Line network level cloud platform escalator prediction and early warning method and system architecture

Abstract

The invention discloses a line network level cloud platform escalator prediction and early warning method and system architecture, and relates to the technical field of escalators, and the method comprises the following steps: obtaining the operation state information of each escalator of each station in each line, finely analyzing the trend degradation monitoring of each escalator part of each station in each line, and determining the corresponding escalator part; and performing analysis and statistics on operation early warning and fault information of each escalator of each station in each line, performing corresponding early warning and fault processing, making a maintenance plan, recording the maintenance process of each maintenance escalator of each station in each line, and analyzing the maintenance effect of each maintenance escalator. The system can analyze and manage the actual operation state and operation and maintenance information of the escalator in the operation of each station in each line network level line, including statistical management of data such as normality, early warning, alarm, fault, maintenance, shutdown and the like, realizes monitoring diagnosis and early warning of the operation state of the escalator, improves the safety and reliability of the escalator, and improves the safety and reliability of the escalator. The sudden fault risk is reduced, and the early warning timeliness and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of escalators, and specifically to a prediction and early warning method and system architecture for escalators at the wire network level cloud platform. Background Art

[0002] As an efficient vertical transportation tool, escalators have been widely used in public places such as urban rail transit, airports, and railway stations, providing great convenience for people's travel and other activities, improving the personnel flow efficiency, and becoming an indispensable important part of modern buildings. With the continuous increase in the usage of escalators, their safety issues have become increasingly prominent.

[0003] The prior art, such as the invention patent with the publication number: CN110937489B, is an online fault monitoring and early warning method and system for escalators. The method includes: obtaining data of multiple sensors; fusing sensor features to obtain real-time signal features of key components; converting the real-time signal features and normal signal features into distribution changes and comparing them to obtain a health value; integrating a status sample library; receiving fault codes sent by the escalator, analyzing the fault codes to obtain a fault status, and sending the fault status data to the client and allocating maintenance work orders.

[0004] The prior art, such as the invention patent with the publication number: CN110002329B, is an online monitoring and early warning system and method for escalators based on a cloud platform. The system includes a wire network cloud platform, a data comprehensive service system located at the wire network central level, a communication system, and a station-level monitoring and early warning system; the station-level monitoring and early warning system is set up in each station; each escalator is provided with an escalator code, and the station-level monitoring and early warning system transmits the component risk early warning information and the escalator code to the wire network cloud platform through the communication system; the data comprehensive service system retrieves the component risk early warning information from the wire network cloud platform and classifies the escalators based on the clustering analysis method.

[0005] In view of the above solutions, it can be seen that the current escalator early warning methods have certain limitations in terms of accuracy and comprehensiveness. Usually, early warning judgments are made based on limited sensor data or a single monitoring dimension. However, in the actual operation scenario of escalators, the factors affecting their safety and performance are complex and diverse and interrelated. The operating states of various components inside the escalator affect each other, and at the same time, external environmental factors will also interfere with it. This complex interaction relationship may cause it to be impossible to accurately capture potential fault hazards only relying on single data or simple methods, thus unable to issue early warnings in a timely and effective manner, resulting in an increased risk of sudden escalator failures and a reduction in the overall operating efficiency and reliability of the escalators. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides an escalator prediction and early warning method and system architecture for a line-level cloud platform, which can effectively solve the problems involved in the above background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, an escalator prediction and early warning method for a line-level cloud platform is provided, including the following steps:

[0008] S1. Count the escalators in operation at each station on each line, conduct preliminary monitoring on the escalators in operation through the on-site intelligent analysis and early warning system, obtain the component operation parameter sets of the escalators in operation at each station on each line, and synchronously count and upload them to the line-level cloud platform.

[0009] S2. Based on the component operation parameter sets of the escalators in operation at each station on each line, the line-level cloud platform analyzes the operation status label information of the escalators in operation at each station on each line, screens the escalators with abnormal operation monitored at each station on each line, and synchronously uploads them to the line-level cloud platform for display based on the operation status label information of the escalators in operation at each station on each line.

[0010] S3. Conduct secondary redundant monitoring on the escalators with abnormal operation monitored at each station on each line, analyze the secondary redundant monitoring abnormal comparison analysis index of the escalators with abnormal operation monitored at each station on each line, judge the maintenance requirement label of the escalators with abnormal operation, and upload it to the line-level cloud platform for early warning reminder. Synchronously screen the escalators that need maintenance at each station on each line, and generate a maintenance management signal to be transmitted to the line-level cloud platform for reminder.

[0011] S4. The on-site intelligent analysis and early warning system receives the maintenance completion signal, thereby counting the escalators that have been maintained at each station on each line, synchronously obtaining the component operation parameters of the escalators that have been maintained at each station on each line, and analyzing the maintenance verification indicators of the escalators that have been maintained at each station on each line for corresponding early warning processing.

[0012] In the second aspect of the present invention, an escalator prediction and early warning system architecture for a line-level cloud platform is provided, including:

[0013] A module for preliminary monitoring of escalators in operation, which is used to count the escalators in operation at each station on each line, conduct preliminary monitoring on the escalators in operation through the on-site intelligent analysis and early warning system, obtain the component operation parameter sets of the escalators in operation at each station on each line, and synchronously count and upload them to the line-level cloud platform.

[0014] The operating status label information analysis module for escalators in operation is used to analyze the operating status label information of escalators in operation at each station on each line by the line-level cloud platform based on the component operation parameter sets of escalators in operation at each station on each line, screen the escalators with abnormal monitored operation at each station on each line, and synchronously upload the operating status label information of escalators in operation at each station on each line to the network-level cloud platform for display.

[0015] The secondary redundant monitoring module for escalators with abnormal monitored operation is used to perform secondary redundant monitoring on escalators with abnormal monitored operation at each station on each line, analyze the secondary redundant monitoring abnormal comparison analysis index of escalators with abnormal monitored operation at each station on each line, judge the maintenance requirement label of escalators with abnormal operation, and upload it to the network-level cloud platform for early warning reminder. Synchronously screen the escalators requiring maintenance at each station on each line, and generate a maintenance management signal to be transmitted to the network-level cloud platform for reminder.

[0016] The maintenance verification module for maintained escalators is used for the on-site intelligent analysis and early warning system to receive the maintenance completion signal, thereby counting the maintained escalators at each station on each line, synchronously obtaining the component operation parameters of the maintained escalators at each station on each line, and analyzing the maintenance verification indicators of the maintained escalators at each station on each line for corresponding early warning processing.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0018] (1) By providing a method and system architecture for escalator prediction and early warning on a network-level cloud platform, the present invention can achieve comprehensive and accurate monitoring and early warning of the operating status of escalators, effectively improving the safety and reliability of escalators. Through multi-parameter comprehensive collection and analysis, potential fault hazards can be captured in advance, reducing the risk of sudden failures. The phased monitoring and early warning mechanism can target the treatment of abnormal escalators, improving the timeliness and accuracy of early warning. At the same time, the optimized maintenance management strategy ensures the reasonable allocation of maintenance resources, improves the scientific nature of maintenance effect evaluation, reduces maintenance costs, and improves the overall operating efficiency of escalators.

[0019] (2) The present invention can improve the scientificity and accuracy of escalator operation management by analyzing the operating status label information of each escalator in operation at each station in each line. By accurately dividing the operating status labels, escalators in different states can be quickly screened out, so that operation and maintenance personnel can quickly focus on abnormal escalators and improve operation and maintenance efficiency. Secondly, it provides a solid data foundation for subsequent monitoring and decision-making, avoiding the limitations of subjective experience judgment. The escalator with the first operating label can maintain routine monitoring and reasonably allocate resources. The escalator with the second operating label triggers secondary redundant monitoring to thoroughly investigate hidden dangers. The escalator with the third operating label can be timely and targeted to prevent the fault from worsening. Ultimately, real-time control of the operating status of the escalator is achieved, operation and maintenance costs are reduced, and the overall operation stability and safety are enhanced.

[0020] (3) The present invention can optimize the maintenance strategy of the escalator by judging the maintenance requirement label of the abnormally operating escalator, improve the pertinence and effectiveness of the maintenance work, reduce resource waste, and improve the utilization rate of the escalator. For the escalator that needs maintenance, it can issue early warning reminders in time, prompting the maintenance personnel to respond quickly and give priority to maintenance, which effectively reduces the probability of sudden failure of the escalator, improves the overall efficiency and quality of the maintenance work, and thus improves the reliability and stability of the entire escalator system.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is an escalator early warning flow chart involved in an embodiment of the present invention.

[0023] Figure 2 The figure is a schematic diagram of a method flow involved in an embodiment of the present invention.

[0024] Figure 3 It is a schematic diagram of system module connection involved in an embodiment of the present invention.

[0025] Figure 4 This is an architecture diagram of the network-level cloud platform escalator prediction and warning system involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Please refer to Figure 1As shown in the figure, it is the automatic escalator warning flow chart involved in the embodiment of the present invention. The specific process is as follows: Obtain the operation status information of each escalator at each station on each line, conduct refined analysis on the trend deterioration monitoring of each escalator component at each station on each line, analyze and statistically count the operation warning and fault information of each escalator at each station on each line, conduct corresponding warning and fault handling, and formulate a maintenance plan. Record the maintenance process of each maintained escalator at each station on each line, and analyze its maintenance effect.

[0028] Please refer to Figure 2 As shown in the figure, the embodiment of the present invention provides a method for predicting and warning escalators at the line network level cloud platform, including the following steps:

[0029] S1. Statistically count each operating escalator at each station on each line, conduct preliminary monitoring on the operating escalators through the in-situ intelligent analysis and warning system, obtain the component operation parameter sets of each operating escalator at each station on each line, and synchronously statistically upload them to the line-level cloud platform.

[0030] In this embodiment, the component operation parameter sets of each operating escalator at each station on each line include component historical operation parameters and component initial operation parameters.

[0031] The component historical operation parameters include the cumulative operation duration, cumulative operation start times, cumulative power consumption, and historical average vibration amplitude of each operating escalator at each station on each line.

[0032] It should be noted that the component historical operation parameters are extracted from the storage data set of the line network level cloud platform.

[0033] The component initial operation parameters include the current value, voltage value, operation speed, and step chain tension of each operating escalator at each station on each line.

[0034] It should be noted that the component initial operation parameters are collected through the built-in sensor set of the operating escalator. The built-in sensor set includes a current sensor, a voltage sensor, a speed sensor, and a tension sensor.

[0035] It should also be noted that the step chain tension refers to the force required to maintain the step chain in a proper working state during the operation of the escalator. It is a key factor to ensure that the step chain can accurately and smoothly drive the steps to run on the guide rail.

[0036] S2, The line-level cloud platform analyzes the operation status label information of each operating escalator at each station on each line based on the component operation parameter sets of each operating escalator at each station on each line, screens each monitored escalator with abnormal operation on each line, and synchronously uploads the operation status label information of each operating escalator at each station on each line to the network-level cloud platform for display.

[0037] In this embodiment, to analyze the operation status label information of each operating escalator at each station on each line, the specific acquisition method is as follows: The operation status label information includes a first operation label, a second operation label, and a third operation label.

[0038] Extract the reference component operation parameter set stored in the database, where the reference component operation parameter set includes component reference historical operation parameters and component reference initial operation parameters.

[0039] Among them, the component reference historical operation parameters include the reference operation duration, reference start-up times, reference power consumption, and historical reference average vibration amplitude of the escalator.

[0040] The component reference initial operation parameters include the rated current, rated voltage, ideal operation speed, and ideal step chain tension of the escalator.

[0041] Based on the component operation parameter sets of each operating escalator at each station on each line and the reference component operation parameter set, analyze and process to obtain the component operation abnormal evaluation indexes of each operating escalator at each station on each line.

[0042] The component operation abnormal evaluation indexes of each operating escalator at each station on each line are used to characterize the degree of abnormal operation during the preliminary monitoring period of each operating escalator and serve as the numerical basis for monitoring the trend of deterioration of each escalator component at each station on each line.

[0043] In a specific embodiment, the process of specifically obtaining the component operation abnormal evaluation indexes of each operating escalator at each station on each line is as follows:

[0044]

[0045] Among them,

[0046]

[0047] Among them, is the component operation abnormal evaluation index of the kth 1 operating escalator at the jth station on the ith line, is the kth 1An evaluation factor for the current abnormal operation of the components of the escalator in operation is the cumulative operation duration of the k 1 th escalator in operation at the j 0 th station on the i th line, a 1 is the reference operation duration of the escalator, 0 is the cumulative number of start-ups of the k th escalator in operation at the j 1 th station on the i 0 th line, b 1 is the reference number of start-ups of the escalator, is the cumulative power consumption of the k 0 th escalator in operation at the j th station on the i 1 th line, c 0 is the reference power consumption of the escalator, is the current value of the k 1 th escalator in operation at the j 0 th station on the i th line, f 1 is the rated current of the escalator, 0 is the voltage value of the k th escalator in operation at the j 1 th station on the i 0 th line, g 1 is the rated voltage of the escalator, 2 is the weight value of the cumulative operation duration, x 3 is the weight value of the cumulative number of start-ups, x 4 is the weight value of the cumulative power consumption, x 5 is the weight value of the historical average vibration amplitude, x 6 is the weight value of the current value, x 7 is the weight value of the voltage value, x 8 is the weight value of the operation speed, x 1 is the weight value of the step chain tension, i is the number of each line, i = 1, 2,..., m 1 where m 2 is the number of lines, j is the number of each station, j = 1, 2,..., m 2 where m 2 is the number of stations, k1 is the number of each operating escalator, k 1 = 1, 2,..., m 4 , m 4 is the number of operating escalators, and e is the natural constant.

[0048] It should be noted that the value ranges of the cumulative operation duration weight, the cumulative operation start times weight, the cumulative power consumption weight, the historical average vibration amplitude weight, the current value weight, the voltage value weight, the operation speed weight, and the step chain tension weight are all between 0 and 1. In this embodiment, the cumulative operation duration weight is a value representing the influence degree of the cumulative operation duration on the component operation abnormality evaluation index of each operating escalator at each station on each line; the cumulative operation start times weight is a value representing the influence degree of the cumulative operation start times on the component operation abnormality evaluation index of each operating escalator at each station on each line; the cumulative power consumption weight is a value representing the influence degree of the cumulative power consumption on the component operation abnormality evaluation index of each operating escalator at each station on each line; the historical average vibration amplitude weight is a value representing the influence degree of the historical average vibration amplitude on the component operation abnormality evaluation index of each operating escalator at each station on each line; the current value weight is a value representing the influence degree of the current value on the component operation abnormality evaluation index of each operating escalator at each station on each line; the voltage value weight is a value representing the influence degree of the voltage value on the component operation abnormality evaluation index of each operating escalator at each station on each line; the operation speed weight is a value representing the influence degree of the operation speed on the component operation abnormality evaluation index of each operating escalator at each station on each line; the step chain tension weight is a value representing the influence degree of the step chain tension on the component operation abnormality evaluation index of each operating escalator at each station on each line. When in use, the preset cumulative operation duration weight, cumulative operation start times weight, cumulative power consumption weight, historical average vibration amplitude weight, current value weight, voltage value weight, operation speed weight, and step chain tension weight can be directly obtained from the database, and their corresponding relationships can be preset mapping relationships. For example, the cumulative operation duration, cumulative operation start times, cumulative power consumption, historical average vibration amplitude, current value, voltage value, operation speed, and step chain tension respectively form a mapping set with the preset cumulative operation duration weight, cumulative operation start times weight, cumulative power consumption weight, historical average vibration amplitude weight, current value weight, voltage value weight, operation speed weight, and step chain tension weight in the database. The real-time cumulative operation duration, cumulative operation start times, cumulative power consumption, historical average vibration amplitude, current value, voltage value, operation speed, and step chain tension are respectively input into the mapping set to obtain the cumulative operation duration weight, cumulative operation start times weight, cumulative power consumption weight, historical average vibration amplitude weight, current value weight, voltage value weight, operation speed weight, and step chain tension weight, and the mapping relationship therein is one-to-one correspondence.

[0049] It should also be noted that, based on the component operation parameter sets and reference component operation parameter sets of each operating escalator at each station on each line, the component operation anomaly evaluation indicators for each operating escalator at each station on each line are obtained through analysis and processing. This is taking into account the influence among these parameters. For example, as the cumulative operation duration increases and the cumulative number of operation starts increases, each component of the escalator will gradually experience fatigue and wear. For example, during the long-term and frequent operation of the step chain, the wear between the chain links will intensify, which will cause a change in the tension of the step chain. Long-time and high-frequency operation will cause the components to generate more heat, resulting in an increase in the cumulative power consumption. When the tension of the step chain is too loose, the steps may experience jamming during operation, and the motor needs to frequently adjust the output torque to overcome this jamming, resulting in a large current fluctuation. The change in the operating speed of the escalator will affect the load characteristics of the motor, and thus affect the current and voltage. When the required operating speed increases, the motor needs to output a larger torque to drive the escalator, which will cause the current to rise. A relatively large historical average vibration amplitude indicates that there are many unstable factors during the operation of the escalator. Long-term vibration will accelerate the loosening and wear of the components. For example, in a vibrating environment, the fixing bolts of the handrail belt drive device may gradually loosen, resulting in a change in the speed difference between the handrail belt and the steps. Vibration will also affect the reliability of the electrical connection. The connection line may experience poor contact during vibration, which will cause an increase in resistance and may cause fluctuations in the current value. A large vibration amplitude will make the force on the step chain uneven, further intensifying the change in the tension of the step chain. At the same time, it will also affect the operating stability of the rollers and accelerate the wear of the rollers. After the rollers are worn, the fit between them and the guide rail deteriorates, which will in turn increase the vibration amplitude.

[0050] In a specific embodiment, the component operation anomaly evaluation indicators for each operating escalator at each station on each line can provide a quantitative evaluation basis for the operating state of the escalator. By comprehensively considering multiple parameters, it can comprehensively reflect the operating conditions of each component of the escalator, which helps the operation and maintenance personnel quickly and accurately determine whether the escalator is in a normal operating state and whether there are potential abnormal risks. For example, if the component operation anomaly evaluation indicator is at a relatively low level, it indicates that each component of the escalator is operating stably, and maintenance can be carried out according to the regular plan, reasonably allocating operation and maintenance resources to avoid waste of resources caused by over-maintenance. Secondly, this indicator can give early warnings of potential failures, thus effectively reducing the probability of sudden failures, ensuring the safe and reliable operation of the escalator, reducing the downtime caused by failures, and improving the overall operating efficiency of the escalator.

[0051] Extract the first verification threshold for component operation anomalies and the second verification threshold for component operation anomalies preset in the database.

[0052] It should be understood that the first verification threshold for component operation anomalies is less than the second verification threshold for component operation anomalies.

[0053] It should be noted that the first verification threshold for abnormal component operation and the second verification threshold for abnormal component operation are extracted from the database and are critical indicators used to characterize whether the escalator operation status is abnormal and the degree of abnormality.

[0054] In a specific embodiment, the setting of the first verification threshold for abnormal component operation and the second verification threshold for abnormal component operation can be determined through a large amount of actual escalator operation test data. By obtaining the operation information of escalators with different usage durations under various working conditions and conducting detailed analysis, the boundary values of the state parameters when the escalator can operate stably in different situations are determined. These boundary values are comprehensively processed, such as taking the average value, weighted average, or setting a certain safety margin according to the actual situation, to obtain the first verification threshold for abnormal component operation and the second verification threshold for abnormal component operation.

[0055] If the abnormal component operation evaluation index of an operating escalator is less than the first verification threshold for abnormal component operation, the operation status label information thereof is recorded as the first operation label. If the abnormal component operation evaluation index of an operating escalator is greater than or equal to the first verification threshold for abnormal component operation and less than or equal to the second verification threshold for abnormal component operation, the operation status label information thereof is recorded as the second operation label. If the abnormal component operation evaluation index of an operating escalator is greater than the second verification threshold for abnormal component operation, the operation status label information thereof is recorded as the third operation label.

[0056] It should be understood that if the operation anomaly evaluation index of a component operating an escalator is less than the first verification threshold for component operation anomaly, it indicates that the current operating conditions of each component of the escalator are good, the fluctuations of various parameters are within the better range of the normal range, the working states of all components are stable, the possibility of failure is extremely low, the overall operation is in a normal state, and it can operate continuously safely and reliably. If the operation anomaly evaluation index of a component operating an escalator is greater than or equal to the first verification threshold for component operation anomaly and less than or equal to the second verification threshold for component operation anomaly, it indicates that some signs worthy of attention have appeared in the operation of the escalator components, and there may be some potential influencing factors causing the component operation parameters to start deviating from the normal range, but it has not reached the level of seriously affecting operation safety and performance. It is necessary to strengthen the monitoring of its operation status, such as increasing the monitoring frequency and the monitoring depth of some key parameters. If the operation anomaly evaluation index of a component operating an escalator is greater than the second verification threshold for component operation anomaly, it indicates that a relatively serious abnormal situation has occurred in the operation of the escalator components, the performance of the components has significantly declined, and the changes in its operation parameters have exceeded the normal allowable range. At this time, there are relatively large safety risks for the escalator to continue operating, and it may malfunction or even cause a safety accident at any time. Emergency measures such as stopping the machine must be taken immediately, and professional maintenance personnel should be arranged for a comprehensive and in-depth overhaul.

[0057] In a specific embodiment, by analyzing the operation status label information of each operating escalator at each station on each line, the scientificity and accuracy of escalator operation management can be improved. By accurately dividing the operation status labels, escalators in different states can be quickly screened out, enabling the operation and maintenance personnel to quickly focus on the abnormal escalators and improving the operation and maintenance efficiency. Secondly, it provides a solid data basis for subsequent monitoring and decision-making, avoiding the limitations of subjective experience judgment. Escalators with the first operation label can maintain regular monitoring and reasonably allocate resources. Escalators with the second operation label trigger secondary redundant monitoring to deeply investigate potential hazards. Escalators with the third operation label can be processed promptly and specifically to prevent the deterioration of faults, ultimately achieving real-time control of the operation status of escalators, reducing operation and maintenance costs, and enhancing the stability and safety of the overall operation.

[0058] S3. Conduct secondary redundant monitoring on each monitored abnormal operating escalator at each station on each line, analyze the secondary redundant monitoring abnormal comparison analysis index of each monitored abnormal operating escalator at each station on each line, determine the maintenance requirement label of the abnormal operating escalator, and upload it to the network-level cloud platform for early warning and reminder. Synchronously screen each escalator requiring maintenance at each station on each line, and generate a maintenance management signal to be transmitted to the network-level cloud platform for reminder.

[0059] In this embodiment, secondary redundant monitoring is performed on each monitored abnormally operating escalator at each station on each line. The specific process is as follows: Based on the operation status label information of each operating escalator at each station on each line, each operating escalator with the operation status label information being the second operation label in each station on each line is extracted and marked as each monitored abnormally operating escalator at each station on each line.

[0060] According to the component operation abnormal evaluation indexes of each operating escalator at each station on each line, the component operation abnormal evaluation indexes corresponding to each monitored abnormally operating escalator at each station on each line are extracted therefrom and marked as the component operation abnormal evaluation indexes of each monitored abnormally operating escalator at each station on each line.

[0061] The difference processing is performed on the component operation abnormal evaluation indexes of each monitored abnormally operating escalator at each station on each line and the second verification threshold of component operation abnormal, and the component operation abnormal deviation factor of each monitored abnormally operating escalator at each station on each line is obtained.

[0062] It should be understood that the difference processing refers to subtracting the second verification threshold of component operation abnormal from the component operation abnormal evaluation indexes of each monitored abnormally operating escalator at each station on each line.

[0063] It should also be understood that the component operation abnormal deviation factor of each monitored abnormally operating escalator at each station on each line helps to more accurately measure the degree of component operation abnormal of the escalator, provides a clear adjustment basis for subsequent secondary redundant monitoring, and maps with the monitoring adjustment parameters corresponding to each component operation abnormal deviation factor interval to obtain the secondary redundant monitoring adjustment parameters, so that the method and focus of secondary redundant monitoring can be flexibly adjusted according to the actual abnormal degree of the escalator. For example, if the deviation factor is large, it indicates that the component operation of the escalator is relatively serious. The monitoring frequency and depth of key components and vulnerable components can be increased in the secondary redundant monitoring to timely discover potential serious fault hazards, avoid the further deterioration of the faults, effectively improve the safety and reliability of the escalator operation, and also help to reasonably allocate maintenance resources to avoid unnecessary over-monitoring or insufficient maintenance.

[0064] The monitoring adjustment parameters corresponding to each component operation abnormal deviation factor interval stored in the database are extracted, and the monitoring adjustment parameters corresponding to the component operation abnormal deviation factor of each monitored abnormally operating escalator at each station on each line are mapped and extracted, and marked as the secondary redundant monitoring adjustment parameters.

[0065] Secondary redundant monitoring is performed on each monitored abnormally operating escalator at each station on each line according to the secondary redundant monitoring adjustment parameters.

[0066] It should be understood that the secondary redundant monitoring adjustment parameter refers to the adjustment of the monitoring frequency of the escalator, and the magnitude of the monitoring frequency is obtained by extracting the abnormal deviation factors of the component operation of each monitored abnormal operating escalator at each station on each line.

[0067] It should also be understood that if the extracted monitoring frequency exceeds the frequency that the monitoring device can withstand, the maximum frequency it can withstand is used for secondary redundant monitoring.

[0068] In this embodiment, the secondary redundant monitoring anomaly comparison and analysis index of each monitored abnormal operating escalator at each station on each line is analyzed. The specific analysis process is as follows: Obtain the secondary redundant monitoring operation parameters of each monitored abnormal operating escalator at each station on each line. The secondary redundant monitoring operation parameters of each monitored abnormal operating escalator at each station on each line include the roller wear amount, the speed difference between the handrail belt and the step, the braking torque of the brake, and the output frequency of the frequency converter of each monitored abnormal operating escalator at each station on each line currently.

[0069] It should be noted that the secondary redundant monitoring operation parameters of each monitored abnormal operating escalator at each station on each line are collected by the built-in sensors of the escalator.

[0070] It should also be noted that the roller wear amount refers to the degree to which the surface material of the escalator step roller gradually wears and the size gradually decreases during the operation of the escalator due to continuous friction with the guide rail and various loads it bears.

[0071] The speed difference between the handrail belt and the step refers to the difference between the running speed of the handrail belt and the running speed of the step during the operation of the escalator.

[0072] The braking torque of the brake refers to the torque generated by the braking device of the escalator to prevent the movement of the step and the handrail belt during operation.

[0073] The output frequency of the frequency converter refers to the frequency signal output by the frequency converter to the motor after converting the input fixed-frequency power supply in the escalator control system.

[0074] Simultaneously obtain the environmental interference parameters of each monitored abnormal operating escalator at each station on each line. The environmental interference parameters of each monitored abnormal operating escalator at each station on each line include the external vibration acceleration, current, voltage, and environmental noise volume of each monitored abnormal operating escalator at each station on each line currently.

[0075] It should be noted that the environmental interference parameters of each monitored abnormal operating escalator at each station on each line are collected by the escalator sensor device.

[0076] Based on the secondary redundant monitoring operation parameters of each monitored abnormally operating escalator at each station on each line and the environmental interference parameters of each monitored abnormally operating escalator at each station on each line, the secondary redundant monitoring evaluation indication parameters of each monitored abnormally operating escalator at each station on each line are obtained through analysis and processing.

[0077] The secondary redundant monitoring evaluation indication parameters of each monitored abnormally operating escalator at each station on each line are used to characterize the degree of abnormal operation of each monitored abnormally operating escalator at each station on each line during secondary redundant monitoring.

[0078] In a specific embodiment, the specific acquisition method of the secondary redundant monitoring evaluation indication parameters of each monitored abnormally operating escalator at each station on each line is as follows:

[0079] Extract the secondary redundant monitoring reference operation parameters of the escalator and the environmental interference reference parameters of the escalator stored in the database. Among them, the secondary redundant monitoring reference operation parameters of the escalator include the reference roller wear amount of the escalator, the reference speed difference between the handrail belt and the steps, the ideal brake braking torque, and the ideal frequency converter output frequency.

[0080] The environmental interference reference parameters of the escalator include the reference external vibration acceleration of the escalator, the rated current, the rated voltage, and the reference environmental noise volume.

[0081] It should be noted that the reference roller wear amount of the escalator, the reference speed difference between the handrail belt and the steps, the ideal brake braking torque, the ideal frequency converter output frequency, the reference external vibration acceleration, and the reference environmental noise volume are obtained by averaging through their corresponding historical data before the database is established.

[0082] The rated current and the rated voltage are given by the manufacturer before the escalator is installed.

[0083]

[0084] Among them, is the secondary redundant monitoring evaluation indication parameter of the kth monitored abnormally operating escalator at the jth station on the ith line, 2 is the operation index of the kth monitored abnormally operating escalator at the jth station on the ith line, is the environmental interference index of the kth monitored abnormally operating escalator at the jth station on the ith line, 2 is the roller wear amount of the kth monitored abnormally operating escalator at the jth station on the ith line, qa is the kth monitored abnormally operating escalator at the jth station on the ith line, 2 is the environmental interference index of the kth monitored abnormally operating escalator at the jth station on the ith line, is the kth monitored abnormally operating escalator at the jth station on the ith line, 2 is the roller wear amount of the kth monitored abnormally operating escalator at the jth station on the ith line, qa 0is the wear amount of the reference roller of the escalator, is the k 2 th difference between the handrail belt and the step speed of the jth station in the ith line for monitoring the escalator with abnormal operation, qb 0 is the reference difference between the handrail belt and the step speed of the escalator, is the k 2 th braking torque of the brake of the jth station in the ith line for monitoring the escalator with abnormal operation, qc 0 is the ideal braking torque of the brake of the escalator, is the k 2 th output frequency of the frequency converter of the jth station in the ith line for monitoring the escalator with abnormal operation, qd 0 is the ideal output frequency of the frequency converter of the escalator, is the k 2 th external vibration acceleration of the jth station in the ith line for monitoring the escalator with abnormal operation, qf 0 is the reference external vibration acceleration of the escalator, is the k 2 th current of the jth station in the ith line for monitoring the escalator with abnormal operation, qg 0 is the rated current of the escalator, is the k 2 th voltage of the jth station in the ith line for monitoring the escalator with abnormal operation, qh 0 is the rated voltage of the escalator, is the k 2 th environmental noise volume of the jth station in the ith line for monitoring the escalator with abnormal operation, qp 0 is the reference environmental noise volume of the escalator, y 1 is the operation index weight value, y 2 is the environmental interference index weight value, i is the number of each line, i = 1, 2,..., m 1 , m 1 is the number of lines, j is the number of each station, j = 1, 2,..., m 2 , m 2 is the number of stations, k 2 is the number of each escalator with abnormal operation monitored, k 2 = 1, 2,..., m 3 , m 3 is the number of escalators with abnormal operation monitored, e is the natural constant.

[0085] It should be understood that the softsign function is a built-in function in Pytnon,

[0086] It should be noted that the value ranges of both the operation index weight and the environmental interference index weight are between 0 and 1. In this embodiment, the operation index weight is a value representing the influence degree of the operation index on the secondary redundant monitoring and evaluation index parameters of each monitored abnormal operation escalator at each station on each line, and the environmental interference index weight is a value representing the influence degree of the environmental interference index on the secondary redundant monitoring and evaluation index parameters of each monitored abnormal operation escalator at each station on each line. When in use, the preset operation index weight and environmental interference index weight can be directly obtained from the database, and their corresponding relationship can be a pre-set mapping relationship. For example, the operation index and the environmental interference index respectively form a mapping set with the preset operation index weight and environmental interference index weight in the database, and the real-time operation index and environmental interference index are respectively input into the mapping set to obtain the operation index weight and the environmental interference index weight, and the mapping relationship therein is one-to-one correspondence.

[0087] It should also be noted that, based on the secondary redundant monitoring operation parameters of each monitored abnormally operating escalator at each station on each line and the environmental interference parameters of each monitored abnormally operating escalator at each station on each line, the secondary redundant monitoring evaluation indication parameters of each monitored abnormally operating escalator at each station on each line are obtained through analysis and processing. This is considering the mutual influence among these parameters. For example, an increase in the roller wear amount will lead to an increase in the running resistance of the escalator. Since the roller is a key support and guiding component for the step operation, after wear, its surface becomes uneven, and the friction with the guide rail increases. This will cause the drive motor to output a greater torque to maintain the normal operation of the step, thereby causing the current to rise. The roller wear may also affect the running stability of the step, and further lead to a change in the speed difference between the handrail belt and the step. The worn roller may cause the step to have slight shaking or jamming during operation, while the operation of the handrail belt is relatively independent. In this case, the speed difference between the handrail belt and the step may exceed the normal range. When the speed difference between the handrail belt and the step is too large, in order to adjust the speed difference, the control system may adjust the output frequency of the frequency converter. If the speed difference is too large, the frequency converter may increase the output frequency to try to speed up the handrail belt or the step to make them match. When the braking torque of the brake is insufficient, the escalator may not be able to brake in time during the stop process, resulting in an overly long sliding distance of the step. This may cause abnormal vibration due to excessive friction between the step and components such as the comb plate, thereby affecting the external vibration acceleration. The output frequency of the frequency converter directly determines the rotational speed of the motor, thus affecting the running speeds of the step and the handrail belt. When the output frequency changes, the speeds of the step and the handrail belt will also change accordingly, which will affect the speed difference between the handrail belt and the step. At the same time, the change in the output frequency of the frequency converter will affect the load characteristics of the motor, and further affect the current and voltage. When the frequency increases, the motor speed increases, the load may increase correspondingly, the current may rise, and the voltage may also fluctuate due to factors such as the counter electromotive force of the motor. Too low voltage may cause the motor to fail to start normally or have an unstable rotational speed during operation, thereby affecting the running speeds of the step and the handrail belt and causing a change in the speed difference between the handrail belt and the step.

[0088] In a specific embodiment, analyzing the secondary redundant monitoring evaluation indication parameters of each monitored abnormally operating escalator at each station on each line can more accurately grasp the actual operating state of the escalator, avoid the one-sidedness brought by single-parameter monitoring, and the indication parameters obtained by comprehensively evaluating these parameters help to timely detect potential fault hazards, take targeted measures in advance, improve the safety and reliability of the escalator operation, reduce sudden shutdown events caused by faults, and at the same time provide a more scientific and accurate basis for maintenance work, optimize the allocation of maintenance resources, and reduce maintenance costs.

[0089] The operation abnormality assessment correction coefficient corresponding to the operation abnormality assessment index interval of each component stored in the database is extracted, and the operation abnormality assessment correction coefficient corresponding to the component operation abnormality assessment index of each monitored abnormal operation escalator at each station in each line is mapped and extracted, and marked as the operation abnormality assessment correction coefficient of each monitored abnormal operation escalator at each station in each line.

[0090] Based on the secondary redundant monitoring and evaluation indicator parameters of each monitored abnormal operation escalator in each station on each line and the operation abnormality evaluation correction coefficient of each monitored abnormal operation escalator in each station on each line, the secondary redundant monitoring anomaly comparison analysis index of each monitored abnormal operation escalator in each station on each line is obtained through analysis and processing.

[0091] The secondary redundant monitoring anomaly comparison and analysis index of each monitored abnormal operation escalator at each station in each line is used to characterize the comprehensive abnormality degree of the two monitorings of each monitored abnormal operation escalator at each station in each line.

[0092] In a specific embodiment, the secondary redundant monitoring anomaly comparison and analysis index of each abnormally running escalator monitored at each station in each line is obtained in the following manner:

[0093]

[0094] in, is the kth station of the jth station in the i-th line 2 Monitoring abnormal operation of escalators secondary redundant monitoring abnormal comparison analysis index, is the kth station of the jth station in the i-th line 2 A secondary redundant monitoring and evaluation indicator parameter for monitoring abnormal escalator operation, is the kth station of the jth station in the i-th line 2 The operation abnormality assessment correction coefficient of the abnormal operation escalator monitored, i is the number of each line, i = 1, 2, ..., m 1 , m 1 is the number of lines, j is the number of stations, j = 1, 2, ..., m 2 , m 2 is the number of stations, k 2 is the number of each escalator monitored for abnormal operation, k 2 =1,2,...,m 3 , m 3 To monitor the number of abnormally operating escalators, e is a natural constant.

[0095] In a specific embodiment, by analyzing the secondary redundant monitoring anomaly comparison analysis index of each monitored abnormal operation escalator at each station on each line, the operating condition of the escalator can be more comprehensively reflected. Compared with single monitoring data, it takes into account the abnormal conditions of the initial monitoring and the influence of various operating parameters and environmental interference factors during the secondary redundant monitoring, providing a more scientific and reliable basis for judgment for maintenance personnel. It can promptly screen out escalators with serious potential safety hazards, give early warnings and arrange precise maintenance work to avoid the occurrence of faults. At the same time, it also helps to reasonably plan maintenance resources, avoid over-maintaining escalators with relatively good operating conditions, improve the efficiency of maintenance work, reduce maintenance costs, and enhance the safety, reliability and operating efficiency of the entire escalator system.

[0096] In a specific embodiment, by judging the maintenance demand label of the abnormal operation escalator, the maintenance strategy of the escalator can be optimized, the pertinence and effectiveness of the maintenance work can be improved, resource waste can be reduced, and the utilization rate of the escalator can be increased. For escalators that require maintenance, early warning reminders can be sent in a timely manner to prompt maintenance personnel to respond quickly and give priority to arranging maintenance for them, effectively reducing the probability of sudden escalator failures, improving the overall efficiency and quality of the maintenance work, and thus enhancing the reliability and stability of the entire escalator system.

[0097] In this embodiment, it is uploaded to the line-level cloud platform for early warning reminders. The uploaded content includes the fault information and maintenance demand information of each monitored abnormal operation escalator at each station on each line. In a specific embodiment, the line-level cloud platform can recommend a maintenance plan based on the fault information and maintenance demand information of each monitored abnormal operation escalator at each station, and monitor the maintenance process of the abnormal operation escalator.

[0098] In this embodiment, the process of judging the maintenance demand label of the abnormal operation escalator is as follows: Extract the preset secondary redundant monitoring anomaly verification threshold in the database.

[0099] It should be noted that the secondary redundant monitoring anomaly verification threshold of the escalator is preset in the database and is a critical index used to measure whether the secondary redundant monitored escalator is faulty.

[0100] In a specific embodiment, there are multiple ways to set the abnormal verification threshold for the secondary redundancy monitoring of escalators. It can be determined through a large amount of actual operation monitoring data. Under different conditions such as passenger flow, operation duration, and load conditions, various operation status parameters of the escalator are collected, and these data are analyzed in detail to determine the boundary values of the status parameters for the normal operation of the escalator and ensuring safety performance in different situations. These boundary values are comprehensively processed, such as taking the average value, weighted average, or setting a certain safety margin according to the actual situation, and then the abnormal verification threshold for the secondary redundancy monitoring of the escalator is obtained.

[0101] If the abnormal index of the secondary redundancy monitoring of a monitored abnormal operation escalator is less than the abnormal verification threshold of the secondary redundancy monitoring of the escalator, the maintenance requirement label of the abnormal operation escalator is marked as no maintenance required. If the abnormal index of the secondary redundancy monitoring of a monitored abnormal operation escalator is greater than or equal to the abnormal verification threshold of the secondary redundancy monitoring of the escalator, the maintenance requirement label of the abnormal operation escalator is marked as maintenance required.

[0102] If the abnormal index of the secondary redundancy monitoring of a monitored abnormal operation escalator is less than the abnormal verification threshold of the secondary redundancy monitoring of the escalator, it indicates that although the escalator was initially determined to be in abnormal operation and underwent secondary redundancy monitoring, after a more in-depth and detailed secondary redundancy monitoring evaluation, its various operation parameters and comprehensive abnormal degree are still in a relatively mild range. Although the current operation state has some fluctuations, it has not reached the level that requires immediate comprehensive maintenance intervention. These minor parameter changes may be caused by some short-term and accidental factors (such as short-term external environmental interference, instantaneous changes in passenger flow, etc.). The key components and overall performance of the escalator can still maintain basic normal operation and will not have an obvious impact on safety and normal use in the short term. At this time, the operation state can continue to be closely observed, and relevant data can be recorded for subsequent analysis to determine whether there is a further change trend. Comprehensive maintenance work is not arranged temporarily to avoid unnecessary shutdowns and resource investments and ensure the utilization rate of the escalator.

[0103] If the abnormal index of the secondary redundancy monitoring of a monitored abnormal operation escalator is greater than or equal to the abnormal verification threshold of the secondary redundancy monitoring of the escalator, it indicates that the comprehensive operation condition of the escalator after secondary redundancy monitoring is poor, and its abnormal degree is relatively serious. It may involve problems such as a decline in the performance of key components, increased wear, and unstable system operation. Continuing to operate poses a significant safety hazard and may cause the escalator to malfunction and stop operating at any time, even endangering the safety of passengers. In this case, professional maintenance personnel must be arranged to conduct a comprehensive inspection and maintenance of the escalator as soon as possible, replace damaged or substandard components in a timely manner, adjust system parameters, eliminate safety hazards, and restore the escalator to a normal, safe, and reliable operation state.

[0104] S4, the in-situ intelligent analysis and early warning system receives the signal indicating the completion of maintenance. Thereby, it counts the escalators that have been maintained at each station on each line, synchronously obtains the component operation parameters of the escalators that have been maintained at each station on each line, and analyzes the maintenance verification indicators of the escalators that have been maintained at each station on each line for corresponding early warning processing.

[0105] In this embodiment, the maintenance verification indicators of the escalators that have been maintained at each station on each line are analyzed. The specific analysis process is as follows: According to the component operation parameters of the escalators that have been maintained at each station on each line, the operation state indication parameters of the escalators that have been maintained at each station on each line are obtained through analysis and processing.

[0106] In a specific embodiment, the operation state indication parameters of the escalators that have been maintained at each station on each line are obtained in the following specific manner:

[0107]

[0108] Among them, is the operation state indication parameter of the k 3 th escalator that has been maintained at the jth station on the ith line, is the current value of the k 3 th escalator that has been maintained at the jth station on the ith line, f 0 is the rated current of the escalator, is the voltage value of the k 3 th escalator that has been maintained at the jth station on the ith line, g 0 is the rated voltage of the escalator, is the operating speed of the k 3 th escalator that has been maintained at the jth station on the ith line, h 0 is the ideal operating speed of the escalator, is the tension force of the step chain of the k 3 th escalator that has been maintained at the jth station on the ith line, p 0 is the ideal tension force of the step chain of the escalator, x 5 is the weight value of the current value, x 6 is the weight value of the voltage value, x 7 is the weight value of the operating speed, x 8 is the weight value of the tension force of the step chain. i is the number of each line, i = 1, 2,..., m 1 , m 1 is the number of lines, j is the number of each station, j = 1, 2,..., m 2 , m 2 is the number of stations, k 3 is the number of each escalator that has been maintained, k 3= 1, 2, ..., m 5 , m 4 where m is the number of escalators that have been maintained, and e is the natural constant.

[0109] Synchronously extract the initial operating parameters of the components corresponding to each maintained escalator among the operating escalators at each station on each line, denoted as the preliminary component operating parameters of each maintained escalator at each station on each line, and analyze to obtain the preliminary operating state indication parameters of each maintained escalator at each station on each line.

[0110] In a specific embodiment, the preliminary operating state indication parameters of each maintained escalator at each station on each line are obtained by extracting the current operating anomaly evaluation factors of the components of the maintained escalators from the current operating anomaly evaluation factors of the components of the operating escalators at each station on each line.

[0111] Perform a difference process on the operating state indication parameters of each maintained escalator at each station on each line and the preliminary operating state indication parameters of each maintained escalator at each station on each line to obtain the maintenance verification indicators of each maintained escalator at each station on each line.

[0112] The said difference process refers to subtracting the preliminary operating state indication parameters of each maintained escalator at each station on each line from the operating state indication parameters of each maintained escalator at each station on each line, and the result of the difference process can be greater than zero, less than zero, or equal to zero.

[0113] The maintenance verification indicators of each maintained escalator at each station on each line are used to characterize the maintenance effect of each maintained escalator at each station on each line.

[0114] In a specific embodiment, through the maintenance verification indicators of each maintained escalator at each station on each line, the actual effect of the maintenance work can be comprehensively evaluated. This indicator is a quantitative manifestation of the change in the operating parameters of the escalator components before and after maintenance. When the maintenance verification indicator is less than zero, it indicates that the maintenance work has precisely solved the previous problems of the escalator, the key operating parameters have been optimized, and the escalator performance has been improved, providing a successful example and experience for subsequent maintenance work. If the maintenance verification indicator is greater than or equal to zero, it means that the maintenance effect fails to meet the expectation, and there may be problems such as inaccurate fault judgment, improper component replacement, or introduction of new interference. At this time, a warning message is generated and uploaded to the cloud platform, and the escalator is shut down to avoid more serious faults caused by improper maintenance.

[0115] In this embodiment, the corresponding warning processing is performed by analyzing the maintenance verification indicators of each maintained escalator at each station on each line, and the specific process is as follows:

[0116] If the maintenance verification index of a maintained escalator is less than zero, it is directly uploaded to the network-level cloud platform for display.

[0117] If the maintenance verification index of a maintained escalator is greater than or equal to zero, the maintained escalator is shut down, a warning message is generated synchronously and uploaded to the network-level cloud platform for display.

[0118] If the maintenance verification index of a maintained escalator is less than zero, it indicates that this maintenance work is relatively successful, precisely targeting the previous problems of the escalator, resulting in significant optimization of the key operating parameters of the escalator. For example, it may be that severely worn components are replaced, the operating parameters of the system are adjusted, so that the overall performance of the escalator is improved, and the operation is more smooth and stable. In this case, directly uploading the information for display is, on the one hand, a positive feedback on the maintenance work, and at the same time provides a successful case and experience reference for subsequent similar maintenance work.

[0119] If the maintenance verification index of a maintained escalator is greater than or equal to zero, it indicates that the maintenance effect fails to meet the expectation and there are certain problems. In order to reduce the operation risk of the maintained escalator, it needs to be shut down, a warning message is generated synchronously and uploaded to the network-level cloud platform for display, which can timely remind relevant personnel to re-examine the maintenance work, conduct in-depth inspection and evaluation of the escalator. At the same time, through in-depth analysis and summary of such situations, the maintenance strategy and process can be continuously optimized, the quality and level of the maintenance work can be improved, and similar problems can be prevented from recurring in the maintenance process of other escalators.

[0120] Please refer to Figure 3 As shown, the embodiment of the present invention provides a prediction and warning system architecture for escalators of a network-level cloud platform, including:

[0121] The preliminary monitoring module for operating escalators is used to count each operating escalator in each station of each line, conduct preliminary monitoring on the operating escalators through the on-site intelligent analysis and warning system, obtain the component operating parameter sets of each operating escalator in each station of each line, and synchronously count and upload them to the line-level cloud platform.

[0122] The operating status label information analysis module for operating escalators is used for the line-level cloud platform to analyze the operating status label information of each operating escalator in each station of each line based on the component operating parameter sets of each operating escalator in each station of each line, screen each monitored abnormal operating escalator in each station of each line, and synchronously upload it to the network-level cloud platform for display based on the operating status label information of each operating escalator in each station of each line.

[0123] The secondary redundant monitoring module for escalators with abnormal operation monitoring is used to conduct secondary redundant monitoring on escalators with abnormal operation monitoring at each station on each line, analyze the secondary redundant monitoring anomaly comparison analysis index of escalators with abnormal operation monitoring at each station on each line, determine the maintenance requirement label for escalators with abnormal operation, and upload it to the network-level cloud platform for early warning reminder. Meanwhile, it screens out escalators requiring maintenance at each station on each line and generates a maintenance management signal to be transmitted to the network-level cloud platform for reminder.

[0124] The maintenance verification module for maintained escalators is used to receive the maintenance completion signal by the on-site intelligent analysis and early warning system, thereby counting the maintained escalators at each station on each line, synchronously obtaining the component operation parameters of the maintained escalators at each station on each line, and analyzing the maintenance verification indicators of the maintained escalators at each station on each line for corresponding early warning processing.

[0125] Please refer to Figure 4 As shown, it is the architecture diagram of the escalator prediction and early warning system of the network-level cloud platform involved in the embodiment of the present invention. In this embodiment, the on-site intelligent analysis and early warning system is installed in the first part of the escalator control cabinet. The line-level cloud platform is used to analyze escalator-related parameters for prediction and early warning, and the network-level cloud platform is used to display relevant information of escalators in operation at each station on each line, including operation information, early warning information, maintenance information, etc.

[0126] In a specific embodiment, by providing a method and system architecture for escalator prediction and early warning of the network-level cloud platform, it is possible to achieve comprehensive and accurate monitoring and early warning of the operating status of escalators, effectively improving the safety and reliability of escalators. Through comprehensive multi-parameter collection and analysis, potential fault hazards can be captured in advance, reducing the risk of sudden failures. The phased monitoring and early warning mechanism can targetedly handle abnormal escalators, improving the timeliness and accuracy of early warning. At the same time, the optimized maintenance management strategy ensures the reasonable allocation of maintenance resources, improves the scientific nature of maintenance effect evaluation, reduces maintenance costs, and improves the overall operating efficiency of escalators.

[0127] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0128] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can well understand and utilize the present invention. As long as it does not deviate from the structure of the present invention or exceed the scope defined by the present invention, it should fall within the protection scope of the present invention.

Claims

1. A network-level cloud platform escalator prediction and early warning method, characterized in that: The following steps are involved: S1, count the escalators in operation at each station on each line, conduct preliminary monitoring of the escalators in operation through the local intelligent analysis and early warning system, obtain the component operation parameter set of each escalator in operation at each station on each line, and synchronously upload the statistics to the line-level cloud platform; S2, the line-level cloud platform analyzes the operating status label information of each escalator in operation at each station in each line based on the component operating parameter set of each escalator in operation at each station in each line, and screens each monitored abnormally operating escalator at each station in each line, and simultaneously uploads the operating status label information of each escalator in operation at each station in each line to the line network-level cloud platform for display; S3: Perform secondary redundant monitoring on each abnormally running escalator monitored at each station on each line, analyze the secondary redundant monitoring abnormal comparison analysis index of each abnormally running escalator monitored at each station on each line, determine the maintenance demand label of the abnormally running escalator, and upload it to the line network-level cloud platform for early warning reminders, simultaneously screen each maintenance-demanding escalator at each station on each line, and generate a maintenance management signal to be transmitted to the line network-level cloud platform for reminders; S4, the on-site intelligent analysis and early warning system receives the maintenance end signal, thereby counting the maintained escalators in each station on each line, synchronously obtaining the component operating parameters of each maintained escalator in each station on each line, analyzing the maintenance verification indicators of each maintained escalator in each station on each line and performing corresponding early warning processing.

2. According to claim 1, a network-level cloud platform escalator prediction and early warning method is characterized by: The component operation parameter set of each escalator in operation at each station in each line includes component historical operation parameters and component initial operation parameters; The component historical operating parameters include the cumulative operating time, cumulative operating start times, cumulative power consumption and historical average vibration amplitude of each escalator in operation at each station on each line; The initial operating parameters of the components include the current current value, voltage value, operating speed and step chain tension of each running escalator at each station in each line.

3. According to claim 2, a network-level cloud platform escalator prediction and early warning method is characterized by: The specific acquisition method of analyzing the running status tag information of each running escalator at each station in each line is as follows: The running status label information includes a first running label, a second running label and a third running label; extracting a reference component operating parameter set stored in a database; Based on the component operation parameter set of each escalator in operation at each station in each line and the reference component operation parameter set, analyzing and processing to obtain the component operation abnormality evaluation index of each escalator in operation at each station in each line; The component operation abnormality evaluation index of each escalator in operation at each station in each line is used to characterize the degree of operation abnormality of each escalator in operation during the preliminary monitoring period, and serves as a numerical basis for monitoring the trend degradation of each escalator component at each station in each line; Extracting a first component operation abnormality verification threshold and a second component operation abnormality verification threshold preset in a database; If the component operation abnormality assessment index of a running escalator is less than the first component operation abnormality verification threshold, its operation status label information is recorded as the first operation label; if the component operation abnormality assessment index of a running escalator is greater than or equal to the first component operation abnormality verification threshold and less than or equal to the second component operation abnormality verification threshold, its operation status label information is recorded as the second operation label; if the component operation abnormality assessment index of a running escalator is greater than the second component operation abnormality verification threshold, its operation status label information is recorded as the third operation label.

4. According to claim 3, a network-level cloud platform escalator prediction and early warning method is characterized by: The specific process of performing secondary redundant monitoring on each abnormally running escalator at each station in each line is as follows: Based on the running status label information of each running escalator at each station in each line, each running escalator with the running status label information of the second running label at each station in each line is extracted, and marked as each monitored abnormal running escalator at each station in each line; According to the component operation abnormality evaluation index of each escalator in operation at each station in each line, the component operation abnormality evaluation index corresponding to each monitored abnormal operation escalator at each station in each line is extracted and marked as the component operation abnormality evaluation index of each monitored abnormal operation escalator at each station in each line; Performing difference processing on the component operation abnormality evaluation index and the component operation abnormality second verification threshold of each monitored abnormal operation escalator at each station in each line, to obtain the component operation abnormality deviation factor of each monitored abnormal operation escalator at each station in each line; Extract the monitoring and adjustment parameters corresponding to the abnormal operation deviation factor intervals of each component stored in the database, and map and extract the monitoring and adjustment parameters corresponding to the abnormal operation deviation factors of each component of each abnormal operation escalator monitored at each station in each line, and mark them as secondary redundant monitoring and adjustment parameters; According to the secondary redundant monitoring adjustment parameters, secondary redundant monitoring is performed on each abnormally operating escalator monitored at each station on each line.

5. According to claim 4, a network-level cloud platform escalator prediction and early warning method is characterized in that: The analysis of the abnormal comparison analysis index of the secondary redundant monitoring of each abnormally running escalator at each station in each line is as follows: Obtain the secondary redundant monitoring operation parameters of each abnormally running escalator monitored at each station in each line, including the roller wear amount, handrail and step speed difference, brake torque and inverter output frequency of each abnormally running escalator monitored at each station in each line; Synchronously obtain the environmental interference parameters of each monitored abnormal operation escalator at each station in each line, wherein the environmental interference parameters of each monitored abnormal operation escalator at each station in each line include the external vibration acceleration, current, voltage and environmental noise volume of each monitored abnormal operation escalator at each station in each line; Based on the secondary redundant monitoring operation parameters of each abnormally operated escalator monitored at each station in each line and the environmental interference parameters of each abnormally operated escalator monitored at each station in each line, the secondary redundant monitoring evaluation indicator parameters of each abnormally operated escalator monitored at each station in each line are obtained by analysis and processing; The secondary redundant monitoring evaluation indicator parameter of each abnormally operated escalator monitored at each station in each line is used to characterize the degree of abnormal operation of each abnormally operated escalator monitored at each station in each line during secondary redundant monitoring; Extract the operation abnormality assessment correction coefficient corresponding to the operation abnormality assessment index interval of each component stored in the database, and map and extract the operation abnormality assessment correction coefficient corresponding to the component operation abnormality assessment index of each monitored abnormal operation escalator at each station in each line, and mark it as the operation abnormality assessment correction coefficient of each monitored abnormal operation escalator at each station in each line; Based on the secondary redundant monitoring evaluation indicator parameters of each abnormally operating escalator monitored at each station in each line and the operation abnormality evaluation correction coefficient of each abnormally operating escalator monitored at each station in each line, the secondary redundant monitoring abnormality comparison analysis index of each abnormally operating escalator monitored at each station in each line is obtained through analysis and processing; The secondary redundant monitoring anomaly comparison and analysis index of each monitored abnormal operation escalator at each station in each line is used to characterize the comprehensive abnormality degree of the two monitorings of each monitored abnormal operation escalator at each station in each line.

6. According to claim 1, a network-level cloud platform escalator prediction and early warning method is characterized by: The specific judgment process of the abnormal operation escalator maintenance demand label is as follows: Extracting the abnormal verification threshold of the escalator secondary redundant monitoring preset in the database; If the secondary redundant monitoring abnormality index of an escalator monitoring abnormal operation is less than the secondary redundant monitoring abnormality verification threshold of the escalator, the maintenance requirement label of the abnormal escalator is marked as requiring no maintenance; if the secondary redundant monitoring abnormality index of an escalator monitoring abnormal operation is greater than or equal to the secondary redundant monitoring abnormality verification threshold of the escalator, the maintenance requirement label of the abnormal escalator is marked as requiring maintenance.

7. According to claim 6, a network-level cloud platform escalator prediction and early warning method is characterized by: The maintenance verification indicators of each maintained escalator at each station on each line are analyzed, and the specific analysis process is as follows: According to the component operation parameters of each maintained escalator at each station on each line, the operation status indicator parameters of each maintained escalator at each station on each line are obtained by analysis and processing; Synchronously extract the initial operating parameters of the components corresponding to each maintained escalator in each running escalator at each station on each line, and analyze and obtain the preliminary operating status indicator parameters of each maintained escalator at each station on each line; Performing difference processing on the operating status indicator parameters of each maintained escalator at each station on each line and the preliminary operating status indicator parameters of each maintained escalator at each station on each line to obtain the maintenance verification index of each maintained escalator at each station on each line; The maintenance verification index of each maintained escalator in each station in each line is used to characterize the maintenance effect of each maintained escalator in each station in each line.

8. According to claim 7, a network-level cloud platform escalator prediction and early warning method is characterized by: The maintenance verification indicators of each maintained escalator at each station in each line are analyzed to perform corresponding early warning processing. The specific process is as follows: If the maintenance verification index of a maintained escalator is less than zero, there is no need for early warning and it will be uploaded to the network-level cloud platform for display; If the maintenance verification index of an escalator that has been maintained is greater than or equal to zero, the escalator that has been maintained will be shut down, and warning information will be generated simultaneously and uploaded to the network-level cloud platform for display.

9. According to claim 5, a network-level cloud platform escalator prediction and early warning method is characterized by: The secondary redundant monitoring anomaly comparison and analysis index of each abnormally running escalator monitored at each station in each line is specifically obtained as follows: Among them, D ijk2 is the secondary redundant monitoring anomaly comparison analysis index of the k2th abnormal escalator at the jth station on the i-th line, C ijk2 is the secondary redundant monitoring evaluation indicator parameter of the k2th abnormal escalator at the jth station on the i-th line, α ijk2 is the operation abnormality assessment correction coefficient of the k2th monitored abnormal operation escalator in the jth station in the ith line, i is the number of each line, i=1,2,...,m1, m1 is the number of lines, j is the number of each station, j=1,2,...,m2, m2 is the number of stations, k2 is the number of each monitored abnormal operation escalator, k2=1,2,...,m3, m3 is the number of monitored abnormal operation escalators, and e is a natural constant.

10. A network-level cloud platform escalator prediction and warning system architecture, characterized by: include: The preliminary monitoring module for running escalators is used to count the running escalators at each station on each line, conduct preliminary monitoring of the running escalators through the local intelligent analysis and early warning system, obtain the component operation parameter set of each running escalator at each station on each line, and synchronously upload the statistics to the line-level cloud platform; The running status tag information analysis module of the running escalator is used for the line-level cloud platform to analyze the running status tag information of each running escalator at each station in each line based on the component running parameter set of each running escalator at each station in each line, and screen each monitored abnormal running escalator at each station in each line, and simultaneously upload the running status tag information of each running escalator at each station in each line to the line network-level cloud platform for display; The secondary redundancy monitoring module for monitoring abnormally running escalators is used to perform secondary redundancy monitoring on each abnormally running escalator at each station on each line, analyze the secondary redundancy monitoring abnormality comparison analysis index of each abnormally running escalator at each station on each line, determine the maintenance demand label of the abnormally running escalator, and upload it to the line network-level cloud platform for early warning reminders, simultaneously screen each maintenance-demanding escalator at each station on each line, and generate a maintenance management signal to be transmitted to the line network-level cloud platform for reminders; The maintenance verification module of the maintained escalator is used for receiving the maintenance end signal of the on-site intelligent analysis and early warning system, thereby counting the maintained escalators at each station in each line, synchronously obtaining the component operating parameters of each maintained escalator at each station in each line, analyzing the maintenance verification indicators of each maintained escalator at each station in each line and performing corresponding early warning processing.

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