CN117689250A - Quantitative evaluation method for engineering drawing design quality - Google Patents
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Abstract
The invention discloses a quantitative evaluation method for design quality of engineering drawings, and relates to the technical field of quantitative evaluation methods for quality. One embodiment of the present invention includes: taking the influence of different defects on the design quality and the difference of each item in the aspects of difficulty, scale and the like into consideration, and calculating the defect index of each item. Probability modeling is carried out on the distribution of the defect indexes, the probability of each item being better than that of the similar items is converted, and the transverse comparison of the quality of the similar items is realized. Whether the overall quality of the item has changed substantially is determined by comparing the average defect indices of the items over different time periods to help confirm whether the relevant management measures are effective.
Description
Technical Field
The invention belongs to the technical field of quality quantitative evaluation methods, and particularly relates to an engineering drawing design quality quantitative evaluation method.
Background
The design defects of engineering drawings are of a plurality of types, and the influence degree of the design defects on the final engineering quality is different. Different projects have great differences in the probability of various design defects due to different difficulties and scales. In the quality evaluation system based on the defect number statistics, the above-mentioned difference is often not considered, but the defect number of each item is simply counted, and the quality thereof is evaluated. This practice is difficult to get popular acceptance by the designer and is prone to striking the designer's enthusiasm for work.
And part of enterprises take the defect rate as an index of quality assessment, namely, the ratio of the defect number of a designer to the drawing amount of the designer is inspected, and the lower the ratio is, the lower the defect rate is. However, this approach also only takes the project size into consideration, failing to take into account the differences in the difficulty of each project and its drawbacks. Some enterprises consider the difference of different defects on the basis of the above, introduce the concepts of defect weight and weighted defect number, but still fail to consider other factors including project difficulty.
The engineering drawing is an important basis for guiding engineering manufacture or construction, and the design quality is an important factor influencing the final engineering quality. In the construction of engineering drawing design quality, the number of drawing defects is an important component, and the lower the number of defects is, the higher the quality is. In order to reduce design defects in engineering drawings, many engineering design enterprises have established specialized aesthetic institutions that seek for possible defects therein in order to quantify statistics and comparisons of quality of each item. However, due to the various differences of difficulty, scale and defect types among projects, simply comparing the number of design defects transversely often has unfairness, so that many management measures formulated on the basis of the unfairness cannot be successfully executed; the statistical results obtained by the method aiming at different time periods are difficult to truly reflect the variation trend of the overall quality of the project, and are unfavorable for checking the effectiveness of related quality management measures. Therefore, a multi-factor quantitative evaluation method and system for the design quality of engineering drawings need to be established, quality distribution and change trend are reflected, and analysis and decision support are provided for enterprises to develop relevant quality management measures.
Disclosure of Invention
The invention provides a quantitative evaluation method and a quantitative evaluation system for design quality of engineering drawings based on probability distribution modeling. Taking the influence of different defects on the design quality and the difference of each item in the aspects of difficulty, scale and the like into consideration, and calculating the defect index of each item. Probability modeling is carried out on the distribution of the defect indexes, and the probability (namely, mass percent) of each item better than that of the similar items is converted, so that the transverse comparison of the quality of the similar items is realized. Whether the overall quality of the item has changed substantially is determined by comparing the average defect indices of the items over different time periods to help confirm whether the relevant management measures are effective.
In view of this, according to an aspect of the embodiment of the present invention, there is provided an engineering drawing design quality quantitative evaluation method, including:
step S1: obtaining the defect number of each project, the weight of each defect, the difficulty coefficient and the scale coefficient;
step S2: calculating the weighted defect number of each item according to the defect number of each item classification statistics and the weight of each type of defect;
step S3: on the basis of the weighted defect number of each item, correcting the weighted defect number by adding difficulty coefficient and scale coefficient of each item to obtain defect index of the item, wherein the item defect index should be reduced along with the increase of the item scale, the item defect index should be reduced along with the increase of the item difficulty, the relation between the item defect index and the item difficulty coefficient and the scale coefficient should be independent, and the defect index obtained after each item correction should accord with the minimum scattering principle;
step S4: on the basis of the defect indexes of each item, checking the distribution rule of the defect indexes to obtain a probability density function y=f (k), wherein k is the defect index, and y is the probability density corresponding to the defect index;
step S5: on the basis of probability density function of defect indexes of each item, the probability of the defect indexes of each item being superior to the similar items is calculated by using the defect indexes of each item, namely the mass percentage q i ＝1-∫f(k i )dk；
Step S6: according to the defect indexes of each item in different time periods, respectively calculating the average defect index of each item;
step S7: and judging whether the overall quality of each item in the period is changed substantially or not by using a difference significance test according to the average defect indexes of each item in different periods.
Optionally, step S1 is preceded by at least one further step of:
step A: quantifying the difficulty level of each item according to the existing standard, and determining the difficulty coefficient of each item;
and (B) step (B): determining the types of defects to be identified according to the quality inspection requirements, and setting corresponding weights according to the influence of different defects on the design quality;
step C: checking drawings of each item, searching possible defects in the drawings, and classifying and counting the number of the defects;
step D: and (5) counting the design scale of each project to obtain the scale coefficient of each project.
Optionally, step S2 further includes:
weight distribution is carried out on the weights of various defects, and the weighted defect quantity k of item i i The calculation method of (1) is that
Wherein w is j Is the weight of the j-th class defect, c j Is the number of type j defects and n is the number of defect types.
Optionally, step S3 further includes:
the correction method comprises the following steps:
wherein d i Sum s i The difficulty coefficient and the scale coefficient of the item i are respectively, and the difficulty correction coefficient a and the scale correction coefficient b are two undetermined constant coefficients.
Alternatively, the normal distribution compliance of the defect index is checked using the Anderson-Darling method.
Optionally, in step S5, the mass percentage q is
Where μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution.
Optionally, step S7 further includes: the difference of defect index distribution between any two time periods is subjected to significance test by adopting double-sample t-test, so that whether the overall quality is substantially changed is judged.
Optionally, the method further comprises:
step S71: calculating a statistic t;
wherein,and->The defect index mean values of the two time periods are respectively; θ 1 And theta 2 Respectively the overall mean of the two time periods, < > in case the defect index satisfies the lognormal distribution>μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution;
and->The defect index variance, m, for two time periods respectively 1 And m 2 The number of items for each of the two time periods is taken from the coefficient dof=min (m 1 ,m 2 )；
Step S72: on the basis of t, the cumulative probability p is calculated and the assumption that the overall quality level has not changed for both time periods is rejected when p is smaller than the threshold.
Optionally, a dynamic quality evaluation method based on historical data may be adopted, when the number of items in the current period is less than a preset number and is insufficient to support the construction of the statistical model, the quality evaluation may be performed on the items in the current period by means of the defect index distribution model in the previous period, and when the number of items in the current period is not less than the preset number, the data in the current period is used to reconstruct the statistical model, and the statistical model is updated at any time as new item data is added.
The quantitative evaluation method and the quantitative evaluation system for the design quality of the multi-factor engineering drawing can more objectively and accurately compare the design quality of different projects, reflect the quality distribution and the change trend, are beneficial to enterprises to carry out related management measures or systems, and stimulate designers to improve the design quality; the method is favorable for identifying whether the overall quality of the project is changed substantially, and provides powerful support for analyzing and checking the effectiveness of various quality management systems or measures.
Drawings
FIG. 1 is a flow chart of an engineering drawing design quality quantitative evaluation method according to an embodiment of the invention;
FIG. 2 is a flowchart of a method for quantitatively evaluating design quality of engineering drawings according to an embodiment of the present invention;
fig. 3-1 and 3-2 are defect index distributions of different time periods of an engineering drawing design quality quantitative evaluation method according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the invention provides a quantitative evaluation method for the design quality of engineering drawings, which comprises the following steps:
step S1: obtaining the defect number of each project, the weight of each defect, the difficulty coefficient and the scale coefficient;
step S2: calculating the weighted defect number of each item according to the defect number of each item classification statistics and the weight of each type of defect;
step S3: on the basis of the weighted defect number of each item, correcting the weighted defect number by adding difficulty coefficient and scale coefficient of each item to obtain defect index of the item, wherein the item defect index should be reduced along with the increase of the item scale, the item defect index should be reduced along with the increase of the item difficulty, the relation between the item defect index and the item difficulty coefficient and the scale coefficient should be independent, and the defect index obtained after each item correction should accord with the minimum scattering principle;
step S4: on the basis of the defect indexes of each item, checking the distribution rule of the defect indexes to obtain a probability density function y=f (k), wherein k is the defect index, and y is the probability density corresponding to the defect index; the method is suitable for transverse comparison between the quality of each item in the same period;
step S5: on the basis of probability density function of defect indexes of each item, the probability of the defect indexes of each item being superior to the similar items is calculated by using the defect indexes of each item, namely the mass percentage q i ＝1-∫f(k i ) dk; the method is suitable for transverse comparison between the quality of each item in the same period;
step S6: according to the defect indexes of each item in different time periods, respectively calculating the average defect index of each item; the method is suitable for transverse comparison between the overall quality of the projects in different time periods;
step S7: and judging whether the overall quality of each item in the time period is changed substantially or not by utilizing a difference significance test according to the average defect indexes of each item in different time periods, and being applicable to transverse comparison between the overall quality of the items in different time periods.
Further, step S1 is preceded by at least one of the following steps:
step A: quantifying the difficulty level of each item according to the existing standard, and determining the difficulty coefficient of each item;
and (B) step (B): determining the types of defects to be identified according to the quality inspection requirements, and setting corresponding weights according to the influence of different defects on the design quality;
step C: checking drawings of each item, searching possible defects in the drawings, and classifying and counting the number of the defects;
step D: and (5) counting the design scale of each project to obtain the scale coefficient of each project.
Further, step S2 further includes:
weight distribution is carried out on the weights of various defects, and the weighted defect quantity k of item i i The calculation method of (1) is that
Wherein w is j Is the weight of the j-th class defect, c j Is the number of type j defects and n is the number of defect types.
Further, step S3 further includes:
the correction method comprises the following steps:
wherein d i Sum s i The difficulty coefficient and the scale coefficient of the item i are respectively, and the difficulty correction coefficient a and the scale correction coefficient b are two undetermined constant coefficients.
Further, the normal distribution compliance of the defect index was checked using the Anderson-Darling method.
Further, in step S5, the mass percentage q is
Where μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution.
Further, step S7 further includes: the difference of defect index distribution between any two time periods is subjected to significance test by adopting double-sample t-test, so that whether the overall quality is substantially changed is judged.
Further, the method further comprises the following steps:
step S71: calculating a statistic t;
wherein,and->The defect index mean values of the two time periods are respectively; θ 1 And theta 2 Respectively the overall mean of the two time periods, < > in case the defect index satisfies the lognormal distribution>μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution;
and->The defect index variance, m, for two time periods respectively 1 And m 2 The number of items for each of the two time periods is taken from the coefficient dof=min (m 1 ,m 2 )；
Step S72: on the basis of t, the cumulative probability p is calculated and the assumption that the overall quality level has not changed for both time periods is rejected when p is smaller than the threshold.
Further, a dynamic quality evaluation method based on historical data can be adopted, when the number of items in the time period is less than the preset number and is insufficient to support the construction of the statistical model, the quality evaluation of the items in the time period can be carried out by means of the defect index distribution model in the previous time period, and when the number of the items in the time period is not less than the preset number, the data in the time period is adopted to reconstruct the statistical model and updated at any time along with the addition of new item data.
Examples
Defects in the building design are difficult to avoid. There are many kinds of defects, and their influence on the design quality is different. In addition, as project difficulty and scale increase, the probability of defects in the design drawing increases. Referring to the flow shown in FIG. 2, the result of the construction professional's annual examination is performed
Aiming at the problems, the existing project defect statistical analysis method is improved. Which mainly comprises the following aspects.
(1) Introducing defect weights and weighting defect numbers (k i )
Defect weights are a distinction between different types of problems. For each type of defect found during the mapping process, the weights of each type of defect are assigned as follows, in combination with the relevant experience, see table 1. Weighted defect number k of item i i The calculation method of (1) is as follows:
wherein w is j Is the weight of the j-th class defect, c j Is the number of type j defects and n is the number of defect types.
TABLE 1 weight assignment for different defects
(2) Introduction of defect index (K) i )
The defect index is based on project difficulty and scale versus weighted defect number (k i ) Is used for correcting the numerical value of the (a). The larger the defect index, the more serious the problem of the project. The specific correction method comprises the following steps:
wherein d i Sum s i The difficulty coefficient and the scale coefficient of item i, respectively. The difficulty correction coefficient a and the scale correction coefficient b are two undetermined constant coefficients, and a and b can be obtained by a differential method on the assumption that the defect indexes of m items accord with the minimum dispersion principle, namely the sample variance s2 is minimum. And (3) the following steps:
when sigma 2 takes the minimum value, it can be found that:
wherein,
difficulty coefficient d of each item i Can be determined with reference to table 2 according to different item categories. Scale factor s of each item i The standard drawing number or building area of the project can be selected. When the number of standard drawings or the building area shows a difference of the number level, the natural logarithmic value thereof can be adopted.
TABLE 2 difficulty factors for different project categories
| Item category | A | B | C | D |
| Difficulty coefficient | 10 | 9 | 6 | 3 |
(3) Defect index (K) i ) Probability fitting and verification of the distribution.
Obtaining defect index K of each item i On the basis of which the distribution can be fitted and checked. Since the defect index is positive, it generally conforms to a lognormal distribution. The compliance can be checked using Anderson-Darling. As shown in fig. 3-1 and 3-2, the defect indexes of the construction professions of a certain enterprise 2018 and 2019 conform to the lognormal distribution, and the p values of the Anderson-Darling test are respectively 0.95 and 0.09, which are both greater than the threshold value of rejecting hypothesis 0.05.
(4) Project design quality evaluation based on distribution rule
On the basis of obtaining the distribution rule of the defect indexes of each item, a quantitative index (mass percentage) of the quality evaluation based on probability can be obtained, and the item quality is compared across years.
As shown in fig. 2 and 3, the defect index distribution of each item in 2018 and 2019 of the construction profession accords with the lognormal distribution, and on the basis of obtaining the distribution parameters mu and sigma thereof, the probability that each item is superior to other items in quality, namely the quality percentage q, can be calculated.
Where μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution. Namely:
the mass percent more stably reflects the mass difference between items than the absolute defect index. For example, the defect index is also 1.0, better than the 90% project in 2018; only 10% of the projects were better in 2019.
(5) Overall mass transverse comparison between arbitrary two years
Although the defect index distribution of each item in each year does not meet the normal distribution of the standard, as long as the number is large (more than 30), the defect index distribution difference between any two years can be subjected to the significance test by adopting the double-sample t-test, so as to judge whether the overall quality is substantially changed. The specific method comprises the following steps:
first, calculate the statistic t
Wherein,and->Defect index mean values of two years respectively; θ 1 And theta 2 Which are the overall average of the two years, respectively. At the defectIn the case where the index satisfies the lognormal distribution, θ 1 And theta 2 The method can be obtained by the following formula: />
Wherein: μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution.
And->The defect index variance, m, for two years respectively 1 And m 2 The number of items for two years, respectively. Is further taken from the coefficient of degree of freedom dof=min (m 1 ,m 2 )。
Second, hypothesis testing.
On the basis of t, the student profile (student distribution) can be used to calculate the cumulative probability p and reject the assumption that the overall quality level has not changed for two years at p < 0.05.
As shown in table 3, there was a statistically significant difference in average defect index (p-value greater than 0.05) between years of construction and structural expertise.
TABLE 3 results of construction and Structure professional average Defect index differential significance test (double sample t-test)
(6) Dynamic quality assessment based on historical data
In order to timely reflect the quality state of the project, a designer is reminded as early as possible when bad trend occurs, rather than only reviewing and summarizing the design quality of the present year when summarized at the end of the year, a dynamic quality evaluation method based on historical data can be adopted. In particular, when the number of items in the current year is small (less than 30) and is insufficient to support the construction of a statistical model, the quality of the items in the current year can be evaluated by means of the defect index distribution model in the previous year. When the number of items in the current year is enough, the data in the current year is adopted to reconstruct the statistical model, and the statistical model is updated at any time along with the addition of new item data.
The invention carries out quantitative evaluation on the design quality of the engineering drawing by modeling the probability distribution. The method can more objectively reflect the quality difference of different projects, can more accurately reflect the overall quality change condition of different time periods, and is beneficial to timely adjusting and improving related quality management measures.
The original defect number is converted into a defect index by introducing parameters such as weighted defect number, difficulty coefficient, scale coefficient, difficulty correction coefficient, scale correction coefficient and the like. The defect indexes comprise comprehensive consideration of project difficulty, scale and defect types, and the defect indexes of each project relate to related information of other projects in the same time period in the calculation process, so that the true quality level of the project can be reflected more accurately.
And determining the difficulty correction coefficient and the scale correction coefficient according to the defect index minimum dispersion principle. The correction coefficient obtained by the method is completely objective, is not influenced by parameter unit selection and subjective consciousness, and is a natural expression of the internal relation of the data set.
The difference in magnitude between the scale factor and the difficulty factor is eliminated by taking the logarithm. The scale coefficient and the difficulty coefficient obtained by the method are more beneficial to reducing the dispersion of the defect index and improving the success rate of probability distribution modeling.
The foregoing is merely a detailed description of the invention, which is not a matter of routine skill in the art. However, the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. The quantitative evaluation method for the design quality of the engineering drawing is characterized by comprising the following steps of:
step S1: obtaining the defect number of each project, the weight of each defect, the difficulty coefficient and the scale coefficient;
step S2: calculating the weighted defect number of each item according to the defect number of each item classification statistics and the weight of each type of defect;
step S3: on the basis of the weighted defect number of each item, correcting the weighted defect number by adding difficulty coefficient and scale coefficient of each item to obtain defect index of the item, wherein the item defect index should be reduced along with the increase of the item scale, the item defect index should be reduced along with the increase of the item difficulty, the relation between the item defect index and the item difficulty coefficient and the scale coefficient should be independent, and the defect index obtained after each item correction should accord with the minimum scattering principle;
step S4: on the basis of the defect indexes of each item, checking the distribution rule of the defect indexes to obtain a probability density function y=f (k), wherein k is the defect index, and y is the probability density corresponding to the defect index;
step S5: on the basis of probability density function of defect indexes of each item, the probability of the defect indexes of each item being superior to the similar items is calculated by using the defect indexes of each item, namely the mass percentage q i ＝1-∫f(k i )dk；
Step S6: according to the defect indexes of each item in different time periods, respectively calculating the average defect index of each item;
step S7: and judging whether the overall quality of each item in the period is changed substantially or not by using a difference significance test according to the average defect indexes of each item in different periods.
2. The method according to claim 1, characterized in that step S1 is preceded by at least one further step of:
step A: quantifying the difficulty level of each item according to the existing standard, and determining the difficulty coefficient of each item;
and (B) step (B): determining the types of defects to be identified according to the quality inspection requirements, and setting corresponding weights according to the influence of different defects on the design quality;
step C: checking drawings of each item, searching possible defects in the drawings, and classifying and counting the number of the defects;
step D: and (5) counting the design scale of each project to obtain the scale coefficient of each project.
3. The method according to claim 1, wherein step S2 further comprises:
weight distribution is carried out on the weights of various defects, and the weighted defect quantity k of item i i The calculation method of (1) is that
Wherein w is j Is the weight of the j-th class defect, c j Is the number of type j defects and n is the number of defect types.
4. A method according to claim 3, wherein step S3 further comprises:
the correction method comprises the following steps:
wherein d i Sum s i The difficulty coefficient and the scale coefficient of the item i are respectively, and the difficulty correction coefficient a and the scale correction coefficient b are two undetermined constant coefficients.
5. The method of claim 4, wherein the normal distribution compliance of the defect index is verified using an Anderson-Darling method.
6. The method according to claim 4, wherein in step S5, the mass percentage q is
Where μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution.
7. The method according to claim 1, wherein step S7 further comprises: the difference of defect index distribution between any two time periods is subjected to significance test by adopting double-sample t-test, so that whether the overall quality is substantially changed is judged.
8. The method as recited in claim 7, further comprising:
step S71: calculating a statistic t;
wherein,and->The defect index mean values of the two time periods are respectively; θ 1 And theta 2 Respectively the overall mean of the two time periods, < > in case the defect index satisfies the lognormal distribution>μ and σ are the log mean and log standard deviation, respectively, of the log normal distribution;
and->The defect index variance, m, for two time periods respectively 1 And m 2 The number of items for each of the two time periods is taken from the coefficient dof=min (m 1 ,m 2 )；
Step S72: on the basis of t, the cumulative probability p is calculated and the assumption that the overall quality level has not changed for both time periods is rejected when p is smaller than the threshold.
9. The method of claim 7, wherein a dynamic quality evaluation method based on historical data is adopted, when the number of items in the period is less than a preset number and is insufficient to support the construction of a statistical model, the quality evaluation of the items in the period can be carried out by means of a defect index distribution model in the previous period, and when the number of items in the period is not less than the preset number, the statistical model is reconstructed by adopting the data in the period and updated at any time with the addition of new item data.
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