Elevator component service life prediction method and system based on big data

Abstract

The present application relates to a method and system for predicting the service life of elevator components based on big data, and to the field of elevator maintenance technology. The method includes obtaining component standard data and component usage intervals, dividing the component usage intervals into several unit intervals and obtaining component interval usage data, and defining excess data and stable data; defining starting data and determining the excess interval with the starting data as the starting point, and determining the excess quantity of each unit interval in the excess interval according to the excess data; determining adjustment parameters according to the excess quantity; determining actual data according to the adjustment parameters and the corresponding excess data, and determining overall usage data according to the actual data and stable data; determining the overall usage time according to the component usage interval, and determining the remaining usage time according to the component standard data, the overall usage data and the overall usage time. The present application has the effect of being able to make a relatively accurate prediction of the service life of each component of the elevator.

Description

Technical Field

[0001] The present application relates to the field of elevator maintenance technology, and in particular to a method and system for predicting the service life of elevator components based on big data. Background Art

[0002] Each component of the elevator has a corresponding service life during use. Before the component reaches the end of its service life, the component needs to be replaced to reduce abnormal situations in the elevator and improve the safety of elevator use. Therefore, it is necessary to predict the service life of each component of the elevator.

[0003] In the related art, when predicting the service life of various components of an elevator, the usage data of the components (including the number of times used, the length of time used, the load weight, etc.) is generally considered. Taking the traction wire rope as an example, when predicting the service life of the traction wire rope, the ratio of the elevator's usage data to the preset standard data is calculated to determine the working fatigue of the traction wire rope and then predict the specific service life.

[0004] In the above-mentioned related technologies, when performing life prediction, each usage data is compared and analyzed with the corresponding standard data to determine the fatigue status of the components, but different usage conditions of the components will lead to different corresponding fatigue conditions. For example, multiple heavy-weight cargo transportations are required within a predicted time period. The impact of continuous transportation of cargo without time intervals and separate transportation of cargo with time intervals on the components is absolutely different. However, from the perspective of the overall time period analysis, the usage data of the elevator is the same. At this time, the overall usage status of the elevator is not accurately determined, and it is impossible to accurately predict the service life of each component of the elevator. Summary of the invention

[0005] In order to more accurately predict the service life of various elevator components, the present application provides a method and system for predicting the service life of elevator components based on big data.

[0006] In the first aspect, the present application provides a method for predicting the service life of elevator components based on big data, which adopts the following technical solution:

[0007] A method for predicting the service life of elevator components based on big data, comprising:

[0008] Get the component installation time and installed component type;

[0009] Determine component standard data corresponding to the installed component type according to a preset standard matching relationship;

[0010] Establish a component usage interval based on the component installation time and the current time point, and divide the component usage interval into several unit intervals according to the preset unit time length;

[0011] Obtaining component interval usage data in a unit interval, defining component interval usage data greater than a preset set limit data as excess data, and defining the remaining component interval usage data as stable data;

[0012] The excess data of the component interval usage data of the previous unit interval as stable data is defined as the starting data, and the unit intervals corresponding to the continuous and uninterrupted excess data are combined with the starting data as the starting point to determine the excess interval, and the excess data is counted in the excess interval to determine the excess quantity of each unit interval;

[0013] Determine the adjustment parameters corresponding to the excess quantity according to the preset adjustment matching relationship;

[0014] Calculate the actual data based on the adjustment parameters and the corresponding excess data, and sum the actual data and the stable data to determine the overall usage data;

[0015] The overall usage time is determined according to the component usage range, and the theoretical total time is determined by calculation based on the component standard data, the overall usage data and the overall usage time, and the difference between the theoretical total time and the overall usage time is calculated to determine the remaining usage time.

[0016] Optionally, after the interval usage data is determined, the elevator component service life prediction method based on big data also includes:

[0017] Calculate based on interval usage data and preset allowable deviation data to determine similar usage range;

[0018] The interval usage data within a similar usage range is defined as similar data, and the similar data is counted to determine the number of data similarities;

[0019] Count all interval usage data to determine the overall data quantity, and calculate the data similarity ratio based on the data similarity quantity and the overall data quantity;

[0020] The interval usage data in which the data similarity ratio is less than the preset benchmark demand ratio is defined as accidental data, and the overall usage data obtained after the accidental data is eliminated is defined as the overall data after elimination, and the overall usage duration obtained after the unit interval corresponding to the accidental data is eliminated is defined as the overall duration after elimination;

[0021] The unit usage value is determined by calculation based on the overall data after elimination and the overall duration after elimination, and the theoretical total duration is determined by calculation based on the unit usage value, component standard data and the overall usage data without eliminating accidental data.

[0022] Optionally, after the data similarity ratio is determined, the elevator component service life prediction method based on big data also includes:

[0023] The interval usage data whose data similarity ratio is not less than the benchmark demand ratio is defined as frequently occurring data, and the similar data within the similar usage range corresponding to the frequently occurring data is defined as bound data;

[0024] Determine the binding interval according to the unit intervals corresponding to any two binding data, and define the binding interval containing only binding data within the binding interval as a stable interval;

[0025] Counting the binding data within the stable interval to determine the stable number of interval data, and counting all the binding data to determine the overall binding number;

[0026] Calculate the proportion of stable bindings based on the number of stable data and the number of overall bindings;

[0027] Determine whether there is a situation where the proportion of stable binding is greater than the preset extreme upper limit proportion;

[0028] If there is no situation where the proportion of stable binding is greater than the proportion of the extreme upper limit, the determined frequent data is maintained;

[0029] If there is a situation where the proportion of stable binding is greater than the proportion of the extreme upper limit, the determined frequent data will be converted into accidental data.

[0030] Optionally, after the theoretical total duration is determined, the elevator component service life prediction method based on big data also includes:

[0031] Establishing a detection interval with the current time point as the end point and a width of the preset detection duration on the preset time axis;

[0032] Acquire interval usage data and overall usage data of each elevator component in a corresponding component usage interval in a detection interval, and define the interval usage data as historical interval data, and define the overall usage data as historical overall data;

[0033] Performing a summation calculation based on the current interval usage data to determine the original total interval data, and performing a summation calculation based on the historical interval data to determine the historical total interval data;

[0034] Performing a difference calculation based on the original total interval data and the historical total interval data to determine the first deviation data, and performing a difference calculation based on the historical overall data and the current overall usage data to determine the second deviation data;

[0035] Performing calculation based on the first deviation data and the second deviation data to determine a similarity value;

[0036] Determine the similarity value with the largest value according to the preset sorting rules, and obtain the actual total duration of the elevator component corresponding to the similarity value;

[0037] The actual total time and the theoretical total time are calculated to determine the comprehensive total time, and the difference between the comprehensive total time and the overall usage time is calculated to determine the remaining usage time.

[0038] Optionally, after the similarity value is determined, the elevator component service life prediction method based on big data further includes:

[0039] Determine whether the maximum similarity value is greater than a preset benchmark similarity value;

[0040] If the maximum similarity value is greater than the baseline similarity value, the actual total duration is determined;

[0041] If the maximum similarity value is not greater than the baseline similarity value, a difference calculation is performed based on the theoretical total time and the overall usage time to determine the remaining usage time.

[0042] Optionally, if the maximum similarity value is greater than the benchmark similarity value, the elevator component service life prediction method based on big data further includes:

[0043] Determine whether there are at least two elevator components with the largest similarity values ​​and are the same;

[0044] If there are not at least two elevator components with the largest similarity values ​​that are the same, the actual total duration is determined based on the unique elevator component;

[0045] If there are at least two elevator components with the largest similarity values ​​and the same value, the actual total duration corresponding to each elevator component is defined as the candidate total duration;

[0046] Randomly select one of all the alternative total durations as the fixed total duration, and calculate the deviation total duration based on the fixed total duration and the remaining alternative total durations;

[0047] The total deviation duration with the smallest value is determined according to the sorting rules, and the fixed total duration corresponding to the total deviation duration and the theoretical total duration are calculated to determine the comprehensive total duration.

[0048] Optionally, the step of calculating the comprehensive total duration based on the actual total duration and the theoretical total duration includes:

[0049] Calculate the difference between the actual total duration and the theoretical total duration to determine the actual theoretical deviation duration;

[0050] Determine whether the actual theoretical deviation duration is greater than the preset permitted duration;

[0051] If the actual theoretical deviation duration is greater than the permitted duration, the theoretical total duration shall be determined as the comprehensive total duration;

[0052] If the actual theoretical deviation duration is not greater than the permitted duration, the adjusted deviation duration is determined based on the actual theoretical deviation duration and the corresponding similarity value, and the target total duration is determined based on the actual total duration and the adjusted deviation duration;

[0053] The target total duration and the theoretical total duration are averaged to determine the overall total duration.

[0054] In the second aspect, the present application provides an elevator component service life prediction system based on big data, which adopts the following technical solutions:

[0055] A big data-based elevator component service life prediction system, comprising:

[0056] An acquisition module is used to acquire component installation time and installed component type;

[0057] A processing module, connected to the acquisition module, for storing and processing information;

[0058] The processing module determines the component standard data corresponding to the installed component type according to the preset standard matching relationship;

[0059] The processing module establishes a component usage interval according to the component installation time and the current time point, and divides the component usage interval into a plurality of unit intervals according to a preset unit time length;

[0060] The acquisition module acquires the component interval usage data in the unit interval, and enables the processing module to define the component interval usage data greater than the preset limit data as excess data, and define the remaining component interval usage data as stable data;

[0061] The processing module defines the excess data of the component interval usage data of the previous unit interval as the starting data, and combines the unit intervals corresponding to the continuous and uninterrupted excess data with the starting data as the starting point to determine the excess interval, and counts the excess data in the excess interval to determine the excess quantity of each unit interval;

[0062] The processing module determines the adjustment parameters corresponding to the excess quantity according to the preset adjustment matching relationship;

[0063] The processing module performs calculations based on the adjustment parameters and the corresponding excess data to determine the actual data, and performs summation calculations based on the actual data and the stable data to determine the overall usage data;

[0064] The processing module determines the overall usage time according to the component usage interval, and calculates according to the component standard data, the overall usage data and the overall usage time to determine the theoretical total time, and calculates the difference between the theoretical total time and the overall usage time to determine the remaining usage time.

[0065] In summary, the present application includes at least one of the following beneficial technical effects:

[0066] When predicting the life of elevator components, the current continuous large load data is analyzed and processed to obtain more accurate component usage data, so as to make a more accurate prediction of the service life of each elevator component;

[0067] When analyzing usage data, accidental data that does not have an objective regularity can be excluded, thereby further improving the accuracy of component life prediction;

[0068] The accuracy of component life prediction can be further improved by comparing and analyzing the current elevator components with the usage conditions of other elevator components. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of the elevator component service life prediction method based on big data.

[0070] Figure 2 It is a flow chart of the method for determining and eliminating accidental data.

[0071] Figure 3 It is a flow chart of the common data analysis method.

[0072] Figure 4 It is a flow chart of the reference analysis method for similar elevator components.

[0073] Figure 5 It is a flowchart of the similarity value limiting method.

[0074] Figure 6 It is a flow chart of the elevator parts screening processing method.

[0075] Figure 7 It is a flow chart of the comprehensive total duration calculation method.

[0076] Figure 8 It is a module flow chart of the elevator component service life prediction method based on big data. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 8It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0078] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0079] The embodiment of the present application discloses a method for predicting the service life of elevator components based on big data. In the process of predicting the service life of elevator components, the total time that the elevator components can be used under theoretical circumstances can be determined by combining the usage of other elevator components and the usage of the current elevator components, thereby determining the remaining available time, so as to more accurately predict the service life of the elevator components.

[0080] Reference Figure 1 The method flow of the elevator component service life prediction method based on big data includes the following steps:

[0081] Step S100: Obtain component installation time and installed component type.

[0082] The component installation time is the time when the elevator component currently required for life prediction is installed, that is, the time when the elevator component begins to be used. The installed component type is the type of elevator component installed, such as door machine belts, traction wire ropes, functional relays, etc., where the installed component type also includes the corresponding model.

[0083] Step S101: Determine component standard data corresponding to the installed component type according to a preset standard matching relationship.

[0084] The component standard data refers to the life data that the elevator component can reach under normal use. For example, the functional relay has the corresponding component standard data of the standard number of uses, that is, the number of times it can be used. Different types of installed components correspond to different component standard data. The standard matching relationship between the two is entered and stored by the staff in advance according to the conditions of each component.

[0085] Step S102: establishing a component usage interval according to the component installation time and the current time point, and dividing the component usage interval into a plurality of unit intervals according to a preset unit time length.

[0086] The component usage interval is the time interval during which the elevator component is used. The unit duration is a fixed duration set by the staff, such as 1 minute. Establishing the unit interval can effectively divide the component usage interval, which is convenient for analyzing the specific usage of elevator components at different time points.

[0087] Step S103: Obtain component interval usage data in the unit interval, and define component interval usage data greater than a preset limit data as excess data, and define the remaining component interval usage data as stable data.

[0088] Component interval usage data refers to the data used by the elevator component within a unit interval. For example, the corresponding data of a functional relay is the number of times it is used within a unit interval. The set limit data refers to the minimum component interval usage data set by the staff to determine that the elevator component is in a heavy load operation. At this time, excess data and stable data are defined to distinguish between different component interval usage data, which is convenient for subsequent analysis.

[0089] Step S104: The excess data of the component interval usage data of the previous unit interval as stable data is defined as the starting data, and the unit intervals corresponding to the continuous and uninterrupted excess data are combined with the starting data as the starting point to determine the excess interval, and the excess data is counted in the excess interval to determine the excess quantity of each unit interval.

[0090] The starting data is defined to distinguish different excess data for the convenience of subsequent analysis; the excess interval is the interval formed by the combination of unit intervals corresponding to all continuous and uninterrupted starting data determined backward from the starting data, that is, the interval formed from the unit interval corresponding to the starting data to the unit interval where stable data appears; the excess quantity is the quantity of excess data contained between each unit interval and the unit interval corresponding to the starting data. For example, if the width of the excess interval is 5 units, the excess quantity of the second unit interval is 2, and the excess quantity of the fifth unit interval is 5.

[0091] Step S105: determining adjustment parameters corresponding to the excess quantity according to a preset adjustment matching relationship.

[0092] Different excess quantities indicate different operating pressures of components. When the excess quantity is larger, it means that the component will be under heavy load for a longer time, and the loss caused by continuing to use the component will also be greater. Therefore, it is necessary to determine the corresponding adjustment parameters to adjust the excess data. Different excess quantities correspond to different adjustment parameters, and the adjustment matching relationship between the two is determined in advance by the staff; among them, the adjustment matching relationship only needs to ensure that the larger the excess quantity, the larger the adjustment parameter. Therefore, the adjustment matching relationship can be represented by, where is the adjustment parameter, and are all preset parameters for calculation, is the excess quantity, generally 1.

[0093] Step S106: Calculate according to the adjustment parameters and the corresponding excess data to determine the actual data, and perform a sum calculation according to the actual data and the stable data to determine the overall usage data.

[0094] The actual data is the data reflecting the actual wear and tear of elevator components, which is determined by multiplying the adjustment parameters by the excess data. The overall usage data is the life data of the elevator components, which is determined by adding all the actual data to all the stable data.

[0095] Step S107: Determine the overall usage time according to the component usage interval, calculate according to the component standard data, the overall usage data and the overall usage time to determine the theoretical total time, and calculate the difference between the theoretical total time and the overall usage time to determine the remaining usage time.

[0096] The overall usage time is the interval span of the component usage interval, that is, the total usage time of the elevator component. The theoretical total usage time is the usage time that the elevator component can achieve under the current elevator component usage conditions. The calculation formula is, where is the theoretical total time, is the overall usage time, is the component standard data, and is the overall usage data; the remaining usage time is the theoretical time that the elevator component can still be used, that is, the predicted service life of the elevator component, which is determined by subtracting the overall usage time from the theoretical total time.

[0097] Reference Figure 2 After the interval usage data is determined, the elevator component service life prediction method based on big data also includes:

[0098] Step S200: performing calculations based on the interval usage data and preset allowable deviation data to determine a similar usage range.

[0099] The permissible deviation data is the maximum deviation allowed when the interval usage data set by the staff is relatively similar as a whole. The similar usage range is the position of the remaining data that is relatively similar to the interval usage data, that is, the two data values ​​obtained by subtracting and adding the permissible deviation data from the interval usage data are the range determined by the endpoints.

[0100] Step S201: defining interval usage data within a similar usage range as similar data, and counting the similar data to determine the number of data similarities.

[0101] Similar data is defined to distinguish usage data of different intervals for the convenience of subsequent analysis; the number of similar data is the total number of similar data that is similar to the usage data of a single interval.

[0102] Step S202: Counting all interval usage data to determine the overall data quantity, and calculating based on the data similarity quantity and the overall data quantity to determine the data similarity ratio.

[0103] The overall data quantity is the total quantity of data used in all intervals, that is, the total quantity of unit intervals; the data similarity ratio is the overall data ratio of the remaining similar data that is similar to the current interval usage data, which is determined by dividing the data similarity quantity by the overall data quantity.

[0104] Step S203: define the interval usage data whose data similarity ratio is less than the preset benchmark demand ratio as accidental data, and define the overall usage data obtained after eliminating the accidental data as the overall data after elimination, and define the overall usage time obtained after eliminating the unit interval corresponding to the accidental data as the overall time after elimination.

[0105] The benchmark demand ratio is the minimum data similarity ratio set by the staff to determine that the current interval usage data is not accidental data. When the data similarity ratio is less than the benchmark demand ratio, it means that the interval usage data is accidental data and cannot reflect the habits of the elevator components in daily use. At this time, it is defined as accidental data to distinguish different interval usage data for subsequent analysis. When the accidental data is eliminated, the overall usage data can be re-determined. At this time, the overall data after elimination and the overall duration after elimination are defined separately to distinguish the data that can reflect the usage habits of elevator components for subsequent analysis.

[0106] Step S204: Calculate the unit usage value based on the overall data after elimination and the overall duration after elimination, and calculate the theoretical total duration based on the unit usage value, component standard data, and overall usage data without eliminating accidental data.

[0107] The unit usage value is the value obtained by dividing the overall data after elimination by the overall duration after elimination. The overall usage data obtained by subtracting the accidental data that has not been eliminated from the standard data of the component can be used to know the status of the remaining usable data, and then the remaining data can be divided by the unit usage value to get a more accurate theoretical total duration.

[0108] Reference Figure 3 After the data similarity ratio is determined, the elevator component service life prediction method based on big data also includes:

[0109] Step S300: defining interval usage data whose data similarity ratio is not less than the benchmark demand ratio as frequently occurring data, and defining similar data within the similar usage range corresponding to the frequently occurring data as bound data.

[0110] When the data similarity ratio is not less than the benchmark demand ratio, there may be a situation where the usage data of this interval is continuously abnormal. For example, the school elevator will not be used during the summer vacation. At this time, a large amount of interval usage data will be obtained, but these cannot reflect the usage of elevator components when students are in school. Therefore, frequently sent data is defined to distinguish the usage data of different intervals for subsequent analysis; at the same time, bound data is defined to distinguish the similar data determined by the frequently sent data for subsequent analysis.

[0111] Step S301: determining a binding interval according to unit intervals corresponding to any two binding data, and defining a binding interval containing only binding data as a stable interval.

[0112] The binding interval is an interval formed by the unit intervals of the two binding data as the interval endpoints, and a stable interval is defined to facilitate analysis of situations where the above situation may occur.

[0113] Step S302: Counting the binding data in a stable interval to determine the stable quantity of interval data, and counting all the binding data to determine the overall binding quantity.

[0114] The stable quantity of interval data is the total quantity of bound data in the stable interval, and the overall binding quantity is the total quantity of all determined binding data.

[0115] Step S303: Calculate the stable binding ratio based on the stable data quantity and the overall binding quantity.

[0116] The stable binding ratio is the ratio of bound data in the stable interval to all bound data, which is determined by dividing the number of stable data by the total number of bindings.

[0117] Step S304: determine whether there is a situation where the stable binding ratio is greater than the preset extreme upper limit ratio.

[0118] The extreme upper limit ratio is the minimum stable binding ratio set by the staff to determine that most of the binding data are within the stable interval. The purpose of the judgment is to find out whether there is a situation where most of the binding data are divided by a stable interval, so as to find out whether there may be a situation where the usage data of an interval is continuously abnormal.

[0119] Step S3041: If there is no situation where the stable binding ratio is greater than the extreme upper limit ratio, the determined frequent data is maintained.

[0120] When there is no situation where the stable binding ratio is greater than the extreme upper limit ratio, it means that the above example situation does not exist, and the determined frequent data can be maintained.

[0121] Step S3042: If there is a situation where the stable binding ratio is greater than the extreme upper limit ratio, the determined frequent data is converted into occasional data.

[0122] When the proportion of stable binding is greater than the proportion of the extreme upper limit, it means that the above-mentioned example exists, that is, the frequent data at this time cannot actually reflect the usage habits of elevator components. At this time, it can be converted into accidental data for analysis.

[0123] Reference Figure 4 After the theoretical total duration is determined, the elevator component service life prediction method based on big data also includes:

[0124] Step S400: establishing a detection interval on a preset time axis with the current time point as the end point and a width of a preset detection duration.

[0125] The time axis is a coordinate axis formed by the combination of various time points. The time axis points from the time points that have been reached to the time points that have not been reached. The detection time is the time set by the staff to obtain the data of the elevator components used in historical situations. The detection interval is established to facilitate the acquisition of various data.

[0126] Step S401: obtaining interval usage data and overall usage data of each elevator component in a corresponding component usage interval in a detection interval, and defining the interval usage data as historical interval data, and defining the overall usage data as historical overall data.

[0127] Each elevator component is a component of the same use environment and the same model as the elevator component with the currently required predicted service life, such as elevator components in the elevator in the same building; the corresponding component usage interval is the time interval from the beginning of use of the elevator component to the time span corresponding to the consistency of the component usage interval, for example, the component usage interval of the currently analyzed elevator component is 6.1-7.1, when the component installation time of an elevator component in the detection interval is 4.12, the corresponding component usage interval is 4.12-5.12; historical interval data and historical overall data are defined to distinguish the data obtained from the remaining elevator components, which is convenient for subsequent analysis.

[0128] Step S402: performing a sum calculation based on the current interval usage data to determine the original total interval data, and performing a sum calculation based on the historical interval data to determine the historical total interval data.

[0129] The original total interval data is the sum of all interval usage data of the elevator component with the current predicted life, and the historical total interval data is the sum of all historical interval data determined for the same elevator component.

[0130] Step S403: performing a difference calculation based on the original total interval data and the historical total interval data to determine the first deviation data, and performing a difference calculation based on the historical overall data and the current overall usage data to determine the second deviation data.

[0131] The first deviation data is the difference between the original total interval data and the historical total interval data, and the difference is an absolute value. Similarly, the second deviation data is the difference between the historical overall data and the current overall usage data, and the difference is also an absolute value.

[0132] Step S404: performing calculation according to the first deviation data and the second deviation data to determine a similarity value.

[0133] The similarity value is a numerical value reflecting the similarity of the usage habits of two elevator components. The larger the value is, the more similar the usage habits of the two elevator components are. The calculation formula is, where is the similarity value, is the preset fixed parameter for calculation, is the first deviation data, is the second deviation data, is the fixed parameter used to perform numerical conversion on the first deviation data, and is the fixed parameter used to perform numerical conversion on the second deviation data.

[0134] Step S405: Determine the similarity value with the largest value according to the preset sorting rule, and obtain the actual total duration of the elevator component corresponding to the similarity value.

[0135] The sorting rule is a method set by the staff to sort the values, such as the bubble method. The sorting rule can be used to determine the similarity value with the largest value, that is, the elevator component corresponding to the similarity value is closest to the usage habit of the elevator component whose service life is currently required to be predicted. At this time, the elevator component can be used for comparison and reference processing; the actual total time is the time the referenced component is used in actual circumstances.

[0136] Step S406: Calculate the actual total duration and the theoretical total duration to determine the comprehensive total duration, and calculate the difference between the comprehensive total duration and the overall usage duration to determine the remaining usage duration.

[0137] The comprehensive total duration is obtained by analyzing the theoretical total duration and the actual total duration, which can represent the overall service life of the elevator components currently predicted. It can be obtained by calculating the average value of the two, or by the method of steps S700-S702. The remaining usage time that is more representative of the remaining service life of the elevator components can be obtained by subtracting the overall usage time from the comprehensive total duration.

[0138] Reference Figure 5 After the similarity value is determined, the elevator component service life prediction method based on big data also includes:

[0139] Step S500: Determine whether the maximum similarity value is greater than a preset reference similarity value.

[0140] The benchmark similarity value is the minimum similarity value set by the staff to determine that the usage habits of two elevator components are relatively similar. The purpose of the judgment is to find out whether the most similar elevator components meet the most basic similarity requirements.

[0141] Step S5001: If the maximum similarity value is greater than the reference similarity value, the actual total duration is determined.

[0142] When the maximum similarity value is greater than the reference similarity value, it means that the currently determined elevator components meet the similarity requirements, and the actual total duration can be determined normally.

[0143] Step S5002: If the maximum similarity value is not greater than the reference similarity value, a difference calculation is performed based on the theoretical total duration and the overall usage duration to determine the remaining usage duration.

[0144] When the maximum similarity value is not greater than the benchmark similarity value, it means that the currently determined elevator component does not meet the similarity requirements. At this time, the service life of the current elevator component cannot be predicted by other elevator components. At this time, the remaining usage time can be determined normally according to the determined theoretical total time.

[0145] Reference Figure 6 , if the maximum similarity value is greater than the benchmark similarity value, the elevator component service life prediction method based on big data also includes:

[0146] Step S600: Determine whether there are at least two elevator components with the largest similarity values ​​and are the same.

[0147] The purpose of the judgment is to find out whether there are multiple elevator components that meet the requirements, so as to determine the actual total duration used for calculation.

[0148] Step S6001: If there are not at least two elevator components with the largest similarity values ​​that are the same, the actual total duration is determined based on the unique elevator component.

[0149] When there are not at least two elevator components with the largest similarity values ​​that are the same, it means that there is only one elevator component that meets the requirements, and the actual total duration can be determined based on the elevator component.

[0150] Step S6002: If there are at least two elevator components with the largest similarity values ​​and which are the same, the actual total duration corresponding to each elevator component is defined as the candidate total duration.

[0151] When there are at least two elevator components with the largest and identical similarity values, it means that there are multiple elevator components that meet the requirements. At this time, there will be multiple actual total durations that can be used. Therefore, the corresponding actual total durations are defined as alternative total durations to distinguish different actual total durations, which is convenient for subsequent analysis.

[0152] Step S601: randomly selecting an alternative total duration from all the alternative total durations as the fixed total duration, and performing calculations based on the fixed total duration and the remaining alternative total durations to determine the deviation total duration.

[0153] The deviation total duration is the difference between the fixed total duration and the remaining alternative total durations, and the calculation formula is, where is the deviation total duration, is the defined fixed total duration, and is the i-th alternative total duration.

[0154] Step S602: Determine the total deviation duration with the smallest value according to the sorting rule, and calculate the fixed total duration corresponding to the total deviation duration and the theoretical total duration to determine the comprehensive total duration.

[0155] The sorting rules can be used to determine the total deviation duration with the smallest value, that is, the distance between the currently determined fixed total duration and the remaining alternative total durations is the smallest, that is, the current fixed total duration can better represent the actual total durations of the remaining elevator components that meet the requirements. At this time, the fixed total duration and the theoretical total duration can be used to calculate the comprehensive total duration; among them, when there are still multiple deviation total durations that meet the requirements, randomly select a fixed total duration corresponding to a deviation total duration that meets the requirements for calculation.

[0156] Reference Figure 7 The steps for calculating the total duration based on the actual duration and the theoretical duration to determine the comprehensive duration include:

[0157] Step S700: Calculate the difference between the actual total duration and the theoretical total duration to determine the actual theoretical deviation duration.

[0158] The theoretical deviation duration is the difference between the actual total duration and the theoretical total duration, and the difference is an absolute value.

[0159] Step S701: Determine whether the actual theoretical deviation duration is greater than the preset permitted duration.

[0160] The permitted duration is the maximum actual theoretical deviation duration allowed when the actual total duration set by the staff does not deviate much from the theoretical total duration. The purpose of the judgment is to find out whether the actual total duration determined is significantly different from the theoretical total duration.

[0161] Step S7011: If the actual theoretical deviation duration is greater than the permitted duration, the theoretical total duration is determined as the comprehensive total duration.

[0162] When the actual theoretical deviation duration is greater than the permitted duration, it indicates that the deviation between the two is large. At this time, it is impossible to determine the usage of the current elevator components by referring to the other elevator components. In this case, the theoretical total duration is normally determined as the comprehensive total duration for subsequent calculations.

[0163] Step S7012: If the actual theoretical deviation duration is not greater than the permitted duration, the adjusted deviation duration is determined based on the actual theoretical deviation duration and the corresponding similarity value, and the target total duration is determined based on the actual total duration and the adjusted deviation duration.

[0164] When the actual theoretical deviation duration is not greater than the permitted duration, it means that the deviation between the two is not large. At this time, the actual total duration can be used for calculation; the adjusted deviation duration is the value obtained by multiplying the actual theoretical deviation duration by the similarity value, and the target total duration is the value obtained by adding or subtracting the adjusted deviation duration from the actual total duration. When the actual total duration is greater than the theoretical total duration, it is calculated by subtracting the adjusted deviation duration from the actual total duration, otherwise it is calculated by adding the adjusted deviation duration to the actual total duration.

[0165] Step S702: Calculate the average of the target total duration and the theoretical total duration to determine the comprehensive total duration.

[0166] A more appropriate comprehensive total duration can be determined by calculating the average of the target total duration and the theoretical total duration, thereby enabling a more accurate prediction of the service life of elevator components.

[0167] Reference Figure 8 Based on the same inventive concept, an embodiment of the present invention provides an elevator component service life prediction system based on big data, including:

[0168] An acquisition module is used to acquire component installation time and installed component type;

[0169] A processing module, connected to the acquisition module, for storing and processing information;

[0170] The processing module determines the component standard data corresponding to the installed component type according to the preset standard matching relationship;

[0171] The processing module establishes a component usage interval according to the component installation time and the current time point, and divides the component usage interval into a plurality of unit intervals according to a preset unit time length;

[0172] The acquisition module acquires the component interval usage data in the unit interval, and enables the processing module to define the component interval usage data greater than the preset limit data as excess data, and define the remaining component interval usage data as stable data;

[0173] The processing module defines the excess data of the component interval usage data of the previous unit interval as the starting data, and combines the unit intervals corresponding to the continuous and uninterrupted excess data with the starting data as the starting point to determine the excess interval, and counts the excess data in the excess interval to determine the excess quantity of each unit interval;

[0174] The processing module determines the adjustment parameters corresponding to the excess quantity according to the preset adjustment matching relationship;

[0175] The processing module performs calculations based on the adjustment parameters and the corresponding excess data to determine the actual data, and performs summation calculations based on the actual data and the stable data to determine the overall usage data;

[0176] The processing module determines the overall usage time according to the component usage interval, calculates according to the component standard data, the overall usage data and the overall usage time to determine the theoretical total time, and performs a difference calculation according to the theoretical total time and the overall usage time to determine the remaining usage time;

[0177] An accidental data elimination module is used to determine and eliminate accidental data, so as to facilitate more accurate determination of the usage habits of elevator components;

[0178] Frequent data analysis module, used to process and analyze frequent data, so as to transform frequent data that is actually accidental data;

[0179] A similar reference analysis module is used to determine the use of elevator components in other historical situations so as to analyze the current elevator components by comparing them with the reference;

[0180] A similarity value limiting module is used to limit the value required to be reached by the maximum similarity value;

[0181] An elevator component screening module is used to screen multiple elevator components that meet the requirements in historical situations;

[0182] The comprehensive total duration calculation module is used to calculate a more accurate comprehensive total duration.

[0183] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A method for predicting the service life of elevator components based on big data, characterized in that: include: Get the component installation time and installed component type; Determine component standard data corresponding to the installed component type according to a preset standard matching relationship; Establishing a component usage interval according to the component installation time and the current time point, and dividing the component usage interval into a number of unit intervals according to a preset unit time length; Obtaining component interval usage data in a unit interval, defining component interval usage data greater than a preset set limit data as excess data, and defining the remaining component interval usage data as stable data; The excess data of the component interval usage data of the previous unit interval as stable data is defined as the starting data, and the unit intervals corresponding to the continuous and uninterrupted excess data are combined with the starting data as the starting point to determine the excess interval, and the excess data is counted in the excess interval to determine the excess quantity of each unit interval; Determine the adjustment parameters corresponding to the excess quantity according to the preset adjustment matching relationship; Calculate the actual data based on the adjustment parameters and the corresponding excess data, and sum the actual data and the stable data to determine the overall usage data; The overall usage time is determined according to the component usage range, and the theoretical total time is determined by calculation based on the component standard data, the overall usage data and the overall usage time, and the difference between the theoretical total time and the overall usage time is calculated to determine the remaining usage time.

2. The elevator component service life prediction method based on big data according to claim 1 is characterized in that: After the interval usage data is determined, the elevator component service life prediction method based on big data also includes: Calculate based on interval usage data and preset allowable deviation data to determine similar usage range; The interval usage data within a similar usage range is defined as similar data, and the similar data is counted to determine the number of data similarities; Count all interval usage data to determine the overall data quantity, and calculate the data similarity ratio based on the data similarity quantity and the overall data quantity; The interval usage data in which the data similarity ratio is less than the preset benchmark demand ratio is defined as accidental data, and the overall usage data obtained after the accidental data is eliminated is defined as the overall data after elimination, and the overall usage duration obtained after the unit interval corresponding to the accidental data is eliminated is defined as the overall duration after elimination; The unit usage value is determined by calculation based on the overall data after elimination and the overall duration after elimination, and the theoretical total duration is determined by calculation based on the unit usage value, component standard data and the overall usage data without eliminating accidental data.

3. The elevator component service life prediction method based on big data according to claim 2 is characterized in that: After the data similarity ratio is determined, the elevator component service life prediction method based on big data also includes: The interval usage data whose data similarity ratio is not less than the benchmark demand ratio is defined as frequently occurring data, and the similar data within the similar usage range corresponding to the frequently occurring data is defined as bound data; Determine the binding interval according to the unit intervals corresponding to any two binding data, and define the binding interval containing only binding data within the binding interval as a stable interval; Counting the binding data within the stable interval to determine the stable number of interval data, and counting all the binding data to determine the overall binding number; Calculate the proportion of stable bindings based on the number of stable data and the number of overall bindings; Determine whether there is a situation where the proportion of stable binding is greater than the preset extreme upper limit proportion; If there is no situation where the proportion of stable binding is greater than the proportion of the extreme upper limit, the determined frequent data is maintained; If there is a situation where the proportion of stable binding is greater than the proportion of the extreme upper limit, the determined frequent data will be converted into accidental data.

4. The elevator component service life prediction method based on big data according to claim 1 is characterized in that: After the theoretical total duration is determined, the elevator component service life prediction method based on big data also includes: Establishing a detection interval with the current time point as the end point and a width of the preset detection duration on the preset time axis; Acquire interval usage data and overall usage data of each elevator component in a corresponding component usage interval in a detection interval, and define the interval usage data as historical interval data, and define the overall usage data as historical overall data; Performing a summation calculation based on the current interval usage data to determine the original total interval data, and performing a summation calculation based on the historical interval data to determine the historical total interval data; Performing a difference calculation based on the original total interval data and the historical total interval data to determine the first deviation data, and performing a difference calculation based on the historical overall data and the current overall usage data to determine the second deviation data; Performing calculation based on the first deviation data and the second deviation data to determine a similarity value; Determine the similarity value with the largest value according to the preset sorting rules, and obtain the actual total duration of the elevator component corresponding to the similarity value; The actual total time and the theoretical total time are calculated to determine the comprehensive total time, and the difference between the comprehensive total time and the overall usage time is calculated to determine the remaining usage time.

5. The elevator component service life prediction method based on big data according to claim 4 is characterized in that: After the similarity value is determined, the elevator component service life prediction method based on big data also includes: Determine whether the maximum similarity value is greater than a preset benchmark similarity value; If the maximum similarity value is greater than the baseline similarity value, the actual total duration is determined; If the maximum similarity value is not greater than the baseline similarity value, a difference calculation is performed based on the theoretical total time and the overall usage time to determine the remaining usage time.

6. The elevator component service life prediction method based on big data according to claim 5 is characterized in that: If the similarity value with the largest value is greater than the benchmark similarity value, the elevator component service life prediction method based on big data also includes: Determine whether there are at least two elevator components with the largest similarity values ​​and are the same; If there are not at least two elevator components with the largest similarity values ​​that are the same, the actual total duration is determined based on the unique elevator component; If there are at least two elevator components with the largest similarity values ​​and the same value, the actual total duration corresponding to each elevator component is defined as the candidate total duration; Randomly select one of all the alternative total durations as the fixed total duration, and calculate the deviation total duration based on the fixed total duration and the remaining alternative total durations; The total deviation duration with the smallest value is determined according to the sorting rules, and the fixed total duration corresponding to the total deviation duration and the theoretical total duration are calculated to determine the comprehensive total duration.

7. The elevator component service life prediction method based on big data according to claim 6 is characterized in that: The steps to calculate the total duration based on the actual total duration and the theoretical total duration to determine the combined total duration include: Calculate the difference between the actual total duration and the theoretical total duration to determine the actual theoretical deviation duration; Determine whether the actual theoretical deviation duration is greater than the preset permitted duration; If the actual theoretical deviation duration is greater than the permitted duration, the theoretical total duration shall be determined as the comprehensive total duration; If the actual theoretical deviation duration is not greater than the permitted duration, the adjusted deviation duration is determined based on the actual theoretical deviation duration and the corresponding similarity value, and the target total duration is determined based on the actual total duration and the adjusted deviation duration; The target total duration and the theoretical total duration are averaged to determine the overall total duration.

8. A big data-based elevator component service life prediction system, characterized in that: include: An acquisition module is used to acquire component installation time and installed component type; A processing module, connected to the acquisition module, for storing and processing information; The processing module determines the component standard data corresponding to the installed component type according to the preset standard matching relationship; The processing module establishes a component usage interval according to the component installation time and the current time point, and divides the component usage interval into a plurality of unit intervals according to a preset unit time length; The acquisition module acquires the component interval usage data in the unit interval, and enables the processing module to define the component interval usage data greater than the preset limit data as excess data, and define the remaining component interval usage data as stable data; The processing module defines the excess data of the component interval usage data of the previous unit interval as the starting data, and combines the unit intervals corresponding to the continuous and uninterrupted excess data with the starting data as the starting point to determine the excess interval, and counts the excess data in the excess interval to determine the excess quantity of each unit interval; The processing module determines the adjustment parameters corresponding to the excess quantity according to the preset adjustment matching relationship; The processing module performs calculations based on the adjustment parameters and the corresponding excess data to determine the actual data, and performs summation calculations based on the actual data and the stable data to determine the overall usage data; The processing module determines the overall usage time according to the component usage interval, and calculates according to the component standard data, the overall usage data and the overall usage time to determine the theoretical total time, and calculates the difference between the theoretical total time and the overall usage time to determine the remaining usage time.

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