CN117409553B - Abnormal alarm method and alarm device based on Internet of things - Google Patents
Abnormal alarm method and alarm device based on Internet of things Download PDFInfo
- Publication number
- CN117409553B CN117409553B CN202311530560.3A CN202311530560A CN117409553B CN 117409553 B CN117409553 B CN 117409553B CN 202311530560 A CN202311530560 A CN 202311530560A CN 117409553 B CN117409553 B CN 117409553B
- Authority
- CN
- China
- Prior art keywords
- eccentric
- alarm
- value
- domain
- acceleration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000001133 acceleration Effects 0.000 claims abstract description 240
- 230000004907 flux Effects 0.000 claims abstract description 41
- 238000005299 abrasion Methods 0.000 claims description 29
- 238000001514 detection method Methods 0.000 claims description 16
- 238000012544 monitoring process Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 description 24
- 239000006185 dispersion Substances 0.000 description 12
- 230000005856 abnormality Effects 0.000 description 11
- 238000004891 communication Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 6
- 238000000605 extraction Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012790 confirmation Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000006855 networking Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 230000000246 remedial effect Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/187—Machine fault alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/001—Alarm cancelling procedures or alarm forwarding decisions, e.g. based on absence of alarm confirmation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application provides an abnormal alarm method and alarm device based on the internet of things, the eccentric acceleration of elevator is acquired, the eccentric acceleration domain is obtained, the eccentric release base is extracted, the eccentric wear alarm circulation value is determined according to the eccentric release base, when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarm instruction is sent to the control center of the internet of things, when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux, the eccentric voltage discrete law is determined according to the eccentric acceleration domain to increase the eccentric wear alarm circulation value, the wear alarm flow-increasing value is obtained, when the wear alarm flow-increasing value is higher than the wear characteristic stop valve flux, a second overhaul alarm instruction is sent to the control center of the internet of things, when the wear alarm flow-increasing value is lower than the wear characteristic stop valve flux, the alarm time stamp of the control center of the internet of things is updated, the situation that when the abnormal alarm is sent out, the time tension reserved for the elevator passengers to withdraw is avoided, and the passengers are trapped in the elevator is caused.
Description
Technical Field
The application relates to the technical field of Internet of things alarm, and in particular relates to an abnormal alarm method and an alarm device based on the Internet of things.
Background
The abnormality of the safety equipment may indicate a potential problem or failure, by setting an abnormality alarm, the abnormality can be found early before the problem is worsened, so that necessary measures are taken to prevent accidents, in the scenes of elevators, mechanical equipment and the like, if the safety equipment is abnormal, accidents may be caused, life and health of personnel are threatened, in addition, the abnormality alarm can prompt the personnel and operators to take action in advance, potential danger is avoided, potential accidents and losses are prevented, and even if the safety equipment is slightly abnormal, remedial measures can be taken before the accidents occur.
In the prior art, the abnormal alarm of safety equipment such as an elevator is usually realized through direct detection of various sensors, the sensors can sense various physical quantity parameters inside and outside the elevator, and abnormal alarm information is sent when the sensors detect abnormal (oversized or undersized) physical quantity parameters, so that elevator passengers are informed of evacuation, but the mode cannot send alarm signals in advance before the elevator breaks down, and when abnormal alarm is sent, the time reserved for the evacuation of the elevator passengers is tense, so that passengers are easy to be trapped in the elevator.
Disclosure of Invention
The application provides an abnormal alarm method and an alarm device based on the Internet of things, which are used for solving the technical problem that when an abnormal alarm is sent out, the time reserved for an elevator passenger to withdraw is tense, so that the passenger is trapped in the elevator.
The application adopts the following technical scheme to solve the technical problems:
in a first aspect, the present application provides an anomaly alarm method based on the internet of things, where the method may be executed by a network device, or may also be executed by a chip configured in the network device, and the application is not limited thereto.
Specifically, the method comprises the following steps:
after an alarm time stamp of the control center of the Internet of things reaches an abnormal alarm period value, acquiring eccentric acceleration of an elevator to obtain an eccentric acceleration domain;
extracting eccentric outliers from the eccentric acceleration domain to obtain all eccentric outliers in the eccentric acceleration domain;
determining an eccentric wear alarm circulation value according to each eccentric deviation base;
when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarm instruction is sent to an internet of things control center, and when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux, an eccentric voltage discrete law is determined according to the eccentric acceleration domain;
Increasing the flow of the eccentric wear alarm flow value according to the eccentric voltage discrete law to obtain a wear alarm flow increasing value;
and when the abrasion alarm increasing value is higher than the abrasion characteristic stop valve flux, a second overhaul alarm instruction is sent to the Internet of things control center, and when the abrasion alarm increasing value is lower than the abrasion characteristic stop valve flux, an alarm time stamp of the Internet of things control center is updated.
With reference to the first aspect, in some implementations of the first aspect, extracting the eccentric outliers from the eccentric acceleration domain, obtaining all the eccentric outliers in the eccentric acceleration domain specifically includes:
determining a main domain trend signal in the eccentric acceleration domain;
acquiring time coordinates corresponding to each second derivative zero point in the main domain trend signal, and taking the time coordinates corresponding to each second derivative zero point as each eccentric departure pointer of an eccentric acceleration domain;
and obtaining each eccentric deviation base according to each eccentric deviation pointer and the eccentric acceleration domain.
With reference to the first aspect, in certain implementations of the first aspect, determining the main domain trend signal in the eccentric acceleration domain specifically includes:
Acquiring a maximum value point set in the eccentric acceleration domain;
acquiring a minimum value point set in the eccentric acceleration domain;
fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the eccentric acceleration domain and the average signal as a first main domain trend signal;
acquiring a main domain trend value of the first main domain trend signal, judging whether the main domain trend value of the first main domain trend signal is lower than a main domain trend judgment threshold, and taking the first main domain trend signal as a main domain signal when the main domain trend value of the first main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
when the main domain trend value of the first main domain trend signal is high and Yu Zhu domain trend judging threshold value is high, acquiring a maximum value point set in the first main domain trend signal; acquiring a minimum value point set in the first main domain trend signal;
fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the first main domain trend signal and the average signal as a second main domain trend signal;
Acquiring a main domain trend value of the second main domain trend signal, judging whether the main domain trend value of the second main domain trend signal is lower than a main domain trend judgment threshold, and taking the second main domain trend signal as a main domain signal when the main domain trend value of the second main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
and when the main domain trend value of the second main domain trend signal is higher than the Yu Zhu domain trend judging threshold, acquiring the next main domain trend signal, judging whether the main domain trend value of the next main domain trend signal is lower than the main domain trend judging threshold, repeating the steps until a main domain trend signal with the main domain trend value lower than the main domain trend judging threshold is found, and taking the main domain trend signal with the main domain trend value lower than the main domain trend judging threshold as the main domain trend signal in the eccentric acceleration domain.
With reference to the first aspect, in some implementations of the first aspect, acquiring a main domain trend value of the first main domain trend signal specifically includes:
acquiring the first main domain trend signal A 1 (t)；
Acquiring a detection time T corresponding to a first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n ；
According to the first main domain trend signal A 1 (T) detecting time T corresponding to the first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n Determining a main domain trend value of the first main domain trend signal, wherein the main domain trend value of the first main domain trend signal is determined according to the following formula:
wherein lambda is 1 And t is a time independent variable and dt is the derivative of the time independent variable.
With reference to the first aspect, in certain implementations of the first aspect, an eccentric acceleration of the elevator is acquired using an acceleration sensor, resulting in an eccentric acceleration domain.
With reference to the first aspect, in some implementations of the first aspect, acquiring an eccentric acceleration of an elevator, the obtaining an eccentric acceleration domain specifically includes:
acquiring eccentric acceleration of the elevator at equal intervals, and mapping each eccentric acceleration value into a time domain space according to acquisition time corresponding to the eccentric acceleration to obtain an eccentric acceleration set;
and obtaining an eccentric acceleration domain according to the eccentric acceleration set.
With reference to the first aspect, in certain implementation manners of the first aspect, a process of obtaining an eccentric acceleration domain according to the eccentric acceleration set is specific:
Acquiring each eccentric acceleration point in the eccentric acceleration set;
acquiring monitoring moments corresponding to the eccentric acceleration points respectively;
determining the eccentric acceleration domain according to each eccentric acceleration point in the eccentric acceleration set and the monitoring time corresponding to each eccentric acceleration point, wherein the eccentric acceleration domain can be determined according to the following formula when the eccentric acceleration domain is concretely implemented:
wherein A (t) is the eccentric acceleration domain, a i For the ith eccentric acceleration point, l in the set of eccentric accelerations i (T) is a transition function, T i For the monitoring time corresponding to the ith eccentric acceleration point in the eccentric acceleration set, T c For the c-th eccentricity in the set of eccentric accelerationsAnd the monitoring time corresponding to the acceleration points is n, the number of the eccentric acceleration points in the eccentric acceleration set is n, and t is the time domain independent variable of the eccentric acceleration domain.
In a second aspect, the application provides an alarm device based on thing networking, alarm device is including the unusual alarm unit of thing networking, the unusual alarm unit of thing networking includes:
the eccentric acceleration domain acquisition module is used for acquiring the eccentric acceleration of the elevator after the alarm time stamp of the control center of the Internet of things reaches an abnormal alarm period value to obtain an eccentric acceleration domain;
The eccentric outlier acquisition module is used for extracting eccentric outliers from the eccentric acceleration domain to obtain all eccentric outliers in the eccentric acceleration domain;
the eccentric wear alarm circulation value determining module is used for determining an eccentric wear alarm circulation value according to each eccentric deviation base;
the eccentric voltage discrete law determining module is used for sending a first overhaul alarm instruction to the control center of the Internet of things when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, and determining an eccentric voltage discrete law according to the eccentric acceleration domain when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux;
the abrasion alarm current-increasing value determining module is used for increasing the eccentric abrasion alarm current-increasing value according to the eccentric voltage discrete law to obtain an abrasion alarm current-increasing value;
and the alarm restarting module is used for sending a second overhaul alarm instruction to the control center of the Internet of things when the abrasion alarm increasing value is higher than the abrasion characteristic stop valve flux, and updating an alarm time stamp of the control center of the Internet of things when the abrasion alarm increasing value is lower than the abrasion characteristic stop valve flux.
In a third aspect, the present application provides a computer terminal device, where the computer terminal device includes a memory and a processor, where the memory stores codes, and the processor is configured to obtain the codes and execute the anomaly alarm method based on the internet of things.
In a fourth aspect, the present application provides a computer readable storage medium storing at least one computer program loaded and executed by a processor to implement the operations performed by the above-described internet of things-based anomaly alarm method.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the abnormal alarming method and the alarming device based on the Internet of things, firstly, after an alarming time stamp of a control center of the Internet of things reaches an abnormal alarming period value, eccentric acceleration of an elevator is acquired, an eccentric acceleration domain is obtained, eccentric outliers are extracted from the eccentric acceleration domain, all eccentric outliers in the eccentric acceleration domain are obtained, eccentric wear alarming circulation values are determined according to the eccentric outliers, when the eccentric wear alarming circulation values are higher than wear characteristic stop valve flux, a first overhaul alarming command is sent to the control center of the Internet of things, when the eccentric wear alarming circulation values are lower than the wear characteristic stop valve flux, an eccentric voltage discrete law is determined according to the eccentric acceleration domain, the eccentric wear alarming circulation values are increased according to the eccentric voltage discrete law, a second overhaul alarming command is sent to the control center of the Internet of things, when the wear alarming circulation values are lower than the wear characteristic stop valve flux, the alarm time stamp of the control center of the Internet of things is updated, the eccentric wear alarming circulation values and the wear alarming circulation values are determined regularly, the wear alarming circulation values are compared with the wear characteristic stop valve flux, and the elevator can be prevented from being aged down before the elevator is in a fault state according to comparison, and passengers can be prevented from getting bad when the elevator is in a riding.
Drawings
FIG. 1 is an exemplary flow chart of an anomaly alarm method based on the Internet of things, according to some embodiments of the present application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software of an IOT anomaly alarm unit shown in accordance with some embodiments of the present application;
fig. 3 is a schematic structural diagram of a computer terminal device for implementing an anomaly alarm method based on the internet of things according to some embodiments of the present application.
Detailed Description
According to the method, after the alarm time stamp of the control center of the Internet of things reaches an abnormal alarm period value, the eccentric acceleration of the elevator is acquired, an eccentric acceleration domain is obtained, eccentric outliers are extracted from the eccentric acceleration domain, all the eccentric outliers in the eccentric acceleration domain are obtained, the eccentric wear alarm circulation value is determined according to the eccentric outliers, when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarm instruction is sent to the control center of the Internet of things, when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux, the eccentric voltage dispersion is determined according to the eccentric acceleration domain, the eccentric wear alarm circulation value is increased according to the eccentric voltage dispersion law, a wear alarm increase value is obtained, when the wear alarm increase value is lower than the wear characteristic stop valve flux, the alarm time stamp of the control center of the Internet of things is updated, the eccentric wear alarm circulation value and the wear alarm increase value are regularly determined, and compared with the wear characteristic stop valve flux, the alarm instruction can be sent before the safety failure of the elevator occurs according to comparison results, and the alarm increase value is prevented from being carried out by passengers when the elevator is stressed, and passengers feel bad when the elevator is in the elevator.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of an anomaly alarm method based on the internet of things according to some embodiments of the present application, the anomaly alarm method 100 based on the internet of things mainly includes the following steps:
in step S101, after the alarm time stamp of the control center of the internet of things reaches the abnormal alarm period value, the eccentric acceleration of the elevator is collected, and the eccentric acceleration domain is obtained.
It should be noted that, the internet of things control center is a core system for realizing centralized monitoring and remote control of an elevator system through internet of things technology, and is used for realizing real-time monitoring of data such as running state, fault information, elevator position and the like of an elevator, so that operation and maintenance personnel can know the running state of the elevator at any time, and potential problems can be found in advance.
It should be noted that, the alarm time stamp is a time stamp of a timer built in the control center of the internet of things, and is used for detecting whether an interval between a current time and a last elevator abnormality detection time has reached a set abnormality alarm period value, where the abnormality alarm period value is a preset abnormality alarm detection period time of an elevator maintenance personnel according to a cycle flow of an elevator operation, and optionally, in some embodiments, the abnormality alarm period value is preset to 168h.
It should be noted that, the eccentric wheel is located in the elevator door, also called an elevator door wheel or a door guide wheel, and is a key component of the elevator door system, and is usually located on the door of the elevator car, so as to guide and support the opening and closing motion of the elevator door, the design of the eccentric wheel can reduce the friction force of the door, make the door slide more easily and stably, and ensure the reliable operation of the elevator door, but after the elevator equipment ages, the eccentric wheel wears down under long-time operation, and the closing speed of the elevator door is too fast, so that the infrared sensor can not detect whether an object exists in the elevator door or not even close the door before the sensor detects the object, increasing the risk of clamping passengers or objects, and the wear of the eccentric wheel also affects the operation effect of the brake system, so that the door cannot stop rapidly, and increasing the safety risk in emergency.
In some embodiments, the eccentric acceleration is the acceleration of the eccentric wheel on the elevator car door in the opening and closing process of the elevator door, in some embodiments, an acceleration sensor in the internet of things system may be used to collect the eccentric acceleration of the elevator to obtain an eccentric acceleration set, for example, the acceleration sensor is installed on the eccentric wheel on the elevator car door and used for monitoring and recording the acceleration change of the eccentric wheel in the opening and closing process of the elevator door in real time to obtain the eccentric acceleration of different monitoring moments, and in some other embodiments, the eccentric acceleration may also be obtained through other devices or equipment capable of realizing acceleration collection, which is not limited herein.
In some embodiments, acquiring the eccentric acceleration of the elevator, the obtaining the eccentric acceleration domain specifically includes:
acquiring eccentric acceleration of the elevator at equal intervals, and mapping each eccentric acceleration value into a time domain space according to acquisition time corresponding to the eccentric acceleration to obtain an eccentric acceleration set; the time domain space is a two-dimensional space, and coordinates of signal points in the time domain space are an eccentric acceleration value and a time domain independent variable value (detection time) respectively.
And obtaining an eccentric acceleration domain according to the eccentric acceleration set.
It should be noted that, the eccentric acceleration set is a point set formed by mapping eccentric acceleration under different monitoring moments into a time domain space, and in specific implementation, in order to improve continuity and integrity of data, it is necessary to fit discrete eccentric acceleration sets to obtain continuous eccentric acceleration domains in a time domain, so as to facilitate extraction of eccentric outliers of subsequent eccentric acceleration domains, where the eccentric acceleration domains are continuous eccentric acceleration signals in the time domain space.
Alternatively, in some embodiments, the process of obtaining the eccentric acceleration domain according to the eccentric acceleration set may be implemented by the following steps:
Acquiring each eccentric acceleration point in the eccentric acceleration set;
acquiring monitoring moments corresponding to the eccentric acceleration points respectively;
determining the eccentric acceleration domain according to each eccentric acceleration point in the eccentric acceleration set and the monitoring time corresponding to each eccentric acceleration point, wherein the eccentric acceleration domain can be determined according to the following formula when the eccentric acceleration domain is concretely implemented:
wherein A (t) is the eccentric acceleration domain, a i For the ith eccentric acceleration point, l in the set of eccentric accelerations i (T) is a transition function, T i For the monitoring time corresponding to the ith eccentric acceleration point in the eccentric acceleration set, T c And n is the number of the eccentric acceleration points in the eccentric acceleration set, and t is the time domain independent variable of the eccentric acceleration domain.
In step S102, the eccentric outlier extraction is performed on the eccentric acceleration domain, so as to obtain all eccentric outliers in the eccentric acceleration domain.
Preferably, in some embodiments, the eccentric outlier extraction is performed on the eccentric acceleration domain, and the following manner may be adopted to obtain all eccentric outliers in the eccentric acceleration domain:
Determining a main domain trend signal in the eccentric acceleration domain;
acquiring time coordinates corresponding to each second derivative zero point in the main domain trend signal, and taking the time coordinates corresponding to each second derivative zero point as each eccentric departure pointer of an eccentric acceleration domain;
and obtaining each eccentric deviation base according to each eccentric deviation pointer and the eccentric acceleration domain.
The time coordinate is a value of the detection time corresponding to a point on the main domain trend signal.
When the method is specifically implemented, the time coordinates can be substituted into the main domain trend signal, so that the unique corresponding eccentric outlier is obtained, and then the coordinates of each eccentric outlier are substituted into the main domain trend signal in the same mode, so that each eccentric outlier is obtained, and the eccentric outlier is a signal point on the main domain trend signal.
Preferably, determining the main domain trend signal in the eccentric acceleration domain may be specifically implemented in the following manner:
acquiring a maximum value point set in the eccentric acceleration domain;
acquiring a minimum value point set in the eccentric acceleration domain;
fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the eccentric acceleration domain and the average signal as a first main domain trend signal;
Acquiring a main domain trend value of the first main domain trend signal, judging whether the main domain trend value of the first main domain trend signal is lower than a main domain trend judgment threshold, and taking the first main domain trend signal as a main domain signal when the main domain trend value of the first main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
when the main domain trend value of the first main domain trend signal is high and Yu Zhu domain trend judging threshold value is high, acquiring a maximum value point set in the first main domain trend signal; acquiring a minimum value point set in the first main domain trend signal;
fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the first main domain trend signal and the average signal as a second main domain trend signal;
acquiring a main domain trend value of the second main domain trend signal, judging whether the main domain trend value of the second main domain trend signal is lower than a main domain trend judgment threshold, and taking the second main domain trend signal as a main domain signal when the main domain trend value of the second main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
And when the main domain trend value of the second main domain trend signal is higher than the Yu Zhu domain trend judging threshold, acquiring the next main domain trend signal, judging whether the main domain trend value of the next main domain trend signal is lower than the main domain trend judging threshold, repeating the steps until a main domain trend signal with the main domain trend value lower than the main domain trend judging threshold is found, and taking the main domain trend signal with the main domain trend value lower than the main domain trend judging threshold as the main domain trend signal in the eccentric acceleration domain.
Alternatively, in some embodiments, the main domain trend value of the first main domain trend signal may be obtained according to the following formula:
wherein lambda is 1 A is the main domain trend value of the first main domain trend signal 1 (T) is the first main domain trend signal, T 1 And T n And respectively detecting the detection time corresponding to the first eccentric acceleration value and the last eccentric acceleration point in the eccentric acceleration set, wherein t is a time independent variable, and dt is the differentiation of the time independent variable.
The eccentric acceleration main domain trend signal is obtained by decomposing the eccentric acceleration domain, so that the average characteristic in the eccentric acceleration domain is filtered, so that the variation characteristic of the eccentric acceleration value is more obvious, and it is noted that the first main domain trend signal A in the above formula can be obtained 1 And (t) replacing the main domain trend signals with other main domain trend signals, thereby acquiring main domain trend values of other main domain trend signals until the main domain trend signals with the main domain trend values lower than the main domain trend judgment threshold are found, wherein the main domain trend signals with the main domain trend values lower than the main domain trend judgment threshold are used as the eccentric acceleration main domain trend signals, and the main domain trend judgment threshold can be calibrated as a constant according to experience in specific implementation.
In step S103, an eccentric wear warning circulation value is determined from each eccentric outlier.
In some embodiments, in the process of determining the eccentric wear warning circulation value according to each eccentric outlier, all eccentric outliers obtained through extraction are connected in the same way through connecting two adjacent eccentric outliers in time sequence in a time domain space through a straight line, so that an acceleration center fold line is obtained, and further, the eccentric acceleration average value of the acceleration center fold line is used as the eccentric wear warning circulation value;
the eccentric outliers are signal points with the most severe main domain trend signal abnormal change in the eccentric acceleration domain, acceleration center broken lines are obtained through linear connection of the eccentric outliers, broken line average values are obtained, and after the distance between each eccentric outlier and the adjacent eccentric outlier is taken as a weight, all the eccentric outliers are weighted and fused, so that an eccentric wear alarm circulation value reflecting the overall change characteristic of the eccentric acceleration is obtained.
And in step S104, when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarm instruction is sent to an Internet of things control center, and when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux, an eccentric voltage discrete law is determined according to the eccentric acceleration domain.
It should be noted that, in the present application, the eccentric wear alarm circulation value is obtained according to the eccentric outlier extracted in the eccentric acceleration domain, and reflects the average value of the more obvious part change characteristics in the eccentric acceleration set, when the eccentric wear alarm circulation value is too high, it indicates that the elevator door closing speed is too fast due to the fact that the dynamic friction factor of the hub slides down due to the wear of the eccentric wheel, and at this time, the elevator safety accident is easy to be caused, in some embodiments, the wear characteristic stop valve flux is calibrated as a constant according to the historical experience, and the wear characteristic stop valve flux is a judgment threshold value for comparing with the eccentric wear alarm circulation value to judge whether the elevator needs to perform abnormal alarm.
Reasonably, in some embodiments, when the eccentric wear alarm circulation value is higher than a preset wear characteristic alarm threshold value, an elevator stop alarm is sent to the control center, and meanwhile, the elevator is controlled to send a stop alarm through an alarm device such as an alarm bell to prevent an elevator user from entering, namely, the elevator stops running immediately, so that the wear is prevented from being aggravated continuously and safety accidents are caused, and a first overhaul alarm instruction is sent to the control center through a communication module carried on the elevator, so that maintenance personnel are informed of abnormal conditions of the wear of the eccentric wheel, and emergency maintenance measures are needed to be taken.
The eccentric wheel abrasion abnormal fault information is used for describing the type of abnormal faults of the elevator; an eccentric emergency maintenance request is used to indicate that emergency maintenance is required, as wear problems can affect the normal operation and safety of the elevator; the eccentric wear warning circulation value information is used for providing detailed eccentric wear warning circulation value information and helping maintenance personnel to know the severity of the fault condition.
Alternatively, in some embodiments, the process of determining the decentration voltage dispersion law according to the decentration acceleration domain may specifically be as follows:
connecting a test probe of an oscilloscope with an armature positive electrode and an armature negative electrode of an eccentric wheel motor, and acquiring an eccentric voltage curve through the oscilloscope, wherein the eccentric voltage curve is the same as a time variable definition domain of the eccentric acceleration domain;
and determining an eccentric voltage discrete law according to the eccentric voltage curve and the eccentric acceleration domain.
Wherein, according to the eccentric voltage curve and the eccentric acceleration domain, determining the eccentric voltage discrete law can be realized by adopting the following steps:
acquiring a detection time T corresponding to a first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n ；
Acquiring the eccentric acceleration domain A (t);
acquiring the eccentric voltage curve U (t);
according to the detection time T corresponding to the first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n Determining an eccentric voltage dispersion law, wherein the eccentric voltage dispersion law is determined according to the following formula:
where η is an eccentric voltage discrete law, t is a time independent variable, dt is a derivative of the time independent variable, σ is a correction coefficient of the eccentric voltage discrete law, and the calibration is constant.
It should be noted that, in the present application, the dispersion law of the eccentric voltage is a value of the relative dispersion degree of the armature voltage and the eccentric acceleration of the eccentric wheel motor in the same time interval, when the dispersion law of the eccentric voltage is larger, it indicates that the coupling property between the armature voltage and the eccentric acceleration of the eccentric wheel motor in the same time interval is not high, and because the armature voltage of the eccentric wheel motor directly controls the eccentric wheel torque in the elevator operation process, when the dispersion law of the eccentric voltage is too high, it generally indicates that the mass distribution of the eccentric wheel is uneven when rotating, resulting in that the center of mass and the axle center of the wheel are not coincident, thereby causing unbalance of the rotating component, thereby generating unstable conditions of vibration and force when rotating at high speed, and possibly causing occurrence of safety accidents.
In some embodiments, the dispersion law of the eccentric voltage is a dispersion degree quantized value of the armature voltage change relative to the eccentric acceleration change of an eccentric motor directly connected with the elevator eccentric, and it is noted that the eccentric motor is a direct current motor or an alternating current induction motor for transmitting power to the eccentric through a transmission system.
In step S105, the eccentric wear alarm circulation value is increased according to the eccentric voltage dispersion law, so as to obtain a wear alarm circulation value.
Optionally, in some embodiments, the eccentric wear alert circulation value is increased according to the eccentric voltage discrete law, and in a process of obtaining a wear alert circulation value, the wear alert circulation value may be a product between the eccentric voltage discrete law and the eccentric wear alert circulation value.
When the eccentric wear alarm circulating value is lower than the wear characteristic alarm threshold value, and if the elevator continues to work, the eccentric wheel imbalance causes rapid wear of the eccentric wheel, and a safety accident is easily caused before an alarm time stamp reaches the next abnormal alarm period value, so that an eccentric voltage discrete law between an armature voltage and an eccentric acceleration is required to be obtained, the eccentric wear alarm circulating value is subjected to flow increasing correction according to the eccentric voltage discrete law to obtain an alarm increasing value, and whether the elevator stops to a control center or not is judged according to the wear alarm increasing value.
And in step S106, when the abrasion alarm increasing value is higher than the abrasion characteristic stop valve flux, a second overhaul alarm instruction is sent to the control center of the Internet of things, and when the abrasion alarm increasing value is lower than the abrasion characteristic stop valve flux, an alarm time stamp of the control center of the Internet of things is updated.
It should be noted that, in this application, the second overhaul alarm instruction is different from the first overhaul alarm instruction in that the type of elevator abnormal fault in the second overhaul alarm instruction is eccentric unbalanced abnormal fault information, the second overhaul alarm instruction includes: eccentric imbalance abnormal fault information, eccentric emergency maintenance request and wear alarm current increasing value information.
The eccentric wheel unbalance abnormal fault information is used for describing the type of abnormal faults of the elevator; the eccentric emergency maintenance request is used to emphasize the need for emergency maintenance, as wear problems can affect the normal operation and safety of the elevator; the wear warning current-increasing value information is used for providing detailed fault characteristic information and helping maintenance personnel to know the severity of fault conditions.
In some embodiments, after receiving the maintenance instruction, the control center of the internet of things may send a maintenance request to an elevator maintenance personnel, and when in specific implementation, after receiving the maintenance instruction, the control center of the internet of things sends the maintenance request to the elevator maintenance personnel, the following manner may be specifically adopted:
The control center first receives service instructions from the elevator installation, which instructions contain the abnormality warning information of the installation, the details of the fault and the emergency maintenance request.
Information arrangement and confirmation: the control center of the internet of things can sort and confirm the received overhaul instructions so as to ensure that accurate information, such as equipment identification, fault types, alarm levels and the like, is acquired.
Service personnel information: and searching stored information about elevator maintenance personnel, such as contact information, positions and the like, in a database of the control center of the Internet of things.
Selecting a notification mode: the control center of the Internet of things selects a proper notification mode, such as a short message, a telephone, an application notification and the like, according to the condition of the equipment and the arrangement of maintenance personnel.
Sending an overhaul request: and the control center of the Internet of things sends an overhaul request to corresponding elevator overhaulers by using the selected communication mode. This may be an immediate notification containing detailed information about the fault, the floor on which it is located, the equipment identity, etc.
Confirmation of receipt: once the elevator service personnel receives the request, the receipt is confirmed by a confirmation notice or system reply and is ready for the corresponding maintenance work.
In the application, the alarm time stamp is a time stamp of a built-in timer of the elevator, after the alarm time stamp reaches an abnormal alarm period value, the elevator starts abnormal alarm self-checking according to the abnormal alarm method based on the Internet of things, and gives an alarm or continues to work according to a self-checking result, in some embodiments, when the abrasion alarm increasing value is lower than a preset abrasion characteristic alarm threshold value, the elevator is indicated to work normally, the alarm time stamp can be updated at the moment, in particular, the time stamp of the built-in timer of the elevator is set to zero, and further after the alarm time stamp reaches the abnormal alarm period value again, the elevator starts the abnormal alarm self-checking of the next period, gives an alarm or continues to work according to the self-checking result, so that maintenance personnel of the elevator can give an abnormal alarm before the abnormal fault of the elevator occurs, and give a maintenance personnel to carry out maintenance personnel, and the situation that the elevator is trapped due to the fact that the time of withdrawing the elevator is tensed when the abnormal alarm is given is avoided.
In addition, in another aspect of the present application, in some embodiments, the present application provides an alarm device based on the internet of things, where the alarm device based on the internet of things includes an abnormal alarm unit of the internet of things, referring to fig. 2, which is a schematic diagram of exemplary hardware and/or software of the abnormal alarm unit of the internet of things according to some embodiments of the present application, the abnormal alarm unit 200 of the internet of things includes: the eccentric acceleration domain acquisition module 201, the eccentric outlier acquisition module 202, the eccentric wear alert circulation value determination module 203, the eccentric voltage discrete law determination module 204, the wear alert rise value determination module 205 and the alert restart module 206 are respectively described as follows:
the eccentric acceleration domain acquisition module 201, in some specific embodiments of the present application, the eccentric acceleration domain acquisition module 201 is mainly configured to acquire an eccentric acceleration of an elevator after an alarm timestamp of an internet of things control center reaches an abnormal alarm period value, so as to obtain an eccentric acceleration domain;
the eccentric outlier acquisition module 202, in some specific embodiments of the present application, the eccentric outlier acquisition module 202 is mainly configured to perform eccentric outlier extraction on the eccentric acceleration domain, so as to obtain all eccentric outliers in the eccentric acceleration domain;
The eccentric wear alert circulation value determination module 203, in some specific embodiments of the present application, the eccentric wear alert circulation value determination module 203 is mainly configured to determine an eccentric wear alert circulation value according to each eccentric outlier;
the eccentric voltage discrete law determining module 204, in some specific embodiments of the present application, the eccentric voltage discrete law determining module 204 is mainly configured to send a first overhaul alarm instruction to the control center of the internet of things when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, and determine an eccentric voltage discrete law according to the eccentric acceleration domain when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux;
the wear alarm current-increasing value determining module 205, in some specific embodiments of the present application, the wear alarm current-increasing value determining module 205 is mainly configured to increase the eccentric wear alarm current-increasing value according to the eccentric voltage discrete law, so as to obtain a wear alarm current-increasing value;
the alarm restarting module 206, in some specific embodiments of the present application, the alarm restarting module 206 is mainly configured to send a second overhaul alarm instruction to the control center of the internet of things when the wear alarm increasing value is higher than the wear characteristic stop valve flux, and update an alarm timestamp of the control center of the internet of things when the wear alarm increasing value is lower than the wear characteristic stop valve flux.
In addition, the application also provides computer terminal equipment, which comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to acquire the codes and execute the abnormality alarming method based on the Internet of things.
In some embodiments, reference is made to fig. 3, which is a schematic structural diagram of a computer terminal device according to some embodiments of the present application, where an anomaly alarm method based on the internet of things is applied. The anomaly alarm method based on the internet of things in the above embodiment can be implemented by a computer terminal device shown in fig. 3, where the computer terminal device includes at least one communication bus 301, a communication interface 302, a processor 303, and a memory 304.
The processor 303 may be a general purpose central processing unit (central processing unit, CPU), application Specific Integrated Circuit (ASIC) or one or more of the methods for controlling the execution of the internet of things based anomaly alarm methods herein.
Communication bus 301 may include a pathway to transfer information between the aforementioned components.
Memory 304 may be, but is not limited to, read-only Memory (ROM) or other type of static storage device that can store static information and instructions, random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, but may also be electrically erasable programmable read-only Memory (EEPROM), compact disc read-only Memory (compact disc read-only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 304 may be stand alone and be coupled to the processor 303 via the communication bus 301. Memory 304 may also be integrated with processor 303.
The memory 304 is used for storing program codes for executing the embodiments of the present application, and the processor 303 controls the execution. The processor 303 is arranged to execute program code stored in the memory 304. One or more software modules may be included in the program code. The determination of the eccentric wear alert circulation value in the above-described embodiments may be implemented by one or more software modules in the processor 303 and program code in the memory 304.
The communication interface 302 uses any transceiver-like device for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local areanetworks, WLAN), etc.
Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.
In a specific implementation, as an embodiment, the computer terminal device may include a plurality of processors, where each of the processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The computer terminal device may be a general purpose computer terminal device or a special purpose computer terminal device. In a specific implementation, the computer terminal device may be a desktop, a laptop, a web server, a palmtop (personal digital assistant, PDA), a mobile handset, a tablet, a wireless terminal device, a communication device, or an embedded device. The embodiment of the application is not limited to the type of the computer terminal equipment.
In addition, in other aspects of the present application, there is provided a computer readable storage medium storing at least one computer program loaded and executed by a processor to implement the operations performed by the above-described anomaly alarm method based on the internet of things.
In summary, in the abnormal alarming method and the alarming device based on the internet of things disclosed in the embodiments of the present application, firstly, after the alarming timestamp of the control center of the internet of things reaches an abnormal alarming period value, the eccentric acceleration of the elevator is collected, an eccentric acceleration domain is obtained, eccentric outliers are extracted from the eccentric acceleration domain, all the eccentric outliers in the eccentric acceleration domain are obtained, an eccentric wear alarming circulation value is determined according to each eccentric outlier, when the eccentric wear alarming circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarming command is sent to the control center of the internet of things, when the eccentric wear alarming circulation value is lower than the wear characteristic stop valve flux, an eccentric voltage discrete law is determined according to the eccentric acceleration domain, the eccentric wear alarming circulation value is increased according to the eccentric voltage discrete law, a wear alarming circulation value is obtained, when the wear alarming circulation value is lower than the wear characteristic stop valve flux, the alarm timestamp of the control center of the internet of things is updated, the first overhaul alarming command is regularly determined, the wear alarming circulation value and the wear alarming circulation value is compared with the wear characteristic stop valve, the elevator can be prevented from being broken before the elevator is ridden, and the elevator is prevented from being ridden by an abnormal alarming command.
The foregoing is merely exemplary embodiments of the present application, and detailed technical solutions or features that are well known in the art have not been described in detail herein. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present application, and these should also be regarded as the protection scope of the present application, which does not affect the effect of the implementation of the present application and the practical applicability of the patent.
The scope of the claims should be determined by the terms of the claims, and the description is intended to be construed as including the terms of the claims, as would be understood by those skilled in the art without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. An anomaly alarm method based on the Internet of things is characterized by comprising the following steps:
after an alarm time stamp of the control center of the Internet of things reaches an abnormal alarm period value, acquiring eccentric acceleration of an elevator to obtain an eccentric acceleration domain;
Extracting eccentric outliers from the eccentric acceleration domain to obtain all eccentric outliers in the eccentric acceleration domain;
determining an eccentric wear alarm circulation value according to each eccentric deviation base;
when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, a first overhaul alarm instruction is sent to an Internet of things control center, and when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux, an eccentric voltage discrete law is determined according to the eccentric acceleration domain;
increasing the flow of the eccentric wear alarm flow value according to the eccentric voltage discrete law to obtain a wear alarm flow increasing value;
when the abrasion alarm increasing value is higher than the abrasion characteristic stop valve flux, a second overhaul alarm instruction is sent to the control center of the Internet of things, and when the abrasion alarm increasing value is lower than the abrasion characteristic stop valve flux, an alarm time stamp of the control center of the Internet of things is updated;
extracting eccentric outliers from the eccentric acceleration domain to obtain all the eccentric outliers in the eccentric acceleration domain specifically comprises the following steps:
determining a main domain trend signal in the eccentric acceleration domain;
acquiring time coordinates corresponding to each second derivative zero point in the main domain trend signal, and taking the time coordinates corresponding to each second derivative zero point as each eccentric departure pointer of an eccentric acceleration domain;
Obtaining each eccentric deviation base according to each eccentric deviation pointer and the eccentric acceleration domain;
the determining of the main domain trend signal in the eccentric acceleration domain specifically comprises:
acquiring a maximum value point set in the eccentric acceleration domain;
acquiring a minimum value point set in the eccentric acceleration domain;
fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the eccentric acceleration domain and the average signal as a first main domain trend signal;
acquiring a main domain trend value of the first main domain trend signal, judging whether the main domain trend value of the first main domain trend signal is lower than a main domain trend judgment threshold, and taking the first main domain trend signal as a main domain signal when the main domain trend value of the first main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
when the main domain trend value of the first main domain trend signal is high and Yu Zhu domain trend judging threshold value is high, acquiring a maximum value point set in the first main domain trend signal; acquiring a minimum value point set in the first main domain trend signal;
Fitting the maximum value point set and the minimum value point set to obtain a maximum signal and a minimum signal, obtaining an average signal of the maximum signal and the minimum signal, and taking a difference signal of the first main domain trend signal and the average signal as a second main domain trend signal;
acquiring a main domain trend value of the second main domain trend signal, judging whether the main domain trend value of the second main domain trend signal is lower than a main domain trend judgment threshold, and taking the second main domain trend signal as a main domain signal when the main domain trend value of the second main domain trend signal is lower than the main domain trend judgment threshold to obtain an eccentric acceleration main domain trend signal;
when the main domain trend value of the second main domain trend signal is higher than the Yu Zhu domain trend judging threshold, acquiring the next main domain trend signal, judging whether the main domain trend value of the next main domain trend signal is lower than the main domain trend judging threshold, repeating the steps until a main domain trend signal with the main domain trend value lower than the main domain trend judging threshold is found, and taking the main domain trend signal with the main domain trend value lower than the main domain trend judging threshold as the main domain trend signal in the eccentric acceleration domain;
The obtaining the main domain trend value of the first main domain trend signal specifically includes:
acquiring the first main domain trend signal A 1 (t)；
Acquiring a detection time T corresponding to a first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n ；
According to the first main domain trend signal A 1 (T) detecting time T corresponding to the first eccentric acceleration point in the eccentric acceleration set 1 Detection time T corresponding to last eccentric acceleration point n Determining a main domain trend value of the first main domain trend signal, wherein the main domain trend value of the first main domain trend signal is determined according to the following formula:
wherein lambda is 1 T is a time independent variable and dt is the differentiation of the time independent variable;
collecting the eccentric acceleration of the elevator, and obtaining the eccentric acceleration domain specifically comprises the following steps:
acquiring eccentric acceleration of the elevator at equal intervals, and mapping each eccentric acceleration value into a time domain space according to acquisition time corresponding to the eccentric acceleration to obtain an eccentric acceleration set;
obtaining an eccentric acceleration domain according to the eccentric acceleration set;
the process of obtaining the eccentric acceleration domain according to the eccentric acceleration set specifically comprises the following steps:
Acquiring each eccentric acceleration point in the eccentric acceleration set;
acquiring monitoring moments corresponding to the eccentric acceleration points respectively;
determining the eccentric acceleration domain according to each eccentric acceleration point in the eccentric acceleration set and the monitoring time corresponding to each eccentric acceleration point, wherein the eccentric acceleration domain is determined according to the following formula:
wherein A (t) is the eccentric acceleration domain, a i For the ith eccentric acceleration point, l in the set of eccentric accelerations i (T) is a transition function, T i For the monitoring time corresponding to the ith eccentric acceleration point in the eccentric acceleration set, T c N is the number of the eccentric acceleration points in the eccentric acceleration set, and t is the time domain independent variable of the eccentric acceleration domain;
wherein, confirm the eccentric wear warning circulation value specifically includes according to each eccentric departure base: connecting adjacent eccentric outliers in time sequence in a time domain space through a straight line to obtain an acceleration center fold line, and taking an eccentric acceleration average value of the acceleration center fold line as an eccentric wear alarm circulation value;
Wherein the wear alarm current increasing value is the product of the eccentric voltage discrete law and the eccentric wear alarm circulating value.
2. The method according to claim 1, characterized in that the eccentric acceleration of the elevator is acquired with an acceleration sensor, resulting in an eccentric acceleration field.
3. An alarm device based on the internet of things, which alarms by adopting the method of claim 1, and is characterized in that the alarm device comprises an abnormal alarm unit of the internet of things, and the abnormal alarm unit of the internet of things comprises:
the eccentric acceleration domain acquisition module is used for acquiring the eccentric acceleration of the elevator after the alarm time stamp of the control center of the Internet of things reaches an abnormal alarm period value to obtain an eccentric acceleration domain;
the eccentric outlier acquisition module is used for extracting eccentric outliers from the eccentric acceleration domain to obtain all eccentric outliers in the eccentric acceleration domain;
the eccentric wear alarm circulation value determining module is used for determining an eccentric wear alarm circulation value according to each eccentric deviation base;
the eccentric voltage discrete law determining module is used for sending a first overhaul alarm instruction to the control center of the Internet of things when the eccentric wear alarm circulation value is higher than the wear characteristic stop valve flux, and determining an eccentric voltage discrete law according to the eccentric acceleration domain when the eccentric wear alarm circulation value is lower than the wear characteristic stop valve flux;
The abrasion alarm current-increasing value determining module is used for increasing the eccentric abrasion alarm current-increasing value according to the eccentric voltage discrete law to obtain an abrasion alarm current-increasing value;
and the alarm restarting module is used for sending a second overhaul alarm instruction to the control center of the Internet of things when the abrasion alarm increasing value is higher than the abrasion characteristic stop valve flux, and updating an alarm time stamp of the control center of the Internet of things when the abrasion alarm increasing value is lower than the abrasion characteristic stop valve flux.
4. A computer terminal device, characterized in that it comprises a memory storing a code and a processor configured to acquire the code and to execute the anomaly alarm method based on the internet of things according to any one of claims 1 or 2.
5. A computer-readable storage medium storing at least one computer program, wherein the computer program is loaded and executed by a processor to implement the operations performed by the internet of things-based anomaly alarm method of any one of claims 1 or 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311530560.3A CN117409553B (en) | 2023-11-16 | 2023-11-16 | Abnormal alarm method and alarm device based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311530560.3A CN117409553B (en) | 2023-11-16 | 2023-11-16 | Abnormal alarm method and alarm device based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117409553A CN117409553A (en) | 2024-01-16 |
| CN117409553B true CN117409553B (en) | 2024-03-26 |
Family
ID=89499844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311530560.3A Active CN117409553B (en) | 2023-11-16 | 2023-11-16 | Abnormal alarm method and alarm device based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117409553B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107381271A (en) * | 2017-07-31 | 2017-11-24 | 深圳市盛路物联通讯技术有限公司 | Intelligent elevator abnormality eliminating method and device based on Internet of Things |
| CN110182663A (en) * | 2019-07-03 | 2019-08-30 | 广州广日电梯工业有限公司 | The pre- diagnostic method of elevator guide shoe and pre- diagnostic system |
| KR20200136171A (en) * | 2019-05-27 | 2020-12-07 | 투비씨앤씨 주식회사 | Elevator monitoring system for customer safety based on Internet of Things, and monitoring method thereof |
| CN113682919A (en) * | 2021-09-22 | 2021-11-23 | 赵福杰 | Intelligent repair elevator based on Internet of things |
| CN218058026U (en) * | 2022-08-12 | 2022-12-16 | 江苏省溧阳中等专业学校 | Elevator safety real-time monitoring and early warning system based on Internet of things |
| CN116281479A (en) * | 2023-04-04 | 2023-06-23 | 南京枫火网络科技有限公司 | Elevator fault monitoring method and system based on Internet of things |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11094186B2 (en) * | 2019-07-10 | 2021-08-17 | Johnson Controls Tyco IP Holdings LLP | Systems and methods for managing alarm data of multiple locations |
-
2023
- 2023-11-16 CN CN202311530560.3A patent/CN117409553B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107381271A (en) * | 2017-07-31 | 2017-11-24 | 深圳市盛路物联通讯技术有限公司 | Intelligent elevator abnormality eliminating method and device based on Internet of Things |
| KR20200136171A (en) * | 2019-05-27 | 2020-12-07 | 투비씨앤씨 주식회사 | Elevator monitoring system for customer safety based on Internet of Things, and monitoring method thereof |
| CN110182663A (en) * | 2019-07-03 | 2019-08-30 | 广州广日电梯工业有限公司 | The pre- diagnostic method of elevator guide shoe and pre- diagnostic system |
| CN113682919A (en) * | 2021-09-22 | 2021-11-23 | 赵福杰 | Intelligent repair elevator based on Internet of things |
| CN218058026U (en) * | 2022-08-12 | 2022-12-16 | 江苏省溧阳中等专业学校 | Elevator safety real-time monitoring and early warning system based on Internet of things |
| CN116281479A (en) * | 2023-04-04 | 2023-06-23 | 南京枫火网络科技有限公司 | Elevator fault monitoring method and system based on Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117409553A (en) | 2024-01-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110059325B (en) | Vehicle fault early warning system and corresponding vehicle fault early warning method | |
| CN107843426B (en) | Method and device for monitoring residual life of bearing | |
| CN104044969B (en) | elevator fault early warning method and system | |
| CN111823952B (en) | Battery cell temperature diagnosis method, storage medium and electronic equipment | |
| CN108238538B (en) | Fault monitoring method and fault monitoring system of escalator | |
| CN103034802B (en) | A kind of automatic tour inspection system based on intelligent rules coupling and method | |
| Kang et al. | Condition monitoring of power transformer on-load tap-changers. Part 2: Detection of ageing from vibration signatures | |
| EP2174197A1 (en) | System and method for predictive maintenance of a battery assembly using temporal signal processing | |
| CN111907370B (en) | Power battery temperature monitoring method and device, vehicle and storage medium | |
| US20220388810A1 (en) | Method, device, and early warning system for monitoring elevator health state | |
| CN110294398B (en) | Escalator remote monitoring control system and method | |
| CN110562818B (en) | Monitoring system and monitoring method for evaluating elevator running quality by time dimension | |
| CN108919776A (en) | A kind of assessment of failure method and terminal | |
| CN111137773B (en) | Method and system for detecting faults in an elevator system | |
| CN109896384B (en) | Traction elevator health state characteristic parameter extraction method based on big data analysis | |
| CN117409553B (en) | Abnormal alarm method and alarm device based on Internet of things | |
| CN109725220B (en) | Detection method, system and device for transformer oil cooling loop | |
| KR20170123324A (en) | Relay device and program | |
| CN113311329A (en) | Method and system for detecting insulation fault of stator core of hydraulic generator and storage medium | |
| KR20200058132A (en) | Railway vehicle major component and system diagnosis apparatus | |
| CN111071889B (en) | Elevator state recognition system and method based on Internet of things | |
| CN109687584B (en) | Power transmission internet of things communication network access optimization method | |
| CN108387800B (en) | Multi-dimensional judgment method for main loop state of storage battery pack | |
| CN111606169B (en) | Method, device and system for detecting faults of traction ropes of elevator car | |
| CN114155692A (en) | Equipment fault reporting method, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |