CN111268529A - Equipment data exception management system and method based on remote monitoring - Google Patents
Equipment data exception management system and method based on remote monitoring Download PDFInfo
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- CN111268529A CN111268529A CN202010109963.0A CN202010109963A CN111268529A CN 111268529 A CN111268529 A CN 111268529A CN 202010109963 A CN202010109963 A CN 202010109963A CN 111268529 A CN111268529 A CN 111268529A
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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
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Abstract
The invention discloses an equipment data abnormity management system and method based on remote monitoring, wherein the abnormity management system comprises an elevator noise database, an elevator noise acquisition module and an elevator abnormity judgment module, the elevator noise database comprises an elevator normal noise database and an elevator abnormity noise database, the elevator normal noise database is used for storing the sound produced during normal operation of an elevator, the elevator abnormity noise database is used for storing the sound produced during abnormal operation of the elevator, the elevator noise acquisition module is used for acquiring noise M1 in an elevator car, noise M2 at the outer top of the elevator car and noise M3 at a tractor of the elevator when the elevator is loaded, and the elevator abnormity judgment module judges whether the elevator is abnormal according to the noise M1, the noise M2 and the noise M3.
Description
Technical Field
The invention relates to the field of equipment management, in particular to a system and a method for equipment data exception management based on remote monitoring.
Background
In modern social economic activities, a vertical lift elevator is an indispensable transportation device capable of rapidly and effectively transporting passengers to different floors. However, in general, the elevator generates noise during operation, and this noise may not only give unpleasant feelings to passengers, but in many cases, may become a main factor of elevator failure. In order to reduce the failure probability of the elevator when a passenger takes the vertical elevator and increase the comfort of the passenger taking the elevator, the noise generated in the operation process of the elevator should be analyzed.
Disclosure of Invention
The invention aims to provide a system and a method for managing equipment data abnormity based on remote monitoring, which aim to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the utility model provides an equipment data abnormal management system based on remote monitoring, abnormal management system includes elevator noise database, elevator noise collection module and elevator abnormity judgement module, elevator noise database includes elevator normal noise database and elevator abnormal noise database, the sound that makes when elevator normal operating is used for storing to elevator normal operating, the sound that makes when elevator abnormal operating is used for storing to elevator abnormal noise database, elevator noise collection module is used for gathering noise M1 in the elevator car, noise M2 of elevator car top and the noise M3 of the hauler department of elevator when elevator load, elevator abnormity judgement module judges whether there is the abnormality according to noise M1, noise M2 and noise M3.
Preferably, the elevator abnormity judging module comprises a spectrum characteristic obtaining module, a matching analysis module and a noise evaluation module, wherein the spectrum characteristic obtaining module is used for obtaining spectrum characteristics of the noise M1, the noise M2 and the noise M3, the matching analysis module comprises a normal noise matching analysis module, an abnormal noise matching analysis module and an abnormity analysis module, the normal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator normal noise database or not and carrying out comprehensive evaluation on the elevator noise by elevator passengers under the condition that the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator normal noise database or not, the abnormal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator abnormal noise, and informing an elevator maintenance person to overhaul the elevator when the noise spectrum characteristics are matched with the spectrum characteristics in the abnormal noise database of the elevator, wherein the abnormality analysis module is used for analyzing the abnormality of noise when the spectrum characteristics of the noise at one position among the noise M1, the noise M2 and the noise M3 are not matched with the spectrum characteristics in the abnormal noise database of the elevator, the noise evaluation module comprises a passenger number recording module, a passenger scoring module, an average evaluation score calculation module, a comprehensive evaluation calculation module and a noise evaluation module, the passenger number recording module is used for recording the number of passengers in the process of ascending or descending of the load of the elevator, the passenger scoring module is used for scoring each passenger of the elevator according to 0 to 10 to evaluate the noise of the elevator, the average evaluation score calculation module is used for calculating the frequency of the elevator in the process of ascending or descending of the load of the elevator, the elevator noise evaluation system comprises an evaluation calculation module, a noise evaluation module and an elevator maintenance personnel, wherein the evaluation calculation module is used for calculating the comprehensive evaluation of elevator noise by the elevator passengers in a preset time period, the noise evaluation module judges whether the elevator noise influences the riding experience according to the calculation result of the evaluation calculation module, and the elevator maintenance personnel are informed to maintain the elevator when the elevator noise influences the riding experience greatly.
Preferably, the abnormality analysis module comprises a similarity comparison module, a noise pre-judging module and a subsequent noise matching module, wherein the similarity comparison module respectively compares the spectral characteristics of the noise with the spectral characteristics in a normal noise database of the elevator, the noise pre-judging module is used for pre-judging the noise as suspected normal noise when the similarity between the spectral characteristics in the normal noise database of the elevator and the spectral characteristics of the noise reaches more than ninety percent, the subsequent noise matching module is used for acquiring corresponding noise in the next preset time period when the noise is pre-judged as the suspected normal noise, and storing the suspected normal noise into the normal noise database of the elevator or the abnormal noise database of the elevator or informing an elevator maintenance person to overhaul and maintain the elevator according to the corresponding noise in the next preset time period.
A device data exception management method based on remote monitoring comprises the following steps:
step S1: presetting an elevator noise database which comprises an elevator normal noise database and an elevator abnormal noise database;
step S2: the method comprises the steps of collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a hoisting machine of the elevator when the elevator is loaded, and judging whether the elevator is abnormal or not according to frequency changes of the noise M1, the noise M2 and the noise M3 during the operation of the elevator.
Preferably, the step S2 further includes the following steps:
step S21: collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a tractor of the elevator when the elevator is loaded;
step S22: acquiring the frequency spectrum characteristics of the noise M1, the noise M2 and the noise M3;
step S23: the spectral signature of noise M1, noise M2, and noise M3 is matched to the spectral signature in the elevator noise database,
if the frequency spectrum characteristic of noise at one position among the noise M1, the noise M2 and the noise M3 is matched with the frequency spectrum characteristic in the abnormal noise database of the elevator, stopping running after the elevator runs for the time, and informing elevator maintenance personnel to overhaul the elevator;
if the frequency spectrum characteristics of the three noises of the noise M1, the noise M2 and the noise M3 are matched with the frequency spectrum characteristics in the normal noise database of the elevator in a preset time period, obtaining the comprehensive evaluation of the elevator noise by elevator passengers, and judging whether to inform elevator maintenance personnel to maintain the elevator according to the comprehensive evaluation;
if the spectral characteristics of the noise at one place among the noise M1, the noise M2, and the noise M3 do not match the spectral characteristics in the elevator noise database, the abnormality analysis is performed on the noise.
Preferably, the step S23 of obtaining the evaluation of the elevator noise by the elevator passenger and determining whether to notify the elevator maintenance staff to maintain the elevator according to the evaluation includes the following steps:
step S231: obtaining the evaluation of each elevator passenger on the elevator noise when the elevator runs upwards or runs downwards every time in the preset time period, wherein the elevator passenger evaluates the elevator noise according to a score of 0 to 10, 0 represents that the elevator noise has a great influence on the riding experience, and 10 represents that the elevator noise has no influence on the riding experience;
step S232: calculating the average evaluation score of all passengers of the elevator on the noise of the elevator in the process of one-time load ascending or load descending;
step S233: calculating the comprehensive evaluation of the elevator passengers to the elevator noise in the preset time period, wherein the comprehensive evaluation is the weighted sum of the average evaluation scores in the ascending or descending process of the elevator load each time in the preset time period, and the weighted weight is the total number of people in the ascending or descending process of the elevator load in the preset time period;
step S234: judging the noise condition of the elevator according to the comprehensive evaluation,
when the comprehensive evaluation is greater than or equal to 7, the influence of the elevator noise on the riding experience is small;
and when the comprehensive evaluation is less than 7, the influence of the elevator noise on the riding experience is large, and an elevator maintenance person is informed to maintain the elevator.
Preferably, the analyzing the abnormality of the noise in step S23 includes: the frequency spectrum characteristics of the noise are compared with the frequency spectrum characteristics in the normal noise database of the elevator respectively, if the similarity between the frequency spectrum characteristics in the normal noise database of the elevator and the frequency spectrum characteristics of the noise reaches more than ninety percent, the noise is judged to be suspected normal noise, corresponding noise in the next preset time period is obtained, and the suspected normal noise is stored in the normal noise database of the elevator or the abnormal noise database of the elevator or elevator maintenance personnel is informed to overhaul and maintain the elevator according to the corresponding noise in the next preset time period.
Optimally, the step of storing the suspected normal noise into an elevator normal noise database or an elevator abnormal noise database or informing an elevator maintenance person to overhaul and maintain the elevator according to the corresponding noise in the next preset time period comprises the following steps:
if the noise of the next preset time period is the same as the suspected normal noise of the previous preset time period, storing the suspected normal noise into an elevator normal noise database; if the noise of the next preset time period is different from the suspected normal noise of the previous preset time period, judging that the noise of the next preset time period is matched with the frequency spectrum characteristics of the elevator noise database, and if the noise of the next preset time period is matched with the frequency spectrum characteristics in the elevator normal noise database, storing the suspected normal noise of the previous preset time period into the elevator normal noise database; if the noise in the next preset time period is matched with the frequency spectrum characteristics in the abnormal noise database of the elevator, storing the suspected normal noise in the previous preset time period into the abnormal noise database of the elevator; otherwise, informing the elevator maintenance personnel to carry out maintenance on the elevator.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the noise M1 in the elevator car, the noise M2 at the top outside the elevator car and the noise M3 at the tractor of the elevator in the preset time period are collected, and the frequency spectrum characteristics of the noises are obtained, and as long as the frequency spectrum characteristics of one noise in the three noises are matched with the frequency spectrum characteristics in the abnormal noise database of the elevator, the elevator maintenance personnel are informed to overhaul the elevator, so that the probability of elevator failure in the process of taking the elevator by the passengers is reduced; the elevator noise taking comprehensive evaluation method has the advantages that the elevator noise taking comprehensive evaluation of the elevator taking personnel to the elevator noise in the preset time period is calculated, the elevator noise taking experience of the elevator taking personnel is judged, and the elevator maintenance personnel is informed to maintain the elevator when the elevator noise affects the elevator taking experience greatly, so that the elevator taking personnel have better taking experience.
Drawings
FIG. 1 is a schematic block diagram of a remote monitoring-based device data anomaly management system according to the present invention;
fig. 2 is a schematic flow chart of a device data anomaly management method based on remote monitoring according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, in the embodiment of the invention, an equipment data abnormality management system and method based on remote monitoring includes an elevator noise database, an elevator noise acquisition module and an elevator abnormality judgment module, where the elevator noise database includes an elevator normal noise database and an elevator abnormal noise database, the elevator normal noise database is used for storing sounds generated during normal operation of an elevator, the elevator abnormal noise database is used for storing sounds generated during abnormal operation of the elevator, the elevator noise acquisition module is used for acquiring noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a traction machine of the elevator when the elevator is loaded, and the elevator abnormality judgment module judges whether the elevator is abnormal according to noise M1, noise M2 and noise M3.
The elevator abnormity judging module comprises a spectrum characteristic obtaining module, a matching analysis module and a noise evaluation module, wherein the spectrum characteristic obtaining module is used for obtaining spectrum characteristics of noise M1, noise M2 and noise M3, the matching analysis module comprises a normal noise matching analysis module, an abnormal noise matching analysis module and an abnormity analysis module, the normal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in an elevator normal noise database or not and carrying out comprehensive evaluation on elevator noise by elevator passengers under the condition that the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator normal noise database or not, the abnormal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator abnormal noise database or not, and informing an elevator maintenance person to overhaul the elevator when the noise spectrum characteristics are matched with the spectrum characteristics in the abnormal noise database of the elevator, wherein the abnormality analysis module is used for analyzing the abnormality of noise when the spectrum characteristics of the noise at one position among the noise M1, the noise M2 and the noise M3 are not matched with the spectrum characteristics in the abnormal noise database of the elevator, the noise evaluation module comprises a passenger number recording module, a passenger scoring module, an average evaluation score calculation module, a comprehensive evaluation calculation module and a noise evaluation module, the passenger number recording module is used for recording the number of passengers in the process of ascending or descending of the load of the elevator, the passenger scoring module is used for scoring each passenger of the elevator according to 0 to 10 to evaluate the noise of the elevator, the average evaluation score calculation module is used for calculating the frequency of the elevator in the process of ascending or descending of the load of the elevator, the elevator noise evaluation system comprises an evaluation calculation module, a noise evaluation module and an elevator maintenance personnel, wherein the evaluation calculation module is used for calculating the comprehensive evaluation of elevator noise by the elevator passengers in a preset time period, the noise evaluation module judges whether the elevator noise influences the riding experience according to the calculation result of the evaluation calculation module, and the elevator maintenance personnel are informed to maintain the elevator when the elevator noise influences the riding experience greatly.
The abnormity analysis module comprises a similarity comparison module, a noise pre-judging module and a subsequent noise matching module, wherein the similarity comparison module respectively compares the frequency spectrum characteristics of the noise with the frequency spectrum characteristics in an elevator normal noise database, the noise pre-judging module is used for pre-judging the noise as suspected normal noise when the similarity between the frequency spectrum characteristics in the elevator normal noise database and the frequency spectrum characteristics of the noise reaches more than ninety percent, the subsequent noise matching module is used for acquiring corresponding noise in the next preset time period when the noise is pre-judged as the suspected normal noise, and storing the suspected normal noise into the elevator normal noise database or the elevator abnormal noise database or informing an elevator maintenance person to overhaul and maintain the elevator according to the corresponding noise in the next preset time period.
A device data exception management method based on remote monitoring comprises the following steps:
step S1: presetting an elevator noise database which comprises an elevator normal noise database and an elevator abnormal noise database;
step S2: the method comprises the steps of collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a tractor of the elevator when the elevator is loaded, and judging whether the elevator is abnormal or not according to frequency changes of the noise M1, the noise M2 and the noise M3 during the operation of the elevator:
step S21: collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a tractor of the elevator when the elevator is loaded;
step S22: acquiring the frequency spectrum characteristics of the noise M1, the noise M2 and the noise M3;
step S23: the spectral signature of noise M1, noise M2, and noise M3 is matched to the spectral signature in the elevator noise database,
if the frequency spectrum characteristic of noise at one position among the noise M1, the noise M2 and the noise M3 is matched with the frequency spectrum characteristic in the abnormal noise database of the elevator, stopping running after the elevator runs for the time, and informing elevator maintenance personnel to overhaul the elevator;
if the frequency spectrum characteristics of the three noises of the noise M1, the noise M2 and the noise M3 are matched with the frequency spectrum characteristics in the normal noise database of the elevator in a preset time period, obtaining the comprehensive evaluation of the elevator noise by elevator passengers, and judging whether to inform elevator maintenance personnel to maintain the elevator according to the comprehensive evaluation;
the step S23 of obtaining the evaluation of the elevator noise by the elevator passenger, and determining whether to notify the elevator maintenance staff to maintain the elevator according to the evaluation includes:
step S231: obtaining the evaluation of each elevator passenger on the elevator noise when the elevator runs upwards or runs downwards every time in the preset time period, wherein the elevator passenger evaluates the elevator noise according to a score of 0 to 10, 0 represents that the elevator noise has a great influence on the riding experience, and 10 represents that the elevator noise has no influence on the riding experience;
step S232: calculating the average evaluation score of all passengers of the elevator on the noise of the elevator in the process of one-time load ascending or load descending;
step S233: calculating the comprehensive evaluation of the elevator passengers to the elevator noise in the preset time period, wherein the comprehensive evaluation is the weighted sum of the average evaluation scores in the ascending or descending process of the elevator load each time in the preset time period, and the weighted weight is the total number of people in the ascending or descending process of the elevator load in the preset time period;
step S234: judging the noise condition of the elevator according to the comprehensive evaluation,
when the comprehensive evaluation is greater than or equal to 7, the influence of the elevator noise on the riding experience is small;
when the comprehensive evaluation is less than 7, the influence of the elevator noise on the riding experience is large, and an elevator maintenance person is informed to maintain the elevator;
if the spectral characteristics of the noise existing at one of the noise M1, the noise M2, and the noise M3 do not match the spectral characteristics in the elevator noise database, the noise is analyzed for abnormality,
the abnormality analysis of the noise includes: compare the spectral feature of this noise respectively with the spectral feature in the normal noise database of elevator, if there is the spectral feature in the normal noise database of elevator and the spectral feature similarity of this noise to reach more than ninety percent, then judge this noise is suspected normal noise to obtain corresponding noise in the next preset time quantum, according to corresponding noise in the next preset time quantum will be suspected normal noise and deposit elevator normal noise database or elevator unusual noise database or inform elevator maintainer to overhaul the elevator and maintain:
if the noise of the next preset time period is the same as the suspected normal noise of the previous preset time period, storing the suspected normal noise into an elevator normal noise database; if the noise of the next preset time period is different from the suspected normal noise of the previous preset time period, judging that the noise of the next preset time period is matched with the frequency spectrum characteristics of the elevator noise database, and if the noise of the next preset time period is matched with the frequency spectrum characteristics in the elevator normal noise database, storing the suspected normal noise of the previous preset time period into the elevator normal noise database; if the noise in the next preset time period is matched with the frequency spectrum characteristics in the abnormal noise database of the elevator, storing the suspected normal noise in the previous preset time period into the abnormal noise database of the elevator; otherwise, informing the elevator maintenance personnel to carry out maintenance on the elevator.
When the frequency spectrum characteristic of the noise with one position in the three noises is not matched with the frequency spectrum characteristic in the elevator noise database, the noise is subjected to abnormality analysis, the category of the noise is judged by collecting the noise in the next preset time period, and the noise is stored in the elevator noise database under the condition that the noise is normal or abnormal, so that the noise types of the elevator noise database are enriched, and the matching judgment of the subsequent noise is facilitated.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. The utility model provides an equipment data exception management system based on remote monitoring which characterized in that: the abnormal management system comprises an elevator noise database, an elevator noise acquisition module and an elevator abnormal judgment module, wherein the elevator noise database comprises an elevator normal noise database and an elevator abnormal noise database, the elevator normal noise database is used for storing the sound made in the normal operation of an elevator, the elevator abnormal noise database is used for storing the sound made in the abnormal operation of the elevator, the elevator noise acquisition module is used for acquiring the noise M1 in an elevator car, the noise M2 at the outer top of the elevator car and the noise M3 at a traction machine of the elevator when the elevator is loaded, and the elevator abnormal judgment module judges whether the elevator is abnormal or not according to the noise M1, the noise M2 and the noise M3.
2. The system for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 1, wherein: the elevator abnormity judging module comprises a spectrum characteristic obtaining module, a matching analysis module and a noise evaluation module, wherein the spectrum characteristic obtaining module is used for obtaining spectrum characteristics of noise M1, noise M2 and noise M3, the matching analysis module comprises a normal noise matching analysis module, an abnormal noise matching analysis module and an abnormity analysis module, the normal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in an elevator normal noise database or not and carrying out comprehensive evaluation on elevator noise by elevator passengers under the condition that the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator normal noise database or not, the abnormal noise matching analysis module is used for analyzing whether the spectrum characteristics of the noise M1, the noise M2 and the noise M3 are matched with the spectrum characteristics in the elevator abnormal noise database or not, and informing an elevator maintenance person to overhaul the elevator when the noise spectrum characteristics are matched with the spectrum characteristics in the abnormal noise database of the elevator, wherein the abnormality analysis module is used for analyzing the abnormality of noise when the spectrum characteristics of the noise at one position among the noise M1, the noise M2 and the noise M3 are not matched with the spectrum characteristics in the abnormal noise database of the elevator, the noise evaluation module comprises a passenger number recording module, a passenger scoring module, an average evaluation score calculation module, a comprehensive evaluation calculation module and a noise evaluation module, the passenger number recording module is used for recording the number of passengers in the process of ascending or descending of the load of the elevator, the passenger scoring module is used for scoring each passenger of the elevator according to 0 to 10 to evaluate the noise of the elevator, the average evaluation score calculation module is used for calculating the frequency of the elevator in the process of ascending or descending of the load of the elevator, the elevator noise evaluation system comprises an evaluation calculation module, a noise evaluation module and an elevator maintenance personnel, wherein the evaluation calculation module is used for calculating the comprehensive evaluation of elevator noise by the elevator passengers in a preset time period, the noise evaluation module judges whether the elevator noise influences the riding experience according to the calculation result of the evaluation calculation module, and the elevator maintenance personnel are informed to maintain the elevator when the elevator noise influences the riding experience greatly.
3. The system for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 2, wherein: the abnormity analysis module comprises a similarity comparison module, a noise pre-judging module and a subsequent noise matching module, wherein the similarity comparison module respectively compares the frequency spectrum characteristics of the noise with the frequency spectrum characteristics in an elevator normal noise database, the noise pre-judging module is used for pre-judging the noise as suspected normal noise when the similarity between the frequency spectrum characteristics in the elevator normal noise database and the frequency spectrum characteristics of the noise reaches more than ninety percent, the subsequent noise matching module is used for acquiring corresponding noise in the next preset time period when the noise is pre-judged as the suspected normal noise, and storing the suspected normal noise into the elevator normal noise database or the elevator abnormal noise database or informing an elevator maintenance person to overhaul and maintain the elevator according to the corresponding noise in the next preset time period.
4. A device data exception management method based on remote monitoring is characterized in that: the exception management method comprises the following steps:
step S1: presetting an elevator noise database which comprises an elevator normal noise database and an elevator abnormal noise database;
step S2: the method comprises the steps of collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a hoisting machine of the elevator when the elevator is loaded, and judging whether the elevator is abnormal or not according to frequency changes of the noise M1, the noise M2 and the noise M3 during the operation of the elevator.
5. The method for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 4, wherein: the step S2 further includes the following method:
step S21: collecting noise M1 in an elevator car, noise M2 at the top outside the elevator car and noise M3 at a tractor of the elevator when the elevator is loaded;
step S22: acquiring the frequency spectrum characteristics of the noise M1, the noise M2 and the noise M3;
step S23: the spectral signature of noise M1, noise M2, and noise M3 is matched to the spectral signature in the elevator noise database,
if the frequency spectrum characteristic of noise at one position among the noise M1, the noise M2 and the noise M3 is matched with the frequency spectrum characteristic in the abnormal noise database of the elevator, stopping running after the elevator runs for the time, and informing elevator maintenance personnel to overhaul the elevator;
if the frequency spectrum characteristics of the three noises of the noise M1, the noise M2 and the noise M3 are matched with the frequency spectrum characteristics in the normal noise database of the elevator in a preset time period, obtaining the comprehensive evaluation of the elevator noise by elevator passengers, and judging whether to inform elevator maintenance personnel to maintain the elevator according to the comprehensive evaluation;
if the spectral characteristics of the noise at one place among the noise M1, the noise M2, and the noise M3 do not match the spectral characteristics in the elevator noise database, the abnormality analysis is performed on the noise.
6. The method for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 5, wherein: the step S23 of obtaining the evaluation of the elevator noise by the elevator passenger and determining whether to notify the elevator maintenance staff to maintain the elevator includes the following steps:
step S231: obtaining the evaluation of each elevator passenger on the elevator noise when the elevator runs upwards or runs downwards every time in the preset time period, wherein the elevator passenger evaluates the elevator noise according to a score of 0 to 10, 0 represents that the elevator noise has a great influence on the riding experience, and 10 represents that the elevator noise has no influence on the riding experience;
step S232: calculating the average evaluation score of all passengers of the elevator on the noise of the elevator in the process of one-time load ascending or load descending;
step S233: calculating the comprehensive evaluation of the elevator passengers to the elevator noise in the preset time period, wherein the comprehensive evaluation is the weighted sum of the average evaluation scores in the ascending or descending process of the elevator load each time in the preset time period, and the weighted weight is the total number of people in the ascending or descending process of the elevator load in the preset time period;
step S234: judging the noise condition of the elevator according to the comprehensive evaluation,
when the comprehensive evaluation is greater than or equal to 7, the influence of the elevator noise on the riding experience is small;
and when the comprehensive evaluation is less than 7, the influence of the elevator noise on the riding experience is large, and an elevator maintenance person is informed to maintain the elevator.
7. The method for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 5, wherein: the analyzing the abnormality of the noise in step S23 includes: the frequency spectrum characteristics of the noise are compared with the frequency spectrum characteristics in the normal noise database of the elevator respectively, if the similarity between the frequency spectrum characteristics in the normal noise database of the elevator and the frequency spectrum characteristics of the noise reaches more than ninety percent, the noise is judged to be suspected normal noise, corresponding noise in the next preset time period is obtained, and the suspected normal noise is stored in the normal noise database of the elevator or the abnormal noise database of the elevator or elevator maintenance personnel is informed to overhaul and maintain the elevator according to the corresponding noise in the next preset time period.
8. The method for managing the abnormal data of the equipment based on the remote monitoring as claimed in claim 6, wherein: the step of storing the suspected normal noise into an elevator normal noise database or an elevator abnormal noise database or informing an elevator maintenance person to overhaul and maintain the elevator according to the corresponding noise in the next preset time period comprises the following steps:
if the noise of the next preset time period is the same as the suspected normal noise of the previous preset time period, storing the suspected normal noise into an elevator normal noise database; if the noise of the next preset time period is different from the suspected normal noise of the previous preset time period, judging that the noise of the next preset time period is matched with the frequency spectrum characteristics of the elevator noise database, and if the noise of the next preset time period is matched with the frequency spectrum characteristics in the elevator normal noise database, storing the suspected normal noise of the previous preset time period into the elevator normal noise database; if the noise in the next preset time period is matched with the frequency spectrum characteristics in the abnormal noise database of the elevator, storing the suspected normal noise in the previous preset time period into the abnormal noise database of the elevator; otherwise, informing the elevator maintenance personnel to carry out maintenance on the elevator.
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| CN115009944A (en) * | 2022-05-26 | 2022-09-06 | 浙江鑫梯互联科技有限公司 | Intelligent real-time monitoring system for elevator running state |
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| CN113844969B (en) * | 2020-06-25 | 2022-12-02 | 三菱电机株式会社 | Elevator remote monitoring system |
| CN115009944A (en) * | 2022-05-26 | 2022-09-06 | 浙江鑫梯互联科技有限公司 | Intelligent real-time monitoring system for elevator running state |
Also Published As
| Publication number | Publication date |
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| CN112408135B (en) | 2022-06-07 |
| CN111268529B (en) | 2021-01-19 |
| CN112408135A (en) | 2021-02-26 |
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