Project real-time progress monitoring method and system based on BIM and Internet of Things fusion

Abstract

The embodiment of the invention relates to the technical field of engineering monitoring, and particularly discloses an engineering real-time progress monitoring method and system based on BIM and Internet of Things fusion. According to the embodiment of the invention, corresponding completion BIM data and planning BIM data are matched; differential comparison is carried out, a plurality of differential monitoring images are extracted, and corresponding differential monitoring point positions and differential monitoring parameters are determined; after the planned construction is finished, progress monitoring is carried out; and monitoring and comparing the plurality of progress monitoring images, judging whether planning is completed or not, and generating a progress monitoring report. According to the method, space coordinates of difference monitoring images are analyzed, component type parameter templates are bound, adjacent monitoring point position integration parameter requirements are combined, a parameter linkage rule and a dynamic regulation and control strategy are established in combination with construction process logic, and finally a self-checking mechanism is embedded to output a monitoring parameter set capable of being self-corrected. Precise and efficient configuration of differential monitoring point positions and parameters is achieved, the manual intervention requirement is remarkably reduced, and the automation level of engineering monitoring is improved.

Description

Technical Field

[0001] This invention belongs to the field of engineering monitoring technology, and in particular relates to a method and system for real-time engineering progress monitoring based on the integration of BIM and the Internet of Things. Background Technology

[0002] Engineering monitoring refers to the technical activities of continuously or periodically observing and recording key parameters of an engineering structure, foundation, environment, and construction process throughout the entire process of engineering construction, operation, and maintenance, using technologies such as measurement, sensing, data acquisition, and analysis, and analyzing and evaluating their changing patterns. Its purpose is to promptly grasp the engineering status, predict potential risks, guide construction and operation and maintenance decisions, and ensure the safety, stability, and durability of the project.

[0003] Real-time project progress monitoring is a technology that mainly monitors the completion status of construction tasks, procedures, and resource input.

[0004] In existing technologies, real-time project progress monitoring cannot be effectively integrated with BIM data, and cannot achieve synchronous updates and automatic comparisons between dynamic information on the construction site and the project model. This results in a low level of automation and intelligence in real-time project progress monitoring, often requiring staff to manually observe, record, and analyze on-site. This is not only inefficient but also susceptible to errors due to subjective human judgment. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for real-time engineering progress monitoring based on the integration of BIM and the Internet of Things, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for real-time project progress monitoring based on the integration of BIM and IoT, the method specifically includes the following steps: Obtain the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completed BIM data and planning BIM data; The planning BIM data and the completed BIM data are compared for differences, multiple difference monitoring images are extracted, and the corresponding difference monitoring points and difference monitoring parameters are determined. After the planning and construction are completed, based on the Internet of Things, progress monitoring is carried out according to multiple difference monitoring points and multiple difference monitoring parameters to obtain multiple progress monitoring images; Based on multiple difference monitoring images, the multiple progress monitoring images are monitored and compared to determine whether the plan has been completed and a progress monitoring report is generated.

[0007] A real-time project progress monitoring system based on the integration of BIM and the Internet of Things, the system comprising: The BIM data processing unit is used to acquire the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completed BIM data and planning BIM data. The differential comparison processing unit is used to perform differential comparison between the planned BIM data and the completed BIM data, extract multiple differential monitoring images, and determine the corresponding differential monitoring points and differential monitoring parameters. The construction progress monitoring unit is used to monitor the progress based on the Internet of Things after the planned construction is completed, according to multiple difference monitoring points and multiple difference monitoring parameters, and to acquire multiple progress monitoring images. The monitoring and comparison processing unit is used to monitor and compare multiple progress monitoring images based on multiple difference monitoring images, determine whether the plan has been completed, and generate a progress monitoring report.

[0008] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention analyzes the spatial coordinates of differential monitoring images and binds them to component type parameter templates. It integrates parameter requirements from adjacent monitoring points, establishes parameter linkage rules and dynamic control strategies in conjunction with construction process logic, and finally embeds a self-verification mechanism to output a self-correcting monitoring parameter set. This enables precise and efficient configuration of differential monitoring points and parameters, significantly reduces the need for manual intervention, and improves the automation level of engineering monitoring.

[0009] 2. This invention dynamically evaluates the priority of differential monitoring points and merges spatially adjacent points. Based on the optimized sequence, it generates a non-overlapping basic inspection path and embeds a response mechanism for inserting new points and reordering parameters to output an adaptive engineering inspection route. This achieves optimal allocation of monitoring resources and real-time response to dynamic changes in construction, significantly improving the efficiency of progress monitoring.

[0010] 3. This invention visualizes and marks deviations on the planning baseline image and associates them with anomaly impact chains by feature matching and stage adaptive threshold judgment of progress monitoring images. It generates a decision tree by combining dynamic weight scoring and regional linkage analysis, and finally compiles a traceable planning achievement report. This enables objective and accurate judgment of project progress completion and rapid anomaly location, ensuring synchronous updates between the construction site and the model and reliable decision-making. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0012] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0013] Figure 2 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0014] Figure 3 A structural block diagram of the BIM data processing unit in the system provided by an embodiment of the present invention is shown.

[0015] Figure 4 A structural block diagram of the differential comparison processing unit in the system provided by an embodiment of the present invention is shown.

[0016] Figure 5 A structural block diagram of the construction progress monitoring unit in the system provided by an embodiment of the present invention is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Understandably, real-time project progress monitoring primarily involves monitoring the completion status of construction tasks, procedures, and resource input. However, current technologies for real-time project progress monitoring cannot effectively integrate with BIM data, nor can they achieve synchronized updates and comparisons between dynamic information from the construction site and the project model. This results in low levels of automation and intelligence in real-time project progress monitoring, often requiring on-site manual observation, recording, and analysis. This is not only inefficient but also susceptible to errors due to subjective human judgment.

[0019] To address the aforementioned issues, this invention acquires the engineering BIM data of the target construction project, updates the construction completion data and construction planning data, and matches the corresponding completed BIM data and planned BIM data. It then performs a differential comparison between the planned BIM data and the completed BIM data, extracts multiple differential monitoring images, and determines the corresponding differential monitoring points and parameters. After the planned construction is completed, based on the Internet of Things (IoT), progress monitoring is performed according to the multiple differential monitoring points and parameters, acquiring multiple progress monitoring images. Based on these multiple differential monitoring images, the progress monitoring images are compared to determine whether the plan has been completed, and a progress monitoring report is generated. This method can match completed BIM data and planned BIM data, perform differential comparisons, extract multiple differential monitoring images, perform progress monitoring and comparison, and determine whether the plan has been completed. It achieves synchronous updates and automatic comparison between the construction site and the engineering model, with a high degree of automation and intelligence, eliminating the need for manual observation, recording, and analysis, effectively improving the efficiency and accuracy of real-time project progress monitoring.

[0020] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0021] Specifically, the real-time project progress monitoring method based on the integration of BIM and IoT includes the following steps: Step S101: Obtain the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completion BIM data and planning BIM data.

[0022] In this embodiment of the invention, a target construction project is determined. After the design of the target construction project is completed, complete engineering BIM data of the target construction project is obtained. During the construction process of the target construction project, construction completion data and construction planning data are periodically updated and obtained. Then, according to the construction completion data, corresponding completed BIM data is matched from the engineering BIM data. Based on the engineering BIM data and construction planning data, the completed BIM data is supplemented with planning construction to generate planning BIM data.

[0023] In a preferred embodiment of the present invention, the steps of obtaining the engineering BIM data of the target construction project, updating the construction completion data and construction planning data, and matching the corresponding completed BIM data and planning BIM data specifically include the following steps: Step S1011: Obtain the engineering BIM data of the target construction project; Step S1012: Update construction completion data and construction planning data; Step S1013: Match the corresponding completed BIM data according to the construction completion data; Step S1014: Based on the project BIM data and the construction planning data, supplement the completed BIM data with planning construction data to generate planning BIM data.

[0024] Furthermore, the real-time project progress monitoring method based on the integration of BIM and IoT also includes the following steps: Step S102: Compare the planned BIM data with the completed BIM data to extract multiple difference monitoring images and determine the corresponding difference monitoring points and difference monitoring parameters.

[0025] In this embodiment of the invention, based on the completed BIM data, the planning BIM data is compared to identify multiple construction difference areas between the planning BIM data and the completed BIM data. The difference ratio of the multiple construction difference areas is identified, and multiple main difference areas are selected from the multiple construction difference areas. Then, the difference monitoring images corresponding to the multiple main difference areas are extracted from the planning BIM data, and the difference monitoring points and difference monitoring parameters corresponding to the multiple difference monitoring images are determined.

[0026] It is understandable that the construction difference area is the area with spatial changes after the construction is completed according to the plan; the main difference area is the construction difference area selected from multiple construction difference areas, which has changes in space and occupies 50%-70% of the entire space.

[0027] In a preferred embodiment of the present invention, the step of comparing the planned BIM data with the completed BIM data, extracting multiple difference monitoring images, and determining the corresponding difference monitoring points and parameters specifically includes the following steps: Step S1021: Based on the completed BIM data, perform a differential comparison on the planned BIM data to identify multiple construction difference areas; Step S1022: Select multiple major difference areas from the multiple construction difference areas; Step S1023: Extract difference monitoring images corresponding to multiple major difference areas from the planning BIM data; Step S1024: Determine the difference monitoring points and difference monitoring parameters corresponding to the multiple difference monitoring images.

[0028] In a preferred embodiment of the present invention, determining the difference monitoring points and difference monitoring parameters corresponding to the plurality of difference monitoring images specifically includes the following steps: Step S10241: For the extracted difference monitoring images, identify the corresponding boundary vertex position information in the BIM 3D space and generate an initial monitoring point set; Step S10242: Associate the spatial coordinate identifiers in the initial monitoring point set with the BIM component database to generate a monitoring point configuration table; wherein, associating the spatial coordinate identifiers in the initial monitoring point set with the BIM component database includes: associating beam and column structure points with displacement deformation monitoring templates; associating decoration engineering points with dimensional accuracy monitoring templates; and associating equipment installation points with positioning deviation monitoring templates. Step S10243: Merge the monitoring points in the monitoring point configuration table that are spatially adjacent (the straight-line distance is less than the preset threshold for adjacent points) and have the same type of monitored physical quantity to generate an optimized core monitoring point list; wherein, the same type of monitored physical quantity means that the monitoring template category associated with the points is consistent; the merging includes: using the geometric center coordinates of the aggregation area as the coordinates of the new monitoring point, and integrating the monitoring parameter requirements of the merged points, wherein the integration means taking the most stringent value among the monitoring parameter requirements of each merged point as the monitoring parameter requirements of the new point; Step S10244: Preset construction process logic, establish parameter association rules based on the preset construction process logic, and generate a group of differential parameter linkage rules; wherein, establishing parameter association rules based on the preset construction process logic includes: when the structural displacement monitoring value exceeds the preset proportion of the design allowable displacement value, adding stress and strain monitoring parameters for the associated support body; when the dimensional deviation monitoring value exceeds the preset range of the design allowable deviation value, activating the positioning coordinate verification monitoring parameters for adjacent or associated embedded parts; Step S10245: Increase the monitoring frequency level for the construction area during the first construction period and decrease the monitoring frequency level for the construction area during the second construction period to generate a dynamic monitoring and control instruction set. The first construction period is the near-term planned time, and the second construction period is the far-term planned time.

[0029] Step S10246: Integrate the parameter type template of the monitoring point configuration table, the core point coordinates of the optimized core monitoring point list, the differential parameter linkage rule group, and the dynamic monitoring and control instruction set to generate an executable monitoring parameter configuration package. Step S10247: Configure the verification function in the executable monitoring parameter configuration package to obtain a self-correcting final monitoring parameter set. Use the self-correcting final monitoring parameter set as the difference monitoring points and difference monitoring parameters corresponding to the difference monitoring images. The verification function configured in the executable monitoring parameter configuration package includes: reducing the monitoring intensity for points with stable data, and triggering an anomaly review mechanism for points with conflicting parameters.

[0030] In this embodiment, the present invention accurately generates an initial set of monitoring points (including spatial coordinate identifiers) by identifying the boundary vertex position information of the difference monitoring images in the BIM 3D space. Subsequently, the point coordinate identifiers are associated with a preset BIM component database: specifically, the system's built-in rule base associates beam and column structure points with a "displacement deformation monitoring template" containing deflection, settlement, and tilt monitoring items; points involving walls, floors, and ceilings are associated with a "dimensional accuracy monitoring template" containing flatness, verticality, and dimensional deviation monitoring items; and electromechanical equipment and pipeline installation points are associated with a "positioning deviation monitoring template" containing plane coordinate deviation, elevation deviation, and angle deviation monitoring items, generating a detailed monitoring point configuration table. To solve the problem of point redundancy, the system merges points in the configuration table that are spatially adjacent (e.g., the straight-line distance is less than a set threshold, such as 2 meters) and have the same monitoring target: the geometric center coordinates of these points are calculated as new coordinate points, and all monitoring parameter requirements of the merged points are integrated (e.g., merging multiple adjacent wall flatness monitoring points into a representative point, whose monitoring requirements cover the range of all original points). Based on a pre-defined construction process logic knowledge base (such as "abnormal structural displacement may cause changes in the stress of the support system" and "decorative dimensions exceeding tolerances require verification of embedded part positioning"), parameter association rules are established: when structural displacement exceeds a threshold, stress and strain monitoring parameters of the associated support body are added; when decorative dimension deviations exceed limits, positioning verification parameters of nearby embedded parts are activated, forming a group of differential parameter linkage rules. Simultaneously, the system dynamically adjusts the monitoring frequency according to the construction schedule: for areas planned for construction in the near future (e.g., one week), the monitoring frequency level is increased (e.g., from once a day to three times a day); for areas planned for construction in the long term (e.g., one month), the frequency is decreased (e.g., from once a day to once every three days), generating a dynamic monitoring and control instruction set. Finally, the parameter templates of the monitoring point configuration table, the optimized coordinates of the core monitoring point list, the differential parameter linkage rule group, and the dynamic monitoring and control instruction set are integrated to form an executable monitoring parameter configuration package. A verification function is embedded in this package: for points whose monitoring data are consistently within the acceptable range, the monitoring intensity is reduced (e.g., by reducing frequency or simplifying parameter items); for points with logical conflicts between monitoring parameters (e.g., excessive displacement but normal stress) or abnormal data jumps, an anomaly verification mechanism is triggered (e.g., by immediately adding another measurement or notifying manual verification). Ultimately, a final monitoring parameter set with self-correcting capabilities is output, achieving full automation and intelligence from point identification to parameter configuration, optimization, linkage, control, and verification.

[0031] Furthermore, the real-time project progress monitoring method based on the integration of BIM and IoT also includes the following steps: Step S103: After the planning and construction are completed, based on the Internet of Things, progress monitoring is carried out according to multiple difference monitoring points and multiple difference monitoring parameters to obtain multiple progress monitoring images.

[0032] In this embodiment of the invention, after the planned construction is completed, an engineering inspection route is planned according to multiple difference monitoring points. Progress monitoring is then controlled according to the engineering inspection route. Based on IoT technology, multiple corresponding difference monitoring parameters are matched at the multiple difference monitoring points along the engineering inspection route. Monitoring and image capture control is then performed according to these parameters, and progress monitoring images and feedback signals corresponding to the multiple difference monitoring points are received. By matching and analyzing the progress monitoring images and feedback signals, it is determined whether the feedback transmission is normal. If there is a feedback signal but no progress monitoring image, the feedback transmission is deemed abnormal. At this time, collaborative IoT analysis is performed based on the planned BIM data, and collaborative transmission devices near the corresponding major difference areas are selected. The collaborative transmission devices are used to receive the collaboratively transmitted progress monitoring images, ensuring that progress monitoring images corresponding to all major difference areas can be obtained.

[0033] In a preferred embodiment of the present invention, the step of acquiring multiple progress monitoring images by performing progress monitoring based on the Internet of Things (IoT) according to multiple difference monitoring points and multiple difference monitoring parameters after the completion of the planned construction specifically includes the following steps: Step S1031: After the construction is completed, plan the engineering inspection route according to the multiple difference monitoring points. Step S1032: Conduct inspection control to monitor progress according to the engineering inspection route; Step S1033: Based on the Internet of Things, progress monitoring is performed at multiple difference monitoring points according to multiple corresponding difference monitoring parameters to obtain multiple progress monitoring images.

[0034] In a preferred embodiment of the present invention, the step of planning the engineering inspection route according to the multiple difference monitoring points after the completion of the planned construction specifically includes the following steps: Step S10311: According to the multiple difference monitoring points, obtain the coordinate data of each point in the BIM three-dimensional space, including longitude, latitude and elevation values, and generate a set of point coordinates; Step S10312: Associate the set of point coordinates with the difference monitoring parameters, and generate a point dataset based on parameter type classification; wherein, the parameter type classification includes: binding displacement deformation labels to structural points; binding dimensional accuracy labels to decorative points; and binding positioning deviation labels to equipment points. Step S10313: Based on the parameter type labels in the point dataset, assign different basic priority levels to different equipment types of points, and adjust the basic priority levels according to the urgency of regional completion in the construction schedule to obtain the point priority evaluation results. Step S10314: Perform spatial analysis on the set of point coordinates, and use the preset point adjacency threshold to detect points that are close in line distance for merging. At the same time, prioritize merging points with the same parameter type label, use the geometric center coordinates as the new coordinate point, integrate the monitoring requirements of all points in the group, and generate an optimized set of point groups. Step S10315: Based on the point priority evaluation results, sort the optimized point group set according to priority level, and generate an ordered representative point sequence by combining the point coordinates; Step S10316: Connect the ordered sequence of representative points, connect adjacent coordinate points in order to generate a basic path, identify intersecting line segments in the path, and add turning coordinates at the intersection points to eliminate path intersections, thus obtaining the initial inspection route. Step S10317: Embed response rules in the initial inspection route to obtain the final engineering inspection route with adaptive function; wherein, embedding response rules in the initial inspection route includes: when a new monitoring point is added, insert it into the nearest group of the same type; when the point parameters change, trigger priority reordering.

[0035] In this embodiment, the invention first acquires the precise coordinate data (longitude, latitude, and elevation) of all difference monitoring points in the BIM 3D space, forming a point coordinate set. Next, the coordinate set is associated with difference monitoring parameters and categorized based on parameter type: structural points (e.g., beams, columns, slabs) are labeled with "displacement deformation"; decorative points (e.g., walls, floors, doors, windows) are labeled with "dimensional accuracy"; and equipment points (e.g., pumps, fans, electrical cabinets) are labeled with "positioning deviation," forming a point dataset containing coordinates, parameters, and labels. The system performs an initial evaluation based on the label type using preset priority rules. The "displacement deformation" label is given the highest priority (e.g., weight value 5) due to its involvement in structural safety; the "dimensional accuracy" label is given medium priority (e.g., weight value 3) due to its impact on appearance and function; and the "positioning deviation" label is given low priority (e.g., weight value 1). Simultaneously, the system dynamically adjusts the priority of each area in the construction schedule based on its completion urgency (e.g., areas on the critical path, areas near contract milestones): for areas with high urgency, the priority of all points within them is increased by one level (e.g., from medium to high priority); for areas with low urgency, the priority is decreased by one level, generating the final point priority assessment result. Then, the system performs spatial proximity analysis on the point coordinate set, identifying and merging points with a straight-line distance less than a set threshold (e.g., 5 meters), and prioritizing the merging of points with the same parameter type labels. During merging, the geometric center coordinates of all points within the merged group are calculated as the coordinates of the new representative point, and the monitoring requirements of all points within the group are integrated (taking the most stringent requirement or the union), generating an optimized point group set. Based on the point priority assessment result, the optimized groups are sorted from high to low priority, and combined with the spatial coordinates of the group's representative points, an ordered sequence of representative points is generated. The system connects an ordered sequence of representative points, sequentially linking adjacent coordinate points to form basic path segments. It then detects intersecting segments within the path; if an intersection is found, a new turning point is added near the intersection, and the path is replanned to eliminate the intersection, resulting in an initial, intersection-free inspection route. Finally, adaptive response rules are embedded in the initial route: when new monitoring points are added during construction, the system inserts them into an existing group with the closest spatial location and the same parameter type, and updates the coordinates and monitoring requirements of the group's representative points; when the monitoring parameter type or priority of a point changes due to design changes or construction adjustments, it triggers a repricing of the entire point group and path replanning, thereby generating a final engineering inspection route with dynamic response capabilities, ensuring that inspection resources are always focused on the most critical monitoring targets.

[0036] In a preferred embodiment of the present invention, the step of performing progress monitoring at multiple difference monitoring points according to multiple corresponding difference monitoring parameters and obtaining multiple progress monitoring images specifically includes the following steps: Step S10331: Match multiple corresponding difference monitoring parameters at multiple difference monitoring points; Step S10332: Perform monitoring and shooting control according to the multiple difference monitoring parameters; Step S10333: Receive progress monitoring images and feedback signals corresponding to multiple difference monitoring points; Step S10334: When there is a feedback signal but no progress monitoring image, perform collaborative IoT analysis based on the planning BIM data, select a collaborative transmission device, and receive the collaboratively transmitted progress monitoring image.

[0037] Furthermore, the real-time project progress monitoring method based on the integration of BIM and IoT also includes the following steps: Step S104: Based on the multiple difference monitoring images, monitor and compare the multiple progress monitoring images to determine whether the plan has been completed, and generate a progress monitoring report.

[0038] In this embodiment of the invention, multiple progress monitoring images are monitored and compared based on multiple difference monitoring images, multiple monitoring comparison results are recorded, and the multiple monitoring comparison results are identified to determine whether the plan has been completed. If the plan has been completed, information is filled into a preset standard basic report by integrating multiple monitoring comparison results to generate a progress completion monitoring report. If the plan has not been completed, multiple abnormal comparison results are selected from the multiple monitoring comparison results, and information is filled into a preset standard basic report to generate a progress abnormality monitoring report.

[0039] In a preferred embodiment of the present invention, the step of monitoring and comparing multiple progress monitoring images based on multiple difference monitoring images to determine whether the plan has been completed and generating a progress monitoring report specifically includes the following steps: Step S1041: For each difference monitoring image and the corresponding progress monitoring image, extract the structural outline features, key component location identifiers, and color distribution feature identifiers to generate an image feature dataset with feature identifiers. Step S1042: Perform feature matching between the difference monitoring images and progress monitoring images at the same monitoring point in the image feature dataset with feature labels to generate the original comparison data after comparison; wherein, the feature matching includes: contour line feature overlap comparison, key component position offset calculation and color distribution similarity analysis. Step S1043: Obtain the construction stage and adjust the comparison tolerance thresholds accordingly. Then, perform threshold judgment on the original comparison data after comparison based on the adjusted comparison tolerance thresholds to obtain preliminary judgment results. The adjusted comparison tolerance thresholds include: using a preset point threshold in the main structure stage (the preset precise point threshold requires a structural outline overlap of ≥98%, a key component position offset ≤1 / 1000 of the design value, and a color distribution similarity of ≥95%); using a preset medium threshold in the decoration and finishing stage (the preset medium threshold requires a structural outline overlap of ≥90%, a key component position offset ≤1 / 500 of the design value, and a color distribution similarity of ≥85%); and using a preset lenient threshold in the equipment installation stage (the preset lenient threshold requires a structural outline overlap of ≥80%, a key component position offset ≤1 / 200 of the design value, and a color distribution similarity of ≥75%). Step S1044: The preliminary judgment result is fused with the difference monitoring image to obtain the planning reference image. The actual construction deviation is marked on the planning reference image. A green semi-transparent verification layer is superimposed on the passing area, and a red flashing warning box is added to the failing area. At the same time, displacement vector lines are drawn at key offset positions to generate a comparison result image with diagnostic markings. Step S1045: Analyze the warning box area in the comparison result image with diagnostic markers, associate it with the BIM component ID in the difference monitoring image, and bind it with the construction responsibility team and design change record. At the same time, mark the impact level of the deviation on subsequent processes and generate an anomaly focus report with impact analysis. Step S1046: The original comparison data, preliminary judgment results, comparison result images with diagnostic markers, and abnormal focus reports with impact analysis are integrated according to the monitoring points to generate a traceable structured comparison result record. The traceable structured comparison result record is used as the monitoring comparison result. Step S1047: Identify the multiple monitoring comparison results and determine whether the planning has been completed; Step S1048: When determining the completion of the plan, a progress completion monitoring report is generated by integrating multiple monitoring comparison results; Step S1049: When it is determined that the plan has not been completed, select multiple abnormal comparison results from the multiple monitoring comparison results and generate a progress abnormality monitoring report.

[0040] In this embodiment, the present invention applies image processing algorithms to extract key feature identifiers from differential monitoring images (planning baseline) and actual progress monitoring images (construction status) at the same monitoring point: Edge detection algorithms (such as the Canny operator) are used to extract feature point sets of structural contour lines; the location identifiers (center point coordinates) of key components (such as embedded parts and connection nodes) are determined through feature point matching or template recognition; the RGB / HSV value distribution of image pixels is analyzed to generate color distribution feature identifiers (such as histograms and statistical moments) describing the uniformity of material color and texture, forming an image feature dataset with feature identifiers. Next, the two sets of image features at the same monitoring point are matched and compared: the percentage of overlap of structural contour line feature points is calculated (number of overlapping points / total number of points); the offset (Euclidean distance) of the key component location identifiers in the image plane or after mapping back to three-dimensional space is calculated; the similarity of the color distribution feature identifiers of the two images is calculated (such as histogram intersection or cosine similarity), generating raw comparison data containing quantified values ​​(overlap percentage, offset in mm, similarity value). The system acquires information about the current construction stage and calls preset tolerance thresholds based on the characteristics of each stage: In the main structure stage, the strictest "precise point threshold" is used (e.g., outline overlap ≥ 98%, key component position offset ≤ 1 / 1000 of the design value, i.e., millimeter level, color similarity ≥ 95%); in the decoration and finishing stage, a "medium threshold" is used (e.g., outline overlap ≥ 90%, position offset ≤ 1 / 500 of the design value, color similarity ≥ 85%); in the equipment installation stage, a "relaxed threshold" is used (e.g., outline overlap ≥ 80%, position offset ≤ 1 / 200 of the design value, color similarity ≥ 75%). The adjusted tolerance thresholds are then used to judge the original comparison data: if all indicators meet the standards, it is judged as "passed"; if any indicator fails to meet the standards, it is judged as "failed," generating a preliminary judgment result. Then, the preliminary judgment results are visualized and integrated into the difference monitoring image (planning reference map): a semi-transparent green verification layer is overlaid on the areas judged as "passed"; a striking red flashing warning box is added to the areas judged as "failed" (outlining the abnormal area); simultaneously, at the locations where the positional deviation of key components is detected, displacement vector lines (with arrows and offset labels) are drawn from the planned position to the actual position, generating an intuitive comparison result image with diagnostic markers. Further, in-depth analysis is performed on the warning box areas: the unique component ID in the BIM model is associated with this area; the responsible work team information for the construction of this component is bound through the construction management system; and design change records related to this component are retrieved. Simultaneously, based on the type and degree of deviation, combined with the construction procedure logic knowledge base, the impact level of the deviation on subsequent procedures is marked (e.g., "minor - no impact", "moderate - requires adjustment", "severe - requires rework or design review"), generating an anomaly focus report with impact analysis that includes problem location, responsibility tracing, change association, and impact assessment.Finally, the original comparison data, preliminary judgment results, comparison result images with diagnostic markers, and anomaly focus reports are structurally integrated according to the monitoring points to generate a structured comparison result record containing a complete traceability chain.

[0041] In a preferred embodiment of the present invention, identifying the multiple monitoring comparison results and determining whether the plan has been completed specifically includes the following steps: Step S10471: For the traceable structured comparison result records, extract the percentage value of contour line overlap, the position offset of key components, the color distribution similarity rating and the abnormal impact level label respectively to generate a result element dataset with quantitative indicators. Step S10472: Based on the type in the construction stage, configure weight coefficients and generate a stage-adaptive weight configuration table; wherein, the configured weight coefficients include: in the main structure stage, outline weight 60% + position offset weight 30% + color weight 10%; in the decoration and finishing stage, outline weight 40% + position offset weight 30% + color weight 30%; in the equipment installation stage, outline weight 30% + position offset weight 50% + color weight 20%. Step S10473: For each quantitative indicator in the result element dataset with quantitative indicators, perform a graded scoring according to a preset grading standard to generate a result indicator set with scores; wherein, the graded scoring includes: assigning a score value to the contour line overlap percentage value corresponding to the preset interval to perform contour line overlap scoring; assigning a score value to the ratio of position offset to design allowable deviation corresponding to the preset interval to perform position offset scoring; and assigning a score value to the color distribution similarity value corresponding to the preset interval to perform color similarity scoring. Step S10474: Apply the stage-adaptive weight configuration table to the result indicator set with scores, calculate the individual scores and corresponding weight coefficients to generate a comprehensive evaluation score for each monitoring point. Step S10475: Perform correlation analysis on the evaluation scores of adjacent points in the comprehensive evaluation score of each monitoring point to generate an evaluation result set with correlation tags; wherein, the correlation analysis on the evaluation scores of adjacent points in the comprehensive evaluation score of each monitoring point includes: when a high zone (≥4 points) surrounds a low zone (≤2 points), a regional conflict tag is triggered; when the comprehensive score of three consecutive points does not exceed the preset score threshold, a progress stall tag is triggered. Step S10476: Based on the evaluation result set with associated tags, construct decision logic to obtain a preliminary conclusion on the completion status of the plan; wherein, constructing decision logic includes: when the comprehensive evaluation score of the monitoring point exceeds the target score, it is determined to be fully achieved; when the comprehensive evaluation score of the monitoring point (90%) exceeds the target threshold score and there is no regional conflict tag, it is determined to be basically achieved; when there is a progress stall tag and the comprehensive evaluation score of the monitoring point (10%) does not exceed the target threshold score, it is determined to be unachieved; Step S10477: For the result element dataset with quantitative indicators, the comprehensive evaluation score of each monitoring point, the evaluation result set with correlation markers, and the preliminary planning completion status judgment conclusion, generate a final planning judgment report with decision-making basis according to the construction area, and use the final planning judgment report with decision-making basis as the judgment result of whether the planning is completed.

[0042] In this embodiment, the present invention extracts key quantitative indicators from the traceable structured comparison results record of each monitoring point: the percentage value of contour line overlap (e.g., 92.5%), the position offset of key components (e.g., +5.2mm), the color distribution similarity rating (e.g., divided into excellent / good / medium / poor based on similarity value), and the abnormal impact level indicator (e.g., slight / moderate / severe), forming a result element dataset with quantitative indicators. The system calls the preset weight coefficient configuration according to the current construction stage type: in the main structure stage, contour line overlap is given the highest weight (e.g., 60%, because it reflects the structural form), followed by position offset (30%, because it affects stress), and color similarity is the lowest (10%); in the decoration and finishing stage, the weights of the three are relatively balanced (e.g., 40%, 30%, 30%, because both appearance and accuracy are important); in the equipment installation stage, position offset has the highest weight (50%, because it is crucial for precise positioning), followed by contour line (30%), and color similarity is the lowest (20%), generating a stage-adaptive weight configuration table. Standardized grading and scoring are applied to each quantitative indicator in the result element dataset: for example, contour line overlap ≥95% is scored 5 points, 90%-95% is scored 4 points, 85%-90% is scored 3 points, and <85% is scored 2 points; positional offset ≤1 / 2 of the allowable value is scored 5 points, ≤ the allowable value is scored 4 points, ≤1.5 times the allowable value is scored 3 points, and >1.5 times the allowable value is scored 2 points; color similarity is rated as excellent 5 points, good 4 points, average 3 points, and poor 2 points, generating a result indicator set with scores. An adaptive weighting table is applied in the application phase to calculate the weighted sum of the scores for each monitoring point: Comprehensive evaluation score = (contour line score) / (contour line score) (Contour line weight) + (Position offset score) Position offset weight) + (color similarity score) (Color similarity weight) is used to generate a comprehensive evaluation score for each monitoring point (e.g., 4.2 points). Correlation analysis is performed on the comprehensive evaluation scores of adjacent points (spatial distance less than a set value, e.g., 10 meters): if a low-scoring zone (comprehensive score ≤ 2 points) is found to be surrounded by multiple high-scoring zones (comprehensive score ≥ 4 points), a "regional conflict marker" is triggered (potentially indicating a serious local problem); if the comprehensive scores of multiple consecutive (e.g., 3 or more) adjacent points are all below a preset passing threshold (e.g., 3 points), a "progress delay marker" is triggered (potentially indicating a systemic delay or problem). Based on the evaluation result set with associated tags, a pre-set decision logic tree is applied for overall judgment: if the comprehensive evaluation score of all monitoring points exceeds the pre-set target score (e.g., 4 points), it is judged as "fully achieved"; if some (e.g., ≥90%) points have a comprehensive score exceeding the target score and there are no "regional conflict tags", it is judged as "basically achieved"; if there are "progress delay tags" or a small number (e.g., ≥10%) points have a comprehensive score not exceeding the target score (even without regional conflicts), it is judged as "not achieved", thus obtaining a preliminary planning completion status judgment conclusion. Finally, the quantitative indicator set, comprehensive evaluation score, associated tag result set, and preliminary judgment conclusion of all points in the construction area (e.g., floor, zone) are integrated to generate a final planning judgment report with detailed decision-making basis (including various scores, weights, calculation processes, and tag reasons), providing an objective, quantitative, and traceable basis for progress assessment.

[0043] Furthermore, Figure 2 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0044] In another preferred embodiment of the present invention, the real-time project progress monitoring system based on the integration of BIM and the Internet of Things includes: BIM data processing unit 101 is used to acquire the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completed BIM data and planning BIM data.

[0045] In this embodiment of the invention, the BIM data processing unit 101 determines the target construction project. After the target construction project is designed, it acquires complete engineering BIM data of the target construction project. During the construction process of the target construction project, it periodically updates and acquires construction completion data and construction planning data. Then, according to the construction completion data, it matches the corresponding completed BIM data from the engineering BIM data. Based on the engineering BIM data and construction planning data, it supplements the completed BIM data with planning construction to generate planning BIM data.

[0046] Specifically, Figure 3 A structural block diagram of the BIM data processing unit 101 in the system provided by an embodiment of the present invention is shown.

[0047] In a preferred embodiment provided by the present invention, the BIM data processing unit 101 specifically includes: The engineering BIM data acquisition module 1011 is used to acquire the engineering BIM data of the target construction project; The construction data update module 1012 is used to update construction completion data and construction planning data. The completed BIM data matching module 1013 is used to match the corresponding completed BIM data according to the construction completion data; The planning and construction supplement module 1014 is used to supplement the completed BIM data with planning and construction based on the project BIM data and the construction planning data, and generate planning BIM data.

[0048] Furthermore, the real-time project progress monitoring system based on the integration of BIM and the Internet of Things also includes: The differential comparison processing unit 102 is used to perform differential comparison between the planned BIM data and the completed BIM data, extract multiple differential monitoring images, and determine the corresponding differential monitoring points and differential monitoring parameters.

[0049] In this embodiment of the invention, the differential comparison processing unit 102 performs differential comparison on the planning BIM data based on the completed BIM data, determines multiple construction difference areas between the planning BIM data and the completed BIM data, identifies the difference ratio of the multiple construction difference areas, selects multiple main difference areas from the multiple construction difference areas, and then extracts the difference monitoring images corresponding to the multiple main difference areas from the planning BIM data, and determines the difference monitoring points and difference monitoring parameters corresponding to the multiple difference monitoring images.

[0050] Specifically, Figure 4 A structural block diagram of the differential comparison processing unit 102 in the system provided by an embodiment of the present invention is shown.

[0051] In a preferred embodiment of the present invention, the differential comparison processing unit 102 specifically includes: The differentiation comparison module 1021 is used to perform a differentiation comparison on the planning BIM data based on the completed BIM data to determine multiple construction difference areas. The main difference area selection module 1022 is used to select multiple main difference areas from multiple construction difference areas; The difference monitoring image extraction module 1023 is used to extract difference monitoring images corresponding to multiple major difference areas from the planning BIM data; The point parameter determination module 1024 is used to determine the difference monitoring points and difference monitoring parameters corresponding to the multiple difference monitoring images.

[0052] Furthermore, the real-time project progress monitoring system based on the integration of BIM and the Internet of Things also includes: The construction progress monitoring unit 103 is used to monitor the progress based on the Internet of Things after the planned construction is completed, according to multiple difference monitoring points and multiple difference monitoring parameters, and to acquire multiple progress monitoring images.

[0053] In this embodiment of the invention, after the planned construction is completed, the construction progress monitoring unit 103 plans an engineering inspection route according to multiple difference monitoring points, and then performs inspection control for progress monitoring according to the engineering inspection route. Based on Internet of Things (IoT) technology, multiple corresponding difference monitoring parameters are matched at multiple difference monitoring points on the engineering inspection route, and monitoring and shooting control is performed according to multiple difference monitoring parameters. Progress monitoring images and feedback signals corresponding to multiple difference monitoring points are received. By matching and analyzing the progress monitoring images and feedback signals, it is determined whether the feedback transmission is normal. When there is a feedback signal but no progress monitoring image, it is determined that the feedback transmission is abnormal. At this time, collaborative IoT analysis is performed based on the planning BIM data, and collaborative transmission devices near the corresponding main difference areas are selected. The collaborative transmission devices are used to receive the collaboratively transmitted progress monitoring images to ensure that progress monitoring images corresponding to all main difference areas can be obtained.

[0054] Specifically, Figure 5 A structural block diagram of the construction progress monitoring unit 103 in the system provided in an embodiment of the present invention is shown.

[0055] In a preferred embodiment of the present invention, the construction progress monitoring unit 103 specifically includes: The inspection route planning module 1031 is used to plan the engineering inspection route according to multiple differential monitoring points after the construction is completed. The inspection control module 1032 is used for inspection control to monitor progress according to the engineering inspection route; The progress monitoring module 1033 is used to perform progress monitoring at multiple difference monitoring points based on the Internet of Things, according to multiple corresponding difference monitoring parameters, and to acquire multiple progress monitoring images.

[0056] Furthermore, the real-time project progress monitoring system based on the integration of BIM and the Internet of Things also includes: The monitoring and comparison processing unit 104 is used to monitor and compare multiple progress monitoring images based on multiple difference monitoring images, determine whether the plan has been completed, and generate a progress monitoring report.

[0057] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring real-time progress of engineering based on BIM and Internet of Things, characterized in that, The method specifically comprises the following steps: Obtain the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completion BIM data and planning BIM data; Differentially compare the planning BIM data and the completion BIM data, extract a plurality of difference monitoring images, and determine corresponding difference monitoring points and difference monitoring parameters; After the planning construction is completed, based on the Internet of Things, the progress monitoring is performed according to the plurality of difference monitoring points and the plurality of difference monitoring parameters, and a plurality of progress monitoring images are obtained; Based on the plurality of difference monitoring images, the plurality of progress monitoring images are compared and monitored, it is judged whether the planning is completed, and a progress monitoring report is generated. 2.The BIM and Internet of Things (IoT) -based engineering real-time progress monitoring method of claim 1, wherein, The method specifically comprises the following steps: Obtain the engineering BIM data of the target construction project, update the construction completion data and construction planning data, and match the corresponding completion BIM data and planning BIM data specifically comprising the following steps: Obtain the engineering BIM data of the target construction project; Update the construction completion data and construction planning data; Match the corresponding completion BIM data according to the construction completion data; 3.The BIM and Internet of Things (IoT) -based engineering real-time progress monitoring method of claim 2, wherein, Based on the engineering BIM data and the construction planning data, the completion BIM data is supplemented for planning construction, and planning BIM data is generated. The method specifically comprises the following steps: Based on the completion BIM data, the planning BIM data is differentially compared to determine a plurality of construction difference regions; From the plurality of construction difference regions, a plurality of main difference regions are selected; From the planning BIM data, a plurality of difference monitoring images corresponding to the plurality of main difference regions are extracted; 4. The BIM and Internet of Things (IoT) integrated engineering real-time progress monitoring method of claim 3, wherein, Determine the difference monitoring points and the difference monitoring parameters corresponding to the plurality of difference monitoring images. The method specifically comprises the following steps: For the extracted difference monitoring image, identify the corresponding boundary vertex position information in the BIM three-dimensional space to generate an initial monitoring point set; Associate the spatial coordinate identifiers in the initial monitoring point set with the BIM component database to generate a monitoring point configuration table; Merge the points in the monitoring point configuration table that are adjacent in space and have the same type of monitored physical quantity to generate an optimized core monitoring point list; Preset a construction process logic, establish a parameter association rule according to the preset construction process logic, generate a difference parameter linkage rule group, and generate a dynamic monitoring control instruction set by increasing the monitoring frequency level of the construction area of the first construction period and decreasing the monitoring frequency level of the construction area of the second construction period; Integrate the parameter type template of the monitoring point configuration table, the core point coordinates of the optimized core monitoring point list, the difference parameter linkage rule group, and the dynamic monitoring control instruction set to generate an executable monitoring parameter configuration package; The check function is configured in the executable monitoring parameter configuration package, and finally a self-correctable final monitoring parameter set is obtained, and the self-correctable final monitoring parameter set is taken as a difference monitoring point and a difference monitoring parameter corresponding to a difference monitoring image; wherein, the check function is configured in the executable monitoring parameter configuration package, including: reducing the monitoring strength for the point with stable data, and triggering the abnormal review mechanism for the point with parameter conflict.

5. The BIM and Internet of Things (IoT) integrated engineering real-time progress monitoring method of claim 4, wherein, The spatial coordinate identifier in the initial monitoring point set is associated with the BIM component database, including: associating the beam-column structure point with a displacement deformation monitoring template; associating the decoration engineering point with a size precision monitoring template; and associating the equipment installation point with a positioning deviation monitoring template.

6. The BIM and Internet of Things (IoT) fusion-based engineering real-time progress monitoring method of claim 5, wherein, After the planning construction is completed, progress monitoring is performed based on the Internet of Things according to the plurality of difference monitoring points and the plurality of difference monitoring parameters, and a plurality of progress monitoring images are obtained, which specifically includes the following steps: After the planning construction is completed, an engineering inspection route is planned according to the plurality of difference monitoring points; An inspection control for progress monitoring according to the engineering inspection route; Based on the Internet of Things, progress monitoring is performed according to the plurality of difference monitoring points and the plurality of corresponding difference monitoring parameters, and a plurality of progress monitoring images are obtained.

7. The BIM and IoT fusion-based engineering real-time progress monitoring method according to claim 6, characterized in that, The engineering inspection route is planned according to the plurality of difference monitoring points after the planning construction is completed, which specifically includes the following steps: According to the plurality of difference monitoring points, the coordinate data of each point in the BIM three-dimensional space is obtained, including longitude, latitude and elevation value, and a point coordinate set is generated; The point coordinate set is associated with the difference monitoring parameter, and a point data set is generated based on the parameter type classification; wherein, the parameter type classification includes: binding the displacement deformation label to the structure point; binding the size precision label to the decoration point; and binding the positioning deviation label to the equipment point; According to the parameter type label in the point data set, different basic priority levels are assigned to different equipment points, and the basic priority levels are adjusted according to the region completion urgency in the construction progress plan to obtain a point priority evaluation result; The point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is 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set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, and the point coordinate set is subjected to spatial analysis, ​ ​ ​ 8. The BIM and Internet of Things (IoT) integrated engineering real-time progress monitoring method of claim 7, wherein, The progress monitoring at the multiple difference monitoring points according to the multiple corresponding difference monitoring parameters specifically includes the following steps: Matching the multiple corresponding difference monitoring parameters at the multiple difference monitoring points; Performing monitoring shooting control according to the multiple difference monitoring parameters; Receiving the progress monitoring images and feedback signals corresponding to the multiple difference monitoring points; When there is a feedback signal but no progress monitoring image, performing collaborative Internet of Things analysis based on the planning BIM data, selecting a collaborative delivery device, and receiving a collaboratively delivered progress monitoring image. 9.The BIM and IoT fusion-based engineering real-time progress monitoring method according to claim 8, characterized in that, The monitoring and comparison of the multiple progress monitoring images based on the multiple difference monitoring images to determine whether the planning is completed and to generate a progress monitoring report specifically includes the following steps: For each difference monitoring image and corresponding progress monitoring image, structural contour line features, key component position identifiers, and color distribution feature identifiers are extracted respectively to generate an image feature dataset with feature identifiers; Feature matching is performed on the difference monitoring images and progress monitoring images of the same monitoring point in the image feature dataset with feature identifiers to generate original comparison data after comparison; wherein the feature matching includes contour line feature coincidence degree comparison, key component position offset calculation, and color distribution similarity analysis; The construction stage is obtained and the comparison tolerance threshold is adjusted according to the construction stage, and the original comparison data after comparison is threshold judged according to the adjusted comparison tolerance threshold to obtain a preliminary determination result; wherein adjusting the comparison tolerance threshold includes: using a preset point threshold in the main structure stage; using a preset medium threshold in the decoration stage; using a preset loose threshold in the equipment installation stage; The preliminary determination result is fused with the difference monitoring image to obtain a planning reference image, the actual construction deviation is marked on the planning reference image, a green translucent verification layer is added to the passed area, a red flashing warning box is added to the unpassed area, and a displacement vector line is drawn at the key offset position to generate a comparison result image with diagnostic markers; The warning box area in the comparison result image with diagnostic markers is analyzed, the BIM component ID in the difference monitoring image is associated, the construction responsibility team and the design change record are bound, and the influence level of the deviation on the subsequent process is marked to generate an abnormal focusing report with impact analysis; The original comparison data after comparison, the preliminary determination result, the comparison result image with diagnostic markers, and the abnormal focusing report with impact analysis are integrated according to the monitoring points to generate a traceable structured comparison result record, and the traceable structured comparison result record is taken as the monitoring comparison result; The multiple monitoring comparison results are identified to determine whether the planning is completed; When it is determined that the planning is completed, the multiple monitoring comparison results are integrated to generate a progress completion monitoring report; When it is determined that the planning is not completed, multiple abnormal comparison results are selected from the multiple monitoring comparison results, and a progress abnormality monitoring report is generated.

10. The BIM and Internet of Things (IoT) fusion-based engineering real-time progress monitoring method of claim 9, wherein, The identification of the multiple monitoring comparison results to determine whether the planning is completed specifically includes the following steps: For the traceable structured contrast result record, the percentage value of contour line coincidence, the position offset of key components, the color distribution similarity rating and the abnormal influence level are extracted respectively to generate a result element data set with quantitative indicators; Based on the type of construction stage, the weight coefficients corresponding to each quantitative indicator are configured to generate a stage-adaptive weight configuration table; For each quantitative indicator in the result element data set with quantitative indicators, a grading score is performed according to the pre-set grading standard to generate a result indicator set with scores; wherein the grading score includes: performing contour line coincidence score according to the pre-set interval corresponding to the score value assigned according to the percentage value of contour line coincidence; performing position offset score according to the pre-set interval corresponding to the score value assigned according to the ratio of position offset to design allowed deviation; performing color similarity score according to the pre-set interval corresponding to the score value assigned according to the color distribution similarity value; The stage-adaptive weight configuration table is applied to the result indicator set with scores to calculate the single item score and the corresponding weight coefficient to generate the comprehensive evaluation score of each monitoring point; The evaluation scores of adjacent points in the comprehensive evaluation scores of each monitoring point are analyzed to generate an evaluation result set with association marks; wherein the association analysis of the evaluation scores of adjacent points in the comprehensive evaluation scores of each monitoring point includes: when a high-score area surrounds a low-score area, triggering a regional contradiction mark; when the comprehensive score of consecutive points does not exceed the pre-set score threshold, triggering a progress blocking mark; Based on the evaluation result set with association marks, a decision logic is constructed to obtain a preliminary planning completion status determination conclusion; wherein the decision logic includes: when the comprehensive evaluation score of the monitoring point exceeds the target score value, it is determined to be completely achieved; when the comprehensive evaluation score of the monitoring point exceeds the target threshold score value and there is no regional contradiction mark, it is determined to be basically achieved; when there is a progress blocking mark and the comprehensive evaluation score of the monitoring point does not exceed the target threshold score value, it is determined to be not achieved; The result element data set with quantitative indicators, the comprehensive evaluation score of each monitoring point, the evaluation result set with association marks and the preliminary planning completion status determination conclusion are generated into a final planning determination report with decision basis according to the construction area, and the final planning determination report with decision basis is taken as the judgment result of whether the planning is completed.

11. A BIM and Internet of Things (IoT) based real-time progress monitoring system for engineering projects, characterized in that, The system applies the engineering real-time progress monitoring method based on the fusion of BIM and Internet of Things as claimed in any one of claims 1 to 10, and the system comprises: A BIM data processing unit is configured to obtain engineering BIM data of a target construction project, update construction completion data and construction planning data, and match corresponding completion BIM data and planning BIM data; A differential comparison processing unit is configured to perform differential comparison on the planning BIM data and the completion BIM data, extract a plurality of difference monitoring images, and determine corresponding difference monitoring points and difference monitoring parameters; A construction progress monitoring unit is configured to perform progress monitoring based on the Internet of Things according to a plurality of difference monitoring points and a plurality of difference monitoring parameters after the planning construction is completed, and obtain a plurality of progress monitoring images. A monitoring comparison processing unit is configured to monitor the progress monitoring images based on the difference monitoring images, determine whether the plan is completed, and generate a progress monitoring report.

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