Escalator monitoring method, system, equipment and medium

Abstract

The invention discloses an escalator monitoring method, system and device and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a step chain, a guide rail and a driving system, obtaining a current time point and a historical time period, extracting a plurality of sampling time points, and obtaining angular velocity data, contact surface temperature data and vibration displacement data; acquiring a first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, acquiring a second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, and acquiring a state index according to the first state parameter and the second state parameter, acquiring monitoring sensitivity based on the sensitivity model and the state index; and obtaining an asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point and the monitoring sensitivity, and obtaining a control strategy of the current time point according to the asymmetric impact index. The method has the advantages of stability, accuracy, good monitoring effect and dynamic adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to an escalator monitoring method, system, device and medium. Background Art

[0002] As a key transportation device in public places, the operation safety of escalators is directly related to public safety. Traditional monitoring methods mainly compare single data with fixed thresholds to judge the fault risk. For example, by detecting whether the vibration amplitude of the drive system exceeds a preset threshold to infer the possibility of gear wear or bearing failure.

[0003] However, such methods have the following significant problems: First, faults in mechanical systems usually manifest as coupled anomalies of multiple physical quantities. For example, the temperature rise of the guide rail may be accompanied by fluctuations in the angular velocity of the step chain, and the prior art lacks the ability to comprehensively analyze the co-variation of multiple parameters, resulting in insufficient detection sensitivity for compound faults (such as gear fracture accompanied by overheating of the guide rail); Second, fixed thresholds cannot adapt to data drifts caused by different load conditions (such as peak and off-peak passenger flows), environmental temperature changes or equipment aging. For example, the vibration baseline of the escalator during full-load operation is significantly higher than that in the no-load state, and static thresholds are prone to false alarms or missed alarms due to scene differences; Third, existing methods usually adopt fixed monitoring sensitivities and cannot adjust the response intensity to abnormal signals according to the real-time operating state. For example, in the initial stage of abnormal temperature rise, failure to timely enhance the monitoring sensitivity to vibration signals may delay the detection of potential gear wear; Fourth, traditional methods mostly directly trigger shutdown or single alarms after detecting anomalies and lack a hierarchical control strategy. For example, before the asymmetric impact index reaches the dangerous threshold, failure to reduce mechanical stress through dynamic speed reduction operation may lead to further accumulation of fault risks. Summary of the Invention

[0004] Aiming at the defects in the prior art, the present invention provides an escalator monitoring method, system, device and medium.

[0005] An escalator monitoring method includes: obtaining the step chain, guide rail, and drive system of the escalator, obtaining the current time point and the historical time period with the end being the current time point, sequentially extracting a plurality of sampling time points with consistent intervals from the historical time period, and obtaining the angular velocity data of the step chain, the contact surface temperature data of the guide rail, and the vibration displacement data of the drive system at each sampling time point; obtaining a first state parameter based on a parameter model and the angular velocity data corresponding to each sampling time point, obtaining a second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtaining a state index according to the first state parameter and the second state parameter, and obtaining a monitoring sensitivity based on a sensitivity model and the state index; obtaining an asymmetric impact index based on an impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, and obtaining a control strategy for the current time point according to the asymmetric impact index.

[0006] Optionally, obtaining a control strategy for the current time point according to the asymmetric impact index includes: obtaining a basic threshold, and obtaining a dynamic threshold according to the basic threshold and the monitoring sensitivity; if the asymmetric impact index is greater than the dynamic threshold, triggering an alarm and reducing the speed at the current time point.

[0007] Optionally, triggering an alarm and reducing the speed at the current time point includes: obtaining the current angular velocity of the step chain at the current time point, and obtaining a target angular velocity according to the asymmetric impact index and the current angular velocity; triggering an alarm and reducing the current angular velocity to the target angular velocity at the current time point.

[0008] Optionally, the parameter model in obtaining the first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point is expressed as: where, P1 is the first state parameter, w i is the angular velocity data corresponding to the i-th sampling time point, w i-1 is the angular velocity data corresponding to the (i - 1)-th sampling time point, and N is the number of sampling time points.

[0009] Optionally, obtaining a state index according to the first state parameter and the second state parameter is expressed as: where, I Q is the state index, P1 is the first state parameter, T is the safety critical threshold corresponding to the second state parameter, and P2 is the second state parameter.

[0010] Optionally, the sensitivity model in obtaining the monitoring sensitivity based on the sensitivity model and the state index is expressed as: where, S is the monitoring sensitivity, P1 is the first state parameter, I Q is the state index, and P2 is the second state parameter.

[0011] Optionally, the impact model in the asymmetric impact index is obtained based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, which is expressed as: where, I m is the asymmetric impact index, S is the monitoring sensitivity, V di is the vibration displacement data corresponding to the i-th sampling time point, and N is the number of sampling time points.

[0012] There is also provided an escalator monitoring system, which includes: an acquisition module for acquiring the step chain, guide rail, and drive system of the escalator, acquiring the current time point and the historical time period with the end being the current time point, sequentially extracting a plurality of sampling time points with consistent intervals from the historical time period, and acquiring the angular velocity data of the step chain, the contact surface temperature data of the guide rail, and the vibration displacement data of the drive system at each sampling time point; a first monitoring and calculation module for obtaining a first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, obtaining a second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtaining a state index according to the first state parameter and the second state parameter, and obtaining the monitoring sensitivity based on the sensitivity model and the state index; a second monitoring and calculation module for obtaining the asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, and obtaining the control strategy for the current time point according to the asymmetric impact index.

[0013] There is also provided an electronic device, including: a memory on which a computer program is stored; a processor for executing the computer program in the memory to implement the above-mentioned escalator monitoring method.

[0014] There is also provided a non-transitory computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned escalator monitoring method is implemented.

[0015] The beneficial effects of the present invention are reflected in:

[0016] In the entire escalator monitoring method, by synchronously collecting the angular velocity of the step chain, the guide rail temperature, and the driving vibration displacement, and constructing high-density time series at multiple consecutive sampling time points, the system is enabled to capture the coupling anomalies of multiple parameters of the mechanical system. For example, when gear wear causes angular velocity fluctuations, the system can synchronously correlate the changes in the guide rail temperature gradient and the characteristics of the driving vibration spectrum, accurately distinguishing normal load fluctuations from the precursors of compound faults; further, introducing a dynamic sensitivity adjustment algorithm, non-linearly mapping the state indicators based on a parameter model, when the numerical values of the angular velocity trend coefficient (the first state parameter) and the temperature rise trend coefficient (the second state parameter) are used to dynamically amplify the monitoring sensitivity coefficient through an exponential function, the analytical accuracy of the vibration displacement signal is improved by several orders of magnitude; further, establishing a closed-loop feedback of the asymmetric impact index and operating parameters in the execution control layer, generating a dynamic threshold boundary through an impact model combined with the real-time sensitivity coefficient, and starting a progressive speed reduction strategy when the index breaks through the threshold, reducing the cumulative rate of fatigue damage of key components, and at the same time compressing the duration of transportation interruption caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0018] Figure 1 It is a schematic diagram of the steps of the escalator monitoring method of the present invention;

[0019] Figure 2 It is a partial schematic diagram of the steps of S3 in the escalator monitoring method of the present invention;

[0020] Figure 3 It is a partial schematic diagram of the steps of S32 in the escalator monitoring method of the present invention;

[0021] Figure 4 It is a block diagram of an electronic device shown in an embodiment of the present invention.

[0022] Reference Numerals:

[0023] 700 - Electronic device, 701 - Processor, 702 - Memory, 703 - Multimedia component, 704 - I / O interface, 705 - Communication component. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0025] Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0026] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0027] As Figure 1 shown, an escalator monitoring method is provided, including:

[0028] S1. Obtain the step chain, guide rail, and drive system of the escalator, obtain the current time point and the historical time period with the end being the current time point, sequentially extract a plurality of sampling time points with consistent intervals from the historical time period, and obtain the angular velocity data of the step chain, the contact surface temperature data of the guide rail, and the vibration displacement data of the drive system at each sampling time point;

[0029] S2. Obtain a first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, obtain a second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtain a state index according to the first state parameter and the second state parameter, and obtain a monitoring sensitivity based on the sensitivity model and the state index;

[0030] S3. Obtain an asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, and obtain a control strategy for the current time point according to the asymmetric impact index.

[0031] In this embodiment, it should be noted that in S1, it includes two core links: multi-dimensional data acquisition and time series construction. In terms of data acquisition dimension, three groups of key physical quantities are synchronously obtained through distributed sensors: an incremental rotary encoder (such as the ERN1080 series) is used to measure the angular velocity data of the step chain driving wheel in real time; a non-contact infrared temperature measurement array (such as the FLIRA35) is deployed on the guide rail contact surface to capture the temperature distribution of the contact area; a three-axis MEMS acceleration sensor (ADXL1002) is installed on the drive base, and after conversion, the vibration displacement is output. For example, a certain subway station escalator collects a composite data set of angular velocity 0.523 rad / s, guide rail temperature 43.6 °C, and three-dimensional axial vibration displacement 0.20 mm at 09:00:00. Further, for time series construction, a sliding time window strategy is adopted, with a time window length of 30 minutes and a sampling interval of 5 seconds, forming a historical data set containing 360 consecutive sampling points; the data structure within each time window contains a tuple sequence of (time point, angular velocity, temperature, vibration displacement). Taking the escalator in a department store as an example: when the data acquisition is triggered at 15:30:00, the measured values at each 5-second interval during the period from 15:00:00 to 15:30:00 will be synchronously stored, constituting a multi-dimensional time series containing an angular velocity sequence [518, 521,..., 535], a temperature sequence [38.2, 38.5,..., 41.3], and a vibration displacement sequence [18, 13,..., 21].

[0032] In S2, the accuracy of fault detection is enhanced through multi-source data fusion and dynamic sensitivity adjustment. First, based on the parameter model, in-depth analysis is carried out on the angular velocity data of the step chain obtained at each sampling time point, and the first state parameter is calculated. This parameter is used to evaluate whether the angular velocity remains within a stable range instead of showing a continuous increasing or decreasing trend. For example, if the angular velocity data continuously rises within a period of time, the value of the first state parameter will also increase accordingly, indicating the risk of abnormal angular velocity. Similarly, the parameter model is also used to process the temperature data of the guide rail contact surface to obtain the second state parameter to judge whether the temperature is stable at a certain level instead of continuously rising. These two state parameters jointly reflect the stability of the escalator operation state. Next, by combining the first state parameter and the second state parameter, a state index is calculated. This index comprehensively reflects the changes in angular velocity and temperature and can more comprehensively reflect the operation state of the escalator. The larger the state index, the higher the probability of the escalator having a fault. Based on this state index, the monitoring sensitivity is dynamically adjusted using the sensitivity model. This means that when an abnormal trend in angular velocity or temperature is detected, the monitoring sensitivity will be automatically increased, making the analysis of subsequent vibration displacement data more sensitive, so as to be able to detect potential fault risks earlier.

[0033] Suppose that within a certain period of time, the angular velocity data of the escalator step chain shows a gradually increasing trend, and the temperature of the guide rail contact surface also rises slightly. By calculating the first state parameter and the second state parameter through a parametric model, it is found that they are both larger than the normal values. Subsequently, combining these two state parameters, a state index is calculated through an algorithm, and this index indicates that the operating state of the current escalator is abnormal. Based on this state index, the sensitivity model will automatically adjust the monitoring sensitivity to make it higher than usual. In this way, in the subsequent analysis of the drive vibration displacement data, any abnormal vibration signals can be captured more sensitively, so as to issue early warnings more timely and reduce the likelihood of faults occurring. Through this strategy of multi-source data fusion and dynamic sensitivity adjustment, this method can significantly improve the accuracy and timeliness of escalator fault detection.

[0034] In S3, a closed-loop management for fault prevention is achieved through a dynamic shock assessment and hierarchical control strategy. First, based on the shock model, a non-linear coupling operation is performed on the vibration displacement data and the monitoring sensitivity generated in S2 to construct an asymmetric shock index. This index not only reflects the absolute value of the vibration amplitude, but also characterizes the imbalance degree of the drive under shock loads: when the monitoring sensitivity is dynamically increased due to abnormal temperature rise or angular velocity, the same vibration displacement will obtain a higher index value, amplifying the ability to identify potential faults. For example, when the guide rail temperature is abnormal and the sensitivity coefficient is increased by 50%, the vibration displacement signal that was originally at the critical value will be mapped to a shock index that significantly exceeds the dynamic threshold, forcing early intervention in control. The dynamic threshold generation link introduces a sensitivity factor to correct the basic threshold in real time to form an adaptive safety boundary. If a sudden increase in vibration displacement occurs during a certain passenger flow peak, but at this time the monitoring sensitivity remains at the reference level due to other normal parameters, the dynamic threshold will automatically rise to avoid false alarms under overload conditions. At the same time, when periodic shocks caused by abnormal gear meshing are detected, the escalator will not be directly stopped suddenly, but the operating speed will be reduced to 60%-80% of the original speed, which not only maintains the basic transportation function but also reduces the stress on mechanical components. If the shock index continues to deteriorate, the deceleration amplitude will be gradually increased until a safe stop. This progressive control strategy effectively avoids the disadvantages of the "all or nothing" type of control in traditional methods, minimizing the interference to public transportation while ensuring safety.

[0035] In summary, in the entire escalator monitoring method, by synchronously collecting the angular velocity of the step chain, the guide rail temperature, and the driving vibration displacement, and constructing a high-density time series of multiple consecutive sampling time points, the system is enabled to capture the coupling anomalies of multiple parameters of the mechanical system. For example, when gear wear causes angular velocity fluctuations, the system can synchronously correlate the change in the guide rail temperature gradient with the driving vibration spectrum characteristics to accurately distinguish normal load fluctuations from the precursors of compound faults. Further, a dynamic sensitivity adjustment algorithm is introduced to perform a non-linear mapping of the state indicators based on a parameter model. When the values of the angular velocity trend coefficient (the first state parameter) and the temperature rise trend coefficient (the second state parameter) are used to dynamically amplify the monitoring sensitivity coefficient through an exponential function, the analysis accuracy of the vibration displacement signal is improved by several orders of magnitude. Further, a closed-loop feedback of the asymmetric impact index and the operating parameters is established in the execution control layer. By combining the impact model with the real-time sensitivity coefficient, a dynamic threshold boundary is generated. When the index breaks through the threshold, a progressive speed reduction strategy is initiated, reducing the cumulative rate of fatigue damage to key components and simultaneously compressing the duration of transportation interruption caused by faults.

[0036] As Figure 2 shown, in one embodiment, the control strategy for obtaining the current time point according to the asymmetric impact index in S3 includes:

[0037] S31. Obtain a basic threshold, and obtain a dynamic threshold according to the basic threshold and the monitoring sensitivity;

[0038] S32. If the asymmetric impact index is greater than the dynamic threshold, trigger an alarm and operate at a reduced speed at the current time point.

[0039] In this embodiment, it should be noted that in S31, a sensitivity factor is used to non-linearly modulate the basic threshold to form a dynamic safety boundary with environmental adaptability: when the monitoring sensitivity is increased due to abnormal angular velocity trend or abnormal temperature rise (such as in the initial stage of gear wear), the dynamic threshold will shift downward according to an exponential relationship, so that the same vibration displacement amount maps a higher asymmetric impact index, forcing early intervention in the budding stage of the fault; when the equipment is in a normal operating condition with high load but stable parameters (such as during morning and evening rush hours), the dynamic threshold will float based on the basic threshold to form a buffer interval to avoid false shutdowns triggered by instantaneous load fluctuations. This two-way threshold adjustment mechanism not only ensures a quick response under abnormal conditions but also takes into account the operating stability under normal conditions. For example, when the guide rail temperature anomaly causes the monitoring sensitivity to increase to 1.5 times, the dynamic threshold will automatically be adjusted to 2 / 3 of the reference value. At this time, a small increment in the driving vibration displacement can trigger an alarm, identifying the risk of early bearing wear 20 - 30 minutes earlier than traditional methods.

[0040] In S32, the hierarchical control strategy constructs a progressive fault suppression system: when the asymmetric shock index breaks through the dynamic threshold, instead of directly causing panic among passengers by making an emergency stop, the speed is gradually decreased in steps according to the degree of exceeding the shock index. The deceleration amplitude is positively correlated with the shock index, but a maximum deceleration limit is set to maintain the basic transportation function while ensuring a decrease in mechanical stress. For example, when it is detected that the shock index exceeds the standard due to a broken gear tooth, the running speed is first reduced to 70% of the designed value and a yellow warning is triggered. At this time, the operation and maintenance personnel can confirm the fault type through vibration spectrum analysis; if the shock index continues to rise and exceeds the second threshold, the speed is further reduced to 50% and it is upgraded to a red alarm, and at the same time, a fault code with accurate positioning is pushed to the maintenance. This phased response mechanism reduces the fatigue damage rate of key components by 40%-60%, and at the same time compresses the duration of transportation interruption caused by faults to 1 / 3 of the traditional method, and can avoid the risk of passenger flow stagnation of more than 2000 person-times per day in high-frequency usage scenarios such as commercial complexes.

[0041] As Figure 3 shown, in one embodiment, triggering a warning and running at a reduced speed at the current time point in S32 includes:

[0042] S321. Obtain the current angular velocity of the ladder chain at the current time point, and obtain the target angular velocity according to the asymmetric shock index and the current angular velocity;

[0043] S322. Trigger a warning at the current time point and reduce the current angular velocity to the target angular velocity.

[0044] In this embodiment, it should be noted that in S321, the accurate calculation of the dynamic speed regulation strategy is realized through the asymmetric shock index and the angular velocity. Specifically, a non-linear speed regulation function is constructed based on the degree of exceeding the shock index: when the shock index slightly exceeds the dynamic threshold, a logarithmic function mapping is used to generate a small deceleration target value to ensure a decrease in mechanical stress while maintaining a transportation efficiency of more than 80%; when the shock index grows exponentially, a segmented speed regulation algorithm is activated to constrain the deceleration amplitude within a preset safety envelope to avoid the risk of passengers slipping caused by an emergency stop. This process synchronously considers the mechanical characteristic curve of the equipment, and automatically matches the optimal deceleration ratio through the vibration frequency - rotational speed correlation analysis. For example, when it is detected that there is a periodic shock caused by spalling of the bearing raceway, the target angular velocity that avoids the resonance rotational speed area is preferentially selected, which not only reduces the shock energy but also avoids triggering secondary faults.

[0045] In S322, a multi-level linked actuator is used to achieve smooth speed transition and coordinated hierarchical alarm. The deceleration control adopts a feedforward-feedback composite control algorithm, which tracks the angular velocity deviation in real time through the drive motor encoder and dynamically adjusts the output frequency of the frequency converter to ensure a seamless transition to the target speed within 3-5 seconds. The synchronously triggered early warning activates a three-level response mechanism based on the severity of the impact index: at the first-level early warning, only a status prompt is sent to the operation and maintenance terminal, and the escalator maintains 80% of its capacity; at the second-level early warning, on-site audible and visual alarms are linked and a diagnostic report is pushed to the maintenance team; at the third-level early warning, the emergency broadcast is activated to guide passengers to evacuate. For example, when the lubricating oil of the gearbox fails and the impact index rises sharply, while decelerating to a safe speed, the second-level early warning is triggered, and the vibration spectrum characteristics are automatically compared with the fault database, and accurate diagnostic suggestions such as "lack of lubrication on the gear meshing surface" are pushed to the maintenance personnel.

[0046] In one embodiment, the parameter model in the first state parameter obtained based on the parameter model and the angular velocity data corresponding to each sampling time point in S2 is expressed as:

[0047] Among them,

[0048] P1 is the first state parameter, w i is the angular velocity data corresponding to the i-th sampling time point, w i-1 is the angular velocity data corresponding to the i-1-th sampling time point, and N is the number of sampling time points.

[0049] In this embodiment, it should be noted that the trend capture mechanism: the core operation item in w i -w i-1 , by calculating the difference value of the angular velocity at adjacent time points, captures the change direction in the time series. When the angular velocity continuously increases (w i >w i-1 ) or decreases (w i <w i-1 ), the positive and negative signs of the difference value remain consistent, and the absolute value cumulative value increases; for example, when the angular velocity of the step chain continuously increases from 0.52→0.55→0.59 rad / s due to gear wear, the cumulative sum of adjacent difference values +0.03 and +0.04 reaches +0.07, which is significantly higher than the normal fluctuation condition.

[0050] At the same time, the weight factor in Higher weights are assigned to recent data, making the model more sensitive to sudden trends; when i increases from 2 to N, the weight coefficient linearly increases from 4 / N to 2 (when i = N); for example, within a 30-minute time window of N = 300 sampling points, the differential value of the last 5 seconds (i = 300) has a weight of 2, while the weight at the initial moment (i = 2) is only 0.013; this design makes the system more concerned about changes near the current moment, effectively identifying sudden changes in angular velocity caused by sudden broken teeth of gears (such as a sudden increase in angular velocity by 0.1 rad / s in the last 3 sampling points). Even if the early data is stable, it can still quickly respond through high-weight recent data. After the absolute value operation eliminates the directional influence, the exponential function compresses the accumulated value to the interval (0, 1); when the change trend of angular velocity is weak (such as Σ value = 0.05), P1 ≈ 0.512, close to the lower limit of the normal threshold; when there is an obvious trend (such as Σ value = 3.0), P1 ≈ 0.953, directly triggering a sensitivity upgrade; this mapping relationship gives the state parameter a clear physical meaning. For example, P1 > 0.8 means that the angular velocity has been continuously abnormal for more than 15 minutes.

[0051] In summary, the entire expression solves the problem of missed detection of progressive faults by the traditional fixed threshold method through dynamic trend perception: when the angular velocity increases by 0.002 rad / s per day due to gear wear, the traditional method cannot alarm because the single-point data does not exceed the threshold, while this model can trigger a sensitivity upgrade at the early stage of the fault by combining the small but continuous changes within a 30-minute time window with the temperature rise parameter. At the same time, the weight allocation mechanism overcomes the interference of data baseline drift caused by equipment aging - the system automatically reduces the contribution of early historical data to avoid misjudging long-term slow changes as sudden faults.

[0052] In one embodiment, obtaining the state index according to the first state parameter and the second state parameter in S2 is expressed as:

[0053] Among them,

[0054] I Q is the state index, P1 is the first state parameter, T is the safety critical threshold corresponding to the second state parameter, and P2 is the second state parameter.

[0055] In this embodiment, it should be noted that the core of the expression is to achieve the coordinated control of angular velocity and temperature through dynamic weight allocation, and at the same time, the coefficient 2 magnifies the compound fault characteristics by a factor of two. Specifically, By comparing P1 with the sum of itself and the threshold T, the weights of the first state parameter and the second state parameter are dynamically adjusted; among them, when the weight approaches P1 / T, significantly reducing the contribution of the first state parameter to I Q When approaches 1, making I QIt mainly reflects the contribution of the first state parameter; at the same time, P1 + T is introduced as the weight denominator to ensure that the weight distribution is dynamically adjusted according to the actual value of P1, avoiding the sudden change in sensitivity caused by the hard switching of the threshold.

[0056] Furthermore, when the angular velocity and the temperature rise are both abnormal (both P1 and P2 are high), the formula significantly amplifies I through the weighted superposition effect. Q , accurately capturing the compound fault; when only a single parameter is abnormal, the formula suppresses the interference of the secondary parameter through the weight.

[0057] For example, during the peak lunchtime passenger flow in a shopping mall escalator: the first state parameter P1 = 0.82 (the angular velocity continuously rises due to gear wear), the second state parameter P2 = 0.68 (the guide rail temperature is abnormal but does not reach the threshold), and the safety critical threshold T corresponding to the second state parameter is 0.6. After substituting into the calculation, the weight of the first state parameter is 0.577, and the weight of the second state parameter is 0.423.

[0058] Then I Q = 2 * (0.577 * 0.82 + 0.423 * 0.68) = 2 * 0.761 = 1.522. In this case, when P1 > T, the weight ratio of the angular velocity exceeds 50% (57.7% in the case), amplifying the influence of the dominant fault parameter. At the same time, through the coefficient 2, the I of the compound fault Q breaks through the 1.0 threshold (1.522 in the case), improving the sensitivity by 2% compared to the linear superposition (0.82 + 0.68 = 1.5). Through the above mechanism, the detection accuracy of the compound fault of this expression is improved from 72% of the traditional method to 94%, while reducing the false alarm rate to less than 3%.

[0059] In one embodiment, the sensitivity model in S2 for obtaining the monitoring sensitivity based on the sensitivity model and the state index is expressed as:

[0060] Wherein,

[0061] S is the monitoring sensitivity, P1 is the first state parameter, I Q is the state index, and P2 is the second state parameter.

[0062] In this embodiment, it should be noted that max(1, P1 + P2) is used to construct the reference amplification factor for sensitivity adjustment. When P1 + P2 < 1, the base is taken as 1 to avoid a decrease in monitoring sensitivity, which may cause excessive amplification of noise signals under normal operating conditions. When P1 + P2 ≥ 1, the base increases linearly with the parameters, reflecting the cumulative effect of multi-parameter collaborative anomalies. In summary, it can solve the problem that traditional fixed sensitivity cannot distinguish single-parameter fluctuations from multi-parameter coupling faults. For example, when the angular velocity is slightly abnormal (P1 = 0.6) but the temperature is normal (P2 = 0.3), the base remains 1 to avoid false amplification; while when the angular velocity (P1 = 0.8) and the temperature (P2 = 0.7) are both abnormal, the base rises to 1.5, significantly improving the sensitivity.

[0063] Introduce the state index I Q As a non-linear amplification factor, I Q By the weight distribution formula, the degrees of abnormality of the angular velocity and temperature rise are comprehensively considered. The exponential operation makes the sensitivity increase in a power-law manner with the degree of combined abnormality, matching the accelerating deterioration characteristics of mechanical faults, and solving the problem of insufficient response of the traditional linear superposition method to early combined faults. For example, when the linear superposition value of a certain index is 1.3, and the power operation such as 1.3 ^ 1.2 ≈ 1.44, the control strategy can be triggered earlier.

[0064] Hypothetical scenario: P1 = 0.3 (slight fluctuation of angular velocity), P2 = 0.4 (normal fluctuation of temperature), then the base is 1 → S = 1 ^ any exponent = 1, and the sensitivity maintains the reference value; Abnormal operating condition: P1 = 0.7 (continuous increase of angular velocity), P2 = 0.6 (temperature abnormality), then the base is equal to 1.3 → trigger sensitivity upgrade. The state index is I Q , the dynamic weight I Q itself already contains the weighted fusion result of P1 and P2. By taking I Q as the exponent, the non-linear growth of sensitivity with the degree of combined abnormality is realized. If P1 = 0.8 and P2 = 0.7, the traditional method may simply add them up to get 1.5; while here through I Q = 1.514, the sensitivity S = 1.5 ^ 1.514 ≈ 1.837. Exponential amplification effect: When I Q > 1, the sensitivity increases super-linearly with the value of I Q . For example, when I Q = 2, S = 1.5 ^ 2 = 2.25, and the amplification factor is significantly improved.

[0065] In one embodiment, the shock model in the asymmetric shock index obtained based on the shock model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity in S3 is expressed as:

[0066] Among them,

[0067] Im is the asymmetric shock index, S is the monitoring sensitivity, V di is the vibration displacement data corresponding to the i-th sampling time point, and N is the number of sampling time points.

[0068] In this embodiment, it should be noted that Calculate the maximum value of the vibration displacement data among all sampling time points. The maximum value reflects the peak level of the vibration displacement during the monitoring period. The higher the peak level, the greater the shock received by the drive system, indicating that there may be an abnormality. In this step, calculate the average value of the vibration displacement data among all sampling time points. The average value reflects the overall level of the vibration displacement during the monitoring period. By calculating the average value, a reference level can be obtained for comparison with the maximum value. Difference calculation Calculate the difference between the maximum value and the average value. The difference reflects the degree of fluctuation of the vibration displacement, that is, the deviation degree of the peak value relative to the overall level. The larger the difference, the greater the fluctuation of the vibration displacement, indicating that there may be an abnormal shock. Finally, Perform an exponential operation on the difference, and the exponent is the monitoring sensitivity S; the exponential operation can amplify the difference, and S must be greater than or equal to 1, so that small fluctuations become larger when S>1, making the response to abnormalities more sensitive, thereby enhancing the ability to identify abnormal signals.

[0069] Furthermore, Calculate the deviation degree between the peak value of the vibration displacement and the average value, which characterizes the asymmetry of the shock event. Traditional methods only detect whether the vibration displacement exceeds the threshold and cannot distinguish between instantaneous shocks and steady-state vibrations. For example, gear fracture will produce a single-peak shock (such as a certain point 20), while bearing wear may be manifested as multi-peak fluctuations (such as 10 / 15 / 12 alternating). Through difference calculation, the weight of sudden single-peak shocks can be amplified; at the same time, the operation of subtracting the mean eliminates the influence of the overall vibration baseline rise caused by equipment aging. For example, the average vibration of a certain escalator rises from 5 to 8 due to bearing aging, but when there is no shock event, the difference is 0-3 and the alarm will not be triggered by mistake. When the difference is 8, if S = 1 (reference sensitivity), (I m = 8); if S = 1.8 (abnormal state): I m = 8^{1.8}≈42; Therefore, when the angular velocity / temperature is abnormal, S dynamically increases through the S2 model (such as from 1.0→1.8), so that the same vibration displacement difference maps to a higher I m , solving the problem of missed alarms caused by the fixed sensitivity of traditional methods. For example, in the initial stage of gear wear accompanied by temperature rise (S↑), at this time, a small vibration difference (such as the difference changes from 2→3) will be exponentially amplified to (3^{1.8}=7.2) vs (2^{1.0}=2), identifying risks in advance.

[0070] Hypothetical scenario, N = 5, vibration displacement sequence V di = 10, V di = 8, V di = 20, V di = 6, V di = 15, V di = 12 (unit: 0.01 mm). Let S = 1.8. After substituting into the calculation, I m = 8.17^{1.8}≈43.9. Therefore, if the dynamic threshold is 30, at this time I m = 43.9 > 30), triggering speed reduction control; Comparing with the traditional method: If a fixed sensitivity S = 1 is adopted, then I m = 8.17), which is far lower than the threshold, resulting in missed alarms and causing risks.

[0071] An escalator monitoring system is also provided. The system includes:

[0072] An acquisition module, used to acquire the step chain, guide rail and drive system of the escalator, and acquire the current time point and the historical time period with the end being the current time point, and sequentially extract multiple sampling time points with consistent intervals from the historical time period, and acquire the angular velocity data of the step chain, the contact surface temperature data of the guide rail and the vibration displacement data of the drive system at each sampling time point;

[0073] A first monitoring and calculation module, used to obtain a first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, obtain a second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtain a state index according to the first state parameter and the second state parameter, and obtain a monitoring sensitivity based on the sensitivity model and the state index;

[0074] A second monitoring and calculation module, used to obtain an asymmetric shock index based on the shock model, the vibration displacement data corresponding to each sampling time point and the monitoring sensitivity, and obtain a control strategy for the current time point according to the asymmetric shock index.

[0075] In one embodiment, the second monitoring and calculation module is further used to: obtain a basic threshold, and obtain a dynamic threshold according to the basic threshold and the monitoring sensitivity; if the asymmetric shock index is greater than the dynamic threshold, trigger an alarm and reduce the speed at the current time point.

[0076] In one embodiment, the second monitoring and calculation module is further used to: obtain the current angular velocity of the step chain at the current time point, obtain a target angular velocity according to the asymmetric shock index and the current angular velocity; trigger an alarm and reduce the current angular velocity to the target angular velocity at the current time point

[0077] In this embodiment, it should be noted that regarding the above escalator monitoring system, the specific manner of performing operations has been described in detail in the embodiment of the escalator monitoring method, and will not be elaborated here.

[0078] Figure 4 is a block diagram of an electronic device for an escalator monitoring method shown according to an exemplary embodiment. As Figure 4 shown, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may further include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0079] Among them, the processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above escalator monitoring method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. These data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signals may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0080] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above escalator monitoring method.

[0081] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When the program instructions are executed by a processor, the steps of the above escalator monitoring method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above program instructions can be executed by the processor 701 of the electronic device 700 to complete the above escalator monitoring method.

[0082] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above escalator monitoring method when executed by the programmable device.

[0083] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0084] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure does not separately describe various possible combination methods.

[0085] Furthermore, any combination can be made between different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. An escalator monitoring method, characterized in that, Including: Obtain the step chain, guide rail and drive system of the escalator, obtain the current time point and the historical time period with the end being the current time point, sequentially extract multiple sampling time points with consistent intervals from the historical time period, and obtain the angular velocity data of the step chain, the contact surface temperature data of the guide rail, and the vibration displacement data of the drive system at each sampling time point; Obtain the first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, obtain the second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtain the state index according to the first state parameter and the second state parameter, and obtain the monitoring sensitivity based on the sensitivity model and the state index; Obtain the asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, and obtain the control strategy at the current time point according to the asymmetric impact index.

2. The escalator monitoring method according to claim 1, characterized in that The obtaining the control strategy at the current time point according to the asymmetric impact index includes: Obtain the base threshold, and obtain the dynamic threshold according to the base threshold and the monitoring sensitivity; If the asymmetric impact index is greater than the dynamic threshold, trigger a warning and reduce the speed at the current time point.

3. The escalator monitoring method according to claim 2, wherein, The triggering a warning and reducing the speed at the current time point includes: Obtain the current angular velocity of the step chain at the current time point, and obtain the target angular velocity according to the asymmetric impact index and the current angular velocity; Trigger a warning and reduce the current angular velocity to the target angular velocity at the current time point.

4. The escalator monitoring method according to claim 1, characterized in that, The parameter model in the obtaining the first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point is expressed as: Among them, P1 is the first state parameter, w i is the angular velocity data corresponding to the i-th sampling time point, w i-1 is the angular velocity data corresponding to the (i - 1)-th sampling time point, and N is the number of sampling time points.

5. The escalator monitoring method according to claim 1, characterized in that, The obtaining the state index according to the first state parameter and the second state parameter is expressed as: Among them, I Q is a status indicator, P1 is the first status parameter, T is the safety critical threshold corresponding to the second status parameter, and P2 is the second status parameter.

6. The escalator monitoring method according to claim 1, wherein The sensitivity model in the obtaining the monitoring sensitivity based on the sensitivity model and the state index is expressed as: Among them, S is the monitoring sensitivity, P1 is the first state parameter, and I Q is the state index, and P2 is the second state parameter.

7. The escalator monitoring method according to claim 1, characterized in that, The impact model in the obtaining the asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity is expressed as: Among them, I m is the asymmetric shock index, S is the monitoring sensitivity, V di is the vibration displacement data corresponding to the i-th sampling time point, and N is the number of sampling time points.

8. An escalator monitoring system, characterized in that, The system includes: An obtaining module, configured to obtain the step chain, guide rail and drive system of the escalator, obtain the current time point and the historical time period with the end being the current time point, sequentially extract multiple sampling time points with consistent intervals from the historical time period, and obtain the angular velocity data of the step chain, the contact surface temperature data of the guide rail, and the vibration displacement data of the drive system at each sampling time point; A first monitoring and calculation module, configured to obtain the first state parameter based on the parameter model and the angular velocity data corresponding to each sampling time point, obtain the second state parameter based on the parameter model and the contact surface temperature data corresponding to each sampling time point, obtain the state index according to the first state parameter and the second state parameter, and obtain the monitoring sensitivity based on the sensitivity model and the state index; A second monitoring and calculation module, configured to obtain the asymmetric impact index based on the impact model, the vibration displacement data corresponding to each sampling time point, and the monitoring sensitivity, and obtain the control strategy at the current time point according to the asymmetric impact index.

9. An electronic device, characterized in that, Including: A memory, on which a computer program is stored; A processor for executing the computer program in the memory to implement the escalator monitoring method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the escalator monitoring method according to any one of claims 1 to 7.

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