CN113401760B - Elevator operation fault supervision system based on big data - Google Patents
Elevator operation fault supervision system based on big data Download PDFInfo
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- CN113401760B CN113401760B CN202110732245.3A CN202110732245A CN113401760B CN 113401760 B CN113401760 B CN 113401760B CN 202110732245 A CN202110732245 A CN 202110732245A CN 113401760 B CN113401760 B CN 113401760B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—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
- B66B5/0037—Performance analysers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0087—Devices facilitating maintenance, repair or inspection tasks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
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Abstract
The invention discloses an elevator operation fault supervision system based on big data, which relates to the technical field of elevator operation fault supervision and solves the technical problem of one-sided elevator supervision in the prior art; the elevator monitoring system has the advantages that the elevator monitoring unit is used for detecting the elevator in test operation, the qualified operation coefficient and the qualified environment coefficient interval of the elevator in test operation are collected, the qualified operation coefficient of the elevator is collected through data analysis, the qualified operation coefficient is used as a detection standard, the accuracy of elevator operation monitoring is improved, meanwhile, the qualified environment data interval is also used as the elevator monitoring standard, accurate control is carried out on environmental influence, the elevator is prevented from being affected by the environment to cause faults, the elevator is monitored in use, the elevator is monitored before operation and is monitored in real time, only the current elevator condition can be checked, complete judgment on the actual state of the elevator can not be made, and the elevator supervision efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of elevator operation fault supervision, in particular to an elevator operation fault supervision system based on big data.
Background
The elevator is a vertical elevator which is powered by a motor and is provided with a box-shaped nacelle, is used for people riding or goods carrying in multi-storey buildings, is commonly called an escalator or a moving sidewalk, is fixed lifting equipment for serving specified floors, and along with economic development, the elevator is more and more frequently used due to high-rise building construction, and the demand of the elevator is increasingly increased, so that the supervision of the operation fault of the elevator is particularly important;
the patent with application number CN202010819297X discloses an elevator intelligent supervision and maintenance on demand system and method based on PHM technology, which adopts the following technical scheme: the elevator fault prediction system comprises an elevator data acquisition device, a data analysis module and a supervision maintenance module, wherein the supervision module supervises an elevator according to an abnormal detection model formed by the data analysis module, and the prediction maintenance module predicts the fault of the elevator and updates the maintenance strategy of the elevator according to a prediction reference model formed by the data analysis module;
however, in the above patent, only the current elevator state of the elevator can be checked, and the actual state of the elevator cannot be completely and correctly judged, so that the elevator fault supervision efficiency is reduced; meanwhile, environment data intervals are difficult to collect, and whether real-time environment influences elevator operation or not can not be accurately judged, so that the reliability of elevator operation is reduced; in addition, the elevator can not be predicted before running each time, whether the elevator can run under the current running environment or not is judged, and the elevator can not be effectively avoided before an accident occurs; the trouble problem can not be sent to corresponding maintenance personnel in the trouble shooting, so that the maintenance period of the maintenance personnel is prolonged, and the use quality of a user is greatly reduced;
in view of the above-mentioned technical drawbacks of the integrated type in respect of elevator operation, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an elevator operation fault supervision system based on big data, which can be used for monitoring the operation of an elevator, monitoring before operation and monitoring the operation in real time, solves the technical problem of one-sided elevator supervision in the prior art, not only detects the current state of the elevator, but also can judge the quality of the elevator through multi-aspect detection.
The purpose of the invention can be realized by the following technical scheme:
an elevator operation fault supervision system based on big data comprises a cloud supervision platform, an operation prediction unit, an elevator monitoring unit, a real-time monitoring unit, an alarm unit and a maintenance unit;
the elevator monitoring unit is used for detecting the trial run elevator, acquiring a qualified running coefficient and a qualified environment coefficient interval of the trial run elevator, monitoring the trial run elevator according to the qualified running coefficient of the trial run elevator, generating a qualified temperature interval and a qualified humidity area simultaneously, and sending the qualified running coefficient, the qualified temperature interval and the qualified humidity interval to the operation prediction unit;
the cloud supervision platform marks the operable data acquisition object as an operating elevator after receiving the operable data acquisition object and the qualified operating coefficient, puts the operating elevator into use, generates an operation prediction instruction and sends the operation prediction instruction to an operation prediction unit, and the operation prediction instruction is generated before the operating elevator is used;
the operation prediction unit is used for receiving an operation prediction instruction, predicting each operation of the operation elevator after receiving the operation prediction instruction, generating an environment influence signal or an environment non-influence signal, and sending the environment influence signal or the environment non-influence signal to the cloud supervision platform;
the cloud supervision platform suspends the corresponding running elevator after receiving the environment influence signal, maintains the environment adjusting device of the corresponding running elevator, generates a running instruction and controls the running elevator to run after receiving the environment influence-free signal, generates a real-time monitoring signal and sends the real-time monitoring signal to the real-time monitoring unit, the environment adjusting device is a ventilator, and the ventilator can adjust the temperature value and the humidity value of the running environment of the running elevator;
the real-time monitoring unit monitors the running elevator in real time in the running process after receiving the real-time monitoring signal, generates a compensation abnormal signal or outputs an abnormal signal, and sends the compensation abnormal signal or the output abnormal signal to the alarm unit;
after receiving the compensation abnormal signal or the output abnormal signal, the cloud supervision platform sends the compensation abnormal signal or the output abnormal signal to the alarm unit;
the alarm unit generates an alarm signal after receiving the compensation abnormal signal or the output abnormal signal and sends the alarm signal and the compensation abnormal signal or the output abnormal signal to the maintenance unit;
and after the maintenance unit receives the alarm signal and the compensation abnormal signal or the output abnormal signal, the compensation abnormal signal or the output abnormal signal is sent to maintenance personnel of the corresponding maintenance type.
As a preferred embodiment of the present invention, the detection process of the elevator monitoring unit is as follows:
step S1: randomly selecting an elevator which does not run as a data acquisition object, setting detection time for the data acquisition object, dividing the detection time into i sub-time periods at equal intervals, wherein i is 1, 2, …, n is a positive integer, randomly selecting m sub-time periods in the i sub-time periods as the data acquisition time, the m sub-time periods are different in environmental data, m is a positive integer larger than 5 and m is smaller than n, and the environmental data comprises an environmental temperature value and an environmental humidity value;
step S2: after the data acquisition time is selected, operating the data acquisition object, and acquiring a shaking frequency value and a shaking displacement value of the box body in the operation process of the data acquisition object in the data acquisition time, and respectively marking the corresponding shaking frequency value and the corresponding shaking displacement value as PLm and WYm, wherein the shaking frequency value is represented as the frequency of shaking of the data acquisition object in the operation process, and the shaking displacement value is represented as the displacement generated by shaking of the data acquisition object in the operation process;
step S3: by the formula
Acquiring a running coefficient Xm of a data acquisition object, wherein v1 and v2 are proportionality coefficients, v1 is greater than v2 is greater than 0, and e is a natural constant;
step S4: comparing the operational coefficient of the data acquisition object to an operational coefficient threshold:
if the operation coefficient of the data acquisition object is larger than or equal to the operation coefficient threshold value, judging that the data acquisition object is unqualified in operation, marking the corresponding data acquisition object as an operation-forbidden data acquisition object, and sending the operation-forbidden data acquisition object to the cloud supervision platform;
if the operation coefficient of the data acquisition object is less than the operation coefficient threshold value, judging that the data acquisition object is qualified in operation, marking the corresponding data acquisition object as an operable data acquisition object, marking the operation coefficient of the operable data acquisition object as a qualified operation coefficient, sending the operable data acquisition object and the qualified operation coefficient to a cloud supervision platform, and simultaneously entering step S5;
step S5: the method comprises the steps of obtaining ambient environment data in the running process of a data collection object in data collection time, marking ambient temperature values in the ambient environment data as WD and ambient humidity values as SD, constructing an ambient temperature value set A { WD1, WD2, …, WDm } and an ambient humidity value set B { SD1, SD2, …, SDm } in the data collection time, obtaining a subset with the largest value and a subset with the smallest value in the ambient temperature value set A, respectively marking the corresponding values as WDmax and WDmin, obtaining a subset with the largest value and a subset with the smallest value in the humidity temperature value set B, and respectively marking the corresponding values as SDmax and SDmin, wherein WDmax and WDmin are respectively expressed as an upper qualified temperature limit value and a lower limit value of qualified running of the data collection object, and SDmax and SDmin are respectively expressed as an upper qualified humidity limit value and a lower limit value of qualified running of the data collection object;
step S6: and acquiring a qualified temperature interval and a qualified humidity interval according to the qualified temperature upper limit value and the qualified humidity lower limit value and the qualified humidity upper limit value and the qualified humidity lower limit value, and sending the qualified temperature interval and the qualified humidity interval to an operation prediction unit.
As a preferred embodiment of the present invention, the operation prediction unit specifically predicts as follows:
step SS 1: the method comprises the steps that after an operation prediction instruction is received, the temperature value and the humidity value of the operation environment of an operation elevator box body and the external environment are obtained in real time, if the corresponding numerical value of the operation environment and the external environment is less than 1 in the temperature value and the humidity value of the operation environment of the operation elevator box body, the temperature value and the humidity value of the operation environment are selected, and if the corresponding numerical value of the operation environment and the external environment is more than or equal to 1, the temperature value and the humidity value of the external environment are selected; marking the selected temperature values as YWD and YSD;
step SS 2: by running predictive formulas
Acquiring an environment influence coefficient XS in real time operation of an operating elevator, wherein alpha and beta are a temperature correction factor and a humidity correction factor respectively, the value of alpha is 1.04, and the value of beta is 3.11;
step SS 3: comparing the environment influence coefficient XS in real time operation of the running elevator with an environment influence coefficient threshold value:
if the environment influence coefficient XS is larger than or equal to the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment influences the running, generating an environment influence signal and sending the environment influence signal to the cloud supervision platform;
and if the environment influence coefficient XS is less than the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment does not influence the running, generating an environment non-influence signal and sending the environment non-influence signal to the cloud supervision platform.
As a preferred embodiment of the present invention, the real-time monitoring unit specifically monitors the following processes:
step T1: after the running elevator closes the elevator door, the running elevator is divided into primary running, secondary running and tertiary running;
step T2: performing compensation monitoring on the running elevator, adding the weight in the real-time running elevator box body and the weight of the running elevator box body to obtain the running total mass of the running elevator, obtaining the dead weight adjusting time of the running elevator, judging that the running elevator is abnormal in compensation if the dead weight adjusting time of the running elevator is not less than the closing time of the elevator door corresponding to the running elevator, generating an abnormal compensation signal and sending the abnormal compensation signal to a cloud supervision platform, judging that the running elevator is normal in compensation if the dead weight adjusting time of the running elevator is less than the closing time of the elevator door corresponding to the running elevator, generating a normal compensation signal and sending the normal compensation signal to the cloud supervision platform; wherein, the compensation monitoring is used for primary operation, secondary operation and tertiary operation;
step T3: the method comprises the steps of monitoring output of an operating elevator, dividing the operating process of the operating elevator into three parts of acceleration, constant speed and deceleration, acquiring the constant speed of the real-time operation of the operating elevator, and acquiring the shaking times of the operating elevator from acceleration to constant speed and from constant speed to deceleration; if the constant speed of the running elevator in real time is not equal to the rated constant speed or the frequency of the corresponding shaking times is larger than the shaking time threshold value, judging that the output of the corresponding motor of the running elevator is abnormal, generating an output abnormal signal and sending the output abnormal signal to the cloud supervision platform; otherwise, judging that the output of the running elevator is normal; wherein the output monitoring is only used for the secondary operation and the tertiary operation.
As a preferred embodiment of the present invention, the operation grade division of the elevator in step T1 is as follows:
acquiring the weight in the running elevator box in real time, calculating the ratio of the weight in the running elevator box to the rated bearing capacity of the box, marking the corresponding ratio as a weight ratio, and if the weight ratio is less than or equal to one third, marking the real-time running of the corresponding running elevator as primary running; if the weight ratio is more than one third and less than two thirds, marking the real-time operation of the corresponding operation elevator as secondary operation; and if the weight ratio is more than or equal to two thirds, marking the real-time operation of the corresponding operation elevator as three-level operation.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, a trial run elevator is detected by an elevator monitoring unit, and a qualified running coefficient and a qualified environment coefficient interval of the trial run elevator are collected; the qualified operation coefficient of the elevator is acquired through data analysis, the qualified operation coefficient is used as a detection standard, the accuracy of elevator operation monitoring is improved, and meanwhile, a qualified environmental data interval is also used as an elevator monitoring standard, so that the environmental influence is accurately controlled, and the elevator is prevented from being affected by the environment to break down;
2. in the invention, an operation prediction instruction is received by an operation prediction unit, and each operation of an operation elevator is predicted after the operation prediction instruction is received; the operation prediction instruction is generated before the elevator is operated, and whether the elevator can normally operate each time is predicted, so that the safety performance of the elevator used by a user is improved, the occurrence of elevator accidents is reduced, and unnecessary loss is brought;
3. in the invention, after the real-time monitoring signal is received by the real-time monitoring unit, the running elevator is monitored in real time in the running process; the time of self-weight compensation is reduced, the efficiency of self-weight compensation is improved, and the occurrence of elevator faults is reduced; the motor output is monitored, so that speed abnormity caused by output abnormity is prevented, safety accidents are avoided, and meanwhile, the use quality of a user is effectively enhanced due to stable running speed;
in conclusion, the elevator monitoring system has the advantages that the situation that the current elevator state can only be checked is avoided by monitoring the elevator in use, monitoring before operation and monitoring in real time, so that the actual state of the elevator cannot be completely judged, and the elevator monitoring efficiency is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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.
As shown in fig. 1, an elevator operation fault supervision system based on big data comprises a cloud supervision platform, an operation prediction unit, an elevator monitoring unit, a real-time monitoring unit, an alarm unit and an overhaul unit, wherein the cloud supervision platform is in bidirectional communication connection with the operation prediction unit, the elevator monitoring unit, the real-time monitoring unit and the alarm unit, the operation monitoring unit is in bidirectional communication connection with the elevator monitoring unit, and the alarm unit is in unidirectional communication connection with the overhaul unit;
the elevator monitoring unit is used for detecting the elevator in test operation, the qualified running coefficient and the qualified environmental coefficient interval of the elevator in test operation are collected, the qualified running coefficient of the elevator is collected through data analysis, and the qualified running coefficient is used as a detection standard, the accuracy of elevator operation monitoring is improved, the qualified environmental data interval also serves as an elevator monitoring standard, the environment influence is accurately controlled, the elevator is prevented from being affected by the environment and being out of order, the elevator in test operation is shown as an elevator which is installed but not put into use, and the specific detection process is as follows:
step S1: randomly selecting an elevator which does not run as a data acquisition object, setting detection time for the data acquisition object, dividing the detection time into i sub-time periods at equal intervals, wherein i is 1, 2, …, n is a positive integer, randomly selecting m sub-time periods in the i sub-time periods as the data acquisition time, the m sub-time periods are different in environmental data, m is a positive integer larger than 5 and m is smaller than n, and the environmental data comprises an environmental temperature value and an environmental humidity value;
step S2: after the data acquisition time is selected, operating a data acquisition object, and within the data acquisition time, marking a shaking frequency value and a shaking displacement value of a box body of the data acquisition object in the operation process as PLm and WYm respectively, wherein the shaking frequency value represents the frequency of shaking of the data acquisition object in the operation process, the shaking displacement value represents the displacement generated by shaking of the data acquisition object in the operation process, the box body shakes and has vertical displacement, namely the box body is taken as a level line, and the displacement value has positive and negative, the shaking displacement value acquired by the method is only a displacement value, and the shaking frequency value and the shaking displacement value can be acquired through detection equipment such as a sensor;
step S3: by the formula
Acquiring a running coefficient Xm of a data acquisition object, wherein v1 and v2 are proportionality coefficients, v1 is greater than v2 is greater than 0, and e is a natural constant; the operation coefficient is a numerical value for detecting the qualified operation probability of the data acquisition object by carrying out normalization processing on the parameters of the data acquisition object; the larger the sway frequency value and the sway displacement value obtained by a formula are, the larger the operation coefficient is, and the smaller the probability of representing qualified operation of the data acquisition object is;
step S4: comparing the operational coefficient of the data acquisition object to an operational coefficient threshold:
if the operation coefficient of the data acquisition object is larger than or equal to the operation coefficient threshold value, judging that the data acquisition object is unqualified in operation, marking the corresponding data acquisition object as an operation-forbidden data acquisition object, and sending the operation-forbidden data acquisition object to the cloud supervision platform;
if the operation coefficient of the data acquisition object is less than the operation coefficient threshold value, judging that the data acquisition object is qualified in operation, marking the corresponding data acquisition object as an operable data acquisition object, marking the operation coefficient of the operable data acquisition object as a qualified operation coefficient, sending the operable data acquisition object and the qualified operation coefficient to a cloud supervision platform, and simultaneously entering step S5;
step S5: the method comprises the steps of obtaining ambient environment data in the running process of a data collection object in data collection time, marking ambient temperature values in the ambient environment data as WD and ambient humidity values as SD, constructing an ambient temperature value set A { WD1, WD2, …, WDm } and an ambient humidity value set B { SD1, SD2, …, SDm } in the data collection time, obtaining a subset with the largest value and a subset with the smallest value in the ambient temperature value set A, respectively marking the corresponding values as WDmax and WDmin, obtaining a subset with the largest value and a subset with the smallest value in the humidity temperature value set B, and respectively marking the corresponding values as SDmax and SDmin, wherein WDmax and WDmin are respectively expressed as an upper qualified temperature limit value and a lower limit value of qualified running of the data collection object, and SDmax and SDmin are respectively expressed as an upper qualified humidity limit value and a lower limit value of qualified running of the data collection object;
step S6: acquiring a qualified temperature interval and a qualified humidity interval according to the qualified temperature upper limit value and the qualified humidity lower limit value and the qualified humidity upper limit value and the qualified humidity lower limit value, and sending the qualified temperature interval and the qualified humidity interval to an operation prediction unit;
after receiving the operable data acquisition object and the qualified operation coefficient, the cloud supervision platform marks the operable data acquisition object as an operating elevator, puts the operating elevator into use, generates an operation prediction instruction and sends the operation prediction instruction to an operation prediction unit, and the operation prediction instruction is generated before the operating elevator is used, so that whether the elevator can normally operate each time is predicted, the safety performance of the elevator used by a user is improved, the occurrence of elevator accidents is reduced, and unnecessary loss is brought;
the operation prediction unit is used for receiving the operation prediction instruction, predicting each operation of the elevator after receiving the operation prediction instruction, and taking the qualified temperature interval and the qualified humidity interval sent by the elevator monitoring unit as prediction standards to improve the prediction accuracy, wherein the specific prediction process is as follows:
step SS 1: the method comprises the steps that after an operation prediction instruction is received, the temperature value and the humidity value of the operation environment of an operation elevator box body and the external environment are obtained in real time, if the corresponding numerical value of the operation environment and the external environment is less than 1 in the temperature value and the humidity value of the operation environment of the operation elevator box body, the temperature value and the humidity value of the operation environment are selected, and if the corresponding numerical value of the operation environment and the external environment is more than or equal to 1, the temperature value and the humidity value of the external environment are selected; marking the selected temperature values as YWD and YSD; when the difference value between the operation environment and the external environment is close, the accuracy of elevator operation prediction can be improved by taking the operation environment as the criterion, otherwise, when the difference value between the operation environment and the external environment is large, the influence of the corresponding data of the external environment on the operation environment data can be effectively prevented by taking the external environment as the criterion, so that the prediction result is inaccurate, and the prediction accuracy is reduced;
step SS 2: by running predictive formulas
Acquiring an environment influence coefficient XS in real time operation of an operating elevator, wherein alpha and beta are a temperature correction factor and a humidity correction factor respectively, the value of alpha is 1.04, and the value of beta is 3.11;
the correction factors in the formula are obtained by sampling analysis of technicians in the field, for example, the temperature correction factors, the technicians in the field randomly extract three time periods and monitor the three time periods to obtain real-time temperature values of the surrounding environment of the running elevator in the three time periods, namely 27 ℃, 28 ℃ and 32 ℃, obtain a proper temperature interval of 25-35 ℃ of the surrounding environment of the running elevator, mark the median value in the qualified temperature interval as the optimal temperature, namely 30 ℃, and when the real-time temperature values are in the qualified temperature interval, the real-time temperature values can be adjusted to the optimal temperature through the temperature correction coefficients in the analysis and calculation process, namely the temperature correction coefficients corresponding to the three time periods and the five time periods are 1.11, 1.07 and 0.94, and the average value is 1.04;
step SS 3: comparing the environment influence coefficient XS in real time operation of the running elevator with an environment influence coefficient threshold value:
if the environment influence coefficient XS is larger than or equal to the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment influences the running, generating an environment influence signal and sending the environment influence signal to the cloud supervision platform;
if the environment influence coefficient XS is smaller than the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment does not influence the running, generating an environment non-influence signal and sending the environment non-influence signal to the cloud supervision platform;
after receiving the environment influence signal, the cloud supervision platform suspends the corresponding running elevator, maintains the environment adjusting device of the corresponding running elevator, generates a running instruction and controls the running elevator to run after receiving the environment influence signal, generates a real-time monitoring signal and sends the real-time monitoring signal to the real-time monitoring unit, the environment adjusting device is a ventilator, and the ventilator can adjust the temperature value and the humidity value of the running environment of the running elevator;
after the real-time monitoring unit receives the real-time monitoring signal, carry out real-time supervision to the operation elevator at the operation in-process, under the influence that does not receive the environment, prevent that elevator equipment from appearing unusually, lead to the operating quality to reduce, can in time discover and stop, reduce the harm that the accident brought, concrete monitoring process is as follows:
step T1: after the elevator door is closed, the weight in the running elevator box body is obtained in real time, the ratio of the weight in the running elevator box body to the rated bearing capacity of the box body is calculated, the corresponding ratio is marked as the weight ratio, and if the weight ratio is less than or equal to one third, the real-time running of the corresponding running elevator is marked as primary running; if the weight ratio is more than one third and less than two thirds, marking the real-time operation of the corresponding operation elevator as secondary operation; if the weight ratio is more than or equal to two thirds, marking the real-time operation of the corresponding operation elevator as three-level operation;
step T2: performing compensation monitoring on the running elevator, adding the weight in the real-time running elevator box body and the weight of the running elevator box body to obtain the running total mass of the running elevator, obtaining the dead weight adjusting time of the running elevator, judging that the running elevator is abnormal in compensation if the dead weight adjusting time of the running elevator is not less than the closing time of the elevator door corresponding to the running elevator, generating an abnormal compensation signal and sending the abnormal compensation signal to a cloud supervision platform, judging that the running elevator is normal in compensation if the dead weight adjusting time of the running elevator is less than the closing time of the elevator door corresponding to the running elevator, generating a normal compensation signal and sending the normal compensation signal to the cloud supervision platform; wherein, the compensation monitoring is used for primary operation, secondary operation and tertiary operation; the time of self-weight compensation is reduced, the efficiency of self-weight compensation is improved, and the occurrence of elevator faults is reduced;
step T3: the method comprises the steps of monitoring output of an operating elevator, dividing the operating process of the operating elevator into three parts of acceleration, constant speed and deceleration, acquiring the constant speed of the real-time operation of the operating elevator, and acquiring the shaking times of the operating elevator from acceleration to constant speed and from constant speed to deceleration; if the constant speed of the running elevator in real time is not equal to the rated constant speed or the frequency of the corresponding shaking times is larger than the shaking time threshold value, judging that the output of the corresponding motor of the running elevator is abnormal, generating an output abnormal signal and sending the output abnormal signal to the cloud supervision platform; otherwise, judging that the output of the running elevator is normal; wherein, the output monitoring is only used for the second-level operation and the third-level operation; the motor output is monitored, so that speed abnormity caused by output abnormity is prevented, safety accidents are avoided, and meanwhile, the use quality of a user is effectively enhanced due to stable running speed;
after receiving the compensation abnormal signal or the output abnormal signal, the cloud supervision platform sends the compensation abnormal signal or the output abnormal signal to the alarm unit;
after receiving the compensation abnormal signal or the output abnormal signal, the alarm unit generates an alarm signal and sends the alarm signal and the compensation abnormal signal or the output abnormal signal to the maintenance unit;
after receiving the alarm signal and the compensation abnormal signal or the output abnormal signal, the maintenance unit sends the compensation abnormal signal or the output abnormal signal to maintenance personnel corresponding to maintenance types, wherein the maintenance types comprise electrical maintenance and mechanical maintenance, the compensation abnormal is a mechanical fault, and the output abnormal is an electrical fault; the maintenance time of the maintainers is shortened, and the elevator fault which cannot be efficiently solved by the maintainers is prevented from occurring.
The working principle of the invention is as follows:
when the elevator operation fault supervision system works, a trial operation elevator is detected through an elevator monitoring unit, and a qualified operation coefficient and a qualified environment coefficient interval of the trial operation elevator are collected; receiving an operation prediction instruction through an operation prediction unit, and predicting each operation of the operation elevator after receiving the operation prediction instruction; after receiving the real-time monitoring signal through the real-time monitoring unit, the real-time monitoring unit monitors the running elevator in real time in the running process, generates a compensation abnormal signal or outputs an abnormal signal, and sends the compensation abnormal signal or the output abnormal signal to the alarm unit; after the alarm unit receives the compensation abnormal signal or outputs the abnormal signal, an alarm signal is generated and sent to the maintenance unit together with the compensation abnormal signal or the output abnormal signal; after the alarm signal and the compensation abnormal signal or the output abnormal signal are received through the maintenance unit, the compensation abnormal signal or the output abnormal signal is sent to maintenance personnel of the corresponding maintenance type.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (3)
1. An elevator operation fault supervision system based on big data is characterized by comprising a cloud supervision platform, an operation prediction unit, an elevator monitoring unit, a real-time monitoring unit, an alarm unit and a maintenance unit;
the elevator monitoring unit is used for detecting the trial run elevator, acquiring a qualified running coefficient and a qualified environment coefficient interval of the trial run elevator, monitoring the trial run elevator according to the qualified running coefficient of the trial run elevator, generating a qualified temperature interval and a qualified humidity area simultaneously, and sending the qualified running coefficient, the qualified temperature interval and the qualified humidity interval to the operation prediction unit;
the cloud supervision platform marks the operable data acquisition object as an operating elevator after receiving the operable data acquisition object and the qualified operating coefficient, puts the operating elevator into use, generates an operation prediction instruction and sends the operation prediction instruction to an operation prediction unit, and the operation prediction instruction is generated before the operating elevator is used;
the operation prediction unit is used for receiving an operation prediction instruction, predicting each operation of the operation elevator after receiving the operation prediction instruction, generating an environment influence signal or an environment non-influence signal, and sending the environment influence signal or the environment non-influence signal to the cloud supervision platform;
the cloud supervision platform suspends the corresponding running elevator after receiving the environment influence signal, maintains the environment adjusting device of the corresponding running elevator, generates a running instruction and controls the running elevator to run after receiving the environment influence-free signal, generates a real-time monitoring signal and sends the real-time monitoring signal to the real-time monitoring unit, the environment adjusting device is a ventilator, and the ventilator can adjust the temperature value and the humidity value of the running environment of the running elevator;
the real-time monitoring unit monitors the running elevator in real time in the running process after receiving the real-time monitoring signal, generates a compensation abnormal signal or outputs an abnormal signal, and sends the compensation abnormal signal or the output abnormal signal to the alarm unit;
after receiving the compensation abnormal signal or the output abnormal signal, the cloud supervision platform sends the compensation abnormal signal or the output abnormal signal to the alarm unit;
the alarm unit generates an alarm signal after receiving the compensation abnormal signal or the output abnormal signal and sends the alarm signal and the compensation abnormal signal or the output abnormal signal to the maintenance unit;
after receiving the alarm signal and the compensation abnormal signal or the output abnormal signal, the maintenance unit sends the compensation abnormal signal or the output abnormal signal to maintenance personnel of a corresponding maintenance type;
the specific detection process of the elevator monitoring unit is as follows:
step S1: randomly selecting an elevator which does not run as a data acquisition object, setting detection time for the data acquisition object, dividing the detection time into i sub-time periods at equal intervals, wherein i =1, 2, …, n and n are positive integers, randomly selecting m sub-time periods in the i sub-time periods as the data acquisition time, the m sub-time periods are different in environmental data, m is a positive integer larger than 5 and m is smaller than n, and the environmental data comprise an environmental temperature value and an environmental humidity value;
step S2: after the data acquisition time is selected, operating the data acquisition object, and acquiring a shaking frequency value and a shaking displacement value of the box body in the operation process of the data acquisition object in the data acquisition time, and respectively marking the corresponding shaking frequency value and the corresponding shaking displacement value as PLm and WYm, wherein the shaking frequency value is represented as the frequency of shaking of the data acquisition object in the operation process, and the shaking displacement value is represented as the displacement generated by shaking of the data acquisition object in the operation process;
step S3: acquiring a running coefficient Xm of a data acquisition object through a formula, wherein v1 and v2 are proportionality coefficients, v1 is greater than v2 is greater than 0, and e is a natural constant;
step S4: comparing the operational coefficient of the data acquisition object to an operational coefficient threshold:
if the operation coefficient of the data acquisition object is larger than or equal to the operation coefficient threshold value, judging that the data acquisition object is unqualified in operation, marking the corresponding data acquisition object as an operation-forbidden data acquisition object, and sending the operation-forbidden data acquisition object to the cloud supervision platform;
if the operation coefficient of the data acquisition object is less than the operation coefficient threshold value, judging that the data acquisition object is qualified in operation, marking the corresponding data acquisition object as an operable data acquisition object, marking the operation coefficient of the operable data acquisition object as a qualified operation coefficient, sending the operable data acquisition object and the qualified operation coefficient to a cloud supervision platform, and simultaneously entering step S5;
step S5: the method comprises the steps of obtaining ambient environment data in the running process of a data collection object in data collection time, marking ambient temperature values in the ambient environment data as WD and ambient humidity values as SD, constructing an ambient temperature value set A { WD1, WD2, …, WDm } and an ambient humidity value set B { SD1, SD2, …, SDm } in the data collection time, obtaining a subset with the largest value and a subset with the smallest value in the ambient temperature value set A, respectively marking the corresponding values as WDmax and WDmin, obtaining a subset with the largest value and a subset with the smallest value in the humidity temperature value set B, and respectively marking the corresponding values as SDmax and SDmin, wherein WDmax and WDmin are respectively expressed as an upper qualified temperature limit value and a lower limit value of qualified running of the data collection object, and SDmax and SDmin are respectively expressed as an upper qualified humidity limit value and a lower limit value of qualified running of the data collection object;
step S6: acquiring a qualified temperature interval and a qualified humidity interval according to the qualified temperature upper limit value and the qualified humidity lower limit value and the qualified humidity upper limit value and the qualified humidity lower limit value, and sending the qualified temperature interval and the qualified humidity interval to an operation prediction unit;
the specific prediction process of the operation prediction unit is as follows:
step SS 1: the method comprises the steps that after an operation prediction instruction is received, the temperature value and the humidity value of the operation environment of an operation elevator box body and the external environment are obtained in real time, if the corresponding numerical value of the operation environment and the external environment is less than 1 in the temperature value and the humidity value of the operation environment of the operation elevator box body, the temperature value and the humidity value of the operation environment are selected, and if the corresponding numerical value of the operation environment and the external environment is more than or equal to 1, the temperature value and the humidity value of the external environment are selected; marking the selected temperature values as YWD and YSD;
step SS 2: acquiring an environment influence coefficient XS in real time operation of an elevator to be operated by an operation prediction formula, wherein alpha and beta are a temperature correction factor and a humidity correction factor respectively, the value of alpha is 1.04, and the value of beta is 3.11;
step SS 3: comparing the environment influence coefficient XS in real time operation of the running elevator with an environment influence coefficient threshold value:
if the environment influence coefficient XS is larger than or equal to the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment influences the running, generating an environment influence signal and sending the environment influence signal to the cloud supervision platform;
and if the environment influence coefficient XS is less than the environment influence coefficient threshold value when the running elevator runs in real time, judging that the environment does not influence the running, generating an environment non-influence signal and sending the environment non-influence signal to the cloud supervision platform.
2. The elevator operation fault supervision system based on big data according to claim 1 is characterized in that the real-time monitoring unit has the following specific monitoring processes:
step T1: after the running elevator closes the elevator door, the running elevator is divided into primary running, secondary running and tertiary running;
step T2: performing compensation monitoring on the running elevator, adding the weight in the real-time running elevator box body and the weight of the running elevator box body to obtain the running total mass of the running elevator, obtaining the dead weight adjusting time of the running elevator, judging that the running elevator is abnormal in compensation if the dead weight adjusting time of the running elevator is not less than the closing time of the elevator door corresponding to the running elevator, generating an abnormal compensation signal and sending the abnormal compensation signal to a cloud supervision platform, judging that the running elevator is normal in compensation if the dead weight adjusting time of the running elevator is less than the closing time of the elevator door corresponding to the running elevator, generating a normal compensation signal and sending the normal compensation signal to the cloud supervision platform; wherein, the compensation monitoring is used for primary operation, secondary operation and tertiary operation;
step T3: the method comprises the steps of monitoring output of an operating elevator, dividing the operating process of the operating elevator into three parts of acceleration, constant speed and deceleration, acquiring the constant speed of the real-time operation of the operating elevator, and acquiring the shaking times of the operating elevator from acceleration to constant speed and from constant speed to deceleration; if the constant speed of the running elevator in real time is not equal to the rated constant speed or the frequency of the corresponding shaking times is larger than the shaking time threshold value, judging that the output of the corresponding motor of the running elevator is abnormal, generating an output abnormal signal and sending the output abnormal signal to the cloud supervision platform; otherwise, judging that the output of the running elevator is normal; wherein the output monitoring is only used for the secondary operation and the tertiary operation.
3. The elevator operation fault supervision system based on big data according to claim 2, characterized in that the operation elevator operation grade division in step T1 is as follows:
acquiring the weight in the running elevator box in real time, calculating the ratio of the weight in the running elevator box to the rated bearing capacity of the box, marking the corresponding ratio as a weight ratio, and if the weight ratio is less than or equal to one third, marking the real-time running of the corresponding running elevator as primary running; if the weight ratio is more than one third and less than two thirds, marking the real-time operation of the corresponding operation elevator as secondary operation; and if the weight ratio is more than or equal to two thirds, marking the real-time operation of the corresponding operation elevator as three-level operation.
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| CN114838767B (en) * | 2022-05-06 | 2022-12-20 | 徐州工程学院 | Temperature and humidity intelligent monitoring system and method for cold-chain logistics |
| CN114634075A (en) * | 2022-05-18 | 2022-06-17 | 大汉科技股份有限公司 | Unmanned elevator fault analysis and prediction system based on data analysis |
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