CN111099468B - Elevator early warning maintenance system and method based on cloud AIoT technology - Google Patents
Elevator early warning maintenance system and method based on cloud AIoT technology Download PDFInfo
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- CN111099468B CN111099468B CN202010012586.9A CN202010012586A CN111099468B CN 111099468 B CN111099468 B CN 111099468B CN 202010012586 A CN202010012586 A CN 202010012586A CN 111099468 B CN111099468 B CN 111099468B
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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
- B66B3/00—Applications of devices for indicating or signalling operating conditions of elevators
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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
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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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
- B66B5/0037—Performance analysers
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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
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The application provides elevator early warning dimension system based on high in clouds AIoT technique, including perception layer, edge layer, platform layer and application layer, the perception layer is used for acquireing elevator part basis operating data. The edge layer is used for acquiring basic operation data of the elevator component from the sensing layer and performing edge calculation on the data. The platform layer comprises an IoT access module used for accessing the elevator component basic operation data after edge calculation, a database module used for rolling and storing the data accessed by the IoT access module, and an AI analysis module used for calling the data in the database module to perform AI modeling analysis. The application layer is used for receiving and displaying the analysis result of the AI modeling analysis. This application utilizes thing networking IoT technique, acquires elevator part basis operating data through the perception layer, needn't be limited to elevator control system's sensor and acquires the monitoring data, so can compatible different brands ' elevator, facilitate the use.
Description
[ technical field ] A method for producing a semiconductor device
The application relates to the technical field of elevators, in particular to an elevator early warning maintenance system and method based on a cloud AIoT technology.
[ background of the invention ]
With the development of economy and the continuous mining of city scale, high-rise residences such as houses, hotels, office buildings and the like are increased continuously, and the installation and use number of elevators is larger originally. The elevator brings convenience to people to come in and go out of a high-rise building, and meanwhile casualties and economic losses caused by elevator faults are increased. Therefore, how to effectively monitor the safe operation of the elevator and eliminate various elevator fault hidden dangers in time becomes an important problem which is urgently needed to be solved by elevator manufacturers, users and all levels of labor safety monitoring departments. At present, part of elevator enterprises also have the technology of fault monitoring and early warning maintenance on elevators, but in the prior art, the system monitoring data sources are more sensors installed on the basis of an elevator control system, the application range of the system is limited by elevator brands, and when a user uses a plurality of brands of elevators at the same time, one set of early warning system cannot be compatible with all elevators.
[ summary of the invention ]
In order to enable the early warning maintenance system to be compatible with elevators of different brands for use, the application provides an elevator early warning maintenance system and method based on the cloud AIoT technology.
Elevator early warning dimension system based on high in clouds AIoT technique includes:
the sensing layer is used for acquiring basic operation data of the elevator component;
the edge layer is used for acquiring basic operation data of the elevator component from the sensing layer and performing edge calculation on the data;
the platform layer comprises an IoT access module used for accessing the elevator component basic operation data after edge calculation, a database module used for rolling and storing the data accessed by the IoT access module and an AI analysis module used for calling the data in the database module to perform AI modeling analysis;
and the application layer is used for receiving and displaying the analysis result of the AI modeling analysis.
According to the elevator early warning maintenance system based on the cloud AIoT technology, the AI analysis module restores an elevator operation model by calling basic operation data of elevator components in the database module, performs curve discrete comparison with a preset ideal operation model, obtains an elevator operation health score after weighted average of the dispersion of the elevator operation curve within a certain time, and outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold value.
According to the elevator early warning and maintenance system based on the cloud AIoT technology, the sensing layer comprises a sensor module for converting a physical signal containing basic operation data of an elevator component into an analog weak current signal, an isolation module for isolating and integrating the analog weak current signal into an electric signal with a uniform format, and a signal conditioning module for converting the electric signal with the uniform format into digital quantity data.
As above elevator early warning maintenance system based on high in clouds AIoT technique, the edge layer includes:
the edge calculation module is used for performing edge calculation on the basic operation data of the elevator component;
the data gateway module is used for integrating and receiving the elevator component basic operation data acquired by the sensing layer and converting the elevator component basic operation data which accord with the preset sensor measurement range into a protocol of a docking platform layer after edge calculation;
the wireless transmission module is used for transmitting the elevator component basic operation data acquired by the sensing layer to the data gateway module;
the restarting module is used for outputting a restarting signal to the corresponding sensor module to restart the corresponding sensor module when the basic operation data of the elevator component exceeds the preset sensor measuring range after the edge calculation;
and the fault reporting module is used for outputting a shielding signal to the corresponding sensor module to shield the corresponding sensor module and reporting the fault information of the corresponding sensor module to the platform layer when the basic operation data of the elevator component obtained by the edge calculation still exceeds the preset sensor measurement range after the sensor module is restarted and in the preset time.
According to the elevator early warning and maintenance system based on the cloud AIoT technology, the application layer comprises a Web end and an APP end.
The application also discloses an elevator early warning maintenance method based on the cloud AIoT technology, which comprises the following steps:
the sensing layer obtains basic operation data of the elevator component;
the edge layer acquires basic operation data of the elevator component from the sensing layer and carries out edge calculation on the data;
the platform layer accesses the elevator component basic operation data after the edge calculation through an IoT access module, and performs AI modeling analysis by rolling and storing the data accessed through the IoT access module through a database module and calling the data in the database module through an AI analysis module;
and the application layer receives and displays the analysis result of the AI modeling analysis.
In the above elevator early warning and maintenance method based on cloud AIoT technology, in the step of accessing the elevator component basic operation data after edge calculation through the IoT access module by the platform layer, and performing AI modeling analysis by rolling and storing the data accessed through the IoT access module through the database module and calling the data in the database module through the AI analysis module, the method includes the following steps:
the AI analysis module restores an elevator operation model by calling the basic operation data of the elevator components in the database module, performs curve dispersion comparison with a preset ideal operation model, obtains an elevator operation health score after weighting and averaging the elevator operation curve dispersion within a certain time, and outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold value.
In the above elevator early warning and maintenance method based on cloud AIoT technology, the step of obtaining the basic operation data of the elevator component at the sensing layer includes the following steps:
the sensor module converts a physical signal containing basic operation data of the elevator component into an analog weak current signal;
the isolation module isolates and integrates the analog weak current signals into electric signals with a uniform format;
the signal conditioning module converts the electric signals with the uniform format into digital quantity data.
In the above elevator early warning and maintenance method based on the cloud AIoT technology, the step of the edge layer acquiring the basic operation data of the elevator component from the sensing layer and performing edge calculation on the data includes the following steps:
the wireless transmission module transmits the basic operation data of the elevator component acquired by the sensing layer to the data gateway module;
the edge calculation module carries out edge calculation on the basic operation data of the elevator components transmitted to the data gateway module;
if the elevator component basic operation data after the edge calculation conforms to the preset sensor measurement range, the data gateway module converts the elevator component basic operation data conforming to the preset sensor measurement range into a protocol of a docking platform layer;
if the basic operation data of the elevator component after the edge calculation exceeds the preset sensor measurement range, the restarting module outputs a restarting signal to the corresponding sensor module to restart the corresponding sensor module;
and if the basic operation data of the elevator component obtained by edge calculation still exceeds the preset sensor measurement range after the sensor module is restarted and in a preset time, the fault reporting module outputs a shielding signal to the corresponding sensor module to shield the corresponding sensor module and reports the fault information of the corresponding sensor module to the platform layer.
According to the elevator early warning and maintenance method based on the cloud AIoT technology, the application layer comprises a Web end and an APP end.
Compared with the prior art, the method has the following advantages:
1. this application utilizes thing networking IoT technique, acquires elevator part basic operation data and uploads the platform layer through the marginal layer in real time and carries out data analysis and obtain elevator operation early warning monitoring result through the perception layer of system self, consequently is independent of the control system of elevator, needn't be limited to the sensor of elevator control system installation itself and acquires the monitoring data, so this system can compatible different brands' elevator, facilitates the use.
2. This application passes through AI intelligent analysis technique, realizes elevator self-perception, self-diagnosis, can carry out one set of intelligent monitoring early warning system of independent thinking, can be suitable for all brands' elevators, can not influential to original elevator control system, and can judge the unusual trend of elevator operation under the special operating mode environment, and the early warning degree of accuracy is high.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below.
Fig. 1 is a hierarchical block diagram of an elevator early warning maintenance system based on cloud AIoT technology;
fig. 2 is a flowchart of an elevator early warning maintenance method based on the cloud AIoT technology.
[ detailed description ] embodiments
As shown in fig. 1, the elevator early warning maintenance system based on the cloud AIoT technology includes a perception layer 1, an edge layer 2, a platform layer 3 and an application layer 4.
The sensing layer 1 is used for acquiring basic operation data of the elevator component. Specifically, the sensing layer 1 comprises a sensor module 11 for converting a physical signal containing basic operation data of an elevator component into an analog weak current signal, an isolation module 12 for isolating and integrating the analog weak current signal into an electric signal with a uniform format, and a signal conditioning module 13 for converting the electric signal with the uniform format into digital quantity data. In actual device system deployment, the sensor modules 11 are mainly distributed in four areas, namely an elevator machine room, a hoistway, a car and a pit, and acquire physical signals containing basic operation data in related elevator components in real time to obtain operation key parameters of the related elevator components, specifically, the sensor modules 11 mainly acquire operation data of an elevator host, car operation data, hoistway environment data, steel rope tension data and door machine operation data. In addition, a sensor data acquisition device is correspondingly arranged in the arrangement area of the sensor module 11, the sensor data acquisition device comprises an isolation module 12 and a signal conditioning module 13 in the sensing layer 1, and the sensor module 11 is connected into the sensor data acquisition devices of the respective areas in a field bus mode so as to transmit the converted analog weak current signals containing the basic operation data of the elevator components to the sensor data acquisition devices. In this embodiment, acquire elevator part basis operating data through the perception layer, be independent of the control system of elevator, needn't be subject to the sensor of elevator control system installation itself and acquire the monitoring data, so this system can compatible different brands' elevator, facilitate the use, and the sensor module of this embodiment distributes in a plurality of positions of elevator hardware system, guarantees that the trouble early warning scope has good expansibility, effective discernment special environment operating mode, more can accurately discover elevator potential safety hazard under the special environment operating mode.
And the edge layer 2 is used for acquiring basic operation data of the elevator component from the sensing layer 1 and performing edge calculation on the data. Specifically, the edge layer 2 includes an edge calculation module 23, a data gateway module 22, a wireless transmission module 21, a restart module 24, and an error reporting module 25.
The edge calculation module 23 is used for performing edge calculation on the basic operation data of the elevator component so as to clean the ineffectively acquired data acquired by the filtering sensor module 11 and reduce the data load of the subsequent platform layer.
The data gateway module 22 is configured to integrate and receive the elevator component basic operation data acquired by the sensing layer 1, and convert the elevator component basic operation data meeting a predetermined sensor measurement range into a protocol for interfacing with the platform layer 3 after edge calculation, for example, into an internet protocol, so as to conveniently upload the elevator basic operation data to the platform layer through an internet interface such as 5G/4G/WiFi/RJ 45.
The wireless transmission module 21 is configured to transmit the basic operation data of the elevator component acquired by the sensing layer 1 to the data gateway module 22, and the wireless transmission module 21 transmits the basic operation data of the elevator component to the data gateway module 22 after the signal conditioning module 13 converts the basic operation data of the elevator component into digital data. Specifically, the sensor data acquisition device also comprises a wireless transmission module 21 with an edge layer, so as to conveniently transmit the basic operation data of the elevator component acquired by the sensing layer to the data gateway module in a wireless manner. The wireless transmission module 21 may employ wireless technologies such as, but not limited to, ZigBee, LoRa, Nb-Iot, WiFi, 433, etc.
In actual device system deployment, the system of this embodiment further includes a data transmission gateway in signal connection with the sensor data acquisition device, where the data transmission gateway includes a wireless transmission module 21 on an edge layer, the wireless transmission module 21 on the data transmission gateway and the wireless transmission module 21 on the sensor data acquisition device form wireless signal transmission, and the data transmission gateway further includes a data gateway module 22 and an edge calculation module 23 on the edge layer 2.
In order to effectively implement the function of the self-diagnosis sensor module, the edge layer 2 further includes a restart module 24 and an error reporting module 25.
The restart module 24 is configured to output a restart signal to the corresponding sensor module 11 to restart the corresponding sensor module 11 when the elevator component basic operation data exceeds the predetermined sensor measurement range after the edge calculation. The fault reporting module 25 is configured to output a shielding signal to the corresponding sensor module 11 to shield the corresponding sensor module 11 and report fault information of the corresponding sensor module 11 to the platform floor 3 when the basic operation data of the elevator component obtained through edge calculation after the sensor module 11 is restarted and after a predetermined time, for example, 1 minute, still exceeds a predetermined sensor measurement range. The system has the function of self-diagnosis of the sensor module, can effectively eliminate the sensor module with faults, and ensures the accuracy of the monitoring result of the system.
In actual device system deployment, the restart module 24 and the failure reporting module 25 are disposed within the data transmission gateway.
The platform layer 3 comprises an IoT access module 31 for accessing the elevator component basic operation data after edge calculation, a database module 32 for rolling and storing the data accessed by the IoT access module 31, and an AI analysis module 33 for calling the data in the database module 32 to perform AI modeling analysis. Specifically, the AI analysis module 33 restores the elevator operation model by calling the basic operation data of the elevator components in the database module 32, performs curve discrete comparison with a preset ideal operation model, obtains an elevator operation health score after weighted averaging of the elevator operation curve dispersion within a certain time period, such as 5 minutes, and outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold, and the AI analysis module 33 can establish a host operation model, a car operation model, a hoistway environment model, a steel rope tension model and a door motor operation model. In actual device system deployment, the cloud platform server comprises three modules of a platform layer, and the cloud platform server can be deployed on a public cloud or a local server of a user.
The application layer 4 is used for receiving and displaying an analysis result of the AI modeling analysis, and the application layer 4 includes a Web end 41 and an APP end 42. Web end 41 is used for the monitoring center of operation and maintenance unit or property unit, APP end 42 is used for the operation and maintenance personnel cell-phone outfit, two kinds of applications can both see the elevator real-time physical examination report that contains AI modeling analysis result, wherein APP end application still possesses functions such as receiving the maintenance worksheet and uploading the maintenance record to the platform layer, after operation and maintenance unit or property unit learned the early warning analysis result through Web end 41, can dispatch the maintenance worksheet to APP end 42 thereby notify the maintenance personnel to the elevator field maintenance, the maintenance personnel can see the elevator real-time running condition on APP end in order to conveniently prepare required accessory tool. And after the on-site maintenance is finished, the maintenance record is uploaded to the platform layer in real time to form an operation and maintenance information closed-loop system.
The embodiment utilizes thing networking IoT technique, acquires elevator part basic operation data and uploads the platform layer through the marginal layer in real time and carries out data analysis and obtain elevator operation early warning monitoring result through the perception layer of system self, consequently is independent of the control system of elevator, needn't be limited to the sensor of elevator control system installation itself and acquires the monitoring data, so this system can compatible different brands' elevator, facilitates the use. And the embodiment passes through AI intelligent analysis technique, realizes elevator self-perception, self-diagnosis, can carry out one set of intelligent monitoring early warning system of independent thinking, can be suitable for the elevator of all brands, can not influence original elevator control system, and can judge the unusual trend of elevator operation under the special operating mode environment, and the early warning degree of accuracy is high.
The embodiment also discloses an elevator early warning maintenance method based on the cloud AIoT technology, which comprises the following steps:
and S101, the sensing layer 1 acquires basic operation data of the elevator component.
In the step, the sensing layer 1 converts physical signals containing basic operation data of the elevator component into analog weak current signals through the sensor module 11, isolates and integrates the analog weak current signals into electric signals with a uniform format through the isolation module 12, and converts the electric signals with the uniform format into digital quantity data through the signal conditioning module 13, so that the basic operation data of the elevator component is effectively obtained.
And S102, the edge layer 2 acquires basic operation data of the elevator component from the sensing layer 1 and carries out edge calculation on the data.
The method specifically comprises the following steps:
the wireless transmission module 21 transmits the elevator component basic operation data acquired by the sensing layer 1 to the data gateway module 22.
The edge calculation module 23 performs an edge calculation on the elevator component base operating data transmitted to the data gateway module 22.
If the edge calculated elevator component base operational data meets the predetermined sensor measurement range, the data gateway module 22 converts the elevator component base operational data meeting the predetermined sensor measurement range into a protocol for the docking platform layer 3.
If the basic operation data of the elevator component after the edge calculation exceeds the preset sensor measurement range, the restart module 24 outputs a restart signal to the corresponding sensor module 11 to restart the corresponding sensor module 11.
If the basic operation data of the elevator component obtained through edge calculation after the sensor module 11 is restarted for a predetermined time, for example, 1 minute, still exceeds a predetermined sensor measurement range, the fault reporting module 25 outputs a shielding signal to the corresponding sensor module 11 to shield the corresponding sensor module 11 and reports fault information of the corresponding sensor module 11 to the platform floor 3.
S103, the platform layer 3 accesses the elevator component basic operation data after the edge calculation through the IoT access module 31, and performs AI modeling analysis by rolling and storing the data accessed through the IoT access module 31 through the database module 32 and calling the data in the database module 32 through the AI analysis module 33.
In this step, the AI analysis module 33 restores the elevator component basic operation data in the database module 32 into an elevator operation model, performs curve discrete comparison with a preset ideal operation model, obtains an elevator operation health score after weighting and averaging the elevator operation curve dispersion within a certain time, and outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold.
And S104, receiving and displaying an analysis result of the AI modeling analysis by the application layer 4.
In this step, the application layer 4 includes a Web end 41 and an APP end 42.
The foregoing is illustrative of one embodiment provided in connection with the detailed description and is not intended to limit the disclosure to the particular embodiments described. Similar or identical methods, structures, etc. as used herein, or several technical deductions or substitutions made on the premise of the idea of the present application, should be considered as the protection scope of the present application.
Claims (4)
1. Elevator early warning dimension system based on high in clouds AIoT technique, its characterized in that includes:
the sensing layer is used for acquiring basic operation data of the elevator component;
the edge layer is used for acquiring basic operation data of the elevator component from the sensing layer and performing edge calculation on the data;
the platform layer comprises an IoT access module used for accessing the elevator component basic operation data after edge calculation, a database module used for rolling and storing the data accessed by the IoT access module and an AI analysis module used for calling the data in the database module to perform AI modeling analysis;
the application layer is used for receiving and displaying the analysis result of the AI modeling analysis;
the sensing layer comprises a sensor module, an isolation module and a signal conditioning module, wherein the sensor module is used for converting physical signals containing basic operation data of the elevator component into analog weak current signals, the isolation module is used for isolating and integrating the analog weak current signals into electric signals in a uniform format, and the signal conditioning module is used for converting the electric signals in the uniform format into digital quantity data;
the AI analysis module restores an elevator operation model by calling the basic operation data of the elevator components in the database module, performs curve dispersion comparison with a preset ideal operation model, obtains an elevator operation health score after weighting and averaging the dispersion of the elevator operation curve within a certain time, outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold value, and establishes a host operation model, a car operation model, a hoistway environment model, a steel rope tension model and a door motor operation model;
the edge layer includes:
the edge calculation module is used for performing edge calculation on the basic operation data of the elevator component;
the data gateway module is used for integrating and receiving the elevator component basic operation data acquired by the sensing layer and converting the elevator component basic operation data which accord with the preset sensor measurement range into a protocol of a docking platform layer after edge calculation;
the wireless transmission module is used for transmitting the elevator component basic operation data acquired by the sensing layer to the data gateway module;
the restarting module is used for outputting a restarting signal to the corresponding sensor module to restart the corresponding sensor module when the basic operation data of the elevator component exceeds the preset sensor measuring range after the edge calculation;
and the fault reporting module is used for outputting a shielding signal to the corresponding sensor module to shield the corresponding sensor module and reporting the fault information of the corresponding sensor module to the platform layer when the basic operation data of the elevator component obtained by the edge calculation still exceeds the preset sensor measurement range after the sensor module is restarted and in the preset time.
2. The cloud AIoT technology-based elevator early warning and maintenance system according to claim 1, wherein the application layer comprises a Web end and an APP end.
3. An elevator early warning maintenance method based on a cloud AIoT technology is characterized by comprising the following steps:
the sensing layer obtains basic operation data of the elevator component;
the edge layer acquires basic operation data of the elevator component from the sensing layer and carries out edge calculation on the data;
the platform layer accesses the elevator component basic operation data after the edge calculation through an IoT access module, and performs AI modeling analysis by rolling and storing the data accessed through the IoT access module through a database module and calling the data in the database module through an AI analysis module;
the application layer receives and displays an analysis result of the AI modeling analysis;
in the step of obtaining basic operation data of the elevator component at the sensing layer, the method comprises the following steps:
the sensor modules are distributed in four areas, namely an elevator machine room, a well, a car and a pit, and convert physical signals containing basic operation data of elevator components into analog weak current signals;
the isolation module isolates and integrates the analog weak current signals into electric signals with a uniform format;
the signal conditioning module converts the electric signals with the uniform format into digital quantity data;
the method comprises the following steps that the platform layer accesses the basic operation data of the elevator component after edge calculation through an IoT access module, and the platform layer stores the data accessed through the IoT access module in a rolling mode through a database module and calls the data in the database module through an AI analysis module to perform AI modeling analysis, wherein the steps comprise the following steps:
the AI analysis module restores an elevator operation model by calling the basic operation data of the elevator components in the database module, performs curve dispersion comparison with a preset ideal operation model, obtains an elevator operation health score after weighting and averaging the dispersion of the elevator operation curve within a certain time, outputs an early warning analysis result when the elevator operation health score is lower than a preset early warning threshold value, and establishes a host operation model, a car operation model, a hoistway environment model, a steel rope tension model and a door motor operation model;
the step that the edge layer obtains basic operation data of the elevator component from the sensing layer and carries out edge calculation on the data comprises the following steps:
the wireless transmission module transmits the basic operation data of the elevator component acquired by the sensing layer to the data gateway module;
the edge calculation module carries out edge calculation on the basic operation data of the elevator components transmitted to the data gateway module;
if the elevator component basic operation data after the edge calculation conforms to the preset sensor measurement range, the data gateway module converts the elevator component basic operation data conforming to the preset sensor measurement range into a protocol of a docking platform layer;
if the basic operation data of the elevator component after the edge calculation exceeds the preset sensor measurement range, the restarting module outputs a restarting signal to the corresponding sensor module to restart the corresponding sensor module;
and if the basic operation data of the elevator component obtained by edge calculation still exceeds the preset sensor measurement range after the sensor module is restarted and in a preset time, the fault reporting module outputs a shielding signal to the corresponding sensor module to shield the corresponding sensor module and reports the fault information of the corresponding sensor module to the platform layer.
4. The cloud AIoT technology-based elevator early warning and maintenance method according to claim 3, wherein the application layer comprises a Web end and an APP end.
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| CN111422718B (en) * | 2020-05-29 | 2021-09-17 | 中国科学院福建物质结构研究所 | Elevator Internet of things edge calculation system and method |
| CN111847163A (en) * | 2020-06-04 | 2020-10-30 | 广州特种机电设备检测研究院 | Elevator running state monitoring system, method and device |
| CN112340556B (en) * | 2020-10-27 | 2023-04-11 | 日立楼宇技术（广州）有限公司 | Elevator operation data acquisition system and method |
| CN112520528B (en) * | 2020-12-23 | 2021-06-22 | 江苏梯卫士网络科技有限公司 | Automatic monitoring elevator fault detection system and detection method thereof |
| CN114827182A (en) * | 2021-01-21 | 2022-07-29 | 迈动云智(常熟)信息科技有限公司 | Intelligent park management platform based on ET brain |
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