CN108083044A - A kind of elevator based on big data analysis maintenance system and method on demand - Google Patents
A kind of elevator based on big data analysis maintenance system and method on demand Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
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- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The present invention provides a kind of elevator based on big data analysis maintenance system on demand, including data source, data access module and data processing module, data access module obtains data from data source and is parsed and distributed, and data processing module receives the data of data access module distribution and stored, modeled and analyzed；Data access module includes data resolution unit, data cleansing unit and file distributing unit.The present invention also provides a kind of elevator based on big data analysis maintenance methods on demand.The present invention uses the relevant elevator data of maintenance and elevator safety operation fault data of internet of things equipment acquisition, with reference to the fault condition during elevator routine use and the online situation of internet of things equipment etc., realize that by risk, the quantizating index mathematical model by situation maintenance, data supporting is provided for maintenance reform for the daily maintenance Model Establishment elevator for implementing Internet of Things+maintenance according to elevator safety operation risk demand.
Description
Technical field
It is needed the present invention relates to elevator maintenance technical field more particularly to based on the prediction maintenance of elevator Internet of Things big data analysis
The model and system asked.
Background technology
Recent year economy is advanced by leaps and bounds, and city enters the skyscraper Fast Construction phase, and elevator goes out as skyscraper
The important vertical transportation instrument entered, also welcomes eruptive growth, at the same time, with the rapid growth of elevator quantity, elevator dimension
Protect the market demand it is surging, traditional maintenance mode occur man-machine mismatch, harmful competition, maintenance work do it is unreal, do well
The phenomenon that, great threat is generated to the safety and reliability of elevator maintenance.
Also have to the research in terms of elevator maintenance, such as Chinese patent application CN201410609531.0 at present, disclose
It protects in a kind of computational methods of elevator maintenance time, the calculation protected by first quarter moon or year, half a year protects, season protects, first quarter moon
Four modes for protecting unification are protected, plan the elevator maintenance time.
The technical solution shortcoming：The mode that the program relies primarily on unified time calculating arranges elevator maintenance plan, this
Kind mode extensive management, it is to be directed to some quality preferably or make that can not be directed to the phenomenon that single ladder do fine-grained management, cause
With elevator infrequently, elevator running state is good, but resource is arranged to carry out maintenance, occupies this maintenance money very in short supply
Source；It is larger for elevator safety risk, early warning can not be but made in time, event may have occurred before conventional maintenance plan arrival
Barrier, simultaneously because the low level management of maintenance resource, enough maintenance resources can not be obtained by further resulting in the high elevator of failure risk.
The content of the invention
The problem of present invention is in background technology, provides a kind of elevator based on big data analysis maintenance system on demand,
Based on internet of things data acquisition technology, a large amount of parameters of elevator run data are gathered, with reference to elevator history maintenance data and number of faults
According to, using big data technology analytical technology, realize elevator maintenance prediction, by the mode of traditional temporally maintenance, be adjusted to by
The maintenance mode on demand of elevator operation risk, operating status.
For this purpose, the present invention uses following technical scheme：A kind of elevator based on big data analysis maintenance system on demand, including
Data source, data access module and data processing module, data access module obtained from data source data and carry out parsing and
Distribution, data processing module receive the data of data access module distribution and are stored, modeled and analyzed；Number in data source
According to including elevator basic data, car movement data, elevator faults data and elevator maintenance data；Data access module includes number
According to resolution unit, data cleansing unit and file distributing unit, wherein data resolution unit obtains related data from data source,
Data cleansing unit is sent to after parsing, the data after the cleaning of data cleansing unit are sent to number by file distributing unit
It is handled accordingly according to processing module.
Further, in data source, elevator basic data includes elevator brand, elevator model and elevator commissioning date,
Car movement data includes elevator speed, elevator running temperature, elevator running frequency and elevator vibration amplitude, elevator faults
Data include elevator faults type and time of failure, and elevator maintenance data include the elevator maintenance date and maintenance situation is anti-
Feedback.
Further, in data access module, data resolution unit will be received by unified data transmission entrance
Data according to unified communications protocol and unified data standard, realize that data access collects management, data cleansing unit is determined
Adopted cleaning rule, is filtered dirty data, realizes preliminary data quality management, file distributing unit will be after access and cleaning
Data sending is stored and analyzed to data processing module.
Further, in data processing module, data storage cell stores mass data and is used with providing analysis, data
Modeling unit can simultaneously carry out the self-teaching of model by establishing elevator faults prediction model to the analysis of historical data, prediction
The related data of elevator operation, substitutes into model and is calculated in analytic unit access certain time, and output elevator maintenance is estimated
Date.
The present invention also provides a kind of elevator based on big data analysis, maintenance method, this method use above system on demand,
And two flows are assessed including real-time online early warning analysis and offline maintenance date, offline maintenance date assessment is pre- with real-time online
The result or intermediate result of alert analysis are foundation, wherein, real-time online early warning analysis sends alarm for emergency case, and notifies
Service personnel carries out maintenance inspection, specifically includes following steps：
A1, data preparation：Car movement data is accessed in real time and pulls the historical data of elevator normal operation in preceding certain time,
Data item includes elevator speed, elevator operation acceleration, lift car temperature and elevator vibration amplitude, and data item is collected
Afterwards by elevator brand and type classification input outlier detection model；
A2, early warning detection：The local outlier generated by analyzing real time data compared with historical data, by the data after analysis
It brings failure risk Early-warning Model into and carries out risk profile, failure risk Early-warning Model is by calculating output risk factor；
A3, data storage：It is stored respectively to Outlier Data storehouse, for belonging to Outlier Data according to whether data belong to Outlier Data
Store to Outlier Data storehouse, for being not belonging to the storing to non-Outlier Data storehouse of Outlier Data；
Maintenance before offline maintenance date assessment occurs for failure is reminded, and specifically includes following steps：
B1, elevator faults data are obtained, according to the gap periods that elevator brand and model statistical average failure occur, obtained on demand
Maintenance gap periods average；
The elevator operation risk coefficient drawn in B2, the gap periods of statistics nearly a period of time by real-time online early warning analysis flow
Average, and be ranked up from high to low；
B3, to single elevator, according to risk factor size, on the basis of maintenance gap periods average on demand, provided with reference to maintenance
Source was elongated or is shortened to the maintenance cycle, and the high elevator of risk factor suitably shortens the maintenance cycle, the low elevator of risk factor
It is appropriate to elongate the maintenance cycle.
Further, in step A2 early warning detection further include risk factor confirmation, in particular to：It is pre- in failure risk
After surveying model output risk factor, forecast analysis unit obtains the implement general plan coefficient in nearly 5 minutes, to these real-time risk systems
Number does average value processing, if the average of risk factor is more than threshold value in real time, exports fault pre-alarming.
Further, in the early warning detecting step of step A2, the local outlier detection model pair based on density is passed through
Real time data and historical data are compared to obtain Outlier Data storehouse, and the local outlier detection model based on density passes through
Local outlier factor algorithm describes data sample and other samples using various statistics, distance, density quantizating index
Alienation degree so as to judge the intensity of anomaly of data, distinguishes outlier and non-outlier.
Further, the specific algorithm of failure risk prediction model is in step A2：
（1.1）Peel off to the part of elevator speed, elevator operation acceleration, elevator running temperature and vibration amplitude factor root
Average computation is weighted according to formula 1；
Formula 1,
Wherein, x1 ..., xn are observational variables, and w1....wn is weighted value；
Observational variable x1 ..., xn are calculated by the local outlier detection model based on density；
（1.2）The time point broken down by elevator faults data acquisition；
（1.3）From Outlier Data storehouse obtain this time point before 1 it is small when in data；
（1.4）Data in when small to above-mentioned 1 carry out accounting and analyze to obtain corresponding data item weight.
Further, the threshold value compared with real-time risk factor is obtained by risk factor threshold value Calculating model, risk
The specific algorithm of coefficient threshold Calculating model is：
（2.1）The time point broken down by elevator faults data acquisition；
（2.2）Before obtaining this time point from Outlier Data storehouse and non-Outlier Data storehouse 1 it is small when data；
（2.3）Bring the data of acquisition into accident analysis prediction model, the failure risk system that must be out of order before occurring in 5 minutes
Number, takes the minimum value of these coefficients as risk threshold value；
（2.4）Tuning is modified to threshold value by lasting fault detect.
The beneficial effects of the invention are as follows：The relevant elevator data of maintenance and elevator that the present invention is gathered with internet of things equipment
Safe operation fault data with reference to the fault condition during elevator routine use and the online situation of internet of things equipment etc., is realized
Implement the daily maintenance pattern of Internet of Things+maintenance according to elevator safety operation risk demand, i.e., high to security performance, accident analysis
Low elevator, by elevator manufacturing enterprise by remotely monitoring the inspection of progress equipment, safeguarding and to the failure on-call maintenance of discovery, pin
Elevator low to security performance, failure risk is high, by maintenance company arrange in time maintenance staff visit overhauled, maintenance, will
Maintenance based on time, project converts the maintenance based on status of equipment or based on risk, tentatively establish elevator by risk, by situation
The quantizating index mathematical model of maintenance provides data supporting for maintenance reform.
Description of the drawings
Fig. 1 is the system block diagram of the present invention.
Fig. 2 is the flow chart of real-time analysis and early warning flow.
Fig. 3 is the schematic diagram of local outlier detection model.
Fig. 4 is the flow chart of offline maintenance date assessment.
Specific embodiment
Specific embodiments of the present invention are described in further details below in conjunction with attached drawing, it is noted that implement
Example is intended merely to more understand that the illustrates technical scheme, is not limitation of the invention.
Embodiment 1, a kind of elevator based on big data analysis maintenance system on demand.
As shown in Figure 1, including data source, data access module and data processing module, data access module is from data source
Middle acquisition data are simultaneously parsed and distributed, data processing module receive the data of data access module distribution and stored,
Modeling and analysis；Data in data source include elevator basic data, car movement data, elevator faults data and elevator maintenance
Data；Data access module includes data resolution unit, data cleansing unit and file distributing unit, wherein data resolution unit
Related data is obtained from data source, data cleansing unit is sent to after parsing, the data after the cleaning of data cleansing unit,
Data processing module is sent to by file distributing unit to be handled accordingly.
In data source, elevator basic data includes elevator brand, elevator model and elevator commissioning date, elevator operation number
According to including elevator speed, elevator running temperature, elevator running frequency and elevator vibration amplitude, elevator faults data include electricity
Terraced fault type and time of failure, elevator maintenance data include the elevator maintenance date and maintenance situation is fed back.
In data access module, data resolution unit by unified data transmission entrance, by the data received by
According to unified communications protocol and unified data standard, realize that data access collects management, data cleansing unit definition cleaning rule
Then, dirty data is filtered, realizes preliminary data quality management, file distributing unit is by the data sending after access and cleaning
It is stored and is analyzed to data processing module.
In data processing module, data storage cell stores mass data and is used with providing analysis, data modeling unit
The self-teaching of model, forecast analysis unit can be simultaneously carried out by establishing elevator faults prediction model to the analysis of historical data
The related data of elevator operation in certain time is accessed, substitutes into model and is calculated, the date is estimated in output elevator maintenance.
Embodiment 2, a kind of elevator based on big data analysis maintenance method on demand.
As shown in Fig. 2, the method for the present embodiment is based on the system of embodiment 1, comprising two core processes, wherein one
A is real-time online early warning analysis flow, abnormality detection is done for the operation data and historical data accessed in real time, for exception
Data are paid close attention to and alarm, and O＆M unit goes to scene to carry out maintenance inspection after receiving the report for police service；Second flow is to tie up offline
Date estimation flow is protected, according to elevator faults data, maintenance data and car movement data, establishes mathematical formulae, assessment is single
Platform elevator needs on the date of maintenance, individually below illustrate the two flows.
（One）Real-time online early warning analysis flow.
Real-time analysis and early warning flow makes alarm, and service personnel is arranged to carry out in time mainly for unexpected abnormality situation
Maintenance inspection, by accident prevention in possible trouble, specific process chart below figure 2.
Real-time analysis and early warning flow mainly divides three big stages, as the 1.1-1.4 steps identified in Fig. 2 belong to data standard
Standby stage, 2.1-2.6 steps belong to fault pre-alarming detection-phase, and 3.1-3.4 steps belong to phase data memory.
Specifically, in data preparation stage, predominantly subsequent model analysis prepares data, including the electricity accessed in real time
Terraced running state data and pull nearest 1 it is small when elevator normal operation historical data（It is pulled from non-Outlier Data storehouse）.Number
Include elevator speed according to item, run acceleration information, car temperature data, vibration amplitude data, with electricity after tidal data recovering
The outlier detection model of terraced brand and type classification input based on density.
Outlier detection model based on density mainly goes to retouch using various statistics, distance, density quantizating index
Alienation degree of the data sample with other samples is stated, and then judges the intensity of anomaly of data, is illustrated in fig. 3 shown below, passes through vision
It intuitively faces, for the point of C1 set, whole spacing, density, deployment conditions more uniformity, it is believed that be same
Cluster；For the point of C2 set, cluster is equally regarded as, o1, o2 point are relatively isolated, are exactly that we will analyze the exception come
Point or discrete point.Outlier detection model based on density passes through local outlier factor algorithm-Local Outlier Factor
(LOF) Identifying Outliers of the totally different set of this density deployment conditions of C1 and C2 are realized, algorithm Universal efficient adapts to electricity
The identification of terraced operation exception status data.
Lof algorithmic formulas are as follows：LOFk(p)=∑o∈Nk(p)lrdk(o)lrdk(p)|Nk(p)|=∑o∈Nk(p) lrdk(o)|Nk(p)|/lrdk(p)
The part that Lof algorithms export P points by as above formula peels off the factor（LOFk(p)）IfLOFk(p)Closer 1, explanation
Its neighborhood dot density of p is similar, and p may belong to cluster together with neighborhood；If this ratio is more less than 1, illustrate that the density of p is higher than
Its neighborhood dot density, p are point off density；If this ratio is more more than 1, illustrate that the density of p is less than its neighborhood dot density, p more can
It can be abnormal point.
LOF algorithms have ripe realization in a variety of programming languages such as java, python, and the present invention is only with this
Ripe algorithm, therefore do not make detailed algorithm derivation, it will be understood by those skilled in the art that knowing that this algorithm can simultaneously utilize this calculation
It is conventional technical means that method, which derive,.
In fault pre-alarming detection-phase, this stage by analyze generated in real time data compared with historical data it is local from
Group's point, and then bring the data after analysis into failure risk Early-warning Model and carry out risk profile, model is by calculating output risk
Coefficient.The excessive risk event that single occurs is it is possible that caused by for factors such as some wrong data, it is impossible to which directly evidence occurs
Failure risk can just make accurate judgement, it is therefore desirable to obtain nearest 5, it is necessary to the appearance excessive risk event continued for some time
Minute real time data risk factor, does it average value processing, average is if greater than threshold value（By the way that " risk factor threshold value calculates mould
Type " is calculated）, then fault pre-alarming is exported.
Wherein, failure risk prediction model is mainly by elevator speed, operation acceleration, temperature and vibration amplitude
The peel off Weighted Average Algorithm of the factor of part show that conceptual data peels off coefficient, is referred to as risk factor, risk factor
Size may determine whether push alarm.Weighted formula is as follows：
X1 ... ... therein, xn are observational variables, and w1 ... ..., wn are weighted values.Bring formula into failure risk prediction model
In, x is respectively that elevator speed locally peels off factor x1 there are four, and operation acceleration locally peels off factor x2, temperature office
Portion peels off factor x3, and vibration amplitude peels off factor x4, and weighted value equally exists four, is respectively that elevator speed locally peels off
The factor accounts for risk factor weight w1, and the elevator operation acceleration factor that locally peels off accounts for risk factor weight w2, and temperature locally peels off
The factor accounts for risk factor weight w3, and the vibration amplitude factor that locally peels off accounts for risk factor weight w4.
X1....x4 is calculated by " the local outlier detection model based on density " in formula, and weight then needs to pass through
Show that specific practice is as follows for historical failure situation correlation analysis：
（1）The time point broken down by elevator faults data acquisition.
（2）From before " Outlier Data storehouse " middle acquisition this time point 1 data when small.
（3）Data are carried out with accounting analysis and draws corresponding data item weight, as elevator speed Outlier Data sum accounts for
The 15% of total Outlier Data sum, then the elevator speed factor that locally peels off account for risk factor weight w1 equal to 0.15.
Risk factor threshold value Calculating model exports risk factor by off-line analysis, is a set of to be done by historical failure data
Learn the model of evidence, specific implementation step is as follows：
（1）The time point broken down by elevator faults data acquisition.
（2）From before " Outlier Data storehouse " and " non-Outlier Data storehouse " middle acquisition this time point 1 data when small.
（3）Accident analysis prediction model is brought into obtaining data out, the failure risk of 5 minutes before the generation that must be out of order
Coefficient takes these coefficient minimum values as risk threshold value.
（4）Tuning is carried out to threshold value subsequently through lasting detection fault pre-alarming accuracy.
（Two）Offline maintenance date estimation flow.
The maintenance that above-mentioned real-time first analysis and early warning flow is mainly used to solve before catastrophic failure is reminded, but normalization
Maintenance work（Calculate the maintenance cycle according to maintenance date assessment models）Still need to carry out, be on the one hand used for collecting more
Maintenance and elevator faults data optimize for model learning, are on the other hand also to solve real-time analysis and early warning flow to be not covered by scene
Maintenance inspection, the new fault scenes or data rule found during normality maintenance are by Continuous optimization to real-time analysis and early warning stream
Cheng Zhong, the maintenance of normality can be analyzed offline according to acquired data, and idiographic flow is as shown in Figure 4.
（1）Elevator faults data are obtained, and according to elevator brand and model statistical average failure origination interval cycle, as
Maintenance gap periods average on demand.
（2）Elevator operation risk Coefficient Mean in nearest gap periods is counted, is ranked up operation from high to low.
（3）For single elevator according to risk factor, on the basis of maintenance gap periods average on demand, provided with reference to maintenance
Operation is elongated the cycle or is shortened in source, and the high elevator of risk factor suitably shortens the low elevator of maintenance cycle, risk factor
It is appropriate to elongate the maintenance cycle.
Claims (9)
1. a kind of elevator based on big data analysis maintenance system on demand, it is characterized in that, including data source, data access module and
Data processing module, data access module obtain data from data source and are parsed and distributed, and data processing module receives
The data of data access module distribution are simultaneously stored, modeled and analyzed；Data in data source include elevator basic data, electricity
Ladder operation data, elevator faults data and elevator maintenance data；Data access module includes data resolution unit, data cleansing list
Member and file distributing unit, wherein data resolution unit obtain related data from data source, data cleansing are sent to after parsing
Unit, the data after the cleaning of data cleansing unit are sent to data processing module by file distributing unit and carry out accordingly
Processing.
2. a kind of elevator based on big data analysis according to claim 1 maintenance system on demand, it is characterized in that, in data
In source, elevator basic data includes elevator brand, elevator model and elevator commissioning date, and car movement data is run including elevator
Speed, elevator running temperature, elevator running frequency and elevator vibration amplitude, elevator faults data include elevator faults type and event
Hinder time of origin, elevator maintenance data include the elevator maintenance date and maintenance situation is fed back.
3. a kind of elevator based on big data analysis according to claim 1 maintenance system on demand, it is characterized in that, in data
In AM access module, data resolution unit is assisted the data received according to unified communication by unified data transmission entrance
It negotiates peace unified data standard, realizes that data access collects management, data cleansing unit defines cleaning rule, to dirty data progress
Filtering, realizes preliminary data quality management, and file distributing unit is by the data sending after access and cleaning to data processing module
It is stored and is analyzed.
4. a kind of elevator based on big data analysis according to claim 1 maintenance system on demand, it is characterized in that, in data
In processing module, data storage cell stores mass data and is used with providing analysis, and data modeling unit passes through to historical data
Analysis establish elevator faults prediction model and can simultaneously carry out the self-teaching of model, in forecast analysis unit access certain time
The related data of elevator operation, substitutes into model and is calculated, and the date is estimated in output elevator maintenance.
5. a kind of elevator based on big data analysis maintenance method on demand, it is characterized in that, appoint in this method application claim 1-4
Elevator maintenance system, and two flows are assessed including real-time online early warning analysis and offline maintenance date on demand described in one,
The offline maintenance date is assessed using the result of real-time online early warning analysis or intermediate result as foundation, wherein, real-time online early warning point
Analysis sends alarm for emergency case, and service personnel is notified to carry out maintenance inspection, specifically includes following steps：
A1, data preparation：Car movement data is accessed in real time and pulls the historical data of elevator normal operation in preceding certain time,
Data item includes elevator speed, elevator operation acceleration, lift car temperature and elevator vibration amplitude, and data item is collected
Afterwards by elevator brand and type classification input outlier detection model；
A2, early warning detection：The local outlier generated by analyzing real time data compared with historical data, by the data after analysis
It brings failure risk Early-warning Model into and carries out risk profile, failure risk Early-warning Model is by calculating output risk factor；
A3, data storage：It is stored respectively to Outlier Data storehouse, for belonging to Outlier Data according to whether data belong to Outlier Data
Store to Outlier Data storehouse, for being not belonging to the storing to non-Outlier Data storehouse of Outlier Data；
Maintenance before offline maintenance date assessment occurs for failure is reminded, and specifically includes following steps：
B1, elevator faults data are obtained, according to the gap periods that elevator brand and model statistical average failure occur, obtained on demand
Maintenance gap periods average；
The elevator operation risk coefficient drawn in B2, the gap periods of statistics nearly a period of time by real-time online early warning analysis flow
Average, and be ranked up from high to low；
B3, to single elevator, according to risk factor size, on the basis of maintenance gap periods average on demand, provided with reference to maintenance
Source was elongated or is shortened to the maintenance cycle, and the high elevator of risk factor suitably shortens the maintenance cycle, the low elevator of risk factor
It is appropriate to elongate the maintenance cycle.
6. a kind of elevator based on big data analysis according to claim 5 maintenance method on demand, it is characterized in that, step A2
In early warning detection further include risk factor confirmation, in particular to：After failure risk prediction model exports risk factor, in advance
Survey analytic unit obtains the implement general plan coefficient in nearly 5 minutes, average value processing is done to these real-time risk factors, if real-time wind
The average of dangerous coefficient is more than threshold value, then exports fault pre-alarming.
7. a kind of elevator based on big data analysis according to claim 6 maintenance method on demand, it is characterized in that, in step
In the early warning detecting step of A2, real time data and historical data are compared by the local outlier detection model based on density
Compared with so as to obtain Outlier Data storehouse, the local outlier detection model based on density is by local outlier factor algorithm, using each
Kind statistics, quantizating index describes data sample and the alienation degree of other samples, so as to judge data
Intensity of anomaly, distinguish outlier and non-outlier.
8. a kind of elevator based on big data analysis according to claim 7 maintenance method on demand, it is characterized in that, in step
The specific algorithm of failure risk prediction model is in A2：
（1.1）Peel off to the part of elevator speed, elevator operation acceleration, elevator running temperature and vibration amplitude factor root
Average computation is weighted according to formula 1；
Formula 1,
Wherein, x1 ..., xn are observational variables, and w1....wn is weighted value；
Observational variable x1 ..., xn are calculated by the local outlier detection model based on density；
（1.2）The time point broken down by elevator faults data acquisition；
（1.3）From Outlier Data storehouse obtain this time point before 1 it is small when in data；
（1.4）Data in when small to above-mentioned 1 carry out accounting and analyze to obtain corresponding data item weight.
9. a kind of elevator based on big data analysis according to claim 7 maintenance method on demand, it is characterized in that, it is and real-time
The threshold value that risk factor is compared is obtained by risk factor threshold value Calculating model, the specific calculation of risk factor threshold value Calculating model
Method is：
（2.1）The time point broken down by elevator faults data acquisition；
（2.2）Before obtaining this time point from Outlier Data storehouse and non-Outlier Data storehouse 1 it is small when data；
（2.3）Bring the data of acquisition into accident analysis prediction model, the failure risk system that must be out of order before occurring in 5 minutes
Number, takes the minimum value of these coefficients as risk threshold value；
（2.4）Tuning is modified to threshold value by lasting fault detect.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711168356.6A CN108083044B (en) | 2017-11-21 | 2017-11-21 | Elevator on-demand maintenance system and method based on big data analysis |
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| CN108584598A (en) * | 2018-03-16 | 2018-09-28 | 广州市金度信息科技有限公司 | A kind of elevator faults automatically analyze and method for early warning, storage medium and intelligent terminal |
| EP3823922B1 (en) * | 2018-07-19 | 2022-08-31 | Inventio AG | Method and device for monitoring a person transport installation using a registration device and a digital doppelgänger |
| US11945686B2 (en) | 2018-07-19 | 2024-04-02 | Inventio Ag | Method and device for monitoring a state of a passenger transport system using a digital double |
| CN109165818A (en) * | 2018-08-02 | 2019-01-08 | 国网湖北省电力有限公司电力科学研究院 | A kind of negative point calculating method for electrical equipment risk assessment |
| CN109165818B (en) * | 2018-08-02 | 2022-02-08 | 国网湖北省电力有限公司电力科学研究院 | Negative point calculation method for risk assessment of electrical equipment |
| CN110817628A (en) * | 2018-08-08 | 2020-02-21 | 北京感瞰科技有限公司 | Intelligent fault diagnosis method, device and system for elevator |
| CN109110608A (en) * | 2018-10-25 | 2019-01-01 | 歌拉瑞电梯股份有限公司 | A kind of elevator faults prediction technique based on big data study |
| CN109896384A (en) * | 2019-02-26 | 2019-06-18 | 北京市特种设备检测中心 | Towed elevator health status characteristic parameter extraction method based on big data analysis |
| CN110077928A (en) * | 2019-04-13 | 2019-08-02 | 浙江城际特种设备检测有限公司 | A kind of method of elevator detection |
| US11993480B2 (en) | 2019-04-30 | 2024-05-28 | Otis Elevator Company | Elevator shaft distributed health level with mechanic feed back condition based monitoring |
| CN110155840A (en) * | 2019-05-22 | 2019-08-23 | 安徽奥里奥克科技股份有限公司 | A kind of elevator data acquisition management system |
| CN110271929A (en) * | 2019-06-14 | 2019-09-24 | 山东科技大学 | A kind of city elevator maintenance QA system based on big data driving |
| CN110436291A (en) * | 2019-07-03 | 2019-11-12 | 北京中铁电梯工程有限公司 | A kind of elevator real time execution maintenance examination and repair system |
| CN110371815A (en) * | 2019-07-04 | 2019-10-25 | 安徽中科福瑞科技有限公司 | A kind of on-demand maintenance system of elevator based on Internet of Things |
| CN112633617A (en) * | 2019-09-24 | 2021-04-09 | 北京国双科技有限公司 | Maintenance strategy generation method and device, computer equipment and readable storage medium |
| US11993488B2 (en) | 2019-09-27 | 2024-05-28 | Otis Elevator Company | Processing service requests in a conveyance system |
| CN110687851A (en) * | 2019-10-31 | 2020-01-14 | 广东安可云科技有限公司 | Terminal operation monitoring system and method |
| CN110844731A (en) * | 2019-11-15 | 2020-02-28 | 上海市特种设备监督检验技术研究院 | Elevator function safety real-time monitoring system and method |
| CN111027934A (en) * | 2019-12-10 | 2020-04-17 | 深圳市通用互联科技有限责任公司 | Elevator maintenance task data processing method and device, computer equipment and storage medium |
| CN111243250A (en) * | 2019-12-25 | 2020-06-05 | 东软集团股份有限公司 | Maintenance early warning method, device and equipment based on alarm data |
| CN111392539B (en) * | 2020-03-25 | 2021-08-20 | 虏克电梯有限公司 | Elevator fault early warning device |
| CN111392539A (en) * | 2020-03-25 | 2020-07-10 | 虏克电梯有限公司 | Elevator fault early warning device |
| CN111240306A (en) * | 2020-04-26 | 2020-06-05 | 南京市产品质量监督检验院 | Self-adaptive distribution transformer fault diagnosis system and diagnosis method thereof |
| CN112357709A (en) * | 2020-04-28 | 2021-02-12 | 广东梯云科技有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
| CN111252641A (en) * | 2020-04-28 | 2020-06-09 | 广东梯云科技有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
| CN111611546A (en) * | 2020-05-18 | 2020-09-01 | 南京市特种设备安全监督检验研究院 | Elevator maintenance-on-demand working quality evaluation method based on Internet of things data extraction and calculation |
| CN111611546B (en) * | 2020-05-18 | 2023-06-20 | 南京市特种设备安全监督检验研究院 | Elevator on-demand maintenance work quality evaluation method based on Internet of things data extraction and calculation |
| CN111559680A (en) * | 2020-05-25 | 2020-08-21 | 陕西省特种设备检验检测研究院 | Elevator intelligent inspection method based on big data |
| CN111874775A (en) * | 2020-06-12 | 2020-11-03 | 朱凯 | Elevator safety monitoring system based on big data |
| CN111717753A (en) * | 2020-06-29 | 2020-09-29 | 浙江新再灵科技股份有限公司 | Self-adaptive elevator fault early warning system and method based on multi-dimensional fault characteristics |
| CN111994749A (en) * | 2020-08-14 | 2020-11-27 | 揭阳市聆讯软件有限公司 | Elevator intelligent supervision and on-demand maintenance system and method based on PHM technology |
| CN114314228A (en) * | 2020-09-29 | 2022-04-12 | 思维实创(哈尔滨)科技有限公司 | Calculation method of elevator maintenance period model based on big data |
| CN112390105A (en) * | 2020-10-28 | 2021-02-23 | 日立楼宇技术（广州）有限公司 | Elevator operation detection method and device, computer equipment and storage medium |
| CN112830358A (en) * | 2020-12-30 | 2021-05-25 | 重庆厚齐科技有限公司 | System and method for predicting elevator maintenance cycle on demand by machine learning |
| CN112830357A (en) * | 2020-12-30 | 2021-05-25 | 重庆厚齐科技有限公司 | System and method for measuring and calculating elevator health value based on Internet of things and big data |
| CN112830357B (en) * | 2020-12-30 | 2022-05-17 | 重庆厚齐科技有限公司 | System and method for measuring and calculating elevator health value based on Internet of things and big data |
| CN112830358B (en) * | 2020-12-30 | 2022-05-20 | 重庆厚齐科技有限公司 | System and method for predicting elevator maintenance cycle on demand by machine learning |
| CN112811275A (en) * | 2020-12-30 | 2021-05-18 | 重庆厚齐科技有限公司 | Elevator maintenance-on-demand period measuring and calculating system and method based on Internet of things |
| CN112777442A (en) * | 2021-02-03 | 2021-05-11 | 浙江新再灵科技股份有限公司 | Elevator safety region risk prediction method based on Internet of things big data |
| CN113071966A (en) * | 2021-04-26 | 2021-07-06 | 平安国际智慧城市科技股份有限公司 | Elevator fault prediction method, device, equipment and storage medium |
| CN113233275A (en) * | 2021-04-27 | 2021-08-10 | 重庆千跬科技有限公司 | Automatic matching system for target elevator and scattered maintenance personnel |
| CN113213296A (en) * | 2021-04-27 | 2021-08-06 | 重庆千跬科技有限公司 | Automatic matching method for target elevator and scattered maintenance personnel |
| CN115001943A (en) * | 2022-05-27 | 2022-09-02 | 深圳小湃科技有限公司 | Equipment fault identification method and equipment based on big data and storage medium |
| CN115001943B (en) * | 2022-05-27 | 2024-03-22 | 深圳小湃科技有限公司 | Equipment fault identification method, equipment and storage medium based on big data |
| CN114671314A (en) * | 2022-05-30 | 2022-06-28 | 凯尔菱电（山东）电梯有限公司 | Safety monitoring method for elevator |
| CN115231410A (en) * | 2022-07-22 | 2022-10-25 | 成都市旭永升机电设备有限公司 | Elevator periodic maintenance, monitoring and management cloud system based on intellectualization |
| CN115258867A (en) * | 2022-08-12 | 2022-11-01 | 杭州电子科技大学 | Elevator maintenance system and method according to needs |
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Application publication date: 20180529 Assignee: Jiangsu xinzailing Intelligent Technology Co., Ltd Assignor: ZHEJIANG XINZAILING TECHNOLOGY Co.,Ltd. Contract record no.: X2020980000583 Denomination of invention: Elevator as-required maintenance system and method based on big data analysis Granted publication date: 20191224 License type: Common License Record date: 20200311 |
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Denomination of invention: A method of elevator maintenance system based on big data Effective date of registration: 20220419 Granted publication date: 20191224 Pledgee: CITIC Bank Limited by Share Ltd. Hangzhou Xiaoshan branch Pledgor: ZHEJIANG XINZAILING TECHNOLOGY Co.,Ltd. Registration number: Y2022330000551 |