Quantification method and device for usability of 3D CAD software based on extended interval number

Abstract

The present invention discloses a method and device for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals. The method first determines the usability index set of the three-dimensional CAD software, and then performs standardization to obtain standardized data, original interval number, standardized sample number, and standardized probability density, respectively. The concentration and trend of the sample data are obtained according to the probability density after standardization, and the expansion prediction angle is calculated and corrected to generate the number of expansion intervals. The secondary indexes are determined according to the number of expansion intervals, the weights of the secondary indexes are determined and weighted summed to obtain the primary indexes, and the weights of the primary indexes are determined to finally obtain the quantification results of the usability of the three-dimensional CAD software. The present invention fully considers the distribution characteristics of the sample data and the global degree of the sample, and can accurately quantify the usability of the three-dimensional CAD software.

Description

Technical Field

[0001] The present invention relates to the field of quantification of usability of three-dimensional CAD software, and in particular to a method and device for quantifying usability of three-dimensional CAD software based on an extended interval number. Background Art

[0002] As basic industrial software, 3D CAD software is widely used in many industries such as shipbuilding, aviation, and machinery. For 3D CAD software with similar functions and performance, users tend to choose software that is simple to operate, user-friendly, and easy to use as their design tools. Therefore, accurately quantifying the usability of 3D CAD software is very necessary for users to choose suitable software. The usability index data of 3D CAD software is given by users who participate in the test. The sample data values ​​have a certain range of variation. The existing usability index description method that only uses the boundary information of the current sample data to establish the interval number does not fully utilize the distribution characteristics of the sample data. At the same time, the users who participated in the test only accounted for a small part of the theoretical maximum value of 3D CAD software usage. The usability index data directly obtained based on the test samples lacks global representativeness. Summary of the invention

[0003] The purpose of the present invention is to provide a 3D CAD software usability quantification method and device based on extended interval numbers, in order to address the problem that the existing use of interval numbers to describe the usability index of 3D CAD software neither fully explores the uncertainty distribution law of current sample data nor considers the possible impact of unknown samples on the current interval number range, thereby resulting in inaccurate quantification results of the 3D CAD software usability. The method fully considers the uncertainty distribution law of sample data and its globality, and can accurately quantify the usability of 3D CAD software.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] The 3D CAD software usability quantification method based on the extended interval number considers the global degree of the sample and the distribution characteristics of the sample data, describes each usability index by establishing the extended interval number, adjusts its weight based on the uncertainty degree of each index, and then calculates the final usability quantification value. It specifically includes the following steps:

[0006] S1: Determine the usability index set of 3D CAD software and obtain the initial data of the index; the usability index set includes the first-level usability index U i (1≤i≤I) and its subordinate secondary index U ij (1≤i≤I,1≤j≤J i ), where I is the number of first-level usability indicators, J i (1≤i≤I) is the first-level usability index U iThe initial data of each secondary indicator is obtained from the users who participated in the test.

[0007] S2: Normalize the initial data to obtain standardized data, and establish the original interval number through the standardized data; for the nth sample x in N initial data samples n , the corresponding standardized data Where maxX is the theoretical maximum value of the data; the original interval number I is established by standardizing the data o =[I ol ,I or ]= oc ,I ow >, where the left border Right border Interval number midpoint I oc =(I ol +I or ) / 2, interval width I ow =I or -I ol .

[0008] S3: Standardize the number of samples corresponding to each standardized data value; for the mth standardized data among the M standardized data The corresponding number n m , its standardized quantity

[0009] S4: Use normal distribution to fit the probability density of standardized data and standardize the probability density; the initial probability density function after fitting Where x is the independent variable. In the specific example, it represents the continuous standardized data. * represents the discrete standardized data, μ(x * ), σ 2 (x * ) are the mean and variance of the standardized data samples, and the standardized probability density function

[0010] S5: According to the standardized probability density, the concentration and trend of the sample data are calculated. The concentration is used to reflect the concentration of the sample; the trend is used to reflect the difference between the sample characteristics and the characteristics described by the original interval number; the concentration Trend degree d pt =2(μ(x * )-I oc ) / I ow .

[0011] ​S6: Calculate the expansion prediction angle. First, calculate the expansion rotation angle according to the concentration and trend of the sample data. The expansion rotation angle D is the size of the normalized data as the x-axis, and the standardized sample quantity and standardized probability density as the y-axis. Construct a standardized sample data statistical information graph. Rotate the left and right boundaries of the standardized probability density curve along their tangents downward by D and extend them to intersect with the x-axis. The angle between the left boundary extension line and the negative direction of the x-axis is the left expansion prediction angle D. l The angle between the right boundary extension line and the positive direction of the x-axis is the right expansion prediction angle D r , when D l , D r When it is greater than 90°, adjust the rotation direction of the corresponding extension line to make it equal to 90°; the extended rotation angle D = arcsin(|d pt |) / d pc , if d pt > 0, the extended rotation angle is counterclockwise. If d pt <0, the expansion rotation angle is clockwise, d pc represents the concentration, d pt Indicates the degree of trend.

[0012] S7: Based on the globality of the sample, the expansion prediction angle is corrected. First, the globality coefficient of the sample data is calculated. Then, the left and right global expansion prediction angles are calculated based on the globality coefficient and the left and right expansion prediction angles. N, where N all is the theoretical maximum number of users participating in the test, N represents the number of data samples; the left global expansion prediction angle D' l =(1-c g )D l +c g 90°, right global expansion prediction angle D' r =(1-c g )D r +c g 90°.

[0013] S8: Generate the extended interval number. First, select the value with the minimum standardized sample number and standardized probability density at the boundary of the original interval number as the extension starting point. Determine the extension slope through the global expansion prediction angle. Make the intersection of the extension line and the x-axis as the left boundary of the extended interval number. el and right boundary I er , the number of expansion intervals I e =[I el ,I er ]= ec ,I ew >, where the midpoint of the extended interval is I ec =(I el +I​er ) / 2 reflects the usability index characteristics of considering the global nature of the sample, and expands the width of the interval number I ew =I er -I el Reflects the global prediction uncertainty considering the global nature of the sample;

[0014] S9: Determine each secondary index according to the number of expansion intervals, and for each secondary index of ease of use U ij , establish the expansion interval number through its original data samples The value of this indicator in is the average value of the normalized sample data, and the uncertainty of the indicator value is c g Represents the global coefficient of sample data;

[0015] S10: Determine the initial weight of each secondary indicator using the analytic hierarchy process Then for each first-level index U i Construct the matrix M i , the element in the rth row and kth column of the matrix Normalize each column of the matrix to get a new matrix Elements Then the secondary index U ij The final weight

[0016] S11: According to the values ​​of each secondary indicator and its uncertainty, the weighted sum is used to obtain the value of the primary indicator and its uncertainty; i , its value Its uncertainty For the first level indicator U i , its value The corresponding uncertainty Then construct the matrix S, whose rth row and kth column element Normalize each column of the matrix to get a new matrix Elements The first-level index U i The final weight The quantitative results of the usability of 3D CAD software are The value range is 0 to 1.

[0017] The device for quantifying the usability of three-dimensional CAD software based on the number of extended intervals includes one or more processors for implementing the method for quantifying the usability of three-dimensional CAD software based on the number of extended intervals.

[0018] The advantages and beneficial effects of the present invention are:

[0019] The present invention provides a method and device for quantifying the usability of 3D CAD software based on the expansion interval number. When quantifying the usability of 3D CAD software, the distribution law and globality of sample data can be fully considered, and the usability of 3D CAD software can be accurately quantified. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments;

[0021] Figure 1 is a flow chart of the method of the present invention;

[0022] Figure 2 is a schematic diagram of statistical information of sample data after standardization processing in the present invention;

[0023] Figure 3 It is a schematic diagram of the concentration and trend of sample data in the present invention;

[0024] Figure 4 It is a schematic diagram of the expansion rotation angle and expansion prediction angle of the sample data in the present invention;

[0025] Figure 5 It is a schematic diagram of the global expansion prediction angle of sample data in the present invention;

[0026] Figure 6 It is a schematic diagram of the number of expansion intervals generated by sample data in the present invention;

[0027] Figure 7 It is a structural diagram of the device of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more clear. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] The method for quantifying the usability of 3D CAD software based on the extended interval number proposed in the present invention uses the extended interval number to represent each usability index, calculates its weight based on the uncertainty of each index, and determines the final usability quantification result. The flow chart of the method is as follows Figure 1 As shown, the specific steps include:

[0030] S1: Determine the usability index set of 3D CAD software, including the first-level usability index U i (1≤i≤I) and its subordinate secondary index U ij (1≤i≤I,1≤j≤J i ), where I is the number of first-level usability indicators, J i (1≤i≤I) is the first-level usability index U iThe initial data of each secondary indicator is obtained from the users who participated in the test.

[0031] S2: Normalize the initial data to obtain standardized data. For the nth sample x among the N initial data samples, n , the corresponding standardized data Where maxX is the theoretical maximum value of the data; the original interval number I is established by standardizing the data o =[I ol ,I or ]= oc ,I ow >, where the left border Right border Midpoint I oc =(I ol +I or ) / 2 , Width I ow =I or -I ol .

[0032] S3: Standardize the number of samples corresponding to each data value. For the mth data among the M standardized data The corresponding number n m , its standardized quantity

[0033] S4: Use normal distribution to fit the probability density of standardized data and perform standardization. The initial probability density function after fitting Where x is the independent variable. In the specific example, it represents the continuous standardized data. * represents the discrete standardized data, μ(x * ), σ 2 (x * ) are the mean and variance of the standardized data samples, and the standardized probability density function

[0034] S5: Calculate the concentration and trend of sample data. Reflects the concentration of the sample; trend degree d pt =2(μ(x * )-I oc ) / I ow , reflecting the degree of difference between the sample characteristics and the characteristics described by the original interval number.

[0035] S6: Calculate the expansion prediction angle. First, calculate the expansion rotation angle according to the concentration and trend of the sample data. The expansion rotation angle D = arcsin(|d pt |) / d pc ​, if d pt > 0, the extended rotation angle is counterclockwise. If d pt <0, the expansion rotation angle is clockwise; the left and right boundaries of the standardized probability density curve are rotated D along their tangents and extended to intersect with the x-axis. The angle between the left boundary extension line and the negative direction of the x-axis is the left expansion prediction angle D l The angle between the right boundary extension line and the positive direction of the x-axis is the right expansion prediction angle D r , when D l , D r When it is greater than 90°, the rotation direction of the corresponding extension line should be adjusted to make it equal to 90°.

[0036] S7: Based on the globality of the sample, the extended prediction angle is corrected. First, the globality coefficient of the sample data is calculated. in N all is the theoretical maximum number of users participating in the test, and N represents the number of data samples; then the global expansion prediction angle is calculated, and the left global expansion prediction angle D' l =(1-c g )D l +c g 90°, right global expansion prediction angle D' r =(1-c g )D r +c g 90°.

[0037] S8: Generate the extended interval number. First, select the value with the smaller standardized sample number and standardized probability density at the boundary of the original interval number as the extension starting point. Determine the extension slope through the global expansion prediction angle. The intersection of the extension line and the x-axis is the left boundary of the extended interval number. el and right boundary I er , the number of expansion intervals I e =[I el ,I er ]= ec ,I ew >, where the midpoint of the extended interval is I ec =(I el +I er ) / 2 reflects the usability index characteristics of considering the global nature of the sample, and expands the width of the interval number I ew =I er -I el Reflects the global prediction uncertainty considering the global nature of the sample.

[0038] S9: Determine each secondary index according to the number of expansion intervals, and for each secondary index of ease of use U ij , establish the expansion interval number through its original data samples The value of this indicator​ in is the average value of the normalized sample data, and the uncertainty of the indicator value is

[0039] S10: Determine the initial weight of each secondary indicator using the analytic hierarchy process Then for each first-level index U i Construct the matrix M i , the element in the rth row and kth column of the matrix Normalize each column of the matrix to get a new matrix Elements Then the secondary index U ij The final weight

[0040] S11: According to the weighted sum of the secondary index values ​​and their uncertainties, the primary index value and its uncertainty are obtained. i , its value Its uncertainty For the first level indicator U i , its value The corresponding uncertainty Then construct the matrix S, whose rth row and kth column element Normalize each column of the matrix to get a new matrix Elements The first-level index U i The final weight The quantitative results of the usability of 3D CAD software are The value range is 0 to 1.

[0041] The method of the present invention is described below with reference to specific embodiments.

[0042] A specific 3D CAD software was selected for usability quantification, and operability, aesthetics, and ease of learning were selected as three first-level usability indicators. Each first-level indicator is composed of a series of specific second-level indicators. The data of each second-level usability indicator is directly given by the score value of the current usability test. The indicators and their initial weights determined by the hierarchical analysis method are shown in Table 1.

[0043] Table 1. 3D CAD usability indicators and their initial weights

[0044]

[0045] The value range of each secondary index is 0 to 10. The higher the value, the higher the usability level of the corresponding index. 11For example, the test obtained 100 sample data. The theoretical maximum number of users of this 3D CAD software is 1,000,000. The global coefficient of the sample is 0.333. The obtained scoring data is shown in Table 2.

[0046] Table 2. Original sample data of sketching operation of secondary indicator of 3D CAD usability

[0047] Data Value 7.0 7.5 8.0 8.5 Corresponding quantity 35 31 22 12

[0048] The initial data of the sample is normalized, and then the sample size and the normalized probability density function fitted with the normal distribution are standardized. The results are as follows: Figure 2 As shown, the concentration and trend of the sample data are calculated as follows Figure 3 As shown, in Figure 4 In the above example, the expansion rotation angle is calculated based on the concentration and trend. The two boundaries of the standardized probability density curve are rotated along the extension line and extended to the x-axis to obtain the expansion prediction angle. At this time, the right expansion prediction angle is greater than 90°, so it is adjusted to 90°. After that, the global expansion prediction angle is calculated based on the global coefficient. Since the right preference prediction angle is already 90°, no matter what the value of the global coefficient is, the right global expansion prediction angle is 90°. The obtained global expansion prediction angle is as follows: Figure 5 As shown; select the probability density function at the boundary of the original interval number as the extension starting point, determine the extension slope through the global expansion prediction angle, and draw an extension line to obtain the expansion interval number as shown in Figure 6 shown.

[0049] According to the number of expansion intervals, the corresponding secondary indicators of 3D CAD software usability can be obtained, and the initial weights of each secondary indicator obtained using the hierarchical analysis method can be adjusted accordingly. The results are shown in Table 3.

[0050] Table 3. Secondary indicators and weights of 3D CAD software usability

[0051]

[0052] As shown in Table 4, the secondary usability index based on the number of expansion intervals takes into account the possible impact of the global finiteness of the sample, performs variable weight calculation on the initial weight to obtain the final weight, uses the weighted sum to obtain each primary index, and then uses the same method to determine the final quantification result of the usability of the 3D CAD software, which is 0.722.

[0053] Corresponding to the above-mentioned embodiment of the method for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals, the present invention also provides an embodiment of the device for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals.

[0054] See also Figure 7The device for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals provided in an embodiment of the present invention includes one or more processors for implementing the method for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals in the above embodiment.

[0055] The embodiment of the device for quantifying the ease of use of three-dimensional CAD software based on the number of expansion intervals of the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From the hardware level, if Figure 7 As shown in the figure, it is a hardware structure diagram of any device with data processing capability where the three-dimensional CAD software usability quantification device based on the expansion interval number of the present invention is located. Figure 7 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0056] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0057] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0058] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals in the above embodiment is implemented.

[0059] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0060] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention shall be included in the protection scope of the invention.

Claims

1. A quantification method for the usability of 3D CAD software based on the expansion interval number, characterized by The steps include: S1: Determine the usability index set of 3D CAD software and obtain the initial data of the index; S2: Normalize the initial data to obtain standardized data, and establish the original interval number through the standardized data; S3: Standardize the number of samples corresponding to each standardized data value; S4: Use normal distribution to fit the probability density of the standardized data and standardize the probability density; S5: Calculate the concentration and trend of the sample data based on the standardized probability density. The concentration is used to reflect the concentration of the sample; the trend is used to reflect the degree of difference between the sample characteristics and the characteristics described by the original interval number. S6: Calculate the expansion prediction angle. First, calculate the expansion rotation angle according to the concentration and trend of the sample data. The expansion rotation angle D is the size of the normalized data as the x-axis, and the standardized sample quantity and standardized probability density as the y-axis. Construct a standardized sample data statistical information graph. Rotate the left and right boundaries of the standardized probability density curve along their tangents downward by D and extend them to intersect with the x-axis. The angle between the left boundary extension line and the negative direction of the x-axis is the left expansion prediction angle D. l The angle between the right boundary extension line and the positive direction of the x-axis is the right expansion prediction angle D r , when D l , D r If it is greater than 90°, adjust the rotation direction of the corresponding extension line to make it equal to 90°; The extended rotation angle D = arcsin (d pt |) / d pc , if d pt > 0, the extended rotation angle is counterclockwise. If d pt <0, the expansion rotation angle is clockwise, d pc represents the concentration, d pt Indicates the degree of trend; S7: correcting the expansion prediction angle based on the globality of the sample, first calculating the globality coefficient of the sample data, and then calculating the left and right global expansion prediction angles according to the globality coefficient and the left and right expansion prediction angles; S8: Generate the number of extended intervals. First, select the value with the minimum standardized sample quantity and standardized probability density at the boundary of the original interval as the starting point of the extension. Determine the extension slope through the global extension prediction angle and make the intersection of the extension line and the x-axis. The intersection of the extension line and the x-axis is the left boundary of the extended interval number. el and right boundary I er , expand the number of intervals The midpoint of the extended interval is I ec =(I el +I er ) / 2 reflects the usability index characteristics of considering the global nature of the sample, and expands the width of the interval number I ew =I er -I el Reflects the global prediction uncertainty considering the global nature of the sample; S9: Determine each secondary indicator according to the number of expansion intervals. For each secondary indicator of ease of use, establish the number of expansion intervals through its original data sample. Calculate the value and uncertainty of the secondary indicator through the number of expansion intervals, the average value of the normalized sample data, and the global coefficient of the sample data. S10: Determine the initial weight of each secondary indicator using the analytic hierarchy process. According to the uncertainty of the secondary indicator, construct a matrix for each primary indicator and normalize each column of the matrix. Calculate the final weight of the secondary indicator using the elements of the new matrix and the initial weight of the secondary indicator. S11: Use the hierarchical analysis method to determine the initial weight of each first-level indicator, and obtain the value of the first-level indicator and its uncertainty by taking the weighted sum of the value, uncertainty and final weight of each second-level indicator. According to the uncertainty of the first-level indicator, a matrix is ​​constructed for the first-level indicator and each column of the matrix is ​​normalized. The final weight of the first-level indicator is calculated using the elements of the new matrix and the initial weight of the first-level indicator. The quantitative result of the usability of the 3D CAD software is obtained through the final weight of the first-level indicator and the value of the first-level indicator.

2. The method for quantifying the ease of use of three-dimensional CAD software based on the expansion interval number according to claim 1, characterized in that In S1, the usability index set includes the first-level usability index U i (1≤i≤I) and its subordinate secondary index U ij (1≤i≤I,1≤j≤Ji), where I is the number of first-level usability indicators, J is i (1≤i≤I) is the first-level usability index U i The number of secondary usability indicators under the above conditions, the initial data of each secondary indicator is obtained from the users who participated in the test; In S9, the number of expansion intervals is established as The value of this indicator in is the average value of the normalized sample data, and the uncertainty of the indicator value is c g Represents the global coefficient of sample data; In S10, for each primary index U i Construct the matrix M i , the element in the rth row and kth column of the matrix Normalize each column of the matrix to get a new matrix Elements Then the secondary index U ij The final weight represents the initial weight; In S11, the first-level index U i , its value The corresponding uncertainty Then construct the matrix S, whose rth row and kth column element Normalize each column of the matrix to get a new matrix Elements The first-level index U i The final weight The quantitative results of the usability of 3D CAD software are The value range is 0 to 1.

3. The method for quantifying the ease of use of three-dimensional CAD software based on the expansion interval number according to claim 1 is characterized in that In S2, for the nth sample x among the N initial data samples, n , the corresponding standardized data in is the theoretical maximum value of the data; the original interval number I is established by standardizing the data o =[I ol ,I or ]= oc ,I ow >, where the left border Right border Interval number midpoint I oc =(I ol +I or ) / 2, interval width I ow =I or -I ol ;​ In S4, the initial probability density function after fitting Where x represents the continuous standardized data, x * represents the discrete standardized data, μ(x * ), σ 2 (x * ) are the mean and variance of the standardized data samples, and the standardized probability density function 4. The method for quantifying the ease of use of three-dimensional CAD software based on the expansion interval number according to claim 3 is characterized in that The concentration of S5 5. The method for quantifying the ease of use of three-dimensional CAD software based on the expansion interval number according to claim 3 is characterized in that The trend degree d in S5 pt =2(μ(x * )-I oc ) / I ow .

6. The method for quantifying the ease of use of three-dimensional CAD software based on the expansion interval number according to claim 1, characterized in that In S3, for the mth standardized data among the M standardized data, The corresponding number n m , its standardized quantity 7. The method for quantifying the usability of three-dimensional CAD software based on the expansion interval number according to claim 1, characterized in that The global coefficients in S7 in N all is the theoretical maximum number of users participating in the test, N represents the number of data samples; the left global expansion prediction angle D' l =(1-c g )D l +c g 90°, right global expansion prediction angle D' r =(1-c g )D r +c g 90°.

8. A device for quantifying the usability of three-dimensional CAD software based on the number of expansion intervals, characterized in that: The method comprises one or more processors for implementing a three-dimensional CAD software usability quantification method based on an extended interval number as described in any one of claims 1 to 7.

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