Fuzzy theory-based intelligent rail driving ability evaluation method

Abstract

The invention relates to the technical field of intelligent rail driving ability evaluation, and discloses an intelligent rail driving ability evaluation method based on a fuzzy theory, and the method comprises the steps: obtaining intelligent rail driving data and intelligent rail driving ability evaluation data of an intelligent rail driver; obtaining intelligent rail driving ability evaluation index information from the intelligent rail driving data and the intelligent rail driving ability evaluation data, and determining hierarchical intelligent rail driving ability evaluation index scores; establishing a judgment matrix of hierarchical hierarchical intelligent rail driving ability evaluation indexes by adopting an analytic hierarchy process, and calculating weight values of the hierarchical hierarchical intelligent rail driving ability evaluation indexes; and calculating an intelligent rail driving ability evaluation score by adopting a fuzzy comprehensive evaluation method according to the hierarchical intelligent rail driving ability evaluation index score and the corresponding weight value. According to the invention, the intelligent rail driving ability of the intelligent rail driver can be evaluated more comprehensively and accurately.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart rail driving ability evaluation, and particularly relates to a smart rail driving ability evaluation method based on fuzzy theory. BACKGROUND

[0002] With the use of intelligent rail rapid transit system (referred to as smart rail) in some cities in China, smart rail has become an important part of modern urban transportation system. Smart rail uses automation technology and intelligent control system to achieve efficient, safe and low-energy transportation, greatly improving the operation efficiency of urban public transportation. However, the safe and stable operation of smart rail cannot be separated from the high quality and professional ability of the driver, and the suitability of the driver has become one of the key factors for the efficient and smooth operation of smart rail.

[0003] The suitability of smart rail driver generally refers to the ability of the driver to effectively complete his duties and respond to emergencies in the actual operation process. This ability not only includes the basic operation skills of the driver, but also involves his familiarity with traffic regulations, system operation and monitoring ability, emergency response ability, safety awareness and physical and mental quality, etc. In the traditional driver evaluation system, only the operation skills and theoretical knowledge of the driver are concerned, while the comprehensive ability of the driver in emergency disposal, psychological quality and stress coping is ignored, which makes the existing evaluation method unable to fully reflect the suitability of the driver.

[0004] At present, the ability evaluation method of most urban public transportation drivers focuses on single-dimensional evaluation. For example, the theoretical knowledge of the driver is evaluated by written test, and the operation skills of the driver are tested by simple simulation test or driving operation test. These evaluation methods cannot meet the comprehensive requirements of the driver in actual work, and cannot effectively evaluate the comprehensive ability of the driver in complex environment. Further, emergency handling ability and psychological quality are one of the key abilities of smart rail driver. When encountering equipment failure, traffic accident and other emergencies, the driver can quickly and calmly respond, take appropriate measures in time, and ensure the safety of train and passengers' life and property. However, the existing evaluation system usually ignores this point, and does not include the emergency disposal ability and stress coping ability of the driver in the evaluation range. SUMMARY

[0005] In view of the above deficiencies in the prior art, the present application provides a smart rail driving ability evaluation method based on fuzzy theory.

[0006] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows:

[0007] A smart rail driving ability evaluation method based on fuzzy theory, comprising the following steps:

[0008] Obtaining the smart rail driving data and the smart rail driving ability evaluation data of the smart rail driver;

[0009] Obtaining the smart rail driving ability evaluation index information from the smart rail driving data and the smart rail driving ability evaluation data, and determining the hierarchical level smart rail driving ability evaluation index score;

[0010] Establishing the judgment matrix of the hierarchical level smart rail driving ability evaluation index by using the analytic hierarchy process, and calculating the weight value of the hierarchical level smart rail driving ability evaluation index;

[0011] According to the hierarchical level smart rail driving ability evaluation index score and the corresponding weight value, the fuzzy comprehensive evaluation method is used to calculate the smart rail driving ability evaluation score.

[0012] The present application has the following beneficial effects:

[0013] The present application can more comprehensively and accurately evaluate the smart rail driving ability of the smart rail driver by constructing the hierarchical level smart rail driving ability evaluation system, and the accurate quantification of the driver's on-job ability by the fuzzy comprehensive evaluation method avoids the fuzziness and inconsistency of the subjective score in the traditional evaluation, thereby improving the accuracy of the smart rail driving ability evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a kind of smart rail driving ability evaluation method flow chart based on fuzzy theory. DETAILED DESCRIPTION

[0015] The specific embodiments of the present application are described below to facilitate the understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0016] As Figure 1 shown, the smart rail driving ability evaluation method based on fuzzy theory provided by the present application embodiment comprises the following steps S1 to S4:

[0017] S1, obtaining the smart rail driving data and the smart rail driving ability evaluation data of the smart rail driver;

[0018] In this embodiment, the traditional evaluation system is too one-sided, focusing only on the driver's operational skills or theoretical knowledge, and failing to comprehensively assess the driver's overall qualities in terms of emergency response, safety awareness, and psychological qualities. Therefore, step S1 constructs a comprehensive evaluation system that covers all core capabilities of intelligent rail transit drivers, especially in terms of emergency response capabilities and psychological qualities, to ensure that their ability to adapt to complex working environments is scientifically evaluated.

[0019] Step S1 establishes a multi-level intelligent rail transit driver competency evaluation system, clarifying the hierarchical structure of the target layer, criterion layer, and scheme layer, and ensuring that the evaluation indicators can comprehensively cover the driver's key abilities such as theoretical knowledge, skills, safety awareness, and physical and mental qualities.

[0020] The intelligent rail transit driver competency evaluation system comprehensively covers key factors affecting driver competency, adhering to the basic principles of comprehensiveness, scientific rigor, operability, and comparability, and constructs a hierarchical comprehensive evaluation system. This system adopts a structure of target layer—criteria layer—solution layer, including:

[0021] (1) Target layer (Intelligent rail transit driver suitability assessment).

[0022] (2) Criteria Level (Core Dimensions Affecting Drivers' Job Competency): Theoretical knowledge, skills and qualities, safety awareness, and physical and mental qualities.

[0023] (3) Scheme layer (specific assessment content): composed of various factors of the response criteria layer.

[0024] The specific evaluation index system is shown in Table 1:

[0025] Table 1. Evaluation Index System for the Suitability of Intelligent Rail Transit Drivers

[0026]

[0027] In this embodiment, the intelligent rail transit driving data obtained in step S1 includes simulated operation data of intelligent rail transit driving and train speed data;

[0028] The intelligent rail transit (IRT) driving capability assessment data includes textual data on IRT driving capability assessment and physiological indicator data.

[0029] S2. Obtain intelligent rail transit capability assessment index information from intelligent rail transit driving data and intelligent rail transit capability assessment data, and determine the hierarchical intelligent rail transit capability assessment index scores.

[0030] In this embodiment, step S2 obtains intelligent rail transit capability assessment index information from intelligent rail transit driving data and intelligent rail transit driving capability assessment data, and determines the hierarchical intelligent rail transit driving capability assessment index scores, including:

[0031] The assessment score of intelligent rail transit driving capability is used as the target layer indicator; theoretical knowledge score, skill quality score, safety awareness score, and physical and mental quality score are used as the criterion layer indicators; intelligent system operation score, equipment operation and fault diagnosis score, traffic regulations and safety regulations score, emergency response score, and professional ethics score are used as the scheme layer indicators corresponding to the theoretical knowledge indicators of the criterion layer; basic operation score, emergency operation score, equipment management and maintenance score, and system operation and monitoring score are used as the scheme layer indicators corresponding to the skill quality indicators of the criterion layer; safe operation standard score, dangerous driving behavior score, and danger perception ability score are used as the scheme layer indicators corresponding to the safety awareness indicators of the criterion layer; and physical health score and mental health score are used as the scheme layer indicators corresponding to the safety awareness indicators of the criterion layer.

[0032] Based on the text data of the intelligent rail transit capability assessment, the scores of intelligent system operation, equipment operation and fault diagnosis, traffic regulations and safety provisions, emergency response and professional ethics in the scheme layer are determined according to the theoretical knowledge indicators of the criterion layer.

[0033] Based on the simulated operation data of intelligent rail transit, the basic operation score, emergency operation score, equipment management and maintenance score, and system operation and monitoring score of the corresponding scheme layer are determined according to the skill literacy indicators of the criterion layer.

[0034] Based on the simulated operation data and train speed data of intelligent rail transit, the safety awareness index at the criterion level is determined to correspond to the safety operation standard score, dangerous driving behavior score, and danger perception ability score at the scheme level.

[0035] Based on the textual data and physiological indicator data of the intelligent rail transit capability assessment, the physiological health score and mental health score of the scheme layer are determined corresponding to the safety awareness index of the criterion layer.

[0036] This embodiment uses a test paper to accurately assess the theoretical knowledge level of intelligent rail transit (IRT) drivers, evaluating their theoretical knowledge at the scheme-level indicators to obtain textual data for assessing their theoretical knowledge capabilities. This method allows for the calculation of scores for the corresponding theoretical knowledge indicators, providing accurate data support for subsequent fuzzy comprehensive evaluation. The test paper settings are shown in Table 2.

[0037] Table 2. File Design Related Settings

[0038]

[0039] The performance indicators for each scheme level are as follows:

[0040] Intelligent Rail Transit System Operation: This test primarily assesses the driver's mastery of the intelligent rail transit system, focusing on their understanding of the system interface, operating procedures, and common operational processes, including but not limited to the following systems: HMI system, braking system, train control system, onboard signaling system, onboard wireless communication system, and automatic tracking system.

[0041] Equipment operation and fault diagnosis: This focuses on assessing the driver's ability to operate and diagnose various equipment on the intelligent rail transit vehicle. The driver must be familiar with the equipment's operating procedures, daily maintenance requirements, and the diagnosis and handling of common faults.

[0042] Traffic Regulations and Safety Rules: This test assesses the driver's understanding and mastery of traffic regulations and safe operating procedures for the intelligent rail system. It focuses on how the driver complies with relevant traffic laws and regulations in daily work and ensures passenger safety and traffic order.

[0043] Emergency Response: This mainly assesses the driver's ability to respond to emergencies, examining whether the driver can respond quickly and accurately when equipment malfunctions, traffic accidents, or other unexpected events occur.

[0044] Professional ethics: This assessment aims to evaluate the driver's professional ethics and sense of responsibility.

[0045] Skills and competence directly impact the operational accuracy and safety of intelligent rail transit (IRT) drivers. This embodiment proposes a comprehensive simulated practical assessment scheme, aiming to evaluate drivers' various capabilities in actual operation through systematic scenario simulation, thereby ensuring they possess the comprehensive qualities to complete tasks safely and efficiently.

[0046] The practical examination room is set up as follows:

[0047] Based on on-site research of the driver testing sites in the training departments, and combined with the four scheme-level indicators under the skills and competencies described in this article—basic operation, system operation and monitoring, equipment management and maintenance, and emergency operation—a practical testing room that meets the assessment requirements was designed. The testing room design follows the driver's driving process from start to finish, primarily assessing the driver's basic operation, system operation, and monitoring abilities. In addition, an emergency response area and an equipment management and maintenance area were set up in the latter half of the testing room. The emergency response area simulates unexpected events to assess the driver's emergency response and handling capabilities; the equipment management and maintenance area is located at the finish line, focusing on assessing the driver's equipment fault inspection and maintenance capabilities.

[0048] The evaluation criteria for each indicator at the solution level are as follows:

[0049] Building upon the aforementioned practical testing environment design, this section details the assessment items and standards for each scheme-level indicator, as shown in Tables 3, 4, 5, and 6. The scoring for each scheme-level indicator will be based on the driver's performance in simulated practical operations, covering four main aspects: basic operation, system operation and monitoring, emergency operation, and equipment management and maintenance. Assessors will score each item according to established standards, combined with simulated data from the driver's intelligent rail transit operation, ensuring the fairness and accuracy of the scoring. The final score reflects the driver's comprehensive ability in each operation.

[0050] Table 3 Basic Operation Evaluation Criteria

[0051]

[0052] Table 4 System Operation and Monitoring Evaluation Criteria

[0053]

[0054] Table 5 Emergency Operation Evaluation Standards

[0055]

[0056] Table 6 Equipment Management and Maintenance Evaluation Standards

[0057]

[0058] The safety awareness assessment is as follows:

[0059] Strict adherence to safe operating procedures by drivers is crucial to ensuring the safe operation of the intelligent rail transit system. Therefore, this embodiment uses simulated driving data from drivers on the intelligent rail transit system. If a driver violates any safety regulations, professional intelligent rail transit driving examiners will observe their operational performance and deduct points according to the safety assessment standards, as shown in Table 7. The final score reflects the driver's adherence to safety awareness and their operational execution capabilities throughout the entire driving process.

[0060] Table 7 Safe Operating Procedures

[0061]

[0062] Considering the operational differences between intelligent rail transit (IRT) drivers and private car drivers, this embodiment focuses on assessing dangerous driving behavior based on simulated IRT driving data and train speed data, centering on changes in train speed and acceleration during the driving process, with a particular emphasis on speeding and sudden acceleration changes. Dangerous driving identification employs a threshold-setting method, setting thresholds for speeding and sudden acceleration changes based on existing data, as shown in Table 8. The presence of dangerous driving behavior is determined by analyzing the train speed-time curves during simulated driving. If the set thresholds are exceeded, points are deducted according to the dangerous driving assessment standards, as shown in Table 9, with a maximum score of 100 points. The final score reflects the driver's safe driving performance in actual operation.

[0063] Table 8 Thresholds for Dangerous Driving Factors

[0064]

[0065] Table 9. Standards for Assessing Dangerous Driving

[0066]

[0067] Regarding speeding, based on the speed limits for urban buses stipulated in the "Road Traffic Law of the People's Republic of China," the speeding threshold is set as follows: Duration exceeds This can be considered speeding. For sudden speed changes, referencing recommended thresholds for sudden speed changes in heavy-duty trucks abroad, and considering the weight of the intelligent rail transit train, the threshold is set as follows: ,Right now Duration exceeds This can be identified as a sudden change in speed.

[0068] Danger perception ability refers to a driver's ability to identify and react to potential hazards in traffic situations. This embodiment designs a self-assessment scale for danger perception ability suitable for intelligent rail transit drivers, as shown in Table 10. Drivers use the assessment scale to evaluate themselves, and the final score reflects the driver's ability to identify and respond to potential hazards in a timely manner in complex environments.

[0069] Table 10 Self-Assessment Scale for Danger Perception Ability

[0070]

[0071] The physical and mental health assessment is as follows:

[0072] Considering the working environment of intelligent rail transit (IRT) drivers, this embodiment establishes physiological health assessment standards suitable for IRT drivers based on their physiological health indicators, as shown in Tables 11, 12, 13, and 14. Assessment factors include heart rate, blood pressure, blood glucose, and blood lipids. By analyzing the driver's medical examination report and combining it with the established assessment standards, the driver's final score in terms of physiological health can be obtained.

[0073] Table 11 Heart Rate Assessment Standards

[0074]

[0075] Table 12 Blood Pressure Assessment Criteria

[0076]

[0077] Table 13 Blood Glucose Assessment Standards

[0078]

[0079] Table 14 Blood Lipid Assessment Standards

[0080]

[0081] To address the mental health issues of intelligent rail transit (IRT) drivers, the Symptom Checklist-90 (SCL-90) was used to obtain textual data assessing drivers' ability to drive IRT. This scale contains 10 rating factors and 90 items, each item divided into 5 levels from mild to severe. The rating dimensions are shown in Table 15, and the scoring criteria are shown in Table 16.

[0082] Table 15 Assessment Dimensions of the Symptom Self-Rating Scale

[0083]

[0084] Table 16 Scoring criteria for the Symptom Self-Rating Scale

[0085]

[0086] Finally, the mental health score is converted into a 100-point scale using Equation 1, and this score is used as the data input for the fuzzy comprehensive evaluation of the driver's mental health.

[0087]

[0088] S3. Use the analytic hierarchy process (AHP) to establish a judgment matrix for the hierarchical intelligent rail transit driving capability assessment index, and calculate the weight values ​​of the hierarchical intelligent rail transit driving capability assessment index.

[0089] In this embodiment, step S3 uses the analytic hierarchy process (AHP) to establish a judgment matrix for the hierarchical intelligent rail transit driving capability assessment indicators, and calculates the weight values ​​of the hierarchical intelligent rail transit driving capability assessment indicators, including:

[0090] Based on expert knowledge data, the relative importance of evaluation indicators for intelligent rail transit driving capabilities at each level is determined, and a judgment matrix from the target layer to the criterion layer and a judgment matrix from the criterion layer to the scheme layer are constructed.

[0091] Calculate the indicator weights from the target layer to the criterion layer based on the judgment matrix from the target layer to the criterion layer, and calculate the indicator weights from the criterion layer to the solution layer based on the judgment matrix from the criterion layer to the solution layer.

[0092] Multiply the indicator weights from the target layer to the criterion layer by the indicator weights from the criterion layer to the solution layer to obtain the comprehensive indicator weights from the target layer to the solution layer.

[0093] The analytic hierarchy process (AHP) used in this embodiment is commonly used to solve complex decision-making problems. It decomposes the problem into multiple levels and determines the relative importance of factors at each level by comparing them in pairs, thus determining the weight of each factor. The weights of each level are then aggregated to arrive at the final decision. This embodiment uses AHP by inviting 10 experts in the field of intelligent rail transit (including intelligent rail transit drivers, trainers, and technicians) to complete a questionnaire to determine the relative importance of indicators at each level. The survey results are compared with a 1-9 scale, and the judgment matrices of the 10 experts are combined into a single judgment matrix using the mean method. The weights of each indicator are further calculated, and finally, a consistency check is performed. The specific calculation process is as follows:

[0094] First, the survey results of the 10 experts were compared with the 1-9 scale in Table 17 to obtain the indicator judgment matrix for each expert. (in ).

[0095] Table 17 1-9 Scale

[0096]

[0097] Then, the average score of importance among the indicators is calculated using the mean method of the following formula, and one judgment matrix from the target layer to the criterion layer and four judgment matrices from the criterion layer to the solution layer are constructed.

[0098]

[0099] in, Indicates the first Indicators in the expert's judgment matrix With indicators By taking the average of the comparison values, the opinions of experts can be effectively integrated, and the impact of extreme scores can be reduced.

[0100] The synthesized judgment matrix The weights of each indicator are calculated using the following root-finding method. .

[0101]

[0102] in, It is an indicator The weights; It is the first in the judgment matrix Line 1 The element of the column represents the first element. The first indicator and the first The relative importance of each indicator; It determines the size of the matrix, representing the number of indicators.

[0103] To ensure the consistency of the judgment matrix, a consistency check is required to verify the reasonableness of the expert scores. The consistency check in the Analytic Hierarchy Process (AHP) includes calculating the consistency index (…). ) and consistency ratio ( The specific steps are as follows:

[0104] Consistency Indicators By determining the largest eigenvalue of the matrix Dimensions of the matrix To calculate:

[0105]

[0106] Among them, the largest eigenvalue .

[0107] Consistency ratio The formula used to determine whether the consistency of a matrix meets the requirements is as follows:

[0108]

[0109] in, It is a random consistency index with a fixed value that depends on the dimension n of the judgment matrix. The specific values ​​are shown in Table 18.

[0110] Table 18 value

[0111]

[0112] like If the judgment matrix passes the consistency test, it indicates that the expert scores are consistent and the weights of each indicator are reasonable; if Then the judgment matrix needs to be adjusted and the weights recalculated until... until.

[0113] Through the above steps, the specific scale values, index weights, and consistency test results of each layer's judgment matrix can be obtained, as detailed in Tables 19, 20, 21, 22, and 23.

[0114] Table 19 From Target Layer to Criterion Layer ( The judgment matrix, weights, and test results of ()

[0115]

[0116] Table 20 From Target Layer to Criterion Layer ( The judgment matrix, weights, and test results of ()

[0117]

[0118] Table 21 From Target Layer to Criterion Layer ( The judgment matrix, weights, and test results of ()

[0119]

[0120] Table 22 From Target Layer to Criterion Layer ( Judgment matrix and weights

[0121]

[0122] Table 23 From Target Layer to Criterion Layer ( Judgment matrix and weights

[0123]

[0124] Finally, the weight vectors of each judgment matrix are obtained. Next, the criterion layer weights will be... With the corresponding scheme layer weights Multiplying these values, as shown in Table 24, yields the overall weights of the criterion-level indicators relative to the target-level indicators, in the following order:

[0125]

[0126] Therefore, the degree of influence of each indicator at the scheme level on the driver's job suitability, from largest to smallest, is as follows: basic operation, system operation and monitoring, equipment operation and fault diagnosis, intelligent system operation, physical health, emergency operation, dangerous driving behavior, safe operation procedures, mental health, traffic laws and safety regulations, equipment management and maintenance, emergency response (theory), hazard perception ability, and professional ethics.

[0127] Table 24. Calculation Method of Comprehensive Weight of Each Indicator at the Solution Layer Relative to the Target Layer

[0128]

[0129] S4. Based on the scores of the hierarchical intelligent rail transit driving capability assessment indicators and their corresponding weight values, the fuzzy comprehensive evaluation method is used to calculate the intelligent rail transit driving capability assessment score.

[0130] In this embodiment, step S4 calculates the intelligent rail transit driving capability assessment score using the fuzzy comprehensive evaluation method based on the hierarchical intelligent rail transit driving capability assessment index scores and corresponding weight values, including:

[0131] Based on the set intelligent rail transit driving capability assessment level, the membership degree value of each criterion layer indicator is represented by a trapezoidal membership function, and the fuzzy evaluation matrix of each criterion layer indicator is obtained based on the scores of the hierarchical intelligent rail transit driving capability assessment indicators.

[0132] Based on the fuzzy evaluation matrix of each criterion-level indicator and the comprehensive indicator weights from the target layer to the scheme layer, calculate the comprehensive evaluation score of each criterion-level indicator.

[0133] The intelligent rail transit driving capability assessment score is calculated based on the comprehensive evaluation score of each criterion level indicator and the fuzzy boundary value of each evaluation level score.

[0134] In this embodiment, the factor set and evaluation set of the evaluation object are first established.

[0135] Let set Include Subset ,and ,because To evaluate the indicator factors, indicators are constructed from the target layer to the criterion layer. The evaluation set, for convenience, is simply referred to as the criterion indicator evaluation set. Similarly, a hierarchy can be established from the criterion layer to the solution layer. The evaluation set, referred to as the scheme indicator evaluation set, is used to establish a hierarchical structure based on the intelligent rail transit driver suitability evaluation index system in Table 1.

[0136] Target layer: {Intelligent Rail Transit Suitability Assessment}.

[0137] Criteria and Indicator Layer: ={Theoretical knowledge, skills and qualities, safety awareness, physical and mental well-being}.

[0138] Solution Indicator Layer:

[0139] ={Intelligent system operation, equipment operation and fault diagnosis, traffic regulations and safety provisions, emergency response, professional ethics};

[0140] ={Basic Operations, System Operations and Monitoring, Emergency Operations, Equipment Management and Maintenance};

[0141] ={Safe operating procedures, dangerous driving behaviors, and the ability to perceive danger};

[0142] ={physical health, mental health}.

[0143] Then, create a collection of comments. The corresponding set of fractions is The collection of comments The specific meanings of "these are" are "excellent, good, average, qualified, and poor".

[0144] Secondly, construct the fuzzy evaluation matrix.

[0145] According to the commentary collection The classification (Excellent, Good, Average, Satisfactory, Poor) is used, and the membership degree values ​​of each option layer index are represented by a trapezoidal membership function, resulting in the membership degree matrix of each option layer index, also called the fuzzy evaluation matrix. Based on the boundary values ​​of the five rating levels, a step-type membership function is constructed. This function can effectively describe the fuzzy transition between different rating levels, as shown below:

[0146] The corresponding membership function is:

[0147]

[0148] The corresponding membership function is:

[0149]

[0150] The corresponding membership function is:

[0151]

[0152] The corresponding membership function is:

[0153]

[0154] The corresponding membership function is:

[0155]

[0156] Based on the evaluation scheme and standards, scores for each scheme-level indicator of the assessed driver are obtained. These scores are then substituted into the membership function of each rating level, and the membership degrees of the scheme-level indicators are statistically analyzed to obtain the fuzzy comprehensive evaluation matrix. The membership statistics are shown in Table 25.

[0157] Table 25 Membership Degree Statistics

[0158]

[0159] Based on the membership statistics in Table 25, the fuzzy evaluation matrix of each target layer index is obtained. The four fuzzy evaluation matrices are shown in the following forms:

[0160]

[0161]

[0162]

[0163]

[0164] Based on the weight vectors of each indicator at the scheme layer and the indicators at the criterion layer The scores of the assessed drivers for each scheme-level indicator are then used to input the scores into the membership functions of each rating level, thus obtaining the fuzzy rating matrix for each scheme-level indicator. .

[0165] Finally, the comprehensive evaluation score is calculated using the fuzzy evaluation formula:

[0166]

[0167] in, This represents the comprehensive weight vector of the evaluation indicators. The evaluation level membership matrix represents the evaluation indicators. Represents the evaluation membership row vector, " " indicates a generalized fuzzy composition operator, the same below.

[0168]

[0169] in, This indicates the score for the evaluation result. This represents the fuzzy boundary value of the scores for each evaluation level. .

[0170] This invention uses a scheme-level indicator evaluation scheme and evaluation criteria to conduct simulated tests on intelligent rail transit drivers, evaluating their scores for each scheme-level indicator. Then, the scores are substituted into the membership functions of each rating level to statistically analyze the membership degrees of the scheme-level indicators, thereby obtaining a fuzzy evaluation matrix. The membership statistics are shown in Table 26:

[0171] Table 26 Attribute Statistics Table

[0172]

[0173] Based on the membership statistics in Table 26, the fuzzy evaluation matrix of each target layer index is obtained. The four fuzzy evaluation matrices are shown in the following forms:

[0174]

[0175]

[0176]

[0177]

[0178] The evaluation of the theoretical knowledge of the criterion layer is as follows:

[0179]

[0180]

[0181] The driver's theoretical knowledge rating is " The score was 83.0416.

[0182] The criteria-level skills and competence assessment is as follows:

[0183]

[0184]

[0185] The driver's skill level is rated as " The score was 87.2240.

[0186] The safety awareness assessment at the criterion level is as follows:

[0187]

[0188]

[0189] The driver's safety awareness rating is " The score was 87.7514.

[0190] The criteria for evaluating physical and mental well-being are as follows:

[0191]

[0192]

[0193] The driver's physiological fitness rating is " The score was 86.8000.

[0194] The evaluation of the trial work ability of the target level is as follows:

[0195] Criterion layer index membership matrix :

[0196]

[0197]

[0198]

[0199] The driver's job competency assessment level is " The score was 86.0456.

[0200] The scores for each criterion-level indicator and target-level score are shown in Table 27.

[0201] Table 27: Criterion Level Indicators and Target Level Scores

[0202]

[0203] The present invention has the following advantages:

[0204] 1. Comprehensiveness

[0205] The evaluation system proposed in this invention covers multiple core competency dimensions of intelligent rail transit drivers, comprehensively assessing their theoretical knowledge, skills, safety awareness, and physical and mental well-being. Unlike existing single-dimensional evaluation methods (such as those that only examine theoretical knowledge or operational skills), this invention can more accurately reflect the overall quality of drivers, ensuring that the evaluation results are more scientific and comprehensive.

[0206] 2. Precise quantification

[0207] By employing clear scoring criteria and a fuzzy comprehensive evaluation method, this invention achieves precise quantification of drivers' job suitability, avoiding the ambiguity and inconsistency of subjective scoring in traditional evaluations. Each competency indicator is carefully designed and weighted, providing an operable and transparent scoring mechanism.

[0208] 3. Highly targeted

[0209] This invention specifically designs evaluation standards for the working characteristics of intelligent rail transit drivers, with particular emphasis on assessing crucial abilities in practical work, such as emergency response capabilities and mental health. Existing technologies often neglect drivers' emergency response capabilities and psychological qualities. This invention, through specific simulation assessments and psychological evaluations, ensures that drivers can respond promptly to emergencies in actual operation.

[0210] 4. Scalability

[0211] The evaluation method of this invention has strong scalability, allowing for adjustments to the indicators and weights in the evaluation system according to actual needs, adapting to the specific requirements of different types of intelligent rail systems and drivers. Furthermore, based on the flexibility of fuzzy evaluation, the evaluation content can be further enriched by combining other evaluation dimensions or technical means in the future, improving the accuracy and applicability of the evaluation.

[0212] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0216] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for assessing intelligent rail transit driving capabilities based on fuzzy theory, characterized in that, Includes the following steps: Acquire intelligent rail transit driving data and intelligent rail transit driving ability assessment data from intelligent rail transit drivers; Information on intelligent rail transit capability assessment indicators is obtained from intelligent rail transit driving data and intelligent rail transit capability assessment data, and scores for the hierarchical intelligent rail transit capability assessment indicators are determined. The hierarchical analytic hierarchy process (AHP) is used to establish a judgment matrix for the hierarchical intelligent rail transit (IRT) driving capability assessment index, and the weight values ​​of the hierarchical IRT driving capability assessment index are calculated. Based on the scores of the hierarchical intelligent rail transit driving capability assessment indicators and their corresponding weight values, the fuzzy comprehensive evaluation method is used to calculate the intelligent rail transit driving capability assessment score.

2. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 1, characterized in that, Intelligent rail transit driving data includes simulated driving data and train speed data; The intelligent rail transit (IRT) driving capability assessment data includes textual data on IRT driving capability assessment and physiological indicator data.

3. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 2, characterized in that, Intelligent rail transit (IRT) driving capability assessment index information is obtained from IRT driving data and IRT driving capability assessment data to determine hierarchical IRT driving capability assessment index scores, including: The assessment score of intelligent rail transit driving capability is used as the target layer indicator; theoretical knowledge score, skill quality score, safety awareness score, and physical and mental quality score are used as the criterion layer indicators; intelligent system operation score, equipment operation and fault diagnosis score, traffic regulations and safety regulations score, emergency response score, and professional ethics score are used as the scheme layer indicators corresponding to the theoretical knowledge indicators of the criterion layer; basic operation score, emergency operation score, equipment management and maintenance score, and system operation and monitoring score are used as the scheme layer indicators corresponding to the skill quality indicators of the criterion layer; safe operation standard score, dangerous driving behavior score, and danger perception ability score are used as the scheme layer indicators corresponding to the safety awareness indicators of the criterion layer; and physical health score and mental health score are used as the scheme layer indicators corresponding to the safety awareness indicators of the criterion layer. Based on the text data of the intelligent rail transit capability assessment, the scores of intelligent system operation, equipment operation and fault diagnosis, traffic regulations and safety provisions, emergency response and professional ethics in the scheme layer are determined according to the theoretical knowledge indicators of the criterion layer. Based on the simulated operation data of intelligent rail transit, the basic operation score, emergency operation score, equipment management and maintenance score, and system operation and monitoring score of the corresponding scheme layer are determined according to the skill literacy indicators of the criterion layer. Based on the simulated operation data and train speed data of intelligent rail transit, the safety awareness index at the criterion level is determined to correspond to the safety operation standard score, dangerous driving behavior score, and danger perception ability score at the scheme level. Based on the textual data and physiological indicator data of the intelligent rail transit capability assessment, the physiological health score and mental health score of the scheme layer are determined corresponding to the safety awareness index of the criterion layer.

4. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 3, characterized in that, A hierarchical judgment matrix for intelligent rail transit (IRT) driving capability assessment indicators is established using the analytic hierarchy process (AHP). The weight values ​​of these indicators are calculated, including: Based on expert knowledge data, the relative importance of evaluation indicators for intelligent rail transit driving capabilities at each level is determined, and a judgment matrix from the target layer to the criterion layer and a judgment matrix from the criterion layer to the scheme layer are constructed. Calculate the indicator weights from the target layer to the criterion layer based on the judgment matrix from the target layer to the criterion layer, and calculate the indicator weights from the criterion layer to the solution layer based on the judgment matrix from the criterion layer to the solution layer. Multiply the indicator weights from the target layer to the criterion layer by the indicator weights from the criterion layer to the solution layer to obtain the comprehensive indicator weights from the target layer to the solution layer.

5. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 4, characterized in that, Consistency indicators and consistency ratios are used to verify the consistency of indicator weights from the target layer to the criterion layer and from the criterion layer to the scheme layer.

6. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 5, characterized in that, Consistency indicators and consistency ratios are used to verify the consistency of indicator weights from the target layer to the criterion layer and from the criterion layer to the solution layer, including: The consistency index is calculated based on the maximum eigenvalue and dimension of the index weight matrix; A random consistency index is set based on the dimensions of the index weight matrix, and the consistency ratio is calculated in combination with the consistency index. Determine if the consistency ratio is less than the set threshold; if so, the consistency test is passed; otherwise, adjust the judgment matrix and recalculate the indicator weights.

7. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 6, characterized in that, Based on the hierarchical intelligent rail transit (IRT) driving capability assessment index scores and corresponding weight values, the fuzzy comprehensive evaluation method is used to calculate the IRT driving capability assessment score, including: Based on the set intelligent rail transit driving capability assessment level, the membership degree value of each criterion layer indicator is represented by a trapezoidal membership function, and the fuzzy evaluation matrix of each criterion layer indicator is obtained based on the scores of the hierarchical intelligent rail transit driving capability assessment indicators. Based on the fuzzy evaluation matrix of each criterion-level indicator and the comprehensive indicator weights from the target layer to the scheme layer, calculate the comprehensive evaluation score of each criterion-level indicator. The intelligent rail transit driving capability assessment score is calculated based on the comprehensive evaluation score of each criterion level indicator and the fuzzy boundary value of each evaluation level score.

8. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 7, characterized in that, Based on the established intelligent rail transit driving capability assessment levels, a trapezoidal membership function is used to represent the membership values ​​of each criterion level indicator, including: The intelligent rail transit driving capability assessment levels are set as Level I, Level II, Level III, Level IV, and Level V; The membership values ​​of each criterion layer index are represented by a trapezoidal membership function, specifically as follows: The membership function corresponding to level I is: ； The membership function corresponding to level II is: ； The membership function corresponding to level III is: ； The membership function corresponding to level IV is: ； The membership function corresponding to level V is: ； Where x represents the score of the hierarchical intelligent rail transit driving capability assessment index.

9. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 8, characterized in that, Based on the fuzzy evaluation matrix of each criterion-level indicator and the comprehensive indicator weights from the target layer to the solution layer, the comprehensive evaluation score of each criterion-level indicator is calculated, including: The fuzzy evaluation matrix of each criterion-level indicator is fuzzily synthesized with the corresponding row matrix in the comprehensive indicator weights from the target layer to the scheme layer to obtain the comprehensive evaluation score of each criterion-level indicator, as follows: ； in, The comprehensive evaluation score of the criteria-level indicators. The weights of the comprehensive indicators from the target layer to the solution layer. This is the fuzzy evaluation matrix of the criteria layer indicators.

10. The intelligent rail transit driving capability assessment method based on fuzzy theory according to claim 9, characterized in that, The intelligent rail transit capability assessment score is calculated based on the comprehensive evaluation score of each criterion level indicator and the fuzzy boundary value of each evaluation level, as follows: ； in, The score is used to assess the intelligent rail transit driving capability. This represents the fractional fuzzy boundary value.

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