Bridge construction tower deviation control method and system based on image detection

Abstract

The application discloses a bridge construction tower deviation control method and system based on image detection, relates to the technical field of tower deviation control, image data acquisition and multi-source information access, image feature extraction and effectiveness screening, geometric alignment and attitude conclusion generation, multi-source collaborative quality evaluation and trend judgment, control instruction generation and execution, closed-loop review and evidence chain reservation, the application forms unified input by fusing monitoring images, BIM baseline, illumination, crane operation and meteorological data, extracts credible edges and removes occlusion and artifacts, improves alignment accuracy, generates a graded conclusion based on attitude parameters and fitting ratios, obtains a final result by weight weighting synthesis, judges the trend in combination with continuous periods, ensures stable and reliable determination, unifies the control instruction set field, and the reply is traceable, forms a differential evaluation report and a versioned evidence chain archive after execution, realizes whole-process closed-loop control and quality traceability.

Description

Technical Field

[0001] This invention relates to the field of tower deflection control technology, and in particular to a method and system for controlling tower deflection during bridge construction based on image detection. Background Technology

[0002] During bridge construction, the verticality control of the main tower is directly related to the overall stability of the structure and construction safety. Currently, common monitoring methods mainly rely on total stations or laser theodolites to measure single points. When wind-induced swaying or thermal disturbances occur during construction, the results of single-point measurements need to be repeated multiple times for confirmation, leading to delays in judgment. On-site hoisting equipment, temporary components, and personnel activities can all obstruct the measurement points, causing monitoring interruptions or result deviations. Under low light conditions, the effectiveness of laser and optical measurements is significantly reduced, making it impossible to continuously track the tower's attitude at night. Traditional methods often rely on only a single device or single-point data, failing to integrate information such as meteorological conditions, construction progress, and equipment operating status, making it difficult to achieve stable monitoring and trend judgment throughout the entire process. Therefore, how to establish a continuous, interference-resistant, and traceable tower deviation monitoring and control method through image detection combined with multi-source data analysis without relying on additional hardware modifications has become an urgent problem to be solved in the field of bridge construction.

[0003] Currently, Chinese invention patent application number CN202310567252.1 discloses an automatic correction method for cable towers during bridge construction. During bridge construction, the method monitors the deformation of the cable tower in real time and adjusts the extension and retraction of the cables on both sides of the tower accordingly. This ensures that the deformation value of the cable tower remains within a preset range, guaranteeing the verticality of the cable tower and the balance of forces on both sides, thus solving the construction risk problem caused by cable tower misalignment. Simultaneously, during the adjustment of the extension and retraction of the cables on both sides of the cable tower, the stress on all cables is monitored and controlled in real time, ensuring that the stress on all cables is within the corresponding preset stress range. This avoids a large deviation between the actual stress and the design stress of the cables, preventing premature failure of the cables during later bridge use. The entire adjustment process is integrated into the bridge construction process, reducing the construction cycle and ensuring construction progress.

[0004] The aforementioned technologies are insufficient for continuous monitoring, dynamic evaluation, and closed-loop control of the tower's attitude during construction. This invention establishes a reliable edge detection and differential evaluation mechanism by integrating multi-source data, thereby enabling real-time detection, trend judgment, and traceable correction of attitude deviations. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are difficult to achieve continuous monitoring, dynamic evaluation and closed-loop control of tower attitude during construction. This invention establishes a reliable edge determination and differential evaluation mechanism by integrating multi-source data, thereby realizing real-time detection, trend judgment and traceable correction of attitude deviation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A method for controlling tower deflection during bridge construction based on image detection includes the following steps:

[0008] Step S1: Read the construction site monitoring images and main tower related images and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset.

[0009] Step S2: Identify the outer contour of the main tower, segment boundary lines and image shadow areas in the image; generate predicted shadow areas based on sunlight parameters and compare them with the image shadow areas to retain reliable edges; at the same time, generate and remove occluded areas based on crane operation records and output a set of reliable edges.

[0010] Step S3: Align the trusted edge set with the BIM geometric baseline data and output the matching degree data. Based on the relative relationship between the trusted edge set and the design vertical line, determine and output the attitude parameters of roll direction, pitch direction and top horizontal offset direction. Then, generate a hierarchical attitude conclusion based on the continuity of alignment.

[0011] Step S4: Based on the set of credible edges, sunlight parameters, image shadow areas, occlusion areas and meteorological records, the image clarity and edge validity, consistency of illumination and shadow, occlusion situation and on-site environment are judged and weighted to generate image conclusions. The image conclusions are weighted and synthesized to generate the final attitude conclusion. The continuous trend or fluctuation state is judged and output based on the final attitude conclusions in the continuous period.

[0012] Step S5: Based on the attitude conclusion and trend judgment, generate a set of control instructions including differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment. The control instructions are expressed in a unified field. Input the execution object, amplitude level and execution sequence to the construction control terminal, and record the receipt information and status mark.

[0013] Step S6: After execution, acquire new periodic images and repeat the processing to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, save image screenshots, credible edge markers, attitude conclusions and control receipts, and archive versioned evidence chains.

[0014] Preferably, step S1 includes the following sub-steps:

[0015] Step S101: Read the construction site monitoring images and main tower related images, and add corresponding shooting time, shooting location information, lens orientation information, lens angle information and image resolution data to the construction site monitoring images and main tower related images;

[0016] Step S102: Synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and weather records, and establish the correspondence between BIM geometric baseline data, solar illumination parameters, crane operation records and weather records and shooting time and shooting location information;

[0017] Step S103: Time and location are unified for the construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records, and a unified dataset is output. The time unification uses the same time base to align the construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records. The location unification uses the BIM geometric baseline data as a location reference to normalize and describe the lens orientation information and lens viewing angle information.

[0018] Preferably, step S2 includes the following sub-steps:

[0019] Step S201: Identify the outer contour of the main tower, segment boundary lines, and image shadow areas in the construction site monitoring images and related images of the main tower;

[0020] During the identification process, the construction site monitoring images and related images of the main tower are preprocessed, and the location information of the segment boundary lines is extracted.

[0021] Step S202: Generate a predicted shadow region based on the sunlight parameters, compare the shadow region in the image with the predicted shadow region, and retain the edges that are consistent with the predicted shadow region as reliable edges;

[0022] During the comparison process, the light and shadow conditions corresponding to the sunlight parameters are matched with the shadow area of ​​the image. If the shadow area of ​​the image matches the predicted shadow area, it is marked as reliable; otherwise, it is regarded as a false edge and is removed.

[0023] Step S203: Generate occlusion areas based on crane operation records and remove occlusion areas, outputting a set of reliable edges;

[0024] Among them, the slewing angle and trolley displacement data in the crane operation record are mapped onto the plane of the construction site monitoring image and the main tower related image, forming an area that obscures the orientation of the tower body. The edges of the area that obscures the orientation of the tower body are not included in the calculation.

[0025] Preferably, step S3 includes the following sub-steps:

[0026] Step S301: Align the trusted edge set with the BIM geometric baseline data;

[0027] During the alignment process, the reliable edge segments in the reliable edge set are compared one by one with the outer contour of the BIM geometric baseline data, and the matching degree data is output. Reliable edge segments with matching degree data greater than the preset first matching degree threshold are marked.

[0028] Step S302: Determine the relative relationship between the outer contour of the tower body and the designed vertical line, and output the attitude parameters, including the roll direction, pitch direction and top horizontal offset direction.

[0029] During the judgment process, the set of credible edges is used as the basis for the outer contour of the tower body, and the design vertical line in the BIM geometric baseline data is used as a comparison reference. By comparing the relative positions of the credible edge line segments and the design vertical line segment by segment, it is confirmed whether the tower body has an overall tilt or top offset, and the judgment result is described in directional language.

[0030] Step S303: Generate a hierarchical description based on the continuity of alignment to form an attitude conclusion;

[0031] When generating a hierarchical description, a set of credible edges is used as the basis for the outer contour, and BIM geometric baseline data is used as the design baseline reference. The degree of continuous fit between the set of credible edges and the BIM geometric baseline data is compared, and the attitude conclusion is classified into levels, including normal, close to the threshold, and beyond the limit. Combined with attitude parameters, the set of credible edges, BIM geometric baseline data, and attitude parameters are compared and cross-validated. First, the overall fit ratio between the credible edges and the BIM geometric baseline data is determined, and then the attitude parameters are combined for analysis. The analysis includes confirming the direction and degree of the offset item by item, and outputting the attitude conclusion.

[0032] Preferably, step S4 includes the following sub-steps:

[0033] Step S401: Based on the assignment rules, determine and output the image conclusion:

[0034] Image sharpness and edge validity are based on a set of reliable edges;

[0035] If the credible edge continuously matches the BIM geometric baseline data within the tower's height range and the coverage ratio is not less than half, it is considered clear.

[0036] If the fit is only partially high and the coverage is between one-quarter and half, it is considered general.

[0037] A coverage rate of less than one-quarter is considered poor.

[0038] The consistency between lighting and shadow is based on sunlight parameters and the shadow area of ​​the image;

[0039] If the boundary of the image shadow area and the boundary of the predicted shadow area are within a range where the positional difference does not exceed 10 pixels or the angular deviation does not exceed 5 degrees, they are considered to be consistent and recorded as passed.

[0040] If the difference exceeds the above range, it will be judged as inconsistent and recorded as unsuccessful;

[0041] Occlusion is determined based on the occlusion area; an image located within the occlusion area is considered occluded.

[0042] The on-site environment was based on meteorological records;

[0043] Good visibility and moderate or lower wind speeds are considered usable conditions.

[0044] Moderate visibility or high wind speed is considered an environment with limited visibility.

[0045] Based on the above criteria: images that simultaneously satisfy the conditions of being clear, passable, unobstructed, and usable in the environment are given high weight.

[0046] A weight is assigned to a condition that satisfies any three of the criteria or simultaneously satisfies clarity and passability.

[0047] Any instances that are obscured, fail to pass through, or have poor clarity will be assigned a low weight.

[0048] If the monitoring images at the construction site and the images related to the main tower correspond to key process nodes in the construction progress, but do not meet the high weight condition, then the corresponding image conclusions shall be given priority within the same level without changing the level boundary.

[0049] Step S402: The image conclusions are weighted and synthesized to generate the final pose conclusion;

[0050] During weighted synthesis, image conclusions with weights greater than a preset high weight threshold are used as the main reference. Image conclusions with weights greater than a preset medium weight threshold and less than a preset high weight threshold, and image conclusions with weights less than a preset medium weight threshold are included in a preset ratio to generate the final weighted result. The final weighted result is then output as the final pose conclusion.

[0051] Step S403: When a consistent offset direction appears within a continuous period, it is marked as a continuous trend;

[0052] When the direction of offset changes frequently, it is marked as a fluctuating state;

[0053] The time interval of the continuous cycle is consistent with the acquisition frequency of the construction site monitoring images;

[0054] When judging trends, if the final conclusion of two or more consecutive periods is in the same direction and the level is close to or exceeds the preset threshold, it is marked as a continuous deviation trend and prompts to enter the control level.

[0055] If the direction changes repeatedly within multiple cycles and the level remains normal or close to below the preset threshold, it is marked as a fluctuation state, and a prompt is made to enter the observation level.

[0056] Output trend judgment.

[0057] Preferably, step S5 includes the following sub-steps:

[0058] Step S501: Generate a control command set based on the attitude conclusion and trend judgment;

[0059] The control instruction set determines the action level and priority object based solely on attitude conclusions and trend judgments, and describes each control instruction as action type, execution object, amplitude level, execution order, and verification method.

[0060] Step S502, the control instruction set includes differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment, the differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment are formed into control instruction entries with a unified field;

[0061] Step S503: Input the execution object, amplitude level, and execution sequence, and input the control instruction set into the construction control terminal for execution;

[0062] Before input, the control command items are arranged according to the execution order and sent to the construction control terminal one by one. The acknowledgment information of the construction control terminal is received and recorded. Each acknowledgment is linked to the corresponding control command and its attitude conclusion. At the same time, the status markers of executed, pending, and reviewed are attached to the command control command items.

[0063] Preferably, step S6 includes the following sub-steps:

[0064] Step S601: After execution, collect the monitoring images of the construction site and related images of the main tower for the new cycle, and repeat the processing.

[0065] During the data acquisition process, the execution receipt returned by the construction control terminal is matched with the timestamps of the newly acquired construction site monitoring images and main tower related images. At the same time, steps S1 to S5 are run again to generate data output consistent with the previous cycle.

[0066] Step S602: Generate a differential evaluation report and record the changes in attitude conclusions before and after execution;

[0067] The prime difference assessment report includes the difference in attitude conclusions before and after execution, compares the changing trends of roll direction, pitch direction and top horizontal offset, and records trend labels;

[0068] Step S603: Save the image screenshot, trusted edge markers, pose conclusions and control receipts, and generate a versioned evidence chain archive.

[0069] The image screenshots are labeled with corresponding credible edge markers, the attitude conclusions are labeled with level tags, and the control receipts contain information on the action execution object, amplitude level, and sequence. The image screenshots, credible edge markers, attitude conclusions, and control receipts are packaged into a unified archive file and labeled with the version number and generation time.

[0070] Preferably, the differential evaluation report includes posture conclusions before and after execution, trend labels, a list of images involved in the conclusions, weight scores, and control feedback;

[0071] The image list used in the conclusion includes the shooting time, shooting location, and availability weight of each construction site monitoring image and related images of the main tower. The control receipt includes the execution object, amplitude level, sequence, and feedback information from the construction control terminal for each control instruction item.

[0072] Preferably, the control instruction set is divided into observation level, fine-tuning level and correction level, corresponding to normal, near threshold and over-limit states respectively, and the action object, amplitude level and verification method are clearly specified in the control instruction entries.

[0073] A bridge construction tower deflection control system based on image detection includes a data acquisition and access module, a feature extraction and filtering module, an alignment posture generation module, a collaborative evaluation and judgment module, an instruction generation and execution module, and a verification and evidence retention module.

[0074] The data acquisition and access module is used to read construction site monitoring images and main tower related images and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset.

[0075] The feature extraction and filtering module is used to identify the outer contour of the main tower, segment boundary lines and image shadow areas in the image, generate predicted shadow areas based on sunlight parameters and compare them with the image shadow areas to retain reliable edges, and generate and remove occluded areas based on crane operation records, and output a set of reliable edges.

[0076] The alignment attitude generation module is used to align the credible edge set with the BIM geometric baseline data and output matching degree data. Based on the relative relationship between the credible edge set and the design vertical line, it judges and outputs attitude parameters of roll direction, pitch direction and top horizontal offset direction, and then generates hierarchical attitude conclusions based on the continuity of alignment.

[0077] The collaborative evaluation and judgment module is used to judge and assign weights to image clarity and edge effectiveness, illumination and shadow consistency, occlusion situation and on-site environment based on the set of credible edges, sunlight parameters, image shadow area, occlusion area and meteorological records, generate image conclusions, weightedly synthesize the image conclusions to generate the final attitude conclusion, and judge and output the continuous trend or fluctuation state based on the final attitude conclusion within the continuous period.

[0078] The instruction generation and execution module is used to generate a set of control instructions based on posture conclusions and trend judgments, including differential adjustment of climbing formwork cylinders, adjustment of temporary cable tension, and adjustment of outer tie rod tension. The control instructions are expressed in a unified field, and the execution object, amplitude level, and execution sequence are input to the construction control terminal, and the feedback information and status mark are recorded.

[0079] The verification evidence retention module is used to collect new cycle images after execution and repeat the processing process to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, it saves image screenshots, credible edge markers, attitude conclusions and control receipts, and archives versioned evidence chains.

[0080] The beneficial effects of this invention are as follows: This invention integrates monitoring images, BIM baselines, lighting, crane operation data, and meteorological data to form a unified input, extracts reliable edges and removes occlusions and artifacts, improving alignment accuracy. It generates hierarchical conclusions based on posture parameters and fitting ratios, and obtains the final result through weighted synthesis. It also combines continuous cycles to make trend judgments to ensure stable and reliable judgments. The generated control instruction set has unified fields and traceable acknowledgments. After execution, it generates a differential evaluation report and a versioned evidence chain archive, realizing closed-loop control and quality traceability throughout the entire process. Attached Figure Description

[0081] Figure 1 A flowchart illustrating the steps of a bridge construction tower deflection control method based on image detection, as provided in one embodiment of the present invention;

[0082] Figure 2 This is a basic flowchart of a bridge construction tower deflection control system based on image detection, provided as an embodiment of the present invention. Detailed Implementation

[0083] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0084] Example 1, referring to Figure 1 This paper presents a method for controlling tower deflection during bridge construction based on image detection, comprising the following steps:

[0085] Step S1: Read the construction site monitoring images and main tower related images and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset.

[0086] Step S2: Identify the outer contour of the main tower, segment boundaries, and image shadow areas in the image. Generate a predicted shadow area based on the sunlight parameters and compare it with the image shadow area to retain reliable edges. At the same time, generate and remove occluded areas based on the crane operation records, and output a set of reliable edges.

[0087] Step S3: Align the trusted edge set with the BIM geometric baseline data and output the matching degree data. Based on the relative relationship between the trusted edge set and the design vertical line, determine and output the attitude parameters of roll direction, pitch direction and top horizontal offset direction. Then, generate a hierarchical attitude conclusion based on the continuity of alignment.

[0088] Step S4: Based on the set of credible edges, sunlight parameters, image shadow areas, occlusion areas, and meteorological records, the image clarity and edge validity, consistency of illumination and shadow, occlusion situation, and on-site environment are judged and weighted to generate image conclusions. The image conclusions are weighted and synthesized to generate the final attitude conclusion. The continuous trend or fluctuation state is judged and output based on the final attitude conclusions in the continuous period.

[0089] Step S5: Based on the attitude conclusion and trend judgment, generate a set of control instructions including differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment. The control instructions are expressed in a unified field. Input the execution object, amplitude level and execution sequence to the construction control terminal, and record the receipt information and status mark.

[0090] Step S6: After execution, acquire new periodic images and repeat the processing to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, save image screenshots, credible edge markers, attitude conclusions and control receipts, and archive versioned evidence chains.

[0091] This invention integrates monitoring images, BIM baselines, lighting, crane operation data, and meteorological data to form a unified input. It extracts reliable edges and removes occlusions and artifacts to improve alignment accuracy. Based on posture parameters and fitting ratios, it generates hierarchical conclusions, which are then weighted and synthesized to obtain the final result. It also incorporates continuous periodic trend judgment to ensure stable and reliable judgment. The generated control instruction set has unified fields and traceable acknowledgments. After execution, it generates a differential evaluation report and a versioned evidence chain archive, achieving closed-loop control and quality traceability throughout the entire process.

[0092] Step S1 includes the following sub-steps:

[0093] Step S101: Read the construction site monitoring images and related images of the main tower, and add corresponding shooting time, shooting location information, lens orientation information, lens angle information and image resolution data to the construction site monitoring images and related images of the main tower.

[0094] Step S101 adds information such as shooting time, shooting location, lens orientation, lens angle and resolution to the construction site monitoring images and main tower images to achieve full-element recording of image data, providing complete basic information for subsequent screening of usable images and alignment operations.

[0095] Step S102: Synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records, and establish the correspondence between BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records and shooting time and shooting location information.

[0096] Step S102 establishes a time and location correspondence between BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records and image data, thereby achieving accurate matching of multi-source information and enabling various types of data to support subsequent geometric alignment and reliable edge judgment under a unified reference.

[0097] Step S103: Time and location are unified for construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and a unified dataset is output. Time unification uses the same time base to align the construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records. Location unification uses BIM geometric baseline data as a location reference to normalize the description of lens orientation information and lens viewing angle information.

[0098] Step S103 unifies the time and location of images and multi-source data, and outputs a unified dataset containing source and alignment information, ensuring that subsequent processing steps can run under a consistent time base and spatial reference, thereby improving the overall accuracy and traceability of the calculation.

[0099] Step S1 involves collecting monitoring images from the construction site and relevant images of the main tower, and combining them with BIM geometric baselines, solar illumination parameters, crane operation records, and meteorological records to form a unified input dataset. This enables synchronous access and normalization of multi-source information. This step ensures that subsequent feature extraction and attitude determination processes are performed under a unified temporal and spatial reference, thereby improving data integrity and consistency and reducing errors caused by differences in data sources.

[0100] Step S2 includes the following sub-steps:

[0101] Step S201: Identify the outer contour of the main tower, segment boundary lines, and image shadow areas in the construction site monitoring images and related images of the main tower.

[0102] During the identification process, the monitoring images of the construction site and related images of the main tower are preprocessed, and the location information of the segment boundary lines is extracted.

[0103] Step S201 identifies the outer contour of the main tower, segment boundaries, and image shadow areas, and preprocesses the image during the identification process to ensure the continuity of edge extraction and the accuracy of segment positions, laying the foundation for subsequent baseline alignment and offset determination.

[0104] Step S202: Generate a predicted shadow region based on the sunlight parameters, compare the shadow region in the image with the predicted shadow region, and retain the edges that are consistent with the predicted shadow region as reliable edges.

[0105] During the comparison process, the lighting conditions corresponding to the sunlight parameters are matched with the shadow area of ​​the image. If the shadow area of ​​the image matches the predicted shadow area, it is marked as reliable; otherwise, it is considered a false edge and is removed.

[0106] Step S202 generates a predicted shadow area using sunlight parameters and compares it with the shadow area in the image. This effectively distinguishes between real edges and false edges caused by illumination and reflection, retaining only the edges that match the prediction as reliable edges, thereby improving the reliability and anti-interference ability of edge extraction.

[0107] Step S203: Generate occlusion areas based on crane operation records and remove occlusion areas, outputting a set of reliable edges.

[0108] Among them, the slewing angle and trolley displacement data in the crane operation record are mapped onto the plane of the construction site monitoring image and the main tower related image, forming an area that obscures the orientation of the tower body. The edges of the area that obscures the orientation of the tower body are not included in the calculation.

[0109] Step S203 maps the slewing angle and trolley displacement data from the crane operation record onto the image plane, generates occluded areas and removes corresponding edges, ensuring that the output set of reliable edges is not affected by the occlusion of construction equipment, thereby providing a stable input for subsequent geometric alignment.

[0110] Step S2 extracts features and filters validity from construction site monitoring images and relevant images of the main tower, generating a reliable edge set while ensuring image clarity and information integrity. This step effectively eliminates false edges and invalid data caused by lighting differences, glare interference, and construction obstructions, providing accurate and stable input data for subsequent geometric alignment and attitude conclusion generation.

[0111] Step S3 includes the following sub-steps:

[0112] Step S301: Align the trusted edge set with the BIM geometric baseline data.

[0113] During the alignment process, the reliable edge segments in the reliable edge set are compared one by one with the outer contour of the BIM geometric baseline data, and the matching degree data is output. Reliable edge segments with matching degree data greater than the preset first matching degree threshold are marked.

[0114] Step S301 effectively identifies edge segments that highly match the design baseline by comparing the set of reliable edges with the BIM geometric baseline segment by segment and outputting the matching degree data. This process improves alignment accuracy and eliminates interfering edges with low matching degree, providing stable input for subsequent attitude parameter determination.

[0115] Step S302: Determine the relative relationship between the outer contour of the tower body and the designed vertical line, and output the attitude parameters, including the roll direction, pitch direction and top horizontal offset direction.

[0116] During the judgment process, the set of credible edges is used as the basis for the outer contour of the tower body, and the design vertical line in the BIM geometric baseline data is used as a comparison reference. By comparing the relative positions of the credible edge line segments and the design vertical line segment by segment, it is confirmed whether the tower body has an overall tilt or top offset, and the judgment results are described in directional language.

[0117] Step S302, by using a set of reliable edges as the basis for the tower's outer contour and the designed vertical line as a reference, can output attitude parameters including roll direction, pitch direction, and top horizontal offset direction. This effect ensures the directional expression of the tower's tilt and offset, allowing the offset characteristics to be clearly quantified and determined.

[0118] Step S303: Generate a hierarchical description based on the continuity of alignment to form an attitude conclusion.

[0119] When generating a hierarchical description, a set of credible edges is used as the basis for the outer contour, and BIM geometric baseline data is used as the design baseline reference. The degree of continuous fit between the set of credible edges and BIM geometric baseline data is compared, and the attitude conclusion is classified into levels, including normal, close to the threshold, and exceeding the limit. Combined with attitude parameters, the set of credible edges, BIM geometric baseline data, and attitude parameters are compared and cross-validated. First, the overall fit ratio between the credible edges and BIM geometric baseline data is determined, and then the attitude parameters are combined for analysis. The analysis includes confirming the direction and degree of offset item by item, and outputting the attitude conclusion.

[0120] Step S303 generates a hierarchical description based on alignment continuity, classifying the attitude conclusions into normal, near-threshold, and out-of-limit categories. This is then cross-validated using attitude parameters, comprehensively reflecting the degree of conformity and offset characteristics between the tower's outer contour and the design baseline. This ensures the accuracy and traceability of the attitude conclusions, providing an operational grading standard for subsequent monitoring and risk warning.

[0121] Step S3 aligns the trusted edge set with the BIM geometric baseline data, combines this with the design vertical lines to determine attitude parameters, and generates a hierarchical attitude conclusion based on continuity and fit. This step enables multi-level comparison between the actual outer contour extracted from the image and the design baseline, ensuring the accuracy of attitude determination and providing a quantifiable structural health assessment through hierarchical conclusions. This provides a reliable basis for early identification and subsequent warning of tower deformation.

[0122] Step S4 includes the following sub-steps:

[0123] Step S401: Based on the assignment rules, determine and output the image conclusion:

[0124] Image sharpness and edge validity are based on a set of reliable edges.

[0125] If the credible edge continuously matches the BIM geometric baseline data within the tower's height range and the coverage ratio is not less than half, it is considered clear.

[0126] If the fit is only localized and the coverage is between one-quarter and half, it is considered general.

[0127] Coverage of less than one-quarter is considered poor.

[0128] The consistency of lighting and shadows is based on the sunlight parameters and the shadow areas of the image.

[0129] If the boundary of the image shadow area and the boundary of the predicted shadow area are within a range where the positional difference does not exceed 10 pixels or the angular deviation does not exceed 5 degrees, then they are considered to be consistent and recorded as passed.

[0130] If the difference exceeds the above range, it is judged as inconsistent and recorded as unacceptable.

[0131] Occlusion is determined by the occlusion area; an image located within the occlusion area is considered occluded.

[0132] The on-site environment was based on meteorological records.

[0133] Good visibility and moderate or lower wind speeds are considered usable conditions.

[0134] Average visibility or high wind speed is considered an environment with limited visibility.

[0135] Based on the above criteria, images that simultaneously satisfy the criteria of being "clear", "passable", "unobstructed", and "usable in the environment" are given high weight.

[0136] Assign weights to those that satisfy any three of the criteria, or simultaneously satisfy both "clarity" and "pass".

[0137] Any entry marked as "obstructed," "failed," or of poor clarity will be assigned a low weight.

[0138] If the monitoring images at the construction site and the relevant images of the main tower correspond to key process nodes in the construction progress, but do not meet the high weight conditions, then the corresponding image conclusions shall be given priority within the same level without changing the level boundaries.

[0139] Step S401, through a step-by-step assessment of image clarity, consistency of lighting and shadows, occlusion, and the site environment, classifies the construction site monitoring images and main tower-related images into different levels and assigns different weights based on comprehensive conditions. This ensures that high-quality images can still be selected as reliable input even in complex environments, while prioritizing the adoption of images from key process nodes, thereby enhancing the rationality and engineering applicability of the conclusions.

[0140] Step S402: The image conclusions are weighted and synthesized to generate the final pose conclusion.

[0141] During weighted synthesis, image conclusions with weights greater than a preset high weight threshold are used as the main reference. Image conclusions with weights greater than a preset medium weight threshold but less than a preset high weight threshold and image conclusions with weights less than a preset medium weight threshold are included in a preset ratio to generate the final weighted result. The final weighted result is then output as the final pose conclusion.

[0142] Step S402 generates the final pose conclusion by weighted synthesis of the conclusions from images with different weights. This effect ensures that the determination result does not overly rely on a single image, but rather integrates the conclusions from multiple image sources to form a stable weighted result, thereby significantly improving the robustness and consistency of the final pose conclusion.

[0143] Step S403: When a consistent offset direction appears within a continuous period, it is marked as a continuing trend.

[0144] When the direction of offset changes frequently, it is marked as a fluctuating state.

[0145] The time interval of the continuous cycle is consistent with the acquisition frequency of the monitoring images at the construction site.

[0146] When judging trends, if the final conclusion of two or more consecutive cycles is in the same direction and the level is close to or exceeds the preset threshold, it is marked as a continuous deviation trend and prompts to enter the control level.

[0147] If the direction changes repeatedly over multiple periods and the level remains normal or close to or below the preset threshold, it is marked as a fluctuating state, and a prompt is made to enter the observation level.

[0148] Output trend judgment.

[0149] Step S403 identifies continuous offset trends or fluctuations by comparing attitude conclusions over consecutive periods, and then determines the appropriate control or observation level based on the conclusion level. This ensures that dynamic monitoring of the tower's attitude goes beyond instantaneous judgment; it identifies potential hazards through trend analysis and triggers tiered responses, thereby expanding from static detection to dynamic early warning.

[0150] Step S4 comprehensively assesses image quality, lighting and shadow consistency, occlusion, and the on-site environment, assigns different weights, and performs weighted synthesis to ultimately output attitude conclusions and trend judgments. This step realizes a complete logic from single-image judgment to multi-source information fusion, which can eliminate low-reliability data, highlight the importance of key process node images, and, combined with continuous periodic trend analysis, provide a more robust, dynamic, and controllable judgment basis for tower condition monitoring.

[0151] Step S5 includes the following sub-steps:

[0152] Step S501: Generate a control command set based on the attitude conclusion and trend judgment.

[0153] The control instruction set uses attitude conclusions and trend judgments as the sole basis to determine the action level and priority object, and describes each control instruction as a unified field of "action type, execution object, amplitude level, execution order, and verification method".

[0154] Step S501 generates a unified set of control instructions based on attitude conclusions and trend judgments, specifying the action type, execution object, amplitude level, execution sequence, and verification method. This ensures a one-to-one correspondence between control measures and monitoring results, avoids ambiguous or inconsistent instruction transmission, and guarantees the targeted and hierarchical nature of control actions.

[0155] Step S502, the control instruction set includes differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment, and the differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment are formed into control instruction entries with a unified field.

[0156] Step S502 standardizes the differential adjustment of the climbing formwork cylinder, the temporary cable tension adjustment, and the outer tie rod tension adjustment into independent instruction entries, recording them in a unified field format. This ensures that the execution of the three main control methods can be directly invoked and has a standardized description, thereby improving the compatibility and compatibility between different control measures.

[0157] Step S503: Input the execution object, amplitude level, and execution sequence, and input the control instruction set into the construction control terminal for execution.

[0158] Before input, the control command items are arranged according to the execution order and sent to the construction control terminal one by one. The acknowledgment information of the construction control terminal is received and recorded. Each acknowledgment is linked to the corresponding control command and its attitude conclusion. At the same time, the status markers "executed, pending execution, and reviewed" are attached to the command control command items.

[0159] Step S503 involves sequentially arranging the control command items before execution and issuing them one by one to the construction control terminal. Simultaneously, it receives acknowledgments, establishes reference relationships, and appends status information to each command. This achieves closed-loop tracking and traceable management of the entire execution process, ensuring the transparency, verifiability, and reproducibility of control actions.

[0160] Step S5 generates a set of control instructions based on attitude conclusions and trend judgments, standardizes and itemizes them, and then issues them to the construction control terminal for execution, realizing a closed loop from data analysis to control actions. This step ensures that control measures can accurately correspond to the direction and degree of tower offset, and improves the operability, traceability, and execution reliability of control instructions through unified field specifications and a feedback mechanism, thereby achieving dynamic correction and closed-loop control of the tower attitude during bridge construction.

[0161] Step S6 includes the following sub-steps:

[0162] Step S601: After execution, collect the new cycle of construction site monitoring images and main tower related images, and repeat the processing process.

[0163] During the data acquisition process, the execution receipt returned by the construction control terminal is matched with the timestamps of the newly acquired construction site monitoring images and main tower related images. At the same time, steps S1 to S5 are run again to generate data output consistent with the previous cycle.

[0164] After step S601 is completed, it acquires the construction site monitoring images and main tower related images for the new cycle and establishes a correspondence between them and the feedback from the construction control terminal. This ensures the synchronization of data acquisition and control actions in time, and achieves a continuous closed loop of monitoring, analysis and control by repeating steps S1 to S5.

[0165] Step S602: Generate a differential evaluation report and record the changes in attitude conclusions before and after execution.

[0166] The prime difference assessment report includes the difference in attitude conclusions before and after execution, compares the changing trends of roll direction, pitch direction and top horizontal offset, and records trend labels.

[0167] Step S602 generates a differential assessment report, recording the changes in attitude conclusions before and after execution, comparing in detail the differences in roll direction, pitch direction, and top horizontal offset, and attaching trend labels. This effect allows the offset correction effect during construction to be quantified, facilitating the construction team to quickly determine the effectiveness of adjustment measures.

[0168] Step S603: Save the image screenshot, trusted edge markers, pose conclusions and control receipts, and generate a versioned evidence chain archive.

[0169] The image screenshots are labeled with corresponding credible edge markers, the attitude conclusions are labeled with level tags, and the control receipts contain information on the action execution object, amplitude level, and sequence. The image screenshots, credible edge markers, attitude conclusions, and control receipts are packaged into a unified archive file and labeled with the version number and generation time.

[0170] Step S603 saves image screenshots, trusted edge markers, attitude conclusions, and control receipts, and packages them into a versioned chain of evidence archive. This ensures that all key data is completely saved in each cycle and appended with version numbers and timestamps, thereby enabling traceability and auditing of the entire construction process and ensuring that quality control and responsibility definition are based on evidence.

[0171] Step S6 involves re-acquiring monitoring images and main tower images after execution, and then running the data processing flow again in conjunction with the feedback information to form a closed-loop verification mechanism. This step enables dynamic evaluation and trend confirmation of the effectiveness of control measures, and ensures the verifiability, comparability, and traceability of each round of attitude control through differential evaluation reports and versioned evidence chain archiving, providing complete data support for continuous monitoring and subsequent construction quality review.

[0172] The differential evaluation report includes posture conclusions before and after execution, trend labels, a list of images involved in the conclusions, weighted scores, and control feedback.

[0173] The image list used in the conclusion includes the shooting time, shooting location, and availability weight of each construction site monitoring image and related images of the main tower. The control receipt includes the execution object, amplitude level, sequence, and feedback information from the construction control terminal for each control instruction item.

[0174] The control instruction set is divided into observation level, fine-tuning level and correction level, which correspond to normal, near threshold and over-limit states respectively, and the action object, amplitude level and verification method are clearly specified in the control instruction entries.

[0175] Example 2, refer to Figure 2 This paper presents a bridge construction tower deflection control system based on image detection, including a data acquisition and access module, a feature extraction and filtering module, an alignment posture generation module, a collaborative evaluation and judgment module, an instruction generation and execution module, and a verification and evidence retention module.

[0176] The data acquisition and access module is used to read monitoring images of the construction site and related images of the main tower and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset.

[0177] The feature extraction and filtering module is used to identify the outer contour of the main tower, segment boundaries and image shadow areas in the image. It generates predicted shadow areas based on sunlight parameters and compares them with the image shadow areas to retain reliable edges. At the same time, it generates and removes occluded areas based on crane operation records and outputs a set of reliable edges.

[0178] The alignment attitude generation module is used to align the trusted edge set with the BIM geometric baseline data and output matching degree data. Based on the relative relationship between the trusted edge set and the design vertical line, it determines and outputs attitude parameters such as roll direction, pitch direction and top horizontal offset direction, and then generates hierarchical attitude conclusions based on the continuity of alignment.

[0179] The collaborative evaluation and judgment module is used to judge and assign weights to image clarity and edge validity, illumination and shadow consistency, occlusion situation and on-site environment based on the set of credible edges, sunlight parameters, image shadow area, occlusion area and meteorological records, generate image conclusions, weighted synthesis of image conclusions to generate final attitude conclusions, and judge and output the continuous trend or fluctuation state based on the final attitude conclusions in continuous periods.

[0180] The instruction generation and execution module is used to generate a set of control instructions based on attitude conclusions and trend judgments, including differential adjustment of climbing formwork cylinders, adjustment of temporary cable tension, and adjustment of outer tie rod tension. The control instructions are expressed in a unified field, and the execution object, amplitude level, and execution sequence are input to the construction control terminal, and the acknowledgment information and status flags are recorded.

[0181] The verification evidence retention module is used to collect new cycle images after execution and repeat the processing process to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, it saves image screenshots, credible edge markers, attitude conclusions and control receipts, and archives versioned evidence chains.

[0182] This invention unifies and aligns construction site monitoring images, BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records to form a unified input dataset. This effectively reduces the risk of single data source failure. By comparing predicted shadow areas with image shadow areas and identifying occlusion areas, a set of reliable edges is formed. This effectively eliminates false edges caused by thermal disturbance, reflection, or occlusion, ensuring the accuracy of geometric alignment. Through continuous alignment analysis of the reliable edge set and the BIM geometric baseline, attitude parameters of roll, pitch, and horizontal offset are output. Combined with the fit ratio and amplitude differences, hierarchical attitude conclusions are generated, providing clear input for subsequent control. The conclusions for each image are based on factors such as weather, occlusion, illumination, and construction progress. By assigning weights and generating final attitude conclusions through weighted synthesis, the dominant role of high-quality images is highlighted while taking into account diverse data sources, thus enhancing the comprehensiveness of the conclusions. Based on attitude conclusions from continuous periods, continuous offset trends and fluctuation states can be determined, thereby prompting the control or observation level at an early stage to avoid misjudgments and delays. The generated control instruction set uses a unified field, covering action type, execution object, amplitude level, execution order, and verification method, and establishes a reference relationship with the feedback from the construction control end to ensure that the execution process of the instructions is transparent and traceable. After execution, a differential evaluation report is generated, and image screenshots, reliable edge markers, attitude conclusions, and control feedback are saved to form a versioned archive file, which can be used for quality acceptance and support subsequent accountability.

[0183] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling tower deflection during bridge construction based on image detection, characterized in that, Includes the following steps: Step S1: Read the construction site monitoring images and main tower related images and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset. Step S2: Identify the outer contour of the main tower, segment boundary lines and image shadow areas in the image; generate predicted shadow areas based on sunlight parameters and compare them with the image shadow areas to retain reliable edges; at the same time, generate and remove occluded areas based on crane operation records and output a set of reliable edges. Step S3: Align the trusted edge set with the BIM geometric baseline data and output the matching degree data. Based on the relative relationship between the trusted edge set and the design vertical line, determine and output the attitude parameters of roll direction, pitch direction and top horizontal offset direction. Then, generate a hierarchical attitude conclusion based on the continuity of alignment. Step S4: Based on the set of credible edges, sunlight parameters, image shadow areas, occlusion areas and meteorological records, the image clarity and edge validity, consistency of illumination and shadow, occlusion situation and on-site environment are judged and weighted to generate image conclusions. The image conclusions are weighted and synthesized to generate the final attitude conclusion. The continuous trend or fluctuation state is judged and output based on the final attitude conclusions in the continuous period. Step S5: Based on the attitude conclusion and trend judgment, generate a set of control instructions including differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment. The control instructions are expressed in a unified field. Input the execution object, amplitude level and execution sequence to the construction control terminal, and record the receipt information and status mark. Step S6: After execution, acquire new periodic images and repeat the processing to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, save image screenshots, credible edge markers, attitude conclusions and control receipts, and archive versioned evidence chains.

2. The bridge construction tower deflection control method based on image detection as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Read the construction site monitoring images and main tower related images, and add corresponding shooting time, shooting location information, lens orientation information, lens angle information and image resolution data to the construction site monitoring images and main tower related images; Step S102: Synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and weather records, and establish the correspondence between BIM geometric baseline data, solar illumination parameters, crane operation records and weather records and shooting time and shooting location information; Step S103: Time and location are unified for the construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records, and a unified dataset is output. The time unification uses the same time base to align the construction site monitoring images, main tower related images, BIM geometric baseline data, solar illumination parameters, crane operation records, and meteorological records. The location unification uses the BIM geometric baseline data as a location reference to normalize and describe the lens orientation information and lens viewing angle information.

3. The bridge construction tower deflection control method based on image detection as described in claim 2, characterized in that, Step S2 includes the following sub-steps: Step S201: Identify the outer contour of the main tower, segment boundary lines, and image shadow areas in the construction site monitoring images and related images of the main tower; During the identification process, the construction site monitoring images and related images of the main tower are preprocessed, and the location information of the segment boundary lines is extracted. Step S202: Generate a predicted shadow region based on the sunlight parameters, compare the shadow region in the image with the predicted shadow region, and retain the edges that are consistent with the predicted shadow region as reliable edges; During the comparison process, the light and shadow conditions corresponding to the sunlight parameters are matched with the shadow area of ​​the image. If the shadow area of ​​the image matches the predicted shadow area, it is marked as reliable; otherwise, it is regarded as a false edge and is removed. Step S203: Generate occlusion areas based on crane operation records and remove occlusion areas, outputting a set of reliable edges; Among them, the slewing angle and trolley displacement data in the crane operation record are mapped onto the plane of the construction site monitoring image and the main tower related image, forming an area that obscures the orientation of the tower body. The edges of the area that obscures the orientation of the tower body are not included in the calculation.

4. The bridge construction tower deflection control method based on image detection as described in claim 3, characterized in that, Step S5 includes the following sub-steps: Step S501: Generate a control command set based on the attitude conclusion and trend judgment; The control instruction set determines the action level and priority object based solely on attitude conclusions and trend judgments, and describes each control instruction as action type, execution object, amplitude level, execution order, and verification method. Step S502, the control instruction set includes differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment, the differential adjustment of climbing formwork cylinder, temporary cable tension adjustment and outer tie rod tension adjustment are formed into control instruction entries with a unified field; Step S503: Input the execution object, amplitude level, and execution sequence, and input the control instruction set into the construction control terminal for execution; Before input, the control command items are arranged according to the execution order and sent to the construction control terminal one by one. The acknowledgment information of the construction control terminal is received and recorded. Each acknowledgment is linked to the corresponding control command and its attitude conclusion. At the same time, the status markers of executed, pending, and reviewed are attached to the command control command items.

5. The bridge construction tower deflection control method based on image detection as described in claim 4, characterized in that, Step S6 includes the following sub-steps: Step S601: After execution, collect the monitoring images of the construction site and related images of the main tower for the new cycle, and repeat the processing. During the data acquisition process, the execution receipt returned by the construction control terminal is matched with the timestamps of the newly acquired construction site monitoring images and main tower related images. At the same time, steps S1 to S5 are run again to generate data output consistent with the previous cycle. Step S602: Generate a differential evaluation report and record the changes in attitude conclusions before and after execution; The prime difference assessment report includes the difference in attitude conclusions before and after execution, compares the changing trends of roll direction, pitch direction and top horizontal offset, and records trend labels; Step S603: Save the image screenshot, trusted edge markers, pose conclusions and control receipts, and generate a versioned evidence chain archive. The image screenshots are labeled with corresponding credible edge markers, the attitude conclusions are labeled with level tags, and the control receipts contain information on the action execution object, amplitude level, and sequence. The image screenshots, credible edge markers, attitude conclusions, and control receipts are packaged into a unified archive file and labeled with the version number and generation time.

6. The bridge construction tower deflection control method based on image detection as described in claim 5, characterized in that, The differential evaluation report includes posture conclusions before and after execution, trend labels, a list of images involved in the conclusions, weight scores, and control feedback; The image list used in the conclusion includes the shooting time, shooting location, and availability weight of each construction site monitoring image and related images of the main tower. The control receipt includes the execution object, amplitude level, sequence, and feedback information from the construction control terminal for each control instruction item.

7. A bridge construction tower deflection control system based on image detection, applied in a bridge construction tower deflection control method based on image detection as described in any one of claims 1-6, characterized in that, It includes a data acquisition and access module, a feature extraction and filtering module, an alignment posture generation module, a collaborative evaluation and judgment module, an instruction generation and execution module, and a verification and evidence retention module; The data acquisition and access module is used to read construction site monitoring images and main tower related images and their additional information, synchronously access BIM geometric baseline data, solar illumination parameters, crane operation records and meteorological records, and unify the time and location of all data to output a unified dataset. The feature extraction and filtering module is used to identify the outer contour of the main tower, segment boundary lines and image shadow areas in the image, generate predicted shadow areas based on sunlight parameters and compare them with the image shadow areas to retain reliable edges, and generate and remove occluded areas based on crane operation records, and output a set of reliable edges. The alignment attitude generation module is used to align the credible edge set with the BIM geometric baseline data and output matching degree data. Based on the relative relationship between the credible edge set and the design vertical line, it judges and outputs attitude parameters of roll direction, pitch direction and top horizontal offset direction, and then generates hierarchical attitude conclusions based on the continuity of alignment. The collaborative evaluation and judgment module is used to judge and assign weights to image clarity and edge effectiveness, illumination and shadow consistency, occlusion situation and on-site environment based on the set of credible edges, sunlight parameters, image shadow area, occlusion area and meteorological records, generate image conclusions, weightedly synthesize the image conclusions to generate the final attitude conclusion, and judge and output the continuous trend or fluctuation state based on the final attitude conclusion within the continuous period. The instruction generation and execution module is used to generate a set of control instructions based on posture conclusions and trend judgments, including differential adjustment of climbing formwork cylinders, adjustment of temporary cable tension, and adjustment of outer tie rod tension. The control instructions are expressed in a unified field, and the execution object, amplitude level, and execution sequence are input to the construction control terminal, and the feedback information and status mark are recorded. The verification evidence retention module is used to collect new cycle images after execution and repeat the processing process to generate attitude conclusion differences and trend labels before and after execution to form a differential evaluation report. At the same time, it saves image screenshots, credible edge markers, attitude conclusions and control receipts, and archives versioned evidence chains.

Images