Building engineering construction progress information management method based on BIM

Abstract

The invention discloses a BIM (Building Information Modeling)-based building engineering construction progress information management method, relates to the technical field of construction progress management, and solves the problem that no intelligent monitoring processing mode is adopted to confirm the construction progress in real time. Identifying gradient pixel points by confirming gradient features of the pixel points, and further determining gradient contour lines and related feature difference values; according to the analysis method based on the gradient features, detail changes in the construction image can be sensitively captured, the current construction position and progress are accurately judged in combination with the previously determined image features, and the accuracy of construction progress monitoring is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction progress management, and particularly to a BIM-based construction progress information management method for construction projects. Background Art

[0002] A building engineering model is an engineering data model based on three-dimensional digital technology and integrating various relevant information of a building engineering project; it not only contains geometric information of a building, such as shape, size, position, etc., but also covers a large amount of non-geometric information, such as material properties, cost information, progress plan, equipment parameters, maintenance requirements, etc.; simply speaking, a BIM model is an all-round digital expression of a building engineering project and can provide rich data support for each stage of the whole life cycle of the project.

[0003] After the BIM building construction model is completed, based on preset progress parameters, the construction data of different construction sections are displayed in real time. The application with the patent publication number CN107818430A discloses a BIM-based construction progress information management method for construction projects. The specific steps of this BIM-based construction progress information management method are as follows: obtaining information; system modeling; real-time monitoring; construction period prediction; data synchronization. A BIM-based 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. This BIM-based construction progress information management system can convert the information monitored in real time into data instructions in the modeling, so as to vividly display the construction process. Through the monitoring of the construction of the project, it can timely remind the project operation and give instructions for the construction of the project.

[0004] During the process of progress management of its BIM building construction model, generally based on the output progress of the corresponding construction model and the specific progress reported actually, it is evaluated whether the progress is abnormal. For the construction sections with abnormal progress, personnel allocation is required, and it is evaluated whether the construction progress is misreported. However, such a processing method requires a large amount 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 the loss of manpower and reduce the work burden of monitoring personnel. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a BIM-based construction progress information management method for construction projects, 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 BIM-based construction project progress information management method includes the following steps:

[0007] Step 1: Initially check the progress data information shown in 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 has a large gap, and calibrate the construction sections to be processed. The specific sub-steps are as follows:

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

[0009] Calibrate the progress data reported in real time for the corresponding construction section as HD i , identify JD i and HD i to see if they satisfy: |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 is performed. If not, this construction section is calibrated as a construction section to be processed;

[0010] Step 2: Based on the calibrated construction sections to be processed, confirm the past progress data of this construction section to be processed from the reported progress data information, and based on the changing trend of such past progress data, identify whether the progress data information reported at the current moment is normal. If it is normal, calibrate this construction section to be processed as a normal construction section. If it is not normal, calibrate this construction section to be processed as an abnormal construction section. The specific method is as follows:

[0011] S21: Based on the calibrated construction section to be processed, confirm the past progress data reported for this construction section to be processed, and confirm the different progress data associated with different times. Sort several groups of progress data according to the time sequence, and generate a progress data change curve belonging to this construction section to be processed based on the sorted progress data. The horizontal axis of this curve is the time line, and the vertical axis is the progress data;

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

[0013] S23. Calibrate 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 calibrate it as Sc. 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 for the current construction section i Whether it satisfies: HD i ∈ [Jsmin, Jsmax]. If it satisfies, calibrate the marked construction section to be processed as a normal construction section and re - perform the monitoring process; If HD i does not satisfy HD i ∈ [Jsmin, Jsmax], then calibrate the marked construction section to be processed as an abnormal construction section;

[0015] Step 3. For the marked abnormal construction section, use an aerial drone to survey the construction image of this abnormal construction section. From the previously collected construction images, 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 construction image surveyed by the aerial drone to evaluate the specific construction distance of the current abnormal construction section.

[0016] Preferably, in Step 1, the BIM building construction model is a preset model, and there are different unit construction progress for different construction sections in the BIM building construction model. The unit construction progress is a preset progress, which is the construction progress completed per unit time. The BIM building construction model displays the construction progress data of different construction sections in real - time according to the preset unit construction progress.

[0017] Preferably, in Step 3, the specific method for confirming the image features between the constructed area and the unconstructed area from the construction image is as follows:

[0018] From the determined construction image, identify the image boundary between the constructed area and the unconstructed area in this construction image. This image boundary has been pre - calibrated in the construction image and is pre - calibrated by the operator;

[0019] Identify the different pixel values associated with different pixel points on both sides of the image boundary, confirm several groups of pixel values associated with the pixel points on one side of the image boundary, select the minimum pixel value and the maximum pixel value from the confirmed several groups of pixel values, and use the selected minimum pixel value and maximum pixel value as the first pixel feature;

[0020] Then, synchronously confirm several groups of pixel values associated with the pixel points on the other side of the image boundary, select the minimum pixel value and the maximum pixel value from the confirmed several groups of pixel values, and use the selected minimum pixel value and maximum pixel value as the second pixel feature;

[0021] From the determined first pixel feature and second pixel feature: label the minimum pixel value in the first pixel feature as T1min, the maximum pixel value as T1max, the minimum pixel value in the second pixel feature as T2min, and the maximum pixel value as T2max. Then, use: |T1max - T2max|, |T1min - T2min|, |T1min - T2max|, |T1max - T2min| to confirm four groups of differences, and select the minimum value and the maximum value from the four groups of differences to determine the image feature between the constructed area and the unconstructed area.

[0022] Preferably, in the third step, based on the confirmed image feature, the specific method for real-time processing of the survey and construction image of the high-altitude unmanned aerial vehicle is as follows:

[0023] Based on the determined abnormal construction section, confirm the starting construction point and the ending construction point of this abnormal construction section. Use the starting construction point as the initial take-off point, the ending construction point as the take-off end point, and the specific road section of the abnormal construction section as the flight section to confirm the flight route of this high-altitude unmanned aerial vehicle;

[0024] Make the high-altitude unmanned aerial vehicle fly at high altitude according to the confirmed flight route, and during the flight, conduct real-time survey and collection of the construction images of the abnormal construction section, and process the real-time surveyed and collected construction images:

[0025] From the collected construction images, confirm the gradient feature of the internal pixel points. Based on this gradient feature, confirm whether this pixel point is a gradient pixel point, label the pixel value of the corresponding pixel point as Xs, then confirm the pixel values of the adjacent pixel points around this pixel point, and label them as H1 - H8. Use Gx 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] Then use to determine the gradient feature G associated with this pixel point k , where k represents different pixel points;

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

[0029] Identify the gradient pixel points that continuously appear in this construction image, and based on the continuously appearing gradient pixel points, confirm the gradient contour line. Process the pixel values associated with the pixel points on both sides of the gradient contour line. Perform mean processing on several groups of pixel values associated with the pixel points on one side of the gradient contour line to confirm the first associated feature. Perform mean processing on several groups of pixel values associated with the pixel points on the other side of the gradient contour line to confirm the second associated feature. Use: |First associated feature - Second associated feature| = Feature difference to confirm the feature difference associated with this gradient contour line;

[0030] If the feature difference ∈ Image features, directly lock the high-altitude position of the high-altitude drone, then determine the construction position, lock the actual construction progress based on the distance between the construction position and the starting construction point, and display the locked actual construction progress; If the feature difference ∉ Image features, continuously confirm the gradient pixel points of the subsequent construction images until the actual construction progress is determined.

[0031] The present invention provides a method for managing construction progress information based on BIM. Compared with the prior art, it has the following beneficial effects:

[0032] By analyzing the past progress data of the construction section to be processed, the present invention can effectively confirm whether the progress data reported at the current moment meets the standard, effectively confirm whether the construction progress of the corresponding construction section to be processed is normal, and evaluate whether there is an abnormal construction progress situation. Such an evaluation method can effectively ensure the specific accuracy of the evaluation and improve the overall effect of the progress evaluation;

[0033] Identify the image features between the constructed area and the unconstructed area from the construction image. This analysis method based on pixel values fully considers the differences between the pixel points on both sides of the image boundary. By calculating multiple groups of differences and selecting the maximum and minimum values to determine the features, it can accurately capture the essential differences between the constructed and unconstructed areas in the image, providing a reliable basis for accurately judging the construction progress subsequently;

[0034] When performing real-time processing on the construction images surveyed by the high-altitude drone, identify the gradient pixel points by confirming the gradient features of the pixel points, and then determine the gradient contour line and related feature differences; This analysis method based on gradient features can keenly capture the detailed changes in the construction images, and combined with the previously determined image features, 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 method flow of the present invention;

[0036] Figure 2 Schematic diagram for evaluating the construction section to be processed in the present invention. Specific embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Please refer to Figure 1 , this application provides a BIM-based construction progress information management method for construction projects, including the following steps:

[0039] Step 1: Initially check the progress data information displayed in 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 has a large gap, and calibrate the construction section to be processed. Among them, the BIM construction model is a preset model, formulated in advance by relevant personnel, and there are different unit construction progress for different construction sections in the BIM construction model. The unit construction progress is a preset progress, which is the construction progress completed per unit time. The BIM construction model displays the construction progress data of different construction sections in real time according to the preset unit construction progress. The specific sub-steps for identifying whether the progress data information has a large gap are as follows:

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

[0041] Calibrate the progress data reported in real time for the corresponding construction section as HD i (Since there may be false reporting, it is necessary to evaluate the reported progress data information to confirm whether the construction section is abnormal);

[0042] Identify JD i and HD i to see if they satisfy: |JD i -HD i |≤Y1, where Y1 is a preset value, and its specific value is determined by the operator according to experience, representing the standard value of the difference between progress. If it is satisfied, no processing is required. If it is not satisfied, this construction section is calibrated as the construction section to be processed;

[0043] Specifically, the construction section to be processed is where there are relevant anomalies in the progress data of the corresponding construction section, with a large deviation from the progress data associated in the model. Either the progress is slow and there is construction delay, or the progress is too fast, and there may be progress problems compared to the construction model. An overly fast progress may involve false reporting of progress, and relevant confirmation is also required to identify whether there are real anomalies in this construction section to be processed.

[0044] Step 2: Based on the identified construction section to be processed, confirm the past progress data of this construction section to be processed 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 it is normal, mark this construction section to be processed as a normal construction section; if it is not normal, mark this construction section to be processed as an abnormal construction section. The specific method for marking is as follows:

[0045] S21: Based on the identified construction section to be processed, confirm the reported past progress data of this construction section to be processed (excluding the progress data HD reported currently i ), and confirm the different progress data associated with different moments. Sort several groups of progress data according to the time sequence, and generate a progress data change curve belonging to this construction section to be processed based on the sorted progress data. The horizontal axis of this curve is the time line, and the vertical axis is 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 latter node - progress data of the former node, and select the maximum trend and the minimum trend from the confirmed several 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. 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 whether the progress data HD reported for the current construction section i satisfies: HD i ∈[Jsmin, Jsmax]. If it satisfies, mark the identified construction section to be processed as a normal construction section and re - conduct monitoring and processing; if it does not satisfy, mark the identified construction section to be processed as an abnormal construction section;

[0049] Specifically, the abnormal construction sections belong to the relevant construction sections with abnormal reported progress. Such construction sections first have a large difference from the progress data output by the BIM building model, and the change trends generated in this stage of such construction sections have not occurred in the past periods. That is, such construction sections may be in a relatively fast or slow construction progress in the current stage, resulting in a large abnormality in this construction section in the current stage. Therefore, the construction progress of such construction sections is in an abnormal state in the current stage, so the construction progress needs to be recalibrated, and then the drone is dispatched to confirm whether there is false reporting of progress in the current construction section.

[0050] Step 3: For the calibrated abnormal construction sections, use an aerial drone to survey the construction images of this abnormal construction section. From the previously collected construction images, confirm the image features between the constructed area and the unconstructed area in the construction image, and then, based on the determined image features, perform real-time processing on the construction images surveyed by the aerial drone to evaluate the specific construction distance of the current abnormal construction section, so as to determine the specific construction progress of this abnormal construction section;

[0051] Among them, the specific method for confirming the image features between the constructed area and the unconstructed area from the construction images is as follows:

[0052] From the determined construction image, identify the image boundary between the constructed area and the unconstructed area within this construction image. This image boundary has been pre-calibrated in the construction image by the operator in advance;

[0053] Identify the different pixel values associated with different pixel points on both sides of the image boundary, confirm several groups of pixel values associated with the pixel points on one side of the image boundary, select the minimum pixel value and the maximum pixel value from the confirmed several groups of pixel values, and use the selected minimum pixel value and maximum pixel value as the first pixel feature;

[0054] Then, synchronously confirm several groups of pixel values associated with the pixel points on the other side of the image boundary, select the minimum pixel value and the maximum pixel value from the confirmed several groups of pixel values, and use the selected minimum pixel value and maximum pixel value as the second pixel feature;

[0055] From the determined first pixel feature and second pixel feature: calibrate the minimum pixel value in the first pixel feature as T1min, the maximum pixel value as T1max, the minimum pixel value in the second pixel feature as T2min, and the maximum pixel value as T2max. Use: |T1max - T2max|, |T1min - T2min|, |T1min - T2max|, |T1max - T2min| to confirm four groups of differences, and select the minimum value and the maximum value from the four groups of differences to determine the image features between the constructed area and the unconstructed area;

[0056] Specifically, there are significant differences between the constructed area and the unconstructed area, which can be clearly identified on the image, and the pixel differences between their boundaries are also relatively obvious. Therefore, based on the specific differences between the corresponding pixels, the corresponding construction interruption nodes can be identified, and finally the specific confirmation of the construction progress can be completed;

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

[0058] Based on the determined abnormal construction section, confirm the starting construction point and the ending construction point of this abnormal construction section. Take the starting construction point as the initial takeoff point, the ending construction point as the takeoff end point, and the specific section of the abnormal construction section as the flight section to confirm the flight route of this high-altitude drone;

[0059] Make the high-altitude drone fly at high altitude according to the confirmed flight route, and during the flight, conduct real-time survey and collection of the construction images of the abnormal construction section, and process the construction images collected in real-time:

[0060] From the collected construction images, confirm the gradient features of the internal pixel points. Based on this gradient feature, confirm whether this pixel point is a gradient pixel point, calibrate the pixel value of the corresponding pixel point as Xs, and then confirm the pixel values of the adjacent pixel points around this pixel point, and calibrate them as H1 - H8, and use Gx k = (-1)×H1 + 0×H2 + 1×H3 + (-2)×H4 + 0×Xs + 2×H5 + (-1)×H6 + 0×H7 + 1×H8;

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

[0062] Then use to determine the gradient feature G associated with this pixel point k , where k represents different pixel points;

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

[0064] Identify the gradient pixel points that continuously appear in this construction image, and based on the continuously appearing gradient pixel points, confirm the gradient contour line. Process the pixel values associated with the pixel points on both sides of the gradient contour line. Perform mean processing on several groups of pixel values associated with the pixel points on one side of the gradient contour line to confirm the first associated feature. Perform mean processing on several groups of pixel values associated with the pixel points on the other side of the gradient contour line to confirm the second associated feature. Use: |First associated feature - Second associated feature| = Feature difference to confirm the feature difference associated with this gradient contour line;

[0065] If the feature difference ∈ Image feature, directly lock the high-altitude position of the high-altitude drone, then determine the construction position, lock the actual construction progress based on the distance between the construction position and the starting construction point, and display the locked actual construction progress for external relevant personnel to view;

[0066] If the feature difference ∉ Image feature, continuously confirm the gradient pixel points of the subsequent construction images until the actual construction progress is determined.

[0067] Identify the image features between the constructed area and the unconstructed area from the construction image through detailed steps. This pixel-value-based analysis method fully considers the differences in pixel points on both sides of the image boundary. By calculating multiple groups of differences and selecting the maximum and minimum values to determine the features, it can accurately capture the essential differences between the constructed and unconstructed areas in the image, providing a reliable basis for accurately judging the construction progress subsequently.

[0068] Real-time process the survey image: When real-time processing the construction image surveyed by the high-altitude drone, identify the gradient pixel points by confirming the gradient features of the pixel points, and then determine the gradient contour line and the related feature differences. This gradient-feature-based analysis method can keenly capture the detailed changes in the construction image. Combining with the previously determined image features, it can accurately judge the current construction position and progress, greatly improving the accuracy of construction progress monitoring.

[0069] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the well-known prior art in the art.

[0070] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced 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 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 section to be processed; Step 2: Based on the marked construction section to be processed, confirm the past progress data of the construction section to be processed 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 construction section to be processed as a normal construction section; if abnormal, mark the construction section to be processed as an abnormal construction section; Step 3: For the marked abnormal construction section, use a high-altitude UAV to survey the construction image of this abnormal construction section, and confirm the image features between the constructed area and the unconstructed area in the construction image from the construction images collected in the past. Then, based on the determined image features, the survey construction image of the high-altitude UAV is processed in real time to assess the specific construction distance of the current abnormal construction section.

2. A method for managing construction progress information of a building project based on BIM according to claim 1, characterized in that: In the step 1, 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 the 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.

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 building construction model is calibrated as JD i , where i represents different construction sections; The progress data reported in real time by 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.

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 marked construction section to be processed, confirm the past progress data reported by the construction section to be processed, and confirm the different progress data associated with different times, 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, wherein the horizontal coordinate axis of the curve is the timeline and the vertical coordinate axis is the progress data; S22, based on the confirmed progress data change curve, confirm the change trend between adjacent data: confirm different progress data associated with adjacent nodes, adopt 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, calibrate the progress data of the terminal node of the progress data change curve as JM, identify the time length associated with the terminal node from the current moment and calibrate 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 reported by the current construction section HD i Satisfied: HD i ∈[Jsmin, Jsmax], if it is satisfied, the marked construction section to be processed is marked as a normal construction section and the monitoring process is performed 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: In step 3, the specific method of confirming the image features between the constructed area and the unconstructed area from the construction image is: From the determined construction image, an image boundary between a constructed area and an unconstructed area in the construction image is identified, where the image boundary has been pre-calibrated in the construction image by an operator; Identify different pixel values ​​associated with different pixel points on both sides of the image boundary, confirm several groups of pixel values ​​associated with the pixel points on one side of the image boundary, select a minimum pixel value and a maximum pixel value from the confirmed several groups of pixel values, and use the selected minimum pixel value and maximum pixel value as the first pixel feature; Then, a plurality of groups of pixel values ​​associated with the pixel points on the other side of the image boundary are synchronously confirmed, a minimum pixel value and a maximum pixel value are selected from the confirmed plurality of 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: calibrate the minimum pixel value in the first pixel feature as T1min, the maximum pixel value as T1max, calibrate the minimum pixel value in the second pixel feature as T2min, and the maximum pixel value as T2max, use: |T1max-T2max|, |T1min-T2min|, |T1min-T2max|, |T1max-T2min| to confirm four sets of differences, and select the minimum and maximum values ​​from the four sets of differences to determine the image features between the constructed area and the unconstructed area.

7. A method for managing construction progress information of a building project based on BIM according to claim 6, characterized in that: In the step 3, based on the confirmed image features, the specific method of real-time processing of the survey and construction image of the high-altitude UAV is as follows: Based on the determined 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 route 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 confirmed 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 k =(-1)×H1+0×H2+1×H3+(-2)×H4+0×Xs+2×H5+(-1)×H6+0×H7+1×H8; 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. k >Y2, then this pixel is marked as a gradient pixel; Identify the gradient pixel points that appear continuously in the construction image, and confirm the gradient contour line based on the gradient pixel points that appear continuously, process the pixel values ​​associated with the pixel points associated with both sides of the gradient contour line, perform average processing on several groups of pixel values ​​associated with the pixel points on one side of the gradient contour line, confirm the first associated feature, perform average processing on several groups of pixel values ​​associated with the pixel points on the other side of the gradient contour line, confirm the second associated feature, and use: |first associated feature-second associated feature|=feature difference to confirm the 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.

8. A method for managing construction progress information of a building project based on BIM according to claim 7, 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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