Escalator abnormal work intervention method and application thereof

Abstract

The invention discloses an escalator abnormal work intervention method and application thereof. According to the scheme, multi-source equipment monitoring data such as vibration, temperature, current, rotating speed, linear speed and braking torque of a power component and a moving component of an escalator are synchronously obtained through a sensor group; meanwhile, on-site use state data such as passenger flow quantity, unit step level load, left and right load unbalance degree, congestion retention state and abnormal elevator taking behavior of the escalator working site are obtained; on the basis of the data, according to the scheme, an equipment side health characterization vector and a use side state characterization vector are constructed based on methods of envelope demodulation, spectral analysis, temperature rise rate, normalization processing and the like; and through weighted linear combination, Sigmoid mapping and quantification methods, risk levels are given in real time, differential intervention strategies such as speed reduction, current limiting, direction locking, smooth speed reduction and acousto-optic prompt are automatically selected according to different risk levels, and active early warning and grading disposal of abnormal working conditions are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of escalator safety technology and special equipment management technology, and particularly relates to an escalator abnormal operation intervention method and application thereof. BACKGROUND

[0002] As a vertical transportation equipment widely used in modern public places, the operation safety of escalator is directly related to the safety of passengers' life and property and the operation efficiency of public places. At present, the existing escalator safety monitoring mainly relies on independent hardware fault alarm system and simple passenger flow statistical equipment. When mechanical failure or escalator accident occurs, the system can only passively trigger emergency stop or sound and light alarm, and lacks active risk assessment and hierarchical intervention based on mechanical state and on-site use condition. In addition, the traditional monitoring system usually only focuses on the vibration, temperature, current and other parameters of power components (such as drive motor, speed reducer and brake), ignores the influence of passenger flow load characteristics and passenger behavior on equipment safety during operation, and is difficult to accurately locate and differentially process abnormal situations. Especially in high passenger flow, congestion or abnormal escalator behavior scenes, the existing system cannot adjust the operation parameters or limit the passenger flow in time, which is easy to cause secondary failure or cause personnel injury. SUMMARY

[0003] Therefore, the purpose of the present application is to provide an escalator abnormal operation intervention method and application thereof based on multi-factor fusion, reliable implementation, flexible response feedback and strong application operability.

[0004] In order to achieve the above technical purpose, the technical scheme adopted by the present application is as follows: An escalator abnormal operation intervention method applied in the operation process of an escalator, comprising: S01, in response to a work start signal of the escalator, monitoring the working state of power components and / or moving components of the escalator to obtain equipment monitoring data; at the same time, monitoring the on-site use state of the escalator to obtain on-site state data; S02, based on the equipment monitoring data, representing the mechanical health state of the escalator to obtain a health representation vector on the equipment side; and based on the on-site state data, representing the passenger flow load state to obtain a use state representation vector on the use side; S03, jointly analyzing the health representation vector and the use state representation vector, calculating the risk level index at the current time according to the preset condition, and obtaining the operation risk level of the escalator, which is used to represent the abnormal degree of the escalator; S04, judging the operation risk level according to a preset condition, and generating an abnormal information when the operation risk level exceeds a preset risk threshold, the abnormal information including an abnormal type, an abnormal involved component position, the operation risk level, and / or an intervention suggestion corresponding to the operation risk level; S05, acquiring the abnormal information, determining and executing a preset corresponding work intervention strategy according to the abnormal information, so as to adjust the working parameters and / or the operation state of the escalator.

[0005] As a possible implementation, further, in the scheme S01, the power component includes one or more of a drive motor, a speed reducer, a main shaft bearing, and a brake of the escalator, and the motion component includes one or more of a step chain, a handrail belt driving mechanism, and a handrail belt of the escalator.

[0006] As a possible implementation, further, the equipment monitoring data in the scheme is collected by a sensor group, and the collected data at least includes vibration data, motor current data, temperature data, rotation speed data, step chain linear speed data, handrail belt tension data, handrail belt linear speed data, differential speed between the step chain linear speed and the handrail belt linear speed, and brake torque data; wherein the collection objects of the vibration data, the motor current data, and the temperature data are the drive motor; and the collection object of the rotation speed data is the drive motor, the speed reducer, and / or the main shaft bearing.

[0007] As a possible implementation, further, the field state data in the scheme is used to represent the passenger flow and load usage of the field where the escalator is located, and the field state data at least includes one or more of passenger flow quantity, unit step load, left-right load imbalance degree of the step, passenger congestion and retention state, and abnormal boarding behavior flag.

[0008] On the basis of the above scheme, as a relatively preferred implementation, preferably, in the scheme S01, the equipment monitoring data The definitions are as follows: wherein, is a sensor number, is a vibration time domain signal collected by a vibration sensor, i.e., vibration data, is a surface temperature of the drive motor collected by a temperature sensor, i.e., temperature data, is a power supply current parameter of the drive motor collected by a current sensor, i.e., motor current data, is an angular speed of the main shaft bearing collected by a rotation speed sensor, i.e., rotation speed data, is a handrail belt tension collected by a tension / tension force sensor, i.e., handrail belt tension data, These are the linear velocity data of the ladder chain and the linear velocity data of the handrail belt, respectively, collected and processed by the position sensor group. The braking torque of the brake, i.e., the braking torque data, is estimated from the known speed change and moment of inertia, and is defined as follows: in, This is the equivalent moment of inertia. The angular acceleration of the spindle bearing during braking is measured using an angular velocity sensor. The on-site status data The definition is as follows: in, For passenger flow, The unit pedal load is the load collected by the pedal load detection sensor. This refers to the imbalance of load on the left and right sides of the pedal. Passengers are stuck in a state of congestion. This is a sign of abnormal elevator use. pedal left and right load imbalance The formula is defined as follows: in, These are the loads on the left and right sides of the same escalator step, respectively, both of which are collected by the step load detection sensor. The passenger congestion and delay status This data was collected directly from the escalator area using an external visual passenger flow monitoring device. Abnormal elevator behavior indicators It is a binarized signal. It is generated by external visual passenger flow monitoring devices and determined by the status of the emergency stop button on the escalator.

[0009] As a preferred implementation method, preferably, in step S02 of this solution, the mechanical health status of the escalator is characterized based on the equipment monitoring data to obtain a health characterization vector on the equipment side, including: Based on the device monitoring data Feature extraction is performed on the vibration data to obtain envelope demodulation and spectral analysis data for the vibration data corresponding to each vibration sensor, as well as the maximum amplitude of the fault band. In the envelope demodulation, the vibration data is subjected to Hilbert transform, the formula of which is defined as follows: Spectrum analysis analyzes the envelope of the demodulated signal. Perform FFT transformation to obtain the envelope spectrum. Then based on the envelope spectrum From characteristic frequency band Extract the maximum amplitude of the fault band. , which is defined as follows: wherein, is the vibration sensor collected vibration time domain signal, is the Hilbert transform operator, is the imaginary unit, and f is the frequency, is the fault indication frequency band corresponding to the driving motor or its driving main shaft; based on the equipment monitoring data temperature data to calculate the temperature rise rate of the driving motor , which is defined as follows: wherein, is the sampling time interval of the temperature sensor, , are the surface temperatures of the driving motor at time t, ; based on the equipment monitoring data motor current data to calculate the RMS value of the driving motor at time window T , which is defined as follows: wherein, is the current value of the driving motor at time t, is the current effective value, and T is the time window; based on the equipment monitoring data main shaft bearing angular velocity, step chain linear velocity data, handrail belt linear velocity data to calculate the rotation speed deviation, speed differential; The formula of the rotation speed deviation is defined as follows: The formula of the speed differential is defined as follows: wherein, is the angular velocity of the main shaft bearing, i.e., the rotation speed data, is the rated angular velocity of the main shaft bearing, which is a constant value, are the step chain linear velocity data and the handrail belt linear velocity data, respectively; Collect data representing the mechanical health status of the escalator based on the equipment monitoring data to generate a health representation vector on the equipment side, which is defined as follows: wherein, when the data is collected, each component of the health representation vector is also normalized to map to [0, 1]; In step S02, the passenger flow load status is characterized based on the on-site status data to obtain a usage status characterization vector on the user side, including: Based on the aforementioned on-site status data Passenger flow Unit pedal load Normalize them separately to obtain the relevant normalization parameters. , Then, the left and right load imbalance of the pedals will also be considered. Directly used as dimensionless data Then, the load congestion index is calculated. Its formula is defined as follows: in, , , These are the weighting coefficients. ; The on-site status data Passenger congestion and delays As a congestion factor, combined with abnormal elevator riding behavior indicators Calculate congestion and behavioral indicators Its formula is defined as follows: in, , These are the weighting coefficients. ; Data representing passenger flow load status based on the aforementioned on-site status data is collected to generate a usage status representation vector on the user side, defined as follows: Among these, state representation vectors are used during data aggregation. Each component is also normalized to map it to [0, 1].

[0010] As a preferred implementation method, step S03 of this solution preferably includes: The health representation vector With the aforementioned state representation vector Perform joint analysis to obtain linear combination scores. Its formula is defined as follows: in, , These are the device-side feature weight vector and the user-side feature weight vector, respectively. Then based on the linear combination fraction By using Sigmoid mapping and quantification to calculate the risk level index at the current moment, the operational risk level of the escalator is obtained, and the formula is defined as follows: in, For continuous risk probabilities, they are mapped to [0, 1]. This is the mapping slope adjustment factor. For the Sigmoid function, The final operational risk level is used to characterize the degree of abnormality of the escalator, and it is quantified to 0-9, wherein the higher the value of the operational risk level, the higher the risk. This is a rounding function used to extend continuous risk to a discrete risk level range {0, 1, ..., 9}.

[0011] As a preferred implementation method, step S04 of this solution preferably includes: The operational risk level With preset threshold To make a comparison, if If this occurs, it will trigger the generation of abnormal information, which includes the abnormality type, the location of the component involved in the abnormality, the operational risk level, and / or the intervention suggestions corresponding to the operational risk level. Wherein, according to the health representation vector With the aforementioned state representation vector The anomaly type is determined to be the equipment-side degradation type and / or load risk type; Based on the health representation vector The fault band has the largest amplitude. Identify the components involved in the anomaly, and then further determine the location of the anomaly based on the components; The intervention recommendations are obtained by querying a pre-built mapping table, and different intervention strategies are applied according to the operational risk level.

[0012] As a preferred implementation method, preferably, in step S05 of this solution, the work intervention strategy includes at least one of the following: reducing the operating speed, restricting passenger flow, locking the operating direction, smoothly decelerating to a stop at a preset deceleration rate, and issuing on-site audio-visual reminders and / or guidance broadcasts.

[0013] As a preferred implementation method, this solution further includes: S6. Record the operation monitoring information, including the equipment monitoring data, the on-site status data, the operation risk level, the abnormal information, and the work intervention strategy, and use them for subsequent operation and maintenance analysis and strategy optimization.

[0014] Based on the above, the present scheme also proposes an escalator abnormal work intervention system, which applies the method described above, which includes: A data acquisition unit is configured to monitor the working state of the power components and / or moving components of the escalator in response to a working start signal of the escalator to obtain equipment monitoring data; at the same time, the on-site use state of the escalator is also monitored to obtain on-site state data; A data processing unit is configured to characterize the mechanical health state of the escalator based on the equipment monitoring data to obtain a health characterization vector on the equipment side; and characterize the passenger flow load state based on the on-site state data to obtain a use state characterization vector on the use side; A data analysis unit is configured to jointly analyze the health characterization vector and the use state characterization vector, calculate a risk level index at the current time according to a preset condition, and obtain an operation risk level of the escalator, which is used to represent the abnormal degree of the escalator; A data judgment unit is configured to judge the operation risk level according to a preset condition, and generate abnormal information when the operation risk level exceeds a preset risk threshold, the abnormal information including abnormal type, abnormal involved component position, operation risk level and / or intervention suggestion corresponding to the operation risk level; An escalator control unit is configured to obtain abnormal information, determine and execute a preset corresponding work intervention strategy according to the abnormal information to adjust the working parameters and / or operation state of the escalator; An operation and maintenance management unit is configured to record the operation monitoring information, record the equipment monitoring data, the on-site state data, the operation risk level, the abnormal information and the work intervention strategy, and use them for subsequent operation and maintenance analysis and strategy optimization.

[0015] Based on the above, the present scheme also proposes an escalator work management method, which includes obtaining a health inspection instruction of the escalator and executing the method described above.

[0016] Compared with the prior art, the application has the beneficial effects that: the scheme synchronously acquires vibration, temperature, current, rotating speed, linear speed, braking torque and other multi-source equipment monitoring data of the power component and the moving component of the escalator through the sensor group, and also acquires passenger flow quantity, unit step load, left-right load imbalance, congestion and retention state, abnormal riding behavior and other on-site use state data of the escalator working site; on the basis of the above data, the scheme constructs the equipment side health characterization vector and the use side state characterization vector through envelope demodulation, spectrum analysis, temperature rise rate, normalization processing and other methods; and then the risk level is given in real time through weighted linear combination, Sigmoid mapping and quantization method, and different intervention strategies such as speed reduction, current limiting, direction locking, smooth deceleration, sound and light prompting are automatically selected according to different risk classification, so that active early warning and graded disposal of abnormal working conditions are realized.

[0017] In summary, the scheme has the following advantages: (1) comprehensiveness: the scheme considers mechanical health and passenger flow use state at the same time, multi-dimensionally integrates, and risk assessment is more comprehensive; (2) initiative: the scheme can trigger graded intervention before the risk exceeds the threshold, avoiding operation interruption and safety hazards caused by passive shutdown; (3) accuracy: the scheme realizes abnormal component positioning and risk type discrimination through multi-sensor data feature extraction and quantitative mapping, and the intervention suggestion is more targeted.

[0018] (4) traceability: the whole process operation data and intervention record of the scheme can be stored and uploaded to a remote platform, supporting subsequent operation and maintenance analysis, fault tracing and model parameter optimization. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a brief implementation flowchart of the escalator abnormal working intervention method of the scheme; Figure 2 is a brief unit module connection diagram of the escalator abnormal working intervention system of the scheme. DETAILED DESCRIPTION

[0021] The application will be described in further detail below with reference to the drawings and embodiments. It is particularly pointed out that the following embodiments are only for illustrating the application, but not for limiting the scope of the application. Similarly, the following embodiments are only part of the embodiments of the application, but not all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the application.

[0022] As shown in Figure 1 , the embodiment of the application provides an escalator abnormal operation intervention method, which is applied to an escalator operation process, and includes the following steps: S01, in response to a work start signal of the escalator, monitoring the working state of the power component and / or the motion component of the escalator to obtain equipment monitoring data; at the same time, the on-site use state of the escalator is also monitored to obtain on-site state data; S02, based on the equipment monitoring data, the mechanical health state of the escalator is characterized to obtain a health characterization vector on the equipment side; and based on the on-site state data, the passenger flow load state is characterized to obtain a use state characterization vector on the use side; S03, the health characterization vector and the use state characterization vector are jointly analyzed, and the risk level index of the current time is calculated according to the preset condition to obtain the operation risk level of the escalator, which is used to represent the abnormal degree of the escalator; S04, the operation risk level is judged according to the preset condition, and when the operation risk level exceeds the preset risk threshold, an abnormal information is generated, which includes the abnormal type, the abnormal involved component position, the operation risk level and / or the intervention suggestion corresponding to the operation risk level; S05, obtaining the abnormal information, determining and executing the preset corresponding work intervention strategy according to the abnormal information to adjust the working parameters and / or the operation state of the escalator.

[0023] As a possible implementation, further, in the S01 of the present application, the power component includes one or more of the drive motor, the speed reducer, the main shaft bearing and the brake of the escalator, and the motion component includes one or more of the step chain, the handrail belt driving mechanism and the handrail of the escalator.

[0024] Correspondingly, in the aspect of data collection on the equipment side, as a possible implementation, further, the equipment monitoring data in the present solution is collected by a sensor group, and the collected data at least includes vibration data, motor current data, temperature data, rotating speed data, step chain linear speed data, handrail belt tension data, handrail belt linear speed data, differential speed between step linear speed and handrail belt linear speed, and brake torque data; wherein, the collection objects of the vibration data, motor current data and temperature data are driving motors; and the collection object of the rotating speed data is a driving motor, a speed reducer and / or a main shaft bearing.

[0025] In the aspect of data collection on the use side, as a possible implementation, further, the field state data in the present solution is used to represent the passenger flow and load use condition of the field where the escalator is located, and the field state data at least includes one or more of passenger flow quantity, unit step load, left-right load imbalance degree of step, passenger congestion and retention state, and abnormal boarding behavior mark.

[0026] On the basis of the above solution, as a relatively preferred implementation, preferably, in the present solution S01, the equipment monitoring data are defined as follows: wherein, is a sensor number, is a vibration time domain signal collected by a vibration sensor, i.e. vibration data, is a surface temperature of a driving motor collected by a temperature sensor, i.e. temperature data, is a power supply current parameter of a driving motor collected by a current sensor, i.e. motor current data, is an angular speed of a main shaft bearing collected by a rotating speed sensor, i.e. rotating speed data, is a handrail belt tension collected by a tension / tension force sensor, i.e. handrail belt tension data, are step chain linear speed data and handrail belt linear speed data collected and processed by a position sensor group, is a brake torque of a brake, i.e. brake torque data, which is obtained by estimating a known rotating speed change and an inertia, and is defined as follows: wherein, is an equivalent transmission inertia, is an angular acceleration of the main shaft bearing measured by an angular speed sensor during braking.

[0027] In the present solution, the part of collecting data on the equipment side can be obtained by using existing technical sensors or other auxiliary detection devices, and the present solution is characterized by ingeniously applying it to the escalator scene, and therefore the principle will not be described in detail.

[0028] Correspondingly, the field state data is defined as follows: wherein, is the passenger flow quantity, is the unit step load collected by the step load detection sensor, is the left-right step load imbalance degree, is the passenger congestion and retention state, is the abnormal riding behavior flag; the left-right step load imbalance degree is defined as follows: wherein, respectively are the left and right loads of the same step of the escalator, which are both collected by the step load detection sensor; the passenger congestion and retention state is directly collected by the external visual passenger flow monitoring device in the field area of the escalator; the abnormal riding behavior flag is a binary signal, which is generated by the external visual passenger flow monitoring device and determined by the emergency stop button state of the escalator.

[0029] In this scheme, the part of collecting data on the use side can be obtained by using existing technical sensors or other auxiliary detection devices. The application of this scheme in the escalator scene is ingenious, and the principle will not be described in detail.

[0030] In order to more reliably and accurately extract the characteristics of the running state of the escalator from the equipment monitoring data on the equipment side and the field state data on the use side, as a preferred selection implementation, preferably, in step S02 of the present scheme, the mechanical health state of the escalator is characterized based on the equipment monitoring data, and a health characterization vector on the equipment side is obtained, including: characterizing the mechanical health state of the escalator based on the equipment monitoring data including vibration data, to obtain envelope demodulation and spectrum analysis data of vibration data corresponding to each vibration sensor, and maximum amplitude of fault band, in envelope demodulation, the vibration data is subjected to Hilbert transform, which is defined as follows: spectrum analysis is performed by FFT transform on the envelope of the envelope demodulated signal envelope , and then based on the envelope spectrum , the maximum amplitude of the fault band is extracted from the characteristic frequency band , which is defined as follows: wherein, is the vibration sensor the collected vibration time domain signal, is the Hilbert transform operator, is the imaginary unit, and f is the frequency, is the fault indication frequency band corresponding to the driving motor or its driving spindle; based on the equipment monitoring data temperature data to calculate the temperature rise rate of the driving motor which is defined as follows: wherein, is the sampling time interval of the temperature sensor, , are the surface temperatures of the driving motor at time t, ; based on the equipment monitoring data motor current data to calculate the RMS value of the driving motor at time window T which is defined as follows: wherein, is the current value of the driving motor at time t, is the current effective value, and T is the time window; based on the equipment monitoring data spindle bearing angular velocity, step chain linear velocity data, handrail belt linear velocity data to calculate the rotation speed deviation, speed differential; the formula of the rotation speed deviation is defined as follows: the formula of the speed differential is defined as follows: wherein, is the angular velocity of the spindle bearing, i.e., the rotation speed data, is the rated angular velocity of the spindle bearing, which is a constant value, are the step chain linear velocity data and the handrail belt linear velocity data, respectively; collect data representing the mechanical health status of the escalator based on the equipment monitoring data to generate the health representation vector on the equipment side, which is defined as follows: wherein, when the data is collected, each component of the health representation vector is also normalized to map to [0, 1]; In step S02, the passenger flow load state is characterized based on the field state data, and a use state characterization vector of the use side is obtained, including: based on the field state data the number of passengers , the unit platform load , and the left-right load imbalance , respectively, are normalized to obtain the related normalized parameters , ; then the platform left-right load imbalance is directly taken as a dimensionless data , and the load congestion degree index is calculated , and the formula is defined as follows: , is a weight coefficient ; The passenger congestion and retention state in the field state data is taken as a congestion coefficient, combined with the abnormal elevator behavior flag to calculate the congestion and retention and behavior flag index , and the formula is defined as follows: wherein , are weight coefficients ; The data representing the characterization of the passenger flow load state based on the field state data is collected to generate a use state characterization vector of the use side, which is defined as follows: wherein each component of the use state characterization vector is also normalized when the data is collected, so as to be mapped to [0, 1].

[0031] Based on the above extracted characterization vectors of the mechanical equipment and the field use, as a preferred selection implementation, preferably, step S03 of the present scheme includes: The health characterization vector is analyzed in combination with the use state characterization vector to obtain a linear combination score , and the formula is defined as follows: wherein , are respectively a device-side feature weight vector and a use-side feature weight vector; Then, based on the linear combination score ​, the risk level index of the current moment is obtained by Sigmoid mapping and quantization calculation, and the running risk level of the escalator is obtained, and the formula definition is as follows: wherein, is a continuous risk probability, which is mapped to [0, 1], is a mapping slope adjustment factor, is a Sigmoid function, is the final running risk level, which is used to represent the abnormality degree of the escalator and is quantized to 0-9, wherein the greater the numerical value of the running risk level is, the higher the risk is; is a rounding function, which is used to expand the continuous risk to the discrete risk level interval {0, 1, ……9}.

[0032] In the aspect of abnormal situation judgment, as a preferred selection implementation manner, preferably, the step S04 of the scheme comprises: comparing the running risk level with a preset threshold value if , triggering the generation of abnormal information, generating abnormal information, the abnormal information includes abnormal type, abnormal component position, running risk level and / or intervention suggestion corresponding to the running risk level; wherein, according to the health representation vector and the use state representation vector , it is judged that the abnormal type is the degradation type and / or the load risk type of the equipment side; according to the maximum amplitude of the fault zone in the health representation vector , the component involved in the abnormality is determined, and then the abnormal position is further determined according to the component; the intervention suggestion is obtained by querying the pre-constructed mapping table, which corresponds to different intervention strategies according to the running risk level. As a preferred selection implementation manner, preferably, in the step S05 of the scheme, the work intervention strategy at least includes: reducing the running speed, limiting the passenger flow to enter, locking the running direction, smoothly decelerating to stop according to the preset deceleration, and at least one of the following: issuing a scene sound and light reminder and / or guiding broadcast.

[0033] As an example, assuming that the numerical value of the preset threshold value

[0034] is 5, the running risk level of the scheme corresponding to different intervention strategies can include the following:

[0035] ​In order to facilitate operation and maintenance management, and at the same time provide guidance and reference for subsequent maintenance, as a relatively optimal selection implementation, preferably, the scheme further comprises: S6, executing running monitoring information recording, recording the equipment monitoring data, the field state data, the running risk level, the abnormal information and the work intervention strategy, which are used for subsequent operation and maintenance analysis and strategy optimization.

[0036] Based on the escalator abnormal work intervention method described above, the embodiment scheme can also be applied to an escalator work management method, which executes the method described above according to the health inspection instructions or other related instructions of the escalator.

[0037] In combination with Figure 2 Based on the above, the scheme also proposes an escalator abnormal work intervention system, which applies the method described above, and comprises: A data acquisition unit is configured to respond to a work start signal of the escalator, monitor the working state of the power components and / or motion components of the escalator to obtain equipment monitoring data, and at the same time, monitor the field use state of the escalator to obtain field state data; A data processing unit is configured to represent the mechanical health state of the escalator based on the equipment monitoring data to obtain a health representation vector on the equipment side, and represent the passenger flow load state based on the field state data to obtain a use state representation vector on the use side; A data analysis unit is configured to jointly analyze the health representation vector and the use state representation vector, calculate a risk level index at the current time according to a preset condition, and obtain a running risk level of the escalator, which is used to represent the abnormal degree of the escalator; A data judgment unit is configured to judge the running risk level according to a preset condition, generate abnormal information when the running risk level exceeds a preset risk threshold, and the abnormal information includes abnormal type, abnormal involved component position, running risk level and / or intervention suggestion corresponding to the running risk level; An escalator control unit is configured to obtain abnormal information, determine and execute a preset corresponding work intervention strategy according to the abnormal information, so as to adjust the working parameters and / or running state of the escalator; An operation and maintenance management unit is configured to execute running monitoring information recording, record the equipment monitoring data, the field state data, the running risk level, the abnormal information and the work intervention strategy, which are used for subsequent operation and maintenance analysis and strategy optimization.

[0038] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0039] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0040] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for intervening in abnormal operation of an escalator, applied during the operation of an escalator, characterized in that, It includes: S01. In response to the start signal of the escalator, monitor the working status of the power components and / or moving parts of the escalator to obtain equipment monitoring data; at the same time, monitor the on-site usage status of the escalator to obtain on-site status data. S02. Based on the equipment monitoring data, characterize the mechanical health status of the escalator to obtain a health characterization vector on the equipment side; and based on the on-site status data, characterize the passenger flow load status to obtain a usage status characterization vector on the user side. S03. Perform joint analysis on the health representation vector and the usage status representation vector, calculate the risk level index at the current moment according to preset conditions, and obtain the operational risk level of the escalator. The operational risk level is used to characterize the degree of abnormality of the escalator. S04. Determine the operational risk level according to preset conditions. When the operational risk level exceeds a preset risk threshold, generate abnormal information. The abnormal information includes the abnormality type, the location of the component involved in the abnormality, the operational risk level, and / or the intervention suggestion corresponding to the operational risk level. S05. Obtain abnormal information, determine and execute the preset corresponding work intervention strategy based on the abnormal information, so as to adjust the working parameters and / or operating status of the escalator.

2. The escalator malfunction intervention method as described in claim 1, characterized in that, In S01, the power component includes one or more of the following: drive motor, reducer, main shaft bearing, and brake of the escalator; and the moving component includes one or more of the following: step chain, handrail drive mechanism, and handrail belt of the escalator. The equipment monitoring data is collected through a sensor array, and the collected data includes at least vibration data, motor current data, temperature data, rotational speed data, step chain linear speed data, handrail belt tension data, handrail belt linear speed data, the difference between the step linear speed and the handrail belt linear speed, and braking torque data; wherein, the vibration data, motor current data, and temperature data are collected from the drive motor; and the rotational speed data are collected from the drive motor, reducer, and / or main shaft bearing. The on-site status data is used to characterize the passenger flow and load usage at the site where the escalator is located. The on-site status data includes at least one of the following: passenger flow, unit step load, left and right step load imbalance, passenger congestion and stagnation status, and abnormal escalator behavior indicators.

3. The escalator malfunction intervention method as described in claim 1 or 2, characterized in that, In S01, the device monitoring data is defined as follows: in, Number the sensor. The vibration data refers to the time-domain signal of vibration collected by the vibration sensor. The temperature data refers to the surface temperature of the drive motor collected by the temperature sensor. The current sensor collects the power supply current parameters of the drive motor, i.e., the motor current data. The angular velocity of the spindle bearing, i.e., the rotational speed data, is collected by the speed sensor. The tension of the handrail belt is collected by a tension / force sensor, i.e., the tension data of the handrail belt. These are the linear velocity data of the ladder chain and the linear velocity data of the handrail belt, respectively, collected and processed by the position sensor group. The braking torque of the brake, i.e., the braking torque data, is estimated from the known speed change and moment of inertia, and is defined as follows: in, This is the equivalent moment of inertia. The angular acceleration of the spindle bearing during braking is measured using an angular velocity sensor. The on-site status data The definition is as follows: in, For passenger flow, The unit pedal load is the load collected by the pedal load detection sensor. This refers to the imbalance of load on the left and right sides of the pedal. Passengers are stuck in a state of congestion. This is a sign of abnormal elevator use. pedal left and right load imbalance The formula is defined as follows: in, These are the loads on the left and right sides of the same escalator step, respectively, both of which are collected by the step load detection sensor. The passenger congestion and delay status This data was collected directly from the escalator area using an external visual passenger flow monitoring device. Abnormal elevator behavior indicators It is a binarized signal. It is generated by external visual passenger flow monitoring devices and determined by the status of the emergency stop button on the escalator.

4. The escalator malfunction intervention method as described in claim 3, characterized in that, In step S02, the mechanical health status of the escalator is characterized based on the equipment monitoring data, and the resulting health characterization vector on the equipment side includes: Based on the device monitoring data Feature extraction is performed on the vibration data to obtain envelope demodulation and spectral analysis data for the vibration data corresponding to each vibration sensor, as well as the maximum amplitude of the fault band. In the envelope demodulation, Hilbert transform is performed on the vibration data, and its formula is defined as follows: Spectrum analysis analyzes the envelope of the demodulated signal. Perform FFT transformation to obtain the envelope spectrum. Then based on the envelope spectrum From characteristic frequency band Extract the maximum amplitude of the fault band. Its definition is as follows: in, Vibration sensor The collected vibration time-domain signal, For the Hilbert transform operator, The imaginary unit is f, where f is the frequency. This is the fault indication frequency band for the corresponding drive motor or its drive spindle; Based on the device monitoring data The temperature data is used to calculate the heating rate of the drive motor. Its definition is as follows: in, The sampling time interval of the temperature sensor. , These represent the time t and t of the drive motor, respectively. Surface temperature at that time; Based on the device monitoring data The RMS value of the drive motor is calculated using the motor current data in the data. Its formula is defined as follows: in, This represents the current value of the drive motor at time t. Where is the effective value of the current, and T is the time window; Based on the device monitoring data The angular velocity of the main shaft bearing, the linear velocity of the ladder chain, and the linear velocity of the handrail belt are used to calculate the speed deviation and speed difference. Speed ​​deviation The formula is defined as follows: Speed ​​differential The formula is defined as follows: in, This refers to the angular velocity of the main spindle bearing, i.e., the rotational speed data. The rated angular velocity of the main spindle bearing is a constant value. These are the speed data for the ladder chain and the speed data for the handrail belt; Data representing the mechanical health status of escalators based on equipment monitoring data is collected to generate a health representation vector on the equipment side, defined as follows: Among them, during data aggregation, the health representation vector Each component is also normalized to map it to [0, 1]. In step S02, the passenger flow load status is characterized based on the on-site status data to obtain a usage status characterization vector on the user side, including: Based on the aforementioned on-site status data Passenger flow Unit pedal load Normalize them separately to obtain the relevant normalization parameters. , Then, the left and right load imbalance of the pedals will also be considered. Directly used as dimensionless data Then, the load congestion index is calculated. Its formula is defined as follows: in, , , These are the weighting coefficients. ; The on-site status data Passenger congestion and delays As a congestion factor, combined with abnormal elevator riding behavior indicators Calculate congestion and behavioral indicators Its formula is defined as follows: in, , These are the weighting coefficients. ; Data representing passenger flow load status based on the aforementioned on-site status data is collected to generate a usage status representation vector on the user side, defined as follows: Among these, state representation vectors are used during data aggregation. Each component is also normalized to map it to [0, 1].

5. The escalator malfunction intervention method as described in claim 4, characterized in that, Step S03 includes: The health representation vector With the aforementioned state representation vector Perform joint analysis to obtain linear combination scores. Its formula is defined as follows: in, , These are the device-side feature weight vector and the user-side feature weight vector, respectively. Then based on the linear combination fraction By using Sigmoid mapping and quantification to calculate the risk level index at the current moment, the operational risk level of the escalator is obtained, and the formula is defined as follows: in, For continuous risk probabilities, they are mapped to [0, 1]. This is the mapping slope adjustment factor. For the Sigmoid function, The final operational risk level is used to characterize the degree of abnormality of the escalator, and it is quantified to 0-9, wherein the higher the value of the operational risk level, the higher the risk. This is a rounding function used to extend continuous risk to a discrete risk level range {0, 1, ..., 9}.

6. The escalator malfunction intervention method as described in claim 5, characterized in that, Step S04 includes: The operational risk level With preset threshold To make a comparison, if If this occurs, it will trigger the generation of abnormal information, which includes the abnormality type, the location of the component involved in the abnormality, the operational risk level, and / or the intervention suggestions corresponding to the operational risk level. Wherein, according to the health representation vector With the aforementioned state representation vector The anomaly type is determined to be the equipment-side degradation type and / or load risk type; Based on the health representation vector The fault band has the largest amplitude. Identify the components involved in the anomaly, and then further determine the location of the anomaly based on the components; The intervention recommendations are obtained by querying a pre-built mapping table, and different intervention strategies are applied according to the operational risk level.

7. The escalator malfunction intervention method as described in claim 6, characterized in that, In step S05, the work intervention strategy includes at least one of the following: reducing the operating speed, restricting passenger flow, locking the operating direction, smoothly decelerating to a stop at a preset deceleration rate, and issuing on-site audio-visual reminders and / or guidance broadcasts.

8. The escalator malfunction intervention method as described in claim 2, characterized in that, It also includes: S6. Record the operation monitoring information, including the equipment monitoring data, the on-site status data, the operation risk level, the abnormal information, and the work intervention strategy, and use them for subsequent operation and maintenance analysis and strategy optimization.

9. An escalator malfunction intervention system, wherein the method described in any one of claims 1 to 8 is characterized in that, It includes: The data acquisition unit is used to respond to the start signal of the escalator and monitor the working status of the power components and / or moving parts of the escalator to obtain equipment monitoring data; at the same time, it also monitors the on-site usage status of the escalator to obtain on-site status data. The data processing unit is used to characterize the mechanical health status of the escalator based on the equipment monitoring data, and obtain a health characterization vector about the equipment side. Based on the on-site status data, the passenger flow load status is characterized to obtain a usage status characterization vector for the user side; The data analysis unit is used to jointly analyze the health representation vector and the usage status representation vector, calculate the risk level index at the current moment according to preset conditions, and obtain the operational risk level of the escalator. The operational risk level is used to characterize the degree of abnormality of the escalator. The data judgment unit is used to judge the operational risk level according to preset conditions. When the operational risk level exceeds the preset risk threshold, abnormal information is generated. The abnormal information includes the abnormality type, the location of the component involved in the abnormality, the operational risk level, and / or the intervention suggestion corresponding to the operational risk level. The escalator control unit is used to acquire abnormal information, determine and execute preset corresponding work intervention strategies based on the abnormal information, so as to adjust the working parameters and / or operating status of the escalator. The operation and maintenance management unit is used to record operation monitoring information, including equipment monitoring data, on-site status data, operational risk level, abnormal information, and work intervention strategies, for use in subsequent operation and maintenance analysis and strategy optimization.

10. A method for managing the operation of an escalator, characterized in that, It includes: Obtain a health inspection instruction from the escalator and execute the method described in any one of claims 1 to 8.

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