CN106348119B - Isolated elevator operation safety monitoring system and method based on Internet of things - Google Patents
Isolated elevator operation safety monitoring system and method based on Internet of things Download PDFInfo
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- CN106348119B CN106348119B CN201610838260.5A CN201610838260A CN106348119B CN 106348119 B CN106348119 B CN 106348119B CN 201610838260 A CN201610838260 A CN 201610838260A CN 106348119 B CN106348119 B CN 106348119B
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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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- 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
Abstract
The invention relates to an isolated elevator operation safety monitoring system and method based on the Internet of things, which comprises a data acquisition module, a data processing module and a data abnormity decision module; the data acquisition module is used for acquiring the running state information of the elevator; the data processing module is used for carrying out logic processing on the acquired information and identifying the running state of the elevator through an intelligent algorithm; and the data abnormity decision module is used for early warning when the elevator breaks down. By adopting the technologies of the Internet of things, the intelligent identification technology, the photoelectric sensing technology and the like, and developing an isolated elevator operation signal acquisition module, the parameters of the elevator, such as the operation speed, the direction, the position of the floor and the like, are acquired in real time; the parameters are monitored in real time, the running state of the elevator is obtained through intelligent identification, an alarm is given when a fault occurs, and support is provided for elevator safety evaluation.
Description
Technical Field
The invention relates to the field of elevator safety detection, in particular to an isolated elevator operation safety monitoring system and method based on the Internet of things.
Background
In recent years, with the rapid development of Chinese economy and the continuous deepening of urbanization and urbanization construction, the use amount of elevators is rapidly increased. As a special device directly related to life safety of people, safe operation of elevators is receiving wide attention. The safety of the elevator is mainly determined by the regular inspection of quality monitoring department and the regular maintenance of maintenance unit. Under the conditions of large quantity of elevators and shortage of maintenance and management staff at present, how to timely find elevator faults and take effective measures to process the faults becomes a subject concerned by elevator supervision departments and maintenance units. Pilot runs in multiple cities show that: the elevator running state monitoring device makes meaningful exploration for improving the elevator running safety, and particularly plays a positive role in improving the rescue speed of the elevator in emergency. Therefore, the national quality control bureau special equipment safety supervision agency requires "promote application test point of internet of things technology in the supervision work of special equipment such as elevators and cranes" and "develop elevator fault statistics work test point for installing remote monitoring systems" in "2013 special equipment safety supervision and energy saving supervision work key point". Therefore, along with the increasingly obvious role played by the elevator running state monitoring device in elevator supervision and elevator emergency rescue, the market demand of the elevator running state monitoring device is gradually opened, and the elevator running state monitoring device has a good market demand prospect.
At present, the acquisition of elevator running state information mainly passes through interface protocol, gathers elevator control system signal of telecommunication and installs the three kinds of modes of sensor additional, and above-mentioned three kinds of modes all receive many-sided restriction in the implementation process, if need the open interface protocol of manufacturer, disturb original elevator control system easily, are difficult to the installation and gather the not accurate scheduling problem of sensing information, have influenced the accuracy of elevator running state monitoring devices monitoring to a certain extent, have restricted the popularization and application of relevant product. Therefore, the development of an electrical isolation type running state monitoring device which is easy to install, high in applicability and high in running state recognition rate is urgently needed.
Disclosure of Invention
In view of the above, it is necessary to provide an isolated elevator operation safety monitoring system and method based on the internet of things, which adopt technologies such as the internet of things technology, the intelligent identification technology, the photoelectric sensing technology and the like, and by developing an isolated elevator operation signal acquisition module, obtain parameters such as the operation speed, the direction, the floor position where the elevator is located and the like in real time; the parameters are monitored in real time, the running state of the elevator is obtained through intelligent identification, an alarm is given when a fault occurs, and support is provided for elevator safety evaluation.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an isolated elevator operation safety monitoring system based on the Internet of things comprises a data acquisition module, a data processing module and a data abnormity decision module;
the data acquisition module is used for acquiring the running state information of the elevator;
the data processing module is used for carrying out logic processing on the acquired information and identifying the running state of the elevator through an intelligent algorithm;
and the data abnormity decision module is used for early warning when the elevator breaks down.
Preferably, the elevator monitoring system further comprises a data center server for storing and monitoring elevator running state data information.
Preferably, the data acquisition module comprises a current sensor, an alarm detection switch, a laser range finder and a resistance voltage division circuit;
the current sensor is used for detecting a real-time current signal in the running process of the elevator;
the alarm detection switch is used for collecting whether the elevator car is occupied;
the laser range finder is used for acquiring parameter information of normal operation, maintenance operation, abnormal elevator operation speed and the like of the elevator;
and the resistance voltage division circuit is used for monitoring whether the elevator has power failure.
Preferably, the intelligent algorithm comprises an elevator floor searching algorithm and an elevator state identification algorithm;
the elevator floor searching algorithm is used for quickly searching the position of the floor where the elevator arrives at the moment after the elevator arrives at a certain floor;
the elevator state recognition algorithm is used for calculating the actual running speed of the elevator car according to the collected information and matching the actual running speed with the theoretical running speed of the elevator car, so that the state of the elevator is obtained.
Preferably, the elevator floor searching algorithm adopts a dichotomy algorithm, and comprises the following specific steps:
set x e (a, b), sequence { xkThe convergence is in equation f (x), where a, b are the floor numbers of the lowest and highest floors of the elevator, respectively, and f (x) is [ a, b ]]Above is a discrete function, let epsilon>0 is given precision requirement, when | xk-x*If | < ε, x is obtainedkThe number of floors where the elevator is located is calculated as follows;
note a1＝a，b1＝b；
The first step is as follows: k is 1, calculate x1＝(a1+b1) And/2, measuring the distance f (x) from the floor to the top of the building at the moment from the distance measuring instrument, and f (a)1) Distance of the bottommost floor from the top of the hoistway, f (b)1) The distance from the highest floor to the top of the well; if f (x)1)<f(x)<f(a1) Then x (number of floors where elevator is located)*Must be in [ a1，x1]Record a2＝a1，b2＝x1(ii) a If f (b)1)<f(x)<f(x1) Then x (number of floors where elevator is located)*Must be in the interval [ x1，b1]Record a2＝x1，b2＝b1(ii) a Combining the two cases to obtain root-containing interval with half of reduced length2，b2]I.e. f (b)2)<f(x)<f(a2) And b is2－a2＝1/2(b1－a1)；
Step k, calculating in half: repeating the above calculation process, and calculating half-by-half to obtain root-containing region in step 1, …, and k-1
And satisfies the following conditions:
f(bk)<f(x)<f(ak) I.e. x ∈ [ a ]k，bk]；
Now the kth step subsection is calculated:
computing
Then x (number of floors where elevator is located) is*∈[ak+1，bk+1]And is provided with
Derivation of
The obtained half-fraction number k>[㏑(b-a)-㏑ε]/㏑2；
Obtaining a sequence { x ] from the dichotomykGet it ready
This value is the real root of f (x), i.e. the number of floors the elevator is in.
Preferably, the elevator state identification algorithm includes:
(1) speed W for theoretical state of elevatori(i 1,2,3 … n), a vector M of n elements may be usediTo show that:
Mi＝[m1m2m3…mn-2mn-1mn](1)
wherein, the element mjIs the magnitude of a certain point in speed and the time interval △ t between two adjacent elements is nearly equal, there is therefore a unique feature vector M for each elevator state to be monitorediCan be used as a matching template of the input signal;
(2) real-time monitoring distance Y between elevator car and car roofi(i ═ 1,2,3 … n), a vector Y containing n elements may be usediTo show that:
Yi＝[y1y2y3…yn-2yn-1yn](2)
wherein the element yjDistance of elevator car to ceiling, ynIs the latest distance value, y, currently acquired1Is the first (n-1) △ t1Distance value of time, time interval △ t of two adjacent sampling points1Equal and equal to △ t;
③ to monitor the travel speed of the elevator, the rate of change of elevator distance is introduced:
V＝(yi+1–yi)/△t (3)
detecting a speed change value of the elevator, judging that the running speed of the elevator is abnormal when the running speed of the elevator is greater than the theoretical speed, and judging that the elevator runs normally when the running speed of the elevator is within the range of the normal running speed; and when the interval of the elevator running speed range is in the maintenance running range interval, judging that the elevator is in maintenance running.
Preferably, the data abnormity decision module comprises an external alarm unit and a network alarm unit;
the external alarm unit comprises a power amplifier and a buzzer;
the network alarm unit adopts a GPRS network of a mobile phone module, and when the device monitors that the elevator is abnormal, the device can actively send alarm data in real time through the GPRS network.
Preferably, the data processing system further comprises a data communication module, wherein the data communication module is used for enabling the data acquisition module, the data processing module and the data abnormity decision module to be in communication connection, and the data communication module comprises a short-distance wireless communication unit, a GPRS unit and a WIFI unit.
Preferably, the elevator monitoring system further comprises a data center server for storing and monitoring elevator running state data information.
A method for monitoring the running safety of an elevator according to the system comprises the following steps:
s1, initializing and sending a remote control instruction;
s2, acquiring and calculating sensing information, and identifying the real-time running state of the elevator through an intelligent algorithm according to the acquired sensing information;
s3, performing characteristic identification, namely judging the safety state information of the elevator operation through the characteristic identification of the real-time operation state data of the elevator, and judging the fault information of closing and closing of the elevator door, normal operation, maintenance operation, abnormal elevator operation speed and the like;
and S4, storing alarm and state data.
Compared with the prior art, the invention has the beneficial effects that: by developing an isolated elevator running signal acquisition module, parameters such as the running speed, the direction and the position of a floor where the elevator is located are acquired in real time; the parameters are monitored in real time, the running state of the elevator is obtained through intelligent identification, an alarm is given when a fault occurs, and support is provided for elevator safety evaluation. The device can realize real-time acquisition of elevator running state data, intelligent state and fault identification, intelligent decision and alarm and the like. And (3) recognizing: normal operation, maintenance operation, power failure, people trapping, abnormal speed and 5 states and faults.
Drawings
FIG. 1 is a functional block diagram of the system architecture of the present invention;
fig. 2 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 shows an isolated elevator operation safety monitoring system based on the internet of things, which comprises a data acquisition module, a data processing module and a data anomaly decision module;
the data acquisition module is used for acquiring the running state information of the elevator;
the data processing module is used for carrying out logic processing on the acquired information and identifying the running state of the elevator through an intelligent algorithm;
and the data abnormity decision module is used for early warning when the elevator breaks down.
The elevator monitoring system also comprises a data center server used for storing and monitoring the elevator running state data information.
In this embodiment, the data acquisition module includes a current sensor, an alarm detection switch, a laser range finder, and a resistance voltage divider circuit; the data processing module is internally provided with a central processing unit STM32, sensing signals are acquired through RS485 communication and RS232 communication and then are sent to the central processing unit, and the central processing unit STM32 is used for processing, state recognition, exception decision and the like of the sensing signals;
the alarm detection switch is used for acquiring whether the elevator car is trapped by people or not; whether the elevator is trapped by people or not is mainly to detect whether people exist in the elevator car or not, and besides the monitoring process that a camera visually sees real-time images, the infrared sensor is better selected. An infrared sensor is a sensor that measures by using infrared rays emitted from an object. Infrared light may be referred to as infrared light and has absorption, reflection, interference, refraction, and the like properties. Objects existing in the world, such as numerical control devices, steamers, people, etc., all emit infrared light, except that the wavelength of each object differs depending on the temperature of its object. The body temperature of a human being is approximately 37 degrees, and far infrared rays of 9 to 10 μm are emitted. The infrared sensor can detect infrared rays in the wave band and convert the infrared rays into corresponding electric signals, and whether human existence is detected or not can be known through the electric signals to the central processing unit STM 32.
The laser range finder is used for acquiring parameter information of normal operation, maintenance operation, abnormal elevator operation speed and the like of the elevator; a laser range finder is an instrument for measuring the distance to a target by using laser. The laser range finder has the working principle that a fine laser beam is emitted to a target through an optical lens, a photoelectric element receives the laser beam reflected by the target, and the time of the laser to and fro is calculated through a high-precision pulse chip at the inner side, so that the distance from an observer to the target is calculated. The error of the laser rangefinder is only one fifth to one hundred times that of other optical rangefinders. The laser range finder is selected to measure the distance and obtain the real-time speed of the elevator through differential operation.
And the resistance voltage division circuit is used for monitoring whether the elevator has power failure. Because the whole set of device is connected with the power supply of the elevator, when the elevator is powered off, the device is dead, and only the standby power supply can be started. The voltage can be monitored by a resistance voltage dividing circuit, namely, a resistor is connected in series in the circuit, but the voltage change is not influenced. Then a measuring point is connected between the two resistors and is used for detecting whether the power supply of the elevator is dead or not, and if the power supply of the elevator is dead, the power failure of the elevator is indicated.
In this embodiment, elevator brake performance remote self-diagnosis can also be performed, including: after reference position information between a distance measurement sensing module (namely a laser distance meter) and a fixed reference point and a proportionality coefficient between a rotating position of a traction motor encoder and a vertical movement distance of the elevator are obtained, the elevator is controlled to run at a constant speed, when the elevator reaches a rated speed and approaches a braking preset position, the elevator is braked, and meanwhile, a real-time distance between the distance measurement sensing module and the fixed reference point and a real-time rotating position of the traction motor encoder are collected in the braking process of the elevator, so that braking performance parameters of the elevator are obtained through calculation, the obtained braking performance parameters are compared and judged with a preset safety interval, and self-diagnosis is carried out on the braking performance of the elevator. The method can automatically test and obtain various brake performance parameters of the elevator without manual measurement, can perform remote self-diagnosis on the brake performance of the elevator, has high automation degree and high accuracy, is quick, and can be widely applied to the field of diagnosis of the brake performance of the elevator.
The current sensor is used for detecting a real-time current signal in the running process of the elevator;
preferably, the intelligent algorithm comprises an elevator floor searching algorithm and an elevator state identification algorithm;
the elevator floor searching algorithm is used for quickly searching the position of the floor where the elevator arrives at the moment after the elevator arrives at a certain floor;
the elevator state recognition algorithm is used for calculating the actual running speed of the elevator car according to the collected information and matching the actual running speed with the theoretical running speed of the elevator car, so that the state of the elevator is obtained.
In this embodiment, the elevator floor search algorithm adopts a dichotomy algorithm, and each floor of the elevator is firstly calibrated, so that an array is established, but the numbers in the array are not regular, because the distance between each floor of the elevator is not constant, and then the dichotomy search is performed on the array, so that the efficiency can be improved, and the specific steps are as follows:
set x e (a, b), sequence { xkThe convergence is in equation f (x), where a, b are the floor numbers of the lowest and highest floors of the elevator, respectively, and f (x) is [ a, b ]]Above is a discrete function, let epsilon>0 is given precision requirement, when | xk-x*If | < ε, x is obtainedkThe number of floors where the elevator is located is calculated as follows;
note a1＝a，b1＝b；
The first step is as follows: k is 1, calculate x1＝(a1+b1) And/2, measuring the distance f (x) from the floor to the top of the building at the moment from the distance measuring instrument, and f (a)1) Distance of the bottommost floor from the top of the hoistway, f (b)1) The distance from the highest floor to the top of the well; if f (x)1)<f(x)<f(a1) Then root (elevator building)Number of layers) x*Must be in [ a1，x1]Record a2＝a1，b2＝x1(ii) a If f (b)1)<f(x)<f(x1) Then x (number of floors where elevator is located)*Must be in the interval [ x1，b1]Record a2＝x1，b2＝b1(ii) a Combining the two cases to obtain root-containing interval with half of reduced length2，b2]I.e. f (b)2)<f(x)<f(a2) And b is2－a2＝1/2(b1－a1)；
Step k, calculating in half: repeating the above calculation process, and calculating half-by-half to obtain root-containing region in step 1, …, and k-1
And satisfies the following conditions:
f(bk)<f(x)<f(ak) I.e. x ∈ [ a ]k，bk]；
Now the kth step subsection is calculated:
computing
Then x (number of floors where elevator is located) is*∈[ak+1，bk+1]And is provided with
Derivation of
The obtained half-fraction number k>[㏑(b-a)-㏑ε]/㏑2；
Obtaining a sequence { x ] from the dichotomykGet it ready
This value is true of f (x)I.e. the number of floors the elevator is in.
In the embodiment, the conversion of the coordinate axis is carried out according to the distance between the elevator and the top based on the acquisition of the laser range finder; matching the real-time conversion result, namely the running speed of the car with the theoretical speed so as to obtain the state of the elevator, wherein the elevator state identification algorithm comprises the following steps:
(1) speed W for theoretical state of elevatori(i 1,2,3 … n), a vector M of n elements may be usediTo show that:
Mi＝[m1m2m3…mn-2mn-1mn](1)
wherein, the element mjIs the magnitude of a certain point in speed and the time interval △ t between two adjacent elements is nearly equal, there is therefore a unique feature vector M for each elevator state to be monitorediCan be used as a matching template of the input signal;
(2) real-time monitoring distance Y between elevator car and car roofi(i ═ 1,2,3 … n), a vector Y containing n elements may be usediTo show that:
Yi＝[y1y2y3…yn-2yn-1yn](2)
wherein the element yjDistance of elevator car to ceiling, ynIs the latest distance value, y, currently acquired1Is the first (n-1) △ t1Distance value of time, time interval △ t of two adjacent sampling points1Equal and equal to △ t;
③ to monitor the travel speed of the elevator, the rate of change of elevator distance is introduced:
V＝(yi+1–yi)/△t (3)
detecting a speed change value of the elevator, judging that the running speed of the elevator is abnormal when the running speed of the elevator is greater than the theoretical speed, and judging that the elevator runs normally when the running speed of the elevator is within the range of the normal running speed; and when the interval of the elevator running speed range is in the maintenance running range interval, judging that the elevator is in maintenance running.
Preferably, the data abnormity decision module comprises an external alarm unit and a network alarm unit;
in this embodiment, the external alarm unit includes a power amplifier and a buzzer; the power amplification module is externally connected through the central processing unit STM32, and then the buzzer is connected. When the occurrence device detects that the elevator has a fault, whether the network is smooth or not and whether the network is transmitted to the server or not, the alarm can be sounded at the first time. The sound will only be turned off when the maintenance personnel manually turn off the device on the spot or remotely turn off the buzzer through the data center server. The purpose of this is to ensure that the elevator is out of order to be noticed by surrounding people, and to inform others that the elevator is out of order in the first moment.
The network alarm unit adopts a GPRS network of a mobile phone module, and when the device monitors that the elevator is abnormal, the device can actively send alarm data in real time through the GPRS network.
Preferably, the data processing system further comprises a data communication module, wherein the data communication module is used for enabling the data acquisition module, the data processing module and the data abnormity decision module to be in communication connection, and the data communication module comprises a short-distance wireless communication unit, a GPRS unit and a WIFI unit. When the device monitors that the elevator is abnormal, the device actively sends data to the data center server in real time through the GPRS network. Only after the data center server receives the data, the information is returned to the device to inform the isolated elevator operation safety intelligent monitoring device that the server has received the information. At this time, the monitoring device stops sending abnormal information to the server, and turns off the alarm and sends maintenance personnel in time. Here, the monitoring device returns data only when the server is accessed at ordinary times, and automatically transmits data when a failure occurs. This is done to save traffic.
The embodiment also provides a method for monitoring the running safety of the elevator according to the system, which comprises the following steps as shown in fig. 2:
s1, initializing and sending a remote control instruction;
s2, acquiring and calculating sensing information, and identifying the real-time running state of the elevator through an intelligent algorithm according to the acquired sensing information;
s3, performing characteristic identification, namely judging the safety state information of the elevator operation through the characteristic identification of the real-time operation state data of the elevator, and judging the fault information of closing and closing of the elevator door, normal operation, maintenance operation, abnormal elevator operation speed and the like;
and S4, storing alarm and state data.
The method for judging whether the elevator door is in fault comprises the following steps: controlling an elevator door motor to complete a plurality of door opening-closing actions, and acquiring a real-time current signal of the elevator door motor by adopting a current sensor so as to obtain standard operation parameters of the elevator door motor; collecting a real-time current signal of an elevator door motor by adopting a current sensor; judging whether a maximum value appears in the collected real-time current signal, if so, acquiring the moment when the maximum value appears as the starting moment of the execution action of the elevator door motor; calculating to obtain real-time operation parameters of an elevator door motor according to the acquired real-time current signals, further combining the obtained standard operation parameters of the elevator door motor to judge whether the elevator door motor has a fault, and if so, continuing to execute; and carrying out fault alarm and outputting and displaying a corresponding fault type. The method is simple in implementation mode, strong in operability, high in accuracy and rapid, and can be widely applied to the field of safety detection of elevator door systems.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. An isolated elevator operation safety monitoring system based on the Internet of things is characterized by comprising a data acquisition module, a data processing module and a data abnormity decision module;
the data acquisition module is used for acquiring the running state information of the elevator;
the data processing module is used for carrying out logic processing on the acquired information and identifying the running state of the elevator through an intelligent algorithm;
the data abnormity decision module is used for early warning when the elevator fails;
the intelligent algorithm comprises an elevator floor searching algorithm and an elevator state identification algorithm;
the elevator floor searching algorithm is used for quickly searching the position of the floor where the elevator arrives at the moment after the elevator arrives at a certain floor;
the elevator state recognition algorithm is used for calculating the actual running speed of the elevator car according to the collected information and matching the actual running speed with the theoretical running speed of the elevator car so as to obtain the state of the elevator;
the elevator floor searching algorithm adopts a dichotomy algorithm, and comprises the following specific steps:
set x e (a, b), sequence { xkThe convergence is in equation f (x), where a, b are the floor numbers of the lowest and highest floors of the elevator, respectively, and f (x) is [ a, b ]]Above is a discrete function, let epsilon>0 is given precision requirement, when | xk-x*|<When epsilon is present, x is obtainedkThe number of floors where the elevator is located is calculated as follows;
note a1＝a，b1＝b；
The first step is as follows: k is 1, calculate x1＝(a1+b1) And/2, measuring the distance f (x) from the floor to the top of the building at the moment from the distance measuring instrument, and f (a)1) Distance of the bottommost floor from the top of the hoistway, f (b)1) The distance from the highest floor to the top of the well; if f (x)1)<f(x)<f(a1) Then x (number of floors where elevator is located)*Must be in [ a1，x1]Record a2＝a1，b2＝x1(ii) a If f (b)1)<f(x)<f(x1) Then x (number of floors where elevator is located)*Must be in the interval [ x1，b1]Record a2＝x1，b2＝b1(ii) a Combining the two cases to obtain root-containing interval with half of reduced length2，b2]I.e. f (b)2)<f(x)<f(a2) And b is2－a2＝1/2(b1－a1)；
Step k, calculating in half: repeating the above calculation process, and calculating half-by-half to obtain root-containing region in step 1, …, and k-1
And satisfies the following conditions:
f(bk)<f(x)<f(ak) I.e. x ∈ [ a ]k，bk]；
Now the kth step subsection is calculated:
computing
Then x (number of floors where elevator is located) is*∈[ak+1，bk+1]And is provided with
Derivation of
The obtained half-fraction number k>[㏑(b-a)-㏑ε]/㏑2；
Obtaining a sequence { x ] from the dichotomykGet it ready
This value is the real root of f (x), i.e. the number of floors the elevator is in.
2. The isolated elevator operation safety monitoring system based on the internet of things of claim 1, wherein the data acquisition module comprises a current sensor, an alarm detection switch, a laser range finder and a resistance voltage division circuit;
the current sensor is used for detecting a real-time current signal in the running process of the elevator;
the alarm detection switch is used for acquiring whether the elevator car is occupied or not;
the laser range finder is used for acquiring the information of parameters of normal operation, maintenance operation and abnormal operation speed of the elevator;
and the resistance voltage division circuit is used for monitoring whether the elevator has power failure.
3. The isolated form elevator operation safety monitoring system based on thing networking of claim 1, characterized in that, the elevator state recognition algorithm includes:
(1) speed W for theoretical state of elevatori(i 1,2,3 … n), a vector M of n elements may be usediTo show that:
Mi＝[m1m2m3… mn-2mn-1mn](1)
wherein, the element mjIs the amplitude of a certain point of speed and the time interval △ t between two adjacent elements is nearly equal, so that for each elevator state to be monitored there is a unique characteristic vector MiCan be used as a matching template of the input signal;
(2) real-time monitoring distance Y between elevator car and car roofi(i ═ 1,2,3 … n), a vector Y containing n elements may be usediTo show that:
Yi＝[y1y2y3… yn-2yn-1yn](2)
wherein the element yjDistance of elevator car to ceiling, ynIs the latest distance value, y, currently acquired1Is the first (n-1) △ t1Distance value of time, time interval △ t of two adjacent sampling points1Equal and equal to △ t;
③ to monitor the travel speed of the elevator, the rate of change of elevator distance is introduced:
V＝(yi+1–yi)/△t (3)
detecting a speed change value of the elevator, judging that the running speed of the elevator is abnormal when the running speed of the elevator is greater than the theoretical speed, and judging that the elevator runs normally when the running speed of the elevator is within the range of the normal running speed; and when the interval of the elevator running speed range is in the maintenance running range interval, judging that the elevator is in maintenance running.
4. The isolated elevator operation safety monitoring system based on the internet of things of claim 1, wherein the data anomaly decision module comprises an external alarm unit and a network alarm unit;
the external alarm unit comprises a power amplifier and a buzzer;
the network alarm unit adopts a GPRS network of a mobile phone module, and when the device monitors that the elevator is abnormal, the device can actively send alarm data in real time through the GPRS network.
5. The isolated elevator operation safety monitoring system based on the internet of things of claim 1, further comprising a data communication module for enabling the data acquisition module, the data processing module and the data anomaly decision module to be in communication connection, wherein the data communication module comprises a short-distance wireless communication unit, a GPRS unit and a WIFI unit.
6. The isolated elevator operation safety monitoring system based on the internet of things of claim 1, further comprising a data center server for storing and monitoring elevator operation state data information.
7. Method for the safety monitoring of the operation of an elevator with a system according to any one of claims 1 to 6, characterized in that it comprises the following steps:
s1, initializing and sending a remote control instruction;
s2, acquiring and calculating sensing information, and identifying the real-time running state of the elevator through an intelligent algorithm according to the acquired sensing information;
s3, performing feature recognition, namely judging safety state information of elevator operation and judging fault information of normal operation, overhaul operation and abnormal elevator operation speed through the feature recognition of the real-time elevator operation state data;
and S4, storing alarm and state data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610838260.5A CN106348119B (en) | 2016-09-20 | 2016-09-20 | Isolated elevator operation safety monitoring system and method based on Internet of things |
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| US11535491B2 (en) * | 2019-03-28 | 2022-12-27 | Otis Elevator Company | Verification of trapped passenger alarm |
| CN110065866A (en) * | 2019-04-25 | 2019-07-30 | 永大电梯设备（中国）有限公司 | A kind of integrated forecasting system of the exception monitoring based on SMT |
| CN114291676B (en) * | 2021-12-08 | 2024-01-02 | 杭州优迈科技有限公司 | Elevator general logic analysis method, device, system, equipment and storage medium |
| CN114803763B (en) * | 2022-06-30 | 2022-09-09 | 锐创软件技术(启东)有限公司 | Variable speed goods elevator abnormity detection method based on neural network |
| CN114861834B (en) * | 2022-07-04 | 2022-09-30 | 深圳新闻网传媒股份有限公司 | Method for continuously updating data information of big data storage system |
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