CN113511572A - Elevator maintenance quality evaluation method and system based on big data and storage medium - Google Patents

Elevator maintenance quality evaluation method and system based on big data and storage medium Download PDF

Info

Publication number
CN113511572A
CN113511572A CN202110608622.2A CN202110608622A CN113511572A CN 113511572 A CN113511572 A CN 113511572A CN 202110608622 A CN202110608622 A CN 202110608622A CN 113511572 A CN113511572 A CN 113511572A
Authority
CN
China
Prior art keywords
maintenance
elevator
fault
statistical
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110608622.2A
Other languages
Chinese (zh)
Other versions
CN113511572B (en
Inventor
张巍
李刚
林创鲁
欧阳徕
葛友明
叶亮
李丽宁
罗永通
莫绍孟
劳伟文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Special Equipment Testing And Research Institute Guangzhou Special Equipment Accident Investigation Technology Center Guangzhou Elevator Safety Operation Monitoring Center
Original Assignee
Guangzhou Academy of Special Equipment Inspection and Testing
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Academy of Special Equipment Inspection and Testing filed Critical Guangzhou Academy of Special Equipment Inspection and Testing
Priority to CN202110608622.2A priority Critical patent/CN113511572B/en
Publication of CN113511572A publication Critical patent/CN113511572A/en
Application granted granted Critical
Publication of CN113511572B publication Critical patent/CN113511572B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Algebra (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Maintenance And Inspection Apparatuses For Elevators (AREA)
  • Indicating And Signalling Devices For Elevators (AREA)

Abstract

The method calculates the maintenance quality evaluation score of a maintenance unit to be tested according to maintenance elevator fault control efficiency data, maintenance elevator fault treatment efficiency data, maintenance quality control data, maintenance customer satisfaction data, maintenance elevator internet of things monitoring capability data, statistic elevator fault control efficiency data, statistic elevator fault treatment efficiency data, statistic maintenance quality control data, statistic customer satisfaction data and statistic elevator internet of things monitoring capability data. The method and the device for evaluating the maintenance quality of the maintenance unit to be tested calculate the maintenance data of the maintenance unit to be tested and the statistical data of all the maintenance units, and calculate the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance data and the statistical data. But this application wide application in elevator safety technical field.

Description

Elevator maintenance quality evaluation method and system based on big data and storage medium
Technical Field
The application relates to the technical field of elevator safety, in particular to an elevator maintenance quality evaluation method and system based on big data and a storage medium.
Background
At present, the maintenance and the service of the elevator are carried out according to special equipment safety supervision regulations, and the elevator is cleaned, lubricated, adjusted and checked at least once every 15 days. The aim of this regulation is to ensure that the elevator can be maintained at the most basic level, but it is difficult to adapt to elevators of different quality levels or different use environments. The elevator is used as loss type equipment, and the elevator equipment can be divided into an early fault period, a sporadic fault period and a fault loss period according to the attenuation rule of the elevator performance. When the elevator enters a loss period, the failure rate of equipment gradually rises, and the performance gradually declines, so that preventive maintenance at the stage is very important. Along with the accumulation of time, if the elevator is still subjected to preventive maintenance once every 15 days, the risk of elevator safety accidents is increased, namely, serious safety accidents can occur at the time point of preventive maintenance; in addition, when the elevator just enters the loss period, the abrasion of the elevator equipment is not serious, and preventive maintenance can be carried out in an interval of more than 15 days according to related experience, so that the maintenance cost is reduced.
The maintenance on demand is to make a maintenance plan by combining the actual state of each elevator and implement targeted and effective maintenance. The actual state of the elevator comprises the actual service life, the running condition, the maintenance record, the current state of component wear, the service environment, the management quality and the like of the elevator. The on-demand maintenance mode of the Internet of things and maintenance realizes the on-demand maintenance mode combining online inspection and maintenance of the Internet of things and field maintenance through a remote elevator monitoring technical means, so that the elevator maintenance industry develops towards humanization and science and technology more and more. How to supervise the maintenance quality of the elevator in a new mode brings about wide attention of passengers, use units and market supervision departments.
Along with the gradual construction of an elevator intelligent supervision platform, the collection, analysis and utilization of elevator related data become possible, the evaluation of the elevator maintenance quality at present is generally based on the maintenance data of a single elevator and is evaluated based on a threshold value, and the evaluation mode needs to manually set the threshold value, so that the existing elevator maintenance quality evaluation is not accurate enough.
Disclosure of Invention
In view of the above, an object of the present application is to provide an elevator maintenance quality evaluation method, system and storage medium based on big data, so as to improve the accuracy of elevator maintenance quality evaluation.
The first technical scheme adopted by the application is as follows:
a quality evaluation method for elevator maintenance based on big data comprises the following steps:
collecting maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator internet of things function condition data of a maintenance unit to be tested;
collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units;
calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data;
calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data;
calculating maintenance quality control data according to the maintenance quality data;
calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers;
calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things;
calculating and counting elevator fault control efficiency data according to the statistical elevator fault data;
calculating statistical elevator fault handling efficiency data according to the statistical fault handling data;
calculating statistical maintenance quality control data according to the statistical maintenance quality data;
calculating statistical customer satisfaction data according to the statistical customer evaluation data;
calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data;
and calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator internet of things monitoring capability data.
Further, the step of calculating a maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistical elevator fault control efficiency data, the statistical elevator fault treatment efficiency data, the statistical maintenance quality control data, the statistical customer satisfaction data and the statistical elevator internet of things monitoring capability data specifically includes:
calculating a maintenance elevator fault control efficiency index according to the maintenance elevator fault control efficiency data and the statistical elevator fault control efficiency data;
calculating a maintenance elevator fault disposal efficiency index according to the maintenance elevator fault disposal efficiency data and the statistical elevator fault disposal efficiency data;
calculating a maintenance quality control index according to the maintenance quality control data and the statistical maintenance quality control data;
calculating a maintenance customer satisfaction index according to the maintenance customer satisfaction data and the statistical customer satisfaction data;
calculating a maintenance elevator Internet of things monitoring capability index according to the maintenance elevator Internet of things monitoring capability data and the statistic elevator Internet of things monitoring capability data;
and calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault treatment efficiency index, the maintenance quality control index, the maintenance customer satisfaction index and the maintenance elevator Internet of things monitoring capability index.
Further, the step of calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault handling efficiency index, the maintenance quality control index, the maintenance customer satisfaction index and the maintenance elevator internet of things monitoring capability index specifically includes:
obtaining a maintenance elevator fault control efficiency weight vector, a maintenance elevator fault treatment efficiency weight vector, a maintenance quality control weight vector, a maintenance customer satisfaction weight vector and a maintenance elevator internet of things monitoring capability weight vector;
and calculating a maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault treatment efficiency index, the maintenance quality control index, the maintenance customer satisfaction index, the maintenance elevator internet of things monitoring capability index, the maintenance elevator fault control efficiency weight vector, the maintenance elevator fault treatment efficiency weight vector, the maintenance quality control weight vector, the maintenance customer satisfaction weight vector and the maintenance elevator internet of things monitoring capability weight vector.
Further, the calculation formula of the maintenance elevator fault rate index is as follows:
Figure BDA0003095083050000031
wherein, g1To maintain the elevator fault rate index, PfiIn order to maintain the failure rate of the elevator,
Figure BDA0003095083050000032
the failure rate of the elevator is counted.
Further, the elevator maintenance quality evaluation method based on big data further comprises the following steps:
and when the maintenance quality evaluation score is smaller than a preset score threshold, generating maintenance quality alarm information.
Further, the calculation formula of the man-trapping and rescue timeliness rate of the maintenance elevator is as follows:
PRi＝TRi/TRmax
wherein, PRiMaintenance of elevator entrapment rescue timeliness, TRiRescue response time, T, for maintaining elevator drowsinessRmaxThe elevator is maintained to be trapped and rescue the allowed maximum response time.
Further, the elevator maintenance quality evaluation method based on big data further comprises the following steps:
calculating the average value of the maintenance quality evaluation scores and the standard deviation of the maintenance quality evaluation scores of all maintenance units;
calculating a maintenance quality evaluation score standard value according to the maintenance quality evaluation score, the maintenance quality evaluation score average value and the maintenance quality evaluation score standard deviation;
the calculation formula of the maintenance quality evaluation score standard value is as follows:
Figure BDA0003095083050000041
wherein, SzyFor quality assessment score standard value of maintenance, Z and Z are constants, SyIn order to maintain the quality evaluation score,
Figure BDA0003095083050000042
to maintain the quality evaluation score average, SσAnd the standard deviation of the maintenance quality evaluation score is obtained.
The second technical scheme adopted by the application is as follows:
an elevator maintenance quality evaluation system based on big data comprises:
the system comprises an acquisition module, a maintenance module and a maintenance module, wherein the acquisition module is used for acquiring maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator Internet of things function condition data of a maintenance unit to be tested; collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units;
the calculation module is used for calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data; calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data; calculating maintenance quality control data according to the maintenance quality data; calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers; calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things; calculating and counting elevator fault control efficiency data according to the statistical elevator fault data; calculating statistical elevator fault handling efficiency data according to the statistical fault handling data; calculating statistical maintenance quality control data according to the statistical maintenance quality data; calculating statistical customer satisfaction data according to the statistical customer evaluation data; calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data;
and the evaluation module is used for calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator internet of things monitoring capability data.
The third technical scheme adopted by the application is as follows:
an elevator maintenance quality evaluation system based on big data comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method.
The fourth technical scheme adopted by the application is as follows:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method.
According to the method and the device, the maintenance data of the maintenance unit to be tested and the statistical data of all the maintenance units are calculated, and the maintenance quality evaluation score of the maintenance unit to be tested is calculated according to the maintenance data and the statistical data.
Drawings
Fig. 1 is a flow chart of an elevator maintenance quality evaluation method based on big data according to an embodiment of the present application.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood.
The present application will now be described in further detail with reference to the accompanying drawings and specific examples. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art. Further, for several described in the following embodiments, it is denoted as at least one.
As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any combination of one or more of the associated listed items.
In the elevator maintenance quality evaluation method, event data and statistical data such as the elevator Internet of things, maintenance, inspection and detection, emergency disposal, user complaints and the like in a certain period and a certain range are collected by a monitoring platform, elevator operation quality evaluation indexes are extracted and calculated, and elevator maintenance quality levels are evaluated from five dimensions of elevator fault control efficiency, elevator fault disposal efficiency, maintenance quality control, customer evaluation and elevator Internet of things monitoring capacity. According to the method, an elevator maintenance quality evaluation index system is established, and an elevator maintenance quality evaluation model is established by adopting a multi-target effect comprehensive evaluation method so as to realize large data driven elevator maintenance quality evaluation.
As shown in fig. 1, an embodiment of the present application provides a method for evaluating elevator maintenance quality based on big data, including:
s100, collecting maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator Internet of things function condition data of a maintenance unit to be tested; collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units;
s200, calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data; calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data; calculating maintenance quality control data according to the maintenance quality data; calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers; calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things; calculating and counting elevator fault control efficiency data according to the statistical elevator fault data; calculating statistical elevator fault handling efficiency data according to the statistical fault handling data; calculating statistical maintenance quality control data according to the statistical maintenance quality data; calculating statistical customer satisfaction data according to the statistical customer evaluation data; calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data;
s300, calculating a maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator Internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator Internet of things monitoring capability data.
Specifically, feature data of links such as elevator internet of things, maintenance, inspection and detection, emergency disposal, user complaints and the like in a statistical time period of an elevator monitoring platform need to be collected. The extracted feature data includes: the system comprises elevator fault times, elevator sleepiness times, elevator total operation times, elevator fault interval time, elevator fault time, elevator sleepiness rescue response time, elevator sleepiness rescue allowed maximum response time, elevator maintenance interval time, elevator maintenance maximum allowed interval time, elevator inspection unqualified items, elevator total inspection item numbers, elevator internet of things function inspection unqualified item numbers, elevator internet of things function total inspection item numbers, elevator supervision spot check unqualified item numbers, elevator supervision spot check total item numbers, user complaint times, satisfaction degree scores, elevator internet of things fault early warning function scores, elevator internet of things fault detection function scores, elevator internet of things predictive maintenance support scores and the like.
And then extracting the total time of the statistical time period, the total operation times of the statistical time period and the continuous normal operation times of the elevator before the fault event occurs according to the statistical data.
The duration of the event is calculated as:
TEh＝TEs-TE0(E＝F,P,C)
wherein, TEhIs the duration of the event, TEsAs the release time of the event, TE0Is the time of occurrence of the event.
The duration of the fault event is calculated as:
TFh＝TFs-TF0
wherein, TFhFor the duration of a fault event, TFsFor the release time of a fault event, TF0Is the time of occurrence of the fault event.
The duration of the ith fault event is calculated as:
TFhi＝TFsi-TF0i
wherein, TFhiDuration of the ith fault event, TFsiIs the release time of the ith fault event, TF0iIs the time of occurrence of the ith failure event.
The duration of the i-th drowsiness event is calculated by the formula:
TPhi＝TPsi-TP0i
wherein, TPhiDuration of the i-th distress event, TPsiIs the release time of the i-th trapped event, TP0iIs the occurrence time of the i-th distress event.
The calculation formula of the ith elevator maintenance interval time is as follows:
TMi＝Tmi-Tmi-1
wherein, TMiInterval time for i-th elevator maintenance, TmiFor the ith elevator maintenance time, Tmi-1For the i-1 th elevator maintenance time.
The maximum allowable interval time for maintaining the elevator is the maximum time allowed between two maintenance, and can be recorded as TMmax。
Therefore, the calculation formula of the ith time of elevator maintenance is as follows:
PMi＝TMi/TMmax
wherein, PMiAnd maintaining the timeliness rate for the ith elevator.
The number of unqualified items in the elevator inspection is the total number of unqualified items in the elevator inspection, and can be recorded as Ntf(ii) a The total number of elevator inspection items is the total number of elevator inspection items and can be recorded as Nt(ii) a The number of unqualified items of the function inspection of the elevator Internet of things is the total number of unqualified items of the function inspection of the Internet of things terminal, and can be recorded as Niottf(ii) a The total number of the elevator Internet of things function inspection items is the total number of items detected by the terminal functions of the Internet of things, and can be recorded as Niott。
The calculation formula of the i-th elevator trapping rescue response time is as follows:
TRi＝TDi-TCi
wherein, TRiResponse time for i-th elevator trapped rescue, TDiMoment T of arrival of rescue workers for ith elevator trapped personCiAnd at the moment of alarming for the elevator drowsiness rescue for the ith time.
The maximum response time allowed by elevator trapped rescue is the maximum response time allowed by trapped emergency rescue and can be recorded as TRmax。
The calculation formula of the elevator trapping and rescuing timeliness at the ith time is as follows:
PRi＝TRi/TRmax
wherein, PRiAnd the elevator is trapped and rescued for the ith time.
The number of complaints of the user is the total number of complaint events in the statistical time period, and can beIs marked as Nc。
The total running times of the elevator are the total running times of the elevator in the statistical time period, and the calculation formula is as follows:
NS＝Ne-N0
wherein N isSFor the total number of elevator runs, NeCumulative number of runs of elevator for statistical time period end time, N0The running times of the elevators are accumulated for the start time of the statistical time period.
Number of continuous normal operation times N of elevator before ith eventniThe calculation formula of (2) is as follows:
Nni＝N=N-N0i
wherein N iseiCumulative number of runs for elevator of i-th event, N0iThe number of runs is accumulated for the elevator of the i-1 th event. Total elevator operating time TSThe calculation formula of (2) is as follows:
TS＝Te-Ts
wherein, TeTo count the end time of the time period, TsThe start time of the statistical time period.
After the characteristic data are obtained through calculation, an elevator maintenance quality evaluation multi-target control model needs to be established; and classifying the extracted characteristic data, and evaluating the elevator maintenance quality level from five dimensions of elevator fault control efficiency, elevator fault treatment efficiency, maintenance quality control, customer satisfaction and elevator Internet of things monitoring capacity. Wherein:
A. the elevator fault control efficiency data comprises elevator fault rate, average elevator fault interval time and elevator trapping rate;
B. the elevator fault handling efficiency data comprises average elevator fault duration and elevator people trapping and rescuing timeliness;
C. the maintenance quality control data comprises a maintenance timeliness rate, an inspection failure rate, an elevator Internet of things function inspection failure rate and an elevator supervision spot check failure rate;
D. the customer satisfaction data comprises a customer complaint rate and a satisfaction average score;
E. the elevator internet of things monitoring capability data comprises an elevator internet of things fault early warning function average score, an elevator internet of things fault detection function average score and an elevator internet of things predictive maintenance support average score.
The above feature data are defined as follows:
A：
maintenance elevator failure rate PfiThe calculation formula of (2) is as follows:
Pfi＝Nfi/NSi
wherein N isfiNumber of maintenance elevator failures for i-th maintenance unit, NSiThe total running times of the maintenance elevator of the ith maintenance unit.
Statistic elevator fault rate
Figure BDA0003095083050000081
The calculation formula of (2) is as follows:
Figure BDA0003095083050000082
wherein the content of the first and second substances,
Figure BDA0003095083050000083
the number of elevator faults is counted for all maintenance units,
Figure BDA0003095083050000084
the total running times of the elevator are counted by all maintenance units, and n is the number of the maintenance units.
Maintenance elevator mean fault interval time TFVThe calculation formula of (2) is as follows:
Figure BDA0003095083050000091
wherein N is maintenance elevator fault frequency TFiTo maintain the interval between the i-th fault and the i-1 st fault of the elevator. Counting mean time between failures of elevator
Figure BDA0003095083050000092
The calculation formula of (2) is as follows:
Figure BDA0003095083050000093
wherein S is the total failure times of all elevators of all maintenance units in the statistical time period.
Maintenance elevator trapping rate PKiThe calculation formula of (2) is as follows:
PKi＝NKi/NSi
wherein N isKiNumber of elevator sleepers to be maintained, NSiTo maintain the total running times of the elevator.
Statistic elevator trapping rate
Figure BDA0003095083050000094
The calculation formula of (2) is as follows:
Figure BDA0003095083050000095
B：
maintenance elevator fault average time length TFUiThe calculation formula of (2) is as follows:
Figure BDA0003095083050000096
wherein N isfiMaintenance elevator failure times T for maintenance unit to be testedFhiThe duration of the ith failure event. Counting the mean time of elevator fault
Figure BDA0003095083050000097
The calculation formula of (2) is as follows:
Figure BDA0003095083050000098
wherein S is the total failure times of all elevators of all maintenance units in the statistical time period.
Maintenance elevator trapped rescue timeliness PKiThe calculation formula of (2) is as follows:
Figure BDA0003095083050000099
wherein R is the number of people trapped in the maintenance elevator, PRjAnd the elevator is trapped and rescued for the jth time.
Statistics of elevator trapped person rescue timeliness
Figure BDA00030950830500000910
The calculation formula of (2) is as follows:
Figure BDA0003095083050000101
and K is the total number of people trapped by all elevators of all maintenance units in the statistical time period.
C：
Maintenance timeliness PMViThe calculation formula of (2) is as follows:
Figure BDA0003095083050000102
wherein W is the maintenance frequency of the ith maintenance unit in the statistical time period, PMjAnd maintaining the timeliness rate for the j-th elevator.
Statistics of maintenance timeliness
Figure BDA0003095083050000103
The calculation formula of (2) is as follows:
Figure BDA0003095083050000104
and B is the total maintenance frequency of all maintenance units in the statistical time period.
Maintenance protectorInspection reject ratio PtfThe calculation formula of (2) is as follows:
Ptf＝Ntfi/Nti
wherein N istfiNumber of unqualified elevator inspection items of i-th dimension unit, NtiThe total number of the inspection items of the elevator of the ith dimension unit.
Statistical test failure rate
Figure BDA0003095083050000105
The calculation formula of (2) is as follows:
Figure BDA0003095083050000106
function inspection failure rate P of maintenance elevator internet of thingsiotfThe calculation formula of (2) is as follows:
Piotf＝Niotfi/Nioti
wherein N isiotfiUnqualified number of elevator internet of things function inspection items of i-th maintenance unit, NiotiThe total number of the elevator internet of things function test items of the ith maintenance unit.
Statistics elevator thing networking functional test defective rate
Figure BDA0003095083050000107
The calculation formula of (2) is as follows:
Figure BDA0003095083050000108
maintenance elevator supervision spot check failure rate PccfThe calculation formula of (2) is as follows:
Pccf＝Nccfi/Ncci
wherein N isccfiNumber of unqualified items for elevator supervision spot check of ith maintenance unit, NcciThe elevator supervision spot check total item number of the ith maintenance unit.
Statistical elevator supervision spot check failure rate
Figure BDA0003095083050000111
The calculation formula of (2) is as follows:
Figure BDA0003095083050000112
D：
maintenance user complaint rate PtsfThe calculation formula of (2) is as follows:
Ptsf＝Ntsi/Nsi
wherein N istsiNumber of complaints of the user for the ith maintenance unit, NsiThe total number of elevator maintenance for the ith maintenance unit.
Counting the complaint rate of users
Figure BDA0003095083050000113
The calculation formula of (2) is as follows:
Figure BDA0003095083050000114
the average score of the maintenance satisfaction degree is GiI.e. the ith maintenance unit maintenance satisfaction survey score.
Statistical satisfaction mean score
Figure BDA0003095083050000115
The calculation formula of (2) is as follows:
Figure BDA0003095083050000116
the average score of the fault early warning function of the maintenance elevator internet of things is GWiNamely scoring of the fault early warning function of the elevator internet of things of the ith maintenance unit.
Statistics of average score of fault early warning function of elevator internet of things
Figure BDA0003095083050000117
The calculation formula of (2) is as follows:
Figure BDA0003095083050000118
the average score of the fault detection function of the maintenance elevator internet of things is GMiNamely the grade of the detection function of the elevator internet of things of the ith maintenance unit.
Statistics of average fault detection function score of elevator internet of things
Figure BDA0003095083050000119
The calculation formula of (2) is as follows:
Figure BDA00030950830500001110
the predictive maintenance support average score of the elevator internet of things is GYiI.e. the scoring of the predictive maintenance support capability of the elevator internet of things of the ith maintenance unit.
Statistics of elevator internet of things predictive maintenance support average division number
Figure BDA0003095083050000121
The calculation formula of (2) is as follows:
Figure BDA0003095083050000122
in some embodiments, the weights of five dimensions of elevator maintenance quality level evaluation are evaluated by adopting an expert evaluation method. Weight vector Q (a) of five dimensions of elevator fault control efficiency, elevator fault treatment efficiency, maintenance quality control, customer satisfaction and elevator internet of things monitoring capability1,a2,a3,a4,a5) Wherein
Figure BDA0003095083050000123
Specifically, an expert evaluation method is adopted to control the elevator fault rate and the elevator mean fault related to the efficiency of the elevator fault controlAllocating weight to the interval time and the elevator trapping rate to obtain a weight vector Q1＝(a11,a12,a13)。
The same method is adopted to distribute weight to the elevator fault average time length and the elevator trapped rescue time rate related to the elevator fault processing efficiency to obtain a weight vector Q2＝(a21,a22)。
By the same method, weights are distributed to the maintenance timeliness rate, the inspection failure rate, the elevator Internet of things function inspection failure rate and the elevator supervision spot check failure rate related to maintenance quality control to obtain a weight vector Q3＝(a31,a32,a33,a34)。
The same method is adopted to distribute weight to the user complaint rate and the satisfaction average score related to the customer satisfaction to obtain a weight vector Q4＝(a41,a42)。
By adopting the same method, weights are distributed to the elevator Internet of things fault early warning function average score, the elevator Internet of things fault detection function average score and the elevator Internet of things predictive maintenance support average score related to the monitoring capability of the elevator Internet of things, so that a weight vector Q is obtained5＝(a51,a52,a53)。
Because the data structures of the elevator fault rate, the average fault interval time of the elevator and the elevator passenger trapping rate are different, the elevator fault rate index, the average fault interval time index of the elevator and the elevator passenger trapping rate index are adopted to replace the data structures, wherein:
fault rate index g for elevator1The calculation formula of (2) is as follows:
Figure BDA0003095083050000124
mean time between failure index g for elevator2The calculation formula of (2) is as follows:
Figure BDA0003095083050000125
elevator entrapment rate index g3The calculation formula of (2) is as follows:
Figure BDA0003095083050000126
in the elevator fault handling efficiency, an elevator fault average time length index and an elevator people trapping rescue timeliness rate index are adopted for replacement. The method specifically comprises the following steps:
mean time length index h of elevator fault1The calculation formula of (2) is as follows:
Figure BDA0003095083050000131
elevator trapped person rescue timeliness index h2The calculation formula of (2) is as follows:
Figure BDA0003095083050000132
also, the maintenance timeliness index k1The calculation formula of (2) is as follows:
Figure BDA0003095083050000133
inspection reject ratio index k2The calculation formula of (2) is as follows:
Figure BDA0003095083050000134
elevator thing networking functional test defective rate k3The calculation formula of (2) is as follows:
Figure BDA0003095083050000135
elevator supervision and spot check disqualification rate index k4The calculation formula of (2) is as follows:
Figure BDA0003095083050000136
customer complaint rate index m1The calculation formula of (2) is as follows:
Figure BDA0003095083050000137
satisfaction mean fraction index m2The calculation formula of (2) is as follows:
Figure BDA0003095083050000138
elevator Internet of things fault early warning function average score index r1The calculation formula of (2) is as follows:
Figure BDA0003095083050000139
elevator Internet of things fault detection function average fraction index r2The calculation formula of (2) is as follows:
Figure BDA0003095083050000141
predictive maintenance support average score index r for elevator internet of things3The calculation formula of (2) is as follows:
Figure BDA0003095083050000142
after all the above characteristic data are converted, each index mean value tends to 1.
Obtaining a characteristic matrix related to elevator fault control efficiency:
G＝[g1,g2,g3]T
also, a feature matrix related to elevator fault handling efficiency is obtained:
H＝[h1,h2]T
and similarly, obtaining a characteristic matrix related to quality maintenance management and control:
K＝[k1,k2,k3,k4]T
also, a customer satisfaction related feature matrix is obtained:
M＝[m1,m2]T
similarly, obtaining a characteristic matrix related to the monitoring capability of the Internet of things of the elevator:
R＝[r1,r2,r3]T
therefore, the evaluation results of each dimension of the maintenance quality can be obtained:
Q1*G＝a11g1+a12g2+a12g3
Q2*H＝a21h1+a22h2
Q3*K＝a31k1+a32k2+a32k3
Q4*M＝a41m1+a42m2
Q5*R＝a51r1+a52r2+a53r3
this results in a column vector:
Figure BDA0003095083050000143
and finally, establishing an elevator maintenance quality evaluation model:
Figure BDA0003095083050000144
and obtaining the maintenance quality evaluation score of each maintenance unit according to the elevator maintenance quality evaluation model, and obtaining the ranking and the relative position of each maintenance unit in the maintenance quality evaluation of all maintenance units.
And standardizing the quality evaluation scores according to the distribution of the quality evaluation indexes of the maintenance units. The standardization processing method comprises the following steps:
Figure BDA0003095083050000151
wherein, SzyFor quality assessment score standard value of maintenance, Z and Z are constants, SyIn order to maintain the quality evaluation score,
Figure BDA0003095083050000152
to maintain the quality evaluation score average, SσAnd the standard deviation of the maintenance quality evaluation score is obtained.
The distribution of the maintenance quality evaluation scores after the standardization treatment is the same as the distribution shape of the original evaluation result, and the sequence of the distribution of the maintenance quality evaluation scores of the elevators of all maintenance units can not be changed.
After the standardization processing, the position of the maintenance quality of a certain maintenance unit can be seen from the evaluation result of the maintenance quality of the elevator maintenance unit.
And setting a rating threshold and a risk warning mechanism according to the actual condition of the distribution of the maintenance quality evaluation result of the elevator maintenance unit after standardized processing and the elevator safety supervision requirement.
The following ranking mechanism was employed: and setting the thresholds SL and SH through system setting. When SzyWhen the maintenance quality of the elevator maintenance unit is higher than or equal to SH, the maintenance quality of the elevator maintenance unit is judged to be level 1; when SzyAnd when the maintenance quality of the elevator is lower than SL, the maintenance quality of the elevator maintenance unit is judged to be 3 grades.
According to the method, the maintenance quality grades of the elevator maintenance unit are divided into 1 grade, 2 grades and 3 grades. According to the actual situation, corresponding management can be implemented.
The application also provides an elevator maintenance quality evaluation system based on big data, includes:
the system comprises an acquisition module, a maintenance module and a maintenance module, wherein the acquisition module is used for acquiring maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator Internet of things function condition data of a maintenance unit to be tested; collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units;
the calculation module is used for calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data; calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data; calculating maintenance quality control data according to the maintenance quality data; calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers; calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things; calculating and counting elevator fault control efficiency data according to the statistical elevator fault data; calculating statistical elevator fault handling efficiency data according to the statistical fault handling data; calculating statistical maintenance quality control data according to the statistical maintenance quality data; calculating statistical customer satisfaction data according to the statistical customer evaluation data; calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data;
and the evaluation module is used for calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator internet of things monitoring capability data.
The contents in the above method embodiments are all applicable to the embodiment of the apparatus system, the functions specifically implemented by the embodiment of the system are the same as those in the above method embodiments, and the advantageous effects achieved by the embodiment of the system are also the same as those achieved by the above method embodiments.
The embodiment of the application also provides an elevator maintenance quality evaluation system based on big data, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method.
The contents in the above method embodiments are all applicable to the embodiment of the apparatus system, the functions specifically implemented by the embodiment of the system are the same as those in the above method embodiments, and the advantageous effects achieved by the embodiment of the system are also the same as those achieved by the above method embodiments.
In addition, a storage medium is further provided, where processor-executable instructions are stored, and when executed by a processor, the processor-executable instructions are configured to perform the steps of the method for processing mutual information according to any one of the above-mentioned method embodiments. For the storage medium, it may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. It can be seen that the contents in the foregoing method embodiments are all applicable to this storage medium embodiment, the functions specifically implemented by this storage medium embodiment are the same as those in the foregoing method embodiments, and the advantageous effects achieved by this storage medium embodiment are also the same as those achieved by the foregoing method embodiments.
It should be appreciated that the layers, modules, units, platforms, and/or the like included in an embodiment system of the application may be implemented or embodied by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Moreover, the data processing flows performed by the layers, modules, units, and/or platforms included in the system embodiments of the present application may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The data processing flows correspondingly performed by the layers, modules, units and/or platforms included in the system of embodiments of the present application may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the system may be implemented in any type of computing platform operatively connected to a suitable connection, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. The data processing flows correspondingly executed by the layers, modules, units and/or platforms included in the system of the present application may be implemented in machine readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and/or write storage medium, a RAM, a ROM, etc., so that it may be read by a programmable computer, and when the storage medium or device is read by a computer, may be used to configure and operate the computer to perform the processes described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The present application also includes the computer itself when programmed according to the methods and techniques described herein.
The above description is only a preferred embodiment of the present application, and the present application is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present application should be included in the protection scope of the present application as long as the technical effects of the present application are achieved by the same means. Various modifications and variations of the technical solution and/or embodiments thereof are possible within the protective scope of the present application.

Claims (10)

1. A method for evaluating the maintenance quality of an elevator based on big data is characterized by comprising the following steps:
collecting maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator internet of things function condition data of a maintenance unit to be tested; the maintenance elevator fault data comprise maintenance elevator fault times, maintenance elevator trapping times, maintenance elevator total operation times and maintenance elevator fault interval time; the maintenance elevator fault handling data comprises maintenance elevator fault time, maintenance elevator trapped rescue response time and maintenance elevator trapped rescue allowed maximum response time; the maintenance quality data of the maintenance system comprises maintenance interval time of the maintenance system elevator, maximum allowable maintenance interval time of the maintenance system elevator, the number of unqualified maintenance inspection items of the maintenance system elevator, the total number of inspection items of the maintenance system elevator, the number of unqualified inspection items of the function inspection of the Internet of things of the maintenance system elevator, the total number of inspection items of the function inspection items of the Internet of things of the maintenance system elevator, the number of unqualified inspection items of the supervision and spot check of the maintenance system elevator and the total number of supervision and spot check items of the maintenance system elevator; the customer evaluation data comprises complaint times of a maintenance user and a maintenance satisfaction degree score; the function condition data of the maintenance elevator internet of things comprises a maintenance elevator internet of things fault early warning function score, a maintenance elevator internet of things fault detection function score and a maintenance elevator internet of things predictive maintenance support score;
collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units; the statistics of the elevator fault data comprises statistics of elevator fault times, statistics of elevator trapping times, statistics of total elevator running times and statistics of elevator fault interval time; the statistic elevator fault handling data comprises statistic elevator fault time, statistic elevator trapped rescue response time and statistic elevator trapped rescue allowable maximum response time; the statistical maintenance quality data comprises statistical elevator maintenance interval time, statistical elevator maintenance maximum allowable interval time, statistical elevator inspection unqualified item number, statistical elevator total inspection item number, statistical elevator Internet of things function inspection unqualified item number, statistical elevator Internet of things function total inspection item number, statistical elevator supervision random inspection unqualified item number and statistical elevator supervision random inspection total item number; the statistical customer evaluation data comprises statistical customer complaint times and statistical satisfaction scores; the method comprises the following steps of counting elevator internet of things function condition data, including a fault early warning function score, a fault detection function score and a predictive maintenance support score of the elevator internet of things;
calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data; the maintenance elevator fault control efficiency data comprises maintenance elevator fault rate, maintenance elevator mean fault interval time and maintenance elevator crowd rate; the maintenance elevator fault rate is calculated according to the maintenance elevator fault frequency and the maintenance elevator total operation frequency; the maintenance elevator mean fault interval is obtained by calculation according to the maintenance elevator fault interval time and the maintenance elevator fault times; the man trapping rate of the maintenance elevator is calculated according to the man trapping times of the maintenance elevator and the total running times of the maintenance elevator;
calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data; the maintenance elevator fault handling efficiency data comprises maintenance elevator fault average time and maintenance elevator trapped rescue time rate; the maintenance elevator fault average time length is obtained by calculation according to the maintenance elevator fault time and the maintenance elevator fault times; the maintenance elevator drowsiness rescue timeliness rate is calculated according to the maintenance elevator drowsiness rescue response time and the maintenance elevator drowsiness rescue allowed maximum response time;
calculating maintenance quality control data according to the maintenance quality data; the maintenance quality control data comprises maintenance timeliness rate, maintenance inspection unqualified rate, maintenance elevator Internet of things function inspection unqualified rate and maintenance elevator supervision and spot inspection unqualified rate; the maintenance timeliness rate is obtained by calculation according to the maintenance interval time of the maintenance elevator and the maximum allowable maintenance interval time of the maintenance elevator; the maintenance inspection disqualification rate is obtained by calculation according to the number of unqualified maintenance elevator inspection items and the total number of maintenance elevator inspection items; the failure rate of the function test of the Internet of things of the maintenance elevator is calculated according to the number of failure items of the function test of the Internet of things of the maintenance elevator and the total number of the function test items of the Internet of things of the maintenance elevator; the failure rate of the supervision and spot check of the maintenance elevator is calculated according to the number of failure items of the supervision and spot check of the maintenance elevator and the total number of the supervision and spot check items of the maintenance elevator;
calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers; the maintenance customer satisfaction data comprises maintenance user complaint rate and maintenance satisfaction average score; the complaint rate of the maintenance user is calculated according to the total running times of the maintenance elevator and the complaint times of the maintenance user; the maintenance satisfaction degree average score is obtained by calculation according to the maintenance satisfaction degree score;
calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things; the maintenance elevator internet of things monitoring capability data comprises a maintenance elevator internet of things fault early warning function average score, a maintenance elevator internet of things fault detection function average score and a maintenance elevator internet of things predictive maintenance support average score; the maintenance elevator Internet of things fault early warning function average score is obtained by calculation according to the maintenance elevator Internet of things fault early warning function score; the maintenance elevator Internet of things fault detection function average score is obtained by calculation according to the maintenance elevator Internet of things fault detection function score; the predictive maintenance support average score of the maintenance elevator internet of things is obtained by calculation according to the predictive maintenance support score of the maintenance elevator internet of things;
calculating and counting elevator fault control efficiency data according to the statistical elevator fault data; the statistic elevator fault control efficiency data comprises statistic elevator fault rate, statistic elevator mean fault interval time and statistic elevator trapping rate; the statistical elevator fault rate is obtained by calculation according to the statistical elevator fault times and the statistical elevator total operation times; the statistical elevator mean fault interval is obtained by calculation according to the statistical elevator fault interval time and the statistical elevator fault times; the statistic elevator trapping rate is calculated according to the statistic elevator trapping times and the statistic elevator total operation times;
calculating statistical elevator fault handling efficiency data according to the statistical fault handling data; the statistic elevator fault handling efficiency data comprises statistic elevator fault average time length and statistic elevator trapped rescue timeliness; the statistical elevator fault average time length is obtained by calculation according to the statistical elevator fault time and the statistical elevator fault times; the statistics of the elevator trapping rescue timeliness is obtained by calculation according to the statistics of the elevator trapping rescue response time and the statistics of the maximum elevator trapping rescue allowable response time;
calculating statistical maintenance quality control data according to the statistical maintenance quality data; the statistical maintenance quality control data comprises a statistical maintenance timeliness rate, a statistical inspection disqualification rate, a statistical elevator Internet of things function inspection disqualification rate and a statistical elevator supervision spot check disqualification rate; the statistical maintenance timeliness rate is obtained by calculation according to the statistical elevator maintenance interval time and the statistical elevator maintenance maximum allowable interval time; the statistical inspection disqualification rate is obtained by calculation according to the statistical elevator inspection disqualification item number and the statistical elevator total inspection item number; the unqualified rate of the function test of the elevator internet of things is calculated according to the unqualified item number of the function test of the elevator internet of things and the total item number of the function test of the elevator internet of things; the statistical elevator supervision spot check failure rate is obtained by calculation according to the statistical elevator supervision spot check failure item number and the statistical elevator supervision spot check total item number;
calculating statistical customer satisfaction data according to the statistical customer evaluation data; the statistical customer satisfaction data comprises statistical customer complaint rate and statistical satisfaction average score; the statistical user complaint rate is obtained by calculation according to the statistical total elevator running times and the statistical user complaint times; the statistical satisfaction degree average score is obtained by calculation according to the statistical satisfaction degree score;
calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data; the statistic elevator internet of things monitoring capability data comprises a statistic elevator internet of things fault early warning function average score, a statistic elevator internet of things fault detection function average score and a statistic elevator internet of things predictive maintenance support average score; the statistical elevator Internet of things fault early warning function average score is obtained by calculation according to the statistical elevator Internet of things fault early warning function score; the statistical elevator Internet of things fault detection function average score is obtained by calculation according to the statistical elevator Internet of things fault detection function score; the statistical elevator internet of things predictive maintenance support average score is calculated according to the statistical elevator internet of things predictive maintenance support score;
and calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator internet of things monitoring capability data.
2. The elevator maintenance quality evaluation method based on big data according to claim 1, wherein the step of calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistical elevator fault control efficiency data, the statistical elevator fault treatment efficiency data, the statistical maintenance quality control data, the statistical customer satisfaction data, and the statistical elevator internet of things monitoring capability data specifically comprises:
calculating a maintenance elevator fault control efficiency index according to the maintenance elevator fault control efficiency data and the statistical elevator fault control efficiency data; the maintenance elevator fault control efficiency index comprises a maintenance elevator fault rate index, a maintenance elevator mean fault interval time index and a maintenance elevator crowd rate index; the maintenance elevator fault rate index is obtained by calculation according to the maintenance elevator fault rate and the statistical elevator fault rate; the maintenance elevator mean fault interval time index is obtained by calculation according to the maintenance elevator mean fault interval time and the statistical elevator mean fault interval time; the maintenance elevator trapping rate index is obtained by calculation according to the maintenance elevator trapping rate and the statistical elevator trapping rate;
calculating a maintenance elevator fault disposal efficiency index according to the maintenance elevator fault disposal efficiency data and the statistical elevator fault disposal efficiency data; the maintenance elevator fault handling efficiency index comprises a maintenance elevator fault average time length index and a maintenance elevator trapped rescue time rate index; the maintenance elevator fault average time length index is obtained by calculation according to the maintenance elevator fault average time length and the statistical elevator fault average time length; the maintenance elevator trapped rescue timeliness index is obtained by calculation according to the maintenance elevator trapped rescue timeliness and the statistic elevator trapped rescue timeliness;
calculating a maintenance quality control index according to the maintenance quality control data and the statistical maintenance quality control data; the maintenance quality control index comprises a maintenance timeliness rate index, a maintenance inspection disqualification rate index, a maintenance elevator Internet of things function inspection disqualification rate index and a maintenance elevator supervision spot check disqualification rate index; the maintenance and maintenance timeliness rate index is obtained by calculation according to the maintenance and maintenance timeliness rate and the statistical maintenance timeliness rate; the maintenance inspection disqualification rate index is obtained by calculating the maintenance inspection disqualification rate and the statistical inspection disqualification rate to obtain the maintenance elevator Internet of things function inspection disqualification rate index; the maintenance elevator supervision random inspection disqualification rate index is obtained by calculation according to the maintenance elevator supervision random inspection disqualification rate and the statistic elevator supervision random inspection disqualification rate;
calculating a maintenance customer satisfaction index according to the maintenance customer satisfaction data and the statistical customer satisfaction data; the maintenance customer satisfaction index comprises a maintenance user complaint rate index and a maintenance satisfaction average score index; the complaint rate index of the maintenance user is obtained by calculation according to the complaint rate of the maintenance user and the complaint rate of the statistical user; the maintenance satisfaction degree average score index is obtained by calculation according to the maintenance satisfaction degree average score and the statistics maintenance satisfaction degree average score;
calculating a maintenance elevator Internet of things monitoring capability index according to the maintenance elevator Internet of things monitoring capability data and the statistic elevator Internet of things monitoring capability data; the maintenance elevator internet of things monitoring capability index comprises a maintenance elevator internet of things fault early warning function average score index, a maintenance elevator internet of things fault detection function average score index and a maintenance elevator internet of things predictive maintenance support average score index; the maintenance elevator Internet of things fault early warning function average score index is obtained by calculation according to the maintenance elevator Internet of things fault early warning function average score and the statistical elevator Internet of things fault early warning function average score; the maintenance elevator Internet of things fault detection function average score index is obtained by calculation according to the maintenance elevator Internet of things fault detection function average score and the statistical elevator Internet of things fault detection function average score; the index of the predictive maintenance support average of the elevator internet of things is obtained by calculation according to the predictive maintenance support average of the elevator internet of things and the statistical predictive maintenance support average of the elevator internet of things;
and calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault treatment efficiency index, the maintenance quality control index, the maintenance customer satisfaction index and the maintenance elevator Internet of things monitoring capability index.
3. The elevator maintenance quality evaluation method based on big data according to claim 2, wherein the step of calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault handling efficiency index, the maintenance quality control index, the maintenance customer satisfaction index and the maintenance elevator internet of things monitoring capability index specifically comprises:
obtaining a maintenance elevator fault control efficiency weight vector, a maintenance elevator fault treatment efficiency weight vector, a maintenance quality control weight vector, a maintenance customer satisfaction weight vector and a maintenance elevator internet of things monitoring capability weight vector;
and calculating a maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency index, the maintenance elevator fault treatment efficiency index, the maintenance quality control index, the maintenance customer satisfaction index, the maintenance elevator internet of things monitoring capability index, the maintenance elevator fault control efficiency weight vector, the maintenance elevator fault treatment efficiency weight vector, the maintenance quality control weight vector, the maintenance customer satisfaction weight vector and the maintenance elevator internet of things monitoring capability weight vector.
4. The elevator maintenance quality evaluation method based on big data according to claim 2, characterized in that the calculation formula of the maintenance elevator fault rate index is as follows:
Figure FDA0003095083040000051
wherein, g1To maintain the elevator fault rate index, PfiIn order to maintain the failure rate of the elevator,
Figure FDA0003095083040000052
the failure rate of the elevator is counted.
5. The big data-based quality evaluation method for maintaining the elevator according to claim 1, further comprising:
and when the maintenance quality evaluation score is smaller than a preset score threshold, generating maintenance quality alarm information.
6. The elevator maintenance quality evaluation method based on big data according to claim 1, wherein the calculation formula of the man-trapping and rescue timeliness rate of the maintenance elevator is as follows:
PRi＝TRi/TRmax
wherein, PRiMaintenance of elevator entrapment rescue timeliness, TRiRescue response time, T, for maintaining elevator drowsinessRmaxThe elevator is maintained to be trapped and rescue the allowed maximum response time.
7. The big data-based quality evaluation method for maintaining the elevator according to claim 1, further comprising:
calculating the average value of the maintenance quality evaluation scores and the standard deviation of the maintenance quality evaluation scores of all maintenance units;
calculating a maintenance quality evaluation score standard value according to the maintenance quality evaluation score, the maintenance quality evaluation score average value and the maintenance quality evaluation score standard deviation;
the calculation formula of the maintenance quality evaluation score standard value is as follows:
Figure FDA0003095083040000061
wherein, SzyFor quality assessment score standard value of maintenance, Z and Z are constants, SyIn order to maintain the quality evaluation score,
Figure FDA0003095083040000062
to maintain the quality evaluation score average, SσAnd the standard deviation of the maintenance quality evaluation score is obtained.
8. An elevator maintenance quality evaluation system based on big data is characterized by comprising:
the system comprises an acquisition module, a maintenance module and a maintenance module, wherein the acquisition module is used for acquiring maintenance elevator fault data, maintenance elevator fault disposal data, maintenance quality data, maintenance customer evaluation data and maintenance elevator Internet of things function condition data of a maintenance unit to be tested; the maintenance elevator fault data comprise maintenance elevator fault times, maintenance elevator trapping times, maintenance elevator total operation times and maintenance elevator fault interval time; the maintenance elevator fault handling data comprises maintenance elevator fault time, maintenance elevator trapped rescue response time and maintenance elevator trapped rescue allowed maximum response time; the maintenance quality data of the maintenance system comprises maintenance interval time of the maintenance system elevator, maximum allowable maintenance interval time of the maintenance system elevator, the number of unqualified maintenance inspection items of the maintenance system elevator, the total number of inspection items of the maintenance system elevator, the number of unqualified inspection items of the function inspection of the Internet of things of the maintenance system elevator, the total number of inspection items of the function inspection items of the Internet of things of the maintenance system elevator, the number of unqualified inspection items of the supervision and spot check of the maintenance system elevator and the total number of supervision and spot check items of the maintenance system elevator; the customer evaluation data comprises complaint times of a maintenance user and a maintenance satisfaction degree score; the function condition data of the maintenance elevator internet of things comprises a maintenance elevator internet of things fault early warning function score, a maintenance elevator internet of things fault detection function score and a maintenance elevator internet of things predictive maintenance support score;
collecting the statistical elevator fault data, the statistical elevator fault treatment data, the statistical maintenance quality data, the statistical customer evaluation data and the statistical elevator Internet of things function condition data of all maintenance units; the statistics of the elevator fault data comprises statistics of elevator fault times, statistics of elevator trapping times, statistics of total elevator running times and statistics of elevator fault interval time; the statistic elevator fault handling data comprises statistic elevator fault time, statistic elevator trapped rescue response time and statistic elevator trapped rescue allowable maximum response time; the statistical maintenance quality data comprises statistical elevator maintenance interval time, statistical elevator maintenance maximum allowable interval time, statistical elevator inspection unqualified item number, statistical elevator total inspection item number, statistical elevator Internet of things function inspection unqualified item number, statistical elevator Internet of things function total inspection item number, statistical elevator supervision random inspection unqualified item number and statistical elevator supervision random inspection total item number; the statistical customer evaluation data comprises statistical customer complaint times and statistical satisfaction scores; the method comprises the following steps of counting elevator internet of things function condition data, including a fault early warning function score, a fault detection function score and a predictive maintenance support score of the elevator internet of things;
the calculation module is used for calculating maintenance elevator fault control efficiency data according to the maintenance elevator fault data; the maintenance elevator fault control efficiency data comprises maintenance elevator fault rate, maintenance elevator mean fault interval time and maintenance elevator crowd rate; the maintenance elevator fault rate is calculated according to the maintenance elevator fault frequency and the maintenance elevator total operation frequency; the maintenance elevator mean fault interval is obtained by calculation according to the maintenance elevator fault interval time and the maintenance elevator fault times; the man trapping rate of the maintenance elevator is calculated according to the man trapping times of the maintenance elevator and the total running times of the maintenance elevator; calculating maintenance elevator fault disposal efficiency data according to the maintenance elevator fault disposal data; the maintenance elevator fault handling efficiency data comprises maintenance elevator fault average time and maintenance elevator trapped rescue time rate; the maintenance elevator fault average time length is obtained by calculation according to the maintenance elevator fault time and the maintenance elevator fault times; the maintenance elevator drowsiness rescue timeliness rate is calculated according to the maintenance elevator drowsiness rescue response time and the maintenance elevator drowsiness rescue allowed maximum response time; calculating maintenance quality control data according to the maintenance quality data; the maintenance quality control data comprises maintenance timeliness rate, maintenance inspection unqualified rate, maintenance elevator Internet of things function inspection unqualified rate and maintenance elevator supervision and spot inspection unqualified rate; the maintenance timeliness rate is obtained by calculation according to the maintenance interval time of the maintenance elevator and the maximum allowable maintenance interval time of the maintenance elevator; the maintenance inspection disqualification rate is obtained by calculation according to the number of unqualified maintenance elevator inspection items and the total number of maintenance elevator inspection items; the failure rate of the function test of the Internet of things of the maintenance elevator is calculated according to the number of failure items of the function test of the Internet of things of the maintenance elevator and the total number of the function test items of the Internet of things of the maintenance elevator; the failure rate of the supervision and spot check of the maintenance elevator is calculated according to the number of failure items of the supervision and spot check of the maintenance elevator and the total number of the supervision and spot check items of the maintenance elevator; calculating satisfaction data of the maintenance customers according to the evaluation data of the maintenance customers; the maintenance customer satisfaction data comprises maintenance user complaint rate and maintenance satisfaction average score; the complaint rate of the maintenance user is calculated according to the total running times of the maintenance elevator and the complaint times of the maintenance user; the maintenance satisfaction degree average score is obtained by calculation according to the maintenance satisfaction degree score; calculating the monitoring capacity data of the maintenance elevator internet of things according to the data of the function condition of the maintenance elevator internet of things; the maintenance elevator internet of things monitoring capability data comprises a maintenance elevator internet of things fault early warning function average score, a maintenance elevator internet of things fault detection function average score and a maintenance elevator internet of things predictive maintenance support average score; the maintenance elevator Internet of things fault early warning function average score is obtained by calculation according to the maintenance elevator Internet of things fault early warning function score; the maintenance elevator Internet of things fault detection function average score is obtained by calculation according to the maintenance elevator Internet of things fault detection function score; the predictive maintenance support average score of the maintenance elevator internet of things is obtained by calculation according to the predictive maintenance support score of the maintenance elevator internet of things; calculating and counting elevator fault control efficiency data according to the statistical elevator fault data; the statistic elevator fault control efficiency data comprises statistic elevator fault rate, statistic elevator mean fault interval time and statistic elevator trapping rate; the statistical elevator fault rate is obtained by calculation according to the statistical elevator fault times and the statistical elevator total operation times; the statistical elevator mean fault interval is obtained by calculation according to the statistical elevator fault interval time and the statistical elevator fault times; the statistic elevator trapping rate is calculated according to the statistic elevator trapping times and the statistic elevator total operation times; calculating statistical elevator fault handling efficiency data according to the statistical fault handling data; the statistic elevator fault handling efficiency data comprises statistic elevator fault average time length and statistic elevator trapped rescue timeliness; the statistical elevator fault average time length is obtained by calculation according to the statistical elevator fault time and the statistical elevator fault times; the statistics of the elevator trapping rescue timeliness is obtained by calculation according to the statistics of the elevator trapping rescue response time and the statistics of the maximum elevator trapping rescue allowable response time; calculating statistical maintenance quality control data according to the statistical maintenance quality data; the statistical maintenance quality control data comprises a statistical maintenance timeliness rate, a statistical inspection disqualification rate, a statistical elevator Internet of things function inspection disqualification rate and a statistical elevator supervision spot check disqualification rate; the statistical maintenance timeliness rate is obtained by calculation according to the statistical elevator maintenance interval time and the statistical elevator maintenance maximum allowable interval time; the statistical inspection disqualification rate is obtained by calculation according to the statistical elevator inspection disqualification item number and the statistical elevator total inspection item number; the unqualified rate of the function test of the elevator internet of things is calculated according to the unqualified item number of the function test of the elevator internet of things and the total item number of the function test of the elevator internet of things; the statistical elevator supervision spot check failure rate is obtained by calculation according to the statistical elevator supervision spot check failure item number and the statistical elevator supervision spot check total item number; calculating statistical customer satisfaction data according to the statistical customer evaluation data; the statistical customer satisfaction data comprises statistical customer complaint rate and statistical satisfaction average score; the statistical user complaint rate is obtained by calculation according to the statistical total elevator running times and the statistical user complaint times; the statistical satisfaction degree average score is obtained by calculation according to the statistical satisfaction degree score;
calculating and counting elevator internet of things monitoring capacity data according to the elevator internet of things function condition data; the statistic elevator internet of things monitoring capability data comprises a statistic elevator internet of things fault early warning function average score, a statistic elevator internet of things fault detection function average score and a statistic elevator internet of things predictive maintenance support average score; the statistical elevator Internet of things fault early warning function average score is obtained by calculation according to the statistical elevator Internet of things fault early warning function score; the statistical elevator Internet of things fault detection function average score is obtained by calculation according to the statistical elevator Internet of things fault detection function score; the statistical elevator internet of things predictive maintenance support average score is calculated according to the statistical elevator internet of things predictive maintenance support score;
and the evaluation module is used for calculating the maintenance quality evaluation score of the maintenance unit to be tested according to the maintenance elevator fault control efficiency data, the maintenance elevator fault treatment efficiency data, the maintenance quality control data, the maintenance customer satisfaction data, the maintenance elevator internet of things monitoring capability data, the statistic elevator fault control efficiency data, the statistic elevator fault treatment efficiency data, the statistic maintenance quality control data, the statistic customer satisfaction data and the statistic elevator internet of things monitoring capability data.
9. An elevator maintenance quality evaluation system based on big data is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
CN202110608622.2A 2021-06-01 2021-06-01 Elevator maintenance quality evaluation method and system based on big data and storage medium Active CN113511572B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110608622.2A CN113511572B (en) 2021-06-01 2021-06-01 Elevator maintenance quality evaluation method and system based on big data and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110608622.2A CN113511572B (en) 2021-06-01 2021-06-01 Elevator maintenance quality evaluation method and system based on big data and storage medium

Publications (2)

Publication Number Publication Date
CN113511572A true CN113511572A (en) 2021-10-19
CN113511572B CN113511572B (en) 2023-04-07

Family

ID=78065214

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110608622.2A Active CN113511572B (en) 2021-06-01 2021-06-01 Elevator maintenance quality evaluation method and system based on big data and storage medium

Country Status (1)

Country Link
CN (1) CN113511572B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114638553A (en) * 2022-05-17 2022-06-17 四川观想科技股份有限公司 Maintenance quality analysis method based on big data
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

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110315490A1 (en) * 2010-06-29 2011-12-29 Empire Technology Development Llc Intelligent elevator safety monitor
JP2012218886A (en) * 2011-04-08 2012-11-12 Hitachi Building Systems Co Ltd Maintenance work management system of elevator
CN105752783A (en) * 2016-01-29 2016-07-13 重庆和航安全技术服务有限公司 Elevator quality comprehensive evaluation method and system
JP2017111481A (en) * 2015-12-14 2017-06-22 株式会社日立ビルシステム Quality evaluation and education system for use in elevator maintenance work
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
CN110562818A (en) * 2019-07-18 2019-12-13 广州盛识信息技术有限公司 monitoring system and monitoring method for evaluating elevator running quality by time dimension
CN112079223A (en) * 2020-11-03 2020-12-15 南京市特种设备安全监督检验研究院 Elevator maintenance-as-needed working quality evaluation method
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

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110315490A1 (en) * 2010-06-29 2011-12-29 Empire Technology Development Llc Intelligent elevator safety monitor
JP2012218886A (en) * 2011-04-08 2012-11-12 Hitachi Building Systems Co Ltd Maintenance work management system of elevator
JP2017111481A (en) * 2015-12-14 2017-06-22 株式会社日立ビルシステム Quality evaluation and education system for use in elevator maintenance work
CN105752783A (en) * 2016-01-29 2016-07-13 重庆和航安全技术服务有限公司 Elevator quality comprehensive evaluation method and 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
CN110562818A (en) * 2019-07-18 2019-12-13 广州盛识信息技术有限公司 monitoring system and monitoring method for evaluating elevator running quality by time dimension
CN112079223A (en) * 2020-11-03 2020-12-15 南京市特种设备安全监督检验研究院 Elevator maintenance-as-needed working quality evaluation method
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

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114638553A (en) * 2022-05-17 2022-06-17 四川观想科技股份有限公司 Maintenance quality analysis method based on big data
CN114638553B (en) * 2022-05-17 2022-08-12 四川观想科技股份有限公司 Maintenance quality analysis method based on big data
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

Also Published As

Publication number Publication date
CN113511572B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
CN113511572B (en) Elevator maintenance quality evaluation method and system based on big data and storage medium
CN105752783B (en) Quality of elevator integrated evaluating method and system
CN115081926B (en) Operation safety early warning method and system suitable for intelligent power plant
CN111144727B (en) Urban rail transit arrival passenger flow toughness evaluation system and method
CN112713649B (en) Power equipment residual life prediction method based on extreme learning machine
CN102801739A (en) Network risk determining and evidence obtaining method based on cloud computing environment
CN108928744B (en) Container crane online diagnosis and maintenance system based on big data
CN107403279B (en) Oil transfer pump working condition self-adaptive state early warning system and method
CN104425046B (en) Method and system for quantizing operation security indexes of nuclear power station
CN109809149B (en) Fault early warning system and method for industrial production equipment
CN108038631B (en) Risk assessment method for lithium ion battery
CN117670028A (en) Dust removal system risk dynamic assessment and grading early warning method based on Internet of things monitoring
CN115394036A (en) Monitoring and early warning method and system for building fire
CN113723827B (en) Operation risk diagnosis and operation management and control method and system for subway electromechanical equipment
CN115818388A (en) Elevator operation condition adverse risk monitoring system
US20130253975A1 (en) System and method for inspecting and assessing risk of mechanical equipment and facilities
CN115965234B (en) Comprehensive quantitative evaluation method and system for production operation risk based on double factors
CN114021969B (en) Agricultural enterprise safety production risk index analysis system
CN115520741A (en) Elevator operation monitoring and early warning method and system based on neural network and storage medium
US20130253974A1 (en) System And Method For Inspecting And Assessing Risk of Mechanical Equipment And Facilities
CN114372668A (en) Hoisting equipment dynamic risk assessment method and device based on real-time data
RU2709155C1 (en) Safety assessment system for hazardous production facility
CN115713228A (en) Petrochemical device risk analysis method and device and storage medium
CN113537699A (en) Elevator safety risk assessment method, system and storage medium
CN113697623B (en) Elevator maintenance early warning system and method based on deep learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20240422

Address after: 510663 No.9, Keke Road, Huangpu District, Guangzhou City, Guangdong Province

Patentee after: Guangzhou Special Equipment Testing and Research Institute (Guangzhou Special Equipment Accident Investigation Technology Center Guangzhou Elevator Safety Operation Monitoring Center)

Country or region after: China

Address before: 6 / F, Liurong building, 65 Liurong Road, Yuexiu District, Guangzhou, Guangdong 510180

Patentee before: GUANGZHOU ACADEMY OF SPECIAL EQUIPMENT INSPECTION & TESTING

Country or region before: China

TR01 Transfer of patent right