Method for automatically measuring and calculating project progress of transformer substation

Abstract

The invention relates to the field of construction engineering management, and discloses a substation engineering progress automatic measurement and calculation method, which comprises the following steps: S1) constructing a three-dimensional BIM model containing a geometric model and attribute information by obtaining substation engineering related data, and marking key feature points for subsequent progress comparison; s2) arranging a camera at a construction site, acquiring a construction video stream in real time and transmitting the construction video stream to the central server; s3) performing denoising and key frame extraction on the video image, and extracting image feature points and descriptors by using a deep learning algorithm; s4) matching the image feature points with corresponding points in the BIM model, calculating the deviation between the actual progress and the planned progress, and generating construction progress data; and S5) setting an early warning level according to the construction deviation, generating a progress report, and sending alarm information in multiple modes. According to the method, automatic measurement and calculation of the construction progress are realized by combining the BIM model and video monitoring, and manual intervention is reduced.

Description

Technical Field

[0001] The invention relates to the field of construction engineering management, and in particular to a method for automatically calculating the progress of a transformer substation project. Background Art

[0002] In substation construction projects, construction progress management is a key link in project delivery on schedule, and is directly related to the overall efficiency and quality of power engineering. Traditional substation construction management mainly relies on project planning, on-site inspections and manual records, and adjusts construction strategies by regularly comparing actual progress with planned progress. However, with the expansion of substation projects and the increase in complexity, construction progress management is facing huge technical challenges.

[0003] Traditional methods mainly rely on manual inspections of construction sites and recording progress information, and then manually compiling the data into reports. Due to the complexity of construction scenes and scattered areas, manual inspections are difficult to fully cover, and data collection and compilation requires a lot of time and human resources, which is inefficient. At the same time, manual records may be affected by personal experience or subjective judgment, which can easily lead to data errors or omissions. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides a method for automatically calculating the progress of a substation project, aiming to improve the problem that manual inspection is difficult to fully cover, and data collection and organization require a lot of time and human resources, resulting in low efficiency.

[0005] In a first aspect, the present invention provides the following technical solution, a method for automatically calculating the progress of a substation project, comprising the following steps:

[0006] S1) BIM model construction: by acquiring relevant data of the substation project, a three-dimensional BIM model containing geometric model and attribute information is constructed, and key feature points are marked for subsequent progress comparison;

[0007] S2) Video monitoring data collection: deploying cameras at the construction site to collect construction video streams in real time and transmit them to the central server; also includes collecting on-site construction status data through drone tilt photography and laser point cloud scanning;

[0008] S3) Image data processing: denoising and key frame extraction of video images, and using deep learning algorithms to extract image feature points and descriptors;

[0009] S4) Progress comparison and analysis: match the image feature points with the corresponding points in the BIM model, calculate the deviation between the actual progress and the planned progress, and generate construction progress data;

[0010] S5) Result feedback and early warning: Set early warning levels according to construction deviations, generate progress reports and send alarm information through various means.

[0011] Preferably, the S1) comprises the following steps:

[0012] S101) Obtaining architectural design drawings, construction drawings and bills of quantities, and digitizing them;

[0013] S102) Using BIM modeling software to construct a three-dimensional geometric model of the substation, the model includes the main building, equipment installation location, cable trench and control room;

[0014] S103) assigning attribute information to the components in the geometric model, wherein the attribute information includes material, size, weight and construction plan time;

[0015] S104) marking key feature points in the model, which are used for subsequent progress comparison, and exporting the BIM model into a standard format file.

[0016] Preferably, the step S2) comprises the following steps:

[0017] S201) Planning the number, type and installation location of cameras according to the monitoring requirements of the construction site;

[0018] S202) Arranging cameras in the construction area and configuring camera parameters, including resolution, frame rate and focal length;

[0019] S203) obtaining high-resolution images of the construction site through oblique photography by a drone, and collecting three-dimensional data of the construction status through laser point cloud scanning;

[0020] S204) transmitting the video stream of the camera and the data collected by the drone to the central server via a streaming platform, wherein the streaming platform supports RTSP, RTMP and HLS protocols;

[0021] S205) The transmitted multi-source data is decoded, format-converted, and stored as image data or point cloud data for subsequent analysis.

[0022] Preferably, the step S3) comprises the following steps:

[0023] S301) extracting key frames from the transmitted video stream, where the extracted key frames are images of the construction status at a specific time point;

[0024] S302) performing denoising, brightness enhancement and edge refinement on the key frame image to improve image quality;

[0025] S303) Using a convolutional neural network to extract edge, corner and texture area feature points in the image, and generate feature descriptors that describe the feature points.

[0026] Preferably, the step S4) comprises the following steps:

[0027] S401) using a KD tree algorithm to quickly match image feature points with BIM model feature points;

[0028] S402) applying the RANSAC algorithm to remove erroneous matching points to improve matching accuracy;

[0029] S403) calculating the deviation between the actual progress and the planned progress by comparing the difference between the construction status in the image and the planned status in the BIM model;

[0030] S404) Generate a progress report, including the completed parts, unfinished parts and deviation cause analysis.

[0031] Preferably, the step S5) comprises the following steps:

[0032] S501) Setting the warning level according to the construction deviation, including the following warning rules:

[0033] If the deviation is less than 5%, it is marked in green, indicating that the construction is normal;

[0034] Deviations of 5%-10% are marked in yellow, triggering a warning alert;

[0035] If the deviation exceeds 10%, it will be marked in red and trigger an emergency warning;

[0036] S502) sending alarm information to relevant personnel through sound alarm, SMS notification and email;

[0037] S503) The construction progress data is visualized in the form of charts, 3D animations and texts, so that project managers can intuitively understand the construction status.

[0038] Preferably, it also includes:

[0039] S6) Regularly update the BIM model to reflect the latest construction status, including adding new feature points and adjusting the construction plan;

[0040] S7) is linked with the smart construction site management platform and combined with IoT equipment data to perform intelligent optimization of construction management.

[0041] In a second aspect, the present invention provides the following technical solution, a substation project progress automatic calculation system, comprising:

[0042] BIM modeling module, which is used to obtain the design drawings, construction drawings and bill of quantities of the substation project, build a three-dimensional geometric model including the main building, equipment installation location, cable trench and control room, assign component attribute information and mark key feature points;

[0043] The video acquisition module is used to collect the construction video stream through the camera deployed at the construction site, configure the camera parameters and transmit the video stream to the central server through the streaming platform;

[0044] The image processing module is used to extract key frames and preprocess images of the video stream, extract image feature points through deep learning algorithms and generate feature descriptors;

[0045] Progress comparison module, used to match image feature points with feature points in the BIM model, analyze the deviation between the construction progress and the planned progress, and generate a construction progress report;

[0046] The feedback and warning module is used to set the warning level according to the progress deviation, and feedback the warning information through sound alarm, SMS notification or email, and display the construction progress status in the form of charts, 3D models and animations.

[0047] In a third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for automatically calculating the progress of a substation project when executing the computer program.

[0048] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned method for automatically calculating the progress of a substation project.

[0049] The present invention has the following beneficial effects:

[0050] 1. In the present invention, by combining BIM model and video monitoring, the automatic calculation of construction progress is realized, reducing human intervention; the deep learning algorithm is used to identify and compare the construction status in the monitoring image, dynamically generate construction progress report and deviation analysis, and adjust the construction plan in real time, which greatly improves the efficiency and scientificity of construction management.

[0051] 2. In the present invention, the feature points of the monitoring image are extracted by a convolutional neural network and matched with the key points in the BIM model to ensure the high accuracy of progress measurement; the BIM model is dynamically updated to reflect the latest status of the construction, and the reliability and adaptability of construction progress analysis are effectively improved by combining feature point recognition and error elimination technology.

[0052] 3. In the present invention, the warning level (green, yellow, red) is automatically divided according to the deviation of the construction progress, and the relevant personnel are notified by sound, text message and email; the system can also predict the risk of delay and put forward optimization suggestions, and dynamically display the construction status through three-dimensional visualization to help the construction team quickly locate problems and formulate countermeasures.

[0053] 4. In the present invention, the system is linked with the smart construction site management platform, accessing the Internet of Things devices, workers' attendance and material flow data to form a comprehensive information network of the construction status; by real-time monitoring of equipment status and personnel attendance, the construction resource allocation is intelligently optimized to improve the efficiency of on-site management. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A method flow chart of the method for automatically calculating the progress of a substation project proposed by the present invention;

[0055] Figure 2 It is a video monitoring data acquisition flow chart of the method for automatically calculating the progress of a substation project proposed by the present invention;

[0056] Figure 3 This is a system architecture diagram of the substation project progress automatic calculation system proposed in the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be described clearly and completely below 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 creative work are within the scope of protection of the present invention.

[0058] Embodiment 1

[0059] Reference Figure 1-Figure 2 In a first embodiment of the present invention, the present invention provides a method for automatically calculating the progress of a substation project, comprising the following steps:

[0060] S1) BIM model construction: by acquiring relevant data of the substation project, a three-dimensional BIM model containing geometric model and attribute information is constructed, and key feature points are marked for subsequent progress comparison;

[0061] S2) Video monitoring data collection: deploying cameras at the construction site to collect construction video streams in real time and transmit them to the central server; also includes collecting on-site construction status data through drone tilt photography and laser point cloud scanning;

[0062] S3) Image data processing: denoising and key frame extraction of video images, and using deep learning algorithms to extract image feature points and descriptors;

[0063] S4) Progress comparison and analysis: match the image feature points with the corresponding points in the BIM model, calculate the deviation between the actual progress and the planned progress, and generate construction progress data;

[0064] S5) Result feedback and early warning: Set early warning levels according to construction deviations, generate progress reports and send alarm information through various means.

[0065] Specifically, this method combines deep learning algorithms with BIM models to achieve intelligent and automated calculation of construction progress, reduce manual dependence, and greatly improve management efficiency. The system uses monitoring images to match feature points with BIM models, dynamically updates the construction status, and ensures high accuracy and real-time progress calculation. The multi-level early warning mechanism (green, yellow, and red) combined with real-time feedback helps the construction team quickly respond to delay risks and make optimization and adjustment suggestions. Through the multi-dimensional data linkage of smart construction sites, such as equipment status, attendance information, and material flow monitoring, the system realizes the intelligent allocation and optimization management of construction resources, and comprehensively improves the collaborative efficiency and risk control capabilities of the construction site.

[0066] The S1) comprises the following steps:

[0067] S101) Obtaining architectural design drawings, construction drawings and bills of quantities, and digitizing them;

[0068] S102) Using BIM modeling software to construct a three-dimensional geometric model of the substation, the model includes the main building, equipment installation location, cable trench and control room;

[0069] S103) assigning attribute information to the components in the geometric model, wherein the attribute information includes material, size, weight and construction plan time;

[0070] S104) marking key feature points in the model, which are used for subsequent progress comparison, and exporting the BIM model into a standard format file.

[0071] Specifically, S1.BIM model construction

[0072] By acquiring relevant data of the substation project, a three-dimensional BIM model containing geometric models and attribute information is constructed, and key feature points are marked for subsequent progress comparison.

[0073] S101) Data collection and digitization

[0074] Obtaining architectural design drawings and construction drawings:

[0075] Obtain design blueprints, construction plans, and equipment layouts from the substation project team.

[0076] Make sure the drawings cover all critical areas (such as transformer installation area, cable laying paths, control room, etc.).

[0077] Bill of Quantities and Material Information Arrangement:

[0078] Obtain a list of all material specifications, quantities and supply cycles to clarify the availability of each resource.

[0079] Digital processing:

[0080] Use a CAD tool or scanner to convert paper drawings into digital files, making sure the format is compatible with BIM modeling software.

[0081] S102) 3D model creation

[0082] BIM modeling software selection:

[0083] Use professional modeling software such as Revit and Navisworks to build 3D models.

[0084] Geometry Modeling:

[0085] Build the model layer by layer according to the architectural design drawings, including the main building structure such as foundation, columns, beams, walls, floors, etc.

[0086] Accurately model equipment installation locations such as transformers and cable junction boxes.

[0087] Spatial structure planning:

[0088] Divide the model into multiple independent area modules (such as equipment installation area, cable trench, control room) to facilitate subsequent feature annotation and progress comparison.

[0089] S103) Attribute assignment and feature point marking

[0090] Attribute information added:

[0091] Each model component should be added with information such as physical properties (such as material, size, weight), time properties (planned construction stage), and functional properties (purpose).

[0092] Feature point annotation:

[0093] Add feature point markers to key areas such as equipment installation points, segmentation points of cable paths, floor interfaces, etc., and assign unique identifiers to support progress comparison.

[0094] S104) Model export and sharing

[0095] Model format conversion:

[0096] Convert models to standard formats (such as IFC files) for compatibility with other systems and subsequent data import.

[0097] Collaborative sharing:

[0098] Upload the model to the project management platform to achieve multi-party collaboration.

[0099] Described S2) comprises the following steps:

[0100] S201) Planning the number, type and installation location of cameras according to the monitoring requirements of the construction site;

[0101] S202) Arranging cameras in the construction area and configuring camera parameters, including resolution, frame rate and focal length;

[0102] S203) obtaining high-resolution images of the construction site through oblique photography by a drone, and collecting three-dimensional data of the construction status through laser point cloud scanning;

[0103] S204) transmitting the video stream of the camera and the data collected by the drone to the central server via a streaming platform, wherein the streaming platform supports RTSP, RTMP and HLS protocols;

[0104] S205) The transmitted multi-source data is decoded, format-converted, and stored as image data or point cloud data for subsequent analysis.

[0105] Specifically, S2. Video surveillance data collection

[0106] Cameras are deployed at the construction site to collect construction video streams in real time and transmit them to the central server. At the same time, construction status data is collected by combining drone oblique photography and laser point cloud scanning technology.

[0107] Camera layout planning

[0108] Regional division

[0109] Equipment installation area:

[0110] Monitor the precise installation process of equipment such as transformers and switchgear, and capture key images of equipment connection points and installation status.

[0111] Material storage area:

[0112] Monitor the storage and distribution of materials to ensure that the materials entry and usage processes are controllable and reduce material management errors.

[0113] Main construction area:

[0114] Monitor the dynamic operation status of the main structure construction, including civil engineering, support installation and line laying areas.

[0115] Determine the number and type of cameras

[0116] Short-distance area: Use hemispherical cameras, which are suitable for close-range monitoring of equipment installation points and material storage areas.

[0117] Medium and long-distance areas: Use gun-type cameras to meet the need for a wide field of view, suitable for large-scale monitoring of the main construction area.

[0118] Calculate the number of cameras required based on the size, shape and field of view requirements of each monitoring area and deploy them appropriately.

[0119] Camera and drone installation and parameter configuration

[0120] Camera Installation

[0121] Arrange cameras according to the plan to ensure coverage of all key construction areas with no blind spots.

[0122] The camera should be installed at a height that avoids obstruction and its wind resistance should be appropriately increased to cope with the special conditions of the construction environment.

[0123] Camera parameter configuration

[0124] Set the resolution to 1080p or higher to ensure that the details of the picture are clearly visible.

[0125] The frame rate is set to above 15 frames per second to ensure the smoothness and continuity of the monitoring image.

[0126] Adjust the focus distance and shooting angle according to the needs of different monitoring areas.

[0127] Drone Installation and Configuration

[0128] Use drones to perform oblique photography to collect high-resolution images from multiple angles in the construction area, capturing complex areas or blind spots that are difficult to cover.

[0129] Combined with laser point cloud scanning technology, three-dimensional status data of the construction area is generated to obtain accurate construction site height, distance and shape information.

[0130] Transmission and storage of video streams and multi-source data

[0131] Streaming Media Platform Deployment

[0132] The video stream captured by the camera is transmitted to the streaming platform in real time through RTSP, RTMP and other protocols, and uploaded to the central server to ensure real-time transmission and stability of the data.

[0133] The data collected by drones and laser scanning equipment is uploaded to the central server via wireless transmission or regular export.

[0134] Decoding and format conversion

[0135] Decode and convert the camera video stream to generate image files such as JPEG and PNG suitable for processing.

[0136] The drone images are stitched together to generate a high-resolution panoramic view of the construction site.

[0137] Perform 3D modeling on laser point cloud data to generate a real-time construction 3D model that matches the BIM model.

[0138] Data storage and management

[0139] All video streams, images and point cloud data are stored in the central server and archived by region and stage to facilitate subsequent analysis and retrieval.

[0140] The S3) comprises the following steps:

[0141] S301) extracting key frames from the transmitted video stream, where the extracted key frames are images of the construction status at a specific time point;

[0142] S302) performing denoising, brightness enhancement and edge refinement on the key frame image to improve image quality;

[0143] S303) Using a convolutional neural network to extract edge, corner and texture area feature points in the image, and generate feature descriptors that describe the feature points.

[0144] Specifically, S3. Image data processing

[0145] Denoise and extract key frames from video images, and use deep learning algorithms to extract image feature points and descriptors.

[0146] S301) Key frame extraction

[0147] Time point selection:

[0148] Extract keyframes from your video by construction phase, such as when equipment is installed or material is laid.

[0149] Extraction method:

[0150] Use the FFmpeg tool to automatically capture image frames at set time intervals to ensure representativeness.

[0151] S302) Image preprocessing

[0152] Quality Optimization:

[0153] The extracted images are denoised to remove uneven light or dust interference.

[0154] Use an edge detection algorithm (such as Canny) to enhance the clarity of building edges.

[0155] S303) Feature point extraction and descriptor generation

[0156] Feature point extraction:

[0157] A convolutional neural network (CNN) is used to analyze the regions with rich textures and obvious edges in the image and generate the coordinates of feature points.

[0158] Descriptor generation:

[0159] A descriptor is generated for the surrounding area of ​​each feature point, recording pixel intensity and direction information.

[0160] Described S4) comprises the following steps:

[0161] S401) using a KD tree algorithm to quickly match image feature points with BIM model feature points;

[0162] S402) applying the RANSAC algorithm to remove erroneous matching points to improve matching accuracy;

[0163] S403) calculating the deviation between the actual progress and the planned progress by comparing the difference between the construction status in the image and the planned status in the BIM model;

[0164] S404) Generate a progress report, including the completed parts, unfinished parts and deviation cause analysis.

[0165] Specifically, S4. Progress comparison and analysis

[0166] Match image feature points with corresponding points in the BIM model, calculate the deviation between the actual progress and the planned progress, and generate construction progress data.

[0167] S401) Feature point matching

[0168] Matching method:

[0169] The KD tree algorithm is used to quickly search for the best matching pairs of image feature points and BIM model feature points.

[0170] False match removal:

[0171] The RANSAC algorithm is used to eliminate matching pairs with large errors to ensure reliable results.

[0172] S402) Deviation Analysis

[0173] Progress calculation:

[0174] Compare the positions and states of the matching points, and calculate the number and area ratio of completed and unfinished areas.

[0175] Deviation Report:

[0176] Automatically generate variance data including percentage completed and reasons for delay.

[0177] Described S5) comprises the following steps:

[0178] S501) Setting the warning level according to the construction deviation, including the following warning rules:

[0179] If the deviation is less than 5%, it is marked in green, indicating that the construction is normal;

[0180] Deviations of 5%-10% are marked in yellow, triggering a warning alert;

[0181] If the deviation exceeds 10%, it will be marked in red and trigger an emergency warning;

[0182] S502) sending alarm information to relevant personnel through sound alarm, SMS notification and email;

[0183] S503) The construction progress data is visualized in the form of charts, 3D animations and texts, so that project managers can intuitively understand the construction status.

[0184] Specifically, S5. Result feedback and early warning

[0185] Set warning levels based on construction deviations, generate progress reports and send alarm information in a variety of ways.

[0186] S501) Warning rule setting

[0187] Deviation less than 5%: Green, indicating normal.

[0188] Deviation is 5%-10%: Yellow, trigger warning.

[0189] Deviation > 10%: Red, triggering an emergency warning.

[0190] S502) Feedback method implementation

[0191] Provides sound alarm, SMS notification and email sending functions to push deviation information to project managers in real time.

[0192] S503) Visual display

[0193] The 3D model is used to dynamically display completed areas and deviation points, and the progress status is presented intuitively.

[0194] Also includes:

[0195] S6) Regularly update the BIM model to reflect the latest construction status, including adding new feature points and adjusting the construction plan;

[0196] S7) is linked with the smart construction site management platform and combined with IoT equipment data to perform intelligent optimization of construction management.

[0197] Specifically, S6. Regularly update the BIM model dynamically

[0198] Update the BIM model regularly to reflect the latest status of construction, including adding new feature points and adjusting the construction plan.

[0199] S601) Obtaining feedback data from the construction site

[0200] Data source:

[0201] Extract construction completion data from the monitoring system, such as installed equipment locations, laid cable paths, etc.

[0202] Obtain progress reports, issue records, and change requests submitted by the construction team.

[0203] Data Integration:

[0204] Combine real-time monitoring data with manual feedback data to form a comprehensive data table of construction completion status.

[0205] S602) Update BIM model geometry

[0206] Complete the zone update:

[0207] Mark completed construction areas in the BIM model and reflect completion status through color or status marking.

[0208] Unfinished regional adjustments:

[0209] Adjust the construction plan of the unfinished areas according to the actual progress and rearrange the construction sequence or duration.

[0210] S603) Add new feature points

[0211] Key Area Expansion:

[0212] If new unplanned areas are added during construction (such as installation points for new equipment or changed cable routes), corresponding feature points need to be added to the BIM model.

[0213] Feature point marking:

[0214] Assign unique identifiers to newly added feature points and associate them with updated construction plans and progress information.

[0215] S604) Dynamic construction plan adjustment

[0216] Adjustment rules:

[0217] Adjust the subsequent construction plan based on actual construction progress deviations and new requirements.

[0218] Program Feedback:

[0219] The updated plan is synchronized to the on-site construction team through the construction management platform to ensure consistency in implementation.

[0220] S7. Link with smart construction site management platform

[0221] The progress measurement system is linked with the smart construction site management platform and combined with IoT equipment data to achieve intelligent optimization of construction management.

[0222] S701) IoT device data access

[0223] Equipment status monitoring:

[0224] Access the operating status data of on-site equipment (such as cranes, welding equipment, etc.) to monitor in real time whether the equipment is operating normally.

[0225] Worker attendance information:

[0226] Connect to the construction site attendance system to obtain workers' attendance status in real time and analyze whether construction resource allocation meets demand.

[0227] Material flow tracking:

[0228] Through RFID, QR code and other technologies, the storage location, transportation route and usage of key materials can be tracked to ensure that materials are supplied on time.

[0229] S702) Linkage data analysis

[0230] Construction resource optimization:

[0231] Analyze the utilization of on-site resources based on attendance data, equipment status and construction progress, and propose optimized deployment plans.

[0232] Schedule risk prediction:

[0233] Combine historical construction data with current progress to predict possible delay risks and provide risk avoidance suggestions.

[0234] S703) System linkage feedback

[0235] Dynamic data synchronization:

[0236] Synchronize the optimization suggestions and monitoring data generated by the smart construction site platform to the BIM model and progress measurement system to achieve data sharing.

[0237] Multi-party collaborative management:

[0238] Link the construction team, supervision unit and project management to share construction status and optimization plans in real time to improve management efficiency.

[0239] S704) Intelligent Optimization Decision

[0240] Automated Scheduling:

[0241] Based on real-time data, optimized construction plans or adjustment plans are automatically generated and pushed to relevant persons in charge.

[0242] Alarm mechanism:

[0243] If equipment abnormalities, resource shortages or progress delays are detected, an early warning will be automatically triggered and relevant personnel will be notified through multiple channels.

[0244] Embodiment 2:

[0245] Reference Figure 3 In a second embodiment of the present invention, the present invention provides a substation engineering progress automatic calculation system, comprising:

[0246] BIM modeling module, which is used to obtain the design drawings, construction drawings and bill of quantities of the substation project, build a three-dimensional geometric model including the main building, equipment installation location, cable trench and control room, assign component attribute information and mark key feature points;

[0247] The video acquisition module is used to collect the construction video stream through the camera deployed at the construction site, configure the camera parameters and transmit the video stream to the central server through the streaming platform;

[0248] The image processing module is used to extract key frames and preprocess images of the video stream, extract image feature points through deep learning algorithms and generate feature descriptors;

[0249] Progress comparison module, used to match image feature points with feature points in the BIM model, analyze the deviation between the construction progress and the planned progress, and generate a construction progress report;

[0250] The feedback and warning module is used to set the warning level according to the progress deviation, and feedback the warning information through sound alarm, SMS notification or email, and display the construction progress status in the form of charts, 3D models and animations.

[0251] Embodiment 3

[0252] The third embodiment of the present invention is based on the same inventive concept and proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for automatically calculating the progress of a substation project in the above embodiment are implemented.

[0253] Embodiment 4

[0254] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer device, the terminal includes: a processor, a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the substation project progress automatic calculation method of the above embodiment.

[0255] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0256] The following is an introduction in conjunction with specific embodiments:

[0257] Example 5: Dandong Substation Project

[0258] Project Background

[0259] A substation project in Dandong involved multiple complex electrical equipment installation and cable laying tasks.

[0260] The construction sites are relatively scattered, the equipment types are diverse, and progress control is difficult.

[0261] Implementation process

[0262] BIM model construction

[0263] Collect architectural design drawings and construction plans for Dandong substation, including detailed drawings of transformer area, cable trench and control room.

[0264] Use BIM modeling software to build a three-dimensional model of the substation, and add feature points of key areas such as equipment installation points and cable paths to the model.

[0265] Add attribute information such as material, size, weight and construction phase plan to each component in the model, and export it to IFC format file for subsequent progress comparison.

[0266] Video surveillance data collection

[0267] Deploy high-definition cameras in the equipment installation area and main construction area, and plan the coverage of each camera to ensure that there are no blind spots in monitoring.

[0268] Set the camera resolution (1080p) and frame rate (20 frames per second) and transmit the video stream to the central server in real time through the streaming platform.

[0269] Image data processing

[0270] Extract key frame images from construction monitoring videos, such as images after transformer installation or images at the time when cable laying is completed.

[0271] A deep learning model (CNN) is used to extract feature points from images, generate feature point descriptors, and mark cable connection points and equipment installation status.

[0272] Progress comparison and analysis

[0273] Match the image feature points with the corresponding feature points in the BIM model, and quickly search for matching points using the KD tree algorithm.

[0274] The RANSAC algorithm is used to eliminate false matches, calculate the construction completion rate, and generate a progress report. The report includes a list of completed construction areas and unfinished areas.

[0275] Result feedback and early warning

[0276] Set warning levels based on construction deviations: if equipment installation is delayed by more than 10% of the planned time, a red emergency alarm is triggered and the project manager is notified via SMS.

[0277] The progress deviation information is dynamically displayed through the 3D model, and the completed and unfinished areas are intuitively marked.

[0278] Dynamic update and optimization

[0279] Update the BIM model regularly, add the status of completed areas and adjust the subsequent construction plan based on delays.

[0280] Linked with the smart construction site management platform, it integrates equipment status data and optimizes construction resource allocation, such as deploying workers to speed up progress.

[0281] Example 6: A substation equipment expansion project in Jiangsu

[0282] Project Background

[0283] This project involves the expansion of the existing substation, including the installation of new transformers and cables. The construction environment is complex and involves the protection of existing equipment.

[0284] Implementation process

[0285] BIM model construction

[0286] Build a comprehensive BIM model of the existing substation and expansion area, marking new equipment installation points and protection areas for existing equipment.

[0287] Add detailed construction phase attribute information to the extension model, such as specific task nodes and time schedule.

[0288] Video surveillance data collection

[0289] Dome cameras are deployed in the expansion area to focus on monitoring the transformer hoisting process and cable laying quality.

[0290] Set up a real-time monitoring system and upload video data to the central server through a streaming platform.

[0291] Image data processing

[0292] Image enhancement processing is performed on the construction images of the expansion area to remove noise caused by construction dust and improve image quality.

[0293] The SIFT algorithm is used to extract feature points of the expansion area, such as the bolt connection points and cable interfaces at the transformer installation location.

[0294] Progress comparison and analysis

[0295] Compare the actual construction progress of the expansion area with the planned progress of the BIM model, marking the completed parts (such as transformer base installation) and delayed parts (such as cable connection).

[0296] Generate construction progress reports for expansion areas and make optimization recommendations, such as adjusting construction resources to speed up cable laying tasks.

[0297] Result feedback and early warning

[0298] For tasks with construction progress delayed by more than 5%, a yellow warning will be issued through the smart construction site platform, and the construction manager will be advised to take remedial measures.

[0299] Dynamically display the completion progress of the expansion area in the 3D model, and use animation to demonstrate the reasons for construction delays and corrective plans.

[0300] Example 7: Full-cycle monitoring project of a new energy substation

[0301] Project Background

[0302] This project is a newly built new energy substation with a long project cycle and many participating units, and the construction progress requires full-cycle monitoring.

[0303] Implementation process

[0304] BIM model construction

[0305] Build a complete 3D model based on the project construction plan, including detailed information of all construction stages, and mark feature points such as equipment installation locations, floor segments, and key cable paths in the model.

[0306] The model is imported into the smart construction site management platform to support subsequent data interaction and dynamic updates.

[0307] Video surveillance data collection

[0308] Multi-angle cameras are deployed in the main construction area, material storage area and main passages of the construction site, and drone aerial photography is used to regularly capture large areas.

[0309] The video stream is transmitted in real time and combined with drone images to form a high-resolution construction image database.

[0310] Image data processing

[0311] The images collected by the drone are stitched together to form a panoramic view.

[0312] A deep learning model is used to automatically extract feature points, with special attention paid to the installation status and positioning accuracy of large equipment (such as main transformers).

[0313] Progress comparison and analysis

[0314] Compare the stitched panoramic image with the BIM model at multiple points to analyze the equipment installation accuracy and the progress of the construction area.

[0315] Automatically generate construction deviation reports, predict possible delay risks in the next phase, and make adjustment suggestions.

[0316] Result feedback and early warning

[0317] Dynamically update construction progress information on the smart construction site management platform, and push progress analysis results to the construction party and supervision unit in real time.

[0318] If construction in a certain area is found to be delayed, multiple levels of alarms (such as on-site alarms and system alarms) will be triggered, and the project management team will be notified to adjust the plan.

[0319] Dynamic update and optimization

[0320] Combined with real-time data, the construction completion status in the BIM model is dynamically updated, and subsequent construction plans are adjusted to optimize resource allocation.

[0321] Link IoT devices to monitor equipment status and personnel attendance on construction sites to ensure that construction resources match schedule requirements.

[0322] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically calculating the progress of a substation project, characterized in that: The following steps are involved: S1) BIM model construction: by acquiring relevant data of the substation project, a three-dimensional BIM model containing geometric model and attribute information is constructed, and key feature points are marked for subsequent progress comparison; S2) Video monitoring data collection: deploying cameras at the construction site to collect construction video streams in real time and transmit them to the central server; also includes collecting on-site construction status data through drone tilt photography and laser point cloud scanning; S3) Image data processing: denoising and key frame extraction of video images, and using deep learning algorithms to extract image feature points and descriptors; S4) Progress comparison and analysis: match the image feature points with the corresponding points in the BIM model, calculate the deviation between the actual progress and the planned progress, and generate construction progress data; S5) Result feedback and early warning: Set early warning levels according to construction deviations, generate progress reports and send alarm information through various means.

2. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: The S1) comprises the following steps: S101) Obtaining architectural design drawings, construction drawings and bills of quantities, and digitizing them; S102) Using BIM modeling software to construct a three-dimensional geometric model of the substation, the model includes the main building, equipment installation location, cable trench and control room; S103) assigning attribute information to the components in the geometric model, wherein the attribute information includes material, size, weight and construction plan time; S104) marking key feature points in the model, which are used for subsequent progress comparison, and exporting the BIM model into a standard format file.

3. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: Described S2) comprises the following steps: S201) Planning the number, type and installation location of cameras according to the monitoring requirements of the construction site; S202) Arranging cameras in the construction area and configuring camera parameters, including resolution, frame rate and focal length; S203) obtaining high-resolution images of the construction site through oblique photography by a drone, and collecting three-dimensional data of the construction status through laser point cloud scanning; S204) transmitting the video stream of the camera and the data collected by the drone to the central server via a streaming platform, wherein the streaming platform supports RTSP, RTMP and HLS protocols; S205) The transmitted multi-source data is decoded, format-converted, and stored as image data or point cloud data for subsequent analysis.

4. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: The S3) comprises the following steps: S301) extracting key frames from the transmitted video stream, where the extracted key frames are images of the construction status at a specific time point; S302) performing denoising, brightness enhancement and edge refinement on the key frame image to improve image quality; S303) Using a convolutional neural network to extract edge, corner and texture area feature points in the image, and generate feature descriptors that describe the feature points.

5. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: Described S4) comprises the following steps: S401) using a KD tree algorithm to quickly match image feature points with BIM model feature points; S402) applying the RANSAC algorithm to remove erroneous matching points to improve matching accuracy; S403) calculating the deviation between the actual progress and the planned progress by comparing the difference between the construction status in the image and the planned status in the BIM model; S404) Generate a progress report, including the completed parts, unfinished parts and deviation cause analysis.

6. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: Described S5) comprises the following steps: S501) Setting the warning level according to the construction deviation, including the following warning rules: If the deviation is less than 5%, it is marked in green, indicating that the construction is normal; Deviations of 5%-10% are marked in yellow, triggering a warning alert; If the deviation exceeds 10%, it will be marked in red and trigger an emergency warning; S502) sending alarm information to relevant personnel through sound alarm, SMS notification and email; S503) The construction progress data is visualized in the form of charts, 3D animations and texts, so that project managers can intuitively understand the construction status.

7. The method for automatically calculating the progress of a substation project according to claim 1, characterized in that: Also includes: S6) Regularly update the BIM model to reflect the latest construction status, including adding new feature points and adjusting the construction plan; S7) is linked with the smart construction site management platform and combined with IoT equipment data to perform intelligent optimization of construction management.

8. The substation project progress automatic calculation system is characterized by: The method for automatically calculating the progress of a substation project according to any one of claims 1 to 7 comprises: BIM modeling module, which is used to obtain the design drawings, construction drawings and bill of quantities of the substation project, build a three-dimensional geometric model including the main building, equipment installation location, cable trench and control room, assign component attribute information and mark key feature points; The video acquisition module is used to collect the construction video stream through the camera deployed at the construction site, configure the camera parameters and transmit the video stream to the central server through the streaming platform; The image processing module is used to extract key frames and preprocess images of the video stream, extract image feature points through deep learning algorithms and generate feature descriptors; Progress comparison module, used to match image feature points with feature points in the BIM model, analyze the deviation between the construction progress and the planned progress, and generate a construction progress report; The feedback and warning module is used to set the warning level according to the progress deviation, and feedback the warning information through sound alarm, SMS notification or email, and display the construction progress status in the form of charts, 3D models and animations.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for automatically calculating the progress of a substation project as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for automatically calculating the progress of a substation project as described in any one of claims 1 to 7 is implemented.

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