An elevator operation stability monitoring system

Abstract

The application discloses an elevator operation stability monitoring system, which collects a transverse amplitude offset angle, a single floor rate change ratio, a noise change ratio and a longitudinal amplitude offset angle through a signal collection module, inputs the same into a dynamic stability detection model for analog output, and simultaneously, the dynamic stability detection model is subjected to deep neural optimization, analog output of the dynamic stability detection model is realized through change of a constant adaptive value, and the accuracy of stability representation value output is improved. The system provided by the application does not have a high requirement for a signal collection device, has low maintenance cost in the later period, can greatly reduce the central processing unit (CPU) operation on the basis of ensuring continuous output of stability values, reduces the cost of system configuration, and is favorable for large-scale elevator operation safety testing and application.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of elevator safety technology, in particular to an elevator operation stability monitoring system. BACKGROUND

[0002] As a common tool for people to go up and down in shopping malls and office buildings, the elevator is set in the elevator shaft of the building and is assisted by the lifting driving mechanism and the guide rail. Since the elevator will have some matching wear after long-term use, the instability of the matching will cause the elevator to vibrate greatly and increase the probability of elevator safety accidents during high-speed up and down. Therefore, it is necessary to monitor the abnormal state.

[0003] At present, the technology for monitoring the running state of the elevator is still rare, and the existing disclosed operation detection method involves a large amount of operation model, such as patent authorized announcement No. CN107194053B - intelligent elevator control system operation fault prediction method, which plays a role in detecting the running fault of the elevator. On the one hand, the corresponding detection model operation has a large amount of calculation, and the cost of the configured MCU central processor is too high, and on the other hand, the installation technology requirement of the elevator signal acquisition device is high, and the maintenance cost is large. Therefore, it is of great practical significance to develop a system with small operation amount, low installation technology and suitable for general elevator operation stability detection based on signal detection. SUMMARY

[0004] The purpose of this part is to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part and the abstract and title of the specification to avoid obscuring the purpose of this part, the abstract and the title of the specification, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above problems existing in the existing elevator operation stability detection method, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is to solve the problem that the existing elevator operation stability detection method has a large amount of calculation of the corresponding detection model, the cost of the configured MCU central processor is too high, and the installation technology requirement of the elevator signal acquisition device is high and the maintenance cost is large.

[0007] To solve the above technical problems, the application provides the following technical scheme: an elevator operation stability monitoring system, comprising: a signal acquisition module, which acquires an operation parameter signal through a signal sensor arranged in an elevator car; a signal transmission module, which is in data connection with the signal acquisition module, receives the operation parameter signal, and transmits the operation parameter signal to a data processing module through a wireless data network; the data processing module, which is in wireless data connection with the signal transmission module, receives the operation parameter signal, performs stability analysis on the input operation parameter signal through a constructed dynamic stability detection model, and outputs a dynamic stability value in real time; and an early warning judgment module, which is in data connection with the data processing module, acquires the dynamic stability value, and performs elevator stability safety early warning when the dynamic stability value reaches a set alarm threshold.

[0008] As a preferred scheme of the elevator operation stability monitoring system, the signal sensor specifically comprises: a lateral amplitude detection unit, which is embedded in the central top of the elevator car, acquires a lateral amplitude offset angle a of the elevator in real time through a built-in lateral amplitude detector; a single-layer operation speed calculation unit, which is embedded in the central top of the elevator car, acquires a single-layer operation speed in the elevator operation process in real time, and synchronously acquires a single-layer speed change ratio β; a noise change unit, which is embedded in the central top of the elevator car, acquires noise in the elevator operation process in real time through a built-in sound signal collector, and synchronously acquires a noise change ratio γ during operation; and a longitudinal amplitude detection unit, which is embedded in the central top of the elevator car, acquires a longitudinal amplitude offset angle δ of the elevator in real time through a built-in longitudinal amplitude detector.

[0009] As a preferred scheme of the elevator operation stability monitoring system, the single-layer operation speed calculation unit acquires a single-layer operation speed in the elevator operation process specifically as follows: a signal transmitter is arranged at the top of each layer of the elevator shaft; a signal receiver for receiving the signal transmitted by the signal transmitter is embedded in the single-layer operation speed calculation unit; when the elevator runs to the corresponding floor, the signal receiver receives the corresponding signal in real time; and a processor built in the single-layer operation speed calculation unit acquires the interval time of signal reception of each layer in real time, and acquires the single-layer speed change ratio β according to the uninterrupted interval time.

[0010] As a preferred scheme of the elevator operation stability monitoring system, wherein: the data processing module performs stability analysis on the input operation parameter signal through the constructed dynamic stability detection model, and outputs a dynamic stability value in real time, specifically including: S1: constructing a dynamic stability detection model; S2: inputting the real-time collected operation parameter signal into the dynamic stability detection model for operation to obtain an initial simulation steady-state value; S3: constructing a deep neural optimization network model; S4: inputting the initial simulation steady-state value into the deep neural optimization network model, continuously outputting a simulation steady-state value through the change of a corresponding constant adaptive value ε, repeatedly executing until the output result is satisfactory, and obtaining the dynamic stability value.

[0011] As a preferred scheme of the elevator operation stability monitoring system, wherein: the operation formula of the constructed dynamic stability detection model is specifically:

[0012]

[0013] Wherein, μ is an initial simulation steady-state value; α is a lateral amplitude offset angle (degree); β is a single-layer rate change ratio; λ is a noise change ratio; δ is a longitudinal amplitude offset angle (degree); ε is a constant adaptive value; x and dx are integral operations.

[0014] As a preferred scheme of the elevator operation stability monitoring system, wherein: the operation formula of the constructed deep neural optimization network model is specifically:

[0015]

[0016] Wherein, L represents a simulation steady-state value, μ is an initial simulation steady-state value, and ε is a constant adaptive value.

[0017] Wherein, ε is selected as any constant in (0, 10).

[0018] As a preferred scheme of the elevator operation stability monitoring system, wherein: when the root mean square error between the simulation steady-state value L output by the deep neural optimization network model and the initial simulation steady-state value μ is less than a set threshold value, the output result is defined as satisfactory.

[0019] As a preferred scheme of the elevator operation stability monitoring system, wherein: the set threshold value is set to 0.25.

[0020] The beneficial effects of the present application: the present application provides an elevator operation stability monitoring system, through the signal acquisition module collects the lateral amplitude offset angle, single layer rate change ratio, noise change ratio and longitudinal amplitude offset angle, inputs it into the dynamic stability detection model for analog output, at the same time, the present application optimizes the dynamic stability detection model, changes the constant adaptive value to simulate the output of the dynamic stability detection model, improves the accuracy of the stability representation value output, the system provided by the present application does not make high requirements for the signal acquisition device, the maintenance cost is low, at the same time, the present application can greatly reduce the central processing unit operation on the basis of ensuring the continuous output of stability value, reduces the cost of system configuration, is conducive to the test and application of large-scale elevator operation safety. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0022] Figure 1 The system module diagram of the elevator operation stability monitoring system provided by the present application.

[0023] Figure 2 The method flow chart of the single layer operation speed calculation unit provided by the present application to obtain the operation speed of single layer in the elevator operation process.

[0024] Figure 3 The method flow chart of the data processing module provided by the present application to analyze the stability of the input operation parameter signal through the constructed dynamic stability detection model, and to output the dynamic stability value in real time. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0026] The existing elevator operation stability detection method has high operation calculation power of the corresponding detection model, high cost of the configured MCU central processor, high technical requirements for the placement of the elevator signal acquisition device and high maintenance cost, therefore, it has important practical significance to develop a system with small operation calculation power, low placement technology and suitable for general elevator operation stability detection based on signal detection.

[0027] Referring to Figure 1 The application provides an elevator operation stability monitoring system, comprising:

[0028] The signal acquisition module 100 obtains the operation parameter signal through the signal sensor arranged in the elevator car;

[0029] The signal transmission module 200 is in data connection with the signal acquisition module 100, receives the operation parameter signal and transmits it to the data processing module 300 through a wireless data network;

[0030] The data processing module 300 is in wireless data connection with the signal transmission module 200, receives the operation parameter signal and performs stability analysis on the input operation parameter signal through the constructed dynamic stability detection model to output the dynamic stability value in real time;

[0031] The early warning judgment module 400 is in data connection with the data processing module 300, obtains the dynamic stability value and performs elevator stability safety early warning when the dynamic stability value reaches the set alarm threshold.

[0032] It should be additionally explained that the alarm threshold of the early warning judgment module 400 is set by the user, for example, can be set to 10, 20, 30 and the like, and the larger the set value is, the lower the alarm degree is.

[0033] Specifically, the signal sensor specifically comprises:

[0034] The lateral amplitude detection unit is embedded in the central part of the top of the elevator car, and the lateral amplitude offset angle a of the elevator during operation is obtained in real time through the built-in lateral amplitude detector;

[0035] The single-layer operation rate calculation unit is embedded in the central part of the top of the elevator car, and the operation rate of the single layer during the operation of the elevator is obtained in real time, and the single-layer rate change ratio β is obtained synchronously;

[0036] The noise change unit is embedded in the central part of the top of the elevator car, and the noise during the operation of the elevator is obtained in real time through the built-in sound signal collector, and the noise change ratio γ during operation is obtained synchronously;

[0037] The longitudinal amplitude detection unit is embedded in the central top of the elevator car, and the longitudinal amplitude offset angle δ during the running of the elevator is obtained in real time through the built-in longitudinal amplitude detector.

[0038] It should be noted that the lateral amplitude detection unit and the longitudinal amplitude detection unit include a fluorescent sheet, a shell, a laser emitter, a beam expander assembly, a dichroic mirror, a light detector, a photoelectric converter, a controller, and a power supply. The fluorescent sheet is multiple and has a circular sheet structure. The fluorescent sheet is provided with metal mixed powder or rare earth ion doped powder, which presents a fluorescent excitation state under the irradiation of laser of a preset wavelength. The shell is a box-shaped shell structure and is arranged on the top of the elevator car. An installation cavity is formed in the shell, and a window is arranged on the side of the elevator shaft facing the fluorescent sheet. A glass sheet is arranged on the window, and a high-transmission film is coated on the glass sheet. The laser emitter is arranged on the side of the installation cavity away from the window through a mounting bracket. The beam expander assembly and the dichroic mirror are sequentially arranged in the direction from the laser emitter to the window. The dichroic mirror is arranged at an angle, and the beam expander assembly can be composed of a plano-concave lens and a plano-convex lens arranged in sequence along the light path. The light detector is arranged on the reflection light path of the incident light received by the glass sheet in the direction of the dichroic mirror. The photoelectric converter is connected with the light detector and the controller and is used to convert the optical signal received by the light detector into an electrical signal and transmit it to the controller. The controller is configured to convert the optical signal received by the light detector multiple times into coordinate values and generate amplitude data. The controller is also connected with the laser emitter and the power supply and is used to control the start and stop of the laser emitter.

[0039] Specifically, please refer to Figure 2 The single-layer running rate calculation unit obtains the running rate of the single layer during the running of the elevator, which is specifically:

[0040] S1: A signal transmitter is arranged at the top of each floor of the elevator shaft.

[0041] S2: A signal receiver is embedded in the single-layer running rate calculation unit to receive the signals sent by the signal transmitter.

[0042] S3: When the elevator runs to the corresponding floor, the signal receiver receives the corresponding signal in real time.

[0043] S4: The processor built in the single-layer running rate calculation unit obtains the time interval of the signals received by each floor in real time, and obtains the single-layer rate change ratio β according to the uninterrupted time interval.

[0044] Further, please refer to Figure 3 The data processing module 300 performs stability analysis on the input running parameter signal through the constructed dynamic stability detection model, and outputs the dynamic stability value in real time, which specifically includes:

[0045] S1: Construct a dynamic stability detection model.

[0046] S2: input the real-time collected operating parameter signal into the dynamic stability detection model for operation, and obtain an initial simulated steady-state value;

[0047] S3: construct a deep neural optimization network model;

[0048] S4: input the initial simulated steady-state value into the deep neural optimization network model, continuously output simulated steady-state values through change of a corresponding constant adaptive value ε, repeatedly execute until the output result is satisfactory, and obtain a dynamic stability value.

[0049] Further, the operation formula of the constructed dynamic stability detection model is specifically as follows:

[0050]

[0051] Wherein, μ is the initial simulated steady-state value; α is the lateral amplitude offset angle (degree); β is the single-layer rate change proportion; λ is the noise change proportion; δ is the longitudinal amplitude offset angle (degree); ε is the constant adaptive value; x and dx are integral operations.

[0052] Further, the operation formula of the constructed deep neural optimization network model is specifically as follows:

[0053]

[0054] Wherein, L represents the simulated steady-state value, μ is the initial simulated steady-state value, and ε is the constant adaptive value.

[0055] Wherein, ε is selected as any constant in (0, 10).

[0056] Specifically, when the root mean square error of the simulated steady-state value L output by the deep neural optimization network model and the initial simulated steady-state value μ is less than a set threshold, the output result is defined as satisfactory.

[0057] Wherein, the set threshold is set to 0.25.

[0058] The application provides an elevator operation stability monitoring system, which collects the lateral amplitude offset angle, the single-layer rate change proportion, the noise change proportion and the longitudinal amplitude offset angle through a signal collection module, inputs them into a dynamic stability detection model for simulated output, and simultaneously, the application performs deep neural optimization on the dynamic stability detection model, performs simulated output on the dynamic stability detection model through change of a constant adaptive value, improves the accuracy of the stability representation value output, the system provided by the application does not make high requirements on the signal collection device, has low maintenance cost in the later period, simultaneously, the application can greatly reduce the central processing unit operation on the basis of ensuring continuous output of the stability value, reduces the cost of system configuration, and is beneficial to large-scale elevator operation safety testing and application.

[0059] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all modifications and equivalents should be included in the scope of the claims of the present application.

Claims

1. An elevator operation stability monitoring system, characterized in that: include: A signal acquisition module (100) acquires an operating parameter signal through a signal sensor configured in the elevator car; A signal transmission module (200) is data-connected to the signal acquisition module (100), receives the operating parameter signal, and transmits it to the data processing module (300) via a wireless data network; A data processing module (300) is wirelessly connected to the signal transmission module (200), receives the operating parameter signal, performs stability analysis on the input operating parameter signal through a constructed dynamic stability detection model, and outputs a dynamic stability value in real time; An early warning judgment module (400) is data-connected to the data processing module (300), obtains the dynamic stability value, and issues an elevator stability safety early warning when the dynamic stability value reaches a set alarm threshold; Wherein, the signal sensor specifically includes: The transverse amplitude detection unit is embedded in the center of the top of the elevator car. It uses the built-in transverse amplitude detector to obtain the transverse amplitude deviation angle α of the elevator in real time during operation. The single-layer running rate calculation unit is embedded in the center of the elevator car top, and obtains the running rate of the single layer in real time during the elevator operation, and simultaneously obtains the single-layer rate change ratio β; The noise change unit is embedded in the center of the elevator car top. It uses the built-in sound signal collector to obtain the noise during the elevator operation in real time and simultaneously obtain the noise change ratio γ during operation. The longitudinal amplitude detection unit is embedded in the center of the top of the elevator car. It uses the built-in longitudinal amplitude detector to obtain the longitudinal amplitude deviation angle δ of the elevator in real time during operation. The single-floor running rate calculation unit obtains the running rate of a single floor during the operation of the elevator as follows: A signal transmitter is installed at the top of each floor of the elevator shaft; The single-layer operation rate calculation unit is internally provided with a signal receiver that receives the signal sent by the signal transmitter; When the elevator reaches the corresponding floor, the signal receiver receives the corresponding signal in real time; The processor built into the single-layer operation rate calculation unit obtains the interval time of receiving signals of each layer in real time, and obtains the single-layer rate change ratio β based on the uninterrupted interval time; The data processing module (300) performs stability analysis on the input operating parameter signal through the constructed dynamic stability detection model, and outputs the dynamic stability value in real time, specifically including: S1: Construct a dynamic stability detection model; S2: inputting the real-time collected operating parameter signal into the dynamic stability detection model for calculation to obtain an initial simulated steady-state value; S3: Build a deep neural optimization network model; S4: Inputting the initial simulated steady-state value into the deep neural optimization network model, continuously outputting the simulated steady-state value by changing the corresponding constant adaptation value ε, and repeatedly executing until the output result is satisfactory, thereby obtaining the dynamic stability value; The calculation formula of the constructed dynamic stability detection model is specifically as follows: ； Wherein, μ is the initial simulation steady-state value; α is the lateral amplitude offset angle (degrees); β is the single-layer rate change ratio; λ is the noise change ratio; δ is the longitudinal amplitude offset angle (degrees); ε is the constant adaptation value; x and dx are integral operations; The calculation formula of the deep neural optimization network model constructed is specifically as follows: ； Where L represents the simulated steady-state value, μ is the initial simulated steady-state value, and ε is the constant adaptation value; Among them, ε is selected as an arbitrary constant in (0,10); Among them, when the root mean square error between the simulated steady-state value L output by the deep neural optimization network model and the initial simulated steady-state value μ is less than a set threshold, the output result is defined as satisfactory.

2. The elevator operation stability monitoring system according to claim 1, characterized in that: The threshold value is set to 0.25.

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