A method for managing construction progress information of building projects based on BIM

Abstract

The present invention discloses a construction progress information management method for a building project based on BIM. The present invention relates to the technical field of construction progress management and solves the problem of not adopting an intelligent monitoring and processing method to confirm the construction progress in real time. The present invention processes the construction images surveyed by a high-altitude unmanned aerial vehicle in real time, identifies gradient pixels by confirming the gradient features of the pixels, and then determines the gradient contour line and the related feature difference. The analysis method based on the gradient feature can keenly capture the detail changes in the construction image, and accurately judge the current construction position and progress in combination with the previously determined image features, thereby greatly improving the accuracy of construction progress monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of construction progress management, and in particular to a method for managing construction progress information of a building project based on BIM. Background Art

[0002] The construction engineering model is an engineering data model based on three-dimensional digital technology, which integrates various relevant information of the construction engineering project. It not only contains the geometric information of the building, such as shape, size, location, etc., but also covers a large amount of non-geometric information, such as material properties, cost information, schedule, equipment parameters, maintenance requirements, etc.; in simple terms, the BIM model is a full-scale digital expression of the construction engineering project, which can provide rich data support for all stages of the project's life cycle.

[0003] After the BIM building construction model is completed, the construction data of different construction sections are displayed in real time based on the preset progress parameters. The application with patent publication number CN107818430A discloses a BIM-based construction project construction progress information management method. The specific steps of the BIM-based construction project construction progress information management method are as follows: obtaining information; system modeling; real-time monitoring; construction period prediction; data synchronization. A BIM-based construction project construction progress information management system, the BIM-based construction project construction progress information management system includes: a data processing center, a positioning system, an image acquisition module, a material usage detection unit, an alarm device, a modeling system and a communication module. The BIM-based construction project construction progress information management system can convert the real-time monitoring information into data indications in the modeling, so as to vividly display the construction process. By monitoring the construction project, it can timely remind the project operation and give instructions to the construction project.

[0004] In the process of progress management, the BIM construction model is generally used to assess whether its progress is abnormal based on the output progress of the corresponding construction model and the specific progress actually reported. For construction sections with abnormalities, personnel deployment is required to assess whether its construction progress is falsely reported. However, this type of processing method consumes a lot of manpower and does not adopt an intelligent monitoring and processing method to confirm the construction progress in real time, so as to fully reduce manpower loss and reduce the workload of monitoring personnel. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a construction progress information management method for building projects based on BIM, which solves the problem of not adopting an intelligent monitoring and processing method to confirm the construction progress in real time.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for managing construction progress information of a building project based on BIM, comprising the following steps:

[0007] Step 1: Perform a preliminary check on the progress data information displayed by the BIM building construction model and the progress data information reported by relevant personnel during the actual construction process, identify and confirm whether the reported progress data information is too different, and calibrate the construction sections to be processed. The specific sub-steps are:

[0008] The real-time progress data of different construction sections in the BIM construction model are calibrated as JD i , where i represents different construction sections;

[0009] The progress data reported in real time for the corresponding construction section is calibrated as HD i , identify JD i with HD i Satisfied: |JD i -HD i |≤Y1, where Y1 is a preset value, representing the standard value of the difference between progress. If it is satisfied, no processing will be performed. If it is not satisfied, this construction section will be marked as a construction section to be processed;

[0010] Step 2: Based on the marked pending construction section, confirm the past progress data of the pending construction section from the reported progress data information, and based on the change trend of such past progress data, identify whether the progress data information reported at the current moment is normal. If normal, mark the pending construction section as a normal construction section; if abnormal, mark the pending construction section as an abnormal construction section. The specific method is as follows:

[0011] S21. Based on the identified pending construction section, confirm the past progress data reported by the pending construction section, confirm different progress data associated with different moments, sort the groups of progress data according to a chronological order, and generate a progress data change curve for the pending construction section based on the sorted progress data, where the horizontal axis of the curve is the timeline and the vertical axis is the progress data.

[0012] S22. Based on the confirmed progress data change curve, confirm the change trend between adjacent data: confirm the different progress data associated with adjacent nodes, use the change trend = progress data of the next node - progress data of the previous node, and select the maximum trend and the minimum trend from the confirmed groups of change trends;

[0013] S23. Mark the progress data of the end node of the progress data change curve as JM, identify the time length associated with the end node from the current moment and mark it as Sc, and use: Jsmax = JM + (Sc × maximum trend) and Jsmin = JM + (Sc × minimum trend) to predict the progress data interval [Jsmin, Jsmax] associated with the current moment;

[0014] S24. Identify the progress data HD reported by the current construction section i Whether it meets: HD i ∈[Jsmin, Jsmax], if it is satisfied, the construction section to be processed will be marked as a normal construction section and the monitoring process will be re-performed; if HD i Not satisfied with HD i ∈[Jsmin, Jsmax], the marked construction section to be processed is marked as an abnormal construction section;

[0015] Step 3: For the marked abnormal construction section, use a high-altitude drone to survey the construction image of this abnormal construction section. From the construction images collected in the past, confirm the image features between the constructed area and the unconstructed area in the construction image. Then, based on the determined image features, perform real-time processing on the survey construction image of the high-altitude drone to assess the specific construction distance of the current abnormal construction section.

[0016] Preferably, in step one, the BIM building construction model is a preset model, and there are different unit construction progresses for different construction sections in the BIM building construction model. The unit construction progress is a preset progress, which is the construction progress completed in each group of unit time. The BIM building construction model displays the construction progress data of different construction sections in real time based on the preset unit construction progress.

[0017] Preferably, in step 3, the specific method of confirming the image features between the constructed area and the unconstructed area from the construction image is:

[0018] Identifying, from the determined construction image, an image boundary between a constructed area and an unconstructed area within the construction image, wherein the image boundary has been pre-calibrated within the construction image by an operator;

[0019] Identifying different pixel values ​​associated with different pixel points on both sides of an image boundary, confirming several groups of pixel values ​​associated with the pixel points on one side of the image boundary, selecting a minimum pixel value and a maximum pixel value from the confirmed several groups of pixel values, and using the selected minimum pixel value and maximum pixel value as a first pixel feature;

[0020] Then, multiple groups of pixel values ​​associated with the pixel point on the other side of the image boundary are simultaneously confirmed, and a minimum pixel value and a maximum pixel value are selected from the confirmed multiple groups of pixel values, and the selected minimum pixel value and maximum pixel value are used as the second pixel feature;

[0021] From the determined first pixel features and second pixel features: the minimum pixel value in the first pixel feature is calibrated as T1min, the maximum pixel value is calibrated as T1max, the minimum pixel value in the second pixel feature is calibrated as T2min, and the maximum pixel value is calibrated as T2max, and four sets of difference values ​​are confirmed using: |T1max-T2max|, |T1min-T2min|, |T1min-T2max|, and |T1max-T2min|, and the minimum and maximum values ​​are selected from the four sets of difference values ​​to determine the image features between the constructed area and the unconstructed area.

[0022] Preferably, in step 3, based on the confirmed image features, the specific method of performing real-time processing on the survey and construction image of the high-altitude UAV is:

[0023] Based on the identified abnormal construction section, the starting construction point and the end construction point of the abnormal construction section are confirmed. The starting construction point is used as the initial takeoff point, the end construction point is used as the takeoff end point, and the specific section of the abnormal construction section is used as the flight section to confirm the flight path of the high-altitude UAV.

[0024] The high-altitude UAV is made to fly at high altitude according to the confirmed flight route, and during the flight, the construction images of the abnormal construction section are surveyed and collected in real time, and the construction images collected in real time are processed:

[0025] From the collected construction image, the gradient characteristics of the internal pixels are confirmed. Based on the gradient characteristics, it is determined whether the pixel is a gradient pixel. The pixel value of the corresponding pixel is calibrated as Xs. Then the pixel values ​​of the adjacent pixels around the pixel are confirmed and calibrated as H1-H8. Gx is used. k =(-1)×H1+0×H2+1×H3+(-2)×H4+0×Xs+2×H5+(-1)×H6+0×H7+1×H8;

[0026] Among them Gy k =(-1)×H1+(-2)×H2+(-1)×H3+0×H4+0×Xs+0×H5+1×H6+2×H7+1×H8;

[0027] Re-adopt Determine the gradient feature G associated with this pixel k , where k represents different pixels;

[0028] If Gk ≤Y2, no calibration is performed, where Y2 is the preset value. If G k >Y2, then this pixel is marked as a gradient pixel;

[0029] Identify gradient pixel points that appear continuously in the construction image, and confirm a gradient contour line based on the continuously appearing gradient pixel points, process pixel values ​​associated with pixel points associated with both sides of the gradient contour line, average several groups of pixel values ​​associated with pixel points on one side of the gradient contour line to confirm a first associated feature, average several groups of pixel values ​​associated with pixel points on the other side of the gradient contour line to confirm a second associated feature, and use: |first associated feature - second associated feature| = feature difference to confirm a feature difference associated with the gradient contour line;

[0030] If the feature difference ∈ image feature, the high-altitude position of the high-altitude UAV is directly locked, and then the construction position is determined. The actual construction progress is locked based on the distance between the construction position and the starting construction point, and the locked actual construction progress is displayed; if the feature difference Image features, the gradient pixel points of subsequent construction images are continuously confirmed until the actual construction progress is determined.

[0031] The present invention provides a method for managing construction project progress information based on BIM. Compared with the existing technology, it has the following advantages:

[0032] The present invention analyzes the past progress data of the construction section to be processed and confirms whether the progress data reported at the current moment meets the standards. It can effectively confirm whether the construction progress of the corresponding construction section to be processed is normal and assess whether there are any abnormalities in its construction progress. This assessment method can effectively ensure the specific accuracy of its assessment and improve the overall effect of its progress assessment.

[0033] Identify the image features between constructed and unconstructed areas in construction images. This pixel-value-based analysis method fully considers the differences between pixels on both sides of the image boundary. By calculating multiple sets of differences and selecting the maximum value to determine features, it can accurately capture the essential differences between constructed and unconstructed areas in the image, providing a reliable basis for subsequent accurate judgment of construction progress.

[0034] When performing real-time processing of construction images surveyed by high-altitude drones, gradient pixels are identified by confirming the gradient features of the pixels, and then the gradient contour lines and related feature differences are determined. This gradient feature-based analysis method can keenly capture detailed changes in construction images, and combined with previously determined image features, it can accurately judge the current construction position and progress, greatly improving the accuracy of construction progress monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the process of the present invention;

[0036] Figure 2 This is a schematic diagram of the assessment of the construction section to be processed according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] See also Figure 1 , this application provides a construction project construction progress information management method based on BIM, including the following steps:

[0039] Step 1: Perform a preliminary check on the progress data information displayed by the BIM construction model and the progress data information reported by relevant personnel during the actual construction process, identify and confirm whether the reported progress data information is too different, and calibrate the construction sections to be processed. Among them, the BIM construction model is a preset model, which is prepared in advance by relevant personnel, and there are different unit construction progresses for different construction sections in the BIM construction model. The unit construction progress is the preset progress, which is the construction progress completed in each unit time. The BIM construction model displays the construction progress data of different construction sections in real time based on the preset unit construction progress. The specific sub-steps for identifying whether the progress data information is too different are as follows:

[0040] The real-time progress data of different construction sections in the BIM construction model are calibrated as JD i , where i represents different construction sections;

[0041] The progress data reported in real time for the corresponding construction section is calibrated as HD i (Because there are cases of false reporting, it is necessary to conduct an information assessment of the reported progress data to confirm whether there are any abnormalities in the construction section);

[0042] Identify JD i with HD i Satisfied: |JD i -HD i |≤Y1, where Y1 is a preset value. Its specific value is determined by the operator based on experience and represents the standard value of the difference between progress. If it meets the standard, no processing is performed. If it does not meet the standard, the construction section is marked as a construction section to be processed.

[0043] Specifically, the pending construction section is the one where the progress data of the corresponding construction section has related anomalies, and the deviation is large from the progress data associated with the model. Either the progress is slow and there are construction delays, or the progress is too fast. Compared with the construction model, there may be progress problems. The progress that is too fast may be a false report of progress. Relevant confirmation is also required to identify whether there are real anomalies in this pending construction section.

[0044] Step 2: Based on the marked pending construction section, confirm the past progress data of the pending construction section from the reported progress data information, and based on the change trend of such past progress data, identify whether the progress data information reported at the current moment is normal. If normal, mark the pending construction section as a normal construction section; if abnormal, mark the pending construction section as an abnormal construction section. The specific method of marking is as follows:

[0045] S21. Based on the identified pending construction section, confirm the past progress data reported for the pending construction section (excluding the currently reported progress data HD i ), and confirm the different progress data associated with different moments, sort the groups of progress data according to the chronological order, and generate a progress data change curve belonging to the construction section to be processed based on the sorted progress data, with the horizontal axis of the curve being the timeline and the vertical axis being the progress data;

[0046] S22. Based on the confirmed progress data change curve, confirm the change trend between adjacent data: confirm the different progress data associated with adjacent nodes, use the change trend = progress data of the next node - progress data of the previous node, and select the maximum trend and the minimum trend from the confirmed groups of change trends;

[0047] S23. Mark the progress data of the end node of the progress data change curve as JM, identify the time length associated with the end node from the current moment and mark it as Sc, and use: Jsmax = JM + (Sc × maximum trend) and Jsmin = JM + (Sc × minimum trend) to predict the progress data interval [Jsmin, Jsmax] associated with the current moment;

[0048] S24. Identify the progress data HD reported by the current construction section i Whether it meets: HD i ∈[Jsmin, Jsmax], if it is satisfied, the construction section to be processed is marked as a normal construction section and the monitoring process is re-performed; if it is not satisfied, the construction section to be processed is marked as an abnormal construction section;

[0049] Specifically, the abnormal construction section belongs to the related construction section with abnormal progress reports. First of all, this type of construction section has a large difference from the progress data output by the BIM building model, and the change trend of this type of construction section in this stage has not appeared in the previous time period. That is, this type of construction section may be in a faster construction progress or a slower construction progress in the current stage, resulting in a large abnormality in this construction section in the current stage. Therefore, the current construction progress of this type of construction section is in an abnormal state, so the construction progress needs to be recalibrated, and then the drone will be dispatched to confirm whether there is any false progress reporting in the current construction section.

[0050] Step 3: For the identified abnormal construction section, a high-altitude drone is used to survey the construction image of this abnormal construction section. The image features between the constructed and unconstructed areas in the construction image are identified from the construction images collected in the past. Based on the determined image features, the high-altitude drone survey construction image is processed in real time to assess the specific construction distance of the current abnormal construction section, thereby determining the specific construction progress of this abnormal construction section.

[0051] The specific method for identifying the image features between the constructed area and the unconstructed area from the construction image is as follows:

[0052] Identifying, from the determined construction image, an image boundary between a constructed area and an unconstructed area within the construction image, wherein the image boundary has been pre-calibrated within the construction image by an operator;

[0053] Identifying different pixel values ​​associated with different pixel points on both sides of an image boundary, confirming several groups of pixel values ​​associated with the pixel points on one side of the image boundary, selecting a minimum pixel value and a maximum pixel value from the confirmed several groups of pixel values, and using the selected minimum pixel value and maximum pixel value as a first pixel feature;

[0054] Then, multiple groups of pixel values ​​associated with the pixel point on the other side of the image boundary are simultaneously confirmed, and a minimum pixel value and a maximum pixel value are selected from the confirmed multiple groups of pixel values, and the selected minimum pixel value and maximum pixel value are used as the second pixel feature;

[0055] From the determined first pixel feature and the second pixel feature: the minimum pixel value in the first pixel feature is calibrated as T1min, the maximum pixel value is calibrated as T1max, the minimum pixel value in the second pixel feature is calibrated as T2min, and the maximum pixel value is calibrated as T2max, four sets of difference values ​​are determined using: |T1max-T2max|, |T1min-T2min|, |T1min-T2max|, and |T1max-T2min|, and the minimum and maximum values ​​are selected from the four sets of difference values ​​to determine the image features between the constructed area and the unconstructed area;

[0056] Specifically, there are significant differences between the constructed and unconstructed areas, which can be clearly seen in the image. The pixel differences between their boundaries are also quite obvious. Therefore, based on the specific differences between the corresponding pixels, the corresponding construction discontinuity nodes can be identified, and the construction progress can be confirmed.

[0057] Based on the confirmed image features, the specific method for real-time processing of the survey and construction images of high-altitude drones is as follows:

[0058] Based on the identified abnormal construction section, the starting construction point and the end construction point of the abnormal construction section are confirmed. The starting construction point is used as the initial takeoff point, the end construction point is used as the takeoff end point, and the specific section of the abnormal construction section is used as the flight section to confirm the flight path of the high-altitude UAV.

[0059] The high-altitude UAV is made to fly at high altitude according to the confirmed flight route, and during the flight, the construction images of the abnormal construction section are surveyed and collected in real time, and the construction images collected in real time are processed:

[0060] From the collected construction image, the gradient characteristics of the internal pixels are confirmed. Based on the gradient characteristics, it is determined whether the pixel is a gradient pixel. The pixel value of the corresponding pixel is calibrated as Xs. Then the pixel values ​​of the adjacent pixels around the pixel are confirmed and calibrated as H1-H8. Gx is used. k =(-1)×H1+0×H2+1×H3+(-2)×H4+0×Xs+2×H5+(-1)×H6+0×H7+1×H8;

[0061] Among them Gy k =(-1)×H1+(-2)×H2+(-1)×H3+0×H4+0×Xs+0×H5+1×H6+2×H7+1×H8;

[0062] Re-adopt Determine the gradient feature G associated with this pixel k , where k represents different pixels;

[0063] If G k ≤Y2, no calibration is performed, where Y2 is a preset value, and its specific value is determined by the operator based on experience. k >Y2, then this pixel is marked as a gradient pixel;

[0064] Identify gradient pixel points that appear continuously in the construction image, and confirm a gradient contour line based on the continuously appearing gradient pixel points, process pixel values ​​associated with pixel points associated with both sides of the gradient contour line, average several groups of pixel values ​​associated with pixel points on one side of the gradient contour line to confirm a first associated feature, average several groups of pixel values ​​associated with pixel points on the other side of the gradient contour line to confirm a second associated feature, and use: |first associated feature - second associated feature| = feature difference to confirm a feature difference associated with the gradient contour line;

[0065] If the feature difference ∈ image feature, the high-altitude position of the high-altitude drone is directly locked, and then the construction position is determined. The actual construction progress is locked based on the distance between the construction position and the starting construction point, and the locked actual construction progress is displayed for external relevant personnel to view;

[0066] If the characteristic difference Image features, the gradient pixel points of subsequent construction images are continuously confirmed until the actual construction progress is determined.

[0067] Detailed steps are taken to identify the image features between constructed and unconstructed areas from construction images. This pixel-value-based analysis method fully considers the differences between pixels on both sides of the image boundary. By calculating multiple sets of difference values ​​and selecting the maximum value to determine the features, it can accurately capture the essential differences between constructed and unconstructed areas in the image, providing a reliable basis for subsequent accurate judgment of construction progress.

[0068] Real-time processing of survey images: When processing construction images captured by high-altitude drones in real time, the system identifies gradient pixels by confirming their gradient characteristics, and then determines gradient contours and related feature differences. This gradient-based analysis method can keenly capture detailed changes in construction images. Combined with previously determined image features, it accurately determines the current construction location and progress, significantly improving the accuracy of construction progress monitoring.

[0069] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0070] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for managing construction progress information of a building project based on BIM, characterized in that: The following steps are involved: Step 1: Perform a preliminary check on the progress data displayed by the BIM construction model and the progress data reported by relevant personnel during the actual construction process, identify and confirm whether the reported progress data is too different, and calibrate the construction sections to be processed; Step 2: Based on the marked pending construction section, confirm the past progress data of the pending construction section from the reported progress data information, and based on the change trend of such past progress data, identify whether the progress data information reported at the current moment is normal. If normal, mark the pending construction section as a normal construction section; if abnormal, mark the pending construction section as an abnormal construction section; Step 3: For the identified abnormal construction section, a high-altitude drone is used to survey the construction image of this abnormal construction section. The image features between the constructed and unconstructed areas in the construction image are identified from the previously collected construction images. Based on the determined image features, the high-altitude drone survey construction image is processed in real time to assess the specific construction distance of the current abnormal construction section. The specific method for identifying the image features between the constructed area and the unconstructed area from the construction image is as follows: identifying, from the determined construction image, an image boundary between a constructed area and an unconstructed area within the construction image, wherein the image boundary has been pre-marked within the construction image; Identifying different pixel values ​​associated with different pixel points on both sides of an image boundary, confirming several groups of pixel values ​​associated with the pixel points on one side of the image boundary, selecting a minimum pixel value and a maximum pixel value from the confirmed several groups of pixel values, and using the selected minimum pixel value and maximum pixel value as a first pixel feature; Then, multiple groups of pixel values ​​associated with the pixel point on the other side of the image boundary are simultaneously confirmed, and a minimum pixel value and a maximum pixel value are selected from the confirmed multiple groups of pixel values, and the selected minimum pixel value and maximum pixel value are used as the second pixel feature; From the determined first pixel feature and the second pixel feature: the minimum pixel value in the first pixel feature is calibrated as T1min, the maximum pixel value is calibrated as T1max, the minimum pixel value in the second pixel feature is calibrated as T2min, and the maximum pixel value is calibrated as T2max, four sets of difference values ​​are determined using: |T1max-T2max|, |T1min-T2min|, |T1min-T2max|, and |T1max-T2min|, and the minimum and maximum values ​​are selected from the four sets of difference values ​​to determine the image features between the constructed area and the unconstructed area; Based on the confirmed image features, the specific method for real-time processing of the survey and construction images of high-altitude drones is as follows: Based on the identified abnormal construction section, the starting construction point and the end construction point of the abnormal construction section are confirmed. The starting construction point is used as the initial takeoff point, the end construction point is used as the takeoff end point, and the specific section of the abnormal construction section is used as the flight section to confirm the flight path of the high-altitude UAV. The high-altitude UAV is made to fly at high altitude according to the confirmed flight route, and during the flight, the construction images of the abnormal construction section are surveyed and collected in real time, and the construction images collected in real time are processed: From the collected construction image, the gradient characteristics of the internal pixels are confirmed. Based on the gradient characteristics, it is determined whether the pixel is a gradient pixel. The pixel value of the corresponding pixel is calibrated as Xs. Then the pixel values ​​of the adjacent pixels around the pixel are confirmed and calibrated as H1-H8. Gx is used. k =(-1)×H1+0×H2+1×H3+(-2)×H4+0×Xs+2×H5+(-1)×H6+0×H7+1×H8; Among them Gy k =(-1)×H1+(-2)×H2+(-1)×H3+0×H4+0×Xs+0×H5+1×H6+2×H7+1×H8; Re-adopt Determine the gradient feature G associated with this pixel k , where k represents different pixels; If G k ≤Y2, no calibration is performed, where Y2 is the preset value. If G k >Y2, then this pixel is marked as a gradient pixel; Identify gradient pixel points that appear continuously in the construction image, and confirm a gradient contour line based on the continuously appearing gradient pixel points, process pixel values ​​associated with pixel points associated with both sides of the gradient contour line, average several groups of pixel values ​​associated with pixel points on one side of the gradient contour line to confirm a first associated feature, average several groups of pixel values ​​associated with pixel points on the other side of the gradient contour line to confirm a second associated feature, and use: |first associated feature - second associated feature| = feature difference to confirm a feature difference associated with the gradient contour line; If the feature difference ∈ image feature, the high-altitude position of the high-altitude drone is directly locked, and then the construction position is determined. The actual construction progress is locked based on the distance between the construction position and the starting construction point, and the locked actual construction progress is displayed.

2. A method for managing construction progress information of a building project based on BIM according to claim 1, characterized in that: In step one, the BIM building construction model is a preset model, and different unit construction progresses exist for different construction sections in the BIM building construction model. The unit construction progress is a preset progress, which is the construction progress completed in each unit time. The BIM building construction model displays the construction progress data of different construction sections in real time based on the preset unit construction progress.

3. A method for managing construction progress information of a building project based on BIM according to claim 1, characterized in that: In step 1, the specific sub-steps for identifying whether the progress data information has a large gap are: The real-time progress data of different construction sections in the BIM construction model are calibrated as JD i , where i represents different construction sections; The progress data reported in real time for the corresponding construction section is calibrated as HD i , identify JD i with HD i Satisfied: |JD i -HD i |≤Y1, where Y1 is a preset value, representing the standard value of the difference between progress. If it is met, no processing is performed. If it is not met, this construction section is marked as a construction section to be processed.

4. A method for managing construction progress information of a building project based on BIM according to claim 1, characterized in that: In step 2, the specific method of calibrating the construction section to be processed is: S21. Based on the identified pending construction section, confirm the past progress data reported by the pending construction section, confirm different progress data associated with different moments, sort the groups of progress data according to a chronological order, and generate a progress data change curve for the pending construction section based on the sorted progress data, where the horizontal axis of the curve is the timeline and the vertical axis is the progress data. S22. Based on the confirmed progress data change curve, confirm the change trend between adjacent data: confirm the different progress data associated with adjacent nodes, use the change trend = progress data of the next node - progress data of the previous node, and select the maximum trend and the minimum trend from the confirmed groups of change trends; S23. Mark the progress data of the end node of the progress data change curve as JM, identify the time length associated with the end node from the current moment and mark it as Sc, and use: Jsmax = JM + (Sc × maximum trend) and Jsmin = JM + (Sc × minimum trend) to predict the progress data interval [Jsmin, Jsmax] associated with the current moment; S24. Identify the progress data HD reported by the current construction section i Whether it meets: HD i ∈[Jsmin, Jsmax], if it is satisfied, the marked construction section to be processed will be marked as a normal construction section and the monitoring process will be carried out again.

5. A method for managing construction progress information of a building project based on BIM according to claim 4, characterized in that: If HD i Not satisfied with HD i ∈[Jsmin, Jsmax], the marked construction section to be processed is marked as an abnormal construction section.

6. A method for managing construction progress information of a building project based on BIM according to claim 1, characterized in that: If the characteristic difference Image features, the gradient pixel points of subsequent construction images are continuously confirmed until the actual construction progress is determined.

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