An electric power practical training user experience evaluation method and system based on analytic hierarchy process weight analysis

Abstract

The application discloses a power training user experience evaluation method and system based on analytic hierarchy process weight analysis, relates to the technical field of power virtual simulation training evaluation and user experience quantitative evaluation, constructs a three-level hierarchical evaluation system, sets seven guideline dimensions and 13 quantitative indexes, and fuses multi-source heterogeneous data. Industry experts are organized by using a 1-9 scale method to construct a judgment matrix, a geometric mean method is used to synthesize the matrix and calculate index weights, and effective weights are screened through consistency verification. After the collected data is cleaned and standardized, the average score of the statistical indexes is calculated, the weighted score is calculated in combination with the weights, the experience advantages and disadvantages are analyzed in double dimensions, the system short board is accurately positioned, and a training optimization scheme containing adaptive difficulty adjustment, multi-terminal adaptation and the like is output. The application realizes the standardization and quantification of power training experience, integrates problem attribution and scheme output, and can be widely used for iterative evaluation of various digital power simulation training.

Description

Technical Field

[0001] This invention relates to the fields of power virtual simulation training evaluation and user experience quantitative evaluation technology, and more specifically to a power training user experience evaluation method and system based on weight analysis of the analytic hierarchy process. Background Technology

[0002] With the advancement of digital and intelligent transformation in the power industry, virtual simulation training has become a core method for skills training of power industry practitioners. Compared with traditional offline hands-on training, power virtual simulation training has significant advantages such as zero safety risk, repeatable training, full scenario coverage, and low cost. It is widely used in core power skills training scenarios such as substation switching operations, line fault inspection, and electrical equipment operation and maintenance. User experience, as a core indicator for measuring the practicality, adaptability, and training effectiveness of power virtual training systems, directly determines the trainees' training enthusiasm, skills mastery efficiency, and final training results. Accurately quantifying and evaluating the user experience of power training is of crucial guiding significance for optimizing training systems, iterating training programs, and improving the quality of power job training.

[0003] Currently, existing power training user experience evaluation technologies have many industry-specific technical shortcomings, as follows: First, the evaluation indicator system is fragmented, lacks a single dimension, and lacks a systematic hierarchical structure. Existing evaluation methods often focus solely on basic usability indicators such as system lag and operational smoothness, neglecting core experience dimensions such as interactive realism, content adaptability, learning convenience, safety perception, emotional experience, and training effectiveness. This lack of a multi-level, comprehensive, and standardized evaluation indicator system results in incomplete evaluation dimensions, failing to fully reflect the real user experience in power training scenarios. Consequently, the evaluation results are one-sided and have low reference value.

[0004] Second, the assignment of indicator weights is highly subjective and lacks quantitative precision. Traditional user experience assessments often use equal weights and subjective manual weighting, failing to differentiate the importance of indicators based on the characteristics of the power industry and the actual needs of frontline practitioners. Significant differences in subjective perception among evaluators make it impossible to quantify the varying impacts of each experience indicator on the overall training experience, resulting in a large discrepancy between the assessment results and the actual training experience, hindering the accurate optimization of the training system.

[0005] Third, the data collection methods are limited, and the integration of multi-source heterogeneous data is low. The current assessment relies solely on single questionnaire survey data, without integrating multi-source information such as system backend log data, physiological monitoring data, eye-tracking data, and manual observation data. Quantitative and qualitative data are disconnected, failing to take into account both the user's subjective experience and the objective operating status of the system. The assessment data is too thin in dimensions and lacks authenticity.

[0006] Fourth, the evaluation results only provide scores without attribution or optimization guidance. Existing technologies can only output general user experience scores, failing to accurately pinpoint the shortcomings and weaknesses of the training system. They cannot specifically analyze issues such as difficulty gradients, terminal adaptation, interaction design, and safety guidance, nor can they output optimization solutions adapted to power training scenarios. The evaluation is disconnected from training optimization, resulting in extremely poor practicality.

[0007] Existing studies have attempted to use the Analytic Hierarchy Process (AHP) for experience evaluation, but they generally suffer from problems such as non-standard hierarchical structure construction, insufficient expert sample size, lack of consistency verification, and low accuracy of weight calculation. They have not customized indicator systems and weight models for the specific scenarios of power training, and cannot adapt to the high safety, high professionalism, and strong practical characteristics of power training scenarios. To date, there is no quantitative evaluation scheme for user experience that is suitable for digital power training, integrates multi-source data, accurately assigns weights, and can be optimized by attribution. Summary of the Invention

[0008] In view of this, the present invention provides a method and system for evaluating the user experience of power training based on the weight analysis of the analytic hierarchy process, in order to solve the problems existing in the background technology.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A method for evaluating the user experience of power training based on weight analysis using the analytic hierarchy process (AHP) includes: S1. Construct a three-level hierarchical evaluation index system for user experience in power training, which consists of the target layer, the criterion layer and the index layer from high to low. S2, construct a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training based on the analytic hierarchy process, and conduct pairwise importance comparison and scoring of each index in the criteria layer and the index layer respectively to generate expert judgment matrices; S3, synthesize the expert judgment matrices using the geometric mean method and calculate the weight of each indicator, then combine the consistency check to screen the final indicator weights. S4 collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores to identify shortcomings in the experience and generate optimization solutions.

[0010] Preferably, the criteria layer in S1 includes seven dimensions: system availability, interactive realism, content adaptability, learning convenience, emotional experience, safety perception, and training effectiveness analysis. The indicator layer corresponds to thirteen specific evaluation indicators, where system availability corresponds to the frequency of system crashes and lags, and operation response latency; interactive realism corresponds to the device operation simulation score and environmental immersion score; content adaptability corresponds to the smoothness of the connection between virtual and real content and the reasonableness of the difficulty gradient; learning convenience corresponds to the usability score of the functional interface and the efficiency of cross-terminal synchronization; emotional experience corresponds to the degree of relief of operation anxiety and the learning sense of accomplishment score; safety perception corresponds to the effectiveness of virtual risk warnings and the clarity of safe operation guidance; and training effectiveness analysis corresponds to the degree of improvement in work skills.

[0011] Preferably, S2 specifically includes: S201 assigns hierarchical labels to the three-tiered evaluation index system for user experience in power training, with the target layer labeled A0 and the seven criteria dimensions labeled A0 and A10 respectively. i (i=1,2,3…7), the underlying indicator layer corresponding to each criterion layer is labeled as A. ij This forms a standardized hierarchical structure model; S202, using the scaling method to compare the importance of each level of indicators pairwise, independently constructing a judgment matrix, and collecting multiple original scoring matrices; S203, perform preliminary consistency screening on all original scoring matrices, eliminate logically contradictory samples, provide feedback to experts for correction of matrices that fail the preliminary verification, and obtain the judgment matrices of each expert.

[0012] Preferably, S3 specifically includes: S301, the geometric mean method is used to synthesize the judgment matrices of each expert group into a comprehensive judgment matrix of each expert group; S302, based on the comprehensive judgment matrix of each expert group, the geometric mean method is used to calculate the weight vector of each level of indicators. This involves sequentially performing matrix element row multiplication, nth-order power extraction, and vector normalization to calculate the relative weight w of each criterion level. i Combined with the weights W of each indicator layer ij ; S303, Perform matrix consistency verification, and calculate the maximum eigenvalue of the matrix, consistency index CI, random consistency index RI, and consistency ratio CR respectively; S304: Filter valid matrices with CR < 0.1, confirm the validity of the indicator weights at each level, and output the final indicator weights.

[0013] Preferably, S4 specifically includes: S401: Select front-line power training personnel as the evaluation sample and collect raw data of all underlying indicators in batches. S402, Remove abnormal data from the original data, fill in a small number of missing data, unify the data units of all indicators, complete the data scoring according to the preset scoring standards of each indicator, and obtain the original score value of each sample indicator. S403. Based on the original scores of each sample indicator, calculate the average score of each sample indicator for all samples, and combine it with the final indicator weight to calculate the weighted score of each indicator, generating a user experience average score dataset and a weighted score dataset.

[0014] Preferably, the calculation of the relative weights w of each criterion layer i Specifically, it includes: ; Among them, a ij To determine the importance comparison results between index i and index j in the matrix.

[0015] Preferably, S303 specifically includes: ; ; ; in, denoted as the largest eigenvalue of the matrix, CI as the consistency index, RI as the random consistency index, CR as the consistency ratio, AW as the value obtained by summing the product of the judgment matrix and the weight vector row by row, and n as the order of the judgment matrix.

[0016] A power training user experience evaluation system based on weight analysis using the analytic hierarchy process (AHP), comprising: The system construction unit constructs a three-level hierarchical evaluation index system for the user experience of power training, which is divided into the target layer, the criterion layer and the index layer from high to low. The matrix generation unit constructs a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training based on the analytic hierarchy process. It then performs pairwise importance comparison and scoring on each index in the criteria layer and the index layer to generate expert judgment matrices. The weight calculation unit synthesizes the expert judgment matrices using the geometric mean method and calculates the weight of each indicator, then combines the consistency check to screen the final indicator weights. The optimization solution generation unit collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores, identifies shortcomings in the user experience, and generates optimization solutions.

[0017] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for evaluating the user experience of power training based on the weight analysis of the analytic hierarchy process, with the following beneficial effects: 1. A comprehensive three-tiered evaluation index system specifically designed for power industry training has been constructed, addressing the shortcomings of traditional evaluation dimensions. This invention, combining the high safety, strong practical application, and emphasis on effectiveness characteristics of power virtual training, innovatively establishes a hierarchical evaluation system comprising 7 major criteria dimensions and 13 core indicators. This system covers all scenarios, including system performance, interactive experience, content adaptation, learning convenience, emotional perception, safety experience, and training effectiveness. It completely breaks through the limitations of traditional evaluations that only focus on basic system usability, enabling a comprehensive and multi-dimensional quantification of the real-world user experience level in power industry training. The completeness and industry adaptability of the evaluation are significantly improved.

[0018] 2. Accurate weight quantification based on large-sample expert data and the AHP algorithm completely avoids subjective assignment bias. This invention employs a joint scoring method by multiple front-line power industry experts. Weights are calculated through matrix synthesis and geometric mean, coupled with a rigorous consistency verification mechanism. This ensures that all indicator weights align with the actual needs of power training, accurately distinguishing the priority of different indicators' impact on user experience. It solves the evaluation distortion problem caused by traditional manual equal weighting and subjective weighting, resulting in evaluation results that are significantly more scientific and accurate than traditional solutions.

[0019] 3. Integrating multi-source heterogeneous data to achieve a comprehensive evaluation of subjective and objective experiences. This invention integrates five types of data: objective system log data, user physiological monitoring data, eye-tracking data, subjective questionnaire data, and manual observation data. It overcomes the limitations of single-questionnaire evaluation, accurately reflecting the objective operational performance of the training system while precisely capturing users' emotional changes, immersive experiences, and learning feelings during hands-on practice. The data dimensions are rich, and the evaluation results are authentic and reliable, effectively solving the technical problems of one-sided and insufficiently authentic traditional evaluation data.

[0020] 4. This invention integrates assessment, attribution, and optimization, making it highly practical. It not only outputs accurate comprehensive user experience scores and sub-scores for each dimension, but also accurately identifies core weaknesses by combining indicator weights and scoring data. It allows for customized optimization solutions for power training scenarios, including intelligent difficulty adaptation, enhanced basic training, and expansion into multiple fault scenarios. This overcomes the shortcomings of traditional assessments that "only score, without analysis or optimization," providing direct data support and technical guidance for the iteration of power virtual training systems, optimization of training programs, and upgrading of training content.

[0021] 5. High degree of standardization and strong scalability, adaptable to various power training scenarios. The evaluation model of this invention has a clear hierarchical structure, unified weight calculation standards, and standardized data collection process. It can be adapted to various virtual power training scenarios such as transformer switching operations, line fault inspection, and substation operation and maintenance. At the same time, the index thresholds can be fine-tuned according to the differences in training scenarios to meet the training and evaluation needs of power practitioners at different levels. It has a wide range of applications and strong reusability. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 A flowchart of the method steps provided by the present invention; Figure 2 This is a schematic diagram of the system architecture provided by the present invention; Figure 3 A bar chart showing the weights of the evaluation indicators provided for this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention discloses a method for evaluating the user experience of power training based on the weight analysis of the analytic hierarchy process (AHP). Figure 1 As shown, it includes: S1, Construct a three-tiered evaluation index system for the user experience of power training, such as Figure 2 As shown, from highest to lowest, they are the target layer, the criterion layer, and the indicator layer; S2, based on the analytic hierarchy process, constructs a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training. It compares and scores the importance of each index in the criteria layer and the index layer, and generates expert judgment matrices. S3, synthesize the expert judgment matrices using the geometric mean method and calculate the weight of each indicator, then combine the consistency check to screen the final indicator weights. S4 collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores to identify shortcomings in the experience and generate optimization solutions.

[0026] In one specific embodiment, identifying user experience shortcomings and generating optimization solutions includes: combining average and weighted scores to analyze the strengths and weaknesses of the training system's user experience from two dimensions; selecting the most impactful shortcomings based on indicator weights and priorities; addressing core issues commonly found in power training systems, such as unreasonable difficulty gradients, lack of cross-terminal adaptation, and insufficient refinement of safety guidance; and generating a tailored, layered optimization solution tailored to the characteristics of power training, including intelligent adaptive difficulty adjustment, strengthening of basic training modules, and expansion of multi-scenario collaborative training; and integrating all evaluation data, scores, shortcomings analysis, and optimization suggestions to generate a complete quantitative evaluation report on the user experience of power training.

[0027] In one specific embodiment, the criteria layer in S1 includes seven dimensions: system availability, interactive realism, content adaptability, learning convenience, emotional experience, safety perception, and training effectiveness analysis. The indicator layer corresponds to thirteen specific evaluation indicators, among which system availability corresponds to the frequency of system crashes and lags, and operation response latency; interactive realism corresponds to the device operation simulation score and environmental immersion score; content adaptability corresponds to the smoothness of the connection between virtual and real content and the reasonableness of the difficulty gradient; learning convenience corresponds to the usability score of the functional interface and the efficiency of cross-terminal synchronization; emotional experience corresponds to the degree of relief of operation anxiety and the learning sense of accomplishment score; safety perception corresponds to the effectiveness of virtual risk warnings and the clarity of safe operation guidance; and training effectiveness analysis corresponds to the degree of improvement in work skills.

[0028] In one specific embodiment, S2 specifically includes: S201 assigns hierarchical labels to the three-tiered evaluation index system for user experience in power training, with the target layer labeled A0 and the seven criteria dimensions labeled A0 and A10 respectively. i (i=1,2,3…7), the underlying indicator layer corresponding to each criterion layer is labeled as A. ij This forms a standardized hierarchical structure model; S202, using the scaling method to compare the importance of each level of indicators pairwise, independently constructing a judgment matrix, and collecting multiple original scoring matrices; S203, perform preliminary consistency screening on all original scoring matrices, eliminate logically contradictory samples, provide feedback to experts for correction of matrices that fail the preliminary verification, and obtain the judgment matrices of each expert.

[0029] In one specific embodiment, S3 specifically includes: S301, the geometric mean method is used to synthesize the judgment matrices of each expert group into a comprehensive judgment matrix of each expert group; S302, based on the comprehensive judgment matrix of each expert group, calculates the weight vector of each level of indicators using the geometric mean method, and sequentially performs matrix element row multiplication, nth-order power extraction, and vector normalization to calculate the relative weight w of each criterion level.i Combined with the weights W of each indicator layer ij ; S303, Perform matrix consistency verification, and calculate the maximum eigenvalue of the matrix, consistency index CI, random consistency index RI, and consistency ratio CR respectively; S304: Filter valid matrices with CR < 0.1, confirm the validity of the indicator weights at each level, and output the final indicator weights.

[0030] In one specific embodiment, S4 specifically includes: S401: Select front-line power training personnel as the evaluation sample and collect raw data of all underlying indicators in batches. S402: Remove abnormal data from the original data, fill in a small number of missing data, unify the data units of all indicators, complete the data scoring according to the preset scoring standards of each indicator, and obtain the original score value of each sample indicator. S403: Based on the original scores of each sample indicator, calculate the average score of each sample indicator for all samples, and combine the final indicator weights to calculate the weighted score of each indicator, generating a user experience average score dataset and a weighted score dataset.

[0031] In one specific embodiment, the relative weights w of each criterion layer are calculated. i Specifically, it includes: ; (1) Among them, a ij To determine the importance comparison results between index i and index j in the matrix.

[0032] In one specific embodiment, S303 specifically includes: ; (2) ; (3) ; (4) in, denoted as the largest eigenvalue of the matrix, CI as the consistency index, RI as the random consistency index, CR as the consistency ratio, AW as the value obtained by summing the product of the judgment matrix and the weight vector row by row, and n as the order of the judgment matrix.

[0033] A power training user experience evaluation system based on weight analysis using the analytic hierarchy process (AHP), comprising: The system construction unit constructs a three-level hierarchical evaluation index system for the user experience of power training, which is divided into the target layer, the criterion layer and the index layer from high to low. The matrix generation unit constructs a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training based on the analytic hierarchy process. It compares and scores the importance of each index in the criteria layer and the index layer, and generates expert judgment matrices. The weight calculation unit synthesizes the expert judgment matrices using the geometric mean method and calculates the weights of each indicator, then combines consistency verification to screen the final indicator weights. The optimization solution generation unit collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores, identifies shortcomings in the user experience, and generates optimization solutions.

[0034] In Specific Embodiment 1, a virtual power simulation training scenario of a new power distribution system is used as the application carrier, and on-the-job training personnel in the power industry are selected as evaluation samples to provide a complete and detailed implementation description of the evaluation method, model and device of the present invention.

[0035] Example 1: Detailed Implementation Process of User Experience Evaluation Method in Power Training

[0036] Step 1: Establish a standardized three-tiered evaluation index system. Based on Tables 1 and 2, build a user experience evaluation system for power training. The target layer is defined as the quality of the user experience in power training. The criteria layer includes seven dimensions: system usability, interaction realism, content adaptability, learning convenience, emotional experience, safety perception, and training effectiveness analysis, further refined into 13 underlying quantitative indicators. Clarify the data collection methods, quantitative standards, and scoring thresholds for each indicator, and unify the overall evaluation standards.

[0037] Table 1 Preset Table of User Experience Quality Evaluation Indicators

[0038] Table 2 Hierarchical Structure Model for User Experience Evaluation in Power Training

[0039] The data for each indicator is collected through a multi-source fusion method, covering six major collection channels: automatic collection of system platform logs, collection of data from eye-tracking devices, monitoring of heart rate and physiological devices, student questionnaires, expert interviews, and manual records by teachers. The data covers all dimensions of the training system's operating status, user operation behavior, physiological and emotional changes, subjective experience feedback, and training effect feedback. Each indicator is matched with a specific quantitative evaluation threshold and grading standard.

[0040] Step 2: Construct the AHP hierarchical model and expert judgment matrix

[0041] The indicators at each level were standardized and labeled: target level A0, seven criteria levels A1-A7, and each underlying indicator Aij. Several senior training experts from the power industry were invited to use a 1-9 scale to score the importance of each level of indicator pairwise, generating individual judgment matrices. The initial consistency of each matrix was verified, and any scoring matrices with logical contradictions were returned to the experts for correction, ensuring that all individual matrices were valid and usable.

[0042] Step 3: Matrix composition, weight calculation and consistency verification

[0043] The geometric mean of the effective expert matrix was calculated to synthesize the judgment matrix of the expert group at the criterion level (Table 3). The relative weights w of each criterion level were calculated using the geometric mean method according to equation (1). i Combined with the index layer weight W ij Retrieve the corresponding order RI values ​​from Table 4, and calculate the CR values ​​of each matrix using equations (2), (3), and (4). All matrix CR values ​​are less than 0.1, and all pass the consistency check. Finally, output the final index weight system. See Table 5 for details of the results of each dimension and index weight (example).

[0044] Table 3. Criterion Layer Expert Group Judgment Matrix

[0045] Table 4. Values ​​of Matrix Order RI

[0046] Table 5 Weights of User Experience Quality Evaluation Indicators in Power Training

[0047] To gain a more intuitive understanding of the overall indicator weights, Figure 3 The weighted indicators were ranked using a bar chart. Overall, in the evaluation of user experience quality in power training, the indicators ranked by importance were: job skill improvement (0.139), seamless integration of virtual and real content (0.113), and reasonableness of difficulty gradient (0.105). Experts participating in the power training were most concerned about job skill improvement, reflecting their focus on the training's impact on job skills. Furthermore, evaluation indicators such as immersive environment, ease of use of the functional interface, and operational response latency also had relatively high weightings, indicating that users have increasingly higher demands for system usability and smoothness. A smooth and easy-to-use power training simulation system can effectively improve training efficiency.

[0048] Step 4: Multi-source data acquisition and standardization processing

[0049] Using frontline power industry trainees as the test sample, comprehensive data collection was conducted, including objective system operation data, user physiological data, subjective questionnaire scores, and manual observation records. The training system automatically recorded system lag frequency and operation response delays in the background; environmental immersion data was collected using eye-tracking devices; and operational anxiety fluctuation data was obtained using heart rate monitoring devices. After the training, user subjective scores were collected through standardized questionnaires, supplemented by teacher observation records. All raw data was cleaned, abnormal data caused by equipment malfunctions were removed, a small number of missing data were supplemented, and scores were assigned according to the scoring criteria after standardization of dimensions. The average score for each indicator was then calculated.

[0050] Step 5: Weighted Score Calculation and Experience Result Analysis

[0051] By combining the composite weights of each indicator with the sample average score, the weighted score of each indicator is calculated, generating a user experience average score data table and a weighted score data table. Simultaneously, a weighted sorting bar chart, an average score distribution chart, and a weighted score distribution chart are drawn.

[0052] Step 6: Attributing weaknesses and generating customized optimization solutions

[0053] To address the shortcomings in identification, optimization strategies were developed based on the characteristics of power engineering training: First, an intelligent difficulty-adaptive mechanism was established to automatically adjust the complexity of training tasks based on student operation time and error frequency, setting a 90% mastery threshold. If the threshold is not met, supplementary training is automatically triggered. Expert decision trees are introduced to simulate the evolution logic of real accidents, optimizing fault difficulty settings. Second, basic training modules were strengthened by reducing the entry difficulty through interactive animations, designing closed-loop training for single skills, and simulating various practical errors and corresponding safety feedback. Third, multi-device collaborative training scenarios were expanded, adding sudden fault scenarios such as lightning strikes and accidental operation. Simultaneously, mobile and tablet clients were developed to achieve multi-terminal data synchronization, addressing the shortcomings in cross-terminal compatibility.

[0054] Step 7: Automatically generate the evaluation report

[0055] By integrating indicator system parameters, expert weight data, sample scoring data, visualization charts, weakness analysis results, and optimization and improvement plans, a complete quantitative evaluation report on the user experience of power training is automatically generated, which supports online viewing and file export.

[0056] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant parts can be found in the method section.

[0057] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the user experience of power training based on the weight analysis of the analytic hierarchy process (AHP), characterized in that, include: S1. Construct a three-level hierarchical evaluation index system for user experience in power training, which consists of the target layer, the criterion layer and the index layer from high to low. S2, construct a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training based on the analytic hierarchy process, and conduct pairwise importance comparison and scoring of each index in the criteria layer and the index layer respectively to generate expert judgment matrices; S3, synthesize the expert judgment matrices using the geometric mean method and calculate the weight of each indicator, then combine the consistency check to screen the final indicator weights. S4 collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores to identify shortcomings in the experience and generate optimization solutions.

2. The method for evaluating user experience in power training based on weight analysis using the analytic hierarchy process (AHP) as described in claim 1, characterized in that, The S1 criteria layer includes seven dimensions: system usability, interactive realism, content adaptability, learning convenience, emotional experience, security perception, and training effectiveness analysis. The indicator layer corresponds to thirteen specific evaluation indicators, among which system usability corresponds to the frequency of system crashes and lags, and operation response latency; interactive realism corresponds to the device operation simulation score and environmental immersion score; content adaptability corresponds to the smoothness of the connection between virtual and real content and the reasonableness of the difficulty gradient; learning convenience corresponds to the usability score of the functional interface and the efficiency of cross-terminal synchronization; and emotional experience corresponds to the degree of relief of operation anxiety and the learning sense of accomplishment score. Safety perception corresponds to the effectiveness of virtual risk warnings and the clarity of safe operation guidance; Analysis of training effectiveness corresponds to the degree of improvement in job skills.

3. The power training user experience evaluation method based on weight analysis using the analytic hierarchy process (AHP) as described in claim 1, characterized in that... S2 specifically includes: S201 assigns hierarchical labels to the three-tiered evaluation index system for user experience in power training, with the target layer labeled A0 and the seven criteria dimensions labeled A0 and A10 respectively. i (i=1,2,3…7), the underlying indicator layer corresponding to each criterion layer is labeled as A. ij This forms a standardized hierarchical structure model; S202, using the scaling method to compare the importance of each level of indicators pairwise, independently constructing a judgment matrix, and collecting multiple original scoring matrices; S203, perform preliminary consistency screening on all original scoring matrices, eliminate logically contradictory samples, provide feedback to experts for correction of matrices that fail the preliminary verification, and obtain the judgment matrices of each expert.

4. The method for evaluating user experience in power training based on weight analysis using the analytic hierarchy process (AHP) as described in claim 1, characterized in that, S3 specifically includes: S301, the geometric mean method is used to synthesize the judgment matrices of each expert group into a comprehensive judgment matrix of each expert group; S302, based on the comprehensive judgment matrix of each expert group, the geometric mean method is used to calculate the weight vector of each level of indicators. This involves sequentially performing matrix element row multiplication, nth-order power extraction, and vector normalization to calculate the relative weight w of each criterion level. i Combined with the weights W of each indicator layer ij ; S303, Perform matrix consistency verification, and calculate the maximum eigenvalue of the matrix, consistency index CI, random consistency index RI, and consistency ratio CR respectively; S304: Filter valid matrices with CR < 0.1, confirm the validity of the indicator weights at each level, and output the final indicator weights.

5. The power training user experience evaluation method based on weight analysis using the analytic hierarchy process (AHP) as described in claim 4, characterized in that, S4 specifically includes: S401: Select front-line power training personnel as the evaluation sample and collect raw data of all underlying indicators in batches. S402, Remove abnormal data from the original data, fill in a small number of missing data, unify the data units of all indicators, complete the data scoring according to the preset scoring standards of each indicator, and obtain the original score value of each sample indicator. S403. Based on the original scores of each sample indicator, calculate the average score of each sample indicator for all samples, and combine it with the final indicator weight to calculate the weighted score of each indicator, generating a user experience average score dataset and a weighted score dataset.

6. The power training user experience evaluation method based on weight analysis using the analytic hierarchy process (AHP) as described in claim 4, characterized in that, The calculation of the relative weights w of each criterion layer i Specifically, it includes: ； Among them, a ij To determine the importance comparison results between index i and index j in the matrix.

7. The power training user experience evaluation method based on weight analysis using the analytic hierarchy process (AHP) as described in claim 6, characterized in that... Specifically, S303 includes: ； ； ； in, denoted as the largest eigenvalue of the matrix, CI as the consistency index, RI as the random consistency index, CR as the consistency ratio, AW as the value obtained by summing the product of the judgment matrix and the weight vector row by row, and n as the order of the judgment matrix.

8. A power training user experience evaluation system based on weight analysis using the Analytic Hierarchy Process (AHP), applied to the power training user experience evaluation method based on weight analysis using the AHP as described in any one of claims 1-7, characterized in that, include: The system construction unit constructs a three-level hierarchical evaluation index system for the user experience of power training, which is divided into the target layer, the criterion layer and the index layer from high to low. The matrix generation unit constructs a hierarchical structure model for the three-level hierarchical evaluation index system of user experience in power training based on the analytic hierarchy process. It then performs pairwise importance comparison and scoring on each index in the criteria layer and the index layer to generate expert judgment matrices. The weight calculation unit synthesizes the expert judgment matrices using the geometric mean method and calculates the weight of each indicator, then combines the consistency check to screen the final indicator weights. The optimization solution generation unit collects data from frontline power industry trainees as evaluation samples and processes them in a standardized manner. Finally, it calculates a weighted comprehensive score based on the final indicator weights and sample scores, identifies shortcomings in the user experience, and generates optimization solutions.

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