A prediction and early warning method for an escalator brake device

Abstract

The present invention relates to the field of escalator early warning technology, and in particular to a prediction and early warning method for an escalator brake device. The method comprises the following steps: installing a brake dynamic distance monitoring sensor at a position close to the escalator brake device and performing voltage change contact influence monitoring and dynamic distance value conversion on the motor brake action stroke of the escalator brake device, obtaining the dynamic distance value of the escalator brake action stroke, and transmitting it to an intelligent analysis and early warning system; performing time series change trend analysis and brake device operation abnormal state analysis on the dynamic distance value of the escalator brake action stroke, and performing abnormal early warning maintenance strategy analysis at the same time, so as to generate an escalator brake device operation abnormal early warning maintenance strategy, and upload it to a preset escalator intelligent operation and maintenance big data platform, so as to perform corresponding escalator brake device abnormal early warning operation and maintenance work. The present invention can realize the safety abnormal early warning process of the escalator.

Description

Technical Field

[0001] The invention relates to the technical field of escalator early warning, and in particular to a prediction and early warning method for an escalator brake device. Background Art

[0002] The escalator motor brake generally refers to an electromagnetic mechanical brake, which is composed of a static brake pad, a dynamic brake disc (fixed on the motor shaft side) and a spring. The coil power supply is synchronized with the motor power supply. When the motor stops running, the brake coil loses power at the same time. The brake spring pushes the brake pad to lock the motor shaft, and the brake function is completed to avoid the motor from failing to start or the motor automatically sliding when it stops running. As an important part of modern transportation, the safety and reliability of escalators are of vital importance. The brake device is one of the key components of escalators, which is used to quickly stop the escalator in an emergency to ensure the safety of passengers. However, traditional escalator brake devices often face problems such as friction plate wear, spring failure and thruster failure in actual operation. These problems often cause the brake device to respond slowly or fail, and fail to hold the brake motor, resulting in out-of-control accidents. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a prediction and early warning method for an escalator brake device to solve at least one of the above technical problems.

[0004] To achieve the above object, a prediction and early warning method for an escalator brake device comprises the following steps:

[0005] Step S1: by installing a brake dynamic distance monitoring sensor at a position close to the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change contact impact on the motor brake action stroke of the escalator brake device, the escalator brake stroke voltage change impact optimization data is obtained;

[0006] Step S2: convert the dynamic distance value of the escalator brake stroke voltage change optimization data through the circuit board of the brake dynamic distance monitoring sensor to obtain the dynamic distance value of the escalator brake action stroke; connect and transmit the dynamic distance value of the escalator brake action stroke to the intelligent analysis and early warning system through a dedicated cable;

[0007] Step S3: analyzing the time series change trend of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to obtain the time series change trend of the escalator brake action stroke distance; analyzing the abnormal operation state of the brake device for the motor brake action stroke corresponding to the escalator brake device based on the time series change trend of the escalator brake action stroke distance to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear;

[0008] Step S4: Analyze the abnormal operation status of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device; upload the abnormal operation warning maintenance strategy for the escalator brake device to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding abnormal operation warning operation and maintenance work of the escalator brake device.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: by installing a brake dynamic distance monitoring sensor near the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change of the motor brake action stroke of the escalator brake device in real time, the real-time change data of the escalator brake stroke voltage is obtained;

[0011] Step S12: plotting the voltage change fluctuation of the escalator brake stroke voltage real-time change data to generate a waveform diagram of the escalator brake stroke voltage real-time change;

[0012] Step S13: measuring the dynamic contact torque of different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points;

[0013] Step S14: performing voltage change fluctuation contact recoil influence correction analysis on the escalator brake stroke voltage real-time change waveform diagram based on the dynamic contact torque data of the brake sensor at different brake contact points, so as to obtain the escalator brake stroke voltage change fluctuation contact recoil influence correction coefficient;

[0014] Step S15: optimizing the voltage change correction effect on the escalator brake stroke voltage real-time change data according to the escalator brake stroke voltage change fluctuation contact recoil correction coefficient to obtain the escalator brake stroke voltage change effect optimization data.

[0015] Further, step S13 includes the following steps:

[0016] Step S131: performing spatial distribution position positioning processing on different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor;

[0017] Step S132: performing contact stress distribution analysis on corresponding brake real-time contact points in the brake dynamic distance monitoring sensor based on the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor, to obtain contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor;

[0018] Step S133: performing dynamic contact torque measurement on the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points.

[0019] Further, step S133 includes the following steps:

[0020] The contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor are finely divided into stress azimuth distribution to obtain stress distribution data at different brake contact points in different azimuths in the brake dynamic distance monitoring sensor;

[0021] Based on the stress distribution data of different brake contact points in different positions in the brake dynamic distance monitoring sensor, the corresponding brake real-time contact points are subjected to contact stress response distribution fitting processing to generate a contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor;

[0022] The stress effect evaluation and analysis is performed on the contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor to obtain the dynamic change effect of the contact stress at different brake contact points in the brake dynamic distance monitoring sensor.

[0023] Based on the dynamic change effect of contact stress at different brake contact points in the brake dynamic distance monitoring sensor, the contact torque of the corresponding contact stress distribution data is quantitatively calculated to obtain the dynamic contact torque data of the brake sensor at different brake contact points.

[0024] Further, step S14 includes the following steps:

[0025] Step S141: performing time-series synchronization processing on the dynamic contact torque data of the brake sensor at different brake contact points based on the time-series dimension range in the real-time change waveform of the escalator brake stroke voltage, so as to obtain the dynamic contact torque data of the brake sensor at different brake contact points under the same time-series dimension;

[0026] Step S142: performing dynamic torque influence modal modeling on the dynamic contact torque data of the brake sensor at different brake contact points in the same time series dimension to obtain a dynamic contact torque influence modal model;

[0027] Step S143: performing voltage fluctuation influence characteristic mapping analysis on the real-time change waveform of the escalator brake stroke voltage based on the dynamic contact torque influence modal model to obtain the escalator brake stroke voltage fluctuation torque influence characteristic data;

[0028] Step S144: performing a recoil effect time domain analysis on the escalator brake stroke voltage real-time change waveform diagram according to the escalator brake stroke voltage fluctuation torque influence characteristic data, and obtaining the escalator brake stroke voltage fluctuation recoil effect time domain data;

[0029] Step S145: Based on the time domain data of the recoil effect of the escalator brake stroke voltage fluctuation, a recoil influence correction analysis is performed on the real-time change waveform of the escalator brake stroke voltage to obtain the escalator brake stroke voltage change fluctuation contact recoil influence correction coefficient.

[0030] Further, step S144 includes the following steps:

[0031] Perform frequency domain distribution conversion analysis on the characteristic data of voltage fluctuation torque influence on the escalator brake stroke to obtain the frequency domain distribution data of voltage fluctuation torque influence on the escalator brake stroke;

[0032] The high-frequency oscillation feature extraction and processing are performed on the frequency domain distribution data of the voltage fluctuation torque effect on the escalator brake stroke to obtain the high-frequency oscillation feature data of the voltage fluctuation torque effect;

[0033] According to the high-frequency oscillation characteristic data of the voltage fluctuation torque affecting the voltage, a time-domain recoil simulation is performed on the voltage change fluctuation in the real-time change waveform of the escalator brake stroke voltage to obtain the time-domain recoil simulation data of the escalator brake stroke voltage fluctuation;

[0034] The dynamic phase synchronization relationship between the escalator brake stroke voltage fluctuation and the time-domain recoil effect is obtained by performing a recoil effect dynamic phase synchronization analysis on the escalator brake stroke voltage fluctuation time-domain recoil simulation data.

[0035] Based on the dynamic phase synchronization relationship between the escalator brake stroke voltage fluctuation and the time domain recoil effect, the voltage change fluctuation in the real-time change waveform of the escalator brake stroke voltage is analyzed in the time domain to obtain the time domain data of the escalator brake stroke voltage fluctuation recoil effect.

[0036] Further, step S2 includes the following steps:

[0037] Step S21: performing noise filtering processing on the optimization data of the influence of the escalator brake stroke voltage change to obtain the escalator brake stroke voltage change noise filtering data;

[0038] Step S22: performing time-series point value extraction processing on the escalator brake stroke voltage change noise filtering data to obtain the escalator brake stroke voltage change value at each time-series point;

[0039] Step S23: obtaining the historical voltage change value and the historical dynamic travel distance of the escalator brake device through the circuit board of the brake dynamic distance monitoring sensor, and performing voltage-distance relationship regression fitting analysis on the historical voltage change value and the historical dynamic travel distance of the escalator brake device to obtain the linear regression fitting relationship between the escalator brake voltage value and the dynamic travel distance;

[0040] Step S24: converting the escalator brake stroke voltage change value at each time point into a dynamic distance value according to the linear regression fitting relationship between the escalator brake voltage value and the dynamic stroke distance to obtain the escalator brake action stroke dynamic distance value;

[0041] Step S25: The dynamic distance value of the escalator brake action stroke is connected and transmitted to the intelligent analysis and early warning system through a dedicated cable.

[0042] Further, step S3 includes the following steps:

[0043] Step S31: plotting a distance value time series change curve of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to generate a time series change curve of the dynamic distance value of the escalator brake stroke;

[0044] Step S32: analyzing the time series change trend of the dynamic distance value time series change curve of the escalator brake stroke to obtain the time series change trend of the escalator brake action stroke distance;

[0045] Step S33: Based on the time series change trend of the escalator brake action stroke distance, the motor brake action stroke corresponding to the escalator brake device is analyzed for the abnormal operation state of the brake device to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear.

[0046] Further, step S33 includes the following steps:

[0047] Compare and judge the time series variation trend of the escalator brake action travel distance. If it is judged that the time series variation trend of the escalator brake action travel distance shows irregular fluctuation, the abnormal operation state of the escalator brake device corresponding to the motor brake action travel is determined as the abnormal state of brake thruster failure.

[0048] If it is determined that the time series change trend of the escalator brake action stroke distance shows a sudden decrease or unstable change trend, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined to be an abnormal state of brake spring failure;

[0049] If it is determined that the time series change trend of the escalator brake action stroke distance increases and the time delay to reach a stable state is obtained, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined as an abnormal wear state of the brake friction plate.

[0050] Further, step S4 includes the following steps:

[0051] Step S41: performing abnormal warning processing on the abnormal operation state of the escalator brake device to obtain abnormal operation warning information data of the escalator brake device;

[0052] Step S42: performing abnormal maintenance strategy suggestion analysis on the abnormal operation warning information data of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device;

[0053] Step S43: Upload the escalator brake device abnormal operation warning maintenance strategy to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding escalator brake device abnormal operation warning maintenance work.

[0054] Beneficial effects of the present invention:

[0055] Compared with the prior art, the escalator brake device prediction and early warning method proposed by the present invention has the beneficial effect that by installing a brake dynamic distance monitoring sensor near the escalator brake device, and using the voltage measuring end of the sensor to monitor the voltage change of the motor brake action stroke of the brake device in real time, the voltage change data of the escalator brake stroke can be obtained in real time, and the state of the motor brake of the escalator during the braking process can be timely understood. This monitoring can accurately capture the working state of the brake device, including the dynamic changes of its contact points, to ensure that the system can accurately respond to various operating conditions. The acquisition of real-time voltage data can help maintenance personnel to promptly discover equipment failures or performance degradation problems. For example, abnormal fluctuations in voltage values ​​indicate wear or damage to the contact points of the brake device. This information is of great significance for preventive maintenance and reducing equipment failures, thereby providing basic data guarantee for the subsequent dynamic distance conversion process. By optimizing the impact correction of the voltage change data obtained from real-time monitoring, the accuracy and analysis effect of the voltage data can be greatly improved. This optimization process provides a more realistic measurement result for the voltage change data by considering and correcting the impact of recoil, thereby improving the credibility of the overall data. The optimized data can more accurately reflect the actual working status of the escalator brake device, thereby helping technicians to perform more accurate dynamic distance conversion measurements. By optimizing the data, the error caused by contact recoil can be reduced, making the data analysis results more in line with actual operating conditions, thereby providing a scientific basis for the efficient and safe operation of the escalator. Secondly, by using the circuit board of the brake dynamic distance monitoring sensor to obtain historical voltage change values ​​and historical dynamic travel distance regression fitting analysis, the quantitative relationship between voltage change and dynamic travel distance is analyzed, and according to this regression quantitative relationship, the escalator brake stroke voltage change value at each time point in the escalator brake stroke voltage change optimization data is converted into a dynamic distance value. The voltage change data can be converted into specific travel distance information. This process makes the abstract data of voltage change concrete and easy to understand, and provides actual distance data for real-time monitoring and analysis, which can not only enhance the accurate grasp of the status of escalator equipment, but also help identify abnormal conditions in the journey, and realize accurate dynamic tracking and fault warning. The dynamic distance value of the escalator brake action stroke is also connected and transmitted to the intelligent analysis and early warning system through the use of special cables. This step can transmit the processed dynamic distance value to the intelligent analysis and early warning system, which can realize real-time monitoring and automatic alarm. The reliability and security of data transmission are guaranteed by special cables, so that the intelligent analysis system can quickly receive the latest data and analyze it. This real-time transmission and early warning function not only improves the safety of the escalator, but also can timely discover potential problems and avoid failures, thereby improving the stability of the brake device and the abnormal early warning response process.Then, the time series trend analysis of the dynamic distance of the escalator brake action travel is performed by using an intelligent analysis and early warning system. The core of this process is to deeply understand the operating status of the escalator brake device through the analysis of the time series trend. This analysis is not limited to observing the changes in a single data point, but also includes the study of the entire data trend. By analyzing these time series trends, the behavior patterns of the brake device under different working conditions can be identified. This trend analysis can reveal whether the brake device is working as expected, whether there is a gradual performance degradation, or whether it is abnormal under certain specific operating conditions. These trend information is of guiding significance for maintenance personnel to formulate effective maintenance strategies, and can help predict the occurrence of equipment failures and take corresponding preventive measures, thereby improving the overall reliability and safety of the brake device. At the same time, the abnormal state of the brake device is analyzed by analyzing the motor brake action stroke corresponding to the escalator brake device based on the time-series change trend of the escalator brake action stroke distance. The purpose of this analysis is to identify the specific abnormal state of the brake device, including the failure of the brake thruster, the failure of the brake spring, and the wear of the brake friction plate. The key to this step is to compare the time-series change trend with the known fault mode to identify the potential abnormal state. Through this trend-based abnormal state analysis, potential problems of the brake device can be discovered in the early stage. For example, if the trend analysis shows irregular fluctuations, this means that the brake thruster is faulty, causing the motor to stop rotating, unable to operate, or even burn out the motor. If it is found that the dynamic change of the stroke tends to decrease suddenly or change unstably, it means that the brake spring fails and cannot return to the normal state. On the other hand, if it is found that the stroke distance increases during the operation and the time to reach the stable state is delayed, it indicates that the friction plate is seriously worn, which will cause the motor to not brake safely and fail to hold the motor, thus losing control and causing an accident. This abnormal state analysis can not only help predict the failure of the escalator brake device, but also reduce the impact of the escalator brake device failure on the operation through timely detection and intervention, thereby improving the operation efficiency and safety of the escalator brake device. Finally, by analyzing the abnormal maintenance strategy recommendations for the abnormal operation state of the escalator brake device, the goal of this process is to generate specific maintenance strategies based on the early warning information data to ensure the normal operation and long-term stability of the equipment. Detailed maintenance strategy recommendation analysis can help the operation and maintenance team develop targeted and effective maintenance plans. At this stage, the generation of maintenance strategies involves in-depth analysis of abnormal early warning data, including factors such as the root cause of the abnormality, the scope of impact, and the expected repair time.Through this analysis, the most appropriate maintenance measures can be identified, such as whether a certain component needs to be replaced, or just adjusted or cleaned. More importantly, this strategic recommendation can optimize resource allocation, allowing the operation and maintenance team to solve the problem in the shortest time and at the lowest cost. The strategic recommendation also includes specific maintenance steps, required tools and materials, and expected completion time, etc., to ensure that maintenance work is carried out according to scientific procedures. In addition, the abnormal operation warning maintenance strategy of the escalator brake device is uploaded to the preset escalator intelligent operation and maintenance big data platform. The key to this process is to perform the corresponding operation and maintenance work through the big data platform, so as to achieve efficient management and maintenance of the escalator, collect and analyze maintenance data from different escalators, and provide a comprehensive operation and maintenance view and data support. This centralized data management enables the operation and maintenance team to monitor the status of each device in real time, make timely adjustments and optimizations, and ensure that abnormal problems are quickly resolved. This information sharing not only improves maintenance efficiency, but also strengthens communication and collaboration among all parties, thereby improving the overall operation and management level of the escalator. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0057] Figure 1 A schematic diagram of the steps of the escalator brake device prediction and early warning method of the present invention;

[0058] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0059] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0060] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0062] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0063] To achieve this, please refer to Figures 1 to 3 The present invention provides a prediction and early warning method for an escalator brake device, the method comprising the following steps:

[0064] Step S1: by installing a brake dynamic distance monitoring sensor at a position close to the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change contact impact on the motor brake action stroke of the escalator brake device, the escalator brake stroke voltage change impact optimization data is obtained;

[0065] Step S2: convert the dynamic distance value of the escalator brake stroke voltage change optimization data through the circuit board of the brake dynamic distance monitoring sensor to obtain the dynamic distance value of the escalator brake action stroke; connect and transmit the dynamic distance value of the escalator brake action stroke to the intelligent analysis and early warning system through a dedicated cable;

[0066] Step S3: analyzing the time series change trend of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to obtain the time series change trend of the escalator brake action stroke distance; analyzing the abnormal operation state of the brake device for the motor brake action stroke corresponding to the escalator brake device based on the time series change trend of the escalator brake action stroke distance to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear;

[0067] Step S4: Analyze the abnormal operation status of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device; upload the abnormal operation warning maintenance strategy for the escalator brake device to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding abnormal operation warning operation and maintenance work of the escalator brake device.

[0068] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the automatic escalator brake device prediction and early warning method of the present invention. In this example, the automatic escalator brake device prediction and early warning method includes the following steps:

[0069] Step S1: by installing a brake dynamic distance monitoring sensor at a position close to the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change contact impact on the motor brake action stroke of the escalator brake device, the escalator brake stroke voltage change impact optimization data is obtained;

[0070] In an embodiment of the present invention, a corresponding brake dynamic distance monitoring sensor is installed near the escalator brake device. The brake dynamic distance monitoring sensor should be configured with a voltage measuring end, which can detect the brake action stroke of the brake device motor in real time, and ensure that the voltage measuring end of the sensor is in close contact with the motor brake assembly, and by starting the monitoring program in the voltage measuring end of the brake dynamic distance monitoring sensor, the voltage change data of the motor brake action stroke in the escalator brake device is collected in real time, and the data is transmitted to the data processing system through the sensor. In addition, by using data drawing software or special tools, the real-time monitored real-time change data of the escalator brake stroke voltage is plotted on a chart in chronological order, the X-axis of the chart represents time, and the Y-axis represents the voltage change value. During the drawing process, it is ensured that the chart can accurately reflect the fluctuation of the voltage value to form a clear voltage change waveform diagram. At the same time, by using a professional torque sensor or a dynamic load measuring instrument installed on the brake device, and by measuring the contact torque at different real-time contact points of the brake in the brake dynamic distance monitoring sensor in real time, the contact torque at different contact points is measured to ensure that the sensor can It is able to record the torque data in real time and transmit these data to the data processing system, and by combining the dynamic contact torque data of the brake sensor at different brake contact points obtained by the previous analysis, the corresponding voltage fluctuation contact recoil effect in the voltage change waveform is corrected and analyzed, so that the contact torque data is compared and analyzed with the voltage change waveform by using the correction algorithm. The correction process includes calculating the influence of the contact recoil on the voltage change and determining the correction coefficient to eliminate the error caused by the contact recoil, so as to ensure that the waveform accurately reflects the real voltage change. Then, by combining the correction coefficient obtained after the previous correction, the corresponding escalator brake stroke voltage real-time change data is corrected for the influence of the voltage change, so that the correction coefficient is applied to the voltage real-time change data to adjust the influence of the voltage change, and the data processing system is used for optimization calculation to ensure that the optimized data can accurately reflect the actual brake stroke voltage change. The optimized data is used for further analysis and monitoring to ensure that the performance of the escalator brake system is stable and can provide real and effective voltage change information, and finally the escalator brake stroke voltage change optimization data is corrected.

[0071] Step S2: convert the dynamic distance value of the escalator brake stroke voltage change optimization data through the circuit board of the brake dynamic distance monitoring sensor to obtain the dynamic distance value of the escalator brake action stroke; connect and transmit the dynamic distance value of the escalator brake action stroke to the intelligent analysis and early warning system through a dedicated cable;

[0072] In an embodiment of the present invention, by using a time series data analysis tool (such as the NumPy library in Python), the escalator brake stroke voltage change impact optimization data is divided into time series to extract the voltage change value at each time point, and the data at each time point is precisely processed by an interpolation method (such as linear interpolation or spline interpolation) to ensure that the value at the time series point is accurate, and by using the circuit board of the brake dynamic distance monitoring sensor to collect the historical voltage change value and the corresponding dynamic travel distance data of the escalator brake device, so as to import these data into a regression analysis tool (such as the regression analysis function of MATLAB or the Scikit-learn library in Python), and apply the linear regression model to fit the relationship between the voltage change value and the dynamic travel distance, so as to obtain a linear regression fitting equation, and by combining the linear regression fitting relationship between the voltage value and the dynamic travel distance obtained by the previous analysis, the escalator brake stroke voltage change value at each time point is converted into a specific dynamic distance value, so as to use the linear relationship formula in the regression equation to substitute the voltage value into the linear regression fitting equation to calculate the corresponding dynamic distance value, thereby converting the escalator brake action stroke dynamic distance value. Then, a dedicated cable is used to connect to the input port of the intelligent analysis and early warning system to ensure the stability and accuracy of data transmission. During the data transmission process, a serial communication interface (such as RS-485) is used for real-time data transmission, and the communication protocol is used to ensure data integrity. Finally, the dynamic distance value of the escalator brake action stroke is connected and transmitted to the intelligent analysis and early warning system, and the dynamic distance value is processed for further analysis and early warning to ensure the normal operation of the escalator.

[0073] Step S3: analyzing the time series change trend of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to obtain the time series change trend of the escalator brake action stroke distance; analyzing the abnormal operation state of the brake device for the motor brake action stroke corresponding to the escalator brake device based on the time series change trend of the escalator brake action stroke distance to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear;

[0074] In an embodiment of the present invention, a data visualization tool (such as Python's Matplotlib library or MATLAB's drawing function) is used in an intelligent analysis and early warning system to draw a time-series change curve of the dynamic distance value of the escalator brake action stroke previously converted, so as to draw a time-series change curve of the distance value, which shows the time-series change curve of the dynamic distance value within a certain time range, and the previously drawn dynamic distance value time-series change curve is imported into a data analysis platform (such as Python's Pandas library or MATLAB's timing toolbox), and the dynamic distance value time-series change curve is smoothed and trend detected by applying a time-series analysis algorithm (such as a sliding average method or an autoregressive integral sliding average model ARIMA). In the analysis process, the data processing tool is used to identify the change pattern in the curve, such as an upward, downward or fluctuating change trend, so as to analyze and obtain the time-series change trend of the escalator brake action stroke distance. Then, by comparing and judging the time series change trend of the escalator brake action travel distance obtained by the previous analysis, the specific abnormality type can be judged by comparing the time series change trend of the normal state. If the change trend shows irregular fluctuations, it is marked as an abnormal state of brake thruster failure; if the curve shows a sudden decrease or unstable fluctuations, it is marked as an abnormal state of brake spring failure; if the trend shows that the travel distance gradually increases and reaches a stable state, it is marked as an abnormal state of brake friction plate wear. Through these analyses, a detailed abnormal operation state of the escalator brake device is generated, including the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear.

[0075] Step S4: Analyze the abnormal operation status of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device; upload the abnormal operation warning maintenance strategy for the escalator brake device to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding abnormal operation warning operation and maintenance work of the escalator brake device.

[0076] In an embodiment of the present invention, the previously determined abnormal operation state of the escalator brake device (including abnormal states such as failure of the brake thruster, failure of the brake spring, and wear of the brake friction plate) is transmitted to the edge computing device, and the device uses a real-time data processing algorithm (such as an abnormality detection algorithm) to analyze the corresponding abnormal operation state, and generates corresponding abnormal warning information in response, including a detailed description of the abnormal type, occurrence time, degree of abnormality and the impact of the abnormality, and uses a data analysis platform (such as the Pandas library in MATLAB or Python) to perform data cleaning and statistical analysis to extract specific patterns and trends of the abnormalities, and uses a rule engine or a decision tree algorithm to recommend abnormal maintenance strategies for the corresponding abnormal operation warning information data to generate maintenance strategy recommendations. These maintenance strategies are based on historical data and equipment maintenance records, involving the priority of the problem, the existing repair steps and the required resources. The results of the strategy recommendation analysis form a detailed maintenance strategy document, including troubleshooting procedures and preventive measures, thereby generating an abnormal operation warning maintenance strategy for the escalator brake device. Then, the previously generated escalator brake device operation abnormal warning maintenance strategy is uploaded to the preset escalator intelligent operation and maintenance big data platform using the data interface. The platform uses cloud computing technology (such as AWS or Azure) to support data storage and processing. The upload process uses RESTful API or data transmission protocol (such as MQTT or kafka) to send the strategy data to the platform. After receiving the strategy, the platform uses its operation and maintenance management system to automatically assign maintenance tasks, notify relevant maintenance personnel, and record the progress of the task. The escalator operation and maintenance system performs necessary maintenance operations such as replacing parts or adjusting system settings by executing the recommendations in the strategy. The entire process is monitored and reported through the platform's dashboard to ensure that abnormal problems are handled in a timely manner, thereby performing the corresponding escalator brake device abnormal warning operation and maintenance work.

[0077] Further, step S1 includes the following steps:

[0078] Step S11: by installing a brake dynamic distance monitoring sensor near the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change of the motor brake action stroke of the escalator brake device in real time, the real-time change data of the escalator brake stroke voltage is obtained;

[0079] Step S12: plotting the voltage change fluctuation of the escalator brake stroke voltage real-time change data to generate a waveform diagram of the escalator brake stroke voltage real-time change;

[0080] Step S13: measuring the dynamic contact torque of different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points;

[0081] Step S14: performing voltage change fluctuation contact recoil influence correction analysis on the escalator brake stroke voltage real-time change waveform diagram based on the dynamic contact torque data of the brake sensor at different brake contact points, so as to obtain the escalator brake stroke voltage change fluctuation contact recoil influence correction coefficient;

[0082] Step S15: optimizing the voltage change correction effect on the escalator brake stroke voltage real-time change data according to the escalator brake stroke voltage change fluctuation contact recoil correction coefficient to obtain the escalator brake stroke voltage change effect optimization data.

[0083] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0084] Step S11: by installing a brake dynamic distance monitoring sensor near the escalator brake device, and using the voltage measuring end of the brake dynamic distance monitoring sensor to monitor the voltage change of the motor brake action stroke of the escalator brake device in real time, the real-time change data of the escalator brake stroke voltage is obtained;

[0085] In an embodiment of the present invention, a corresponding brake dynamic distance monitoring sensor is installed near the escalator brake device. The brake dynamic distance monitoring sensor should be configured with a voltage measuring end, which can detect the brake action stroke of the brake device motor in real time and ensure that the voltage measuring end of the sensor is in close contact with the motor brake assembly. At the same time, by starting the monitoring program in the voltage measuring end of the brake dynamic distance monitoring sensor, the voltage change data of the brake action stroke of the motor in the escalator brake device is collected in real time, and the data is transmitted to the data processing system through the sensor. These data will be used to draw the real-time changes of the brake voltage, accurately reflect the relationship between the brake action stroke and the voltage change, and finally obtain the real-time change data of the escalator brake stroke voltage.

[0086] Step S12: plotting the voltage change fluctuation of the escalator brake stroke voltage real-time change data to generate a waveform diagram of the escalator brake stroke voltage real-time change;

[0087] In an embodiment of the present invention, the collected real-time change data of the escalator brake stroke voltage are passed through a data processing system, and by using data drawing software or special tools, the real-time change data of the escalator brake stroke voltage are plotted on a graph in chronological order. The X-axis of the graph represents time, and the Y-axis represents the voltage change value. During the drawing process, it is ensured that the graph can accurately reflect the fluctuation of the voltage value and form a clear voltage change waveform diagram. These waveform diagrams will be used for subsequent correction analysis to accurately evaluate the impact of voltage changes on the brake device, and finally draw and generate the real-time change waveform diagram of the escalator brake stroke voltage.

[0088] Step S13: measuring the dynamic contact torque of different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points;

[0089] In an embodiment of the present invention, a professional torque sensor or a dynamic load measuring instrument is installed on the brake device, and the contact torque is measured in real time at different real-time contact points of the brake in the brake dynamic distance monitoring sensor to measure the contact torque at different contact points, thereby ensuring that the sensor can record the torque data in real time and transmit the data to the data processing system, thereby obtaining the contact torque data of the brake at each contact point, and finally obtaining the dynamic contact torque data of the brake sensor at different brake contact points.

[0090] Step S14: performing voltage change fluctuation contact recoil influence correction analysis on the escalator brake stroke voltage real-time change waveform diagram based on the dynamic contact torque data of the brake sensor at different brake contact points, so as to obtain the escalator brake stroke voltage change fluctuation contact recoil influence correction coefficient;

[0091] In an embodiment of the present invention, by combining the dynamic contact torque data of the brake sensor at different brake contact points obtained in the previous analysis, the corresponding voltage fluctuation contact recoil effect in the real-time change waveform of the escalator brake stroke voltage is corrected and analyzed, so that the contact torque data and the voltage change waveform are compared and analyzed by using a correction algorithm. The correction process includes calculating the impact of contact recoil on voltage change and determining a correction coefficient, which is applied to the voltage waveform to eliminate errors caused by contact recoil, ensuring that the waveform accurately reflects the actual voltage change, and finally obtaining the correction coefficient for the contact recoil impact of the escalator brake stroke voltage change fluctuation.

[0092] Step S15: optimizing the voltage change correction effect on the escalator brake stroke voltage real-time change data according to the escalator brake stroke voltage change fluctuation contact recoil correction coefficient to obtain the escalator brake stroke voltage change effect optimization data.

[0093] In an embodiment of the present invention, the voltage change impact correction is performed on the corresponding escalator brake stroke voltage real-time change data by combining the escalator brake stroke voltage change fluctuation contact recoil impact correction coefficient obtained after previous correction, so that the impact correction coefficient is applied to the real-time voltage change data to adjust the impact of the voltage change, and the data processing system is used for optimization calculation to ensure that the optimized data can accurately reflect the actual brake stroke voltage change, and the optimized data is used for further analysis and monitoring to ensure that the performance of the escalator brake system is stable and can provide real and effective voltage change information, and finally the escalator brake stroke voltage change impact optimization data is corrected.

[0094] Further, step S13 includes the following steps:

[0095] Step S131: performing spatial distribution position positioning processing on different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor;

[0096] Step S132: performing contact stress distribution analysis on corresponding brake real-time contact points in the brake dynamic distance monitoring sensor based on the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor, to obtain contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor;

[0097] Step S133: performing dynamic contact torque measurement on the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points.

[0098] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0099] Step S131: performing spatial distribution position positioning processing on different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor;

[0100] In an embodiment of the present invention, by locating the spatial distribution positions of different real-time brake contact points in the brake dynamic distance monitoring sensor, it is necessary to use high-precision three-dimensional space measurement equipment to collect data. For example, a laser scanner or a laser rangefinder can be used to scan each contact point of the brake system to obtain its three-dimensional coordinate data. These devices can provide high-precision spatial positioning information in a short time, and input the obtained three-dimensional coordinate data into a computer system, and use a special positioning algorithm to process the data. The positioning algorithm can be a fitting algorithm based on the least squares method, or other spatial optimization algorithms, which are used to determine the specific spatial position of each contact point, so that the precise distribution position of each brake contact point in space can be obtained, and finally the spatial real-time distribution position of different brake contact points in the brake dynamic distance monitoring sensor is obtained.

[0101] Step S132: performing contact stress distribution analysis on corresponding brake real-time contact points in the brake dynamic distance monitoring sensor based on the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor, to obtain contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor;

[0102] In an embodiment of the present invention, by combining the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor obtained by previous positioning analysis, a distribution statistical analysis of contact stress is performed on the corresponding brake real-time contact points in the brake dynamic distance monitoring sensor to establish a stress distribution model of the contact points. The stress data of each contact point can be collected in real time by a stress sensor installed in the brake system, and stress measurement is applied to each contact point by using a piezoelectric stress sensor. These sensors can record the changes in contact stress in real time, and the measured stress data is input into a computer system. The data is processed using stress analysis software. These software are usually based on finite element analysis (FEA) technology, which can simulate and analyze the stress distribution of contact points under different working conditions, and finally obtain the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor.

[0103] Step S133: performing dynamic contact torque measurement on the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points.

[0104] In an embodiment of the present invention, the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor obtained by the previous analysis are processed and analyzed by using data processing software, so as to finely divide the corresponding stress azimuth distribution and generate a detailed stress distribution diagram, which shows the stress distribution of different brake contact points at various angles, accurately describes the stress distribution characteristics of each contact point, and performs response fitting analysis on the corresponding brake real-time contact points by combining the stress distribution data at different orientations obtained by the previous analysis. The specific operation includes using a mathematical model, such as a finite element analysis (FEA) model, to simulate the stress response of the actual contact point, so as to simulate and generate the corresponding fitting field by inputting the stress distribution data obtained in the previous analysis into the fitting algorithm, so as to show the stress response of each brake contact point in the actual operation. How to respond to different stress changes in the contact stress response distribution fitting field generated by the previous simulation is also analyzed by using the stress effect analysis tool to evaluate the stress effect, so as to evaluate the dynamic changes of stress at each contact point through the stress effect analysis tool. This involves the use of special stress analysis software to calculate the dynamic change effect of stress by inputting the fitting field data, and to perform quantitative calculation of the contact torque on the corresponding contact stress distribution data by combining the dynamic change effect of contact stress obtained by the previous analysis, so as to combine the dynamic stress data with the geometric parameters of the brake contact point and calculate the actual contact torque. The torque calculation process needs to use matrix operations or integral calculation methods to summarize the stress data of each contact point into the overall contact torque data, and finally obtain the dynamic contact torque data of the brake sensor at different brake contact points.

[0105] Further, step S133 includes the following steps:

[0106] The contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor are finely divided into stress azimuth distribution to obtain stress distribution data at different brake contact points in different azimuths in the brake dynamic distance monitoring sensor;

[0107] In an embodiment of the present invention, a high-precision stress sensor is used to measure the stress distribution of the brake contact point. When using the stress sensor, the sensor is fixed on different contact points of the brake dynamic distance monitoring sensor to measure the stress data of each contact point in various orientations. In a specific implementation, the sensor includes a strain gauge or an optical fiber sensor. These sensors can provide high-resolution stress data. Repeated measurements are required at different orientations of the sensor to ensure the accuracy and comprehensiveness of the stress distribution data. The measured data are processed and analyzed by using data processing software, and the stress orientation distribution is finely divided to generate a detailed stress distribution diagram. The diagram shows the stress distribution of different brake contact points at various angles, accurately describes the stress distribution characteristics of each contact point, and finally obtains the stress distribution data of different brake contact points in the brake dynamic distance monitoring sensor at different orientations.

[0108] Preferably, contact stress response distribution fitting processing is performed on the corresponding real-time brake contact points based on stress distribution data of different brake contact points in different positions in the brake dynamic distance monitoring sensor, so as to generate a contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor;

[0109] In an embodiment of the present invention, a response fitting analysis is performed on the corresponding real-time brake contact point by combining the stress distribution data of different brake contact points at different positions in the brake dynamic distance monitoring sensor obtained by previous analysis. The specific operation includes using a mathematical model, such as a finite element analysis (FEA) model, to simulate the stress response of the actual contact point, so as to input the stress distribution data obtained in the previous analysis into a fitting algorithm, which can be a nonlinear least squares method or other advanced fitting techniques. The fitting process is performed using a high-performance computing platform to ensure the accuracy of the calculation, so that the simulated fitting field shows how each brake contact point responds to different stress changes in actual operation, which can be further used to analyze dynamic stress behavior, and finally generate a contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor.

[0110] Preferably, a stress effect evaluation and analysis is performed on the contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor to obtain the contact stress dynamic change effect at different brake contact points in the brake dynamic distance monitoring sensor;

[0111] In an embodiment of the present invention, a stress effect analysis tool is used to evaluate and analyze the stress effect of the contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor previously simulated and generated, so as to evaluate the dynamic change of stress at each contact point through the stress effect analysis tool. This involves using special stress analysis software to calculate the dynamic change of stress by inputting fitting field data. During the evaluation process, dynamic factors in the actual operating environment need to be considered, such as temperature changes, load fluctuations, etc. These factors will affect the distribution and change of stress, which can help determine whether the stress recorded by the sensor meets expectations under various working conditions and whether there are abnormal changes, and then evaluate its impact on equipment performance, and finally obtain the dynamic change effect of contact stress at different brake contact points in the brake dynamic distance monitoring sensor.

[0112] Preferably, contact torque quantification calculation is performed on the corresponding contact stress distribution data based on the dynamic change effect of contact stress at different brake contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points.

[0113] In an embodiment of the present invention, the contact torque is quantitatively calculated for the corresponding contact stress distribution data by combining the dynamic change effect of the contact stress at different brake contact points in the brake dynamic distance monitoring sensor obtained by previous analysis, so as to combine the dynamic stress data with the geometric parameters of the brake contact point to calculate the actual contact torque. The torque calculation process requires the use of matrix operations or integral calculation methods to aggregate the stress data of each contact point into overall contact torque data, and finally obtain the dynamic contact torque data of the brake sensor at different brake contact points.

[0114] Further, step S14 includes the following steps:

[0115] Step S141: performing time-series synchronization processing on the dynamic contact torque data of the brake sensor at different brake contact points based on the time-series dimension range in the real-time change waveform of the escalator brake stroke voltage, so as to obtain the dynamic contact torque data of the brake sensor at different brake contact points under the same time-series dimension;

[0116] In an embodiment of the present invention, the dynamic contact torque data corresponding to the brake sensor at different brake contact points are synchronously processed in time by using the time dimension range within the real-time change waveform of the escalator brake stroke voltage drawn and generated previously, so as to obtain the voltage change waveform within a period of time, and perform time decomposition on it. By comparing the voltage change waveform with the dynamic contact torque data of the brake sensor at different contact points, a synchronization processing algorithm is used to time-align these torque data with the voltage waveform. Specifically, an interpolation algorithm can be used to perform fine processing on the time dimension to ensure the synchronization of data at different contact points at the same time point, to ensure that the torque changes at each contact point can be accurately displayed under the same time dimension, and finally the dynamic contact torque data of the brake sensor at different brake contact points under the same time dimension are obtained.

[0117] Step S142: performing dynamic torque influence modal modeling on the dynamic contact torque data of the brake sensor at different brake contact points in the same time series dimension to obtain a dynamic contact torque influence modal model;

[0118] In an embodiment of the present invention, modal modeling is performed on the dynamic contact torque data of the brake sensor at different brake contact points in the same timing dimension after previous timing synchronization, so as to perform dynamic torque modal modeling of the data by applying a dynamic system analysis tool, such as the system identification toolbox in MATLAB, and a mathematical model of torque influence is established based on the torque data at different contact points. By using a modal analysis method (such as principal component analysis PCA or factor analysis), the main modes influencing the torque are extracted, and then a modal model of contact torque influence is established. The model can describe the influence characteristics of the torque data at different contact points on the voltage fluctuation, and finally a dynamic contact torque influence modal model is obtained.

[0119] Step S143: performing voltage fluctuation influence characteristic mapping analysis on the real-time change waveform of the escalator brake stroke voltage based on the dynamic contact torque influence modal model to obtain the escalator brake stroke voltage fluctuation torque influence characteristic data;

[0120] In an embodiment of the present invention, a mapping analysis of the voltage fluctuation influence characteristics of the real-time change waveform of the escalator brake stroke voltage is performed by combining the previously established dynamic contact torque influence modal model, so that the established modal model and the voltage waveform data are input and matched, and the influence characteristics of the voltage fluctuation are extracted through a feature mapping algorithm, such as linear regression analysis or neural network mapping, and the voltage waveform data is input into the model, and finally the escalator brake stroke voltage fluctuation torque influence characteristic data is analyzed.

[0121] Step S144: performing a recoil effect time domain analysis on the escalator brake stroke voltage real-time change waveform diagram according to the escalator brake stroke voltage fluctuation torque influence characteristic data, and obtaining the escalator brake stroke voltage fluctuation recoil effect time domain data;

[0122] In an embodiment of the present invention, a time domain statistical analysis of the recoil effect is performed on the corresponding escalator brake stroke voltage real-time change waveform diagram by combining the escalator brake stroke voltage fluctuation torque influence characteristic data obtained in the previous analysis, so that the obtained escalator brake stroke voltage fluctuation torque influence characteristic data is input into a time domain analysis tool, such as the time domain signal processing toolbox in MATLAB, and a time domain analysis algorithm (such as autocorrelation analysis or time domain convolution) is used to analyze the recoil effect in the voltage fluctuation, thereby generating time domain data of the recoil effect, which can show the instantaneous recoil effect in the voltage fluctuation, and finally obtain the time domain data of the escalator brake stroke voltage fluctuation recoil effect.

[0123] Step S145: Based on the time domain data of the recoil effect of the escalator brake stroke voltage fluctuation, a recoil influence correction analysis is performed on the real-time change waveform of the escalator brake stroke voltage to obtain the escalator brake stroke voltage change fluctuation contact recoil influence correction coefficient.

[0124] In an embodiment of the present invention, a recoil effect correction algorithm, such as a least squares method or a Kalman filter algorithm, is used in combination with the time domain data of the recoil effect of the escalator brake stroke voltage fluctuation obtained by previous analysis to perform a correction analysis of the recoil effect on the corresponding escalator brake stroke voltage real-time change waveform to correct the recoil effect in the voltage fluctuation, and a correction coefficient is calculated by comparing the voltage waveform before and after correction. The coefficient can be used for subsequent correction processing to finally obtain the escalator brake stroke voltage change fluctuation contact recoil effect correction coefficient.

[0125] Further, step S144 includes the following steps:

[0126] Perform frequency domain distribution conversion analysis on the characteristic data of voltage fluctuation torque influence on the escalator brake stroke to obtain the frequency domain distribution data of voltage fluctuation torque influence on the escalator brake stroke;

[0127] In an embodiment of the present invention, the frequency domain conversion is performed on the characteristic data of the influence of the voltage fluctuation torque on the escalator brake stroke previously analyzed by using the Fast Fourier Transform (FFT) tool, so as to convert these time domain data into the frequency domain, and analyze the influence of the voltage fluctuation torque on the escalator brake stroke. The frequency domain distribution data will show the amplitude and phase information of different frequency components, reflecting the main frequency characteristics of the voltage fluctuation. The converted frequency domain data is visualized and processed by using a spectrum analyzer or special software, and finally the frequency domain distribution data of the influence of the voltage fluctuation torque on the escalator brake stroke is obtained.

[0128] Preferably, high-frequency oscillation feature extraction processing is performed on the frequency domain distribution data of the voltage fluctuation torque effect on the escalator brake stroke to obtain high-frequency oscillation feature data of the voltage fluctuation torque effect;

[0129] In an embodiment of the present invention, a high-pass filter is used to filter out the low-frequency components in the frequency domain distribution data of the voltage fluctuation torque affecting the escalator brake stroke, and only the high-frequency components are retained. By applying algorithms such as Hilbert transform or other high-frequency feature extraction algorithms, the high-frequency oscillation characteristics of the voltage fluctuation torque can be extracted. This process is completed through specific signal processing software or a digital signal processor, and the purpose is to capture the high-frequency oscillation pattern in the voltage change and represent it as high-frequency oscillation feature data. These feature data will be further used for time domain recoil simulation processing to ultimately obtain the high-frequency oscillation feature data of the voltage fluctuation torque.

[0130] Preferably, the voltage fluctuation in the real-time voltage change waveform of the escalator brake stroke is subjected to a time-domain recoil simulation process according to the voltage fluctuation torque affecting the high-frequency oscillation characteristic data, so as to obtain the time-domain recoil simulation data of the voltage fluctuation in the escalator brake stroke;

[0131] In an embodiment of the present invention, a time-domain recoil simulation is performed on the corresponding voltage change fluctuation in the real-time change waveform of the escalator brake stroke voltage by combining the high-frequency oscillation characteristic data of the voltage fluctuation torque obtained by previous analysis, so as to establish a mathematical model of voltage change based on the high-frequency oscillation characteristic data, and simulate the corresponding voltage change fluctuation by applying a time-domain recoil algorithm, which involves a simulation or numerical solution method for a nonlinear system. During the simulation process, the recoil effect of the voltage change is calculated and its time-domain performance is recorded. The time-domain recoil simulation data generated by using simulation software or engineering calculation tools can accurately reflect the dynamic response of the voltage fluctuation in the time domain, and finally obtain the time-domain recoil simulation data of the escalator brake stroke voltage fluctuation.

[0132] Preferably, a dynamic phase synchronization analysis of the recoil effect is performed on the time-domain recoil simulation data of the escalator brake stroke voltage fluctuation to obtain a dynamic phase synchronization relationship between the escalator brake stroke voltage fluctuation and the time-domain recoil effect;

[0133] In an embodiment of the present invention, by performing a synchronous analysis of the dynamic phase of the recoil effect on the time-domain recoil simulation data of the voltage fluctuation in the escalator brake stroke obtained by the previous analysis, by using a phase synchronization analysis tool and applying a phase locking algorithm (Phase Locking Algorithm) to analyze the phase relationship between the voltage fluctuation and the time-domain recoil effect, their phase synchronization at different time points can be revealed, and the time-domain recoil simulation data is compared with the actual voltage fluctuation data. By calculating the phase difference and correlation, the dynamic phase synchronization relationship between them is determined. This process helps to identify the time consistency of the voltage fluctuation and the recoil effect and their mutual influence relationship, and finally the dynamic phase synchronization relationship between the voltage fluctuation in the escalator brake stroke and the time-domain recoil effect is obtained.

[0134] Preferably, based on the dynamic phase synchronization relationship between the escalator brake stroke voltage fluctuation and the time domain recoil effect, the voltage change fluctuation in the real-time change waveform of the escalator brake stroke voltage is analyzed in the time domain to obtain the escalator brake stroke voltage fluctuation recoil effect time domain data.

[0135] In an embodiment of the present invention, a time domain analysis of the recoil effect is performed on the corresponding voltage change fluctuation in the real-time change waveform diagram of the escalator brake stroke voltage by combining the dynamic phase synchronization relationship between the escalator brake stroke voltage fluctuation and the time domain recoil effect obtained by previous analysis, so as to decompose the voltage change fluctuation by utilizing the phase synchronization relationship obtained by previous analysis, and by applying a time domain analysis algorithm, the voltage fluctuation data and the phase synchronization relationship of the time domain recoil effect are combined to perform a detailed time domain recoil effect analysis, so as to generate time domain data of the recoil effect of the voltage fluctuation by using signal processing tools or analysis software, and these data demonstrate the time domain recoil effect of the voltage fluctuation, and finally obtain the time domain data of the recoil effect of the escalator brake stroke voltage fluctuation.

[0136] Further, step S2 includes the following steps:

[0137] Step S21: performing noise filtering processing on the optimization data of the influence of the escalator brake stroke voltage change to obtain the escalator brake stroke voltage change noise filtering data;

[0138] In an embodiment of the present invention, the optimized data of the escalator brake stroke voltage change influence obtained by the previous influence optimization analysis is input into the noise filtering module, and the noise is filtered by using a digital filter, such as a Kalman filter or a moving average filter, wherein the Kalman filter reduces the influence of random noise on the data through a prediction and correction process, and the moving average filter smoothes the fluctuation by calculating the average value of the data points. The data after filtering is recorded as noise filtered data to remove the influence of system noise and irregular fluctuations, and finally the escalator brake stroke voltage change noise filtered data is obtained.

[0139] Step S22: performing time-series point value extraction processing on the escalator brake stroke voltage change noise filtering data to obtain the escalator brake stroke voltage change value at each time-series point;

[0140] In an embodiment of the present invention, by utilizing a time series data analysis tool (such as the NumPy library in Python), the noise filtered data of the escalator brake stroke voltage change is segmented according to a time series to extract the voltage change value at each time point, and the data at each time point is precisely processed by an interpolation method (such as linear interpolation or spline interpolation) to ensure that the value at the time series point is accurate, and finally the escalator brake stroke voltage change value at each time series point is obtained.

[0141] Step S23: obtaining the historical voltage change value and the historical dynamic travel distance of the escalator brake device through the circuit board of the brake dynamic distance monitoring sensor, and performing voltage-distance relationship regression fitting analysis on the historical voltage change value and the historical dynamic travel distance of the escalator brake device to obtain the linear regression fitting relationship between the escalator brake voltage value and the dynamic travel distance;

[0142] In an embodiment of the present invention, the historical voltage change values ​​and corresponding dynamic travel distance data of the escalator brake device are collected by using a circuit board of a brake dynamic distance monitoring sensor, and these data are imported into a regression analysis tool (such as the regression analysis function of MATLAB or the Scikit-learn library in Python), and a linear regression model is applied to fit the relationship between the voltage change value and the dynamic travel distance, thereby obtaining a linear regression fitting equation. During the regression analysis process, the regression coefficient is calculated, and the goodness of fit (such as the R-squared value) is evaluated to determine the accuracy of the linear relationship, and finally a linear regression fitting relationship between the escalator brake voltage value and the dynamic travel distance is obtained.

[0143] Step S24: converting the escalator brake stroke voltage change value at each time point into a dynamic distance value according to the linear regression fitting relationship between the escalator brake voltage value and the dynamic stroke distance to obtain the escalator brake action stroke dynamic distance value;

[0144] In an embodiment of the present invention, the escalator brake stroke voltage change value at each timing point is converted into a specific dynamic distance value by combining the linear regression fitting relationship between the escalator brake voltage value and the dynamic travel distance obtained by previous analysis, and the voltage value is substituted into the linear regression fitting equation by using the linear relationship formula in the regression equation to calculate the corresponding dynamic distance value. Through this process, the voltage data after noise filtering is mapped to the dynamic distance data, and these conversion results are used as the dynamic travel distance values, and finally the dynamic distance value of the escalator brake action stroke is obtained.

[0145] Step S25: The dynamic distance value of the escalator brake action stroke is connected and transmitted to the intelligent analysis and early warning system through a dedicated cable.

[0146] In an embodiment of the present invention, a dedicated cable is used to connect to the input port of the intelligent analysis and early warning system to ensure the stability and accuracy of data transmission. During the data transmission process, a serial communication interface (such as RS-232 or RS-485) is used for real-time data transmission, and the communication protocol is used to ensure data integrity. Finally, the dynamic distance value of the escalator brake action stroke is connected and transmitted to the intelligent analysis and early warning system and the dynamic distance value is processed for further analysis and early warning to ensure the normal operation of the escalator.

[0147] Further, step S3 includes the following steps:

[0148] Step S31: plotting a distance value time series change curve of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to generate a time series change curve of the dynamic distance value of the escalator brake stroke;

[0149] In an embodiment of the present invention, a data visualization tool (such as Python's Matplotlib library or MATLAB's drawing function) is used in an intelligent analysis and early warning system to draw a time-series change curve of the dynamic distance value of the escalator brake action stroke previously converted, so as to draw a time-series change curve of the distance value. This curve shows the changing trend of the brake action stroke distance within a certain time range, and finally generates a time-series change curve of the dynamic distance value of the escalator brake stroke.

[0150] Step S32: analyzing the time series change trend of the dynamic distance value time series change curve of the escalator brake stroke to obtain the time series change trend of the escalator brake action stroke distance;

[0151] In an embodiment of the present invention, the previously drawn and generated time series change curve of the dynamic distance value of the escalator brake stroke is imported into a data analysis platform (such as Python's Pandas library or MATLAB's timing toolbox), and the time series analysis algorithm (such as the sliding average method or the autoregressive integral moving average model ARIMA) is applied to the time series change curve of the dynamic distance value of the escalator brake stroke is smoothed and trend detected. During the analysis process, the data processing tool is used to identify the change pattern in the curve, such as the rising, falling or fluctuating change trend, and finally the time series change trend of the escalator brake action stroke distance is analyzed.

[0152] Step S33: Based on the time series change trend of the escalator brake action stroke distance, the motor brake action stroke corresponding to the escalator brake device is analyzed for the abnormal operation state of the brake device to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear.

[0153] In an embodiment of the present invention, a comparison is made by comparing the time series change trends of the escalator brake action travel distance obtained by previous analysis, so as to judge the specific abnormality type by comparing the time series change trends of the normal state. If the change trend shows irregular fluctuations, it is marked as an abnormal state of brake thruster failure; if the curve shows a sudden decrease or unstable fluctuations, it is marked as an abnormal state of brake spring failure; if the trend shows that the travel distance gradually increases and reaches a stable state, it is marked as an abnormal state of brake friction plate wear. Through these analyses, a detailed abnormal operation state of the escalator brake device is generated, including the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear.

[0154] Further, step S33 includes the following steps:

[0155] Compare and judge the time series variation trend of the escalator brake action travel distance. If it is judged that the time series variation trend of the escalator brake action travel distance shows irregular fluctuation, the abnormal operation state of the escalator brake device corresponding to the motor brake action travel is determined as the abnormal state of brake thruster failure.

[0156] In an embodiment of the present invention, by comparing and judging the time series change trend of the escalator brake action travel distance obtained by previous analysis, if it is judged that the change trend on the time series change curve of the escalator brake action travel distance shows irregular fluctuations, that is, there are significant random fluctuations rather than regular changes on the time series change curve, then the abnormal operating state of the motor brake action stroke corresponding to the escalator brake device is judged as an abnormal state of failure of the brake thruster, and this fluctuation indicates a failure or damage of the internal mechanical components of the thruster.

[0157] Preferably, if it is determined that the time series change trend of the escalator brake action stroke distance shows a sudden decrease or unstable change trend, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined as an abnormal state of brake spring failure;

[0158] In an embodiment of the present invention, if it is determined that the change trend on the time-series change curve of the escalator brake action travel distance shows a sudden decrease or unstable change trend (for example, a sudden drop or frequent fluctuations appear in the time-series change curve), it can be determined as an abnormal state of brake spring failure. This situation is caused by the weakening of the elasticity of the spring or the breakage of the spring. In order to confirm the abnormal state, a spectrum analysis tool (such as the spectrum analysis function in MATLAB) is further used to analyze the frequency components in the data, find the frequency characteristics of the sudden change, and compare them with the frequency characteristics of the normal state, so as to confirm that it is a spring failure.

[0159] Preferably, if it is determined that the time series change trend of the escalator brake action stroke distance shows an increase and a time delay to reach a stable state, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined as an abnormal wear state of the brake friction plate.

[0160] In an embodiment of the present invention, if it is determined that the change trend on the time series change curve of the travel distance of the escalator brake action is that the travel distance continues to increase and eventually reaches a stable state, and the establishment of the stable state requires a significant time delay, it indicates that there is an abnormal state of wear of the brake friction plate. This phenomenon is due to the change in friction caused by the wear of the friction plate surface, which causes the response time of the brake action to become longer. By using a dynamic time series analysis tool (such as the time series analysis package in the R language or the statsmodels library in Python), the travel data is subjected to time delay and trend analysis to detect the establishment time of this stable state, and further confirm the wear condition of the friction plate. If it is confirmed that the change in the data conforms to the expected wear pattern, it can be determined that it is an abnormal state of wear of the brake friction plate.

[0161] Further, step S4 includes the following steps:

[0162] Step S41: performing abnormal warning processing on the abnormal operation state of the escalator brake device to obtain abnormal operation warning information data of the escalator brake device;

[0163] In an embodiment of the present invention, the previously determined abnormal operating state of the escalator brake device (including abnormal states such as failure of the brake thruster, failure of the brake spring, and wear of the brake friction plate) is transmitted to the edge computing device, and the device uses a real-time data processing algorithm (such as an abnormality detection algorithm) to analyze the corresponding abnormal operating state, and generates corresponding abnormal warning information in response, including a detailed description of the abnormality type, occurrence time, degree of abnormality and the impact of the abnormality, and finally obtains the abnormal operating warning information data of the escalator brake device.

[0164] Step S42: performing abnormal maintenance strategy suggestion analysis on the abnormal operation warning information data of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device;

[0165] In an embodiment of the present invention, data cleaning and statistical analysis are performed by using a data analysis platform (such as the Pandas library in MATLAB or Python) to extract specific patterns and trends of anomalies, and a rule engine or a decision tree algorithm is used to recommend abnormal maintenance strategies for the corresponding operation abnormality warning information data to generate maintenance strategy recommendations. These maintenance strategies are based on historical data and equipment maintenance records, involving the priority of the problem, existing repair steps and required resources. For example, if the anomaly is an excessively high temperature, it may be recommended to check the cooling system or replace heat-sensitive components. The results of the strategy recommendation analysis form a detailed maintenance strategy document, including troubleshooting procedures and preventive measures, and ultimately generate an escalator brake device operation abnormality warning maintenance strategy.

[0166] Step S43: Upload the escalator brake device abnormal operation warning maintenance strategy to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding escalator brake device abnormal operation warning maintenance work.

[0167] In an embodiment of the present invention, a previously generated escalator brake device operation abnormality warning maintenance strategy is uploaded to a preset escalator intelligent operation and maintenance big data platform using a data interface. The platform uses cloud computing technology (such as AWS or Azure) to support data storage and processing. The upload process uses a RESTful API or a data transmission protocol (such as MQTT) to send the policy data to the platform. After the platform receives the strategy, it uses its operation and maintenance management system to automatically assign maintenance tasks, notify relevant maintenance personnel, and record the progress of the task. The escalator operation and maintenance system performs necessary maintenance operations such as replacing parts or adjusting system settings by executing the recommendations in the strategy. The entire process is monitored and reported through the platform's dashboard to ensure that abnormal problems are handled in a timely manner, thereby performing the corresponding escalator brake device abnormality warning operation and maintenance work.

[0168] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0169] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A monitoring, analysis and early warning method for an escalator brake device, characterized in that: The following steps are involved: Step S1: by installing a brake dynamic distance monitoring sensor at a position close to the escalator brake device, and using the resistance measuring end of the brake dynamic distance monitoring sensor to monitor the contact influence of resistance change on the motor brake action stroke of the escalator brake device, the optimization data of the resistance change influence on the escalator brake stroke is obtained. Step S1 includes the following steps: Step S11: installing a brake dynamic distance monitoring sensor near the escalator brake device, and using the resistance measuring end of the brake dynamic distance monitoring sensor to monitor the resistance change of the motor brake action stroke of the escalator brake device in real time, so as to obtain the real-time change data of the escalator brake stroke resistance; Step S12: plotting the resistance change fluctuation of the escalator brake stroke resistance real-time change data to generate the escalator brake stroke resistance real-time change fluctuation graph; Step S13: measuring the dynamic contact torque of different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points; Step S14: performing resistance change fluctuation contact recoil influence correction analysis on the escalator brake stroke resistance real-time change fluctuation diagram based on the dynamic contact torque data of the brake sensor at different brake contact points, so as to obtain the escalator brake stroke resistance change fluctuation contact recoil influence correction coefficient; Step S15: optimizing the resistance change effect correction of the escalator holding brake stroke resistance real-time change data according to the escalator holding brake stroke resistance change fluctuation contact recoil effect correction coefficient, and obtaining the escalator holding brake stroke resistance change effect optimization data; Step S2: convert the optimization data of the influence of the escalator brake stroke resistance change through the circuit board of the brake dynamic distance monitoring sensor to a dynamic distance value, and obtain the dynamic distance value of the escalator brake action stroke; and transmit the dynamic distance value of the escalator brake action stroke to the intelligent analysis and early warning system through a dedicated cable; Step S3: analyzing the time series change trend of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to obtain the time series change trend of the escalator brake action stroke distance; analyzing the abnormal operation state of the brake device for the motor brake action stroke corresponding to the escalator brake device based on the time series change trend of the escalator brake action stroke distance to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear; Step S4: Analyze the abnormal operation status of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device; upload the abnormal operation warning maintenance strategy for the escalator brake device to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding abnormal operation warning operation and maintenance work of the escalator brake device.

2. The monitoring, analyzing and early warning method for an escalator brake device according to claim 1 is characterized in that: Step S13 includes the following steps: Step S131: performing spatial distribution position positioning processing on different brake real-time contact points in the brake dynamic distance monitoring sensor to obtain the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor; Step S132: performing contact stress distribution analysis on corresponding brake real-time contact points in the brake dynamic distance monitoring sensor based on the spatial real-time distribution positions of different brake contact points in the brake dynamic distance monitoring sensor, to obtain contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor; Step S133: performing dynamic contact torque measurement on the contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor to obtain dynamic contact torque data of the brake sensor at different brake contact points.

3. The monitoring, analyzing and early warning method for an escalator brake device according to claim 2 is characterized in that: Step S133 includes the following steps: The contact stress distribution data at different brake contact points in the brake dynamic distance monitoring sensor are finely divided into stress azimuth distribution to obtain stress distribution data at different brake contact points in different azimuths in the brake dynamic distance monitoring sensor; Based on the stress distribution data of different brake contact points in different positions in the brake dynamic distance monitoring sensor, the corresponding brake real-time contact points are subjected to contact stress response distribution fitting processing to generate a contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor; The stress effect evaluation and analysis is performed on the contact stress response distribution fitting field at different brake contact points in the brake dynamic distance monitoring sensor to obtain the dynamic change effect of the contact stress at different brake contact points in the brake dynamic distance monitoring sensor. Based on the dynamic change effect of contact stress at different brake contact points in the brake dynamic distance monitoring sensor, the contact torque of the corresponding contact stress distribution data is quantitatively calculated to obtain the dynamic contact torque data of the brake sensor at different brake contact points.

4. The monitoring, analyzing and early warning method for an escalator brake device according to claim 1 is characterized in that: Step S14 includes the following steps: Step S141: performing time-series synchronization processing on the dynamic contact torque data of the brake sensor at different brake contact points based on the time-series dimension range in the real-time change fluctuation diagram of the escalator brake stroke resistance, so as to obtain the dynamic contact torque data of the brake sensor at different brake contact points under the same time-series dimension; Step S142: performing dynamic torque influence modal modeling on the dynamic contact torque data of the brake sensor at different brake contact points in the same time series dimension to obtain a dynamic contact torque influence modal model; Step S143: performing resistance fluctuation influence characteristic mapping analysis on the real-time change fluctuation diagram of the escalator brake stroke resistance based on the dynamic contact torque influence modal model to obtain the escalator brake stroke resistance fluctuation torque influence characteristic data; Step S144: performing a recoil effect time domain analysis on the real-time change fluctuation diagram of the escalator brake stroke resistance according to the escalator brake stroke resistance fluctuation torque influence characteristic data, and obtaining the escalator brake stroke resistance fluctuation recoil effect time domain data; Step S145: Based on the time domain data of the recoil effect of the escalator brake travel resistance fluctuation, a recoil influence correction analysis is performed on the real-time change fluctuation diagram of the escalator brake travel resistance to obtain the escalator brake travel resistance change fluctuation contact recoil influence correction coefficient.

5. The monitoring, analyzing and early warning method for an escalator brake device according to claim 4 is characterized in that: Step S144 includes the following steps: Perform frequency domain distribution conversion analysis on the characteristic data of the escalator brake stroke resistance fluctuation torque influence, and obtain the frequency domain distribution data of the escalator brake stroke resistance fluctuation torque influence; Perform high-frequency oscillation feature extraction processing on the frequency domain distribution data of the influence of the resistance fluctuation torque on the escalator brake stroke to obtain the high-frequency oscillation feature data of the influence of the resistance fluctuation torque; According to the high-frequency oscillation characteristic data of the resistance fluctuation torque affecting the resistance change, a time-domain recoil simulation is performed on the resistance change fluctuation in the real-time change fluctuation diagram of the escalator brake stroke resistance to obtain the time-domain recoil simulation data of the escalator brake stroke resistance fluctuation; The dynamic phase synchronization relationship between the escalator brake stroke resistance fluctuation and the time domain recoil effect is obtained by performing a recoil effect dynamic phase synchronization analysis on the escalator brake stroke resistance fluctuation time domain recoil simulation data. Based on the dynamic phase synchronization relationship between the escalator brake stroke resistance fluctuation and the time domain recoil effect, the recoil effect time domain analysis is performed on the resistance change fluctuation in the real-time change fluctuation diagram of the escalator brake stroke resistance, and the time domain data of the escalator brake stroke resistance fluctuation recoil effect is obtained.

6. The monitoring, analyzing and early warning method for an escalator brake device according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing noise filtering processing on the optimization data of the influence of the escalator brake stroke resistance change to obtain the escalator brake stroke resistance change noise filtering data; Step S22: performing time-series point value extraction processing on the escalator brake stroke resistance change noise filtering data to obtain the escalator brake stroke resistance change value at each time-series point; Step S23: obtaining the historical resistance change value and the historical dynamic travel distance of the escalator brake device through the circuit board of the brake dynamic distance monitoring sensor, and performing resistance-distance relationship regression fitting analysis on the historical resistance change value and the historical dynamic travel distance of the escalator brake device to obtain a linear regression fitting relationship between the escalator brake resistance value and the dynamic travel distance; Step S24: converting the escalator brake stroke resistance change value at each time point into a dynamic distance value according to the linear regression fitting relationship between the escalator brake resistance value and the dynamic stroke distance to obtain the escalator brake action stroke dynamic distance value; Step S25: The dynamic distance value of the escalator brake action stroke is connected and transmitted to the intelligent analysis and early warning system through a dedicated cable.

7. The monitoring, analyzing and early warning method for an escalator brake device according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: plotting a distance value time series change curve of the dynamic distance value of the escalator brake action stroke through the intelligent analysis and early warning system to generate a time series change curve of the dynamic distance value of the escalator brake stroke; Step S32: analyzing the time series change trend of the dynamic distance value time series change curve of the escalator brake stroke to obtain the time series change trend of the escalator brake action stroke distance; Step S33: Based on the time series change trend of the escalator brake action stroke distance, the motor brake action stroke corresponding to the escalator brake device is analyzed for the abnormal operation state of the brake device to generate the abnormal operation state of the escalator brake device, wherein the abnormal operation state of the escalator brake device includes the abnormal state of brake thruster failure, the abnormal state of brake spring failure and the abnormal state of brake friction plate wear.

8. The monitoring, analyzing and early warning method for an escalator brake device according to claim 7 is characterized in that: Step S33 includes the following steps: Compare and judge the time series variation trend of the escalator brake action travel distance. If it is judged that the time series variation trend of the escalator brake action travel distance shows irregular fluctuation, the abnormal operation state of the escalator brake device corresponding to the motor brake action travel is determined as the abnormal state of brake thruster failure. If it is determined that the time series change trend of the escalator brake action stroke distance shows a sudden decrease or unstable change trend, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined to be an abnormal state of brake spring failure; If it is determined that the time series change trend of the escalator brake action stroke distance increases and the time delay to reach a stable state is obtained, the abnormal operation state of the escalator brake device corresponding to the motor brake action stroke is determined as an abnormal wear state of the brake friction plate.

9. The monitoring, analyzing and early warning method for an escalator brake device according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing abnormal warning processing on the abnormal operation state of the escalator brake device to obtain abnormal operation warning information data of the escalator brake device; Step S42: performing abnormal maintenance strategy suggestion analysis on the abnormal operation warning information data of the escalator brake device to generate an abnormal operation warning maintenance strategy for the escalator brake device; Step S43: Upload the escalator brake device abnormal operation warning maintenance strategy to the preset escalator intelligent operation and maintenance big data platform to perform the corresponding escalator brake device abnormal operation warning maintenance work.

Images