A construction management method and system based on BIM

Abstract

The present application relates to the field of data processing technology, and in particular to a BIM-based construction management method and system. The method includes: obtaining the dust concentration and wind speed of the monitoring points at the construction site during the target time period; determining the distortion value of the target sub-time period of the target time period; determining the fuzzy membership of the dust concentration in the sub-time period to obtain the weighted fuzzy membership corresponding to the target sub-time period; determining the fuzzy entropy of the target time period based on the weighted fuzzy membership corresponding to different sub-time periods to obtain an abnormality factor; when the abnormality factor is greater than a preset threshold, obtaining a BIM model pre-created for the construction site, displaying dust prompt information in the BIM model, and managing the dust environment of the construction site. Through the above technical solution, the dust environment of the construction site can be effectively managed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a BIM-based construction management method and system. Background Art

[0002] BIM (Building Information Modeling) is a digital design and management tool that simulates actual construction projects by creating 3D models. BIM models can include not only the building's geometric information but also the properties, functions, and other relevant information of its components. For example, using BIM software's rendering and visualization capabilities, the distribution of dust concentrations at different locations within a building can be displayed within the 3D BIM model, providing users with a more intuitive understanding of dust conditions within the building.

[0003] In related technologies, for example, the Chinese patent application document with publication number CN117151652A provides a BIM-based construction management system, including: a BIM model building module, a construction area division module, a construction information acquisition module, a construction information synchronization module, a construction information processing module, a construction information analysis module, a construction information evaluation module, and a construction information safety supervision module. By collecting basic information of the building environment in each monitoring sub-area of ​​the target construction area and synchronously transmitting the data to the three-dimensional building model, the material stacking safety monitoring index, the construction environment safety monitoring index and the construction environment impact index are calculated to realize the monitoring of dust concentration, construction days, noise and vibration intensity at the construction site.

[0004] However, the dust concentration at the construction site monitored by the sensor may be affected by the wind at the construction site, resulting in a certain difference between the monitored dust concentration and the actual dust concentration. Therefore, when using the obtained dust concentration to manage the construction site, it is difficult to effectively manage the dust environment at the construction site. Summary of the Invention

[0005] In order to overcome the problem in related technologies that it is difficult to effectively manage the dust environment at the construction site, the present application provides a BIM-based construction management method and system.

[0006] According to a first aspect of an embodiment of the present application, a BIM-based construction management method is provided, comprising: obtaining dust concentration and wind speed at monitoring points at a construction site during a target time period; determining a distortion value for a target sub-time period within the target time period; the distortion value being determined based on a correlation between dust concentration and wind speed within the target sub-time period, and a degree of fluctuation of wind speed within the target sub-time period; determining the fuzzy membership of the dust concentration for each sub-time period within the target time period using a membership function, and performing a weighted average of the fuzzy memberships of other sub-time periods using the distortion values ​​of different sub-time periods to obtain a weighted fuzzy membership corresponding to the target sub-time period; determining a fuzzy entropy for the target time period based on the weighted fuzzy memberships corresponding to the different sub-time periods, and determining an abnormality factor using the product of the fuzzy entropy and the average value of the dust concentration within the target time period; and obtaining a BIM model pre-created for the construction site when the abnormality factor is greater than a preset threshold, displaying dust prompt information in the BIM model, and managing the dust environment at the construction site.

[0007] In this way, the distortion value of the dust concentration at the construction site within the target sub-time period of the target time period is determined. The distortion value is determined based on the correlation between the dust concentration and the wind speed within the target sub-time period, as well as the degree of fluctuation of the wind speed within the target sub-time period, so that the distortion value can represent the degree to which the monitored dust concentration is affected by the wind speed; the fuzzy membership of the dust concentration in each sub-time period within the target time period is determined using the membership function to determine the abnormality factor, which can eliminate the influence of wind speed on the monitoring results of dust concentration, and the abnormality factor can better reflect the actual abnormal situation of dust concentration; displaying dust prompt information in the BIM model can realize visual reminder of dust; managing the dust environment at the construction site can reduce the dust concentration at the construction site and ensure the health of workers at the construction site.

[0008] Optionally, the distortion value of the target sub-time period is determined by: , where V is the distortion value of the target sub-time period; Z is the difference value, which is used to characterize the difference between the minimum dust concentration in the target sub-time period and the minimum dust concentration in the previous sub-time period; is the fluctuation degree value, which is used to characterize the fluctuation degree of wind speed in the target sub-time period; is a correlation value used to characterize the degree of matching between the ranking information of dust concentration and the ranking information of wind speed in the target sub-time period; a is a preset positive number.

[0009] In this way, the distortion value is determined based on the degree of match between the ranking information of dust concentration and the ranking information of wind speed in the target sub-time period, as well as the degree of difference between the minimum dust concentration in the target sub-time period and the minimum dust concentration in the previous sub-time period. The distortion value of the target sub-time period can better characterize the probability of the influence of dust concentration on wind speed in the target sub-time period.

[0010] Optionally, the relevance value of the target sub-time period is determined by: , T is the correlation value of the target sub-time period, M is the number of moments in the target sub-time period, is the ranking of the dust concentration at the mth moment in the target sub-time period among all dust concentrations in the target sub-time period, is the ranking of the wind speed at the mth moment in the target sub-time period among all wind speeds in the target sub-time period.

[0011] In this way, the ranking of the dust concentration at the time point in the target sub-time period among all the dust concentrations in the target sub-time period is compared with the ranking of the wind speed at the time point in the target sub-time period among all the wind speeds in the target sub-time period, so as to determine the correlation value used to characterize the degree of matching between the ranking information of the dust concentration in the target sub-time period and the ranking information of the wind speed. The correlation value can better reflect the probability that the dust concentration at the monitoring point is affected by the wind speed in the target sub-time period.

[0012] Optionally, the difference degree value is determined by: , where Z is the difference value, norm is the normalization function, is the average value of dust concentration at the monitoring point during the historical period, is the minimum value of dust concentration in the target sub-time period, It is the minimum value of dust concentration in the sub-time period before the target sub-time period.

[0013] In this way, by comparing the minimum value of the dust concentration in the target sub-time period with the minimum value of the dust concentration in the previous sub-time period of the target sub-time period; and comparing the average value of the dust concentration at the monitoring point in the historical time period with the minimum value of the dust concentration in the target sub-time period, the obtained difference degree value can more comprehensively reflect the probability that the dust concentration in the target sub-time period is affected by the wind speed.

[0014] Optionally, the fluctuation degree value is determined by: , P is the fluctuation degree value, R is the number of wind speed types in the target sub-time period, is the frequency ratio of the rth wind speed in the target sub-time period, and ln is a logarithmic function with a natural constant as the base.

[0015] In this way, the fluctuation degree value can better characterize the fluctuation degree of wind speed in the target sub-time period, so as to determine the probability that the dust concentration at the monitoring point in the target sub-time period is affected by wind speed in combination with the fluctuation degree of wind speed in the target sub-time period.

[0016] Optionally, the distortion values ​​of different sub-time periods are used to perform weighted averaging on the fuzzy memberships of other sub-time periods to obtain a weighted fuzzy membership corresponding to a target sub-time period, including: using the inverse of the exponential function of the distortion value of the sub-time period as the weight value corresponding to the sub-time period, and using the weight values ​​of other sub-time periods except the target sub-time period to perform weighted summation on the fuzzy memberships of other sub-time periods except the target sub-time period to obtain a weighted fuzzy membership corresponding to the target sub-time period.

[0017] Optionally, the product of the fuzzy entropy and the average value of the dust concentration in the target time period is used to determine the abnormality factor, including: , where G is the anomaly factor and E is the fuzzy entropy of the target time period. is the normalization function, is the average value of dust concentration during the target time period, It is the average value of dust concentration at the monitoring point during the historical period.

[0018] Optionally, the dust environment at the construction site is managed, including: using dust suppression equipment to suppress dust at the construction site where the monitoring point is located; or restricting the operation of construction equipment at the construction site where the monitoring point is located to reduce the rate at which the construction equipment generates dust.

[0019] According to a second aspect of an embodiment of the present application, a BIM-based construction management system is provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the steps of the BIM-based construction management method provided in the first aspect of the present application are implemented.

[0020] The technical solution provided by the embodiments of the present application may include the following beneficial effects: by determining the distortion value of the dust concentration at the construction site within the target sub-time period of the target time period, the distortion value is determined based on the correlation between the dust concentration and the wind speed within the target sub-time period, and the degree of fluctuation of the wind speed within the target sub-time period, so that the distortion value can characterize the degree to which the monitored dust concentration is affected by the wind speed; using the membership function to determine the fuzzy membership of the dust concentration in each sub-time period within the target time period to determine the abnormality factor, it is possible to eliminate the influence of wind speed on the monitoring results of dust concentration, and the abnormality factor can better reflect the actual abnormal situation of dust concentration; displaying dust prompt information in the BIM model can realize visual reminder of dust; managing the dust environment at the construction site can reduce the dust concentration at the construction site and ensure the health of workers at the construction site.

[0021] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] Figure 1 is a flowchart illustrating a BIM-based construction management method according to an exemplary embodiment;

[0024] Figure 2 The figure is a structural diagram of a BIM-based construction management system according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0026] First, a brief introduction is given to the application scenario of the embodiment of the present application. In the application scenario of the present application, the dust concentration at the construction site can be obtained through sensors, and the dust concentration can be visualized in the BIM model constructed for the construction site. However, the dust concentration at the construction site may be affected by the wind speed at the construction site. For example, the dust at the dust concentration sensor may be blown to other places, causing the dust concentration monitored by the sensor to decrease.

[0027] The dust that is blown by the wind to places other than the dust concentration sensors will still pose a threat to the health of workers at the construction site where the sensors are located. Moreover, since the building is still under construction, it is difficult to install dust concentration sensors at all locations, making it difficult for the obtained dust concentration to accurately reflect the actual dust conditions at the construction site. Therefore, it is difficult to effectively manage the dust environment at the construction site.

[0028] In order to solve the above technical problems, the present invention provides a BIM-based construction management method and system. Figure 1 is a flowchart of a BIM-based construction management method according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0029] In step S101, the dust concentration and wind speed at a monitoring point at a construction site during a target time period are obtained.

[0030] A dust concentration sensor can be installed at the monitoring point of the construction site to obtain the dust concentration at the monitoring point; by installing a wind speed sensor at the monitoring point of the construction site, the wind speed at the monitoring point can be obtained.

[0031] Since the dust concentration monitored by the dust concentration sensor may be affected by wind speed, obtaining the dust concentration and wind speed at the same monitoring point on the construction site helps to avoid the influence of wind speed on the dust concentration, thereby obtaining a dust concentration that better reflects the actual dust concentration at the monitoring point.

[0032] In step S102 , for a target sub-time period within a target time period, a distortion value of the target sub-time period is determined.

[0033] The distortion value is determined based on the correlation between dust concentration and wind speed within the target sub-time period, as well as the degree of fluctuation of wind speed within the target sub-time period; the distortion value is used to characterize the extent to which the dust concentration at the monitoring point is affected by wind speed within the target sub-time period.

[0034] The target sub-time period can be any sub-time period within the target time period; for example, for a time period of 10 minutes, the time period can be divided into 30 sub-time periods of equal length in chronological order, and the length of each sub-time period is equal to 20 seconds. The target sub-time period can be any sub-time period among these 30 sub-time periods; the length of the target time period and the length of the sub-time periods of the target time period can be adaptively set according to actual needs.

[0035] The larger the distortion value of the target sub-time period, the more the dust concentration at the monitoring point is affected by the wind speed during the target sub-time period; conversely, the smaller the distortion value of the target sub-time period, the less the dust concentration at the monitoring point is affected by the wind speed during the target sub-time period.

[0036] When the dust concentration at the monitoring point is affected by wind speed, the amount by which the dust concentration at the monitoring point is affected by wind speed is related to the wind speed at the monitoring point; when the supervisor does not deal with the dust environment at the monitoring point, the dust concentration at the monitoring point has a certain correlation with the wind speed at the monitoring point.

[0037] When the supervisory personnel deal with the dust environment at the monitoring point, such as sprinkling water on the dust at the monitoring point, the correlation between the dust concentration at the monitoring point and the wind speed at the monitoring point decreases. Therefore, by determining the distortion value of the target sub-time period, it is possible to reflect the extent to which the dust concentration at the monitoring point is affected by the wind speed within the target sub-time period, or it is possible to reflect the probability that the dust concentration at the monitoring point has not been dealt with.

[0038] In one embodiment, the distortion value of the target sub-time period is determined by: , where V is the distortion value of the target sub-time period; Z is the difference value, which is used to characterize the difference between the minimum dust concentration in the target sub-time period and the minimum dust concentration in the previous sub-time period; is the fluctuation degree value, which is used to characterize the fluctuation degree of wind speed in the target sub-time period; is a correlation value used to characterize the degree of matching between the ranking information of dust concentration and the ranking information of wind speed in the target sub-time period; a is a preset positive number.

[0039] The difference degree value reflects the change in dust concentration in the target sub-time period relative to the previous sub-time period. Compared with the previous sub-time period of the target sub-time period, if the change in the minimum value of the dust concentration is large, it means that the dust concentration has changed significantly.

[0040] The fluctuation degree value is used to characterize the degree of fluctuation of the wind speed within the target sub-time period. Since wind speed can affect the dust concentration monitored by the dust concentration sensor, the greater the degree of fluctuation of the wind speed within the target sub-time period, the more unstable the wind speed at the monitoring point, and the greater the probability that the dust concentration monitored by the dust concentration sensor will be affected by the wind speed.

[0041] The correlation value is used to characterize the degree of match between the ranking information of dust concentration and the ranking information of wind speed within the target sub-time period. The larger the correlation value within the target sub-time period, the greater the degree of match between the ranking information of dust concentration and the ranking information of wind speed within the target sub-time period.

[0042] Since wind speed mainly causes the reduction of dust concentration monitored by the dust concentration sensor, the greater the wind speed at the monitoring point, the greater the impact of the wind speed on the dust concentration monitored by the dust concentration sensor. When the dust concentration monitored by the dust concentration sensor is only affected by wind speed, the maximum wind speed in the same sub-time period usually corresponds to the minimum dust concentration, or the minimum wind speed in the same sub-time period usually corresponds to the maximum dust concentration, so that the ranking information of dust concentration in the target sub-time period and the ranking information of wind speed in the target sub-time period are less matched. Therefore, the matching degree between the ranking information of dust concentration in the target sub-time period and the ranking information of wind speed in the target sub-time period can reflect the probability that the dust concentration in the target sub-time period is affected by wind speed.

[0043] Among them, the larger the similarity value of the target sub-time period, the higher the degree of match between the ranking information of dust concentration in the target sub-time period and the ranking information of wind speed in the target sub-time period, and the lower the probability that the dust concentration in the target sub-time period is affected by the wind speed; on the contrary, the smaller the similarity value of the target sub-time period, the lower the degree of match between the ranking information of dust concentration in the target sub-time period and the ranking information of wind speed in the target sub-time period, and the higher the probability that the dust concentration in the target sub-time period is affected by the wind speed.

[0044] In this way, the distortion value is determined based on the degree of match between the ranking information of dust concentration and the ranking information of wind speed in the target sub-time period, as well as the degree of difference between the minimum dust concentration in the target sub-time period and the minimum dust concentration in the previous sub-time period. The distortion value of the target sub-time period can better characterize the probability of the influence of dust concentration on wind speed in the target sub-time period.

[0045] a is a preset positive number. The value of the preset positive number can be set according to actual needs. The existence of the preset positive number can avoid the situation where the denominator of the calculation formula of the distortion value is 0 when the correlation value is 0, thereby ensuring the effective calculation process of the distortion value.

[0046] In one embodiment, the relevance value of the target sub-time period is determined by: , T is the correlation value of the target sub-time period, M is the number of moments in the target sub-time period, is the ranking of the dust concentration at the mth moment in the target sub-time period among all dust concentrations in the target sub-time period, is the ranking of the wind speed at the mth moment in the target sub-time period among all wind speeds in the target sub-time period.

[0047] When the dust concentration at the monitoring point is affected by the wind speed within the target sub-time period, the greater the wind speed at the monitoring point within the target sub-time period, the lower the dust concentration monitored by the dust concentration sensor at the monitoring point.

[0048] For example, for a target sub-time period with 10 included moments, the wind speed ranks first at a certain moment in the target sub-time period. If the dust concentration corresponding to the wind speed ranked first in the same sub-time period is ranked 10, the ranking information of the dust concentration in the target sub-time period and the ranking information of the wind speed in the target sub-time period will be less matched, and the dust concentration in the target sub-time period is more likely to be affected by the wind speed.

[0049] For another example, for a target sub-time period with 10 included moments, the wind speed ranks first at a certain moment within the target sub-time period. If the dust concentration corresponding to the wind speed ranked first in the same sub-time period is ranked 5, the ranking information of the dust concentration within the target sub-time period and the ranking information of the wind speed within the target sub-time period are highly matched. There is a high probability that the dust concentration within the target sub-time period is not affected by the wind speed, or there is a high probability that the dust concentration within the target sub-time period is also affected by the dust treatment measures. The dust concentration monitored by the dust concentration sensor within the target sub-time period can better reflect the actual dust concentration in the environment where the monitoring point is located.

[0050] In this way, the ranking of the dust concentration at the time point in the target sub-time period among all the dust concentrations in the target sub-time period is compared with the ranking of the wind speed at the time point in the target sub-time period among all the wind speeds in the target sub-time period, so as to determine the correlation value used to characterize the degree of matching between the ranking information of the dust concentration in the target sub-time period and the ranking information of the wind speed. The correlation value can better reflect the probability that the dust concentration at the monitoring point is affected by the wind speed in the target sub-time period.

[0051] In step S103, the fuzzy membership of the dust concentration in each sub-time period within the target time period and the distortion values ​​of different sub-time periods are determined respectively using the membership function, and the fuzzy membership of other sub-time periods is weighted averaged to obtain the weighted fuzzy membership corresponding to the target sub-time period.

[0052] The membership function can be used to determine the fuzzy membership of the dust concentration in each sub-time period within the target time period. The fuzzy membership can be used to characterize the probability of the dust concentration exceeding the standard. The fuzzy membership of the dust concentration in each sub-time period within the target time period is positively correlated with the dust concentration in the sub-time period. For example, the membership function can be a piecewise function or an exponential function. The embodiments of the present application do not limit the specific membership function.

[0053] The greater the dust concentration in the sub-time period within the target time period, the greater the fuzzy membership corresponding to the dust concentration in the sub-time period; conversely, the smaller the dust concentration in the sub-time period within the target time period, the smaller the fuzzy membership corresponding to the dust concentration in the sub-time period.

[0054] For example, using the distortion values ​​of different sub-time periods, the fuzzy memberships of other sub-time periods are weighted averaged to obtain the weighted fuzzy membership corresponding to the target sub-time period, including: using the inverse of the exponential function of the distortion value of the sub-time period as the weight value corresponding to the sub-time period, using the weight values ​​of other sub-time periods except the target sub-time period, and performing weighted summation on the fuzzy memberships of other sub-time periods except the target sub-time period to obtain the weighted fuzzy membership corresponding to the target sub-time period.

[0055] After determining the weight values ​​and the variables to be weighted summed, the weighted summation result can be obtained. For example, the distortion values ​​of different sub-time periods are used as weights to weight the fuzzy memberships of other sub-time periods. The process includes: ,in, is the weighted fuzzy membership corresponding to the target sub-time period, S is the number of sub-time periods other than the target sub-time period within the target time period, and exp is an exponential function with a natural constant as the base. is the distortion value of the ath sub-time period other than the target sub-time period within the target time period, is the fuzzy membership of the ath sub-time period other than the target sub-time period within the target time period.

[0056] The dust concentration value in the sub-time period with a smaller distortion value can better match the actual dust concentration at the monitoring point. By processing the distortion value with the inverse of the exponential function, a larger weight can be set for the sub-time period with a smaller distortion value, thereby increasing the contribution of the dust concentration in the sub-time period that better matches the actual dust concentration at the monitoring point to the weighted average process.

[0057] In step S104, the fuzzy entropy of the target time period is determined according to the weighted fuzzy memberships corresponding to different sub-time periods, and the abnormality factor is determined by multiplying the fuzzy entropy by the average value of the dust concentration in the target time period.

[0058] The content of the fuzzy entropy of the target time period is determined according to the weighted fuzzy membership corresponding to different sub-time periods within the target time period. The embodiment of the present application will not be repeated here; the fuzzy entropy of the target time period can characterize the complexity of the weighted fuzzy membership of different sub-time periods.

[0059] For a period of time when the average dust concentration is greater than the historical dust concentration average, the greater the fuzzy entropy of the dust concentration in the period, the greater the credibility of the dust concentration exceeding the standard at the monitoring point in the period, and the greater the need to treat the dust environment at the monitoring point.

[0060] In one embodiment, the abnormality factor is determined by multiplying the fuzzy entropy by the average value of the dust concentration in the target time period, including: , where G is the anomaly factor and E is the fuzzy entropy of the target time period. is the normalization function, is the average value of dust concentration during the target time period, It is the average value of dust concentration at the monitoring point during the historical period.

[0061] The fuzzy entropy of the target time period is obtained based on the weighted fuzzy membership of different sub-time periods. The weighted fuzzy membership is obtained by weighted averaging the distortion value of the sub-time period on the fuzzy membership of the sub-time period. The weighted fuzzy membership can reflect the level of dust concentration at the monitoring point in the sub-time period while excluding the interference of wind speed on dust concentration. Therefore, the fuzzy entropy can reflect the credibility of dust concentration exceeding the standard in the target time period while excluding the interference of wind speed on dust concentration.

[0062] In this way, by considering the fuzzy entropy of the target time period and comparing the average value of the dust concentration in the target time period with the average value of the dust concentration at the monitoring point in the historical time period, the abnormal factor can better reflect the situation of exceeding the dust concentration standard, so as to manage the dust environment at the monitoring point where the dust concentration exceeds the standard.

[0063] In step S105, when the abnormal factor is greater than a preset threshold, a BIM model pre-created for the construction site is obtained, dust prompt information is displayed in the BIM model, and the dust environment of the construction site is managed.

[0064] The dust prompt information is used to indicate that the dust concentration at the monitoring point exceeds the standard. For example, in the BIM model, the dust prompt information can be displayed in eye-catching colors such as red, orange, or tangerine. For example, the dust prompt information can be a text prompt message with the content "The dust concentration at the monitoring point exceeds the standard, please deal with it in time."

[0065] Through the pre-created BIM model, users can more intuitively understand the information of different locations in the construction site. For example, in addition to displaying the engineering structure of the construction site, the BIM model can also show the location of the construction workers in the construction site and the dust concentration information at the monitoring point.

[0066] When the abnormal factor is greater than the preset threshold, it means that the dust concentration at the monitoring point exceeds the standard. Obtaining a BIM model created in advance for the construction site and displaying dust prompt information in the BIM model will help to deploy personnel near the monitoring point. For example, personnel near the monitoring point can be evacuated to other locations where the dust concentration does not exceed the standard to carry out work; or personnel working near the monitoring point or about to go to the monitoring point to work can be prompted to wear dust protection masks.

[0067] When the abnormal factor is greater than the preset threshold, managing the dust environment at the construction site can reduce the dust concentration in the environment where the monitoring point is located, avoiding the threat posed by higher dust concentration to the health of workers near the monitoring point.

[0068] Through the BIM-based construction management method provided in the embodiment of the present application, the distortion value of the dust concentration at the construction site within the target sub-time period of the target time period is determined. The distortion value is determined based on the correlation between the dust concentration and the wind speed within the target sub-time period, and the degree of fluctuation of the wind speed within the target sub-time period, so that the distortion value can characterize the degree to which the monitored dust concentration is affected by the wind speed; the fuzzy membership of the dust concentration in each sub-time period within the target time period is determined by using the membership function to determine the abnormality factor, which can eliminate the influence of wind speed on the monitoring results of dust concentration, and the abnormality factor can better reflect the actual abnormal situation of dust concentration; displaying dust prompt information in the BIM model can realize visual reminder of dust; managing the dust environment at the construction site can reduce the dust concentration at the construction site and ensure the health of workers at the construction site.

[0069] In one embodiment, managing the dust environment at a construction site includes: using dust suppression equipment to suppress dust at the construction site where the monitoring point is located; or restricting the operation of construction equipment at the construction site where the monitoring point is located to reduce the rate at which the construction equipment generates dust.

[0070] The dust suppression equipment can be provided with a fixed or movable nozzle for spraying water mist or chemical dust suppressants to reduce the dust concentration at the monitoring point; alternatively, the dust suppression equipment can absorb air and use a filter component to filter the dust to suppress the dust concentration at the monitoring point. The embodiments of the present application do not limit the specific type of dust suppression equipment.

[0071] Construction equipment at a construction site will increase the dust concentration in the air during operation, such as a cutting table for cutting wooden formwork or equipment for mixing cement. By restricting the operation of construction equipment at the construction site where the monitoring point is located, the rate at which the construction equipment generates dust can be effectively reduced, ensuring the safety of workers at the construction site.

[0072] In this way, by managing the dust environment at the construction site, the dust concentration in the environment where the monitoring point is located can be effectively reduced, avoiding the threat posed by excessive dust to the health of workers at the construction site.

[0073] In one embodiment, the difference degree value is determined by: , where Z is the difference value, norm is the normalization function, is the average value of dust concentration at the monitoring point during the historical period, is the minimum value of dust concentration in the target sub-time period, It is the minimum value of dust concentration in the sub-time period before the target sub-time period.

[0074] By comparing the minimum value of the dust concentration in the target sub-time period with the minimum value of the dust concentration in the sub-time period before the target sub-time period, the change in dust concentration in the target sub-time period relative to the previous sub-time period can be understood.

[0075] Here, the objects of comparison are the minimum values ​​of dust concentration in different sub-time periods. The minimum value of dust concentration in the same sub-time period is more likely to be caused by the effect of wind speed. Therefore, by comparing the minimum value of dust concentration in the target sub-time period with the change in the minimum value of dust concentration in the previous sub-time period, it is easier to find the different effects of wind speed on dust concentration in different sub-time periods.

[0076] The greater the difference between the minimum values ​​of dust concentration in different sub-time periods, the greater the probability that wind speed affects the dust concentration at the monitoring point, and the greater the probability that the dust concentration monitored by the dust concentration sensor in the sub-time period cannot reflect the actual dust concentration in the environment at the monitoring point.

[0077] By comparing the average value of the dust concentration at the monitoring point during the historical time period with the minimum value of the dust concentration during the target sub-time period, the effect of wind speed on the dust concentration at the monitoring point can be determined with reference to the dust concentration during the historical time period.

[0078] For example, when the dust concentration remains unchanged, the greater the impact of wind speed on the dust concentration monitored by the dust concentration sensor, the smaller the dust concentration obtained by the dust concentration sensor, and the greater the difference between the average value of the dust concentration at the monitoring point during the historical time period and the minimum value of the dust concentration during the target sub-time period.

[0079] In this way, by comparing the minimum value of the dust concentration in the target sub-time period with the minimum value of the dust concentration in the previous sub-time period of the target sub-time period; and comparing the average value of the dust concentration at the monitoring point in the historical time period with the minimum value of the dust concentration in the target sub-time period, the obtained difference degree value can more comprehensively reflect the probability that the dust concentration in the target sub-time period is affected by the wind speed.

[0080] In one embodiment, the fluctuation degree value is determined by: , P is the fluctuation degree value, R is the number of wind speed types in the target sub-time period, is the frequency ratio of the rth wind speed in the target sub-time period, and ln is a logarithmic function with a natural constant as the base.

[0081] The frequency ratio of wind speed in the target sub-time period is between 0 and 1. When a logarithmic function with a natural constant as the base is used to perform logarithmic operations on values ​​between 0 and 1, the obtained logarithmic operation result is a negative number. The negative sign in the calculation formula of the fluctuation degree value can ensure that the fluctuation degree value is a positive number.

[0082] The more diverse the types of wind speeds within the target sub-time period, or the more diverse the differences in the frequency ratios of different types of wind speeds, the larger the obtained fluctuation degree value. Therefore, the fluctuation degree value can better characterize the fluctuation degree of the wind speed within the target sub-time period.

[0083] In this way, the fluctuation degree value can better characterize the fluctuation degree of wind speed in the target sub-time period, so as to determine the probability that the dust concentration at the monitoring point in the target sub-time period is affected by wind speed in combination with the fluctuation degree of wind speed in the target sub-time period.

[0084] Figure 2 FIG1 is a structural diagram of a BIM-based construction management system 1000 according to an exemplary embodiment. Figure 2 The BIM-based construction management system 1000 includes: a processor 1100 and a memory 1200, wherein the memory 1200 stores computer program instructions, and when the computer program instructions are executed by the processor 1100, all or part of the steps of the BIM-based construction management method in this application are implemented.

[0085] It should be understood that the features of some embodiments of the various applications described herein may be combined with each other unless specifically stated otherwise.

[0086] Although terms such as "first," "second," and "third" may be used herein to describe various components, parts, regions, layers, or sections, these components, parts, regions, layers, or sections are not limited to these terms. Rather, these terms are used only to distinguish one component, part, region, layer, or section from another component, part, region, layer, or section. Therefore, without departing from the teachings of the examples described herein, a first component, part, region, layer, or section mentioned in the examples may also be referred to as a second component, part, region, layer, or section.

[0087] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In this description, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0088] Furthermore, the word "exemplary" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word exemplary is intended to present concepts in a concrete manner. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or."

[0089] Likewise, although the present application has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. With particular regard to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise indicated, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific functions of the described components, even if not structurally equivalent to the disclosed structures.

[0090] Additionally, while particular features of the present application may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and advantageous for any given or particular application.

[0091] Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein, and the description and examples are to be considered merely as exemplary.

[0092] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A BIM-based construction management method, characterized in that: include: Obtain dust concentration and wind speed at monitoring points at construction sites during target time periods; For a target sub-time period within a target time period, determining a distortion value of the target sub-time period; The distortion value is determined based on the correlation between dust concentration and wind speed within the target sub-time period, as well as the degree of wind speed fluctuation within the target sub-time period; The distortion value of the target sub-time period is determined as follows: , where V is the distortion value of the target sub-time period; Z is the difference value, which is used to characterize the difference between the minimum dust concentration in the target sub-time period and the minimum dust concentration in the previous sub-time period; is the fluctuation degree value, which is used to characterize the fluctuation degree of wind speed in the target sub-time period; is a correlation value used to characterize the degree of matching between the ranking information of dust concentration and the ranking information of wind speed in the target sub-time period; a is a preset positive number; The relevance value for the target sub-time period is determined by, among other things: , T is the correlation value of the target sub-time period, M is the number of moments in the target sub-time period, is the ranking of the dust concentration at the mth moment in the target sub-time period among all dust concentrations in the target sub-time period, is the ranking of the wind speed at the mth moment in the target sub-time period among all wind speeds in the target sub-time period; The difference degree value is determined as follows: , where Z is the difference value, norm is the normalization function, is the average value of dust concentration at the monitoring point during the historical period, is the minimum value of dust concentration in the target sub-time period, The minimum value of dust concentration in the sub-time period before the target sub-time period; Compare the minimum dust concentration in the target sub-time period with the minimum dust concentration in the sub-time period immediately before the target sub-time period; and compare the average dust concentration at the monitoring point in the historical time period with the minimum dust concentration in the target sub-time period. The obtained difference degree value can more comprehensively reflect the probability that the dust concentration in the target sub-time period is affected by wind speed. The volatility value is determined as follows: , P is the fluctuation degree value, R is the number of wind speed types in the target sub-time period, is the frequency ratio of the rth wind speed in the target sub-time period, and ln is a logarithmic function with a natural constant as the base; The fluctuation degree value represents the fluctuation degree of wind speed in the target sub-time period. The fluctuation degree of wind speed in the target sub-time period can better determine the probability that the dust concentration at the monitoring point in the target sub-time period is affected by wind speed; The fuzzy membership of the dust concentration in each sub-time period within the target time period is determined by using the membership function. The fuzzy membership of other sub-time periods is weighted averaged using the distortion values ​​of different sub-time periods to obtain the weighted fuzzy membership corresponding to the target sub-time period. According to the weighted fuzzy membership corresponding to different sub-time periods, the fuzzy entropy of the target time period is determined, and the abnormal factor is determined by multiplying the fuzzy entropy by the average value of the dust concentration in the target time period. When the abnormal factor is greater than the preset threshold, a BIM model created in advance for the construction site is obtained, dust prompt information is displayed in the BIM model, and the dust environment of the construction site is managed.

2. The BIM-based construction management method according to claim 1, characterized in that: Using the distortion values ​​of different sub-time periods, the fuzzy membership of other sub-time periods is weighted averaged to obtain the weighted fuzzy membership corresponding to the target sub-time period, including: The inverse of the exponential function of the distortion value of the sub-time period is used as the weight value corresponding to the sub-time period. The weight values ​​of other sub-time periods except the target sub-time period are used to perform weighted summation on the fuzzy membership of other sub-time periods except the target sub-time period to obtain the weighted fuzzy membership corresponding to the target sub-time period.

3. The BIM-based construction management method according to claim 1, characterized in that: The product of fuzzy entropy and the average value of dust concentration in the target time period is used to determine abnormal factors, including: , where G is the anomaly factor and E is the fuzzy entropy of the target time period. is the normalization function, is the average value of dust concentration during the target time period, It is the average value of dust concentration at the monitoring point during the historical period.

4. The BIM-based construction management method according to claim 1, characterized in that: Manage the dust environment at construction sites, including: Use dust suppression equipment to suppress dust at the construction site where the monitoring point is located; or restrict the operation of construction equipment at the construction site where the monitoring point is located to reduce the rate at which the construction equipment generates dust.

5. A BIM-based construction management system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the BIM-based construction management method according to any one of claims 1 to 4 is implemented.

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