CN111559680A - Elevator intelligent inspection method based on big data - Google Patents
Elevator intelligent inspection method based on big data Download PDFInfo
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- CN111559680A CN111559680A CN202010448680.9A CN202010448680A CN111559680A CN 111559680 A CN111559680 A CN 111559680A CN 202010448680 A CN202010448680 A CN 202010448680A CN 111559680 A CN111559680 A CN 111559680A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0087—Devices facilitating maintenance, repair or inspection tasks
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- Indicating And Signalling Devices For Elevators (AREA)
Abstract
The invention discloses an elevator intelligent inspection method based on big data, which comprises a data acquisition unit, a communication system unit, a data diagnosis platform and a visual display unit, wherein the data acquisition unit acquires real-time data, transmits the acquired data to an open Plant-based real-time database system through the communication system unit, then uses the data diagnosis platform to perform centralized management and analysis on the real-time data in the database system, performs data compression and long-term historical storage, performs diagnosis and situation analysis on an elevator system at the same time, displays the result through the visual display unit, and updates the early warning state of equipment. The situation of the equipment is analyzed through the existing algorithm analysis, and the result and the real-time data are displayed in the system by utilizing a visualization technology.
Description
Technical Field
The invention belongs to the technical field of elevator equipment state monitoring application, and particularly relates to an elevator intelligent inspection method based on big data.
Background
With the development of new-generation information communication technologies such as the internet of things and cloud computing, the automation and informatization levels of elevator equipment are increasingly improved, and massive equipment operation real-time and state monitoring data can be acquired and stored.
The method faces the situation of how to clearly and effectively convey and communicate and interact to realize deep insight on redundant and complex data sets, how to change the condition that the prior information system construction emphasizes system functions and neglects data benefits, and the big data technology comes up with the end, and provides an effective means for visualization of elevator running state monitoring and fault diagnosis.
Disclosure of Invention
The invention provides an elevator intelligent inspection method based on big data, which organically combines the key technology of big data at the front edge with the online monitoring technology of elevator equipment, and solves the problems in the elevator equipment monitoring field such as massive monitoring data storage, massive monitoring data real-time analysis, massive monitoring data real-time diagnosis and the like; real-time data are pushed to an openPlant real-time database through an efficient bidirectional data transmission mechanism, interfaces with various control systems on site can be safely and stably realized, the acquired data can be efficiently compressed and stored for a long time, and convenient and easy-to-use client application and a universal data interface are provided, so that management and decision-making personnel can timely and comprehensively know the running condition of the current equipment, can review past historical events, timely find problems existing in the running process and improve the utilization rate of the equipment; and analyzing the situation of the equipment through algorithm analysis, and displaying the result and the real-time data in the system by using a visualization technology.
In order to achieve the purpose, the invention adopts the following specific scheme:
an elevator intelligent inspection method based on big data comprises the following steps:
s1: a sensor group and an elevator monitoring data acquisition unit of the elevator system acquire the running state data information of the elevator in real time and transmit the data information to a communication system in real time;
s2: the high-speed bidirectional data transmission communication system transmits the acquired data to a data diagnosis platform based on big data;
s3: centralized management and analysis are carried out on the real-time monitoring data and the offline state of the equipment;
s4: pushing the stored data to a message queue Kafka, and analyzing the stored data and the transmitted data so as to perform real-time monitoring and fault diagnosis on the equipment;
s5: if the diagnosis result is that the fault occurs, on one hand, the visual display unit alarms in time, and on the other hand, the communication system transmits fault information to the equipment monitoring unit and updates the detection state; and if the monitoring is failure-free, continuously monitoring the data in real time.
Preferably, the storage and management method for the massive original data in step S3 includes:
s31: the state information data detected by a plurality of elevator devices are safely and stably realized by adopting an open Plant-based real-time database system and interfaces with various on-site control systems, and the collected data are subjected to high-efficiency data compression and long-term historical storage.
S32: and adopting a BDM CEP and Storm parallel processing mechanism based on platforms to the mass monitoring original data and the device offline state data.
Preferably, the elevator intelligent inspection method adopts a detection system, the detection system comprises a data acquisition unit, a communication system unit, a data diagnosis platform and a visual display unit, the data acquisition unit acquires real-time data, the acquired data is transmitted to an open Plant-based real-time database system through the communication system unit, then the data diagnosis platform is used for carrying out centralized management and analysis on the real-time data in the database system, carrying out data compression and long-term historical storage, diagnosing and analyzing the elevator system at the same time, displaying results through the visual display unit and updating the early warning state of equipment.
Preferably, the communication system unit mainly comprises GPRS and CDMA, mobile communication and satellite communication modes.
Preferably, the communication system unit is capable of bidirectional and efficient information transmission.
Preferably, the visualization display unit mainly comprises a real-time monitoring module, a historical event query module and a data analysis and early warning module.
Preferably, the database system pushes the stored data to a message queue Kafka, the data in Kafka is processed by two processing platforms BDM CEP and Storm in parallel, and the data is analyzed by adopting the existing elevator fault diagnosis algorithm, so that real-time monitoring and fault diagnosis are carried out on the equipment.
Preferably, the data acquisition unit consists of an elevator monitoring data acquisition unit and a plurality of sensor groups.
Preferably, the plurality of sensor groups comprise a flat sensor, an infrared human body sensor, a base station sensor and a door switch sensor; the elevator monitoring data acquisition group comprises a video monitoring and emergency calling system.
Preferably, the satellite communication uses analog modulation, frequency division multiplexing and frequency division multiple access technologies, the mobile communication uses wireless local area network system, satellite system and wireless local loop system technologies, and the technologies are matched and supplemented according to the coverage condition and the operation cost of mobile wireless signals in the well.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the elevator intelligent inspection method based on the big data, the key technology of the big data at the front edge is organically combined with the online monitoring technology of the elevator equipment, so that the problems in the elevator equipment monitoring field such as massive monitoring data storage, massive monitoring data real-time analysis and massive monitoring data real-time diagnosis are solved.
2. The elevator intelligent inspection method based on big data pushes the real-time data to the openPlant real-time database through an efficient two-way data transmission mechanism, can safely and stably realize interfaces with various on-site control systems, can perform efficient data compression and long-term historical storage on the acquired data, and provides convenient and easy-to-use client application and a universal data interface, so that management and decision-making personnel can timely and comprehensively know the running condition of the current equipment.
3. According to the elevator intelligent inspection method based on big data, the visual display unit is arranged, so that past historical events can be reviewed, problems in operation can be found in time, and the utilization rate of equipment is improved. The situation of the equipment is analyzed through the existing algorithm analysis, and the result and the real-time data are displayed in the system by utilizing a visualization technology.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flow chart of the elevator intelligent inspection method based on big data.
Fig. 2 is a block diagram of a calculation and query framework of an elevator intelligent inspection method based on big data.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described in detail and completely with reference to the accompanying drawings. It is to be understood that the described embodiments are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
As shown in fig. 1-2, an elevator intelligent inspection method based on big data comprises the following steps:
s1: a sensor group and an elevator monitoring data acquisition unit of the elevator system acquire the running state data information of the elevator in real time and transmit the data information to a communication system in real time;
s2: the communication system for high-speed bidirectional data transmission transmits the original data to a data diagnosis platform based on big data;
s3: centralized management and analysis are carried out on the real-time monitoring data and the offline state of the equipment;
s4: pushing the stored data to a message queue Kafka, and analyzing the data by adopting the conventional elevator fault diagnosis algorithm, so as to carry out real-time monitoring and fault diagnosis on the equipment;
s5: if the diagnosis result is that the fault occurs, on one hand, the visual display unit alarms in time, and on the other hand, the communication system transmits fault information to the equipment monitoring unit and updates the detection state; and if the monitoring is failure-free, continuously monitoring the data in real time.
As for the storage and management method adopted in step S3 for the massive raw data, the method includes:
s31, adopting an open Plant-based real-time database system to safely and stably realize interfaces with various control systems on site for the state information data detected by a plurality of elevator devices, and performing high-efficiency data compression and long-term historical storage on the acquired data;
s32: and adopting a BDM CEP and Storm parallel processing mechanism based on platforms to the mass monitoring original data and the device offline state data.
The intelligent elevator inspection method adopts a detection system which comprises a data acquisition unit, a communication system unit, a data diagnosis platform and a visual display unit, wherein the data acquisition unit acquires real-time data, transmits the acquired data to an open Plant-based real-time database system through the communication system unit, then uses the data diagnosis platform to perform centralized management and analysis on the real-time data in the database system, performs data compression and long-term historical storage, diagnoses and situation analysis on an elevator system, displays the result through the visual display unit, and updates the early warning state of equipment.
The communication system unit can realize bidirectional and efficient information transmission.
The visual display unit mainly comprises a real-time monitoring module, a historical event query module and a data analysis and early warning module.
The database system pushes the stored data to a message queue Kafka, the data in the Kafka can be processed in parallel by two processing platforms BDM CEP and Storm, and the data are analyzed by adopting the existing elevator fault diagnosis algorithm, so that real-time monitoring and fault diagnosis are carried out on the equipment.
The data acquisition unit consists of an elevator monitoring data acquisition unit and a plurality of sensor groups.
The communication system unit mainly comprises GPRS and CDMA, mobile communication and satellite communication modes.
The satellite communication uses analog modulation, frequency division multiplexing and frequency division multiple access technologies, the mobile communication uses wireless local area network systems, satellite systems and wireless local loop system technologies, and the satellite communication and the mobile communication are matched and supplemented according to the coverage condition of mobile wireless signals in a well and the operation cost.
Example 2
An elevator intelligent inspection method based on big data comprises the following steps:
s1: a sensor group and an elevator monitoring data acquisition unit of the elevator system acquire the running state data information of the elevator in real time and transmit the data information to a communication system in real time;
s2: the communication system for high-speed bidirectional data transmission transmits the original data to a data diagnosis platform based on big data;
s3: centralized management and analysis are carried out on the real-time monitoring data and the offline state of the equipment;
s4: pushing the stored data to a message queue Kafka, and analyzing the data by adopting the conventional elevator fault diagnosis algorithm, so as to carry out real-time monitoring and fault diagnosis on the equipment;
s5: if the diagnosis result is that the fault occurs, on one hand, the visual display unit alarms in time, and on the other hand, the communication system transmits fault information to the equipment monitoring unit and updates the detection state; and if the monitoring is failure-free, continuously monitoring the data in real time.
As for the storage and management method adopted in step S3 for the massive raw data, the method includes:
s31, adopting an open Plant-based real-time database system to safely and stably realize interfaces with various control systems on site for the state information data detected by a plurality of elevator devices, and performing high-efficiency data compression and long-term historical storage on the acquired data;
s32: and adopting a BDM CEP and Storm parallel processing mechanism based on platforms to the mass monitoring original data and the device offline state data.
The intelligent elevator inspection method adopts a detection system which comprises a data acquisition unit, a communication system unit, a data diagnosis platform and a visual display unit, wherein the data acquisition unit acquires real-time data, transmits the acquired data to an open Plant-based real-time database system through the communication system unit, then uses the data diagnosis platform to perform centralized management and analysis on the real-time data in the database system, performs data compression and long-term historical storage, diagnoses and situation analysis on an elevator system, displays the result through the visual display unit, and updates the early warning state of equipment.
The communication system unit can realize bidirectional and efficient information transmission; the visual display unit mainly comprises a real-time monitoring module, a historical event query module and a data analysis and early warning module; the database system pushes the stored data to a message queue Kafka, the data in the Kafka can be processed in parallel by two processing platforms BDM CEP and Storm, and the data are analyzed by adopting the existing elevator fault diagnosis algorithm, so that real-time monitoring and fault diagnosis are carried out on the equipment; the data acquisition unit consists of an elevator monitoring data acquisition unit and a plurality of sensor groups.
The communication system unit mainly comprises GPRS and CDMA, mobile communication and satellite communication modes; the satellite communication uses analog modulation, frequency division multiplexing and frequency division multiple access technologies, the mobile communication uses wireless local area network systems, satellite systems and wireless local loop system technologies, and the satellite communication and the mobile communication are matched and supplemented according to the coverage condition of mobile wireless signals in a well and the operation cost.
The plurality of sensor groups comprise a flat sensor, an infrared human body sensor, a base station sensor and a door switch sensor; the elevator monitoring data acquisition group comprises a video monitoring and emergency calling system;
the leveling sensor is used for acquiring the horizontal data of the elevator and judging whether the elevator is inclined or not; the infrared human body sensor is used for collecting the number of people in the elevator; the base station sensor is used for judging which base station the elevator is closest to so that the elevator has problems and informs the closest base station of processing; the door opening and closing sensor acquires the opening and closing time of the elevator door and whether the opening and closing are stable; the video monitoring is used for monitoring some other problems generated by non-elevator self factors in the elevator; the emergency call system is used to communicate the failure of an elevator.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention; any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims (8)
1. An elevator intelligent inspection method based on big data is characterized by comprising the following steps:
s1: a sensor group and an elevator monitoring data acquisition unit of the elevator system acquire the running state data information of the elevator in real time and transmit the data information to a communication system in real time;
s2: the communication system is a high-speed bidirectional data transmission system, and transmits acquired data information to a data diagnosis platform based on big data;
s3: centralized management and analysis are carried out on the real-time monitoring data and the offline state of the equipment;
s4: pushing the stored data to a message queue Kafka, and analyzing the data by adopting the conventional elevator fault diagnosis algorithm, so as to carry out real-time monitoring and fault diagnosis on the equipment;
s5: if the diagnosis result is that the fault occurs, on one hand, the visual display unit alarms in time, and on the other hand, the communication system transmits fault information to the equipment monitoring unit and updates the detection state; and if the monitoring is failure-free, continuously monitoring the data in real time.
2. The intelligent elevator inspection method based on big data as claimed in claim 1, characterized in that: as for the storage and management method adopted in step S3 for the massive raw data, the method includes:
s31, adopting an open Plant-based real-time database system to safely and stably realize interfaces with various control systems on site for the state information data detected by a plurality of elevator devices, and performing high-efficiency data compression and long-term historical storage on the acquired data;
s32: and adopting a BDM CEP and Storm parallel processing mechanism based on platforms to the mass monitoring original data and the device offline state data.
3. The intelligent elevator inspection method based on big data as claimed in claim 1, characterized in that: the intelligent elevator inspection method adopts a detection system which comprises a data acquisition unit, a communication system unit, a data diagnosis platform and a visual display unit, wherein the data acquisition unit acquires real-time data, transmits the acquired data to an open Plant-based real-time database system through the communication system unit, then uses the data diagnosis platform to perform centralized management and analysis on the real-time data in the database system, performs data compression and long-term historical storage, diagnoses and situation analysis on an elevator system, displays the result through the visual display unit, and updates the early warning state of equipment.
4. The intelligent elevator inspection method based on big data as claimed in claim 3, characterized in that: the method is characterized in that: the communication system unit can realize bidirectional and efficient information transmission.
5. The intelligent elevator inspection method based on big data as claimed in claim 3, characterized in that: the visual display unit mainly comprises a real-time monitoring module, a historical event query module and a data analysis and early warning module.
6. The intelligent elevator inspection method based on big data as claimed in claim 3, characterized in that: the data acquisition unit consists of an elevator monitoring data acquisition unit and a plurality of sensor groups.
7. The plurality of sensor groups of claim 6 comprising a flat bed sensor, an infrared body sensor, a base station sensor, and a door switch sensor; the elevator monitoring data acquisition group comprises a video monitoring and emergency calling system.
8. The intelligent elevator inspection method based on big data as claimed in claim 3, characterized in that: the database system pushes the stored data to a message queue Kafka, the data in the Kafka can be processed in parallel by two processing platforms BDM CEP and Storm, and the data are analyzed by adopting the existing elevator fault diagnosis algorithm, so that real-time monitoring and fault diagnosis are carried out on the equipment.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104944240A (en) * | 2015-05-19 | 2015-09-30 | 重庆大学 | Elevator equipment state monitoring system based on large data technology |
| CN105645209A (en) * | 2016-03-03 | 2016-06-08 | 宁夏电通物联网科技股份有限公司 | Maintenance system and maintenance method for elevators based on big data support of Internet of Things |
| CN206901517U (en) * | 2017-03-28 | 2018-01-19 | 武汉创新智特科技股份有限公司 | A kind of zone elevator management system based on big data |
| CN108083044A (en) * | 2017-11-21 | 2018-05-29 | 浙江新再灵科技股份有限公司 | A kind of elevator based on big data analysis maintenance system and method on demand |
| KR20190135634A (en) * | 2018-05-29 | 2019-12-09 | 송광섭 | The elevator of the real time crime monitoring system |
-
2020
- 2020-05-25 CN CN202010448680.9A patent/CN111559680A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104944240A (en) * | 2015-05-19 | 2015-09-30 | 重庆大学 | Elevator equipment state monitoring system based on large data technology |
| CN105645209A (en) * | 2016-03-03 | 2016-06-08 | 宁夏电通物联网科技股份有限公司 | Maintenance system and maintenance method for elevators based on big data support of Internet of Things |
| CN206901517U (en) * | 2017-03-28 | 2018-01-19 | 武汉创新智特科技股份有限公司 | A kind of zone elevator management system based on big data |
| CN108083044A (en) * | 2017-11-21 | 2018-05-29 | 浙江新再灵科技股份有限公司 | A kind of elevator based on big data analysis maintenance system and method on demand |
| KR20190135634A (en) * | 2018-05-29 | 2019-12-09 | 송광섭 | The elevator of the real time crime monitoring system |
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Application publication date: 20200821 |
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