring underground geological vibration information, a flowmeter for acquiring pipeline water flow in an underground drainage system, a personnel positioning device for positioning underground personnel in real time, and an equipment sensor for acquiring state information of corresponding underground equipment;
carrying out visual display and auditory broadcast on the fault event and the position information thereof as well as the real-time video data, and transmitting the fault event and the position information thereof as well as the real-time video data to a management center through a communication channel;
the management center comprises an intelligent management platform, wherein the intelligent management platform monitors the fully mechanized mining working condition, stores various real-time working parameter information, analyzes and calculates the various real-time working parameter information, and acquires operation guidance and safety guidance;
acquiring a working video of a target area, acquiring an enhanced video from the working video by adopting an AR (augmented reality) technology, and transmitting the enhanced video to a management center;
the management center marks the enhanced video and generates an operation instruction for the enhanced video;
the marked enhanced video and the operation instruction are transmitted back to the target area;
monitoring the communication channel and setting an improvement node, monitoring in real time when the communication channel is used, and improving the communication quality by adjusting the improvement node; and monitoring and recording the communication quality of the improved communication channel, and analyzing the recorded problems to obtain the reason of the reduction of the communication quality of the communication channel.
2. The AI fault monitoring method for big data based smart mines according to claim 1, wherein the safety guidance includes escape route guidance;
acquiring an operation video of a target area, acquiring an enhanced video from the operation video by adopting an AR technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video and generates an operation instruction for the enhanced video; transmitting the annotated enhanced video and the operation instructions back to the target area comprises:
the operation video is an underground live video, an AR technology is adopted for the underground live video to obtain an enhanced video, and the enhanced video is transmitted to a management center; and the management center marks an escape path on the enhanced video and generates an escape instruction, and the marked enhanced video and the escape instruction are transmitted back to the underground live video sending point.
3. The AI fault monitoring method for big data based intelligent mine according to claim 1, wherein the enhanced video comprises video picture data and voice data, and the operation instruction comprises voice data and picture data;
the remote command center marks the enhanced video, and the operation instruction for generating the enhanced video comprises the following steps: and the remote command center acquires the target frames of the video picture data, performs graphic annotation and character annotation on each target frame, and generates voice data and annotated picture data for the enhanced video.
4. The AI fault monitoring method for big data based smart mines according to claim 1, wherein the acquiring of the working video of the target area, the acquiring of the enhanced video of the working video by AR technology, and the transmitting to the management center further comprises:
storing the enhanced video and generating a prompt signal associated with the enhanced video;
and the management center acquires the stored enhanced video through the prompt signal.
5. The AI fault monitoring method for big data based intelligent mine according to claim 1, wherein the visually displaying and audibly broadcasting the fault event and its location information, and the real-time video data to the management center through the communication channel further comprises:
and when the occurrence duration time of the fault event exceeds a second preset time or the distance between the position of the fault event and the real-time picture acquisition point is less than a second preset distance, automatically stopping the work of the work platform.
6. The AI fault monitoring method for big-data based smart mines according to claim 1, wherein the acquiring of the real-time frames of the target area, the building of the deep learning model based on the convolutional neural network, and the predicting of the fault event and the occurrence location of the fault event of the target area where the real-time frames are located by using the deep learning model comprises:
collecting real-time video data in a target area by taking a first preset distance as an interval;
collecting a plurality of source picture frames of the real-time video data at intervals of first preset time, inputting the source picture frames into a detection network based on a convolutional neural network, carrying out fault event parameter labeling on an interested region of the source picture frames to obtain a material set, and dividing the material set into a training set, a verification set and a test set;
establishing a deep learning model of the source picture frame, inputting the training set and the verification set into the deep learning model for training, learning, identifying and judging the type of the fault event;
inputting the test set into the trained deep learning model for testing to obtain a fault event of the real-time video data, and connecting the fault event with the source picture frame where the fault event is located to obtain a fault event occurring in the fault event picture frame;
and obtaining the distance between the fault event and the real-time video data acquisition point according to the projection relation between the real three-dimensional coordinates in the target area and the image plane of the fault event picture frame and the coordinates of the fault event occurrence point in the fault event picture frame.
7. The AI fault monitoring method for big-data based smart mines according to claim 1, wherein the establishing of the deep learning model of the source frame, the deep learning model including a segmentation task and a classification task, further includes:
the interesting regions comprise a plurality of regions with different content parameters, a channel-based attention mechanism is introduced into the deep learning model, and the weights of the different interesting regions of the source picture frame are adjusted through the channel-based attention mechanism, so that the weights of the different interesting regions in different fault events are different.
8. The AI fault monitoring method for big data based intelligent mine according to claim 1, wherein the visually displaying and audibly broadcasting the fault event and its location information, and the real-time video data to a management center comprises:
the positions of visual display and auditory broadcast are as follows: the real-time video data acquisition points and/or the management center and/or the coal mining machine and/or the top of the target area and/or the entrance of the coal mining well are/is arranged at intervals of a second preset distance.
9. The AI fault monitoring method of big data based intelligent mine as in claim 1, wherein the intelligent management platform monitors the fully mechanized mining operation, stores and analyzes the real-time operation parameters, and obtains operation guidance and safety guidance, comprising:
carrying out standardization processing on the stored real-time working parameter information;
transmitting the standardized data information to a cloud for storage and encryption;
calculating and acquiring decision content aiming at the standardized data information according to a preset decision guiding algorithm, wherein the decision content comprises an operation decision and a safety guidance decision;
and a large data analysis systematization environment is constructed by using a data warehouse technology, and data are stored, processed and updated in real time.
10. An AI fault monitoring system of an intelligent mine based on big data is characterized by comprising a terminal, a management center, a display unit and an audio playing unit, wherein the terminal comprises a camera module, a deep learning module, a detection device, a processing module and a communication module;
the method comprises the steps that a real-time picture of a target area is collected through a camera module, a deep learning model is established through a convolution neural network based on the deep learning module, and a fault event of the target area where the real-time picture is located and the occurrence position of the fault event are predicted through the deep learning model;
and/or detecting and acquiring a fault event of the operation platform through a plurality of real-time detection devices, and then calculating or detecting and acquiring the occurrence position of the fault event;
the operation platform comprises a coal mining system, a transportation system, a power supply system, a ventilation system, a water supply and drainage system, a hydraulic system, a harmful gas monitoring system, a mine pressure analysis system, a vibration analysis system and a personnel positioning system;
the detection device includes: the system comprises an electric power parameter acquisition device for acquiring related electric power parameters of a high-voltage switch of an underground substation, a gas sensor for acquiring underground gas concentration, a pressure sensor for acquiring pressure of a working face hydraulic support, a vibration analysis device for acquiring underground geological vibration information, a flowmeter for acquiring pipeline water flow in an underground drainage system, a positioning device for positioning underground personnel in real time and an equipment sensor for acquiring state information of corresponding underground equipment;
the terminal establishes a communication channel through the communication module, visually displays the fault event and the position information thereof as well as the real-time video data on the display unit, auditorily broadcasts the fault event and the position information thereof on the audio playing unit, and transmits the fault event and the position information thereof as well as the real-time video data to a management center through the communication channel;
the management center comprises an intelligent management platform, wherein the intelligent management platform comprises a monitoring platform for monitoring the fully mechanized coal mining working condition, a storage platform for storing various real-time working parameter information, and an analysis platform for analyzing and calculating the various real-time working parameter information to obtain operation guidance and safety guidance;
the terminal also comprises an AR processing module, a signaling transmission module and a data labeling module; acquiring a working video of a target area through the camera module, acquiring an enhanced video from the working video by the AR processing module through an AR technology, and transmitting the enhanced video to a management center; the management center marks the enhanced video through the data marking module and generates an operation instruction for the enhanced video through the signaling transmission module; the marked enhanced video and the operation instruction are transmitted back to the terminal of the target area;
monitoring the communication channel and setting an improvement node, wherein the communication module comprises a monitoring unit, the monitoring unit is used for monitoring in real time when the communication channel is used, and the communication quality is improved by adjusting the improvement node; the communication module further comprises a storage unit, the monitoring unit monitors the improved communication quality of the communication channel and records the communication quality through the storage unit, and the recorded problems are analyzed to obtain the reason for the reduction of the communication quality of the communication channel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110726252.2A CN113313428A (en) | 2021-06-29 | 2021-06-29 | AI fault monitoring method and system of smart mine based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110726252.2A CN113313428A (en) | 2021-06-29 | 2021-06-29 | AI fault monitoring method and system of smart mine based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113313428A true CN113313428A (en) | 2021-08-27 |
Family
ID=77380712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110726252.2A Pending CN113313428A (en) | 2021-06-29 | 2021-06-29 | AI fault monitoring method and system of smart mine based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113313428A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113343910A (en) * | 2021-06-29 | 2021-09-03 | 上海煤科信息科技有限公司 | Big data-based image processing method and system for smart mine |
| CN114037298A (en) * | 2021-11-15 | 2022-02-11 | 国能神东煤炭集团有限责任公司 | Intelligent mining comprehensive management system for coal mine |
| CN116643206A (en) * | 2023-07-27 | 2023-08-25 | 北京天阳睿博科技有限公司 | High-voltage leakage monitoring and fault positioning system for coal mine full-power network |
| CN116843303A (en) * | 2023-08-30 | 2023-10-03 | 中铁十九局集团第六工程有限公司 | Production control method and system for intelligent beam field |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105513232A (en) * | 2016-02-04 | 2016-04-20 | 珠海智城信息技术有限公司 | Safe emergency dredging method and system |
| CN105741473A (en) * | 2016-04-28 | 2016-07-06 | 江苏科技大学 | Compartment environment monitoring, fire alarm early-warning and escape indicating system, and operation method thereof |
| CN106683301A (en) * | 2017-01-03 | 2017-05-17 | 京东方科技集团股份有限公司 | Escape route generation method, device and system |
| CN106709511A (en) * | 2016-12-08 | 2017-05-24 | 华中师范大学 | Urban rail transit panoramic monitoring video fault detection method based on depth learning |
| CN108416294A (en) * | 2018-03-08 | 2018-08-17 | 南京天数信息科技有限公司 | A kind of fan blade fault intelligent identification method based on deep learning |
| CN110568829A (en) * | 2019-08-30 | 2019-12-13 | 西安合智宇信息科技有限公司 | Mine full production chain intelligence management and control system |
| CN110708179A (en) * | 2018-07-10 | 2020-01-17 | 福建省天奕网络科技有限公司 | Block chain-based data communication bridging method and storage medium |
| CA3094424A1 (en) * | 2019-06-21 | 2020-12-21 | China University Of Mining And Technology | Safety monitoring and early-warning method for man-machine interaction behavior of underground conveyor belt operator |
| US20210048487A1 (en) * | 2019-08-12 | 2021-02-18 | Wuhan University | Power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification |
| KR20210063591A (en) * | 2019-11-25 | 2021-06-02 | 씨앤에스 주식회사 | Evacuation route providing system and method using the same |
| CN112906664A (en) * | 2021-04-06 | 2021-06-04 | 昭通亮风台信息科技有限公司 | Neural network-based method and system for detecting abnormal events in tunnel |
-
2021
- 2021-06-29 CN CN202110726252.2A patent/CN113313428A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105513232A (en) * | 2016-02-04 | 2016-04-20 | 珠海智城信息技术有限公司 | Safe emergency dredging method and system |
| CN105741473A (en) * | 2016-04-28 | 2016-07-06 | 江苏科技大学 | Compartment environment monitoring, fire alarm early-warning and escape indicating system, and operation method thereof |
| CN106709511A (en) * | 2016-12-08 | 2017-05-24 | 华中师范大学 | Urban rail transit panoramic monitoring video fault detection method based on depth learning |
| CN106683301A (en) * | 2017-01-03 | 2017-05-17 | 京东方科技集团股份有限公司 | Escape route generation method, device and system |
| CN108416294A (en) * | 2018-03-08 | 2018-08-17 | 南京天数信息科技有限公司 | A kind of fan blade fault intelligent identification method based on deep learning |
| CN110708179A (en) * | 2018-07-10 | 2020-01-17 | 福建省天奕网络科技有限公司 | Block chain-based data communication bridging method and storage medium |
| CA3094424A1 (en) * | 2019-06-21 | 2020-12-21 | China University Of Mining And Technology | Safety monitoring and early-warning method for man-machine interaction behavior of underground conveyor belt operator |
| US20210048487A1 (en) * | 2019-08-12 | 2021-02-18 | Wuhan University | Power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification |
| CN110568829A (en) * | 2019-08-30 | 2019-12-13 | 西安合智宇信息科技有限公司 | Mine full production chain intelligence management and control system |
| KR20210063591A (en) * | 2019-11-25 | 2021-06-02 | 씨앤에스 주식회사 | Evacuation route providing system and method using the same |
| CN112906664A (en) * | 2021-04-06 | 2021-06-04 | 昭通亮风台信息科技有限公司 | Neural network-based method and system for detecting abnormal events in tunnel |
Non-Patent Citations (2)
| Title |
|---|
| 唐子惠等: "《医学人工智能导论》", vol. 1, 上海科学技术出版社, pages: 221 - 222 * |
| 唐子惠等: "《物权数字化——中国经济第四极》", vol. 1, 30 April 2021, 上海科学技术出版社, pages: 161 - 163 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113343910A (en) * | 2021-06-29 | 2021-09-03 | 上海煤科信息科技有限公司 | Big data-based image processing method and system for smart mine |
| CN114037298A (en) * | 2021-11-15 | 2022-02-11 | 国能神东煤炭集团有限责任公司 | Intelligent mining comprehensive management system for coal mine |
| CN116643206A (en) * | 2023-07-27 | 2023-08-25 | 北京天阳睿博科技有限公司 | High-voltage leakage monitoring and fault positioning system for coal mine full-power network |
| CN116843303A (en) * | 2023-08-30 | 2023-10-03 | 中铁十九局集团第六工程有限公司 | Production control method and system for intelligent beam field |
| CN116843303B (en) * | 2023-08-30 | 2023-11-17 | 中铁十九局集团第六工程有限公司 | Production control method and system for intelligent beam field |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113313428A (en) | AI fault monitoring method and system of smart mine based on big data | |
| KR101949525B1 (en) | Safety management system using unmanned detector | |
| CN112288984A (en) | Three-dimensional visual unattended substation intelligent linkage system based on video fusion | |
| CN106208395B (en) | Unattended substation emergency monitoring system | |
| CN113343910A (en) | Big data-based image processing method and system for smart mine | |
| CN202649815U (en) | Transformer substation panorama automation inspection system based on robot technology | |
| CN103248878B (en) | A kind of mode identification method, equipment and system of fully-mechanized mining working unusual condition | |
| CN211406109U (en) | Portable video mobile monitoring system with AI recognition function | |
| CN103544739A (en) | B/S (Browser/Server) architecture-based polling method of visual subway equipment polling management system | |
| CN110725711B (en) | Video-based drilling system and auxiliary drilling test method | |
| CN113027530A (en) | Coal mine safety monitoring system and monitoring method based on internet data interaction | |
| CN110244679B (en) | Intelligent building site management system based on SAAS platform and application method thereof | |
| CN103309323B (en) | Inspection device method for supervising and the system of tunnel environment | |
| CN110821560A (en) | Tunnel inspection system | |
| CN110568310A (en) | Visual online monitoring system for power transmission line | |
| CN111626636A (en) | Industrial safety emergency management platform based on big data analysis technology | |
| CN112906441B (en) | Image recognition system and method for exploration and maintenance in communication industry | |
| CN116009024A (en) | Attached lifting scaffold monitoring simulation application system based on BIM technology | |
| CN104966140A (en) | Mobile engineer station with power grid working cooperatively | |
| CN113269656A (en) | Intelligent monitoring management system of oil gas well factory based on thing networking | |
| CN115661964A (en) | Intelligent inspection system for comprehensive pipe gallery | |
| CN113922502B (en) | Intelligent video operation and maintenance management system and management method | |
| CN205666910U (en) | Tour and alarm management system | |
| CN114274143A (en) | Intelligent identification method for centralized control operation defect image of intelligent robot group in thermal power plant | |
| CN113037984A (en) | Oil and gas station yard safety combined monitoring system and method based on fog calculation |
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 |