CN116142914B - Elevator on-demand maintenance system and method based on Internet of things and big data - Google Patents
Elevator on-demand maintenance system and method based on Internet of things and big data Download PDFInfo
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- CN116142914B CN116142914B CN202310212308.1A CN202310212308A CN116142914B CN 116142914 B CN116142914 B CN 116142914B CN 202310212308 A CN202310212308 A CN 202310212308A CN 116142914 B CN116142914 B CN 116142914B
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- 238000012423 maintenance Methods 0.000 title claims abstract description 330
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 230000007246 mechanism Effects 0.000 claims description 49
- 238000013075 data extraction Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 description 8
- 230000009467 reduction Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B50/00—Energy efficient technologies in elevators, escalators and moving walkways, e.g. energy saving or recuperation technologies
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- Maintenance And Inspection Apparatuses For Elevators (AREA)
- Indicating And Signalling Devices For Elevators (AREA)
Abstract
The invention provides an elevator on-demand maintenance system and method based on the Internet of things and big data. The elevator on-demand maintenance method comprises the following steps: classifying the elevators according to the matching and parameters to obtain a plurality of elevator types; acquiring historical maintenance data information and historical operation parameter data of each elevator through a large data platform; setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model; and monitoring the current operation parameters of each elevator in real time, calculating the operation parameter operation performance integrated value of each elevator in real time by utilizing the operation parameter and integrated value acquisition model, and sending maintenance prompt to a maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance integrated value reaches or exceeds the elevator maintenance operation performance integrated value corresponding to the elevator type to which the elevator belongs. The system comprises modules corresponding to the method steps.
Description
Technical Field
The invention provides an elevator on-demand maintenance system and method based on the Internet of things and big data, and belongs to the technical field of elevator maintenance.
Background
With the increase of high-rise buildings, the use amount of the elevator is large, the elevator is generally installed in the building, and an elevator car can be used for personnel to carry or load. Generally, the elevator car moves on a rigid rail which is vertical to the horizontal plane or has an inclination angle smaller than 15 degrees with the plumb line to move up and down, so that convenience is brought to people to go up and down the stairs. The elevator also bears personnel safety problem at present, so that the elevator needs to be maintained and protected every time when running for a period, the conventional elevator maintenance process needs to be checked periodically according to fixed time, the checking time is inconsistent with the actual model and running parameters of the elevator, the maintenance efficiency is reduced due to the fact that the maintenance times are too low, elevator faults cannot be found timely, or the labor consumption is increased due to the fact that the maintenance times are too high, and the maintenance cost is increased.
Disclosure of Invention
The invention provides an elevator on-demand maintenance system based on the Internet of things and big data and a method thereof, which are used for solving the problems that in the prior art, the maintenance time is inconsistent with the actual model and operation parameters of an elevator, the maintenance frequency is too low, the maintenance efficiency is reduced, elevator faults cannot be found in time, or the labor consumption is increased due to too many maintenance frequencies, and the maintenance cost is increased:
An elevator on-demand maintenance method based on the Internet of things and big data, the elevator on-demand maintenance method comprises the following steps:
classifying the elevators according to the matching and parameters to obtain a plurality of elevator types;
acquiring historical maintenance data information and historical operation parameter data of each elevator through a large data platform;
setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model;
and monitoring the current operation parameters of each elevator in real time, calculating the operation parameter operation performance integrated value of each elevator in real time by utilizing the operation parameter and integrated value acquisition model, and sending maintenance prompt to a maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance integrated value reaches or exceeds the elevator maintenance operation performance integrated value corresponding to the elevator type to which the elevator belongs.
Further, classifying the elevators according to the matching and the parameters to obtain a plurality of elevator types, including:
scanning through an internet of things platform to obtain brands of all elevators supervised in the internet of things platform;
classifying the elevators for the first time according to brands to obtain a first elevator set;
All elevator models in each first elevator set are extracted, and elevators with the same signal are classified into one elevator set to form a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
Further, the historical maintenance data information and the historical operation parameter data of each elevator are obtained through the big data platform, and the method comprises the following steps:
collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by using a big data platform;
extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
collecting historical operation parameter data of elevators of brands corresponding to the first elevator set by utilizing a big data platform;
and extracting historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
Further, setting an elevator maintenance operation performance comprehensive value corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model, including:
extracting maintenance time information contained in the historical maintenance data information set;
according to the moment corresponding to the maintenance time information, searching the operation parameter data of the elevator before the moment corresponding to the maintenance time information in the historical operation parameter data set, wherein the operation parameter data of the elevator before the moment corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the maintenance time information when the elevator is required to be shut down;
the comprehensive value acquisition model sets the comprehensive value of the elevator maintenance operation performance corresponding to each elevator type.
Further, the integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the electric traction mechanism of the elevatorThe elevator parameters do not accord with the number of the types of the operation parameters of the preset operation parameter standard value; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the electric traction mechanism of the elevator do not accord with the preset operation parameter standard value.
An elevator on-demand maintenance system based on the internet of things and big data, the elevator on-demand maintenance system comprising:
the classification module is used for classifying the elevators according to the matching and parameters to obtain a plurality of elevator types;
the data acquisition module is used for acquiring historical maintenance data information and historical operation parameter data of each elevator through the large data platform;
the comprehensive value acquisition module is used for setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data and combining a comprehensive value acquisition model;
the maintenance judging module is used for monitoring the current operation parameters of each elevator in real time, utilizing the operation parameters to combine with the comprehensive value acquisition model to calculate the operation parameter operation performance comprehensive value of each elevator in real time, and sending maintenance prompt to the maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs.
Further, the classification module includes:
the brand acquisition module is used for scanning and acquiring brands of all elevators supervised in the internet of things platform through the internet of things platform;
the first set acquisition module is used for classifying the elevators for the first time according to brands to obtain a first elevator set;
the second set acquisition module is used for extracting all elevator types in each first elevator set, classifying the elevators with the same signals into one elevator set, and forming a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
Further, the data acquisition module includes:
the first data acquisition module is used for collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by utilizing a big data platform;
the first data set acquisition module is used for extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
the second data acquisition module is used for collecting historical operation parameter data of the elevators of the brands corresponding to the first elevator set by utilizing the big data platform;
The second data set acquisition module is used for extracting the historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
Further, the integrated value acquisition module includes:
the time information extraction module is used for extracting maintenance time information contained in the historical maintenance data information set;
the operation parameter data extraction module is used for searching the operation parameter data of the elevator before the time corresponding to the maintenance time information in the historical operation parameter data set according to the time corresponding to the maintenance time information, wherein the operation parameter data of the elevator before the time corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the time when the maintenance of the elevator is required to be shut down;
and the comprehensive value calculation module is used for setting the comprehensive value of the elevator maintenance operation performance corresponding to each elevator type by the comprehensive value acquisition model.
Further, the integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the number of types of operation parameters of the elevator of which the elevator parameters do not accord with the preset operation parameter standard values; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the electric traction mechanism of the elevator do not accord with the preset operation parameter standard value.
The invention has the beneficial effects that:
according to the elevator on-demand maintenance system and the elevator on-demand maintenance method based on the Internet of things and the big data, the types of elevators are obtained by classifying different models of different elevator brands, the big data platform is used for collecting historical actual operation parameter data and historical actual maintenance parameter data of various elevator types, the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types are used for formulating elevator maintenance operation performance comprehensive values, maintenance monitoring is carried out on each elevator type in a mode of comparing the elevator maintenance operation performance comprehensive values with the operation parameter operation performance comprehensive values of each elevator operation, the requirement information of the elevator types is automatically obtained, the accuracy and the monitoring efficiency of elevator maintenance monitoring can be effectively improved, the maintenance time machine of each elevator is matched with the actual condition of elevator operation of the elevator, the obtained rationality of the maintenance time machine is further improved, and the maintenance time machine is ensured to be in accordance with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. The problem of preventing the fixed time maintenance mode from leading to the fact that the checking time is inconsistent with the actual model and the operation parameters of the elevator, leading to the fact that the maintenance frequency is too low, leading to the reduction of maintenance efficiency, failing to discover elevator faults in time, and simultaneously, preventing the problem of increasing labor consumption caused by the fact that the maintenance frequency is too high and maintenance cost is increased.
Drawings
Fig. 1 is a flow chart of an elevator on-demand maintenance method of the present invention;
fig. 2 is a system block diagram of an elevator on-demand maintenance system according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The elevator on-demand maintenance method based on the Internet of things and big data provided by the embodiment of the invention, as shown in fig. 1, comprises the following steps:
s1, classifying the elevators according to matching and parameters to obtain a plurality of elevator types;
s2, acquiring historical maintenance data information and historical operation parameter data of each elevator through a big data platform;
s3, setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model;
and S4, monitoring the current operation parameters of each elevator in real time, calculating the operation parameter operation performance comprehensive value of each elevator in real time by utilizing the operation parameter and comprehensive value acquisition model, and sending maintenance prompt to a maintainer terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs.
The working principle of the technical scheme is as follows: firstly, classifying the elevators according to matching and parameters to obtain a plurality of elevator types; then, acquiring historical maintenance data information and historical operation parameter data of each elevator through a big data platform; then, setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model; and finally, monitoring the current operation parameters of each elevator in real time, calculating the operation parameter operation performance comprehensive value of each elevator in real time by utilizing the operation parameter and comprehensive value acquisition model, and sending maintenance prompt to a maintainer terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs.
The technical scheme has the effects that: according to the elevator on-demand maintenance method based on the Internet of things and the big data, the types of elevators are obtained by classifying different models of different elevator brands, the big data platform is used for collecting historical actual operation parameter data and historical actual maintenance parameter data of various elevator types, the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types are used for formulating elevator maintenance operation performance comprehensive values, maintenance monitoring is carried out on each elevator type in a mode of comparing the elevator maintenance operation performance comprehensive values with the operation parameter operation performance comprehensive values of each elevator operation, the requirement information of the elevator types is automatically obtained, the accuracy and the monitoring efficiency of elevator maintenance monitoring can be effectively improved, the maintenance time machine of each elevator is matched with the actual condition of elevator operation of the elevator, the obtained rationality of the maintenance time machine is further improved, and the maintenance time machine is ensured to be in accordance with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. The problem of preventing the fixed time maintenance mode from leading to the fact that the checking time is inconsistent with the actual model and the operation parameters of the elevator, leading to the fact that the maintenance frequency is too low, leading to the reduction of maintenance efficiency, failing to discover elevator faults in time, and simultaneously, preventing the problem of increasing labor consumption caused by the fact that the maintenance frequency is too high and maintenance cost is increased.
In one embodiment of the invention, classifying the elevators according to the matching and parameters to obtain a plurality of elevator types comprises:
s101, scanning through an Internet of things platform to obtain brands of all elevators supervised in the Internet of things platform;
s102, classifying the elevators for the first time according to brands to obtain a first elevator set;
s103, extracting all elevator models in each first elevator set, classifying the elevators with the same signals into one elevator set, and forming a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
The working principle of the technical scheme is as follows: firstly, scanning and acquiring brands of all elevators supervised in an internet of things platform through the internet of things platform; then, classifying the elevators for the first time according to brands to obtain a first elevator set; finally, all elevator models in each first elevator set are extracted, and the elevators with the same signals are classified into one elevator set to form a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
The technical scheme has the effects that: through the mode, the elevator classification efficiency and the elevator classification rationality can be effectively improved, and further the convenience of the operation data detection of the follow-up elevators is effectively improved.
In one embodiment of the invention, the historical maintenance data information and the historical operation parameter data of each elevator are obtained through a big data platform, and the method comprises the following steps:
s201, collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by using a big data platform;
s202, extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
s203, collecting historical operation parameter data of elevators of brands corresponding to the first elevator set by using a big data platform;
s204, extracting historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
The working principle of the technical scheme is as follows: firstly, collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by utilizing a big data platform; extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type; then, collecting historical operation parameter data of elevators of brands corresponding to the first elevator set by utilizing a big data platform; and extracting historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
The technical scheme has the effects that: by the method, the acquisition efficiency of the historical maintenance data information and the historical operation parameter data of each elevator type can be effectively improved, and the convenience of operation data detection of the follow-up elevators is further effectively improved.
According to one embodiment of the invention, an elevator maintenance operation performance integrated value corresponding to each elevator type is set according to the historical maintenance data information and the historical operation parameter data combined with an integrated value acquisition model, and the elevator maintenance operation performance integrated value setting method comprises the following steps:
s301, extracting maintenance time information contained in the historical maintenance data information set;
s302, according to the moment corresponding to the maintenance time information, searching the operation parameter data of the elevator before the moment corresponding to the maintenance time information in the historical operation parameter data set, wherein the operation parameter data of the elevator before the moment corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the maintenance time information when the elevator is required to be shut down;
s303, setting an elevator maintenance operation performance comprehensive value corresponding to each elevator type by the comprehensive value acquisition model.
Wherein the integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the number of types of operation parameters of the elevator of which the elevator parameters do not accord with the preset operation parameter standard values; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i represents electricity showing the electric traction mechanism of the elevatorAnd in the operation parameter types which do not accord with the preset operation parameter standard value, the ith operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value.
The working principle of the technical scheme is as follows: firstly, extracting maintenance time information contained in the historical maintenance data information set; then, according to the moment corresponding to the maintenance time information, searching the operation parameter data of the elevator before the moment corresponding to the maintenance time information in the historical operation parameter data set, wherein the operation parameter data of the elevator before the moment corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the maintenance time information when the elevator is required to be shut down; and finally, setting an elevator maintenance operation performance comprehensive value corresponding to each elevator type by the comprehensive value acquisition model.
The technical scheme has the effects that: according to the elevator on-demand maintenance method based on the Internet of things and the big data, the types of elevators are obtained by classifying different models of different elevator brands, the big data platform is used for collecting historical actual operation parameter data and historical actual maintenance parameter data of various elevator types, the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types are used for formulating elevator maintenance operation performance comprehensive values, maintenance monitoring is carried out on each elevator type in a mode of comparing the elevator maintenance operation performance comprehensive values with the operation parameter operation performance comprehensive values of each elevator operation, the requirement information of the elevator types is automatically obtained, the accuracy and the monitoring efficiency of elevator maintenance monitoring can be effectively improved, the maintenance time machine of each elevator is matched with the actual condition of elevator operation of the elevator, the obtained rationality of the maintenance time machine is further improved, and the maintenance time machine is ensured to be in accordance with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. The problem of preventing the fixed time maintenance mode from leading to the fact that the checking time is inconsistent with the actual model and the operation parameters of the elevator, leading to the fact that the maintenance frequency is too low, leading to the reduction of maintenance efficiency, failing to discover elevator faults in time, and simultaneously, preventing the problem of increasing labor consumption caused by the fact that the maintenance frequency is too high and maintenance cost is increased.
Meanwhile, the elevator maintenance operation performance comprehensive value and the operation parameter operation performance comprehensive value of each elevator set by the comprehensive value acquisition model can be combined with the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types, so that the accuracy of elevator maintenance operation performance comprehensive value and the accuracy of elevator operation parameter operation performance comprehensive value acquisition are effectively improved, the accuracy and the rationality of the acquisition of each elevator maintenance time machine are further improved, and the maintenance time machine is ensured to accord with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. On the other hand, by increasing the maintenance times, the self-adaptive change of the comprehensive value of the maintenance operation performance of the elevator can be realized, so that the self-adaptive correction of the maintenance machine is improved, and the matching and self-adaptive adjustment of Gao Weibao time and the actual model and operation parameters of the elevator are further improved.
The embodiment of the invention provides an elevator on-demand maintenance system based on the Internet of things and big data, as shown in fig. 2, the elevator on-demand maintenance system comprises:
The classification module is used for classifying the elevators according to the matching and parameters to obtain a plurality of elevator types;
the data acquisition module is used for acquiring historical maintenance data information and historical operation parameter data of each elevator through the large data platform;
the comprehensive value acquisition module is used for setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data and combining a comprehensive value acquisition model;
the maintenance judging module is used for monitoring the current operation parameters of each elevator in real time, utilizing the operation parameters to combine with the comprehensive value acquisition model to calculate the operation parameter operation performance comprehensive value of each elevator in real time, and sending maintenance prompt to the maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs.
The working principle of the technical scheme is as follows: firstly, classifying the elevators according to matching and parameters through a classification module to obtain a plurality of elevator types; then, the data acquisition module is utilized to acquire historical maintenance data information and historical operation parameter data of each elevator through a large data platform; then, setting elevator maintenance operation performance comprehensive values corresponding to each elevator type by adopting a comprehensive value acquisition module according to the historical maintenance data information and the historical operation parameter data and combining a comprehensive value acquisition model; and finally, monitoring the current operation parameters of each elevator in real time by using a maintenance judging module, calculating the operation parameter operation performance comprehensive value of each elevator in real time by using the operation parameter combined comprehensive value acquisition model, and sending a maintenance prompt to a maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs.
The technical scheme has the effects that: according to the elevator on-demand maintenance system based on the Internet of things and the big data, the types of elevators are obtained by classifying different models of different elevator brands, the big data platform is used for collecting historical actual operation parameter data and historical actual maintenance parameter data of various elevator types, the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types are used for formulating an elevator maintenance operation performance comprehensive value, maintenance monitoring is carried out on each elevator type in a mode of comparing the elevator maintenance operation performance comprehensive value with the operation parameter operation performance comprehensive value of each elevator operation, the requirement information of the elevator type is automatically obtained, the accuracy and the monitoring efficiency of elevator maintenance monitoring can be effectively improved, the maintenance time machine of each elevator is matched with the actual condition of elevator operation of the elevator, the obtained rationality of the maintenance time machine is further improved, and the maintenance time machine is ensured to be in accordance with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. The problem of preventing the fixed time maintenance mode from leading to the fact that the checking time is inconsistent with the actual model and the operation parameters of the elevator, leading to the fact that the maintenance frequency is too low, leading to the reduction of maintenance efficiency, failing to discover elevator faults in time, and simultaneously, preventing the problem of increasing labor consumption caused by the fact that the maintenance frequency is too high and maintenance cost is increased.
In one embodiment of the present invention, the classification module includes:
the brand acquisition module is used for scanning and acquiring brands of all elevators supervised in the internet of things platform through the internet of things platform;
the first set acquisition module is used for classifying the elevators for the first time according to brands to obtain a first elevator set;
the second set acquisition module is used for extracting all elevator types in each first elevator set, classifying the elevators with the same signals into one elevator set, and forming a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
The working principle of the technical scheme is as follows: firstly, acquiring brands of all elevators supervised in an internet of things platform through scanning of the internet of things platform by utilizing a brand acquisition module; then, adopting a first set acquisition module to classify the elevators for the first time according to brands to obtain a first elevator set; and finally, utilizing a second set acquisition module to extract all elevator types in each first elevator set, classifying the elevators with the same signals into one elevator set, and forming a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
The technical scheme has the effects that: through the mode, the elevator classification efficiency and the elevator classification rationality can be effectively improved, and further the convenience of the operation data detection of the follow-up elevators is effectively improved.
In one embodiment of the present invention, the data acquisition module includes:
the first data acquisition module is used for collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by utilizing a big data platform;
the first data set acquisition module is used for extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
the second data acquisition module is used for collecting historical operation parameter data of the elevators of the brands corresponding to the first elevator set by utilizing the big data platform;
the second data set acquisition module is used for extracting the historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
The working principle of the technical scheme is as follows: firstly, collecting historical maintenance data information of elevators of brands corresponding to a first elevator set by using a big data platform through a first data acquisition module; then, extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set by using a first data set acquisition module to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type; later, the historical operation parameter data of the elevators of the brands corresponding to the first elevator set are collected by using a big data platform through a second data acquisition module; and finally, extracting historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set by adopting a second data set acquisition module to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
The technical scheme has the effects that: by the method, the acquisition efficiency of the historical maintenance data information and the historical operation parameter data of each elevator type can be effectively improved, and the convenience of operation data detection of the follow-up elevators is further effectively improved.
In one embodiment of the present invention, the integrated value acquisition module includes:
the time information extraction module is used for extracting maintenance time information contained in the historical maintenance data information set;
the operation parameter data extraction module is used for searching the operation parameter data of the elevator before the time corresponding to the maintenance time information in the historical operation parameter data set according to the time corresponding to the maintenance time information, wherein the operation parameter data of the elevator before the time corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the time when the maintenance of the elevator is required to be shut down;
and the comprehensive value calculation module is used for setting the comprehensive value of the elevator maintenance operation performance corresponding to each elevator type by the comprehensive value acquisition model.
Wherein the integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the number of types of operation parameters of the elevator of which the elevator parameters do not accord with the preset operation parameter standard values; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the electric traction mechanism of the elevator do not accord with the preset operation parameter standard value.
The working principle of the technical scheme is as follows: firstly, extracting maintenance time information contained in the historical maintenance data information set through a time information extraction module; then, according to the moment corresponding to the maintenance time information, the operation parameter data extraction module is utilized to search the operation parameter data of the elevator before the moment corresponding to the maintenance time information in the historical operation parameter data set, wherein the operation parameter data of the elevator before the moment corresponding to the maintenance time information is the last operation parameter of the elevator corresponding to the moment when the elevator is required to be shut down for maintenance; and finally, setting elevator maintenance operation performance comprehensive values corresponding to each elevator type by adopting a comprehensive value acquisition model of a comprehensive value calculation module.
The technical scheme has the effects that: according to the elevator on-demand maintenance system based on the Internet of things and the big data, the types of elevators are obtained by classifying different models of different elevator brands, the big data platform is used for collecting historical actual operation parameter data and historical actual maintenance parameter data of various elevator types, the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types are used for formulating an elevator maintenance operation performance comprehensive value, maintenance monitoring is carried out on each elevator type in a mode of comparing the elevator maintenance operation performance comprehensive value with the operation parameter operation performance comprehensive value of each elevator operation, the requirement information of the elevator type is automatically obtained, the accuracy and the monitoring efficiency of elevator maintenance monitoring can be effectively improved, the maintenance time machine of each elevator is matched with the actual condition of elevator operation of the elevator, the obtained rationality of the maintenance time machine is further improved, and the maintenance time machine is ensured to be in accordance with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. The problem of preventing the fixed time maintenance mode from leading to the fact that the checking time is inconsistent with the actual model and the operation parameters of the elevator, leading to the fact that the maintenance frequency is too low, leading to the reduction of maintenance efficiency, failing to discover elevator faults in time, and simultaneously, preventing the problem of increasing labor consumption caused by the fact that the maintenance frequency is too high and maintenance cost is increased.
Meanwhile, the elevator maintenance operation performance comprehensive value and the operation parameter operation performance comprehensive value of each elevator set by the comprehensive value acquisition model can be combined with the historical actual operation parameter data and the historical actual maintenance parameter data of various elevator types, so that the accuracy of elevator maintenance operation performance comprehensive value and the accuracy of elevator operation parameter operation performance comprehensive value acquisition are effectively improved, the accuracy and the rationality of the acquisition of each elevator maintenance time machine are further improved, and the maintenance time machine is ensured to accord with the actual model and the operation parameters of the elevator; the maintenance frequency can be reduced to the maximum extent, the labor resource cost is saved, and the maintenance efficiency, the maintenance accuracy and the fault discovery timeliness can be improved under the condition of reducing the maintenance frequency to the maximum extent. On the other hand, by increasing the maintenance times, the self-adaptive change of the comprehensive value of the maintenance operation performance of the elevator can be realized, so that the self-adaptive correction of the maintenance machine is improved, and the matching and self-adaptive adjustment of Gao Weibao time and the actual model and operation parameters of the elevator are further improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The elevator on-demand maintenance method based on the Internet of things and big data is characterized by comprising the following steps of:
classifying the elevators according to the matching and parameters to obtain a plurality of elevator types;
acquiring historical maintenance data information and historical operation parameter data of each elevator through a large data platform;
setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined comprehensive value acquisition model;
the method comprises the steps of monitoring current operation parameters of each elevator in real time, calculating operation parameter operation performance comprehensive values of each elevator in real time by utilizing an operation parameter combination comprehensive value acquisition model, and sending maintenance prompts to a maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance comprehensive values reach or exceed elevator maintenance operation performance comprehensive values corresponding to elevator types to which the elevators belong;
the integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the number of types of operation parameters of the elevator of which the elevator parameters do not accord with the preset operation parameter standard values; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i represents that in the type of the operation parameter of the electric traction mechanism of the elevator, the elevator parameter does not accord with the preset operation parameter standard value, the i-th operation parameter does not accord with the preset operation parameter standard valueAnd an operation parameter standard value corresponding to the operation parameter type of the standard value.
2. The on-demand maintenance method of elevators according to claim 1, wherein classifying the elevators according to the match and parameters to obtain a plurality of elevator types comprises:
scanning through an internet of things platform to obtain brands of all elevators supervised in the internet of things platform;
classifying the elevators for the first time according to brands to obtain a first elevator set;
all elevator models in each first elevator set are extracted, and elevators with the same signal are classified into one elevator set to form a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
3. The on-demand maintenance method of elevators according to claim 2, wherein acquiring the historical maintenance data information and the historical operation parameter data of each elevator through the big data platform comprises:
Collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by using a big data platform;
extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
collecting historical operation parameter data of elevators of brands corresponding to the first elevator set by utilizing a big data platform;
and extracting historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
4. The elevator on-demand maintenance method of claim 1, wherein setting elevator maintenance operation performance integrated values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data combined integrated value acquisition model comprises:
extracting maintenance time information contained in the historical maintenance data information set;
According to the moment corresponding to the maintenance time information, searching the operation parameter data of the elevator before the moment corresponding to the maintenance time information in the historical operation parameter data set;
the comprehensive value acquisition model sets the comprehensive value of the elevator maintenance operation performance corresponding to each elevator type.
5. An elevator on-demand maintenance system based on the internet of things and big data, which is characterized in that the elevator on-demand maintenance system comprises:
the classification module is used for classifying the elevators according to the matching and parameters to obtain a plurality of elevator types;
the data acquisition module is used for acquiring historical maintenance data information and historical operation parameter data of each elevator through the large data platform;
the comprehensive value acquisition module is used for setting elevator maintenance operation performance comprehensive values corresponding to each elevator type according to the historical maintenance data information and the historical operation parameter data and combining a comprehensive value acquisition model;
the maintenance judging module is used for monitoring the current operation parameters of each elevator in real time, utilizing the operation parameters to combine with the comprehensive value acquisition model to calculate the operation parameter operation performance comprehensive value of each elevator in real time, and sending maintenance prompt to a maintenance personnel terminal through the Internet of things when the elevator maintenance operation performance comprehensive value reaches or exceeds the elevator maintenance operation performance comprehensive value corresponding to the elevator type to which the elevator belongs;
The integrated value acquisition model:
wherein W represents the comprehensive value of elevator maintenance operation performance corresponding to each elevator type; w (W) i Representing the running performance integrated value of the running parameter corresponding to the ith elevator in each elevator type; m represents the number of operation parameter types that the elevator parameters of the elevator cause machine mechanism do not accord with the preset operation parameter standard value; h 01 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which do not accord with the preset operation parameter standard value in the elevator parameters of the elevator machine mechanism; h 1 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator machine mechanism do not accord with the preset operation parameter standard value; p represents the number of operation parameter types of the elevator car mechanism of the elevator, wherein the elevator parameters do not accord with the preset operation parameter standard values; h 02 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the elevator car mechanism of the elevator, wherein the i is not accord with the preset operation parameter standard value; h 2 i represents an operation parameter standard value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types which show that the elevator parameters of the elevator car mechanism of the elevator do not accord with the preset operation parameter standard value; q represents the number of types of operation parameters of the elevator of which the elevator parameters do not accord with the preset operation parameter standard values; h 03 i represents the actual operation parameter value corresponding to the operation parameter type which does not accord with the preset operation parameter standard value in the operation parameter types of the electric dragging mechanism of the elevator, wherein the elevator parameter does not accord with the preset operation parameter standard value; h 3 i denotes that the i-th one of the types of operation parameters indicating that the elevator parameters of the electric traction mechanism of the elevator do not conform to the preset operation parameter standard values does not conform to the preset operation parameter standard valuesAnd an operation parameter standard value corresponding to the operation parameter type of the set operation parameter standard value.
6. The elevator on-demand maintenance system of claim 5, wherein the classification module comprises:
the brand acquisition module is used for scanning and acquiring brands of all elevators supervised in the internet of things platform through the internet of things platform;
the first set acquisition module is used for classifying the elevators for the first time according to brands to obtain a first elevator set;
The second set acquisition module is used for extracting all elevator types in each first elevator set, classifying the elevators with the same signals into one elevator set, and forming a plurality of second elevator sets, wherein each second elevator set corresponds to one elevator type.
7. The elevator on-demand maintenance system of claim 6, wherein the data acquisition module comprises:
the first data acquisition module is used for collecting historical maintenance data information of elevators of brands corresponding to the first elevator set by utilizing a big data platform;
the first data set acquisition module is used for extracting historical maintenance data information corresponding to each elevator model in the historical maintenance data information according to the elevator model corresponding to the second elevator set to form historical maintenance data information sets, wherein each historical maintenance data information set corresponds to one elevator type;
the second data acquisition module is used for collecting historical operation parameter data of the elevators of the brands corresponding to the first elevator set by utilizing the big data platform;
the second data set acquisition module is used for extracting the historical operation parameter data corresponding to each elevator model in the historical operation parameter data according to the elevator model corresponding to the second elevator set to form historical operation parameter data sets, wherein each historical operation parameter data set corresponds to one elevator type.
8. The elevator on-demand maintenance system of claim 5, wherein the integrated value acquisition module comprises:
the time information extraction module is used for extracting maintenance time information contained in the historical maintenance data information set;
the operation parameter data extraction module is used for searching the operation parameter data of the elevator before the time corresponding to the maintenance time information in the historical operation parameter data set according to the time corresponding to the maintenance time information;
and the comprehensive value calculation module is used for setting the comprehensive value of the elevator maintenance operation performance corresponding to each elevator type by the comprehensive value acquisition model.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6330935B1 (en) * | 1998-01-09 | 2001-12-18 | Kone Corporation | Maintenance method for an elevator installation and elevator installation |
| CN107150938A (en) * | 2017-07-24 | 2017-09-12 | 郑州明慧信息技术有限公司 | It is a kind of to reduce the maintaining method of elevator faults hidden danger |
| CN108529380A (en) * | 2018-03-27 | 2018-09-14 | 深圳市永达电子信息股份有限公司 | Elevator safety prediction technique and system |
| JP2019156624A (en) * | 2018-03-16 | 2019-09-19 | 東芝エレベータ株式会社 | Elevator controller, remote diagnosis system of elevator door and remote diagnosis method of elevator door |
| 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 |
| CN111252641A (en) * | 2020-04-28 | 2020-06-09 | 广东梯云科技有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
| CN113071966A (en) * | 2021-04-26 | 2021-07-06 | 平安国际智慧城市科技股份有限公司 | Elevator fault prediction method, device, equipment and storage medium |
| CN113968528A (en) * | 2021-10-09 | 2022-01-25 | 浙江新再灵科技股份有限公司 | On-demand maintenance monitoring method and system |
| CN115009948A (en) * | 2022-06-24 | 2022-09-06 | 凌波技术有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
-
2023
- 2023-02-24 CN CN202310212308.1A patent/CN116142914B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6330935B1 (en) * | 1998-01-09 | 2001-12-18 | Kone Corporation | Maintenance method for an elevator installation and elevator installation |
| CN107150938A (en) * | 2017-07-24 | 2017-09-12 | 郑州明慧信息技术有限公司 | It is a kind of to reduce the maintaining method of elevator faults hidden danger |
| JP2019156624A (en) * | 2018-03-16 | 2019-09-19 | 東芝エレベータ株式会社 | Elevator controller, remote diagnosis system of elevator door and remote diagnosis method of elevator door |
| CN108529380A (en) * | 2018-03-27 | 2018-09-14 | 深圳市永达电子信息股份有限公司 | Elevator safety prediction technique 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 |
| CN111252641A (en) * | 2020-04-28 | 2020-06-09 | 广东梯云科技有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
| CN112357709A (en) * | 2020-04-28 | 2021-02-12 | 广东梯云科技有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
| CN113071966A (en) * | 2021-04-26 | 2021-07-06 | 平安国际智慧城市科技股份有限公司 | Elevator fault prediction method, device, equipment and storage medium |
| CN113968528A (en) * | 2021-10-09 | 2022-01-25 | 浙江新再灵科技股份有限公司 | On-demand maintenance monitoring method and system |
| CN115009948A (en) * | 2022-06-24 | 2022-09-06 | 凌波技术有限公司 | Elevator maintenance-on-demand intelligent management system and management method thereof |
Non-Patent Citations (3)
| Title |
|---|
| 基于物联网技术的电梯按需维保评估方法的研究;林尧;《质量技术监督研究》;第2卷(第68期);第40-43页 * |
| 林尧.基于物联网技术的电梯按需维保评估方法的研究.《质量技术监督研究》.2020,第2卷(第68期),第40-43页. * |
| 柴志坤.《城市住宅小区安全风险防控》.同济大学出版社,2021,(第1版),第245页. * |
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