Elevator quality safety level measuring method and system based on weighted KL-TOPSIS

Abstract

The invention relates to an elevator quality safety level measuring method and system based on weighted KL-TOPSIS, and the method comprises the steps: obtaining and preprocessing all quality safety index data of a target elevator, and constructing an elevator quality safety index data set; the elevator quality safety index data set is substituted into a preset elevator quality safety adaptability evaluation index system, a weighted judgment matrix is constructed, and the elevator quality safety adaptability evaluation index system comprises first-level quality safety indexes, second-level quality safety indexes and corresponding weights; the maximum value and the minimum value of each quality safety index are determined based on the weighted judgment matrix, and the Euclidean space distance of the target elevator is calculated based on the maximum value and the minimum value; and calculating the relative closeness degree of the target elevator and a preset optimal scheme based on the Euclidean space distance, namely a quality safety level measurement result of the target elevator. According to the method, the accuracy of quality safety level measurement in the full life cycle of the elevator can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator full-life cycle safety risk identification, and particularly to a method and system for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS. Background Art

[0002] As an indispensable vertical transportation tool in people's daily life and production activities, elevators play a key role in the people's livelihood infrastructure. The safety issue of elevators has increasingly become the focus of public attention and has also been widely reported by the media, becoming a key link in the national public safety system. In the context of data-driven goals, the technology for measuring the quality and safety level highly relies on data analysis and intelligent algorithms to evaluate and improve the quality of products or services. The core of the technology lies in the utilization of big data, machine learning, and artificial intelligence, etc., relying on extensive data collection, including structured, unstructured, and semi-structured data from multiple channels such as production processes, quality inspections, and consumer feedback, providing a rich information basis for evaluating the quality and safety level to support decision-making and process optimization. The data-driven technology for measuring the quality and safety level can also extract key quality features, trends, and patterns from the data by applying advanced techniques such as text mining, natural language processing, and statistical analysis, providing valuable insights for improving product design and production processes. It can also use machine learning algorithms to establish prediction models to predict the future quality and safety level. The established models are trained and optimized based on historical data, capable of automatically identifying the key factors affecting quality and predicting their impact on future quality levels. In terms of decision analysis, the data-driven technology for measuring the quality and safety level provides decision-makers with a comprehensive view by integrating information from multiple data sources.

[0003] Currently, practical applications of elevator quality and safety measurement methods face numerous challenges and shortcomings. First, data collection and processing are limited. These methods often rely too heavily on specific data sources, such as maintenance records and inspection reports, while neglecting the integration of multiple sources like consumer feedback and social media. Furthermore, their processing capabilities for unstructured or semi-structured data are limited, potentially leading to the omission of key information. Second, the incompleteness of measurement indicators and models is a significant issue. Existing methods often focus on explicit indicators such as failure rate and repair frequency, while ignoring quality characteristics such as operational smoothness, noise control, and passenger comfort. Furthermore, model design fails to fully account for the variability among elevator brands and models, impacting the accuracy and applicability of the measurement. Furthermore, challenges in implementation and supervision are equally significant. These include high technical barriers to implementation, insufficient personnel training, and the absence or incompleteness of regulatory mechanisms, which cast doubt on the authority and comparability of measurement results. Finally, the lag in technological updates and development is also a major bottleneck. Existing measurement methods are unable to keep up with the rapid iteration of elevator technology, and there are blind spots in the safety performance assessment of new elevators. At the same time, the application of innovative technologies such as big data and artificial intelligence needs to be further explored to achieve intelligent and accurate assessment of elevator quality and safety levels. Therefore, in order to improve the comprehensiveness and accuracy of elevator quality and safety level measurement, it is necessary to continuously strengthen technological innovation, improve data collection and processing mechanisms, enrich measurement indicators and models, and keep up with technological development trends to promote the continuous optimization and upgrading of the elevator quality and safety assessment system. A weighted KL-TOPSIS elevator quality and safety level measurement method and system is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for measuring the quality and safety level of elevators based on weighted KL-TOPSIS, which deeply mines and analyzes massive amounts of text data related to elevator quality and safety. By integrating an advanced weighted KL divergence algorithm with the TOPSIS multi-attribute decision analysis method, the accuracy of the quality and safety level measurement throughout the entire life cycle of elevators is improved, and the scope of identification of the causes of quality and safety risks is broadened.

[0005] To achieve the above objectives, on the one hand, the present invention provides a method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS, comprising:

[0006] Obtain and preprocess the quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator dataset;

[0007] Substituting the elevator quality and safety indicator data set into a preset elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, wherein the elevator quality and safety adaptability evaluation index system includes first-level quality and safety indicators, second-level quality and safety indicators, and corresponding weights;

[0008] Determining the maximum and minimum values of each quality and safety indicator based on the weighted judgment matrix, and calculating the Euclidean space distance of the target elevator based on the maximum and minimum values;

[0009] The relative closeness between the target elevator and a preset optimal solution is calculated based on the Euclidean space distance, that is, the quality safety level measurement result of the target elevator.

[0010] Optionally, obtaining and preprocessing various quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator dataset includes:

[0011] Obtain various quality and safety indicator data of the target elevator;

[0012] Clean the data of the various quality and safety indicators, remove outliers and missing values, and standardize the cleaned data to obtain the processed data of the various quality and safety indicators;

[0013] The processed quality and safety indicator data are sorted into quality and safety indicator data of different dimensions to construct the elevator quality and safety indicator data set.

[0014] Optionally, the first-level quality and safety indicators of the elevator quality and safety adaptability evaluation index system include reliability index B1, maintenance efficiency index B2, performance index B3, safety performance index B4, energy efficiency and environmental protection index B5, and management and maintenance index B6, and the second-level quality and safety indicators include mean time between failures B 11 , failure rate B 12 , mean time to repair B 21 , Fault handling timeliness rate B 22 , running speed B 31 , load capacity B 32 , starting acceleration and braking deceleration B 33 , leveling accuracy B 34 , noise and vibration B 35 、Emergency Braking System B 41 , fall protection device B 42 、Emergency Measures B 43 , energy efficiency grade B 51 , Environmental performance B 52 、Maintenance status B 61 , Regular inspection pass rate B 62 .

[0015] Optionally, the corresponding weights of the first-level quality and safety indicators and the second-level quality and safety indicators of the elevator quality and safety adaptability evaluation index system are determined by combining the AHP method and PCA.

[0016] Optionally, constructing the weighted judgment matrix includes:

[0017] Based on each quality and safety index and the corresponding weights in the elevator quality and safety adaptability evaluation index system, multiply the data in the elevator quality and safety index dataset by the corresponding weights to obtain weighted values, and construct the weighted judgment matrix based on the weighted values. The weighted judgment matrix is:

[0018] Z = (z ij ) m×n ;

[0019] where z ij is an element of the weighted judgment matrix, z ij = x ij w j , x ij is an unweighted data element of the elevator quality and safety index, w j is the weight, 1 ≤ i ≤ m, 1 ≤ j ≤ n, where m and n are the number of indicators and the number of evaluation individuals respectively.

[0020] Optionally, determining the maximum and minimum values of each quality and safety index based on the weighted judgment matrix includes:

[0021]

[0022] where is the maximum value of quality and safety index j, is the minimum value of quality and safety index j.

[0023] Optionally, calculating the Euclidean space distance of the target elevator based on the maximum and minimum values includes:

[0024]

[0025]

[0026] where is the Euclidean space distance from the target elevator to the maximum value, is the Euclidean space distance from the target elevator to the minimum value.

[0027] Optionally, calculating the relative closeness degree of the target elevator to the preset optimal solution based on the Euclidean space distance includes:

[0028]

[0029]

[0030]

[0031] where Ci is the relative proximity degree of the target elevator to the preset optimal solution, is the proximity degree of the target elevator to the maximum value, is the proximity degree of the target elevator to the minimum value, and η is the decision maker's preference coefficient.

[0032] On the other hand, the present invention also provides a weighted KL-TOPSIS-based elevator quality and safety level measurement system, including:

[0033] A data acquisition and processing module, configured to acquire various quality and safety index data of the target elevator and perform preprocessing to construct an elevator quality and safety index data set;

[0034] A weighted judgment matrix construction module, configured to substitute the elevator quality and safety index data set into the elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, where the elevator quality and safety adaptability evaluation index system includes primary quality and safety indexes, secondary quality and safety indexes, and corresponding weights;

[0035] A Euclidean space distance calculation module, configured to determine the maximum and minimum values of each quality and safety index based on the weighted judgment matrix, and calculate the Euclidean space distance of the target elevator based on the maximum and minimum values;

[0036] A quality and safety level measurement module, configured to calculate the relative proximity degree of the target elevator to the preset optimal solution based on the Euclidean space distance, that is, the quality and safety level measurement result of the target elevator.

[0037] The beneficial effects of the present invention are:

[0038] A weighted KL-TOPSIS-based elevator quality and safety level measurement method and system proposed by the present invention significantly improves the accuracy and comprehensiveness of elevator quality and safety assessment. By integrating the advanced weighted KL divergence algorithm and the TOPSIS multi-attribute decision analysis method, it can objectively and accurately measure the information differences between elevator quality and safety indexes, effectively capture the subtle differences and importance weights between indexes, and overcome the subjectivity and insufficient information utilization limitations of traditional measurement methods. At the same time, it can comprehensively consider multiple quality and safety indexes, realize the objective and quantitative evaluation of the elevator quality and safety level, broaden the identification range of quality and safety risk causes, and provide a more comprehensive and reliable decision-making basis for elevator managers.

[0039] The present invention designs an efficient, accurate and automated process, which can deeply mine and analyze a large amount of text data related to elevator quality and safety, greatly improving the efficiency of data processing and analysis, and providing strong support for the intelligent and refined management of the elevator industry. By continuously monitoring the trend of elevator quality and safety, the system can timely detect potential safety hazards, provide strong technical support for elevator maintenance, fault warning and risk management, and help ensure the safety of the public taking the elevator. The present invention not only improves the accuracy and comprehensiveness of the measurement of elevator quality and safety level, but also promotes the intelligent and refined development of elevator industry quality and safety management, which is of great significance for improving the overall safety level of the elevator industry, promoting elevator technology innovation and progress.

[0040] Compared with the prior art, the elevator safety risk factor identification process proposed by the present invention has achieved significant optimization, and the risk cause identification result shows unprecedented accuracy. This innovation not only provides solid technical support for the intelligent identification, warning and prevention and control of elevator safety risks, but also strongly promotes the advancement of public safety governance capabilities and governance systems towards modernization. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a flow chart of a method for measuring elevator quality and safety level based on weighted KL-TOPSIS according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic structural diagram of a system for measuring elevator quality and safety level based on weighted KL-TOPSIS according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0046] At present, in the process of measuring the quality and safety level of elevators, theoretical research and practical work are mainly carried out from perspectives such as qualitative analysis, safety checklist analysis, system analysis, grounded theory analysis, and data mining analysis. In the specific application process, technical methods mostly adopt knowledge-driven and model-driven methods such as accident case analysis, induction of work experience, sorting of relevant literature, system research, expert interviews, analytic hierarchy process, fault tree analysis, and structural equation analysis to identify and analyze elevator safety risk factors. There is still a need to further promote the research on elevator quality and safety level measurement models and methods based on data-driven.

[0047] (1) Identifying elevator quality and safety risk factors based on elevator safety accident cases mainly analyzes real elevator safety accidents through statistical methods and case study methods, and then identifies elevator safety risk factors, which is truly reliable. However, the ability to process unstructured or semi-structured data is limited. These data often contain rich elevator operation information and quality characteristics, but existing processing technologies are difficult to effectively extract and utilize this information. Therefore, it is necessary to improve the data collection and processing mechanism, broaden the data source channels, and enhance the data processing ability to ensure the accuracy and reliability of the measurement.

[0048] (2) There are deficiencies in the design of indicators and models in existing elevator quality and safety level measurement methods. On the one hand, the measurement indicators focus too much on explicit indicators such as failure rate and maintenance frequency, while ignoring quality characteristics such as running smoothness, noise control, and passenger comfort. These implicit indicators also have an important impact on the quality and safety level of elevators, but existing methods do not fully cover them. On the other hand, the model design does not fully consider the differences between elevators of different brands and models, resulting in limited comparability and applicability of the measurement results among different elevators. Therefore, it is necessary to enrich the measurement indicators and models, incorporate more implicit indicators, and conduct differential design for elevators of different brands and models to improve the comprehensiveness and accuracy of the measurement.

[0049] (3) In terms of implementation and supervision, elevator quality and safety level measurement methods also face challenges. On the one hand, the technical threshold in the implementation process is high and personnel training is insufficient, which restricts the smooth progress of the measurement work. On the other hand, the lack or imperfectness of the supervision mechanism makes the authority and comparability of the measurement results questioned. In addition, the lag in technology update and development is also a major bottleneck. Existing measurement methods are difficult to keep up with the rapid iteration of elevator technology, and there are blind spots in the safety performance assessment of new elevators. Therefore, it is necessary to strengthen the implementation and supervision efforts, improve the technical level of personnel, perfect the supervision mechanism, keep up with the trend of technological development, and promote the continuous optimization and upgrading of the elevator quality and safety assessment system. At the same time, actively explore the application of innovative technologies such as big data and artificial intelligence in elevator quality and safety assessment to achieve intelligent and accurate assessment and improve the measurement efficiency and accuracy.

[0050] Therefore, in order to solve the above technical problems, on the one hand, this embodiment provides a method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS, including:

[0051] Obtain and preprocess the quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator dataset;

[0052] Substituting the elevator quality and safety indicator data set into a preset elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, wherein the elevator quality and safety adaptability evaluation index system includes first-level quality and safety indicators, second-level quality and safety indicators, and corresponding weights;

[0053] Determining the maximum and minimum values of each quality and safety indicator based on the weighted judgment matrix, and calculating the Euclidean space distance of the target elevator based on the maximum and minimum values;

[0054] The relative closeness between the target elevator and a preset optimal solution is calculated based on the Euclidean space distance, that is, the quality safety level measurement result of the target elevator.

[0055] Specifically, this embodiment integrates the advanced weighted KL divergence algorithm with the TOPSIS multi-attribute decision analysis method to objectively and accurately measure the information differences between elevator quality and safety indicators, effectively capturing the subtle differences and importance weights between indicators. This overcomes the subjectivity and information underutilization limitations of traditional measurement methods. Furthermore, it comprehensively considers multiple quality and safety indicators to achieve an objective and quantitative assessment of elevator quality and safety levels, broadening the scope of identifying the causes of quality and safety risks and providing elevator managers with a more comprehensive and reliable basis for decision-making.

[0056] Furthermore, various quality and safety indicator data of the target elevator are obtained and preprocessed to construct an elevator quality and safety indicator dataset, including:

[0057] Obtain various quality and safety indicator data of the target elevator;

[0058] Clean the data of the various quality and safety indicators, remove outliers and missing values, and standardize the cleaned data to obtain the processed data of the various quality and safety indicators;

[0059] The processed quality and safety indicator data are sorted into quality and safety indicator data of different dimensions to construct the elevator quality and safety indicator data set.

[0060] Furthermore, the primary quality and safety indicators of the elevator quality and safety adaptability evaluation index system include reliability indicator B1, maintenance efficiency indicator B2, performance indicator B3, safety performance indicator B4, energy efficiency and environmental protection indicator B5, and management and maintenance indicator B6. The secondary quality and safety indicators include mean time between failures B 11 , failure rate B 12 , mean time to repair B 21 , timely rate of fault handling B 22 , running speed B 31 , load capacity B 32 , starting acceleration and braking deceleration B 33 , leveling accuracy B 34 , noise and vibration B 35 , emergency braking system B 41 , anti - falling device B 42 , emergency measures B 43 , energy efficiency grade B 51 , environmental protection performance B 52 , maintenance situation B 61 , qualified rate of regular inspection B 62 .

[0061] Furthermore, the corresponding weights of the primary quality and safety indicators and secondary quality and safety indicators of the elevator quality and safety adaptability evaluation index system are determined by combining the AHP method and PCA.

[0062] Furthermore, constructing the weighted judgment matrix includes:

[0063] Based on each quality and safety indicator and the corresponding weight in the elevator quality and safety adaptability evaluation index system, multiply the data in the elevator quality and safety indicator dataset by the corresponding weight to obtain the weighted values. Based on the weighted values, construct the weighted judgment matrix, and the weighted judgment matrix is:

[0064] Z=(z ij ) m×n ;

[0065] where z ij is an element of the weighted judgment matrix, z ij =x ij w j , x ij is an unweighted data element of the elevator quality and safety indicator, w j is the weight, 1≤i≤m, 1≤j≤n, and m and n are the number of indicators and the number of evaluation individuals respectively.

[0066] Furthermore, determining the maximum and minimum values of each quality and safety indicator based on the weighted judgment matrix includes:

[0067]

[0068]

[0069] Among them, is the maximum value of the quality safety index j, is the minimum value of the quality safety index j.

[0070] Furthermore, calculating the Euclidean space distance of the target elevator based on the maximum value and the minimum value includes:

[0071]

[0072]

[0073] Among them, is the Euclidean space distance from the target elevator to the maximum value, is the Euclidean space distance from the target elevator to the minimum value.

[0074] Furthermore, calculating the relative closeness degree of the target elevator and the preset optimal scheme based on the Euclidean space distance includes:

[0075]

[0076]

[0077]

[0078] Among them, C i is the relative closeness degree of the target elevator and the preset optimal scheme, is the closeness degree of the target elevator and the maximum value, is the closeness degree of the target elevator and the minimum value, and η is the decision maker preference coefficient.

[0079] Next, in combination with Figure 1 a detailed description of a method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS provided in this embodiment is given, specifically including:

[0080] Obtain the quality and safety index data of the elevator; clean and standardize the quality and safety index data of the elevator; according to the importance and relevance of each index, assign corresponding weights to each index for the standardized quality and safety index data of the elevator; multiply the standardized index data by the weights to construct a weighted judgment matrix; according to the weighted judgment matrix, determine the maximum and minimum values of each index respectively as the positive ideal solution and the negative ideal solution, and calculate the Euclidean space distance between each elevator quality and safety level evaluation object and the positive ideal solution and the negative ideal solution; according to the distance calculation results, calculate the relative closeness of each evaluation object to the optimal solution, which is used as the measurement result of the elevator quality and safety level.

[0081] The specific process is as follows:

[0082] Obtain the quality and safety index data of the elevator;

[0083] Specifically, collect the specific content that can clarify the quality and safety index data of the elevator. These indicators need to include multiple aspects such as the operating status, failure rate, maintenance records, safety performance, and energy efficiency performance of the elevator. Among them, the data index sources include official websites, industry websites, professional forums and other channels to obtain relevant elevator safety standards and specifications, and integrate and store the collected quality and safety index data. An elevator quality and safety database can be established to store the collected data according to different indicators.

[0084] Clean and standardize the quality and safety index data of the elevator;

[0085] Specifically, use the pandas library in the Python language to clean the elevator quality and safety index data based on the collected data. Secondly, use the functions provided by the numpy library to standardize the cleaned data, convert it into dimensionless values, and form a more standardized, consistent and comparable data set, which will be used as the input of the weighted KL-TOPSIS measurement method to calculate the quality and safety level of the elevator.

[0086] According to the importance and relevance of each index, assign corresponding weights to each index for the standardized quality and safety index data of the elevator;

[0087] Specifically, based on the quality and safety index data of the elevator and according to the importance and relevance of each index, divide it into an adaptability evaluation index system including first-level indicators and second-level indicators from six aspects: reliability, maintenance efficiency, performance, safety, energy efficiency and environmental protection, management and maintenance, as shown in Table 1.

[0088] Table 1

[0089]

[0090]

[0091] Invite 4 experts to score each index to construct a judgment matrix, and use the OWA-AHP hierarchical analysis method to calculate the subjective weights of each index. An index system including first-level indexes, second-level indexes and corresponding weights is formed, and the importance and relevance of each index are reflected by the distribution of weights. Among them, determining the index weights includes the following steps:

[0092] (1) By constructing a judgment matrix with the AHP method, invite experts to make pairwise comparisons of each index, so as to calculate the initial weights of each index and obtain the judgment matrix Q. These initial weights form a weight matrix Bn×s, where n represents the number of indexes and s represents the number of evaluation objects; calculate the eigenvalues and eigenvectors of QW = λ max W, and W is the normalized eigenvector corresponding to the eigenvalue λ max . If the judgment matrix meets the criteria of the consistency test, then the components of the vector W obtained by calculation accurately represent the index weights corresponding to the n elements in this level. However, if the judgment matrix fails to pass the consistency test, it is necessary to reconstruct this judgment matrix until it meets the consistency requirements. Then perform the hierarchical total sorting, multiply the weights of the previous level by the weights of the next level, so as to obtain the total weights of the elements of the next level relative to the entire evaluation system. This process starts from the highest level and proceeds layer by layer until the final weights of all elements in the evaluation system are obtained.

[0093] (2) Based on the principal component analysis method (PCA) to determine the objective weights, use the statistical software SPSS to analyze the data, and obtain the eigenvalues of the principal components of each index accordingly. Then construct a correlation coefficient matrix that reveals the strength and direction of the linear relationship between different indexes. Based on the eigenvalues and the correlation coefficient matrix, a principal component model can be established, understand the main sources of variation in the data, and attribute these variations to a few principal components. Finally, deduce the weights of each index from the variance contribution rates of each selected principal component. Among them, it specifically includes the following steps:

[0094] ① Use the difference method to keep the trends of the evaluation indexes consistent;

[0095] ② Calculate the correlation coefficient matrix F and eigenvalues and eigenvectors;

[0096] ③ Calculate the contribution rate of the correlation coefficient matrix and determine the number of principal components;

[0097] ④ Normalize the weights.

[0098] Multiply the standardized index data by the weights to construct a weighted judgment matrix;

[0099] Specifically, the standardized index data represents the relative performance of the elevator in various quality and safety indicators. By multiplying the standardized data by the corresponding weights, the weighted index values can be obtained, and these values constitute the weighted judgment matrix. In the weighted judgment matrix, each index is adjusted according to its weight, which enables a more accurate reflection of the actual performance of the elevator in various indicators during the subsequent evaluation process. As one of the input data, it provides a basis for the application of the TOPSIS evaluation method. Its construction includes the following steps:

[0100] (1) Design the matrix structure. According to the evaluation items and weights, design a suitable matrix structure. The rows of the matrix usually represent different evaluation objects, and the columns represent different evaluation items;

[0101] (2) Fill in the data. Fill the processed data into the matrix. For each evaluation object, fill in the corresponding numerical value according to its performance in each evaluation item;

[0102] (3) Apply the weight of each evaluation item to the matrix. Multiply the numerical value of each evaluation item by its corresponding weight to obtain the weighted numerical value.

[0103] According to the weighted judgment matrix, determine the maximum and minimum values of each index respectively as the positive ideal solution and the negative ideal solution, and calculate the Euclidean space distances between each elevator quality and safety level evaluation object and the positive ideal solution and the negative ideal solution;

[0104] Specifically, based on the weighted judgment matrix, judge the weighted performance of each elevator quality and safety level evaluation object in each index. These weighted performances reflect the actual level of the elevator in various quality and safety indicators and have been adjusted according to the importance of the indicators. On this basis, determine the maximum and minimum values of each index respectively as the positive ideal solution and the negative ideal solution. The positive ideal solution represents the elevator quality and safety level with all indicators reaching the optimal state, while the negative ideal solution represents the elevator quality and safety level with all indicators reaching the worst state. Then calculate the Euclidean space distances between each elevator quality and safety level evaluation object and the positive ideal solution and the negative ideal solution, which is an objective evaluation criterion used to measure the closeness between each elevator quality and safety level evaluation object and the ideal solution, reflecting not only the actual level of the elevator in various quality and safety indicators but also considering the relative importance between the indicators.

[0105] According to the distance calculation results, calculate the relative closeness of each evaluation object to the optimal solution, and use this as the measurement result of the elevator quality and safety level;

[0106] The specific calculation process is as follows:

[0107] (1) Calculate the weighted decision matrix.

[0108] Z = (z ij ) m×n ;

[0109] where z ij is an element of the weighted judgment matrix, and z ij = x ij w j , x ij is an unweighted data element of the elevator quality safety index, and w j is the weight, 1 ≤ i ≤ m, 1 ≤ j ≤ n, where m and n are the number of indicators and the number of evaluation individuals, respectively.

[0110] (2) Determine the positive and negative ideal solutions.

[0111]

[0112]

[0113] where is the maximum value of the quality safety index j, is the minimum value of the quality safety index j.

[0114] (3) KL distance from the evaluation object to the positive and negative ideal solutions.

[0115]

[0116]

[0117] where is the Euclidean space distance from the target elevator to the maximum value, is the Euclidean space distance from the target elevator to the minimum value

[0118] (4) Closeness of the evaluation object to the positive and negative ideal solutions.

[0119]

[0120]

[0121] where: η is the preference coefficient of the decision maker, is the closeness between the evaluation object and the ideal object; is the distance between the evaluation object and the ideal object.

[0122] (5) Calculate the relative closeness.

[0123]

[0124] where C iis the relative closeness of the target elevator to the preset optimal solution, C i The larger the value, the closer the evaluation object is to the ideal object.

[0125] To further optimize the technical solution, the method proposed in this embodiment further includes sorting the elevators according to the relative closeness, and interpreting and evaluating the sorting results in combination with the actual situation;

[0126] Specifically, first, it is necessary to construct an evaluation index system for the quality and safety of elevators according to the actual situation. This system may cover multiple aspects such as the traction system, door system, safety protection device, suspension device, and electrical control system. Each index reflects different aspects of the quality and safety of elevators and plays an indispensable role in the overall evaluation. Sort the elevators according to the relative closeness. The sorting results can intuitively show the differences in the quality and safety levels of different elevators. In combination with the actual situation, the sorting results can be interpreted and evaluated. For example, for the elevators ranked at the top, the reasons for their high quality and safety levels can be analyzed, while for the elevators ranked at the bottom, attention should be paid to the existing problems and deficiencies, and corresponding improvement measures and suggestions should be put forward.

[0127] On the other hand, this embodiment provides a weighted KL-TOPSIS elevator quality and safety level measurement system, as Figure 2 shown, including:

[0128] A data acquisition and processing module, configured to acquire various quality and safety index data of the target elevator and perform preprocessing to construct an elevator quality and safety index data set;

[0129] A weighted judgment matrix construction module, configured to substitute the elevator quality and safety index data set into the elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, where the elevator quality and safety adaptability evaluation index system includes first-level quality and safety indexes, second-level quality and safety indexes, and corresponding weights;

[0130] A Euclidean space distance calculation module, configured to determine the maximum and minimum values of each quality and safety index based on the weighted judgment matrix, and calculate the Euclidean space distance of the target elevator based on the maximum and minimum values;

[0131] A quality and safety level measurement module, configured to calculate the relative closeness of the target elevator to the preset optimal solution based on the Euclidean space distance, that is, the quality and safety level measurement result of the target elevator.

[0132] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A weighted KL-TOPSIS-based elevator quality and safety level measurement method, characterized in that include: Obtain and preprocess the quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator dataset; Substituting the elevator quality and safety indicator data set into a preset elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, wherein the elevator quality and safety adaptability evaluation index system includes first-level quality and safety indicators, second-level quality and safety indicators, and corresponding weights; Determining the maximum and minimum values of each quality and safety indicator based on the weighted judgment matrix, and calculating the Euclidean space distance of the target elevator based on the maximum and minimum values; The relative closeness between the target elevator and a preset optimal solution is calculated based on the Euclidean space distance, that is, the quality safety level measurement result of the target elevator.

2. The method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS according to claim 1, wherein Obtain and preprocess the quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator dataset, including: Obtain various quality and safety indicator data of the target elevator; Clean the data of the various quality and safety indicators, remove outliers and missing values, and standardize the cleaned data to obtain the processed data of the various quality and safety indicators; The processed quality and safety indicator data are sorted into quality and safety indicator data of different dimensions to construct the elevator quality and safety indicator data set.

3. The elevator quality and safety level measurement method based on weighted KL-TOPSIS according to claim 1, wherein, The first-level quality and safety indicators of the elevator quality and safety adaptability evaluation index system include reliability indicator B1, maintenance efficiency indicator B2, performance indicator B3, safety performance indicator B4, energy efficiency and environmental protection indicator B5, management and maintenance indicator B6. The second-level quality and safety indicators include mean time between failures B 11 , failure rate B 12 , mean time to repair B 21 , timely rate of fault handling B 22 , running speed B 31 , load capacity B 32 , starting acceleration and braking deceleration B 33 , leveling accuracy B 34 , noise and vibration B 35 , emergency braking system B 41 , anti-falling device B 42 , emergency measures B 43 , energy efficiency grade B 51 , environmental protection performance B 52 , maintenance condition B 61 , passing rate of regular inspection B 62 .

4. The elevator quality and safety level measurement method based on weighted KL-TOPSIS according to claim 1, characterized in that The corresponding weights of the first-level quality and safety indicators and the second-level quality and safety indicators of the elevator quality and safety adaptability evaluation index system are determined by combining the AHP method and the PCA method.

5. The method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS according to claim 1, wherein Constructing the weighted judgment matrix includes: Based on the quality and safety indicators and corresponding weights in the elevator quality and safety adaptability evaluation index system, the data in the elevator quality and safety index data set are multiplied by the corresponding weights to obtain weighted values. Based on the weighted values, the weighted judgment matrix is constructed. The weighted judgment matrix is: Z = (z ij ) m×n ; Among them, z ij is an element of the weighted judgment matrix, and z ij = x ij w j , where x ij is an unweighted data element of the elevator quality and safety index, w j is the weight, 1 ≤ i ≤ m, 1 ≤ j ≤ n, and m and n are the number of indicators and the number of evaluation individuals respectively.

6. The elevator quality and safety level measurement method based on weighted KL-TOPSIS according to claim 5, wherein Determining the maximum and minimum values of various quality and safety indicators based on the weighted judgment matrix includes: Among them, is the maximum value of the quality and safety index j, is the minimum value of the quality and safety index j.

7. The elevator quality and safety level measurement method based on weighted KL-TOPSIS according to claim 6, characterized in that Calculating the Euclidean space distance of the target elevator based on the maximum value and the minimum value includes: Among them, is the Euclidean space distance from the target elevator to the maximum value, is the Euclidean space distance from the target elevator to the minimum value.

8. The method for measuring the quality and safety level of an elevator based on weighted KL-TOPSIS according to claim 7, wherein Calculating the relative proximity between the target elevator and the preset optimal solution based on the Euclidean space distance includes: Among them, C i is the relative proximity of the target elevator to the preset optimal solution, is the proximity of the target elevator to the maximum value, is the proximity of the target elevator to the minimum value, and η is the decision maker's preference coefficient.

9. A weighted KL-TOPSIS-based elevator quality and safety level measurement system, characterized in that, include: The data acquisition and processing module is used to obtain and pre-process the quality and safety indicator data of the target elevator to construct an elevator quality and safety indicator data set; A weighted judgment matrix construction module is used to substitute the elevator quality and safety indicator data set into the elevator quality and safety adaptability evaluation index system to construct a weighted judgment matrix, wherein the elevator quality and safety adaptability evaluation index system includes first-level quality and safety indicators, second-level quality and safety indicators and corresponding weights; a Euclidean space distance calculation module, configured to determine the maximum and minimum values of various quality and safety indicators based on the weighted judgment matrix, and calculate the Euclidean space distance of the target elevator based on the maximum and minimum values; The quality and safety level measurement module is used to calculate the relative closeness between the target elevator and the preset optimal solution based on the Euclidean space distance, that is, the quality and safety level measurement result of the target elevator.

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