3D CAD software performance measurement method and device based on self-reducing reverse cloud generator

Abstract

The present invention discloses a 3D CAD software performance measurement method and device based on a self-reducing inverse cloud generator, determines a 3D CAD software performance measurement index set and performs standardized processing, inputs the obtained standardized data into a self-reducing inverse cloud generator to solve the cloud model digital features of each performance index data, calculates each performance index value and its uncertainty through the digital features, and adjusts the weight of each index accordingly, and then calculates the final performance measurement value. The proposed method fully considers the complex uncertain factors contained in the 3D CAD software performance test data in the 3D CAD software performance measurement process and accurately characterizes them, and can achieve accurate measurement of the 3D CAD software performance.

Description

Technical Field

[0001] The present invention relates to the field of 3D CAD software performance measurement, and in particular to a 3D CAD software performance measurement method and device based on a self-reducing inverse cloud generator. Background Art

[0002] 3D CAD software is widely used in many industries such as shipbuilding, aviation, and machinery. Its performance has an important impact on design efficiency. Accurately measuring the performance of 3D CAD software helps 3D CAD manufacturers understand the gap between their product performance and the international advanced level, so as to iterate and improve the software products. It is of great significance to promote the performance improvement of 3D CAD software products. The performance index data of 3D CAD software is obtained through a large number of tests. Due to the performance fluctuations of 3D CAD software itself and the instability of the test environment, the performance index data contains complex uncertainties. The complex uncertainty factors contained in the performance index test data can be subdivided through the digital features of the cloud model. However, due to the relatively small interference of the instability of the test environment and the small number of actual test data samples, directly using the existing reverse cloud generator to solve the performance index may result in the result of the super entropy calculation being an imaginary number, that is, the problem of failure to solve the digital features of the cloud model, which makes the performance measurement process of the 3D CAD software unable to be completed. Summary of the invention

[0003] In the existing methods of describing the performance index of 3D CAD software based on digital features of cloud models, due to the relatively small interference of the test environment and the small number of sample points, there may be a situation where the result of the super entropy calculation is an imaginary number, which leads to the failure of the solution of the digital features of the cloud model. The purpose of the present invention is to provide a 3D CAD software performance measurement method based on a self-reducing inverse cloud generator, which takes into account the complex uncertainty factors contained in the performance index test data, and at the same time avoids the problem of possible failure of solving the digital features of the cloud model through the self-reducing inverse cloud generator, and can achieve accurate measurement of the performance of 3D CAD software.

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

[0005] A 3D CAD software performance measurement method based on a self-reducing inverse cloud generator is proposed. The digital features of the cloud model of each performance index data are solved by the self-reducing inverse cloud generator. The values ​​of each performance index and its uncertainty are calculated by the digital features, and the weights of each index are adjusted accordingly to determine the final performance measurement results. The specific steps are as follows:

[0006] The performance measurement method of 3D CAD software based on self-reducing reverse cloud generator includes the following steps:

[0007] S1: Determine the performance measurement index set of 3D CAD software, including the first-level performance index Ui (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 performance indicators, J i (1≤i≤I) is the first-level performance index U i The number of secondary performance indicators under the above conditions is obtained, and the original test data of each secondary performance indicator is obtained through the automatic testing program.

[0008] S2: Standardize the original test data to obtain standardized data, and calculate the performance secondary index U ij The nth sample among the total N initial test samples If U ij is a benefit indicator, namely U ij The larger the value, the better the performance of the corresponding 3D CAD software. If U ij is a cost-type indicator, namely U ij The larger the value, the worse the corresponding 3D CAD software performance. in The same performance index U of all the 3D CAD software participating in the test is ij The maximum and minimum values ​​of the test data.

[0009] S3: input the standardized data into the self-reducing inverse cloud generator to solve the digital characteristics of the cloud model;

[0010] S3.1: Standardize data As cloud droplet sample points, they are input into the reverse cloud generator to calculate their expected Ex ij 、Entropy ij and sample variance S 2 ij , where the expectation Indicates the performance level of the indicator; entropy Indicates the uncertainty of the performance level of the indicator itself; variance Then calculate the super entropy of the cloud droplet sample point It indicates that the instability of the test environment causes uncertainty interference to the performance measurement of this indicator;

[0011] S3.2: If the super entropy He ij is a real number, then the expected Ex is returned. ij 、Entropy ij , and super entropy He ij Go to S4, otherwise, go to S3.3;

[0012] S3.3: Maintain Expectations Ex ij 、Entropy ijThe cloud droplet sample points are kept constant according to the expected Ex ij The distance is reordered, and the sample variance is recalculated after subtracting the cloud droplet sample point closest to the expected distance And count the number of sample points M that have been subtracted, and recalculate the super entropy,

[0013] S4: Determine the value of each secondary performance indicator and its uncertainty based on the digital characteristics of the cloud model, and calculate the performance secondary indicator U ij , and its performance index value is a ij =Ex ij , whose uncertainty is

[0014] S5: Calculate the weight of each secondary indicator and use the hierarchical analysis method to determine the initial weight of each secondary indicator Then, for each first-level indicator 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 Adjusted weight

[0015] S6: Calculate the first-level performance index U of 3D CAD software i The value of entropy Super Entropy The uncertainty Construct a matrix S whose rth row and kth column element Normalize each column of the matrix to get a new matrix S * Elements The final weight of the first-level indicator is Indicates the initial weight of the first-level indicator.

[0016] S7: The performance measurement results of 3D CAD software are The value range is 0 to 1.

[0017] The 3D CAD software performance measurement device based on the self-reducing inverse cloud generator includes one or more processors for implementing the 3D CAD software performance measurement method based on the self-reducing inverse cloud generator.

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

[0019] The 3D CAD software performance measurement method and device based on the self-reducing inverse cloud generator provided by the present invention can fully consider the complex uncertainty contained in the 3D CAD software test data when measuring the performance of the 3D CAD software, and can accurately measure the performance of the 3D CAD software. 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 It is a structural diagram of the device of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] like Figure 1 As shown, the 3D CAD software performance measurement method based on the self-reducing inverse cloud generator proposed in the present invention uses the self-reducing inverse cloud generator to solve the cloud model digital features of each performance index data, calculates each performance index value and its uncertainty through the digital features, and adjusts the weight of each index accordingly to determine the final performance measurement result. Specifically, it includes the following steps:

[0025] S1: Determine the performance measurement index set of 3D CAD software, including the first-level performance 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 performance indicators, J i (1≤i≤I) is the first-level performance index U i The number of secondary performance indicators under the above conditions is obtained, and the original test data of each secondary performance indicator is obtained through the automatic testing program.

[0026] S2: Standardize the original test data to obtain standardized data, and calculate the performance secondary index U ij The nth sample among the total N initial test samples If U ij is a benefit indicator, namely U ij The larger the value, the better the performance of the corresponding 3D CAD software. If U ij is a cost-type indicator, namely U ij The larger the value, the worse the corresponding 3D CAD software performance. in The same performance index U of all the 3D CAD software participating in the test is ij The maximum and minimum values ​​of the test data.

[0027] S3: input the standardized data into the self-reducing inverse cloud generator to solve the digital characteristics of the cloud model;

[0028] S3.1: Standardize data As cloud droplet sample points, they are input into the reverse cloud generator to calculate their expected Ex ij 、Entropy ij and sample variance S 2 ij , where the expectation Indicates the performance level of the indicator; entropy Indicates the uncertainty of the performance level of the indicator itself; variance Then calculate the super entropy of the cloud droplet sample point It indicates that the instability of the test environment causes uncertainty interference to the performance measurement of this indicator;

[0029] S3.2: If the super entropy He ij is a real number, then the expected Ex is returned. ij 、Entropy ij , and super entropy He ij Go to S4, otherwise, go to S3.3;

[0030] S3.3: Maintain Expectations Ex ij 、Entropy ij The cloud droplet sample points are kept constant according to the expected Ex ij The distance is reordered, and the sample variance is recalculated after subtracting the cloud droplet sample point closest to the expected distance And count the number of sample points M that have been subtracted, and recalculate the super entropy, Enter S3.2;

[0031] S4: Determine the value of each secondary performance indicator and its uncertainty based on the digital characteristics of the cloud model, and calculate the performance secondary indicator U ij , and its performance index value is a ij =Ex ij , whose uncertainty is

[0032] S5: Calculate the weight of each secondary indicator and use the hierarchical analysis method to determine the initial weight of each secondary indicator Then, for each first-level indicator 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 Adjusted weight

[0033] S6: Calculate the first-level performance index U of 3D CAD software i The value of entropy Super Entropy The uncertainty Construct a matrix S whose rth row and kth column element Normalize each column of the matrix to get a new matrix S * Elements The final weight of the first-level indicator is Indicates the initial weight of the first-level indicator.

[0034] S7: The performance measurement results of 3D CAD software are The value range is 0 to 1.

[0035] The method of the present invention is described below with reference to specific embodiments.

[0036] Specific 3D CAD software was selected for performance measurement, and environment configuration, model loading, model saving, Boolean operation, 3D modeling, and large assembly were selected as six first-level performance indicators. Each first-level indicator is composed of a series of specific second-level indicators. The original test data of each second-level performance indicator was obtained through an automatic test program. Each indicator and its initial weight determined by the hierarchical analysis method are shown in Table 1.

[0037] Table 1. 3D CAD performance evaluation indicators and their initial weights

[0038]

[0039]

[0040] The performance secondary index P 51 For example, 8 3D models used to test the stretching performance of 3D CAD software are used as test cases, covering several commonly used stretching scenarios. 50 sets of raw data of stretching time are obtained from the 3D CAD software through automated testing. Each set of raw test data includes the time consumption of stretching 8 3D models. The raw test data and its standardized data are shown in Table 2.

[0041] Table 2. Performance indicators P 51 The original test data and its standardized data

[0042]

[0043] The standardized data is used as cloud droplet sample points and input into the self-reducing inverse cloud generator to obtain the cloud model digital characteristic expectation Ex 51 、Entropy 51 and super entropy He 51 , and calculate the secondary performance index P of 3D CAD software 51 The value and uncertainty of the index are calculated, and the weight of the index is adjusted accordingly. The same method is used to calculate the cloud model digital characteristics and final weights of all secondary performance indicators. The results are shown in Table 3.

[0044] Table 3. Digital features and weights of cloud models for each secondary performance index of 3D CAD software

[0045]

[0046]

[0047] According to the weights of the secondary performance indicators of the 3D CAD software, the digital characteristics of the cloud model are weighted and summed to obtain the digital characteristics of the cloud model of the primary performance indicator, and the weights of the primary performance indicators are adjusted accordingly. The digital characteristics and weights of the cloud model of each primary performance indicator are shown in Table 4.

[0048] Table 4. Digital features and weights of cloud models for each first-level performance index of 3D CAD software

[0049]

[0050] The cloud model digital features of each first-level performance indicator are weighted and summed using their final weights to obtain the cloud model digital features for 3D CAD performance evaluation. The calculated performance measurement result is 0.830, indicating that the 3D CAD software has better performance.

[0051] Corresponding to the aforementioned embodiment of the 3D CAD software performance measurement method based on the self-reducing reverse cloud generator, the present invention also provides an embodiment of the 3D CAD software performance measurement device based on the self-reducing reverse cloud generator.

[0052] See also Figure 2 The three-dimensional CAD software performance measurement device based on the self-reducing inverse cloud generator provided in an embodiment of the present invention includes one or more processors for implementing the three-dimensional CAD software performance measurement method based on the self-reducing inverse cloud generator in the above embodiment.

[0053] The embodiment of the three-dimensional CAD software performance measurement device based on the self-reducing inverse cloud generator of the present invention can be applied to any device with data processing capabilities, and the any 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 a hardware perspective, if Figure 2 As shown, it is a hardware structure diagram of any device with data processing capability where the three-dimensional CAD software performance measurement device based on the self-reducing reverse cloud generator of the present invention is located, except Figure 2 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.

[0054] 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.

[0055] 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.

[0056] 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 three-dimensional CAD software performance measurement method based on the self-reducing inverse cloud generator in the above embodiment is implemented.

[0057] 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.

[0058] 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 3D CAD software performance measurement method based on a self-reducing inverse cloud generator, characterized in that The steps include: S1: Determine the performance measurement index set of 3D CAD software, including the first-level index and its subordinate second-level index, and obtain the original test data of each second-level performance index; S2: Standardize the original test data to obtain standardized data; S3: Input the normalized number into the self-decreasing inverse cloud generator to solve the digital characteristics of the cloud model, including the following steps: S3.1: Input the standardized data as cloud droplet sample points into the reverse cloud generator, calculate its expectation, which is used to represent the performance level of the indicator, calculate the entropy and variance through the expectation and the standardized data, and the entropy is used to represent the uncertainty of the performance level of the indicator itself. Through the entropy and variance, calculate the super entropy of the cloud droplet sample points, which is used to represent the uncertainty interference caused by the instability of the test environment on the performance measurement of the indicator; S3.2: If the super entropy is a real number, return the expectation, entropy, and super entropy and enter S4, otherwise, enter S3.3; S3.3: Keeping the expectation and entropy unchanged, re-order the cloud droplet sample points according to the distance from the expectation, subtract the cloud droplet sample point closest to the expectation, recalculate the sample variance, count the number of sample points that have been subtracted, recalculate the super entropy, and enter S3.2; S4: Determine the value of each secondary performance indicator and its uncertainty based on the digital characteristics of the cloud model. For the secondary performance indicator, take the expectation as the value of the secondary indicator, and obtain the uncertainty of the secondary indicator through entropy and super entropy. S5: Calculate the weight of each secondary indicator, determine the initial weight of each secondary indicator using the analytic hierarchy process, construct a matrix for each primary indicator based on the uncertainty of the secondary indicator, normalize each column of the matrix, and calculate the adjusted weight of the secondary indicator by comparing each element of the new matrix with the initial weight of the secondary indicator; S6: Calculate the weight of each first-level indicator, use the analytic hierarchy process to determine the initial weight of each first-level indicator, use the final weights to weight the sum of each second-level indicator value, entropy, and super entropy, and use the weighted sum of the first-level indicator expectation as the value of the first-level indicator, obtain the uncertainty of the first-level indicator through the weighted sum of the entropy and super entropy of the first-level indicator, construct a matrix for each first-level indicator according to the uncertainty of the first-level indicator, normalize each column of the matrix, and calculate the final weight of the first-level indicator by obtaining the initial weights of each element of the new matrix and the first-level indicator; S7: The first-level index expectation and entropy are weighted and summed using their final weights to obtain the performance measurement results of the 3D CAD software.

2. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 1 is characterized in that The indicator set in S1 includes the first-level indicator 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 performance indicators, J i (1≤i≤I) is the first-level performance index U i The number of secondary performance indicators under .

3. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 2 is characterized in that In S2, the secondary performance index U ij The nth sample among the total N initial test samples If U ij is a benefit indicator, namely U ij The larger the value, the better the performance of the corresponding 3D CAD software. If U ij is a cost-type indicator, namely U ij The larger the value, the worse the corresponding 3D CAD software performance. in The same performance index U of all the 3D CAD software involved in the test is ij The maximum and minimum values ​​of the test data.

4. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 3 is characterized in that In S3.1, the standardized data As cloud droplet sample points, they are input into the reverse cloud generator to calculate their expected Ex ij 、Entropy ij and sample variance S 2 ij , where the expectation Indicates the performance level of the indicator; entropy Indicates the uncertainty of the performance level of the indicator itself; variance Then calculate the excess entropy He of the cloud droplet sample point ij , It indicates that the instability of the test environment causes uncertainty interference to the performance measurement of this indicator; In S3.3, the number of sample points that have been subtracted is M, and the recalculated super entropy is 5. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 1 is characterized in that In S4, the performance index value is a ij =Ex ij , whose uncertainty is Among them, Ex ij Express expectations, ij represents entropy, He ij It represents super entropy.

6. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 5 is characterized in that In S5, a matrix M is constructed for each primary index. 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 The adjusted weight of the secondary index is Indicates the initial weight of the secondary indicator.

7. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 6 is characterized in that In S6, the first-level performance index U of the three-dimensional CAD software is calculated. i The value of entropy Super Entropy The uncertainty Construct a matrix S whose rth row and kth column element Normalize each column of the matrix to get a new matrix S * Elements The final weight of the first-level indicator is Indicates the initial weight of the first-level indicator.

8. The 3D CAD software performance measurement method based on the self-reducing inverse cloud generator according to claim 7 is characterized in that In S7, the performance measurement result of the 3D CAD software is: The value range is 0 to 1.

9. A 3D CAD software performance measurement device based on a self-reducing reverse cloud generator, characterized in that: It includes one or more processors for implementing the three-dimensional CAD software performance measurement method based on a self-reducing inverse cloud generator as described in any one of claims 1-8.

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