CN116823057B - User experience effect evaluation method and system based on work efficiency analysis - Google Patents
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Abstract
The invention discloses a user experience effect evaluation method and a system based on work efficiency analysis, comprising the steps of acquiring an evaluation index of a product to be evaluated and on-site acquisition experience data, wherein the experience data comprises work efficiency data and parameter adjustment data, inputting the experience data into a filter to extract characteristic indexes influencing the experience effect, extracting corresponding characteristic data according to the characteristic indexes, inputting the experience data into a first screening model to obtain an accurate index, inputting the accurate index into a second screening model to obtain a characteristic index influencing the experience effect, determining the characteristic data weight, preprocessing the characteristic data, inputting the weight and the preprocessed characteristic data into an evaluation algorithm to obtain a comprehensive evaluation score.
Description
Technical Field
The invention relates to the technical field of work efficiency evaluation, in particular to a user experience effect evaluation method and system based on work efficiency analysis.
Background
The work efficiency evaluation technology is more and more widely applied in the field of user experience effects, and can help a user experience effect evaluation system to timely and efficiently analyze evaluation indexes, so that accurate analysis of experience data is realized. At present, the user experience effect evaluation based on the work efficiency analysis has the common characteristics of huge quantity, various types, high information density, multidisciplinary comprehensiveness and the like, and the qualitative analysis and judgment of the work efficiency evaluation technology has more uncertain factors, so that the evaluation result has larger subjectivity. Although some user experience effect evaluation systems based on the work efficiency analysis are constructed, and some professional work efficiency evaluation simulation software tools exist, subjective problems in the user experience effect evaluation work based on the work efficiency analysis cannot be effectively solved.
Disclosure of Invention
The invention aims to provide a user experience effect evaluation system based on work efficiency analysis.
In order to achieve the above purpose, the invention is implemented according to the following technical scheme:
the invention comprises the following steps:
the method comprises the steps of A, obtaining evaluation indexes of products to be evaluated and on-site acquisition experience data, wherein the experience data comprise work efficiency data and parameter adjustment data;
b, inputting the experience data into a screener to extract characteristic indexes influencing the experience effect, extracting corresponding characteristic data according to the characteristic indexes, wherein the screener comprises a first screening model and a second screening model, inputting the experience data into the first screening model to obtain accurate indexes, and inputting the accurate indexes into the second screening model to obtain the characteristic indexes influencing the experience effect;
and C, determining the weight of the characteristic data, preprocessing the characteristic data, and inputting the weight and the preprocessed characteristic data into an evaluation algorithm to obtain a comprehensive evaluation score.
Further, the evaluation indexes are comprehensively selected according to a compound method, wherein the compound method comprises a target guiding method, an experience method and a special method, and indexes mentioned by the three methods are selected as the evaluation indexes according to the characteristics of the evaluation object and the evaluation purpose.
Further, the method for extracting the characteristic index affecting the experience effect comprises the following steps:
inputting the experience data into a first screening model, adjusting the related scores of the data to the work efficiency data according to the support degree calculation parameters, calculating the related scores of all non-empty sets, screening out indexes with the related scores being more than or equal to a threshold value of 0.1 as accurate indexes, and sequencing;
inputting the accurate indexes into a second screening model, taking the largest index of the accurate indexes as a reference index, constructing a reference sequence according to the data of the reference index, taking the other indexes as comparison, and constructing a corresponding comparison sequence according to the data of the other indexes;
carrying out dimensionless treatment on the reference sequence and the comparison sequence;
calculating a correlation coefficient according to the reference sequence and the comparison sequence after dimensionless treatment;
calculating the correlation degree according to the correlation coefficient, and outputting an index with the correlation degree larger than 0.6 as a characteristic index:
wherein the correlation score is P (X-Y), the probability of the occurrence index X in the evaluation index is P (X), the probability of the occurrence index X containing the evaluation index X and the experience data Y is P (X-Y), and the correlation coefficient is ζ i (k) The degree of correlation is r i The reference number is x 0 (k) The comparison number is x i (k) The resolution coefficient is ρ, the minimum difference of two stages is minmin, and the maximum difference of two stages is maxmax.
Further, the method for determining the characteristic data weight comprises the following steps:
calculating the relevance of the characteristic data by using cosine similarity, evaluating according to the relevance and the corresponding characteristic index to obtain an evaluation result, and constructing a decision matrix by the evaluation result;
deblurring the correlation to obtain an accurate value, constructing a correlation matrix according to the correlation, and multiplying the accurate value by the correlation matrix of the corresponding column to obtain a weighted decision matrix;
determining positive ideal solution and negative ideal solution of an evaluation index in the user experience effect evaluation system of the work efficiency analysis, and calculating positive ideal solution distance and negative ideal solution distance;
calculating weights of the other indexes:
V＝[v ij ] m×n ＝[e j ×r ij ] m×n
wherein the weight is ω j The ideal distance isNegative ideal solution distance is->The accurate value isThe positive ideal solution is the maximum value combination of the evaluation indexes is V + The minimum value combination in the negative ideal solution as the evaluation index is V - Correlation r of j rows ij The weighted decision matrix is V, and the weighted decision value V ij Negative ideal weighted decision value +.>Positive ideal weighted decision value +.>Accurate value e j There are n evaluation indexes j and m users i.
Further, the method for preprocessing the characteristic data comprises the following steps:
taking the reciprocal of the characteristic data, and carrying out standardization processing on the reciprocal characteristic data to obtain standardized data;
dividing standardized data into seven grades according to the relevance ranking, and taking the partial large-scale Cauchy distribution and the logarithmic function as membership functions to carry out quantization treatment to obtain the preprocessed data:
wherein the feature data after taking the reciprocal is X, the standardized data is X, and the maximum value of the feature data after taking the reciprocal is X max Taking the minimum value of the feature data after reciprocal as X min The standardized data are divided into seven grades of data according to the relevance ranking, wherein the data are x, undetermined coefficients alpha, beta and b, and the preprocessed characteristic data f (x).
Further, the method for obtaining the comprehensive evaluation score comprises the following steps:
correspondingly multiplying the weight and the preprocessed characteristic data to obtain an evaluation score and outputting a result:
wherein the comprehensive evaluation score is E, and the weight is omega j The feature data after preprocessing is f (x j ) There are n evaluation indexes j.
In a second aspect, a user experience effect evaluation system based on work efficiency analysis comprises
The acquisition module is used for: the method comprises the steps of acquiring evaluation indexes of products to be evaluated and acquiring experience data on site, wherein the experience data comprise work efficiency data and parameter adjustment data;
and a screening module: the experience data input filter is used for extracting characteristic indexes affecting experience effects, and corresponding characteristic data are extracted according to the characteristic indexes;
and an evaluation module: and the characteristic data weight is determined, the characteristic data is preprocessed, and the weight and the preprocessed characteristic data are input into an evaluation algorithm to obtain a comprehensive evaluation score.
The beneficial effects of the invention are as follows:
the invention relates to a user experience effect evaluation method and a system based on work efficiency analysis, and compared with the prior art, the invention has the following technical effects:
1. according to the invention, through the steps of acquiring the index, acquiring the index data, screening the index and determining the weight, the objectivity of analysis can be improved, so that the analysis accuracy is improved, the system scientizes the analysis, the analysis accuracy and speed can be greatly improved, the working efficiency is improved, the real-time analysis of the user experience effect of the work efficiency analysis can be realized, the user experience effect evaluation of the work efficiency analysis corresponding to different users is timely given, the important significance is provided for the user experience effect evaluation system based on the work efficiency analysis, and the system can adapt to the user experience effect evaluation requirements of the work efficiency analysis of different users and different environments and has a certain universality.
2. The method can comprehensively consider the work efficiency experience effect of the user and the correlation of the user, converts the evaluation problem into the score problem by using the evaluation algorithm, obtains the characteristic index by screening the evaluation index, and realizes the accurate control of the evaluation index. The method not only can improve analysis precision, but also has better interpretability, and can be directly applied to a user experience effect evaluation system based on work efficiency analysis.
Drawings
Fig. 1 is a flowchart illustrating steps of a method for evaluating user experience effect based on work efficiency analysis.
Detailed Description
The invention is further described below in the following description of specific embodiments, which are presented for purposes of illustration and description, but are not intended to be limiting.
The invention relates to a user experience effect evaluation method and a system based on work efficiency analysis, wherein the method and the system comprise the following steps:
as shown in fig. 1, in this embodiment, the steps include:
the method comprises the steps of A, obtaining evaluation indexes of products to be evaluated and on-site acquisition experience data, wherein the experience data comprise work efficiency data and parameter adjustment data;
in actual evaluation, in the present embodiment, an automobile seat is selected as an evaluation object, evaluation indexes include a seat size, a backrest angle, a supporting force and a degree of adjustment, data include seat data and user data, wherein the seat data includes the size, the backrest angle, the supporting force and the degree of adjustment, the user data refers to the degree of satisfaction of a user on the automobile seat, and two sets of data are provided by continuously adjusting the evaluation indexes or changing the user to collect different data, the first set of data includes the size of 930mm, the backrest angle of 107 degrees, the supporting force of 475kg, the degree of adjustment of 300 and the degree of satisfaction of 0.86, and the second set of data includes the size of 970mm, the backrest angle of 92 degrees, the supporting force of 500kg, the degree of adjustment of 200 and the degree of satisfaction of 0.72;
b, inputting the experience data into a screener to extract characteristic indexes influencing the experience effect, extracting corresponding characteristic data according to the characteristic indexes, wherein the screener comprises a first screening model and a second screening model, inputting the experience data into the first screening model to obtain accurate indexes, and inputting the accurate indexes into the second screening model to obtain the characteristic indexes influencing the experience effect;
and C, determining the weight of the characteristic data, preprocessing the characteristic data, and inputting the weight and the preprocessed characteristic data into an evaluation algorithm to obtain a comprehensive evaluation score.
In this embodiment, the evaluation index is comprehensively selected according to a composite method, where the composite method includes a target guidance method, an empirical method, and a specific method, and indexes mentioned by the three methods are selected as the evaluation index according to the characteristics of the evaluation object and the evaluation purpose.
In this embodiment, the method for extracting the feature index affecting the experience effect includes:
inputting the experience data into a first screening model, adjusting the related scores of the data to the work efficiency data according to the support degree calculation parameters, calculating the related scores of all non-empty sets, screening out indexes with the related scores being more than or equal to a threshold value of 0.1 as accurate indexes, and sequencing;
inputting the accurate indexes into a second screening model, taking the largest index of the accurate indexes as a reference index, constructing a reference sequence according to the data of the reference index, taking the other indexes as comparison, and constructing a corresponding comparison sequence according to the data of the other indexes;
carrying out dimensionless treatment on the reference sequence and the comparison sequence;
calculating a correlation coefficient according to the reference sequence and the comparison sequence after dimensionless treatment;
calculating the correlation degree according to the correlation coefficient, and outputting an index with the correlation degree larger than 0.6 as a characteristic index:
wherein the correlation score is P (X-Y), the probability of the occurrence index X in the evaluation index is P (X), the probability of the occurrence index X containing the evaluation index X and the experience data Y is P (X-Y), and the correlation coefficient is ζ i (k) The degree of correlation is r i The reference number is x 0 (k) The comparison number is x i (k) The resolution coefficient is rho, the minimum difference value of two stages is minmin, and the maximum difference value of two stages is maxmax;
in actual evaluation, the satisfaction is X, the evaluation index is Y, if P (X-Y) =0.1, the experience effect and the height time of the driver are independent, namely, the satisfaction is not associated with the evaluation index, and the rule of satisfaction-evaluation index is not established; if P (X.fwdarw.Y) >0.1, then the rule "X.fwdarw.Y" is a valid association; if P (X→Y) <0.1, then the rule "X→Y" is an invalid association; the degree of correlation of the backrest angle is 0.6377, the degree of correlation of the supporting force is 0.8219, the degree of correlation of the adjusting degree is 0.7201, the degree of correlation of the size is 0.5502, and the screened characteristic index is the supporting force adjusting degree backrest angle.
In this embodiment, the method for determining the feature data weight includes:
calculating the relevance of the characteristic data by using cosine similarity, evaluating according to the relevance and the corresponding characteristic index to obtain an evaluation result, and constructing a decision matrix by the evaluation result;
deblurring the correlation to obtain an accurate value, constructing a correlation matrix according to the correlation, and multiplying the accurate value by the correlation matrix of the corresponding column to obtain a weighted decision matrix;
determining positive ideal solution and negative ideal solution of an evaluation index in the user experience effect evaluation system of the work efficiency analysis, and calculating positive ideal solution distance and negative ideal solution distance;
calculating weights of the other indexes:
V＝[v ij ] m×n ＝[e j ×r ij ] m×n
wherein the weight is ω j The ideal distance isNegative ideal solution distance is->The accurate value isThe positive ideal solution is the maximum value combination of the evaluation indexes is V + The minimum value combination in the negative ideal solution as the evaluation index is V - Correlation r of j rows ij The weighted decision matrix is V, and the weighted decision value V ij Negative ideal weighted decision value +.>Positive ideal weighted decision value +.>Accurate value e j The number of the evaluation indexes is j, n, and the number of the users is i, m；
In the actual evaluation, the weight of the adjustment degree was 0.411, the weight of the back angle was 0.385, and the weight of the supporting force was 0.247.
In this embodiment, the method for preprocessing the feature data includes:
taking the reciprocal of the characteristic data, and carrying out standardization processing on the reciprocal characteristic data to obtain standardized data;
dividing standardized data into seven grades according to the relevance ranking, and taking the partial large-scale Cauchy distribution and the logarithmic function as membership functions to carry out quantization treatment to obtain the preprocessed data:
wherein the feature data after taking the reciprocal is X, the standardized data is X, and the maximum value of the feature data after taking the reciprocal is X max Taking the minimum value of the feature data after reciprocal as X min Dividing standardized data into seven grades of data according to the relevance ranking into x, undetermined coefficients alpha, beta and b, and preprocessing the characteristic data f (x);
in the actual evaluation, two sets of data were given for preprocessing, the first set of preprocessed data comprising a supporting force of 0.71, an adjustment of 0.85 and a backrest angle of 0.47, and the second set of preprocessed data comprising a supporting force of 0.62, an adjustment of 0.22 and a backrest angle of 0.40.
In this embodiment, the method for obtaining a comprehensive evaluation score includes:
correspondingly multiplying the weight and the preprocessed characteristic data to obtain an evaluation score and outputting a result:
wherein the comprehensive evaluation score is E, and the weight is omega j The feature data after preprocessing is f (x j ) N evaluation indexes are j;
in the actual evaluation, the first group of comprehensive evaluation scores was 0.70567, the second group of comprehensive evaluation scores was 0.39756, and the first group of scores was 0.70567.
In a second aspect, a user experience effect evaluation system based on work efficiency analysis comprises
The acquisition module is used for: the method comprises the steps of acquiring evaluation indexes of products to be evaluated and acquiring experience data on site, wherein the experience data comprise work efficiency data and parameter adjustment data;
and a screening module: the experience data input filter is used for extracting characteristic indexes affecting experience effects, and corresponding characteristic data are extracted according to the characteristic indexes;
and an evaluation module: and the characteristic data weight is determined, the characteristic data is preprocessed, and the weight and the preprocessed characteristic data are input into an evaluation algorithm to obtain a comprehensive evaluation score.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The user experience effect evaluation method based on the work efficiency analysis is characterized by comprising the following steps of:
the method comprises the steps of A, obtaining evaluation indexes of products to be evaluated and on-site acquisition experience data, wherein the experience data comprise work efficiency data and parameter adjustment data;
b, inputting the experience data into a screener to extract characteristic indexes influencing the experience effect, extracting corresponding characteristic data according to the characteristic indexes, wherein the screener comprises a first screening model and a second screening model, inputting the experience data into the first screening model to obtain accurate indexes, and inputting the accurate indexes into the second screening model to obtain the characteristic indexes influencing the experience effect; the method for extracting the characteristic index influencing the experience effect comprises the following steps:
inputting the experience data into a first screening model, adjusting the related scores of the data to the work efficiency data according to the support degree calculation parameters, calculating the related scores of all non-empty sets, screening out indexes with the related scores being more than or equal to a threshold value of 0.1 as accurate indexes, and sequencing;
inputting the accurate indexes into a second screening model, taking the largest index of the accurate indexes as a reference index, constructing a reference sequence according to the data of the reference index, taking the other indexes as comparison, and constructing a corresponding comparison sequence according to the data of the other indexes;
carrying out dimensionless treatment on the reference sequence and the comparison sequence;
calculating a correlation coefficient according to the reference sequence and the comparison sequence after dimensionless treatment;
calculating the correlation degree according to the correlation coefficient, and outputting an index with the correlation degree larger than 0.5 as a characteristic index:
；
wherein the correlation score of the evaluation index X to the experience data Y is PThe probability of the index evaluation index X appearing in the evaluation index is +.>The probability of containing the evaluation index X and the experience data Y is +.>The correlation coefficient is +.>The degree of correlation is +.>The reference number is->The comparison number is +.>Resolution factor of +.>Minimum difference minmin of two stages and maximum difference maxmax of two stages;
c, determining the weight of the characteristic data, preprocessing the characteristic data, and inputting the weight and the preprocessed characteristic data into an evaluation algorithm to obtain a comprehensive evaluation score; comprising the following steps:
correspondingly multiplying the weight and the preprocessed characteristic data to obtain an evaluation score and outputting a result:
wherein the comprehensive evaluation score is E, and the weight isThe characteristic data after pretreatment is +.>There are n evaluation indexes j.
2. The method for evaluating the effect of user experience based on the work efficiency analysis according to claim 1, wherein the evaluation index is comprehensively selected according to a combination method, the combination method comprises a target guiding method, an experience method and a special method, and indexes mentioned by the three methods are selected as the evaluation index according to the characteristics of the evaluation object and the evaluation purpose.
3. The method for evaluating the user experience effect based on the ergonomic analysis according to claim 1, wherein the method for determining the feature data weight comprises the following steps:
calculating the relevance of the characteristic data by using cosine similarity, evaluating according to the relevance and the corresponding characteristic index to obtain an evaluation result, and constructing a decision matrix by the evaluation result;
deblurring the correlation to obtain an accurate value, constructing a correlation matrix according to the correlation, and multiplying the accurate value by the correlation matrix of the corresponding column to obtain a weighted decision matrix;
determining positive ideal solution and negative ideal solution of an evaluation index in the user experience effect evaluation system of the work efficiency analysis, and calculating positive ideal solution distance and negative ideal solution distance;
calculating weights of the other indexes:
wherein the weight isThe ideal distance is +.>The negative ideal solution distance is +.>The precise value is +.>The positive ideal solution is the maximum combination in the evaluation index of +.>The negative ideal solution is the minimum value combination in the evaluation index of +.>Correlation of j rows->The weighted decision matrix is V, the weighted decision value +.>Negative ideal weighted decision value +.>Positive ideal weighted decision valueAccurate value->There are n evaluation indexes j and m users i.
4. The method for evaluating the effect of user experience based on the ergonomic analysis of claim 1, wherein said method for preprocessing the feature data comprises:
taking the reciprocal of the characteristic data, and carrying out standardization processing on the reciprocal characteristic data to obtain standardized data;
dividing standardized data into seven grades according to the relevance ranking, and taking the partial large-scale Cauchy distribution and the logarithmic function as membership functions to carry out quantization treatment to obtain the preprocessed data:
wherein the feature data after taking the reciprocal is X, the standardized data is X, and the maximum value of the feature data after taking the reciprocal isTaking the minimum value of the feature data after reciprocal as +.>Dividing standardized data into seven grades according to the relevance ranking into x, and determining coefficients +.>，/>B, feature data after pretreatment +.>。
5. An ergonomic analysis-based user experience effect evaluation system, comprising:
the acquisition module is used for: the method comprises the steps of acquiring evaluation indexes of products to be evaluated and acquiring experience data on site, wherein the experience data comprise work efficiency data and parameter adjustment data;
and a screening module: the experience data input filter is used for extracting characteristic indexes affecting experience effects, and corresponding characteristic data are extracted according to the characteristic indexes; the method for extracting the characteristic index influencing the experience effect comprises the following steps:
inputting the experience data into a first screening model, adjusting the related scores of the data to the work efficiency data according to the support degree calculation parameters, calculating the related scores of all non-empty sets, screening out indexes with the related scores being more than or equal to a threshold value of 0.1 as accurate indexes, and sequencing;
inputting the accurate indexes into a second screening model, taking the largest index of the accurate indexes as a reference index, constructing a reference sequence according to the data of the reference index, taking the other indexes as comparison, and constructing a corresponding comparison sequence according to the data of the other indexes;
carrying out dimensionless treatment on the reference sequence and the comparison sequence;
calculating a correlation coefficient according to the reference sequence and the comparison sequence after dimensionless treatment;
calculating the correlation degree according to the correlation coefficient, and outputting an index with the correlation degree larger than 0.5 as a characteristic index:
；
wherein the correlation score of the evaluation index X to the experience data Y is PThe probability of the index evaluation index X appearing in the evaluation index is +.>The probability of containing the evaluation index X and the experience data Y is +.>The correlation coefficient is +.>The degree of correlation is +.>The reference number is->The comparison number is +.>Resolution factor of +.>Minimum difference minmin of two stages and maximum difference maxmax of two stages;
and an evaluation module: the method comprises the steps of determining the weight of the characteristic data, preprocessing the characteristic data, and inputting the weight and the preprocessed characteristic data into an evaluation algorithm to obtain a comprehensive evaluation score; comprising the following steps:
correspondingly multiplying the weight and the preprocessed characteristic data to obtain an evaluation score and outputting a result:
wherein the comprehensive evaluation score is E, and the weight isThe characteristic data after pretreatment is +.>There are n evaluation indexes j.
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| WO2023097932A1 (en) * | 2021-11-30 | 2023-06-08 | 江苏徐工工程机械研究院有限公司 | Method and system for screening a plurality of simulation results of engineering machinery |
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