Building intelligent 3D imaging safety quality monitoring system

Abstract

The invention relates to the technical field of building construction quality and safety management, and discloses a building intelligent 3D imaging safety quality monitoring system which comprises an intelligent 3D imager. According to the invention, the intelligent 3D imager, the intelligent safety helmet, the WIFI coverage positioning system and other components are integrated for cooperative work, and the intelligent 3D imager generates a high-precision 3D model and compares the BIM model detection deviation; the intelligent safety helmet collects textures, monitors biological characteristics and interacts with the textures; the WIFI system provides network and positioning, and manages the robot; an electronic datum point and a target ensure accurate modeling; the cloud control center optimizes a model, analyzes a video and generates a report, and the system improves the construction quality detection precision through the cooperation of multiple components, achieves the real-time synchronization and visualization of data, strengthens the security management, strengthens the material statistical management, solves the problems of equipment scanning blind areas and collaboration, guarantees the privacy of a user, provides convenient management, and improves the construction quality detection efficiency. The system meets the requirements of intelligentization, high precision and dynamic management and control of the whole process of building construction, and has remarkable advantages.

Description

Technical Field

[0001] The present invention relates to the technical field of construction quality and safety management, and more particularly to a building intelligent 3D imaging safety and quality monitoring system. Background Art

[0002] Traditional building safety and quality inspection relies on manual patrols, which has the following defects: Large human error: The acceptance results are affected by the technical level, experience and subjective judgment of personnel, and the data credibility is low; Lack of real-time performance: On-site data cannot be synchronized in real time, and the acceptance results are difficult to be graphically displayed and traced; Weak security management: In complex environments, personnel permissions are chaotic, safety accidents occur frequently, and unsafe behaviors cannot be effectively identified; Blind area and coordination problems: Traditional equipment has scanning blind areas, and the device protocols of multiple manufacturers are not unified, making it difficult to manage overall.

[0003] The existing technologies cannot meet the intelligent, high-precision and dynamic control requirements of the entire building construction process. Therefore, there is an urgent need for a building intelligent 3D imaging safety and quality monitoring system to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a building intelligent 3D imaging safety and quality monitoring system to solve the problems existing in the above background art.

[0005] The present invention provides the following technical solutions: A building intelligent 3D imaging safety and quality monitoring system, comprising: An intelligent 3D imager, integrating a camera, an environmental sensor, an accelerometer, a gyroscope and a laser rangefinder radar, generating a 3D model with millimeter-level accuracy based on the triangulation principle, and dynamically comparing with a preset BIM model to detect construction deviations; An intelligent safety helmet, equipped with a camera, a biosensor, a Bluetooth module and a voice interaction module, for collecting construction surface texture maps, monitoring the biometric characteristics of operators, binding identities, and interacting with a cloud control center in real time through wireless communication, and releasing construction announcements, work restricted areas, and emergencies through the intelligent helmet every day; A WIFI coverage positioning system, providing full-domain network coverage and centimeter-level positioning functions, supporting the conversion of device protocols of multiple manufacturers, planning the operation restricted areas of robots, and displaying the position of the intelligent safety helmet in the model space in real time; An electronic reference point and an electronic target, providing an absolute coordinate reference for the 3D model to ensure the consistency of the modeling space; An electronic fence, when the wearer of the intelligent helmet strays into the restricted area of the electronic fence, triggering a warning message and persuading to leave, and at the same time guiding the wearer of the intelligent helmet to bypass the prohibited area; Vehicle identification card, a data card that combines Bluetooth and WiFi, exchanges data through Bluetooth and WiFi communications, records vehicle users, permissions, vehicle ownership, material types, weight, on-board material inspection reports, docking departments and contact numbers.

[0006] The cloud control center uses deep learning algorithms to optimize 3D model details, dynamically analyze video data to identify unsafe behaviors, and generate a 3D timeline model and multi-dimensional management reports for the entire construction process.

[0007] Furthermore, the intelligent 3D imager includes two types: fixed and handheld. The fixed imager is deployed in key construction areas, and the handheld imager is used for mobile scanning and blind spot supplementary modeling. The handheld imager can also present a 3D model scene corresponding to the site on the screen, which is used to check and verify the size, specification, quantity and simulate the real scene effect of the building components, and generate a 3D model in the material stacking area of ​​the site, such as: calculating the volume of template stacking, estimating the template area, calculating the volume of stacked steel pipe roots converted to weight, etc.

[0008] Furthermore, the biosensor of the smart safety helmet includes a heart rate monitoring module. When it detects that the bound user's heart rate is abnormal or does not match the bound user's identity, it automatically triggers a voice warning and pushes warning information to the cloud control center.

[0009] Furthermore, the smart safety helmet has an emergency button. When the bound user encounters an emergency and needs help, the emergency button is manually triggered, and the smart safety helmet triggers a voice warning and pushes a warning message to the cloud control center, highlighting the location in the model. The smart safety helmet also sends the location coordinates to the outside.

[0010] Furthermore, the WIFI coverage positioning system communicates with third-party robot equipment through a multi-protocol compatible interface, plans the robot path in real time and prohibits unauthorized devices from entering the operation restricted area. The WIFI coverage positioning system monitors and records the activity trajectory and position of the smart safety helmets in the area. In an emergency, it intelligently plans the evacuation route according to the location and guides it to a safe area.

[0011] Furthermore, the cloud control center includes a dynamic blind spot completion module, which collects blind spot data through the intelligent safety helmet camera, predicts blind spot features with a deep learning algorithm, and generates a complete 3D model.

[0012] Furthermore, the system also includes a permission management module, which realizes dual permission verification by binding face recognition with a safety helmet and binding a mobile phone Bluetooth with a safety helmet, and triggers a voice warning and security linkage response when an unauthorized person enters the monitoring area.

[0013] Furthermore, the cloud control center analyzes video data in real time, extracts personnel movement trajectories, postures, and object state features, identifies unsafe behaviors through a preset algorithm, and pushes warnings to the intelligent safety helmets.

[0014] Furthermore, the cloud control center analyzes video data in real time, differentiates smoke, abnormal temperature rise, horizontal displacement deviation, verticality deviation, and deformation deviation in the monitored area, and pushes warnings at different levels according to the development trend.

[0015] Furthermore, the vehicle identification card manages the vehicles within the project, records the entry time, exit time, material type, weight, on-vehicle material inspection report of the engineering transportation materials, guides the engineering vehicles, and exchanges and records the vehicle trajectories.

[0016] Furthermore, the system supports generating a 3D as-built model of the entire construction process according to the time axis, providing visual data basis for project payment, auditing, and quality traceability.

[0017] Furthermore, the intelligent safety helmet is built with a privacy control module, allowing users to manually turn off the camera function, and recording the attendance data of the operators and remote assistance requests.

[0018] Furthermore, the cloud control center can turn on all cameras in the monitored area through high-level permissions, including the built-in camera of the intelligent safety helmet, to deal with emergencies and urgent events. It can also generate a 3D real-scene safety evacuation path based on the collected videos and 3D models to guide the safe evacuation of staff at each location.

[0019] Furthermore, the cloud control center generates multi-dimensional management reports, including construction progress, quality pass rate, attendance rate, and safety event statistics, and supports real-time viewing and exporting on the mobile terminal. The system can display and search for the location of the intelligent safety helmet in real time in the model space.

[0020] Furthermore, for the model of the intelligent 3D imaging safety and quality monitoring system, after completion and delivery, the model is streamlined to strengthen the logical relationships and force relationships of components, equipment, and pipeline lines, and a 3D model with clear logical relationships and clear force systems is delivered for operation and maintenance reference and query.

[0021] The technical effects and advantages of the present invention: The intelligent 3D imaging safety and quality monitoring system of the present invention effectively solves many problems in traditional building safety and quality inspection. This system integrates multiple components such as an intelligent 3D imager, an intelligent safety helmet, a WIFI coverage positioning system, etc. to work together. The intelligent 3D imager generates a high-precision 3D model and compares it with the BIM model to detect deviations; the intelligent safety helmet collects textures, monitors biometric features and interacts; the WIFI system provides network and positioning, and also manages robots; electronic reference points and targets ensure accurate modeling; the electronic fence demarcates restricted areas in space; the cloud control center optimizes the model, analyzes videos, and generates reports. Through the cooperation of multiple components, the system improves the accuracy of construction quality inspection, realizes real-time data synchronization and visualization, strengthens security management, solves the blind spots of equipment scanning and coordination problems, protects user privacy, provides convenient management, meets the needs of intelligent, high-precision and dynamic control in the whole process of building construction, and has significant advantages.

[0022] Specifically Improve the accuracy of construction quality inspection With the help of the intelligent 3D imager, the present invention integrates a camera, an environmental sensor, an accelerometer, a gyroscope and a laser range finder radar, and generates a 3D model with millimeter-level accuracy based on the principle of triangulation, and dynamically compares it with the preset BIM model. This process can accurately detect construction deviations. Compared with the traditional method relying on manual inspection, the influence of human errors is greatly reduced, making the construction quality inspection data more reliable. For example, in the construction of the main building structure, the size deviation of beams and columns, the verticality deviation of walls, etc. can be accurately identified to ensure that the construction is carried out strictly in accordance with the design requirements, thus improving the overall quality and safety of the building.

[0023] Enhance real-time performance and data visualization Devices such as the intelligent 3D imager and the intelligent safety helmet interact with the cloud control center in real time through the WIFI coverage positioning system, and the on-site data can be synchronized to the cloud in a timely manner. The cloud control center can not only optimize the details of the 3D model using deep learning algorithms, but also generate a 3D time-axis model and multi-dimensional management reports of the entire construction process. Construction personnel and management personnel can view information such as construction progress and quality status at any time, realizing real-time and visual display of data.

[0024] For example: Management personnel can view the 3D model of the project progress in real time through the mobile terminal, intuitively understand the construction conditions at different stages, and facilitate timely decision-making. The 3D time-axis model can also provide visual data basis for project payment, audit and quality traceability, facilitating the subsequent work.

[0025] For example, managers can also use the mobile APP for positioning in combination with cameras to seamlessly integrate the 3D models and structural construction information in the cloud with the on-site scenario, dynamically display the effects, and review the dimensions, specifications, quantities, and positions of components, eliminating the need to search for data in complex drawings for verification. At the same time, the percentage of completion of this construction section, the list of materials to be completed in this construction section, and the list of materials still required in this period can be calculated through the cloud, keeping the engineering material reserve in the best state forever.

[0026] For example, in complex electromechanical projects, various pipelines that have been concealed can be automatically located through end-point positioning to display the pipeline paths, uses, and real scenes before concealment. This avoids missed or misconnected pipelines due to omissions during handovers between different construction sections and processes.

[0027] For example, based on the user group bound to the intelligent safety helmet, different model data and different permission data are displayed in the APP. For example, the electromechanical maintenance team focuses on displaying the routes of concealed temporary pipelines, switch cabinets, control cabinets, valves, etc., quickly guiding the electromechanical maintenance team to handle faults; for example, the environmental safety team focuses on displaying personnel positions, positions of suspicious personnel, suspicious smoke alarms, abnormal temperature rises in areas, etc., quickly guiding environmental safety personnel to handle potential hazards; for example, the steel bar team focuses on displaying the steel bar stacking site, steel bar equipment operation procedures, and more steel bar node information in the model; Strengthen security management The intelligent safety helmet is equipped with a biosensor and a camera, which can monitor the biometric characteristics of operators and, combined with the dynamic analysis of video data by the cloud control center, effectively identify unsafe behaviors. When an abnormal heart rate of an operator is detected, a voice warning is automatically triggered and a warning message is pushed to the cloud control center; at the same time, the cloud control center analyzes the movement trajectories, postures, and object state characteristics of personnel to identify unsafe behaviors and push warnings to the intelligent safety helmet. In addition, the system is also equipped with a permission management module, which uses face recognition and mobile phone Bluetooth binding to the safety helmet to achieve double permission verification. When an unauthorized person enters the monitoring area, a voice warning and a security linkage response are triggered. These measures comprehensively ensure the safety of personnel at the construction site, reduce the occurrence of safety accidents, and improve the level of security management.

[0028] The intelligent safety helmet collects the position coordinates of the helmet at any time through the WIFI coverage positioning system, interacts the coordinate information in real time in the cloud control center, and dynamically displays the coordinate information in the 3D model to understand the working areas and working trajectories of all employees.

[0029] Solve blind spot and coordination problems The intelligent 3D imager is divided into two types: fixed and handheld. The fixed imager is deployed in key construction areas, and the handheld imager is used for mobile scanning and blind area supplementary modeling. The dynamic blind area complement module in the cloud control center collects blind area data through the intelligent safety helmet camera, predicts the blind area features by combining deep learning algorithms, and generates a complete 3D model, effectively solving the scanning blind area problem existing in traditional equipment. At the same time, the WIFI coverage positioning system supports the protocol conversion of multi-vendor devices, can communicate with third-party robot devices, plan the robot path in real time and prohibit unauthorized devices from entering the operation restricted area, realizing the overall management and collaborative operation of multiple devices and improving the construction efficiency.

[0030] Protecting user privacy and providing convenient management The intelligent safety helmet is built-in with a privacy control module, allowing users to manually turn off the camera function, fully protecting the privacy of operators. At the same time, the intelligent safety helmet can also record the attendance data and remote assistance requests of operators. The multi-dimensional management reports generated by the cloud control center, including construction progress, quality qualification rate, attendance rate and safety event statistics, etc., and support real-time viewing and export on the mobile side, providing a convenient tool for construction management and improving the management efficiency.

[0031] In summary, the building intelligent 3D imaging safety and quality monitoring system of the present invention comprehensively solves the problems existing in traditional building safety and quality inspection, meets the intelligent, high-precision and dynamic management and control requirements of the whole process of building construction, and has remarkable technical effects and practical value. Brief Description of the Drawings

[0032] Figure 1 It is the system architecture diagram of the present invention; Figure 2 It is the schematic diagram of the application of the implementation example of the present invention in virtual reality: Figure 3 It is the brief logic diagram of the present invention. Detailed Description of the Invention

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] It can be understood that the terms "first", "second", etc. used in this application can be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0035] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. Embodiment

[0036] System Deployment and Hardware Installation WIFI Coverage and Positioning System Deployment At the construction site, in the early stage, a professional wireless signal surveying software is used to conduct a detailed survey of the signal strength and interference source distribution in different areas. According to the survey results, WIFI base stations are arranged in a layered and zoned manner. In the main construction area of the building, base stations are set at certain intervals, and directional antennas are used to adjust the signal coverage angle to ensure stable signals in the indoor and outdoor construction areas. For outdoor open areas, such as around large foundation pits and material stacking areas, aiming to meet the signal strength and positioning accuracy, the base station spacing is flexibly adjusted according to the triangular or quadrilateral grid layout to achieve full-area coverage without dead angles.

[0037] When selecting the positioning module, the cost, accuracy, and on-site environment are comprehensively considered. Although the UWB technology has high accuracy, it may be interfered in areas with a lot of metal and strong signal reflection. At this time, Bluetooth beacons are combined for complementary positioning. When installing the UWB positioning tags, they are firmly fixed at specific positions on smart devices and personnel safety helmets, avoiding occlusion, and calibration compensation is carried out to reduce positioning errors caused by human occlusion and differences in equipment installation positions. Bluetooth beacons are deployed at key nodes inside the building, such as stairwells and elevator lobbies, to assist in achieving more accurate indoor positioning. At the same time, a redundant backup network is built, with standby base stations and emergency power supplies set up to ensure the continuity of the network and positioning services, and automatic switching can occur in case of a main network failure to ensure the normal operation of the system.

[0038] Electronic Datum Point and Target Setting Before installing the electronic datum points, on-site measurements are carried out with the help of a high-precision total station, and the measurement accuracy is accurate to the sub-millimeter level. According to the coordinate system of the architectural design drawings, each datum point is repeatedly measured and confirmed to ensure that the absolute coordinate accuracy error is within ±0.5 mm. During installation, special embedded parts are used to firmly embed the electronic datum points into the concrete foundation or stable building structure to ensure that they do not displace or loosen during the construction process. After installation, a protective shell is used to protect the datum points to prevent damage caused by construction collisions and erosion by bad weather.

[0039] The electronic targets are customized according to the characteristics of the construction area and the needs of 3D modeling. In complex structural construction areas, such as large bridge piers and special-shaped building components, targets with unique optical identification and electronic signal reflection devices are designed to enhance the recognition effect of intelligent 3D imagers. The target installation position is accurately calculated to ensure that imagers at different angles can effectively identify it. At the same time, the electronic reference points and targets are calibrated and checked regularly, and the coordinates are re-measured using professional measuring equipment to ensure that their accuracy always meets the requirements, providing reliable coordinate reference points for 3D modeling.

[0040] Intelligent 3D imager installation When the fixed imager is installed on the top of a tower crane or scaffolding, a special shock-absorbing mounting bracket is used to reduce the impact of equipment vibration on imaging accuracy. When installed on a tower crane, the installation angle and position are adjusted to cover the entire construction surface, and combined with the rotation and lifting functions of the tower crane, a larger range of dynamic scanning can be achieved. When installed on the top of a scaffolding, the scaffolding construction and dismantling process is considered, and a fixture that can be quickly disassembled and reinstalled while ensuring installation accuracy is designed.

[0041] Before handheld imagers are assigned to quality inspectors, they are personalized. According to the usage habits of quality inspectors, the layout of the device's operating buttons and the menu display mode are adjusted. At the same time, they are equipped with portable charging devices and data storage modules to facilitate long-term use at the construction site. For different construction scenarios, such as narrow spaces and high-altitude operations, customized auxiliary tools are provided for handheld imagers, such as extension poles and wide-angle lens accessories, to facilitate the acquisition of image data at special locations.

[0042] Utilize 3D imaging to calculate materials, such as: calculate the volume of template stacking, estimate the template area, calculate the volume of stacked steel pipe roots and convert them into weight, and plan the site layout and line water pipe layout more reasonably.

[0043] Smart safety helmet configuration Before the smart safety helmets are distributed, they undergo comprehensive performance testing of components such as the built-in camera, heart rate sensor, voice module, Bluetooth module, and emergency call. The heart rate sensor is calibrated using professional sensor calibration equipment to ensure that the measurement accuracy error is within ±2 times / minute.

[0044] The smart safety helmet can also enable intercom, group chat and call the other party's camera video, allowing barrier-free communication between floors under the noise of construction equipment, avoiding safety accidents caused by poor communication and erroneous operation. For example: multi-person collaborative cable laying, equipment command hoisting, etc. When using face recognition and mobile phone Bluetooth binding permissions, ensure the security of identity verification. Set up face recognition devices at the entrance of the construction site, bind the face and the IP of the intelligent safety helmet, and the safety helmet can also be retrieved through the mobile phone. When an unauthorized safety helmet or an intelligent safety helmet with a mismatched IP appears in the construction area, a voice warning and a security linkage response will be triggered. When workers enter, identity recognition and permission verification will be automatically completed, and unauthorized personnel will not be able to enter. Set up multiple encryption mechanisms for the intelligent safety helmet to encrypt and transmit and store the collected data end-to-end, ensuring data security and preventing privacy leakage. At the same time, regularly update the software system of the helmet to improve the device performance and functions, and add new safety monitoring and interaction functions.

[0045] Data Collection and Model Construction Surface Texture Map Collection When construction workers wear intelligent helmets to collect surface texture data, the system automatically adjusts the collection strategy according to the construction progress and regional characteristics. During the construction stage of the main building structure, focus on collecting the overall texture of walls, beams and columns; during the decoration stage, conduct refined collection of the surface texture of decorative materials. To improve the collection efficiency and quality, develop an intelligent collection guidance program. Through the built-in display screen and voice prompts of the helmet, guide construction workers to collect at appropriate distances and angles.

[0046] During the collection process, use image enhancement algorithms to process the collected images in real time to improve image clarity and contrast. At the same time, adopt image stitching technology to seamlessly stitch the images collected from different angles into a complete texture map, ensuring the continuity and accuracy of the texture. The collected data is uploaded to the on-site record control center in real time via WIFI. During the upload process, adopt data compression technology to reduce the data transmission volume, improve the transmission speed, and ensure that the data quality is not lost.

[0047] 3D Scanning and Preliminary Modeling Install a fixed 3D scanning device under the tower crane arm. Use the swing of the tower crane to continuously repeat scanning from various angles. Continuously correct and improve the 3D model data through AI algorithms, and add timeline information at the time interval set by the administrator. During the scanning process, combine cameras, environmental sensors, accelerometers, gyroscopes and lidar, and calculate and optimize through AI algorithms to ensure clear and accurate scanning data can be obtained day and night.

[0048] When the handheld imager performs indoor or local fine scanning, it obtains the position information of the current scanning area in the overall model through data interaction with the fixed imager, realizing seamless data fusion. For complex structures such as reinforced concrete joints and pipe-intensive areas, adopt a multi-view scanning method to collect data from different angles, and use a point cloud fusion algorithm to merge the point cloud data from multiple views to construct a more complete and accurate local model.

[0049] Cloud Model Optimization When the on-site record control center uploads data to the cloud control center, asynchronous transmission and resume interrupted transfer technologies are adopted to ensure the stability and integrity of data transmission. After receiving the data, the cloud control center uses deep learning algorithms to optimize the 3D model. During the optimization process, the generative adversarial network (GAN) technology is introduced to generate more realistic and delicate model details and improve the visualization effect of the model.

[0050] When comparing with the BIM model, not only the geometry, dimensions, positions, and quantities of components are compared, but also the material properties and spatial position relationships of components are comprehensively compared. For deviation data, hierarchical classification management is adopted. According to the size, type, and impact degree on project quality of the deviation, it is divided into minor deviations, general deviations, and serious deviations. Different warnings are triggered according to the deviation level and urgency. For different levels of deviations, reports with different levels of detail are generated to provide accurate basis for subsequent rectification work.

[0051] Real-time Monitoring and Safety Management Behavior Recognition and Warning When security cameras and smart helmets cooperate in monitoring, multi-camera fusion technology is used to fuse the images of cameras at different positions, expand the monitoring range, and reduce monitoring blind spots. In the behavior recognition algorithm, the technology based on spatio-temporal convolutional network (STCN) is adopted, which can not only identify the static postures of personnel, but also analyze the movement trajectories and action sequences of personnel, improving the accuracy and real-time performance of behavior recognition.

[0052] Security cameras and smart helmets cooperate in monitoring, and at the same time, the IP of the face and the smart safety helmet are bound. When there is no authorized safety helmet worn or the safety helmet does not match the face in the construction area and the monitoring range, voice warnings and security linkage responses are triggered.

[0053] When unsafe behaviors or emergencies are detected, while the cloud control center sends voice warnings to the corresponding helmets, it attracts the attention of construction workers through various methods such as vibration reminders and flashing lights. When a pop-up warning appears on the management terminal, detailed warning information is displayed, including the time, location, personnel information, and behavior type of the unsafe behavior, and handling suggestions, relevant safety specification links, and intelligent evacuation route guidance are provided to facilitate the timely handling by management personnel. At the same time, the unsafe behavior events are recorded in the safety management database for statistical analysis to find out the high-incidence areas and time periods of safety hazards and provide data support for formulating targeted safety management measures.

[0054] Biometric Monitoring The heart rate sensor built into the intelligent helmet uses photoplethysmography (PPG) technology and combines advanced filtering algorithms to remove noise generated by movement, electromagnetic interference, etc., and accurately measure the physiological state of workers. When an abnormal heart rate is detected, the system determines the type of abnormality based on the preset heart rate change curve and threshold. For example, a rapid heart rate may be due to high-intensity labor, emotional stress, or physical illness, while a slow heart rate may be due to physical fatigue or cardiovascular problems.

[0055] According to different types of abnormalities, the system takes different countermeasures. For mild abnormalities, such as a brief increase in heart rate, the helmet emits a gentle voice prompt, advising the worker to take appropriate rest. For severe abnormalities, such as a heart rate continuously exceeding the dangerous threshold, the system immediately notifies the on-site safety officer and medical emergency personnel, and provides the real-time location and health information of the worker to ensure timely rescue. At the same time, the biometric data of the worker is recorded in the health management file to provide a reference for long-term health assessment and labor arrangement.

[0056] Robot collaborative management When establishing communication with third-party robots, develop a general communication protocol conversion middleware that is compatible with the communication protocols of multiple robots. Through the middleware, realize remote control and status monitoring of the robots, and obtain the working status, location information, and task progress of the robots in real time. When delimiting the robot operation restricted area, use 3D modeling technology to construct the virtual boundary of the restricted area and associate it with the positioning systems of the intelligent 3D imager and the intelligent safety helmet.

[0057] When the wearer of the intelligent helmet strays into the electronic fence restricted area, the system first warns the person to leave through the helmet voice and vibration, and at the same time sends a stop command to the robot. If the person does not respond in time, the system automatically controls the robot to adjust the working mode, such as reducing the working speed and pausing dangerous actions, to avoid collision accidents. In addition, during the operation of the robot, the distance between the robot and the surrounding personnel and equipment is monitored in real time. When the distance is too close, a warning signal is issued to ensure construction safety.

[0058] Blind area dynamic complement and model calibration Blind area identification and complement The cloud control center uses machine learning algorithms to deeply analyze historical scan data, considering not only the position, perspective, and scan range of the fixed imager, but also the environmental factors at the construction site, such as equipment layout and personnel activities, to predict the location of the blind area. Adopt an object detection algorithm based on deep learning to identify objects that may cause occlusion, such as large construction equipment and building components, to more accurately determine the scope of the blind area.

[0059] When instructing the nearby smart helmets to turn on the cameras to supplement the data collection, through the intelligent task allocation algorithm, the most suitable helmet is selected to execute the task according to factors such as the position, power, and task priority of the helmet. During the collection process, image enhancement and super-resolution reconstruction technologies are used to improve the quality of the supplementary data. After the collection is completed, a data fusion algorithm is used to fuse the supplementary data with the original 3D model to ensure the integrity and accuracy of the model.

[0060] Model consistency verification After the daily construction is completed, when the system compares the 3D model with the BIM design drawing, a comparison method based on feature point matching and semantic segmentation is adopted. First, the key feature points in the model, such as the edges and corners of components, are extracted for precise matching, and then semantic segmentation technology is used to classify and identify different components in the model, and the attributes and positional relationships of the components are compared.

[0061] When generating the deviation report, not only the numerical values of the deviations are listed, but also the positions and shapes of the deviations in the model are displayed in a visual way, which is convenient for the management personnel to understand intuitively. For areas with large deviations, detailed rectification suggestions and simulated rectification effects are provided to help the construction personnel formulate rectification plans. At the same time, the deviation data is fed back into the quality control system to optimize the construction technology and construction process to avoid similar deviations from occurring again.

[0062] Data management and application 3D time-axis model generation When the cloud updates the 3D model daily according to the construction progress, version control technology is adopted to record the time, updated content, and updated personnel information of each model update. Through the time-axis model, not only the 3D model status of each stage during the construction process can be viewed, but also comparative analysis of different version models can be carried out to understand the project changes.

[0063] To facilitate user viewing and operation, a 3D model visualization interface based on WebGL technology is developed, which supports directly browsing the 3D time-axis model in the browser without installing additional software. In the visualization interface, a variety of interactive functions are provided, such as model zooming, rotation, translation, and component information query, historical version switching, etc., to meet the needs of different users.

[0064] Multi-dimensional report output When the quality report counts the qualified rate of components and the completion rate of deviation rectification, data mining technology is adopted to deeply analyze the quality data. Not only the overall qualified rate and completion rate are counted, but also classified statistics are carried out according to multiple dimensions such as construction area, construction team, and component type to find out the distribution law of quality problems. For areas or teams with large quality fluctuations, key analysis is carried out to find out the reasons and formulate improvement measures.

[0065] When the safety report records the warning event types, frequencies, and handling results, the association analysis algorithm is used to find the association relationships between different safety events, such as the connection between a certain unsafe behavior and a specific construction environment or equipment failure. By analyzing these association relationships, safety risks can be predicted in advance, preventive measures can be taken, and the probability of safety accidents can be reduced.

[0066] When the attendance report automatically generates personnel attendance records based on helmet positioning data, intelligent analysis of the attendance data is carried out in combination with the construction task arrangement and working time requirements. It not only records the personnel's attendance time but also analyzes the working trajectories and staying times of personnel at the construction site to judge whether there are situations such as loafing on the job or changing posts. At the same time, when docking the attendance data with the salary system, the salary is automatically calculated and paid according to different attendance situations and work performances.

[0067] Mobile interaction When managers view the real-time model through the mobile phone client, the adaptive loading technology is adopted to automatically adjust the loading accuracy and display effect of the model according to the mobile phone network condition and device performance. When receiving warning information, through the message push mechanism of the mobile phone, the warning information is timely pushed to the managers to ensure that the information is not missed. At the same time, a warning information classification reminder function is set on the mobile phone client, and managers can set different reminder methods and priorities for different types of warning information according to their own needs.

[0068] When remotely controlling the helmet camera for video inspections or assisting workers in solving technical problems, the real-time video stream transmission technology is adopted to ensure the smoothness and real-time nature of the video images. During the video call, the voice enhancement and noise reduction technology is used to improve the call quality. At the same time, the gesture operation function of the mobile phone client is developed, and managers can control the helmet camera through simple gesture operations, such as adjusting the shooting angle, zooming in and out of the picture, etc., to improve the operation convenience. The reservation device virtual queuing system can also be realized through mobile interaction. The virtual queuing system queues according to the progress plan, team priority level, and urgency set by the administrator. The team can view its own queuing progress and the construction location of the equipment in real time, reasonably arrange time, and when the equipment is idle, the system will conduct a full-staff broadcast to make the equipment use more efficiently and reasonably. For example, it avoids the fighting incidents that often occur when teams scramble for tower cranes at the construction site.

[0069] The mobile phone client can utilize the space coordinates of the App and the intelligent helmet, and pre-display the space structure or reproduce the hidden equipment, components, and pipelines, etc. by scanning the area with the mobile phone camera. This makes the life cycle of the system cover the entire life cycle from construction to operation until the end.

[0070] System maintenance and expansion Protocol compatibility upgrade When reserving open interfaces in the cloud, follow international standards and industry specifications, such as Internet of Things communication protocols MQTT, CoAP, etc., to ensure the universality and compatibility of the interfaces. Regularly pay attention to the development trends of third-party devices, and update the protocol conversion module in a timely manner to support the access of newly emerging devices. Before accessing new devices, conduct strict compatibility tests, including communication stability tests, data interaction tests, function coordination tests, etc., to ensure that the normal operation of the system is not affected after the new devices are accessed.

[0071] Establish a device access management platform to centrally manage all third-party devices accessing the system. On the platform, record the basic information, access time, usage status, maintenance records, etc. of the devices to facilitate device monitoring and management. At the same time, set up a device permission management mechanism to assign different permissions to devices according to their types, functions, and usage scenarios to ensure the security of the system.

[0072] Algorithm iteration When continuously optimizing the deep learning model based on historical data, adopt online learning and incremental learning techniques to enable the model to learn new data in real time and continuously improve its performance. Regularly collect new data from the construction site, including safety event data, quality deviation data, personnel behavior data, etc., and train and optimize the model. At the same time, invite domain experts to evaluate and guide the model, and adjust the model structure and parameters according to the experts' opinions to improve the accuracy and reliability of the model.

[0073] Pay attention to the latest research results in the field of artificial intelligence and introduce new algorithms and technologies into the system in a timely manner. For example, upgrade the object detection algorithm based on deep learning from the traditional convolutional neural network to a more advanced Transformer-based object detection algorithm to improve the accuracy of behavior recognition and object detection. When introducing new algorithms, conduct sufficient tests and validations to ensure their compatibility and stability with the existing system.

[0074] Implementation example Take the construction of a certain high-rise building as an example: In the foundation pit stage, first conduct a comprehensive survey and planning of the construction site to determine the installation positions of the electronic reference points. Use high-precision measuring instruments to measure and mark the key positions of the foundation pit, then install the electronic reference points, and ensure their coordinate accuracy through laser calibration.

[0075] The fixed imager is installed at the high points around the tower crane and the foundation pit to scan the foundation structure and generate an initial 3D model. During the scanning process, adjust and optimize the parameters of the imager to ensure the acquisition of high-quality and high-precision scanning data.

[0076] During the decoration construction, workers wear helmets to collect the wall texture and use hand-held imagers to scan the steel bar joints. When collecting data, the construction personnel strictly follow the operation specifications to ensure the accuracy and integrity of the collected data.

[0077] The cloud compares the model with the BIM design daily to promptly detect deviations during the construction process. For example, when it is detected that a column on a certain floor is offset by 10 mm, the system immediately pushes a rectification instruction. The management personnel can view the deviation location through the mobile terminal, organize relevant personnel for analysis and discussion, and formulate a correction plan.

[0078] When it is found that a worker is not wearing a safety rope, the helmet emits a warning message in real time and records the event in the safety report. The safety management personnel conduct safety education and training for the relevant workers based on the safety report to improve the workers' safety awareness.

[0079] During the decoration stage, use the mobile client APP to check the concealed pipelines to avoid damage to the concealed lines during the subsequent installation of components.

[0080] During the completion stage, the timeline model completely records the entire construction process, providing a visual basis for the government quality supervision department and auditing. The government quality supervision and auditing personnel can intuitively understand the construction progress and quality status through the timeline model, and complete a comprehensive and accurate quality assessment and cost audit report for the project.

[0081] During the operation stage, use the mobile client APP to query the routing of faulty cables, the location of faulty equipment, the power supply routing, etc. The above embodiments only represent several implementation manners of the present invention, and the description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0082] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent 3D imaging safety and quality monitoring system for buildings, characterized in that: Including: An intelligent 3D imager, integrating a camera, an environmental sensor, an accelerometer, a gyroscope and a laser rangefinder radar, generating a 3D model with millimeter-level accuracy based on the triangulation principle, and dynamically comparing it with a preset BIM model to detect construction deviations; An intelligent safety helmet, equipped with a camera, a biosensor, a Bluetooth module and a voice interaction module, used to collect construction surface texture maps, monitor the biometric characteristics of operators, bind identities, and interact with the cloud control center in real time through wireless communication. Construction announcements, work restricted areas, and emergencies are released through the intelligent helmet every day; A WIFI coverage positioning system, providing full-domain network coverage and centimeter-level positioning functions, supporting protocol conversion for multi-vendor devices, planning restricted areas for robot operations, and displaying the position of the intelligent safety helmet in real time within the model space; An electronic reference point and an electronic target, providing an absolute coordinate reference for the 3D model to ensure the consistency of the modeling space; An electronic fence, when the wearer of the intelligent helmet strays into the restricted area of the electronic fence, triggering a warning message and persuading to leave, and at the same time guiding the wearer of the intelligent helmet to bypass the prohibited area; A vehicle identification card, a data card combining Bluetooth and WiFi, communicating and interacting data through Bluetooth and WiFi, recording vehicle users, permissions, vehicle ownership, material types, weights, vehicle-mounted material inspection reports, docking departments and contact numbers; A cloud control center, using deep learning algorithms to optimize the details of the 3D model, dynamically analyzing video data to identify unsafe behaviors, and generating a 3D timeline model and multi-dimensional management reports for the entire construction process.

2. The building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The intelligent 3D imager includes two types: fixed and handheld. The fixed imager is installed in key construction areas, and the handheld imager is used for mobile scanning and blind area supplementary modeling. The handheld imager can also present a 3D model scene corresponding to the site on the screen, used to check and verify the dimensions, specifications, quantities of building components and simulate the actual scene effect, and generate a 3D model in the site material stacking area.

3. The building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The biosensor of the intelligent safety helmet includes a heart rate monitoring module. When an abnormal heart rate of the bound user is detected or the identity does not match the bound user, a voice warning is automatically triggered and a warning message is pushed to the cloud control center.

4. An intelligent building 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The WIFI coverage positioning system communicates with third-party robot devices through a multi-protocol compatible interface, plans the robot path in real time and prohibits unauthorized devices from entering the operation restricted area. The WIFI coverage positioning system monitors and records the activity trajectories and positions of intelligent safety helmets in the area, and intelligently plans an evacuation route according to the location in case of an emergency and guides to a safe area.

5. A building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The cloud control center includes a dynamic blind area completion module, collecting blind area data through the camera of the intelligent safety helmet, predicting blind area characteristics combined with deep learning algorithms, and generating a complete 3D model.

6. The architectural intelligent 3D imaging safety and quality monitoring system according to claim 1, wherein: The system also includes a permission management module, realizing dual permission verification through face recognition binding with the safety helmet and mobile phone Bluetooth binding with the safety helmet. When an unauthorized person enters the monitoring area, a voice warning and a security linkage response are triggered.

7. The building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The cloud control center analyzes video data in real time, extracts personnel movement trajectories, postures, and object state features, identifies unsafe behaviors through preset algorithms, and pushes warnings to intelligent safety helmets.

8. The building intelligent 3D imaging safety and quality monitoring system according to claim 1, wherein: The system supports generating a 3D completion model of the entire construction process according to the timeline, providing visual data basis for project payment, auditing, and quality traceability.

9. The building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The intelligent safety helmet is built with a privacy control module that allows users to manually turn off the camera function and records the attendance data of operators and remote assistance requests.

10. A building intelligent 3D imaging safety and quality monitoring system according to claim 1, characterized in that: The cloud control center generates multi-dimensional management reports, including construction progress, quality pass rate, attendance rate, and safety event statistics, and supports real-time viewing and exporting on the mobile side. The system can display and search the positions of intelligent safety helmets in real time in the model space.

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