Project progress monitoring management system and method based on image recognition

Abstract

The invention belongs to the technical field of project management, and discloses a project progress monitoring management system and method based on image recognition, which extracts physical quantity progress, resource and manpower, and quality and compliance multi-dimensional characteristic parameters based on images, and fuses the physical quantity progress, resource and manpower, and quality and compliance multi-dimensional characteristic parameters. All dimension coefficients are quantified through weighted geometric averaging and a short plate punishment mechanism, a progress monitoring index is comprehensively generated, a grading early warning mechanism is triggered in combination with a threshold value, the problems that traditional progress management depends on manpower and is low in efficiency are solved, and high-precision and dynamic project progress monitoring is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering management, and in particular relates to an engineering progress monitoring management system and method based on image recognition. Background Art

[0002] In traditional construction projects, progress monitoring and management primarily relies on manual inspections, on-site record-keeping, and regular reporting. This approach has numerous drawbacks, including the low efficiency of manual inspections, which makes it difficult to achieve real-time, comprehensive monitoring of large-scale projects. Manual record-keeping and reporting are prone to information errors and omissions, leading to inaccurate progress information. Furthermore, manual management costs are high, and as the scale of projects expands, the manpower input and management difficulty increase significantly.

[0003] Although there are currently some engineering progress monitoring and management systems based on image recognition, the following problems still exist: 1. Multi-dimensional data is fragmented, making comprehensive evaluation difficult. Key parameters such as progress, resources, and quality are usually managed independently by different systems, lacking collaborative analysis; 2. Only the overall progress percentage is focused on, ignoring the "shortboard effect" (such as the lag in a certain link dragging down the overall situation); therefore, the present invention proposes an engineering progress monitoring and management system and method based on image recognition. Summary of the Invention

[0004] The purpose of the present invention is to provide a project progress monitoring management system and method based on image recognition to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A project progress monitoring and management system based on image recognition, comprising: an image acquisition module, an image preprocessing module, a feature extraction and analysis module, and a progress evaluation module; The image acquisition module is used to acquire real-time images of the construction site; The image preprocessing module is used to process the image and improve the image quality; The feature extraction and analysis module is used to extract key features from the image and analyze the key features; The progress assessment module is used to assess whether the project progress is lagging behind or ahead of schedule based on the analysis results of key features.

[0006] As a further description of the technical solution of the present invention, the image acquisition module includes a high-definition camera and a drone, which are set up at multiple key locations on the construction site. The high-definition camera is used to capture real-time images of the construction scene in a fixed area, and the drone is used to capture all-round and multi-angle images of the construction site according to the set route; The image preprocessing module receives the original image data transmitted by the image acquisition module, performs grayscale processing on the image, converts the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; performs noise reduction processing, uses algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; and performs normalization processing on the image, adjusts the size and brightness of the image to a unified standard for subsequent processing; The key features extracted from the image in the feature extraction and analysis module include: physical dimension parameters, resource and manpower parameters, and quality and compliance parameters; The progress analysis module receives the recognition result transmitted by the feature extraction and analysis module, obtains the project progress plan data from the database module, and evaluates whether the project progress is qualified.

[0007] As a further description of the technical solution of the present invention, the working process of the feature extraction and analysis module includes: Extract the physical dimension parameter characteristics of the current construction, including: the completion ratio of the project, the number of components installed, and the working surface expansion rate; The obtained construction physical quantity dimension parameters are normalized, and a mathematical model of the engineering physical quantity progress evaluation coefficient is constructed based on the processed parameters. The expression is: ; Where, is the project completion ratio, The number of components installed, is the working surface expansion rate, 、 and are the weight coefficients corresponding to the completion ratio of the project, the number of components installed and the working surface expansion rate, is the correction factor.

[0008] As a further description of the technical solution of the present invention, the working process of the feature extraction and analysis module also includes: Extract the construction resource and manpower parameter characteristics at the current moment, including: the matching degree of the number of machines on site, the rationality of the personnel density, and the matching degree of material consumption; The obtained construction resource and manpower parameters are normalized, and a mathematical model of construction resource and manpower evaluation coefficients is constructed based on the processed parameters. The expression is: ; Where, Matching degree of mechanical presence, is the rationality of personnel density, S is the matching degree of material consumption, 、 and are the weight coefficients corresponding to the matching degree of the number of machines on site, the rationality of personnel density and the matching degree of material consumption, represents the output maximum term function, is the correction factor.

[0009] As a further description of the technical solution of the present invention, the working process of the feature extraction and analysis module also includes: Extract the current construction quality and compliance parameter characteristics, including: construction defect detection rate, safety compliance rate, and process standard compliance; The obtained quality and compliance parameters are normalized, and a mathematical model of quality and compliance assessment coefficients is constructed based on the processed parameters. The expression is: ; Where, is the construction defect detection rate, is the safety compliance rate, For process standard compliance, 、 、 are the weight coefficients corresponding to the construction defect detection rate, safety compliance rate and process standard compliance, is the correction factor.

[0010] As a further description of the technical solution of the present invention, the project completion ratio Q is equal to the actual completion amount / planned amount, the number of components installed is equal to the actual installation number / planned installation number, and the working surface expansion rate is equal to the active construction area / planned expansion area; The matching degree of the number of machines present =The actual number of machines on site / the planned number of machines required, the rationality of the personnel density L is equal to 1- , the material consumption matching degree S is equal to the actual consumption quantity / planned consumption quantity; The construction defect detection rate D is equal to the actual number of detected defects / the total number of defects that should be detected, the safety compliance rate SC is equal to the number of inspection items that meet safety regulations / the total number of inspection items, and the process standard compliance rate Equal to the proportion of measurement points that meet process standards.

[0011] As a further description of the technical solution of the present invention, the working process of the progress assessment module includes: The project progress monitoring coefficient is constructed based on the engineering physical quantity progress evaluation coefficient, construction resource and manpower evaluation coefficient, and quality and compliance evaluation coefficient. The expression is: ; Where, and They represent the weight coefficients corresponding to the construction resource and manpower evaluation coefficient and the quality and compliance evaluation coefficient respectively; Will The threshold value set by the system 、 and Compare, < < , ≥ Indicates that the project is ahead of schedule, maintains the current plan, and optimizes resource allocation. ≤ < Indicates that the project progress is normal, regular monitoring is carried out, and attention is paid to the short board parameters. ≤ < Indicates that the project progress is lagging behind, analyze the root cause, < It means that the project progress is seriously delayed and work has been suspended for rectification.

[0012] A method for project progress monitoring and management based on image recognition, comprising the following steps: Step S1, data acquisition: The high-definition camera is used to collect real-time images of the construction scene in a fixed area, and the drone is used to collect all-round and multi-angle images of the construction site according to the set route; Step S2, data processing: grayscale the image, converting the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; performing noise reduction processing, using algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; normalizing the image, adjusting the image size and brightness to a unified standard for subsequent processing; Step S3, feature extraction and analysis: extract key feature parameters, physical dimension parameters, resource and manpower parameters, and quality and compliance parameters from the image, and analyze the extracted feature parameters; Step S4: Progress monitoring: Based on the analysis results of key characteristic parameters, assess whether the project progress is lagging behind or ahead of schedule.

[0013] Beneficial effects of the present invention: 1. Improve monitoring efficiency: The image acquisition module automatically collects construction site images and uses image recognition and analysis technology to quickly process image data, realizing real-time and automatic monitoring of project progress, greatly improving monitoring efficiency and reducing the time and labor costs of manual inspections.

[0014] 2. Improve information accuracy: By extracting feature parameters from image information, various target objects and construction status at the construction site can be accurately identified, avoiding information errors and omissions that may occur during manual recording and reporting, and ensuring the accuracy of project progress information.

[0015] 3. Intelligent analysis and prediction: Through the short board parameter penalty mechanism (such as insufficient resources or quality defects), risks can be discovered in advance and resource allocation can be optimized, which helps to take measures to solve potential problems in advance and ensure the smooth progress of the project.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a structural diagram of the project progress monitoring and management system based on image recognition of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, a project progress monitoring and management system based on image recognition is disclosed, the system includes: an image acquisition module, an image preprocessing module, a feature extraction and analysis module and a progress evaluation module; The image acquisition module is used to acquire real-time images of the construction site; The image preprocessing module is used to process the image and improve the image quality; The feature extraction and analysis module is used to extract key features from the image and analyze the key features; The progress assessment module is used to assess whether the project progress is lagging behind or ahead of schedule based on the analysis results of key features.

[0021] Through the above technical solution, The image acquisition module includes a high-definition camera and a drone, which are set up at multiple key locations on the construction site. The high-definition camera is used to capture real-time images of the construction scene in a fixed area, and the drone is used to capture all-round and multi-angle images of the construction site according to the set route; The image preprocessing module receives the original image data transmitted by the image acquisition module, performs grayscale processing on the image, converts the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; performs noise reduction processing, uses algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; and performs normalization processing on the image, adjusts the size and brightness of the image to a unified standard for subsequent processing; The key features extracted from the image in the feature extraction and analysis module include: physical dimension parameters, resource and manpower parameters, and quality and compliance parameters; The progress analysis module receives the recognition result transmitted by the feature extraction and analysis module, obtains the project progress plan data from the database module, and evaluates whether the project progress is qualified.

[0022] The working process of the feature extraction and analysis module includes: Extract the physical dimension parameter characteristics of the current construction, including: the completion ratio of the project, the number of components installed, and the working surface expansion rate; The project completion ratio is based on the comparison of actual values ​​and planned values ​​of earthwork volume, concrete volume, and pipeline laying length calculated based on image segmentation; The number of components installed is based on the identified number of prefabricated components (e.g., bridge segments, prefabricated wall panels) compared with the plan; The working surface expansion rate is based on the proportion of active construction areas identified by aerial imagery; The project completion ratio Q is equal to the actual completion amount / planned amount, the number of components installed is equal to the actual installation number / planned installation number, and the working surface expansion rate is equal to the active construction area / planned expansion area; The obtained construction physical quantity dimension parameters are normalized, and a mathematical model of the engineering physical quantity progress evaluation coefficient is constructed based on the processed parameters. The expression is: ; Where, is the project completion ratio, The number of components installed, is the working surface expansion rate, 、 and are the weight coefficients corresponding to the completion ratio of the project, the number of components installed and the working surface expansion rate, is the correction factor.

[0023] Through the above technical solution, this embodiment provides a method for extracting and analyzing the characteristics of construction physical quantity dimension parameters. The extracted construction physical quantity dimension parameter characteristics are: engineering quantity completion ratio: the ratio of the actual completion quantity to the planned quantity, the number of components installed: the ratio of the actual number of installed components to the planned number of installed components, and the working surface expansion rate: the ratio of the active construction area to the planned expansion area. After normalizing the above parameters, a mathematical model for the engineering physical quantity progress evaluation coefficient is constructed. , where the denominator introduces the short board effect When any parameter is low, the denominator increases, the P value decreases, and the sensitivity to progress lag is strengthened. It is a weighted linear combination that reflects the overall progress. By quantifying the physical progress parameters, combining weights and correction factors, it dynamically evaluates the project progress, paying special attention to the impact of the weakest links (such as the failure to expand the working face or delayed component installation), and providing basic indicators for subsequent comprehensive progress monitoring.

[0024] The working process of the feature extraction and analysis module also includes: Extract the construction resource and manpower parameter characteristics at the current moment, including: the matching degree of the number of machines on site, the rationality of the personnel density, and the matching degree of material consumption; The matching ratio of the number of machines on site is determined by image recognition of the ratio of the number of construction equipment to the planned number, to judge the rationality of resource coordination; The rationality of the personnel density is based on video analysis of the ratio of personnel in the work area to the optimal number, optimizing labor allocation (avoiding idleness or insufficient labor); The material consumption matching degree is based on the use and arrival records of different materials obtained through video, and the consumption ratio to the planned quantity is estimated based on the use and arrival records to detect waste or shortage; The matching degree of the number of machines present =The actual number of machines on site / the planned number of machines required, the rationality of the personnel density L is equal to 1- , the material consumption matching degree S is equal to the actual consumption quantity / planned consumption quantity; The obtained construction resource and manpower parameters are normalized, and a mathematical model of construction resource and manpower evaluation coefficients is constructed based on the processed parameters. The expression is: ; Where, Matching degree of mechanical presence, is the rationality of personnel density, S is the matching degree of material consumption, 、 and are the weight coefficients corresponding to the matching degree of the number of machines on site, the rationality of personnel density and the matching degree of material consumption, represents the output maximum term function, is the correction factor.

[0025] Through the above technical solution, this embodiment provides a method for extracting and analyzing the characteristics of construction resource and manpower parameters. The matching characteristics of the number of machines on site are: actual number of machines / planned number of machines required, the rationality characteristics of personnel density are: 1-|actual number of personnel - optimal number of personnel| / optimal number of personnel, and the matching characteristics of material consumption are: actual consumption / planned consumption. After normalizing the above parameters, a mathematical model for the construction resource and manpower evaluation coefficient is constructed. , where The geometric mean reflects equilibrium. It is a key modifier for engineering management technology used to quantify the risk of resource shortage in engineering management technology. Its core purpose is to penalize the shortage of machinery, manpower or materials. Control the intensity of punishment.

[0026] The working process of the feature extraction and analysis module also includes: Extract the current construction quality and compliance parameter characteristics, including: construction defect detection rate, safety compliance rate, and process standard compliance; The construction defect detection rate is determined by the accuracy of identifying problems such as cracks, deformation, and misalignment; The safety compliance rate is based on the image recognition compliance rate of safety helmet wearing and scaffolding erection specifications; The degree of compliance with the workmanship standards is based on comparing images with construction specifications; The construction defect detection rate D is equal to the actual number of detected defects / the total number of defects that should be detected, the safety compliance rate SC is equal to the number of inspection items that meet safety regulations / the total number of inspection items, and the process standard compliance rate Equal to the proportion of measurement points that meet the process standards; The obtained quality and compliance parameters are normalized, and a mathematical model of quality and compliance assessment coefficients is constructed based on the processed parameters. The expression is: ; Where, is the construction defect detection rate, is the safety compliance rate, For process standard compliance, 、 、 are the weight coefficients corresponding to the construction defect detection rate, safety compliance rate and process standard compliance, is the correction factor.

[0027] Through the above technical solution, this embodiment provides a method for extracting and analyzing the characteristics of quality and compliance assessment parameters, including the construction defect detection rate characteristic: the actual number of defects detected / the total number of defects that should be detected; the safety compliance rate characteristic: the proportion of inspection items that meet safety regulations; and the process standard compliance characteristic: the proportion of measurement points that meet process standards. After normalizing the above parameters, a mathematical model for quality and compliance assessment coefficients is constructed. , where The geometric mean reflects equilibrium. Is it a penalty for insufficient machinery, manpower or materials? Adjust the severity of penalties.

[0028] The working process of the progress assessment module includes: The project progress monitoring coefficient is constructed based on the engineering physical quantity progress evaluation coefficient, construction resource and manpower evaluation coefficient, and quality and compliance evaluation coefficient. The expression is: ; Where, and They represent the weight coefficients corresponding to the construction resource and manpower evaluation coefficient and the quality and compliance evaluation coefficient respectively; Will The threshold value set by the system 、 and Compare, < < , ≥ Indicates that the project is ahead of schedule, maintains the current plan, and optimizes resource allocation. ≤ < Indicates that the project progress is normal, regular monitoring is carried out, and attention is paid to the short board parameters. ≤ < Indicates that the project progress is lagging behind, analyze the root cause, < It means that the project progress is seriously delayed and work has been suspended for rectification.

[0029] Specific analysis of shortcoming parameters includes: If P is low: Check whether the project completion rate matches the plan, and whether there are any design changes or construction efficiency issues. If R is low: Analyze the specific areas of machinery, manpower, and material shortages (such as insufficient crane utilization or delayed rebar supply). If Q is low: Identify the defect type (such as cracks or unqualified welds), strengthen process briefings, or replace subcontractors.

[0030] Through the above technical solution, this embodiment constructs a project progress monitoring coefficient based on the engineering physical quantity progress evaluation coefficient, the construction resource and manpower evaluation coefficient, and the quality and compliance evaluation coefficient. , where P is the base number and nonlinear coupling is achieved by weighted sum of exponential functions R and Q. Then, we set The threshold value set by the system 、 and Compare, < < , ≥ Indicates that the project is ahead of schedule, maintains the current plan, and optimizes resource allocation. ≤ < Indicates that the project progress is normal, regular monitoring is carried out, and attention is paid to the short board parameters. ≤ < Indicates that the project progress is lagging behind, analyze the root cause, < It means that the project progress is seriously delayed and work has been suspended for rectification.

[0031] A method for project progress monitoring and management based on image recognition, comprising the following steps: Step S1, data acquisition: The high-definition camera is used to collect real-time images of the construction scene in a fixed area, and the drone is used to collect all-round and multi-angle images of the construction site according to the set route; Step S2, data processing: grayscale the image, convert the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; perform noise reduction processing, use algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; normalize the image to adjust the size and brightness of the image to a unified standard for subsequent processing Step S3, feature extraction and analysis: extract key feature parameters, physical dimension parameters, resource and manpower parameters, and quality and compliance parameters from the image, and analyze the extracted feature parameters; Step S4: Progress monitoring: Based on the analysis results of key characteristic parameters, assess whether the project progress is lagging behind or ahead of schedule.

[0032] It should be noted that the formulas in this application are all dimensionless and numerically calculated. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The thresholds, correction factors and weight coefficients involved in this application are all empirical values, and are selected and set by technical personnel in this field according to actual conditions.

[0033] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A project progress monitoring and management system based on image recognition, characterized in that: The system includes: an image acquisition module, an image preprocessing module, a feature extraction and analysis module and a progress assessment module; The image acquisition module is used to acquire real-time images of the construction site; The image preprocessing module is used to process the image and improve the image quality; The feature extraction and analysis module is used to extract key features from the image and analyze the key features; The progress assessment module is used to assess whether the project progress is lagging behind or ahead of schedule based on the analysis results of key features.

2. The project progress monitoring and management system based on image recognition according to claim 1 is characterized in that: The image acquisition module includes a high-definition camera and a drone, which are set up at multiple key locations on the construction site. The high-definition camera is used to capture real-time images of the construction scene in a fixed area, and the drone is used to capture all-round and multi-angle images of the construction site according to the set route; The image preprocessing module receives the original image data transmitted by the image acquisition module, performs grayscale processing on the image, converts the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; performs noise reduction processing, uses algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; and performs normalization processing on the image, adjusts the size and brightness of the image to a unified standard for subsequent processing; The key features extracted from the image in the feature extraction and analysis module include: physical dimension parameters, resource and manpower parameters, and quality and compliance parameters; The progress analysis module receives the recognition result transmitted by the feature extraction and analysis module, obtains the project progress plan data from the database module, and evaluates whether the project progress is qualified.

3. The project progress monitoring and management system based on image recognition according to claim 2 is characterized in that: The working process of the feature extraction and analysis module includes: Extract the physical dimension parameter characteristics of the current construction, including: the completion ratio of the project, the number of components installed, and the working surface expansion rate; The obtained construction physical quantity dimension parameters are normalized, and a mathematical model of the engineering physical quantity progress evaluation coefficient is constructed based on the processed parameters. The expression is: ； Where, is the project completion ratio, The number of components installed, is the working surface expansion rate, 、 and are the weight coefficients corresponding to the completion ratio of the project, the number of components installed and the working surface expansion rate, is the correction factor.

4. The project progress monitoring and management system based on image recognition according to claim 3 is characterized in that: The working process of the feature extraction and analysis module also includes: Extract the construction resource and manpower parameter characteristics at the current moment, including: the matching degree of the number of machines on site, the rationality of the personnel density, and the matching degree of material consumption; The obtained construction resource and manpower parameters are normalized, and a mathematical model of construction resource and manpower evaluation coefficients is constructed based on the processed parameters. The expression is: ； Where, Matching degree of mechanical presence, is the rationality of personnel density, S is the matching degree of material consumption, 、 and are the weight coefficients corresponding to the matching degree of the number of machines on site, the rationality of personnel density and the matching degree of material consumption, represents the output maximum term function, is the correction factor.

5. The project progress monitoring and management system based on image recognition according to claim 4 is characterized in that: The working process of the feature extraction and analysis module also includes: Extract the current construction quality and compliance parameter characteristics, including: construction defect detection rate, safety compliance rate, and process standard compliance; The obtained quality and compliance parameters are normalized, and a mathematical model of quality and compliance assessment coefficients is constructed based on the processed parameters. The expression is: ； Where, is the construction defect detection rate, is the safety compliance rate, For process standard compliance, 、 、 are the weight coefficients corresponding to the construction defect detection rate, safety compliance rate and process standard compliance, is the correction factor.

6. The project progress monitoring and management system based on image recognition according to claim 5 is characterized in that: The project completion ratio Q is equal to the actual completion amount / planned amount, the number of components installed is equal to the actual installation number / planned installation number, and the working surface expansion rate is equal to the active construction area / planned expansion area; The matching degree of the number of machines present =The actual number of machines on site / the planned number of machines required, the rationality of the personnel density L is equal to 1- , the material consumption matching degree S is equal to the actual consumption quantity / planned consumption quantity; The construction defect detection rate D is equal to the actual number of detected defects / the total number of defects that should be detected, the safety compliance rate SC is equal to the number of inspection items that meet safety regulations / the total number of inspection items, and the process standard compliance rate Equal to the proportion of measurement points that meet process standards.

7. The project progress monitoring and management system based on image recognition according to claim 1 is characterized in that: The working process of the progress assessment module includes: The project progress monitoring coefficient is constructed based on the engineering physical quantity progress evaluation coefficient, construction resource and manpower evaluation coefficient, and quality and compliance evaluation coefficient. The expression is: ； Where, and They represent the weight coefficients corresponding to the construction resource and manpower evaluation coefficient and the quality and compliance evaluation coefficient respectively; Will The threshold value set by the system 、 and Compare, < < , ≥ Indicates that the project is ahead of schedule, maintains the current plan, and optimizes resource allocation. ≤ < Indicates that the project progress is normal, regular monitoring is carried out, and attention is paid to the short board parameters. ≤ < Indicates that the project progress is lagging behind, analyze the root cause, < It means that the project progress is seriously delayed and work has been suspended for rectification.

8. A management method for a project progress monitoring and management system based on image recognition according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: Step S1, data acquisition: The high-definition camera is used to collect real-time images of the construction scene in a fixed area, and the drone is used to collect all-round and multi-angle images of the construction site according to the set route; Step S2, data processing: grayscale the image, converting the color image into a grayscale image to reduce the amount of data and highlight the key information of the image; performing noise reduction processing, using algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve image quality; normalizing the image, adjusting the image size and brightness to a unified standard for subsequent processing; Step S3, feature extraction and analysis: extract key feature parameters, physical dimension parameters, resource and manpower parameters, and quality and compliance parameters from the image, and analyze the extracted feature parameters; Step S4: Progress monitoring: Based on the analysis results of key characteristic parameters, assess whether the project progress is lagging behind or ahead of schedule.

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