BIM-based building construction management method and system

Abstract

The invention relates to the technical field of data processing, in particular to a BIM-based building construction management method and system. The method comprises the steps of obtaining dust concentration and wind speed of a monitoring point of a building construction site in a target time period; determining a distortion degree value of a target sub-time period of the target time period; determining a fuzzy membership degree of the dust concentration of the sub-time period to obtain a weighted fuzzy membership degree corresponding to the target sub-time period; according to the weighted fuzzy membership degrees corresponding to different sub-time periods, determining the fuzzy entropy of the target time period to obtain abnormal factors; and under the condition that the abnormal factor is greater than a preset threshold value, acquiring a BIM model pre-created for the building construction site, displaying dust prompt information in the BIM model, and managing the dust environment of the building construction site. According to the technical scheme, the dust environment of the construction site can be effectively managed.

Description

Technical Field

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

[0002] BIM (Building Information Modeling) is a digital design and management tool. BIM creates a three-dimensional model to simulate an actual building project. The BIM model can not only contain the geometric information of the building, but also contain the attributes, functions and other relevant information of building components. For example, through the rendering and visualization functions of BIM software, the distribution of dust concentration at different positions of the corresponding building can be displayed in the BIM three-dimensional model, so that users can more intuitively understand the dust conditions at different positions in the building.

[0003] In related technologies, for example, in the Chinese patent application document with the publication number CN117151652A, a BIM-based building construction management system is provided, including: a BIM model establishment 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 security supervision module. By collecting the basic building environment information of each monitoring sub-area in the target building construction area and synchronously transmitting the data to the building three-dimensional 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 the dust concentration, the number of construction days, the noise and the vibration intensity at the construction site.

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

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

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

[0007] In this way, the distortion value of the dust concentration at the building construction site within the target sub-time period of the target time period is determined. The distortion value is determined according to the correlation between the dust concentration and the wind speed within the target sub-time period, and the fluctuation degree 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; using the membership function to respectively determine the fuzzy membership of the dust concentration in each sub-time period within the target time period and determining the abnormal factor can exclude the influence of the wind speed on the monitoring result of the dust concentration, and the abnormal factor can better reflect the actual abnormal situation of the dust concentration; displaying the dust prompt information in the BIM model can achieve visual reminder of the dust; managing the dust environment at the building construction site can reduce the dust concentration at the construction site and ensure the physical health of the operators at the construction site.

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

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

[0010] Optionally, the correlation value of the target sub-time period is determined by the following method, including: , where T is the correlation value of the target sub-time period, M is the number of moments within the target sub-time period, is the ranking of the dust concentration at the m-th moment within the target sub-time period among all the dust concentrations within the target sub-time period, is the ranking of the wind speed at the m-th moment within the target sub-time period among all the wind speeds within the target sub-time period.

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

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

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

[0014] Optionally, the fluctuation degree value is determined by the following method: , where P is the fluctuation degree value, R is the number of types of wind speeds within the target sub-time period, is the frequency proportion of the r-th type of wind speed appearing within the target sub-time period, and ln is the logarithmic function with the natural constant as the base.

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

[0016] Optionally, using the distortion degree values of different sub-time periods to perform weighted averaging on the fuzzy membership degrees of other sub-time periods to obtain the weighted fuzzy membership degree corresponding to the target sub-time period, including: taking the reciprocal of the exponential function of the distortion degree 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 membership degrees of other sub-time periods except the target sub-time period to obtain the weighted fuzzy membership degree corresponding to the target sub-time period.

[0017] Optionally, determining the anomaly factor by using the product of the fuzzy entropy and the average value of the dust concentration within the target time period, including: , where G is the anomaly factor, E is the fuzzy entropy of the target time period, is the normalization processing function, is the average value of the dust concentration within the target time period, is the average value of the dust concentration at the monitoring point within the historical time period.

[0018] Optionally, managing the dust environment at the construction site of a building includes: performing dust suppression treatment on the construction site where the monitoring point is located by using dust suppression equipment; or restricting the operation of the construction equipment at the construction site where the monitoring point is located to reduce the speed at which the construction equipment generates dust.

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

[0020] The technical solutions 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 during the target sub-period within the target period, the distortion value is determined based on the correlation between the dust concentration and the wind speed during the target sub-period and the fluctuation degree of the wind speed during the target sub-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 respectively determine the fuzzy membership of the dust concentration in each sub-period within the target period to determine the abnormal factor can exclude the influence of the wind speed on the monitoring result of the dust concentration, and the abnormal factor can better reflect the actual abnormal situation of the dust concentration; Displaying the dust prompt information in the BIM model can achieve visual reminder of the dust; Managing the dust environment at the construction site can reduce the dust concentration at the construction site and ensure the physical health of the construction workers at the construction site.

[0021] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0023] Figure 1 is a flowchart of a BIM-based construction management method shown according to an exemplary embodiment; Figure 2 is a schematic structural diagram of a BIM-based construction management system shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0025] First, a brief introduction to the application scenario of the embodiments of the present application is given. In the application scenario of the present application, the dust concentration at the construction site can be obtained through sensors and visually displayed 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.

[0026] The dust that is blown by the wind to other places outside the dust concentration sensor still poses a threat to the physical health of the construction workers at the construction site where the sensor is located. Moreover, since the building is still under construction, it is difficult to install dust concentration sensors at all locations, making it difficult to accurately reflect the actual dust situation at the construction site based on the obtained dust concentration. Therefore, it is difficult to effectively manage the dust environment at the construction site.

[0027] To address the above technical problems, an embodiment of the present application provides a BIM-based building construction management method and system. Figure 1 It is a flowchart of a BIM-based building construction management method shown according to an exemplary embodiment, as Figure 1 shown. The method includes the following steps.

[0028] In step S101, obtain the dust concentration and wind speed at the monitoring points at the building construction site during the target time period.

[0029] A dust concentration sensor can be installed at the monitoring points at the building construction site to obtain the dust concentration at the monitoring points; by installing a wind speed sensor at the monitoring points at the building construction site, the wind speed at the monitoring points can be obtained.

[0030] Since the dust concentration monitored by the dust concentration sensor may be affected by the wind speed, obtaining the dust concentration and wind speed at the same monitoring point at the building construction site helps to avoid the influence of the wind speed on the dust concentration, thereby obtaining a dust concentration that can better reflect the actual dust concentration at the monitoring point.

[0031] In step S102, for the target sub-time period within the target time period, determine the distortion value of the target sub-time period.

[0032] 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; the distortion value is used to characterize the degree to which the dust concentration at the monitoring point is affected by the wind speed within the target sub-time period.

[0033] 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 duration in chronological order, and the duration of each sub-time period is equal to 20 seconds. The target sub-time period can be any one of these 30 sub-time periods; the duration of the target time period and the duration of the sub-time periods of the target time period can be adaptively set according to actual needs.

[0034] The greater the distortion value of the target sub - time period, the greater the degree to which the dust concentration at the monitoring point is affected by the wind speed within the target sub - time period; conversely, the smaller the distortion value of the target sub - time period, the smaller the degree to which the dust concentration at the monitoring point is affected by the wind speed within the target sub - time period.

[0035] When the dust concentration at the monitoring point is affected by the wind speed, the amount by which the dust concentration at the monitoring point is affected by the wind speed is related to the wind speed at the monitoring point; in the case where the regulatory personnel do not process the dust environment at the monitoring point, there is a certain correlation between the dust concentration at the monitoring point and the wind speed at the monitoring point.

[0036] In the case where the regulatory personnel process 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 degree 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 processed.

[0037] In one embodiment, the distortion value of the target sub - time period is determined in the following manner: , where V is the distortion value of the target sub - time period; Z is the degree - of - difference value, used to characterize the degree of difference between the minimum dust concentration within the target sub - time period and the minimum dust concentration within the previous sub - time period; is the degree - of - fluctuation value, used to characterize the degree of fluctuation of the wind speed within the target sub - time period; is the correlation value, used to characterize the degree of matching between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period; a is a preset positive number.

[0038] The degree - of - difference value reflects the change in the dust concentration of 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 amount of the minimum value of the dust concentration is large, it indicates that the dust concentration has changed significantly.

[0039] The degree - of - fluctuation value is used to characterize the degree of fluctuation of the wind speed within the target sub - time period; since the 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 is affected by the wind speed.

[0040] The correlation value is used to characterize the degree of matching between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period. The greater the correlation value within the target sub - time period, the greater the degree of matching between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period.

[0041] Since the wind speed mainly causes a decrease in the 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 the 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, resulting in a low matching degree between 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. Therefore, the matching degree between 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 can reflect the probability that the dust concentration in the target sub-time period is affected by the wind speed.

[0042] Among them, the greater the similarity value of the target sub-time period, the higher the matching degree between 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, 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 matching degree between 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, and the higher the probability that the dust concentration in the target sub-time period is affected by the wind speed.

[0043] In this way, the distortion value is determined according to the matching degree between the ranking information of the dust concentration in the target sub-time period and the ranking information of the wind speed, and the difference degree 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 represent the probability that the dust concentration is affected by the wind speed in the target sub-time period.

[0044] a is a preset positive number, and 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 distortion value calculation formula is 0 when the value of the correlation value is 0, ensuring the effective progress of the distortion value calculation process.

[0045] In one embodiment, the correlation value of the target sub-time period is determined in the following manner, including: , where 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 m-th moment in the target sub-time period among all the dust concentrations in the target sub-time period, is the ranking of the wind speed at the m-th moment in the target sub-time period among all the wind speeds in the target sub-time period.

[0046] 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.

[0047] For example, for a target sub - time period containing 10 moments, if the wind speed ranks first at a certain moment within the target sub - time period, and if the dust concentration corresponding to the wind speed ranked first within the same sub - time period ranks 10th, the matching degree between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period is low, and there is a high probability that the dust concentration within the target sub - time period is affected by the wind speed.

[0048] Another example, for a target sub - time period containing 10 moments, if the wind speed ranks first at a certain moment within the target sub - time period, and if the dust concentration corresponding to the wind speed ranked first within the same sub - time period ranks 5th, the matching degree between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period is high. 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 of the environment where the monitoring point is located.

[0049] In this way, compare the ranking of the dust concentration of the moments within the target sub - time period among all the dust concentrations within the target sub - time period with the ranking of the wind speed of the moments within the target sub - time period among all the wind speeds within the target sub - time period, so as to determine the correlation value used to characterize the matching degree between the ranking information of the dust concentration and the ranking information of the wind speed within the target sub - time period. The correlation value can better reflect the probability that the dust concentration at the monitoring point is affected by the wind speed within the target sub - time period.

[0050] In step S103, use the membership function to respectively determine the fuzzy membership degree of the dust concentration of each sub - time period within the target time period, and the distortion degree values of different sub - time periods, and perform weighted averaging on the fuzzy membership degrees of other sub - time periods to obtain the weighted fuzzy membership degree corresponding to the target sub - time period.

[0051] The membership function can be used to respectively determine the fuzzy membership degree of the dust concentration of each sub - time period within the target time period. The fuzzy membership degree can be used to characterize the probability of dust concentration exceeding the standard. The fuzzy membership degree of the dust concentration of each sub - time period within the target time period is positively correlated with the dust concentration of the sub - time period. For example, the membership function can be a piece - wise function or an exponential function, etc. The embodiments of the present application do not limit the specific membership function.

[0052] The greater the dust concentration in a sub - time period within the target time period, the greater the fuzzy membership degree corresponding to the dust concentration in the sub - time period; conversely, the smaller the dust concentration in a sub - time period within the target time period, the smaller the fuzzy membership degree corresponding to the dust concentration in the sub - time period.

[0053] For example, using the distortion degree values of different sub - time periods, the weighted average of the fuzzy membership degrees of other sub - time periods is obtained to get the weighted fuzzy membership degree corresponding to the target sub - time period, including: taking the reciprocal of the exponential function of the distortion degree value of a 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 membership degrees of other sub - time periods except the target sub - time period to obtain the weighted fuzzy membership degree corresponding to the target sub - time period.

[0054] Based on determining the weight value and the variables to be weighted and summed, the result of weighted summation can be obtained; for example, the process of using the distortion degree values of different sub - time periods as weights to weight the fuzzy membership degrees of other sub - time periods includes: , where is the weighted fuzzy membership degree corresponding to the target sub - time period, S is the number of other sub - time periods in the target time period except the target sub - time period, exp is the exponential function with the natural constant as the base, is the distortion degree value of the a - th other sub - time period in the target time period except the target sub - time period, is the fuzzy membership degree of the a - th other sub - time period in the target time period except the target sub - time period.

[0055] The dust concentration value in a sub - time period with a smaller distortion degree value can better match the actual dust concentration of the monitoring point. By processing the distortion degree value with the reciprocal of the exponential function, a larger weight can be set for the sub - time period with a smaller distortion degree value, improving the contribution of the dust concentration in the sub - time period that better matches the actual dust concentration of the monitoring point to the weighted average process.

[0056] In step S104, according to the weighted fuzzy membership degrees corresponding to different sub - time periods, the fuzzy entropy of the target time period is determined, and the product of the fuzzy entropy and the average value of the dust concentration in the target time period is used to determine the anomaly factor.

[0057] The content of determining the fuzzy entropy of the target time period according to the weighted fuzzy membership degrees corresponding to different sub - time periods in the target time period will not be elaborated in this embodiment of the present application; the fuzzy entropy of the target time period can characterize the complexity of the weighted fuzzy membership degrees of different sub - time periods.

[0058] For a certain time period in which the average dust concentration is greater than the historical average dust concentration, the greater the fuzzy entropy of the dust concentration within the time period, the greater the credibility that the dust concentration at the monitoring point exceeds the standard within the time period, and the more necessary it is to treat the dust environment at the monitoring point.

[0059] In one embodiment, the abnormal factor is determined by using the product of the fuzzy entropy and the average value of the dust concentration within the target time period, including: , where G is the abnormal factor, E is the fuzzy entropy of the target time period, is the normalization processing function, is the average value of the dust concentration within the target time period, is the average value of the dust concentration at the monitoring point within the historical time period.

[0060] The fuzzy entropy of the target time period is obtained based on the weighted fuzzy membership degrees of different sub-time periods. The weighted fuzzy membership degree is obtained by weighted averaging the distortion degree values of the sub-time periods for the fuzzy membership degrees of the sub-time periods. The weighted fuzzy membership degree can reflect the level of the dust concentration at the monitoring point within the sub-time period while excluding the interference of wind speed on the dust concentration. Therefore, the fuzzy entropy can reflect the credibility of the dust concentration exceeding the standard within the target time period while excluding the interference of wind speed on the dust concentration.

[0061] In this way, by considering the fuzzy entropy of the target time period and comparing the average value of the dust concentration within the target time period with the average value of the dust concentration at the monitoring point within the historical time period, the abnormal factor can better reflect the situation of the dust concentration exceeding the standard, so as to manage the dust environment at the monitoring points where the dust concentration exceeds the standard.

[0062] In step S105, when the abnormal factor is greater than the preset threshold, obtain the BIM model pre-created for the construction site, display the dust prompt information in the BIM model, and manage the dust environment at the construction site.

[0063] The dust prompt information is used to prompt that the dust concentration at the monitoring point location exceeds the standard. For example, the dust prompt information can be displayed in eye-catching colors such as red, orange, or orange in the BIM model. For example, the dust prompt information can be a text prompt information with the content "The dust concentration at the monitoring point exceeds the standard, please deal with it in time".

[0064] Through the pre-created BIM model, users can more intuitively know the information of different positions at the construction site. For example, in addition to displaying the engineering structure of the construction site, the BIM model can also display the positions of construction workers at the construction site and the dust concentration information of the monitoring points.

[0065] When the abnormal factor is greater than the preset threshold, it indicates that the dust concentration at the monitoring point exceeds the standard. Obtaining the pre-created BIM model for the construction site and displaying dust prompt information in the BIM model helps to deploy the personnel near the monitoring point. For example, evacuating the personnel near the monitoring point to other locations where the dust concentration does not exceed the standard for operation; or, prompting the personnel operating near the monitoring point or the personnel about to go to the monitoring point for operation to wear dust protection masks.

[0066] When the abnormal factor is greater than the preset threshold and the dust environment at the construction site is managed, the dust concentration in the environment where the monitoring point is located can be reduced, avoiding the threat posed by the high dust concentration to the physical health of the operating personnel near the monitoring point.

[0067] Through the BIM-based construction management method provided by the embodiments of the present application, the distortion value of the dust concentration at the construction site during the target sub-period within the target period is determined. The distortion value is determined according to the correlation between the dust concentration and the wind speed during the target sub-period and the fluctuation degree of the wind speed during the target sub-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 degree of the dust concentration in each sub-period within the target period is determined by using the membership function to determine the abnormal factor, which can eliminate the influence of the wind speed on the monitoring result of the dust concentration, and the abnormal factor can better reflect the actual abnormal situation of the dust concentration; displaying the dust prompt information in the BIM model can realize the visual reminder of the dust; managing the dust environment at the construction site can reduce the dust concentration at the construction site and ensure the physical health of the operating personnel at the construction site.

[0068] In one embodiment, managing the dust environment at the construction site includes: using dust suppression equipment to perform dust suppression treatment on the construction site where the monitoring point is located; or, restricting the operation of the construction equipment at the construction site where the monitoring point is located to reduce the speed of dust generation by the construction equipment.

[0069] The dust suppression equipment can be provided with fixed or movable nozzles for spraying water mist or chemical dust suppressants to reduce the dust concentration at the monitoring point; or, the dust suppression equipment can absorb air and use a filter component to filter the dust to achieve the suppression of the dust concentration at the monitoring point. The embodiments of the present application do not limit the specific type of the dust suppression equipment.

[0070] The construction equipment at the construction site will increase the dust concentration in the air during operation. For example, a cutting bed for cutting wooden templates or equipment for cement mixing. By restricting the operation of the construction equipment at the construction site where the monitoring point is located, the speed of dust generation by the construction equipment can be effectively reduced, ensuring the safety of the operating personnel at the construction site.

[0071] 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, and the threat posed by excessive dust to the physical health of the workers at the construction site can be avoided.

[0072] In one embodiment, the degree-of-difference value is determined in the following manner: , where Z is the degree-of-difference value, norm is the normalization function, is the average value of the dust concentration at the monitoring point during the historical time period, is the minimum value of the dust concentration during the target sub-time period, is the minimum value of the dust concentration during the sub-time period immediately preceding the target sub-time period.

[0073] By comparing the minimum value of the dust concentration during the target sub-time period with the minimum value of the dust concentration during the sub-time period immediately preceding the target sub-time period, the change amount of the dust concentration in the target time period relative to the previous time period can be understood.

[0074] Here, the objects of comparison are the minimum values of the dust concentration in different sub-time periods. The minimum value of the dust concentration within the same sub-time period is more likely to be generated under the action of the wind speed. Therefore, by comparing the change amount of the minimum value of the dust concentration during the target sub-time period relative to the minimum value of the dust concentration during the sub-time period immediately preceding it, it is easier to discover the different influences of the wind speed on the dust concentration in different sub-time periods.

[0075] The greater the difference between the minimum values of the dust concentration in different sub-time periods, the greater the probability that the 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 within the sub-time period cannot reflect the actual dust concentration of the environment where the monitoring point is located.

[0076] 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 influence of the wind speed on the dust concentration at the monitoring point can be determined with reference to the dust concentration during the historical time period.

[0077] For example, in the case where the dust concentration remains unchanged, the greater the influence of the 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.

[0078] In this way, by comparing the minimum value of the dust concentration within the target sub-time period with the minimum value of the dust concentration within the previous sub-time period of the target sub-time period; and by comparing the average value of the dust concentration at the monitoring point within the historical time period with the minimum value of the dust concentration within the target sub-time period, the obtained difference degree value can more comprehensively reflect the probability that the dust concentration within the target sub-time period is affected by the wind speed.

[0079] In one embodiment, the fluctuation degree value is determined in the following manner: , where P is the fluctuation degree value, R is the number of types of wind speed within the target sub-time period, is the frequency proportion of the r-th type of wind speed that appears within the target sub-time period, and ln is the logarithmic function with the natural constant as the base.

[0080] The frequency proportion of the wind speed that appears within the target sub-time period is between 0 and 1. When performing logarithmic operations on the values between 0 and 1 using the logarithmic function with the natural constant as the base, the obtained logarithmic operation results are negative values. Through the negative sign in the calculation formula of the fluctuation degree value, it can be ensured that the value of the fluctuation degree value is positive.

[0081] The more diverse the types of wind speed within the target sub-time period, or the more diverse the differences between the frequency proportions of different types of wind speed that appear, the larger the value of 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.

[0082] In this way, the fluctuation degree value can better characterize the fluctuation degree of the wind speed within the target sub-time period, so as to determine the probability that the dust concentration at the monitoring point within the target sub-time period is affected by the wind speed in combination with the fluctuation degree of the wind speed within the target sub-time period.

[0083] Figure 2 FIG. 20 is a schematic structural diagram of a BIM-based building construction management system 1000 shown according to an exemplary embodiment. Refer to Figure 2 , the BIM-based building construction management system 1000 includes: a processor 1100 and a memory 1200. The memory 1200 stores computer program instructions, and when the computer program instructions are executed by the processor 1100, all steps or part of the steps of the BIM-based building construction management method in the present application are implemented.

[0084] It should be understood that unless otherwise specifically indicated, the features of various embodiments of the present application described herein can be combined with each other.

[0085] 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. Instead, these terms are only used to distinguish one component, part, region, layer, or section from another. Thus, the first component, part, region, layer, or section referred to in the examples described herein may also be referred to as the second component, part, region, layer, or section without departing from the teachings of the various examples.

[0086] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description herein, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0087] 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 being advantageous as compared to other aspects or designs. Instead, 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".

[0088] 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 the specification and drawings. In particular with respect to the various functions performed by the above-described components (e.g., elements, resources, etc.), unless otherwise noted, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if not structurally equivalent to the disclosed structure.

[0089] In addition, although a particular feature of the present application may have been disclosed with respect to only one of several implementations, such a feature may, as may be desired and advantageous for any given or particular application, be combined with one or more other features of other implementations.

[0090] Other embodiments of the present application will be readily apparent to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed herein. The specification and examples are to be considered exemplary only.

[0091] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

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 in the target sub-time period, as well as the degree of fluctuation of wind speed in the target sub-time period; The fuzzy membership of the dust concentration in each sub-time period within the target time period is determined by using the membership function, and the fuzzy membership of other sub-time periods is weighted averaged by 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 a 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: The distortion value of the target sub-time period is determined in the following way: , where V is the distortion value of the target sub-time period; Z is the difference degree 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, which is used to characterize the matching degree 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.

3. The BIM-based construction management method according to claim 2 is characterized in that: 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, It 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.

4. The BIM-based construction management method according to claim 2 is characterized in that: The difference degree value is determined in the following manner: , 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.

5. The BIM-based construction management method according to claim 2, characterized in that: The fluctuation degree value is determined in the following manner: , 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.

6. The BIM-based construction management method according to claim 1, characterized in that: 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: 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 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.

7. The BIM-based construction management method according to claim 1, characterized in that: The abnormal factors are determined by multiplying the fuzzy entropy with the average value of the dust concentration in the target time period, including: , where G is the abnormal factor, 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.

8. 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 speed at which the construction equipment generates dust.

9. 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 8 is implemented.

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