Operation safety index evaluation method based on improved Uninorm operator

Abstract

The invention discloses an operation safety index evaluation method based on an improved Uninorm operator, and the method comprises the steps: carrying out the analysis and processing of obtained related operation data, and building a more accurate evaluation system; the subjective weight of each index is calculated by using a linguistic variable-based optimal worst method (BWM), the objective weight of the index is calculated by using an entropy evaluation method, the two weights are combined by using an improved Uninorm operator to obtain the combined weight of the index, an evaluation matrix based on an improved triangular fuzzy number is constructed, and the evaluation matrix is used for evaluating the index. Quantifying the safety level of the traffic operation form of the transportation means taking a rail transit train, a road automobile or a park automatic navigation vehicle as the transportation means; according to the invention, the accuracy and authenticity of traffic operation evaluation can be improved, so that the current social development requirements can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic operation safety feature evaluation using rail transit trains, road vehicles, or park automatic navigation vehicles as transportation tools, and in particular to an operation safety index evaluation method based on obtaining combination weights using an improved Uniform operator. Background Art

[0002] With the advancement of modern cities, the importance of various types of transportation in cities continues to increase. However, the growing transportation network and passengers' travel needs have brought huge challenges to the safety of transportation operations, and its safety issues have also been increasingly valued. In the existing technology, most of the evaluation of traffic operation indicators adopts a single method for weight calculation, that is, only subjective or objective methods are used, which leads to errors in evaluation accuracy and lacks research on the reliability of operation safety evaluation. Summary of the invention

[0003] In order to solve the problems existing in the existing methods and make the evaluation results more scientifically and accurately reflect the current rail transit operation safety level, the present invention provides a more accurate evaluation method, which provides an effective reference for transportation operation safety evaluation.

[0004] The technical solution of the present invention is as follows: An operational safety index evaluation method based on an improved Uniform operator includes the following steps: Step 1: Select comprehensive operational data for analysis and build an operational safety indicator system; Step 2: The best-worst method (BWM) based on linguistic variables was used to determine the subjective weight of each indicator; Step 3: Based on historical operation data, the entropy method is proposed to calculate the objective weight of the indicator; Step 4: Use the improved Uninorm operator to combine the two weights of steps S2 and S3 to obtain the combined weight of the indicator; Step 5: Construct an evaluation matrix based on improved triangular fuzzy numbers and obtain the operational safety evaluation results by combining weights.

[0005] Furthermore, in step 1, the traffic operation safety index system includes four first-level indicators, and the first-level indicators include operation-related personnel factors, environmental factors, equipment factors, and management factors.

[0006] Furthermore, the four first-level indicators can be divided into ten second-level indicators. Operation-related personnel factors include physical reasons, non-compliant behaviors, and unintentional behaviors; environmental factors include natural environment and social environment; equipment factors include vehicle systems, communication systems, and power supply systems; management factors include skill training and emergency rescue systems.

[0007] Furthermore, in step 2, the subjective weight is determined by using the best-worst method (BWM) based on linguistic variables, including the following steps: Determine the evaluation index set ; Combined with relevant expert analysis, determine the best indicators The worst indicator ; Determining the best indicators With indicators The comparison vector of ; Determine the indicator The worst indicator The comparison vector of ; The comparison vectors are and ; Indicators Relative to the indicator The importance of Indicators Relative to the indicator The importance of.

[0008] Establish a BWM model to calculate the weight carrier. The BWM model can be established as follows:

[0009] Get the weight vector ; Furthermore, in step 3, an entropy method is proposed to calculate the objective weight of the indicator, including the following steps: ; Calculation Sample In the indicator The proportion of all samples , reflecting the relative importance of the sample in this indicator. Among them, is the total number of samples; ; in is the index entropy value; is the normalization factor, ensuring that the entropy value range is between [0, 1], and the entropy value Reflects the indicator The degree of discreteness, entropy value The larger the entropy value, the higher the uncertainty of the indicator, the less information it provides, and the The smaller it is, the lower the uncertainty of the indicator is and the more information it provides; ; in Is the entropy weight of the secondary indicator. Calculate indicators based on entropy value The weight reflects the importance of the indicator in the overall evaluation. The smaller the entropy value, the greater the weight. The larger the entropy value, the more information the indicator provides; the larger the entropy value, the greater the weight The smaller it is, the less information the indicator provides.

[0010] Furthermore, in step 4, the combined weight is calculated using the improved Uninorm operator, including the following steps: For any subjective weight With objective weight , the improved Uninorm operator can be written as: ; is a neutral element, and the final weight indicator matrix is ; The influence of subjective and objective weights was strengthened and normalized; The influence of the sum of subjective and objective weights is balanced, the joint effect is reflected, and normalization is also performed; It helps to limit the dominance of a certain larger weight value on the final combination result, making the result more balanced.

[0011] Further, in step 5, the operational safety evaluation result is obtained by combining the weights, including the following steps: Index given by operation experts The triangular fuzzy fraction of ; Defined as ; , and Respectively represent the lower limit, upper limit and most likely value of the fuzzy number; ; represents weighted triangular fuzzy number; ; Score the final evaluation; Set an evaluation standard and get the evaluation result based on the evaluation score.

[0012] The design idea of ​​the present invention: The present invention analyzes and processes the acquired relevant operation data to establish a more accurate evaluation system; adopts the best-worst method (BWM) based on linguistic variables to calculate the subjective weight of each indicator, proposes an entropy method to calculate the objective weight of the indicator, combines the two weights using the improved Uninorm operator to obtain the combined weight of the indicator, constructs an evaluation matrix based on improved triangular fuzzy numbers, and quantifies the safety level of the transportation operation form of transportation means such as rail transit trains, road cars, or park automatic navigation vehicles; the present invention can improve the accuracy and authenticity of the evaluation of transportation operations to better meet the current social development needs.

[0013] The beneficial effects of the present invention are: The present invention uses the BWM method and the entropy weight method to calculate the weights of subjective and objective indicators respectively, and then uses the improved Uninorm operator to calculate the comprehensive weight to obtain the evaluation result. Prior to this, a safety indicator system covering all aspects of traffic operation is constructed to provide relevant personnel with a reasonable and efficient evaluation plan, and to provide the accuracy of the operational safety evaluation results to better meet the needs of urban development and residents' travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a safety indicator system diagram of the present invention; Figure 2 It is a safety index evaluation flow chart of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings.

[0016] An operational safety index evaluation method based on an improved Uniform operator includes the following steps: Step 1: Classify the acquired operational data and select representative indicators to ensure that the data acquired by the system is comprehensive, accurate, and real-time, providing reliable data support for subsequent analysis and decision-making; like Figure 1 As shown, the traffic operation safety index system includes four first-level indicators, which include operation-related personnel factors, environmental factors, equipment factors, and management factors. The four first-level indicators can be subdivided into ten second-level indicators. Operation-related personnel factors include physical reasons, non-compliant behaviors, and unintentional behaviors; environmental factors include natural environment and social environment; equipment factors include vehicle systems, communication systems, and power supply systems; management factors include skill training and emergency rescue systems; Based on historical operational event data, the standardized index In the process of standardization of the sample values ​​of primary and secondary indicators, the number of indicators which are equal to 4 and 10 respectively; ; sample Indicators The standardized result of the value is Indicates; Sample Indicators Value express; Is an indicator The set of all sample values ​​of .

[0017] Step 2: If Figure 2 As shown, based on BWM calculation weights, the evaluation index set is determined ; Combined with relevant expert analysis, select the best indicator among all indicators The worst indicator ; Determining the best indicators With indicators The comparison vector BO ; Determine the indicator The worst indicator The comparison vector OW ; The comparison vectors are and ; Indicators Relative to the indicator The importance of Indicators Relative to the indicator The importance of Establish a BWM model to calculate the weight carrier. The BWM model can be established as follows: ; Convert the above model into a linear model: ; is the weight of the best indicator, is the weight of the worst indicator, is the weight of all indicators; is the weight error, The smaller it is, the smaller the error of the weight is, and the more reliable the calculated data is; Solve the formula to get the weight vector ; Conduct consistency tests on the decision preference information of each indicator to ensure the reliability of the preference information; ; The consistency ratio (CR) is used to measure the reliability of decision preference information. CI is the consistency index. CI is the value when the consistency of preference information is the lowest. The maximum value of .

[0018] The larger the CR value, the lower the consistency. When CR = 0, the preference information is completely consistent. When CR < 0.1, the preference information meets the consistency requirement: Table 1 Consistency index values

[0019] Represents the best indicator Relative to the worst indicator The importance of Step 3: If Figure 2 Calculate the weights using the entropy weight method, using the standardized results obtained in step 1 ; ; For the Secondary indicators The proportion of the first-level indicators, , ; ; Index The entropy value ; ; For indicators The weight of According to the entropy weight method, the weights of the primary and secondary indicators are obtained.

[0020] Step 4: Figure 2 The improved Uninorm operator is used to calculate the index combination weight; Uninorm operators are mappings U that satisfy monotonicity, associativity, and commutativity: ; For any subjective weight With objective weight ; Taking into account the dominant role of larger weight values, the Uninorm operator can be rewritten to calculate the combined weight as follows: ; The final weight indicator matrix is .

[0021] Step 5: Establish scoring criteria: Table 2 Scoring criteria

[0022] Defined as , represents the index given by each city's traffic operation experts The maximum fuzzy score of , and Respectively represent the lower limit, upper limit and most likely value of the fuzzy number; based on And the indicator weight matrix , weighted triangular fuzzy number It can be calculated as follows: ; Transformed triangular fuzzy number ; ; Numeric It is the operational safety indicator result.

[0023] Table 3 Urban rail transit operation safety level

[0024] The following examples collect rail transit operation safety reports and safety accident related literature, and analyze the relevant data in combination with rail transit operation characteristics.

[0025] Based on the experience analysis of rail transit experts, the best and worst indicators are selected.

[0026] Table 4 Selection of the best and worst indicators

[0027] A1 represents the first level indicator, i.e., the operation-related personnel factors, and so on; A11 represents the first second-level indicator (physical reasons) under the first first-level indicator (operation-related personnel factors), and so on.

[0028] Determine the comparison vector between the best first-level index and the worst first-level index , ; Determine the comparison vector between the best secondary index and the worst secondary index , , , , , , , ; Substituting the comparison vector into the BWM model can obtain the indicator weight.

[0029] Table 5 Subjective weights of first-level indicators

[0030] Table 6 Subjective weights of secondary indicators

[0031] Table 7 Subjective final weights of operating indicators

[0032] The rail transit operation safety reports and safety accident-related documents were collected, and 607 incidents that occurred in the domestic urban rail transit system from 2018 to 2021 were classified into 18 categories; Table 8 Rail transit operation safety incidents

[0033] The weight of the first-level indicators is obtained through classification statistics; Table 9 Objective weights of first-level indicators

[0034] Similarly, the weights of further secondary indicators are calculated by classification; Table 10 Secondary index weights

[0035] Finally, the objective weights of the operational characteristic indicators are obtained.

[0036] Table 11 Objective weights of operational characteristic indicators

[0037] ,The comprehensive weight is obtained by improving the Uninorm operator.

[0038] Table 12 Comprehensive weights of operational characteristic indicators

[0039] Combining Table 2 with three urban rail transit operation experts, each first-level indicator was scored and the scores were converted into a fuzzy matrix; ; According to Table 12, based on the fuzzy matrix and the weight matrix, the weighted triangular fuzzy number is obtained:

[0040] Compute the average of weighted triangular fuzzy numbers: ; The operational safety score can be obtained by defuzzifying the average triangular fuzzy number: ;

[0041] Compared with Table 3, combined with the evaluation criteria, it shows that the region has good infrastructure, advanced vehicles and signal systems, and also shows that the proposed urban rail transit operation safety evaluation method is effective.

[0042] Through the description of the above implementation methods, technicians in this field can clearly understand each implementation method. The constructed index system strictly follows the principles of scientificity, objectivity, comprehensiveness, and practicality, and has a scientific and reliable calculation method and data source, which fully reflects the operational characteristics. On this basis, the subjective and objective weights are calculated, and the comprehensive weights of the indicators are calculated based on the improved Uninorm operator, which can effectively avoid the impact of the unbalanced coordination between subjective and objective factors. Finally, the triangular fuzzy method is used to obtain the comprehensive evaluation results. The evaluation results have higher stability and credibility, which is conducive to improving the protection of operational safety and helping managers to formulate targeted measures for control.

[0043] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. An operational safety index evaluation method based on an improved Uninorm operator, characterized in that: The following steps are involved: Step 1: Select operational data for analysis and build a traffic operation safety indicator system; Step 2: Use the best-worst method (BWM) based on linguistic variables to determine the subjective weight of each indicator; Step 3: Based on historical operation data, the entropy method is proposed to calculate the objective weight of the indicator; Step 4: Use the improved Uninorm operator to combine the two weights of step 2 and step 3 to obtain the combined weight of the indicator; Step 5: Construct an evaluation matrix based on improved triangular fuzzy numbers and obtain the operational safety evaluation results by combining weights.

2. According to claim 1, an operational safety index evaluation method based on an improved Uninorm operator is characterized in that: In step 1, a traffic operation safety indicator system is constructed, including the following steps: 1.

1. The traffic operation safety index system includes four first-level indicators, which include operation-related personnel factors, environmental factors, equipment factors, and management factors; the four first-level indicators are subdivided into ten second-level indicators as follows: Operation-related personnel factors include physical reasons, non-compliant behaviors, and unintentional behaviors; environmental factors include natural environment and social environment; equipment factors include vehicle systems, communication systems, and power supply systems; management factors include skills training and emergency rescue systems; 1.

2. Standardized index based on historical operation event data The sample values ​​of the first-level indicators and the second-level indicators are standardized: ； sample Indicators The standardized result of the value is Representation; Sample Indicators Value express; Is an indicator The set of all sample values ​​of .

3. The operation safety index evaluation method based on the improved Uninorm operator according to claim 1 is characterized in that: In step 2, the subjective weight is determined using the best-worst method (BWM) based on linguistic variables, which includes the following steps: 2.

1. Determine the evaluation index set , determine the best indicator The worst indicator ; Determine the best indicator With indicators The comparison vector BO , determine the indicator The worst indicator The comparison vector OW ; The comparison vectors are and ; Indicators Relative to the indicator The importance of Indicators Relative to the indicator The importance of Indicates the total number of all indicators; 2.

2. Establish a BWM model to calculate the weight carrier. The BWM model is established as follows: ； Get the weight vector ; in, is the weight of the best indicator, is the weight of the worst indicator, is the weight of all indicators, Indicates the sum of all indicators in the current indicator set.

4. The operation safety index evaluation method based on the improved Uninorm operator according to claim 1 is characterized in that: In step 3, based on the historical operation data, the entropy method is proposed to calculate the objective weight of the indicator, including the following steps: 3.

1. Calculate the sample ratio: ； Calculation Sample In the indicator The proportion of all samples , reflects the relative importance of the sample in this indicator, where is the total number of samples, Representation sample Indicators The standardized result of the value; 3.

2. Calculate entropy value: ； in is the index entropy value; is the normalization factor, ensuring that the entropy value range is between [0, 1], and the entropy value Reflects the indicator The degree of discreteness, entropy value The larger the entropy value, the higher the uncertainty of the indicator, the less information it provides, and the The smaller it is, the lower the uncertainty of the indicator is and the more information it provides; 3.

3. Calculate indicator weights: ； in is the entropy weight of the secondary indicator, and the indicator is calculated based on the entropy value The weight reflects the importance of the indicator in the overall evaluation. The smaller the entropy value, the greater the weight. The larger the entropy value, the more information the indicator provides; the larger the entropy value, the greater the weight The smaller it is, the less information the indicator provides. Indicates the sum of the metrics of the current metric set.

5. The operation safety index evaluation method based on the improved Uninorm operator according to claim 1 is characterized in that: In step 4, the combined weight is calculated using the improved Uninorm operator, including the following steps: For any subjective weight With objective weight , the improved Uninorm operator is written as: ； in, The Uninorm operator is used to calculate the subjective weight With objective weight The linear combination expression of is a neutral element, and the final weight indicator matrix is .

6. The operation safety index evaluation method based on the improved Uninorm operator according to claim 1 is characterized in that: In step 5, the operational safety evaluation results are obtained by combining the weights. The following steps are involved: 5.

1. Given index The triangular fuzzy fraction of ; Defined as ; in , and Represent the lower limit, upper limit and most likely value of the fuzzy number respectively; ； in represents the weighted triangular fuzzy number, where , is the weight; 5.

2. Set an evaluation standard and obtain the evaluation result based on the evaluation score; ； in Score for the final evaluation.

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