Prototype Software User Experience Evaluation Method

Abstract

The present invention discloses a prototype software user experience evaluation method, comprising the steps of: S1. quantifying objective data of user experience to obtain objective data quantization values; S2. setting several evaluation indicators of the prototype software; S3. performing subjective scoring on several evaluation indicators respectively to obtain several subjective scoring values; S4. calculating the correlation between several subjective scoring values ​​and objective data quantization values ​​respectively to obtain several correlation coefficients; S5. determining the correlation degree of several correlation coefficients respectively to obtain several correlation degrees; S6. calculating the score of each evaluation indicator respectively according to several correlation degrees; S7. taking the sum of the product of each evaluation indicator score and the corresponding weight coefficient as the total evaluation score; S8. evaluating the level of the prototype software with the total evaluation score. The present invention can use objective data to verify the subjective validity, so that the evaluation result is more credible and accurate.

Description

Technical Field

[0001] The present invention relates to the field of user experience evaluation, and in particular to a prototype software user experience evaluation method. Background Art

[0002] Prototype software is software that exists in the design stage and has not yet been coded. Prototype software has the same functional interface and interactive operation method as the final product. By conducting user experience evaluation on prototype software, user experience issues that exist in the software can be discovered early in the design stage to help improve the software design.

[0003] The existing prototype software evaluation method mainly obtains users' subjective evaluation or subjective evaluation scores on software content, functions, interactions and interfaces through questionnaire surveys. There is a lack of objective data support during the user's use of the prototype software. These objective data include objective performance data such as the operation steps, mouse clicks, and mouse movement distance of the user to complete a given task, as well as emotional data of the user's facial expressions during the use of the software. Due to the lack of objective data support, the existing prototype software evaluation has the problem of strong subjectivity. Summary of the invention

[0004] In view of this, the purpose of the present invention is to overcome the defects in the prior art and provide a prototype software user experience evaluation method, which can use objective data to verify the subjective validity, so that the evaluation results are more credible and accurate.

[0005] The prototype software user experience evaluation method of the present invention comprises the following steps:

[0006] S1. Collect objective data of user experience, and quantify the objective data of user experience to obtain the quantitative value of the objective data;

[0007] S2. Setting the evaluation index system of the prototype software; the evaluation index system includes several evaluation indicators;

[0008] S3. Conduct subjective scoring on several evaluation indicators respectively to obtain several subjective scoring values;

[0009] S4. Calculate the correlation between several subjective scoring values ​​and objective data quantification values ​​respectively to obtain several correlation coefficients;

[0010] S5. respectively determine the correlation degree of several correlation coefficients to obtain several correlation degrees;

[0011] S6. Calculate the scores of each evaluation indicator according to a number of correlation levels;

[0012] S7. Set a weight coefficient for each evaluation indicator, calculate the product of each evaluation indicator score and the corresponding weight coefficient, and take the sum of each product as the total evaluation score;

[0013] S8. Evaluate the level of the prototype software based on the total evaluation score.

[0014] Furthermore, the objective user experience data includes operation trajectory data and expression data;

[0015] The operation trajectory data includes the moving range, the longest operation interval time, the number of clicks, the average moving speed and the total moving distance;

[0016] The expression data includes negative expression data and non-negative expression data.

[0017] Furthermore, the objective data of user experience is quantified to obtain the objective data quantification value, which specifically includes:

[0018] Calculate the deviation rate ds between the moving range and the moving range reference value;

[0019] Calculate the deviation rate dt of the longest operation interval time and the longest operation interval time reference value;

[0020] Calculate the deviation rate dm between the number of clicks and the reference value of the number of clicks;

[0021] Calculate the deviation rate dv between the moving average speed and the moving average speed reference value;

[0022] Calculate the deviation rate dl between the total moving distance and the total moving distance reference value;

[0023] Calculate the proportion k of non-negative expressions.

[0024] Furthermore, the deviation rate ds between the moving range and the moving range reference value is determined according to the following formula:

[0025]

[0026] Wherein, S is the relative value of the moving range and the moving range reference value, and S=|s1-s2|; s1 is the moving range; s2 is the moving range reference value;

[0027] Determine the deviation rate dt of the longest operation interval time from the longest operation interval time reference value according to the following formula:

[0028]

[0029] Wherein, T is the relative value of the longest operation interval time and the longest operation interval time reference value, and T=|t1-t2|; t1 is the longest operation interval time; t2 is the longest operation interval time reference value;

[0030] The deviation rate dm between the number of clicks and the baseline number of clicks is determined according to the following formula:

[0031]

[0032] Wherein, M is the relative value of the number of clicks and the reference value of the number of clicks, and M=|m1-m2|; m1 is the number of clicks; m2 is the reference value of the number of clicks;

[0033] The deviation rate dv between the moving average speed and the moving average speed reference value is determined according to the following formula:

[0034]

[0035] Wherein, V is the relative value of the moving average speed and the moving average speed reference value, and V=|v1-v2|; v1 is the moving average speed; v2 is the moving average speed reference value;

[0036] Determine the deviation rate dl between the total moving distance and the total moving distance reference value according to the following formula:

[0037]

[0038] Wherein, L is the relative value of the total moving distance and the total moving distance reference value, and L=|l1-l2|; l1 is the total moving distance; l2 is the total moving distance reference value;

[0039] The proportion k of non-negative expressions is determined according to the following formula:

[0040]

[0041] Among them, po is the number of non-negative expressions, and ne is the number of negative expressions.

[0042] Furthermore, the several evaluation indicators include content indicators, functional indicators, interaction indicators and interface indicators;

[0043] The content indicators include preference and understandability;

[0044] The functional indicators include the clarity of hierarchical logic and the difficulty of operation;

[0045] The interaction index includes interaction efficiency and interaction accuracy.

[0046] Furthermore, the correlations between several subjective rating values ​​and objective data quantification values ​​are calculated respectively to obtain several correlation coefficients, including:

[0047] Calculate the correlation between the subjective rating value of preference and the proportion k of non-negative expression components to obtain the correlation coefficient ρ1;

[0048] Calculate the correlation between the subjective score of understandability and the deviation rate ds to obtain the correlation coefficient ρ2;

[0049] Calculate the correlation between the subjective rating value of the hierarchical logic clarity and the deviation rate dt to obtain the correlation coefficient ρ3;

[0050] Calculate the correlation between the subjective score value of the operation difficulty and the deviation rate dm to obtain the correlation coefficient ρ4;

[0051] Calculate the correlation between the subjective score value of interaction efficiency and the deviation rate dv to obtain the correlation coefficient ρ5;

[0052] Calculate the correlation between the subjective score value of the interaction accuracy and the deviation rate dl to obtain the correlation coefficient ρ6;

[0053] Calculate the correlation between the subjective rating value of the interface index and the proportion k of the non-negative expression component, and obtain the correlation coefficient ρ7;

[0054] Further, the correlation degrees of several correlation coefficients are determined respectively to obtain several correlation degrees, which specifically includes the following steps:

[0055] a1. Determine the correlation coefficient interval A1 to which the correlation coefficient ρ1 belongs, and the correlation degree corresponding to the correlation coefficient interval A1 is used as the correlation degree of the correlation coefficient ρ1;

[0056] a2. By analogy with step a1, the correlation degrees of correlation coefficient ρ2, correlation coefficient ρ3, correlation coefficient ρ4, correlation coefficient ρ5, correlation coefficient ρ6 and correlation coefficient ρ7 are obtained respectively.

[0057] Furthermore, the scores of each evaluation indicator are calculated according to a number of correlation levels, which specifically includes the following steps:

[0058] b1. If the correlation coefficient ρ1 is extremely strongly correlated, strongly correlated, or moderately correlated, the subjective rating value of the preference is used as the score of the preference;

[0059] If the correlation coefficient ρ1 is weakly correlated, the preference score is determined according to the following formula:

[0060]

[0061] Among them, U1 is the score of preference, S1 is the subjective score of preference, S_O is the objective score, and n is the setting coefficient;

[0062] If the degree of relevance is no relevance, the preference has no score;

[0063] b2. Follow step b1 and obtain the scores of understandability, hierarchy logic clarity, operation difficulty, interaction efficiency, interaction accuracy and interface indicators.

[0064] Further, the scores of each evaluation indicator are calculated according to the following steps:

[0065] c1. The average of the sum of scores of several indicators in the content indicator is taken as the content indicator score;

[0066] c2. Follow step c1 and analogy to obtain the functional index score, interaction index score and interface index score respectively.

[0067] Furthermore, the interface indicators include color matching, interface style, aesthetics and layout rationality; the average of the sum of the subjective scoring values ​​of color matching, interface style, aesthetics and layout rationality is taken as the subjective scoring value of the interface indicator.

[0068] The beneficial effects of the present invention are as follows: a prototype software user experience evaluation method disclosed by the present invention sets a number of evaluation indicators and combines subjective evaluation with objective evaluation, uses objective evaluation data to verify the validity of subjective scoring, and then obtains a final evaluation score, and performs a grade evaluation on the prototype software according to the evaluation score, making the evaluation result more credible and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0070] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0071] The present invention is further described below in conjunction with the accompanying drawings, as shown in the drawings:

[0072] The prototype software user experience evaluation method of the present invention comprises the following steps:

[0073] S1. Collect objective data of user experience, and quantify the objective data of user experience to obtain the quantitative value of objective data; wherein, the test user can enter the existing user experience evaluation service cloud platform client through the evaluation link and invitation code through online self-evaluation, and the user experience evaluation service cloud platform automatically collects objective data of user experience during the online self-evaluation of the test user;

[0074] S2. Setting the evaluation index system of the prototype software; the evaluation index system includes several evaluation indicators;

[0075] S3. Subjectively score several evaluation indicators respectively to obtain several subjective score values; wherein the subjective score is scored by the test user according to a set scoring range; the scoring range can be scored on a 5-point scale, and the scores from low to high can be 1, 2, 3, 4, 5;

[0076] S4. Calculate the correlation between several subjective scoring values ​​and objective data quantification values ​​respectively to obtain several correlation coefficients;

[0077] S5. respectively determine the correlation degree of several correlation coefficients to obtain several correlation degrees;

[0078] S6. Calculate the scores of each evaluation indicator according to a number of correlation levels;

[0079] S7. Set a weight coefficient for each evaluation indicator, calculate the product of each evaluation indicator score and the corresponding weight coefficient, and take the sum of each product as the total evaluation score; wherein, the weight coefficient can be set by the main test personnel according to the actual situation, or can be set by the test user. If the weight coefficient is set by the test user, the average of the weight statistics set by several test users is used as the final weight coefficient;

[0080] S8. Evaluate the level of the prototype software based on the total evaluation score. For example, taking the total evaluation score as 100 points, the relationship between the level of the prototype software and the total evaluation score is as follows:

[0081] Very good: 90-100 points; Good: 80-89 points; Average: 60-79 points; Poor: 30-59 points; Very poor: 0-29 points.

[0082] In this embodiment, the objective user experience data includes operation trajectory data and expression data;

[0083] The operation trajectory data includes the moving range, the longest operation interval time, the number of clicks, the average moving speed and the total moving distance;

[0084] The expression data includes negative expression data and non-negative expression data. The negative expressions include anger, hate, fear and sadness; the non-negative expressions include neutral, happy and surprised.

[0085] The cloud platform automatically collects the movement trajectory data of the finger or mouse when the subject operates through the existing finger or mouse trajectory tracking system; the finger or mouse movement trajectory data is presented in two ways: operation click heat zone map and operation trajectory overlay map;

[0086] The operation click heat map reflects all the areas clicked by the subjects in the software interface; the different colors of the clicked areas reflect the number of times the subjects clicked the menu. The closer the color is to red, the more clicks, and the closer it is to green, the fewer clicks. This can intuitively show the frequency of operation clicks by the subjects. The click heat map can be a click heat map of a single subject or a superimposed click heat map of all subjects.

[0087] The operation trajectory overlay chart records the overlay chart of all operation trajectories of the subject's finger or mouse in the software interface during the operation task. It reflects the order of clicks and the distance between operation points during the operation task. The operation trajectory overlay chart can be a single subject's operation trajectory overlay chart or a superposition chart of all subjects' operation trajectories.

[0088] When the subject is performing the assessment task, the high-definition camera on the subject's computer records the subject's facial expression video during the operation. After completing the assessment task, the cloud platform uses the existing expression recognition system based on deep learning algorithms to analyze the facial expression video and automatically identify the main expression components during the subject's operation.

[0089] In this embodiment, the objective data of user experience is quantified to obtain the objective data quantization value, which specifically includes:

[0090] Calculate the deviation rate ds between the moving range and the moving range reference value;

[0091] Calculate the deviation rate dt of the longest operation interval time and the longest operation interval time reference value;

[0092] Calculate the deviation rate dm between the number of clicks and the reference value of the number of clicks;

[0093] Calculate the deviation rate dv between the moving average speed and the moving average speed reference value;

[0094] Calculate the deviation rate dl between the total moving distance and the total moving distance reference value;

[0095] Calculate the proportion k of non-negative expressions.

[0096] In the process of processing the operation trajectory data, the deviation rate is used to evaluate the difference level between the test subject's operation trajectory data value and the reference value. Among them, the several reference values ​​are set by the main test personnel according to the actual situation.

[0097] According to the deviation rate, the difference between the finger or mouse operation trajectory data value and the reference value is objectively scored. The corresponding relationship between the objective score value and the deviation rate is shown in Table 1:

[0098] Table 1

[0099] Objective rating value 1 2 3 4 5 Deviation rate d d＞60％ 40％＜d≤60％ 20％＜d≤40％ 5％＜d≤20％ 0≤d≤5％

[0100] The deviation rate ds between the moving range and the moving range reference value is determined according to the following formula:

[0101]

[0102] Wherein, S is the relative value of the moving range and the moving range reference value, and S=|s1-s2|; s1 is the moving range; s2 is the moving range reference value;

[0103] Determine the deviation rate dt of the longest operation interval time from the longest operation interval time reference value according to the following formula:

[0104]

[0105] Wherein, T is the relative value of the longest operation interval time and the longest operation interval time reference value, and T=|t1-t2|; t1 is the longest operation interval time; t2 is the longest operation interval time reference value;

[0106] The deviation rate dm between the number of clicks and the baseline number of clicks is determined according to the following formula:

[0107]

[0108] Wherein, M is the relative value of the number of clicks and the reference value of the number of clicks, and M=|m1-m2|; m1 is the number of clicks; m2 is the reference value of the number of clicks;

[0109] The deviation rate dv between the moving average speed and the moving average speed reference value is determined according to the following formula:

[0110]

[0111] Wherein, V is the relative value of the moving average speed and the moving average speed reference value, and V=|v1-v2|; v1 is the moving average speed; v2 is the moving average speed reference value;

[0112] Determine the deviation rate dl between the total moving distance and the total moving distance reference value according to the following formula:

[0113]

[0114] Wherein, L is the relative value of the total moving distance and the total moving distance reference value, and L=|l1-l2|; l1 is the total moving distance; l2 is the total moving distance reference value;

[0115] The proportion k of non-negative expressions is determined according to the following formula:

[0116]

[0117] Among them, po is the number of non-negative expressions, and ne is the number of negative expressions.

[0118] By statistically analyzing the facial expression data of each test user, the overall proportion of the seven facial expression components in the evaluation task can be obtained. In this embodiment, the statistical results of the proportion of the seven facial expressions in the three evaluation tasks are shown in Table 2:

[0119] Table 2

[0120]

[0121] In this embodiment, the several evaluation indicators include content indicators, functional indicators, interaction indicators and interface indicators;

[0122] The content indicators include preference and understandability;

[0123] The functional indicators include the clarity of hierarchical logic and the difficulty of operation;

[0124] The interaction index includes interaction efficiency and interaction accuracy;

[0125] The interface indicators include color matching, interface style, aesthetics and layout rationality.

[0126] Among them, content indicators, functional indicators, interaction indicators and interface indicators constitute the first-level evaluation indicators, and preference and understandability, hierarchical logic clarity and operation difficulty, interaction efficiency and interaction accuracy, color matching, interface style, aesthetics and layout rationality constitute the second-level evaluation indicators. By subjectively scoring the second-level evaluation indicators, the subjective scoring values ​​of several second-level evaluation indicators are obtained.

[0127] In this embodiment, the correlations between several subjective scoring values ​​and objective data quantization values ​​are calculated respectively to obtain several correlation coefficients, specifically including:

[0128] Calculate the correlation between the subjective rating value of preference and the proportion k of non-negative expression components to obtain the correlation coefficient ρ1;

[0129] Calculate the correlation between the subjective score of understandability and the deviation rate ds to obtain the correlation coefficient ρ2;

[0130] Calculate the correlation between the subjective rating value of the hierarchical logic clarity and the deviation rate dt to obtain the correlation coefficient ρ3;

[0131] Calculate the correlation between the subjective score value of the operation difficulty and the deviation rate dm to obtain the correlation coefficient ρ4;

[0132] Calculate the correlation between the subjective score value of interaction efficiency and the deviation rate dv to obtain the correlation coefficient ρ5;

[0133] Calculate the correlation between the subjective score value of the interaction accuracy and the deviation rate dl to obtain the correlation coefficient ρ6;

[0134] The correlation between the subjective rating of the interface index and the proportion k of the non-negative expression components is calculated to obtain the correlation coefficient ρ7; the average value of the sum of the subjective rating of color matching, the subjective rating of interface style, the subjective rating of aesthetics, and the subjective rating of layout rationality is taken as the subjective rating of the interface index;

[0135] The above-mentioned correlation calculation uses the existing correlation calculation formula:

[0136]

[0137] Among them, ρ (X,Y) is the correlation coefficient between variables X and Y. In the actual calculation process, the corresponding correlation coefficient can be obtained by replacing the variables.

[0138] In this embodiment, the correlation degrees of several correlation coefficients are determined respectively to obtain several correlation degrees, which specifically includes the following steps:

[0139] a1. Determine the correlation coefficient interval A1 to which the correlation coefficient ρ1 belongs, and use the correlation degree corresponding to the correlation coefficient interval A1 as the correlation degree of the correlation coefficient ρ1; usually, the corresponding relationship between the correlation coefficient interval and the correlation degree is shown in Table 3:

[0140] Table 3

[0141] Correlation coefficient interval [0.8,1.0] [0.6,0.8) [0.4,0.6) [0.2,0.4) [0.0,0.2) Degree of relevance Very strong correlation Strong correlation Moderately related Weak correlation No related

[0142] a2. By analogy with step a1, the correlation degrees of correlation coefficient ρ2, correlation coefficient ρ3, correlation coefficient ρ4, correlation coefficient ρ5, correlation coefficient ρ6 and correlation coefficient ρ7 are obtained respectively.

[0143] The scores of each evaluation indicator are calculated according to several correlation levels, which specifically includes the following steps:

[0144] b1. If the correlation coefficient ρ1 is extremely strongly correlated, strongly correlated, or moderately correlated, the subjective rating value of the preference is used as the score of the preference;

[0145] If the correlation coefficient ρ1 is weakly correlated, the preference score is determined according to the following formula:

[0146]

[0147] Wherein, U1 is the score of preference, S1 is the subjective score of preference, S_O is the objective score, and n is the setting coefficient; the value of n is 2;

[0148] If the degree of relevance is no relevance, the preference has no score;

[0149] b2. Follow step b1 and obtain the scores of understandability, hierarchy logic clarity, operation difficulty, interaction efficiency, interaction accuracy and interface indicators.

[0150] Among them, since the correlation parameters involved in the correlation coefficient ρ1 and the correlation coefficient ρ7 both have a non-negative expression component ratio k, when the correlation degree between the correlation coefficient ρ1 and the correlation coefficient ρ7 is weakly correlated, the objective score value S_O is determined according to Table 4.

[0151] Table 4

[0152]

[0153] Since the correlation parameters involved in the correlation coefficients ρ2, ρ3, ρ4, ρ5 and ρ6 all have a deviation rate d, when the correlation coefficients ρ2, ρ3, ρ4, ρ5 and ρ6 are weakly correlated, the objective score value S_O is determined according to Table 1.

[0154] In this embodiment, the scores of each evaluation indicator are calculated according to the following steps:

[0155] c1. The average of the sum of scores of several indicators in the content indicator is taken as the content indicator score;

[0156] c2. By analogy with step c1, the function index score, interaction index score and interface index score are obtained respectively. If the subjective score adopts a 5-point system, in order to make the score meet the 100-point system, each evaluation index score can be multiplied by 20. That is, the evaluation index score can be multiplied by a multiple according to the subjective scoring system, so that the evaluation index score meets the 100-point system.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A prototype software user experience evaluation method, characterized by: The steps include: S1. Collect objective data of user experience, and quantify the objective data of user experience to obtain the quantitative value of the objective data; The objective user experience data includes operation trajectory data and expression data; the operation trajectory data includes movement range, longest operation interval time, number of clicks, average movement speed and total movement distance; the expression data includes negative expression data and non-negative expression data; Quantify the objective data of user experience to obtain the objective data quantification value, including: Calculate the deviation rate ds between the moving range and the moving range reference value; Calculate the deviation rate dt of the longest operation interval time and the longest operation interval time reference value; Calculate the deviation rate dm between the number of clicks and the reference value of the number of clicks; Calculate the deviation rate dv between the moving average speed and the moving average speed reference value; Calculate the deviation rate dl between the total moving distance and the total moving distance reference value; Calculate the proportion k of non-negative expressions; S2. Setting the evaluation index system of the prototype software; the evaluation index system includes several evaluation indicators; S3. Conduct subjective scoring on several evaluation indicators respectively to obtain several subjective scoring values; S4. Calculate the correlation between several subjective scoring values ​​and objective data quantification values ​​respectively to obtain several correlation coefficients; S5. respectively determine the correlation degree of several correlation coefficients to obtain several correlation degrees; S6. Calculate the scores of each evaluation indicator according to a number of correlation levels; S7. Set a weight coefficient for each evaluation indicator, calculate the product of each evaluation indicator score and the corresponding weight coefficient, and take the sum of each product as the total evaluation score; S8. Evaluate the level of the prototype software based on the total evaluation score.

2. The prototype software user experience evaluation method according to claim 1 is characterized by: The deviation rate ds between the moving range and the moving range reference value is determined according to the following formula: Wherein, S is the relative value of the moving range and the moving range reference value, and S=|s1-s2|; s1 is the moving range; s2 is the moving range reference value; Determine the deviation rate dt of the longest operation interval time from the longest operation interval time reference value according to the following formula: Wherein, T is the relative value of the longest operation interval time and the longest operation interval time reference value, and T=|t1-t2|; t1 is the longest operation interval time; t2 is the longest operation interval time reference value; The deviation rate dm between the number of clicks and the baseline number of clicks is determined according to the following formula: Wherein, M is the relative value of the number of clicks and the reference value of the number of clicks, and M=|m1-m2|; m1 is the number of clicks; m2 is the reference value of the number of clicks; The deviation rate dv between the moving average speed and the moving average speed reference value is determined according to the following formula: Wherein, V is the relative value of the moving average speed and the moving average speed reference value, and V= |v1-v2|; v1 is the moving average speed; v2 is the moving average speed reference value; Determine the deviation rate dl between the total moving distance and the total moving distance reference value according to the following formula: Wherein, L is the relative value of the total moving distance and the total moving distance reference value, and L=|l1-l2|; l1 is the total moving distance; l2 is the total moving distance reference value; The proportion k of non-negative expressions is determined according to the following formula: Among them, po is the number of non-negative expressions, and ne is the number of negative expressions.

3. The prototype software user experience evaluation method according to claim 1 is characterized by: The several evaluation indicators include content indicators, functional indicators, interaction indicators and interface indicators; The content indicators include preference and understandability; The functional indicators include the clarity of hierarchical logic and the difficulty of operation; The interaction index includes interaction efficiency and interaction accuracy.

4. The prototype software user experience evaluation method according to claim 3 is characterized by: The correlations between several subjective rating values ​​and objective data quantification values ​​are calculated respectively to obtain several correlation coefficients, including: Calculate the correlation between the subjective rating value of preference and the proportion k of non-negative expression components to obtain the correlation coefficient ρ1; Calculate the correlation between the subjective score of understandability and the deviation rate ds to obtain the correlation coefficient ρ2; Calculate the correlation between the subjective rating value of the hierarchical logic clarity and the deviation rate dt to obtain the correlation coefficient ρ3; Calculate the correlation between the subjective score value of the operation difficulty and the deviation rate dm to obtain the correlation coefficient ρ4; Calculate the correlation between the subjective score value of interaction efficiency and the deviation rate dv to obtain the correlation coefficient ρ5; Calculate the correlation between the subjective score value of the interaction accuracy and the deviation rate dl to obtain the correlation coefficient ρ6; The correlation between the subjective score of the interface index and the proportion k of the non-negative expression components was calculated to obtain the correlation coefficient ρ7.

5. The prototype software user experience evaluation method according to claim 4 is characterized by: Determining the correlation degrees of several correlation coefficients respectively to obtain several correlation degrees specifically includes the following steps: a1. Determine the correlation coefficient interval A1 to which the correlation coefficient ρ1 belongs, and the correlation degree corresponding to the correlation coefficient interval A1 is used as the correlation degree of the correlation coefficient ρ1; a2. By analogy with step a1, the correlation degrees of correlation coefficient ρ2, correlation coefficient ρ3, correlation coefficient ρ4, correlation coefficient ρ5, correlation coefficient ρ6 and correlation coefficient ρ7 are obtained respectively.

6. The prototype software user experience evaluation method according to claim 5 is characterized by: The scores of each evaluation indicator are calculated according to several correlation levels, which specifically includes the following steps: b1. If the correlation coefficient ρ1 is extremely strongly correlated, strongly correlated, or moderately correlated, the subjective rating value of the preference is used as the score of the preference; If the correlation coefficient ρ1 is weakly correlated, the preference score is determined according to the following formula: Among them, U1 is the score of preference, S1 is the subjective score of preference, S_O is the objective score, and n is the setting coefficient; If the degree of relevance is no relevance, the preference has no score; b2. Follow step b1 and obtain the scores of understandability, hierarchy logic clarity, operation difficulty, interaction efficiency, interaction accuracy and interface indicators.

7. The prototype software user experience evaluation method according to claim 6 is characterized by: Calculate the scores of each evaluation indicator according to the following steps: c1. The average of the sum of scores of several indicators in the content indicator is taken as the content indicator score; c2. Follow step c1 and analogy to obtain the functional index score, interaction index score and interface index score respectively.

8. The method for evaluating the user experience of prototype software according to claim 6, characterized in that: The interface indicators include color matching, interface style, aesthetics and layout rationality; the average of the sum of the subjective scoring values ​​of color matching, interface style, aesthetics and layout rationality is taken as the subjective scoring value of the interface indicators.

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