CN116560340A - Fault remote session guidance diagnosis system - Google Patents

Fault remote session guidance diagnosis system Download PDF

Info

Publication number
CN116560340A
CN116560340A CN202310543167.1A CN202310543167A CN116560340A CN 116560340 A CN116560340 A CN 116560340A CN 202310543167 A CN202310543167 A CN 202310543167A CN 116560340 A CN116560340 A CN 116560340A
Authority
CN
China
Prior art keywords
fault
data
guidance
room
session
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.)
Granted
Application number
CN202310543167.1A
Other languages
Chinese (zh)
Other versions
CN116560340B (en
Inventor
李晨
徐云生
曾依明
郭小冬
曾超
李峘
赵梓轩
聂道翔
黄杰
刘雪峰
舒鹏成
吴启山
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Three Gorges Changdian Big Data Technology Yichang Co ltd
Three Gorges High Technology Information Technology Co ltd
Three Gorges Technology Co ltd
Original Assignee
Three Gorges Changdian Big Data Technology Yichang Co ltd
Three Gorges High Technology Information Technology Co ltd
Three Gorges Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Three Gorges Changdian Big Data Technology Yichang Co ltd, Three Gorges High Technology Information Technology Co ltd, Three Gorges Technology Co ltd filed Critical Three Gorges Changdian Big Data Technology Yichang Co ltd
Priority to CN202310543167.1A priority Critical patent/CN116560340B/en
Publication of CN116560340A publication Critical patent/CN116560340A/en
Application granted granted Critical
Publication of CN116560340B publication Critical patent/CN116560340B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24065—Real time diagnostics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a fault remote session guidance diagnosis system, which comprises: the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit; the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for the guidance of a later fault guidance session room; the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room according to the request guidance instruction and the analysis report, providing corresponding simulated picture data for the fault guidance session room based on the analysis report, and performing fault guidance for operation and maintenance personnel by a specialist in the fault guidance session room. Therefore, real-time communication guidance is carried out on the fault elimination site, the complex fault problem is solved in time, and site operation safety risk management and control is further enhanced.

Description

Fault remote session guidance diagnosis system
Technical Field
The invention relates to the technical field of diagnosis, in particular to a fault remote session guidance diagnosis system.
Background
In the fault elimination process, operation and maintenance personnel often encounter various fault problems, common fault problem operation and maintenance personnel can carry out fault elimination processing through a fault manual, but for some problems with higher difficulty or unusual problems, the operation and maintenance personnel do not know how to process, so that the fault elimination process is extremely time-consuming, and the availability of equipment is reduced. For these faults, operators often communicate by telephone or take pictures on site to ask other more experienced operators or manufacturers for the inquiry. Complex fault handling by photographing or telephonic communication, problems exist including: when the network information is not good, the network information cannot be communicated in time, so that the fault cannot be eliminated; the failure phenomenon cannot be accurately described through telephone communication, so that other operation and maintenance personnel cannot accurately guide on-site personnel to perform defect elimination work; the photo is limited by the problems of pixels, definition and transmission, so that the problem that the scene cannot be accurately displayed in time is often caused, and the progress of work for eliminating the defect is delayed.
Accordingly, there is a need to provide a fault remote session guidance diagnostic system.
Disclosure of Invention
The invention provides a fault remote session guidance diagnosis system, which aims to solve the problem that in the fault elimination process in the prior art, operation and maintenance personnel often encounter various fault problems, and the common fault problem operation and maintenance personnel can perform fault elimination processing through a fault manual, but for some problems with higher difficulty or unusual problems, the operation and maintenance personnel do not know how to process, so that the fault elimination process is extremely time-consuming, and the availability of equipment is reduced. For these faults, operators often communicate by telephone or take pictures on site to ask other more experienced operators or manufacturers for the inquiry. Complex fault handling by photographing or telephonic communication, problems exist including: when the network information is not good, the network information cannot be communicated in time, so that the fault cannot be eliminated; the failure phenomenon cannot be accurately described through telephone communication, so that other operation and maintenance personnel cannot accurately guide on-site personnel to perform defect elimination work; the photo is limited by the problems of pixels, definition and transmission, which often results in the problem that the scene cannot be accurately displayed in time, and the problem of delaying the progress of the work of eliminating the defects.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a fault remote session guidance diagnostic system comprising: the intelligent internet of things cloud platform comprises a fault field control unit, an intelligent internet of things cloud platform and a remote guidance unit;
the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit;
the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room;
the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room according to the request guidance instruction and the analysis report, providing corresponding simulation picture data for the fault guidance session room based on the analysis report, and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room.
Wherein the fault site control unit includes: the system comprises a data acquisition module, a data preprocessing module and a first terminal of a session room;
the data acquisition module is used for acquiring data corresponding to a current fault point when an operation and maintenance person encounters a fault problem in the inspection;
the data preprocessing module is used for extracting data corresponding to the current fault point to perform first characteristic data, preprocessing the extracted first characteristic data and transmitting the preprocessed first characteristic data to the intelligent Internet of things cloud platform;
the first terminal of the conversation room is used for displaying the first characteristic data in real time, and an operation and maintenance person obtains a fault defect elimination instruction scheme provided by an expert through the first terminal of the conversation room.
Wherein, wisdom thing allies oneself with management unit includes: the system comprises a database, a fault evaluation module and a remote collaboration management module;
the database is used for storing historical fault point data, fault manual data and analysis report data, and provides corresponding data support for a fault guidance session room;
the fault evaluation module is used for extracting key data corresponding to the first characteristic data and the fault manual data, acquiring second characteristic data, analyzing and evaluating the second characteristic data and acquiring an analysis report;
the remote collaboration management module is used for constructing a matched fault guidance session room for the fault field control unit and the remote guidance unit.
Wherein the remote instruction unit comprises: the second terminal of the conversation room and the remote instruction operation module;
the second terminal of the conversation room is used for displaying analysis report data in real time, and the corresponding conversation room expert provides current fault guidance for operation and maintenance personnel through the second terminal of the conversation room;
the remote guidance operation module is used for operating the second terminal interface of the current session room and the data to be transmitted corresponding to the session room expert.
Wherein, the data acquisition module includes: a fault point pre-judging sub-module;
when operation and maintenance personnel acquire data corresponding to the current fault point, the fault point pre-judging submodule starts a work task, a function map is constructed based on fault manual data in the intelligent Internet of things management unit and the current acquisition data, the position of the current fault point is predicted through the function map, and automatic amplification and ultra-clear shooting is carried out on the position of the current predicted fault point in a shooting process through shooting equipment in the data acquisition module.
Wherein the fault assessment module comprises: a fault assessment model;
the fault evaluation model comprises a key data extraction layer, a fault state identification layer, a fault mode classification layer, a fault maintenance decision layer and a barrier layer to be diagnosed by an expert;
the fault state identification layer carries out data identification processing on the first characteristic data in the key data extraction layer, a mode level to which the current fault belongs is obtained through identification, a decision tree is constructed through the fault mode classification layer, association of the first characteristic data and fault manual data is constructed through the decision tree, second characteristic data is obtained based on an association model, the second characteristic data is analyzed through the fault maintenance decision layer, a corresponding analysis report is obtained, and fault problems to be solved by an expert are extracted through the barrier layer to be diagnosed by the expert.
Wherein constructing the decision tree through the failure mode classification layer comprises:
constructing a machine learning sample data set and an attribute set based on a database, if all samples in the machine learning sample data set belong to equipment fault operation data, a single-node decision tree is formed, and the equipment fault operation data is returned as the category of the machine learning sample data set; if the attribute set is empty or the same value of the machine learning sample data set on the attribute set is obtained, determining that the decision tree is a single-node decision tree, and returning to the most categories in the machine learning sample data set; if the single-node decision tree is not determined, selecting the attribute of the operation fault equipment as the optimal partition; and dividing the machine learning sample data set into a plurality of non-empty machine learning sample data sets according to the corresponding data of any one of the optimal dividing attributes and the corresponding condition of the current acquired data, and finally constructing a decision tree model.
The fault guiding session room is created by remotely connecting a first terminal of the session room and a second terminal of the session room;
the first terminal of the conversation room comprises a working area where operation and maintenance personnel are located and a classification layer where faults are located; the second terminal of the session room comprises a plurality of expert group terminals, and the terminals mark the expert as good as the maintenance fault field, the session guiding task amount, the maintenance success level and the current work arrangement of the expert;
after an operation and maintenance person sends a request instruction through a first terminal of the conversation room, the intelligent Internet of things cloud platform performs intelligent matching of a second terminal of the conversation room according to the current request instruction, acquires an expert terminal with high current maintenance success level, and constructs a fault instruction conversation room.
When the first terminal of the session room and the second terminal of the session room communicate, a format of a communication message is constructed, and corresponding encapsulation and analysis logic is adopted in a communication layer to acquire the encapsulation and the unwrapping of the message; the flow direction of the corresponding data of the conversation room comprises two-way communication of fault point data in a fault field control unit under intelligent guidance and a remote guidance unit, and two-way communication of a conversation room first terminal and a conversation room second terminal under remote guidance, and a specific flow direction of a message is defined by setting a message format, wherein the message format structure comprises a total length, a message type, a request type and a message content;
the total length is the total length of the data of the message, the message type prescribes the flow direction of the message, and the request type determines which logic processing module in the intelligent internet of things cloud platform should process the message content, and the message content is the core data of the message which needs to be processed.
When the first terminal of the session room and the second terminal of the session room communicate, unified authentication is carried out through the service gateway;
when an operation and maintenance person sends a request instruction to a second terminal of the session room through the first terminal of the session room, a layer of filter is added to the service gateway to authenticate the request, the authentication process is to judge whether the request is a request in a white list, if yes, the authentication is not needed, the next filter is directly entered, if not, the authentication is needed, if not, the request is indicated, whether the request carries a login token and the carried token is legal, if the request carries a legal token, the authentication is passed, the request instruction is successfully sent, otherwise, the authentication failure abnormality is directly returned, and the request instruction is sent failure.
Compared with the prior art, the invention has the following advantages:
a fault remote session guidance diagnostic system comprising: the intelligent internet of things cloud platform comprises a fault field control unit, an intelligent internet of things cloud platform and a remote guidance unit; the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit; the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room; the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room according to the request guidance instruction and the analysis report, providing corresponding simulation picture data for the fault guidance session room based on the analysis report, and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room. Therefore, real-time communication guidance is carried out on the fault elimination site, the complex fault problem is solved in time, and the site operation safety risk management and control is further enhanced; through corresponding expert guidance, timeliness and accuracy of fault and other problems in operation and maintenance work are improved, multistage linkage in work is realized, and guarantee is provided for high efficiency of operation and maintenance personnel.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a fault remote session guidance diagnostic system in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a fault remote session guidance diagnostic system in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of a fault site control unit in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides a fault remote session guidance diagnosis system, which comprises: the intelligent internet of things cloud platform comprises a fault field control unit, an intelligent internet of things cloud platform and a remote guidance unit;
the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit;
the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room;
the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room according to the request guidance instruction and the analysis report, providing corresponding simulation picture data for the fault guidance session room based on the analysis report, and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room.
The working principle of the technical scheme is as follows: the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit; the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room; the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room (referred to as a video session room) according to the request guidance instruction and the analysis report, providing corresponding simulated picture data for the fault guidance session room based on the analysis report (the video session room can display the simulated picture data), and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room.
The beneficial effects of the technical scheme are as follows: the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit; the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room; the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room (referred to as a video session room) according to the request guidance instruction and the analysis report, providing corresponding simulated picture data for the fault guidance session room based on the analysis report (the video session room can display the simulated picture data), and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room. Therefore, real-time communication guidance is carried out on the fault elimination site, the complex fault problem is solved in time, and the site operation safety risk management and control is further enhanced; through corresponding expert guidance, timeliness and accuracy of fault and other problems in operation and maintenance work are improved, multistage linkage in work is realized, and guarantee is provided for high efficiency of operation and maintenance personnel.
In another embodiment, the fail-site control unit includes: the system comprises a data acquisition module, a data preprocessing module and a first terminal of a session room;
the data acquisition module is used for acquiring data corresponding to a current fault point when an operation and maintenance person encounters a fault problem in the inspection;
the data preprocessing module is used for extracting data corresponding to the current fault point to perform first characteristic data, preprocessing the extracted first characteristic data and transmitting the preprocessed first characteristic data to the intelligent Internet of things cloud platform;
the first terminal of the conversation room is used for displaying the first characteristic data in real time, and an operation and maintenance person obtains a fault defect elimination instruction scheme provided by an expert through the first terminal of the conversation room.
The working principle of the technical scheme is as follows: the data acquisition module is used for acquiring data corresponding to a current fault point when an operation and maintenance person encounters a fault problem in inspection (the data comprises video recording of work of the operation equipment through video recording equipment, photographing data of fault sites of the operation equipment through photographing equipment and key operation parameter acquisition data of the operation equipment through a sensor); the data preprocessing module is used for extracting data corresponding to the current fault point to perform first characteristic data (the first characteristic data refers to operation videos, pictures and operation parameters of the fault point, other irrelevant fault data are removed, analysis and processing of the fault point data are facilitated in the later period), and the extracted first characteristic data are preprocessed and then transmitted to the intelligent Internet of things cloud platform; the first terminal of the session room is used for displaying the first characteristic data in real time, and the operation and maintenance personnel obtains a fault defect elimination instruction scheme provided by an expert through the first terminal of the session room (the first terminal of the session room refers to terminal equipment used by the operation and maintenance personnel).
The beneficial effects of the technical scheme are as follows: the data acquisition module is used for acquiring data corresponding to the current fault point when operation and maintenance personnel encounters a fault problem in the inspection process, so that the accuracy of the acquired data and the definition of pixels are ensured; the data preprocessing module is used for extracting data corresponding to the current fault point to perform first characteristic data, preprocessing the extracted first characteristic data and transmitting the preprocessed first characteristic data to the intelligent Internet of things cloud platform, guaranteeing the accuracy of data to be transmitted and improving the progress of defect elimination work; the first terminal of the session room is used for displaying the first characteristic data in real time, an operation and maintenance person obtains a fault defect elimination guiding scheme provided by an expert through the first terminal of the session room, and the expert guides the fault point to point, so that scene requirements such as accident tracing are effectively met, and field operation safety risk management and control is further strengthened.
In another embodiment, the intelligent thing networking management unit includes: the system comprises a database, a fault evaluation module and a remote collaboration management module;
the database is used for storing historical fault point data, fault manual data and analysis report data, and provides corresponding data support for a fault guidance session room;
the fault evaluation module is used for extracting key data corresponding to the first characteristic data and the fault manual data, acquiring second characteristic data, analyzing and evaluating the second characteristic data and acquiring an analysis report;
the remote collaboration management module is used for constructing a matched fault guidance session room for the fault field control unit and the remote guidance unit.
The working principle of the technical scheme is as follows: the database is used for storing historical fault point data, fault manual data and analysis report data, and provides corresponding data support for a fault guidance session room; the fault evaluation module is used for extracting key data corresponding to the first characteristic data and the fault manual data (carrying out data analysis on the first characteristic data, matching an analysis result with the fault manual data, determining a fault type), acquiring second characteristic data (referring to the fault type), carrying out analysis and evaluation on the second characteristic data (analyzing the type of the fault, analyzing different points in the fault manual data and analyzing the different points) and acquiring an analysis report; the remote collaboration management module is used for constructing a matched fault guidance session room for the fault field control unit and the remote guidance unit (the session room second terminal in the remote guidance unit comprises a plurality of second terminals which are intelligently matched by expert according to the fault type, difficulty and current working state of the expert).
The beneficial effects of the technical scheme are as follows: the database is used for storing historical fault point data, fault manual data and analysis report data, and provides corresponding data support for a fault guidance session room; the fault evaluation module is used for extracting key data corresponding to the first characteristic data and the fault manual data, acquiring second characteristic data, analyzing and evaluating the second characteristic data and acquiring an analysis report; the remote collaboration management module is used for constructing a matched fault guidance session room for the fault field control unit and the remote guidance unit. The method has the advantages of effectively meeting scene requirements such as accident tracing and the like and further strengthening field operation safety risk management and control.
In another embodiment, the remote instruction unit includes: the second terminal of the conversation room and the remote instruction operation module;
the second terminal of the conversation room is used for displaying analysis report data in real time, and the corresponding conversation room expert provides current fault guidance for operation and maintenance personnel through the second terminal of the conversation room;
the remote guidance operation module is used for operating the second terminal interface of the current session room and the data to be transmitted corresponding to the session room expert.
The working principle of the technical scheme is as follows: the second terminal of the conversation room is used for displaying analysis report data in real time, and the corresponding conversation room expert provides current fault guidance for operation and maintenance personnel through the second terminal of the conversation room; the remote guidance operation module is used for operating the second terminal interface of the current session room and the data to be transmitted corresponding to the session room expert.
The beneficial effects of the technical scheme are as follows: the second terminal of the conversation room is used for displaying analysis report data in real time, and the corresponding conversation room expert provides current fault guidance for operation and maintenance personnel through the second terminal of the conversation room; the remote guidance operation module is used for operating the second terminal interface of the current session room and the data to be transmitted corresponding to the session room expert. The method has the advantages of effectively meeting scene requirements such as accident tracing and the like and further strengthening field operation safety risk management and control.
In another embodiment, the data acquisition module comprises: a fault point pre-judging sub-module;
when operation and maintenance personnel acquire data corresponding to the current fault point, the fault point pre-judging submodule starts a work task, a function map is constructed based on fault manual data in the intelligent Internet of things management unit and the current acquisition data, the position of the current fault point is predicted through the function map, and automatic amplification and ultra-clear shooting is carried out on the position of the current predicted fault point in a shooting process through shooting equipment in the data acquisition module.
The working principle of the technical scheme is as follows: when operation and maintenance personnel acquire data corresponding to the current fault point, the fault point pre-judging submodule starts a work task, a function map is constructed based on fault manual data in the intelligent Internet of things management unit and the current acquisition data, the position of the current fault point is predicted through the function map, and automatic amplification and ultra-clear shooting is carried out on the position of the current predicted fault point in a shooting process through shooting equipment in the data acquisition module.
The beneficial effects of the technical scheme are as follows: when operation and maintenance personnel acquire data corresponding to the current fault point, the fault point pre-judging submodule starts a work task, a function map is constructed based on fault manual data in the intelligent Internet of things management unit and the current acquisition data, the position of the current fault point is predicted through the function map, and automatic amplification and ultra-clear shooting is carried out on the position of the current predicted fault point in a shooting process through shooting equipment in the data acquisition module. Therefore, the problem of definition of the fault photo is effectively solved, the problem of site accurate display is effectively solved, and the progress of defect elimination work is improved.
In another embodiment, the fault assessment module includes: a fault assessment model;
the fault evaluation model comprises a key data extraction layer, a fault state identification layer, a fault mode classification layer, a fault maintenance decision layer and a barrier layer to be diagnosed by an expert;
the fault state identification layer carries out data identification processing on the first characteristic data in the key data extraction layer, a mode level to which the current fault belongs is obtained through identification, a decision tree is constructed through the fault mode classification layer, association of the first characteristic data and fault manual data is constructed through the decision tree, second characteristic data is obtained based on an association model, the second characteristic data is analyzed through the fault maintenance decision layer, a corresponding analysis report is obtained, and fault problems to be solved by an expert are extracted through the barrier layer to be diagnosed by the expert.
The working principle of the technical scheme is as follows: the fault state identification layer carries out data identification processing on the first characteristic data in the key data extraction layer, a mode level to which the current fault belongs is obtained through identification, a decision tree is constructed through the fault mode classification layer, association of the first characteristic data and fault manual data is constructed through the decision tree, second characteristic data is obtained based on an association model, the second characteristic data is analyzed through the fault maintenance decision layer, a corresponding analysis report is obtained, and fault problems to be solved by an expert are extracted through the barrier layer to be diagnosed by the expert.
The beneficial effects of the technical scheme are as follows: the fault state identification layer carries out data identification processing on the first characteristic data in the key data extraction layer, a mode level to which the current fault belongs is obtained through identification, a decision tree is constructed through the fault mode classification layer, association of the first characteristic data and fault manual data is constructed through the decision tree, second characteristic data is obtained based on an association model, the second characteristic data is analyzed through the fault maintenance decision layer, a corresponding analysis report is obtained, and fault problems to be solved by an expert are extracted through the barrier layer to be diagnosed by the expert. And convenience is provided for an expert to analyze fault reasons and provide fault solutions, and the progress of defect elimination work is improved.
In another embodiment, building a decision tree through a failure mode classification layer includes:
constructing a machine learning sample data set and an attribute set based on a database, if all samples in the machine learning sample data set belong to equipment fault operation data, a single-node decision tree is formed, and the equipment fault operation data is returned as the category of the machine learning sample data set; if the attribute set is empty or the same value of the machine learning sample data set on the attribute set is obtained, determining that the decision tree is a single-node decision tree, and returning to the most categories in the machine learning sample data set; if the single-node decision tree is not determined, selecting the attribute of the operation fault equipment as the optimal partition; and dividing the machine learning sample data set into a plurality of non-empty machine learning sample data sets according to the corresponding data of any one of the optimal dividing attributes and the corresponding condition of the current acquired data, and finally constructing a decision tree model.
The working principle of the technical scheme is as follows: constructing a machine learning sample data set and an attribute set based on a database, if all samples in the machine learning sample data set belong to equipment fault operation data, a single-node decision tree is formed, and the equipment fault operation data is returned as the category of the machine learning sample data set; if the attribute set is empty or the same value of the machine learning sample data set on the attribute set is obtained, determining that the decision tree is a single-node decision tree, and returning to the most categories in the machine learning sample data set; if the single-node decision tree is not determined, selecting the attribute of the operation fault equipment as the optimal partition; and dividing the machine learning sample data set into a plurality of non-empty machine learning sample data sets according to the corresponding data of any one of the optimal dividing attributes and the corresponding condition of the current acquired data, and finally constructing a decision tree model.
The beneficial effects of the technical scheme are as follows: constructing a machine learning sample data set and an attribute set based on a database, if all samples in the machine learning sample data set belong to equipment fault operation data, a single-node decision tree is formed, and the equipment fault operation data is returned as the category of the machine learning sample data set; if the attribute set is empty or the same value of the machine learning sample data set on the attribute set is obtained, determining that the decision tree is a single-node decision tree, and returning to the most categories in the machine learning sample data set; if the single-node decision tree is not determined, selecting the attribute of the operation fault equipment as the optimal partition; and dividing the machine learning sample data set into a plurality of non-empty machine learning sample data sets according to the corresponding data of any one of the optimal dividing attributes and the corresponding condition of the current acquired data, and finally constructing a decision tree model. And convenience is provided for an expert to analyze fault reasons and provide fault solutions, and the progress of defect elimination work is improved.
In another embodiment, the fault guiding session room is created by remotely connecting the session room first terminal and the session room second terminal;
the first terminal of the conversation room comprises a working area where operation and maintenance personnel are located and a classification layer where faults are located; the second terminal of the session room comprises a plurality of expert group terminals, and the terminals mark the expert as good as the maintenance fault field, the session guiding task amount, the maintenance success level and the current work arrangement of the expert;
after an operation and maintenance person sends a request instruction through a first terminal of the conversation room, the intelligent Internet of things cloud platform performs intelligent matching of a second terminal of the conversation room according to the current request instruction, acquires an expert terminal with high current maintenance success level, and constructs a fault instruction conversation room.
The working principle of the technical scheme is as follows: the first terminal of the conversation room comprises a working area where operation and maintenance personnel are located and a classification layer where faults are located; the second terminal of the session room comprises a plurality of expert group terminals, and the terminals mark the expert as good as the maintenance fault field, the session guiding task amount, the maintenance success level and the current work arrangement of the expert; after an operation and maintenance person sends a request instruction through a first terminal of the conversation room, the intelligent Internet of things cloud platform performs intelligent matching of a second terminal of the conversation room according to the current request instruction, acquires an expert terminal with high current maintenance success level, and constructs a fault instruction conversation room. And convenience is provided for an expert to analyze fault reasons and provide fault solutions, and the progress of defect elimination work is improved.
The beneficial effects of the technical scheme are as follows: the first terminal of the conversation room comprises a working area where operation and maintenance personnel are located and a classification layer where faults are located; the second terminal of the session room comprises a plurality of expert group terminals, and the terminals mark the expert as good as the maintenance fault field, the session guiding task amount, the maintenance success level and the current work arrangement of the expert; after an operation and maintenance person sends a request instruction through a first terminal of the conversation room, the intelligent Internet of things cloud platform performs intelligent matching of a second terminal of the conversation room according to the current request instruction, acquires an expert terminal with high current maintenance success level, and constructs a fault instruction conversation room. And convenience is provided for an expert to analyze fault reasons and provide fault solutions, and the progress of defect elimination work is improved.
In another embodiment, when the first terminal of the session room and the second terminal of the session room communicate, a format of a communication message is constructed, and corresponding encapsulation and analysis logic is adopted in a communication layer to obtain the encapsulation and the unwrapping of the message; the flow direction of the corresponding data of the conversation room comprises two-way communication of fault point data in a fault field control unit under intelligent guidance and a remote guidance unit, and two-way communication of a conversation room first terminal and a conversation room second terminal under remote guidance, and a specific flow direction of a message is defined by setting a message format, wherein the message format structure comprises a total length, a message type, a request type and a message content;
the total length is the total length of the data of the message, the message type prescribes the flow direction of the message, and the request type determines which logic processing module in the intelligent internet of things cloud platform should process the message content, and the message content is the core data of the message which needs to be processed.
The working principle of the technical scheme is as follows: adopting corresponding encapsulation and analysis logic in a communication layer to obtain the encapsulation and the unwrapping of the message; the flow direction of the corresponding data of the conversation room comprises two-way communication of fault point data in a fault field control unit under intelligent guidance and a remote guidance unit, and two-way communication of a conversation room first terminal and a conversation room second terminal under remote guidance, and a specific flow direction of a message is defined by setting a message format, wherein the message format structure comprises a total length, a message type, a request type and a message content; the total length is the total length of the data of the message, the message type prescribes the flow direction of the message, and the request type determines which logic processing module in the intelligent internet of things cloud platform should process the message content, and the message content is the core data of the message which needs to be processed.
In order to increase the speed of communication transmission data, a CRC algorithm structure table is adopted for checking, the data stream arranged according to bytes is expressed as a mathematical polynomial in the CRC algorithm structure table, and the data stream is B n B n-1 B n-2 ......B 1 B 0 The CRC algorithm expression is:
H＝B n *256 n +B n-1 *256 n-1 +B n-2 *256 n-2 +...+B 1 *256+B 0
where H represents the CRC algorithm expression, "+" represents the exclusive OR symbol, 256 represents 8 bits to the left, 256 n The power n of 256 is represented.
And CRC redundant bits of data bytes or data blocks transmitted and received in communication are calculated through a CRC table look-up function, so that the data transmission speed is effectively improved.
The beneficial effects of the technical scheme are as follows: adopting corresponding encapsulation and analysis logic in a communication layer to obtain the encapsulation and the unwrapping of the message; the flow direction of the corresponding data of the conversation room comprises two-way communication of fault point data in a fault field control unit under intelligent guidance and a remote guidance unit, and two-way communication of a conversation room first terminal and a conversation room second terminal under remote guidance, and a specific flow direction of a message is defined by setting a message format, wherein the message format structure comprises a total length, a message type, a request type and a message content; the total length is the total length of the data of the message, the message type prescribes the flow direction of the message, and the request type determines which logic processing module in the intelligent internet of things cloud platform should process the message content, and the message content is the core data of the message which needs to be processed. The stability of remote communication is ensured.
In another embodiment, when the first terminal of the session room and the second terminal of the session room communicate, unified authentication is performed through the service gateway;
when an operation and maintenance person sends a request instruction to a second terminal of the session room through the first terminal of the session room, a layer of filter is added to the service gateway to authenticate the request, the authentication process is to judge whether the request is a request in a white list, if yes, the authentication is not needed, the next filter is directly entered, if not, the authentication is needed, if not, the request is indicated, whether the request carries a login token and the carried token is legal, if the request carries a legal token, the authentication is passed, the request instruction is successfully sent, otherwise, the authentication failure abnormality is directly returned, and the request instruction is sent failure.
The working principle of the technical scheme is as follows: when an operation and maintenance person sends a request instruction to a second terminal of the session room through the first terminal of the session room, a layer of filter is added to the service gateway to authenticate the request, the authentication process is to judge whether the request is a request in a white list, if yes, the authentication is not needed, the next filter is directly entered, if not, the authentication is needed, if not, the request is indicated, whether the request carries a login token and the carried token is legal, if the request carries a legal token, the authentication is passed, the request instruction is successfully sent, otherwise, the authentication failure abnormality is directly returned, and the request instruction is sent failure.
The beneficial effects of the technical scheme are as follows: when an operation and maintenance person sends a request instruction to a second terminal of the session room through the first terminal of the session room, a layer of filter is added to the service gateway to authenticate the request, the authentication process is to judge whether the request is a request in a white list, if yes, the authentication is not needed, the next filter is directly entered, if not, the authentication is needed, if not, the request is indicated, whether the request carries a login token and the carried token is legal, if the request carries a legal token, the authentication is passed, the request instruction is successfully sent, otherwise, the authentication failure abnormality is directly returned, and the request instruction is sent failure. The safety of communication is ensured.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. A fault remote session guidance diagnostic system, comprising: the intelligent internet of things cloud platform comprises a fault field control unit, an intelligent internet of things cloud platform and a remote guidance unit;
the fault site control unit is used for collecting fault point data, preprocessing the collected fault point data and then sending a request instruction to the remote instruction unit;
the intelligent internet of things cloud platform is used for storing historical data and fault manual data, analyzing the current fault point data, acquiring an analysis report and providing technical support for guidance of a later fault guidance session room;
the remote guidance unit is used for receiving the request guidance instruction, constructing a fault guidance session room according to the request guidance instruction and the analysis report, providing corresponding simulation picture data for the fault guidance session room based on the analysis report, and performing fault guidance on operation and maintenance personnel by a special person in the fault guidance session room.
2. The fault remote session guidance diagnostic system of claim 1, wherein the fault site control unit comprises: the system comprises a data acquisition module, a data preprocessing module and a first terminal of a session room;
the data acquisition module is used for acquiring data corresponding to a current fault point when an operation and maintenance person encounters a fault problem in the inspection;
the data preprocessing module is used for extracting data corresponding to the current fault point to perform first characteristic data, preprocessing the extracted first characteristic data and transmitting the preprocessed first characteristic data to the intelligent Internet of things cloud platform;
the first terminal of the conversation room is used for displaying the first characteristic data in real time, and an operation and maintenance person obtains a fault defect elimination instruction scheme provided by an expert through the first terminal of the conversation room.
3. The fault remote session guidance diagnostic system of claim 1, wherein the intelligent thing networking management unit comprises: the system comprises a database, a fault evaluation module and a remote collaboration management module;
the database is used for storing historical fault point data, fault manual data and analysis report data, and provides corresponding data support for a fault guidance session room;
the fault evaluation module is used for extracting key data corresponding to the first characteristic data and the fault manual data, acquiring second characteristic data, analyzing and evaluating the second characteristic data and acquiring an analysis report;
the remote collaboration management module is used for constructing a matched fault guidance session room for the fault field control unit and the remote guidance unit.
4. The fault remote session guidance diagnostic system of claim 1, wherein the remote guidance unit comprises: the second terminal of the conversation room and the remote instruction operation module;
the second terminal of the conversation room is used for displaying analysis report data in real time, and the corresponding conversation room expert provides current fault guidance for operation and maintenance personnel through the second terminal of the conversation room;
the remote guidance operation module is used for operating the second terminal interface of the current session room and the data to be transmitted corresponding to the session room expert.
5. The fault remote session guidance diagnostic system of claim 2, wherein the data acquisition module comprises: a fault point pre-judging sub-module;
when operation and maintenance personnel acquire data corresponding to the current fault point, the fault point pre-judging submodule starts a work task, a function map is constructed based on fault manual data in the intelligent Internet of things management unit and the current acquisition data, the position of the current fault point is predicted through the function map, and automatic amplification and ultra-clear shooting is carried out on the position of the current predicted fault point in a shooting process through shooting equipment in the data acquisition module.
6. The fault remote session guidance diagnostic system of claim 3, wherein the fault assessment module comprises: a fault assessment model;
the fault evaluation model comprises a key data extraction layer, a fault state identification layer, a fault mode classification layer, a fault maintenance decision layer and a barrier layer to be diagnosed by an expert;
the fault state identification layer carries out data identification processing on the first characteristic data in the key data extraction layer, a mode level to which the current fault belongs is obtained through identification, a decision tree is constructed through the fault mode classification layer, association of the first characteristic data and fault manual data is constructed through the decision tree, second characteristic data is obtained based on an association model, the second characteristic data is analyzed through the fault maintenance decision layer, a corresponding analysis report is obtained, and fault problems to be solved by an expert are extracted through the barrier layer to be diagnosed by the expert.
7. The fault remote session guidance diagnostic system of claim 6, wherein constructing a decision tree through a fault mode classification layer comprises:
constructing a machine learning sample data set and an attribute set based on a database, if all samples in the machine learning sample data set belong to equipment fault operation data, a single-node decision tree is formed, and the equipment fault operation data is returned as the category of the machine learning sample data set; if the attribute set is empty or the same value of the machine learning sample data set on the attribute set is obtained, determining that the decision tree is a single-node decision tree, and returning to the most categories in the machine learning sample data set; if the single-node decision tree is not determined, selecting the attribute of the operation fault equipment as the optimal partition; and dividing the machine learning sample data set into a plurality of non-empty machine learning sample data sets according to the corresponding data of any one of the optimal dividing attributes and the corresponding condition of the current acquired data, and finally constructing a decision tree model.
8. The fault remote session guidance diagnostic system of claim 3, wherein the fault guidance session room is created by remotely connecting a session room first terminal, a session room second terminal;
the first terminal of the conversation room comprises a working area where operation and maintenance personnel are located and a classification layer where faults are located; the second terminal of the session room comprises a plurality of expert group terminals, and the terminals mark the expert as good as the maintenance fault field, the session guiding task amount, the maintenance success level and the current work arrangement of the expert;
after an operation and maintenance person sends a request instruction through a first terminal of the conversation room, the intelligent Internet of things cloud platform performs intelligent matching of a second terminal of the conversation room according to the current request instruction, acquires an expert terminal with high current maintenance success level, and constructs a fault instruction conversation room.
9. The fault remote session guidance diagnosis system according to claim 8, wherein when the first terminal of the session room and the second terminal of the session room communicate, a format of a communication message is constructed, and corresponding encapsulation and parsing logic is adopted in the communication layer to obtain the encapsulation and unpacking of the message; the flow direction of the corresponding data of the conversation room comprises two-way communication of fault point data in a fault field control unit under intelligent guidance and a remote guidance unit, and two-way communication of a conversation room first terminal and a conversation room second terminal under remote guidance, and a specific flow direction of a message is defined by setting a message format, wherein the message format structure comprises a total length, a message type, a request type and a message content;
the total length is the total length of the data of the message, the message type prescribes the flow direction of the message, and the request type determines which logic processing module in the intelligent internet of things cloud platform should process the message content, and the message content is the core data of the message which needs to be processed.
10. The fault remote session guidance diagnostic system of claim 9, wherein the unified authentication is performed by the service gateway while the first session room terminal and the second session room terminal are in communication;
when an operation and maintenance person sends a request instruction to a second terminal of the session room through the first terminal of the session room, a layer of filter is added to the service gateway to authenticate the request, the authentication process is to judge whether the request is a request in a white list, if yes, the authentication is not needed, the next filter is directly entered, if not, the authentication is needed, if not, the request is indicated, whether the request carries a login token and the carried token is legal, if the request carries a legal token, the authentication is passed, the request instruction is successfully sent, otherwise, the authentication failure abnormality is directly returned, and the request instruction is sent failure.
CN202310543167.1A 2023-05-15 2023-05-15 Fault remote session guidance diagnosis system Active CN116560340B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310543167.1A CN116560340B (en) 2023-05-15 2023-05-15 Fault remote session guidance diagnosis system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310543167.1A CN116560340B (en) 2023-05-15 2023-05-15 Fault remote session guidance diagnosis system

Publications (2)

Publication Number Publication Date
CN116560340A true CN116560340A (en) 2023-08-08
CN116560340B CN116560340B (en) 2023-12-01

Family

ID=87501502

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310543167.1A Active CN116560340B (en) 2023-05-15 2023-05-15 Fault remote session guidance diagnosis system

Country Status (1)

Country Link
CN (1) CN116560340B (en)

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103823458A (en) * 2014-03-17 2014-05-28 广东华南计算技术研究所 Remote diagnosis device, method and system for equipment
CN108805300A (en) * 2018-05-30 2018-11-13 上海理工大学 Numerically controlled machine remote live collaboration fault diagnosis and maintenance system
US20190097872A1 (en) * 2017-09-25 2019-03-28 Dasan Zhone Solutions, Inc. System and method for remote maintenance
CN110830463A (en) * 2019-10-30 2020-02-21 腾讯科技（深圳）有限公司 Third party authorized login method and device
CN111683219A (en) * 2020-07-07 2020-09-18 中国电建集团华东勘测设计研究院有限公司 Remote diagnosis system and method for equipment faults of hydropower station
CN111708798A (en) * 2020-06-30 2020-09-25 浙江运达风电股份有限公司 Wind turbine generator fault diagnosis and processing method and system
CN112116243A (en) * 2020-09-17 2020-12-22 南方电网海南数字电网研究院有限公司 Electrical equipment defect management system based on AHP and remote support
CN113610236A (en) * 2021-08-05 2021-11-05 南京业恒达智能系统股份有限公司 Mine electromechanical equipment fault diagnosis support system based on crowd sensing
CN113747111A (en) * 2021-09-14 2021-12-03 广东电网有限责任公司 Interactive power transformation defect elimination remote video consultation support system based on AR glasses
CN114036041A (en) * 2021-10-21 2022-02-11 南京航空航天大学 SCADE model combination verification environment hypothesis automatic generation method based on machine learning
CN115047841A (en) * 2021-03-09 2022-09-13 北京唐浩电力工程技术研究有限公司 Wind turbine generator system remote fault diagnosis system based on cloud platform
KR102448790B1 (en) * 2021-11-09 2022-09-29 주식회사 우진기전 Failure diagnosis and speed response camera automatic control device
CN115372377A (en) * 2022-08-23 2022-11-22 江苏量为石科技股份有限公司 Distribution network line fault detection positioning device
CN115598683A (en) * 2022-10-11 2023-01-13 江苏电力信息技术有限公司（Cn） Automatic cruise detection method of unmanned aerial vehicle for line fault maintenance

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103823458A (en) * 2014-03-17 2014-05-28 广东华南计算技术研究所 Remote diagnosis device, method and system for equipment
US20190097872A1 (en) * 2017-09-25 2019-03-28 Dasan Zhone Solutions, Inc. System and method for remote maintenance
CN108805300A (en) * 2018-05-30 2018-11-13 上海理工大学 Numerically controlled machine remote live collaboration fault diagnosis and maintenance system
CN110830463A (en) * 2019-10-30 2020-02-21 腾讯科技（深圳）有限公司 Third party authorized login method and device
CN111708798A (en) * 2020-06-30 2020-09-25 浙江运达风电股份有限公司 Wind turbine generator fault diagnosis and processing method and system
CN111683219A (en) * 2020-07-07 2020-09-18 中国电建集团华东勘测设计研究院有限公司 Remote diagnosis system and method for equipment faults of hydropower station
CN112116243A (en) * 2020-09-17 2020-12-22 南方电网海南数字电网研究院有限公司 Electrical equipment defect management system based on AHP and remote support
CN115047841A (en) * 2021-03-09 2022-09-13 北京唐浩电力工程技术研究有限公司 Wind turbine generator system remote fault diagnosis system based on cloud platform
CN113610236A (en) * 2021-08-05 2021-11-05 南京业恒达智能系统股份有限公司 Mine electromechanical equipment fault diagnosis support system based on crowd sensing
CN113747111A (en) * 2021-09-14 2021-12-03 广东电网有限责任公司 Interactive power transformation defect elimination remote video consultation support system based on AR glasses
CN114036041A (en) * 2021-10-21 2022-02-11 南京航空航天大学 SCADE model combination verification environment hypothesis automatic generation method based on machine learning
KR102448790B1 (en) * 2021-11-09 2022-09-29 주식회사 우진기전 Failure diagnosis and speed response camera automatic control device
CN115372377A (en) * 2022-08-23 2022-11-22 江苏量为石科技股份有限公司 Distribution network line fault detection positioning device
CN115598683A (en) * 2022-10-11 2023-01-13 江苏电力信息技术有限公司（Cn） Automatic cruise detection method of unmanned aerial vehicle for line fault maintenance

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘斌, 刘伟彦, 孙正义, 黄华: "基于Web的钻井工程远程故障诊断系统", 石油机械, no. 11 *

Also Published As

Publication number Publication date
CN116560340B (en) 2023-12-01

Similar Documents

Publication Publication Date Title
CN111722714A (en) Digital substation metering operation inspection auxiliary method based on AR technology
CN111307823B (en) System and method for detecting typical visual defects of power transformation equipment based on edge cloud cooperation
CN108279428A (en) Map datum evaluating apparatus and system, data collecting system and collecting vehicle and acquisition base station
CN110807460B (en) Transformer substation intelligent patrol system based on image recognition and application method thereof
CN109685389A (en) Elevator faults work dispatching method, device, server, storage medium and system
CN110309967A (en) Prediction technique, system, equipment and the storage medium of customer service session grading system
CN111652406A (en) Method and system for predicting running state of power grid equipment under cloud-edge cooperation
US11231980B1 (en) Method, device and system for fault detection
CN115272027A (en) Low-carbon emission data monitoring system for double-carbon management
CN116560340B (en) Fault remote session guidance diagnosis system
CN110597130A (en) Unattended substation remote information acquisition system
CN117348557B (en) Automatic control system and control method
CN116580362B (en) Transmission operation cross-system fusion data acquisition method and digital asset processing system
CN117082098A (en) Mobile digital informatization acquisition system
CN110532153A (en) A kind of business level user's operation experience visualization system
CN115880803A (en) Intelligent inspection system and method
CN115187086A (en) Intelligent management and control system for emergency management
CN111565125B (en) Method for acquiring message passing through network traffic path
CN107730422A (en) One kind is based on face recognition online testing analysis system
CN114218430A (en) Remote cooperative equipment operation and maintenance system, method and device
CN114553678A (en) Diagnosis method for soft SLB traffic problem of cloud network
CN114282782A (en) Wisdom energy consumption management system based on visual and internet of things of 3D
CN113655770A (en) Automobile fault diagnosis teaching system and method
CN112116108A (en) Expert system and method for failure analysis of power equipment
CN112132819A (en) Communication network management monitoring method based on artificial intelligence

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