CN116664100A - BIM+AI-based intelligent operation and maintenance management system - Google Patents
BIM+AI-based intelligent operation and maintenance management system Download PDFInfo
- Publication number
- CN116664100A CN116664100A CN202310513979.1A CN202310513979A CN116664100A CN 116664100 A CN116664100 A CN 116664100A CN 202310513979 A CN202310513979 A CN 202310513979A CN 116664100 A CN116664100 A CN 116664100A
- Authority
- CN
- China
- Prior art keywords
- electrical equipment
- risk identification
- bim
- electrical
- real
- 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
- 238000012423 maintenance Methods 0.000 title claims abstract description 56
- 238000012545 processing Methods 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims description 29
- 238000007726 management method Methods 0.000 claims description 27
- 230000005856 abnormality Effects 0.000 claims description 21
- 230000036541 health Effects 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 17
- 239000006185 dispersion Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 7
- 230000008439 repair process Effects 0.000 claims description 5
- 208000006011 Stroke Diseases 0.000 claims description 4
- 238000009434 installation Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 230000000306 recurrent effect Effects 0.000 claims 1
- 230000004044 response Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009776 industrial production Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G06Q50/08—Construction
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Mathematical Physics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Emergency Management (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application discloses a BIM+AI-based intelligent operation and maintenance management system, which belongs to the technical field of digital management, and comprises the following components: the BIM module is used for acquiring the information of the electrical equipment; the AI module is used for acquiring the first parameter information and the second parameter information; the first data processing module is used for generating a first risk identifier and a second risk identifier of the electrical equipment; the comprehensive data processing module is used for generating a third high risk identifier, a third medium risk identifier and a third low risk identifier; the feedback module marks the corresponding three-dimensional model according to the third high risk identification level and sends out an alarm prompt; the system can accelerate the response speed of maintenance work, improve the maintenance efficiency, effectively improve the safety and reliability of the electrical equipment, discover problems in time before the electrical equipment fails, and facilitate the intelligent operation and maintenance management of the electrical equipment.
Description
Technical Field
The application relates to the technical field of digital management, in particular to a BIM+AI-based intelligent operation and maintenance management system.
Background
BIM technology is a comprehensive, digital and informationized technical means, and can realize the whole process informationized management of buildings and facilities, including construction, operation and maintenance. Through BIM technique, can carry out all-round, three-dimensional management and control to building and facility, improve the operating efficiency and the security of facility, AI technique can obtain more valuable data information through the analysis and the processing to sensor data.
With the continuous improvement of the industrial automation level and the rapid development of artificial intelligence technology, an intelligent operation and maintenance management system has become an integral part of the industrial production process. In building management, safe and stable operation of electrical equipment is critical for industrial production and civil electricity utilization. The causes of the faults of the electrical equipment are various, the existing information of the faults of the electrical equipment is lagged, the faults of the electrical equipment can be generally found after the faults of the electrical equipment, for example, the electrical equipment such as an elevator and an air conditioner can be generally found by maintenance personnel after the shutdown is found, the faults of the electrical equipment can bring a certain degree of physical and psychological damage and property loss to a user due to accidents, the time of the faults of the electrical equipment cannot be accurately judged in the traditional manual detection and manual inspection mode, and potential safety hazards possibly existing in the electrical equipment cannot be found in time.
Chinese patent publication No. CN110119851B discloses a method and system for intelligent prediction of building electromechanical system faults, which discloses building a BIM model by creating a building; real-time monitoring is carried out on important electromechanical equipment by adopting the technology of the Internet of things, and monitoring data are dynamically acquired, wherein the important electromechanical equipment is central and sub-area control and power equipment of an electromechanical system; collecting report repair work order information, carrying out semantic recognition on each report repair work order, and determining report repair space and related electromechanical systems or equipment by matching with BIM information; adopting principal component analysis and a neural network algorithm to establish an electromechanical equipment fault prediction model and performing machine learning; the network training uses a cross-validation method until the obtained artificial neural network model is accurate: and (3) performing fault prediction by using an artificial neural network model, and performing key inspection on potential fault notification maintenance personnel. According to the scheme, the building electromechanical equipment fault can be accurately predicted, the sudden fault of the building electromechanical equipment is reduced by 20%, the stable operation of a large public building is ensured, and the operation and maintenance cost is reduced.
Therefore, the application provides a BIM+AI-based intelligent operation and maintenance management system.
Disclosure of Invention
The application aims to solve the defects in the prior art and provides a BIM+AI-based intelligent operation and maintenance management system
In order to achieve the above purpose, the present application adopts the following technical scheme:
the BIM module is used for storing and displaying a three-dimensional model of the building, the three-dimensional model records information of electrical equipment in the building, and the information of the electrical equipment comprises information of the installation position of the electrical equipment;
the AI module is used for being connected to the sensor on the electrical equipment body and the electrical equipment control program, receiving data sent by the sensor on the electrical equipment body and the electrical equipment control program, and summarizing and obtaining first parameter information and second parameter information corresponding to the electrical equipment according to the received data;
the first data processing module is used for analyzing the first parameter information to generate a real-time early warning value of the electrical equipment, and generating a first risk identifier for the corresponding electrical equipment according to the magnitude of the real-time early warning value of the electrical equipment, wherein the first risk identifier comprises a first high risk identifier, a first medium risk identifier and a first low risk identifier;
the first data processing module is used for analyzing the second parameter information to generate an electrical equipment health coefficient, and generating a second risk identifier for the corresponding electrical equipment according to the size of the electrical equipment health coefficient, wherein the second risk identifier comprises a second high risk identifier, a second medium risk identifier and a second low risk identifier;
the comprehensive processing module is used for analyzing the first high risk identifier, the first medium risk identifier and the first low risk identifier, and obtaining a third high risk identifier, a third medium risk identifier and a third low risk identifier according to the second high risk identifier, the second medium risk identifier and the second low risk identifier;
the feedback module is used for marking the three-dimensional model according to the third high risk mark, the third medium risk mark and the third low risk mark and sending out an alarm reminding at the corresponding position according to the corresponding position information of the electrical equipment, wherein the alarm reminding at least comprises the position information of the electrical equipment;
the BIM module, the AI module, the first data processing module, the comprehensive processing module and the feedback module are connected through wires or wirelessly.
Further first parameter information includes an electrical device temperature value WDZi, a humidity value SDZi, a frequency of use PCi, a period of use ZQi, and a usable life NXi;
the second parameter information includes the maintenance number wbi, the major repair number zbi, the accident occurrence number ywi, and the operation time sci of the electric device.
Further, the specific analysis process for generating the real-time early warning value of the electrical equipment is as follows:
normalizing the first parameter information corresponding to each electrical device in a dimensionless processing mode; obtaining real-time early warning value of electrical equipment，
；
Wherein a1, a2, a3, a4 are respectively the temperature value WDZi, the humidity value SDZi, the used frequency PCi, the difference between the usable life and the used periodIs a first weighting constant of the correlation of +.>The early warning value at the ith moment of the electrical equipment is represented, W0 represents a standard temperature value, and S0 represents a standard humidity value.
Further, the specific analysis process for generating the first high risk identifier, the first risk identifier and the first low risk identifier is as follows:
gradient thresholds YJ1 and YJ2 are set, wherein YJ2 is greater than YJ1; real-time early warning value of electrical equipmentComparison analysis with gradient thresholds YJ1 and YJ2, if electricalReal-time early warning value +.>Generating a first high risk identification greater than YJ 2; if the real-time early warning value of the electrical equipment is->Generating a first stroke risk identifier by YJ1 or YJ 2; if the real-time early warning value of the electrical equipmentLess than YJ1 generates a first low risk indicator.
Further, the specific analysis process for generating the health coefficient of the electrical equipment is as follows:
calculating the second parameter information in a dimensionless weighting mode to calculate the health coefficient of the electrical equipment；
；
Wherein b1, b2, b3, b4 are second weighting constants of the maintenance times wbi, the major maintenance times zbi, the accident occurrence times ywi and the working time sci of the electrical equipment respectively.
Further, the specific analysis process for generating the second high risk identifier, the second medium risk identifier and the second low risk identifier is as follows:
setting second gradient early-warning values SW1 and SW2, wherein SW1 is smaller than SW2, and generating a second high-risk mark if the health coefficient of the electrical equipment is larger than SW 2; if the health coefficient of the electrical equipment is greater than or equal to SW1 and less than or equal to SW2, generating a second risk identification; and generating a second low risk identification if the electrical device health coefficient is less than SW 1.
Further, the specific analysis process of the third high risk identifier, the third medium risk identifier or the third low risk identifier is as follows:
any one of the electrical devices has a first high risk identification and a second high risk identification as an A situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a B situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a C situation, any one of the electrical devices has a first high risk identification and a second high risk identification or a first low risk identification and a second high risk identification as a D situation, any one of the electrical devices has a first medium risk identification and a second medium risk identification as an E situation, any one of the electrical devices has a first medium risk identification and a second low risk identification as an F situation, and any one of the electrical devices has a first low risk identification and a second low risk identification as a G situation;
setting the electrical device with the situation A as a third highest risk identification; setting the electrical equipment with the B situation, the C situation and the D situation as a third risk identification; the third lowest risk indicator is set for electrical devices having E, F and G conditions.
Further, a BIM+AI-based intelligent operation and maintenance management system further comprises:
the historical data analysis module is used for recording all the real-time early warning values of the electrical equipment exceeding the gradient threshold YJ2, forming historical real-time early warning value data of the electrical equipment, and generating frequent abnormality and sporadic abnormality according to the analysis of the historical real-time early warning value data of the electrical equipment;
the BIM module, the AI module, the first data processing module, the comprehensive processing module, the feedback module and the historical data analysis module are connected through wires or wirelessly, so that data transmission among the modules is realized.
Further, the common exception and the occasional exception generation logic is as follows:
counting all historical electrical equipment real-time early warning value data exceeding a gradient threshold YJ2 generated by the histories of the electrical equipment of the same brand and the same type;
counting the accumulated duration T of all the historical electrical equipment real-time early warning value data exceeding the gradient threshold YJ 2;
solving according to the historical electrical equipment real-time early warning value data of all the electrical equipment of the same brand and the same type to obtain the average value of the threshold value YJ2；
Calculating the dispersion S of historical real-time early warning value data of each electrical device according to a dispersion calculation formula;
calculating the frequent even coefficient Yn of each electric device of the same brand and the same type, and constructing a frequent even coefficient set Em (Y1, Y2, …, yn) of the electric devices of the same brand and the same type according to the Chang Ou coefficient Yn;
setting a constant coefficient threshold Em for a constant coefficient set of electrical devices of the same brand and type 0 The Yn value is compared with Em 0 Comparing, if Yn is less than or equal to Em 0 Then a frequent abnormality is formed if Yn is greater than Em 0 A sporadic anomaly is generated.
Further, the comprehensive processing module generates a fourth high risk identifier if the electrical equipment has frequent abnormality, generates a fifth high risk identifier if the electrical equipment has frequent abnormality, generates a corresponding instruction, and marks the corresponding BIM module in a grading manner through the electrical equipment position information.
Compared with the prior art, the application has the beneficial effects that:
1. the BIM+AI-based intelligent operation and maintenance management system provided by the application can be used for analyzing and processing real-time data, monitoring the risk identification of the electrical equipment in real time, feeding back the risk identification to maintenance personnel in time through the feedback module, accelerating the response speed of maintenance work, improving the maintenance efficiency, finding problems in time before the electrical equipment fails, improving the safety and reliability of the electrical equipment, preventing the problems, facilitating the intelligent operation and maintenance management of the electrical equipment, adopting a simple calculation mode of the real-time early warning value of the electrical equipment, saving the computer calculation force, and carrying out real-time prediction and alarm on the electrical equipment.
2. According to the application, the first data processing module is used for collecting the data in the BIM module and the first parameter information and the second parameter information processed by the AI module, the data comprehensive processing module is used for judging the risk identification of the electrical equipment in real time, the risk identification is marked and counted on the BIM model through the feedback module, the maintenance work is conveniently arranged by the maintenance personnel according to the risk identification, and the high-level risk identification is fed back to the maintenance personnel at the first time, so that the electrical equipment can be subjected to maintenance processing at the first time.
3. According to the application, the dispersion of the real-time early warning value data of the historical electrical equipment is constructed by utilizing the historical data of the electrical equipment, the constant coefficient set of the electrical equipment with the same brand and the same type is constructed, the electrical equipment with the third high risk identifier is further divided into the fourth high risk identifier and the fifth high risk identifier by setting the constant coefficient threshold value, and corresponding instructions are marked and distinguished on the BIM module and timely sent to maintenance personnel, so that further refined management is facilitated.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application.
FIG. 1 is a block diagram of an overall structure of a BIM+AI-based intelligent operation and maintenance management system according to an embodiment of the present application;
FIG. 2 is a block diagram of an overall structure of a BIM+AI-based intelligent operation and maintenance management system according to an embodiment of the present application;
in the figure, a BIM module is 100; 200. an AI module; 300. a first data processing module; 400. a comprehensive data processing module; 500. a feedback module; 600. and a historical data analysis module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
In the description of the present application, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present application and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
Example 1
Referring to fig. 1, the embodiment discloses a smart operation and maintenance management system based on bim+ai, which includes a BIM module 100, an AI module 200, a first data processing module 300, an integrated data processing module 400, and a feedback module 500, wherein the BIM module 100, the AI module 200, the first data processing module 300, the integrated data processing module 400, and the feedback module 500 are connected by wires or wirelessly.
The BIM module 100 is configured to store and display a three-dimensional model of a building, where the three-dimensional model records information of electrical devices in the building, and the information of the electrical devices includes a model number, a specification, a service life and an installation position of the electrical devices.
It should be noted that the BIM (BuildingInformationModeling) model, also called building information model, is based on a three-dimensional model of a building, and can integrate information of various aspects of the building into one model, which can help to improve the use efficiency of the building and reduce the operation cost, but the BIM model is a static data model when delivered, and does not have autonomous learning ability, and needs to be recorded by combining various sensors and manpower.
The AI module 200 is called artificial intelligence (Artificial Intelligence), and the technology can perform crawler finishing under artificial instructions to obtain historical data of the electrical equipment, or can gather first parameter information and second parameter information from sensors, electrical equipment control programs and three-dimensional models which are directly connected to the electrical equipment body; the first parameter information includes a temperature value WDZi, a humidity value SDZi, a used frequency PCi, a used period ZQi, and a usable life NXi; the temperature value and the humidity value are acquired by corresponding sensors arranged on the electrical equipment, and the used frequency, the used period and the usable period are acquired from related operation monitoring programs of the electrical equipment; the temperature value of the electrical equipment has a larger influence on the electrical equipment in the operation process, and in the prior art, the electrical equipment is overheated due to the fact that the temperature value is too high, elements of the electrical equipment are damaged, and even the probability of fire is larger; too low a temperature value may reduce the operating efficiency and lifetime of the electrical equipment, even causing the electrical equipment to fail; the humidity value refers to the humidity condition of the periphery of the electrical equipment in the operation process of the electrical equipment, the electrical equipment is possibly short-circuited or failed in insulation due to the fact that the humidity is too high, even fire is caused, the electrical equipment is possibly static due to the fact that the humidity is too low, normal operation of the electrical equipment is affected, the standard humidity value operation condition can be described by general electrical equipment, the service life of the electrical equipment is limited, the maximum service times can be limited in the service life of the electrical equipment, and the probability of the problem occurring in the later period of the electrical equipment is increased when the service times are increased.
The first data processing module 300 analyzes the first parameter information to generate an electrical device real-time early warning value, and generates a first risk identifier for the corresponding electrical device according to the magnitude of the electrical device real-time early warning value, wherein the first risk identifier comprises a first high risk identifier, a first medium risk identifier and a first low risk identifier.
The specific analysis process for generating the real-time early warning value of the electrical equipment is as follows: acquiring first parameter information of the electrical equipment through an AI technology, and carrying out normalization processing on the first parameter information corresponding to each electrical equipment in a dimensionless processing mode; obtaining real-time early warning value of electrical equipmentThe specific treatment process is as follows:
；
wherein a1, a2, a3, a4 are the temperature value WDZi, the humidity value SDZi, the frequency of use PCi, the difference between the usable life and the period of useIs a first weighting constant of the correlation of +.>Indicating the i-th moment of the electrical apparatusThe early warning value, W0 represents a standard temperature value, S0 represents a standard humidity value, wherein W0 is an average value of an upper limit temperature value and a lower limit temperature value marked in an electric equipment use specification, and S0 represents an average value of a lower limit humidity value and a lower limit humidity value of an electric equipment experimental environment of the standard humidity value marked in the electric equipment use specification.
The specific analysis process for generating the first high risk identification, the first risk identification and the first low risk identification is as follows:
specifically, gradient thresholds YJ1 and YJ2 are set, wherein YJ2 is greater than YJ1; real-time early warning value of electrical equipmentComparing analysis with gradient threshold values YJ1 and YJ2, if +.>Generating a first high risk identity, < > greater than YJ2>Generating a first risk identifier of YJ1 or more and YJ2 or less, and ++>Generating a first low risk identification less than YJ1;
it is to be noted that,the smaller the value is, the larger the probability of the fault of the electrical equipment is, wherein the first weighting constant is used for balancing the proportion of each item of data in the formula, so that the accuracy of a calculation result is ensured. In addition, the BIM mode is a static data mode, the temperature value WDZi and the humidity value SDZi have a service period ZQi, and the data of the service period NXi are changed in real time, the data sent by the sensor on the electrical equipment body and the electrical equipment control program are acquired and processed through the existing AI technology, the first parameter information and the second parameter information corresponding to the electrical equipment are obtained by summarizing the data acquired and processed according to the AI technology, and the data acquisition process is the prior art and is not repeated herein. It should be noted that the temperatureThe temperature and humidity values refer to the temperature of the core components in the electrical device, such as the temperature and humidity values of the motor in the electrical device.
In addition, the maintenance number wbi, the major maintenance number zbi, the accident occurrence number ywi, and the operation time sci of the electrical equipment are directly related to the health status of the electrical equipment, and these parameters are referred to as second parameter information.
The first data processing module 300 further analyzes the second parameter information to generate an electrical device health coefficient, and generates a second risk identifier for the corresponding electrical device according to the size of the electrical device health coefficient, where the second risk identifier includes a second high risk identifier, a second medium risk identifier, and a second low risk identifier.
The specific analysis process for generating the health coefficient of the electrical equipment is as follows:
calculating the second parameter information in a dimensionless weighting mode to calculate the health coefficient of the electrical equipmentThe method is characterized by comprising the following steps:
；
wherein b1, b2, b3, b4 are second weighting constants of the maintenance times wbi, the major maintenance times zbi, the accident occurrence times ywi and the working time sci of the electrical equipment, and max is a maximum value calculation function.
The specific analysis process for generating the second high risk identification, the second risk identification and the second low risk identification is as follows:
setting second gradient early-warning values SW1 and SW2, wherein SW1 is smaller than SW2, if the health coefficient of the electrical equipment isGreater than SW2, generating a second high risk indicator if the electrical device health coefficient +.>When the electric equipment is greater than or equal to SW1 and less than or equal to SW2, generating a second risk identification, if the electric equipment is healthyCount->Generating a second low risk indicator less than SW 1;
it should be noted that, the number of maintenance times wbi of the electrical device is generally regular, or the electrical device is specially maintained after an accident, and the longer the number of maintenance times is, the longer the service time of the electrical device is, the greater the probability of occurrence of a problem is; the larger the number zbi of major maintenance is, the larger the probability of accident occurrence of the electrical equipment is, the larger the value ywi of the accident occurrence number is, the larger the probability of accident risk of the electrical equipment is, and similarly, the larger the value sci of the working time is, the longer the working time of the electrical equipment is, and the larger the probability of failure of the electrical equipment is. It should be further noted that, as long as any one of these values is greater than the second gradient warning value SW1, it indicates that there is a greater probability of occurrence of a fault in the electrical apparatus.
The comprehensive data processing module 400 analyzes the second low risk identifier to obtain a third high risk identifier, a third medium risk identifier or a third low risk according to the first high risk identifier, the first medium risk identifier, the first low risk identifier, the second high risk identifier and the second medium risk identifier, and the specific logic is as follows: any one of the electrical devices has a first high risk identification and a second high risk identification as an A situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a B situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a C situation, any one of the electrical devices has a first high risk identification and a second high risk identification or a first low risk identification and a second high risk identification as a D situation, any one of the electrical devices has a first medium risk identification and a second medium risk identification as an E situation, any one of the electrical devices has a first medium risk identification and a second low risk identification as an F situation, and any one of the electrical devices has a first low risk identification and a second low risk identification as a G situation;
setting the electrical device with the situation A as a third highest risk identification; setting an electrical device with a situation A, a situation B, a situation C and a situation D as a third risk identifier; the third lowest risk indicator is set for electrical devices having E, F and G conditions.
It should be further noted that the third high risk identification level is higher than the first high risk identification and the second high risk identification level, that is, the probability that the electrical equipment corresponding to the third high risk identification fails is greater than the electrical equipment corresponding to the first high risk identification and the second high risk identification; the third risk indicator and the third low risk indicator are explained above.
The feedback module 500 marks the three-dimensional model according to the position information of the corresponding electrical equipment after the third high risk mark, the third medium risk mark and the third low risk mark, and sends out alarm reminding at the corresponding position, so that maintenance personnel can more intuitively acquire the position of the corresponding electrical equipment, and meanwhile, the corresponding electrical equipment corresponding to different grades is subjected to grading treatment, so that the inspection work efficiency is improved, the risk occurrence possibility is reduced, and the service life of the corresponding electrical equipment is prolonged.
Example 2
Referring to fig. 2, based on the first embodiment, this embodiment is further optimized for the foregoing solution, and this embodiment disclosure provides a bim+ai-based intelligent operation and maintenance management system, where the system further includes:
the historical data analysis module 600 is used for recording all the real-time early warning values of the electrical equipment exceeding the gradient threshold value YJ2Forming historical electrical equipment real-time early warning value data, and analyzing and generating frequent abnormality and sporadic abnormality according to the historical electrical equipment real-time early warning value data, wherein the specific processing logic of the frequent abnormality and the sporadic abnormality is as follows:
firstly, counting all historical electrical equipment real-time early warning value data exceeding a gradient threshold YJ2 generated by the histories of all electrical equipment of the same brand and the same typeThe method comprises the steps of carrying out a first treatment on the surface of the Wherein, the liquid crystal display device comprises a liquid crystal display device,the historical real-time early warning value data of the electrical equipment are data generated by 24-point cut-off of the day before the electrical equipment operates; counting historical electrical equipment real-time early warning value data of electrical equipment of the same brand and the same typeIs provided for the accumulated duration T;
solving according to the historical electrical equipment real-time early warning value data of all the electrical equipment of the same brand and the same type to obtain the average value of the threshold value YJ2；
Calculating the dispersion S of the historical real-time early warning value data of each electrical device through a dispersion calculation formula, wherein the smaller the dispersion S is, the more concentrated the historical electrical device real-time early warning value data is, the larger the probability of the electrical device faults is, the more discrete the historical electrical device real-time early warning value data is, the more sporadic the historical electrical device real-time early warning value data is, and the specific calculation formula of the dispersion S is as follows:
；
in the above formula, i=1 represents a starting point of the real-time early warning value data of the historical electrical equipment, and t represents the times of exceeding the gradient threshold YJ2 of the histories of the electrical equipment of the same brand and the same type.
Calculating the frequent even coefficient Yn of the electrical equipment of each same brand of same type of electrical equipment, and constructing a frequent even coefficient set Em (Y1, Y2, …, yn) of the electrical equipment of the same brand of same type according to the Chang Ou coefficient Yn, wherein m in Em is expressed as electrical equipment of different brand of same type;
the method for calculating the constant even coefficient Yn of the electrical equipment comprises the following steps:
；
in the above, Y1, Y2, … and Yn represent the common even coefficient of the electrical equipment of the same brand type 1,2, … and n electrical equipment, c1 is the average value of the data of the real-time early warning value of the historical electrical equipmentC2, c3 is the proportionality coefficient of the accumulated time T of the real-time early-warning value data of the historical electrical equipment, c4 is a constant item larger than zero. Wherein, the larger the value of Yn indicates that frequent abnormality of the electrical device is more frequent, the larger the probability of occurrence of failure of the electrical device is, and the smaller the value of Yn indicates that frequent abnormality of the electrical device is more frequent, the probability of occurrence of failure of the electrical device is relatively smaller.
Setting a constant coefficient threshold Em for a constant coefficient set of electrical devices of the same brand and type 0 The Yn value is compared with Em 0 Comparing, if Yn is less than or equal to Em 0 Then a frequent abnormality is formed if Yn is greater than Em 0 A sporadic anomaly is generated. The BIM module 100, the AI module 200, the first data processing module 300, the comprehensive data processing module 400, the feedback module 500 and the historical data analysis module 600 are connected through a wire or a wireless.
The comprehensive data processing module 400 further analyzes and processes the received third high risk identifier, frequent abnormality and sporadic abnormality to obtain a fourth high risk identifier and a fifth high risk identifier, the comprehensive data processing module 400 generates corresponding instructions, the corresponding BIM modules are marked in a grading manner through the position information of the electrical equipment on the BIM modules, and the first high risk identifier and the second high risk identifier are sent to maintenance personnel in a warning reminding mode through short messages and the like, wherein the warning reminding at least comprises the position information of the electrical equipment.
The specific generation logic is as follows:
if the electrical equipment has sporadic abnormality, a fourth high risk identification is generated, and if the electrical equipment has sporadic abnormality, a fifth high risk identification is generated.
It is particularly noted that the fourth high risk identification is higher than the fifth high risk identification, which is higher than the third high risk identification. The comprehensive data processing module 400 stops the operation of the electrical equipment in time according to the existing position of the electrical equipment on the BIM model and the risk identification, and simultaneously sends an alarm instruction to maintenance personnel, so that the electrical equipment of the type can be overhauled in time, and the damage caused by possible faults is avoided; in addition, when the same type of electrical equipment with the same brand is used, the electrical equipment can be further classified according to the risk identification, so that the maintenance plan rationality can be conveniently developed.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. BIM+AI-based intelligent operation and maintenance management system, which is characterized by comprising:
the BIM module (100) is used for storing and displaying a three-dimensional model of the building, wherein the three-dimensional model records information of electrical equipment in the building, and the information of the electrical equipment comprises information of the installation position of the electrical equipment;
the AI module (200) is used for being connected to the sensor on the electrical equipment body and the electrical equipment control program, receiving data sent by the sensor on the electrical equipment body and the electrical equipment control program, and summarizing and obtaining first parameter information and second parameter information corresponding to the electrical equipment according to the received data;
the first data processing module (300) is used for analyzing the first parameter information to generate a real-time early warning value of the electrical equipment, and generating a first risk identifier for the corresponding electrical equipment according to the magnitude of the real-time early warning value of the electrical equipment, wherein the first risk identifier comprises a first high risk identifier, a first medium risk identifier and a first low risk identifier;
the first data processing module (300) is used for analyzing the second parameter information to generate an electrical equipment health coefficient, and generating a second risk identifier for the corresponding electrical equipment according to the size of the electrical equipment health coefficient, wherein the second risk identifier comprises a second high risk identifier, a second medium risk identifier and a second low risk identifier;
the comprehensive data processing module (400) is used for analyzing the second high risk identification, the second medium risk identification and the second low risk identification to obtain a third high risk identification, a third medium risk identification and a third low risk identification according to the first high risk identification, the first medium risk identification and the first low risk identification;
the feedback module (500) is used for marking the three-dimensional model according to the third high risk mark, the third medium risk mark and the third low risk mark and sending out an alarm reminding at the corresponding position according to the corresponding position information of the electrical equipment, wherein the alarm reminding at least comprises the position information of the electrical equipment;
the BIM module (100), the AI module (200), the first data processing module (300), the comprehensive data processing module (400) and the feedback module (500) are connected through wires or wirelessly.
2. The bim+ai-based intelligent operation and maintenance management system according to claim 1, wherein the first parameter information includes an electrical device temperature value WDZi, a humidity value SDZi, a frequency of use PCi, a period of use ZQi, and a usable life NXi;
the second parameter information includes the maintenance number wbi, the major repair number zbi, the accident occurrence number ywi, and the operation time sci of the electric device.
3. The bim+ai-based intelligent operation and maintenance management system according to claim 2, wherein the specific analysis process for generating the real-time early warning value of the electrical equipment is as follows:
carrying out normalization processing on the first parameter information corresponding to each electrical device after passing through dimensionless values; obtaining real-time early warning value of electrical equipment，
；
Wherein a1, a2, a3, a4 are respectively the temperature value WDZi, the humidity value SDZi, the used frequency PCi, the difference between the usable life and the used periodIs related to (a)Is>The early warning value at the ith moment of the electrical equipment is represented, W0 represents a standard temperature value, and S0 represents a standard humidity value.
4. The bim+ai-based intelligent operation and maintenance management system of claim 3, wherein the specific analysis process for generating the first high risk identification, the first risk identification, and the first low risk identification is as follows:
gradient thresholds YJ1 and YJ2 are set, wherein YJ2 is greater than YJ1; real-time early warning value of electrical equipmentComparing and analyzing with gradient threshold values YJ1 and YJ2, and if the electric equipment real-time early-warning value +.>Generating a first high risk identification greater than YJ 2; if the real-time early warning value of the electrical equipment is->Generating a first stroke risk identifier by YJ1 or YJ 2; if the real-time early warning value of the electrical equipment is->Less than YJ1 generates a first low risk indicator.
5. The bim+ai-based intelligent operation and maintenance management system according to claim 2, wherein the specific analysis process for generating the health coefficients of the electrical devices is as follows:
calculating the second parameter information in a dimensionless weighting mode to calculate the health coefficient of the electrical equipment；
；
Wherein b1, b2, b3, b4 are second weighting constants of the maintenance times wbi, the major maintenance times zbi, the accident occurrence times ywi and the working time sci of the electrical equipment respectively.
6. The bim+ai-based intelligent operation and maintenance management system of claim 5, wherein the specific analysis process for generating the second high risk indicator, the second risk indicator, and the second low risk indicator is as follows:
setting second gradient early-warning values SW1 and SW2, wherein SW1 is smaller than SW2, and if the health coefficient of the electrical equipment is smaller than the first gradient early-warning valueGreater than SW2, generating a second high risk identification; if the health coefficient of the electrical equipment is->When the stroke risk is greater than or equal to SW1 and less than or equal to SW2, generating a second stroke risk mark; if the health coefficient of the electrical equipment is->Less than SW1 generates a second low risk indicator.
7. The bim+ai-based intelligent operation and maintenance management system of claim 1, wherein the third high risk identification, the third risk identification, or the third low risk identification is specifically analyzed as follows:
any one of the electrical devices has a first high risk identification and a second high risk identification as an A situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a B situation, any one of the electrical devices has a first high risk identification and a second low risk identification as a C situation, any one of the electrical devices has a first high risk identification and a second high risk identification or a first low risk identification and a second high risk identification as a D situation, any one of the electrical devices has a first medium risk identification and a second medium risk identification as an E situation, any one of the electrical devices has a first medium risk identification and a second low risk identification as an F situation, and any one of the electrical devices has a first low risk identification and a second low risk identification as a G situation;
setting the electrical device with the situation A as a third highest risk identification; setting the electrical equipment with the B situation, the C situation and the D situation as a third risk identification; the third lowest risk indicator is set for electrical devices having E, F and G conditions.
8. The bim+ai-based intelligent operation and maintenance management system according to claim 4, further comprising:
a historical data analysis module (600) for recording all the real-time early warning values of the electrical equipment exceeding the gradient threshold YJ2Forming historical electrical equipment real-time early warning value data, and analyzing and generating frequent abnormality and sporadic abnormality according to the historical electrical equipment real-time early warning value data;
the BIM module (100), the AI module (200), the first data processing module (300), the comprehensive data processing module (400), the feedback module (500) and the historical data analysis module (600) are connected through wires or wirelessly, so that data transmission among the modules is realized.
9. The bim+ai-based intelligent operation and maintenance management system of claim 8, wherein the recurrent exception and sporadic exception generation logic is configured to:
counting all historical electrical equipment real-time early warning value data exceeding a gradient threshold YJ2 generated by the histories of the electrical equipment of the same brand and the same type;
counting the accumulated duration T of all the historical electrical equipment real-time early warning value data exceeding the gradient threshold YJ 2;
solving according to the historical electrical equipment real-time early warning value data of all the electrical equipment of the same brand and the same type to obtain the average value of the threshold value YJ2；
Calculating the dispersion S of historical real-time early warning value data of each electrical device according to a dispersion calculation formula;
calculating the frequent even coefficient Yn of each electric device of the same brand and the same type, and constructing a frequent even coefficient set Em (Y1, Y2, …, yn) of the electric devices of the same brand and the same type according to the Chang Ou coefficient Yn;
setting a constant coefficient threshold Em for a constant coefficient set of electrical devices of the same brand and type 0 The Yn value is compared with Em 0 Comparing, if Yn is less than or equal to Em 0 Then a frequent abnormality is formed if Yn is greater than Em 0 A sporadic anomaly is generated.
10. The bim+ai-based intelligent operation and maintenance management system of claim 8, further comprising:
and the comprehensive data processing module (400) generates a fourth high risk identifier if the electrical equipment has frequent abnormality, generates a fifth high risk identifier if the electrical equipment has frequent abnormality, generates a corresponding instruction, and marks the corresponding BIM module in a grading manner through the position information of the electrical equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310513979.1A CN116664100A (en) | 2023-05-09 | 2023-05-09 | BIM+AI-based intelligent operation and maintenance management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310513979.1A CN116664100A (en) | 2023-05-09 | 2023-05-09 | BIM+AI-based intelligent operation and maintenance management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116664100A true CN116664100A (en) | 2023-08-29 |
Family
ID=87727016
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310513979.1A Pending CN116664100A (en) | 2023-05-09 | 2023-05-09 | BIM+AI-based intelligent operation and maintenance management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116664100A (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0954613A (en) * | 1995-08-11 | 1997-02-25 | Toshiba Corp | Plant facility monitor device |
| CN102521709A (en) * | 2011-12-22 | 2012-06-27 | 上海建科工程咨询有限公司 | Building construction quality safety online risk management system |
| WO2016170551A2 (en) * | 2015-04-21 | 2016-10-27 | Grade S.R.L. | Command and control system for optimal risk management |
| US20170147613A1 (en) * | 2014-06-30 | 2017-05-25 | Schneider Electric It Corporation | Data center modeling for facility operations |
| JP2017224133A (en) * | 2016-06-15 | 2017-12-21 | Ｎｅｃフィールディング株式会社 | Maintenance work procedure data management device, system, method and program |
| CN111476385A (en) * | 2020-05-25 | 2020-07-31 | 欧碧芳 | Building facility maintenance supervisory systems based on BIM |
| US20200286016A1 (en) * | 2013-03-15 | 2020-09-10 | Alertenterprise, Inc. | Systems, structures, and processes for interconnected devices and risk management |
| CN112862218A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Power equipment out-of-service management system |
| CN113837462A (en) * | 2021-09-22 | 2021-12-24 | 广东信通通信有限公司 | Fault risk trend analysis method and device, server and storage medium |
| CN114609462A (en) * | 2022-03-22 | 2022-06-10 | 江苏国电南自海吉科技有限公司 | Intelligent early warning diagnosis model for comprehensive multi-characteristic parameters of electrical equipment state |
| CN115062953A (en) * | 2022-06-13 | 2022-09-16 | 陈浩 | Intelligent header type transformer substation safety early warning method, system and storage medium |
| JP2022150786A (en) * | 2021-03-26 | 2022-10-07 | 株式会社Ｎｔｔファシリティーズ | Notification system |
| CN115407712A (en) * | 2022-08-04 | 2022-11-29 | 营口理工学院 | Intelligent maintenance system for hydraulic station of steel mill and working process |
| CN116029116A (en) * | 2022-12-28 | 2023-04-28 | 广东电网有限责任公司湛江供电局 | Intelligent substation wisdom management and control digital twin system |
-
2023
- 2023-05-09 CN CN202310513979.1A patent/CN116664100A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0954613A (en) * | 1995-08-11 | 1997-02-25 | Toshiba Corp | Plant facility monitor device |
| CN102521709A (en) * | 2011-12-22 | 2012-06-27 | 上海建科工程咨询有限公司 | Building construction quality safety online risk management system |
| US20200286016A1 (en) * | 2013-03-15 | 2020-09-10 | Alertenterprise, Inc. | Systems, structures, and processes for interconnected devices and risk management |
| US20170147613A1 (en) * | 2014-06-30 | 2017-05-25 | Schneider Electric It Corporation | Data center modeling for facility operations |
| WO2016170551A2 (en) * | 2015-04-21 | 2016-10-27 | Grade S.R.L. | Command and control system for optimal risk management |
| JP2017224133A (en) * | 2016-06-15 | 2017-12-21 | Ｎｅｃフィールディング株式会社 | Maintenance work procedure data management device, system, method and program |
| CN111476385A (en) * | 2020-05-25 | 2020-07-31 | 欧碧芳 | Building facility maintenance supervisory systems based on BIM |
| CN112862218A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Power equipment out-of-service management system |
| JP2022150786A (en) * | 2021-03-26 | 2022-10-07 | 株式会社Ｎｔｔファシリティーズ | Notification system |
| CN113837462A (en) * | 2021-09-22 | 2021-12-24 | 广东信通通信有限公司 | Fault risk trend analysis method and device, server and storage medium |
| CN114609462A (en) * | 2022-03-22 | 2022-06-10 | 江苏国电南自海吉科技有限公司 | Intelligent early warning diagnosis model for comprehensive multi-characteristic parameters of electrical equipment state |
| CN115062953A (en) * | 2022-06-13 | 2022-09-16 | 陈浩 | Intelligent header type transformer substation safety early warning method, system and storage medium |
| CN115407712A (en) * | 2022-08-04 | 2022-11-29 | 营口理工学院 | Intelligent maintenance system for hydraulic station of steel mill and working process |
| CN116029116A (en) * | 2022-12-28 | 2023-04-28 | 广东电网有限责任公司湛江供电局 | Intelligent substation wisdom management and control digital twin system |
Non-Patent Citations (5)
| Title |
|---|
| NING SUN等: "The Application of Digital-Twin in Bogie Fault Monitoring and Early Warning", IEEE, 6 March 2023 (2023-03-06) * |
| 万军;: "基于室内GIS系统的单位消防安全管理设计与应用", 中国应急救援, no. 05, 20 September 2017 (2017-09-20) * |
| 伍朝辉等: "交通场景数字孪生构建与虚实融合应用研究", 系统仿真学报, vol. 33, no. 2, 18 February 2022 (2022-02-18), pages 295 - 305 * |
| 王林秋;郑磊;张永武;: "基于PHM的复杂机电系统健康管理技术研究", 计算机测量与控制, no. 01, 25 January 2018 (2018-01-25) * |
| 赵泽荣;: "监视系统设备运维智能控制平台研究", 现代信息科技, no. 09, 10 May 2020 (2020-05-10) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109001649B (en) | Intelligent power supply diagnosis system and protection method | |
| CN113255795B (en) | Equipment state monitoring method based on multi-index cluster analysis | |
| CN105261079A (en) | Portable intelligent electric power inspection device | |
| CN106888205A (en) | A kind of non-intrusion type is based on the PLC method for detecting abnormality of power consumption analysis | |
| CN110620906A (en) | Electric power construction safety management system based on thing networking | |
| CN115640915B (en) | Intelligent gas pipe network compressor safety management method and Internet of things system | |
| CN109809149B (en) | Fault early warning system and method for industrial production equipment | |
| CN113063611A (en) | Equipment monitoring management method and system | |
| CN113177646B (en) | Power distribution equipment online monitoring method and system based on self-adaptive edge proxy | |
| CN111717753A (en) | Self-adaptive elevator fault early warning system and method based on multi-dimensional fault characteristics | |
| CN115524002B (en) | Operation state early warning method, system and storage medium of power plant rotating equipment | |
| CN103885406A (en) | Industry gas production equipment fault intelligent diagnosis and monitoring system based on multi-data fusion | |
| CN114244866A (en) | Production equipment supervisory systems based on thing networking | |
| CN117235443A (en) | Electric power operation safety monitoring method and system based on edge AI | |
| CN115060312A (en) | Building material safety monitoring system based on artificial intelligence | |
| CN115145788A (en) | Detection data generation method and device for intelligent operation and maintenance system | |
| CN106081958A (en) | A kind of derrick crane on-line monitoring system | |
| CN117474357A (en) | Power distribution room operation and maintenance management method and system based on deep learning | |
| CN116664100A (en) | BIM+AI-based intelligent operation and maintenance management system | |
| CN115328986A (en) | Power plant safety early warning data analysis processing method and system | |
| CN116523498A (en) | AI-based power facility inspection method, system, device and storage medium | |
| CN113702764B (en) | Overhead cable breakage monitoring and distinguishing method | |
| CN108147244A (en) | Elevator maintenance system based on Internet of Things | |
| CN110758478B (en) | Pre-warning system and method for railway signal equipment | |
| CN112365201A (en) | Neural network-based infrastructure construction monitoring and early warning system and method |
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 |