CN116703657A - Building engineering construction management system based on BIM model - Google Patents

Building engineering construction management system based on BIM model Download PDF

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CN116703657A
CN116703657A CN202310987264.XA CN202310987264A CN116703657A CN 116703657 A CN116703657 A CN 116703657A CN 202310987264 A CN202310987264 A CN 202310987264A CN 116703657 A CN116703657 A CN 116703657A
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characteristic value
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CN116703657B (en
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王超
林长慧
张俊华
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Karamay Dingtai Construction Group Co ltd
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Abstract

The invention relates to the technical field of data processing, in particular to a building construction engineering management system based on a BIM (building information modeling) model, which is used for obtaining delay degree and delay condition of unit time in a history period, calculating the relation of characteristic values of all delay conditions in a combination mode in the history period to obtain basic relevance among the delay conditions, obtaining a detail adjustment value according to the characteristic value difference of any two delay conditions in the combination mode, obtaining relevance weight values among the two delay conditions according to the delay degree, the characteristic value occupation ratio and the detail adjustment value, and adjusting the relevance of the two delay conditions in the combination mode by utilizing the relevance weight values, and obtaining regression coefficients of a regression model by adjusting prediction reference coefficients according to the relevance. According to the invention, the relevance among delay conditions is researched, and the regression coefficient of the regression model is optimized, so that the prediction result is more accurate, and the house construction engineering construction management is facilitated.

Description

Building engineering construction management system based on BIM model
Technical Field
The invention relates to the technical field of data processing, in particular to a building construction engineering management system based on a BIM model.
Background
The BIM model can help designers, architects, engineers and other related personnel to visually arrange the process and various information in the whole design stage, so that construction information is recorded continuously in real time, and the information is complete and reliable, keeps information updated continuously and can be accessed. The BIM model is generally used for predicting the delay degree of the following construction period according to various delay conditions occurring on the same day, and correspondingly adjusting according to the prediction result.
In the prior art, when the prediction is performed through a conventional multiple linear regression algorithm, the proportion of each delay condition participated in the historical information is only used as a regression coefficient, and the possible relevance among each delay condition is not considered, so that the deviation between the predicted result and the actually-occurring delay degree is larger, and further, related personnel cannot make timely adjustment and correction, and engineering construction management is affected.
Disclosure of Invention
In order to solve the technical problems that in the whole construction period, the prediction delay degree and the actually occurring delay degree have larger deviation and further influence the engineering construction management due to the fact that the proportion of each delay condition participated in the historical information is used as a regression coefficient to carry out multiple linear regression model fitting, the invention aims to provide a building engineering construction management system based on a BIM model, and the adopted technical scheme is as follows:
The invention provides a building construction engineering management system based on a BIM model, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the following steps are realized when the processor executes the computer program:
obtaining delay degree and delay condition information in each unit time in a preset history period from the history data of the BIM; carrying out data processing on all delay condition information to obtain characteristic values of the same data expression form;
obtaining a combination mode of delay conditions in each unit time, and obtaining basic relevance of the corresponding combination modes according to characteristic values of all delay conditions in each combination mode; obtaining a detail adjustment value according to the difference of the characteristic values of any two delay conditions in the combination mode; under each combination mode, according to the corresponding delay degree in unit time, the characteristic value duty ratio of any two delay conditions and the detail adjustment value of the corresponding two delay conditions, obtaining the association weight value between any two delay conditions in each combination mode; according to the basic relevance corresponding to all the combination modes and the relevance weight value between any two delay cases, obtaining the relevance between any two delay cases;
Obtaining a first prediction reference coefficient according to the characteristic value duty ratio of the target delay condition in the unit time and the delay degree of the corresponding unit time, and taking the delay condition with the characteristic value larger than the characteristic value of the target delay condition as a reference delay condition; obtaining a second prediction reference coefficient of the target delay condition according to the characteristic value difference and the relevance between the reference delay condition and the target delay condition; obtaining a prediction reference coefficient of the target delay condition according to the first prediction reference coefficient and the second prediction reference coefficient; changing target delay conditions to obtain a prediction reference coefficient of each delay condition;
fitting a multiple linear regression model according to the prediction reference coefficient of each delay condition and the real-time delay degree to obtain a future construction period delay degree prediction value; and carrying out construction management according to the predicted value of the construction period delay degree.
Further, the method for acquiring the characteristic value of the delay condition comprises the following steps:
obtaining the characteristic value according to the information difference between the actual information of the delay condition and preset standard information in the unit time; the characteristic value is positively correlated with the information difference.
Further, the method for acquiring the delay degree comprises the following steps:
Obtaining the actual construction progress and the predicted construction progress in unit time; judging that the actual construction progress in unit time is smaller than the predicted construction progress as the incomplete construction progress; calculating the ratio of the actual construction progress to the predicted construction progress when the construction progress is not completed, and carrying out negative correlation mapping normalization to obtain the delay degree in unit time.
Further, obtaining a basic association of all delay cases in each combination mode includes:
carrying out negative correlation mapping and normalization on the number of delay cases in each combination mode to obtain a first normalization value;
obtaining a characteristic value average value in each combination mode, and summing the characteristic value average values of the combination modes in unit time of each combination mode to obtain a construction period characteristic value;
and obtaining basic relevance of all delay conditions in each combination mode according to the first normalization value and the construction period characteristic value.
Further, the step of obtaining the detail adjustment value between any two delay cases in each combination mode includes:
calculating the characteristic value difference of any two delay conditions in each combination mode in each unit time, and calculating the standard deviation of the characteristic value difference; normalizing the difference of the characteristic values to obtain a difference weight;
In the standard deviation calculation formula, the difference weight is used as the weight of the difference value between the average characteristic value difference and the characteristic value difference, the characteristic value difference standard deviation is obtained, and the characteristic value difference standard deviation is used as the detail adjustment value.
Further, obtaining a correlation weight value between any two delay cases in each combination mode includes:
carrying out negative correlation mapping and normalization on the detail adjustment value to obtain a third normalization value;
counting to obtain the characteristic value duty ratio of any two delay conditions in each unit time of each combination mode; carrying out negative correlation mapping and normalization on the delay degree of each unit time to obtain a fourth normalized value, and multiplying the characteristic value duty ratio by the fourth normalized value to obtain an adjusted characteristic value duty ratio; obtaining the average adjustment characteristic value duty ratio of the combination mode in the unit time of each combination mode;
and obtaining the association weight value between any two delay conditions in each combination mode according to the third normalized value and the average adjustment characteristic value duty ratio, wherein the third normalized value and the average characteristic value duty ratio are in positive correlation with the association weight value.
Further, obtaining the relevance of any two delay cases in each combination mode comprises the following steps:
acquiring initial relevance of any two delay cases in each combination mode according to the basic relevance of all delay cases in each combination mode and the relevance weight value between any two delay cases in each combination mode, and summing the initial relevance of all combination modes corresponding to any two delay cases to acquire the relevance; and the basic relevance and the weight value of the relevance are in positive correlation with the initial relevance.
Further, the step of obtaining the prediction reference coefficient for each delay case includes:
taking the product of the delay degree of each unit time with the target delay condition and the characteristic value duty ratio of the target delay condition in each unit time as a delay value in the unit time, accumulating the delay value in each unit time with the target delay condition as a construction period delay value of the target delay condition, and normalizing the construction period delay value to obtain a first prediction reference coefficient;
obtaining an adjustment association according to the characteristic value difference and the association between each reference delay condition and the target delay condition, wherein the characteristic value difference and the association are in positive correlation with the adjustment association; taking the average adjustment relevance of all the reference delay cases as a second prediction reference coefficient of the target delay cases;
And carrying out weighted summation on the first prediction reference coefficient and the second prediction reference coefficient, and carrying out normalization to obtain each delay condition prediction reference coefficient.
Further, performing a multiple linear regression model fit to predict a future time delay, including:
and constructing a multiple linear regression model by taking the characteristic value of the delay condition in unit time as an independent variable and taking the prediction reference coefficient of each delay condition as a regression coefficient, and predicting the delay degree of the construction period according to the multiple linear regression model.
Further, the preset weight value includes that the weight value of the first prediction reference coefficient is set to 0.5, and the weight of the second prediction reference coefficient is set to 0.5.
The invention has the following beneficial effects:
considering that when the delay degree is predicted by utilizing a multiple linear regression model in construction, the problem that the deviation between the prediction delay degree and the actual delay degree of a construction period is larger may occur by only utilizing the participation duty ratio of each delay condition characteristic value in the history information as a regression coefficient. Because the construction period delay is commonly influenced by a plurality of delay cases, the relevance among the delay cases is considered, and therefore, the invention statistically analyzes the relevance among all the delay cases in each combination mode as a basic relevance; when a delay condition characteristic value changes, the delay condition characteristic value with relevance also changes within a certain degree, so that a detail adjustment value between any two delay conditions in each combination mode is calculated according to the difference characteristic of the characteristic values between the two delay conditions; because the combination modes are different, the same two delay cases possibly appear in different combination modes, the weight values of the same two delay cases in all delay cases in each combination mode need to be considered, the association weight value between any two delay cases in each combination mode is obtained through calculation, and then the basic association is adjusted, so that accurate association information is obtained; the regression coefficients in the multiple linear regression equation are adjusted according to the relevance between any two delay conditions, so that the fitting degree of the multiple linear regression equation is optimized, the prediction result is more accurate, and the construction management of building engineering is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an implementation method of a building engineering construction management system based on a BIM model according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a building construction management system based on a BIM model according to the invention, and the detailed description is given below of the specific implementation, structure, characteristics and effects thereof. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Building engineering construction management system embodiment based on BIM model:
the following specifically describes a concrete scheme of the building engineering construction management system based on the BIM model provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation method of a building construction management system based on a BIM model according to an embodiment of the present invention is shown, and the embodiment of the present invention provides a building construction management system based on a BIM model, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor when executing the computer program may implement the following method steps, where the flowchart corresponding to the method steps is shown in fig. 1, and specifically includes:
step S1: obtaining delay degree and delay condition information in each unit time in a preset history period; and carrying out data processing on all delay condition information to obtain the characteristic values of the same data expression form.
The BIM model has the characteristics of digitalization and visualization, is commonly used in the field of building engineering, has a continuously updated construction database, and can be used by related personnel to obtain the delay degree and delay condition information in each unit time in a preset history period. The degree of delay indicates whether the construction progress is completed in a unit time, and the delay condition information indicates a factor that causes the construction progress to be not completed in a history period. Because the delay condition information has different statistical modes and has no unified calculation standard, the data processing is carried out to convert all the characteristic values of the delay condition into the same value range, thereby facilitating the subsequent analysis of the relevance among the delay conditions.
Preferably, in one embodiment of the present invention, the method for acquiring the delay degree includes:
obtaining the actual construction progress and the predicted construction progress in unit time; judging that the actual construction progress in unit time is smaller than the predicted construction progress as the incomplete construction progress; calculating the ratio of the actual construction progress to the predicted construction progress when the construction progress is not completed, and carrying out negative correlation mapping normalization to obtain the delay degree in unit time.
Preferably, in one embodiment of the present invention, all delay condition information is subjected to data processing, so as to obtain feature values in the same data expression form.
And obtaining the characteristic value according to the information difference between the actual information of the delay condition and preset standard information in the unit time, wherein the characteristic value and the information difference are positively correlated. For example:
(1) Material delay supply: according to the recorded predicted delivery time of the material in the unit time, calculating the actual delivery time to obtain the delay time in the unit time, and taking the delay time as the characteristic value of the delay condition, wherein the longer the delay time is, the larger the characteristic value of the delay condition is. That is, in this scenario, the actual information is the actual delivery time, the standard information is the estimated delivery time, the delay time is the information difference, and the larger the information difference is, the larger the feature value is.
(2) Equipment failure: and obtaining the characteristic value of the delay condition according to the maintenance times of the equipment in the history period and the corresponding maintenance or shutdown time length, wherein the longer the maintenance or shutdown time length is, the larger the characteristic value of the delay condition is. In this scenario, the maintenance or downtime period is taken as actual information, and the standard information is set to 0.
(3) Personnel are improperly deployed: the number of historic personnel in the current project is taken as an experience value, the situation that the number of actual participators is smaller than the number of historic personnel in the current day is taken as a delay situation that personnel are improperly allocated in the current day, and the ratio of the number of the actual participators to the number of the historic personnel is subjected to negative correlation mapping to be taken as a characteristic value of the delay situation, wherein the larger the ratio is, the larger the characteristic value of the delay situation is. In this scenario, the standard information is the number of historic people, and the actual information is the number of actual participants.
(4) Rain and snow weather: according to weather information recorded on the same day, the weather evaluation value of the current day can be directly calculated by using the existing weather parameter calculation mode, and the difference between the standard weather evaluation value and the weather evaluation value of the current day is used as the characteristic value of the delay condition, wherein the larger the weather evaluation value is, the larger the characteristic value of the delay condition is. In this scenario, the standard information is a standard weather evaluation value, and the actual information is a weather evaluation value of the current day.
In the embodiment of the invention, all the delay condition characteristic values are normalized, so that the value range of the characteristic values is limited between 0 and 1, and the delay condition characteristic values with the same data expression form are obtained.
Thus, delay condition characteristic values of all the same data expression forms are obtained.
Step S2: obtaining a combination mode of delay conditions in each unit time, and obtaining basic relevance of the corresponding combination modes according to characteristic values of all delay conditions in each combination mode; obtaining a detail adjustment value according to the difference of the characteristic values of any two delay conditions in the combination mode; under each combination mode, according to the corresponding delay degree in unit time, the characteristic value duty ratio of any two delay conditions and the detail adjustment value of the corresponding two delay conditions, obtaining the association weight value between any two delay conditions in each combination mode; and obtaining the relevance between any two delay cases according to the basic relevance corresponding to all the combination modes and the relevance weight value between any two delay cases.
Based on BIM models of a plurality of completed projects, historical construction period records of each completed project are obtained, and a combination mode of delay conditions in each unit time is obtained according to the historical construction period records. For example, only the delay conditions occurring every day exist in the statistical history period Will->Denoted as +.>Combination of species->Two sequence numbers for the delay case, +.>The serial numbers of the combination modes are shown. In the embodiment of the invention, the unit time is set to one day.
The occurrence of construction period delay is commonly influenced by a plurality of delay conditions, so that a certain correlation exists among the delay conditions. When the number of delay cases in the combination mode is smaller and the number of occurrence times of the combination mode is larger, the association between the corresponding delay cases in the combination mode is considered to be stronger; the more the unit time that a plurality of delay cases corresponding to the combination mode appear at the same time, and the characteristic values of the delay cases are higher, the stronger the relevance among the delay cases corresponding to the combination mode is considered, so that the basic relevance of the corresponding combination mode can be obtained through the characteristic values of all the delay cases in each combination mode.
Preferably, in one embodiment of the present invention, the specific step of obtaining the basic relevance of all delay cases in each combination mode includes:
carrying out negative correlation mapping and normalization on the number of delay cases in each combination mode to obtain a first normalization value; obtaining a characteristic value average value in each combination mode, and summing the characteristic value average values of the combination modes in unit time of each combination mode to obtain a construction period characteristic value; and obtaining the basic relevance of all delay conditions in each combination mode according to the first normalization value and the construction period characteristic value. In one embodiment of the invention, the first The basic relevance of all delay cases in the combination mode is as follows:
in the middle ofIndicate->Basic relevance of all delay cases in a combination of species, +.>Indicate->Delay cases number in combination +.>Indicating total number of delay cases, +.>Indicate->The unit time of the combination mode is +.>Represent the firstThe combination of->Characteristic value of individual delay cases, +.>Is indicated at +.>First->The average value of the characteristic values in the combination mode of the two.
In the basic relevance formula, carrying out negative correlation mapping and normalization processing on the delay condition number, and further subtracting the normalization result from 1 to obtain a first normalization value, wherein the larger the first normalization value is, the moreThe fewer the number of delay cases in the combination mode, the +.>In combination mode->The stronger the basic relevance of the seed delay condition; calculating to obtain->The average value of the characteristic values of all delay cases in the combination mode is larger to indicate the +.>The more serious the delay of the combination mode is, the +.>Seed combination formulaSumming the average values of the characteristic values in the unit time to obtain a construction period characteristic value, wherein the larger the construction period characteristic value is, the more->In combination mode- >The stronger the association of the delay cases in the unit time. It should be noted that, the calculation method of the basic relevance of all delay cases in other combination methods is the same as the calculation method described above, so as to obtain the basic relevance of all delay cases in each combination method.
Under each combination mode, when the relevance exists between two delay cases, the characteristic value of one delay case changes, and the characteristic values of other delay cases with relevance are adjusted to a certain degree, so that the characteristic value difference of the two delay cases is kept between a certain degree; the larger the difference of the characteristic values is, the smaller the relevance of the two delay cases is, and the smaller the possibility of making the adjustment is, so that the detail adjustment values of any two delay cases are obtained according to the difference of the characteristic values of any two delay cases in each combination mode.
Preferably, in one embodiment of the present invention, the step of obtaining the detail adjustment value between any two delay cases in each combination includes:
calculating the characteristic value difference of any two delay conditions in each combination mode in each unit time, and calculating the standard deviation of the characteristic value difference; normalizing the difference of the characteristic values to obtain a difference weight; in the standard deviation calculation formula, the difference weight is used as the weight of the difference value between the average characteristic value difference and the characteristic value difference, the characteristic value difference standard deviation is obtained, and the characteristic value difference standard deviation is used as the detail adjustment value. In one embodiment of the invention, the first The detailed adjustment value between two delay cases in a combination mode is as follows:
in the middle ofIndicate->Detail adjustment value between any two delay cases in the combination of the modes, +.>Indicate->Delay cases->Indicate->Delay cases->Indicated as at->The following time of unitThe combination of->Delay of time and->Characteristic value difference between two delay cases of delay cases, < ->Expressed as mean value of the characteristic value differences, +.>Denoted as +.>The unit time of the combination mode is +.>For normalization function->Expressed as a variance weight obtained by normalizing the variance of the feature values.
In a detail adjustment value formula, calculating the difference value of any two delay conditions in each combination mode in unit time, adding an absolute value to obtain the characteristic value difference of any two delay conditions in each unit time, and carrying out standard deviation calculation on the characteristic value difference; normalizing the difference of the characteristic values to obtain a difference weight; in the standard deviation calculation formula, the difference weight is used as the weight of the difference value between the average characteristic value difference and the characteristic value difference, the characteristic value difference standard deviation is obtained, and the characteristic value difference standard deviation is used as the detail adjustment value. The smaller the difference weight is, the stronger the relevance between two delay conditions is represented, and the smaller the possibility of adjustment is needed; in the standard deviation calculation formula, the smaller the standard deviation of the characteristic value difference is, the more stable the characteristic value difference of two delay cases is, namely, when one delay case occurs and the characteristic value is larger, the more likely other delay cases with relevance are generated; the detail adjustment value characterizes the relevance between any two delay cases in each combination mode. It should be noted that, the calculation method of the detail adjustment value between any two delay cases in other combination modes of the present invention is the same as the calculation method described above, so as to obtain the detail adjustment value between any two delay cases in each combination mode.
Because the combination modes are different, the same two delay cases can appear in different combination modes, and the weight of the same two delay cases in all delay cases in each combination mode needs to be considered; under a certain combination mode, the smaller the detail adjustment value is, the stronger the relevance of any two delay conditions is, and the larger the relevance weight value is; counting to obtain the characteristic value duty ratio of any two delay cases in each unit time of each combination mode, wherein the characteristic value duty ratio represents the weight of the two delay cases in each combination mode in all delay cases, and the larger the characteristic value duty ratio is, the smaller the interference of the other delay cases is when judging the relation between the two delay cases, the larger the association weight value between the two delay cases is; the more serious the delay degree is, the higher the occurrence degree of the two delay conditions in the unit time is represented, and the larger the association weight value is. Therefore, the association weight value between any two delay cases in each combination mode is obtained according to the delay degree in unit time, the characteristic value duty ratio of any two delay cases and the detail adjustment value corresponding to the two delay cases.
Preferably, in one embodiment of the present invention, the step of obtaining the association weight value between any two delay cases in each combination mode includes:
carrying out negative correlation mapping and normalization on the detail adjustment value to obtain a third normalization value; counting to obtain the characteristic value duty ratio of any two delay conditions in each unit time of each combination mode; carrying out negative correlation mapping and normalization on the delay degree of each unit time to obtain a fourth normalized value, and multiplying the characteristic value duty ratio by the fourth normalized value to obtain an adjusted characteristic value duty ratio; obtaining the average adjustment characteristic value duty ratio of the combination mode in the unit time of each combination mode; and obtaining the association weight value between any two delay conditions in each combination mode according to the third normalized value and the average adjustment characteristic value duty ratio, wherein the third normalized value and the average characteristic value duty ratio are in positive correlation with the association weight value. The calculation formula of the association weight value between any two delay cases in each combination mode is expressed as follows:
in the middle ofIndicate->The weight value is correlated between any two delay cases in the combination mode, and the weight value is +.>For normalization function->Indicate will be->Carrying out negative correlation mapping and normalization on detail adjustment values between any two delay cases in a combination mode, and carrying out +. >Indicate->The unit time of the combination mode is +.>Indicate->The combination of->Delay cases->Indicate->The combination of->Delay cases->Characterization of->Characteristic value ratio of any two delay conditions in combination modes>Indicating +.>Sum of all delay profile values of the combination means, < +.>Indicate->The combination of the means is in the form of delay per unit time, < >>Indicate->The combination mode normalizes the result at the delay degree of unit time.
In the calculation formula of the associated weight value, carrying out normalization processing on the detail adjustment value between any two delay conditions in each combination mode, and further subtracting the normalization result from 1 to realize negative correlation mapping to obtain a third normalization value; normalizing the delay degree of unit time to obtain a fourth normalized value, multiplying the characteristic value duty ratio by the fourth normalized value to obtain an adjustment characteristic value duty ratio, averaging the adjustment characteristic value duty ratios of the combination modes in the unit time of each combination mode to obtain an average adjustment characteristic value duty ratio, and averaging the adjustment characteristic value duty ratios of the combination modes in the unit time of each combination mode to obtain an average adjustment characteristic value duty ratio, wherein the larger the average adjustment characteristic value duty ratio is, the larger the association weight value between any two delay cases in each combination mode is, and the third normalized value and the average adjustment characteristic value duty ratio are in positive correlation relation with the association weight.
Under each combination mode, the basic association represents the association relation of all delay cases, but in the actual construction situation, the number of combination modes with some delay cases existing at the same time is large, and the number of combination modes with other delay cases existing at the same time is small, so that the association between any two delay cases in the combination modes needs to be considered, delay cases with strong association can often occur at the same time in unit time, and delay cases with weak association can only occasionally occur at the same time in unit time. The association weight value obtained according to the above steps reflects the magnitude relation of the association of any two delay cases in each combination mode, so that certain adjustment is needed to be made on the basic association according to the association weight value when the association between any two delay cases in each combination mode is calculated. The association between any two delay cases is obtained according to the association weight value and the basic association.
Preferably, in an embodiment of the present invention, the step of obtaining the association between any two delay cases in each combination mode includes:
according to the basic relevance of all delay cases in each combination mode and the relevance weight value between any two delay cases in each combination mode, acquiring the initial relevance of any two delay cases in each combination mode, and summing the initial relevance of all combination modes corresponding to any two delay cases because the number of the combination modes is a characteristic value, so as to acquire the relevance, wherein the relevance calculation formula of any two delay cases in each combination mode is as follows:
In the method, in the process of the invention,representing the relevance of any two delay cases in each combination mode>Indicate->Basic relevance of all delay cases of combination means,/-for>Indicate->And associating weight values between any two delay cases in the combination mode.
In the relevance formula, the basic relevance is multiplied by the relevance weight value to obtain the initial relevance of any two delay cases in a combined mode, namelyAnd adding the initial relevance of each combination mode to obtain relevance of any two delay conditions of each combination mode for initial relevance, wherein the larger the basic relevance is, the larger the relevance weight value is, and the higher the initial relevance is.
It should be noted that, the initial relevance calculating manner in the embodiment of the present invention may be replaced by a technical means known to those skilled in the art, which is not limited and described herein in detail.
Step S3: obtaining a first prediction reference coefficient according to the characteristic value duty ratio of the target delay condition in the unit time and the delay degree of the corresponding unit time, and taking the delay condition with the characteristic value larger than the characteristic value of the target delay condition as a reference delay condition; obtaining a second prediction reference coefficient of the target delay condition according to the characteristic value difference and the relevance between the reference delay condition and the target delay condition; obtaining a prediction reference coefficient of the target delay condition according to the first prediction reference coefficient and the second prediction reference coefficient; and changing target delay conditions to obtain a prediction reference coefficient of each delay condition.
When the delay degree of the construction period is predicted by conventionally utilizing a multiple linear regression model, the delay degree of the target delay condition in the whole construction period is used as a prediction reference coefficient. In unit time, the characteristic value occupation ratio of the target delay condition can represent the severity of the target delay condition in unit time, and the larger the delay degree is, the more serious the delay of the construction period in unit time is represented, so that the severity of the target delay condition in the whole construction period can be obtained according to the characteristic value occupation ratio and the delay degree of the target delay condition in unit time, and the prediction reference coefficient of the conventional multiple linear regression model is obtained according to the characteristic value occupation ratio and the delay degree of the corresponding unit time of the target delay condition in unit time.
In step S2, the correlation between any two delay cases in each combination mode is considered to affect the prediction result, so that the prediction reference coefficient needs to be adjusted based on the correlation, and the prediction reference coefficient is used as the first prediction reference coefficient. In all delay cases in unit time, taking delay cases with characteristic values larger than those of target delay cases as reference delay cases, when the characteristic values of the target delay cases are smaller, the characteristic values of the reference delay cases with larger relevance are larger, and the characteristic values of the target delay cases have increased probability, at the moment, the first prediction reference coefficient correspondingly needs to be adjusted to a certain degree, and the adjustment value is taken as a second prediction reference coefficient, so that the second prediction reference coefficient can be obtained according to the characteristic value difference and relevance between the reference delay cases and the target delay cases. And further obtaining a prediction reference coefficient of the target delay condition according to the first prediction reference coefficient and the second prediction reference coefficient, and changing the target delay condition to obtain the prediction reference coefficient of each delay condition.
Preferably, in one embodiment of the present invention, a product of a delay degree of each unit time in which a target delay condition exists and a characteristic value duty ratio of the target delay condition in each unit time is taken as a delay value in the unit time, delay values in each unit time to which the target delay condition belongs are accumulated as a construction period delay value of the target delay condition, the construction period delay value is normalized to obtain a first prediction reference coefficient, and an adjustment association is obtained according to a characteristic value difference and an association between each reference delay condition and the target delay condition; taking the average adjustment relevance of all the reference delay cases as a second prediction reference coefficient of the target delay cases; and carrying out weighted summation on the first prediction reference coefficient and the second prediction reference coefficient, and carrying out normalization to obtain the prediction reference coefficient of each delay condition.
In an embodiment of the present invention, the step of obtaining the prediction reference coefficient for each delay case includes:
the calculation formula of the first prediction reference coefficient is as follows:
in the method, in the process of the invention,representing the first prediction reference coefficient,/for>Indicate->Time of unit->The characteristic value of the delay time is used for generating a delay time,characteristic value ratio of each unit time representing target delay condition, +. >Indicating the degree of delay per unit time, < >>Represents an exponential function based on natural constants, < ->Indicating the unit time to which the target delay situation belongs, < +.>Indicating total number of delay cases, +.>Indicate->First delay condition characteristic value in unit time. Taking the product of the delay degree of each unit time and the characteristic value duty ratio of the target delay condition in each unit time as the delay value in the unit time, and taking the target delay conditionAnd accumulating the delay values in each unit time as a construction period delay value of a target delay condition, normalizing the construction period delay value to obtain a first prediction reference coefficient, and adding the first prediction reference coefficients of all delay conditions to be 1.
The calculation formula of the second prediction reference coefficient is as follows:
in the method, in the process of the invention,representing the second prediction reference coefficient,/for the prediction>Characteristic value representing a target delay profile, +.>Representing a reference delay profile, +_>Representing the characteristic value difference between each reference delay profile and the target delay profile, ++>Representing the association between each reference delay profile and the target delay profile, +.>For normalization function->Representing normalized results of the association between each reference delay profile and the target delay profile, +. >Indicating the total number of reference delay cases. Normalizing the relevance between the reference delay condition and the target delay condition to obtain a fifth normalized value, and differentiating the characteristic value between each reference delay condition and the target delay condition by the characteristic value differenceAnd multiplying the fifth normalized value to obtain an adjustment relevance, wherein the characteristic value difference and the fifth normalized value are in positive correlation with the adjustment relevance, and the average adjustment relevance of all the reference delay cases is taken as a second prediction reference coefficient of the target delay case. When the characteristic value of the target delay condition is smaller, but because the characteristic value of the reference delay condition is larger, the probability of the characteristic value increase exists in the target delay condition, and the larger the second prediction reference coefficient is, the larger the probability of the characteristic value increase of the target delay condition is.
The calculation formula of the prediction reference coefficient of each delay condition is as follows:
in the method, in the process of the invention,representing the first prediction reference coefficient,/for>Representing the second prediction reference coefficient,/for the prediction>Represents an exponential function based on natural constants, < ->Weight value representing the first prediction coefficient, < ->And a weight value representing the second prediction coefficient. And carrying out weighted summation on the preset weight values of the first prediction reference coefficient and the second prediction reference coefficient, and carrying out normalization to obtain the prediction reference coefficient of each delay condition.
Preferably, in the embodiment of the present invention, the weight value of the first prediction reference coefficient is set to 0.5, and the weight value of the second prediction reference coefficient is set to 0.5.
Step S4: fitting a multiple linear regression model according to the prediction reference coefficient of each delay condition and the real-time delay degree to obtain a future time period delay degree prediction value; and carrying out construction management according to the predicted value of the construction period delay degree.
The multiple linear regression model is used as a technical means well known to those skilled in the art, and is commonly used for data prediction in the field of building engineering, and the principle is not described herein in detail, so that related personnel can perform construction management according to the predicted value.
Preferably, in one embodiment of the present invention, a multiple linear regression model is constructed with the eigenvalue of the current day as an independent variable and the prediction reference coefficient of each delay condition as a regression coefficient, and the specific model is as follows:
in the method, in the process of the invention,representing a predicted value of the future time delay>Indicating the extent of delay in construction period per unit time, +.>Characteristic value representing each delay profile, < +.>A prediction reference coefficient representing each delay condition. />And representing the predicted value obtained according to the multiple linear regression model, and adding the predicted value and the time delay of the current day to obtain the predicted value of the time delay of the future time. The related personnel can carry out construction management according to the predicted value of the delay degree of the future construction period.
In summary, the invention obtains the delay degree and delay condition information in each unit time in a preset history period, obtains the combination mode of the delay conditions in each unit time by processing data to obtain the characteristic value of the same data expression form, obtains the basic relevance of the corresponding combination mode according to the characteristic values of all delay conditions in each combination mode, obtains the detail adjustment value according to the characteristic value difference of any two delay conditions in the combination mode, and obtains the relevance weight value between any two delay conditions in each combination mode according to the delay degree, the characteristic value ratio of any two delay conditions and the detail adjustment value of the corresponding two delay conditions in each combination mode, and obtains the relevance between any two delay conditions according to the basic relevance and the relevance weight value.
A building construction engineering construction delay condition correlation analysis method embodiment based on a BIM model:
in the prior art, in the field of construction engineering, when related personnel predict the degree of construction period delay, correlation among delay conditions is sometimes not considered, and the correlation can influence a prediction result, even if construction delay condition correlation analysis is performed, the technical problem that correlation acquisition is inaccurate often exists in the process. In order to solve the technical problem, the embodiment provides a building construction engineering construction delay condition relevance analysis method based on a BIM model, which comprises the following steps:
Step S1: obtaining delay degree and delay condition information in each unit time in a preset history period; and carrying out data processing on all delay condition information to obtain the characteristic values of the same data expression form.
Step S2: obtaining a combination mode of delay conditions in each unit time, and obtaining basic relevance of the corresponding combination modes according to characteristic values of all delay conditions in each combination mode; obtaining a detail adjustment value according to the difference of the characteristic values of any two delay conditions in the combination mode; under each combination mode, according to the corresponding delay degree in unit time, the characteristic value duty ratio of any two delay conditions and the detail adjustment value of the corresponding two delay conditions, obtaining the association weight value between any two delay conditions in each combination mode; and obtaining the relevance between any two delay cases according to the basic relevance corresponding to all the combination modes and the relevance weight value between any two delay cases.
Because the specific implementation process of the steps S1-S2 is already described in detail in the building construction management system based on the BIM model, the detailed description is omitted.
The technical effects of this embodiment are: in the embodiment, the relevance among all delay conditions in each combination mode is statistically analyzed and is taken as the basic relevance; when a delay condition characteristic value changes, the delay condition characteristic value with relevance also changes within a certain degree, so that a detail adjustment value between any two delay conditions in each combination mode is calculated according to the difference characteristic of the characteristic values between the two delay conditions; because the combination modes are different, the same two delay cases possibly appear in different combination modes, the weight values of the same two delay cases in all delay cases in each combination mode need to be considered, the association weight value between any two delay cases in each combination mode is calculated, and then the basic association is adjusted, so that accurate association information is obtained.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.

Claims (10)

1. A building engineering construction management system based on a BIM model, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the following steps when executing the computer program:
obtaining delay degree and delay condition information in each unit time in a preset history period from the history data of the BIM; carrying out data processing on all delay condition information to obtain characteristic values of the same data expression form;
obtaining a combination mode of delay conditions in each unit time, and obtaining basic relevance of the corresponding combination modes according to characteristic values of all delay conditions in each combination mode; obtaining a detail adjustment value according to the difference of the characteristic values of any two delay conditions in the combination mode; under each combination mode, according to the corresponding delay degree in unit time, the characteristic value duty ratio of any two delay conditions and the detail adjustment value of the corresponding two delay conditions, obtaining the association weight value between any two delay conditions in each combination mode; according to the basic relevance corresponding to all the combination modes and the relevance weight value between any two delay cases, obtaining the relevance between any two delay cases;
Obtaining a first prediction reference coefficient according to the characteristic value duty ratio of the target delay condition in the unit time and the delay degree of the corresponding unit time, and taking the delay condition with the characteristic value larger than the characteristic value of the target delay condition as a reference delay condition; obtaining a second prediction reference coefficient of the target delay condition according to the characteristic value difference and the relevance between the reference delay condition and the target delay condition; obtaining a prediction reference coefficient of the target delay condition according to the first prediction reference coefficient and the second prediction reference coefficient; changing target delay conditions to obtain a prediction reference coefficient of each delay condition;
fitting a multiple linear regression model according to the prediction reference coefficient of each delay condition and the real-time delay degree to obtain a future construction period delay degree prediction value; and carrying out construction management according to the predicted value of the construction period delay degree.
2. The building engineering construction management system based on the BIM model according to claim 1, wherein the method for obtaining the characteristic value includes:
obtaining the characteristic value according to the information difference between the actual information of the delay condition and preset standard information in the unit time; the characteristic value is positively correlated with the information difference.
3. The building engineering construction management system based on the BIM model according to claim 1, wherein the obtaining method of the delay degree includes:
obtaining the actual construction progress and the predicted construction progress in unit time; judging that the actual construction progress in unit time is smaller than the predicted construction progress as the incomplete construction progress; calculating the ratio of the actual construction progress to the predicted construction progress when the construction progress is not completed, and carrying out negative correlation mapping normalization to obtain the delay degree in unit time.
4. The building engineering construction management system based on the BIM model according to claim 1, wherein the obtaining of the basic relevance of all delay cases in each combination mode includes:
carrying out negative correlation mapping and normalization on the number of delay cases in each combination mode to obtain a first normalization value;
obtaining a characteristic value average value in each combination mode, and summing the characteristic value average values of the combination modes in unit time of each combination mode to obtain a construction period characteristic value;
and obtaining basic relevance of all delay conditions in each combination mode according to the first normalization value and the construction period characteristic value.
5. The building engineering management system based on the BIM model according to claim 1, wherein the step of obtaining the detail adjustment value between any two delay cases in each combination mode includes:
calculating the characteristic value difference of any two delay conditions in each combination mode in each unit time, and calculating the standard deviation of the characteristic value difference; normalizing the difference of the characteristic values to obtain a difference weight;
in the standard deviation calculation formula, the difference weight is used as the weight of the difference value between the average characteristic value difference and the characteristic value difference, the characteristic value difference standard deviation is obtained, and the characteristic value difference standard deviation is used as the detail adjustment value.
6. The building engineering construction management system based on the BIM model according to claim 5, wherein the obtaining of the association weight value between any two delay cases in each combination mode comprises the following steps:
carrying out negative correlation mapping and normalization on the detail adjustment value to obtain a third normalization value;
counting to obtain the characteristic value duty ratio of any two delay conditions in each unit time of each combination mode; carrying out negative correlation mapping and normalization on the delay degree of each unit time to obtain a fourth normalized value, and multiplying the characteristic value duty ratio by the fourth normalized value to obtain an adjusted characteristic value duty ratio; obtaining the average adjustment characteristic value duty ratio of the combination mode in the unit time of each combination mode;
And obtaining the association weight value between any two delay conditions in each combination mode according to the third normalized value and the average adjustment characteristic value duty ratio, wherein the third normalized value and the average characteristic value duty ratio are in positive correlation with the association weight value.
7. The building engineering construction management system based on the BIM model according to claim 1, wherein the correlation between any two delay cases in each combination mode is obtained, and the system comprises:
acquiring initial relevance of any two delay cases in each combination mode according to the basic relevance of all delay cases in each combination mode and the relevance weight value between any two delay cases in each combination mode, and summing the initial relevance of all combination modes corresponding to any two delay cases to acquire the relevance; and the basic relevance and the weight value of the relevance are in positive correlation with the initial relevance.
8. The building engineering construction management system based on the BIM model according to claim 1, wherein the step of obtaining the prediction reference coefficient for each delay condition includes:
taking the product of the delay degree of each unit time with the target delay condition and the characteristic value duty ratio of the target delay condition in each unit time as a delay value in the unit time, accumulating the delay value in each unit time with the target delay condition as a construction period delay value of the target delay condition, and normalizing the construction period delay value to obtain a first prediction reference coefficient;
Obtaining an adjustment association according to the characteristic value difference and the association between each reference delay condition and the target delay condition, wherein the characteristic value difference and the association are in positive correlation with the adjustment association; taking the average adjustment relevance of all the reference delay cases as a second prediction reference coefficient of the target delay cases;
and carrying out weighted summation on the first prediction reference coefficient and the second prediction reference coefficient and carrying out normalization on the weighted summation to obtain each delay condition prediction reference coefficient.
9. The building engineering construction management system based on the BIM model according to claim 1, wherein the performing of the fitting of the multiple linear regression model to predict the future time delay includes:
and constructing a multiple linear regression model by taking the characteristic value of the delay condition in unit time as an independent variable and taking the prediction reference coefficient of each delay condition as a regression coefficient, and predicting the delay degree of the construction period according to the multiple linear regression model.
10. The building engineering management system based on the BIM model according to claim 8, wherein the preset weight value includes that the weight value of the first prediction reference coefficient is set to 0.5, and the weight value of the second prediction reference coefficient is set to 0.5.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108647753A (en) * 2018-05-04 2018-10-12 桂林电子科技大学 A kind of construction site method for prewarning risk based on BIM and RFID technique
US20180374052A1 (en) * 2016-12-20 2018-12-27 Chetan Mogal Method of improviding building information modeling
CN112052992A (en) * 2020-08-26 2020-12-08 杭州新中大科技股份有限公司 Building engineering project progress prediction system and method based on deep learning
CN112446534A (en) * 2020-11-09 2021-03-05 国网福建省电力有限公司 Construction period prediction method and device for power transmission and transformation project
KR20210047054A (en) * 2019-10-21 2021-04-29 서울시립대학교 산학협력단 Apparatus and method for managing
KR102298260B1 (en) * 2020-11-25 2021-09-07 한국건설기술연구원 the construction managing system based on augmented reality and the construction management method using the same
CN113700053A (en) * 2021-08-13 2021-11-26 湖南方圆工程咨询监理有限公司 BIM-based construction process foundation pit deformation monitoring and early warning method and system
CN115481884A (en) * 2022-09-13 2022-12-16 中铁第四勘察设计院集团有限公司 Engineering progress information visual management method based on engineering structure tree
CN115511467A (en) * 2022-10-12 2022-12-23 天栋建设管理有限公司 Project management method and system based on engineering cost

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180374052A1 (en) * 2016-12-20 2018-12-27 Chetan Mogal Method of improviding building information modeling
CN108647753A (en) * 2018-05-04 2018-10-12 桂林电子科技大学 A kind of construction site method for prewarning risk based on BIM and RFID technique
KR20210047054A (en) * 2019-10-21 2021-04-29 서울시립대학교 산학협력단 Apparatus and method for managing
CN112052992A (en) * 2020-08-26 2020-12-08 杭州新中大科技股份有限公司 Building engineering project progress prediction system and method based on deep learning
CN112446534A (en) * 2020-11-09 2021-03-05 国网福建省电力有限公司 Construction period prediction method and device for power transmission and transformation project
KR102298260B1 (en) * 2020-11-25 2021-09-07 한국건설기술연구원 the construction managing system based on augmented reality and the construction management method using the same
CN113700053A (en) * 2021-08-13 2021-11-26 湖南方圆工程咨询监理有限公司 BIM-based construction process foundation pit deformation monitoring and early warning method and system
CN115481884A (en) * 2022-09-13 2022-12-16 中铁第四勘察设计院集团有限公司 Engineering progress information visual management method based on engineering structure tree
CN115511467A (en) * 2022-10-12 2022-12-23 天栋建设管理有限公司 Project management method and system based on engineering cost

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
包胜;卢富华;赵政烨;沈锐;: "BIM技术在施工进度管理中的应用", 项目管理技术, no. 04 *
王惠文;孟洁;: "多元线性回归的预测建模方法", 北京航空航天大学学报, no. 04 *
胡文发;常永霞;何新华;: "建筑工程项目工期的影响因素与预测模型", 土木工程学报, no. 02 *
袁立刚;李小利;陈俐光;: "基于多元线性回归的沥青路面质量评价指标相关性研究", 施工技术, no. 1 *
陈宝健等: ""基于BIM技术的桥梁工程施工成本控制"", 《福建工程学院学报》 *

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