WO2018232892A1 - 一种智能监控、预测电梯故障的方法及装置 - Google Patents

一种智能监控、预测电梯故障的方法及装置 Download PDF

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Publication number
WO2018232892A1
WO2018232892A1 PCT/CN2017/096965 CN2017096965W WO2018232892A1 WO 2018232892 A1 WO2018232892 A1 WO 2018232892A1 CN 2017096965 W CN2017096965 W CN 2017096965W WO 2018232892 A1 WO2018232892 A1 WO 2018232892A1
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Prior art keywords
elevator
data
running
probability
failure
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PCT/CN2017/096965
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English (en)
French (fr)
Inventor
杜光东
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深圳市盛路物联通讯技术有限公司
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Publication of WO2018232892A1 publication Critical patent/WO2018232892A1/zh

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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B66HOISTING; LIFTING; HAULING
    • B66BELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/02Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B66HOISTING; LIFTING; HAULING
    • B66BELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006Monitoring devices or performance analysers
    • B66B5/0018Devices monitoring the operating condition of the elevator system
    • B66B5/0031Devices monitoring the operating condition of the elevator system for safety reasons
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B66HOISTING; LIFTING; HAULING
    • B66BELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006Monitoring devices or performance analysers
    • B66B5/0037Performance analysers

Definitions

  • the present invention relates to the field of Internet of Things technologies, and in particular, to a method and apparatus for intelligently monitoring and predicting elevator faults.
  • Elevators have become an essential infrastructure in people's lives. However, in recent years, elevators have been continually malfunctioning. For example, when the elevator has not reached the predetermined floor, it suddenly opens. If the user inside the elevator is not aware of the outside scene and walks out of the elevator, it will fall down and fall into a serious injury or even death. Other elevators suddenly stop during the ascent or descent, and then frequently occur when they descend to a certain position. It is always a fear for the user to take the elevator that “has frequent problems”. It greatly reduces the user experience and even poses a threat to the user's personal safety. Then, how to ensure the safety of the user when riding the ladder and try to avoid the occurrence of safety accidents has become a technical problem to be solved urgently.
  • the present invention provides a method and apparatus for intelligently monitoring and predicting elevator faults.
  • the present invention provides a method for intelligently monitoring and predicting an elevator failure, the method comprising:
  • the alarm message is sent to the maintenance company, wherein the alarm message includes location information and number information of the first elevator, and the first elevator is any one of the at least one elevator. .
  • the method for intelligently monitoring and predicting elevator faults collects the running data of the elevator during real-time operation, and predicts the working state of the elevator according to the current running data.
  • the maintenance company shall be notified in time to dispatch personnel to check the elevator position where the failure may occur in advance, and if there is a risk of failure, it shall be dealt with in time. In this way, preventive measures can be taken in advance to avoid personal injury and property damage in the event of an elevator failure.
  • the method further includes:
  • the elevator when it is judged according to the prediction that the elevator may or will be malfunctioning, the elevator is parked on a certain floor in time to facilitate the passenger to step down, and it is prohibited to have another passenger to go up the elevator. This prevents passengers from riding the faulty elevator and causing unnecessary danger.
  • the present invention provides an apparatus for intelligently monitoring and predicting an elevator failure, the apparatus comprising:
  • the collecting unit is configured to collect at least one running data of the elevator during operation
  • a predicting unit configured to predict an operating state of each of the at least one elevator according to the operating data of each of the at least one elevators
  • a processing unit configured to determine, according to an operating state of each of the at least one elevator, a probability that each of the at least one elevator fails;
  • the alarm unit is configured to send an alarm message to the maintenance company when the processing unit determines that the probability of the first elevator failure is greater than the risk probability threshold, wherein the alarm message includes location information and number information of the first elevator, and the first elevator is at least Any elevator in an elevator.
  • the device for intelligently monitoring and predicting elevator faults collects the running data of the elevator during real-time operation, and predicts the working state of the elevator according to the current running data.
  • the maintenance company shall be notified in time to dispatch personnel to check the elevator position where the failure may occur in advance, and if there is a risk of failure, it shall be dealt with in time. In this way, preventive measures can be taken in advance to avoid personal injury and property damage in the event of an elevator failure.
  • the device further includes: a control unit, configured to control the first elevator to stop to a floor closest to the current location of the first elevator, and the elevator is turned on;
  • the alarm unit is also used to issue an alarm prompt so that the passengers in the elevator can get off the elevator and remind the passengers outside the elevator to prohibit the ride.
  • the elevator when it is judged according to the prediction that the elevator may or will be malfunctioning, the elevator is parked on a certain floor in time to facilitate the passenger to step down, and it is prohibited to have another passenger to go up the elevator. This prevents passengers from riding the faulty elevator and causing unnecessary danger.
  • FIG. 1 is a system architecture diagram of intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a signaling flow of a method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of a method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention
  • FIG. 4 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention
  • FIG. 5 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention
  • FIG. 6 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention.
  • FIG. 7 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of an apparatus for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention.
  • FIG. 9 is a schematic structural diagram of another apparatus for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention.
  • FIG. 1 is a schematic structural diagram of a system for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention.
  • the system includes: an intelligent terminal 10, a server 20, an Internet of Things access gateway 30, and an Internet of Things service gateway 40.
  • the smart terminal 10 herein is a local device, and is mainly used to collect operation data of at least one elevator during operation.
  • the smart terminal 10 can be a local PC, an IPAD, or a smartphone or the like.
  • the elevators may be dispersedly arranged.
  • an intelligent terminal 10 may be set to collect operation data of all elevators in a certain area.
  • the collected operational data of all elevators in the area in which it is collected is transmitted to the remote server 20 through the Internet of Things. After the server 20 collects the elevator operation data through the transmission of the smart terminal 10, the operation state of each elevator will be predicted based on the data.
  • the alarm information includes location information and number information of the first elevator.
  • the first elevator is any one of at least one elevator.
  • the smart terminal 10 first registers in the Internet of Things access gateway 30, and after the registration is successful, establishes a communication connection between the smart terminal 10 and the Internet of Things access gateway 30.
  • the smart terminal 10 sends an authentication request to the Internet of Things access gateway 30, and the IoT access gateway 30 sends the authentication request to the server 20 through the Internet of Things service gateway 40. So that the server 20 can authenticate the smart terminal 10 according to the authentication request.
  • the IoT service gateway 40 and the IoT access gateway 30 transmit the response information of the successful authentication to the smart terminal 10.
  • the smart terminal 10 After receiving the response message, the smart terminal 10 establishes a communication connection with the server 20 through the Internet of Things access gateway 30 and the Internet of Things service gateway 40.
  • the embodiment of the present invention further provides a schematic diagram of a signaling flow for intelligently monitoring and predicting elevator faults. Specifically, as shown in FIG. 2, the specific includes:
  • Step 1 The intelligent terminal collects at least one running data when the elevator is running.
  • elevators exist in many places because elevators have become popular. For example, residential areas, commercial areas, schools and hospitals, etc., will be installed with multiple elevators. In order to ensure real-time monitoring of the operation status of elevators in various areas and locations, it is desirable to reduce costs as much as possible.
  • One or more smart terminals can be installed in different areas. The smart terminals installed in each area are only used to collect the operational data of the elevators in the area during operation.
  • Step 2 Each intelligent terminal separately transmits the operation data of the elevator operation collected by itself to the server through the Internet of Things network.
  • an area includes multiple intelligent terminals, and each intelligent terminal separately collects data of different elevators in the area, the data transmission delay is caused in order to prevent congestion when all intelligent terminals transmit data. It is also possible to separately set a main terminal in each area for collecting the running data of the elevator running time collected by other intelligent terminals in the area, and then uniformly summing and compressing, and transmitting to the server through the Internet of Things network.
  • step 3 the server receives at least one running data of the elevator in real time.
  • the running data received by the server is uploaded by different smart terminals, and each smart terminal uploads itself as at least one running data of the elevator running time. Therefore, all the servers receive in real time are the operational data of at least one elevator running time.
  • Step 4 predict an operating state of each of the at least one elevator according to the operating data of each of the at least one elevator during operation.
  • Step 5 Determine, according to an operating state of each of the at least one elevator, a probability that each of the at least one elevators fails.
  • step 6 when it is determined that the probability of the first elevator failure is greater than the risk probability threshold, an alarm message is sent to the maintenance company.
  • the alarm message includes location information and number information of the first elevator.
  • the first elevator here is either one of at least one elevator.
  • the communication connection between the intelligent terminal and the server that collects the elevator operation data in real time is realized through the object network.
  • the server monitors and predicts the running state of the elevator remotely. Once it is determined that the elevator may be faulty and the probability of failure is relatively large, the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to arrive in advance.
  • the elevator position in order to repair the elevator before the elevator fails. Avoid personal injury and property damage caused by elevator failure.
  • the intelligent terminal is used to collect the running data of the elevator in its responsible area in real time.
  • IoT access gateway and IoT service gateway are used to build Establish a communication connection between the smart terminal and the server.
  • the server after collecting the running data of the elevator running collected by different intelligent terminals, predicts the running state of the elevator according to the operating data, and determines the probability that the elevator will fail according to the operating state, and if the probability is large, issues the Alarm information to the maintenance company.
  • the maintenance company In order for the maintenance company to dispatch maintenance personnel to arrive at the elevator position where the failure may occur, to repair or maintain the elevator.
  • the component that plays a crucial role is the server. Therefore, in the following, the method steps performed by the server will be described in detail.
  • FIG. 3 is a schematic flowchart of a method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention. Specifically, as shown in FIG. 3, the method includes:
  • Step 310 Collect at least one operational data of the elevator during operation in real time.
  • the server collects at least one running data of the elevator running time sent by the intelligent terminal in different areas in real time.
  • Step 320 Predict the operating status of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • Step 330 Determine, according to an operating state of each of the at least one elevators, a probability that each of the at least one elevators fails.
  • step 320 the operational status of each of the at least one elevator has been determined. Then, the probability of the elevator malfunctioning can be judged according to the state of each elevator running. For example, when the elevator is running, if multiple parameters are not within the data range when the elevator is in normal operation, the probability that the elevator will fail is relatively large. However, if the elevator is running, only one or a few parameters are not within the data range when the elevator is running, then the probability of the elevator malfunctioning is slightly smaller.
  • Step 340 when it is determined that the probability of the first elevator failure is greater than the risk probability threshold, sending an alarm message to the maintenance company.
  • the probability that the first elevator fails in the embodiment of the present invention is greater than the risk probability threshold
  • an alarm message is sent to the maintenance company.
  • specific location information and number information of the first elevator may be included.
  • the risk threshold here is 60%. The risk threshold below can also refer to this value and will not be described.
  • the method for intelligently monitoring and predicting an elevator fault is provided by the embodiment of the invention, and the communication connection between the intelligent terminal and the server for collecting elevator operation data in real time is realized through the object network.
  • the server monitors and predicts the running state of the elevator remotely. Once it is determined that the elevator may be faulty and the probability of failure is relatively large, the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to arrive in advance.
  • the elevator position in order to repair the elevator before the elevator fails. Avoid personal injury and property damage caused by elevator failure.
  • FIG. 4 is another embodiment provided by the present invention. Schematic diagram of a method for intelligently monitoring and predicting elevator failures, the method comprising:
  • step 410 at least one running data of the elevator running time is collected in real time.
  • the server collects at least one running data of the elevator running time sent by the intelligent terminal in different areas in real time.
  • Step 420 Predict the operating state of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • the running data of the elevator running may include at least one or more of the following parameters:
  • the specific prediction process can include:
  • step a the running data of each elevator in at least one elevator is matched with the data range of the pre-stored elevator during normal operation to obtain a matching result.
  • each elevator will have a corresponding parameter range for the elevator during normal operation, such as elevator operation, acceleration/deceleration time, rated voltage, rated current, rated speed, rated load, and so on.
  • Step b determining an operating state of each of the at least one elevator according to the matching result.
  • the elevator may malfunction. If one or more of the previous running data of the elevator matches the data range of the stored elevator in the normal operation of the elevator, the current running state of the elevator is normal.
  • Step 430 Determine, according to an operating state of each of the at least one elevators, a probability that each of the at least one elevators fails.
  • step 420 the operational status of each of the at least one elevator has been determined. Then, the probability of the elevator malfunctioning can be judged according to the state of each elevator running. For example, when the elevator is running, if multiple parameters are not within the data range when the elevator is in normal operation, the probability that the elevator will fail is relatively large. However, if the elevator is running, only one or a few parameters are not within the data range when the elevator is running, then the probability of the elevator malfunctioning is slightly smaller.
  • Step 440 Send an alarm message to the maintenance company when it is determined that the probability of the first elevator failure is greater than the risk probability threshold.
  • an alarm message is sent to the maintenance company.
  • specific location information and number information of the first elevator may be included.
  • the method for intelligently monitoring and predicting elevator faults realizes a communication connection between an intelligent terminal and a server for collecting elevator running data in real time through an object-link network.
  • the server monitors and predicts the running status of the elevator through remote monitoring.
  • Predicting the running state of the elevator may include matching the running data of the elevator running time with the good timing range of the pre-stored elevator during normal operation, and determining the electric quantity according to the matching result.
  • the running state of the ladder may include matching the running data of the elevator running time with the good timing range of the pre-stored elevator during normal operation, and determining the electric quantity according to the matching result.
  • the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to reach the elevator position where the failure may occur in advance, and strive for The elevator can be repaired before the elevator fails. Avoid personal injury and property damage caused by elevator failure.
  • FIG. 5 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention. Steps 510, 530 to 540 in the method are the same as steps 410, 430 to 440 in the above embodiment. The only difference is step 520, which will not be described here at steps 510, 530 to 540. The detailed description of step 520 is as follows:
  • Step 520 predict an operating state of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • the running data of the elevator running may include at least one or more of the following parameters:
  • the specific prediction process can include:
  • Step c setting a virtual model when the elevator is running, inputting operation data of each elevator operation in at least one elevator into the virtual model for simulation, and obtaining a simulation result.
  • the virtual model is a model built using big data. How to establish is actually the existing technology. It is not detailed here. Using this data model, the current operational data is input into the virtual model for simulation to obtain simulation results.
  • Step d determining an operating state of each of the at least one elevator according to the simulation result.
  • the method for intelligently monitoring and predicting elevator faults realizes a communication connection between an intelligent terminal and a server for collecting elevator running data in real time through an object-link network.
  • the server monitors and predicts the running status of the elevator through remote monitoring.
  • the virtual model of the elevator running time can be set, the data of each elevator running time is input into the virtual model for simulation, and the running state of the elevator is determined according to the simulation result.
  • the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to arrive at the elevator position where the failure may occur in advance, and strive for the failure of the elevator.
  • the elevator could be repaired. Avoid personal injury and property damage caused by elevator failure.
  • FIG. 6 is a schematic flowchart of another method for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention. The method includes:
  • step 610 at least one running data of the elevator running time is collected in real time.
  • the server collects at least one running data of the elevator running time sent by the intelligent terminal in different areas in real time.
  • Step 620 Predict the operating status of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • the running data of the elevator running may include at least one or more of the following parameters:
  • the specific prediction process can include:
  • Step c setting a virtual model when the elevator is running, inputting operation data of each elevator operation in at least one elevator into the virtual model for simulation, and obtaining a simulation result.
  • the virtual model is a model built using big data. How to establish is actually the existing technology. It is not detailed here. Using this data model, the current operational data is input into the virtual model for simulation to obtain simulation results.
  • Step d determining an operating state of each of the at least one elevator according to the simulation result.
  • Step 630 Determine, according to an operating state of each of the at least one elevators, a probability that each of the at least one elevators fails.
  • step 620 the operational status of each of the at least one elevator has been determined. Then, the probability of the elevator malfunctioning can be judged according to the state of each elevator running. For example, when the elevator is running, if multiple parameters are not within the data range when the elevator is in normal operation, the probability that the elevator will fail is relatively large. However, if the elevator is running, only one or a few parameters are not within the data range when the elevator is running, then the probability of the elevator malfunctioning is slightly smaller.
  • the method steps for specifically determining the probability of an elevator failure include:
  • e respectively, set the weighting coefficient corresponding to the acceleration/deceleration time, the flatness longitude, the voltage, the current, the rotational speed, the load and the floor of the first elevator during operation.
  • the weight coefficient can be set according to the previous big data statistics. For example, when the parameters change, it is easy to cause elevator failure. Then, the weight coefficient of the parameter can be set to be slightly larger, and when the parameters change, the number of times the elevator failure occurs is relatively small, then the weight coefficient of the parameter can be set to be slightly smaller.
  • the value of the specific weight coefficient can be set according to the frequency at which the parameters change in the past elevator failure. For example, if the number of elevator failures is 100 and the probability of a failure of the elevator is 20, the weighting factor can be set to 0.2. And so on. The weighting coefficients of these parameters are determined by counting the causes of the failure of the elevator in the past.
  • the setting identifier is 0, and if the parameter does not belong to When the corresponding preset data range when the elevator is running normally, the setting flag is 1.
  • the multiplication is multiplied by the corresponding weight coefficient and summed.
  • the weight coefficient probability is obtained, as shown in step g.
  • Step 640 when it is determined that the probability of the first elevator failure is greater than the risk probability threshold, sending an alarm message to the maintenance company.
  • an alarm message is sent to the maintenance company.
  • specific location information and number information of the first elevator may be included.
  • the method for intelligently monitoring and predicting elevator faults realizes a communication connection between an intelligent terminal and a server for collecting elevator running data in real time through an object-link network.
  • the server remotely monitors and predicts the running state of the elevator, and then determines the probability of the elevator malfunctioning according to the running state of the elevator.
  • the probability of determining the failure of the elevator may be summed according to the operation data identifier of the elevator operation and the weight coefficient corresponding to the operation data respectively, thereby obtaining the probability of the elevator being faulty.
  • the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to reach the elevator position where the failure may occur in advance, and strive to perform the elevator before the elevator fails. Repair. Avoid personal injury and property damage caused by elevator failure.
  • the embodiment of the present invention further provides another schematic diagram of a method for intelligently monitoring and predicting an elevator fault. Specifically, as shown in FIG. 7 , steps 710 to 740 are the same as steps 610-640 in the previous embodiment, and details are not described herein again.
  • Step 750 controlling the first elevator to stop to the floor closest to the current location of the first elevator, the elevator is turned on, and an alarm prompt is issued, so that the passenger in the elevator goes down the elevator, and the passenger outside the elevator is reminded to prohibit the boarding.
  • the elevator is parked in the floor closest to the current location in advance, and then an alarm is issued to inform the passenger to get off the elevator as soon as possible. And remind passengers outside the elevator to prohibit carrying the elevator.
  • the elevator is automatically locked. Prevent other unsuspecting passengers from continuing to take the elevator.
  • step 750 and step 740 an alarm message is sent to the maintenance company.
  • the method for intelligently monitoring and predicting an elevator fault is provided by the embodiment of the invention, and the communication connection between the intelligent terminal and the server for collecting elevator operation data in real time is realized through the object network.
  • the server monitors and predicts the running state of the elevator remotely. Once it is determined that the elevator may be faulty and the probability of failure is relatively large, the alarm information is directly sent to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel to arrive in advance.
  • the elevator position in order to repair the elevator before the elevator fails. Avoid personal injury and property damage caused by elevator failure.
  • the elevator is parked on a certain floor in time to facilitate the passengers to step down, and it is forbidden to have passengers on the elevator. It can also prevent passengers from riding the faulty elevator and causing unnecessary dangerous damage.
  • the embodiment of the invention further provides an apparatus for intelligently monitoring and predicting an elevator fault.
  • FIG. 8 is a schematic structural diagram of an apparatus for intelligently monitoring and predicting an elevator fault according to an embodiment of the present invention. Specifically, as shown in FIG. 8, the apparatus includes: an acquisition unit 810, a prediction unit 820, a processing unit 830, and an alarm unit 840.
  • the collecting unit 810 is configured to collect at least one running data of the elevator during operation.
  • the collecting unit 810 collects at least one running data of the elevator running time sent by the intelligent terminal in different areas in real time.
  • the predicting unit 820 is configured to predict an operating state of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • the running data of the elevator running may include at least one or more of the following parameters:
  • the prediction unit 820 is specifically configured to: match the operation data of each elevator operation of the at least one elevator with the data range of the pre-stored elevator during normal operation, and obtain a matching result.
  • each elevator will have a corresponding parameter range for the elevator during normal operation, such as elevator operation, acceleration/deceleration time, rated voltage, rated current, rated speed, rated load, and so on.
  • the operating state of each of the at least one elevator is determined based on the matching result.
  • the elevator may malfunction. If one or more of the previous running data of the elevator matches the data range of the stored elevator in the normal operation of the elevator, the current running state of the elevator is normal.
  • a processing unit configured to determine, according to an operating state of each of the at least one elevator, a probability that each of the at least one elevator fails.
  • the processing unit 830 can determine the probability of the elevator malfunctioning according to the state of each elevator operation. For example, when the elevator is running, if multiple parameters are not within the data range when the elevator is in normal operation, the probability that the elevator will fail is relatively large. However, if the elevator is running, only one or a few parameters are not within the data range when the elevator is running, then the probability of the elevator malfunctioning is slightly smaller.
  • the processing unit 830 is specifically configured to:
  • the weighting coefficients corresponding to the acceleration/deceleration time, the flatness longitude, the voltage, the current, the rotational speed, the load, and the floor of the first elevator are sequentially set.
  • the weight coefficient can be set according to the previous big data statistics. For example, when the parameters change, it is easy to cause elevator failure. Then, the weight coefficient of the parameter can be set to be slightly larger, and when the parameters change, the number of times the elevator failure occurs is relatively small, then the weight coefficient of the parameter can be set to be slightly smaller.
  • the value of the specific weight coefficient can be set according to the frequency at which the parameters change in the past elevator failure. For example, if the number of elevator failures is 100 and the probability of a failure of the elevator is 20, the weighting factor can be set to 0.2. And so on. The weighting coefficients of these parameters are determined by counting the causes of the failure of the elevator in the past.
  • the first elevator When the first elevator is running, one or more of acceleration/deceleration time, leveling longitude, voltage, current, speed, load and floor are not in the data range of the first elevator during normal operation, one or more are set
  • the identification of the operational data that does not belong to the data range when the first elevator is in normal operation is 1, otherwise the setting identifier is 0.
  • the setting identifier is 0, and if the parameter does not belong to When the corresponding preset data range when the elevator is running normally, the setting flag is 1.
  • the multiplication is multiplied by the corresponding weight coefficient and summed.
  • the weight coefficient corresponding to the running data of the first elevator is multiplied by the identifier corresponding to the running data, and the probability of the first elevator failure is obtained.
  • the alarm unit 840 is configured to send an alarm message to the maintenance company when the processing unit 830 determines that the probability of the first elevator failure is greater than the risk probability threshold.
  • the processing unit 830 determines that one of the at least one elevators, that is, the probability that the first elevator fails in the embodiment of the present invention is greater than the risk probability threshold, the alarm message is sent to the maintenance company.
  • specific location information and number information of the first elevator may be included.
  • the device for intelligently monitoring and predicting the elevator fault is provided by the embodiment of the invention, and the communication connection between the intelligent terminal and the server for collecting the elevator running data in real time is realized through the object network.
  • the processing unit remotely monitors and predicts the running state of the elevator. Once it is determined that the elevator may be faulty and the probability of failure is relatively large, the alarm unit directly sends an alarm message to the maintenance company to which the elevator belongs, so as to maintain the public.
  • the division can dispatch maintenance personnel to reach the elevator position where the failure may occur in advance, and strive to repair the elevator before the elevator fails. Avoid personal injury and property damage caused by elevator failure.
  • the prediction unit 820 mainly obtains a matching result by matching the running data of each elevator in at least one elevator and the data range of the pre-stored elevator during normal operation, and then determining at least one elevator according to the matching result.
  • the predicting unit may also determine the operational status of each of the at least one elevator in the following manner.
  • the virtual model when the elevator is running is set, and the operation data of each elevator in at least one elevator is input into the virtual model for simulation, and the simulation result is obtained.
  • the virtual model is a model built using big data. How to establish is actually the existing technology. It is not detailed here. Using this data model, the current operational data is input into the virtual model for simulation to obtain simulation results.
  • the operating state of each of the at least one elevator is determined based on the simulation result.
  • each functional component cooperates, and what can be achieved is to determine the probability of the elevator being faulted according to the operating state of the elevator.
  • an alarm message is sent to the relevant department for correlation.
  • the department is able to repair the elevator in time.
  • the present invention also provides another means for intelligently monitoring and predicting the elevator failure.
  • the apparatus includes: an acquisition unit 910, a prediction unit 920, a processing unit 930, an alarm unit 940, and a control unit 950.
  • the collecting unit 910 is configured to collect at least one running data of the elevator during operation.
  • the collecting unit 910 collects at least one running data of the elevator running time sent by the intelligent terminal in different areas in real time.
  • the predicting unit 920 is configured to predict an operating state of each of the at least one elevator according to the operating data of each of the at least one elevators.
  • the running data of the elevator running may include at least one or more of the following parameters:
  • the prediction unit 920 is specifically configured to: match the operation data of each elevator operation of the at least one elevator with the data range of the pre-stored elevator during normal operation to obtain a matching result.
  • each elevator will have a corresponding parameter range for the elevator during normal operation, such as elevator operation, acceleration/deceleration time, rated voltage, rated current, rated speed, rated load, and so on.
  • the operating state of each of the at least one elevator is determined based on the matching result.
  • the elevator may malfunction. If one or more of the previous running data of the elevator matches the data range of the stored elevator in the normal operation of the elevator, the current running state of the elevator is normal.
  • the prediction unit predicts the operating state of each of the at least one elevator.
  • the prediction unit may also predict and predict the operating state of each of the at least one elevator by the following prediction manner.
  • the details are as follows: setting a virtual model when the elevator is running, inputting the operation data of each elevator operation of at least one elevator into the virtual model for simulation, and obtaining the simulation result.
  • the virtual model is a model built using big data. How to establish is actually the existing technology. It is not detailed here. Using this data model, the current operational data is input into the virtual model for simulation to obtain simulation results.
  • the operating state of each of the at least one elevator is determined based on the simulation result.
  • a processing unit configured to determine, according to an operating state of each of the at least one elevator, a probability that each of the at least one elevator fails.
  • the processing unit 930 can determine the probability of the elevator malfunctioning according to the state of each elevator operation. For example, when the elevator is running, if multiple parameters are not within the data range when the elevator is in normal operation, the probability that the elevator will fail is relatively large. However, if the elevator is running, only one or a few parameters are not within the data range when the elevator is running, then the probability of the elevator malfunctioning is slightly smaller.
  • processing unit 930 is specifically configured to:
  • the weighting coefficients corresponding to the acceleration/deceleration time, the flatness longitude, the voltage, the current, the rotational speed, the load, and the floor of the first elevator are sequentially set.
  • the weight coefficient can be set according to the previous big data statistics. For example, when the parameters change, it is easy to cause elevator failure. Then, the weight coefficient of the parameter can be set to be slightly larger, and when the parameters change, the number of times the elevator failure occurs is relatively small, then the weight coefficient of the parameter can be set to be slightly smaller.
  • the value of the specific weight coefficient can be set according to the frequency at which the parameters change in the past elevator failure. For example, if the number of elevator failures is 100 and the probability of a failure of the elevator is 20, the weighting factor can be set to 0.2. And so on. The weighting coefficients of these parameters are determined by counting the causes of the failure of the elevator in the past.
  • the first elevator When the first elevator is running, one or more of acceleration/deceleration time, leveling longitude, voltage, current, speed, load and floor are not in the data range of the first elevator during normal operation, one or more are set
  • the identification of the operational data that does not belong to the data range when the first elevator is in normal operation is 1, otherwise the setting identifier is 0.
  • the setting identifier is 0, and if the parameter does not belong to When the corresponding preset data range when the elevator is running normally, the setting flag is 1.
  • the multiplication is multiplied by the corresponding weight coefficient and summed.
  • the weight coefficient corresponding to the running data of the first elevator is multiplied by the identifier corresponding to the running data, and the probability of the first elevator failure is obtained.
  • the alarm unit 940 is configured to send an alarm message to the maintenance company when the processing unit 930 determines that the probability of the first elevator failure is greater than the risk probability threshold.
  • the processing unit 930 determines one of the at least one elevators, that is, the probability that the first elevator fails in the embodiment of the present invention is greater than the risk probability threshold, the alarm message is sent to the maintenance company.
  • specific location information and number information of the first elevator may be included.
  • the control unit 950 is configured to control the first elevator to stop to the floor closest to the current location of the first elevator, and the elevator is turned on.
  • the elevator is parked in the floor closest to the current location in advance, and the elevator is opened to facilitate the passengers to get off the elevator as soon as possible.
  • the alarm unit 940 is also used to issue an alarm prompt so that the passengers in the elevator can get off the elevator and remind the passengers outside the elevator to prohibit the boarding.
  • control unit 950 is further configured to automatically lock the elevator when it is determined that the passenger has all gone down the elevator. Prevent other unsuspecting passengers from continuing to take the elevator.
  • the device for intelligently monitoring and predicting the elevator fault is provided by the embodiment of the invention, and the communication connection between the intelligent terminal and the server for collecting the elevator running data in real time is realized through the object network.
  • the processing unit remotely monitors and predicts the running state of the elevator. Once it is determined that the elevator may be faulty and the probability of failure is relatively large, the alarm unit sends an alarm message directly to the maintenance company to which the elevator belongs, so that the maintenance company can dispatch the maintenance personnel in advance. Arrive at the location of the elevator where the failure may occur, and strive to repair the elevator before the elevator fails. Avoid personal injury and property damage caused by elevator failure. At the same time, in this way, the elevator is parked on a certain floor in time to facilitate the passengers to step down, and it is forbidden to have passengers on the elevator. It can also prevent passengers from riding the faulty elevator and causing unnecessary dangerous damage.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of cells is only a logical function division.
  • multiple units or components may be combined or integrated. Go to another system, or some features can be ignored or not executed.
  • the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • An integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, can be stored in a computer readable storage medium.
  • the technical solution of the present invention contributes in essence or to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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  • Maintenance And Inspection Apparatuses For Elevators (AREA)
  • Indicating And Signalling Devices For Elevators (AREA)

Abstract

一种智能监控、预测电梯故障的方法及装置,通过实时收集至少一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率;当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。避免由于电梯故障造成的人员伤亡和财产损失。

Description

一种智能监控、预测电梯故障的方法及装置 技术领域
本发明涉及物联网技术领域,尤其涉及一种智能监控、预测电梯故障的方法及装置。
背景技术
电梯已经成为人们生活中必不可少的基础设施。但是近几年来,电梯不断发生故障。例如,电梯还没有到达预定的楼层时,突然开门。如果电梯内的用户没有意识到外面的情景,而走出电梯时,直接掉下去摔成重伤,甚至死亡。还有的电梯在上升或者下降过程中突然停止,然后在下降至一定位置等的情况频繁发生。使用户在乘坐“频繁发生问题”的电梯时,总是提心吊胆。大大降低了用户的体验,甚至会对用户的人身安全构成威胁。那么,如何保证用户在乘梯时的安全,尽量避免安全事故的发生,则成为了亟待解决的技术问题。
发明内容
为解决上述技术问题,本发明提供了一种智能监控、预测电梯故障的方法及装置。
第一方面,本发明提供了一种智能监控、预测电梯故障的方法,该方法包括:
实时收集至少一个电梯运行时的运行数据;
分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态;
分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率;
当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息,其中,报警消息中包括第一电梯的位置信息及编号信息,第一电梯为至少一个电梯中的任一个电梯。
本发明实施例提供的一种智能监控、预测电梯故障的方法,实时采集电梯运行时的运行数据,根据当前的运行数据进行预判电梯的工作状态。当预测电梯发生故障的概率较大时,及时通知维修公司派发人员,提前到达可能会发生故障的电梯位置进行检查,如果确实存在发生故障的风险,则及时处理。如此以来,可以提前做好预防措施,尽量的避免在电梯故障时发生的人身伤害和财产损失。
进一步,当根据预测结果确定第一电梯发生故障的概率大于风险概率阈值时,方法还包括:
控制第一电梯停靠至距离第一电梯当前位置最近的楼层,电梯开启,并发出 报警提示,以便电梯中的乘客下电梯,并且提醒电梯外的乘客禁止乘梯。
在上述实施中,当根据预测判断电梯可能或者即将发生故障时,及时将电梯停靠在某一楼层,方便乘客下梯,并且禁止再有乘客上电梯。由此避免乘客乘坐故障电梯,而造成不必要的危险伤害。
第二方面,本发明提供了一种智能监控、预测电梯故障的装置,该装置包括:
采集单元,用于实时收集至少一个电梯运行时的运行数据;
预测单元,用于分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态;
处理单元,用于分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率;
报警单元,用于当处理单元确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息,其中,报警消息中包括第一电梯的位置信息及编号信息,第一电梯为至少一个电梯中的任一个电梯。
本发明实施例提供的一种智能监控、预测电梯故障的装置,实时采集电梯运行时的运行数据,根据当前的运行数据进行预判电梯的工作状态。当预测电梯发生故障的概率较大时,及时通知维修公司派发人员,提前到达可能会发生故障的电梯位置进行检查,如果确实存在发生故障的风险,则及时处理。如此以来,可以提前做好预防措施,尽量的避免在电梯故障时发生的人身伤害和财产损失。
进一步,装置还包括:控制单元,用于控制第一电梯停靠至距离第一电梯当前位置最近的楼层,电梯开启;
报警单元还用于,发出报警提示,以便电梯中的乘客下电梯,并且提醒电梯外的乘客禁止乘梯。
在上述实施中,当根据预测判断电梯可能或者即将发生故障时,及时将电梯停靠在某一楼层,方便乘客下梯,并且禁止再有乘客上电梯。由此避免乘客乘坐故障电梯,而造成不必要的危险伤害。
附图说明
图1为本发明实施例提供的一种智能监控、预测电梯故障的系统架构图;
图2为本发明实施例提供的一种智能监控、预测电梯故障的方法信令流程示意图;
图3为本发明实施例提供的一种智能监控、预测电梯故障的方法流程示意图;
图4为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图;
图5为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图;
图6为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图;
图7为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图;
图8为本发明实施例提供的一种智能监控、预测电梯故障的装置结构示意图;
图9为本发明实施例提供的另一种智能监控、预测电梯故障的装置结构示意图。
具体实施方式
以下描述中,为了说明而不是为了限定,提出了诸如特定系统结构、接口、技术之类的具体细节,以便透切理解本发明。然而,本领域的技术人员应当清楚,在没有这些具体细节的其它实施例中也可以实现本发明。在其它情况中,省略对众所周知的系统、电路以及方法的详细说明,以免不必要的细节妨碍本发明的描述。
图1为本发明实施例提供的一种智能监控、预测电梯故障的系统架构图。
具体如图1所示,该系统包括:智能终端10、服务器20、物联网接入网关30和物联网服务网关40。
需要说明的是,这里的智能终端10为本地设备,主要用于采集至少一个电梯运行时的运行数据。例如,智能终端10可以是本地PC机,IPAD,或者智能手机等等。而电梯可能是分散安置的,为了节省成本,可以设定一个智能终端10采集某一个区域内的所有电梯的运行数据。并将采集到的其负责的区域内所有电梯的运行数据通过物联网络,传输至远程服务器20中。而服务器20通过智能终端10的传输,而收集到电梯运行数据后,将会根据这些数据分别预测每一个电梯的运行状态。如果根据至少一个电梯中每一个电梯的运行状态,确定第一电梯发生故障的概率大于风险概率阈值时,就会向维修公司发出报警信息。其中,报警信息中包括第一电梯的位置信息和编号信息。第一电梯为至少一个电梯中任一个电梯。当然,在智能终端10向服务器20传输电梯运行数据之前,首先需要和远程服务器20之间建立无线通信连接。
智能终端10首先在物联网接入网关30中进行注册,注册成功后,建立智能终端10和物联网接入网关30之间的通信连接。智能终端10向物联网接入网关30发送鉴权请求,物联网接入网关30将该鉴权请求通过物联网服务网关40发送至服务器20中。以便服务器20能够根据鉴权请求对智能终端10进行鉴权。在鉴权成功后,通过物联网服务网关40和物联网接入网关30,发送鉴权成功的响应信息至智能终端10。智能终端10接收到该响应消息后,通过物联网接入网关30和物联网服务网关40,建立和服务器20之间的通信连接。
为更加详细的介绍上述一种智能监控、预测电梯故障的系统中各部件所执行的方法步骤,本发明实施例还提供了一种智能监控、预测电梯故障的方法信令流程示意图。具体如图2所示,具体包括:
步骤1,智能终端采集至少一个电梯运行时的运行数据。
具体的,因为电梯已经普及,所以很多地方都存在电梯。例如住宅区,商业区,学校和医院等等,都将安装多部电梯。为了保证实时监测各个区域,各个位置的电梯的运行状态,又希望尽量的降低成本。则可以在不同的区域,分别安装一个或者多个智能终端。每个区域安装的智能终端只用于采集该区域内的电梯运行时的运行数据。
步骤2,每一个智能终端分别将自身所采集的电梯运行时的运行数据,通过物联网网络传输至服务器。
当然,如果一个区域包括多个智能终端,每个智能终端分别采集该区域内不同电梯的数据时,为了防止所有智能终端传输数据时发生拥堵,而导致的数据传输延时。还可以在每一个区域中分别设置一个主终端,用于收集该区域其他智能终端采集的电梯运行时的运行数据,然后统一汇总并压缩后,通过物联网网络传输至服务器中。
步骤3,服务器实时接收至少一个电梯运行时的运行数据。
服务器接收的运行数据,因为是通过不同智能终端上传的,而每一个智能终端上传的本身就是至少一个电梯运行时的运行数据。所以,服务器实时接收到的均是至少一个电梯运行时的运行数据。
步骤4,分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
步骤5,分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
步骤6,当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
其中,报警消息中包括第一电梯的位置信息和编号信息。而这里的第一电梯既是至少一个电梯中的任一个电梯。
在上述系统中,通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
由上述系统信令流程中可以看出,在该系统中,智能终端用于实时采集其负责区域内的电梯运行时的运行数据。物联网接入网关和物联网服务网关则用于建 立智能终端和服务器之间的通信连接。而服务器,则是收集到不同智能终端采集的电梯运行时的运行数据后,根据运行数据进行预测电梯的运行状态,以及根据运行状态确定电梯将会发生故障的概率,如果概率较大,则发出报警信息至维修公司。以便维修公司能够分派维修人员及时到达可能会发生故障的电梯位置,对电梯进行维修或者维护。以防止电梯损坏时导致的人身伤亡和财产损失的发生。因此可以看出,上述系统中,起到至关作用的部件为服务器。因此,在下文中,将详细介绍服务器所执行的方法步骤。
图3为本发明实施例提供的一种智能监控、预测电梯故障的方法流程示意图。具体如图3所示,该方法包括:
步骤310,实时收集至少一个电梯运行时的运行数据。
具体的,服务器实时收集不同区域的智能终端发送的至少一个电梯运行时的运行数据。
步骤320,分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
步骤330,分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
具体的,在步骤320中,已经确定了至少一个电梯中每一个电梯的运行状态。那么,则可以根据每一个电梯运行时的状态来判断电梯发生故障的概率。例如,电梯运行时,如果多个参数均不在电梯正常运行时的数据范围内,则电梯将要发生故障的概率就会比较大。而如果电梯运行时,只有一个或者少数的参数不在电梯运行时的数据范围内,那么电梯发生故障的概率稍小。
步骤340,当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
具体的,当确定至少一个电梯中某一个电梯,也即是本发明实施例中所说的第一电梯发生故障的概率大于风险概率阈值时,则向维修公司发送报警消息。而在报警消息中,可以包括第一电梯的具体位置信息和编号信息。例如,这里的风险阈值为60%。下文中的风险阈值也可以指的是这个数值,将不在赘述。
本发明实施例提供的一种智能监控、预测电梯故障的方法,通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
为了更进一步的详细介绍本发明的实施方案,本发明实施例还提供了另一种智能监控、预测电梯故障的方法,具体如图4所示,图4为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图,该方法包括:
步骤410,实时收集至少一个电梯运行时的运行数据。
具体的,服务器实时收集不同区域的智能终端发送的至少一个电梯运行时的运行数据。
步骤420,分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
具体的,电梯运行时的运行数据至少可以包括以下参数中的一个或者多个:
电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层。根据这些电梯运行时的运行数据,分别根据每一个电梯运行时的上述运行数据,来预测对应电梯的运行状态。
具体的预测过程可以包括:
步骤a,将至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果。
其中,在进行预测之前,服务器中将存储每一个电梯出厂时的运行参数信息。在出厂时,每个电梯都将存在与之对应的电梯正常运行时的参数范围,例如电梯运行时,加速/减速时间,额定电压,额定电流,额定转速,额定载荷等等。
步骤b,根据匹配结果确定至少一个电梯中每一个电梯的运行状态。
如果电梯此前的运行数据中的一个或者多个和服务器中预存储的电梯正常运行时的数据范围不匹配的话,则说明电梯可能会发生故障。而如果电梯此前运行数据中的一个或者多个和服务器中与存储的电梯正常运行时的数据范围相匹配,则说明电梯当前的运行状态正常。
步骤430,分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
具体的,在步骤420中,已经确定了至少一个电梯中每一个电梯的运行状态。那么,则可以根据每一个电梯运行时的状态来判断电梯发生故障的概率。例如,电梯运行时,如果多个参数均不在电梯正常运行时的数据范围内,则电梯将要发生故障的概率就会比较大。而如果电梯运行时,只有一个或者少数的参数不在电梯运行时的数据范围内,那么电梯发生故障的概率稍小。
步骤440,当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
具体的,当确定至少一个电梯中某一个电梯,也即是本发明实施例中所说的第一电梯发生故障的概率大于风险概率阈值时,则向维修公司发送报警消息。而在报警消息中,可以包括第一电梯的具体位置信息和编号信息。
本发明实施例提供的一种智能监控、预测电梯故障的方法通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态。预测电梯的运行状态可以包括将电梯运行时的运行数据和预存储的电梯正常运行时的好时机范围进行匹配,并且根据匹配结果确定电 梯的运行状态。一旦根据电梯的运行状态确定电梯可能发生故障,且发生故障的概率比较大,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
在本发明实施例提供的另一种智能监控、预测电梯故障的方法中,还提供了另一种预测电梯运行的状态的方法。具体如图5所示,图5为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图。该方法中步骤510,530至540均以上一实施例中步骤410,430至440相同。唯一不同的为步骤520,这里将不在赘述步骤510,530至540。而详细说明步骤520,具体如下:
步骤520,分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
具体的,电梯运行时的运行数据至少可以包括以下参数中的一个或者多个:
电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层。根据这些电梯运行时的运行数据,分别根据每一个电梯运行时的上述运行数据,来预测对应电梯的运行状态。
具体的预测过程可以包括:
步骤c,设置电梯运行时的虚拟模型,将至少一个电梯中每一个电梯运行时的运行数据输入至虚拟模型中进行模拟,获取模拟结果。
其中,虚拟模型就是利用大数据建立的一个模型。具体如何建立是现有技术。这里不在详细介绍。而利用这个数据模型,将当前的运行数据输入到虚拟模型中进行模拟,从而获取模拟结果。
步骤d,根据模拟结果确定至少一个电梯中每一个电梯的运行状态。
实际上而言,就是将采集到的电梯运行状态数据进行分析,然后预测电梯未来的运行状态,并利用预测到的结果来指导电梯未来的运行。
本发明实施例提供的一种智能监控、预测电梯故障的方法通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态。其中,可以通过设置电梯运行时的虚拟模型,将每一个电梯运行时的数据输入至虚拟模型中进行模拟,并根据模拟结果确定电梯的运行状态。并且,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
在本发明的另一个具体实施例中,详细介绍了如何计算第一电梯发生故障的概率。具体如图6所示,图6为本发明实施例提供的另一种智能监控、预测电梯故障的方法流程示意图。该方法包括:
步骤610,实时收集至少一个电梯运行时的运行数据。
具体的,服务器实时收集不同区域的智能终端发送的至少一个电梯运行时的运行数据。
步骤620,分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
具体的,电梯运行时的运行数据至少可以包括以下参数中的一个或者多个:
电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层。根据这些电梯运行时的运行数据,分别根据每一个电梯运行时的上述运行数据,来预测对应电梯的运行状态。
具体的预测过程可以包括:
步骤c,设置电梯运行时的虚拟模型,将至少一个电梯中每一个电梯运行时的运行数据输入至虚拟模型中进行模拟,获取模拟结果。
其中,虚拟模型就是利用大数据建立的一个模型。具体如何建立是现有技术。这里不在详细介绍。而利用这个数据模型,将当前的运行数据输入到虚拟模型中进行模拟,从而获取模拟结果。
步骤d,根据模拟结果确定至少一个电梯中每一个电梯的运行状态。
实际上而言,就是将采集到的电梯运行状态数据进行分析,然后预测电梯未来的运行状态,并利用预测到的结果来指导电梯未来的运行。
步骤630,分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
具体的,在步骤620中,已经确定了至少一个电梯中每一个电梯的运行状态。那么,则可以根据每一个电梯运行时的状态来判断电梯发生故障的概率。例如,电梯运行时,如果多个参数均不在电梯正常运行时的数据范围内,则电梯将要发生故障的概率就会比较大。而如果电梯运行时,只有一个或者少数的参数不在电梯运行时的数据范围内,那么电梯发生故障的概率稍小。
而具体确定电梯发生故障的概率的方法步骤具体包括:
e,分别为第一电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层依次设定与之对应的权重系数。
权重系数可以根据以往大数据统计来设定,例如,哪些参数发生变化时,容易导致电梯故障的发生。那么,可以设定该参数的权重系数稍大,哪些参数发生变化时,电梯故障发生的次数比较少,那么则可以设定该参数的权重系数稍小。而具体设定权重系数的数值,则可以根据统计以往电梯故障时,哪些参数发生变化的频率而设定。例如,如果电梯故障次数为100次,而电压发生改变导致电梯发生故障的概率为20次,那么其权重系数可以设定为0.2。依次类推。通过统计以往电梯发生故障的原因,而确定这些参数的权重系数。
f,当第一电梯运行时,加速/减速时间、平层经度、电压、电流、转速、载荷和楼层中的一个或多个不属于第一电梯正常运行时的数据范围时,设定一个或 多个不属于第一电梯正常运行时的数据范围的运行数据的标识为1,否则设定标识为0。
具体的,为了能够保证计算风险概率更加精确,可以将第一电梯运行时,参数属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为0,而如果参数不属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为1。并将标识与相应的权重系数相乘并做和。最终获取权重系数概率,具体如步骤g。
g,分别将第一电梯运行时的运行数据对应的权重系数乘以该运行数据对应的标识后做和,获取第一电梯发生故障的概率。
步骤640,当确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
具体的,当确定至少一个电梯中某一个电梯,也即是本发明实施例中所说的第一电梯发生故障的概率大于风险概率阈值时,则向维修公司发送报警消息。而在报警消息中,可以包括第一电梯的具体位置信息和编号信息。
需要说明但是,本发明实施例中在预测电梯运行状态时,主要采用的是如图5所示的方法步骤,但是图4所示的方法步骤其实是与图5中所示的预测电梯的运行状态的方法相并列的方案。因此,本实施例中所述的计算第一电梯发生故障的概率方法同样可以应用与图4所示的方法步骤中。
本发明实施例提供的一种智能监控、预测电梯故障的方法通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态,进而根据电梯的运行状态确定电梯发生故障的概率。而确定电梯发生故障的概率可以根据电梯运行时的运行数据标识和与运行数据分别对应的权重系数乘积后做和,由此获取电梯发生故障时的概率。一旦确定电梯发生故障的概率比较大时,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
进一步的,在电梯发生故障之前,必须采取一定措施,来防止电梯发生故障导致的人身伤亡等。那么,本发明实施例还提供了另一种智能监控、预测电梯故障的方法流程示意图。具体如图7所示,其中,步骤710至步骤740,与上一实施例中步骤610-640相同,这里不再赘述。详细介绍一下步骤750:
步骤750,控制第一电梯停靠至距离第一电梯当前位置最近的楼层,电梯开启,并发出报警提示,以便电梯中的乘客下电梯,并且提醒电梯外的乘客禁止乘梯。
具体的,当确定电梯发生故障的概率大于风险概率阈值时,为了避免未来某段时间电梯将会发生故障。所以,提前将电梯停靠在距离当前位置最近的楼层,然后发出报警提示,告知乘客尽快下电梯。并且提醒电梯外的乘客禁止承载电梯。
进一步的,当确定乘客已经全部下电梯后,则将电梯自动锁定。防止其他不知情的乘客继续乘坐该电梯。
另外,还需要说明的是,步骤750和步骤740中向维修公司发送报警消息。这两个步骤其实并不分先后顺序,具体哪个在前,哪个在后,或者同时执行这里不做任何限定。但是,一般而言,是并行实现的。
本发明实施例提供的一种智能监控、预测电梯故障的方法,通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。服务器通过远程监控、预测电梯的运行状态,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。同时通过该种方式,及时将电梯停靠在某一楼层,方便乘客下梯,并且禁止再有乘客上电梯。还可以避免乘客乘坐故障电梯,而造成不必要的危险伤害。
相应地,本发明实施例还提供了一种智能监控、预测电梯故障的装置。
图8为本发明实施例提供的一种智能监控、预测电梯故障的装置结构示意图。具体如图8所示,该装置包括:采集单元810,预测单元820,处理单元830以及报警单元840。
采集单元810,用于实时收集至少一个电梯运行时的运行数据。
具体的,采集单元810实时收集不同区域的智能终端发送的至少一个电梯运行时的运行数据。
预测单元820,用于分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
其中,电梯运行时的运行数据至少可以包括以下参数中的一个或者多个:
电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层。根据这些电梯运行时的运行数据,分别根据每一个电梯运行时的上述运行数据,来预测对应电梯的运行状态。
优选的,预测单元820具体用于:将至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果。
其中,在进行预测之前,服务器中将存储每一个电梯出厂时的运行参数信息。在出厂时,每个电梯都将存在与之对应的电梯正常运行时的参数范围,例如电梯运行时,加速/减速时间,额定电压,额定电流,额定转速,额定载荷等等。
根据匹配结果确定至少一个电梯中每一个电梯的运行状态。
如果电梯此前的运行数据中的一个或者多个和服务器中预存储的电梯正常运行时的数据范围不匹配的话,则说明电梯可能会发生故障。而如果电梯此前运行数据中的一个或者多个和服务器中与存储的电梯正常运行时的数据范围相匹配,则说明电梯当前的运行状态正常。
处理单元,用于分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
具体的,在预测单元820确定了至少一个电梯中每一个电梯的运行状态之后,那么,处理单元830则可以根据每一个电梯运行时的状态来判断电梯发生故障的概率。例如,电梯运行时,如果多个参数均不在电梯正常运行时的数据范围内,则电梯将要发生故障的概率就会比较大。而如果电梯运行时,只有一个或者少数的参数不在电梯运行时的数据范围内,那么电梯发生故障的概率稍小。
优选的,处理单元830具体用于:
分别为第一电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层依次设定与之对应的权重系数。
权重系数可以根据以往大数据统计来设定,例如,哪些参数发生变化时,容易导致电梯故障的发生。那么,可以设定该参数的权重系数稍大,哪些参数发生变化时,电梯故障发生的次数比较少,那么则可以设定该参数的权重系数稍小。而具体设定权重系数的数值,则可以根据统计以往电梯故障时,哪些参数发生变化的频率而设定。例如,如果电梯故障次数为100次,而电压发生改变导致电梯发生故障的概率为20次,那么其权重系数可以设定为0.2。依次类推。通过统计以往电梯发生故障的原因,而确定这些参数的权重系数。
当第一电梯运行时,加速/减速时间、平层经度、电压、电流、转速、载荷和楼层中的一个或多个不属于第一电梯正常运行时的数据范围时,设定一个或多个不属于第一电梯正常运行时的数据范围的运行数据的标识为1,否则设定标识为0。
具体的,为了能够保证计算风险概率更加精确,可以将第一电梯运行时,参数属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为0,而如果参数不属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为1。并将标识与相应的权重系数相乘并做和。最终获取权重系数概率,具体如下:
分别将第一电梯运行时的运行数据对应的权重系数乘以该运行数据对应的标识后做和,获取第一电梯发生故障的概率。
报警单元840,用于当处理单元830确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
具体的,当处理单元830确定至少一个电梯中某一个电梯,也即是本发明实施例中所说的第一电梯发生故障的概率大于风险概率阈值时,则向维修公司发送报警消息。而在报警消息中,可以包括第一电梯的具体位置信息和编号信息。
本发明实施例提供的一种智能监控、预测电梯故障的装置,通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。处理单元通过远程监控、预测电梯的运行状态,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接通过报警单元发出报警信息至该电梯所属维修公司,以便维修公 司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。
在上述实施中,预测单元820主要是通过将至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果,然后根据匹配结果确定至少一个电梯中每一个电梯的运行状态。而在另一个实施方案中,预测单元还可以采用如下方式确定至少一个电梯中每一个电梯的运行状态。
具体如下:
设置电梯运行时的虚拟模型,将至少一个电梯中每一个电梯运行时的运行数据输入至虚拟模型中进行模拟,获取模拟结果。
其中,虚拟模型就是利用大数据建立的一个模型。具体如何建立是现有技术。这里不在详细介绍。而利用这个数据模型,将当前的运行数据输入到虚拟模型中进行模拟,从而获取模拟结果。
根据模拟结果确定至少一个电梯中每一个电梯的运行状态。
实际上而言,就是将采集到的电梯运行状态数据进行分析,然后预测电梯未来的运行状态,并利用预测到的结果来指导电梯未来的运行。
而其他部件所执行的功能均同上一个实施例中所介绍的一种智能监控、预测电梯故障的装置中所执行的功能相同或者类似,这里将不做赘述。
上述装置实施例中,各功能部件协同作用,所能实现的是根据电梯的运行状态,确定电梯发生故障的概率,一旦概率大于某个预设阈值时,则发出报警信息至相关部门,以便相关部门能够及时对电梯维修。但是,考虑到承载电梯的用户的安全,以及即将承载电梯的用户的安全,本发明还提供了另一种智能监控、预测电梯故障的装置。具体如图9所示,该装置包括:采集单元910,预测单元920,处理单元930、报警单元940,以及控制单元950。
采集单元910,用于实时收集至少一个电梯运行时的运行数据。
具体的,采集单元910实时收集不同区域的智能终端发送的至少一个电梯运行时的运行数据。
预测单元920,用于分别根据至少一个电梯中每一个电梯运行时的运行数据,预测至少一个电梯中每一个电梯的运行状态。
其中,电梯运行时的运行数据至少可以包括以下参数中的一个或者多个:
电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层。根据这些电梯运行时的运行数据,分别根据每一个电梯运行时的上述运行数据,来预测对应电梯的运行状态。
优选的,预测单元920具体用于:将至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果。
其中,在进行预测之前,服务器中将存储每一个电梯出厂时的运行参数信息。在出厂时,每个电梯都将存在与之对应的电梯正常运行时的参数范围,例如电梯运行时,加速/减速时间,额定电压,额定电流,额定转速,额定载荷等等。
根据匹配结果确定至少一个电梯中每一个电梯的运行状态。
如果电梯此前的运行数据中的一个或者多个和服务器中预存储的电梯正常运行时的数据范围不匹配的话,则说明电梯可能会发生故障。而如果电梯此前运行数据中的一个或者多个和服务器中与存储的电梯正常运行时的数据范围相匹配,则说明电梯当前的运行状态正常。
当然,上述只是介绍了一种预测单元预测至少一个电梯中每一个电梯的运行状态的方式。而预测单元也可以通过如下预测方式预测预测至少一个电梯中每一个电梯的运行状态。
具体如下:设置电梯运行时的虚拟模型,将至少一个电梯中每一个电梯运行时的运行数据输入至虚拟模型中进行模拟,获取模拟结果。
其中,虚拟模型就是利用大数据建立的一个模型。具体如何建立是现有技术。这里不在详细介绍。而利用这个数据模型,将当前的运行数据输入到虚拟模型中进行模拟,从而获取模拟结果。
根据模拟结果确定至少一个电梯中每一个电梯的运行状态。
实际上而言,就是将采集到的电梯运行状态数据进行分析,然后预测电梯未来的运行状态,并利用预测到的结果来指导电梯未来的运行。
处理单元,用于分别根据至少一个电梯中每一个电梯的运行状态,判断至少一个电梯中每一个电梯发生故障的概率。
具体的,在预测单元920确定了至少一个电梯中每一个电梯的运行状态之后,那么,处理单元930则可以根据每一个电梯运行时的状态来判断电梯发生故障的概率。例如,电梯运行时,如果多个参数均不在电梯正常运行时的数据范围内,则电梯将要发生故障的概率就会比较大。而如果电梯运行时,只有一个或者少数的参数不在电梯运行时的数据范围内,那么电梯发生故障的概率稍小。
优选的,处理单元930具体用于:
分别为第一电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层依次设定与之对应的权重系数。
权重系数可以根据以往大数据统计来设定,例如,哪些参数发生变化时,容易导致电梯故障的发生。那么,可以设定该参数的权重系数稍大,哪些参数发生变化时,电梯故障发生的次数比较少,那么则可以设定该参数的权重系数稍小。而具体设定权重系数的数值,则可以根据统计以往电梯故障时,哪些参数发生变化的频率而设定。例如,如果电梯故障次数为100次,而电压发生改变导致电梯发生故障的概率为20次,那么其权重系数可以设定为0.2。依次类推。通过统计以往电梯发生故障的原因,而确定这些参数的权重系数。
当第一电梯运行时,加速/减速时间、平层经度、电压、电流、转速、载荷和楼层中的一个或多个不属于第一电梯正常运行时的数据范围时,设定一个或多个不属于第一电梯正常运行时的数据范围的运行数据的标识为1,否则设定标识为0。
具体的,为了能够保证计算风险概率更加精确,可以将第一电梯运行时,参数属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为0,而如果参数不属于与之对应的电梯运行正常时的预设数据范围时,则设定标识为1。并将标识与相应的权重系数相乘并做和。最终获取权重系数概率,具体如下:
分别将第一电梯运行时的运行数据对应的权重系数乘以该运行数据对应的标识后做和,获取第一电梯发生故障的概率。
报警单元940,用于当处理单元930确定第一电梯发生故障的概率大于风险概率阈值时,向维修公司发送报警消息。
具体的,当处理单元930确定至少一个电梯中某一个电梯,也即是本发明实施例中所说的第一电梯发生故障的概率大于风险概率阈值时,则向维修公司发送报警消息。而在报警消息中,可以包括第一电梯的具体位置信息和编号信息。
控制单元950,用于控制第一电梯停靠至距离第一电梯当前位置最近的楼层,电梯开启。
具体的,当确定电梯发生故障的概率大于风险概率阈值时,为了避免未来某段时间电梯将会发生故障。所以,提前将电梯停靠在距离当前位置最近的楼层,电梯开启,方便乘客尽快下电梯。
而电梯开启的同时,报警单元940还用于,发出报警提示,以便电梯中的乘客下电梯,并且提醒电梯外的乘客禁止乘梯。
进一步的,控制单元950还用于,当确定乘客已经全部下电梯后,则将电梯自动锁定。防止其他不知情的乘客继续乘坐该电梯。
本发明实施例提供的一种智能监控、预测电梯故障的装置,通过物联网络实现实时收集电梯运行数据的智能终端和服务器之间的通信连接。处理单元通过远程监控、预测电梯的运行状态,一旦确定电梯可能发生故障,且发生故障的概率比较大,则直接通过报警单元发出报警信息至该电梯所属维修公司,以便维修公司能够分派维修人员提前到达可能发生故障的电梯位置,争取在电梯发生故障之前,能够对电梯进行抢修。避免由于电梯故障而导致的人员伤亡和财产损失的发生。同时通过该种方式,及时将电梯停靠在某一楼层,方便乘客下梯,并且禁止再有乘客上电梯。还可以避免乘客乘坐故障电梯,而造成不必要的危险伤害。
读者应理解,在本说明书的描述中,参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中,对上述术语的示意性表述不必针对的是 相同的实施例或示例。而且,描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外,在不相互矛盾的情况下,本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,上述描述的装置和单元的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。
在本申请所提供的几个实施例中,应该理解到,所揭露的装置和方法,可以通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。
作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本发明实施例方案的目的。
另外,在本发明各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分,或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本发明各个实施例方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种智能监控、预测电梯故障的方法,其特征在于,所述方法包括:
    实时收集至少一个电梯运行时的运行数据;
    分别根据所述至少一个电梯中每一个电梯运行时的运行数据,预测所述至少一个电梯中每一个电梯的运行状态;
    分别根据所述至少一个电梯中每一个电梯的运行状态,判断所述至少一个电梯中每一个电梯发生故障的概率;
    当确定第一电梯发生故障的概率大于风险概率阈值时,向所述维修公司发送报警消息,其中,所述报警消息中包括所述第一电梯的位置信息及编号信息,所述第一电梯为所述至少一个电梯中的任一个电梯。
  2. 根据权利要求1所述的方法,其特征在于,所述分别根据所述至少一个电梯中每一个电梯运行时的运行数据,预测所述至少一个电梯中每一个电梯的运行状态,具体包括:
    将所述至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果;
    根据所述匹配结果确定所述至少一个电梯中每一个电梯的运行状态。
  3. 根据权利要求1所述的方法,其特征在于,所述分别根据所述至少一个电梯中每一个电梯运行时的运行数据,预测所述至少一个电梯中每一个电梯的运行状态,具体包括:
    设置所述电梯运行时的虚拟模型,将所述至少一个电梯中每一个电梯运行时的运行数据输入至所述虚拟模型中进行模拟,获取模拟结果;
    根据所述模拟结果确定所述至少一个电梯中每一个电梯的运行状态。
  4. 根据权利要求2或3所述的方法,其特征在于,所述至少一个电梯中每一个电梯运行时的运行数据均包括:电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层;
    判断所述第一电梯发生故障的概率,具体包括:
    分别为所述第一电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层依次设定与之对应的权重系数;
    当所述第一电梯运行时,所述加速/减速时间、平层经度、电压、电流、转速、载荷和楼层中的一个或多个不属于所述第一电梯正常运行时的数据范 围时,
    设定所述一个或多个不属于所述第一电梯正常运行时的数据范围的运行数据的标识为1,否则设定标识为0;
    分别将所述第一电梯运行时的运行数据对应的权重系数乘以该运行数据对应的标识后做和,获取所述第一电梯发生故障的概率。
  5. 根据权利要求1-3所述的方法,其特征在于,所述当根据所述预测结果确定第一电梯发生故障的概率大于风险概率阈值时,向所述维修公司发送报警消息之前,所述方法还包括:
    根据所述第一电梯运行时的运行数据,确定所述电梯发生故障的原因;
    所述向所述维修公司发送报警消息时,所述报警消息中还包括所述电梯发生故障的原因。
  6. 一种智能监控、预测电梯故障的装置,其特征在于,所述装置包括:
    采集单元,用于实时收集至少一个电梯运行时的运行数据;
    预测单元,用于分别根据所述至少一个电梯中每一个电梯运行时的运行数据,预测所述至少一个电梯中每一个电梯的运行状态;
    处理单元,用于分别根据所述至少一个电梯中每一个电梯的运行状态,判断所述至少一个电梯中每一个电梯发生故障的概率;
    报警单元,用于当所述处理单元确定第一电梯发生故障的概率大于风险概率阈值时,向所述维修公司发送报警消息,其中,所述报警消息中包括所述第一电梯的位置信息及编号信息,所述第一电梯为所述至少一个电梯中的任一个电梯。
  7. 根据权利要求6所述的装置,其特征在于,所述预测单元具体用于:
    将所述至少一个电梯中每一个电梯运行时的运行数据和预存储的电梯正常运行时的数据范围进行匹配,获取匹配结果;
    根据所述匹配结果确定所述至少一个电梯中每一个电梯的运行状态。
  8. 根据权利要求6所述的装置,其特征在于,所述预测单元具体用于:
    设置所述电梯运行时的虚拟模型,将所述至少一个电梯中每一个电梯运行时的运行数据输入至所述虚拟模型中进行模拟,获取模拟结果;
    根据所述模拟结果确定所述至少一个电梯中每一个电梯的运行状态。
  9. 根据权利要求7或8所述的装置,其特征在于,所述至少一个电梯 中每一个电梯运行时的运行数据均包括:电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层;
    所述判断单元具体用于,分别为所述第一电梯运行时的加速/减速时间、平层经度、电压、电流、转速、载荷和楼层依次设定与之对应的权重系数;
    当所述第一电梯运行时,所述加速/减速时间、平层经度、电压、电流、转速、载荷和楼层中的一个或多个不属于所述第一电梯正常运行时的数据范围时,设定所述一个或多个不属于所述第一电梯正常运行时的数据范围的运行数据的标识为1,否则设定标识为0;
    分别将所述第一电梯运行时的运行数据对应的权重系数乘以该运行数据对应的标识后做和,获取所述第一电梯发生故障的概率。
  10. 根据权要求6-8任一项所述的装置,其特征在于,所述处理单元还用于,根据所述第一电梯运行时的运行数据,确定所述电梯发生故障的原因;
    所述报警单元向所述维修公司发送报警消息时,所述报警消息中还包括所述电梯发生故障的原因。
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